1st African Conference on
Precision Agriculture

www.PAafrica.org

PROCEEDINGS
8-10 December 2020

PRECISION AGRICULTURE in ACTION for AFRICA

1st African Conference on Precision Agriculture | 8-10 December | 2020i

1st African Conference on Precision Agriculture | 8-10 December | 2020

PROCEEDINGS OF THE
1st African Conference on Precision Agriculture (AfCPA)

8-10 December, 2020

PUBLISHED BY:
African Plant Nutrition InstituteBenguérir, Moroccowww.apni.net

CONFERENCE WEBSITE:
www.paafrica.org
This publication contains the full paper submissions from the AfCPA. These papers, along withall other accepted abstract submissions are available at the conference website.

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1st African Conference on Precision Agriculture | 8-10 December | 2020

1st African Conference on Precision Agriculture (AfCPA)
Organized by:

SCIENTIFIC COMMITTEE:
Steve Phillips APNI, Morocco - Chair
Abdelaziz Belal, NARSS, Egypt
Martin Bosompem, UCC, Ghana
Jeremy Cordingley, Cropnuts, Kenya
Nicodeme Fassinou, UAC, BeninSam Gameda, CIMMYT, Ethiopia
Aboubacar Karim, Investiv, Côte d’Ivoire
Jess Lowenberg-DeBoer, Harper Adams, UK
Hélène Marrou, UM6P, Morocco

Sponsored by:iii

1st African Conference on Precision Agriculture | 8-10 December | 2020

TABLE OF CONTENTSADOPTION OF PRECISION AGRICULTURE#7507 VALIDATION OF CLIMATE SMART AGRICULTURAL TECHNOLOGIES FOR IMPROVEDCROP YIELDS IN SEMI-ARID LANDS OF KENYA
A.O. Esilaba, D. Nyongesa, M. Okoti, A. Micheni, A. Kathuku-Gitonga, D. Mutisya, E. Njiru., D.R. Macharia, R.
Kisilu, J. Kavoi, C. Bett, J. Wambua, M. Njunie, P. Finyange, B.M. Muli, E. Muthiani, C. Nekesa, A. Mzingirwa,
E. Thuranira, B. Rono, P. Gicheru, N. Mangale, J. Mutegi, and. L. Wasilwa ....................................................... 2#7523 VARIABILITY IN MONTHLY RAINFALL AND TEMPERATURE HAS AN INFLUENCE ON
DAILY MILK PRODUCTION IN SAHIWAL COWS IN KENYA
MacDonald Gichuru Githinji, Evans Deyie Ilatsia, Thomas Kainga Muasya, Bockline Omedo Bebe .................. 8#7616 DEVELOPMENT OF LODGING DIRECTION DETERMINATION SYSTEM USING IMAGE
PROCESSING
Arai Y, Morimoto E, Nonami K, Ito T ............................................................................................................... 16#7666 DETERMINANTS OF THE ADOPTION OF AN INTELLIGENT MONITORING SYSTEM AND
EFFECTS ON FARMS PERFORMANCE IN TUNISIA
J. Ben Nasr, H. Chiboub and M. Msaddak ........................................................................................................ 22#7773 THE STATUS OF PRECISION AGRICULTURE AND ITS ADOPTION IN MOROCCO
Rachid Bouabid, Hakim Boulal and Steve Phillips ............................................................................................ 27#7893 THE ROLES OF KEY PUBLIC SERVICES ON THE ADOPTION OF CLIMATE-SMART
AGRICULTURAL TECHNOLOGIES IN COFFEE-BASED FARMING SYSTEM OF ETHIOPIASamuel Diro and Agajie Tesfaye ...................................................................................................................... 33

APPLICATIONS FOR UAVS
#7465 CASHEW TREES DETECTION AND YIELD ANALYSIS USING UAV-BASED MAP
Thierry Roger Bayala, Issouf Ouattara, Sadouanouan Malo and Arto Visala .................................................... 42#7657 AUTONOMOUS HEXACOPTER SPRAYING DRONES FOR PLANTS PROTECTION
Abbes Kailil, Hassan Benaouda, Abdelhakim Mohsine, .................................................................................... 49iv

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CLIMATE SMART AGRICULTURE#7488 SIMULATION OF CASSAVA YIELD UNDER DIFFERENT CLIMATIC SCENARIOS IN
KILEMBWE, SOUTH-KIVU PROVINCE EASTERN DR CONGO
Yamungu Alongo Boniface Byamalong, Majaliwa Mwanjalolo Jackson Gilbert, Anthony Egeru, Dossa Boton
Martin ............................................................................................................................................................. 54#7883 CLIMATE SMART AGRICULTURE: CHALLENGES AND OPPORTUNITIES TO PROMOTETHE SYSTEM IN ETHIOPIA
Getachew Agegnehu and Tilahun Amede .......................................................................................................... 60#7927 CHANGES IN CLIMATIC FACTORS LEAD TO THE CHANGE IN CULTURAL WEDGING OF
RICE IN THE IVORIAN PRE-FOREST ZONE
Y.C. Brou, D.A. Kouassi, K.P-M. Kouakou, and E-O. Tienebo .......................................................................... 67

DECISION SUPPORT SYSTEMS#7549 CROPSAT – OPPORTUNITIES FOR APPLICATIONS IN PRECISION AGRICULTURE IN
AFRICA
O. Alshihabi, I. Nouiri, M. Mechri, H. Angar, K. Piikki, J. Martinsson, and M. Söderström ............................... 74#7594 APPLICATION OF PRECISION AGRICULTURE TECHNOLOGIES IN GRAZING: EXAMPLEOF GOATS BROWSING IN FOREST RANGELANDS OF NORTHERN MOROCCO
Y. Chebli, S. El Otmani, J.L. Hornick, J.F. Cabaraux, M. Chentouf................................................................... 79#7889 APPLICATION OF INFORMATION & COMMUNICATION TECHNOLOGIES (ICTS) INTO
PRECISION FARMING ECOSYSTEM IN ETHIOPIAN DIGITAL AGRICULTURE DEVELOPMENTPERSPECTIVE PLAN
Tadesse Anberbir ............................................................................................................................................. 83

ECONOMICS OF PRECISION AGRICULTURE#7517 TOMATO YIELD AND ECONOMIC PERFORMANCE UNDER ORGANIC AND MINERALFERTILIZER APPLICATIONS IN COASTAL TOGOJean M. Sogbedji and Mouhamadou Lare......................................................................................................... 89

EDUCATION AND OUTREACH INNOVATIONS#7480 SMARTAFRIHUB FOR SMART AGRICULTURE CAPACITY BUILDING IN AFRICA
Charvat K, Miderho Christus, Obot A, Löytty T, Kubickova H .......................................................................... 96v

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MAPPING AND GEOSTATISTICS#7438 PREPARATION OF A PRECISION RIPPING PLAN USING MANUAL VERTICALPENETROMETER MEASUREMENTS
T. Rátonyi, P. Ragán, A. Széles, P Fejér, I. Bácskai, E Harsányi ..................................................................... 103#7445 A CHEAP ALTERNATIVE TO DATA MANAGEMENT AND CREATING OF YIELD MAPS OF
SMALL-PLOT FIELD EXPERIMENTS
P. Ragán, T. Rátonyi, A. Széles, P. Fejér, I. Bácskai, E Harsányi .................................................................... 107#7598 SOME ESSENTIAL NUTRIENTS, ACTIVE LIMESTONE AND PH STATUS OF NORTH AND
CENTER TUNISIAN SOILS
A. Hachana, I. Hemissi, I. Achour, A. Souissi, and B. Sifi................................................................................ 112#7654 DIGITAL MAPPING OF EXCHANGEABLE CATIONS IN SOILS OF SOUTHWESTERNNIGERIA
M.K. Idowua, O.E. Awosiyana and E.A. Adesinab .......................................................................................... 115#7659 SOIL ORGANIC CARBONE MAPPING IN NORTH OF TUNISIA: COMPARISON BETWEENDIFFERENT INTERPOLATION METHODS
M. Barbouchi, H. Bahri, A. Souissi, H. Cheikh Mhammed and M. Annabi ....................................................... 120#7834 COMPARISON OF SOIL TESTING AND SCANNING METHODS FOR IN-FIELD SPATIAL
VARIABILITY ASSESSMENT OF SOIL FERTILITY: IMPLICATIONS FOR PRECISIONAGRICULTURE
Rachid Bouabid, Ibtissam El Hadad, Samir Benmansour ................................................................................ 126#7880 SPATIAL SOIL LOSS RISK ASSESSMENT FOR PROPER INTERVENTION: A CASE OF NERI
WATERSHED OMO-GIBE BASIN SOUTHWESTERN ETHIOPIA
Abebe Hegano, Awdenegest Moges, and Nigatu Wonderade ........................................................................... 132#7891 GEO-STATISTICAL PREDICTION OF SPATIAL DISTRIBUTION OF SALT-AFFECTED SOILSOF MEKI-ZEWAY FARM AREAS IN ETHIOPIA: BASELINE INFORMATION FOR PRACTICALIMPLEMENTATION OF PRECISION AGRICULTURE SYSTEM
Melese Minaleshoa and Girma Kassa............................................................................................................. 138#8019 CARTOGRAPHIE INTERACTIVE DES EXPLOITATIONS DE NOIX DE CAJOU DESPRODUCTEURS DE LA COOPÉRATIVE COPRODIGO DE GOHITAFLA
S. Kone, N. Coulibaly, D.J.M.J. Djina, K. Berté, et A. C. Kambou................................................................... 143

ON-FARM EXPERIMENTATION#7448 SOIL MAPPING WITH THE VERIS U3 SOIL SCANNER IN A PRECISION FARM IN HUNGARY
E. Harsányi1, T. Rátonyi1, A. Széles2, P. Fejér2, I. Bácskai1, P. Ragán1 ............................................................ 150

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POLICY SUPPORT INNOVATIONS#7464 JUST A MOMENT; THE NEED FOR STREAMLINING PRECISION AGRICULTURE DATA IN
AFRICA
Tegbaru B. Gobezie and Asim Biswas ............................................................................................................ 155

PRECISION AGRICULTURE FOR FIELD AND PLANTATION CROPS#7410 MAXIMISATION DE L’EFFICIENCE D’UTILISATION DE L’AZOTE PAR LA TOMATE(SOLANUM LYCOPERSICUM L.) SUR LES FERRASOLS AU SUD DU TOGO
Mouhamadou Lare et Jean Mianikpo Sogbedji ............................................................................................... 160#7505 MAPPING OF SOIL NUTRIENT VARIABILITY IN SOME PLANTATION CROPS INABEOKUTA, OGUN STATE NIGERIA
E.C. Basil, B.A. Senjobi, R.T. Basil, A.O. Tobore ............................................................................................ 169#7540 EVALUATION OF ON-FARM OIL PALM YIELD PARAMETERS IN NIGER DELTA REGION OF
NIGERIA
D.K. Madukwe, F. Ekhator, P. Osayande, V.O. Aduramigba-Modupe, O. Solomon, M.N. Okoye and C.E.
Ikuenobe ........................................................................................................................................................ 173#7545 DEVELOPMENT OF CANOPY MAPPING SYSTEM OF ASIAN PEARS (PYRUS PYRIFOLIA
NAKA) USING TERRESTRIAL LASER SCANNING
Jaehwan Lee, Eiji Morimoto, Kazuyoshi Nonami, Ichizen Matumura, So Sato, Mana Ikebe ............................. 178#7566 EFFECT OF INTERCROPPING SORGHUM WITH COWPEA AND NITROGEN APPLICATIONON GROWTH AND YIELD OF SORGHUM (Sorghum bicolor (L.) MOENCH)
Peter E. Moi, Onesmus M. Kitonyo, George N. Chemining’wa and Josiah M. Kinama .................................... 183#7604 GRAIN YIELD OF TWO PRE-RELEASE RICE VARIETIES INCREASED MARGINALLY WITHHIGHER PLANT DENSITY AND NITROGEN RATE
Wilfred Kioko, Onesmus Kitonyo, George Chemining’wa, Setegn Gebeyehu and Rosemary Murori ................. 188#7667 MAPPING OF MICRONUTRIENTS STATUS IN SOILS UNDER MULTIVARIETAL CITRUS
SINENSIS PRODUCTION FOR PRECISION AGRICULTURE
Bernard N. Okafor, Vincent Aduramigba-Modupe and Olubunmi Denton ....................................................... 192#7933 CARACTERISATION ARCHITECTURALE POUR LA PREDICTION DU POTENTIEL DE
PRODUCTION DES TETES DE CLONES DE COFFEA CAENOPHORA PIERRE EX A. FROEHNER
Kouassi Kouadio Henri, Yao N’guessan Abraham, Kouassi Roland Herve, Angama Djedoux Maxime et Legnate...................................................................................................................................................................... 196

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PRECISION AGRICULTURE FOR SMALLHOLDERS#7414 DIGITAL PLATFORMS FOR BOOSTING FARMER KNOWLEDGE: TWO CASE STUDIES IN
KENYA AND UGANDA
T. Harigaya, J. Zhu, M. Mwanje, E. Bakirdjian, and J. Abuli .......................................................................... 203#7422 MAPPING AFRICAN SOILS AT 30M RESOLUTION – ISDASOIL: LEVERAGING SPATIALAGRONOMY IN FARM-LEVEL ADVISORY FOR SMALLHOLDERS
M. Miller, J. Collinson, K. Shepherd, A. Dobermann and J. Crouch................................................................ 207#7442 AFFORDABILITY OF MECHANISATION SERVICES FOR SMALLHOLDERS IN ZAMBIA BY
AGRODEALER DEVELOPMENT
S. Peets, and S. Woods ................................................................................................................................... 211#7515 SPATIAL VARIABILITY AND MAPPING OF SELECTED SOIL QUALITY INDICATORS FORPRECISION FARMING AT A SMALLHOLDING LEVEL IN MINNA, NIGERIA
B.A. Lawal, M.K.A Adeboye, and P.A. Tsado.................................................................................................. 216#7534 FARMER CHARLIE: PRECISION AGRICULTURE AT SMALLHOLDER FARMERS’ SERVICE
B. Bonnardel, G. Cursoli, ............................................................................................................................... 221#7564 SCALING PRECISION AGRICULTURE IN WEST AFRICA SMALLHOLDER IRRIGATION AND
WATER MANAGEMENT SYSTEMS
Adebayo OlubukolaOke and Vincent Aduramigba-Modupe ............................................................................. 226#7639 CLOSING THE YIELD GAP IN AFRICA THROUGH SOIL ATTRIBUTE MANAGEMENT USINGREMOTE SENSING AND PRECISION AGRICULTURE APPROACHES AT THE FIELD SCALE
K. Khechba, A. Laamrani, A. Chehbouni, D. Dhiba, K.Misbah ....................................................................... 233#7646 POTENTIAL PRECISION AGRICULTURE PRACTICES FOR HIGH QUALITY AND EFFICIENTFRUITS AND VEGETABLES PRODUCTION IN WEST AFRICA: A MINI REVIEW
Nicodeme V. Fassinou Hotegni, Leocade Azonhoumon, Enoch G. Achigan-Dako............................................ 237

PRECISION NUTRIENT MANAGEMENT#7462 MAPPING SPATIAL VARIABILITY OF SOIL NUTRIENT DEFICIENCIES IN SMALLHOLDERVILLAGES – A PREREQUISITE FOR IMPROVED CROP PRODUCTION IN AFRICA
Mats Söderström, Kristin Piikki, Job Kihara, John Mutua, Johanna Wetterlind............................................... 244#7489 PRECISION FARMING USING SPATIAL SOIL VARIABILITY MAPS FOR IMPROVEDBANANA NUTRIENT MANAGEMENT ON A FERRALSOL IN CENTRAL UGANDA
Patrick Musinguzi, Hannah Kabasomi and Twaha A. Basamba ...................................................................... 249#7506 GESTION STRATEGIQUE DES NUTRIMENTS POUR L’AMELIORATION DU RENDEMENT ET
DE LA PROFITABILITE ECONOMIQUE DU GOMBO (Abelmoschus esculentus L.) SUR LES SOLSFERRALITIQUES AU SUD TOGO
Kossi Kotchadjo, Jean. M. Sogbedji ............................................................................................................... 263

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#7513 PRECISION NUTRIENT MANAGEMENT FOR CASSAVA PRODUCTION
Kodjovi S. Ezui, Meklit Tariku Meklit, Adeyemi Olojede, Mutiu Busari, Peter Deusdedit Mlay, Florence
Olowokere, Joy Adiele, Rebecca Enesi, Ademola Adebiyi, Christine Kreye, Stefan Hauser, Frederick Baijukya,
Thompson Ogunsanmi, Theresa Ampadu-Boakye, Shamie Zingore, Bernard Vanlauwe, Pieter Pypers ............ 273#7563 MAPPING AND ASSESSING FERTILITY OF AFRICAN SOILS USING HIGH-RESOLUTION
REMOTE SENSING AND MACHINE LEARNING APPROACHES
Mohammed Hmimou, Ahmed Laamrani, Said Khabba, Faissal Sehbaoui, Abdelghani Chehbouni, Driss Dhiba...................................................................................................................................................................... 278#7579 SOIL FERTILITY MAPPING OF DRY SAVANNAH ZONE OF TOGO
Ganyo Komla, Ablede Komlan, Koudjega Kossi, Ani Sékou, Afawoubo Koffi, Anoumou Dosseh, Mensah Achité,
Assih-Faram Essodésibè, Tchalla-Kpondi M’klwa, Bonfoh Bédibètè, Kpemoua Kossi, Lombo Yao.................. 283#7581 NUTRIENT MANAGEMENT TAILORED TO SMALLHOLDER AGRICULTURE ENHANCESPRODUCTIVITY AND SUSTAINABILITY
Pauline Chivenge, Kazuki Saito, Michelle Anne Bunquin, Sheetal Sharma, Achim Dobermann ....................... 288#7599 IMPACT OF SENSOR-BASED PRECISION NITROGEN MANAGEMENT ON WHEAT YIELDAND NITROGEN USE EFFICIENCY
Abdou A. Soaud. El-Metwally A. El-Metwally, Ali M. Ali and Radwa K. Sayed ............................................... 294#7617 A GEOSTATISTICAL APPROACH TO DEFINE A SOIL FERTILITY INDEX BASED ON THE
MAIN SOIL MACRONUTRIENTS
H.S. Aboelkier, A. Nasrallah, S. Shaddad, and G. Buttafuoco.......................................................................... 302#7619 QUANTIFICATION OF OPTIMAL FERTILIZERS DEMAND FOR WHEAT AND CORN FIELDSIN MOROCCO USING VERY HIGH RESOLUTION REMOTE SENSED IMAGERY AND HYBRIDCOMPUTATIONAL APPROACHES
K. Misbah, A. Laamrani, A. Chehbouni, D. Dhiba, J.Ezzahar, K.Khechba ...................................................... 307#7708 ENHANCING THE USE OF APPROPRIATE FERTILIZERS FOR IMPROVING RICE AND
MAIZE PRODUCTION IN TANZANIA
C.J. Senkoro, K.A. Mtua, C. Komba, S.A. Kazyoba, J.D. Mbogoni and M.M. Msolla ....................................... 313#7785 COMPARATIVE FERTILIZATION EFFECTS ON MAIZE PRODUCTIVITY UNDERCONSERVATION AND CONVENTIONAL TILLAGE ON SANDY SOILS IN A SMALLHOLDERCROPPING SYSTEM IN ZIMBABWE
Nyasha Kafesu, Regis Chikowo and Shamie Zingore....................................................................................... 318#7835 SPATIAL VARIABILITY OF SOIL AND PLANT NUTRIENT STATUS IN RELATION TO THE
OCCURRENCE OF BITTER PIT IN APPLE ORCHARDS IN THE SAIS PLATEAU, MOROCCO
R. Bouabid, H. Labazi and H. Bahri ............................................................................................................... 323

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PRECISION WATER MANAGEMENT#7475 CROP WATER REQUIREMENTS, BIOMASS AND GRAIN YIELDS ESTIMATION FOR SULLA(HEDYSARUM CORONARIUM L.) USING CROP WAT IN SEMI-ARID REGION OF TUNISIA
R. Hajri, M. Rezghui, M. Mechri, and M. Ben Younes ..................................................................................... 330#7595 A PRECISION IRRIGATION APP FOR SMART WATER MANAGEMENT BY FARMERS:“IRRISMART”
A. Abouabdillah, and R Bouabid .................................................................................................................... 335#7645 MONITORING IRRIGATION WATER USE AT LARGE SCALE IRRIGATED AREAS USINGREMOTE SENSING IN WATER SCARCE ENVIRONMENT
M.H. Kharrou, V. Simonneaux, M. Le Page, S. Er-Raki, G. Boulet, J. Ezzahar, S. Khabba, A. Chehbouni........ 338#7660 A REINFORCEMENT LEARNING BASED APPROACH FOR EFFICIENT IRRIGATION WATERMANAGEMENTEl Hachimi Chouaib, Belaqziz Salwa, Khabba Said and Chehbouni Abdelghani ............................................. 343

PROXIMAL AND REMOTE SENSING#7451 ESTIMATING SOIL ORGANIC MATTER FROM CELL PHONE IMAGES
A. Biswas, H. Vasava, Y. Fu, P. Taneja, S. Lin, and P. Daggupati ................................................................... 348

ROBOTICS, AUTOMATION, AND SMALL FARM MECHANIZATION#7493 WILLINGNESS TO PAY FOR DRONE TECHNOLOGY IN THE APPLICATION OF PESTICIDEFOR THE CONTROL OF FALL ARMYWORM
S. Omega, F. Annor-Frimpong, S. Akaba, W. Ghartey, J. Ocran, and I. Asante ............................................... 353#7606 A REVIEW ON SENSOR BASED ROBOTIC AGRICULTURE: IMPROVING TRADITIONALAGRICULTURE PRACTICES
S.C. Karad, Prabhat Kumar and G.U Shinde .................................................................................................. 357#7641 LOCALLY ADAPTED MACHINERY SOLUTIONS FOR SUSTAINABLE INTENSIFICATION OF
CROP-LIVESTOCK SYSTEMS IN TUNISIAUdo Rudiger, Zied Idoudi, Aymen Frija, Mourad Rekik, Monia El Ayed, Hatem Cheikh, and Anis Zaim .......... 369#7648 LIDAR-BASED SOYBEAN CROP SEGMENTATION FOR AUTONOMOUS NAVIGATION
V.A.H. Higuti, A.E.B. Velasquez, M.V. Gasparino, D.V. Magalhães, R.V. Aroca, D.M.B.P. Milori, M. Becker . 376

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SATELLITE IMAGERY#7479 THE VISION OF FUTURE EARTH OBSERVATION FOR AGRICULTURE
Charvat K, Kubickova H, Safar V ................................................................................................................... 382#7531 ORCHARD YIELD ASSESSMENT IN NORTH-EAST OF MOROCCO USING SATELLITEIMAGERY
A. Matese, K. Aberkani, S. Samri, M. Said, S.F. Di Gennaro and P. Toscano .................................................. 388#7629 MONITORING CORN (ZEA MAYS) YIELD USING SENTINEL-2 SATELLITE IMAGES FORPRECISION AGRICULTURE APPLICATIONS
A. Kayad, M. Sozzi, S. Gatto, L. Sartori, F. Pirotti and F. Marinello ............................................................... 393#7681 PREDICTING IN-SEASON SORGHUM YIELD POTENTIAL USING REMOTE SENSINGAPPROACH: A CASE STUDY OF KANO IN SUDAN SAVANNAH AGRO- ECOLOGICAL ZONE,NIGERIAA. Tukur, H.A. Ajeigbe, F.M. Akinseye, I.B. Mohammed, M.M. Badamasi ....................................................... 396

SOFTWARE AND MOBILE APPLICATIONS#7368 SOCIO-DEMOGRAPHIC FACTORS CONTRIBUTING TO ADOPTION OF E-COMMERCE BY
AGRIBUSINESS SMES IN THE UPPER EAST REGION OF GHANA
S. Omega and S. Akaba .................................................................................................................................. 402

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ADOPTION OF PRECISION AGRICULTURE

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#7507 VALIDATION OF CLIMATE SMART AGRICULTURAL TECHNOLOGIESFOR IMPROVED CROP YIELDS IN SEMI-ARID LANDS OF KENYA
A.O. Esilaba*, D. Nyongesa, M. Okoti, A. Micheni, A. Kathuku-Gitonga, D. Mutisya, E. Njiru.,
D.R. Macharia, R. Kisilu, J. Kavoi, C. Bett, J. Wambua, M. Njunie, P. Finyange, B.M. Muli, E.
Muthiani, C. Nekesa, A. Mzingirwa, E. Thuranira, B. Rono, P. Gicheru, N. Mangale, J. Mutegi,and. L. Wasilwa
Kenya Agricultural and Livestock Research Organization, P.O. Box 57811 – 00200, Nairobi,
Kenya, anthony.esilaba@kalro.org
ABSTRACT
Kenya’s Vision 2030 economic strategy identifies agriculture as one of the key sectors todrive the Country’s economy. However, the agricultural sector faces various challenges. For
instance, the sector is predominantly rain-fed and 80% of the land area is arid and semi-arid(ASALs). Due to challenges associated with rain-fed agriculture, the ASALs have the lowest
development indicators and the highest poverty incidence levels. The Kenya Cereals Enhancement
Programme – Climate Resilient Agricultural Livelihoods (KCEP-CRAL) tested and validated
several agricultural technologies with potential to improve crop production in Kenya. This wasachieved through establishment of ‘Mother and Baby’ Climate Smart Agriculture (CSA) trials forsoil and crop management in eight Counties in Eastern and Coastal regions. These Counties
included, Embu, Tharaka-Nithi, Kitui, Machakos, Makueni, Kilifi, Kwale and Taita-Taveta. The
trials were established on station and on-farm during the October-November-December (OND)
season of 2016 (for Eastern region and Taita-Taveta) and May 2018 (March-April-May (MAM)
season for Kwale and Kilifi Counties. The tested CSA technologies were those of Conservation
Agriculture (CA), Integrated Soil Fertility Management (ISFM), fertilizer source/efficacy,
nitrogen, phosphorus, potassium, sulphur and zinc-based fertilizers. The test crops included cereals(maize, sorghum and millet) and pulses (beans, green grams, cowpeas and pigeon peas). In
addition, the project conducted validation trials on push-pull technologies (PPT) for control of Fall
Army Worms (FAW). The crops were tested for their performance, adaptability and acceptability
in these environments. Results from soil characterization and field trials showed that organiccarbon, nitrogen, phosphorus, sulphur and zinc were the most limiting nutrients in most of thecounties. The soil amendments supplying the above listed limited nutrients were either organic,
inorganic or/and organic inorganic combination sources coupled with appropriate crop varieties
and soil and water management practices. The cereal and legume crop yields increased by at least
50-100 percent depending on the agro-ecological zone, crop type and ISFM technologies. It was
concluded that farm-specific characterizations were important towards determining levels offarm/catchment specific application of both macro- and/or micro-nutrients for improved land andcrop productivity. The study recommended adoption of sustainable farm specific management
strategies to improve soil water, fertilizers and labour use efficiencies in the Kenyan ASALs.
Keywords: arid and semi-arid lands; macro and micronutrients; climate resilient agriculture;
inputs use efficiency

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INTRODUCTION
Agricultural sector is not only the driver of Kenyan economy but also the means of
livelihood for over 80% of the rural Kenyans population. Indeed, the Kenya’s Vision 2030
economic strategy identifies agriculture as one of the key sectors to drive the Country’s economy.
However, the agricultural sector faces a myriad of challenges related to aridity which characterizes
over 80% of the total land area. These are compounded by rain-fed agriculture in Kenya which
over the years has failed to adequately support crops to produce high yields. Smallholder farmers
who account for about 80% of the country’s population use inorganic fertilizers in combination
with organic resources. Cereal and legume crop yield gaps are large in the smallholder farming
systems but there is a potential of closing them through adoption of improved crop varieties andsustainable cropping systems. Continuous use of unbalanced inorganic fertilizers without additionof organics has led to increase in soil acidity, nutrient mining and decline in soil organic matter.
In addition, rainfall variability resulting from climate change has negatively affected agricultural
productivity. As one of the climate smart agriculture (CSA) remedies, Integrated Soil Fertility
Management (ISFM) which also embraces adherence to conservation agriculture practices offer
the best options for improving soil fertility and consequently the crop yields within and beyond
the Kenyan ASALs. Based on the above background, a study was conducted in 8 Counties inUpper Eastern, Lower Eastern and Coastal regions to validate the benefits of embracing CSA
principles and practices on soil and crop productivity in the selected sites and counties.
MATERIALS AND METHODS
For almost five years (2016 to 2020), the Kenya Cereals Enhancement Programme –Climate Resilient Agricultural Livelihoods (KCEP-CRAL) validated CSA technologies withpotential to improve soil and crop production in the ASALs of Kenya. This was achieved throughestablishment and monitoring of on-station and on-farm trials following a “Mother and Baby
approach in the Upper Eastern (Embu and Tharaka-Nithi Counties); Lower Eastern (Kitui,
Machakos and Makueni Counties), and the Coastal regions (Taita-Taveta, Kilifi and Kwale
Counties). A soil testing SMART (Soil analysis, Mapping, Recommendation and Transfer tofarmers) approach was used as part of the novel procedures before and during seasonal
establishment of the field trials. The first trials in the list of trials were the CA practices. These setof trials were made up of 24 treatments of residue management, tillage practices and crop varieties
laid out in split-split plot randomized complete plot design. The second set of trials were basedon ISFM treatments that included, a control, inorganic fertilizer at the recommended rates,
farmyard manure (FYM) at the recommended rate and organic and inorganic fertilizer applied at
half the recommended rates. The third set of trials were based on the fertilizer sources as definedin the 4R nutrient stewardship framework. This trial focused on the effect of the various fertilizer
materials on crop productivity and had up to seven treatments composed of five inorganic
fertilizers, farmyard manure and no input (control). The effect of potassium (K) based fertilizer oncrop performance was the fourth trial in the list and tested the efficacy of 0, 40, 80, 120, 160 and
200 kg K2O ha-1. The K application treatments (including the 0 kg K2O ha-1) were provided withthe recommended rates of N and P. The study had the effect of sulphur (S) based fertilizer as thefifth trial. The trials focused on the efficacy of 0, 10, 20, 30 and 40 kg S ha-1. The five S application
rates (including the 0 kg S ha-1) were provided with the recommended rates of N and P. The sixth
trial was of the effect of zinc (Zn) on crop yields. The trial had Zn applied at (0, 5, 10, 15 and 20

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1st African Conference on Precision Agriculture | 8-10 December | 2020

kg Zn ha-1). The five Zn application rates (including the 0 kg Zn ha-1) were provided with therecommended rates of N and P. The sixth treatment in the trial structure was absolute control (noinputs). The crops included in the trials were cereals (maize, sorghum and millet) and associatedpulses (beans, green grams, cowpeas and pigeon peas) and push-pull technologies (PPT) to control
Fall Army Worm (FAW). The crops were tested for their performance, adaptability andacceptability in these environments. Participatory Learning and Action Research (PLAR) was usedduring technology development, dissemination and capacity building of farmers, extension
providers and other partners.
Data Collected and Analysis
The types of datasets that were collected included: crop performance during the growth
period (phonological development) and crop yields (biomass and grains). Data sets were subjectedto the analysis of variance (ANOVA) procedure (SAS, 2002). Differences between treatments
means was separated using LSD at 5% level of significance.
RESULTS AND DISCUSSIONS
Effect of Conservation Agriculture (CA) on Crop Yields
It was evident that both cereal and legume crops grew vigorously under the Zai pit
compared to those planted in zero and conventional tillage structures in all test crops, seasons andregions. Consequently, the crops under Zai gave significantly higher yields (shoot biomass andgrains (Fig. 1). The higher yields under the CA based tillage practice (Zai pits) were attributed tothe effect of in-situ water harvesting and conservation. Likewise, the pits concentrated together
in-situ or applied ex-situ soil fertility nutrients for crop use. Additionally, the process of excavating
the pits softened and also improves the soil depth allowing wider or/and deeper crop rooting for
wider scavenging of limited plant growth resources. The three positive effects of Zai pits improved
the crop resources use efficiency leading higher yields.

Figure 1. Effect of tillage practices on average green grams shoot biomass and grain yields
Effect of Integrated Soil Fertility Management (ISFM) on Crop Yields
In the Upper and lower Eastern, half Fertilizer (20 kg ha-1 N and P) and half manure (2.5 t-1ha ) (HMF) performed well for green grams. However, sorghum did better than green grams whenfull fertilizer rates 40 kg ha-1 N and P (FF) were applied across the regions (Fig. 2). Maize yields

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1st African Conference on Precision Agriculture | 8-10 December | 2020

showed gradual increase in yields from no fertilizer to use of half manure plus half fertilizer thento full manure and the highest yield was achieved under full fertilizer application (Fig. 2.).

Figure 2. Green grams and sorghum response to integrated soil fertility management
technologies

Upper Eastern

Lower Eastern

Figure 3. The effect of different fertilizer sources on sorghum yields in Kenya

Nil

Minjingu

Coast Region

Fertilizer sources

5

Mea Mazao

NPK 17:17:17

DAP

FYM

Mavuno

NPK 23:23:0

Nil

Minjingu

Mea Mazao

NPK 17:17:17

DAP

FYM

Mavuno

NPK 23:23:0

Nil

Minjingu

Mea Mazao

DAP

NPK 17:17:17

FYM

Mavuno

2.5
2
1.5
1
0.5
0
NPK 23:23:0

Sorghum grain yield(t/ha)The Effect of Fertilizer Source and Efficacy on Crop Yields
Higher sorghum and millet grain and biomass yields were obtained with application offertilizers compared to the nil application. Significant effects were found from plots treated withdifferent fertilizer sources in Upper Eastern where Farmyard Manure (FYM), Diammonium
Phosphate (DAP), MEA Mazao and Mavuno planting fertilizers gave the highest yields (Fig. 3).
No significant differences were found in Lower Eastern and Coast regions. However, the increasein yields in plots treated with fertilizers from different sources indicated the need for fertilizer
application in the three regions. The importance of FYM is shown by the significantly higher yields
obtained from the treatment in Upper Eastern.

1st African Conference on Precision Agriculture | 8-10 December | 2020

The Effect of Potassium Fertilizers on Crop Yields
Application of potassium at 80 kg ha-1 had the highest millet yield but was not significantly
different (P<0.05) from 40 kg ha-1 and no application of K (Fig. 4). Under sorghum, the highest
yield response was recorded at 40 kg K ha-1 across the regions which was significantly different(P<0.05) from the other higher K fertilizer rates (Fig. 4). On average, the yields of millet, sorghum,
maize and cowpeas highest at 40 kg K ha-1 (Fig. 4).

Yield (t/ha)

1.5

MilletSorghum

1
0.5
0
Nil

NP0K

NP40K
NP80K NP120K
Potassium rates (kg/ha)

NP160K

NP200K

Figure 4. Average yields of millet and sorghum across Eastern and Coastal regions
The Effect of Sulphur and Zinc Fertilizers on Crop Production
A general trend showed that there was gradual sorghum, millet, maize and cowpea grain
yield increases up to 20 kg S ha-1 followed by a decrease in yield on further addition of S. There
was no significant response of cowpea and maize on sulphur application at the coast region.
Similarly, there was no significant response of green grams, sorghum and maize to zinc fertilizers.
Crop yields increased up to 5 kg Zn ha-1. There is need for long-term trial management to confirm
micronutrient responses.
Push-Pull Technologies (PPT)In addition, the project conducted testing and validation on push-pull technologies (PPT)
for control of Fall Army Worm (FAW). Brachiaria spp and Pannicum maximum grasses for wet
and dry areas (respectively) attracted (pulled) fall armyworm from maize and sorghum while
Clitoria and Lablab dolichos had the highest (40%) repelling (pushing) effect on the pest from thecrops.
CONCLUSIONS
Soil characterization and field trials showed that organic carbon, nitrogen, phosphorus,
sulphur and zinc were the most limiting nutrients in almost all the counties. The soil amendments
supplying the above listed nutrients were either organic, inorganic or/and organic inorganic
combination sources coupled with appropriate crop varieties and soil and water management
practices. The cereal and legume crop yields increased by at least 50-100 percent depending on
the agro ecological zone, crop type and relevant ISFM technologies. It was concluded that farmspecific characterizations were important in determining levels of farm/catchment specific61st African Conference on Precision Agriculture | 8-10 December | 2020

application of both macro- and/or micro-nutrients for improved land and crop productivity. The
study recommended the adoption of sustainable farm specific management strategies to improvesoil water, fertilizers and labour use efficiencies in the Kenyan ASALs.
REFERENCES
FAO. 2018. Climate-smart agriculture training manual − A reference manual for agriculturalextension agents. Rome. 106 pp.
Micheni A. 2015. Dynamics of Soil Properties and Crop Yields under Conservation Agriculture
Practices in a Humic Nitisol, Eastern Kenya. Unpublished PhD Thesis, Jomo Kenyatta
University of Agriculture and Technology (JKUAT), Nairobi.
SAS. 2002. Release 8.2, SAS Institute Inc., Cary, NC, USA.
UNDP. 2016. UNDP’s Gender Equality Seal Certification Programme: Lessons Learned in Latin
America. Women Deliver. http://womendeliver.
UNFCCC. 2007. Report on the workshop on climate related risks and extreme events. Note by thesecretariat. FCCC/SBSTA/2007/7. UNFCCC. Bonn, Germany. 15 pp.
Vanlauwe B. Wendt J, Giller K, Corbeels M, Gerard B, et al. 2014. A fourth principle is required
to define CA in SSA Africa: The appropriate use of fertilizer to enhance crop productivity.
Field Crop Research 155: 10 – 13.
Wall PC, Thierfelder C, Ngwira A, Govaerts B, Nyagumbo I, et al. 2013. Conservation Agriculture
in Eastern and Southern Africa. In Jat RA, Sahrawat KL, Kassam AH (eds.) Conservation
Agriculture: Global Prospects and Challenges. CABI, Wallingford Oxfordshire OX10
8DE, UK.
World Bank. 2018. Kenya Agricultural Productivity Program (KAPP I and II). Independent
Evaluation Group, Project Performance Assessment Report 133838. Washington, DC:
World Bank.

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1st African Conference on Precision Agriculture | 8-10 December | 2020

#7523 VARIABILITY IN MONTHLY RAINFALL AND TEMPERATURE HAS AN
INFLUENCE ON DAILY MILK PRODUCTION IN SAHIWAL COWS IN KENYA
MacDonald Gichuru Githinji1, 2*, Evans Deyie Ilatsia1, Thomas Kainga Muasya2,
Bockline Omedo Bebe2
1
Kenya Agriculture and Livestock Research Organization, Naivasha, Kenya; 2Department of
Animal Sciences, Egerton University, Egerton, Kenya, macgithinji@gmail.com
ABSTRACT
Climate change leads to alteration of environmental conditions directly or indirectly
through anthropogenic activities. The consequences include fluctuations in the mean as well as
variability of recognizable environmental variables with the changes persisting for longer thannormal periods. Climate change poses numerous serious threats to livestock production throughincreased temperature, changes and shifts in rainfall distribution and increased frequency of
extreme weather events. Grazing systems that are dependent on the natural cycle of climaticconditions are expected to be more seriously impacted by climate change. The consequences ofclimate change include increased heat stress, reduced water and feed quality and availability,
increased cases of diseases and pests and or emergence of new ones. As livestock farmers in thetropics continue to bear the brunt of climate change, there is need to understand how the variabilityof identifiable environmental variables influence livestock performance. The objective of thisstudy was to determine the influence of rainfall and temperature of milk yield in Sahiwal cattle in
Kenya. Monthly milk yield records of Sahiwal cows and meteorological data for monthly
minimum and maximum temperature and rainfall for a period of 32 years were extracted fromrecords at the national Sahiwal stud, Naivasha, Kenya. The relationship between the variables wasstudied by multiple regression analysis. Minimum and maximum temperature and monthly rainfall
significantly (P<0.05) affected monthly milk yield. The proportion of total variation accounted forby climatic variables was small (0.5%) but significant. Each individual weather variable accounted
for a small proportion of total variation. Minimum and maximum temperature had a negative effecton monthly milk yield. For every 1oC increase temperature, in monthly milk yield decreased by 1.58 kg and -1.17 kg, respectively. A 1 mm increase in monthly rainfall of monthly caused monthly
milk yield to increase by 0.07 kg. Mitigating strategies are required to alleviate the negative effects
of temperature on monthly milk yield. Sound grazing management and feed conservation could
harness the advantage of the positive effect of rainfall on milk yield.
Keywords: climate change; heat stress, mitigation, rainfall variability
INTRODUCTION
Climate change is attributed directly or indirectly to anthropogenic activities which alter
the makeup of the earth’s atmosphere (UNFCC, 2008). Whether due to natural variability or
human activity, climate change leads to changes in the mean and or variability of the identifiable
properties of climate persisting for longer than normal periods of time (IPCC, 2007). Climatechange poses a serious threat to livestock production through increased temperature, changes andshifts in rainfall distribution and increased frequency of extreme weather events (Rojas-Downing8

1st African Conference on Precision Agriculture | 8-10 December | 2020

et al. 2017). The consequences of climate change include increased heat stress, reduced water(Chapman et al. 2012) and feed quality and availability (Nardone et al. 2010), increased cases of
diseases (Thorton et al. 2009.) and pests and or emergence of new ones Karl et al. 2009). The
greatest adverse effects of climate change will be felt by crop and livestock farmers in developing
countries who are dependent on natural systems (UNDP 2014).
Grazing systems that depend on the natural cycle of climatic conditions are expected to be
more seriously impacted by climate change (Aydinalp and Cresser 2008). Among grazing systems
across the world those found in low altitude arid and semi-arid areas will be affected most severely
as higher temperatures and reduced rainfall reduce feed yields and increased land degradation(Hoffman and Vogel 2008). On the other hand, non-grazing systems are expected to be lessaffected by climate change because housing and other structures allow for greater control ofproduction conditions FAO 2009; Thorton and Gerber 2010). Heat stress is one of the components
of climate change with the most significant direct impact on livestock production.
Recent studies have reported on the temporal and spatial variability of rainfall andtemperature in different countries and ecosystems across the world (Nouaceur et al. 2017; Rustum
et al. 2017). Most these studies have reported either increase or decrease in intensity of rainfall,
increased incidences of drought and rising ambient temperatures (Nouaceur et al 2017; Pedersen
et al. 2010; Kumar et al. 2017). Heat stress decreases feed intake, feed conversion efficiency
leading to reduced milk production, growth, reproduction and increased incidences of diseases andmortality (Thorton 2010; Lacetera 2019; Santos et al. 2019). Poor feed conversion efficiency leads
to increased methane gas emissions (Wagnorn and Hegarty 2011), further fueling global warming.
The objective of this study was to determine the influence of rainfall and temperature of milk yieldin Sahiwal cattle in Kenya.
MATERIALS AND METHODS
Data of this study were collected at the National Sahiwal Stud, Naivasha, Kenya from 1985
to 2012. The study is located at 0o 43'1.8408'' S, 36o 25' 51.6936''E on the floor of the Great Rift
Valley at about 600 m above sea level. The climate of this location is semi-arid with an annual
average rainfall of 600 mm. The rainfall pattern of the area is bimodal, with two distinct peaks
occurring in May and the other one in November. However, the rainfall distribution varies fromyear to year. The average minimum and maximum temperatures are 8oC and 30oC, respectively.
The breed reared at the Stud is the Sahiwal cattle. The breed was brought into the country from
India and Pakistan in the first half of the 20th century. Since then it has been systematically bred
for milk and growth. The Stud is run as a closed nucleus, in which performance and pedigree
recording and genetic evaluation is carried out. The improved germplasm is distributed tocommercial herds mainly breeding bulls and sometimes semen and surplus heifers (Ilatsia et al.
2011). The design of the breeding programme is described in details by Muhuyi et al (1999). The
cows at the stud are mainly raised on natural pastures dominated by star grass (cynodon dactylon)
with mineral salts being provided. The pasture land is dotted by acacia trees with the main genus
being the acacia xanthophloea. The cows are milk twice a day by hand. Average milk yield has
been reported to be 4.5±1.5kg per day per cow. Climatic was collected routinely by neighbouring
flower farm and included ambient temperatures and rainfall. The climatic variable recorded wereminimum and maximum temperatures and monthly rainfall. Milk yields for each cow wererecorded at milking and added to daily and weekly totals. From the weekly totals monthly totals

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1st African Conference on Precision Agriculture | 8-10 December | 2020

were summed up. The monthly milk yield yields were then related to minimum and maximum
temperature and rainfall for the same period.
RESULTSThe effect of fixed factors on monthly milk yield is presented in Table 1. Parity, month of
milking and year were significant at P<0.001. As monthly rainfall increased milk yield increased
significantly (P<0.001) but decreased as minimum and maximum temperature increased. Meanmonthly rainfall and maximum temperature significantly (P<0.001) influenced monthly milkyield. The effect of minimum temperature was significant at P<0.05. For every increase in monthly
rainfall of 1 mm, monthly milk yield increased by 0.07 kg. A 1oC increase in minimum andmaximum temperature lead to a decrease in monthly milk yield of 1.58 kg and 1.17 kg,
respectively.
Table 1. Effect of independent variables, partial sums of squares for monthly milk yield andcoefficients of regression of monthly milk yield on weather variables.
SourceDegrees of freedom

Parity
5
Month
11
Year
31
Minimum temperature
1
Maximum temperature1
Rainfall1
Model
50
Residual
8635
R2
0.10
***P<0.001, **P<0.01, *P<0.05.

Sums of squares
1356776.2***
237527.3***
3126052.4***21520.3*
101775.5**43458.9**46534615.6
41674546.7

Partial regression
coefficients-1.58±0.57**-1.17±0.32***0.07±0.02***

The model accounted for 43% of the total sum of squares for monthly milk yield. Year ofcalving accounted for 6.7% of the total variation in monthly milk yield followed by parity (2.7%),
month of milking (0.5%) and maximum temperature (0.2%). Variables associated with weather
accounted for a small proportion of the total variation (0.4%), thought significant.
Monthly milk yield generally increased significantly (P<0.05) from parity 1 to a peak
between parity 3 and thereafter decreased, with monthly milk yield in parity 2, 3 and 5; 5 and 6;
and 3 and 4 being similar (P>0.05).
Monthly milk yield, rainfall, minimum and maximum temperature generally remained
constant (P>0.05) across the months from January to December (Figure 1).

10Figure 1. Trends of mean monthly rainfall (RF), minimum (AMINT) and maximum (AMAXT)
temperature and milk yield for Sahiwal cattle in Kenya.
The correlations between monthly milk yield and rainfall, average minimum temperature,
average temperature and maximum temperature are shown in Table 2. The correlations between
monthly milk yield and monthly rainfall was positive and low and significantly different from 0(P<0.0001). Monthly milk yield was negatively correlated with average minimum (P>0.05),
average (P<0.05) and maximum temperature (P<0.05). This implies that months receiving highamounts of rain were generally cooler.
Table 2. Correlations between mean monthly milk yield, rainfall, minimum and maximum
temperature at the National Sahiwal Stud, Naivasha, Kenya.
Variable

Monthly
milk yield

Monthly milk
yieldMonthly rainfall
Mean minimum
temperature
Mean maximum
temperature***P<0.001, **P<0.01, *P<0.05.

MonthlyrainfallMean minimum
temperature

Mean maximum
temperature

0.042***-0.017ns-0.046***-0.123***-0.047***--0.112***-

Mean monthly rainfall was positively and significantly correlated (P<0.001) with mean
monthly minimum and negatively and significantly correlated with mean monthly maximum
temperature (P<0.001). The hottest months were also the coldest as indicated by the negative andsignificant correlation (P<0.001) maximum and minimum temperatures. This means that the study
area has a wide diurnal temperature range.

1st African Conference on Precision Agriculture | 8-10 December | 2020

DISCUSSION
Monthly milk yield was positively associated with rainfall as shown by the positive
correlation and regression coefficient. Many studies have reported a significant effect of rainfall
on milk yield (Msechu et al. 1995). The effect of climate change is complex. However, studies
have reported either upward or downward trend in monthly or seasonal rainfall (Rustum et al.
2017; Msechu et al. 1995). For the NSS at Naivasha Kenya, monthly rainfall increased leading toa concomitant increase in milk yield. The regression coefficient of monthly milk yield on rainfall[0.07±0.02kg/mm] reflects the effect of rainfall on pasture growth feed availability, palatability
and nutritive value. The time lag from onset of rains to the maximal response in pasture value wasalso displayed in the current study. However, shorter periods may have been more sensitive inmeasuring this time lag.
The partial regression coefficients of milk yield on temperature indicate the importance of
ambient temperature on the welfare of animals. The loss of 1.58±0.57 and 1.17±0.32 kg milk yieldfor every 1oC increase in minimum and maximum temperature is related to the negative effect ofhigh ambient temperatures on animal behavior and physiological responses of animals. As ambient
temperatures increase metabolic heat production increases (Rhoads et al. 2013) as animals respondby altering their behavior and physiological processes. The changes include changes in feeding
and water seeking behavior, increase in respiration rate, heart rate and rectal temperature (BrownBrandl et al. 2005). The consequence of the behavioral and physiological changes is often reduced
milk yield and growth (Nardone et al. 2010; Berman 2005). The widest monthly temperature rangeof the study site of about 17.9oC occurred in January, February, March, and September toDecember, which were also associated with significantly lower milk production. As a consequenceof climate change, a number of studies have reported significant increase in mean and minimum
average temperature (Asfaw et al.; 2018; Javari 2017). This may explain the greater influence ofmean minimum temperature on milk yield found in the current study.
The results of the current study call for identification of mitigating strategies for pasturebased beef and milk production systems. Some of the strategies suggested include modificationsin the management systems, breeding strategies, policy changes and a change in farmer perception
and adaptive capacity to climate change (Rojas-Downing et al. 2017; IFAD 2010; USDA 2013).
Specifically the mitigation strategies will involve improvement of feeding strategies in terms ofmodifying diet composition, feeding time and frequency (Renaudeau et al. 2012), incorporation ofagroforestry to modify micro-climates in grazing lands (Thorton and Herrero 2010). However evenwhen farmers employ heat stress mitigation strategies, losses of more than 50% of production per
cow have been reported for dairy cattle (Lakew 2017). In most production systems, animals are
rarely exposed to a single environmental stressor. It is likely that the cows at the NSS were exposed
to more stressors than were captured in the current study. Other stressors may be include windspeed, poor nutrition, diseases, pests and humidity. Seijan et al (2013) reported that production
and reproduction was further compromised by poor nutrition, long distances to feeding areas water
sources. Although the Sahiwal cattle are reared within a demarcated area at the NSS, it is likelythat animals walk longer seeking feed and spend more time under shade during hot months, further
affecting production.

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1st African Conference on Precision Agriculture | 8-10 December | 2020

CONCLUSIONS
Climatic variables, minimum and maximum temperature and monthly rainfall significantly(P<0.05) affected monthly milk yield but for a small proportion of total variation (0.5%) throughsignificant. A 1oC increase in minimum and maximum temperature led to a 1.58 kg and 1.17 kg
decrease in monthly milk yield, respectively. A 1 mm increase in monthly rainfall of monthly
caused monthly milk yield to increase by 0.07kg. Mitigating strategies are required to alleviate thenegative effects of temperature on monthly milk yield. Sound grazing management and feed
conservation could harness the advantage of the positive effect of rainfall on milk yield.
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eport%20%2802-04-2013%29b.pdf.
Waghorn GC, Hegarty RS. 2011. Lowering ruminant methane emissions through improved feed
conversion efficiency. Ani. Feed Sci. Tech. 166-167: 291-301.

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1st African Conference on Precision Agriculture | 8-10 December | 2020

#7616 DEVELOPMENT OF LODGING DIRECTION DETERMINATION SYSTEMUSING IMAGE PROCESSING
Arai Y, Morimoto E, Nonami K, Ito T
Tottori University, Tottori Prefecture, Japan
m19j7146c@edu.tottori-u.ac.jp, +81-0857-31-5343
ABSTRACTIn this study, image processing system was developed for application on rice plants todetermine lodging condition, which was contributing factor to declining harvester efficiency byusing combine harvester. Therefore, we developed a system for determination of the lodging
direction by algorithm based on convolutional neural network (CNN). As for deep learning
framework, Pytorch1.1.0 were used to train and test the judging direction. GoogLeNet was used
as a pre-trained CNN model.
Lodging direction was defined as 3 classes in images (i.e. frontward, backward and theothers). Charge Coupled Device Camera (CCD camera) was installed in head feeding typecombine and image was captured in front of a divider (2-10 m). CCD camera’s height anddepression angle were set to 2.5 m and 39 degrees. Image size was 640 x 480 pixels, and thesampling rate was 30 Hz.
After acquisition of images, Region of Interest (ROI) were set to 250 x 250 pixels inimages. These images were cropped in ROI. The images that are contained the lodging rice inmore than half of ROI were selected manually. A total of 17,899 sample images were collected fortraining and validation. 4,997 images were used as training data and 12,902 images were used asvalidation data. As data augmentation, the training data was rotated 90°, 180° and 270°. Then,
training and validation data were labeled manually. 19,988 images were used for training data set
and 12,902 images were for validation, respectively.
The recall was applied for evaluation of the validation. As a result, the recall of frontward,
backward, and others were 89.0%, 97.9%, and 81.5%, respectively. Main reason of the error was
the situation where one image contains various lodging directions. However, total accuracy was
90.6%, so the result indicated that the developed system could be acceptable for determination
lodging direction on combine harvester.
INTRODUCTION
According to the Japanese Census of Agriculture and Forestry in 2015, the number ofgrowers with farmland area of 20 ha or more increased by 38.1 % to 3850 and it means that young
workers will manage a lot of farmland (Static Agriculture Census, 2015).
To solve these problems, the Ministry of Agriculture, Forestry and Fishery of Japan has
launched a plan to promote the development of robots for agricultural use. This also aims atincreasing agricultural productivity and improving self-sufficiency in Japan. Currently, manydifferent types of agricultural robots are under development in Japan, such as a robot wheel-type
tractor (Kise et al., 2002, Yang et al., 2012), a robot crawler-type tractor (Takai et al., 2010), a
robot rice transplant machine (Nagasaka et al., 2004) and a robot combine harvester (Iida et al.,2011). In all the research mentioned above, a real-time kinematic GPS receiver and an IMU/GPS

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1st African Conference on Precision Agriculture | 8-10 December | 2020

compass are required. In addition, to ensure the safety and precision while combine harvesters
operating autonomously, it is also important for combines to capable of identifying the surrounding
environment quickly and accurately. For example, there is a research to distinguish what are
harvested areas from what is unharvest areas using image processing (Yang et al., 2020).
Especially, it is important to estimate lodging in harvesting. The lodging area takes more time andeffort to harvest than the non-lodging area. The operator needs to adjust the cutting speed for
lodging degree and direction. Therefore, the automation of process for adjusting the cutting speed
is an important function for not only the development the of robotic combine harvester but also
the reduction of agricultural workload, as well as proper positioning, which is independent of theoperator’s skill and experience.
From the above, lodging condition was focused on as data that can be get at harvesting by
combine. Lodging is caused by excess of growth because of excess of fertilizer and causes to be
deteriorated grain’s quality, to generate disease and insect pest and to worsen with by efficiency
at harvesting (Ibaraki, 1967; Saito, 1991). Acquisition and accumulation of lodging condition isresult of cultivation management of the year and it can be used not only for sharing information
but also for reference for fertilization after the next year.
Many research studies on prediction of lodging have been studied (Fukuda et al., 1988;
Sato, 2002; Hama et al., 2016; Tanaka et al., 2016). As a method for determination of the lodging
level, Morimoto et al. (2020) judged the level by support vector machine by using image
processing with the CCD camera mounted on the combine. However, there is few reports on
method to visualize lodging direction during harvest.
The objectives of this research were 1) to collect sample image during harvest using
combine harvester, 2) to develop rice lodging direction detection algorithm.
MATERIALS AND METHODS
Method of Sample Image Acquisition and Definition of Region of Interest
In this research, the tablet (FZ-M1, Panasonic) and the head feeding type combine harvester(HJ6123GZCAPLW, Iseki) were used and CCD camera on tablet’s back collected images in frontof a divider. Also, camera’s height anddepression angle were set to 2.5 m and 39
degrees in 2018 (Figure 1). Image size was786,432 pixels and the sampling rate was 30
Hz. 171,877 images were collected.
Figure 2 shows data preparation. Afteracquisition of images, ROI (Region ofInterest) were set to 250 x 250 in images(Figure 3) and cropped in ROI.
Then, the images that are contained therice in more than half of ROI were selected.
Finally, images visually determined to belodging were selected as training andvalidating images. 4,997 images were selected
as training images and 12,902 images were Figure 1. Position of CCD camera installation
selected as validating images, respectively.

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Figure 3. Region of interest

Figure 2. Steps of the data preparation
Development of Lodging Direction Algorithm
Figure 4 shows steps of the proposed algorithm. Firstly, as data augmentation, rotating thetraining images 90°, 180° and 270°. Lodging direction was defined as 3 classes (Figure 5) in animage (frontward, backward and the others), and labeled manually for training and validating
images. As a result, 19,987 images were made as training set and 12,902 images were made as
validation set, respectively. Pytorch0.3 were used to train and test the judging lodging direction.
GoogLeNet was used as a deep learning model. Trained with the training set, and then validating
the model on the validation set, respectively.

Figure 4. Steps of the proposed algorithm
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1st African Conference on Precision Agriculture | 8-10 December | 2020

Model Training and Testing
Lodging direction was judged based on theliner Support Vector Machine leaned in advance.
reproducibility defined as follows was calculatedwith each lodging direction.

Figure 5. Definition of lodging direction
Equation 1.
RESULTS AND DISCUSSION
Processing Result by Real-time Lodging Analysis System
Real-time lodging direction analysis system was applied, and the reproducibility wascalculated. As the result, reproducibility of “frontward” was 97.9%, of “backward” was 81.5%,
and of “others” was 89.0%, respectively (Table 1), and showed that the system was usable as alodging analysis tool.
Table 1. Reproducibility of each lodging direction

Actual class

Prediction
Backward

Frontward

OthersBackward

4,156

19

72

4,247

97.9%

Frontward

310

5,673

385

6,368

89.1%

Others

281

143

1,863

2,287

81.5%

Total

4,747

5,835

2,320

12,902

90.6%

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Total Reproducibility1st African Conference on Precision Agriculture | 8-10 December | 2020

When there was a different direction of lodging in one
image, it is considered to have been the cause of erroneous
determination. Figure 6 is an image of “others”, but it was
misjudged as “frontward”. Especially, it is considered that thehigh reproducibility of backward was a good tendency, because
the head feeding type combine harvester may break whenharvesting in “backward”. In addition, the number of the
misjudgment of "backward" was more "others" than"Frontward". It is considered that the judgment on the borderline
between “backward” and “others” was ambiguous, so we aretrying to get more training data and to improve Figure 6. Image of “others”
reproducibility.
REFERENCES
Fukuda K, Kamada Y, Dakeishi S, 1988. Lodging diagnosis of rice plants by “Fieldtron”, Rep.
Tohoku Br., Crop Sci. Soc. Japan 31: 47-49.
Hama A, Hayazaki Y, Mochizuki A, Tsuruoka Y, Tanaka K, Kondoh A, 2016. Rice growthmonitoring using small UAV and SfM-MVS technique, J. Japan Soc. Hydrol. And Water
Resour, 29(1): 44-54.
Ibaraki T. 1967. A case study on disease injury that frequently occur in lodging rice (in Japanese),
Annual report of the Society of Plant Protection of North Japan, 1967(18): 53.
Iida M, Yamada Y. 2006. Rice harvesting operation using an autonomous combine with a GPS
and a FOG, Proceedings of the Conference of Automation Technology for Off-road
Equipment, ASAE, 125-131.
Matsui. Control of crawler tractor using GPS as navigation sensor, Master Thesis, 2007.
Morimoto E, Inagaki N, Ueta D, Takahashi K, Nonami K. 2020. Estimate of Rice Lodging Basedon Image Processing (Part 2) -Effect Verification of Convolutional Neural Network andLearning to Rank- Journal of JSAM, 82(2): 156-161.
Nagasaka Y, Umeda N, Kanetani Y, Taniwaki K, Sasaki Y, 2004. Autonomous guidance for ricetransplanting using global positioning and gyroscopes, Computers and Electronics inAgriculture 43: 223-234.
Kise M, Noguchi N, Ishii K, Terao H. 2002. Field mobile robot navigated by RTK-GPS and FOG(Part3), Journal of JSAM. 64(2): 102-110.
Saito Y. 1991. Fertilization and lodging (in Japanese), The Hokuriku Crop Science 26: 125-131.
Sato T. 2002. Studies on techniques for lodging prevention of rice variety “Koshihikari” based on
growth diagnosis, The Hokuriku Crop Science 37: 4-9.
Static Agriculture Census. 2015. Ministry of Agriculture and Forestry and Fisheries.
http://www.maff.go.jp/j/tokei/sihyo/data/08.html#1. Accessed Aug. 17, 2017.
Takai R, Barawid O. Jr., Ishii K, Noguchi N. 2010. Development of Crawler-Type Robot Tractor
based on GPS and IMU, Preprint of the IFAC International Conference on
AGRICONTROL 2010 (CD-R), A3-5.
Tanaka K, Kondoh A. 2016. Mapping of rice growth using low altitude remote sensing by
multicopter, Journal of The Remote Sensing Society of Japan 36(4): 373-387.
Yang L, Noguchi N, Ishii K. 2012. Development of a Real Time Multi-lens-based Omnidirectional Stereo Vision, Automation Technology for Off-Road Equipment 35-40.

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1st African Conference on Precision Agriculture | 8-10 December | 2020

Yang L, Iida M, Suguri M, Masuda R. 2020. Regional segmentation of Field Image Based on
Convolutional Neural Network for Rice Combine Harvester, J. of JSAM. 82(1): 47-56.

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1st African Conference on Precision Agriculture | 8-10 December | 2020

#7666 DETERMINANTS OF THE ADOPTION OF AN INTELLIGENT MONITORINGSYSTEM AND EFFECTS ON FARMS PERFORMANCE IN TUNISIA
J. Ben Nasr, H. Chiboub and M. Msaddak
National Agronomic Institute, Tunis, Tunisia
jamelnasr@yahoo.fr +216 96570453
ABSTRACTTechnology has become essential to the farm organization not only for its effectiveness,but also for its strategic development within its competitive environment. The aim of this study is
to assess the extent to which farmers complied with the aspects of precision agriculture fortransforming agricultural extension in developing countries using the latest available technologies
and research methods.
Considering the observation of the rise of Computerized Management Software Packages
and intelligent monitoring in a path of modernization of agricultural techniques, we questioned themain factors influencing the decision to adopt software management, farmers’ perception of thesemanagement tools and their impact on farm performance. Referring to a theoretical framework
focused on innovation and technologies adoption in agriculture, we built the hypothesis of ourresearch: Changes in farming practices, such as the installation of management software, are theresult of a process of innovation. This process begins with a technical proposal, which is graduallyadopted by farmers based on several factors. On the basis of a sample of 50 agricultural specialized
in crop production, with an area of 40 ha or more and spread over the two areas: Grand Tunis andZaghouan governorates, an econometric analysis (Logit model) has been carried to test thishypothesis and to identify the decisions of adoption factors of the technology management; in ourcase the software AgriManager produced by the company Ezzayra.
Initially, through a bivariate analysis, it was possible to remove the variables mostinfluencing the choice of operators to adopt computerized management software. Secondly, toovercome the constraint of the model degree of freedom, an analysis in main components identified
the main components containing the most significant explanatory variables. These were introduced
into the estimated Logit model, which allows several correlations to be drawn between the selectedvariables and farmers' decision to adopt this innovative technique. These are the variables relating
to the characteristics of the holding (total area, crop types, etc. ), of the operator (their age, levelofeducationand
theirformation)
and
their
relational
networks.
Finally, an analysis of farmers' perceptions of such software has made it possible to identify the
expected effects of its adoption in terms of productivity gains, stock management, improving the
profitability and efficiency of the exploitation of scarce resources. The partial cost method and thecomparison of the situation before and after software are used to confirm the positive effect ofintelligent monitoring methods on the economic performance of the farms using this management
method.

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INTRODUCTION
Technology has become essential to the farm organization not only for its effectiveness,but also for its strategic development within its competitive environment (Bucci et al, 2019; Akullo
et al., 2018; Kalirajan and Shand, 2001).
Our research purpose is to contribute to a better understanding of the adoption of newtechnologies by farms. This study determines technology adoption factors and their consequences
for the implementation of management software within large farms to various types of speculation,
particularly arboriculture, market gardening and field crops. In this paper, we answer the following
two research questions: What are the main factors that influence farmers decision about adopting
a new technology for managing agricultural businesses? What changes have been observed infarms that have chosen to use this practice? On the basis of a sample of 50 farmers belonging to
the governorates of Manouba, Ben Arous and Zaghouan chosen as study areas, statistical andeconometric analyses were carried out in order to identify the determinants of decisions to adopt a
new management technology by farms.
MATERIALS AND METHODS
Modelling the Decision to Adopt New Management Technology: Logit model
Logit models are built on the assumption of cumulative logistic distributions allowing anadequate treatment of outliers due to their set ends unlike Probit models (Hurlin, 2003).
We consider a sample of N farms indexed i = 1, .., N. For each farm, we observe whethera certain event has occurred, and we denote yi the coded variable associated with the event.
pi = Prob (yi = 1| Xi) = F (α )where the function F (.) denotes the distribution function. The choice of the distribution function
F (.) is a priori unconstrained for i of the interval [1, N] we have the following form:
Yi =1 if the event has occurred: farm i adopts agricultural management software
0 if the event did not happen for farm i

The decision of adoption a new technology by an operator depends on several factors which
are social, economic, related to the environment, climatic, related to the external market, and / orrelated to the internal characteristics of the operation. The operator's behavior following the choiceof technology adoption is correlated with several explanatory variables Xi which will be takenfrom the principal component analysis.
The variables collected from the database collected are: age, education level, agriculturaltraining, exploitation area, olive groves, type of culture: market gardening and arboriculture or
field crops, irrigation, difficulties in recruiting labor, exportation, information about management
software and sources of this information.
The number of explanatory variables is equal to 17 and the number of observations is 50.
The degree of freedom is dof = 50-17 + 1= 32. This low degree of freedom does not permit a good
estimate of the logit model. Hence, we carry out a principal component analysis to reduce thenumber of variables without affecting the quality of the information.

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1st African Conference on Precision Agriculture | 8-10 December | 2020

Data
Data for farmers are generally unavailable or not up to date. In addition, there is a lack of
information about the management systems adopted by the farms and the conditions affecting thedecision to change agricultural technics. The field study is essential to succeed in filling this gap
and producing the needed data for our research. The conduct of surveys covering technical, socialand economic aspects among operators allows the creation of a database necessary for the analysisof their situation, their characteristics, their responses and attitudes towards the adoption of new
management technologies. The sample is made up of 50 farmers, spread over 2 zones, Grand Tunis(Ben Arous and Mannouba) and Zaghouan. The minimum area is 40ha. The cultures adopted arewidely varied: arboriculture, market gardening and field crops. Among the 50 farms visited, only
22 use software as a means of management, which corresponds to 44% of the sample studied.
RESULTS AND DISCUSSION
The Adoption of New Management Technology in Agriculture Factors - Principal
Component Analysis
This analysis gives 15 components. The first 6 components explain 74% of theinformation. However only 4 first components are significant (Table 1) and will be taken in the
model
Table 1. Component matrix
Component Coefficients
X1 : Age
X2 : Education levelX3 : Agricultural training
X4 : Exploitation area
X5 : Olives
X6 : Market gardening and arboriculture
X7 : Field crops
X8 : Irrigation
X9 : Difficulties in recruiting labor
X10 : Exportation
X11 : Information about management
software
X12 : source 1
X13 : source 2
X14 : source 3
X15 : source 4
Sig of components(* ) : significant

-,466
-,029
,136
,156
,654
,859
-,713
,888
-,101
,278

,253
-,686
-,180
-,236
,322
,078
,261
,098
,202
-,284

,107
-,145
-,068
,950
,016
-,133
,178
-,151
,013
,906

-,275
,440
,281
-,033
-,011
-,269
,133
-,231
-,436
-,089

,275
,252
,595
,053
,026
,170
,203
,120
,392
,005

,185
-,048
,357
,020
,391
,030
,298
-,034
,254
-,029

,335

,382

,107

,610

-,193

-,225

,029
,054
-,203
,160
(* )
0,003

,573 ,165,234 ,030,676 ,190,378 ,113(*)(*)
0,024 0,013,384 ,295,197 ,675-,305 -,128,521 -,288(*)(**)
0,097 0,561

-,196
-,434
-,248
,514
0,361

(** ) : not significant

Each principal component group set of explanatory variables such as:
C 1: age, olive tree plantations, market gardening and arboriculture, field crops and irrigation.
C 2: level of education and sources of information 1 and 3.

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1st African Conference on Precision Agriculture | 8-10 December | 2020

C 3: the total area of exploitation and export.
C 4: difficulties in recruiting labour, information on management software and source of
information.
Logistic Regression: Logit Model
The overall significance test gives a result <5% which reveals the high significance of themodel. The maximum likelihood test gives a result equal to 24.6 which complies with theacceptance standards of the model.
Table 2. Model estimation result
Explanatory variable
Constant
Coefficient
6.663 -4.092 19.343 1.403
2.041
The results of the regression test show that:• - increases with: The young age, the increase in the areas planted with olive trees, market
gardening and arboriculture, the reduction in the area of the farm and irrigation.
• Given the positive correlation proved in the model, this induces an increase in Y whichtends towards 1 signifying a choice for the adoption of management software.
• - decreases with: The increase in the level of education, The non-use of media andcollective interest groups as sources of information. This leads Y to tend towards 1, which
support the choice of adopting management technology.
• - increase in line with Y: The increase in the total area of the farm concerned andexportation.
• - also increases: Have ease of recruiting labor, Be informed about management software,and the influence of neighbouring farmers.
Effect of Adoption on Performance
Overhead costs were not recorded in the operating account, which leads to erroneous
results in the calculation of gross operating income, in cost control and in generating the need forfinancial resources. The figure shows the share of each type of expense in the total cost recognized.

Structural Cost
Operating cost
Non-operating
cost
Figure 1. The part of each type of load in the total cost25

1st African Conference on Precision Agriculture | 8-10 December | 2020

It emerges that by changing the management mode, the farm has managed to reduce theshare of its expenses allocated to the consumption of raw materials, the purchase of inputs,
transport, and energy costs as well as personnel costs. This mainly comes down considering
structural loads.
The personnel costs for the year 2015 were only 2168 TND. Correcting the above costs isessential to be able to compare between the two studied and to take inflation into account.
Each cost will be divided by the GDP deflator for the corresponding year. The GDP deflators forthe years 2015 and 2018 have the respective values 3.4 and 5.6.
•
•
•

•

Results show that operating expenses as well as some production expenses clearlydecreased from 2015 to 2018. The main findings are as follows:
The increase of workers costs with 33% due to the inclusion of labor compensation forpermanent and occasional workers.
The decrease in the costs of delegated work with 9%. The value of this charge is around
5,000 TND (actual cost). This reveals the decrease in subcontracting due to the lack ofworkforce resources for carrying out daily or exceptional work such as repairing
equipment. There is therefore a better allocation of this resource as well as an optimizationof the execution of work orders.
The other consumption charges decrease with 16% due to the savings in mechanicaltraction energy and electrical energy used for carrying out certain production work.
REFERENCES

Akullo D, Maat H, Wals AEJ. 2018. An institutional diagnostics of agricultural innovation; publicprivate partnerships and smallholder production in Uganda, NJAS - Wageningen J. Life
Sciences 84: 6-12, ISSN 1573-5214, https://doi.org/10.1016/j.njas.2017.10.006.
Bucci G, Bentivoglio D, Finco A. 2019. Factors affecting ICT adoption in Agriculture: a casestudy in Italy. Quality - Access to Success 20 : 122-129.
Hurlin C. 2003. Econométrie des Variables Qualitatives. Polycopié de Cours Maîtrised’Econométrie, Université d’Orléans
Kalirajan K. Shand R. 2001, Technology and farm performance: paths of productive efficiencies
over time. Agricultural Economics, 24: 297-306. https://doi.org/10.1111/j.15740862.2001.tb00031.x

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1st African Conference on Precision Agriculture | 8-10 December | 2020

#7773 THE STATUS OF PRECISION AGRICULTURE AND ITS ADOPTION IN
MOROCCORachid Bouabid1, Hakim Boulal2 and Steve Phillips2
1
National School of Agriculture of Meknes; 2African Plant Nutrition Institute,rbouabid@enameknes.ac.ma
ABSTRACTIn Morocco, agriculture represents a major economic sector and has known importantchanges in the last two decades. Nevertheless, despite the many efforts to improve the agriculturalenvironment and boost productivity, precision agriculture (PA) practices are still in theirembryonic stage. Factors affecting PA onset and adoption are multiple, among which (i) the
dominance of smallholder farming and the structure of the farms, (ii) the level of farmer’s
education, perception and ability to use new technologies, (iii) the affordability of relevant
information and specific equipment required, (iv) the lack of training and extension regarding PA,(v) the limited number of PA service providers and their high service cost, (vi) government
restrictions on the use of drones for PA purposes, (vii) limited support in previous government
strategies for hard PA technologies. PA in Morocco did not reach yet to the level of “adoption”,
but still at the level of “presence”. Most farmers are not well informed about PA and its benefits.
Few tentatives of soft PA have been initiated in the past but did not attain satisfactory momentum.
Soil testing, as a basic classical example for soil fertility assessment and nutrient management,
was promoted and subsidised for a long time, but is remain limitedly adopted by farmers on a large
scale. Precision irrigation, a vertical approach in precision farming, is an exception that has known
particular expansion in Morocco as a result of the efforts by the government for promoting drip
irrigation with regard to water scarcity, yet meriting innovative and adapted technologies,
especially for small farmers. Sensing-based assessment of soil properties and crop monitoring(drone carried sensors, soil scanners) were recently introduced, but are still in a trial process andtheir cost is very discouraging, even for large commercial farms. Free online platforms providing
vegetation indices are just being discovered and explored by few curious farmers and used toexperiment making adjusted nutrient rates (even without adapted equipment). The implementationof digital technologies by the sugar industry for affiliated farmers for sowing, input use, yieldmonitoring and traceability is a recent success story that merits to be extrapolated to otherindustrial crops. The present paper raises questions about the factors that delayed the presence andadoption of PA in Morocco and provides insights of its future development.
Keywords: precision agriculture, adoption, developing countries, Morocco
INTRODUCTION
PA, as an integrated crop management system that uses various tools and technologies forassessing and monitoring soil and crop spatial variability and for implementing site-specificapplications, is considered nowadays in many developed countries a common practice rather thanan innovation. On the contrary, in most developing countries, agriculture is still struggling with27

1st African Conference on Precision Agriculture | 8-10 December | 2020

the basics of farming, and is constrained by many factors, such as land tenure and farm structure,
low inputs use, limited farmer’s knowledge, weak extension service and undeveloped markets.
Agriculture, like other industries, is evolving towards a knowledge-based direction. Its
development and competitiveness will be highly driven by new technologies. In most developing
countries, increasing productivity on small-scale farms is of priority concern and is critical forfood security. However, the delay in the introduction, promotion and adoption of new technologies
will enlarge the time gap and leave these countries way behind. PA has known a good emergence
and a rapid spreading in the USA since late 80s, and has also known in recent years a good takeoff in several other countries in Europe, Latin America, China and Australia (Tey and Briandal;
2012; Silva et al, 2015; Kendal et al. 2017, Lowenberg-DeBoer and Erickson, 2019). However, in
Morocco, as in similar developing countries, PA is still poorly known, if not unknown at all, among
farmers and even among a large community of professionals and decision makers.
PA in Morocco
Morocco is a country of which the economy is tightly related to agriculture. Although small
holder farms are dominant (about 65%), medium and large size farms play an important role indriving the changes in farming systems and in adopting new technologies. Past government
agricultural strategies focused on various segments such as crop diversification, improved input
use, adoption of machinery, water management (large infrastructures and in-farm systems), accessto financing, etc. Conversion to drip irrigation was among the main targets, especially with regard
to water scarcity. Although the ‘Green Morocco Plan’ included multiple actions to boost Moroccan
agriculture, the promotion of new technologies, such as PA, was timid. The new ‘Green Generation
Strategy 2020-2030’ explicitly addressed such a dimension and is intended to give particularattention in the future to PA and other digital technologies.
To some extent, small farmers have been practicing insentiently various forms of soft PA
techniques for a long time. Varying seed and fertilizer rates on slope lands, adjusting manure
applications to poor field areas, adapting irrigation water to soil texture, etc. are examples of suchuse. However, these practices are not to the level of technology and advancement to catch-up withthe rapid development expected for a modern agriculture.
Soil testing can help implement appropriate site-specific fertilizer applications if soil
sampling takes into consideration within-field zoning and among-field differences. Soil testing has
been subsidized (50%) and promoted since early 90s. However, its adoption by farmers remained
very limited, until recent years. Despite the overlooking of infield spatial variability required forPA, soil testing remains a good tool for optimizing fertilizer use and increasing productivity.
PA is applied to some extent in high-value crops such as orchards and vignards. Farms
with several fields are managing fertilizers and irrigation according soil heterogeneity, varieties,
age, expected yield, etc. Within field variability is sometimes taken into consideration and sitespecific applications are implemented with available means. Examples include field patches withhigh pH and high lime causing micronutrient deficiencies, for which site-specific soil applicationor foliar sprays are used. Segmenting fields for drip irrigation allows variable rate (VR) irrigation(and fertigation) according to crop requirement at various growth stages. This is often automatedas the cost of irrigation packages are becoming affordable with government incentives. A
smartphone application (IrriSmartOne) was developed recently by a team from the National
School of Agriculture to help farmers better manage irrigation water and engage widely in thepractice of precision irrigation.

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1st African Conference on Precision Agriculture | 8-10 December | 2020

At a large-scale mapping, soil fertility assessment was recently implemented over 7 million
ha of arable land by a joint public-private partnership. This project enabled developing a countrywide reference soil fertility database, generating spatial variability maps of selected soil
parameters, and developing an online DSS platform (fertimap.ma) to help farmers andextensionists make sound fertilizer recommendations for specific crops. Although this project isconsidered a valuable tool, the coarse resolution of the data does not reflect in-field spatial
variability which is the foundation for PA.
In Morocco, PA application in its technological concept is still at an embryonic stage. It is
not yet to the level of “adoption”, but still to the level of “presence”. Most farmers are not wellinformed about it and about its benefits. Few tentatives of soft PA have been initiated in the past
but did not reach satisfactory momentum.
The main manufacturer of white sugar in Morocco (Cosumar) engaged in a farmer
aggregation process to provide technical and financial support and guaranty the absorption of
production. Since 2019, they digitally registered about 80000 farmers to undergo real time
monitoring of agricultural practices (sowing, input use, irrigation, etc.), crop growth and yield, as
well as the planning of harvest and flow to sugar factories. Soil testing is performed for all farmers
and adapted fertilizer recommendations are provided. PA in this example is considering variability
among clusters of farmers rather variability within-field.
Sensing based proximal tools using different technologies (contact resistivity, infrared
reflectance, passive gamma-ray spectroscopy, etc.) were recently introduced by a couple PAservice providers and are in the phase of trials and demonstration in few large commercial farms.
These sensors (if well calibrated using soil testing data) have the advantage of providing rapid,
direct or indirect, assessment of some soil properties and delivering spatial variability maps forquick PA use. However, their adoption has been delayed by several factors, mainly, their high cost,
the required knowledge for information handling, and the unavailability of variable-rateequipment to implement site-specific nutrient management. Al-Moutmir program of the OCP
group recently introduced the SoilOptix tool and is conducting multiple trials in small farm todemonstrate the use of such technology for fertilizer management.
Remote sensing information from satellites or drone-carried sensors, are getting popularamong farmers. Free online platforms providing vegetation indices (VI) are being explored by few
curious farmers to experiment crop monitoring and make adjusted nutrient rates. The example ofthe ‘Onesoil’ platform offers reasonable spatial (10m) and temporal (5 days) resolutions NDVIs
and generates map files that can be used for VR applications. However, to our knowledge, only
two farms in Morocco have GPS guided tractors and VR equipment, and therefore, the use of suchplatforms remains limited to an overall crop monitoring. Drone flying is subject to very strict
licensing in Morocco and therefore remains of limited use.
Although yield mapping using yield monitors have been increasingly used in several
developing countries (South Africa, Turkey and Mexico), this practice is still absent in Morocco
from both machinery and farmers perspectives.
Factors Affecting PA Onset and Adoption in Morocco
The attitude of farmers towards any new technology is strongly driven by the benefits that
this technology can provide, mainly increasing profitability with reasonable investments(financial, technical, time & efforts). The net return of any PA technique should offset the costs ofdedicated/necessary equipment and software, information and data processing, knowledge
upgrade, trained personnel, related services, etc. The question often postulated: ‘Is it worth the29

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hassle’. The logic in this process will differ among farmers, depending on various factors
especially linked to their socio-economic and cultural context.
A rapid and unstructured survey with a short list of farmers and professionals revealed that themain factors behind the limited onset and adoption of PA, in its technological concept, are thefollowing:• Lack of awareness and information: at the level of the farmers, professionals andextensionists• Perception: PA has been perceived as a practice that requires highly advanced knowhow,
hard technologies and heavy investments• Farm structure: small size and fragmentation of farms (fields) are not in favour of in-field
variability-based PA• Level of education: PA requires a minimum level of training and ability to use computers,
process and manage data, understand and manipulate RS information, use smart-apps, etc• Cost of equipment: packages required for PA (data, equipment, software, etc) are still
expensive even for commercial farms. Soft tools are still not available on the market withreasonable prices• Cost of PA services: only a few service providers are present and their services are
expensive and not economically justified• Constraints on the use of flying objects (drones): the use of drones is subject to severe
restriction for use by farmers and service providers• Lack of trained personnel: limited number of PA specialists• Limited government support: PA has not received much attention in previous Ag-strategies
The Future of PA in Morocco
Various soft PATs have been used by small farmers in many developing countries andshowed good promise even in traditional cropping systems (Cook et al., 2003, Mondal et Basu,
2009; Lowenberg-DeBoer and Erickson, 2019). The costs and benefits of using PATs are very
complex (Thompson et al. 2018) and would require time to be demonstrated.
PA in Morocco can be promoted and implemented at various scales (spatial, temporal,
equipment, etc.) by targeting key agricultural practices, low-cost tools, adapted decision support
systems (DSS), extension, data availability, incentives (subsidies) for equipment acquisition, etc.
Future development of PA and its adoption in Morocco needs to take into consideration
the local context, mainly farm size and fragmentation, level of education, limited financial means
to invest in equipment and other related tools. Farms of less than 20 ha represent about 65% ofarable land (20-100 ha: 25% and >100 ha: 10% only). In some regions with a high agriculturalpotential, PATs would be much easily perceived, implemented and more likely adopted.
PA actions need to be adapted to the various categories of farm holders (Table 1). The
spontaneous initiatives need to be capitalized and used to bring new followers. Simple tools (leafcolour charts, handheld sensors, etc.) adopted in countries such as India and South Africa can betested to help grasp the concept of in-field variability and the practice of site-specific interventions.

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Table 1. Adapted PA technologies to different farmer categories
Small farms(soft PA)

•

•
•
•

Medium and largecommercial farms(soft and hard PA)

•
•
•
•
•

Simple soil and crop tests, free available remote sensing information,in situ observations field mapping-, low-cost tools and low cost
adapted equipment for GNSS and VR applications;
Easy-to-use smartphone apps and DSS;
Farmer clustering (aggregation, cooperatives, etc.);
Incentives (subsidies) for equipment and related software
Soil fertility mapping (intensive soil testing, proximal soil scanners,
etc)
Remote sensing (free or via service provider)
Yield monitoring system,
GNSS and VRA equipment,
Advanced smartphone apps and DSS

Field demonstrations and pilot projects (by the extension services or business companies)
can play a major accompanying role to show the usefulness and the added-value of PA, train
leading farmers as early adopters, and expose other potential followers. To train specialists in PA
and related information technologies, academic institutions need to update and tailor their curricula
to offer adapted PA programs. Continuing education programs need to be developed for
professionals willing to invest in PA tools and services. Relevant information on PATs should bemade available to farmers and professionals through appropriate and operative
channels(extension programs, social media, agri-fairs, media, etc). The international agricultural Fair of
Meknes, visited annually by thousands of farmers and professionals, can serve as a platform forpromoting PATs with the effective presence of the industry operating in this segment.
Despite the actual constraints, the future developments of PA technologies and equipment
and their affordability, the availability of open-source relevant information, the competitivity
among service providers, and the fostering of digital technologies are all factors in favour of better
prospects for PA in Morocco. Internet development and the wide use of smartphones by farmers
and extension agents would greatly help raising the awareness towards PA. The degree of future
adoption will depend on several factors:• Government support (including subsidies) and promotion of PA (concrete actions of the‘Green Generation’ strategy)• Change of perceptions for PA• Availability and/or design of adapted strategies• Market availability and affordability of technologies and services• Increased presence of specialized service providers• Farmer’s organisation• Training, extension and advisory for PA use• Creation of business opportunities for PA technologies and services.
• Supporting research and development to develop adapted PATs and assess their feasibility
and benefits.

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The curve of PA adoption can be timely compressed to reduce the gap of PA development
and engage in a rapid adoption with context-fitted solutions.
REFERENCES
Cook SE, O’Brien R, Corner RJ, et al. 2003. Is precision agriculture relevant to developing
countries. Proceedings of the 4th European conference on precision agriculture, vol. 115–
119. The Netherlands: Wageningen Academic Publishers.
Kendall H, Naughton P, Clark B, Taylor J, Li Z, Zhao C, Yang G, et al. 2017. Precision agriculture
in China: Exploring awareness, understanding, attitudes and perceptions of agriculturalexperts and end-users in China. Adv. Anim. Biosci. 8(2): 703–707.
Lowenberg-DeBoer J, Erickson B. 2019. Setting the Record Straight on Precision Agriculture
Adoption. Agronomy Journal 111(4): 1552-1569.
Mondal P, Basu, M. 2009. Adoption of precision agriculture technologies in India and in somedeveloping countries: Scope, present status and strategies. Progress in Natural Science 19:659–666.
Silva Antolini L, Scare RF, Dias A. 2015. Adoption of precision agriculture technologies by
farmers: A systematic literature review and proposition of an integrated conceptual
framework. International Food and Agribusiness Management Association Conference. St.
Paul,
MN,
14–17
June
2015
https://www.ifama.org/resources/files/2015Conference/1259_paper_Antonlini _precision.pdf (Accessed November 2020).
Tey YS, Brindal M. 2012. Factors influencing the adoption of precision agricultural technologies:
A review for policy implications. Prec. Agric. 13: 713–730.
Thompson N, Bir C, Widmar D, Mintert J. 2018. Farmer perceptions of precision agriculture
benefits. J. Agric. Appl. Econ. 51: 142–163. doi:10.1017/aae.2018.27

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1st African Conference on Precision Agriculture | 8-10 December | 2020

#7893 THE ROLES OF KEY PUBLIC SERVICES ON THE ADOPTION OF CLIMATESMART AGRICULTURAL TECHNOLOGIES IN COFFEE-BASED FARMINGSYSTEM OF ETHIOPIASamuel Diro and Agajie Tesfaye
Ethiopian Institute of Agricultural Research; Holeta, Ethiopia
samueldiro85@gmail.com /+251920533748
ABSTRACTThis study explored the adoption status of Climate Smart Agricultural (CSA) practices andfactors that influenced their adoption including the key public services (education, extension, andcommunication devices). The study used quantitative primary data collected from smallholder
farmers from major coffee-growing regions of the country: Oromia and SNNP. A multivariate
probit (MVP) model was used to study factors that influenced the adoption of climate-smart
agricultural technologies namely manure application, minimum tillage, intercropping, use of
improved forage, and physical soil and water management practices. The effect of the educationlevel of the household head, farmers' extension access, and the role of communication technologies
in technology adoption was also observed. The study result showed that 35% of the farmers appliedmanure on their farm plots. Application of minimum tillage was also experienced by 36% of the
farmers. Intercropping, improved forages and, physical soil and water management structures wereadopted by 45, 19, and 47% of the farmers, respectively. The findings of the study also revealed
the positive and significant effect of education, extension (access to extension services andparticipation on field days), and ownership of communication devices specifically radio on the
adoption of climate-smart agricultural practices. However, more than half of the farming
community has not yet adopted improved technologies and practices of these factors. Policymakers
and public authorities must pay due attention to problems affecting effective extension service
provision. The positive effect of radio ownership on technology adoption also suggests the needfor launching more rural FM radio channels to farmers which focus on the provision of agricultural
information and knowledge.
Keywords: climate-smart, extension, forage, intercropping, manure, and multivariate
INTRODUCTION
Adoption starts from knowing the existence of any improved technologies or practices.
Thus, awareness is the main factor that affects the adoption of agricultural technologies. Next toawareness, farmers analyze to try or wait for the use of technologies which is highly affected by
the education level of the household. However, the role of education, extension, andcommunication devices such as radio is neglected in different adoption studies. This study stresses
to show the effects of these variables on the adoption of different climate-smart agricultural
practices. The purpose of the study was to investigate the role of education, extension, andcommunication on the adoption of climate-smart agricultural technologies in the coffee-based
farming system of Ethiopia. Moreover, the study was anticipated to explore demographic, socioeconomic, and institutional factors affecting the adoption of climate-smart agricultural

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1st African Conference on Precision Agriculture | 8-10 December | 2020

technologies, study climate-smart agricultural technologies' adoption status and analyze theinterrelationship between the adoptions of different climate-smart agricultural technologies.
MATERIALS AND METHODS
Study Area and the Data
The study used quantitative primary data collected from smallholder farmers from majorcoffee-growing regions of the country: Oromia and SNNP. Gedeo, Sidama, Kafa, and Sheka zones
from the SNNP region and Ilubabor, Jimma, West Wollega, and Kellem Wollega zones from theOromia region were coffee-producing zones selected for the study.
Sampling and Data Collection
A multistage sampling technique was employed to select the population for the study
which involved both purposive and random sampling techniques. A total of 953 sample households
were selected for the study (584 from SNNP and 369 from Oromia Regions). Data was collectedfrom the sampled households through a structured questionnaire administered.
Data Analysis
Descriptive statistics were used to describe the collected and cleaned data. A multivariate
probit (MVP) model was also used to factors that influenced the adoption of climate-smart
agricultural technologies (Minimum Tillage, Manure Application, and Physical Soil and Water
Management Practices). Farmers adopt a mix of technologies to enhance declining soil fertility
and mitigate climate change. This implies that the adoption decision is inherently multivariate, andattempting univariate modeling would exclude useful economic information about interdependentand simultaneous adoption decisions (Dorfman, 1996). Ignoring these interdependencies can leadto inconsistent policy recommendations (Marenya and Barrett, 2007). Thus, the use of amultivariate probit model is vital.
RESULTS AND DISCUSSION
Farm Household Characteristic
The descriptive result of the study showed of the total sample, 61% of farmers fall under
SNNP and the rest 39% was from the Oromia region. About 90% of farmers were male-headedhouseholds. Farmers' mean age was 42.6 years with a minimum of 21 and a maximum of 90 years.
The average family size of the respondents was 6.3 families. The minimum education level of thehousehold head was 0 and the maximum was 12 years with a mean of 4.8 years. The mean distancefrom homestead to farm plots was 2.6 km with a maximum of 11 km. About 54 and 37% of farmers
have access to radio and credit, respectively. More than 85% of farmers have also access to theextension of soil and water management which is really better despite the quality of the services.
However, only 24% of farmers have participated in field days. About 25% of farmers also
participated in off-farm income-generating activities. The mean total land in the study area was
1.8 hectares. Study area farmers also have on average 4.2 tropical livestock units (TLU). Coffee isthe main cash crop in the study area. The study result showed 60% of farmers have adopted
improved coffee varieties.

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Percentage of farmers (%)

Adoption Patterns of Climate-smart Agricultural Practices
The study result showed that 34 and 37% of farmers in the SNNP and Oromia regions use
manure on their farmland, respectively. The use of minimum tillage was higher at SNNP than in
the Oromia region with an overall mean of 36%. About 20 and 61% of farmers in the Oromia and
SNNP regions used intercropping, respectively (mean = 45%). Only 9% of farmers in Oromia and
25% in SNNP regions used improved forage. The result also exhibited that 49% of respondents in
the Oromia region use physical soil conservation structures which are higher as compared to the
SNNP region (44%). On average, 47% of respondents used soil and water conservation structureson their land along the study regions (Figure 1).
70
60
50
40
30
20
10
0
Oromia
SNNP
Overall

ManureMinimum tillage

Intercropping

Improved forage

37
34
35

24
44
36

20
61
45

9
25
19

OromiaSNNP

PSW
conservation
49
44
47

OverallFigure 1. Adoption status of climate-smart agricultural technologies
Econometric Result
The pairwise coefficients of intercropping and manure application, use of improved forageand manure application, physical soil and water conservation and manure application, physicalsoil and water conservation and minimum tillage, use of improved forage and intercropping,
physical soil and water conservation and intercropping and physical soil and water conservationand the use of improved forage are revealed to be positively and significantly correlated indicatingcomplementarity among the paired practices. However, the pairwise coefficient of minimum
tillage and manure application was negative and significant which implies the substitutability of
the improved paired practices.
Factors Affecting Manure Application
A positive and significant relation was seen between household head age and theapplication of manure. The result is consistent with Ketema and Bauer, (2011). Family size
negatively and significantly affects the use of manure. The result is contrary to Tao et al., (2014)
who found a positive relationship between family size and manure application. Distance fromhomestead to farm plots also affects the use of manure negatively and significantly due to the bulky
nature of manure which hampers the transportation of manure to the further plots. The result is inline with Mesfin et al., (2016). Access to natural resource management extension affects theapplication of manure positively and significantly. The result is in line with Makokha et al., (2001)
and Abebe and Debebe, (2019). Likewise, participation in field days affects the application of35

1st African Conference on Precision Agriculture | 8-10 December | 2020

manure positively and significantly. TLU or livestock ownership affects the use of manure
positively and significantly. The result corroborates with Mesfin et al., (2016). The result also
showed a positive and significant relationship between manure application and participation in offfarm income generation activities. The result contrasts with Makokha et al., (2001) where anegative relationship between manure application and participation in off-farm income-generating
activities was found.
Factors Affecting the Use of Minimum Tillage
The age of the household head affects the adoption of minimum tillage negatively. The
finding is in line with Ketema and Bauer (2012) and Prakash et al., (2018) and contrasts with
Grabowski et al., (2014). The relationship between minimum tillage adoption and mean distanceto farm plots was also positive which corroborates with the finding of Zulu-Mbata et al., (2016).
The education of the household head affects the adoption of minimum tillage positively andsignificantly. The result agrees with Ketema and Bauer (2012), Grabowski et al., (2014), andPrakash et al., (2018). Radio ownership of the household head affects the adoption of minimum
tillage positively and significantly as information from radio enhances the adoption of improved
technologies. Besides, farmers who have access to natural resource management extension
services are more likely to use minimum tillage. The result is in line with Ketema and Bauer,(2012), Marenya et al., (2017), and Prakash et al., (2018). Participation in field days also affects
the adoption of minimum tillage positively and significantly. These three communications andextension services enhance the information exchange on improved agricultural technologies. Land
size affects the adoption of minimum tillage positively and significantly. Grabowski et al., (2014),
Ngoma et al., (2014), Zulu-Mbata et al., (2016), and Prakash et al., (2018) also found the same
result. Marenya et al., (2017) also found a positive relationship between minimum tillage use andland size in Ethiopia which contrasts with the finding of the study in Kenya and Tanzania.
Factors Affecting the Use of Intercropping
The age of the household heads and participation in off-farm income-generating activities
affect the adoption of intercropping positively and significantly. Female-headed households also
use intercropping than male-headed households. The negative relationship between credit accessand adoption of intercropping. Both education and participation in field days affect the adoptionof intercropping positively and significantly which witnessed the positive role of education andextension on the adoption of agricultural technologies. The positive relationship between the useof intercropping and access to extension service agree with Ketema and Bauer, (2012). Land size
and adoption of improved coffee varieties also affect the adoption of intercropping negatively andsignificantly due to the subsistence nature of intercropping, which is also in line with Ketema andBauer, (2012). An increase in family size also enhances farmers to use of intercropping. The result
also corroborates with Ketema and Bauer, (2012) and contrast with Ekepu and Tirivanhu, (2016).
The negative relation between TLU and adoption of intercropping showed that those farmers who
use intercropping have a small land size and they do not have space to rear livestock.
Factors Affecting the Adoption of Improved Forage
Female-headed households have less likely to adopt improved forage than male-headed
counterparts. Female household heads are resource-poor especially land. Thus, they opt to plantother crops than forage to feed their family member. The education level of the household headaffects the adoption of improved forage positively and significantly which is also agreeing with36

1st African Conference on Precision Agriculture | 8-10 December | 2020

Lapar and Ehui, (2003). Extension on natural resource management is directly related to theadoption of improved forage which is in line with Beshir, (2014) and Abebe et al., (2018). Botheducation and extension services affect the adoption of agricultural technologies positively throughenhancing the search, evaluation, decision, and utilization of new information. Mean distance fromfarm plots has a negative and significant effect on the adoption of improved forage. Forage is a
new technology for farmers and farmers plant the grasses in and around the homestead. The result
is consistent with Abebe et al., (2018). TLU also affects the adoption of improved forage
positively. The result also supports the finding of Beshir (2014). Coffee improved variety adoption
affects the adoption of improved forage positively and significantly.
Factors Affecting the Adoption of Physical Conservation Structures
Source of information and knowledge such as ownership of radio and access to extension
services affect the adoption of physical soil and water conservation structures positively andsignificantly. The positive relationship between the adoption of soil and water conservation
structures and extension service was also found by Birhanu and Meseret, (2013); Damtew et al.,(2015); Asfaw and Neka, (2017); Issahaku and Abdulai, (2019) and Wordofa et al., (2020). Better
exposure to education increases farmers’ better understanding of the benefits and constraints.
However, the result contrasts with Belachew et al., (2020). Construction of physical soil and water
conservation structures is capital, time, and labor-intensive. Both coffee variety adoption and TLU
which help the farmer to generate more income have positively and significantly affected theadoption of physical soil and water conservation structures. The result is consistent with Nigussie
et al., (2015); Issahaku and Abdulai, (2019), and Belachew et al., (2020).

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Table 1. Factors affecting adoption of climate-smart agricultural practices
Variables

ManureApplication(MA)

Minimum
Tillage(MT)

Physical soil
and waterManagement(PM)
Region [SNNP]-0.0450.726***
0.950***
0.944***-0.400***(0.094)(0.095)(0.100)(0.124)(0.097)
Sex [Female]
0.042
0.052
0.378**-0.529**-0.118(0.154)(0.155)(0.160)(0.223)(0.153)
Household head age in
0.007*-0.009**0.007*
0.002
0.003
completed years(0.004)(0.004)(0.004)(0.005)(0.004)
Household head education in
0.016
0.037***
0.049***
0.060***
0.007
completed years(0.013)(0.013)(0.014)(0.016)(0.014)
Family size in number-0.039**-0.0150.069***
0.013-0.000(0.019)(0.019)(0.021)(0.022)(0.020)Mean distance from farm
-0.006*0.137***-0.015-0.051*-0.028plots in km(0.027)(0.026)(0.028)(0.031)(0.027)
Access to natural resource
0.491***
0.478***-0.0550.325*
1.034***
management extension [Yes](0.135)(0.141)(0.144)(0.168)(0.128)
Participation on field days
0.372***
0.226**
0.211*
0.192
0.178
[Yes]
(0.105)(0.103)(0.113)(0.123)(0.115)
Radio ownership [Yes]0.100
0.216**
0.096
0.007
0.211**(0.091)(0.090)(0.096)(0.109)(0.094)Tropical Livestock Units
0.011*-0.019-0.067***0.059***
0.038***(TLU) in numbers(0.012)(0.012)(0.013)(0.013)(0.013)******
Total land in hectares-0.0390.099-0.091-0.051-0.027(0.030)(0.029)(0.034)(0.034)(0.031)
Improved coffee adoption-0.154*0.025-0.183*0.032**
0.334***[Yes](0.090)(0.089)(0.095)(0.109)(0.092)
Household credit access-0.231**
0.067-0.207**
0.032
0.001
[Yes]
(0.089)(0.090)(0.097)(0.107)(0.093)Off-farm income-generating
0.401***-0.151
0.528***-0.0240.048
activities [Yes](0.102)(0.104)(0.112)(0.121)(0.108)Constant-0.801***-1.067***-1.868***-4.156***-0.332(0.307)(0.310)(0.332)(0.437)(0.311)
Number of draws = 5; Number of observations = 918; Wald chi2(70) = 668.57; Prob > chi2 = 0.000
Estimated covariance of the correlation matrix
rhoMTMA = -0.158(0.053) ***rhoIFMT = 0.009(0.059)
rhoICMA = 0.227(0.054) ***rhoPMMT = 0.118(0.055) **rhoIFMA = 0.141(0.060) **rhoIFIC = 0.077(0.061)
rhoPMMA = 0.220(0.054) ***rhoPMIC = 0.202(0.057) ***rhoICMT= -0.019(0.056)
rhoPMIF = 0.265(0.061) ***Note: Numbers in the parenthesis is standard error*** (P < 0.01); ** (P < 0.05); * (P < 0.10).

38

Intercropping(IC)

Improved
Forage(IF)

1st African Conference on Precision Agriculture | 8-10 December | 2020

CONCLUSIONS AND POLICY INTERVENTION
The study found a positive and significant effect of education, extension (extension
services and participation on field days), and communication (ownership of radio) on the adoptionof climate-smart agricultural practices. Thus, policymakers and public authorities must pay due
attention to problems affecting effective farmers-extension linkage. Extension service is beyond
expert assistance in the improvement of production and marketing. It also enables a flow of
information and the transfer of knowledge and scientific findings. The agricultural extension
workers have an effective and important role in helping farmers solve agricultural problems. Thus,
extension workers must have a wide knowledge of various agricultural disciplines and they shouldhave the ability to deal with farmers. The farmers' training center system which is partiallyfunctioning currently should also be strengthened to its full capacity. The positive effect of radio
ownership on technology adoption also suggests launching FM channels to farmers which initially
focuses on the provision of agricultural information and knowledge.
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Damtew AA, Maru HA, Mesfin DA. 2015. Determinants of adopting techniques of soil and water
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Ekepu D, Tirivanhu P. 2016. Assessing socio–economic factors influencing adoption of Legumebased multiple cropping systems among smallholder Sorghum farmers in Soroti, Uganda.
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Tillage by Cotton Farmers in Eastern Zambia. Indaba Agricultural Policy ResearchInstitute (IAPRI); Working Paper 87, Lusaka, Zambia.

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Resources Forum 42: 141-158.
Tao H, Morris T, Bravo-Ureta B, Meinert R. 2014. Factors Affecting Manure Applications as
Directed by Nutrient Management Plans at Four Connecticut Dairy Farms. Agronomy
Journal: Agronomy, Soils & Environmental Quality 106(4): 1420-1426.
Wordofa MG, Okoyo EN, Erkalo E. 2020. Factors influencing adoption of improved structuralsoil and water conservation measures in Eastern Ethiopia. Environmental System Research
9(13): 1-11.
Zulu-Mbata O, Chapoto A, Hichaambwa M. 2016. Determinants of Conservation Agriculture
Adoption among Zambian Smallholder Farmers Indaba Agricultural Policy ResearchInstitute Working Paper 114 Lusaka, Zambia. PP: 25.

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APPLICATIONS FOR UAVS

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#7465 CASHEW TREES DETECTION AND YIELD ANALYSIS USING UAV-BASED
MAP
Thierry Roger Bayala1*, Issouf Ouattara2*, Sadouanouan Malo1 and Arto Visala2
LAMDI (Laboratoire d’Algèbre, de Mathematiques Discretes et Informatiques), Université
Nazi Boni, Bobo Dioulasso, Burkina Faso, thierrybayala@gmail.com 2Autonomous Systems
research group, Department of Electrical Engineering and Automation, School of ElectricalEngineering, Aalto University, Espoo, Finland, issouf.ouattara@aalto.fi
1

ABSTRACTIn this study we developed a novel method to detect cashew trees in an orthophoto map
derived from images collected by an unmanned aerial vehicle (UAV). We also suggest a way inwhich these detections can be used to analyze the yield of the cashew farm. The proposed method
uses images analysis to find the tops of trees, to merge different tops located on the same tree, andto segment individual tree. The segmented trees are used in a deep learning framework to know
the exact location of cashew trees. The preliminary cashew detection from UAV-based map is
promising.
This study can be interesting for developing countries where UAV system are nowadays
gaining popularity in agriculture. Given that our method does not require any additional sensor
other than the RGB camera onboard the UAV, this low-cost solution is suitable for small andmedium cashew farmers. The developed method can also be extended to other types of trees, otherthan the cashew.
INTRODUCTION
Native to brazil, cashew was brought to Africa due to its qualities of adaptation to difficult
soil and climatic conditions. It has therefore been used in several regions of Africa as a species ofreforestation since the 1970s. In the 1990s, its world production declined due to the decline inproduction in India. This situation has led the government of Burkina Faso to emphasize theproduction of cashew. Engaging more than 45.000 households with 35.000 tones/year, cashew is
nowadays one of the most exported horticulture crops in the country (Issa et al. 2017).
One of the big challenges faced by farmers and institutions is to predict the yield withaccuracy. The early estimation of the yield is crucial for field management in term of fertilizer andpesticide application in agriculture in general (Geipel et al. 2014), and cashew farming, inparticular. An accurate yield estimate is also important for governmental institutions to accurately
measure the impact of cashew production and take adequate political decisions.
Several approaches have been introduced for yield prediction of species such as corn, wheat,soybean, etc. These approaches include ground-based field surveys, remote sensing-based
methods, or environmental factors techniques (Vuong et al. 2018; Bresilla et al. 2018; Geipel etal. 2014; Wang et al. 2014; Mu et al. 2014; Yang et al. 2019; Maimaitijiang et al. 2020). In Burkina
Faso, the yield estimation process is carried out manually and therefore not precise enough.
In this work we show a new approach to identify cashew trees using deep learning model.
We also propose a model for estimating the yield.

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MATERIALS AND METHODS
Data Collection and Pre-processing
In this study, images have been collected using the DJI Phantom 4 pro v2 drone and the
DJI Inspire 1 drone. Both vehicles have a flight time of approximatively 30 minutes. The image
size from the camera is 5472x3648 for the Phantom 4 pro v2 drone, and 4000x3000 for the Inspire
1.

Figure 1. Study area (11°07'35.5"N and 2° 05'52.5"W), image generated using google earth
online.
The images have been collected in Léo which is a rural town located in the province ofSissili in Burkina Faso. The study area is marked in Fig. 1. The total mapped area is 11.53 hectares.
The images have been collected at the same flight altitude of 36 meters. A total of 1221 images
have been collected using four different flight missions.
The collected images are combined in a structure from motion and photogrammetry
scheme using the Agisoft Metashape Professional v1.5.5 software (Agisoft 2019). From thisprocess, an orthophoto, a digital surface model (DSM), and digital terrain model (DTM) areproduced. The DSM is produced from the point cloud generated from the collected images. It
represents the earth surface and everything on it. The DTM is generated by first classifying thepoint cloud into ground points and non-ground points. Only the ground points are used to generate
the DTM which represents the bare-earth surface. All data in this study are generated using thereference frame WGS 84/ UTM zone 30N (EPSG:32630).
Individual Tree Identification
The individual tree identification (ITD) method in this study follows the steps used in the
study by (Issouf et al. 2020). Here, we present the most important aspects of the method. For moredetail refer to (Issouf et al. 2020; Mohan et al. 2017; Baena et al. 2017). We also present some ofthe differences adopted in this study.

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Using a canopy high model (CHM) computed as the difference between the DSM and theDTM, a local maximum filter is used with fixed window size to find the points which are on theupper surface of each trees. The result of this process is a set of points representing the top part of
the trees in the map.
In the study in (Issouf et al. 2020), the trees which were detected are coniferous trees whichhave conic shape and the maximum filter in this case generally results in a single point representing
the top of the tree. In this study, the trees of concern are deciduous trees which have partially a
locally flat top surface. Therefore, a maximum filter algorithm will detect several points on the topof the trees. These points, on the same tree, need to be grouped together in order to be effectively
used in a subsequent watershed segmentation algorithm to segment out the individual trees. To
group several points on the same tree, we use a simple but effective heuristic detailed in the
Algorithm 1.

These points representing the tops of the individual trees are used as markers in a markercontrolled watershed segmentation algorithm to segment out individual trees in the map (Myer andBeucher 1990; Meyer 2012). The segmented trees are used in the subsequent detection step toseparate the cashew trees from non-cashew trees.
Cashew Tree Detection
To separate the cashew trees from the non-cashew trees in the orthophoto map covering an
area of more than 11 hectares, a deep learning detection algorithm is used with the results of thewatershed segmentation algorithm. A pure classification algorithm could also be used to simply

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classify the segmented trees from the orthophoto map. This might however introduce errors
because the segmentation results might have several trees segmented as one tree.
The Faster R-CNN with Inception v2 deep neural network architecture is used as done in(Issouf et al. 2020). As explained in this study, using the detection algorithm on the segmented
tree images has an advantage over a pure classification. If the segmented tree image contains boththe cashew and other tree species the detection algorithm can correctly identify the cashew tree
while the classification algorithm might not. Using the individual segmented tree images for
detection has an advantage because, there is no need to apply a classification or detection all overthe entire orthophoto map. This helps to avoid going through parts of the orthophoto map that
obviously do not contain any tree.
The deep learning detection algorithm is implemented, trained, and tested using the python
programming language and the TensorFlow object detection API (Huang et al. 2016). A pretrained model is used to speed up the training and to avoid the problem of overfitting given thelimited number of annotated images (Yamashita et al. 2018).
Yield Estimation
For each detected cashew tree, the height and the true area (geographic area) can berespectively estimated from the CHM and the detection process. Using the height and the surface
area, we suggest the use of a K-Nearest Neighbors (KNN) algorithm to estimate the yield of agiven cashew tree. This is, however, only possible if there is enough ground truth data which canserve as training data. These ground truth data can be collected with farmers from current andhistorical yield. In this study, these data are not available to validate the KNN algorithm, andtherefore the result of the yield estimation is not presented. In addition to the surface area value
and the heigh of the cashew tree, other parameters such as the location, the type of soil, the yearof production, etc could also be used in the KNN algorithm in order to account for local
environmental effect and the time effect in the yield estimation.
RESULTSIn the previous Section, the material and method have been presented. The aerial vehicle
platforms used for the data collection have been also presented. The method consists of individual
tree identification step which uses a maximum filter analysis and a watershed segmentation
algorithm, and a deep learning detection algorithm.
The result of fusing the detected points using the Algorithm 1 is presented. Using the
maximum filter analysis several points are detected at the upper surface of each tree. In order tosegment the tree as a single tree using the watershed segmentation algorithm, these points are
merged so that for each tree only one point is used to represent its top. A sample of the result of
the fusion is shown in Fig. 2. Using the merged points as markers in the marker-controlledwatershed segmentation, the trees are segmented, and the result is shown in Fig 3. It can be seenthat in many cases, individual trees are segmented. However, in some other cases, group of trees
are segmented as one tree. This happens because the trees are too closed to each other and havecomparative height.
The segmented trees are cropped with a margin from the map and the resulting images areused as input to the deep learning algorithm. Parts of these images (from an area of the overallmap) have been used for training the neural network. After the training, the images from anotherpart of the map are used to test the neural network. Fig. 4 shows the detection result. In this figure,45

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the yellow patches represent the original segmented trees from the individual tree identification
step. The reddish patches represent the result of the detection of the cashew trees. The deeplearning model can effectively detect the cashew trees among other types of trees. Even when the
cashew tree belongs to a segmented patch which contains another species of tree, the detection
algorithm can still find the cashew tree. This is an advantage over a simple classification techniqueon the segmented patch.
The detection model is also tested with another dataset which has been collected fivemonths earlier (in May 2020) on a portion of the same site. The result is shown in Fig. 5. It can beseen from this result that the model is able to effectively detect the cashew trees. This result is
important because it shows the proposed method is effective on dataset taken at different time andin different conditions.

Fig 2: local maximum detection andmerging result. The points in the blue
rectangles best illustrate the merging
result.

Fig 4: The detection result

Fig 3: The marker-controlled watershedsegmentation result

Fig 5: The detection result of cashew trees.
Yellow patches represent non-cashew trees, thereddish patches are the detected cashew trees.

CONCLUSIONS AND FUTURE WORKIn this study, a method for detecting cashew trees and estimating the yield have beenpresented. The process for detecting the cashew trees includes an individual tree identification anda deep learning detection step. The proposed method can effectively detect the cashew trees among
other trees. The area of the detected trees can be computed because the orthophoto isgeoreferenced. This area along with the heigh can be used in a KNN algorithm for yield estimationfor each tree.
In the future work, the accuracy of our proposed detection method can be better accessedby using ground truth data. Yield data can also be collected to not only train the KNN algorithm(hyperparameter selection) but also evaluate its performance.

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ACKNOWLEDGEMENTS
Through the contribution of authors Issouf Ouattara and Arto Visala, this work is indirectly
supported by the Strategic Research Council at the Academy of Finland Through Integrated
Disruptive Technologies of 3D Digitalization, Robotics, Geospatial Information and Image
Processing/Computing - Point Cloud Ecosystem” (project decision numbers 293389 and 314312).
FasoDrone (www.fasodrone.com) is also acknowledge for contributing in the datacollection and putting computing resources for this study.
REFERENCES
Agisoft R. LLC. 2019. St. Petersburg, Agisoft metashape user manual, professional edition, ver.
1.5 [On-line]. https://www.agisoft.com/pdf/metashape-pro_1_5_en.pdf.
Baena S, Moat J, Whaley O, Boyd DS. 2017. Identifying species from the air: UAVs and the very
high-resolution challenge for plant conservation, PLOSONE 12(11): 1–21,
DOI:10.1371/journal.pone.0188714.
Bresilla K, Perulli GD, Boini A, Morandi B, Grappadelli LC, Manfrini L. 2018. Using Deep
Learning - Convolutional Naural Networks (Cnns) For Real-Time Fruit Detection in Trees,
14th International Conference on Precision Agriculture.
Geipel J, Link J, Claupein W. 2014. Combined spectral and spatial modeling of corn yield basedon aerial images and crop surface models acquired with an unmanned aircraft system.
Remote Sens. 6: 10335–10355.
Huang J, Rathod V, Sun C, Zhu M, Korat-tikara A, Fathi A, Fischer I, Wojna Z, Song Y,
Guadarrama S, Murphy K 2016. Speed/accuracy trade-offs for modern convolutional
object detectors, CoRR, http://arxiv.org/abs/1611.10012.
Issa W, et al. 2017. Diseases of Cashew Nut Plants (Anacardium Occidentale L.) in Burkina Faso,
Advances in Plants and Agriculture Research 6 (February) 7.
Issouf O, Heikki H, Arto V. 2020. Drone based mapping and identification of young spruce standfor semiautonomous cleaning, preprint accepted for IFAC World Congress.
Maimaitijiang M, Sagan V, Sidike P, Hartling, S, Esposito F, Fritsch F. 2020. Soybean yieldprediction from UAV using multimodal data fusion and deep learning. Remote Sensing ofEnvironment 237: 1. doi:10.1016/j.rse.2019.111599
Meyer F, Beucher S. 1990. Morphological segmentation, Journal of Visual Communication andImage Representation 1(1): 21–46. https://doi.org/10.1016/1047-3203(90)90014-M.
Meyer F. 2012. The watershed concept and its use in segmentation: A brief history, CoRR,
http://arxiv.org/abs/1202.0216.
Mohan M, Silva CA, Klauberg C, Jat P, Catts G, Cardil A, Hudak AT, Dia M. 2017. Individual
tree detection from unmanned aerial vehicle (UAV) derived canopy height model in an
open canopy mixed conifer forest, Forests, vol. 8, no. 9, 2017, ISSN: 1999-4907. [Online].
https://www.mdpi.com/1999-4907/8/9/340.
Mu H, Zhou L, Dang X, Yuan B. 2019. Winter Wheat Yield Estimation from Multitemporal
Remote Sensing Images based on Convolutional Neural Networks, 10th International
Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp),
Shanghai, China, pp. 1-4, DOI: 10.1109/Multi-Temp.2019.8866918.
Ngo VM, Le-Khac N-A, Kechadi M-T. 2018. An Efficient Data Warehouse for Crop Yield
Prediction, 14th International Conference on Precision Agriculture.

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Wang L, Tian Y, Yao X, Zhu Y, Cao W. 2014. Predicting grain yield and protein content in wheat
by fusing multi-sensor and multi-temporal remote-sensing images. Field Crop Res. 164:
178–188.
Yamashita R, Nishio M, Do R, Togashi K. 2018. Convolutional neural networks: An overview
and application in radiology, Insights into Imaging 9: 611 –629.
Yang S, Hu L, Wu H, Fan W, Ren H. 2019. Estimation Model of Winter Wheat Yield Based on
UAV Hyperspectral Data, IGARSS 2019 - 2019 IEEE International Geoscience andRemote Sensing Symposium, Yokohama, Japan, pp. 7212-7215, DOI:
10.1109/IGARSS.2019.8898839.

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#7657 AUTONOMOUS HEXACOPTER SPRAYING DRONES FOR PLANTSPROTECTION
Abbes Kailil1, Hassan Benaouda2, Abdelhakim Mohsine3,
1Eng. Doctor in aerospace engineering, Moroccan Industry Services & Engineering SARL,
Morocco. 2 Eng. Doctor in Agriculture, National Institute of Agriculture Research (INRA),
Morocco. 3 Engineer in agriculture, National Office of Agriculture Extension (ONCA), Morocco.
abbes.kailil@moroccanindustry.com, hbenaouda_inra@yahoo.fr, mohsine.aci@gmail.com
ABSTRACT
Farming technologies have considerably evolved over the last decades considering new
agricultural problems and constraints. This paper discusses a Moroccan project based on
autonomous spraying drones developed during the last three years in collaboration between
Moroccan Industry Services & Engineering company, the INRA institute and the ONCA office.
The main purposes of this project is to assist Moroccan farmers improve their performance andenhance their efficiency using chemicals to withstand different diseases, increase crop yields(crops and orchards) and monitor their growth. Also, using our drones helped farmers during thelast two years protect themselves and the environment from chemicals and reduce the use of water.
Many operations and tests have been carried out in cooperation with the two departments (INRAand ONCA) to highlight the efficiency of our system and to develop new processes together
considering this new technology. This paper gives an overview of different experiences conducted,
the main results obtained and the technical descriptions of our hexacopter.
INTRODUCTION
Precision agriculture and digital tools are becoming the pillar parameters for an agriculturethat withstand to all constraints and climate changes. The use of agricultural inputs including
fertilizers, plant protection chemicals and water, is increasing considerably and it is becoming
important to optimize their use in order to protect humans and environment. Currently the chemicaltreatments are done through sprayers dragged by tractors or on small vehicles or by plane in caseof large areas. These conventional methods can be very time-consuming and energy-non-efficient,
polluting and non-water-efficient. They can also cause damage especially at advanced stages ofcultivation. The spraying drone greatly reduces time and cost of treatment, water consumption
based on low volume technology ensuring a stable and high yield, crops need balanced, consistentfertilization and continuous maintenance. It also allows immediate access to the parcels for thetreatment of large crops after the rains fall and after irrigation, especially for clay soils withdifficult access. The use of spraying drones could be the ideal solution for hard-to-reach crops(rice, sugar cane, corn, rapeseed, sunflower). This paper deals with technical characteristics of ourhexacopter (Moroccan Agriculture Spraying Drone MASD-M6) from design to flight tests andhighlights the most important operational phases during the last two years.

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Design and Manufacturing
First Phase: the main purpose was to design and manufacture hexacopter drone achieving all
performance parameters mainly the stability and thrust subsystem. Three prototypes were realized
before reaching the final version.

During these stages we focused on structure mechanical strength and configuration and theflight stability. We used a Linear Quadratic Regulator controller to establish an efficient control
law which satisfies a given specification and maintains sufficient stability and accuracy even under
the strong effects of intrinsic parameters uncertainties.
In the next step we will adapt the fractional controller to enhance the stability performancesand reduce the energy consumption.
In order to reduce the weight and increase the capacity of the drone in term of chemical
payload, we decided to choose the carbon fiber material for the 90% of the structure and all otherparts are made from Aerospace Aluminum.
Second Phase: After flight and crash tests, we started working on the spraying system and nozzles
configurations. Two main categories were adopted: pressure and electrostatic nozzles.
• Pressure nozzles for crops
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• Electrostatic nozzles for orchard
Two pumps are mounted in order to ensure the pressure and flow rates during the spraying
operations.
The main performances of MASD-M6 drone are:• Payload capacity: 25 liters• Spraying area/hour: 8-10 ha
Spraying Operation and Chemical Treatments
Since the final version of the hexcopter drone was tested and approved we started spraying
operations in collaboration with our clients.
The spraying program was supported by our two main partners INRA and ONCA. In thisway, during 2019 year we have treated:• more than 3,000 hectares of rice,• more than 1000 hectares of sunflower• more than 1000 hectares of olive trees• more than 500 hectares of citrus• more than 200 hectares of sugar canes• more than 200 hectares of corn
• more than 200 demonstrations and tests.
As such, in order to raise awareness among all stakeholders about the benefits and added
values of these drones, we organized several awareness and scientific field days in partnershipwith all our partners. We cite as an example:• In partnership with INRA a National Scientific Conference: Smart Agriculture.
• In partnership with the province of SIDI KACEM: Field day with official use ofdrones for wheat treatment with the presence of major agricultural departments(ONCA, DRA, ORMVA, Associations, etc.).
During these operation two main objectives were defined:• the efficiency of the treatments• the droplets distribution and density

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CONCLUSIONS
MASD-M6 drone is an autonomous hexacopter spraying drone for agriculture chemicaltreatments. This project aims to develop R&D and scientific research rather than helping Moroccan
farmers to enhance their efficiencies and improve spraying methods while protecting environmentand humans from chemicals.
In this project, an aerial spraying system with a capacity of 25-liter tank was designed andmanufactured. Field experiments were carried out in order to apply pesticide in agriculture. The
results are excellent and meets the farming constraints and chemical spraying optimal objectives.
However, in order to improve the main performance parameters of the drone many research
programs were developed to continue increasing the stability against the wind effect and spraying
efficiencies in different conditions.
REFERENCES
Gundlach J. 2012. Designing Unmanned Aircraft Systems: A Comprehensive Approach, Aurora
Flight Sciences Manassas, Virginia.
Kailil A, Mrani M. 2004. Low Earth-orbit satellite attitude stabilization with fractional regulators,
International Journal of Scientific Sciences.
Krishna KR. 2018. Agricultural drones a peaceful pursuit, Tylor & Francis, Apple Academic press
Inc.
Quan Q. 2017. Introduction to multicopter design and control, Springer, Singapore.

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CLIMATE SMART AGRICULTURE

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#7488 SIMULATION OF CASSAVA YIELD UNDER DIFFERENT CLIMATICSCENARIOS IN KILEMBWE, SOUTH-KIVU PROVINCE EASTERN DR CONGO
Yamungu Alongo Boniface Byamalong1,2, Majaliwa Mwanjalolo Jackson Gilbert1, Anthony
Egeru1, Dossa Boton Martin1
1
Department of Environmental Management Makerere University, Kampala, Uganda;
2
Univeristé Espoir du Congo, Baraka, DR Congobyamalong@gmail.com or byamalong@caes.mak.ac.ug, +256788319193
ABSTRACT
Climate variability and change are projected to significantly impact agricultural production
across Africa. This study assessed the effects of climate variability and change on cassava yield in
Kilembwe, South-Kivu province Eastern DR Congo. The assessment relies on the DSSAT cropmodel simulation of cassava under current and future climate. The period 1980–2010 was used torepresent the baseline, while future projection covers three periods including the near future(2010–2039), mid-century (2040–2069), and end-century (2070-2099). Climate, soil, and crop
yield and field management data were collected for Kilembwe in the South-Kivu Province. Results
show both Tmin and Tmax are projected to increase over time up to 1.9°C and 1.8°C (RCP 4.5)
and up to 3.91°C and 3.57°C (RCP 8.5) at the End of the Century. Rainfall is also projected toincrease up 10.36 and 9.27%; respectively under RCP 4.5 and RCP 8.5 at the end of the Century.
The projected climate is likely to increase cassava yields between 35.63 to 50.67% for RCP 4.5
and between 30.92 up to 50.16% for RCP 8.5 in Kilembwe. Rainfall increase and temperaturechanges are determining factors of yield increase. Climate variability and change will continue toaffect positively cassava production in Kilembwe. Farmers are therefore encouraged to increase
growing cassava to increase their resilience to climate variability and change.
Keywords: cassava, climate variability and change, DSSAT, Kilembwe
INTRODUCTION
Root crops, cassava (Manihot esculenta Crantz) included are important in the African diet
(Chandrasekara and Kumar 2016). It is estimated that 60% more food will be required by 2050
(compared to 2005) to meet human nutrition needs in Africa (Msowoya and Madani 2016). Rootcrops' contribution to global and regional food security is diverse. Cassava is the world’s fourthmost important source of food energy (carbohydrates) as well as fourth among staple crops in thetropics after rice, sugar, and maize. Its global production is estimated at 183 million tons per year
(FAO STAT, 2014). Worldwide, 800 million people depend on cassava as their primary staple
food. It is estimated that two-thirds or more of its production is used for human food and one thirdor less used feeding animal and industrial purpose (Ferraro et al., 2016). Africa is the continentthat depends most on root and tuber crops in feeding its population. In the humid and sub-humid
areas of tropical Africa, it is either a primary staple food or a secondary co-staple. Cassava is themain staple crop and food in the Democratic Republic of Congo and second staple crop and foodafter rice in Kilembwe, Fizi Territory. Across the current cassava producing situation, predicting
future cassava yields relies on understanding how climate change will affect cassava growth and54

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development remain relevant (Sanginga & Mbabu, 2015). In order to provide the interactions
between climate and crop production under future climate scenarios, crop models are important
tools as used by (Jones et al., 2003; Zinyengere et al., 2015, Muhindo et al. 2016 and Oumarou,
2017) to predict the impacts of climate variability and change on agriculture production. Thesestudies have deployed outputs from global climate models “GCM” datasets associated with the
AgMIP protocol under different climate scenarios (Moore et al. 2012). The Decision Support
System for Agrotechnology Transfer (DSSAT) Version 4.7.5 is a software application programthat comprises crop simulation models for over 32 crops (Hoogenboom et al., 2010). Studies(Sonwa et al., 2014; Fotso-Nguemo et al., 2018; Nonki et al., 2020; Sonkoué et al., 2018) in Central
Africa including in DR Congo have shown that rainfall variability and temperature will be on therise with significant impact on crop production. However, future cassava under different climate
scenarios was still unknown despite it being the main staple food in the DR Congo.
MATERIALS AND METHODSThis study was conducted in Kilembwe located at 0.55°S and 31.53°E in the high altitudeof 1232m of Fizi territory, in the South-Kivu Province, Eastern part of the Democratic Republicof Congo. The climate in Kilembwe is tropical with annual average rainfall of 1500mm and themean temperature varies between 24 and 28°C. The data collected during the survey included the
major crops grown, their yield, prices, management practices, labor cost, inputs cost, adaptation
measures used, planting and harvesting period, the quantity of harvest sold, and consumed by thehousehold. Besides, crop yields per household were estimated in the field and their prices asked in
the local market, due to lack of meteorological stations climate data were downloaded from the
AgMERRA portal (https://data.giss.nasa.gov/impacts/agmipcf/agmerra/). Focus group discussion“FDGs” were organized at the community level. The assessment of the effects of climate
variability and change on cassava yield was done using DSSAT. DSSAT 4.5.7 to simulate
historical and future cassava yields. Historical cassava yield was compared to the future cassava
yield under different scenarios using the T-test. Historical data on Cassava yields were generated
for the period (1980-2010). Man Kendall and linear regression were all used for determining thetrend of historical rainfall, temperature (Tmax and Tmin), and cassava yield (Salmi et al., 2002;
Pohlert, 2020). The cultivar used to simulate cassava yields was a local variety called “Sawasawa”.
For the projection of future cassava yield, three future climate projection periods namely NC (nearcentury/future 2010-2039), MC (mid-century 2040-2069), and EC (End-Century 2070-2099), and
two Representative Concentration Pathways (RCP 4.5 and 8.5) were considered.
RESULTS AND DISCUSSION
Historical Temperature, Rainfall, and Cassava Yield Trends
Figure 1 shows the trend of historical annual rainfall, average annual temperature (Tmax
and Tmin) and cassava yield. Both average annual Tmax and Tmin tended to increase with time.
The gradient for average annual Tmin varied from 0.03°C to 0.06°C and the gradient for averageannual Tmax varied from 0.02°C to 0.06°C. Both Tmin and Tmax varied significantly (p=0.01)
overtime. The peak (1650mm) of the annual rainfall was observed between 2000 and the minimum(920mm) annual rainfall was observed in 1992. Cassava yield tended to decline as indicated in thelinear regression and Sen’s slope was -84.789 Kg/ha.

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Projected Change in Temperature and Rainfall for all Periods and RCPs 4.5 and 8.5
Projected climate change for the three periods and the two RCPs are presented in Table 1.
Results show that the change in average annual maximum and minimum temperature is likely toincrease in all periods. In the Near-Term the increase in maximum and minimum temperature
ranged between 0.71°C and 0.73°C and 0.92°C and 0.98°C for RCP 4.5 and RCP 8.5 respectively.
In the Mid-Century, there is an increase of 1.50°C and 1.53°C for maximum and minimum
temperature for RCP 4.5 and 2.10°C and 2.34°C respectively maximum and minimum temperature
for RCP 8.5. lastly, for the End-Century there was an increase of 1.80°C and 1.90°C and 3.57°C
and 3.91°C for maximum and minimum temperature for RCP 4.5 and RCP 8.5 respectively. The
average rainfall was projected to increase by 4.37%, 1.81%, 6.76%, 8.16%, 10.36%, and 9.27% in
the Near-Term, Mid, and End-Centuries of RCP 4.5 and 8.5 respectively. Both Tmin and Tmax aswell as rainfall are likely to increase over time and the end-century is likely to have the highest
increment in Temperature and rainfall (p<0.05) while Near Future projected the lowest (p<0.05).
The results of this study are showing an increasing trend in temperature and rainfall which
corroborates with the findings of Muhindo et al. (2016) for Kavumu and Luberizi in the SouthKivu Province, Democratic Republic of Congo. The increase in temperature can be linked to theglobal temperature increase induced by greenhouse gas concentration into the atmosphere that has
passed the 400 parts per million (ppm) caused by agricultural activities, deforestation, mining
activities, and other anthropogenic activities.

Figure 1. Historical trend in temperature, rainfall, and cassava yield in Kilembwe56

1st African Conference on Precision Agriculture | 8-10 December | 2020

Table 1. Future changes in rainfall, minimum and maximum temperatures for Kilembwe
RCP 4.5

RCP 8.5

PeriodNear-Term
Mid-century
End-century

ΔTmax
(°C)
0.71
1.53
1.80

ΔTmin
(°C)
0.73
1.53
1.90

Relative change
in Rainfall
(%)
4.37
6.76
10.36

ΔTmax
(°C)
0.92
2.10
3.57

ΔTmin
(°C)
0.98
2.34
3.91

Relative change
in Rainfall
(%)
1.81
8.16
9.27

Projected Increase in Cassava Yield Under Different Climate Regimes
The projected yield changes are shown in Figure 2. All selected five GCMs and RCPs
projected an increase in cassava yield for all periods under different climate regimes. The projected
increase in yield ranged from 35.63% to 50.67% and from 30.92% to 50.16% for RCP 4.5 and
RCP 8.5; respectively (p<0.05). The extent in the yield increase was higher in Hot/Wet for both
RCP 4.5 (p<0.05) and RCP 8.5 not significant (p=0.07) than the rest of the climate regimes while
Hot/Dry for RCP 4.5(p<0.05) and Cool/Dry for RCP 8.5 (p=0.01) projected a lower increase. The
increase in cassava yield in the tropic was also found by Tenge et al. (2012) and Bashaasha et al.
(2012) who found an increase about 60 and 80% in Rwanda and Uganda; respectively. The
increase in cassava yield can be attributed to temperature and rainfall changes which are within
the range of the required value for optimum cassava growth (25 to 29º C) while rainfall for the last
decade was also within the range required rainfall (1000 to 1500 mm) for cassava growth.

Figure 2. Impact of climate change on Cassava yield in NT, MC, EC under RCP 4.5 and RCP 8.5
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CONCLUSIONS AND RECOMMENDATIONS
We observed that both Tmin and Tmax are projected to increase over time up to 0.98°C,
0.92°C for the near future; for the Mid-century 2.34°C, 2.1°C and 3.91°C, 3.57°C for the EndCentury. Rainfall will increase by 10.36% (RCP 4.5) and 9.27% (RCP 8.5) in the End-century.
These observed patterns in rainfall and temperature will lead to an increase in cassava production
by 50.67% and 50.16% RCP 4.5 and 8.5; respectively. We encourage farmers to adopt cassava asa crop because of its responsiveness to climate, this could help the farmer to be food secure despite
the projected climate variability and change.
REFERENCES
Bashaasha B, Thomas, TS, Waithaka M, Kyotalimye M. 2012. “East African Agriculture andClimate Change: A Comprehensive Analysis - Uganda.” (December).
Chandrasekara A, Kumar TJ. 2016. “Roots and Tuber Crops as Functional Foods : A Review on
Phytochemical Constituents and Their Potential Health Benefits.”
Ferraro V, Piccirillo C, Tomlins K, Pintado ME. 2016. “Cassava (Manihot Esculenta Crantz) andYam (Dioscorea Spp.) Crops and Their Derived Foodstuffs : Safety , Security and Nutritional
Value Cassava (Manihot Esculenta Crantz) and Yam (Dioscorea Spp.) Crops and Their
Derived Foodstuffs : Safety , Secu.” 8398.
Food and Agriculture Organization of the United Nations. 2014. “Statistical Yearbook 2014:
EuropeandCentral
Asia
Food
and
Agriculture.”
FAO.
http://www.bfi.org.uk/statisticalyearbook2014/.
Fotso-Nguemo TC, et al. 2018. “Projected Trends of Extreme Rainfall Events from CMIP5 Models
over Central Africa.” Atmospheric Science Letters 19(2).
Hoogenboom G, Jones JW, Porter CH, Wilkens PW, Boote KJ, Hunt LA, Tsuji GY (Eds). 2010.
1 Decision Support System for Agrotechnology Transfer Version 4.5. Volume 1: Overview.
University of Hawaii, Honolulu, HI.
Jones JW, et al. 2003. “The DSSAT Cropping System Model.” European Journal of Agronomy
18(3–4): 235–65.
Moore N, et al. 2012. “East African Food Security as Influenced by Future Climate Change andLand Use Change at Local to Regional Scales.” : 823–44.
Msowoya, K, Madani K. 2016. “Climate Change Impacts on Maize Production in the Warm Heartof Africa.” Water Resources Management. http://dx.doi.org/10.1007/s11269-016-1487-3.
Muhindo I, Majaliwa M, Katusabe A, Walangululu M. 2016. “Projected Impact of Climate Change
on Rice Yield in Two Agro-Ecological Zones in South- Kivu , Democratic Republic ofCongo.” African Journal of Rural Development 1(3): 299–310.
Nonki RM, Lenouo A, Lennard CJ, Tchawoua C. 2020. “Correction to: Assessing Climate Change
Impacts on Water Resources in the Benue River Basin, Northern Cameroon (Environmental
Earth Sciences, (2019), 78, 20, (606), 10.1007/S12665-019-8614-4).” Environmental Earth
Sciences 79(5): 12665. https://doi.org/10.1007/s12665-020-8865-0.
Oumarou Y. 2017. “Perception Paysanne Des Perturbations Pluviométriques et Stratégies d ’
Adaptation Dans Les Systèmes de Culture à Sorgho Repique En Zone Soudano-Sahélienne
Du Cameroun Résumé.”Pohlert T. 2020. “Non-Parametric Trend Tests and Change-Point Detection.” : 1–18.
Salmi T, et al. 2002. Detecting Trends of Annual Values of Atmospheric Pollutants by the Mann-58

1st African Conference on Precision Agriculture | 8-10 December | 2020

Kendall Test and Sen’s Slope Estimates - the Excel Template Application MAKESENS.
Sanginga N, Mbabu A. 2015. “Sanginga, N.; Mbabu, A. Root and Tuber Crops (Cassava, Yam,
Potato and Sweet Potato). In Proceedings of the An Action Plan for African Agricultural
Transformation Conference, Dakar, Senegal, 21–23 October 2015.”
Sonkoue D, Monkam D, Fotso-Nguemo TC, Yepdo ZD, Vondou DA. 2018. “Evaluation and
Projected Changes in Daily Rainfall Characteristics over Central Africa Based on a MultiModel Ensemble Mean of CMIP5 Denis Sonkou E.” (Ipcc 2013).
Sonwa D, et al. 2014. “C Limate Change and Adaptation in C Entral A Frica :” (January).
Tenge NG, Mutabazi A, Thomas TS. 2012. “East African Agriculture and Climate Change : A
Comprehensive Analysis - Rwanda International Food Policy Research Institute ,Washington, DC .”Zinyengere N, Crespo O, Hachigonta A, Tadross M. 2015. “Crop Model Usefulness in Drylandsof Southern Africa : An Application of Crop Model Usefulness in Drylands of Southern
Africa : An Application of DSSAT HAL Id : Hal-01161900.”

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#7883 CLIMATE SMART AGRICULTURE: CHALLENGES AND OPPORTUNITIESTO PROMOTE THE SYSTEM IN ETHIOPIA
Getachew Agegnehu and Tilahun Amede
International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Addis Ababa,
Ethiopia, G.Agegnehu@cgiar.org
ABSTRACTAgriculture is vital to the Ethiopian economy and its development has significant
implications for food security and poverty reduction. Yet, the substantial reliance of the sector on
rain-fed systems has made it vulnerable to variability in rainfall, temperature, and climate change.
Climate-smart agriculture (CSA) is a viable alternative, which combines climate change and foodsecurity through the integration of adaptation and mitigation measures. Two strategies areimportant in the process of climate-friendly agricultural management: agricultural practices canmitigate climate changes by reducing greenhouse gas (GHG) emissions, adapting agriculture to
the noticeable changes through the development of soil and water management, sustainable crop
production, and livestock management. Adaptation to climate change is a major challenge for
Ethiopia. A significant portion of the population is still dependent on highly climate-sensitive
agriculture. Long drought spells during the small rainy season, increased temperatures, andtorrential rains during summer have caused serious distress to agriculture-dependent communities
in many locations. Climate change adaptation interventions needs to be implemented if achieving
food security and promoting sustainable agriculture to end poverty is to be realized. The droughtprone areas in the country are likely to experience more intense and irregular rainfall, affecting
yields of late maturing crops, and posing challenges to vulnerable pastoral and agro-pastoral
populations. If CSA is to be applicable for farmers, cross-disciplinary research and development
supported by policy and socio-economic contexts are essential to transform smallholder
agriculture.
Keywords: Adaptation, climate change, climate smart agriculture, mitigation, sustainableagriculture
INTRODUCTION
Agriculture is still the key sector to meeting the basic needs and livelihoods of most people,
but meeting the food demand for a growing population has been a formidable challenge for thesector. The sector is dominated by smallholder subsistence agriculture, largely rain-fed dependentand most vulnerable to climate change, and responsible for the supply of 95% of agricultural
produce (EPCC 2015), with limited contribution of irrigated agriculture. It is characterized by low
input-output production systems due to low adoption of improved technologies, inadequate
capacity of the agricultural extension service, low adaptive capacity of smallholder farmers to
climate change and limited financial resources for investment in climate change adaptation andmitigation measures to sustainably increase productivity and income, enhance resilience andreduce the adverse effects of climate change. If the business as usual approach is continued, climatechange may decrease GDP by 8-10% by 2050 but if adaptation actions are applied these losses

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could be reduced by half (CIAT:BFS/USAID 2017). The development and dissemination of
improved agricultural technologies are the major driving factors for increasing the productivity
and commercialization of smallholder farming in the country.
Increasing agricultural productivity in a sustainable way to meet the growing demand of
the growing population, while at the same time to adapt to and reduce the GHG emissions are thethree interlinked challenges that the agriculture sector need to overcome. To address thesechallenges, agricultural production and food systems should undergo a complete transformationfrom subsistence farming to a more productive and commercial agriculture through adoption of
CSA. CSA is a strategy to address the challenges of climate change and food security bysustainably increasing productivity, strengthening resilience to adapt to and reduce GHG
emissions and thereby enhancing the achievement of national food security and development goals(FAO 2010). Policy imperatives for CSA include the need to increase crop yields, feed a growing
population, mobilize investments to farmers and reduce GHG emissions. CSA differs from theconventional approaches as it emphasizes the capacity to implement flexible, context-specificsolutions, backed by innovative policy and financing instruments. CSA represents a combinationof practices that have been used in environmental ecology, conservation, climate change, andagriculture (Lipper et al. 2018). However, the relationship between agriculture and climate change
is not well understood. The dual relationship between climate change and agriculture has been
apparent through scientific assessments of the Intergovernmental Panel on Climate Change (IPCC)
and policy reviews of development agencies (Parry et al. 2007). Agricultural systems contributeto and are influenced by climate change, with the majority of impacts being felt by developing
countries. Thus, CSA is accepted globally as a feasible approach to transform and protect theagriculture sector to sustainably increase productivity, enhance resilience and reduce GHG
emissions.
RESULTS AND DISCUSSION
Climate Change
Climate change affects all sections of a society, but the degree of vulnerability of eachgroup within a community varies based on resource possession or wealth status, gender, age andlocation. In Ethiopia, total annual GHG emission has been estimated to be 144 Mt CO2eq(including emissions from land-use change and forestry), approximately 0.3% of global emissions,while per capita emissions are low, amounting to 2 tons of CO2eq annually (World Bank 2016).
The agricultural sector in the country is a major contributor to national emissions, approximately
60% of total emissions. Since Ethiopia has the largest livestock population in Africa (FAO 2016),
most of the agricultural GHG emissions emanate from livestock-related activities (CH4 and N2Oemissions from enteric fermentation and manure, respectively), which account for almost 92% ofagricultural emissions. Thus, the livestock sub-sector is the major emitter of methane (CH4) while
the crop sub-sector mainly releases nitrous oxide (N2O) due to application of N fertilizer (IPCC
2007). As indicated in Fig. 1, most emissions from the forest sector are associated with
deforestation due to agricultural land expansion (FAO 2016), implying that land-use change also
contributes to emissions of CO2 and N2O. Thus, mitigation measure is very relevant on the
reduction of CH4 and N2O, which are the major gases emitted from the agriculture sector.

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Manure left on
pasture, 37%

GHG EMISSION (%)
Manuremanagement, 2%
Burningsavanah,4%
Other,
0.04

Entricfermentation,
53%

Mineralfertilizer , 2%
Manure applied
to soils , 1%
Burning crop
residues, 1%

Figure 1. Sources of greenhouse gas (GHG) emissions in Ethiopia. Adapted from FAO (FAO
2016).
Climate-Smart Technologies and Practices
CSA practices include diverse on-farm practices such as agronomy, agroforestry, livestock,
forestry, land use, pastoral and grazing, water and soil management, and bioenergy (Thorn et al.
2016). CSA practices and technologies should address three core components: sustainably
increasing productivity, supporting farmers’ adaptation to climate change, and reducing GHG
emissions (FAO 2010). There are a wide range of conventional agricultural practices at farm level,
which usually affect soil structure, moisture and fertility as well as contributing to erosion. Planting
the same crop year after year encourages among others certain weeds, pests and diseases. Table 1
compares different aspects of conventional and CSA. CSA practices emphasize the adaptation
aspect more than mitigation. Crop modelling studies indicate adaptation benefits to major crops
such as rice, wheat, and maize. On-farm adaptation would lead to significant improvements toyield, avoiding damage for temperature increases of up to 1-2°C in temperate regions and up to
1.5-3°C in tropical regions (Howden et al. 2007). Generally, agricultural practices that have beenfound to be potentially climate-smart in a wide range of perspectives include, but are not restricted
to agroforestry, improved soil management through conservation agriculture, agricultural water
management such as water harvesting and drip irrigation, integrated livestock and rangeland
management, soil fertility management and improved crop varieties. The practices presented inTable 1 lead to higher productivity and improve food security, but their ability to address
adaptation and mitigation varies. Thus, although these technologies may be considered good forclimate-smart options, it is vital for any CSA solutions to take the condition into account to decide
how they contribute to productivity, adaptation/resilience and mitigation in a given location.

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Table 1. Comparison of conventional and climate smart agriculture.
Aspects
Technologies

Conventional agriculture
Conversion of energy sources fromhuman to animal and fossil fueldependent machinery.

Agricultural
inputsIncreased use of fertilizer, pesticides
andherbicides(also
highlydependent on fossil fuels), andinefficiently applied
Expansion of agricultural land area
throughdeforestation
andconversion from grasslands tocropland.

Land areas

Naturalresources
Production
and marketing

Depletion of natural resources (e.g.
land, water, genetic resources),
which are used in the production
systems.
Increased
specializationinagricultural
production
andmarketing systems.

Climate-smart agriculture
Use of energy efficient technologies for
agricultural power (such as for irrigation water
pumping or tillage). Reduces amount of energy
use in land preparation.
Enhanced efficiency of fertilizer. Optimum
supply of soil nutrients over time and space
matching to the requirements of crops with theright product, rate, time and place.
Intensification on existing land areas as main
source of production increase rather than
expansion to new areas. Promote carbon
sequestration including sustainable land use
management.
Restoration, conservation and sustainable use ofnatural resources in agricultural production
systems. Promote carbon sequestration
including sustainable land use management.
More diversification in production, input andoutput marketing systems.

Summary of key climate-smart agricultural practices
Crop
management

Livestock
management• Intercroppingwith legumes• Crop rotations• New cropvarieties• Improved
storage andprocessing
techniques• Greater crop
diversity• Improved
feeding
strategies• Rotationalgrazing• Fodder crops• Grassland
restoration andconservation• Manuretreatment• Improved
livestockhealth• Animalhusbandry
improvements

Soil andwater
management• Contour
planting• Terraces
and bunds• Planting
pits• Waterstorage• Dams,ponds,
ridges• Improved
irrigation
system(e.g. drip)

Conservation
agriculture

Agroforestry

Food energy
systems• Minimum
tillage• Crop residue
retention aspermanent soil
cover.
• Crop rotation
or association(intercropping)• Balanced
application ofmineral andorganic
fertilizers• Boundary• Biogas
trees and• Production
hedgerowsof energy• Nitrogenplants
fixing trees• Improvedon farms
energy• Multipurposesavingtrees
stoves• Improved
fallow withfertilizer
shrubs• Woodlots• Fruitorchards

Conservation Agriculture
Conservation agriculture (CA) was introduced by the FAO (FAO 2008) as a concept forresource-efficient agricultural crop production based on integrated management of soil, water, andbiological resources combined with external inputs. CA is an approach to farming which can63

1st African Conference on Precision Agriculture | 8-10 December | 2020

sustainably increase yields of crops. The principles of CA include minimal soil disturbance;
maintenance of a mulch of carbon-rich organic matter cover that enrich the soil (e.g. crop residues
including cover crops); crop rotation and intercropping including trees, which could include Nfixing legumes; and balanced application of mineral and organic fertilizers. For instance, adoptionof sustainable intensification practices in Ethiopia increased farmers’ income from USD 99 to
USD 240 (Tesfaye et al. 2016) (Fig. 2).

Additional income (USD/ha)
250
200
150
100

170
99

194

201

V+T

D+T

216

240

120

50
T

D

V

V+D

V+D+T

Integrated agronomic practices

Figure 2. Additional income from adoption of integrated sustainable intensification practices in
Ethiopia. Note: T =Tillage; D =Crop diversification (cereal-legume intercropping, rotation); V=Improved maize variety
Agroforestry Practices
Agroforestry has the potential to contribute to both climate change mitigation andadaptation by sequestering carbon and enhancing resilience of the agricultural systems. Trees in
the agroforestry system can help fight climate change by storing carbon in their biomass.
Therefore, agroforestry is considered as a practice of planting trees with crops to exploit theecological and economic interactions of the different components within the same land
management unit. Agroforestry is widely adopted as a climate-smart practice, due to its potentials
for climate change mitigation, adaptation, increasing crop productivity and thereby improve foodsecurity (Coulibaly et al. 2017). Agroforestry enhances soil organic matter content, agricultural
productivity, carbon sequestration, water retention, agro-biodiversity and farmers’ income (Paulet al. 2017). It is increasingly widespread for restoration of degraded sloping lands, to contributeto food security and for economic development.
Challenges and Opportunities
Major productivity gains are possible in Ethiopia given the large gaps between current
yields and the yields that are achievable with improved inputs and crop management practices
while also maintaining low GHG emissions. The policy framework built largely on the Climate
Resilient Green Economy (CRGE) strategy and an enabling institutional infrastructure would
enable Ethiopia to take major steps towards mainstreaming climate change into agriculturalplanning and integrating the CSA into the agriculture sector. The existence of vast agriculturalland in lowlands, water, diverse crop and soil types and varied agro-ecological zones in the country
would enable to implement different climate smart technologies and practices across the country.

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The presence of trained and skilled human power in the agriculture sector at different levels is also
another opportunity to successfully implement CSA.
The major challenges include shortage of relevant CSA technologies and practices; Weakcoordination among stakeholders working on CSA; lack of regulatory framework for
implementation of policies and strategies for CSA; low adoption of CSA practices due to poordissemination and awareness creation mechanisms; and Shortage of finance and facilities. Poor
accessibility, inadequate technology multiplication and supply, unaffordability of the CSA
technologies and practices are also main challenges for implementation and dissemination of CSApractices.
CONCLUSIONS
The gaps in technology adoption and yield are yet very high in Ethiopia despite concerted
efforts to increase production and productivity through the use of improved agricultural
technologies and practices. There are a number of interplaying challenges from technology tofarmers’ capacity to use climate smart practices to boost production and productivity in thechanging climate. Most importantly, there is inadequate use of agricultural technologies andimproved practices in crops, livestock, and NRM. Minimum requirements need to be set for
packaging CSA practices based on farming systems, agroecology, choices of agricultural
enterprises and access to the market. Agro-ecological based and local-specific research anddevelopment approach has been followed for years for demonstration and scaling up of agricultural
technologies. Without adoption of CSA technologies and innovations, farming communities in
Ethiopia will not be able to deal with the effects of climate change and variabilities. The issue of
CSA practices has to receive due attention in an effort to ensure sustainability of the rural
livelihood system and food security goal of the country in the face of climate change. Thus, current
and emerging policies need to include options to facilitate and accelerate uptake and scaling up
strategies of CSA, and to be informed by research to achieve this.
REFERENCES
CIAT:BFS/USAID. 2017. Climate-Smart Agriculture in Ethiopia. CSA Country Profiles for
Africa Series. International Center for Tropical Agriculture (CIAT); Bureau for Food
Security, United States Agency for International Development (BFS/USAID),
Washington, D.C. 26 p.
Coulibaly JY, Chiputwa B, Nakelse T, Kundhlande G. 2017. Adoption of agroforestry and theimpact on household food security among farmers in Malawi. Agricultural
Systems.155:52-69.
EPCC. 2015. First assessment report, working group II agriculture and food security. Addis Ababa,
Ethiopia.
FAO. 2008. Investing in Sustainable Agricultural Intensification: The Role of Conservation
Agriculture Rome.
FAO. 2010. Climate-smart’agriculture: Policies, practices and financing for food security,
adaptation and mitigation. Rome.
FAO. 2016. Ethiopia Climate-Smart Agriculture Scoping Study. Rome: Food and Agriculture
Organization of the United Nations (FAO). Rome. Available at: www.fao.org/3/ai5518e.pdf.

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Howden SM, Soussana J-F, Tubiello FN, Chhetri N, Dunlop M, Meinke H. 2007. Adapting
agriculture to climate change. Proceedings of the national academy of sciences.104:1969119696.
IPCC. 2007. Agriculture. In Climate change 2007: Mitigation. Working Group III Contribution to
the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC).
In: Cambridge University Press.
Lipper L, McCarthy N, Zilberman D, Asfaw S, Branca G. 2018. Climate Smart Agriculture.
Natural Resource Management and Policy.52:2018.
Parry ML, Canziani OF, Palutikof JP, Van Der Linden PJ, Hanson CE. 2007. IPCC, 2007: climatechange 2007: impacts, adaptation and vulnerability. Contribution of working group II to
the fourth assessment report of the intergovernmental panel on climate change. Cambridge
Uni-versity Press, Cambridge, UK.
Paul C, Weber M, Knoke T. 2017. Agroforestry versus farm mosaic systems–Comparing land-use
efficiency, economic returns and risks under climate change effects. Science of the Total
Environment.587:22-35.
Tesfaye K, Cairns J, Kassie M, Misiko M, Stirling C, Abate T, Prasanna B, Mekuria M, Hailu H,
Bahadur Rahut D. Potential for taking climate smart agricultural practices to scale:Examples from Sub-Saharan Africa. Proceedings of the Symposium on Climate Change
Adaptation in Africa; 2016.
Thorn JP, Friedman R, Benz D, Willis KJ, Petrokofsky G. 2016. What evidence exists for theeffectiveness of on-farm conservation land management strategies for preserving
ecosystem services in developing countries? A systematic map. Environmental
Evidence.5:13.
World Bank. 2016. World Development Indicators: Ethiopia. World Bank, Washington D.C.

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#7927 CHANGES IN CLIMATIC FACTORS LEAD TO THE CHANGE IN CULTURAL
WEDGING OF RICE IN THE IVORIAN PRE-FOREST ZONE
Y.C. Brou, D.A. Kouassi, K.P-M. Kouakou, and E-O. Tienebo
Research and Innovation Unit in Agronomic Sciences and Rural Engineering,
Félix HOUPHOUËT-BOIGNY National Polytechnic Institute,Yamoussoukro, Côte d'Ivoire.
casimir.brou@inphb.ci

ABSTRACT
Climate change in the pre-forest zone of Cote d'Ivoire has led to a mismatch between
cropping periods and new seasons, challenging the sowing periods usually recommended for
rainfed rice cultivation in this area. Our study aims to determine the optimal sowing period for two
rainfed rice varieties cultivated in this pre-forest zone of the country. The agro-climatic analysis
carried out over the period 1980-2017 allowed to determine the optimal dates for sowing rice,which ensures, with the maximum probability, the crop's water satisfaction over its entire cycle.
This analysis showed that for an annual probability of success over 80%, the optimal sowing period
ranges from March 22 to April 26 for the 120-day average cycle rainfed rice variety and, fromMarch 27 to May 11, for the 100-day short cycle rainfed rice variety. These periods allow a goodwater supply for the crop's first cycle, but it is not possible to implement a second cycle with thesame water supply levels in this area.
INTRODUCTION
Rice (Oryza sativa) is a staple food for the whole Ivorian population. The overall use ofrice in Cote d'Ivoire in 2011, compared with 1960, was tenfold increased to 1.43 million tons, with
about 50% of domestic demand not covered by the national production (Bahan et al. 2012; JICA
and JAICAF 2013). Consequently, Cote d'Ivoire relies on massive importations to satisfy the local
demand for rice.
In response to this issue, the country is engaged in an ambitious National Rice Sector
Development Program in order to achieve food self-sufficiency (ONDR, 2012). Emphasis has been
focused on rainfed rice which occupies 86% of the cultivated areas and contributes to 80% of thenational paddy production (Zingore et al. 2014). However, rice productivity is still low andstrongly constrained by the adverse effects of climate changes in the study region where thebimodal four-season rainfall regime is gradually being replaced by a monomodal two-season
rainfall regime (Diomandé et al. 2017).Therefore, an update of the cropping calendar is needed forthe development of rainfed rice cultivation in this area. This study aims to address this problem bydetermining the optimal sowing period for rainfed rice grown in the pre-forest zone of Coted'Ivoire.

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MATERIALS AND METHODS
The department of Yamoussoukro (latitude 6.85 North; longitude 5.29 West; altitude 214m) is the study area.
Two categories of data were used in this study: climatic and agronomic data. Daily climate
data over 38 years (1980 to 2017) were supplied by the SODEXAM weather Station in
Yamoussoukro (latitude North 6.90; longitude East -5.37). The ETP (Potential Evapotranspiration)
values for the area were calculated using the Penman-Monteith formula (Allen et al. 1998).
Agronomic data consist first in soil water capacity (RU), and rhizosphere water holding
capacity (RUR) estimated at 70 mm.
In this study, one medium-cycle of 120 days (IDSA 85) and one short-cycle of 100 days
(Nerica 1) varieties were used. For these two rice varieties, there are four major growing phases,
and each one is characterized by a crop coefficient Kc that determines the phase waterrequirements (Allen 2000).
The agro-climatic analysis approach was used in this study to determine the favorable
sowing period and the optimal sowing period for rice. The optimal sowing period ensures, withthe maximum probability, that both water requirements for the emergence and those for crop's
development and growth (Lhomme and Monteny 1981), particularly the heading-flowering andmaturation phases, are achieved.
The good water supply conditions for the heading-flowering and maturation phases were
determined from the rainfed rice water requirements index I defined by Frère (1987), whose values
should be greater than or equal to 95. Indeed, Frère (1987) developed a method for estimating theindex I of satisfaction of crop water needs in countries where water is a limiting factor in rainfed
agriculture. Index I expresses the degree to which the plant's cumulative water requirements havebeen achieved at a given phase or for the entire growing cycle.
The water balance is based on a relatively simple principle. At the beginning of the rainyseason, the index I was assigned the value 100 on the assumption that at sowing time, the watercontent in the soil is higher than the water requirements of the plants. I value decreases as soon as
water stress occurs. In the event of a water deficit (Di), the I index is reduced by the percentage ofthis deficit in relation to the total water requirements for the season (TMR).
If (Ei/Di) < 0 then I_i=I_(i-1)-(E_i/D_i ×100)/TMR, with TMR = Kci ×ETP
If 0 ≤ Ei/Di ≤ 100 then Ii = Ii-1.

(1)

Kci: crop coefficient of the plant at a phenological stage and a given decade or pentad i (Dancette1983); ETPi: potential evapotranspiration of the decade or pentad I; and Ei: excess water in thesoil of the decade or pentad i.
In the event of excess water in the soil of more than 100 mm, considered as excess water
harmful to the plant, the index will be reduced by three units.
If (Ei /Di) > 100, then Ii = Ii-1 - 3(2)

Depending on the frequency level, we have cases of excellent (I=100%), good (95≤ I ≤ 99),
moderate (80 ≤ I ≤ 94), mediocre (60 ≤ I ≤ 79) and poor (50 ≤ I ≤ 59) water supply (Sarr 2007;
Sarr et al. 2012).

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The analysis of the evolution, as a function of sowing dates, of the likelihood of dry
sequences occurring for more than ten days during the 30 days following sowing, as well as theevolution of the likelihood of good water supply conditions in the heading-flowering andmaturation phases enabled to determine the optimal period for the sowing of rainfed rice in the
study area.
The optimal sowing period was determined graphically from the intersection of three
curves: (i) the evolution curve, as a function of sowing dates, of the likelihood of dry sequences
exceeding ten days occurring, 30 days after sowing of rainfed rice, (ii) the evolution curve, as afunction of sowing dates, of the likelihood of good water supply conditions (I ≥ 95) of the headingflowering phase, and (iii) the evolution curve, as a function of sowing dates, of the likelihood of
good water supply conditions occurring (I ≥ 95) in the maturation phase
RESULTS AND DISCUSSION
Optimal Sowing Period for Average Cycle Rainfed Rice of 120 Days
From the intersection of the three curves (Figure 1), the optimum period for sowing rainfed
rice of 120 days ranges from March 22 to April 26 with a probability of success greater than 80%.
Sowing between March 22 and April 26 will provide a good water supply for a 120-day variety ofrainfed rice in more than 80% of the years.
Flowering with I ≥ 95
Maturity with I ≥95
Probabilities ss >10
Pr=80

100
90

Probabilities (%)

80
70
60
50
40
30
20
10

27-Dec7-Dec

17-Nov

28-Oct8-Oct

18-Sep29-Aug9-Aug

20-Jul30-Jun10-Jun21-May1-May

11-Apr22-Mar

2-Mar

10-Feb

21-Jan1-Jan

0

Fig 3 Graphical determination of the optimal sowing period for average cycle rainfed rice by theintersection of the three curves
I: index I of satisfaction of crop water needs at a given phase or for the entire growing cycle; ss:
dry sequences
Dates of sowing
Optimal sowing period (Pr > 80 %)Figure 1. Graphical determination of the optimal sowing period for average cycle rainfed rice by
the intersection of the three curves. I: index I of satisfaction of crop water needs at a given phaseor for the entire growing cycle; ss: dry sequences.

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Optimal Sowing Period for Short-Cycle Rainfed Rice of 100 Days
For rainfed rice of 100 days, according of the intersection of the three curves (Figure 2), theoptimum period for sowing ranges from March 27 to May 11. Sowing made in this period will
provide a good supply of water for a 100-day variety of rainfed rice with more than 80% of success.

Heading-flowering (I≥95)
Maturity (I≥95)
100

Probability ss > 1090
Probability (%)

80
70
60
50
40
30
20
10

Optimal sowing period (Pr > 80 %)27-Dec7-Dec

17-Nov

28-Oct8-Oct

18-Sep29-Aug9-Aug

20-Jul30-Jun10-Jun21-May1-May

11-Apr22-Mar

2-Mar

10-Feb

21-Jan1-Jan0

Dates of sowing

Figure 2. Graphical determination of the optimal sowing period for short cycle rainfed rice byintersecting the three curves. I: index I of satisfaction of crop water needs at a given phase or forthe entire growing cycle; ss: dry sequences.
In Cote d'Ivoire, with the climate change, the imperative of adapting farming practices toclimate change is well established (Ouédraogo et al. 2010). Our results show that the optimal
sowing period for the considered 120-day variety ranges from March 22 to April 26 when
considering an annual success rate of more than 80%. For 100-day rice variety, it is from March
27 to May 11.
The optimal sowing period for short cycle rice is included in the period indicated in 2005
by the National Center for Agricultural Research (Bouet et al. 2005). However, for 120-day rice
variety, the optimal period determined in our study does not include the month of June suggestedby the National Center for Agricultural Research (Bouet et al. 2005). It appears that the effects ofclimate change since 2005 have once again disrupted the rainfed rice cropping calendar in the
study area. Similar results were obtained by Kouakou et al. (2013) in west-central Cote d'Ivoire.
In addition, the narrowness of the identified optimal sowing periods does not favor a second crop
cycle. Indeed, a possible second crop cycle sown outside the identified optimal periods will beconfronted with a lower probability of success (Probability < 0.8), implying a higher risk of failure.

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REFERENCES
Allen RG. 2000. Using the FAO-56 dual crop coefficient method over an irrigated region as partof an evapotranspiration intercomparison study. Journal of Hydrology 229: 27–41.
https://doi.org/10.1016/S0022-1694(99)00194-8
Allen RG, Pereira LS, Raes D, Smith M. 1998. Crop evapotranspiration-Guidelines for computing
crop water requirements. FAO Irrigation and drainage paper 56 300: 327.
https://www.researchgate.net/publication/235704197_Crop_evapotranspirationGuidelines_for_computing_crop_water_requirementsFAO_Irrigation_and_drainage_paper_56
Bagnouls F, Gaussen H. 1957. Les climats biologiques et leur classification. Annales deGéographie 66: 193–220. https://www.jstor.org/stable/23443505
Bahan F, Kéli J, Yao-Kouamé A, et al. 2012. Caractérisation des associations culturales à base deriz (Oryza sp): cas du Centre-Ouest forestier de la Côte d’Ivoire. Journal of Applied
Biosciences 56: 4118–4132. http://m.elewa.org/JABS/2012/56/8.pdf
Dancette C. 1983. Estimation des besoins en eau des principales cultures pluviales en zone
soudano-sahélienne.
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Diomandé M, Dongo K, Koné B, et al. 2017. Vulnérabilité de l’agriculture pluviale au changementde régime pluviométrique et adaptation des communautés rurales du «V-Baoulé» en Côte
d’Ivoire.
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Frère M. 1987. Suivi agrométeorologique des cultures et prévision des rendements. FAO, Rome,
Italie, p 170
JICA, JAICAF. 2013. Etude de Collecte d’informations dans le secteur agricole en Côte d’Ivoire.
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p236.
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Kouakou KE, Kouakou A, Kouassi FW, et al. 2013. Détermination des périodes optimales desemis du riz pluvial au Centre-ouest de la Côte d’Ivoire. International Journal of Innovation
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719–726.
http://www.ijias.issrjournals.org/abstract.php?article=IJIAS-13-109-03
Lhomme JP, Monteny B. 1981. Une méthode d’analyse agroclimatique pour le calage des cycles
culturaux en zone intertropicale. L’Agronomie Tropicale 36: 334–338.
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ONDR. 2012. Stratégie Nationale Révisée de Développement de la filière Riz en côte d’ivoire(SNDR)
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2020.
ONDR,
Côte
d’Ivoire,p40.
https://riceforafrica.net/downloads/NRDS/Cote_dIvoire_fr.pdf
Ouédraogo M, Dembélé Y, Somé L. 2010. Perceptions et stratégies d’adaptation aux changements
des précipitations: cas des paysans du Burkina Faso. Science et changements
planétaires/Sécheresse
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https://pdfs.semanticscholar.org/b6e3/d12884032821677dcc4f3d1ab1cedce68a78.pdf
Sarr B. 2007. Manuel d’utilisation destiné aux ingénieurs en agrométéorologie. Centre Régional
AGRHYMET, Niamey, Niger
Sarr B, Atta S, Kafando L. 2012. Revue des indices climatiques utilisés dans les systèmesd’assurances agricoles indicielles en Afrique. Science et changements

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planétaires/Sécheresse23:
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Zingore S, Wairegi L, N’Diaye MK. 2014. Guide pour la gestion des systèmes de culture de riz.
Consortium Africain pour la Santé des Sols, Nairobi Le Consortium Africain pour la SantédesSols(ASHC)
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http://africasoilhealth.cabi.org/wpcms/wpcontent/uploads/2016/06/French-Rice-Guide-A4-BW-lowres.pdf

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DECISION SUPPORT SYSTEMS73

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#7549 CROPSAT – OPPORTUNITIES FOR APPLICATIONS IN PRECISIONAGRICULTURE IN AFRICA
O. Alshihabi1, I. Nouiri2, M. Mechri3, H. Angar3, K. Piikki1, J. Martinsson4, and M. Söderström1
1
Swedish University of Agricultural Sciences (SLU), Skara, Sweden; 2National Agronomic
Institute of Tunisia (INAT), Tunis, Tunisia; 3National Institute of Field Crops (INGC), Bou
Salem, Tunisia; 4Dataväxt AB, Grästorp, Sweden, omran.alshihabi@slu.se
ABSTRACTThe present paper aims at describing the CropSAT system, a Sentinel-2-based interactive
decision support system (DSS) that provides vegetation index (VI) maps free-of-charge all acrossthe globe for different applications in precision agriculture. We summarize research results fromthe ongoing developmental process and pointing to opportunities for development and applicationin precision agriculture in Africa. The DSS was initially developed in a research project at theSwedish University of Agricultural Sciences (SLU), and has since its launch in 2015 been
continuously developed in a private-public-partnership between SLU, private companies and theSwedish Board of Agriculture. One of the main applications of CropSAT is providing spatial
variation maps of several VIs to be used in variable rate application (VRA) of any input inagriculture (fertilizers, pesticides or growth regulators). These maps could be either downloaded
in different formats compatible with a wide range of spreaders/sprayers available in the machinery
market or printed out to be used manually or with the help of smart phones localization apps, forexample to support discussion in advisory situations. Such a DSS is an appropriate platform fordeveloping other application using the satellite images like nitrogen uptake estimation, protein
content/yield prediction and water stress assessment. Ongoing research is now running to develop
and integrate models in CropSAT for new applications and the tool is subject of research anddevelopment projects in other countries worldwide. An initial study was carried out to test the DSS
in Tunisia in collaboration between SLU, the National Agronomic Institute of Tunisia (INAT) andthe National Institute of Field Crops (INGC) to assess the feasibility for application under arid andsemi-arid climate where different crops varieties are used. Further development in Tunisia will
focus on integrating crops water status indices in order to use the tool for irrigation water
management. Now CropSAT has continuously updated global coverage with new satellite images(about every three days in North Africa), and is provided in multiple languages including English,
Arabic and French.
Keywords: CropSAT, decision support system, Sentinel-2, precision agriculture, nitrogen
fertilization
INTRODUCTION
Increasing crop yield and improved quality is a main target of nitrogen (N) application. Welladjusted N rates means improving nitrogen use efficiency and in turn better profit and minimized
risk of negative environmental impact associated with non-optimal nutrient application, such as
nitrate leaching (Delin et al., 2014) and emissions of gaseous nitrous oxide (Balafoutis et al., 2017).
The web-based decision support tool CropSAT (Dataväxt AB, Grästorp, Sweden) allows the74

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optimization of the application of fertilizers and pesticides using variable rate application (VRA)
technology at the within-field scale (Söderström et al., 2017, Alshihabi et al., 2020). The system
has global coverage and is free of charge. CropSAT It is a decision support system that was initiallybased on DMC low-cost data with 22m spatial resolution, with 30-m Landsat 8 as backup. From2015 and 2017, the first of two Sentinel-2 satellites became available, with higher spatial resolution(10 m) and some additional spectral bands within the red edge region of the crop canopy
reflectance spectra with 20-m resolution (Figure 1), dedicated to vegetation studies. The vegetationindex (VI) used, after initial performance tests, was the modified soil adjusted vegetation index(MSAVI2; Eq.1; Qi et al. 1994). Other indices were considered but rejected because they reachedsaturation too early in the season (e.g. the normalized difference vegetation index NDVI; Rouse etal. 1973). Common broadband vegetation indices (e.g. NDVI, MSAVI2) are based on thereflectance differences in the NIR and RED regions (ρRED and ρNIR).
1𝑀𝑆𝐴𝑉𝐼2 = *(2 × 𝜌./0 + 1) 4[(2 × 𝜌./0 + 1)6 − 8 × (𝜌./0 − 𝜌09: )]<2(1)

Figure 1. Spectral bands of the satellites used in CropSAT in part of the visible to near-infrared(NIR) region.
MATERIALS AND METHODS
The global version of the CropSAT system follows a simple interactive workflow:
1. First, an area of interest (often a crop field) is delineated by digitizing the area boundaries
manually on the screen.
2. Then, a list of Sentinel-2 images available for that field is previewed and an appropriate
image not affected by the clouds or their shadows is selected.
3. Rates of N fertilizer (or any other product) for five intervals of a VI map calculated fromthe chosen image are specified.
4. A derived VRA map can be modified by interactive tools (e.g. by drawing on the map orby specifying a new mean rate).
5. The VRA map can then be exported in various file formats (for different brands of
spreaders or sprayers) or downloaded as a map for manual use.

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Figure 2. Screenshot from CropSAT, showing a vegetation index map of a field near Kenitra,
Morocco, 01 Feb 2020.
An important question that arises in this type of decision support system, is a non-entirely
new images can be useful? In this system, only the within-field relative variations are important,and the interpretation of the different index values are left to the user (Figure 2). In some cases,
the spatial variation pattern is very stable and a map is more or less similar during a few weeks’time, in other cases, changes in the pattern may occur quicker. Therefore, it is recommended thatusers do field checks to investigate if the map seems to display the current pattern. In CropSAT,
fields with clouds or cloud shadows are automatically removed, and pixels within 15 m of the fieldborders are removed; removed pixels are subsequently recalculated (through averaging) by theremaining neighboring pixels within the field. The level of crop N requirement should preferably
be decided at a few representative locations in the field through user experience or the use of tools
such as the Yara N-Tester (which is based on the Minolta SPAD-meter and measures lighttransmitted by the plant leaf at 650 and 940 nm (e.g. Uddling et al. 2007)) that can assist inproviding an N recommendation to the user. Obtained N values are then inserted into CropSAT
and a VRA file is generated and can be downloaded and used for controlling the spreader. For an
increasing number of countries, there is also a cloud-based solution for transferring VRA files tothe spreader.
RESULTS AND DISCUSSION
Several research studies have been, and are being, carried out in the continuous developmentof the DSS. Methods have been developed for translation of VI maps to N uptake maps (in winterwheat), to N rate maps (oilseed rape) and for spatial grain protein content predictions for harvest
planning (malting barley). So far, these further developments are available in the Swedish versionof CropSAT. However similar research can be carried on in any country for local conditions andcultivars. The system is now widely used, particularly in Scandinavia, as a DSS for VRA of N but
also for VRA of other inputs, e.g. pesticides and growth regulators. In addition to the global
version, there are a number of nationally adapted versions, developed and run in collaboration withlocal organizations in these countries and he tool supports several languages (more than ten
different language, including English, French and Arabic), which opens door for possible further76

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developments and dissemination in Africa and the Middle East region. Sustainable uses of natural
resources in agriculture in arid and semi–arid areas require integrated management of soil andwater. Water scarcity and climate change adaptation/mitigation demand scientifically based DSSs
for site-specific fertilizer application and water management in the agricultural sector. In futureversions of CropSAT, also VIs related to the water status (such as NDWI; McFeeters, 1996) of thecrop could be presented as decision support for variable-rate irrigation and VRA of nitrogen, sothat farmers and authorities can manage their natural resources in a sustainable, profitable andpractical way. The feasibility of the DSS for use under arid and semi-arid climate will be
investigated in a collaboration between SLU and the National Agronomic Institute of Tunisia(INAT) and the National Institute of Field Crops (INGC), two institutions concerned bydeveloping the field crops in the north of Tunisia. The first test on the ground under the Tunisianconditions showed a well functionality of the system under sub-humid to arid climate conditions.
The full adaptation of the system to different climate conditions is planned in Tunisia as a starting
point for a wider dissemination in arid and semi-arid areas.
ACKNOWLEDGEMENTS
CropSAT was developed in a project financed by the Swedish Foundation for Agricultural
Research (SLF; project no. H1233115). Subsequent funding of development/running has been
provided by VGR/SLU (contract: RUN 2018-00141) together with Dataväxt AB (Grästorp,
Sweden) and the Swedish Board of Agriculture (Jönköping, Sweden).
REFERENCES
Alshihabi O, Piikki K, Söderström M. 2020. CropSAT – A Decision Support System for PracticalUse of Satellite Images in Precision Agriculture. In: El Moussati A., Kpalma K., Ghaouth
Belkasmi M., Saber M., Guégan S. (eds) Advances in Smart Technologies Applications
and C ase Studies. SmartIC T 2019. Lecture Notes in Electrical Engineering, vol 684.
Springer, C ham. Available at: https://doi.org/10.1007/978-3-030-53187-4_45 Verified
September 09, 2020.
Balafoutis A, Beck B, Fountas S, Vangeyte J, Wal TVD, Soto I, et al. 2017. Precision agriculture
technologies positively contributing to GHG emissions mitigation, farm produc-tivity andeconomics. Sustainability 9(8): 1339.
Delin S, Stenberg M. 2014. Effect of nitrogen fertilization on nitrate leaching in relation to grain
yield response on loamy sand in Sweden. European Journal of Agronomy 52: 291–296.
McFeeters SK. 1996. The use of normalized difference water index (NDWI) in the delineation ofopen water features. International Journal of Remote Sensing 17: 1425–1432.
Qi J, Chehbouni A, Huete AR, Kerr YH. 1994. Modified Soil Adjusted Vegetation Index(MSAVI). Remote Sensing of Environment 48: 119-126.
Rouse JW Jr, Hass RH, Schell JA, Deering DW. 1973. Monitoring vegetation systems in the Great
Plains with ERTS. In Proceedings Earth Resources Technology Satellite (ERTS)
Symposium, 3rd., Greenbelt, MD, 10–14 Dec 1973 (Vol. I, pp. 309–317). Washington,
DC: NASA SP-351, NASA.
Söderström M, Piikki K, Stenberg M, Stadig H, Martinsson J. 2017. Producing nitrogen (N) uptake
maps in winter wheat by combining proximal crop measurements with Sentinel-2 and77

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DMC satellite images in a decision support system for farmers. SOIL & PLANTSCIENCE, https://doi.org/10.1080/09064710.2017.1324044.
Uddling J, Gelang-Alfredsson J, Piikki K, Pleijel H. 2007. Evaluating the relationship between
leaf chlorophyll concentration and SPAD-502 chlorophyll meter readings. Photosynthesis
Research 91: 37–46.

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#7594 APPLICATION OF PRECISION AGRICULTURE TECHNOLOGIES INGRAZING: EXAMPLE OF GOATS BROWSING IN FOREST RANGELANDS OFNORTHERN MOROCCOa

Y. Cheblia,b, S. El Otmania,b, J.L. Hornickb, J.F. Cabarauxb, M. Chentoufa
National Institute of Agricultural Research (INRA), Tangier, Morocco; b Faculty of Veterinary
Medicine, University of Liège, Liège, Belgium
youssef.chebli@inra.org.ma
ABSTRACTThe aim of this study was to characterize patterns of grazing behavior of goats in a
Mediterranean forest rangeland. We conducted a one-year study in Chefchaouen region, Northern
Morocco, during two contrasting seasons (spring and summer) using new technological tools.
Eight goats were simultaneously fitted with GPS collars and sensors for 3 days during each season.
A calibration study and classification tree analysis were used to predict other grazing activities ofgoats (eating, walking, and standing without grazing). During spring, goats tended to select lower
elevation feeding stations (1070 vs 1200 m), traveled a short distance (5.9 vs 8.5 km), and grazed
in a small area (12 vs 15 ha). Due to low forage availability and stressful conditions during summer,goats increased walking and lying activities at the expense of grazing (eating). These findings
contribute to a better understanding of the grazing behavior of goats in forested rangelands.
Overall, the application of precision agriculture technologies in grazing could be useful for better
spatio-temporal management of herds.
INTRODUCTION
The extensive goat production system is one of the most important components ofagricultural systems in the southern Mediterranean side. In Northern Morocco, forest rangelands
areas have always formed an integral part of goats feeding (Chebli et al., 2018). These forest
pastures constitute an important forage reserve, protecting goats during feed gaps and drought
periods (Chebli et al., 2020a).
Grazing is associated with daily activities significantly different from those of animals in
confinement, such as grazing and traveled distance. Several researches has been undertaken to
explore the potential for precision agriculture in the livestock system. Unfortunately, few studies
have focused on the grazing activities of goats, especially in the woodlands. Effective managementof goats on North Moroccan forest rangelands requires understanding their grazing behavior todevelop targeted decisions to improve grazing and feeding strategies. It is quite difficult tounderstand the grazing behavior of goats, especially in a complex Moroccan forest rangeland. A
challenge for research is to analyze the grazing behavior of goats by using precision agriculture
technologies.
The objective of the present study was to investigate the grazing behavior of goats in a
Mediterranean forest rangeland using new technologies, taking into account two contrastingseasons of the year.

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MATERIALS AND METHODS
The research was carried out in Chefchaouen region, which is located in Northern Morocco(35°15´N; 5°24´W). Eight alpine goats were selected from a representative goat farm breeding aflock of 52 heads grazing year-round. The experiment was conducted during the two contrastingseasons of the year, spring, and summer.
The forage availability was estimated during each season as described by Chebli et al.
(2017 and 2020a). The measurements were undertaken in the last month of each studied season.
To estimate activities of goats during grazing, each one was simultaneously fitted with GPS collars(locomotion activities) and pedometer sensors (steps, the time spent lying and standing) for 3 days
during each studied season. A calibration study and classification tree analysis were used to predictgrazing activities of goats at 5 min intervals as described by Brassard et al. (2016). The
classification and regression tree (CART) analysis were used to construct the classification trees.
Data were analyzed using SAS software (SAS Inst. Cary, NC, USA). All parameters were analyzed
according to PROC MIXED procedure. For all analyses, the significance level was declared at P
< 0.05.
RESULTS AND DISCUSSION
The study area is considered as a degraded forest rangeland. It was mainly covered byheterogeneous vegetation composed of three distinct groups of plant species: herbaceous, shrubs(Arbutus unedo, Calicotome villosa, Cistus spp., Erica arborea, Lavandula stoechas, Myrtus
communis, Phillyrea media, Pistacia lentiscus, Rubus ulmifolius), and trees (Quercus suber andOlea europaea). The results indicated a higher forage availability during spring (1645 kg DM/ha)compared to the summer (911 kg DM/ha). This result can be explained by the growing conditionsof each plant favored mainly by precipitation recorded during winter and early spring. The declines
of forage availability during summer are also reported in similar studies (Schlecht et al., 2009;
Chebli et al., 2020b).
Figure 1 displayed the variation of grazing activities of goats during both studied seasons.
All studied parameters of grazing activities of goats varied significantly with the season (P < 0.05),
which was confirmed by several authors (Safari et al., 2011; Rodrigues et al., 2013). During
summer, goats graze in a large area (15 vs 12 ha) and traveled towards the high altitudes (1200 vs
1070 m) compared to the spring season. Due to the low forage availability recorded during the dry
season (summer), goats search for more favorable feeding stations, which are located mainly inhigh altitudes. This was confirmed by the high vertical distance traveled by goats during summer
compared to the spring (646 vs 205 m). Also, the number of steps was numerically greater (9490
vs 6983) and the horizontal distance traveled by goats was higher (8.5 vs 5.9 km) during the dry
season. This result could be explained by the low abundance of preferred shrubs and herbaceous
during the summer. Similarly, Zampaligré and Schlecht (2017) reported that the abundance ofpalatable species decreased the traveled distance of goats. Moreover, the seasonal changes in goatsgrazing activities could be explained by the duration of grazing day, which was shorter in spring(7 h) and longer during the summer (10 h). The goats prolonged their lying time in summer incomparison to the spring (22 vs 12 %) at the expense of standing duration. In order to increasegrazing (eating) time, the herder extends the duration of grazing day of goats to recover the lost
times allocated for lying and walking during the summer. Besides, increased time of lying could
be also linked to the increasingly stressful conditions (high temperature and humidity) recorded80

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during the dry season. In accordance with many relevant studies, forage availability is the major
determinant of grazing activities (Schlecht et al., 2009; Zampaligré and Schlecht, 2017).
As observed in this study, goats traveled considerable horizontal and vertical distances,which could increase their energy expenditure in contrast to goats in the flat rangeland.

Figure 1. Grazing activities of goats in a forest rangeland of Northern Morocco.
CONCLUSIONS
The results contribute to a better understanding of the grazing behavior of goats in forest
rangelands. These findings could be used as the first guide for future studies and managers
interested in grazing activities of goats. Overall, the application of precision agriculture
technologies in grazing could be useful for better spatio-temporal management of herds.
811st African Conference on Precision Agriculture | 8-10 December | 2020

REFERENCES
Brassard ME, Puchala R, Gipson TA, Sahlu T, Goetsch AL. 2016. Factors influencing estimatesof heat energy associated with activity by grazing meat goats. Livest. Sci. 193:103-109.
Chebli Y, Chentouf M, Hornick JL, Cabaraux JF. 2017. Extensive goat production systems in
Northern Morocco: production and use of pastoral resource. Grassland Science in Europe.
22:131-133.
Chebli Y, Chentouf M, Ozer P, Hornick JL, Cabaraux JF. 2018. Forest and silvopastoral coverchanges and its drivers in Northern Morocco. Appl. Geogr. 101:23-35.
Chebli Y, El Otmani S, Chentouf M, Hornick JL, Bindelle J, Cabaraux JF. 2020a. Foraging
Behavior of Goats Browsing in Southern Mediterranean Forest Rangeland. Animals 10:
196.
Chebli Y, El Otmani S, Hornick JL, Cabaraux JF, Chentouf M. 2020b. Pastoral production anduse of forest rangelands in Western Rif. AFRIMED AJ–Al Awamia 128:1–16.
Rodrigues MM, Oliveira ME, de Moura RL, Rufino MOA, Silva WKA, Nascimento MPSCB.
2013. Forage intake and behavior of goats on Tanzania-grass pasture at two regrowth ages.
Acta Sci. Anim. Sci. 35:37-41.
Safari J, Mushi DE, Kifaro GC, Mtenga LA, Eik LO. 2011. Seasonal variation in chemicalcomposition of native forages, grazing behaviour and some blood metabolites of Small
East African goats in a semi-arid area of Tanzania. Anim. Feed Sci. Technol. 164:62-70.
Schlecht E, Dickhoefer U, Gumpertsberger E, Buerkert A. 2009. Grazing itineraries and forage
selection of goats in the Al Jabal al Akhdar mountain range of Northern Oman. J. Arid
Environ. 73:355-363.
Zampaligré N, Schlecht E. 2017. Livestock foraging behavior on different land use classes along
the semi-arid to sub-humid agro-ecological gradient in West Africa. Environ. Dev. Sustain.
20:731-748.

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#7889 APPLICATION OF INFORMATION & COMMUNICATION TECHNOLOGIES(ICTS) INTO PRECISION FARMING ECOSYSTEM IN ETHIOPIAN DIGITALAGRICULTURE DEVELOPMENT PERSPECTIVE PLAN
Tadesse Anberbir
Ethiopian Institute of Agricultural Research (EIAR), Addis Ababa, Ethiopia,
taddeseanb@gmail.com /tadesse.anberbir@eiar.gov.et
ABSTRACT
Information and Communication Technologies (ICTs) in agriculture is an emerging field
focusing on the enhancement of agriculture and digital technologies for precision farming are now
replacing the traditional farming system. The Ethiopian government recognized the importance
ICTs for the country’s development and ICT in Agriculture is also expected to be a key catalyst
for the transformation of Agriculture so as to contribute for the economic prosperity in the country.
The endorsed and currently enforced ICT policy and the recent Digital Ethiopia strategy 2025 are
the demonstration of its commitment to the development of ICTs in Agriculture and an enabler ofsocioeconomic transformation. Ethiopian Institute of Agricultural Research (EIAR) is a federal
government organ, with a network of 20 research centers and more than 55 sub centers representing
various agro-ecological settings. In EIAR, integrating ICTs into agriculture research, aiming at
generation of context specific evidences to measure values of digital technologies in precision
farming, compared to the analogue method, is considered as a major catalyst for modernizing the
research system. However, there exist major challenges of building ICT infrastructure for sharing
and exchanging agricultural knowledge generated from research at national and regional levels
and there is a huge gap between the agricultural researchers, extension officers and farmers related
to sharing, exchanging and disseminating agricultural knowledge and technologies. This paper
presents the result of a survey on the use of ICTs in agricultural research, challenges, lessons andfuture needs in EIAR and discusses progresses on the application of precision farming technologies
mainly on the use of unmanned aerial vehicles (UAVs for soil moisture nutrient and disease
detection at crop critical growth stages, aiming at spot based and immediate response. The findingsof the survey revealed that the use of ICT for agricultural research is still at its initial stage andlack of ICT policy and awareness about ICT for agriculture (e-Agriculture) by researchers andadministrative staff is the main challenge. The poor ICT infrastructure, lack of centralized
agricultural research information system, access and dissemination of the resources currently
available in EIAR are also critical challenges which limits the full integration of Digital
Agriculture practices. However, the recent initiatives on use of UAVs for precision agriculture
and digitization of the Wheat rust diseases monitoring and early warning processes are believed tohelp breaking the old thinking and revolutionize the traditional way of data collection, analysis
and interpretation. The possible integration of soil-crop-disease diagnoses tools, the remotesensing; including the current 13.75m resolution ETRSS-1 satellite imageries is a good initiativeand so far, the preliminary results showed that precision agriculture practices are very critical for
modernizing the research activities in EIAR and transforming smallholders to smart farmers
through large scale demonstration farming approach.
Keywords: ICT, digital agriculture, precision farming, UAVs, wheat rust surveillance83

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INTRODUCTION
Information and Communication Technologies (ICTs) in agriculture is an emerging field
focusing on the enhancement of agriculture and the advancement in ICT can be utilized forproviding accurate, timely, relevant information and services to the farmers, extension agents andresearchers. ICTs are used to digitalize agricultural processes and precision farming are now
replacing the traditional farming system.
Agriculture is an important sector with the majority of the rural population in developing
countries depend on it. In Ethiopia, agriculture influence the overall economic performance andpoverty reduction and it accounts for nearly 40% of GDP and over 70% of employment.
Accordingly, the Ethiopian government gives special attention to the sector and like in othersectors the government recognized the importance of ICT for modernizing the agricultural
practices and developments. ICT in Agriculture is expected to be a key catalyst for thetransformation of Agriculture so as to contribute for the economic prosperity in the country. The
endorsed and currently enforced ICT policy and the recent Digital Ethiopia strategy 2025 are thedemonstration of its commitment to the development of ICTs in Agriculture and an enabler ofsocioeconomic transformation. Projects such as 8028 IVRS/SMS toll-free hotline for farmers,
Market Information System (MIS), Wheat Rust Surveillance Information System, Livestock
tracking system and e-extension services which are implemented by the Ministry of Agriculture(MoA), Ethiopian Institute of Agriculture (EIAR), Agricultural Transformation Agency (ATA)
and other partners shows some of the efforts that the government commitment to the sector on
using ICT for agriculture so as to digitizing the agricultural processes across the value chain.
However, the sector faces major challenges in enhancing production and to sustain and improve
the livelihood of small holder farmers and despite these and other initiatives, there exist major
challenges of building ICT infrastructure for sharing and exchanging agricultural knowledge
generated from research at national and regional levels and there is a huge gap between the
agricultural researchers, extension officers and farmers related to sharing, exchanging anddisseminating agricultural knowledge and technologies. In general, the digitization of agriculture
in Ethiopia is still far behind most other countries [1] and Precision Agriculture (PA) practices arevery limited and most of the small holder farmers still use traditional farming system.
Therefore, ICT technologies play an important role in addressing these challenges anduplifting the livelihoods of the rural people. This paper reports the result of a survey on the use of
ICTs in agricultural research in EIAR, which was conducted using an online questionnaire andinterviews, and discusses current digital agriculture initiatives and progresses on the applicationof precision farming technologies mainly on the application of unmanned aerial vehicles (UAVs)
and Wheat Rust Early Warning & Advisory System.
Application of ICTs in EIAR
The Ethiopian Agricultural Research System (EARS) consists of Ethiopian Institute ofAgricultural Research (EIAR), seven Regional Agricultural Research Institutes (RARIs), which
are administered by the regional state governments, and Higher Learning Institutions (HLIs).
EIAR is a federal research institutes which coordinates research within the EARS, by taking aleading role in influencing agricultural policy development and conducts research at its federal
research centers. EIAR is one of the oldest and largest agricultural research systems in Africa thatcomprises of 20 research centers and many sub centers located across various ecological zones in84

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the nation. This section presents the results on a survey conducted to assess the current status on
the use of ICTs for agricultural research in EIAR HQs and across 17 research centers.
METHODOLOGY
The survey was collected from researchers in HQs and 17 agricultural research centersthrough online questioners via http://www.qualtrics.com/ and interviews by personally visiting the
research centers. Among the 17 research centers, 12 research centers & HQs participated in thesurvey and in total 43 researchers responded the questioners.
The following were some of the major questions presented via online:
1. What are the benefits of using ICT in Agriculture?
2. What data types do you use or generate? Where do you store your data?
3. Estimate the amount of research data you currently maintain (both4. active and inactive data stored for long term)
5. What are the main challenges you encounter within your work on DataManagement and open research data? (more than one answer possible)
6. In your opinion, what do you consider to be the most significant obstacles
preventing greater use of ICTs in Agricultural research in EIAR?
Survey Analysis
From responses received as to the most significant obstacles preventing greater use of ICTs
in Agricultural research, an overwhelming amount of respondents, (84%), indicated that theirresearch center is not equipped the necessary ICT infrastructure and systems for delivering digital
agriculture services while 16% of respondents indicated they have the necessary equipment/ICT
infrastructure system and manpower for delivering ICT services. And most of the researchers
responded that the main challenges they encountered in the research systems are the difficulty ofaccessing data and lack of data management system (see Figure 1).

Figure 1. Responses for question no. 6 “What are the main challenges you encounter within yourwork on data Management and open research data? (More than one answer possible)”
DISCUSSION
The application/integration of ICTs in agricultural research in EIAR has shown significant
progress over the last years and mainly internet connectivity were improved and EIAR Agri-Net
which connects 17 agricultural research centers were fully implemented. And recently, some
digital agriculture practices has been started. However, there exist major challenges of building
ICT infrastructure for sharing and exchanging agricultural knowledge generated from research and85

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there is a huge gap between the agricultural researchers, extension officers and farmers related tosharing, exchanging and disseminating agricultural knowledge and technologies. The researchsystems face various challenges and mainly the poor ICT infrastructure, lack of centralized
agricultural research information system, access and dissemination of the resources currently
available in EIAR are critical challenges which limits the full integration of Digital Agriculture
practices. Therefore, the ICT infrastructure in all research centers should be upgraded, integrated
application platforms needs to be implemented, awareness should be created and e-Agriculture
policy and strategy should be crafted at institutional level.
Precision Agriculture Practices in EIAR
Digital Agriculture (DA) practice is a gateway to Precision Agriculture (PA) and the muchdreamed transformation of Ethiopian Agriculture research systems. PA is an approach to farmmanagement that uses information technology (IT) to ensure that crops and soil receive exactly
what they need for optimum health and productivity [2]. In EIAR, DA practices is not new exceptthat efforts are scattered here and there. For instance, agro-climate advisory services dissemination
using a tool/platform called Ethiopian Digital Agro Climate Advisory Platform (EDACaP) has
already been under piloting, while the Wheat Rust Surveillance and Early Warning platform andthe Breeding Management System (BMS) in major crops, making an attractive example of thealready incubated digital technologies in the agricultural research system. Accordingly, EIAR has
already started to see important signs of progress, as well as notable areas for further improvement.
The following systems and platforms are implemented in EIAR and greatly supports the initiatives
towards ICT-based precision agriculture services.
Wheat Rust Surveillance Information System (WRSIS)
Wheat rust diseases pose one of the greatest threats to global food security, including
subsistence farmers in Ethiopia. The Wheat Rust Early Warning and Advisory System or Wheat
Rust Surveillance Information System (WRSIS) integrates a web portal and mobile app and it aims
to collect real time data from field, report incidences in advance and provide integrated wheat rustdisease related information for wheat rust disease protection community via SMS and Interactive
Voice Response System (IVRS) and decision makers at the MoA. The framework represents oneof the first advanced crop disease Early Warning Systems (EWSs) implemented in a developing
country and EIAR manages the system at national level (www.WRSIS.gov.et).
Remote Sensing Applications-Drones for Precision Agriculture
EIAR recently acquired two Parrot Bluegrass drones donated by Technical Centre for
Agricultural and Rural Cooperation (CTA) which are designed for end-to-end agricultural
solutions. Currently the drones are used mainly for crops monitoring, wheat rust disease
identification, water stress and nutrient deficiency applications at crop critical growth stages,
aiming at spot based and immediate response for early warning and advisory services forresearchers, farmers and extension agents.
CONCLUSIONS AND RECOMMENDATIONS
The findings of the survey revealed that the poor ICT infrastructure, lack of centralized
agricultural research information system, access and dissemination of the resources currently
available in EIAR are critical challenges which limits the full integration of DA practices.

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However, the recent initiatives on use of Drones or Unmanned Aerial Vehicles (UAVs) for PA
and digitization of the Wheat rust diseases monitoring and early warning processes are believed tohelp breaking the old thinking and revolutionize the traditional way of data collection, analysis
and interpretation. The possible integration of soil-crop-disease diagnoses tools and the remotesensing are a good initiative and so far, the preliminary results showed that precision agriculture
practices are very critical for modernizing the research activities in EIAR.
As a frontrunner in the agriculture sector, EIAR must take maximum advantage of theopportunities presented by technological explosion (capturing the late comer advantage) and fromthe new national digital strategy. DA should be considered as a major catalyst for modernizing the
research system and technologies including Artificial Intelligence (AI), Machine learning (ML),
Robotics, Internet-of-Things (IoT), Sensors and satellite-based communications needs to be
integrated in the existing systems/platforms and so that early warning and advisory services canbe provided to farmers and development agents via expert systems and different channels.
REFERENCES
Allen Sader C, et al. 2019. An early warning system to predict and mitigate wheat rust diseases in
Ethiopia. Environ. Res. Lett. 14 115004.
EIAR. 2020 www.eiar.gov.et/ict
Ethioagroclimate. 2020. https://ethioagroclimate.net/
Manish M, Abhishek S, et al. 2012. Uses of ICT in Agriculture, International Journal of Advanced
Computer Research Vol. 21 (3) March-2012.
Spyros F, et al. 2003. ICT in Precision Agriculture – diffusion of technology: An overview ofPrecision Agriculture. DOI: 10.13140/2.1.1586.5606
Technical Centre for Agricultural and Rural Cooperation (CTA), Digitalization of
African Agriculture Report, https://www.cta.int/en/digitalisation 2018–201987

1st African Conference on Precision Agriculture | 8-10 December | 2020

ECONOMICS OF PRECISION AGRICULTURE

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#7517 TOMATO YIELD AND ECONOMIC PERFORMANCE UNDER ORGANIC AND
MINERAL FERTILIZER APPLICATIONS IN COASTAL TOGOJean M. Sogbedji and Mouhamadou Lare
Ecole Supérieure d’Agronomie, Université de Lomé. 01 BP 1515 Lomé 01 Togo,
mianikpo@yahoo.com
ABSTRACT
Research efforts towards enhancing vegetables production are still needed in Togo. We
assessed tomato (Solanum lycopersicum) yield and associated economic returns under three soil
fertility management strategies in a 3-yr period study. Three tomato varieties were used including:
MONGAL-F1 (V1), SUMO-F1 (V2) and COBRA 26-F1 (V3). The fertilization regimes were: no
fertilizer application as the control (F1), application of 200 kg of N15P15K15 + 100 kg of urea (46%N) corresponding to N76P30K30 ha-1 (F2) and application of farm yard manure (FYM) at the rate of
6 Mg ha-1 (F3). The 3-yr period was segmented into two different cropping periods with three
tomato crops for each period. The first period runs from October to January and the second runs
from the subsequent period of February to May. Tomato fresh fruit yields were collected and wereused to determine the net cash return through a partial budget analysis under each variety –fertilization regime combination. Across tomato varieties, three-crop mean yields were 93 to131%
and 109 to 144% higher for fertilization regimes F2 and F3, respectively, as compared to yieldunder the control (F1), and mean yields with F3 were 7.5% on average superior to those for F2.
Irrespective of fertilization regime, the MONGAL-F1 mean yields were 6 to 24 and 16 to 31%
superior to yields under SUMO-F1 and COBRA 26-F1, respectively, and SUMO-F1 based yields
were 6 to 10% higher than those for COBRA 26-F1. Higher economic returns (typically ranging
from 9500 to 27000 USD ha-1) were recorded when fertilizers were applied, and lower returns(typically in the range of -131 to 4700 USD ha-1) were obtained with no fertilization, with thehighest economic return under the V1F3 combination during the February to May cropping period.
Tomato cropping without external mineral and/or organic nutrient input may not be advised forthe study area.
Keywords: tomato variety, cropping period, ferralsols, fertilization regime, yield and net cash
returnINTRODUCTION
Vegetables production continuously gains importance because of the high nutritional valueof its products. These non-traditional crops could revitalize rural economies and contribute to foodand nutrition security towards achieving the United Nations Sustainable Development Goals(SDGs). Historically, research and extension in Africa has concentrated on staples (cereals)
because of food security. However, to achieve food, nutrition and financial security, vegetables
production should be promoted in sub-Saharan Africa (SSA) because of their importance. A recent
study conducted in several West African countries by IFDC using the IFAD grant No 1174 (IFDC,
2014) established that vegetable cropping highly contributes to provision of revenues to address
key needs including food security, children education, inputs provision for annual crops, and89

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several other social needs for smallholder farmers. Moreover, the value of vegetables (over 3000$
per hectare per year) in Benin, Burkina Faso, Ghana, Niger and Togo is 6 to 10 times that of cereals(150-250$ per hectare per year).
Tomato is one of the most produced vegetables in the world, ranking second after potato(Kalbani et al., 2016). As it is a relatively short duration crop and gives a high yield, it is
economically attractive and the area under cultivation is increasing daily (Naika et al., 2005).
Moreover, tomatoes contribute to a healthy, well balanced diet and having rich in minerals,
vitamins, essential amino acids, sugars and dietary fibers (Kalbani et al., 2016). Although tomato
may be produced throughout the year in coastal western Africa, it tends to be abundantly available
only part of the year, which leads to very low demands with associated non-economic sale prices
along with important postharvest losses.
Some constraints to vegetable production are water deficiency/quality, poor soils, labor,
inadequate information on production and processing, low yielding varieties that are susceptible
to insect pests and diseases, cost of inputs, and poor infrastructure for processing, storage andtransport that contribute to high postharvest losses. In coastal Togo, tomato yields are between 5
and 6 Mg ha-1 (ITRA, 2011), which is drastically below the world average yield of 34 Mg ha-1 asreported by Debela et al. 2016. To secure and sustain the social and economic potential role oftomato cropping in the region, research is needed towards improving its yields and net cash returns.
The objective of this work was to assess the response of three tomato varieties to threefertilization schemes and the effect of cropping timing on both the productivity and economic
profitability of the crop on costal West African ferralsols. The aim was to identify management
practices that enhance and secure tomato cropping contribution to social welfare in this agro
ecosystem.
MATERIAL AND METHODS
Experimental Site
The study was conducted at the University of Lomé Agricultural Research Station in Lomé,
Togo (6°22’N, 1°13’E; altitude = 50 m). The soil type was a rhodic ferralsol locally called “Terresde Barre” that originated from a continental deposit, and covers part of the arable lands in Togo,
Bénin, Ghana, and Nigeria in coastal Western Africa. Annual rainfall typically ranges from 800 to1100 mm and allows for two cropping seasons: a first season from April to July, the main season
with a 25-yr average rainfall of 470 mm, and a second season from September to December witha 25-yr average rainfall of 200 mm. At the onset of this experiment, the site has been undercontinuous mineral (NPK) fertilized maize cropping.
Soil and Crop Management
A 3-yr period (2016-2019) split-plot experiment was settled with three replicates. Three
tomato varieties were the main plot effects and three fertilizer schemes were at the subplot level.
The site was manually plowed and 9 main plots (4 m x 3 m) were laid out in a randomized complete
block design. The three tomato varieties were: (i) MONGAL-F1, V1, (ii) SUMO-F1, V2 and (iii)
COBRA 26-F1, V3. Three fertilizer treatments were applied: (i) no fertilizer application as thecontrol (F1), (ii) application of 200 kg of N15P15K15 + 100 kg of urea (46% N) corresponding to
N76P30K30 ha-1 (F2) and (iii) application of FYM at the rate of 6 Mg ha-1 (F3). Fertilizer treatment
F2 is a recommendation by the national agricultural extension services in Togo, and F3 is arecommended FYM-based organic amendment by IFDC (2014).

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Six tomato crops were performed during the three years of experimentation in two periods
typically embedded in the two cropping seasons. The first period runs from October to January
and the second from the subsequent period of February to May, with three crops for each period.
During each crop period, tomato was transplanted after three weeks of nursing at a density of
37,000 plants ha-1 and weeded as needed. Fertilizer N15P15K15 and FYM rates were applied two
weeks after transplanting (just after the first weeding) while urea was applied four weeks after
transplanting as recommended by the national agricultural research and extension services in theregion. In each cropping period of each of the three years, all fertilizers were manually pointplaced at approximately 8 cm depth. Plants were chemically treated against diseases and insects
and received additional water (apart from rainfall) as needed that was brought through handwatering.
Data Collection and Analysis
Tomato fresh fruit yield was determined under each treatment by harvesting all the plantsfrom each plant bed. The GENSTAT statistical software package was used to run the analysis ofvariance (ANOVA) on the yield data sets and the Duncan test at 5% was used to discriminate
among mean tomato yields. Mean tomato fruit yield data were used to establish a partial financial
budget which represents the net profitability of the production under each variety – fertilization
combination.
Economic Analysis
The profitability of tomato fresh fruit production in each cropping period was estimatedthrough a partial budget (output value minus inputs cost value) analysis. Output consisted of theamount of cash corresponding to the mean fresh fruit yield under each tomato variety – fertilization
scheme combination, which was determined to be sold at 600 CFA (US$1.2) kg-1 and at 800 F
CFA (US$1.6) kg-1, the average sale price for the first and the second crop periods, respectively.
The inputs consisted of the production costs under each combination, including those for soil
preparation, seed, crop nursing and transplanting and related tasks, fertilizer purchase andapplication, crop weeding and crop harvesting and associated tasks. Labor costs were determined
to be 2 000 F CFA (US$4.0) per person-day based on labor records from the experiment, andfertilizer costs were based on ongoing prices which were 220 F CFA kg-1 (US$0.44) for both
N15P15K15 and urea. Farmyard manure cost was determined to be 20 000 F CFA Mg-1 (US$40.0).
RESULTS AND DISCUSSION
Tomato Fresh Fruit Yield
Tomato mean yields were typically between 8 and 30 Mg ha-1 (Table 1) with the lowest
yield under the control fertilizer treatment. This reasonably agrees with mean yield range of 10 to30 Mg ha-1 reported by Tesfay et al. 2018 and Rajya et al. 2015 using a control and various
combinations of organic and inorganic fertilization schemes. The three varieties were clearly
responsive, although differently, to fertilization schemes. Irrespective of tomato variety, three-cropmean yields were consistently highest under fertilization regime F3 and lowest for F1 in both
cropping periods. During the first cropping period and for V1, mean yields increased by 95 and119% under F2 and F3, respectively, as compared to yield under F1, and yield for F3 was 12%
superior to that under F2. For V2, F2 and F3 resulted in mean yield increase by 115 and 139%
under F2 and F3, respectively, as compared to yield under F1, with F3-based yield being 11%91

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higher than that of F2. Mean yields for V3 increased by 93 and 109% under F2 and F3,
respectively, as compared to yield under F1, and yield for F3 was 9% superior to that under F2.
In the second cropping period and for V1, mean yields increased by 131 and 144% under F2 andF3, respectively, as compared to yield under F1, and yield for F3 was 5% superior to that underF2. For V2, F2 and F3 resulted in similar mean yield but 122% higher than yield for F1. Meanyields for V3 increased by 103 and 120% under F2 and F3, respectively, as compared to yieldunder F1, and yield for F3-based fertilization was 8% superior to that under F2. The results of thisstudy demonstrate that enhancement of soil fertility is needed for tomato production in the area ofstudy if high yields are to be achieved, which agrees with research results published by Gorobani
et al. 2017 in the area. Organic (FYM) fertilizing regime proved superiority over mineral (NPK)based fertilization by 7.5% on average. This trend in our yield data sets does not corroborate resultsby Kochakinezhad et al. 2012 who found that the difference between the two classes of fertilizers(organic and chemical) was not very high (yield under chemical fertilizer was 2.2% higher thanthat for organic fertilizer), and concluded that organic fertilizers are competitive and may be asuitable replacement for chemical fertilizer. In our area of study, Gorobani et al. 2017 found nosignificant difference between tomato yield under FYM fertilizing regime and that under inorganic(NPK) based fertilization. The superiority of organic fertilization over the inorganic fertilizationin our study may be explained by the continuous use (six consecutive crops) of organic fertilizer
that might lead to more nutrient released for the crop use.
Table 1. Tomato yield (Mg ha-1) and net cash profit (USD ha-1).
Treatment

Cropping period
October to January__________________________________
Crop 1 Crop 2 Crop 3 Mean
Net
yield
cash
profitFebruary to May____________________________________________
Crop 1
Crop 2
Crop 3Mean
Net cashprofit

V1F1
V1F2
V1F3
Mean

10.64c¶17.94b
22.79a17.12

11.94b
23.10a24.25a19.76

10.81c23.98b26.02a20.27

11.13c21.67b
24.35a
19.05

1386
12464
15304

7.74b
21.97a22.09a17.27

13.03c24.80b
27.49a21.77

11.91b28.88a30.07a23.62

10.90c25.21b
26.55a
20.88

4768
25069
26944

V2F1
V2F2
V2F3
Mean

10.65c21.23b
23.58a18.49

9.71c
19.74b
23.63a17.70

8.83b21.83a22.57a17.75

9.73c
20.93b
23.26a
17.98

-131
11659
14121

8.24c
15.29a13.86b
12.46

11.17b
22.67a23.23a19.02

8.57b
24.06a24.83a19.15

9.33b
20.67a20.64a
16.88

2502
18525
18428

V3F1
V3F2
V3F3
Mean

9.73b
16.06a16.63a14.14

11,15c17.66b
22.58a17.13

8,48b22.94a22.28a17.90

9.79c
18.89b
20.50a
16.39

-58
9464
11149

6.96c
13.47b
14.23a11.55

11.03b
20.49a22.55a18.02

9.36b
21.68a23.35a18.13

9.12c
18.55b20.04a
15.90

2208
15481
17573

¶ Means within the same column followed by the same letter are not significantly different at P = 0.05.

Regardless of fertilization treatment, overall 3-crop mean tomato yields were 19.05, 17.98
and 16.39 Mg ha-1 for the V1, V2 and V3, respectively, in the first cropping period, and 20.88,
16.88 and 15. 90 Mg ha-1 for V1, V2 and V3, respectively, in the second cropping period (Table
1). Overall mean yield with V1 increased by 6 and 16% as compared to yields for V2 and V3,

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respectively, while the V2-based yield was 10% superior to that under V3, during the first cropping
period. In the second cropping period, mean yield for V1 was 24 and 31 % higher as compared toyields with V2 and V3, respectively, and the V2-based yield was 6% over the yield under V3.
These results indicate that within the cropping period, yield potential was consistently highest andlowest for V1 and V3, respectively, and fluctuates between cropping period. The three varieties
responded positively to fertilization scheme with a higher response to organic fertilizer and a better
performance for V1, indicating that the variety-fertilization regime interaction was measurable.
Such variety effects on tomato yield as well as positive crop-fertilization regime interactions werereported by Kochakinezhad et al. 2012 and Ilupeju et al. 2015.
Partial Budget Analysis
Results of the balance of outputs (cash values of tomato fresh fruit mean yield) andcorresponding inputs (total costs associated with production) for the three crops within each
cropping period are presented in Table 1. On a per hectare basis, except the V2F1 and V3F1
combinations during the first cropping period, the balance was positive in all other cases,
indicating that there was profit or net gain. The data sets reveal that higher net returns (typicallyranging from 9500 to 27000 USD ha-1) were recorded when fertilizers were applied, and lower
returns (typically in the range of -131 to 4700 USD ha-1) were obtained with no fertilization. For
the three varieties and within each of the two cropping periods, net returns were consistently higher(15304 to 26944 USD ha-1) with the F3 fertilization regime as compared to returns (9464 to 25069
USD ha-1) when the F2 fertilization regime was used. Net returns were consistently higher for thesecond cropping period (February to May) with values typically ranging from 2200 to 26944 USD
ha-1 in comparison to those (-131 to 15304 USD ha-1) for the first cropping period (October toJanuary) primarily because of the higher tomato sale price in the second cropping period. Overall
the highest net return (26944 USD ha-1) was recorded for the V1 (MONGAL-F1 variety) combinedwith the F3 (FYM-based) fertilization regime.
CONCLUSIONS
Enhancement of soil fertility is needed for tomato production in the area of study if highyields are to be achieved. The three varieties responded positively to fertilization scheme with ahigher response to organic fertilizer and a better yield-based performance for the MONGAL-F1
variety regardless of cropping timing. The economic profitability of tomato cropping was ingeneral evident and strongly affected by fertilization scheme, crop variety and cropping timing.
Tomato production using organic (FYM) based fertilization regime and MONGAL-F1 variety
preferably during the February to May cropping period appears to be the best management
practices that improve the crop yield and maximize the economic returns in the study zone.
REFERENCES
Debela KB, Belew D, and Nego J. 2016. Evaluation of Tomato (Lycopersicon Esculentum Mill.)
Varieties for Growth and Seed Quality under Jimma Condition, South Western Ethiopia.
International Journal of Crop Science and Technology 2(2): 69-77
Gorobani A, Sogbedji JM, Mazinagou M. 2017. Amélioration de la productivité et de la rentabilitééconomique de la tomate (Solanum lycopersicum L.) sur les sols ferrallitiques au sud Togo.
J. Rech. Sci. Univ. Lomé (Togo) 19(2-Spécial): 131-138.

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IFDC. 2014. Mainstreaming pro-poor fertilizer access and innovative practices in West Africa.
IFAD Technical Assistance Grant No. 1174 report. Muscle Shoals, Alabama, U.S.A.
Ilupeju EAO, Akanbi WB, Olaniyi JO, Lawal BA, Ojo MA, Akintokun PO. 2015. Impact oforganic and inorganic fertilizers on growth, fruit yield, nutritional and lycopene contentsof three varieties of tomato (Lycopersicon esculentum (L.) Mill) in Ogbomoso, Nigeria.
Afr. J. Biotechnol. 14(31): 2424-2433
ITRA. 2011. Rapport national sur l'état des ressources phytogénétiques pour l'alimentation etl'agriculture. MAEH, ITRA, Togo
Kalbani FOSA, Salem MA, Cheruth AJ, Kurup SS, Senthilkumar A. 2016. Effect of some organic
fertilizers on growth, yield and quality of tomato (Solanum lycopersicum). International
Letters of Natural Sciences 53:1-9
Kochakinezhad H, Peyvast Gh, Kashi AK, Olfati JA, Asadii A. 2012. A comparison of organicand chemical fertilizers for tomato production. Journal of Organic Systems 7(2): 14-25
Naika S, van Lidt de Jeude J, de Goffau M, Hilmi M, van Dam B. 2005. Cultivation of tomato
production, processing and marketing. Agromisa Foundation and CTA, Wageningen, pp
1–92.
Rajya Laximi P, Saravanan S, Kakshman Naik M. 2015. Effect of organic manures and inorganic
fertilizers on plant growth, yield, fruit quality and shelf life of tomato (Solanum
lycopersicum). International Journal of Agricultural Science and Research 5(2): 7-12
Tesfay T, Gebremariam M, Gebretsadik K, Hagazi M, Girmay S. 2018. Tomato Yield andEconomic Performance Under Vermicompost and Mineral Fertilizer Applications. The
Open Agriculture Journal 12: 262-269.

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1st African Conference on Precision Agriculture | 8-10 December | 2020EDUCATION AND OUTREACH INNOVATIONS

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#7480 SMARTAFRIHUB FOR SMART AGRICULTURE CAPACITY BUILDING IN
AFRICA
Charvat K, Miderho Christus, Obot A, Löytty T, Kubickova H
Wirelessinfo, Cholinska 1048/19, 784 01 Litovel, Czech Republiccharvat@wirelessinfo.cz
ABSTRACTDigital Innovation Hubs (DIH) are multi-actor ecosystems that support farming
communities in their digital transformation by providing a broad variety of services from a onestop shop. Besides providing access to digital technologies and infrastructure, DIH can beconsidered as a social space for a community of practices that helps in networking and connecting
users and suppliers of digital innovations.
With the aim to bring together IT suppliers, the farming sector, technology experts,
investors, science and technology, public bodies, and other relevant actors to understand and solve
real life problems and challenges of the African farmers, SmartAfriHub1 DIH was developed andlaunched at Nairobi2 and Kampala3 INSPIRE Hackathons to support the knowledge transfer andinnovation between the ICT, farming communities and public bodies in Africa. These days,
SmartAfriHub supports the development of African Agriculture Knowledge and Innovation
System regionally and nationally.
This article presents a concept and strategy in which Digital Innovation Hub SmartAfriHub, series of INSPIRE Hackathons and practice-oriented and research-driven
Innovation Experiments build capacity and impact on the systemic change underlying the patterns,
structures and mindset of African agriculture.
INTRODUCTION
Precision agriculture is an important component of the third wave of agricultural revolution
and has enhanced hopes of battling food crises by increasing global food production with the helpof new technological advancements [1].
SmartAfriHub started as a tool to provide farmers with the information to detect andprevent crop disease, weeds and insect damage based on weather forecasts gathered from aerial
surveillance during the Nairobi INSPIRE Hackathon 2019 and Kampala INSPIRE hackathon
2020. The technology was then further developed by adding visual layers from satellites, planes
and drones and leveraged with AI capabilities. Acknowledging that smart farming can create a
massive impact on the agricultural economy in the near future and will be dependent on precision
technologies gave room to integration of SmartAfriHub where new innovations were developed
to tackle Africa agricultural problems.

1

https://www.smartafrihub.com/
https://www.plan4all.eu/nairobi-inspire-hackathon-2019/3https://www.plan4all.eu/kampala-inspire-hackathon-2020/
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Facing the African agricultural problems, capacity building is essential to foster the cocreation to find solutions that lead to emerging practices to advance agriculture and give farmers
precise information. SmartAfriHub DIH has transformed through the INSPIRE hackathons into a
capacity building tool that enables sharing the knowledge and ideas about initiatives andtechnologies so that researchers and practitioners in developing countries can benefit.
At the end, the capacity building activities, embracement of emerging technologies, andadvanced communication, dissemination and innovation in the long term boost a digitaltransformation and systemic change in agriculture- and food systems towards food security
sustainability. The interventions are addressed on the intangible patterns, structures and people's
mindset which have an impact on the way the farm in Africa.
MATERIALS AND METHODSINSPIRE Hackathons - A Platform for Capacity Building and Innovations Prototyping
Starting in 2016, the INSPIRE Hackathon concept was born as a means to support
sustainability and implementation of results of the European Commission Research Framework
Program FP7 and H2020 and also to bring together different project communities to facilitatetransfer of technology and knowledge between the projects, and eventually also to organizations
and companies [2-3].
Capacity Building - To Pave the Path to the Systemic Change
In general, hackathon is a lab and platform for agriculture innovation experiments which
aim to find solutions to real-life problems, leverage ambitiously scientific results to practice, andto apply open innovation practices that rely on co-design, co-creation and sharing (Figure 1).
Capacity building is one of the core values and principles of INSPIRE Hackathon. It is integrated
into all phases of the hackathon: challenges definition, solution ideation, solution piloting, team
activities, webinars, communication and dissemination by using SmartAfriHub and social media.

Figure 1. Practice-oriented learning and co-creation pattern97

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INSPIRE Hackathon Innovation Experiments to Support African Agriculture
INSPIRE Hackathons carry out Innovation Experiments to change agriculture practices,
underlying patterns of behaviour, the structure of the system and mental models. Such Innovation
Experiments form a unique intellectual knowledge hub and social space that leverages scientificresults, non-formal learning and co-creation patterns to new knowledge and practices and concrete
technical results.
Overall, 18 Innovation Experiments (IEs) were defined to tackle the African agriculturalproblems so far (in the Nairobi INSPIRE Hackathon 2019, Kampala INSPIRE Hackathon 2020
and COVID-19 INSPIRE Hackathon 2020).
Agricultural & Earth Observation IEs● Food Security in Relation to Earth Observation (GEOSS and COPERNICUS
Relevance)● Agriculture Innovation Hub for Africa● Citizen Science in Africa to Ground Truth & Exploit Earth Observation data
● SmartAfriHub● Desert Locust [4]● EO4 Food Security● Digitalization of indigenous knowledge in African agriculture for fostering foodsecurity● Developing a blockchain technology to enhance tracking and tracing of food items
throughout the value chain to ensure food security in Africa
Climate data IEs● Climatic Services for Africa● Climate change trends for Africa
Open Data & Technologies IEs● Open Land Use for Africa (OLU4Africa)● IoT Technologies for Africa● Open Data and Data Sharing in Agri-Food Chains in Africa● Smart Points of Interest - Publication of Open Data in Africa as 5-star Linked OpenData
● Open Transport Map (OTM) Applications for Africa● Text Mining & Metadata● Ethical and legal aspects of open data affecting farmers● Interchangeable map compositions in support of collaborative spatial intelligence
Systems Thinking - To Understand the Agriculture World
According to Waters Center [5] systems thinking is “a transformational approach tolearning, problem-solving and understanding the world”. The approach monitors what is tangible,
seeable and what has really happened, but above all systems thinking aim is to grasp what is under
the water surface; what is unseen, what are the patterns of behaviour, how the system is structured,
what factors are influencing into the patterns, and what assumptions, beliefs and values do people
hold about the system.

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Figure 2. Hackathons’ outcomes that advance systemic change
INSPIRE Hackathons generate intermediate outcomes that together help to achieve systemicchange into the agricultural domain. The main outcomes are (Figure 2):● Extends and deepens understanding into the current and potential state of the system● Seeks solutions and answers to true everyday challenges of agricultural practitioners● Strengthens liaison among the hackathon participants● Develops individual and collective capacities
SmartAfriHub - A Platform for Knowledge Transfer
The accelerator for launching the SmartAfriHub DIH was the Nairobi INSPIRE Hackathon
organized by Plan4all association in 2019. The intention was to develop an agriculture innovation
hub for Africa, that becomes a social space for the African agriculture community to shareknowledge and experience between farmers, industry, research community, advisory services andothers involved. During this hackathon, an initial analysis was carried out, first technology
solutions were drawn and the first release version of SmartAfriHub was launched.
A large community around the SmartAfriHub formed already during this hackathon and
one year later, in the fully virtual Kampala INSPIRE Hackathon 2020, there was a huge interest of
the hackathon participants and organisers in developing tactics on how Communities of Practicesof agriculture and digital technologies could “seek, sense and share” needs, problems andknowledge at SmartAfriHub and deliver value to community members, farmers and society of
African countries. Another goal was to explore and test the available SmartAfriHub applications
with the help of a mentor. As the result of this hackathon was the increase of interest in
SmartAfriHub DIH by Communities of Practices RUFORUM - Regional Universities Forum for
Capacity Building in Agriculture4, RCMRD - Regional Centre for Mapping of Resources for4

www.ruforum.org
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Development5, GODAN - Global Open Data for Agriculture & Nutrition 6, P4CDA -Programme
for Capacity Development in Africa7 and Plan4all 8 that support the SmartAfriHub ambition.
SmartAfriHub provides a variety of applications and tools, eg. HSlayers NG enables thecreation and sharing of map compositions, JupyterHub for data cleaning, exploration, analysis andvisualization, statistical modeling and simulations or Layman QGIS plugin that allows to create
and edit layers and create map composition structures on local stations that are possible to uploadto the server. The available tools that support the Digital Innovation Hub in being a community
building are for example Blog, Wiki, Forum, Library and Science Shop.
Nowadays, SmartAfriHub is a technical platform for Communication & Dissemination &Exploitation and Tools for Global Geospatial Information Management (GGIM).
RESULTS AND DISCUSSION
SmartAfriHub platform facilitates building a Community of Practice (CoP) on Precision
Agriculture in Africa.
SmartAfriHub platform embraces systemic change by sharing insights, tools, methods, bestpractices and connections on how CoP of agriculture and digital technologies could explore needs
and problems, share knowledge at SmartAfriHub and deliver value to community members,
farmers and society.
SmartAfriHub embraces open innovation, open data, open sources and open dialogue CoPof Africa agriculture. The platform and the community are the key components for developmentof African Agriculture Knowledge and Information System.
The social spaces which are realized both in SmartAfriHub and in INSPIRE Hackathons
enhance capacity development and particularly informal life-long learning.
The virtual and 3 months lasting hackathon journeys in which the communication took
place over several digital channels was tested also in the COVID-19 lockdown period. This test
proved that remote- and distance learning methods and tools are becoming mainstream.
REFERENCES[1] How Precision Agriculture is Revolutionizing the Agricultural Sector, CropOM, accessible
at https://cropom.com/articles/how-precision-agriculture-is-revolutionizing-theagricultural-sector[2] Bye BL, Charvat K, Mildorf T, Jørgen Berre A. 2018. The INSPIRE Hackathons: A
framework for solving real-life problems innovating with today's knowledge, data andtechnology. EGUGA, 12658.
[3] Bye BL, Mildorf T, Charvat K, Berre AJ. 2017. (Dec). Hackathons as A
Capacity Building Tool for Environmental Applications. In AGU Fall Meeting Abstracts(Vol. 2017, pp. PA13E-07).

5https://www.rcmrd.org/
https://www.godan.info/
7
https://p4cda.net/
8
www.plan4all.eu
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[4] Cherenet E, Houel M, Kamou W, et al. 2020. Mapping and Predicting Desert Locust Invasion[version 1; not peer reviewed]. F1000Research 2020, 9:693 (document)(https://doi.org/10.7490/f1000research.1118028.1)[5] Factors affecting technology adoption for cocoa. Agron J. (2020) 2:12-24. Waters Center for
Systems Thinking, accessible at https://waterscenterst.org/systems-thinking-tools-andstrategies/what-is-systems-thinking/101

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MAPPING AND GEOSTATISTICS

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1st African Conference on Precision Agriculture | 8-10 December | 2020#7438 PREPARATION OF A PRECISION RIPPING PLAN USING MANUALVERTICAL PENETROMETER MEASUREMENTS
T. Rátonyi1, P. Ragán1 A. Széles2, P Fejér2, I. Bácskai1, E Harsányi1
1
National Agricultural Research and Innovation Centre, Institute of Agricultural Engineering,Gödöllő, Hungary; 2University of Debrecen Faculty of Agricultural, Food Sciences andEnvironmental Management, Debrecen, Hungary, ragan.peter@mgi.naik.hu +36 20 214 8127
ABSTRACT
Large weight tractors and farm machinery used in agriculture significantly contribute to
the formation of compacted and thickening layers starting from the soil surface. There are suitabledeep ripping technologies to eliminate harmful soil compaction, which are extremely energy andcost demanding.
The presented study was performed in a complex long-term tillage trial set up on calcareous
chernozem soil in the eastern part of Hungary, west of Debrecen. A vertical, static hand-held“Penetronik” type penetrometer was used to design precision soil ripping. The device determines
the position of the measuring points using DGPS. It measures volumetric soil moisture content
with a capacitive sensor and soil penetration resistance (MPa) with a mechanical sensor which has
a probe with a 60° cone angle.
Strip tillage shows greater looseness than winter ploughed treatment. Soil ripping has apositive effect on soil resistance even after harvest. The polygons were selected from the vector
GIS database that formed the map at a depth of 5 cm, and then their combined area was determinedfor each layer. These areas were plotted as a percentage of the cultivated area.
According to the measured data, compaction should be reduced in 70% of the total area inthe top 10 cm soil layer, where trampling damage due to agro-technical interventions is located. If
a medium deep ripper (45 cm effective working depth) is available as a tillage tool, ripping can be
designed for this maximum depth. The traditionally (winter ploughing) cultivated area had thehighest intervention area. When using precision strip tillage technology, despite the fact that 30%of the area is cultivated within a growing year, a much smaller rate of intervention is required witha medium deep ripper.
INTRODUCTION
Large weight tractors and farm machinery used in agriculture significantly contribute to
the formation of compacted and thickening layers starting from the soil surface (Regman et al.,2018). There are suitable deep ripping technologies to eliminate harmful soil compaction, which
are extremely energy and cost demanding (Birkás, 2006). In precision agriculture, it is possible totreat spatially delimited unfavourable soil patches. Following this principle, the extent of the zones
to be ripped on the field and the depth location of the solid soil layers can also be determined
during the planning of the deep ripping. Penetration resistance determined by a contact mechanicalsensor is one of the most commonly used methods for studying soil compaction-looseness, depth
location and extent of compacted layers, and spatial and temporal changes in soil physicalcondition, which can be compared with a continuous soil resistance measurement method (Birkás,
2006). The mechanical resistance of the soil varies inversely with the soil moisture content and in103

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direct proportion to the bulk density (Champbell and O`Sullivan, 1991). At a given moisture
content, soil resistance increases with increasing bulk density, and with increasing moisture
content at a given bulk density, soil resistance decreases (Ehlers et al., 1983). The bulk density(g×cm-3) of the soil was calculated from the soil resistance and moisture content values using a predefined empirical formula. By means of a penetrometer, measurements with a large number ofrepetitions can be performed, which can be used to create precision soil ripping maps.
MATERIALS AND METHODS
The presented study was performed in a complex long-term tillage trial set up on calcareous
chernozem soil in the eastern part of Hungary, west of Debrecen. A vertical, static hand-held“Penetronik” type penetrometer was used to design precision soil ripping. The device determines
the position of the measuring points using DGPS. It measures volumetric soil moisture content
with a capacitive sensor and soil penetration resistance (MPa) with a mechanical sensor which has
a probe with a 60° cone angle. Soil compaction was determined at 400 measuring points×ha-1 inthe sample area. The experiment was started in 1989 and until 2014, winter ploughing, spring
ploughing and spring shallow cultivation were the three tillage treatments, and the experiment also
includes two irrigation treatments.
In 2015, winter ploughed tillage remained unchanged; instead of spring ploughing, strip
tillage was introduced and spring shallow tillage was replaced by ripping. In the case of winterploughing, the measurement points were determined randomly. In the ripping tillage variant,measurements were performed in a direction perpendicular to the cultivation. In the case of strip
tillage, where 30% of the area is cultivated strip and 70% is uncultivated strip spacing,measurements also took place perpendicular to the cultivation direction in 2018. Depth values forbulk densities above 1.5 g×cm-3 (Birkás, 2002) were sorted and interpolated by the kriging method,
and then displayed using Quantum GIS.
RESULTS AND DISCUSSION
By displaying the precision ripping plan, the demand of different basic crops for ripping
after the maize harvest at the end of a growing period can be observed. The map of the ripping
plan shows that the winter ploughed treatment requires a greater depth of ripping. Strip tillage
shows greater looseness than winter ploughed treatments. Soil ripping has a positive effect on soil
resistance even after harvest (Figure 1).

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Figure 1. Precision ripping plan for different primary tillage and irrigation treatments
The polygons were selected from the vector GIS database that formed the map at a depth of
5 cm, and then their combined area was determined for each layer. These areas were plotted as apercentage of the cultivated area.
According to the measured data, compaction should be reduced in 70% of the total area in the top
10 cm soil layer, where trampling damage due to agro-technical interventions is located. If a
medium deep ripper (45 cm effective working depth) is available as a tillage tool, ripping can be
designed for this maximum depth. In the case of the winter ploughed cultivation at a depth of 45
cm, 16.5% of the area, in strip tillage 3.2% of the area, and in the case of ripping 6.7% must be
cultivated with a medium deep ripper. For a depth of 40 cm to be ripped, 20% should be ripped in
the case of winter ploughing, 5% in strip tillage and 10.4% in the case of ripping cultivation. At acultivation depth of 35 cm, the proportion of the area to be ripped is 23.9% in the case of the winterploughing treatment, 8.7% in strip tillage and 10.4% in the case of the ripped area. In the upper 30
cm layer, the proportion of the area to be cultivated is 28.8% in the case of winter ploughed
treatment, 14.1% in strip tillage and 18.8% in the ripped. (Figure 2).

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Figure 2. Correlation between the area and the depth of planned ripping
It was confirmed that soil mapping based on measurements with a contact mechanicalsensor is suitable for preparing a precision ripping plan. A proper sampling strategy is required forthe measurements, and soil moisture data must also be available to determine soil compaction. The
method and depth of primary tillage determine the proportion and depth of intervention with amedium-depth cultivator relative to the cultivated area. The traditionally (winter ploughing)cultivated area had the highest intervention area. When using precision strip tillage technology,
despite the fact that 30% of the area is cultivated within a growing year, a much smaller rate ofintervention is required with a medium deep ripper.
ACKNOWLEDGEMENTS
The research was supported by the project “Establishing a scale-independent complex
precision consultancy system (GINOP-2.2.1-15-2016-00001)”. The field trial and the analyses
were supported by KITE cPlc.
REFERENCES
Birkás M. 2002. Környezetkímélő és energiatakarékos talajművelés. Szent István Egyetem
Mezőgazdaság és Környezettudományi Kar, Növénytermesztési Intézet Földműveléstani
Tanszék, Gödöllő. Akaprint Nyomdaipari Kft. 16.
Birkás M. 2006. Környezetkímélő alkalmazkodó talajművelés. Szent István Egyetem
Mezőgazdaság és Környezettudományi Kar, Növénytermesztési Intézet Földműveléstani
Tanszék, Gödöllő. Akaprint Nyomdaipari Kft. 90–92.
Champbell DJ, O`Sullivan MF. 1991. The cone penetrometer in relation to trafficability,
compaction, and tillage. Soil Analysis (Physical Methods) K. A. Smith, C.E Mullis, (eds.)
399–429.
Ehlers W, Köpke U, Hesse F, Böhm W. 1983. Penetration resistance and root growth of oats intilled and untilled loess soil. Soil and Tillage Research 3:261–275.
Rehman Z, Khalid U, Farooq K, Mutjaba H. 2018. On yield stress of compacted clays.
International Journal of Geo-Engineering 9:21.

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1st African Conference on Precision Agriculture | 8-10 December | 2020#7445 A CHEAP ALTERNATIVE TO DATA MANAGEMENT AND CREATING OFYIELD MAPS OF SMALL-PLOT FIELD EXPERIMENTS
P. Ragán1, T. Rátonyi1, A. Széles2, P. Fejér2, I. Bácskai1, E Harsányi1
1
National Agricultural Research and Innovation Centre, Institute of Agricultural Engineering,Gödöllő, Hungary; 2University of Debrecen Faculty of Agricultural, Food Sciences andEnvironmental Management, Debrecen, Hungary
ragan.peter@mgi.naik.hu +36 20 214 8127
ABSTRACTDue to the effects of climate change, the spatial and temporal distribution of precipitation
is currently extremely variable. Long-term field trials provide an opportunity to examine the longterm effects of crop production factors and the effect of different crop years can also be analysed.
In the long-term field trial, spatial representation of the data belonging to each plot might benecessary for the purpose of soil heterogeneity analysis, working hypothesis, or even presentation.
The long-term field trial included in the analysis is located in the eastern part of Hungary,east of Debrecen. The long-term experiment was founded in 1989 by Prof. Dr. János Nagy. The
long-term experiment consists of 3 tillage 3 fertilizer treatments 2 forecrop 2 irrigation 3 genotype
2 plant number treatments in 4 repetitions. In the present article, the 2015-2019 period of the longterm trial is examined.
In the average of the 5 examined crop years, there is no statistical difference between the
ripped (10.64 t/ha) and winter ploughed (10.58 t/ha) cultivation. The yield of strip tillage wasstatistically 8.57% lower than that of the winter ploughed treatment.
The statistical evaluation confirmed that the crop year clearly influenced the maize yieldin the average of primary tillage.
INTRODUCTION
Due to the effects of climate change, the spatial and temporal distribution of precipitation iscurrently extremely variable. Long-term field trials provide an opportunity to examine the longterm effects of crop production factors and the effect of different crop years can also be analysed.
In the long-term field trial, spatial representation of the data belonging to each plot might benecessary for the purpose of soil heterogeneity analysis, working hypothesis, or even presentation.
Researchers dealing with long-term field trials usually store the measurement data for a given
experiment in MS Excel or in a database of a statistical software and perform statistical analyses
by means of them. However, in long-term trials, possible treatment modifications or plot mergers
make it difficult to standardize the database. It is difficult to retract a database of several crop years
from the lines of the database for each plot, and it is also difficult to display the measurement data
even for a given year. Yield mapping using GPS is now an available option, however, a plot
harvester equipped with a robotic steering wheel and a GPS-based RTK (real time kinematic)
system requires a large investment. Spatial data originated from yield mapping also require theapplication of GIS.

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MATERIALS AND METHODS
The long-term field trial included in the analysis is located in the eastern part of Hungary,east of Debrecen. The long-term experiment was founded in 1989 by Prof. Dr. János Nagy. The
long-term experiment consists of 3 tillage 3 fertilizer treatments 2 forecrop 2 irrigation 3 genotype
2 plant number treatments in 4 repetitions. In the present article, the 2015-2019 period of the longterm trial is examined. As of 2015, the trial includes the traditional winter ploughed, a ripping
tillage treatment without ploughing, and the RTK-based modern strip tillage treatment introduced
by a company engaged in agricultural trade and services in Hungary. Satellite or UAV-based
remote sensed data or field measurements can also be used to generate the yield map. In the present
article, the experimental plots were digitized based on UAV footage using the open source, free
Quantum GIS software. The longer time series numerical statistical database and the plot yielddata of the given year were integrated into a GIS database.
According to the storage method used in GIS, the layer (*.shp) files in Quantum GIS store
the outline, there are additional 5 files, one of which is the database (*.dbf) file, which stores
attribute table belonging to the layers. This no longer meets the criteria for a proper database forlong-term experiments over several years (Huzsvai, 2012). The input interface of the Quantum
GIS attribute table provides relatively few options for simple data entry. It sorts the attribute line
for the selected element in the given layer to the first position, but it is difficult to enter large
amounts of data one by one for the given parcels. The Open Office Calc software handles .dbf files
easily and correctly, and the column and decimal separators can be specified individually when
opening files with a .csv extension. With the help of the above, the lines of data filtered from theoriginal database were attached to the lines of the .dbf database belonging to the parcel outlines in
the appropriate order. Bypassing the QGIS data entry interface with this procedure, a large amountof data can be attached to each plot outline, and no separate data entry is required other thancreating a database for basic statistics.
RESULTS AND DISCUSSIONThe GIS database created in Quantum GIS, with its large amount and relatively easy toattach data, offers a solution for the proper spatial and temporal identification of each plot, which
is a practical problem in long-term experiments (Figure 1). This makes it easier to track theexperiment, or even any change that might occur in it. The existing GIS database can be exported
in a number of formats, including kml, which can be displayed using Google Earth.
Another solution to display the previously created kml files is to import them into Google
My Maps that is available under Google Drive, where it is possible to view the map using abrowser. With Google Drive and My Maps, these maps can be shared even within a research group,
as well as outlines can be edited, and blank columns that already exist within the attribute table
can be filled with measurement data.
In addition to the GIS visualization, the data of the long-term experiment were processedby numerical statistical method and RStudio. The yield results of the experiment were examinedusing a repeated measurement model and the method of least significant difference (LSD). In thescope of the evaluation, the yield results of the non-irrigated, monoculture maize, 80 kg N/ha 60
kg P2O5/ha 90 kg K2O/ha nutrient levels were examined in the 2015-2019 crop years. There wasno statistically significant difference between the columns marked with the same letter in the
figures.

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In the average of the 5 examined crop years, there is no statistical difference between the
ripped (10.64 t/ha) and winter ploughed (10.58 t/ha) cultivation. The yield of strip tillage wasstatistically 8.57% lower than that of the winter ploughed treatment.

Figure 1. Analog measured and digitized maize yield map of the long-term experiment(Debrecen-Látókép, Hungary, 2019)
The statistical evaluation confirmed that the crop year clearly influenced the maize yieldin the average of primary tillage. According to Gombos and Nagy (2019), the average temperatureof the experimental space is 10.4 °C, the average annual precipitation is 550 mm. The lowest yield(8.96 t/ha) in the average of tillage was measured in the warmer and drier year of 2015. Then, theyield of the 2016 crop year (12.97 t/ha), which was 0.89 °C warmer than average but 267.8 mm
more precipitation, was significantly higher than the other crop years included in the study. It was
4.01 t/ha (+144.8%) higher than the average yield in 2015 and 2.71 t/ha (+126.4%) higher than the
average yield achieved in 2019. The second lowest yield of the study period (9.58 t/ha) was
measured in the 2017 crop year, which was 0.98 °C warmer than the average and had 91.1 mm
more precipitation. In this crop year, maize produced 3.39 t/ha less than before. The yield of the2018 crop year with an average rainfall but warmer than average (+1.34 °C) was 2.83 t/ha lower
than the 2016 average yield. The last year of the study (2019) was 2.74 °C warmer than averageand 191.7 mm less precipitation fell, however, the yield did not differ statistically from the yieldof 2018.
Primary tillage and crop year influenced maize yield together. The lowest yield results ofthe examined period were observed in 2015 (+1.45 °C, -32.5 mm), in this crop year there was no
significant difference in yield between the three tillage variants. In the year 2016, which provided
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an outstanding yield (+0.89 °C, 267.8 mm), the three tillage variants differed statistically. The
highest yield of the examined period was 13.77 t/ha with the winter ploughed primary tillage, while
the second highest yield was achieved in the same year (2016) with the ripped (13.01 t/ha) tillage.
The peak yield of 12.13 t/ha of strip tillage was also measured in the 2016 crop year. In 2017(+0.98 °C, +91.1 mm) there was no statistical difference between the yields of winter ploughed
and ripped tillage, of which the maize yield of strip tillage was 0.73 t/ha less. In 2018, which was
warmer than average (+1.34 °C) but came with average rainfall (+1.5 mm) there was no statisticaldifference between winter ploughed (9.93 t/ha) and strip tillage (9.68 t/ha). In this crop year, thehighest yield was achieved with ripping (10.81 t/ha). In the dry 2019 (+2.74 °C, -191.7 mm) crop
year, there was no statistical difference between the yield of ripped (10.51 t/ha) and winterploughed (10.31 t/ha) treatments. In this crop year, there was no difference between strip tillage(9.95 t/ha) and winter ploughing (Figure 2).

Figure 2. The effect of primary tillage and crop year on maize yield (Debrecen-Látókép, Hungary,
2015-2019)
The applied GIS method is suitable for the spatial display of data from small-plot longterm experiments, for the management and storage of long time series data. Based on the resultsof the study, the yield of the winter ploughed primary tillage exceeded the strip tillage only twicein the 5 years examined. This also supports the spread of non-ploughing tillage among farmers.
The strip cultivator disturbs only 30% of the soil surface and is therefore more energy efficientthan the other two studied tillage variants.
ACKNOWLEDGEMENTS
The research was supported by the project “Establishing a scale-independent complex
precision consultancy system (GINOP-2.2.1-15-2016-00001)”. The field trial and the analyses
were supported by KITE cPlc.

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REFERENCES
Gombos B, Nagy J. 2019. Az időjárás értékelése kukorica (Zea mays L.) tartamkísérletek
eredményei alapján. Növénytermelés 68.2:5-23
Huzsvai L. 2012. Statisztika Gazdaságelemzők részére, Seneca Books, Debrecen. 10-12.
Quantum GIS Development Team. 2020. Quantum GIS Geographic Information System. Open
Source Geospatial Foundation Project. http://qgis.osgeo.org
R Core Team. 2020. R: A language and environment for statistical computing. R Foundation for
Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.

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#7598 SOME ESSENTIAL NUTRIENTS, ACTIVE LIMESTONE AND PH STATUS OFNORTH AND CENTER TUNISIAN SOILS
A. Hachana1,2,*, I. Hemissi1, I. Achour1, A. Souissi1,2, and B. Sifi1
1
Agronomic Sciences and Techniques Laboratory, National Institute of Agricultural Researchof Tunisia (INRAT), Carthage University, 2049, Ariana, Tunisia. 2Crop Production
Department, National Institute of Agriculture of Tunisia, Carthage University, 1082, Tunis,
Tunisia, amira.hachana@gmail.com
ABSTRACT
Tunisia is a North African country characterized by a Mediterranean climate in the northand Saharan climate in the south part of the country, which resulted in a high geo-morphologicaldiversity of its soils. Indeed, the soils in Tunisia are characterized by high variability of theirfertility status that is affected mainly by abiotic and biotic constraints such as salinity, drought,
erosion and low soil organic carbon (Mhiri, 2018). Thereby, soil fertility is largely linked togeographical location, making recommendation of chemical fertilization for small holder difficult.
This study aims to generate maps at national level characterizing current nutritional status of thesoil in order to facilitate the recommendation of integrated soil fertility management and land use
planning according to the specificity of the soils and agro-climatic conditions.
MATERIALS AND METHODS
Seventy (70) soil samples collected from different agro-ecological zones was analyzed in
the laboratory to determine assimilable phosphorus (P), exchangeable potassium (K), active
limestone content (CaCO3), and pH. In each site, samples were taken from the 0-20 cm groundlayer from four points (North, South, East, West, and Center) forming a square with a diagonal of
10 m. Samples were then homogenized, air-dried and sieved (2 mm).
The content of P was determined according to Olsen method (Gautheyrou and Gautheyrou,
1966), the content of K using the flame photometric method, active limestone according toDrouineau-Galet method (Gautheyrou and Gautheyrou, 1966) and the pH using 1:5 w/v soil
suspensions in the water at 25°C.
To determine the spatial variability of the different parameters (phosphorus, potassium, pH,
and active limestone), a geo-statistical analysis was performed. Semi-variograms have been
constructed and four different soil maps have been generated by the Ordinary Kriging interpolation
method using SPATIAL ANALYST extension of the software “ArcGIS Pro”. The method adopted
is commonly used as interpolation methods, where the spatial prediction of the unmeasured point
X0 is given by predicting the value Z*(X0), which equals the line sum of the observed value Z(Xi).
The formula is described as following (Pham et al., 2019):
D∗(

𝑍 𝑋@ ) = A 𝜆C Z (𝑋C )CEF

With λi = weighting coefficient from the measured position to X0
n = the number of positions within the neighborhood searching.

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The method used allowed to generate a graphical representation of the spatial variability of thestudied parameters from 70 soil sampling points in Tunisia.
RESULTS AND DISCUSSION
Data Description from Soil Samples Collected
Results showed that assimilable soil P content was a major limiting nutrient with values
varying from 31.1 to 208.3 ppm and mean value equal to 96.9 ppm. Soil P dispersion was highwith a coefficient of variation (CV) of 40.6%. In addition, results showed that exchangeable soil
K status was high (mean value of 166.3 ppm) with a high variability (CV of 35.1%) and values
were ranged between 76.9 and 334 ppm. Moreover, pH evaluation showed that soils were alkaline(7.6-8.4). Mean value was of 8.03 and CV was low (1.5%). Soils were mostly calcareous (8.1-24% CaCO3) with a mean of 14.2%. Values were mainly high in the North-East of the country. Only
25 % of the soils had their active limestone content less than 8% showing a high CV (24.2%).
Semivariogram Analysis Results
Based on the semivariogram analysis and according to the ratio of nugget to threshold,which determines the spatial dependence, results showed that phosphorus, potassium and lime andpH have a strong spatial dependence, whereas, pH has a weak spatial dependence.
Spatial Distribution of Soil P, K, Active Ca, and pH
Figures 1, 2, 3 and 4 showed interpolated maps of P and K nutrients availability, active
limestone and pH, respectively. Results showed that P availability was increasing from North Westto Central West and decreasing from North East to Central East. Potassium availability was very
high in North East of Tunisia, very low in the Center East, and good in the remaining areas. Soil
active Ca was very high in the North East of Tunisia and high in the remaining regions. Soil pH
was high alkaline expertly for Nord West which was moderate

Figure 1. Interpolation map ofassimilable P availability in Tunisiansoils.

Figure 2. Interpolation map of
exchangeable K availability in Tunisiansoils.

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Figure 3. Interpolation map of active
limestone content in Tunisian soils.

Figure 4. Interpolation map of pH in
Tunisian soils.

CONCLUSIONS
Based on this research study it is confirmed that the interpolation approach using theordinary kriging method gives expressive and explicative maps of the spatial distribution of soils
nutrients variability as found by other studies in other countries (Wani t al., 2012; Pham et al.,2019; Seminova et al., 2020). The maps generated can be used as decision making tools for
integrated soil fertility management to optimize returns on chemical fertilizers inputs while
preserving ecosystems and improving crop productivity and yields.
REFERENCES
Gautheyrou J, Gautheyrou M. 1966. Soil analysis. Methods used by the Antilles Soils Bureau
Laboratory, 5A ed. Soil Office of Antille.
Mhiri A. 2018. Tunisian agriculture at a crossroads, Boudjebel, M. (Ed.), Simpact, Tunisia.
Pham TG, Kappas M, Huynh CV, Nguyen LHK. 2019. Application of Ordinary Kriging andRegression Kriging Method for Soil Properties Mapping in Hilly Region of Central
Vietnam. ISPRS Int. J. Geo-Inf. 8(3), 147: 1-17.
Smirnova EV Giniyatullin KG, Sahabiev IA,Okunev RV. 2020. Comparison of interpolated soil
nutrients maps obtained with application of various approaches of ordinary kriging. IOP
Conf. Ser. Earth Environ. Sci. 548.
Wani MA, Wani JA, Bhat MA, Kirmani NA, Wani ZM, Bhat SN. 2013. Mapping of Soil
Micronutrients in Kashmir Agricultural Landscape Using Ordinary Kriging and Indicator
Approach. J. Indian Soc. Remote Sens. 41: 319–329.

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#7654 DIGITAL MAPPING OF EXCHANGEABLE CATIONS IN SOILS OF
SOUTHWESTERN NIGERIAa

M.K. Idowua*, O.E. Awosiyana and E.A. Adesinab
Department of Soil Science and Land Resources Management, Faculty of Agriculture, Obafemi
Awolowo University, Ile-Ife, Osun State, Nigeria; bDepartment of Geography, Faculty of
Science, Obafemi Awolowo University, Ile-Ife. Osun State, Nigeria
midowu@oauife.edu.ng; +234 803 8272 331
ABSTRACTThe study created Geo-database and developed a digitized map of soil exchangeable
cations indicating the spatial distribution in soils of southwestern Nigeria. The existing electronicand hard copy records were used for secondary data and geographical coordinates of locations
where the primary data of exchangeable cations were obtained. Geographic information system(GIS) was used to show the regional level perspective of the cations. Topographical maps wereused for extraction and update while shape file of administrative map of each state within theregion was geo-referenced to World Geodetic System of 1984. The cations were interpolated using
kriging technique in ArcGIS® 10.4 environment. The data collected were subjected togeostatistical analysis. Results showed that exchangeable calcium has the highest average
concentration and the spread around the mean value was least in exchangeable potassium.
Exchangeable calcium and magnesium which have skewness values of 0.34 and 0.92 respectively,
were more normally distributed than exchangeable potassium and sodium which has skewness
values of 3.07 and 2.79 respectively. Exchangeable calcium contents were dominantly low inmajor parts of southwestern Nigeria but high at the eastern fringes of Ogun and Lagos States andsouthern fringe of Oyo State. The low soil exchangeable magnesium and K were low while
exchangeable sodium was low to medium. The study concluded that digitized map of
exchangeable cations showing their spatial distribution could be used as digital nutrients map andcould also form basis for precise fertilizer recommendation.
Keywords: exchangeable cations, digitized map, Geographic Information System, soil fertility
INTRODUCTION
Soil fertility depletion has been established as one of the major factors for decline in crop
yield, low quality resulting in poverty among smallholder farmers in Nigeria in particular and subSaharan Africa in particular (Aduayi, 1989, Adepetu et al., 2014; Idowu et al., 2020; Egbebi et al.,2020). The current blanket method of fertilizer application could be the reason that discourage
farmers from using fertilizer. The average fertilizer usage SSA is about 17 kg / ha, which is thelowest in the world. There is the need to used resources including fertilizer in an efficient way
which is one of the philosophies of precision agriculture. Information on soils in African continentand their characteristics are scanty, with its constrain in agricultural development (Omran, 2005;
Lal, 2020). Soil information maps available have not been used for research because they are notavailable in digital formats. Soil models essentially do not yield empirical results due to inadequate115

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basic soil data (Stroosnijder, 2005). This makes geospatial data, methods and tools simply defined
as information with geographical component to be relevant for Agriculture.
It was evident that most of the locations where good quality and reliable soil data wereobtained were not geo-referenced. There is a huge reservoir of soil data on exchangeable cations
which are not available to users in southwest Nigeria. Hence, this study were to collated
exchangeable cations data to produce digital map and developed a digitized map of soil cations
with their spatial distribution in soils of southwestern Nigeria.
MATERIALS AND METHODS
The study was carried out in Southwestern Nigeria located within latitudes 50 52ʹ 52.12ʹʹ N
and 9 12ʹ 44.60ʹʹ N and longitudes 20 38ʹ 42.39ʹʹ E and 60 0ʹ 17.66ʹʹ E. It comprises of Lagos, Ogun,
Oyo, Osun, Ondo and Ekiti states (Figure 1). Mean annual rainfall ranges between 1200 mm and1900 mm and temperature ranges from 26 0C to 32 0C (Akintola, 1986). The secondary data usedwere obtained from electronic publications and hard copies of data record on exchangeable cations.
Topographical maps at the scale of 1:50,000 were used for the extraction of settlement name foreach sample point. The settlement names were updated on the topographical map using google
earth satellite image. The coordinates of the locations where the exchangeable cations and theirrespective values were collected from existing electronic and hardcopy records. Some of theexchangeable cation values with only location but without geographic coordinates (latitude andlongitude) were carefully located and their geographic coordinates collected with the aids of ahand-held Global Positioning System Receiver. Figure 2 shows the soil sample points map of soil
exchangeable cations. The shape file of the administrative map of Southwest Nigeria containing
all the states within the region was geo-referenced to World Geodetic System (WGS, 1984).
GIS was used to show regional and state level perspective of exchangeable cations in southwest
Nigeria. Geo-database was created and the value fields for exchangeable cations (Ca2+, Mg2+, K+
and Na+) were entered into the attribute table in GIS environment. To show spatial variation of theexchangeable cations for southwest region, kriging interpolation was done.
0

(a)

(b)

Figure 1. (a) Administrative Map of the Study Area and (b) Map showing soil sample points

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RESULTS AND DISCUSSION
Exchangeable calcium content of soils in southwest Nigeria was relatively low due to theclimatic conditions and some human factors that contributed to the depletion and loss of the cations(Figure 2). The nature of the parent materials, the soil forming processes and the particle size
distribution as well as soil organic matter content might have also affected the content. High values
which ranges between 10.00 and 20.00 cmol/kg at the extremes of Lagos state shows its dominantconcentration. The low Ca2+ content generally could be related to dominance of 1:1 type of clay
minerals in the soils of Lagos state (Akamigbo, 2001), which was associated with the sandy natureof soils and low organic matter which might have evolved from flooding or colluviation (Essoka
and Namaku, 2007). Exchangeable calcium of Ogun state soils was dominated by low to moderatevalues. The weathering of minerals in these soils was strongly influenced by high rainfall andtemperature that had a significant impact on the weathering of feldspar and mica that played asignificant role in the mineralization of kaolinite which is an important clay mineral of Ogun statesoils for food crop production (Fernandes et al. 2011). High exchangeable calcium in Oyo statemight be due to recycling and fallowing process in soils from accumulation of refuse and waste
disposal (Areola, 1982). The exchangeable calcium content of Ondo State soils was related to themedium grained granite and gneiss as parent rock and materials (Periaswamy and Ashaye, 1982)
and land use types. Low calcium content in Ekiti State indicates that the soils would have low
ability to retain nutrients (Negassa, 2001; Fashina, 2005) due to continuous cultivation, leachingof plant nutrients, weathering, runoff and major crop harvest (Senjobi and Ogunkunle, 2011).
Low exchangeable magnesium (Figure 2) and low to medium for exchangeable potassium
and sodium in this region. This could be linked to deforestation in some secondary forest andreduced soil cover in some parts of the states (Wakene and Negassa, 2001).

Figure 2. Spatial Distribution of (a) Exchangeable Calcium and (b) Exchangeable magnesium ofSouthwest Nigeria Soils Analyzed using GIS
The exchangeable potassium of the soils of this region are dominated by low to moderatevalues but high at the northern and southern fringe of Oyo state and towards the eastern fringe ofOgun state. The low to moderate values might be due to potassium fixation in soils of Southwest
region especially when K fertilizers are applied. Also, some pedogenic and anthropogenic
conditions as well as high rainfall in most part of the regions would have influenced little variationin the exchangeable K content (Kiflu and Beyene, 2013).

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Low to moderate values of exchangeable sodium were also dominant in soils of southwestern Nigeria and high towards the Northern to Central part of Oyo state. It was also high at thefringes of Ogun, Lagos, and Ondo states. This could be due to the selective mobilization of cations
as well as micronutrient content in the soils of some part of southwest Nigeria influences theavailability of exchangeable sodium in different soil types. Restricted leaching of cations in oilpalm dominated states also have a positive influence on exchangeable sodium accumulation on thesoils of southwestern Nigeria (Odjugo, 2015).
CONCLUSIONSThe study concluded that digitized map of exchangeable cations showing their spatial
distribution could serve as a tool for digital nutrients map and also form basis for precise fertilizer
recommendation and precision agriculture in Nigeria.
REFERENCES
Adepetu JA, Adetunji MT, Ige DV. 2014. Soil fertility evaluation and fertilizer recommendation.
Soil Fertility and Crop Nutrition, Jumak Publishers, Ibadan, Nigeria, pp. 523.
Aduayi EA 1989. Making the soils to plants. Inaugural Lecture series 7of the Obafemi Awolowo
University, Ile-Ife, Nigeria. pp. 38.
Akintola O. 1986. Rainfall distribution in Nigeria. Impact publishers, Ibadan, Nigeria. pp 380.
Akamigbo FOR. 2001. The influence of parent materials on the soils of Southeastern Nigeria, East
Afr. Agric & Forest J. 48:81-91.
Areola 1982. Soil variability within land facets in areas of low smooth relief: a case study on the
Gwagwa Plains, Nigeria. Soil Survey and land Evaluation 2(1): 9-13.
Egbebi IA, Oyedele DJ, Idowu MK, Tijani PO, Olakayode AO. 2020. Changes in the physicaland chemical properties of a Ultisol: response to short term fertilizer management.
Journal of Agricultural Research. http://doi.10.23880/oajar./6000240 .
Essoka A, Namaku Y. 2007. Soil Variations along a Toposequence in a Nsorthern Ginnea Savanna
Region of Nigeria. In: Mamman A, Chupp D, Mash S. (eds.) Urbanization Resource
Exploitation and Environmental Stability in Nigeria. 402 - 410.
Fernandes JC, Gamero CA, Rodrigues JGL, Mirás-Avalos JM. 2011. Determination of the quality
index of a Paleudult under sunflower culture and different management systems. Soil Till.
Res. 112:167-174.
Idowu MK, Oyedele DJ, Adekunle OK. 2020. The influence of nitrogen fertilizer application onyields and nitrogen use efficiencies of Solanum macrocarpon and Solanum scabrum inSouthwest Nigeria. Journal of Food and Nutrition Sciences. 11(06):562-570.
http://doi.10.4236/fns.2020.116039
Kiflu A, Beyene S. 2013. Effects of different land use systems on selected soil properties in South
Ethiopia. J. Soil Sci. and Envr. Mgt. 4(5):100-107.
Negassa W. 2001. Assessment of important physicochemical properties of Nitosols under different
management systems in Bako Area, Western Ethopia. M.Sc. Thesis, Alemaya University,
Alemaya. pp 109.
Odjugo PAO. 2015. Valuing the cost of environmental degradation in the face of changing climate:Emphasis on flood and erosion in Benin City, Nigeria. African Journal of Environmental
Science Technology 6(1):17-27.

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Omran EE. 2005. “Spatial Data Infrastructure to Support Land Evaluation Applications in Egypt,”
MSc Thesis Report GIRS-2005-2016, Centre for Geo-Information, Wageningen
University, Wageningen.
Periaswamy SP, Ashaye TI. 1982. Updated classification of some Southwestern Nigeria soils Ife
Journal of Agriculture 4(1&2):25-40.
Senjobi BA, Ogunkunle AO. 2011. Effect of different land use types and their implications on landdegradation and productivity in Ogun State, Nigeria. Journal of Agricultural Biotechnology& Sustainable Development 3(1):7-18.
Stroosnijder L. 2005 “Measurement of Erosion: Is It Possible?” Catena 64(2-3):162-174.
Wakene and Negassa W. 2001. Assessment of Important Physicochemical Properties of Nitosols
under Different Management Systems in Bako Area, Western Ethopia. M.Sc. Thesis,
Alemaya University, Alemaya. pp 109.

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#7659 SOIL ORGANIC CARBONE MAPPING IN NORTH OF TUNISIA:COMPARISON BETWEEN DIFFERENT INTERPOLATION METHODS
M. Barbouchi1, H. Bahri, A. Souissi, H.2,3 Cheikh Mhammed2 and M. Annabi2
1
Laboratoire des Sciences et Techniques Agronomiques, INRGREF-Université de Carthage, Rue
Hédi Karray, 1004, Al Manzah 1, Tunis, Tunisie. 94. 2Laboratoire des Sciences et Techniques
Agronomiques, INRAT-Université de Carthage, Rue Hédi Karray, 1004, Al Manzah 1, Tunis,
Tunisie. 3Institut National Agronomique de Tunis, Université de Carthage, Avenue Charles
Nicolle, Tunis 1082, Tunisia, Barbouchi.meriem@yahoo.fr; haithem.bahri@gmail.com;
hatemcheikh@yahoo.fr; mannabi@gmail.com; souissiamir89@gmail.com
ABSTRACT
Soil organic carbon (SOC) stock is an important carbon pool in terrestrial ecosystems. It
plays an important role in agricultural productivity and is often used as a key indicator of soil
quality whether for soil fertility or climate regulation. SOC stocks are difficult to estimate due tothe large spatial variability. In this way, many different techniques have been conducted forpredicting and mapping SOC content. However, although numerous techniques are in use, there is
still debate on which is most appropriate for regional soil mapping. In this context, this paper
discussed the application of geostatistical method to mapping SOC in North of Tunisia. 1097
samples of SOC were collected over the Northeast region from different sources. The selection ofdata was based on the rigor and credibility of the sources. The available data covered the period
between 2000 and 2014. The sampling was carried out in the top soil (0-30 cm). Data wereanalyzed using geostatistical methods: simple kriging, ordinary kriging, universal kriging andinverse distance weighting (IDW) with the power 1, 2 and 4. These methods were compared using
different performance criteria: normalized root mean square (RMSE), average standard error(ASE) and coefficient of determination (R²). The best model for describing spatially variation ofsoil organic carbon was the ordinary kriging with the lowest error criteria and the highest
coefficient of determination (R²=0.6). The spatial structure of soil organic carbon is well describedby the stable model. The nugget effect indicated that soil organic carbon was highly dependent on
the study area (nugget/sill ratio=20%).
INTRODUCTION
Soil organic carbon is the main component of soil organic matter. As an indicator of soil
health, SOC is important for its contributions to food production, it improves the structural stabilityof the soil by promoting the formation of aggregates which, in combination with porosity, provide
sufficient aeration and water infiltration for plant growth (FAO, 2017). Soil organic carbon (SOC)
is one of the soil properties that is not only key to sustainable soil fertility and productivity, but
has become increasingly important as a major terrestrial carbon reservoir in the face of climatechange (Wiesmeier et al. 2012). As COS is a spatially variable property, COS maps are of greatinterest for agricultural management as well as for environmental research related to terrestrial
sequestration of atmospheric carbon (Liu et al. 2014). In fact, on a global scale, the soil C stockincludes about 1500 Pg (1 Pg=1015 g) of soil organic carbon (SOC) (FAO, 2017). Small changes
in the COS stock can influence atmospheric CO2 concentrations and, in turn, have an impact on120

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global climate (Lal, 2003). It is therefore very important to estimate this stock in order to explore
areas of high and low COS stock potential for effective land-use management decisions. SOC
distribution is affected by many factors, including climate, hydrology, soil type, land use, andothers (McBratney et al, 2003, Cumble et al, 2013, Luo et al 2017), and its spatial variation is often
wide and complex. SOC stocks are difficult to estimate due to the large spatial variability in agiven soil (Cerri, 2007).
Therefore, quantitative evaluation of SOC levels is meaningful to facilitate regional
planning and to provide decision makers with a reference tool. hence, SOC maps are of strong
interest for agricultural management as well as in environmental research related to terrestrial
carbon (Liu et al 2014). One of the most important challenges of digital soil mapping is thedevelopment of methods that allow the characterization of large areas with high resolution. Surface
soils, which form the largest reservoir of the organic carbon, may be able to sequester atmosphericcarbon and thus mitigate climate change. In that way, many different techniques have been
conducted for predicting and mapping SOC content, such as the machine learning model (Ottoy
and al, 2018; Wu and al., 2020), multiple linear regression (Ottoy and al, 2018), the random forestmodel (Nabiollah et al., 2019) and the geostatistical methods (Kumar et al,2012; Gol et al., 2017;
Chabala et al,2017; Phachamphon, 2010). Although numerous techniques are in use, there is still
debate on which is most appropriate for regional soil mapping. In this context, this paper discussed
the application of the different geostatistical methods for the SOC mapping.
MATERIALS AND METHODS
Study Area
The study was conducted in the North of Tunisia. This region is characterized by three
types of bioclimatic stage (humid, sub-humid and semi-arid). Northern regions where forests andfertile agricultural land. This region is divided into two regions; the very rainy regions where therainfall is above 600mm. and the rainy regions where the average annual rainfall amounts between
400 and 600mm.
Soil Organic Carbon Data BaseA total of 1097points were gathered over the north region eleven governorate between
2000 and 2014, mainly by the Soil direction bulletins, analysis laboratories, research work (thesis,
masters, etc.), scientific articles and annals of research institutes. The selection of data was basedon the rigor and credibility of the sources. The data adequately reflect the distribution of the soilin this region. Soil samples were collected within soil profiles in the 0–30cm.
SOC Spatial Modeling
The spatial variability of SOC was studied using different geostatistical methods: ordinary
kriging, simple kriging, universal kriging and IDW. It used to estimate a soil organic Carbone
value at a region for which a variogram is known, using data in the neighborhood of the estimation
location. The predicted SOC at an unsampled location using measured values. In fact, it used todescribe how a soil organic carbon varies over the land surface. It demonstrates mathematicallythe means in which the variance of SOC varied as the distance and direction separating any two
points. The process of modeling semivariogram function fits a semivariogram curve to SOC
empirical data. An automated adjustment procedure was followed during the adjustment of the
SOC semivariogram using the stable model for each geostatistical method. And we applied thepower 1,2 and 4 for the IDW method.

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To validate the spatial prediction of SOC, we used the leave-one-out cross validation. The
indices used during this validation were determination coefficient (R²), root mean square error(RMSE), average standard error (ASE), and standardized RMSE (RMSSE). Thus, for each of thesampled locations, there was a measured value SOC (x0) and a predicted value (SOC(xi)), The R²,
ASE and RMSE described by (Piccini et al., 2013).
RESULTS AND DISCUSSION
Variography and Interpolated Surfaces of SOC
Table 1 shows the variographic parameters. It was observed that the spatial autocorrelation
was high for simple kriging and ordinary kriging with a nugget to sill ratio of 0.22 and 0.2
respectively and medium for universal kriging. The distance at which the sill is reached is 24 km
for OK, 1km for SK and 6.2Km for UK it marks the limit of spatial dependence. This expresses
that the places near to each another have a similar SOC while those more distant tend to have amore different soil on average this is explained by other intrinsic factors which can influence the
SOC in particular, the type of soil, the land Use, agricultural practices etc.…This shows that semivariogram parameters obtained from fitting of experimental semi variogram values werereasonable to describe the spatial variation of SOC. Coupling the predicted and measured SOC ina cross validation indicated a positive relationship (R²=0.6 for OK, 0.4 for both SK and UK). The
three types of kriging showed very similar results but the ordinary kriging showed the best results
R² higher and ASE and RMSE lower this is in accordance with the literature the OK is one of thegeostatistical models that use a set of statistical tools to predict the value of a given soil property(in this case SOC) at a location that was not sampled (Johnston et al., 2001). OK is said to be anexact interpolator in the sense that interpolated values or their local average coincide with thevalues at the sampled locations (Chabala, 2017).
Table 1. Variogram characteristics and indices used for leave-out-one cross validation of kriging
model for soil organic carbon prediction.
Nugget
OK
0.11
SK
0.08
UK
1.57
*NSR: Nugget to Sill ratio

Range
240
10.5
62

Sill
0.51
0.4
3.5

NSR*
0.22
0.2
0.44

R²
0.6
0.49
0.4

RMSE
1.92
1.93
1.98

ASE
0.69
3.02
3.37

For the IDW model results showed higher RMSE and lower R² compared to kriging this iscan be explained by the better performance of geostatistical approach compared to deterministic
one for mapping soil proprieties because the soil properties such as SOC do not depend on the
distance only.

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Table 2. Indices used for leave-out-one cross validation of Inverse distance weight (IDW) model
for soil organic carbon prediction.
IDW

Power
1
2
4

ME
-0.07
-0.06
-0.05

RMSE
2.93
2.99
2.10

R²
0.4
0.34
0.3

Figure 1 illustrates the resulted maps of SOC distribution in North Tunisia based on
different types of interpolation method. The maps show that the KS and KU underestimate the
SOC stocks the SK considered the majority of the area with SOC stocks between 5 and 6Kg/m².
Comparing the 6 resulted maps, the OK represents well the classes of the SOC. OK map show thatthe highest values of the stock are observed in the extreme North-West region in fersiallitic soils
in forests with rainfall greater than 400 mm / year (humid bioclimatic stage) where recorded values
are between 11.5 and 16.5 kg/m². High stocks are also observed in vertisols in forests (between
7.5 and 11.5 kg/m²) in subhumid climates in the Plains of Mateur, cultivated by cereal and fodder
crop. Thus, values between 4 and 6 Kg/m² characterize the alluvial plains of the upper Madjerda
valley formed of vertisols associated with poorly evolved soils of alluvial supply with annual
rainfall greater than 400 mm.

1231st African Conference on Precision Agriculture | 8-10 December | 2020

Figure 1. Maps of soil organic carbon (SOC) spatial distribution, generated based on kriging and
IDW model in North Tunisian site.
REFERENCES
Cerri CEP, Easter M, Paustian K, Killian K, Coleman K, Bernoux M, ... Cerri CC. 2007. Predictedsoil organic carbon stocks and changes in the Brazilian Amazon between 2000 and
2030. Agriculture, ecosystems & environment 122(1): 58-72.
Chabala LM, Mulolwa A, Lung O. 2017. Application of ordinary kriging in mapping soil organiccarbon in Zambia. Pedosphere 27(2): 338-343.
FAO. 2017. Carbone organique du sol, une richesse invisible. Rome.
Göl C, Bulut S, Bolat F. 2017. Comparison of different interpolation methods for spatial
distribution of soil organic carbon and some soil properties in the Black Sea backward
region of Turkey. Journal of African Earth Sciences 134: 85-91.
Johnston K, Ver Hoef JM, Krivoruchko K, Lucas N. 2001. Using ArcGIS Geostatistical Analyst.
ESRI Press, Redlands.
Kumar S, Lal R, Liu D. 2012. A geographically weighted regression kriging approach for mapping
soil organic carbon stock. Geoderma 189: 627-634.
Liu E, Teclemariam SG, Yan C, Yu J, Gu R, Liu S, … Liu Q. 2014. Long-term effects of no-tillage
management practice on soil organic carbon and its fractions in the northern
China. Geoderma 213: 379-384.

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Lal R. 2003. Soil erosion and the global carbon budget. Environment International 29(4): 437-450.
Luo Z, Feng W, Luo Y, Baldock J, Wang E. 2017. Soil organic carbon dynamics jointly controlled
by climate, carbon inputs, soil properties and soil carbon fractions. Global Change
Biology, 23(10): 4430-4439.
McBratney AB, Santos MM, Minasny B. 2003. On digital soil mapping. Geoderma, 117(1-2): 352.
Nabiollahi K, Eskandari S, Taghizadeh-Mehrjardi R, Kerry R, Triantafilis J. 2019. Assessing soilorganic carbon stocks under land-use change scenarios using random forestmodels. Carbon Management 10(1): 63-77.
Ottoy S, Angileri V, Gibert C, Paracchini ML, Pointereau P, Terres JM, ... Dicks LV. 2018.
Impacts of selected Ecological Focus Area options in European farmed landscapes on
climate regulation and pollination services: a systematic map protocol. Environmental
Evidence 7(1): 10.
Phachomphon K, Dlamini P, Chaplot V. 2010. Estimating carbon stocks at a regional level using
soil information and easily accessible auxiliary variables. Geoderma 155(3-4): 372-380.
Wiesmeier M, Spörlein P, Geuß U, Hangen E, Haug S, Reischl A. Kögel-Knabner I. 2012. Soilorganic carbon stocks in southeast Germany (Bavaria) as affected by land use, soil typeand sampling depth. Global Change Biology 18(7): 2233-2245.
Wu Q, Wang Z, Hu X, Zheng T, Yang Z, He F, ... Wang J. 2020. Uncovering the eutectics design
by machine learning in the Al–Co–Cr–Fe–Ni high entropy system. Acta Materialia 182:
278-286.

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1st African Conference on Precision Agriculture | 8-10 December | 2020#7834 COMPARISON OF SOIL TESTING AND SCANNING METHODS FOR INFIELD SPATIAL VARIABILITY ASSESSMENT OF SOIL FERTILITY:IMPLICATIONS FOR PRECISION AGRICULTURE
1

Rachid Bouabid1*, Ibtissam El Hadad1, Samir Benmansour2
National School of Agriculture of Meknes, Morocco; 2Les Domaines Agricoles, Morocco,
rbouabid@enameknes.ac.ma
ABSTRACT

Understanding spatial variability of soil fertility is a key to variable rate nutrient
applications for precision fertilization. The objective of this study was to assess field spatial
variability of soil fertility using two approaches, a gridded soil testing and a proximal sensing
technique. Measurements were performed on a 12-ha field planned for corn. For the first approach,soil samples were taken from 161 geopositioned grid points and were analyzed for pH, electricalconductivity (ECs), organic matter, phosphorus and potassium, while the second approach relied
on a soil scanner (Veris U3) that uses sensors for measuring apparent electrical conductivity (ECa),
pH and organic matter. Three interpolation methods (IDW, Spline and Kriging) were used forcomparative mapping of spatial variability of the selected soil parameters. The findings show that,in the case of the gridded soil testing, the three interpolation methods generated similar results
based on map patterns and quadratic mean errors (QME), with universal kriging giving the least
error. The ECa obtained from Veris scanner data was relatively similar to soil ECs obtained fromgridded soil testing, with a significant correlation (R2=0,67). High salinity levels were depicted byboth methods. However, the maps obtained for organic matter and pH were different, with nosignificant correlation. This can be attributed to the fact that the pH and infrared readings might
be biased by factors such as soil moisture and soil roughness. Although both approaches showed
high contents of phosphorus and potassium in the soil, the trends depicted by their respective maps
were different. These differences have important implications for the management of soil salinity,organic matter (and its contribution to soil N balance by mineralization), as well as for phosphorus
and potassium that would require a drawdown strategy.
Keywords: precision fertilization, spatial variability, spatial interpolation, Veris U3, electricalconductivity, precision agriculture
INTRODUCTION
The heterogeneity of soil properties represents an important source of variability that canaffect crop productivity (Mulla, 1993; Cambardella 1994; Mallarino et al. 2004). It is related tovarious inherent soil factors as well as to agricultural practices (Zwaenepoel and Le Bars, 1997).
Conventional methods of soil testing can reflect soil fertility but overcome its in-field variability.
In the context of precision farming, soil heterogeneity assessment and mapping require welldistributed measurements and appropriate methods of interpolation. High density grid-sampling is
considered most accurate for map representation but can be costly and time-constraining(Mallarino et al. 2004). Various sensing tools have been used for direct (ie. electrical conductivity)
or indirect (ie. organic matter) measurement of some soil properties (Shibusawa, 2006). Their126

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reliability depends on the type of sensor and the specific conditions of their use. Translating pointdata to spatial variability maps is accomplished by spatial interpolation methods, such as IDW,
Spline and Kriging. The latter informs better about spatial autocorrelation and spatial dependence(Tabor et al. 1985; Cambardella et al. 1994). The objectives of this study were (i) to assess soil
spatial variability as a basis for precision fertilization using two methods, gridded laboratory soil
testing and a sensing technique using a soil scanner with multiple sensors, and (ii) to compare threespatial interpolation methods for the case of the grid soil testing.
MATERIALS AND METHODS
The research was conducted on a 12-ha irrigated corn filed located in a farm adopting
precision agriculture near the city of Fes, Morocco. The soil is a Vertic Calcixeroll. For the soil
testing method, 161 regularly spaced composite soil samples were collected (0.2-m depth) on aregular grid in mid-February 2020 before sowing and analyzed for electrical conductivity (ECs)(1:5 soil extract), pH, organic matter (OM), available phosphorus (P) and potassium (K). In thecase of the sensing method, a Veris U3 scanner (Veris Technologies®) contracted by the farm waspassed throughout the field (with 15-m spacing) to collect data for apparent electrical conductivity(ECa), pH and OM. Few samples were collected by the service provider for extrapolations forphosphorus and potassium based on own developed models.
In the case of the lab soil testing, spatial interpolation was performed using Inverse
Distance Weight (IDW), Spline and Kriging using ArcGIS Geostatistical Analyst. Best
optimization parameters and models were used each method and cross validations were based on
QME and map patterns. In the case of kriging, the degree of spatial dependence was evaluatedusing the nugget/sill ratio (Cambardella et al.,1994). In the case of the scanner method, spatial
interpolation was done by the kriging for ECa, pH and OM. Phosphorus and potassium maps were
derived by the Veris service provider and were made available to this study for comparisons. Meanvalues of scanner data situated within a 10 meters radius relative to the same positions of the grid
sampling points were used for the purpose of correlations among the two methods.
RESULTS AND DISCUSSION
Spatial Variability Using the Gridded Soil Testing Data
The soil parameters measured presented different degrees of variability, with pH and OM
showing low CVs (2,6% and 13,2% respectively) and ECs, phosphorus and potassium showing
high CVs (61.7%, 59,7 % and 39.2, respectively). Values ranged from 0,1 to 1,14 ds/m for ECs,
from 7,7 to 8,3 for pH, from 2,04 to 4,35% for OM, from 21,3 to 512 ppm (P2O5) for phosphorus,
and from 226 to 982 ppm (K2O) for potassium. High skewness of ECs, pH P and K were noticed
indicating that these properties have particular high local distribution.
The spatial variability maps obtained with the 3 interpolation methods showed similar
trends and patterns for all the measured parameters. For the purpose of this short article, only maps
with kriging method are presented (Figure 1). Kriging (Universal) showed the smallest MQE.
Overall, the maps revelled the existence of a general NE-SW gradient for all measured parameters.
The ECs map revealed a high salinity area in the SW part of the field (0,5 to 1,14 ds/m),
exceeding corn salt tolerance. The low range (92m) and nugget/sill ratio (0.09) of ECs obtained
from the kriging semi-variogram indicates a strong spatial dependence (<0,25) inferring that127

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variability is affected more by structural extrinsic factors (topography and drainage) than byfarming practices (Tabor et al. 1985; Cambardella et al., 1994; Goovaerts, P., 1998).
Soil pH variations across the field (map not shown) were relatively small (0.6 pH unit).
The alkaline conditions are attributed to the presence of active CaCO3 (2,5 to 7,3%) that tends tobuffet soil pH around 8.2. The high alkaline conditions, in the eastern part of the field, can affectsoil conditions, mainly micronutrient availability, with risks of iron chlorosis.
In general, the soil OM map did not show great variability, with the 2.5-3.5% class being
the dominant. OM accumulation in the western part of the field can be related to residue
management. Although the overall amplitude differences are not too high across the field, shortterms management of residues and organic amendments are needed to level up OM contents.
Kriging variogram gave a range of 594m and nugget/sill ratio of 0,69 indicating a weak spatial
dependence compared to ECs. Similar results were reported by other studies (Miao and Mulla,2006).

Figure 1. Spatial variability maps of ECs, organic matter, phosphorus and potassium based on
grid laboratory soil testing data
The maps of soil available phosphorus were also quite similar among the threeinterpolators, with kriging yielding a smoother map. The P level was in general very high (>50ppm) indicating that the phosphorus fertilization practice leads to a build-up of this nutrient andneeds to be drawdown to a reasonable level. The kriging semi-variogram gave a range of 594
meters and a nugget/sill ratio of 0,13 that indicate large autocorrelation distances and high spatial
dependence, inferring a stronger influence of management practices (Cambardella et al., 1994).
The exchangeable K maps revealed also a general E-W decreasing trend with high levels(>300 ppm K2O) on more than two thirds of the field. These high K levels are most probably
related to the mineralogy of the soil clays which are illite rich (Bouabid et al., 1996). In fact, K
fertilizers were not applied on this field for several years. Kriging semivariogram gave a range of393m, while the nugget/sill ratio was very low (1,6), suggesting a high spatial dependence which
corroborates that variation of K are more related to inherent soil conditions than to fertilizer
practice.
Spatial Variability Maps Using Veris U3 Scanner
The ECa map obtained by the Veris U3 using universal kriging (Figure 2) shows a very
patchy pattern, but still displays a NE-SW gradient similar to that depicted in the map obtained by
the grid laboratory soil testing. The map revels also that the SW part of the field has a high ECa.
Correlation among ECa and ECs was highly significant (R2 of 0.67). Compared to the 1:1 curve, it

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appears that for low salt levels (<0.020 ds/m), the scanner seems to underestimate measurements,and for higher salt levels (>0.020 ds/m), it tends to overestimate EC measurements.
The OM map shows contents within close range compared to the laboratory grid soil testing
map, but with different trends. The <2.5% class being the dominant, and the class >3.5% being
negligeable. Some agreement for the intermediate class (2,5-3,5%) were observed in the middle
part of the field. No significant correlation was obtained with MO obtained with soil the gridlaboratory testing method. This lack of correlation can be attributed to various factors, such as thestate of organic residues decomposition, soil moisture, aggregate heterogeneity and soil surface
roughness (Shonk et al., 1991; Sudduth and Hummel, 1993; Christy, 2008; Bricklemyer and
Brown, 2009; Morgan et al., 2009;).

Figure 2. Spatial variability maps of ECa, organic matter, phosphorus and potassium based on
Veris U3 scanner data
The maps of phosphorus and potassium provided by the Veris service provider (using own
models) and made available to this study by the farm showed also different class extents and trends
compared to those obtained by the gridded soil test method. In the case of P, both methods showthat the class ‘>80 ppm’ was the dominant, but the extents these classes were different. While thegradient of P was relatively E-W in the case of the scanner method, it was rather NE-SW in thecase of the laboratory grid soil testing method (Figure 1 & 2). In the case of potassium, bothmethods revelled high soil K contents, but the trends were also different, especially for classes
higher than 300 ppm. Recommendations in both cases would be toward no, or minimum, P and K
applications in a small part of the field only. The observed differences for both P and K could be
attributed to the models used by the service provider for extrapolations for these two elements
across the field, which rely on a small number of tested soil samples (4 samples for 13 ha). A
greater number of P and K testing could provide a better portrayal of the spatial variability of P
and K using the various Veris U3 sensed parameters.
CONCLUSIONS
Spatial interpolation using IDW, Spline and Kriging generated similar map trend withclose MQEs. Kriging gave smoother limits among the classes adopted. Soil ECs showed small
variations and low spatial dependence compared to the other parameters. Apparent ECa map
obtained using the Veris scanner showed similarity with laboratory grid soil test ECs, with asignificant correlation. However, differences among the two methods were shown for organic
matter, pH, phosphorus and potassium. Differences for OM can be attributed to artefacts insensing by the scanner due to factors such as soil moisture and surface roughness, while those129

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observed for P and K can be attributed to the small number of soil-tested samples used forextrapolations. The spatial variability revealed on this field has important implications for sitespecific management of salinity, organic matter and nutrients.
ACKNOWLEDGEMENTS
The authors acknowledge the Company ‘Les Domaines Agricoles’ for allowing thisresearch to be conducted on one of their fields, as well as for making the Veris U3 data available
for analysis and comparisons.
REFERENCES
Bouabid R, Badraoui M, Bloom PR, Daniane M. 1996. The nature of smectites and associatedinterstratified minerals in soils of the Gharb plain of Morocco. European Journal of Soil
Science 47(2): 165–174.
Brikelmeyer RS, Brown DJ. 2010. On-the-go VisNIR: Potential and limitations for mapping soil
clay and organic carbon. Computers and Electronics in Agriculture 70: 209–216.
Cambardella CA, Moorman TB, Novak JM, Parkin TB, Karlen DL, Turco RF, Konopka AE. 1994.
Field-scale variability of soil properties in central Iowa soils. Soil Science Society America
Journal 58(5): 1501–1511.
Christy CD. 2008. Real-time measurement of soil attributes using on-the-go near infrared
reflectance spectroscopy. Comput. Electron. Agric. 61 (1): 10–19.
Goovaerts P. 1998. Geostatistical tools for characterizing the spatial variability of microbiologicaland physico-chemical soil properties. Biology and Fertility of Soils 27(4): 315–334.
Mallarino AP, Wittry DJ. 2004. Efficacy of grid and zone soil sampling approaches for sitespecific assessment of phosphorus, potassium, pH, and organic matter. Precis. Agric. 5:
131–144.
Miao Y, Mulla DJ, Robert PC. 2006. Spatial Variability of Soil Properties, Corn Quality and Yield
in Two Illinois, USA Fields: Implications for Precision Corn Management. Precision
Agriculture 7(1): 5–20. doi:10.1007/s11119-005-6786-2
Morgan CLS, Waiser TH, Brown DJ, Hallmark CT. 2009. Simulated in situ characterization ofsoil organic and inorganic carbon with visible near-infrared diffuse reflectancespectroscopy. Geoderma 151: (3–4), 249–256.
Mulla DJ. 1993. Mapping and managing spatial patterns in soil fertility and crop yield. In P. C.
Robert, W. E. Larson, & R. Rust (Eds.), Soil Specific Crop Management (pp. 15-26).
Amer. Soc. Agronomy.
Shibusawa S. 2006. Soil Sensors for Precision Farming. In Handbook of Precision Agriculture,
Principles and Applications; Srinivasan, A., Ed.; The Haworth Press: New York, NY, USA,pp. 57–90.
Sudduth KA, Hummel JW. 1993. Soil organic matter, CEC, and moisture sensing with a portable
NIR spectrophotometer. Trans. ASAE 36, 1571–1582.
Shonk JL, Gaultney LD, Schulze DG, Vanscoyoc GE. 1991. Spectroscopic sensing of soil organic
matter content. Trans. ASAE 34 (5) : 1978–1984.
Tabor JA, Warrick AW, Myers DE, Pennington DA. 1985. Spatial variability of nitrate irrigatedcotton: II. Soil nitrate and correlated variables. Agron. J. 49: 390-394.

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Zwaenepoel P, Le Bars JM. 1997. L’agriculture de précision. Ingénieries Eau-AgricultureTerritoires, 12 : 67-79. IRSTEA ; CEMAGREF, 1997, ffhal-00461080.

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1st African Conference on Precision Agriculture | 8-10 December | 2020

#7880 SPATIAL SOIL LOSS RISK ASSESSMENT FOR PROPER INTERVENTION: A
CASE OF NERI WATERSHED OMO-GIBE BASIN SOUTHWESTERN ETHIOPIA
Abebe Hegano1*, Awdenegest Moges2, and Nigatu Wonderade2
1
Southern Agricultural Research Institute (SARI), PO Box 06, Hawassa, Ethiopia
2
Hawassa University, Institute of Technology, PO Box 05, Hawassa, Ethiopia,
abehegeno@gmail.com
ABSTRACT
Soil erosion is one of the biggest global environmental problems resulting in both on-siteand offsite effects. It contributes negatively to agricultural production, quality of source water for
drinking, ecosystem health in land and aquatic environments, and aesthetic value of landscapes.
This study was conducted in Neri watershed, part of Omo Gibe basin with area of 465.46 km2.
RUSLE model supported by a GIS framework is used to assess the average annual soil loss, andcreate a soil erosion hazard map. To this end, data for the model parameters were derived from, aDigital Elevation Model (30*30 m), thirty years (1988-2017) rainfall data at 4 rain gauge stations,soil erodibility data from field samples, Landsat-8 satellite image for cover management andsupport practices. As result, the estimated average annual soil loss in Neri watershed varied from0 at flat lands to 465.16 t ha-1 yr-1at worst condition, with an estimated mean annual soil loss of
9.955 t ha-1 yr-1 and total amount of soil loss is 463365.46 t yr-1 About 54.88% (25536.8 ha) of thewatershed was categorized below moderate classes. The remaining 45.12% (21009.2 ha) of landarea was classified under high to very high classes about several times the maximum tolerable soil
loss. Based on the soil loss hazard map, six sub-watersheds out of eleven Neri sub- watersheds
need prior intervention in terms of integrated cover-management and mechanical conservation
measures. Furthermore, RUSLE results can be refined by analyzing along with sub-watersheds
level real time monitoring for conservation practices.
Keywords: soil erosion, RUSLE, GIS, Neri watershed, Ethiopia
INTRODUCTION
Soil erosion is the one among major threats to the sustainability of environment andproductive capacity of agriculture (Yang et al., 2003; Feng et al., 2010), which makes plant root
depth shallow, removes plant nutrients and losses water (Mahmud et al., 2005). This possibility
can be attributed to the fact that the impact of soil erosion is more damaging on bare land andcultivated land than any other types of land use/land cover. A 17% productivity reduction in the
global scale since the end of world war second (Angima et al., 2003). It results in serious foodinsecurity in many developing countries as it depletes productive soils (Blanco and Lal, 2008).
The severe situation in Ethiopia is quantified by loss of 1 billion USD per year (Sonneveld, 2002)
and is still affecting 50% of the agricultural area. It is the prime contributor to temporary or
permanent decline of the productivity of land (Oldeman et al., 1991). Its effects are also recognized
to be severe threats to the national economy of Ethiopia (Tamene, 2005).
Soil loss by surface runoff is a severe ecological problem occupying 56% (1,100 million
hectares) of the world-wide area as accelerated by human-induced soil degradation as (Bai et al.,132

1st African Conference on Precision Agriculture | 8-10 December | 20202008). In the same manner, about 43% (537,000 km2) of the total highland areas of Ethiopia arehighly affected by soil erosion (Hurni, 1990).
Quantitative understanding of soil loss has got attention of scientific community indifferent parts of Ethiopia. For instance, in the Ethiopian highlands only, an annual soil loss
reaches 200-300 tons ha-1yr-1 (FAO, 1984; Hurni, 1993) and soil loss due to erosion in Ethiopia
amounts to 1493 million tons per annum, of which about 42t ha-1 yr-1 was estimated from cultivatedfields of Ethiopia (Hurni, 2008).
The study area, Neri watershed, lower part of Omo Gibe basin in South Omo zone,
obviously shares the above-mentioned hazards and threats of soil erosion. Despite all these allthese could not go beyond quantified risks of soil loss and its spatial distribution for prioritizedintervention, there was no area specific information investigated particular to this watershed.
Consequently, this study is aimed to assess the average annual soil loss, and create a soil
erosion hazard map within this watershed is needed for decision makers in policy and strategy
formulation and, for natural resource managers by providing a necessary tool to design the rightintervention strategy for the specific climate, soil type, and topography and land use situation.
MATERIALS AND METHODS
Soil erosion modeling study was conducted, Neri watershed is situated in the lower part of
the Omo Ghibe basin southwestern, Ethiopia. Geographically, it lies between 5.630 and 5.930
North, and 36.310 and 36.670 East. The study area is about 465.46 km2.
RUSLE empirical model Renard et al. (1997) supported by a GIS framework is used toassess the average annual soil loss, and create a soil erosion hazard map. To this end, data for themodel parameters were derived from, NMA thirty years (1988-2017) rainfall data at 4 rain gauge
stations, soil erodibility data from field samples, Landsat-8 satellite image (OLI) assessed online
USGS on 1st January 2017, for cover management and support practices. The RUSLE (Renard etal., 1997) model can be expressed as Equation (1);…… ………………………………………………Equation 1
Where, A, stands for computed soil loss per unit area per year (t ha-1 yr-1), R for rainfall
erosivity factor (MJ mm ha-1 h-1yr-1), K for soil erodibility factor (t ha MJ-1 mm-1) , LS for slope
length and steepness factor (dimensionless), C for cover - management factor (dimensionless) and
P for support practice factor (dimensionless) R factor was determined using regression (Equation
1) calibrated by (Kaltenrieder, 2007).
………………………………………………………Equation 2
Where, R is rainfall erosivity factor, P is mean annual rainfall in (mm)
Soil erodibility (K-factor) has been calculated after textural (analysis particle size
distribution) by means of the following formulae which were developed from global data ofmeasured K values, obtained from 225 soil classes (Renard et al. 1997).

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1st African Conference on Precision Agriculture | 8-10 December | 2020……………………………Equation 3
With R2 = 0.983 Where, Dg is the geometric mean particle
size, for each particle size class (clay, silt, sand), di is the maximum diameter (mm), di-1 is theminimum diameter and fi is the corresponding mass fraction.
LS-factor was computed from ASTER DEM by ArcGIS 10.3.1 in raster calculator using
the map algebra expression in (Equation 4) suggested by Mitasova and Mitas (1999)…………………………………………………………………………………..Equation 4
C-factor estimation was done by two maps generated (LULC and NDVI suggested byDurigon et al.(2014) and regression equation (Equation 5) obtained for this particular study
watershed from C-factor tabular values of references and NDVI map was employed.
C= -1.211*(NDVI) + 0.615 ………………………………………………Equation 5
P-factor was obtained from both slope and LULC classes (Wischmeier and Smith, 1978)
because no soil conservation practices. So, conservation practices vary based on LULC and slope.
RESULTS AND DISCUSSION
Revised Universal Soil Loss Equation Model Individual Factors Outputs
Rainfall erosivity values as result of Inverse distance weighted (IDW) interpolation, range
from 713.1 to 754.43 MJmmh-1ha-1yr-1 with mean of 731.87. As the result of kriging interpolationof calculated spatial K-values based on equation (11) and (12). K-factor values in the study
watershed differed from (0.0306 – 0.044) Mg h MJ-1 mm-1. Estimated annual average soil
erodibility factor was 0.0385Mg hMJ-1mm-1. As result of linear regression equation, C-factor
output was found to its maximum value of 0.6141, minimum 0.01056 and average 0.385 and the
P factor values of study area ranges between 0.11 and 1 with mean of 0.591.
Soil Loss Estimation Results
The RUSLE model output pixel level analysis result of Neri watershed ranges from in themean (0-465.16 ton ha-1yr-1) the maximum amount estimated at the mid-eastern parts of thewatershed. The very high pixel values (more than 25 tons ha-1yr-1) were detected in a distributedmanner throughout the watershed. However, the particular maximum pixel value found at muchof the steeper slope banks with high LS factor value where poor surface cover condition. The
average annual soil loss rate is 9.955 tons ha-1yr-1 from the entire watershed area (46546 hectare).
The total amount of soil loss is 463365.46 tons yr-1.
The mean annual soil loss rate from this watershed is higher than the previous research
report compared with the case of Gibe-III Dam Catchment which is 7.47 t /ha/ y by Gera work and
Awdenegest (2014). Conversely, the mean annual soil loss results of current study is lower than
the annual average soil loss under Ethiopian condition which is 12 tons per hectare per year and134

1st African Conference on Precision Agriculture | 8-10 December | 2020

about 42tha-1yr-1 from cultivated highland (Hurni, 1993) could be due to agricultural fields lie lessslopes.,According to WBISPP (2001), the average annual soil erosion rates were classified into
the five priority classes, namely, ‘Very Less (0-03.125), Less (3.125-6.25), High (12.5-25) andVery high (>25) t ha-1 yr-1 to develop soil erosion severity maps.
Table 1. Area and amount of annual soil loss for each severity class (WBISPP, 2001).
Soil loss(t ha-1 year-1)(0 - 3.125)(3.125 - 6.25)(6.25 - 12.5)(12.5 - 25)(> 25)

Equivalent top soil
removal (mm)(<0.2.5)
0.2.5 – 0.5
0.5 - 1
1-2
>2

Soil loss risk
class
Very Less
Less
ModerateHigh
Very High

Area
(ha)
25023.13
6316.2910896.423025.49
1284.67
46546

Area
(%)
53.76%
13.57%
23.41%
6.5%
2.76%
100%

As result, about more than 6.25 tons ha-1yr -1, was (19,241ha.), which comprises 32.67%
of the total study area which zone of a great danger of soil erosion and the remaining 67.33%,(31339.42) hectare is lesser, which is recognized as not.
Prioritization of Intervention areas
According to (Gebreyesus and Kirubel, 2009; FAO and UNEP., 1984), soil loss tolerance(SLT) denotes the maximum allowable soil loss rate that will sustain an economic and a high levelof productivity. (WBISPP, 2001) classification of soil loss classes which is for Southern Ethiopia
was used, which is more than tolerable loss rate category (>6.125 t ha-1 year-1 As result, 7 subwatersheds out of eleven and or 9 kebeles out of 19 were beyond this soil loss tolerance rate.

Figure 1. Soil loss severity classes by sub watershed.
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CONCLUSIONS
The application of RUSLE model with the aid of GIS and remote sensing was applied over
the Neri watershed to generate soil loss hazard map. Moreover, the study was an attempt toestimate average annual soil loss at this watershed and to identify risky areas priority intervention.
The soil loss map produced by overlaying of grid maps of the six factors showed that the soil loss
rate of the watershed ranged from (0 – 465.16 t ha-1yr-1) with a mean annual soil loss rate of 9.95
t ha-1yr-1 and overall total annual amount of 463,365.46t/yr. So that, it is perceptible that thiswatershed is under risk of soil erosion.
As a further matter, for the prioritization of intervention, the soil loss map of baseline
period was used to extract the soil loss per nineteen (19) kebeles administrative units and eleven(11) sub-watersheds (SWs). But sub-watersheds having greater than SLT were chosen. Based on
the analysis, sub-watersheds greater than SLT in their order of area weighted average soil loss
were identified for priority intervention. Based on the findings of this study, to ensure sustainable
resource use, management practice by strong policy measures, erosion minimization in agriculturaland non-agricultural land use classes are of paramount importance
REFERENCES
Angima SD, Stott DE, O‟Neill MK, Ong CK, Weesies GA. 2003. Soil erosion prediction using
RUSLE for central Kenyan highland conditions. Agriculture, ecosystems and environment
97(13): 295- 308.
Bai Z, Dent DL, Olson L, Shaepman ME. 2008. Proxy global assessment of land degradation. Soil
Use and Management 24, 223-234.
Blanco CH, Lal R. 2008. Principles of soil conservation and management. Springer Science andBusiness Media B.V, Berlin P 240.
De Jong, SM, Paracchini ML, Bertolo F, Folving S, Megier J, de Roo APJ. 1999. Regional
assessment of soil erosion using the distributed model SEMMED and remotely sensed data.
Catena 37, 291–308.
Durigon VL, Carvalho DF, Antuness MAH, Oliveira PTS, Fernandess MM. 2014. NDVI timeseries for monitoring RUSLE cover management factor in a tropical watershed.
International Journal of Remote Sensing 35, p.441-453.
Feng X, Wang Y, Chen L, Fu B, Bai G. 2010. Modelling soil erosion and its response to land-use
change in hilly catchments of the Chinese Loess Plateau. Geomorphology 118: 239–248.
Food and Agricultural Organization (FAO) and UNEP. 1984. Provisional Methodology for
Assessment and Mapping of desertification.
Gebreyesus B, Kirubel M. 2009. Estimating Soil Loss Using Universal Soil Loss Equation (USLE)
for Soil Conservation planning at Medego Watershed, Northern Ethiopia. Journal of
American Science 5(1), pp 58-69. Marsland Press.
Gerawork B, Awdenegest M. 2014. Spatial Erosion Hazard Assessment for Proper Intervention in
the case of Gibe-III Dam Catchment, Southwest Ethiopia. MSc Thesis, Haramaya
University.
Hurni H, Herweg K, Portner B, Liniger H. 2008. Soil Erosion and Conservation in Global
Agriculture. In Land Use and Soil Resources. Springer, Dordrecht pp. 41-71.
Hurni, H. 1990. Degradation and conservation of soil resources in the Ethiopian highlands.
Mountain Research and Development 8: 123-130.

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Hurni H. 1993. Land degradation, fine, and land resource scenarios in Ethiopia. In: Pimenta(edition.),
Kaltenrieder J. 2007. Adaptation and Validation of the Universal Soil Loss Equation (USLE) for
the Ethiopian Eritrean Highlands. MSc Thesis, University of Berne, Centre forDevelopment and Environment Geographisches Institute.
Lal R, Elliot W. 1994: Erodibility and erosivity, In Lal, R. (ed.), Soil Erosion research Methods,
St. Lucie Press, Delray Beach, Florida, pp 181-207.
Mahmud Y, Alemu M, Menale K, Pender J. 2005. Cost of Land degradation in Ethiopia: A Critical
Review of Past Studies. Environmental Economics Policy Forum in Ethiopia, andInternational Food Policy Research Institute, Addis Ababa, Ethiopia.
Mitasova H, Mitas Z. 1999. Modeling soil detachment with RUSLE 3D using GIS. IL, USA:
University of Illinois at Urbana –Champaign.
Oldeman LR. 1994. The global extent of land degradation. In: Greenland DJ, Szabolcs I (eds) Land
resilience and sustainable land use. CAB International, Wallingford UK, pp 99–118.
Renard KG, Foster GR, Weesies GA, McCool DK, Yoder DC. 1997. Predicting soil erosion bywater: A guide to conservation planning with the Revised Universal Soil Loss equation,
Agriculture Handbook No. 703, U.S. Department of Agriculture, Washington, D.C., USA.
Smith JL, Halvorson JJ, Papendick RI. 1993. Using Multiple-Variable Indicator Kriging for
Evaluating Soil Quality. Soil Science Society of America Journal 57: 743-749.
Sonneveld BGJS. 2002. Land under Pressure: The Impact of Water Erosion on Food Production
in Ethiopia. Shaker Publishing, Maastricht, the Netherlands.
Tamene L. 2005. Reservoir siltation in the dry lands of Northern Ethiopia: causes, Source areas,
management option. PhD. Thesis, University of Bonn, Germany
Wischmeier WH, Smith DD. 1978. Predicting Rainfall Erosion Losses: A Guide For Conservation
Planning-USDA Agricultural Hand book No 537, Washington, DC.
Woldeamlak B, Teferi E. 2009. Assessment of soil erosion hazard and prioritization for treatment
at the watershed level: Case study in the Chemoga watershed, Blue Nile basin, Ethiopia.
Land. Degrad. Dev. 20: 609–622.
Woody Biomass Inventory and Strategic Planning Project (WBISPP). 2001. Southern Nations.
Yang D, Kanae S, Oki T, Koike T, Musiake K. 2003. Global potential soil erosion with reference
to land use and climate changes. Hydrology Process 17: 2913–2928.

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#7891 GEO-STATISTICAL PREDICTION OF SPATIAL DISTRIBUTION OF SALTAFFECTED SOILS OF MEKI-ZEWAY FARM AREAS IN ETHIOPIA: BASELINEINFORMATION FOR PRACTICAL IMPLEMENTATION OF PRECISIONAGRICULTURE SYSTEM
Melese Minaleshoa and Girma Kassa
Ethiopian Institute of Agricultural Research (EIAR), Debre Zeite Agricultural Research Center,
meleseminaleshoa16@gmail.com
ABSTRACT
Salinity and sodicity induced soil degradation are major soil property related environmental
constraint with severe negative impacts on productivity and sustainability of both rainfed andirrigated agriculture in arid and semi-arid lowlands of Ethiopia. The spatial prediction, data bases
creation and preparation of actionable digital soil salinization/sodiciation pattern maps has a
special importance to enable site-specific management system leading to the establishment andexecution of Digital Agriculture (DA) in general and Precision Agriculture in particular. Spatiallyhigh-resolved digital input layers are the fundamental requirements of space time conscious
variable rate application devices in the Internet of Things (IOT)realm to address the with-in-farmplot soil property variation. This study was conducted at Meki-Zeway farm areas during 20182019 with objective to investigate extent and spatial distribution of salt-affected soils and prepare
the associated digital database and digital maps. A grid sampling scheme was designed and auger
samples were collected at three depths (0-30, 30-60 and 60-90cm). Based on Geo-referenced soil
attribute data related to salinity/sodicity was collected from the field and respective laboratory
values generated. The spatial prediction and mapping of the unsampled surface from laboratory
point values was carried out in opensource GIS environment following geo-statistical interpolation
techniques. Statistical surfaces of the salinity/sodicity indicator preliminary layers including EC,
ESP and pHe and the final salinity/sodicity distribution predicted raster was generated. The results
revealed a widespread occurrence of salt-affected soils of the investigated farm areas being
remarkably varied spatially in terms of extent and types of the problems both in vertical andhorizontal modes. Larger proportion of the area contained pHe values greater than 7.8 with extreme
value of 10.3 and overall range values of ECe and ESP between 0.20-15.30dS/m and 3-58,
respectively for the upper 0-30cm soil depth. With increasing soil depth sodicity and alkalinity
increased consistently whereas salinity followed the reverse depth-wise distribution. Extent ofsoils alkalinity and sodicity was more pronounced South-ward (Zeway farm area) than Meki farmarea (North-ward). Chloride and bicarbonate salts of calcium and sodium predominate in the salt
composition of the saline soils while carbonate and bicarbonate salt of sodium appeared dominantsalts in sodic soils contributing to common alkaline reaction of sodic and saline sodic soils of thearea. Diverse manifestations of spatial heterogeneity in terms of types and severity among different
salt-affected soil classes is highly evident from present study and therefore site-specific, more
precise pixel-by-pixel reclametive and ameliorative measures must be taken to account such
scenarios- with precision agriculture. The study recommended a similar pathway of preparing theremaining other soil properties to be prepared and included to provide a more complete and unified
soil fertility improvement guide and digitally actionable input to AI and IOT tools for effective138

1st African Conference on Precision Agriculture | 8-10 December | 2020

deployment of precision agriculture and underlines the need for further research on investigationof salinity-sodicity casual factors and development of effective management options.
INTRODUCTION
Widespread occurrence of salt-affected soil in Ethiopia has been well documented(Hawando, 1995; Aredehey et al., 2018; Kidane et al., 2006; Fantaw A (2007).). In recent years
salinity and sodicity-induced soil degradation is becoming a major environmental constraint withsevere negative impacts on productivity and sustainability of irrigated agriculture in arid, semiarid and lowlands of the country. Meki-Zeway located in the Central Rift valley zone of the Great
Rift Valley system of Ethiopia where soils of the area are naturally salt-affected and prone tosecondary salinization. According to Meron (2007) buildup and expansion of salinization andsodiciation in particular is becoming potential threat to sustainability of irrigated farms of the area.
Soil salinity and sodicity are spatially variable and temporally highly dynamic. Soil heterogeneity
particularly imposed by mosaic distribution of salinity and sodicity is an important management
challenge. Such heterogeneity predetermines differences in rehabilitation and management
practices. In this regard the spatial prediction, data bases creation and preparation of actionabledigital soil salinization/sodiciation pattern maps has a special importance to enable site-specificmanagement system leading to the establishment and execution of Digital Agriculture (DA) ingeneral and precision agriculture in particular. In past only limited studies has been conducted in
the area which generally focused on soil taxonomic classification and related aspect where detailed
information on extent and spatial distribution of salt-affected soils in the area is lacking. This study,
therefore, was executed with objective to investigate extent and spatial distribution of salt-affectedsoils and prepare the associated digital database and digital maps.
MATERIALS AND METHODS
The study site lies between 7˚ 57' 6.15'' N to 8˚ 9' 4.43'' N Latitude and 38˚ 42' 36.2'' E to38˚51' 1.17'' E Longitude found at 160 km south of Addis Ababa, and at an average altitude of
1628m above sea level. Slope gradients are generally very low, and predominantly lying in therange between 1 and 2%. The area received average annual rainfall of 775 mm and has a meanannual temperature ranging from 12.33 to 26.18°C laying within warm semi-arid lowland agroclimatic zone classification of Ethiopian (MOARD, 2005). The dominant soil type of the studysite is Haplic Andosols, Typic Haploxerands (Zewdie 2004) and it is of sandy loam texture. The
agro-climatic conditions allow farmers to grow onion (Allium cepa), tomato (Solanum
lycopersicum) and maize (Zea mays). A grid sampling scheme was designed and auger samples
were collected at three soil depths (0-30, 30-60 and 60-90cm). Following standard analyticalmethod, collected soil samples were analyzed for particle size distribution, pH, EC, CEC,
exchangeable bases, water soluble cations and anions. ESP was computed as the percentage of
exchangeable Na to the CEC of the soil. The soils were classified into different salt-affected soils
according to the standard guidelines. The spatial prediction and mapping of the unsampled surface
from laboratory point values was carried out in opensource GIS environment following geostatistical interpolation techniques. Statistical surfaces of the salinity/sodicity indicator preliminary
layers including EC, ESP and pHe and the final salinity/sodicity distribution predicted raster was
generated. Data generated was subjected to descriptive statistics. Regression analysis was also
used to examine the relationships between selected soil properties.

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RESULTS AND DISCUSSION
Particle size analysis indicates that the soils of Meki-Zeway irrigated farm areas was sandy
loam to loamy textured soils. The proportion of clay particle varied between 14 to 25% while silt
and sand varied from 13 to 42 and 31 to 70%, respectively. Result revealed that the sand content
in the soils of studied area was very high and silt/clay ratios were greater than 0.76 indicating thatthe soils are relatively young with high degree of weathering potential (Meron, 2007). Soil pH
values of saturation paste extract (pHe) for all soil samples analyzed varied between range valuesof 6.9 to 9.3, 7.2 to 9.7 and 7.9 to 10.3, respectively for the upper (0-30cm), middle (30-60cm) andlower (60-90cm) soil layers and could be rated to lie between neutral to very strongly alkaline inreaction(Table 1). From range values, existence of remarkable spatial variation is evident on bothvertical and horizontal planes. Magnitude of alkalinity and extent of spatial coverage increased in
the direction from North (Meki) to South-ward (Zeway). Close to 3,897ha (40.1%) of farm area
exhibit soil with pHe value that could be rated as moderately alkaline in reaction whereas about
398ha (4.1%) of studied area had pHe value greater than 9.0 and regarded as strongly alkaline inreaction.
Distribution of salinity and extent of severity showed spatially heterogeneous pattern
throughout studied area mostly dominated along irrigated farm areas adjacent to Lake Zeway. The
overall range values of ECe, varied from 0.20 to 15.30, 0.19 to 12.98 and 0.21 to 7.56dS/m withmean value of 3.08, 2.30 and 1.93dS/m, respectively at a soil depth of 0-30, 30-60 and 60-90cm(Table 1). Depth-wise distribution of soil ECe didn’t showed consistent trend of either increasingor decreasing. Nearly 82% (7,969ha) out of the total farmland studied, contained ECe values lessthan 4dS/m and generally categorized as free of excess salt having no adverse effect on growthand productivity of most crops. Close to 17% (1,652ha) had ECe values between 4 and 8dS/m forthe upper 0-30cm soil layer which could be rated as moderately saline soil class whereas only
about 84ha (1%) were regarded as highly saline soil class (ECe between 8 and15 dS/m).
Sodic soils; contains soluble carbonate (CO32-) and bicarbonate (HCO3-) ions of Na+. The
concentrations of Na+ were greater than the accompanying levels of chloride (Cl-) and sulfate(SO42-) that is CNa: (CCl- + CSO42-) ratio greater than 1. Alternatively, the ratio (2CCO32- +CHCO3-) : (CCl- + 2CSO42-) was more than 1 in soil solution phase, expressed as meq/lt which
agrees with Chhabra (2005) observation as cited by Qadir et.al., (2007). The main cause of alkaline
reaction of soils is the hydrolysis of either exchangeable cations or of salts such as Na2CO3 (FAO
(1988). Result implies that NaHCO3 and Na2CO3 were the dominant salts in sodic soils and could
be presumed to be the major soluble salts predominantly contributing to common alkaline reactionof sodic and saline sodic soils of the area. The range values of ESP as a measure of soil sodicity
varied between 3 and 58 with mean value of 13 for the upper 0-30cm soil layer (Table 1). The ESP
consistently increased with increasing soil depth indicating lower soil layer had more sodic
property. Assessment of soils of irrigated lands in Meki Zeway by Mengistu (2001) as cited by
Kidane et.al. 2006 revealed that the soils of the area are sodic in the subsurface horizons. Spatial
distribution of soil with sodic nature followed the same trend to that of soils with alkaline property.

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Figure 1. Spatial distribution of predicted salt-affected soil classes for Meki-Zeway.
Table 1. Mean and range values of soil pH, EC and ESP for Meki-Zeway irrigated farm areas.
Range
and
mean
Min
Max
Mean

pHe
0-30
cm
6.9
9.0
7.9

30-60
cm
7.2
9.7
8.3

ECe (dS/m)
60-90
cm
7.9
10.3
8.7

0-30
cm
0.20
15.30
3.08

30-60
cm
0.19
12.98
2.30

ESP
60-90
cm
0.21
7.56
1.93

0-30
cm
3
58
12

30-60
cm
5
65
22

60-90
cm
7
76
34

CONCLUSIONS
Some limited part of farms under investigation had shown to contain excess accumulationof free salt in which 15% out of the total area could be classified as saline affected soil. The soils
lack appreciable quantities of neutral soluble salts but contain measurable to appreciable quantitiesof alts capable of alkaline hydrolysis, Substantial area of irrigated farms exhibit alkaline soil
property that range from moderate to strongly alkaline reaction and tend to increase with increasing
soil depth. The study also revealed that majority of farm areas contained soils with sodic character
that increased with increasing soil depth and could be regarded as a major soil productivity
constraint. Diverse manifestations of spatial heterogeneity in terms of types and severity among
different salt-affected soil classes is highly evident from present study and therefore site-specific,
more precise pixel-by-pixel reclaiming and ameliorative measures must be taken to account such
scenarios- with precision agriculture. The study recommended a similar pathway of preparing theremaining other soil properties to be prepared and included to provide a more complete and unified
soil fertility improvement guide and digitally actionable input to AI and IOT tools for effective
deployment of precision agriculture. The study also underlines the need for further research on

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investigation of casual factors responsible for buildup of soils with saline and sodic property anddevelopment of effective management option.
REFERENCES
Aredehey G, Libsekal H, Brhane M, Welde K, Giday A, Moral MT. 2018. Top-soil salinity
mapping using geostatistical approach in the agricultural landscape of Timuga irrigation
scheme , South Tigray , Ethiopia. Cogent Food & Agriculture 4(1): 1–13.
https://doi.org/10.1080/23311932.2018.1514959
FAO. 1988. Salt Affected Soils and Their Management. Soil Resources, Management andConservation Service FAO Land and Water Development Division. FAO Soils Bulletin
39, Rome, Italy.
FAO. 2018. Precision agriculture systems on saline and alkaline soils. Soil salinity management
manual. pp 59-61. ISBN 978-92-5-130141-8
Fentaw A. 2007. An overview of Salt Affected Soils and their Management in Ethiopia, a Paper
Presented in the Third International Workshop on Water Management (Water Man)
Project. Third International Workshop on Water Management (Water Man) Project.
Haramaya, Ethiopia.: Haramaya University.
Hawando T. 1995. The survey of the soil and water resources of Ethiopia. UNU/Tokyo.
Kidane G, Abebe F, Heluf G, Fentaw A, Wondimagegne C. 2006. Assessment of salt affected soils
in Ethiopia and recommendations on management options for their sustainable utilization.
A Task Force Report Submitted to the Office of the Deputy Prime Minister and Ministerof Agriculture and Rural Development. Addis Ababa, Ethiopia.
Meron N. 2007. Characterization of salt affected soils in the central Rift Valley and assessing salt
tolerance of different plants: A case study at the southwestern shore of Lake Ziway. Addis
Abeba University.
MOARD. 2005. Aajor Agro-ecological Zones of Ethiopia. Addis Ababa.
Qadir M, Oster JD, Schubert S, Noble AD, Sahrawat KL. 2007. Phytoremediation of Sodic andSaline-Sodic
Soils.
AdvancesinAgronomy
96(07):
197–247.
https://doi.org/10.1016/S0065-2113(07)96006-X

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#8019 CARTOGRAPHIE INTERACTIVE DES EXPLOITATIONS DE NOIX DE CAJOUDES PRODUCTEURS DE LA COOPÉRATIVE COPRODIGO DE GOHITAFLA
S. Kone, N. Coulibaly, D.J.M.J. Djina, K. Berté, et A. C. Kambou
Institut National Polytechnique Félix Houphouët Boigny, Département Gestion Commerce etEconomie Appliquée, Yamoussoukro, Côte d’Ivoire
ksiakawole@gmail.com 00 225 08 48 93 85
RESUME
L’agriculture a connu de profondes mutations : la spécialisation, la réduction de la maind’œuvre agricole, la mécanisation croissante, etc. Ces dernières années, l'intégration destechnologies de l'information et de la communication dans la gestion des exploitations agricoles adonné naissance à l'agriculture de précision. Ces innovations technologiques permettent de piloter
et d’optimiser les principales fonctions (productions, transports, commercialisation,transformation, consommation, etc.) qui structurent les exploitations agricoles. L'agriculture deprécision, lancée aux USA dans les années 80, s'implante aujourd'hui dans les principaux pays
agricoles africains, via la géolocalisation et la cartographie des exploitations agricoles. Lesorganisations professionnelles agricoles, sont confrontées à une montée croissante desproblématiques d’allocation efficiente des ressources et d’organisation du travail en lien avec lesvariabilités spatiales et temporelles des exploitations agricoles. L’organisation spatiale desparcellaires agricoles est généralement caractérisée de manière dynamique par des indicateurs
géographiques, sociologiques, agronomiques et économiques. Notre papier vise à concevoir une
cartographie interactive des exploitations de noix de cajou, afin d’optimiser la gestion desressources au profit des membres de la coopérative COPRODIGO de Gohitafla en Côte d’Ivoire.
Nous avons utilisé des récepteurs GPS pour collecter les données géographiques sur le terrain etréaliser le mapping des parcelles des producteurs. Nous avons utilisé une méthodologie dessystèmes d’informations géographiques qui comprend des traitements et l’analyse des données
géographiques avec la version PI (3.14) du logiciel QGIS. Les résultats nous ont permis d’obtenir
un code source en langage web (disponible hors ligne pour les zones à faible couverture réseau)
que nous avons édité pour obtenir une carte web interactive multicritères des exploitations de noixde cajou. La carte interactive est interprétable par un navigateur web et comporte des champs defiltres sélectifs permettant de caractériser les exploitations de noix de cajou en fonction de plusieurs
indicateurs (date de création, superficie, production, rendement, distance au siège, etc.). La carteinteractive permet à la coopérative de localiser ses exploitations dans le territoire, de mieux gérer
ses informations, d’être efficace dans la prise des décisions stratégiques et opérationnelles,
d’optimiser le groupage et la collecte des productions de noix de cajou afin de leurcommercialisation.
INTRODUCTION
L’agriculture ivoirienne emploie plus de 60% de la population active et représente au moins
34% du PIB (Kone et al., 2019). Le dynamisme agricole de la Côte d’Ivoire continue de surprendre
les marchés de matières premières, notamment la filière de production et de transformation desnoix de cajou (anacarde). La Côte d’Ivoire occupe la place de premier producteur mondial de noix143

1st African Conference on Precision Agriculture | 8-10 December | 2020de cajou. De 8500 tonnes en 1989, la production ivoirienne d’anacarde a dépassé 70 000 tonnes
en 1999, puis a bondi à plus de 800 000 tonnes en 2020. Dans un premier temps, le boom del’anacarde s’est produit principalement en zone de savane. La noix de cajou s’est également
produite vers le sud dès les années 2000, dans les zones cacaoyères de contact forêt-savane tellesque celles de Tanda, mais également dans les anciennes boucles du cacao vers M’Bahiakro à l’Est
et Bouaflé à l’Ouest, et plus récemment, vers 2010, au cœur des régions cacaoyères comme à
Bayota, au nord de Gagnoa (Ruf et al., 2019). La commercialisation des noix de cajou rapporte
plus de 480 millions d’euros aux producteurs. En effet, la noix de cajou fournit des revenus
importants aux paysans et contribue au développement local à travers la création d’emplois directs
et indirects (Kouassi et al., 2020).
La Côte d’Ivoire, a fait le choix de baser son développement agricole sur les organisations
professionnelles agricoles, notamment les coopératives agricoles de production, de collecte, decommercialisation et de transformation. L’utilisation partagée par les agriculteurs de tous les
moyens pour faciliter ou développer leur activité économique permet d’améliorer leur activitééconomique, d’améliorer ou d’accroître leurs performances économiques (Barraud-Didier et al.,2012). La création des coopératives agricoles permet la réalisation de plusieurs avantages, au profit
des producteurs, d'une organisation à grande échelle dans l'agriculture tout en évitant ses coûts detransaction (Tortia et al., 2013). Les coopératives agricoles, jouent un rôle important dans ledéveloppement de l’agriculture et plus précisément la filière anacarde en Côte d’Ivoire.
Par ailleurs, les coopératives agricoles font face à de nombreuses difficultés et défis,notamment celui de l'intégration des technologies de l'information et de la communication dans lagestion stratégique et opérationnelle des exploitations agricoles. L’agriculture de précision offre
de réelles possibilités aux coopératives en termes de caractérisation dynamiques et multicritèresdes exploitations agricoles à travers les systèmes d’information géographique. Les coopératives,font de plus en plus face aux difficultés de mapping et de la typologie des producteurs et de leurs
exploitations pour optimiser les services aux membres de la coopérative. Les coopératives doivent
optimiser la chaîne d'approvisionnement agroalimentaire qui comporte quatre principaux
domaines fonctionnels à savoir : (1) la production, (2) la récolte, (3) le stockage, (4) la distribution
et le transport des produits agricoles (Ahumada et al., 2009 ; Graf Plessen, 2019). Le domaine
fonctionnel de la distribution et du transport des produits agricole nécessite de la part des dirigeantsdes coopératives une bonne connaissance de la localisation géographique des exploitations
agricoles afin de planifier de manière optimale l’affectation des camions pour la collecte et letransport des produits agricoles dans les zones de production et de les livrer dans les zones decommercialisation, de transformation et ou de consommation à moindre coût.
Dans la littérature, la structure et l’organisation spatiale des parcellaires agricoles peuventêtre décrits sur la base d’un indicateur qualitatif simple. Ce type de descripteur se retrouve
principalement dans des études de cas sur de petits territoires et généralement concomitantes avec
des enquêtes directes en exploitation (Marie et al., 2009 ; Pauchard et al., 2016 ; Thomas Puech etal., 2020). Les outils des systèmes d'information géographique (SIG) peuvent aider à élargir lacompréhension des disparités des résultats de santé au sein d'une communauté (Geraghty et al.,2010).
L’objectif général de notre papier est de s’appuyer sur une démarche des systèmesd’information géographique pour réaliser une cartographie interactive qui permet lagéolocalisation multicritère des exploitations de noix de cajou dans la coopérative COPRODIGOde Gohitafla en Côte d’Ivoire. Les données actualisées (surface, fidélité, production, rendement,

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1st African Conference on Precision Agriculture | 8-10 December | 2020âge du vergers, suivi agronomique, état du verger et des équipements, etc.) permettent le suivi
optimal des coopératives en matière de politique managériale, administratif et opérationnel.
L’hypothèse qui sous-tend cette étude est que la caractérisation spatiale et temporelles desexploitations agricoles facilite la gestion stratégique et opérationnelle des coopératives puis accroît
leurs revenus et celui des producteurs de noix de cajou en Côte d’Ivoire. L’intérêt de cette étude
est de contribuer à intégrer les innovations technologiques de l’agriculture de précision à savoir
les technologies de l'information et de la communication pour renforcer la compétitivité desorganisations professionnelles agricoles, afin de jouer un rôle moteur dans le développement desfilières agricoles en Afrique.
MATÉRIELS ET MÉTHODES
Les données ont été collectées, par visite de terrains en 2020, dans les 10 sections de lacoopérative COPRODIGO de Gohitafla, auprès de 249 producteurs qui avaient 394 parcelles. Ces
parcelles représentent une superficie totale de 1335,53 ha, et une moyenne de 3,39 ha par
exploitation agricole. Nous avons obtenu la distance entre chaque parcelle et le siège de lacoopérative. Les calculs de surfaces et de distances ont été réalisés automatiquement grâce à QGIS.
Nous avons obtenu les données grâce aux enquêtes que nous avons effectué sur le terrain. La
méthodologie utilisée, a été inspiré des travaux de plusieurs auteurs (Auda, 2018 ; Bahoken et al.,2016 ; Delsart, 2020 ; Quesseveur, 2001 ; Thevenin, 2002 ; Fao et al., 2020 ; Shaw et al., 2017 ;Boelaert et al., 2002 ; AZZI, 2016 ; King et al., 1989 ; Ocaña et al., 2002 ; Ramos, 2003 ; Vidal etal., 1998 ; Habert, 2000). Des récepteurs (GPS) ont été utilisés pour les délimitations et la collecte
des waypoints des exploitations et des magasins de stockage. Chacune des exploitations a été
physiquement délimitée et géoréférencée. Les informations collectées ont été ensuite extraites enutilisant le logiciel d’extraction de Garmin : Garmin Basecamp. Nous avons procédé par utilisation
des outils de vecteurs de QGIS pour convertir les tracés en polygones. Après l’ajout des données
vecteurs des parcelles et celles des magasins de stockage des sections, nous avons ajouté les
informations sur le réseau routier et les multi-polygones de couche d’arrière-plan que nous avons
obtenue grâce au SIG collaboratifs OpenStreet map. Nous avons extrait ces données sous forme
de fichier d’extension osm grâce au logiciel libre JOSM. Grâce au plugin qgis2web nous avons
réalisé une exportation de notre projet en fichier HTML, CSS et javascript (avec les Framework
libre OpenLayer et Leaflet). Les codes sources du projet ont ensuite été édités pour réaliser notre
cartographie interactive.
RÉSULTATS ET DISCUSSIONS
Les résultats de l’analyse des informations géographiques, ont permis de concevoir une carteinteractive des 394 exploitations agricoles de noix de cajou de la coopérative de Gohitafla (Figure1). Ces 394 exploitations agricoles appartiennent à 249 producteurs répartis dans 10 sections de lacoopérative agricole. Cette carte peut être utilisée au sein de la coopérative même sans avoir accès
à une connexion internet, elle est aussi disponible via une connexion internet pour une consultation
à distance. Elle permet aux dirigeants de la coopérative et aux décideurs politiques de connaitre lagéolocalisation des exploitations agricoles et de s’assurer de l’existence des membres d’une
coopérative agricole. Ces informations participent à la visibilité interne et externe de la coopérative
et contribuent à sa crédibilité vis-à-vis des partenaires techniques, commerciaux et financiers.
L’interface graphique de la carte interactive comprend 7 champs qui comportent des informations145

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importantes sur les producteurs de noix de cajou et leurs exploitations (Figure 1). Les 7 champs
portent sur le Filtre par année de création de l’exploitation, le Filtre par la distance del’exploitation, le Filtre par la superficie de l’exploitation, la Légende de la carte, la Description dela carte, le Titre de la carte et la Situation géographique des exploitations de noix de cajou mappées.
La carte permet une localisation spatiale des exploitations et de comprendre les disparités ainsi
que les défis de production, de collecte et de transport des noix de cajou des producteurs. La carteinteractive, permet de réaliser la sélection spatiale par les superficies des parcelles, les distances
des parcelles au siège de la coopérative et par les années de création des exploitations de noix decajou. La carte permet également de mesurer la superficie des exploitations et les distances entre
deux parcelles.

Figure 1. Interface graphique de la carte interactive de la coopérative COPRODIGO de Gohitafla.
1 Filtre par année de création de l’exploitation. 2 Filtre par la distance de l’exploitation. 3 Filtre
par la superficie de l’exploitation. 4 Légende de la carte. 5 Description de la carte. 6 Titre de lacarte. 7 Situation géographique des exploitations de noix de cajou mappées.
La carte interactive permet d’avoir des précisions fines sur une parcelle données. Le filtrage
par le nom des producteurs permet de localiser en temps réel les informations sur un producteur
donné, à savoir son identifiant, la section de la coopérative à laquelle il appartient, l’année decréation de sa parcelle, la date d’adhésion à la coopérative, sa production, etc. A chaque entité
parcelle de la carte, les informations apparaissent en Popup ou en Infobulle lorsqu’on clique surelle. Ainsi pour chaque parcelle, la carte peut renseigner sur les magasins de sections les plus
proches, le réseau routier afin d’identifier les chemins les plus courts.
Dans la littérature, nos résultats sont similaires à ceux de plusieurs auteurs qui ont
développés des cartes interactives comme outil d’aide à la décision dans plusieurs domaines
d’activités. Plainecassagne et al. (2003) ont conçu une cartographie interactive du domaine
pastoral pyrénéen. Cette carte a permis de créer un espace d'échange d'informations localisées surle domaine pastoral entre les partenaires du massif. Les données actualisées et échangées ont
facilité le suivi technique du pastoralisme (évolution de la fréquentation des estives, des modes de146

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gardiennage, de l'état des équipements, etc.), qui ont aidé à défendre des orientations en matièrede politique pastorale. Thomas Puech et al (2020) ont développé une méthode pour caractériserl’organisation spatiale des parcellaires d’exploitations agricoles. Leurs travaux ont permis deproduire un indicateur qualitatif simple sur l’organisation structurelle des parcellaires
d’exploitations agricoles en croisant les outils de la statistique multivariée et de la géomatique.
Geraghty et al (2010) se sont appuyés sur les systèmes d'information géographique (SIG) pourévaluer les disparités de résultats chez les patients atteint du Diabète de type 2. Cette approche apermis une meilleure prise en charge des patients suivant une cartographie de la pathologie.
REMERCIEMENTS
Cette communication a été réalisée grâce au Projet de Promotion et de Compétitivité de laChaine de valeur de l’Anacarde (PPCA) financé par la Banque Mondiale et piloté par le Conseil
Coton Anacarde (CCA) en Côte d’Ivoire. Les auteurs remercient Dr Coulibaly Adama
Coordonnateur du Projet PPCA et Mme GUI LOU IRIE Epse TA Présidente du Conseild’Administration de la coopérative COPRODIGO de Gohitafla pour leur disponibilité à faciliter
la collecte des données et leurs analyses.
RÉFÉRENCES
Ahumada O, Villalobos JR. 2009. Application of planning models in the agri-food supply chain:
A review. European Journal of Operational Research. 196: 1-20.
Auda Y. 2018. Systèmes d'information géographique avec les logiciels libres grass et qgis.
AZZI L. 2016. Modélisation d’un réseau d’aep et contribution a sa gestion à l’aide d’un systèmed’information géographique. Étude de la rive gauche de la ville de sidi aiche. Université
Mohamed Khider-Biskra.
Bahoken F, et al. 2016. Les réseaux de transport à grande vitesse en france: Construction et apports
d'un système d'information géo-historique.
Barraud-Didier V, Henninger M-C, Anzalone G. 2012. The weakening of the member-cooperative
relationship in france. Études rurales. 190: 119-130.
Boelaert A, Van Der S. 2002. Editorial: Geographical information systems (gis), gimmick or toolfor health district management? Tropical Medicine & International Health. 3: 163-165.
Delsart, B. 2020. Segmentation manuelle de nuage de points au sein d'un système d'information
géographique web 3d.
Fao R, et al. 2020. Le systeme d'information geographique de la fao. XF2006294997.
Geraghty EM, et al. 2010. Using geographic information systems (gis) to assess outcome
disparities in patients with type 2 diabetes and hyperlipidemia. The Journal of the American
Board of Family Medicine. 23: 88-96.
Graf Plessen M. 2019. Coupling of crop assignment and vehicle routing for harvest planning in
agriculture. Artificial Intelligence in Agriculture. 2: 99-109.
Habert, EJL. IRD 2000. Qu’est-ce qu’un système d’information géographique. Laboratoire decartographie appliquée.
King D, Daroussin J, Arrouays D. 1989. Analyse cartographique et systeme d'information
geographique en pedologie.

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Kone S, Coulibaly N, Djina, DJMJ. 2019. Evaluation of short and long run impacts of cotton priceon rural poverty of lialibé and kossou producers in cote d’ivoire. International Journal of
Agricultural Economics. 4: 225-232.
Kouassi KM, Kouakou, LS. 2020. Dynamiques socio-economiques de l’adoption de la culture del’anacarde en milieu rural ivoirien : Cas de la sous-préfecture de languibonou. International
Journal of Progressive Sciences and Technologies. 21: 12.
Marie M, Bensaïd, A, Delahaye D. 2009. Le rôle de la distance dans l’organisation des pratiques
et des paysages agricoles : L’exemple du fonctionnement des exploitations laitières dansl’arc atlantique. CyberGeo: European Journal of Geography.
Ocaña R, Mundó J, Lusitano J. 2002. Les gis comme outils de travail pour la planification etgestion des transports urbains. 153.
Pauchard L, Madeline P, Marie M. 2016. L’échange parcellaire : Une nouvelle étape dansl’aménagement foncier de l’ouest français. 240: 7-24.
Plainecassagne L, Roucolle M. 2003. Une cartographie interactive du domaine pastoral pyrénéen
: Le volet pastoral du sig pyrénées. Revue géographique des Pyrénées et du Sud-Ouest.
Sud-Ouest Européen. 95-99.
Quesseveur E. 2001. Traitement spatial du bruit des transports terrestres.
Ramos, F. École doctorale" Information, Communication, Simulation, Modélisation 2003.
Modélisation et validation d’un système d’information géographique 3d opérationnel.
Ruf F, Kone S, Bebo B. 2019. Le boom de l’anacarde en côte d’ivoire : Transition écologique etsociale des systèmes à base de coton et de cacao. Cahiers Agricultures. 28: 21.
Shaw N, McGuire S. 2017. Understanding the use of geographical information systems (gis) inhealth informatics research: A review. J Innov Health Inform. 24: 940.
Thevenin T. 2002. Quand l'information géographique se met au service des transports publics
urbains: Une approche spatio-temporelle appliquée à l'agglomération bisontine. Besançon.
Puech T., et al. 2020. Une méthode pour caractériser l’organisation spatiale des parcellaires
d’exploitations agricoles Cybergeo : European Journal of Geography [En ligne],
Cartographie, Imagerie, SIG, document 938, mis en ligne le 23 mars 2020, consulté le 19
novembre 2020. URL : http://journals.openedition.org/cybergeo/34181 ; DOI :https://doi.org/10.4000/cybergeo.34181
Tortia EC, Valentinov VL, Iliopoulos C. 2013. Agricultural cooperatives. Journal of
Entrepreneurial and Organizational Diversity. 2: 23-36.
Vidal E, et al. 1998. Teledetection et systemes d'information geographique en irrigation etdrainage; guide methodologique et applications.

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ON-FARM EXPERIMENTATION

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1st African Conference on Precision Agriculture | 8-10 December | 2020#7448 SOIL MAPPING WITH THE VERIS U3 SOIL SCANNER IN A PRECISIONFARM IN HUNGARY
1

E. Harsányi1, T. Rátonyi1, A. Széles2, P. Fejér2, I. Bácskai1, P. Ragán1
National Agricultural Research and Innovation Centre, Institute of Agricultural Engineering,Gödöllő, Hungary; 2University of Debrecen Faculty of Agricultural, Food Sciences andEnvironmental Management, Debrecen, Hungary
ragan.peter@mgi.naik.hu +36 20 214 8127
ABSTRACT

Currently, field crop production faces constant challenges. Extreme climatic conditions,deteriorating circumstances on the field have a negative impact on the quantity and quality ofavailable yields, and the ever-changing agro-economic environment makes the profitability of thesector uncertain
The examined farm is located in the eastern part of Hungary, and as of 2013, it started togradually introduce precision farming in field crop production. The fields of the farm areheterogeneous, there are areas with low productivity on certain fields. The total area is 942
hectares, of which 230 hectares were examined. Measurement of the soil electrical conductivity(ECa) of the upper 60 cm soil layer were performed on designated field plots of the farm with a
Veris U3 soil scanner. Harvesting of the examined areas, grain moisture measurement and yield
mapping were performed by means of a JD S770i type combine harvester; the data were provided
by KITE cPlc. The vector point-like Veris U3 data and yield data were projected onto a 10x10 m
resolution polygon grid prepared from Sentinel 2 raster satellite imagery for numerical statisticalcorrelation analysis in the GIS databaseOn the examined plots there are saline spots and buried watercourses, which, dependingon the crop year, also have a positive and negative effect on the yield of cultivated plants. In the
average of the examined fields (230 ha), the conductivity measured by Veris U3 in 2016 showeda moderately weak (r = 0.29) correlation with the yield mapped by the combine.
INTRODUCTION
Currently, field crop production faces constant challenges. Extreme climatic conditions,deteriorating circumstances on the field have a negative impact on the quantity and quality ofavailable yields (De Benedetto et al., 2013)., and the ever-changing agro-economic environment
makes the profitability of the sector uncertain. Huang et al. (2017) found that an increase in theelectrical conductivity (ECa) of the soil determined between different zones of the field reduced
the yields of cultivated plants at the same pH, regardless of the nitrogen supply of the soil.
According to Kravchenko and Bullock (2002), the variables influencing the yield of crops, thetopographic characteristics of a plot might vary from micro-topography to catchment size.
Precision crop production means site-specific agricultural cultivation tied to geographicalcoordinates. Modern strip tillage technology based on precision technology for crops with widerow spacing is becoming more and more popular in Hungary as well. Strip tillage combines thebenefits of conventional tillage systems with the soil-protecting advantages of no-tillage. Maize,150

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sunflower and rapeseed have all been successfully strip tilled in Hungary. In Hungary, highprecision RTK (Real Time Kinematic) covers 95% of arable land in 2020.
MATERIALS AND METHODSThe examined farm is located in the eastern part of Hungary, and as of 2013, it started togradually introduce precision farming in field crop production. The fields of the farm areheterogeneous, there are areas with low productivity on certain fields. The total area is 942
hectares, of which 230 hectares were examined. Measurement of the soil electrical conductivity(ECa) of the upper 60 cm soil layer were performed on designated field plots of the farm with a
Veris U3 soil scanner. On the 120-hectare field plot of the farm, harmful inland water was drainedusing a 3D RTK application, following the maize harvest in 2018. This plot was examined with a
VERIS U3 soil scanner before and after water management. Agro-technical interventions, their
documentation and mapping were recorded on the on-board computers of a John Deere machines.
Maize, wheat, soybean and sunflower varieties were sown on the examined plots in the examined
period of 2016-2019. The research was performed in cooperation with one of the largest
agricultural integrators and service providers in Hungary. Harvesting of the examined areas, grainmoisture measurement and yield mapping were performed by means of a JD S770i type combine
harvester; the data were provided by KITE cPlc.
The vector point-like Veris U3 data and yield data were projected onto a 10x10 m
resolution polygon grid prepared from Sentinel 2 raster satellite imagery for numerical statisticalcorrelation analysis in the GIS database. A linear and multilinear regression analysis was
performed from the GIS database using RStudio.
RESULTS AND DISCUSSION
On the examined plots there are saline spots and buried watercourses, which, dependingon the crop year, also have a positive and negative effect on the yield of cultivated plants. In the
average of the examined fields (230 ha), the conductivity measured by Veris U3 in 2016 showeda moderately weak (r = 0.29) correlation with the yield (Figure 1) mapped by the combine. In
2017, the correlation between electrical conductivity (ECa) of the soil and yield (t/ha) was
moderately weak (r = 0.4) when examined together on several plots; the correlation was thestrongest from among the analysed years.

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Figure 1. Yield map of the examined field plots (Hungary, Hajdú-Bihar county, 2017)
In 2018, the correlation between ECa and yield data was weak (r = 0.29) on the examinedplots, in 2019 the correlation between yield and ECa values of the soil of the plots was negligible(r = 0.08).
Inland drainage on the 120-ha plot changed moisture conditions, however, the correlation
between the two ECa values measured by Veris remained strong (r=0.79). A moderate (r = 0.55)
correlation was found between the elevation data of the soil surface determined by RTK and theECa values (Figure 2).
On the examined 120-hectare field, the correlation between the 2016 yield data and the ECa
data measured in 2018 was moderately weak (r = 0.38). The correlation between the 2017 harvest
and the 2018 ECa data was also moderately weak (r = 0.32). The yield data of the stock sown andharvested after the soil scan in 2018 were moderately (r = 0.53) related to the ECa values. Afterthe autumn harvest of 2018, the plot drainage of the plot was assessed with a 3D application, thus
the Veris U3 measurements were now repeated in the spring of 2019 with the support of RTK. The
link between ECa and yield data measured this year was weak (r = 0.25). We also found a very
weak (r = 0.2) correlation between the original 2018 ECa distribution of the plot and the 2019yield.

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Figure 2. Surface elevation (m) and soil electrical conductivity (ECa) values on the examinedfield plot (Hungary, Hajdú-Bihar county, 2019)
Examined by multilinear regression, the 2018 ECa and elevation data were moderately (r =0.55) correlated with the 2016 yield data. The 2017 yield data were moderately weakly (r = 0.4)
correlated with the 2017 yield data. In the year of mapping, the correlation between the 2018 yielddata and the altitude and ECa data was moderately close (r = 0.57). The relationship between the2019 ECa, elevation, and yield data was moderately weak (r = 0.3).
Overall, it can be stated that the given crop year greatly influences the ECa correlation
between the crop and the soil, i.e. the soil patches also modify the yield of the crops within thefield to a varying extent, depending on the crop year.
ACKNOWLEDGEMENTS
The research was supported by the project “Establishing a scale-independent complex
precision consultancy system (GINOP-2.2.1-15-2016-00001)”.
REFERENCES
De Benedetto D, Castrignano A, Rinaldi M, Ruggieri S, Santoro F, Figorito B, Gualano S, Huang
ML, Liu X, Wang Z, Liang Z, Wang M, Liu M, Suarez DL. 2017. Interactive effects ofpH, EC and nitrogen on yields and nutrient absorption of rice (Oryza sativa L.) Agricultural
Water Management 194:48-57.
Kravchenko AN, Bullock DG. 2002. Spatial variability of soybean quality data as a function offield topography: I. Spatial data analysis. Crop Science 42:804–815.

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POLICY SUPPORT INNOVATIONS

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#7464 JUST A MOMENT; THE NEED FOR STREAMLINING PRECISIONAGRICULTURE DATA IN AFRICA
Tegbaru B. Gobezie and Asim Biswas*
School of Environmental Sciences, University of Guelph, 50 Stone Road East, N1G 2W1,
Guelph, ON, Canada* biswas@uoguelph.ca, +1 519 824 4120 Extn. 54249
ABSTRACTPrecision agriculture (PA) data sources in the era of digital agriculture are diverse in termsof the range of technology options and the types of data they generate. These include proximal
sensors, unmanned aerial vehicle (UAV), satellites, farm machinery mounted sensors and robotics
to generate static data or real time information (e.g., yield monitoring). Government institutions,scientists and private sectors take the lion’s share in generating PA data at innovation, validationand dissemination phases. At scale-up and wider application phases, farmers also generate
tremendous amount of data that might have privacy and ownership concerns. There are no onesize-fits-all approach in terms of PA technology applications, but there remains a question on betterway of integrating PA data continent-wide in Africa. The data privacy and ownership concerns
have to be addressed while maintaining the integration of PA data at continental scale. The
objective of this paper is to review the existing opportunities and challenges of data harmonization
in PA in Africa and discuss the existing technological advancements in PA data science and theirapplications in the other parts of the world. Finally, we proposed a new PA data sharing andrewarding model – ‘PrecisionLink’ to rationalize data network system through establishing strong
institutions and self-sustaining business model for all of Africa. The model uses AI and blockchain
technology to track and stamp PA data using unique dataset_IDs or PrecisionPrint (like afingerprint), valuing credit amounts using ‘pVouchers’ (eVouchers) and distribute credits between
PA data owner or ‘PrecisionProprietor’, data client or ‘PrecisionClient’ and funders or‘PrecisionPatron’. The system we propose lays the foundation for win-win PA data sharing andself-sustaining business models for smallholder farmers and technology solutions, while ensuringstrong partnership between farmers’ cooperatives, private sectors, scientists, government andfinancial institutions, and countries at high-level. Establishing and networking strong PA datanodes in all of African countries is timely to ensure the future of PA big data application in Africa.
Keywords: precision agriculture, data, AI, blockchain, ‘PrecisionLink’, ‘pVoucher’,‘PrecisionPrint’, ‘PrecisionProprietor’, ‘PrecisionPatron’, ‘PrecisionClient’
INTRODUCTION
The sources of precision agriculture (PA) data include machine-generated, processmediated or human-sourced data that are known to be highly heterogeneous (Wolfert et al., 2017).
Most of the PA applications in Africa are related to improving water use efficiency and input costs
reduction such as water-efficient and climate smart deciduous and fruit farming in South Africa,
Mozambique, Tanzania and Zimbabwe (Ncube et al., 2018). In PA something that perfectly works
for a given farm might not work for adjacent farm. This implicitly indicate that PA applications155

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for the very fragmented pieces of lands of African smallholder farmers that are mainly
characterised by high variabilities between and within farms require more tailored PA solutions.
The diversity of PA data in terms of volume and variety can be considered as important driver of
bigdata in the era of digital agriculture if there are high success rate of PA technology adoption. In
such scenario, shortage of data might not be the question in the future, but sustainable way ofintegrating PA data from different sources continent-wide in Africa is one of the key issues.
Various research results have shown that different data captured from variety of sources can beharmonized using data-fusion techniques for application of PA (Bendre et al., 2015; Ji et al., 2019,2017; Xu et al., 2019). Data-fusion and other state-of-the-art methods address the technical layerthat are very critical in the data value chain. However, the governance layer of such a big and very
diverse data that might have privacy and ownership concerns is the elephant in the room. Data ismerchantable good but key issues related to data marketing in PA are ownership, privacy and lackof sustainable business model (Pierce et al., 2019). Therefore, the objective of this paper is toreview the existing opportunities and major challenges of PA data harmonization in Africa in
relation to existing technological advancements in PA data science and their applications in theother parts of the world. For future application of PA data synchronisation, we proposed a newdata sharing and rewarding model – ‘PrecisionLink’ to network several PA data-hubs forestablishing self-sustaining business model that connects different actors in the PA data landscapefor all member countries in Africa.
CHALLENGES AND OPPORTUNITIES
Looking at data as marketable good and as the new oil in the 21st century, PA data passes
through different stages before reaching the final data marketing stage. Mostly data capture is theinitial stage in the entire process of data value chain and the intermediary stages respectively
include data storage, data transfer, data transformation and data analytics (Wolfert et al., 2017).
Data governance issues, which are one of the major challenges of data sharing, become more
complicated at the later stage due to privacy and ownership concerns that are in turn associatedwith lack of viable business model that motivates data holders to share their data. The otherchallenges in PA are related to the technical realm, especially decisions made at the data capture
stage. Sanches et al. (2018) reported that number of samples usually collected to spatially represent
soil attributes are denser for PA than ordinary field observation methods, however, sometimes
outputs are reported similar for both approaches, e.g., application of apparent electricalconductivity (ECa) to formulate lime recommendation rate. This has a direct implication on thevolume of data required for certain PA solutions and the ultimate adoption rate of the technologies.
The opportunities for PA to advance in Africa are enormous since some reports have shownthat use of Internet of things (IOT) technologies using sensors-based computer visioning assisted
PA have been tested for on-site fertilizer recommendations (OLIVEIRA-JR et al., 2020). Some of
the initiatives like Agricultural Commercialization Cluster (ACC) in Ethiopia lay the foundation
for PA applications on cluster of farms which otherwise could have been fragmented and remainchallenging for smallholder farmers. In addition, there are several enablers that promote the PA
use, which include: (1) the example of using drones and AI to estimate what yield (2) cloudcomputing and freely available high resolution imageries from google earth engine (3) handheld
proximal sensors and breakthrough on automated farm inputs management system, (4) immerging
high-resolution information on biophysical and agronomic advisories (e.g., the iSDA-Soil) and (5)156

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use of planters in PA for weeding by fitting with tines to improve efficiencies in Semi-Arid partof Western Africa (Aune et al., 2017).
THE OUTLOOKConceptualizing PA data in an analogy to produce agricultural commodity for export thatmeet certain standards such as speciality coffee could help to streamline winning business modelfor data sharing. It is like treating PA data as ‘speciality data’ that fulfill traceability in the newframework of PA data sharing and rewarding model called ‘PrecisionLink’. As proposed by Pierce
et al. (2019) data will be tagged using unique identifiers that we designated as ‘PrecisionPrint’(like a figure print) for the case of PA data. The origin and owner of such data could be multiple
farmers or individual farmer - ‘PrecisionProprietor’, and do not have to be limited to a single
source as AI and blockchain technology could be used to link the ‘PrecisionPrint’ and‘PrecisionProprietor’ to track and stamp the data. As shown in the PA data sharing and rewarding
model – ‘PrecisionLink’ (Figure 1), PrecisionPatron that representers funders such as giant input
manufacturing companies, bi-lateral and multilateral organizations, philanthropists, etc. stimulates
the PA that will help them to achieve their objectives in one or another way. PrecisionPatron
mainly provide funding to data client or the ‘PrecisionClient’ that could be PA hardware andsoftware developers using their analytical tools for better decision making. There could be cases
where the PrecisionPatron channel funds to countries or umbrella institutions within countries suchas farmers coops for their PA priorities through ‘PrecisionProprietor’ to promote PA for climatesmart agriculture and the data capture process.

To ensure the smooth functioning of the model, ‘PrecisionProprietor’ will have anagreement to share PA data with ‘PrecisionClient’ that will be valued using pVoucher (likeeVoucher) and there could be no physical monitory transactions. The equivalent pVoucher of data
shared by ‘PrecisionProprietor’ to ‘PrecisionClient’ will help the data owners to get equivalent157

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services by the ‘PrecisionClient’. In return to the funding channelled to ‘PrecisionClient’ by‘PrecisionPatron’ the total values of the ‘pVouchers’ could be used to claim for fulfilment ofagreement made with the ‘PrecisionPatron’.
REFERENCES
Aune JB, Coulibaly A, Giller KE. 2017. Precision farming for increased land and labour
productivity in semi-arid West Africa. A review. Agron. Sustain. Dev. 37: 16.
https://doi.org/10.1007/s13593-017-0424-z
Bendre MR, Thool RC, Thool VR. 2015. Big data in precision agriculture: Weather forecastingfor future farming, In: 1st International Conference on Next Generation Computing
Technologies (NGCT). pp. 744–750. https://doi.org/10.1109/NGCT.2015.7375220
Ji W, Adamchuk V, Chen S, Biswas A, Leclerc M, Viscarra Rossel R. 2017. The use of proximal
soil sensor data fusion and digital soil mapping for precision agriculture, In: Pedometrics
2017, Abstract Book Pedometrics 2017. Wageningen, Netherlands, p. 298 p.
Ji W, Adamchuk VI, Chen S, Mat Su AS, Ismail A, Gan Q, Shi Z, Biswas A. 2019. Simultaneous
measurement of multiple soil properties through proximal sensor data fusion: A case study.
Geoderma 341: 111–128. https://doi.org/10.1016/j.geoderma.2019.01.006
Ncube B, Mupangwa W, French A. 2018. Precision Agriculture and Food Security in Africa, In:
Mensah P, Katerere D, Hachigonta S, Roodt A. (Eds.), Systems Analysis Approach forComplex Global Challenges. Springer International Publishing, Cham, pp. 159–178.
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Oliveira-Jr A, Resende C, Gonçalves J, Soares F, Moreira W. 2020. IoT Sensing Platform for eAgriculture in Africa, in: 2020 IST-Africa Conference (IST-Africa). Presented at the 2020
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PRECISION AGRICULTURE FOR FIELD AND PLANTATION CROPS

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#7410 MAXIMISATION DE L’EFFICIENCE D’UTILISATION DE L’AZOTE PAR LA
TOMATE (SOLANUM LYCOPERSICUM L.) SUR LES FERRASOLS AU SUD DU
TOGOMouhamadou Lare et Jean Mianikpo Sogbedji
Ecole Supérieure d’Agronomie, Université de Lomé. 01 BP 1515 Lomé 01 Togo,
mianikpo@yahoo.com
RESUME
Maximiser l’efficience d’utilisation de l’Azote (N) en culture de tomate est une condition
pour optimiser la production de la culture. Il a été mené sur trois ans, six cultures de tomate
réparties sur deux périodes (Septembre à janvier 2017-2019 et de Février à Mai 2018-2020) sur un
sol ferralitique à la Station d’Expérimentations Agronomiques de Lomé suivant un dispositif
expérimental en blocs aléatoires complets à trois répétitions. Onze formules de fertilisations
couplées à la variété Mongal ont fait l’objet de l’étude. Deux formes d’urées (UO : Urée Ordinaire
et USG : Urée Super Granulée) ont été utilisées aux doses identiques de N (0 ; 30 ; 60 ; 90 ; 120 ;150 kg ha-1) comme fumure d’appoint respectivement le T+, T1, T2, T3, T4, T5 pour UO et T+, T1’,
T2’, T3’, T4’, T5’ pour USG. 200 kg ha-1 de NPK 15-1515 ont été appliqués comme fumure defond pour tous les traitements de fertilisation. La réponse du rendement en fruits frais de tomate àla dose d’urée ordinaire (UO) et d’urée super granulée (USG) a été modélisée. Les modèles
quadratiques obtenus ont servi à déterminer les doses économiques optimales d’application de N.
En première période, la dose économique optimale d’application a été de 131 et 118 kg de N ha-1
respectivement pour l’UO et l’USG. En deuxième période, la dose économique optimale a été de122 et 123 kg de N ha-1 respectivement pour l’UO et l’USG. Toutes ces doses ont permis dedégager des profits économiques variant typiquement entre 10 et 18 millions de F CFA ha-1, avec
une plus grande profitabilité pour l’USG à la deuxième période de production.
Mots clés : tomate, dose économique optimale d’application, rendement économique optimal, uréeordinaire, urée super granulée
ABSTRACT
Maximizing the efficiency of using Nitrogen (N) in tomato cultivation is a condition for
optimizing crop production. It was carried out over three years, six tomato crops spread over two
periods (September to January 2017-2019 and from February to May 2018-2020) on ferralitic soilat the Lomé Agronomic Experimentation Station following an experimental device in complete
random blocks with three repetitions. Eleven fertilization formulas coupled with the Mongal
variety were the subject of the study. Two forms of urea (UO: Ordinary Urea and USG: Super
Granulated Urea) were used at identical doses of N (0; 30; 60; 90; 120; 150 kg ha-1) as
supplemental manure respectively the T + , T1, T2, T3, T4, T5 for UO and T +, T1 ', T2', T3 ', T4',
T5 'for USG. 200 kg ha-1 of NPK 15-1515 was applied as a basic fertilizer for all fertilization
treatments. The response of fresh tomato fruit yield to the dose of ordinary urea (UO) and super
granulated urea (USG) was modeled. The quadratic models obtained were used to determine theoptimal economic application rates of N. In the first period, the optimum economic application160

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rate was 131 and 118 kg of N ha-1 respectively for UO and USG. . In the second period, the optimal
economic dose was 122 and 123 kg of N ha-1 respectively for UO and USG. All these doses madeit possible to generate economic profits typically varying between 10 and 18 million CFA F ha-1,
with greater profitability for the USG in the second production period.
Keywords tomato, optimal economic application rate, optimal economic yield, ordinary urea,
super granulated urea
INTRODUCTION
L’agriculture est la principale source de revenus et d’emplois pour 70% de la population
mondiale pauvre vivant en milieu rural (Banque Mondiale, 2014). Secteur clé des économies
Africaines, elle reste le principal moyen de subsistance pour un continent qui devrait voir sa
population passer de 700 millions de 2007 à 1,2 milliard en 2030 (FAO, 2003). Au Togo, Le
secteur agricole fait vivre les 2/3 de la population active, contribue en moyenne à hauteur de 38%
au PIB et participe pour 20% aux recettes d’exportations (MAEP, 2007). Avec une population qui
devrait avoisiner 9,76 millions d’habitants en 2031 dont 4,79 millions d’hommes et 4,97 millions
de femmes (INSEED, 2015), la recherche de l’équilibre alimentaire s’impose. Pourtant dans ledomaine de maraichage et surtout dans la production de tomate (S. lycopersicum L.), les systèmesde production existant au Togo notamment la culture sur sol en plein champ ou en jardins familiaux
ont toujours aboutit aux rendements relativement faibles qui n’excèdent pas 5 Mg ha-1 malgré
l’utilisation de la fumure (ITRA, 2011; FAOSTAT, 2018). Ces faibles rendements sont dus, aux
contraintes récurrentes dans les différentes régions économiques et qui sont entre autre les attaquesparasitaires et des ravageurs, les maladies, l’appauvrissement des sols, la divagation des animaux,le manque de main d’œuvre, l’approvisionnement en eau, les problèmes foncier et financier(Kanda et al., 2014). Pour Adden et al., 2016, la perte de fertilité des sols est la principale
contrainte de la baisse des rendements. Etant donné que le sol est épuisable, il apparait nécessaired’envisager des modes de gestion qui permettent une exploitation rationnelle et durable des terres(Mandi, 2000). Or l’azote étant le moteur de la croissance végétale (Mustapha, 2012) et parconséquent indispensable à la production de la tomate. Cependant, en raison de divers phénomènesde perte (lixiviation, volatilisation, adsorption sur les colloïdes du sol, immobilisation microbienne
et minéralisation), la quantité d’azote du sol disponible pour la plante peut changer drastiquement
sur une courte période, rendant difficile la formulation de recommandations spécifiques à chaqueagroécosystème (Detchinli and Sogbedji, 2015). Il est important d’optimiser la fertilisation del’azote qui consiste à trouver un bon équilibre entre besoins de la plante, reliquats du sol et apports
exogènes. L’enjeu est de taille tant pour la rentabilité de l’exploitation que pour le bilan
environnemental à l’échelle de la parcelle. Pour évoluer vers une agriculture de précision, il est
nécessaire de concevoir de nouveaux systèmes de culture répondant au mieux à la multiplicité desobjectifs, économiques, environnementaux et sociaux. La modélisation est devenue l’outil
incontournable qui permet de connaître, comprendre, inventer et partager ces nouvelles manièresde produire. C’est dans cette optique que la présente étude intitulée «Maximisation de l’efficienced’utilisation de l’azote par la tomate sur les ferrasols du Sud du Togo» est initiée. L’objectif général
de l’étude est d’optimiser la production en fruits de tomate. Plus précisément, il s’agit : (i) dedéterminer l’effet de la forme et de la dose d’application de l’azote sur le rendement en fruits dela tomate ; (ii) d’étudier la réponse du rendement en fruits de la tomate à la forme et à la dose161

1st African Conference on Precision Agriculture | 8-10 December | 2020

d’application de l’azote ; (iii) d’évaluer la rentabilité économique de l’utilisation de l’azote sous
culture de tomate.
MATERIEL ET METHODES
Site expérimental
Les travaux ont été réalisés dans la région maritime au sud du Togo à la Stationd’Expérimentations Agronomiques de Lomé (SEAL) sise dans l’enceinte de l’Université de Lomé
(UL). Lomé est une ville de l’extrême Sud-Ouest du Togo dont elle est la capitale. Elle est localisée
sur 6°10 latitude nord et 1°10 longitude Est (Detchinli et Sogbedji, 2015). Le climat est celui dusud-Togo, de type subéquatorial à deux saisons sèches et deux saisons pluvieuses. Les
précipitations annuelles varient de 800 à 1200 mm et la température moyenne annuelle est
entre 24 et 30°C. La température moyenne annuelle est de 27 °C (Worou, 2000). Le sol est de
type ferralitique communément appelé « terre de barre », qui s’est développé à partir desdépositions continentales (Saragoni et al., 1992). Sa teneur en potassium (K) est inférieure à 2cmol.kg-1 ; il a un contenu en phosphore total variant de 250 à 300 mg.kg-1, une capacité d’échange
cationique de 3 à 4 méq.kg-1, le N total est de 0,05 - 0,1% avec un rapport C/N de 7 – 11, un pH
de 5,2 à 6,8 (Detchinli et Sogbedji, 2015).
Conduite de l’essai
L’essai a été mené sur trois ans, six cultures de tomate réparties sur deux périodes(Septembre à janvier 2017-2019 et de Février à Mai 2018-2020) sur un sol ferralitique à la Stationd’Expérimentations Agronomiques de Lomé suivant un dispositif expérimental en blocs aléatoires
complets à trois répétitions. Le schéma cultural 50 cm x 60 cm a été adopté, soit une densité depeuplement de 33 333 plants ha-1. Six doses identiques ont été appliquées de part et d’autre del’USG et de l’UO à raison de (0, 30, 60, 90, 120, 150 kg ha-1 respectivement T+, T1, T2, T3, T4, T5
pour UO et T+, T1’, T2’, T3’, T4’, T5’ pour USG) comme fumure de relais et 200 kg ha-1 de NPK :15 15 15 comme fumure de fond.
Collecte et analyse des données
Le rendement en fruits frais de tomate sous chaque traitement et la profitabilité ont étédéterminé à partir des lignes centrales de chaque parcelle élémentaire de tomate dont les fruits ont
été récoltés et pesés. Le rendement potentiel a été calculé par extrapolation à partir du poids desfruits issus des plants récoltés et sur la base de la densité de peuplement de 33 333 plants ha-1. Les
données obtenues ont été traitées à l’aide du tableur Excel pour déterminer les modèles (oufonctions) de la réponse du rendement en fruits frais de tomate aux différents traitements defertilisation appliqués. L’analyse statistique a été réalisée à l’aide du logiciel GenSTAT Version
12.1, et le test de Duncan a été utilisé pour discriminer les rendements au seuil de 5%. La doseéconomique optimale de l’azote est définie comme la dose d’azote pour laquelle la valeurmarchande du rendement marginal correspond au coût marginal de la fumure azotée. Pour lesmodèles quadratiques, elle est calculée en égalant les premières dérivées des fonctions obtenues
aux ratios entre les coûts unitaires des fertilisants et les prix unitaires du produit (NAS, 1961 ;Nelson et al., 1985 ; Sogbedji, 1999 ; Agbangba et al., 2016 ; Detchinli et al., 2017). La tomate aété vendu à 600 F CFA kg-1 et 800 F CFA kg-1 respectivement pour la première période et laseconde. Le prix d’achat de l’azote a été fixé à 695,65 F CFA kg-1 pour l’urée ordinaire (soit 16
000 F CFA le sac de 50 kg d’urée ordinaire à 46 % N) et à 717,39 F CFA kg-1 pour l’urée super162

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granulée (soit 16 500 F CFA le sac de 50 kg d’USG à 46 % N). Le cout de la main d’œuvre est
fixé sur la base de 2000 F CFA par homme-jour.
RESULTATS ET DISCUSSION
Influence de la forme et de la dose d’application de l’azote sur le rendement en fruits detomateLes rendements en fruits frais de tomate enregistrés au cours des deux périodes de culturessont résumés dans le tableau 1 ci-après. L’analyse de la variance a révélé que les approches defertilisations ont eu d’effet significatif sur le rendement en fruits. Les moyennes affectées d’unemême lettre dans une même colonne ne sont pas statistiquement différentes au seuil de 5% selon
le test de Duncan. Les variations des rendements entre les deux périodes de cultures ont presquesuivi les mêmes scénarii avec une augmentation de 2,34% à la deuxième période de culture sur lapremière. Durant les deux périodes de cultures, la forme USG a permis d’obtenir de meilleursrendements par rapport à la forme UO. Ces résultats présentent des similitudes avec ceux obtenus
par ; (IFDC, 2007 ; Detchinli et al, 2017) pour lesquels La technologie de l'USG améliore
l'utilisation efficiente de l'azote en gardant plus longtemps l'azote dans le sol, hors de l'eau desurface où il est plus susceptible de se perdre sous forme gazeuse ou par percolation ou encore parl'écoulement. Plusieurs facteurs expliquent l’efficacité de l’USG dont le principal facteur évoqué
est la disponibilité de l’azote pour la plante tout au long du cycle de production car l’USG a cettefaculté de libérer progressivement ses éléments nutritifs à la plante. Les pertes en azote parvolatilisation, par nitrification et par dénitrification sont réduites de 2/3 avec l’utilisation desengrais azotés super granulés (Segda et al., 2006 ; IFDC, 2008 ). En ce qui concerne les doses
d’applications de l’urée, les rendements ont varié selon les doses d’applications. Durant les deux
périodes de cultures, les rendements ont été croissante pour les deux formes d’urée aux doses de(0 ; 30 ; 60 ; 90 ; 120 kg ha-1) jusqu’à la dose 150 kg ha-1 ou il commence par diminué.
L’augmentation des rendements n'a été de plus en plus petite au fur et à mesure que les doses
d’applications de l’azote augmentent par conséquent elle n’est pas proportionnelle aux doses
d’application d’azote. Ceci se justifie par la loi de Mitscherlich (Mitscherlich, 1909), pour laquelle« Quand on apporte des doses croissantes d'éléments fertilisants, les augmentations de rendement
sont de plus en plus faibles au fur et à mesure que les doses s'élèvent ». Ces rendements sontlargement supérieurs au rendement national qui n’excède pas 5 Mg ha-1 (ITRA, 2011 ; FAOSTAT,2018). Ces résultats sont en adéquation avec celui de (Mustapha, 2012) pour lesquels lafertilisation azotée joue un rôle essentiel sur la croissance des végétaux et le rendement des cultureset qu’elle contribue a augmenté la production agricole tout en ayant un impact sur la qualité desproduits récolté. La détermination de la dose optimale s’impose pour une production optimale etdurable.
Modèles de la réponse du rendement en fruits de tomate à la dose d’azote
La réponse du rendement en fruits frais de tomate à la dose d’azote a donné des modèles
quadratiques, c’est à- dire des fonctions de type Y(X) = aX² + bX + c, avec Y = rendement enfruits frais exprimé en 103 kg ha-1, X = dose d’azote exprimée en kg ha-1, a = un coefficient
quadratique, b = un coefficient linéaire et c = une constante. Les coefficients de détermination (R²)
obtenus sont très élevés (0,9741 à 0,8665), indiquant ainsi d’une part que le rendement en fruits
de tomate est fortement corrélé à la dose d’azote minéral appliqué et d’autre part que tous lesmodèles obtenus sont valides ((Azaïs et Bardet, 2012). Bien que valides, les modèles obtenus

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portent les insuffisances ou limites soulignées par (Agbangba et al., 2016) à savoir notamment ladifficulté d’interprétation agronomique des paramètres (coefficients quadratiques, coefficientslinéaires et constantes). Par exemple, les constantes c des différents modèles ne correspondent pas
exactement aux rendements enregistrés sous le témoin positif (correspondant à x = 0).
Tableau 1. Rendement en fruits de tomate en Mg ha-1.
Période 1

Traitements
T+= N0T1= N30
T2= N60
T3= N90
T4= N120
T5= N150
MoyennePériode 2

UO

USG

UO

USG

18,50 g22,26 f
24,12 e25,68 d27,61 b25,98 cd
22,32

18,50 g22,88 f
24,40 e26,84 bc
31,00 a26,34 cd
23,15

18,67 f
21,58 e24,69 d26,66 c28,23 b26,29 c22,50

18,67 f23,91 d26,03 c27,72 b31,62 a27,77 b
23,87

30
20
y = -0,0005x2 + 0,1318x + 18,508
R² = 0,9741

10
0

0

50

100

150

200

Rendement en (Mg ha-1)

Rendement en (Mg ha-1)

PPDS Période 1 : 1,0512 ; PPDS Période 2 : 1,0512 ; Période 1 : Septembre à Janvier ; Période 2 : Février
à Mai ; 200 kg ha-1 de NPK : 15 15 15 a été associé à tous les traitements ; tous les traitements ayant reçu
le NPK ont également reçu une dose de 0 ; 30 ; 60 ; 90 ; 120 ; 150 de USG : Urée Super Granulée ou UO :Urée Ordinaire.
30
20
y = -0,0006x2 + 0,1477x + 18,254
R² = 0,9731

10
0

0

40
30
y = -0,0007x2 + 0,166x + 18,217
R² = 0,8665

10
0
0

50

100

150

100

150

200

200

Figure 2 : Modèle quadratique durendement en fruits de tomate en
fonction de la dose d’azote minéralappliqué à la 2e période sous
forme UO
Rendement en (Mg ha-1)

Rendement en (Mg ha-1)

Figure 1 : Modèle quadratique durendement en fruits de tomate en
fonction de la dose d’azote minéralappliqué à la 1ere période sous
forme UO

20

50

Dose de N (kg ha-1 )Dose de N (kg ha-1)

30
20

y = -0,0007x2 + 0,173x + 18,884
R² = 0,888

10
0

0

50

100

150

200

Dose de N (kg ha-1 )Dose de N (kg ha-1 )Figure 3 : Modèle quadratique durendement en fruits de tomate en
fonction de la dose d’azote minéralappliqué à la 1ere période sous forme
USG

40

Figure 4 : Modèle quadratique durendement en fruits de tomate en
fonction de la dose d’azoteminéral appliqué à la 2e période
sous forme USG

1641st African Conference on Precision Agriculture | 8-10 December | 2020

Doses économiques optimales de l’azote
Les doses économiques optimales de l’azote obtenues pour l’urée ordinaire et pour l’urée
super granulée ont présenté différentes variations d’amplitude suivant la période de culture(Tableau 2).
Comparativement aux doses économiques optimales obtenues, pour l’urée ordinaire, lesrecommandations en vigueur pour la fertilisation azotée sur culture de maïs (ITRA, 2007) dont les
producteurs ont tendances à étendre sur d’autres cultures vivrières notamment la tomate présentent
des déficits (N kg ha-1) de 54,60 et de 46,33 respectivement pour la première et deuxième périodede cultures pour l’urée ordinaire contre des déficits (N kg ha-1) de 41,71 et 46,93 respectivement
pour la première et deuxième période de cultures pour l’urée super granulée. En première périodede cultures, la dose économique optimale de l’urée super granulée a été inférieure d’environ unsac (de 50 kg) à celle de l’urée ordinaire pour un rendement supérieur d’une tonne environ à celui
obtenu sous urée ordinaire. En deuxième période de cultures, la dose économique optimale del’urée super granulée a été presque identique à celle de l’urée ordinaire pour un rendement
supérieur de plus de deux tonnes à celui obtenu sous urée ordinaire. Ces données indiquent lasupériorité en culture de la tomate de la performance de l’urée super granulée comparativement àl’urée ordinaire, supériorité rapportée par plusieurs autres études sur le maïs et du riz (Yaosse,
2009 ; Laba and Sogbedji, 2015, Detchinli et al., 2017). La technologie du placement profond del’urée (utilisation de l’urée super granulée, USG) est une technologie prometteuse pourl’amélioration de la production de la tomate sur les ferrasols du Sud du Togo. Il ressort de l’étude
que l’utilisation des doses économiques d’azote est indispensable à l’obtention de haut rendement
sous culture de tomate. L’application éventuelle de fortes doses de fumure minérale n’est
envisageable que sur le court terme sur sol suffisamment pourvu en matière organique (parexemple sol mis en jachère pendant plusieurs années) ou en cas de contrainte incontournabled’accès à la fumure organique (Detchinli and Sogbedji, 2014, 2015a).
Tableau 2. Doses économiques optimales de l’azote sous urée ordinaire et sous USG.
Période 1
Période 2
UO
USG
UO
USG
-1
X optimal, kg ha
130,6
117,71
122,33
122,93Q optimal, kg ha-1
283,91
255,89265,94
267,24-1
Y optimal, Mg ha
27,19
28,06
27,34
29,57
-1
X recommandé kg ha
76
76
76
76
-1
Q recommandé kg ha
165,22
165,22
165,22
165,22-1
Déficit en N kg ha
54,60
41,71
46,33
46,93
-1
Déficit en urée kg ha
118,7090,85
100,72102,02-1
Y = rendement en fruits de tomate exprimé en Mg ha , X = dose d’azote exprimée en kg ha-1 ; Q= quantité d’urée en kg ha-1.
Effet de l’utilisation de l’azote sur la rentabilité économique de la culture de tomate.
Les profits à l’hectare générés sur la base des doses économiques optimales ont varié
suivant la période et la forme d’urée. De manière générale, il a été pour la première période de 10
634 978 FCFA sous UO et de 11 221 183 FCFA sous USG soit une supériorité de 5,51% et de 16
248 829 FCFA sous UO et de 18 042 800 FCFA sous USG soit une supériorité de 11,04%. La

1651st African Conference on Precision Agriculture | 8-10 December | 2020

culture de tomate est une spéculation rentable économiquement. Ces résultats sont en adéquation
avec les résultats de Hanson, (2001) pour lesquels la culture de la tomate est une activité
génératrice de revenus. Les profits enregistrés au cours de la seconde période sont supérieurs àceux de la première période malgré que les tendances en termes de rendement soient similaires.
En cette période les prix de vente de la tomate sont très attractifs.
CONCLUSION
La culture de la tomate est une activité génératrice de revenu. La détermination des doseséconomiques optimales d’azote devient alors une nécessité. A l’issue de cet essai conduit à la
Station d’Expérimentations Agronomiques de Lomé ; il ressort que les doses économiques
optimales d’azote sont supérieures à la recommandation en vigueur. A la première période decultures, la dose économique optimale a été de 293,91 et 255,89 respectivement pour l’uréeordinaire (soit 131 kg de N ha-1) et pour l’urée super granulé (soit 118 kg de N ha-1). A la deuxième
période de cultures, la dose économique optimale a été de 265,94 et 267,24 respectivement pourl’urée ordinaire (soit 122 kg de N ha-1) et pour l’urée super granulé (soit 123 kg de N ha-1). Lesformules de fertilisation pour la première période sont N131 P30 K30 pour l’urée ordinaire et N118
P30 K30 pour l’urée super granulée. Pour la deuxième période, elles sont N122 P30 K30 pour l’uréeordinaire et N123 P30 K30 pour l’urée super granulée. Toutes ces doses économiques obtenues durantles deux périodes de cultures sont économiquement rentables avec une plus grande profitabilité aucours de la deuxième période de culture. L’utilisation de l’urée sous la forme USG est plus
avantageuse en termes de rendements et de rentabilité économique que la forme UO.
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Detchinli KS, Sogbedji JM. 2015. Assessment of the profitability and the effects of three maizebased cropping systems on soil health in Western Africa. Ecole Supérieure d’Agronomie,
Université de Lomé, Togo. Publié dans American Journal of Agriculture and Forestry. 2(6): 321-329.
Detchinli KS, Sogbedji JM. 2015a. Yield performance and economic return of maize as affectedby nutrient management strategies on ferralsols in coastal western Africa. European
Scientific Journal 11: 1857–7881.

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Detchinli KS, Sogbedji JM, Atchoglo R, Bona K. 2017. Modélisation des doses économiques
optimales de l’azote en culture du maïs (zea mays l.) sur les sols ferralitiques au Togo. rev.
cames 5(01): 2424-7235.
FAOSTAT. 2018. Production/rendement et surface cultivable de la tomate fraiche. Disponible sur
http://www.fao.org/faostat/fr/#data/QC/visualize. Consulté le 05 juin 2020
Godard C. 2005. Modélisation de la réponse du rendement des grandes cultures et intégration dans
un modèle économique d’offre agricole à l’échelle européenne. Application à l’évaluation
des impacts du changement climatique. Thèse de doctorat. Institut National AgronomiqueParis-Grignon, Ecole Doctorale ABIES, UMR Economie Publique, INRA - INA P-G.
Hanson P, Chen JT, Kuo CG, Morris R, Open RT. 2001. Suggestions sur les pratiques culturales
de la tomate. Learning center, pp 1 - 9.
IFDC. 2003b. Adapting Nutrient Management Technologies Project (ANMAT). A project
supported by International Fund for Agricultural Development (IFAD), Rome. Octobre
2003. 5p.
IFDC Report. 2007. Actualités des travaux et des progrès de l’IFDC- Un Centre International pourla Fertilité des Sols et le Développement Agricole. Vol 32, N° 2, 8p.
IFDC. 2008. An International Center for Soil Fertility and Agricultural Development.
www.ifdc.org. Consulté le 07/06/2008.
INSEE. 2016. Rentabilité économique. https://comptabilite.ooreka.fr// (consulté le 05 juin 2020).
ITRA. 2007. Situation de référence sur les principales céréales cultivées au Togo : maïs, riz,sorgho, mil. Lomé : Institut Togolais de Recherche Agronomique, pp. 9-38. Consulté le 03
juin 2020.
Kanda M, Akpavi S, Wala K, Boundjou GD, Akpagana K. 2014. Diversité des espèces cultures etcontraintes à la production en agriculture maraichère au Togo. Int. J. Biol. Chem. Sci. 8(1):115-127, February, issn 1997-342X (Online).
Laba BS, Sogbedji JM. 2015. Identification of land degradation and climate change resilient soil
and crop management strategies for maize production on West African ferralsols.
International Invention Journal of Agriculture and Soil Science 3(2): 13-20.
MAEP. 2007. Deuxième rapport sur l’état des lieux des ressources phytogénétiques pourl’alimentation
etl’agricultureau
Togo.
Disponible
sur
http://www.fao.org/docrep/013/i1500e/Togo.
Mitscherlich EA. 1909. Das gesetz des minimums und das gesetz des abnehmenden bodenertrages.
38: 537-552.
Mustapha A. 2012. Etude de la nutrition uréique et ammoniacale chez le colza (Brassica napus L.)et développement de nouveaux inhibiteurs d’uréases et de la nitrification. Thèse dedoctorat. Sciences du Vivant [q-bio]. Université de Caen Basse Normandie.
National Academy of Sciences – National Research Council. 1961. Statistical methods of researchin economic aspects of fertilizer response and use. Committee on economic of fertilizer useof the agricultural board, NAS-NRC pub. 918, NAS-NRS,Washington, DC.
Nelson LA, Voss RD, Pesek JT. 1985. Agronomic and statistical evaluation of fertilizer response.
P. 53-90. In O.P. Angelstad (Ed.) Fertilizer technology and use, 3rd ed. ASA, Madison.
RECA (Réseau National des Chambres d'Agriculture du Niger). 2016. La tomate au Niger.
Saragoni H, Poss R, Marquett J, Latrille E. 1992. Fertilisation et succession des cultures vivrières
au sud du Togo: synthèse d'une expérimentation de longue durée sur terres de barre.
Agronomie Tropicale 46 : 107-120.

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1st African Conference on Precision Agriculture | 8-10 December | 2020

Segda Z, Sie M, Mando A, Haefele M, Wopereis M, Kebbeh M, Miezan K, Sedogo M, Guindo S,
2006. Performance d’une gestion intégrée des nutriments pour la production du riz irrigué
dans la plaine de Bagré au Burkina Faso. Agronomie Africaine, No spécial 5, annéeinternationale du riz, 29-39.
Sogbedji JM. 1999. Maize nitrogen utilization and nitrate leaching modeling in Togo and New
York. Ph.D. Thesis, Cornell University, New York, USA.
Worou SK. 2000. Sols dominants du Togo: Corrélation avec la Base de Référence Mondiale. In:
AG/AGL (Ed). Quatorzième réunion du sous-comité ouest et centre africain de corrélation
des sols pour la mise en valeur des terres. AG/AGL. Page 227. Disponible sur
www.fao.org/documents/show_cdr.asp?url_file.htm (17 février 2016).
Yaosse G. 2009. Test de l’efficacité de la technologie du placement en profondeur de l’urée
briquette (UDP) sur le périmètre rizicole irrigué de la vallée de Zio. Mémoire d’Ingénieur
Agronome, Ecole Supérieure d’Agronomie, Université de Lomé. Lomé, Togo.
Annexe 1. Tableau résumant les rendements des six saisons de cultures.
PERIODE 1 : Septembre à Janvier(2017-2019)
Traitements
T+
T1
T2
T3
T4
T5
T1'
T2'
T3'
T4'
T5'
MoyenneCulture 1 Culture 2 Culture 3
19,01 f
18,56 f17,93 h24,41 de21,27 e21,10 g26,21 bc
23,28 cde 22,87 f
25,71 cd
25,92 cd 25,42 de26,31 bc
28,69 b27,83 b25,98 cd
25,81 cd 26,14 cde
23,33 e22,42 e22,89 f25,21 cd
23,07 de24,91 e27,87 b25,69 cd 26,97 b
29,47 a33,45 a30,08 a26,54 bc
26,09 bc
26,37
24,54
23,96
23,43

PERIODE 2: Février à juin
(2018-2020)
Moyenne1
18,50 g22,26 f
24,12 e25,68 d27,61 b25,98 cd
22,88 f
24,40 e26,84 bc
31,00 a26,34 cb
23,98

168

Traitements Culture 1T+
18,42 hT1
22,60 gT2
25,32 eT3
27,37 cd
T4
28,29 bc
T5
25,83 e
T1'
24 f
T2'
26,45 deT3'
27,82 cT4'
32,57 aT5'
29,09 bMoyenne
25,08

Culture 2 Culture 3
19,40 f
18,18 h21,22 f
20,91 g26,43 de22,32 f27,55 bcd 25,05 de
29,13 b27,26 b27,50 bcd 25,54 de
24,75 e22,97 f26,97 cd
24,66 e28,61 bc
26,72 bc
33,47 a28,82 a28,21 bcd 26,01 cd
25,48
23,07

Moyenne2
18,67 g21,58 f
24,69 d26,66 c28,23 b26,29 c23,91 e26,03 c27,72 b31,62 a27,77 b24,54

1st African Conference on Precision Agriculture | 8-10 December | 2020

#7505 MAPPING OF SOIL NUTRIENT VARIABILITY IN SOME PLANTATIONCROPS IN ABEOKUTA, OGUN STATE NIGERIA
E.C. Basil, B.A. Senjobi, R.T. Basil, A.O. Tobore
Department of Soil Science and Land Management, Federal University of Agriculture,
Abeokuta, Ogun State, Nigeria, charlesejike7@gmail.com +2348068904445
ABSTRACTThe activities of man in the soil and the undulating topography have great effects on soil
nutrient variability. For near accurate application of nutrients in precision agriculture in order toreduce wastage of resources, the research was conducted to acquaint the research farm of theinstitute with the nutrient variability at specific location. A detailed soil survey was carried out at
the Teaching and Research Farms of Federal University of Agriculture, Abeokuta. Following
observed soil variations, surface (0-15cm) and sub-surface (15-30 cm) soil samples were taken
viz: Arboretum, Cashew, Oil palm. A Fallow land was also sampled. The samples were subjectedto physical and chemical analyses for some nutrients determination. The results were digitally
mapped using ARC GIS software package. The result showed variability in organic carbon in oilpalm plantation, variability in the bulk density, available phosphorus and organic carbon in thefallow. Cashew plantation recorded a variation in the bulk density while the arboretum hadvariations in the available phosphorus. In order to minimize fertilizer wastage, concern should begiven to these areas of variability.
INTRODUCTION
A great deal of inference can be drawn from the morphology of the soil as they were seenon the field and make interpretation and prediction about its qualities. However, for accurateinterpretation and prediction, especially for modern agriculture and non-agricultural uses of soils,
quantitative data on composition of the soils are needed for characterization (Buol, 1997). Soil is
a vital resource for producing the food and fiber needed to support an increasing world population(Pappendick and Parr, 1992), therefore, characterizing and evaluation of the soil for suitability
purpose for desired crops cannot be over emphasized. The objectives of this study are tocharacterize the soil and map the variability shown in the nutrients in the various plantations.
MATERIALS AND METHODS
The study was conducted at the Teaching and Research Farms of the Directorate of
University Farms (DUFARMS), Federal University of Agriculture, Abeokuta (FUNAAB). The
area is located between latitude 70 12’ N and 70 23’, Longitude 3o 20’ E and 30 23’ and on Elevation
108 m. The vegetation is basically derived savanna, which has been modified by various
agricultural practices over time. The climate of Abeokuta falls between the humid and sub-humid
tropics with mean annual rainfall of about 1113 mm, two peaks distribution pattern and five drymonths in the year. Three different crop plantations and a fallow were considered for this study
namely: Arboretum, Cashew, Oil palm and Fallow. At each of the chosen land use type, an areaof 4 ha was demarcated for the study. Surface (0-15 cm) and sub-surface (15-30 cm) soil samples

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were collected with the aid of soil auger at the intersections of traverses using Rigid Grid method
at 100 m interval. A representative profile pit (2 x 1 x 2 m) was dug at each of the predominate
land types or slope segments and soil types/mapping units encountered at each of the chosen land
use types. The profile pits were described morphologically after FAO (2006) guidelines. They
were sampled and placed in labeled bags and then processed in the laboratory after air-drying. Soilcolour was determined using Munsell colour chart. The air-dried soil samples were, grinded andsieved with a 2 mm mesh sieve and sub-samples were further sieved with 0.5 mm sieve for theorganic carbon and nitrogen determination. The Organic Carbon was determined using Walkley
and Black method. Soil pH in water (1:1) was determined with the use of a glass electrode pH
meter (Mclean,1965). Exchangeable cations were extracted with 1M NH4OAC (pH7.0), sodium
and potassium were determined using flame photometer and exchangeable Mg and calcium by
Atomic Absorption Spectrometer (Spark,1965). Available P was extracted using Bray-1 extractant
followed by Molybdenum blue colorimetric. Exchangeable acidity was determined by the KCl
extraction method (Mclean, 1965). Total nitrogen was determined by the Macro-kjeldahl digestion
method of Jackson (1962). The bulk density was determined by core method. Particle size
distribution analysis was determined by the Bouyoucos hydrometer (1951) method using calgon
as dispersing agent. The data were subjected to analysis of variance (ANOVA) to assess the effectof different land use types on the soil quality indices. Mean value were separated by Duncan’sMultiple Range Test (DMRT) at p<0.05.
RESULTS AND DISCUSSION
Generally, the soil textural fractions of sand, clay and silt varied significantly with thedifferent plantation. The particle size distribution showed that the pedons have very high sandcontents (>800 g/kg) and this decreased with depth across the land uses. The clay contents also
ranged from 120 g/kg in the arboretum to 450 g/kg in the oil palm across the profiles. The soils
were so compacted and as a result, samples could only be taken from the Ap designated horizon.
The high percentage of sand in all the land uses is a good indication of the observable highinfiltration rate (Fagbemi and Udoh 1992; Senjobi 2007). The poor water holding capacity of thesoils which is as a result of coarse texture of the studied soil, enhanced erodibility of the soil which
may have been exposed through cultivation and livestock grazing. Bulk density >1.65g/cm3 may
impede roots and inhibits development and water movement (Aminu et al., 2013). The increase insoil bulk density can be probably attributed to the loss of organic matters through tillage practices.
This is in conformity with Click (2005) and Bahramie et al., (2010). Ogunkunle et al., (2014) also
supported that soils with bulk density values ≥ 1.30 as recorded in almost all the land uses areindicative of compaction prone soils which will hinder root elongation, reduce aeration and impede
water infiltration and movement within the root zone. The total nitrogen content in all the soils ofland use types was moderate (0.11-0.22%) compared to the TSQI used (0.1 - 0.5 %). This is dueto the accumulation of litter falls. This could be as a result of an imbalance in the accumulation oflitter-falls and the rate of decomposition by micro-organisms. This means that the rate ofmineralization is on a reduction trend compared to the rate of accumulation. Though there wasnutrient variability as shown in Figure 1, the maps designated with different colour did show withthe legend the areas that had the nutrients very low, low, moderate or high. Maps with a colour
showed no variation while maps with multiple colours showed variation.

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CONCLUSIONS
It could be inferred from the study that different crop plantation has different soil
characteristics and there were variations in the soil nutrient distribution with space with respect tothe physical, chemical and biological parameters under different plantations. In the oil palm
plantation, most of the nutrients were leached down the profile or probably held up in the clay
content which was highly improved and compacted. The nutrients maps could be used to offer
solutions to nutrient deficiencies and accurate application of nutrients to avoid wastage ofresources.

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Figure 1. Nutrient map of organic carbon in the fallow.
REFERENCES
Bouyoucus GJ. 1962. Hydrometer method improved for making particle size analysis of soil.
Agronomy Journal 53: 464- 465.
Fagbemi A, Udoh EJ. 1992.The Characteristics of 2 Soil Topsequences in the Basement Complex
in the Federal Capital Territory of Nigeria. Ife journal of Agriculture 4 (112): 9-24.
Jackson ML. 1962. Soil Chemical Analysis. Prentice Hall, New York.
Mclean EO. 1965. Aluminum: In methods of soil analysis (ed. C.A. Black) agronomy No.9 part 2.
American Society of Agronomy, Madison. Wisconisin pp. 978-998.
Munsell Color Company. 1975. Munsell soil color charts. Munsell Color Company Inc., Batimore
18, Maryland, USA
Walkley A, Blank IA. 1934.An examination of the Degtjareff method for determining soil organic
matter and proposed modification of the chromic acid titration method.

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1st African Conference on Precision Agriculture | 8-10 December | 2020

#7540 EVALUATION OF ON-FARM OIL PALM YIELD PARAMETERS IN NIGER
DELTA REGION OF NIGERIA
D.K. Madukwe3, *F. Ekhator1, P. Osayande1, V.O. Aduramigba-Modupe2, O. Solomon1, M.N.
Okoye1 and C.E. Ikuenobe1
1
Nigerian Institute for Oil Palm Research (NIFOR) P.M.B 1030, Benin City, Edo State. 2Instituteof Agricultural Research and Training, Obafemi Awolowo University, Ibadan, Oyo state
3
OCP Fertilizer, Nigeria, kenekh2003@yahoo.co.uk
ABSTRACTThe potential yield of oil palm in smallholder farmers’ field has been low due to poor
nutrient management. Yield of over 40 tons of fresh fruit bunch hectare-1 is realizable with NIFOR
tenera material if the plant environment is properly harnessed for the crop beneficiary; however,actual yield of NIFOR tenera material is between 22-25 tons ha-1. A yield survey study was
conducted in small and medium scale farmers’ fields in Niger delta states of Nigeria where oilpalm is predominantly grown to determine the actual yield of oil palm for precise nutritional
management. In each of the states three local governments and five communities’ local
governments were sampled. Three samples were collected from each community. Data collected
were fresh fruit bunch (FFB) weight, palm frond petiole cross section and leaf area. Data collected
were subjected to analysis of variance and means separated using Ducan Multiple Range Test
(DMRT). The results showed that bunch weight palm-1 at harvest, bunch weight palm-1 year-1,
bunch weight tons hectare-1, leaf area and petiole cross section were significantly different in all
the local government and state. Bunch weight palm-1 at harvest, bunch weight palm-1 year-1, bunch
weight tons hectare-1, and petiole cross section were significantly highest in Emohua local
government area with values of 8.99 kg palm-1, 65.7 kg palm-1 year-1, and 9.55 tons hectare-1year1
respectively. Cross river state had the highest bunch weight palm-1 at harvest, bunch weight palm1
year-1, bunch weight tons hectare-1, leaf area and petiole cross section of 8.926 kg palm-1, 8.03
tons hectare-1 year-1, 56.2 kg palm-1 year-1, 5.68 m2, and 19.9 cm2 respectively. There was positive
correlation between bunch weight, leaf area and petioles cross section in all the local governmentarea. Tenera fruit form had the highest percentage of 93.33 in Emohua and the lowest percentage
of 6.67 in Oruk Anam. Nigrescens was the predominant fruit type and 100% was recorded in Itu,
Oruk-Anam and Ahoada. This result suggests that oil palm yield in the area is far lower than the
average yield of well-nourished and managed NIFOR tenera material in Nigeria
Keywords: Evaluation, oil palm, yield parameters, tenera, NIFORINTRODUCTION
Oil palm is a unique tropical crop cultivated mainly for its mesocarp and kernel oil(Woittiez et al., 2017). Crude palm oil is the preferred oil for the diet of Sub-Saharan African
people (Corley and Tinker, 2015). A growing demand for palm oil for food and soap industries isdue to an increase in population in Africa, which is estimated as 770 million. The use of palm oilfor biodiesel has added greatly to the continent’s demand for the commodity (Bakoume et al.,2017). There is a growing global demand for palm oil because of its universal applicability and173

1st African Conference on Precision Agriculture | 8-10 December | 2020

increasing population (Murphy, 2014). Oil palm is a uniquely productive tropical crop with apotential fresh fruit bunch (FFB) and palm oil yield capacity well over 40 tons FFB ha-1yr-1 and
10 tons of palm oil ha-1yr-1 (Murphy, 2014). Actual yields are between 18 to 30 tons FFB ha-1yr-1
and 3-6 tons of palm oil ha-1yr-1 and for NIFOR tenera hybrid it is 22 to 25 tons in Nigeria(Donough et al., 2010; Okomu, 2018; Okwagwu et al 2005). It is a significant crop in Nigeria,
occupying over 2.53 million hectares and production stands at 1million ton yr-1 (Bassey, 2016).
Nigeria’s current palm oil production falls far short of the national local consumption andindustrial uses (Proshare, 2019). The national production deficit estimated at nearly 2 million
metric tonnes is met by importation into the country (Asemota, 2013; Bassey, 2016, Proshare,2019). This deficit could be due to the fact that of the 2.53 million hectares purportedly cultivatedto oil palm in Nigeria, 2.1millions hectares are in the wild and poorly managed (Bassey, 2016).
There is every need for planned and co-ordinated best management practices that will impact on
oil palm farmers’ yield and income and eventually very balanced communities (Corley and Tinker,2015). In order to bridge the palm oil deficit gap and considering the population of Nigeria whichcurrently stands at about 200 million and consumption of palm oil which is estimated at about 3
million metric tons; the need to increase the farm size and adopt best management practices which
promote proper palm nutritional enhancement becomes very pertinent for the development of oilpalm industries in Nigeria and also satisfying the palm oil need of the Nigerian populace.
The stem, fronds and leaf are proper agronomic parameters that determine plant vigour(Rankie and Fairhurst, 1999; Fairhurst and Hardter, 2005). If these parameters are not properly
developed due to nutrient imbalance, fresh fruit bunch yield will be affected. Therefore,
assessment of oil palm agronomic parameters that will help in soil specific and regional fertilizer
formulations for the oil palm becomes very necessary if the aim of meeting the palm oil need of
Nigeria is to be achieved.
This study evaluated oil palm fresh fruit bunch yield, leaf area and petiole cross section in
Niger Delta Region of Nigeria.
MATERIALS AND METHODS
A yield survey study was conducted in small and medium scale farmers’ fields in Niger
delta states of Nigeria where oil palm is predominantly grown to determine the actual yield of oilpalm for precise nutritional management. Three states were marked out for agronomic datacollection, in each of the three states three local governments and five communities’ local
governments were also marked for agronomic data collection. Three samples (fresh fruit bunch
weight, leaf area, and petiole cross section) were collected from each community. The areasampled were three local governments of Cross River state (Biase, Akamkpa and Akpabuyo),
Akwa Ibom state (Abak, Itu and Oruk Anam) and River state (Ahoada, Emouha and Tai). The
samples were collected in three selected farmers’ field in each community on June 28, 2020. The
samples collected were properly labeled and place in samples bags for further processing.
Agronomic data was recorded in a field book and later calculated.
RESULTS AND DISCUSSION
The results of this study showed that bunch weight palm-1 at harvest, bunch weight palm-1
year , bunch weight tons hectare-1, leaf area and petiole cross section were significantly different
in all the local government and state. Bunch weight palm-1 at harvest, bunch weight palm-1 year-1,-1

174

1st African Conference on Precision Agriculture | 8-10 December | 2020bunch weight tons hectare-1, and petiole cross section were significantly highest in Emohua local
government area with values of 8.99 kg palm-1, 65.7 kg palm-1 year-1, and 9.55 tons hectare-1year1
respectively (Table 1). Conversely, bunch weight palm-1 at harvest, bunch weight palm-1 year-1,
bunch weight tons hectare-1 were lowest in Akamkpa local government area with values of 2.07
kg palm-1 30.5 kg palm-1 year-1 4.18 tons hectare-1year-1 respectively; but leaf area and petiole crosssection were lowest in Tai local government area with values of 3.45 m2 and 11.74 cm2 respectively(Table 1). In addition, Cross river state had the highest bunch weight palm-1 at harvest, bunch
weight palm-1 year-1, bunch weight tons hectare-1, leaf area and petiole cross section of 8.926 kg
palm-1, 8.03 tons hectare-1 year-1, 56.2 kg palm-1 year-1, 5.68 m2, and 19.9 cm2 respectively.
However, bunch weight palm-1 at harvest, bunch weight palm-1 year-1, bunch weight tons hectare1
and leaf area was lowest in Akwa Ibom state with values of 3.371 kg palm-1, 5.61 kg palm-1 year1
39.2 tons hectare-1 year-1 and 5.38 m2 respectively (Table 1). Nigrescens was the predominantfruit type in all the local government and 100% was recorded in Itu, Oruk-Anam and Ahoada whilevirescens was recorded in Biase local government, Akpabuyo local government, Akamkpa local
government, Emohua local government and Tai local government with values of 15.38 %, 6.67 %,13.35% and 14.29% respectively (Table 1). There was positive correlation between bunch weight,
leaf area and petioles cross section in all the local government area. Tenera fruit form had thehighest percentage of 93.33 in Emohua local government (Table 2). Fresh fruit bunch weight kg
palm-1 year-1 and fresh fruit bunch weight ton ha-1 were lower than the fresh fruit bunch yield of
NIFOR hybrid tenera probably because most of the palms were left without adequate care andfertilization. During the period of sampling, most of the farms were overgrown with weeds, hardly
pruned, inaccessible and no records of fertilizer application. If oil palm is left unkempt andunfertilized, large amount of nutrient are removed which must be replaced either by recycling ofpalm waste or by addition of inorganic mineral fertilizers; to avoid yield reduction or else
assimilates will be partitioned to vegetative growth during stress instead of reproductive growth(Hartely 1988; Woittiez et al., 2017). Harvesting frequency of once month-1 may also be attributed
to the low bunch yield observed in all locations. Yield increase over 20 % in palm had beenrecorded by reducing harvesting frequency from 30 to 7 days (Donough et al., 2013). Leaf area
was lower than the standard recommended for field palm in all the locations probably because of
the fact that the palms in the area were not properly managed or fertilized. The correlation observedin leaf area, petiole cross section with oil palm fresh fruit bunch yield in all the local government
sampled indicated that any response in vegetative growth due to the partitioning of assimilates willprobably increase fresh fruit bunch yield in these areas. The observed low rate of adoption of
NIFOR elite tenera hybrid materials in almost the local government except Emohua indicated lackof awareness on the benefits of using NIFOR tenera hybrid materials. Virescens has previously
been found to occur at very low frequency in Africa usually 50 in 10, 000 bunches in grove in
Nigeria (Hartley, 1988).This result concluded that bunch yield, leaf area and petiole cross section
were lower than the standard recommended for oil palm. This study recommends oil palm best
management practices and development of appropriate regional specialty fertilizer for the oil palm.

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Table 1. Oil palm fresh bunch yield, leaf area petiole cross section, percentage fruit types andforms local government.
Local
government(state-1)

Biase(cross river)Akpabuyo(cross river)Akamkpa(cross river)
Abak
(Akwa
Ibom)
Itu (Akwa
Ibom)
Oruk Anam
Akwa Ibom
Ahoada(Rivers)
Emohua(Rivers)
Tai (Rivers)
S:E

Bunch
weight(kg)
palm-1atharvest
6.77b

Bunch
weight(kg)
palm-1year-1Bunchweight(tons)ha1
year-1

Leaf
area
(m2)

Petiolecross
section(cm2)

Tenera
(%)

Dura
(%)

Nigrescens(%)

Virescens
(%)

53.8a

7.92ab

4.27bc15.72b

38.50

61.50

84.62

15.38

8.74a

60.9ab

8.43a

6.98a

23.22a

53.33

46.67

93.33

6.67

2.07b

30.5c

4.18c

5.31ab

19.65ab

33.33

66.67

86.67

13.33

5.31b

43.1c

6.10b

5.23ab

17.13b

60.00

40.00

100.000

5.69b47.3bc

6.67a

5.48ab20.05ab

26.67

73.33

100.000

6.57b

54.8ab

7.76ab

5.86ab

21.41ab

6.67

93.33

100.00

0

3.94b

39.0c

5.81bc6.43b

21.13ab

58.33

41.67

100.00

0

8.99a

65.7a

9.55a

6.68ab

25.71a

93.33

6.67

86.67

13.33

4.65
1.551

40.1c
6.92

5.16b
0.995

3.45c
0.598

11.74c
2.489

85.71

14.29

85.71

14.29

Table 2. Relationship between bunch weight, petiole cross section and leaf area across local
government in the three states.
Bunchweight tons
ha-1
Bunch Weight
tonsha-1
Petiole Cross
Section (cm2)
Leaf area (m2)Bunch weightat harvest (kg)Bunch weight(kg)
palm-1 year-1

PearsonCorrelation
N
PearsonCorrelation
N
PearsonCorrelation
N
PearsonCorrelation
N
PearsonCorrelation

N**Correlation is significant at
0.01 level

Petiole Cross
Section (cm2)

Leaf area(m2)

Bunch weightat harvest (kg)Bunch weight(kg) palm-1
year-111350.627**

1

135

135
0.864**

1

135

135

135

0.819**

0.503**

0.472**

1

135

135

135

135

0.928**

0.646**

0.612**

0.841**

1

135

135

135

135

135

0.599

**

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1st African Conference on Precision Agriculture | 8-10 December | 2020

REFERENCES
Asemota O. 2013. Open forum on agricultural biotechnology (OFAB) in Africa, Nigeria chapter:
Modifying the quality of palm oil through biotechnology. 24pp
Bakoume C, Ndiaye O, Okoye MN. 2017. Oil palm development in Africa. The planter 93 (1098):
632-641.
Bassey OI. 2016. Overview of oil palm production in Nigeria, comparative, social andenvironmental impacts: the case of Ekong Amaku Community in Cross River State,
Nigeria. Institute of Social Science, Erasmus University of Rotterdam. 10pp.
Corley RHV, Tinker PB. 2015. The Oil palm. Fifth edition. Black well Science Ltd 9600 Garington
Road, Oxford OX4 2DQ,UK. 562 PP
Donough C, Witt C, Fairhurst T. 2010. Yield intensification in oil palm using BMP as amanagement tool. In: Proceedings of the International Oil Palm Conference, 1–3 June
2010, Yogyakarta, Indonesian Oil Palm Research Institute.
Donough CR, Cock J, Oberthür T,, Indrasuara K, Gatot AR, Dolong T. 2013. Estimating oil
content of commercially harvested oil palm fresh fruit bunches—A step towards increasing
palm oil yields. Poster Presented at the PIPOC 2013 International Palm Oil Congress andExhibition, 19–21 November2013, Kuala Lumpur.
Fairhurst TH, Hardter R. 2005. Oil palm. Management for large and sustainable yields. First
edition. Oxford graphic printer Pte Ltd. 382 pp
Hartley CWS. 1988. The oil palm (Elaeis guineensis Jacq.). Third edition. Longmans Publisher,
London, UK. 112-113.
Murphy DJ. 2014.The future of oil palm as a major global crop: opportunities and challenges.
Journal of oil palm research 26 (1): 1-24.
Okomu. 2018. Okomu daily harvest yield data report from harvested fields.
Okwuagwu CO, Ataga CD, Okolo EC, Ikuenobe CE, Ugbah MM. 2005. The production of NIFOR
elite tenera hybrid planting material. The NIFOR EWS. A technical report. 21 pp
Proshare. 2019. The Nigerian Oil Palm Sector Report. Proshare Ecosystem.
Woitiez LS, van Wijk MT, Slingerland M, van Noordwijk M, Giller KE. 2017. Yield gaps in oil
palm: A quantitative review of contributing factors. Europ. J. Agronomy 8: 57–77.

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1st African Conference on Precision Agriculture | 8-10 December | 2020

#7545 DEVELOPMENT OF CANOPY MAPPING SYSTEM OF ASIAN PEARS (PYRUS
PYRIFOLIA NAKA) USING TERRESTRIAL LASER SCANNING
Jaehwan Lee1*, Eiji Morimoto1, Kazuyoshi Nonami1, Ichizen Matumura1, So Sato2, Mana Ikebe2
1
United Graduate School of Agricultural Sciences, Tottori University, Japan. 2Faculty ofAgriculture, Tottori University, Japan, D19A1106B@edu.tottori-u.ac.jp
ABSTRACT
Bud thinning and Pruning in Asian pears (Pyrus pyrifolia Naka) is necessary to ensure thequality and number of fruits but is time-consuming and heavily dependent on work experience andtime availability. The objectives of this research were 1) establishing a method for measuring point
cloud data of pear trees, 2) to validate the number of fruits prediction based on point cloud data
analysis. Point cloud data of pruning of the orchard were measured by using a Terrestrial Laser
Scanner (TLS; Topcon GLS-2000). The number of measured point cloud points is 18,682,993.
Then, to measure the length of the branches, tree data were extracted from the point cloud data,
and the predicted number of fruits was verified based on 8 pears/m of each branch. For example,
a tree branch with an area of 24 m² (number of points 31,975) has a total length of 22.9 m,
multiplied by 8 to calculate the estimated number of fruits from 3D scanning data. The predicted
number of fruits was 183 and the actual number of fruits was 164. That means it has 19 fewer
fruits than the theoretical number of fruits. So, the measured number of fruits/m² was an averageof 6.8, and the Predicted number of fruits/m² was average at 7.6. The absolute error of the two
results was 0.8/m².
Keywords: orchard management, Asian pears, point cloud data, remote sensing, fruit yieldprediction, bud thinning, pruning
INTRODUCTION
According to the Japanese Census of Agriculture and Forestry in 2015, 74% (i.e. 24,511
people) of farmers in Tottori Prefecture are over 60 years old (Static Agriculture Census, 2015).
Recent cultivation trends have decreased the area of Japanese pears due to the aging of producers,
lack of labor, and aging of pear trees. This may be due to reduced production, lower prices, reduced
employment due to aging, and reduced agricultural land. Japanese pear is more difficult to cultivate
than other fruit trees, and intensive farming is necessary (Yamada, 1983). But cultivation
techniques take decades to acquire with tacit knowledge, and most of the work relies on the sensory
experience of workers (Suenaga, 2009). As a result, orchard data is becoming important for bothskilled and unskilled individuals. For example, research on visualization of agricultural records
using telemetry and sensors is being conducted on rice crops. Morimoto et al. (2017) developed asmart rice transplanter to measure topsoil depth and soil fertility values. Pruning and buds thinning
are the ones that have a large impact on the yield and quality of pear crops. Because, the yields ofpear were closely correlated between pear trees biomass parameters such as canopy area and thetotal length of LAI (Yoshida et al., 2006). So, a study on the characterization of various pruning
methods of apple trees for the preparation of pruning manual for beginners was also conducted(Asada, 2006). Bud thinning is the process of identifying of buds which are thought to be an1781st African Conference on Precision Agriculture | 8-10 December | 2020

abnormality (twin flowers, etc.) or poor growth, and removing them from the tree. Japanese pears
are produced on the basis of 12,000 pears per 10a, and it is said that it is best to have 8 pears per
1m of the branch. The problem, however, is the time-consuming work of walking through theorchard and checking the unusual buds by the workers. The know-how of horticulture of fruit is
the accumulation of experience gained from decades of work experience, and the hurdles oftechnological succession to new farmers. However, the research to prove the correlation such as
the quality according to the work has been carried out, but the research on the recording andutilization of the work contents of the expert has not proceeded. Therefore, in this research, Treedata measurement with 3D scanner data was developed. The objectives of this research were toestablish methods for collecting orchard point cloud data and analyze tree data.
MATERIALS AND METHODS
Terrestrial Laser Scanner (TLS) uses a tripod-mounted, Light Detection and Ranging(LiDAR) to create a high-resolution 3D point cloud of objects. LiDAR technology is one option
to collect data about the canopy geometry of plants in tree crops and it is a method that can measurelarge areas such as forests and orchards in a short time (Karp et al., 2017; Liang et al., 2016). In
this study, the point cloud data of the orchard was measured using a 3D scanner (Topcon's GLS 2000) In March 2018. There is a total of four measuring points, and the number of measured point
cloud points is 18,682,993 (Fig. 1). The point cloud data is used to record the pruning of theorchard. In this study, was used the point cloud data to measure the length of branches.

Figure 1. Point cloud data of orchard.
Using a tripod, a common measurement method for Asian pear orchard measurements, thedata measurement time per measurement point is about 30 minutes. So, to simplify the installationof the TLS, a new type of fixing device Ground-station was made (Fig. 2). The Ground-station
was pinned 30 mm below the ground of the measurement point. The Ground-station is divided into
a fixed iron plate at the bottom and an upper part, which is the instrument fixture. The upper partcan be moved, and the installation height of the 3D TLS is 100 mm from the ground. In March
2018, the instrumentation was started using a Ground-station and the scanning time was 10

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minutes. Instrument points were all measured before scanning by Topcon's LN - 100. Since themeasurement time was fasted by 1/3 than the tripod fix method. As a result, high-density point
cloud data could be obtained in a shorter time.

(b) Ground-station-mounted(a) Tripod-mounted

Figure 2. 3D scanner mount method.
RESULTS AND DISCUSSION
The survey methods were obtained from the orchard in August 2018 with the number offruits for each tree. Also, the point cloud data was obtained in March 2018 after pruning to measure
the length of tree branches using the point cloud data. Sample tree data were extracted to verify
point cloud data measured in the orchard. It is assumed that 8 fruits are produced per 1m, which isthe standard cultivation method of Japanese pears. After measuring the length of tree branches
using point cloud data, the optimum number of fruits was calculated and compared with the total
number of actual productions. Estimates of the number of fruits analyzed the absolute error
between the length of each tree branch and actual the number of fruits. The result was as follows.
For example, the total length of tree A branch was 22.9m and the total length was multiplied by 8
to calculate the predicted number of fruits (Fig. 3). As a result, Tree A produced 164 pears in 2018.
And, estimates of the pear number of fruits based on the length of the branches were 183 and thearea occupied by trees is 24 m². So, the measured number of fruits/m² was average 6.8, and thePredicted number of fruits/m² was average 7.6. The absolute error of the two results was 0.8/m²(Table 1).

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A-3

A-4

A-2

A-1

A-8

A-5

A-6

A-7

Figure 3. Length measuring of the branch of sample tree by point cloud data.
Table 1. Result of predicted number of fruits.
Tree A

Tree B

Tree C

Tree D

Area (m²)

24.0

21.0

21.0

15.0

Branch length (m)

22.9

26.9

21.9

21.0

Predicted fruits

183.1

215.3

174.9

157.7

Measured fruits
Absolute error

164.0
19.1

218.0
2.7

192.0
17.1

124.0
33.7

Predicted fruits/m²
7.6
10.3
Measured fruits/m²
6.8
10.4*Absolute error: (Predicted fruits - Measured yield)

8.3
9.1

10.5
8.3

As a result, the comparison between the number of fruits prediction data and the actual
number of fruits was in the range of an average of 20.65. And, the number of fruits per unit area
could be estimated through analysis of point cloud data. And, the average of the measured andpredicted number of fruits was 8.7 number of fruits /m² and 9.4 number of fruits /m².

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CONCLUSIONSIn this paper, the CMS of Asian pears for estimating the number of fruits during Bud
thinning and Pruning operations using point cloud data was proposed. The results of this study canbe used to determine the number of fruits during thinning operations conducted from December toApril in Asian pear horticulture. We collected 18,682,993 points cloud data containing Asian peartree shapes using TLS. The experimental results showed a similar number of fruits/m² whencompared to different each tree data, but it was confirmed that the measured number of fruits/m²
was less than the predicted number of fruits/m². Thus, the predicted number of fruits/m² may not
be sufficient for practical application. In actual Pruning, however, the worker does not know thetotal length of branches that need to be removed, and the total length of branches remaining. The
number of fruits by the length of the branch can, thus, only be determined on the basis of worker
experience with canopy map. If this information can be mapped, it may be possible to adjust the
number of flower buds to the theoretical number at the time for flower thinning in the next season.
In the future, the producer can probably make next year’s schedule for pruning and buds thinningby using accumulated data over the years. Therefore, we will conduct research to confirm therelationship between the canopy area and the number of pears to be harvested. From the above
results, the proposed CMS proposed a method to efficiently measure parameters necessary for the
management and monitoring of orchards.
REFERENCES
Asada T. 2006. A study of differentially located apple tree spurs. Bulletin of the Faculty ofAgriculture and Life Science-Hirosaki University (Japan).
Karp FHS, et al. 2017. Accuracy assessment of a mobile terrestrial laser scanner for tree
crops. Advances in Animal Biosciences 8(2): 178-182.
Liang X, et al. 2016. Terrestrial laser scanning in forest inventories. ISPRS Journal of
Photogrammetry and Remote Sensing 115: 63-77.
Morimoto E, Hayashi K. 2017. Design of smart agriculture Japan model. Advances in Animal
Biosciences 8(2): 713-717.
Static Agriculture Census, Ministry of Agriculture and Forestry and Fisheries. 2015.
Suenaga S. 2009. Agriculture, forestry and fisheries and knowledge management. Agricultural
management research 46(4): 8-16.
Yamada. 1983. Technological Change and Farm Development of Pear Production: A Case Studyof Pear Farming in Chiba Prefecture 32: 81-91.
Yoshida A, et al. 2006. Effects of various training systems on canopy development, yield and fruit
quality in young filler trees of Japanese pear [Pyrus pyrifolia] Gold Nijisseiki. Horticultural
Research (Japan).

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#7566 EFFECT OF INTERCROPPING SORGHUM WITH COWPEA AND NITROGENAPPLICATION ON GROWTH AND YIELD OF SORGHUM (Sorghum bicolor (L.)
MOENCH)Peter E. Moi, Onesmus M. Kitonyo, George N. Chemining’wa and Josiah M. Kinama
University of Nairobi, Nairobi, Kenya, moipeter799@gmail.com
ABSTRACT
Despite the development of improved varieties, the yield of sorghum has remained
significantly low in dryland environments due to low soil fertility and inappropriate cropping
practices. However, implementation of precision agriculture (PA) within the context of companion
cropping with legumes and fine-tuning the supply of fertilizer nitrogen (N) has the potential toincrease sorghum yield in these environments. The objective of this study was to determine theeffect of intercropping and fertilizer N rate on growth and yield of selected varieties of sorghum.
Results showed that intercropping significantly reduced crop growth rate (CGR) of sorghum by
54% compared with sole crop system. Addition of N increased sorghum CGR by 30% but nodifferences were detected between 40 and 80 kg N ha-1. Under sole crop system, Gadam outyielded Serena by 1.3 t ha-1 in Igoji but there were no differences in yield between the two varietiesin Katumani which was drier than the former. However, intercropping significantly reduced thegrain yield of both sorghum varieties by about 50% irrespective of the cowpea variety. Addition
of N increased grain yield by at least 26% in both sites but yield differences between 40 and 80 kg
N ha-1 were marginal under both sole crop and intercrop systems. Cropping system × N interaction
effects on grain yield were significant in Igoji only, where N increased sorghum grain yield undersole crop system but higher N rates only marginally increased yield under intercrop. Althoughintercropping reduced sorghum yield, present results show that there is potential to exploit
cropping system × N interactions to increase yields, more so in wet environments than in areas
with low rainfall. Lack of significant differences in grain yield between the application of 40 and
80 kg N ha-1 suggests that sorghum yield could be maximized at lower N rates. However, further
studies are needed to establish the economically optimal N rate in sorghum production.
Keywords: cropping system, crop growth rate, drylands, interactions
INTRODUCTION
The increased demand to enhance food production to feed a rapidly growing population
has progressively worsened primary resources such as soil, water and atmosphere (Mariangela etal., 2012). Precision agriculture has been investigated in previous studies and is considered a win–
win solution both for improving crop yields and environmental quality of agriculture (Mariangela
et al., 2012). Sorghum (Sorghum bicolor (L.) Moench) is an essential cereal as a food security cropand a raw material for making malt thus, increasing its productivity could end severe foodinsecurity and increase incomes of smallholder farmers in the dryland environments due to its
unique traits of tolerating moisture stress and high yielding ability in a wide range of soils(Mwadalu and Mwangi, 2013). However, despite the development of improved varieties, the yieldof sorghum has remained significantly low in dryland environments due to low soil fertility and183

1st African Conference on Precision Agriculture | 8-10 December | 2020

inappropriate cropping practices (Kilambya and Witwer, 2013). Therefore, implementation of
precision agriculture approaches in sorghum production within the context of companion croppingwith legumes and variable rate of nitrogen fertilizer application could improve sorghum yields,
fertilizer use efficiency and profitability as well as reduce negative environmental consequences(Mariangela et al., 2012).
Cereal-legume intercropping is a sustainable agricultural practice where more than two
crops are grown simultaneously on the same land (Sibhatu & Belete, 2015). In comparison withsole crop systems, intercropping improves crop diversification, increases crop yields and stability,
especially under low-input conditions, improves soil fertility and conservation, as well as weed
control (Oseni, 2010; Layek et al., 2018). Nitrogen (N) is among the most deficient nutrients inmany agricultural soils for cereal production on a global basis but is essential in crop growth(Yagoub and Abdelsalam, 2010). The N fertilizer application rates in sorghum production often
vary across environments and cropping systems. An earlier study reported that maximum grain
yield of sorghum in cereal-legume intercrop system was attained with addition of 41 kg N ha-1 indry environment (Sibhatu & Belete, 2015). However, Shamme & Raghavaiah (2016) reported that
under sole crop system, the highest sorghum grain yield was recorded with the application of 92
kg N ha-1 while the lowest grain yield was recorded with no nitrogen application. Additionally,
higher crop yields have been attained by increasing N addition across environments (Dobermann,2007). While N is limiting in most agriculture soils, it’s a very mobile element in the soil andsusceptibility to loses through leaching, denitrification, volatilization and runoff thus, farmers haveresorted to excessive application of N fertilizers in the agricultural systems to compensate for theselosses (Shamme and Raghavaiah, 2016). This excessive use of N fertilizers often causes
environmental pollution, weed problems, sorghum susceptibility to diseases, lodging, delayed
maturity thus, precision farming techniques such as appropriate fertilizer placement method at thecorrect rate and right time when the plant needs the nutrients and variable-rate N fertilizers are
required to increase productivity, efficient use of inputs and reduce negative environmental impact(Dobermann, 2007; Mariangela et al., 2012). However, little information exists on the appropriate
N rate for sorghum production in a sorghum-legume intercrop system in dry and medium potential
environments. Thus, the objective of this study was to determine the effect of intercropping andfertilizer N rate on growth and yield of selected varieties of sorghum.
MATERIALS AND METHODS
Two field experiments were simultaneously conducted under rain-fed conditions at the
Kenya Agricultural and Livestock Research Organization (KALRO) stations in Katumani and
Igoji during 2018/2019 short rains. Experiments were laid out in randomized complete block
design with split plot arrangement replicated three times. Treatments comprised two cropping
systems (intercrop vs. sole crop) with two varieties of sorghum (Gadam and Serena) and two
cowpea varieties (M66 and K80), and three rates of fertilizer nitrogen (0, 40 and 80 kg N ha−1).
Fertilizer N was supplied from urea (46% N) and side banded on both sorghum and cowpea infractions of a third at sowing and two-thirds top dressed at tillering stage of sorghum growth. All
treatment plots received 60 P kg ha−1 of basal fertilizer in the form of triple super phosphate thatwas banded on the planting rows of both crops.
Sorghum was sampled for shoot biomass at flowering and physiological maturity and crop
growth rate (CGR) (g m-2 day-1), computed between the two stages. Yield components werecollected from a net area of 16 m2, and included panicle length, grain yield and weight. Data were184

1st African Conference on Precision Agriculture | 8-10 December | 2020

subjected to the analysis of variance using GenStat 14th Edition. Treatment means were comparedand separated using least significant difference (LSD) test at 5%cprobability level.
RESULTS AND DISCUSSION
Crop growth rate (CGR) did not significantly differ between Gadam and Serena in bothsites but cropping system significantly affected this trait in Igoji (P = 0.002) and Katumani (P =0.016) (Table 1). In both sites, intercropping significantly reduced sorghum growth rate by 2.4 g
m-2 day-1, irrespective of the cowpea variety. Addition of N significantly increased sorghum
growth rate only in Igoji (P = 0.002) where application of 80 kg N ha-1 increased overall CGR by
1.6 g m-2 day-1 (30%) compared with control plots but without significant differences between 40
and 80 kg N ha-1. Cropping system × N rate effect CGR was significant in Igoji (P <.001) and
Katumani (P = 0.042). Under sole cropping system, sorghum CGR increased with additional N but
marginal effects were observed under the intercrop system.
Sorghum grain yield was significantly affected by the cropping system (P <.001) in bothsites, N rate in Igoji (P <0.01) and Katumani (P = 0.013) while cropping system × N rateinteractions only occurred in Igoji (P <0.01) (Table 1). Under sole crop system, Gadam out-yielded
Serena by 1.3 t ha-1 in Igoji but there were no differences in yield between the two varieties in
Katumani. However, intercropping significantly reduced the grain yield of both sorghum varieties
by about 50%, irrespective of the cowpea variety. Addition of fertilizer increased grain yield by atleast 26% in both sites but yield differences between 40 and 80 kg N ha-1 were marginal underboth sole crop and intercrop systems. Cropping system × N rate effect on sorghum grain yield in
Igoji revealed that while sorghum grain yield increased with the addition of N under sole crop
system, higher N rates only marginally increased yield under intercropping system. Intercroppingsorghum with either cowpea variety significantly reduced 1000 seed weight. However, the addition
of N and its interaction with cropping system did not affect 1000-seed weight in both sites.
The reduction in the grain yield and CGR of intercropped sorghum could be attributed tointerspecies competition for resources like soil nutrients, sunlight and water in the intercrop system
which affected growth of vegetative and reproductive parts (stems, leaves and panicles) resulting
into low biomass production and grain yield (Oseni, 2010; Makoi et al., 2010; Legwaila et al.,2012; Karanja et al., 2014; Sibhatu and Belete, 2015). However, the increase in CGR and grain
yield with addition of N could be attributed to the important role of N in increasing growth anddevelopment of plant reproductive parts and photosynthetic capacity suggesting that proper rate
and time of N application are critical for meeting crop nutrient needs and increases CGR and grain
yield (Shamme & Raghavaiah, 2016; Yang et al. (2018). The interaction of cropping system ×
nitrogen treatments were significant where the highest CGR and mean grain yield was recorded
under sole but only marginal increase in intercrop system was observed. This could have been
attributed to non-proportional sharing of soil N sources resulting from competition between
sorghum and cowpea and limited fixation of N by the cowpea in an intercrop system resulting to
low grain yield in an intercrop system as opposed to sole cropping system (Jensen et al., 2020).
Therefore, the study findings suggest that Gadam variety was superior to Serena in terms of growthand yield and sole cropping system with addition of N was effective in achieving maximum CGR
and grain yield. However, present results also show that there is potential to exploit cropping
system × N interactions to increase yields, more so in wet environments than in areas with low
rainfall despite grain yield reduction in an intercrop system.

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CONCLUSIONSThe overall findings suggest that sole cropping system and split application of N fertilizer
was effective in increasing sorghum crop growth rate and yield compared with intercropping
system and no fertilizer application. Additionally, although intercropping reduced CGR andsorghum grain yield by about 50%, present results show that there is potential to exploit cropping
system × N interactions to increase yield, more so in wet environments than in areas with low
rainfall. Lack of significant differences in grain yield between the application of 40 and 80 kg N
ha-1 suggests that sorghum yield could be maximized at lower N rates, and further studies areneeded to establish the economically optimal N rate.
Table 1. Crop growth rate (CGR) and grain yield of two sorghum varieties (Gadam and Serena)
grown under sole and intercrop system with two varieties of cowpea (K80 and M66), and at three
N rates at KALRO research stations in Igoji and Katumani during 2018/2019 short rain season.
Treatments

Igoji
CGR
Grain
(g m-2 day-1) yield(t ha-1)

Seedweight(g)

Katumani
CGR
Grain(g m-2 day-1)
yield(t ha-1)

Seedweight (g)Cropping system (CS)Sole Gadam
5.32ab3.15a
31.1a
5.86ab
3.09a
33.6a
Gadam + K80
5.19ab
1.48bc30.7a
3.74bc0.89c
29.1b
Gadam + M66
6.17a
1.27bc
30.9a
7.69a
1.43b
32.9a
Sole Serena
4.35b
1.82b
27.4b
4.31bc
2.76a
30.1b
Serena + K80
4.88ab
1.06bc26.1b
3.44bc1.10bc
26.3c
Serena + M66
1.97c
0.95c
31.0a
1.94c
1.27bc30.7b
P-value
0.002
<.001
0.002
0.011
<.001
<.001
N rate0 kg N ha-1
3.67b
1.46b
30.06a3.60a
1.43b
30.33a-1
40 kg N ha
5.00a
1.57b
29.50a5.11a
1.96a
30.72a80 kg N ha-1
5.27a
1.84a
29.06a4.77a
1.88a
30.28aP-value
0.002
0.015
0.341
0.157
0.013
0.803
P-value CS × N
<.001
<.001
0.236
0.042
0.061
0.761
rate
Means within a column followed by the same letters are statistically similar; CS × N is interaction
between cropping system (CS) and N rate, ns is not significant.
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Karanja SM, Kibe AM, Karogo PN, Mwangi M. 2014. Effects of Intercrop Population Density

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and Row Orientation on Growth and Yields of Sorghum-Cowpea Cropping Systems in Semi
Arid Rongai, Kenya. J. Agric. Sci. 6(5). https://doi.org/10.5539/jas.v6n5p34
Kilambya D, Witwer M. 2013. Analysis of incentives and disincentives for sorghum in Kenya.
Technical notes series, MAFAP, FAO, Rome.
Layek J, Das A, Mitran T, Nath C, Meena Ram S, Yadav G, Shivakumar BG, Kumar S, Lal R.
2018. Cereal+Legume Intercropping: An Option for Improving Productivity and Sustaining
Soil Health. Legumes for Soil Health and Sustainable Management 11: 347-386.
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Legwaila GM, Marokane TK, Mojeremane W. 2012. Effects of Intercropping on the Performanceof Maize and Cowpeas in Botswana. International Journal of Agriculture and Forestry 2(6):307–310. https://doi.org/10.5923/j.ijaf.20120206.07
Makoi J, Chimphango S, Dakora F. 2010. Photosynthesis, water-use efficiency and _13C of fivecowpea genotypes grown in mixed culture and at different densities with sorghum.
Photosynthetica 48: 143–155.
Mariangela D, Pietro R, Francesco M. 2012. Precision nitrogen management of wheat. A review.
Agronomy for Sustainable Development, Springer Verlag/EDP Sciences/INRA 33 (1): 219241. https://doi.org/10.1007/s13593-012-0111-z
Mwadalu R, Mwangi M. 2013. The potential role of sorghum in enhancing food security in semiarid eastern Kenya: A review. Journal of Applied Biosciences 71(1): 5786.
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Oseni TO. 2010. Evaluation of Sorghum-Cowpea Intercrop Productivity in Savanna Agro-ecology
using Competition Indices. Journal of Agricultural Science 2(3): 229.
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Shamme SK, Raghavaiah CV. 2016. Sorghum (Sorghum bicolor L.) Growth, Productivity,
Nitrogen Removal, N Use Efficiencies and Economics in Relation to Genotypes and Nitrogen
Nutrition in Kellem-Wollega Zone of Ethiopia, East Africa. Advances in Crop Science andTechnology 04(03): 3–10. https://doi.org/10.4172/2329-8863.1000218
Sibhatu Berhane, Belete K. 2015. Effect of cowpea density and nitrogen fertilizer on a sorghumcowpea intercropping system in Kobo, northern Ethiopia. International Journal of Agriculture
and Forestry 5(6): 305–317. https://doi.org/10.5923/j.ijaf.20150506.02
Yagoub SO, Abdelsalam AK. 2010. Effect of Nitrogen and Seed Rates on Growth and Yield ofForage Sorghum (Sorghum bicolor L Moench cv. Abusabien). Journal of Science andTechnology 11(2): 123 - 136.

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#7604 GRAIN YIELD OF TWO PRE-RELEASE RICE VARIETIES INCREASEDMARGINALLY WITH HIGHER PLANT DENSITY AND NITROGEN RATE
Wilfred Kioko1*, Onesmus Kitonyo1, George Chemining’wa1, Setegn Gebeyehu2 and Rosemary
Murori2
1
Department of Plant Science and Crop Protection, University of Nairobi; 2International Rice
Research Institute, wmuthini@gmail.com +254720436843
ABSTRACTThe application of precision agriculture (PA) through the optimization of plant populationand management of fertilizer nitrogen (N) can increase yield. Current rice (Oryza sativa) plant
population and N management guidelines in Kenya were developed from research conducted about
three decades. These management guidelines might still be robust and could be applied to recently
released as well as close-to-release varieties. However, the yield potential of new and pipelinevarieties might not be maximized under existing sowing density and N rate. A study was conducted
to investigate the effect plant population and N rate on two pre-release rice varieties, namely
08FAN10 and CSR36. Field experiments were carried out in Ahero and Mwea rice researchstations. Treatments were laid out in a randomized complete block design with split-split plot
arrangement where three N rates (0, 75 and 125 kg N/ha) formed the main plots, varieties wereassigned to the sub-plots while three plant densities (1, 2 and 3 plants per hill, PPH) were allocatedto the sub-sub plots. Nitrogen was applied in the form of urea in two equal splits at 21 and 45 day
after transplanting (DAT). Data were collected on crop growth, yield and N traits, and subjected
to analysis of variance using GenStat 14th edition. Treatment means were compared and separated
the least significant difference at 5% probability level. Variety 08FAN10 out-yielded CSR36 by
47% in Mwea only while the addition of fertilizer nitrogen significantly improved grain yieldcompared with control but there were no marked yield differences between 75 and 125 kg N/ha.
The effects of plant density were marginal with only a 20% increase in grain yield under 3PPH
compared with 1PPH in Mwea only but without differences between 2 and 3PPH. Number of
productive tillers numbers were significantly affected by N rate and variety but there were nodifferences between the three plant densities. In addition, grain yield was a function of the numberof reproductive tillers (R2 = 0.49) in Ahero only. Both two-way and three-way treatment
interactions were not significant for grain yield in both sites.
INTRODUCTION
Since being introduced to Kenya in 1907, rice has become the third most important cereal
crop after maize and wheat (IRRI, 2018). Kenya’s annual rice production is estimated at 112,800
metric tons against a consumption demand of 538,000 metric tons (IRRI, 2018). These deficits
demonstrate the need to increase rice production in the Country. Current farmers’ yields are
estimated on 4.25 t/ha against a yield potential of 7 t/ha (Atera et al., 2018). This large yield gap
stems from lack of improved varieties, poor agronomic practices and water shortages as well as
high cost of inputs and machinery (Atera et al., 2018)). Current rice agronomic practices weredeveloped about three decades ago despite significant advances in rice breeding both locally andglobally. Whilst these practices might be robust with modern varieties, the yield potential of these188

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new and pre-release genotypes might not be maximized. Plant density and N rate are important
precision agriculture tools in rice production (Dong et al., 2012). Counce and Wels (1990) reported
that at an adequate plant population density with a high level of N, rice crops have shown to
produce more tillers, hence a high number of panicles which results to greater grain yield. The
objective of this study was to evaluate the effect of fertilizer N rate and plant density on the yieldof two pre-release rice varieties in Kenya.
MATERIALS AND METHODS
Field experiments were carried out in Ahero and Mwea irrigation schemes in Kenya.
Treatments comprised two pre-release varieties (08 FAN 10 and CSR 36), three plant population
densities and three nitrogen rates (0, 75, and 125 kg N/ha). The treatments were laid out in arandomized complete block design with split split plot arrangement and replicated three times in
Mwea and four times in Ahero. Seedlings were transplanted after 21 days at a spacing of 20 cm
by 20 cm. Fertilizer N was sourced from urea (46% N) and applied in two equal portions at 21 and
45 days after transplanting. One, two and three seedling number per hill were planted to create
different plant population densities, designated as 1PPH, 2PPH and 3PPH. Data was collected oncrop phenology, crop growth traits and yield components, and subjected to analysis of varianceusing GenStat 14th edition software at 5% significance level.
RESULTS AND DISCUSSION
Variety 08 FAN 10 out-yielded CSR 36 by 30% in both sites. Addition of fertilizer N
significantly increased grain yield of rice in both sites compared with control (Table 1). In Ahero
the effect of nitrogen rates on the grain yield was highly significant at p˂0.05. Nitrogen is the mostessential macro nutrient for plant growth; hence the increase in its application, leads to incrementin yields (Dong et al., 2012). There was significant effect of nitrogen rates to the number of
productive tillers in both sites at p˂0.05. Tillering is essential in determining the overall grain yieldin rice (Ling, 2000). This can be attributed to the important role nitrogen plays in cell division inplants (Rajput et al., 1988).
Varietal effect on grain yield and the number of productive tillers was significant at p˂0.05
in the both sites. Higher yields were recorded in Ahero as compared to Mwea. This could be dueto the fact the temperature conditions for Ahero are relatively higher than Mwea. Ying et al.,(1998) reported that rice production is correlated to air temperature and amount of nitrogen
applied, where rice grown in hotter places tend to have a faster crop growth rate than the ones
grown in cooler ones.
The effect of plant per hill on grain yield was highly significant in Mwea at p˂0.05 but notsignificant in Ahero. Likewise, the effect of plants per hill on productive tillers number was not
significant at p˂0.05 in the two sites. There was a marginal increase in grain yield in the two sites
due to the effect of plant per hill although not significant. Nitrogen rate and variety interaction had
a significant effect on grain yield at p˂0.05 in Mwea and a highly significant effect in Ahero.
Nitrogen and variety interaction was not significant on productive tiller number in the two sites.
This can be attributed to the fact that the natural endowments of crop cultivars to optimally utilize
available nutrients and subsequently partition its photosynthates for dry matter accumulation dovary (Mani et al., 2018).

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Nitrogen rate and plant per hill interaction effect was not significant on grain yield andproductive tiller number at p˂0.05 as shown in table below. Variety, plant per hill interaction was
only significant on the productive tillers in Ahero and not significant on grain yield and productive
tillers in Mwea. Nitrogen rate, variety and plants per hill interactions at p˂0.05 were not significant
in the two sites as shown in Table 1. The analysis of variance did not show any significant effecton grain yield and productive tillers numbers.
CONCLUSIONS
Results of the present study imply that, irrespective of rice variety, higher N rates increase
grain yield but an economically optimal rate requires further investigation. The possibility ofmodern pre-release varieties to withstand higher seedling densities without compromising on grain
yield offers the opportunity to reduce weed competition in rice fields. Further fine tuning of plant
population and N rate in modern varieties through the application of PA technologies willcontribute to the reduction of rice yield gaps.
Table 1. Grain yield and number of productive tillers per m2 of two pre-release rice varieties
grown under three N rates and three plant densities in Mwea and Ahero.
Treatment

Mwea
Yield (t/ha)
Tillers/m2

Ahero
Yield (t/ha) Tillers/m2

Variety
08 FAN 10
CSR 36
P value

3.92a
2.12b
˂0.001

11a
11a
0.015

7.08a
6.42b
0.017

12a
11b
0.016

N rate0 kg N/ha
75 kg N/ha
125 kg N/ha
P value

2.15b
3.37a
3.54a
0.015

10b
11a
11a
0.015

5.82b
7.28a
7.15a
˂0.001

11b
11b
12a
0.019

Plants per hill (PPH)
1 PPH
2 PPH
3 PPH
P-value

2.65b
3.08a
3.33a
0.017

11a
11a
11a
0.381

6.69a
6.74a
6.81a
0.942

12a
11b
11b
0.393

<0.001
0.859
0.865
0.665

0.091
0.534
0.031
0.325

Interactions
N rate × variety
0.030
0.637
N rate × plants per hill
0.255
0.653
Variety × plants per hill
0.794
0.491
N rate × variety × plants per hill
0.878
0.680
Means followed by the same letter are not significantly different

1901st African Conference on Precision Agriculture | 8-10 December | 2020

REFERENCES
Artacho P, Bonomeli C, Meza F. 2009. Nitrogen application in irrigated rice grown inMediterranean conditions. Effects on grain yield, dry matter production, nitrogen uptake
and nitrogen use efficiency. J. Plant Nutr. 32: 1574-1593.
Counce PA, Wells BR. 1990. Rice plant population density effect on early-season nitrogenrequirement. J. Prod. Agric. 3(3).
Evan AA, Onyancha FN, Majiwa EBO. 2018. Production and marketing of rice in Kenya:
Challenges and opportunities. J. Dev. Agric, Econ. 10(3): 64-70.
Fageria NK, Moreira A, Coelha AM. 2011. Yield and yield components of upland rice asinfluenced by nitrogen sources. J. Plant Nutr. 34(3): 361-370.
Hezhong D, Li W, Egrinya Eneji A, Zhang D. 2012. Nitrogen rate and Plant density effects onyield and late-season leaf senescence of cotton raised on a saline field. Field Crops Res.
126: 137-144.
IRRI. 2018. Intensifying agricultural productivity through research and development.
Jifeng Y, Shaobing P, Qingrui H, Hong Y, Chongdang Y, Visperas RM, Cassman, KG. 1998.
Comparison of high-yield rice in tropical and subtropical environments. Field Crops Res.
55: 71-84.
Ling QH. 2000. Crop population quality. Shanghai Scientific and Technical Publishers, Shanghai,
China. pp. 32-36.
Mani H, Muhammad A, Mahmoud BA, Jari S, Usman A. 2018. Response of rice varieties tonitrogen fertilization under irrigation at Talata Mafara, Sudan Savanna. JFNA 1(1): 17-20.
Rajput, MMK, Ansari AH, Mehdi S, Hussain AM. 1988. Effect of N and P fertilizers alone and incombanition with organic matter on the growth and yield of Toria Sarhad. J. Agric. Res. 4:
3-6.

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#7667 MAPPING OF MICRONUTRIENTS STATUS IN SOILS UNDER
MULTIVARIETAL CITRUS SINENSIS PRODUCTION FOR PRECISIONAGRICULTURE
1*Bernard N. Okafor, 2Vincent Aduramigba-Modupe and 2Olubunmi Denton
1
National Horticultural Research Institute (NIHORT), Idi-Ishin, Ibadan, Nigeria, 2Institute ofAgricultural Research &Training, Obafemi Awolowo University, Moor Plantation, Ibadan,
Nigeria, kpakpando2009@gmail.com
ABSTRACT
Citrus production in Nigeria is below the world average; and this is caused by poor soil
management among other things. The situation is further acerbated by blanket fertiliser applicationand low application of precision in soil fertility management. A study was carried out on a 34year-old multi varietal citrus orchard under sweet orange (Citrus sinensis) to determine the current
soil fertility status and variability of micronutrients. Soil samples were collected at a sampling
depth of 0-15cm to determine soil micronutrient level. The sampling were done at 7x7m interval
using a hand-held GPS while spatial analysis was done using ArcGIS software. The results showedthat mean value of Mn, Fe, Cu and Zn were as follows: Mn 330.08±88.84; Fe 115.7±30.74; Cu
3.14±0.72 and Zn 8.81±3.65 mg/kg, with a kurtosis of 0.44, -0.53, -0.098 and 0.94 and skewness
of 0.28, 0.43, -0.31 and -0.06 for Mn, Fe, Cu and Zn respectively. Due to the micronutrient
variability of the orchard soils, it is important to use precision agriculture for management in orderto improve yield and soil quality.
Keywords: mapping, micronutrients, citrus, variability, soil fertility
INTRODUCTION
Attainment of sufficiency in citrus production in Nigeria remains a challenge. This is due
to limiting factors such as low availability of quality rootstock material, climate change, lowagricultural input, lack of precision / poor use of fertiliser, high post-harvest losses and low value
addition. Of importance in increasing citrus yield in Nigeria is the need for intensive soil fertility
management as most farms in Nigeria are low in fertility due to low application/ precision in useof fertiliser and blanket fertiliser application. Nigeria currently has a fertiliser index rate of
5.5kg/ha of arable land (Worldbank, 2020). This has grossly affected soil and plant nutrient needs
leading to micronutrient deficiency and crop failure in many cases. Consequently, there is need for
improved micronutrient fertility in Nigerian agriculture. Micronutrients are essential in crop
growth and are usually required in little quantity. Raja (2009) observed that the role ofmicronutrient in crop production include improved crop quality, increase in yield, improved
disease resistance and prevention of physiological disorder. Beyond the supply of micronutrients,
soil organic matter needs to be given serious attention as there is a strong correlation between
organic matter and most crop properties (Ogunkunle and Awotoye, 2011).
Precision in agricultural practice has improved its productivity significantly (Yousefi andRadzari (2014). Continuous cultivation and low application of fertiliser has led to soil fertility
decline (Ogunjimi et al., 2017). Due to the inherent variability of soil properties as a result of land192

1st African Conference on Precision Agriculture | 8-10 December | 2020

use (Borusiewicz, 2016) and climatic change (Pareek, 2017), it is important to use precise
management to ensure soil conservation and improve its quality. Precision agriculture has been a
viable tool for soil variability management (Zude-Sasse et al., 2016) as it helps to reduce low andpoor utilization of farm input due to variation in factor of agricultural production. Shaato et al.,(2005) observed that studies on micronutrient in Nigerian soil are limited. Therefore, this study
was carried out to map micronutrient status of a citrus orchard in order to enhance precision in its
fertility management.
MATERIALS AND METHODS
Soil samples were collected from multi-varietal citrus collection block at National
Horticultural Research Institute (NIHORT), Ibadan. It consists of 12 sweet orange varieties planted
at a spacing of 7m x 7m. The varieties include Bende, Meran, Umudike, Agege, Pineapple andParson Brown among others. Soil samples were taken from 0 - 15 cm depth using a soil auger. The
samples were analyzed for micronutrients using standard laboratory procedures. Data were
subjected to descriptive statistics while spatial analysis was carried out using GIS software
package.
RESULTS AND DISCUSSION
Table 1 shows the descriptive statistics of the micronutrient concentration of the study area.
Micronutrients play a vital role in citrus productivity. As shown in Table 1, the mean values ofMn, Fe and Zn are adequate for citrus production while mean concentration of Cu was indicativeof low nutrient concentration for citrus production as recommended by (Chen et al., 2007).
Improved fertility management of soil micronutrient is important because of its effect on crop
quality and productivity (Patil et al., 2018). Coefficient of variation (CV) is an indicator of thelevel of variability of soil properties. Wilding and Dress (1983), characterized CV values asfollows; 0-15 (low variability), 15-35 (moderate variability) and >35 (high variability). From theforegoing, (Mn, Fe and Cu) were moderately variable while Cu was highly variable. Consequently,
Cu fertility management should be more precise and site specific. Application of fertiliser without
recourse to site specific needs of the soil/crop leads to wastage of resource and poor crop yield(schellberg et al., 2008). Precision agriculture helps to restrict resource use to particular area,
improve yield and farm efficiency; thereby reducing wastage (Chan, 2006). Mapping aids visibility
and presentation of soil properties to enhance farm and soil management. The micronutrient mapsof the citrus orchard are presented in Figures 1-2
Table 1. Descriptive statistics of the orchard.
Micronutrient
Mn
Fe
Cu
Zn

Mean
330.08±88.84
115.7±30.74
3.14±0.72
8.81±3.65

CV
29.91
26.56
22.85
36.83

193

Kurtosis
0.44
-0.53
-0.01
0.94

Skewness
0.28
0.43
-0.31
-0.06

1st African Conference on Precision Agriculture | 8-10 December | 2020

Figure 1. Distribution of Cu within the orchard.’

Figure 2. Distribution of Zn within the orchard.
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Mean value of Cu (3.14 mg/kg) across the orchard indicated low nutrient status but themap (Fig 1) showed particular areas of deficiency. Precision and site-specific management are
required to improve Cu concentration of the orchard, enhance yield, improve soil quality andreduce wastage of fertiliser. Blanket fertiliser application should be avoided.
REFERENCES
Borusiewicz A, Kapela K, Drożyner P, and Marczuk T. 2016. Application of Precision Agriculture
Technology in Podlaskie Voivodeship. Agricultural Engineering 20: 5-11.
Chan CW. 2006. Application of precision agriculture technologies in the tropical greenhouseenvironment. Acta Horti. 710: 479-484D. OI:10.17660/ActaHortic. 2006.710.59 DOI:
10.17660/ActaHortic.2006.710.59.
Chen F, Lu JW, Liu D. 2007. Investigation of soil fertility in citrus orchards of Southern China.
Better Crops 91 3: 24 -27.
Ogunkunle CO, Awotoye O. 2011. Soil Fertility Status under Different Tree Cropping System in
a Southwestern Zone of Nigeria. Notulae Scientia Biologicae 3(2): 123-128
DOI: 10.15835/nsb.3.2.6050
Pareek N. 2017. Climate change impact on soils: adaptation and mitigation. MOJ Eco Environ
Sci ;2(3):136-139. DOI: 10.15406/mojes.2017.02.00026
Patil KD, More SS, Wahane MR, Puranik UY, Khobragade NH. 2018. Micronutrient importancein high tech horticulture. Journal of Pharamcognosy and photochemistry 7(3): 628-635.
Raja MD. 2009. Importance of micronutrient in the changing horticultural scenario in India.
Journal of Horticultural science 4(1): 1-27.
Schellberg J, Hill MJ, Gerhards R, Rothmund M, Braun M. 2008. Precision agriculture on
grassland: Applications, perspectives and constraints. European Journal of Agronomy
29(2-3): 59-71
Shaato R, Ajayi SO, Ojanuga AG. 2005. Total and extractable copper, iron, manganese and zincin major agricultural soils in the Lower Benue Valley, Central Nigeria andtheconcept of extractant efficiency. Nigerian Journal of Chemical Research 59 – 68.
Wilding LP, Dress LR. 1983. Spatial variability and pedology. In: Wilding LP, Smeek NE andHall GF. Eds. Pedogenesis and soil taxonomy. Pudoc wageningen.
Yousefi1 MR, Razdari AM. 2016. Application of GIS and GPS in Precision Agriculture (AReview). International journal of Advanced Biological and Biomedical Research 4(2):473-476.
Zude-Sasse M, Fountas S, Gemtos TA, Abu-Khalaf N. 2016. Applications of precision agriculture
in horticultural crops. Eur. J. Hortic. Sci. 81(2): 78–90 DOI: 10.17660/eJHS.2016/81.2.2

1951st African Conference on Precision Agriculture | 8-10 December | 2020

#7933 CARACTERISATION ARCHITECTURALE POUR LA PREDICTION DU
POTENTIEL DE PRODUCTION DES TETES DE CLONES DE COFFEACAENOPHORA PIERRE EX A. FROEHNER.
1Kouassi Kouadio Henri, 1Yao N’guessan Abraham, 3Kouassi Roland Herve, 1Angama Djedoux
Maxime et 2Legnate
1
Université Jean Lorougnon guédé (Daloa, Côte d’Ivoire), 2Centre national de recherche
agronomique (CNRA, Divo, Côte d’Ivoire), 3Ecole normale de l’Enseignement Supérieur (ENS,
Abidjan, Côte d’Ivoire), atoumanikouadiokan@yahoo.fr
RESUME
Le café demeure jusqu’à ce jour une culture très importante dans le monde etparticulièrement en Côte d’Ivoire. Cette culture est avec les changements climatiques confrontéeà de nombreux facteurs qui limitent sa production. Parmi ces facteurs les plus problématiquessont : la baisse de la fertilité des sols, les insectes et les performances agronomiques de l’espèce
et/ou de la variété cultivée. C’est dans un tel contexte qu’une étude des paramètres de croissancea été réalisée chez 35 variétés de caféier de l’espèce Coffea canephora, dont 11 clones et 24
descendances hybrides. L’objectif est de prédire le potentiel de production des variétés étudiéesen vu d’opérer une sélection plus rigoureuse des variétés à cultiver. Cette étude était basée sur lamodélisation de l’architecture des variétés étudiées à partir du logiciel Grenlab qui est parmi lesplus récents conçu à cet effet. Les résultats ont montré que toutes les variétés ont des probabilitésde développement du tronc et des branches supérieures à 0,82, et celles des ramifications
supérieures à 0,70. Les rapports de rythme sont inférieurs à 1 (W < 1) pour 14 d’entre elles. Pour
17 autres variétés de caféier de petite taille, ces rapports sont supérieurs ou égaux à 1. Ce derniergroupe est plus facile d’accès aux producteurs au cours de la récolte. Par ailleurs, la production
serait négativement influencée par l’élongation compétitive des branches. Les variétés les plus
compétitives sont celles les branches plagiotropes sont de forme retombante ; cela grâce à leurmeilleure adaptation.
Mot clés: architecture, paramètre de croissance, prédiction, production, Coffea canephora, Côte
d’Ivoire
INTRODUCTION
Le café est le produit le plus commercialisé après le pétrole (ICO, 2015). La culture du café
représente un enjeu économique majeur pour les 70 pays producteurs de toute la zone intertropicale
humide (ICO, 2015). Deux espèces de caféiers sont cultivées à l'échelle industrielle (Coffea
arabica L, et Coffea canephora Pierre (Robusta). L'arabica est cultivé en altitude (1600 à 2100 m),
à de basses températures. C'est le café le plus apprécié par les consommateurs à cause de son arômeet son goût agréable. Il représente 70% de l’offre mondiale du café (Montagnon et al., 1992).
Cependant, cette espèce est sensible aux maladies. C canephora, l’autre espèce, est renommée
pour sa robustesse agronomique, d’où son nom commun de Robusta. Il est cultivé principalement
dans les zones tropicales humides de basse altitude et représente 30% de la production mondialede café. En Côte d’Ivoire, la culture du café robusta concerne près de 500 000 planteurs et leurs1961st African Conference on Precision Agriculture | 8-10 December | 2020

familles, et participe à la création de nombreux emplois dans les secteurs secondaires et tertiaires
de l’économie. Quelque soit le mode de culture de l’arbre (graines ou bouturage), le rendement
augmente dans les 3 à 4 années suivant la plantation, puis se stabilise et commence généralement
à diminuer avec de l’âge de l’arbre. La baisse de fertilité des sols sous caféières, le faible rendement
et le vieillissement des vergers, etc. constituent les principales contraintes de la production du caféen Côte d’Ivoire. Face à celles-ci, un programme de sélection de variétés améliorées a été initié
depuis 1960 pour l'amélioration de l'espèce C. canephora. Les principaux critères retenus sont lavigueur et l'architecture (Leroy, 1993), la productivité (Montagnon & al., 2012), la résistance auxaléas climatiques, la synchronisation de la maturité des fruits, etc. La modélisation de lacaféiculture est de plus en plus utilisée par les agronomes pour prédire les récoltes en fonction desconditions environnementales, du point de vue quantitatif et d'optimiser les itinéraires culturaux(De reffye, 2009). Divers modèles agronomiques et écophysiologiques ont été développés : lemodèle LIGNUM (Gawain, 2009) et récemment, le modèle GreenLab (Qi et al, 2010). Ce dernier
modèle cumulant architecture et croissance végétative, apparaissant comme le plus avancé de tousa été utilisé dans le cadre du présent travail dont l’objectif est de modéliser des architectures decaféiers afin d’identifier celles qui prédisent le mieux le rendement chez de nouvelles variétés encours de sélection. Spécifiquement, les activités consistaient à établir une corrélation entre lesparamètres de l’architecture des variétés et leur potentiel réel de production et à identifier lesparamètres architecturaux prédisant le mieux le potentiel de production.
MATERIEL ET METHODES
Zonze de l’étude
Les travaux présentés dans ce mémoire ont été réalisés sur une parcelle expérimentale dela station CNRA de Divo (Fig. 1). Cette station est située entre (5°46’04.07N, 5° 13’22.09W), à
17 km de la ville de Divo (route de Guitry) et à 200 km du nord-ouest d’Abidjan. Elle est dans unezone de climat tropical humide à quatre saisons. Deux saisons des pluies dont une grande (AvrilJuillet) et une petite (Octobre-Novembre), puis deux saisons sèches dont une grande (DécembreMars) et une petite (Aout-Septembre). Elle a une pluviométrie moyenne de 1223 mm/an. Les
températures oscillent entre 21 °C et 35 °C en moyenne par an. L’hygrométrie est relativementélevée avec un taux d’humidité qui dépasse parfois 80 %. Les sols sont ferralitiques (Kassin et al.,2018). Cette région contribue significativement à la production de café et de cacao en Côte
d’Ivoire.
MétodeL’étude partait sur 35 variétés de C.canephora en cours de sélection, dont 11 clones et 24
descendances hybrides. Les plants analysés sont tous âgés de 3 ans, depuis la date de leur dernier
recépage en décembre 2017. L’expérience a été mise en place en utilisant des pancartes pourmarquer les arbres sélectionnés, d’une balance de précision électronique (SARTORIUS) pourpeser les récoltes, de sachets en plastique pour la récolte des cerises mûres. Toutes les mesures ont
été réalisées sur une parcelle portant le numéro A18-2003. Le nombre total d’arbres analysés est
de 347, à raison de 7 à 10 arbres choisis au hasard par variété, suivant leur taux de survie dansl’essai. L’analyse des cimes a été réalisée pour chaque arbre individuel, et sa production en cerises
fraiches récoltées et pesées au laboratoire. Toutes les données collectées ont constitué en une basede données pour les analyses. L’analyse des cimes a consisté au comptage du nombre dephytomères sur chaque axe, de l’apex à la base (tige principale (A1), rameaux plagiotropes sur

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chaque nœud (A2). Cette démarche permet d’avoir par cohorte, des phytomères de même âge etdonc, qui sont comparables (Fig. 2). Différents codes ont été utilisés pour caractériser les axes.
Chaque nœud a été caractérisé par la présence ou non de ramifications à l’aisselle de chacune deses feuilles : « 0 » pour l’absence de ramification «1», «3» ou «10 » indique la présence, à l’aisselled’une feuille, d’un rameau qui comporte respectivement 1, 3 ou 10 phytomères ; «999» symbolise
une branche vivante dont le nombre de phytomères n’a pas été relevé.

Figure 1. Plagiotrope primaire montrant les phytomères des branches secondaires.
La production a été déterminée par plante. Les cerises mûres sur chaque arbre ont été
récoltées à chaque passage et pesées au laboratoire. La détermination des paramètres de croissanceet de développement a été réalisée par le logiciel MATLAB. Le programme Gloups-Dev a calculé
les paramètres P, B, W et A. (P : probabilités de développement du tronc, B : probabilité dedéveloppement des branches A : probabilité de ramification W : rapport de rythme dedéveloppement)
RESULTATS
Estimations des paramètres de croissance et de développement à partir du programme«gloup-dev » de 35 variétés de Coffea. Canephora
Toutes les valeurs obtenues sont faibles. Les écarts observés entre variétés pour le mêmeparamètre ne sont pas statistiquement significatifs. La probabilité de croissance du tronc (P) est
comprise entre 0,82 et 0,98. Celle des branches (B) est entre 0,86 et 0,96.
Estimation du rapport de rythme de croissance et de développement du cafféier
Le rapport de rythme (W) varie de 0,82 à 1,16 et la probabilité d’apparition des branches
sur le tronc (A), de 0,74 à 0,97. Plusieurs variétés affichent la même valeur lorsqu’on considère
les paramètres individuellement. Par contre, lorsque les 4 paramètres sont considérés ensemble lesvariétés sont assez différentes les unes des autres. Les rapports de rythme de développement desbranches par rapport au tronc (W) sont, pour 14 des variétés étudiées, inférieurs à 1 (W < 1) etpour 17 autres, supérieurs ou égaux à 1 (W ≥ 1).
Corrélation entre croissance et production du caféier
Les corrélations significatives s’observent entre P et W (P = 0,6133) d’une part, et entre W
et B (P = -0,5251) d’autre part. Dans le dernier cas la corrélation observée est négative entre laprobabilité de croissance des branches (B) et la production, quoiqu’elle ne soit pas statistiquement198

1st African Conference on Precision Agriculture | 8-10 December | 2020

significative. Aucune différence n’a été observée entre les paramètres de croissance et deproduction suivant les générations de sélection. Par contre, une légère augmentation deproductivité des premières aux dernières générations de sélection, a été observée autant pour les
hybrides que pour les clones.
DISCUSSION
Les paramètres de développement des axes des 35 variétés de caféiers ont montré desprobabilités de développement du tronc (P) et des branches (B) élevées, supérieures à 80 %. Ces
données indiquent le faible taux d’échec dans l’émission des phytomères. Comme l’a montré
Mathieu (2006). Celui-ci a mentionné que chez les arbres tropicaux à développement continu, laplupart des cycles de développement aboutissent à l’émission de phytomères à partir de tous lesméristèmes. Les caféiers, comme beaucoup d’autres espèces tropicales, sont caractérisés par undéveloppement continu. La ramification, tout comme le nombre de phytomères des branches, sontdes critères de sélection importants. Les caféiers avec des branches qui font moins de pause sontles idéotypes les plus recherchés par la sélection. Les rapports de rythme de développement desbranches par rapport au tronc (W) inférieurs à 1 (W < 1) pour 14 des variétés étudiées, etsupérieurs ou égaux à 1 (W ≥ 1) pour 17 autres, montre que le tronc des 14 premières variétés ont
une croissance plus rapide que les branches et sont des caféier de grande taille. Les 17 variétéspour lesquelles W est supérieur ou égal à 1 représentent des caféiers de petite taille qui offre aux
producteurs l’avantage d’accès facile aux branches pendant la récolte du café. Les valeurs del’intensité de ramification (A) obtenues dans cette étude sont toutes supérieures à 0,7. Ces valeurssont très élevées par rapport aux valeurs trouvées par Ouattara (2013) sur la même espèce (A<0,5). Ces écarts pourraient s’expliquer par la différence d’âge des caféiers, et les conditions
climatiques dans lesquelles les travaux se sont déroulés. Les valeurs observées sur de jeunes
individus seraient, selon Andrianasolo, (2012) influencées par « l’effet de base », les conditions
de l’environnement, les effets maternels. Les caféiers étudiés ici étant plus âgés, les valeursobtenues seraient donc, caractéristiques de chacune des variétés étudiées. La valeur maximale(0,97) est obtenue avec le clone 588, l’un des plus ramifiés qui soient actuellement connus. La
valeur la plus faible (0,74) est obtenue avec l’hybride 900 de seconde génération. Les valeurs lesplus élevées sont celles relevées sur des caféiers dont les méristèmes axillaires sont toujours
fonctionnels pour générer une ramification (Hallé, 1978). L’idéal de la sélection architecturale ducaféier serait de rechercher les variétés ou les arbres qui ramifient le plus. La présente étude amontré deux corrélations très significatives, entre P et W d’une part (positive) et entre B et W
d’autre part (négative). L’estimation de telles corrélations entre paramètres de croissance à partir
des résultats de Ouattara (2013), Okoma (2019) confirme la forte liaison entre P et W maiségalement, entre P et B. Par ailleurs, aucun paramètre architectural ne montre de liaison
significative avec la production (Y). La valeur la plus élevée s’observe avec P, le paramètre decroissance du tronc. En effet, quoique ce soient uniquement les branches qui portent les fruits chezcette espèce, leur mise en place a lieu à chaque nœud de la tige principale, celui-ci dépendant dufonctionnement du méristème apical. Le contraire du comportement du 528 s’observe avec lavariété 126, connue pour la rigidité de son bois et de ses plagiotropes. Grande victime de cetteforte compétition avec ses proches voisins, cette variété affiche l’une des productions les plus
faibles de la parcelle d’essai.

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CONCLUSION
Les écarts observés entre variétés pour chacun des paramètres estimés sont très faibles et
non significatifs statistiquement. L’étude a permit d’identifier 17 variétés de petite taille, avec desplagiotropes plus longues. Cette architecture présente des branches plus faciles d’accès pendant larécolte du café. Les paramètres de croissance et de production ne sont pas corrélés mais les valeursobtenues montrent que la probabilité d’élongation du tronc est plus fortement liée à la production.
Cependant, la compétions générée par l’élongation des branches au cours de la croissanceinfluence négativement la production des caféiers. Dans cette compétition, les variétés qui ont desplagiotropes de forme retombante présentent les meilleures adaptations et ont une production plusélevée. Par ailleurs il est possible par GreenLab à partir des paramètres de croissancecaractéristiques de l’espèce C. canephora de faire des simulations de densités de plantation lesmieux adaptées aux nouvelles variétés sélectionnées de caféiers. Une telle stratégie permettrait,
non seulement un gain considérable de temps en matière de mise au point de techniques culturales
du caféier, mais également une économie substantielle de moyens.
REFERENCES
Andrianasolo DN. 2012. Génétique des populations et modèles d’architecture et de production
végétale. Application à la préservation des ressources génétiques des Mascarocoffea. Thèsede Doctorat, Département de Biologie des Organismes, Université de Montpellier II(Montpellier, France), 208 p.
De Reffye P. 2009. Modélisation en biologie végétale. Production végétale et architecture desplantes. Académie d'Agriculture de France - 2009. Séance du 28 janvier,Gawain J. 2009. Modélisation d’image agronomique application à la reconnaissance d’adventices
par un pulvérisateur localisé 54: 102-180.
Hallé F, Oldeman RAA, Tomlinson PB. 1978. Eds. Tropical trees and forests :an architectural
analysis. Berlin : Springer-Verlag, 441pp.
ICO 2015. International Coffee Organization. Trade Statistics, http://www.ico.org/trade_statistics. asp. consulté le 23/02/2016.
IRD 2010. A new history of coffee. Scientific bulletin no 347; April 2010.
Kassin KE, Kouame B, Coulibaly K, Tahi GM, N’guessan WP, Aka AR, Assi ME, Guiraud BH,
Kone B, Yao GF. 2018. Prospects for sustainable cocoa farming from the rainfall balancein the last thirty years at Lôh-Djiboua and Gôh post-pioneer regions, Côte d’Ivoire. Journalof Soil Science and Environmental Management 9(11): 188-198.
Leroy T. 1993. Diversité, paramètres génétiques et amélioration par sélection récurrente
réciproque du caféier Coffeacanephora Pierre, Thèse de doctorat, Ecole Nationale
agronomie de Renne, 147 p.
Montagnon C, Cubry P, Leroy T. 2012. Amélioration génétique du caféier Coffeacanephora Pierreconnaissances acquises, stratégies et perspectives. Cahier Agriculture, 21(2): 143-153.
Montagnon C, Leroy T, and Yapo A. 1992. Diversité génotypique et phénotypique de quelquesgroupes de caféiers (Coffeacanephora) en collection, Conséquences leur utilisation ensélection, Café, Cacao, Thé, 36(3): 187–198.
Okoma P. 2019. Modélisation et identification de QTL de l’architecture et de la production debiomasse chez six espèces de caféiers Africain, Thèse de doctorat, Université Jean
Lorougnon Guede de Daloa (Cote D’Ivoire).

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Ouattara Y. 2013. Étude des paramètres de croissance, d’architecture et de l’ajustement de labiomasse chez cinq espèces de caféiers (coffeasp). Mémoire pour l’obtention du diplômede master en sciences agronomiques, spécialité : protection des végétaux et del’environnement, 67 p.
Qi R, Ma Y, Hu B, De Reffye P, Cournède PH. 2010. Optimization of source-sinkdynamics inplant growth for ideotypebreeding a case study on maize. Computers and Electronics inAgriculture 71: 96-105.

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PRECISION AGRICULTURE FOR SMALLHOLDERS

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#7414 DIGITAL PLATFORMS FOR BOOSTING FARMER KNOWLEDGE: TWO
CASE STUDIES IN KENYA AND UGANDA
T. Harigaya, J. Zhu, M. Mwanje, E. Bakirdjian, and J. Abuli
Precision Agriculture for Development, Nairobi, Kenya
ebakirdjian@precisionag.org
ABSTRACTThe aim of this paper is to share rich insights on issues, and to identify knowledge gaps
present on farms in two contexts in East Africa. This paper describes the experience of Precision
Agriculture for Development (PAD), an international NGO, in developing and implementing
digital advisory platforms for smallholder farmers in Kenya and Uganda. In Kenya, PAD has
developed and deployed MoA-INFO, a large-scale SMS platform, on behalf of the Ministry ofAgriculture, Livestock, and Fisheries. MoA-INFO provides free agronomic recommendations tomore than 460,000 Kenyan smallholder farmers. The two-way SMS platform allows for thecollection of data from farmers which, in turn, informs the customization of agronomic advice
provided to farmers. In Uganda, PAD has developed an IVR (Interactive Voice Response) service
to provide free agronomic advice to coffee farmers in Western Uganda. The service consists of a
combination of push calls (pre-recorded audio messages) sent to farmers and an automated callcenter which allows farmers to record questions and receive answers from an agronomist for free
and in their local language.
INTRODUCTION
Approximately 80% of African farms, equivalent to 33 million farms, are two hectares orless in size. Many smallholder farmers do not have access to the resources and information theyneed to improve productivity and yields on their farms. Lack of knowledge of Good Agronomic
Practices (GAPs) means that many farmers fail to realize their full yield potential. Moreover,extension workers, who are responsible for disseminating GAPs to farmers, are spread too thin tobe effective. For example, as of March of 2019, there was one extension worker for every 1,800
coffee farmers in Uganda. This paper describes the experience of Precision Agriculture forDevelopment (PAD), an international NGO, in developing and implementing two digital advisory
platforms for smallholder farmers in Kenya and Uganda.
Precision Agriculture for Development (PAD) works with partners to develop, pilot, test,
and scale mobile phone-based solutions that deliver appropriate information to smallholder
farmers, with the goal of increasing agricultural productivity, farmer incomes, and environmental
sustainability. Three core principles inform our approach: 1) the application of insights frombehavioral economics and user-centered design to create appropriate and actionable agricultural
messages, 2) an iterative and interactive process of innovating, testing, and refining through A/B
trials and rigorous impact evaluations, and 3) operating at scale. In our model, scaling, innovating,and learning work together.
In 2018, PAD worked with the Kenyan Ministry of Agriculture, Livestock, and Fisheries(MoALF) to launch an advisory service (MoA-INFO) to provide farmers with free SMS advisory
messages, delivered in English and Swahili, throughout the growing seasons. This service was203

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initially launched to address the Fall Armyworm crisis sweeping the African continent, but has
since been expanded to incorporate content across 10 value chains on GAPs from pre-planting topost-harvest. In addition, the service provides decision support tools to assist farmers to makeinformed decisions about which maize and bean seeds to plant, which fertilisers to apply, andwhether pesticides are appropriate based on the scale of pest infestation. This service has reachedmore than 460,000 farmers. To implement this service, PAD partnered with Safaricom to register
users from among its existing subscriber base, and with the Kenya Agriculture and LivestockResearch Organization (KALRO) and the Centre for Agriculture and Biosciences International(CABI) to develop agronomic content.
As part of the Uganda Coffee Agronomy Training Program (UCAT https://www.ucat.coffee), PAD has built a coffee GAPs advisory service and automated call center
for a sub-sample of robusta coffee farmers in Uganda. Selected farmers receive weekly advisory
training on GAPs and have access to an automated question & answer IVR (Interactive Voice
Response) platform. Farmers are able to: (1) listen to advisory messages again and access pastmessages (2) record a question for a local agronomist (3) listen to the answer to their question
given by the agronomist and (4) listen to past questions and answers they have asked. The platform
is available to selected coffee farmers free of charge in four languages and can be accessed throughany basic feature phone.
MATERIALS AND METHODS
To assess the needs of farmers and characterize their interests and priorities, we first present
an overview of farmer interactions on the MoA-INFO and UCAT digital platforms. In bothsettings, we have conducted multiple rounds of surveys with users to collect feedback on content,
which in turn is used to develop more relevant agronomic advice.
PAD uses A/B testing to iterate the design of the MoA-INFO service. By assessing relative
engagement against changes in framing, timing or type of required response PAD is able toimplement rapid updates to improve user experience and engagement. PAD also conducts phonesurveys with farmers to assess their knowledge and practices to better understand what information
is most important to them, and to assess how they are internalizing the MoA-INFO
recommendations. PAD also pilots other channels for sharing information with farmers. In AprilJune 2020, PAD offered an information service for banana farmers via IVR and in NovemberDecember 2020, PAD is operating a service for tomato farmers on Telegram, a messaging app.
In Uganda, PAD designed several experiments in an effort to improve farmer engagementwith the platform. To increase the pick-up rate of weekly advisory calls, we used an A/B test to
identify whether it was more efficient to send farmers a reminder twenty-four hours or one hour
before receiving the call. We used a similar methodology to determine whether farmers were more
likely to listen to the full advisory call if the recording's voice matched the gender of the farmer.
Finally, we tested whether providing farmers with non-financial incentives (airtime credit)
increased their propensity to record a question for our agronomists.
We also present results from different types of experiments (RCT, A/B trials)

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RESULTS & DISCUSSION
Kenya
In one A/B test administered on the MoA-INFO platform, we assessed two messages
inviting users to conduct a self-survey of their field for Fall Armyworm. We randomized a regularmessage9, which advertises the tool keyword (“CHECK” or “ANGALIA”), and a message whichinstead takes the users directly into the monitoring tool, bypassing the first question. Of the twodifferent message phrasings, the second, “make it easy”10, formulation stimulated two percent
more recipients to initiate the tool. Moreover, of farmers who initiated the tool in response to either
message, those responding to the “make it easy” message were more likely to complete the test.
Overall farmers responding to the “make it easy” message demonstrated a 6% higher completion
rate when compared to farmers who initiated the tool after having received the “regular message”.
We also investigated the timing of these invitations (7 AM, 12 PM, 3 PM and 6 PM).
Messages sent at 6 PM returned the highest number of users who accessed the monitoring tool, but
not by a great deal (1 to 2% improvement over other times). The midday message, while eliciting
the lowest commencement rate, resulted in the highest completion rate across the four times.
Intuitively, midday is a time when it is most likely that farmers will be in their fields; a requirement
for undertaking and completing the assessment tool. The midday message resulted in significantly
better information generation than the 7 AM message. Comparisons in the completion rate between
the 3 PM and 6 PM rates were not statistically significant, but both were below the completion
rates associated with the midday call.
Based on the results of the A/B test, the service was re-designed to send the advisory toolat midday using the “make it easy” phrasing. In conjunction with improved farmer targeting, themonitoring tool was pushed to farmers 77,568 times in the 2019 Kenyan short rains season(October-January). A sample of 50,779 farmers received messages (some received it more thanonce); 16,234 invitations were accepted for the survey (21% of invitations; 32% as a share of
farmers) and 5,213 surveys were completed (10.3% of farmers, but 32% of those who initiated thetool). Of the 5,213 farmers who completed the test, 1,934 were encouraged not to use pesticides
because it would not be cost effective for them while the rest were encouraged to use pesticides toaddress the infestation. The phrasing of the “make-it-easy” message has also been adapted for use
in other messages sent to farmers inviting them to receive content.
Table 1 below shows results from a pilot MoA-INFO polling survey, which highlight
how questions on user knowledge, adoption, and reasons for non-adoption of priority
recommendations, can be combined to identify recommendations that might be most susceptible
to an information intervention. In this case, we highlighted misconceptions around maizethinning and planting fertilizer-use to be particularly fertile areas for shifting behavior withinformation. The survey was administered to 214 randomly selected MoA-INFO maize farmers.

9

Regular message:
Hi. Now is a great time to scout your maize for Fall Armyworm! Send CHECK to 40130 for help scouting
your maize.
10‘Make it easy’ message:
Are you ready to assess the extent of Fall Armyworm (FAW) damage on your farm and receive advice? You
will need to go to your shamba [farm]. A. Yes, B. No

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Table 1. Summary of survey responses of MoA-INFO users (N= 214)
Knowledge

Adoption

Reasons for Non-Adoption

Reasons for Suboptimal Adoption

Recommendations % Farmers N%# Non- Financial
Time
Information Unavail- Other Financial Limited
Aware ofAdoption adopters constraints constraints constraints ability reasons constraints availability
Best
(N)
(N)
(N)
(N)
(N)
(N)
(N)
Practices
Early Planting

68.5

212

58.5

88

34.2%
(14)

36.6%
(15)

2.4%
(1)

2.4%
(1)

24.4%
(10)

Manure/Compost
Use

76.2

214

48.1

111

3.6%
(4)

9%
(10)

10.8%
(12)

72.1%
(80)

4.5%
(5)

Manure/Compost
Quantity

103

28.2

74

Planting Fertilizer
Use

214

79.0

45

37.8%
(17)

2.2%
(1)

48.9%
(22)

0%
(0)

11.1%
(5)

Planting Fertilizer
Quantity

57.9

169

57.4

72

Maize Thinning

73.0

125

30.4

87

0%
(0)

3.5%
(3)

62.1%
(54)

-

34.5%
(30)

Weeding

74.3

214

90.2

21

0%
(0)

33.3%
(7)

23.8%
(5)

-

42.7%
(9)

214

68.2

68

61.3%
(38)

1.6%
(1)

22.6%
(14)

0%
(0)

14.5%
(9)

146

57.5

62

45%
(27)

20%
(12)

18.3%
(11)

-

16.7%
(11)

Top-dressing
Fertilizer Use
Top-dressing
Fertilizer
Application

72.0

91.3%
(63)

1.5%
(1)

95.5%
(42)

2.3%
(1)

UgandaIn Uganda 4,407 coffee farmers are registered on our platform. Since the beginning of the
programme in October 2019, 1,311 questions have been recorded by farmers and answered by ouragronomist team. The pick-up rate of advisory IVR messages varies between 70% and 75%, and
76% of users listen to at least 90% of the message content. In terms of user satisfaction,
approximately 80% of surveyed farmers (N=40) reported discussing the content of our messages
with their family and friends, which helps to diffuse our advisory content beyond the populationof registered farmers.
We have designed several A/B tests to improve farmer engagement on the UCAT service.
For example, we assessed the effect of sending a dramatised version of our weekly message
relative to the regular, non-dramatised, framing (dramatised messages emphasize the risk of not
applying the recommendations in the message, while non-dramatised messages put the emphasison the benefits). We found that the pick-up rate was 3 percentage points lower among recipientsof dramatised messages.
We have also assessed whether sending a reminder to farmers increases the likelihood of
their picking up our calls. Running the experiment for 8 weeks, we found that receiving a reminder
increases the pick-up rate by 9%. The impact was mainly driven by reminders received 24 hours
in advance, as opposed to reminders received one hour in advance.

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#7422 MAPPING AFRICAN SOILS AT 30M RESOLUTION – ISDASOIL:
LEVERAGING SPATIAL AGRONOMY IN FARM-LEVEL ADVISORY FOR
SMALLHOLDERS
M. Miller, J. Collinson, K. Shepherd, A. Dobermann and J. Crouch
Innovative Solutions for Decision Agriculture (iSDA), www.isda-africa.com
with T. Hengl, EnvirometriX and S. Haefele, Rothamsted Research
jonathan.crouch@isda-africa.com
ABSTRACT
Field level soil data has been the foundation of agronomic advisory, but traditional methods
involving on-farm sampling are too expensive for a large proportion of African smallholders.
Building on the work of the African Soil Information Service (AfSIS), Innovative Solutions for
Decision Agriculture (iSDA) and partners have created an agronomic soil database which covers
the entire African continent at a spatial resolution of 30 m. At this resolution each soil property
prediction map results in over 24 billion pixels.
“iSDAsoil” combines remote sensing data and other geospatial information with point
samples subjected to spectral analysis and traditional wet chemistry reference analysis – and isfreely available via a user-friendly web interface, with full data available under an open license(CC-BY 4.0) and developer API at: https://www.isda-africa.com/isdasoil/
Our digital maps cover 17 agronomically important soil properties at two depths andinclude soil texture, soil pH, macronutrients (soil organic carbon, nitrogen, phosphorous, andpotassium, magnesium), micronutrients, CEC, electrical conductivity etc. Each property includes
estimates of uncertainty and from these raw properties a fertility capability classification (FCC)
has been calculated for each 30m point, enabling users to identify yield limiting constraints in soil
properties on a per-field basis.
iSDAsoil is designed to encourage sharing and we hope that the owners of other soil andagronomic data, in industry and academia, will share their datasets to help create ever moreaccurate maps. The mapping infrastructure has been designed to allow fast updates for regular
addition of new data. We view these agronomy data stacks as the starting point, rather than an endproduct per se. Our aim is to catalyse opportunities to develop and deliver products and services
supporting low-cost agronomic advisory for millions of smallholders across Africa – as well as
bring greater efficiency to agribusinesses supporting smallholder value chains.
Together with its partners across Africa and elsewhere, iSDA is building site-specificfertilizer recommendation services, yield prediction and risk-return tools based on iSDAsoil. These
are currently being validated through a network of on-farm trials in Ghana. iSDA is also designing
end-to-end solutions for commercial aggregators and off-takers in Kenya, Ghana and Rwanda,
each based on a different blend of technologies including iSDAsoil. This paper will provide an
overview of these and other applications including consortium data-sharing opportunities andplans for a central referencing and calibration service.

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INTRODUCTION
Soil has long been appreciated as an important factor in determining crop yields. However,until recently soil information for Africa has only been available at relatively coarse resolutions(Jones et al., 2013). In high income countries, farmers frequently pay for lab-based soil testing tounderstand their constraints in soil fertility down to the field level and even finer levels of detail.
But smallholders in Africa have access to neither the testing infrastructure, nor the finances toroutinely adopt this approach. As a result, it is impossible for most farmers in Africa to consider
the current soil nutrient status of their fields when making management decisions, such as the typeand quantity of fertiliser to apply.
More recently, continental scale soil sampling initiatives, such as the AfSIS project (fundedby the Bill and Melinda Gates Foundation) have allowed for the creation of a pan-African databaseof soil information (Leenaars et al., 2014). Coupled with high resolution satellite information,
predictive soil mapping (PSM) has now become possible; this uses machine learning algorithms
to predict soil properties on a consistent spatial scale (Malone at al., 2017). Hengl et al. (2015,2017) and Vagen et al. (2016) pioneered this approach for Africa, initially at a resolution of 250m.
These maps proved useful for strategy formulation and policy-making, but the resolution is toocoarse for interface with farm-level advisory.
iSDAsoil, at 30m resolution, bridges the gap to farm-level information. It is available
through Open and FAIR data access principles in order to empower researchers, businesses,
governments and others to make data-informed decisions for improving the productivity of African
agriculture, ranging from the field to the country level.
The data platform is designed to encourage sharing and we hope that the owners of othersoil and agronomic data, in industry and academia, will share their datasets to help create evermore accurate maps. With this in mind, the mapping infrastructure was designed to allow fast
updates for regular addition of new data. As well as being able to explore the data visually at theiSDAsoil website, the data can be queried via a free API, and downloaded in bulk via Zenodo.
We hope to create a virtuous cycle, whereby contributors of point data will benefit fromincreasingly accurate maps as more data is added. While additional soil sampling will always be
required, it is hoped that users of iSDAsoil will be able to focus on incorporating the data into theirapplications and products.
MATERIALS AND METHODSiSDAsoil is a collection of spatial predictions of soil nutrients and properties, based on thepredictive soil mapping (PSM) techniques described in Hengl and Macmillan. The first step in thisprocess was to standardise all the input soil data as follows:• Ensuring all measured units were in the same format• Harmonizing properties measured using different techniques• Removing any erroneous datapoints
An extensive selection of covariates was then used to predict soil properties across Africa. We
apply a multiscale ensemble machine learning approach that combines input covariates at two
different geospatial resolutions:• Coarse scale: 250m (includes climatic and vegetation variables based on MODIS satellite
products and similar)
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Fine scale: 30m (includes digital terrain model (DTM) derivatives, as well as Sentinel-2
satellite and Landsat-7/8 cloud-free composite images)

We then applied five regression modelling algorithms:• Random forest as implemented in the Ranger package• Gradient boosting as implemented in the XGBoost package• Cubist regression models as implemented in the Cubist package• Neural network algorithms as implemented in the deepnet package• GLM with Lasso or Elasticnet Regularization as implemented in the glmnet package
All programming was implemented using R software for statistical computing and open-source
packages and applications for spatial analysis. For each property, we ran model fine tuning, feature
selection (to reduce the number of covariates) and ensemble machine learning, using theSuperLearner algorithm as implemented in the mlr package. Predictions at the coarse (250m) andfine (30m) resolutions were generated independently, then merged using ensembling. We firstpredict values at three depths: 0, 20cm and 50cm, then aggregate values to standard depth intervals(0–20cm and 20–50cm). In principle, all steps, except the soil data import and feature selection,
are fully automated. Computing was implemented in a high-performance system with tasks fullyparallelized using the R snowfall package. As training points, we have used a compilation of legacy
soil profiles and point data from various datasets. The total number of training points used depends
on the soil property, but is generally higher than 100,000. Please see our website for further details
at: https://www.isda-africa.com/isdasoil/technical-information/
RESULTS AND DISCUSSIONiSDA's unique focus is combining new low-cost technologies, rigorous on-farm science andviable last mile delivery business models. We are initially focusing on advisory for maize, rice,
potato and coffee but will expand to cover all major crops of importance to smallholders in Africa.
iSDA is currently carrying out a series of on-farm trials to verify the use of iSDAsoil as a “virtual
soil test” for fertiliser recommendations. This purely digital solution offers the possibility of aradically lower cost alternative to physical soil tests, while providing a substantial improvement
on currently available national (blanket) fertilizer recommendations by virtue of providing
recommendations tailored to individual farms. This could potentially allow a route to low-cost yet
customised agronomic advisory for millions of smallholders who could not afford a service basedon a traditional soil test. If this provides a reliable foundation, we will then augment this withhistorical agronomic and economic data in combination to generate probabilistic, risk-based
return-on-investment advisory.
Agronomic advisory for paddy rice, particularly fertilizer recommendation, is extremely
challenging as flooding substantially alters soil chemistry and traditional soil tests tend to giveinconsistent results in diagnosing nutrient deficiencies. iSDA is therefore investigating whether
iSDAsoil can help determine optimum stratification of nutrient omission trials in order to provide
fertilizer recommendations at the community level. This would then be implemented as a publicprivate-partnership with government extension services and commercial aggregators.
The maps in iSDAsoil include prediction uncertainty for all data points. These can be usefulin identifying areas where soil property predictions are of lower accuracy, and thus care should be209

1st African Conference on Precision Agriculture | 8-10 December | 2020

taken in terms of associated conclusions. These areas should also be primary targets for additionalsoil sampling campaigns. We are also better able to understand what covariates are important forpredicting specific soil properties.
Soil infrared spectroscopy is the most promising technology that has emerged over the past
20 years for measuring soil health at low cost. However, this potential is constrained by the lackof regional spectral calibration libraries, and the limited capacity and large inter-lab variability of
conventional soil testing labs in Africa needed to develop the calibrations. To overcome this, iSDA
has joined an initiative to provide a centralised soil reference laboratory service, and a centralised
high quality service for developing spectral calibrations. The initiative has been housed under the
Global Soil Partnership (GSP) and implemented through the Global Soil Laboratory Network,
hosted by FAO, as well as linked to the GSP’s Global Soil Information System (GLOSIS).
There are many other possible uses for iSDAsoil, such as in the fight against climate change.
Carbon budgeting has recently become an important topic as governments and companies attempt
to offset their carbon emissions. An important aspect of carbon budgets is being able to estimate
the amount of carbon stored in the soil. iSDAsoil provides estimates of both organic and total
carbon.
REFERENCES
Jones A. (ed.), et al. 2013. Soil atlas of Africa. Luxembourg: Union Européenne, 176 p. (Joint
Research Centre Soil Atlas Series). ISBN 978-92-79-26715-4.
Leenaars J. 2014. Africa Soil Profiles Database, version 1.2: a compilation of georeferenced andstandardised legacy soil profile data for Sub-Saharan Africa (with dataset). Technicalreport (ISRIC — World Soil Information, 2014).
Hengl T, et al. 2015. Mapping Soil Properties of Africa at 250 m Resolution: Random Forests
Significantly Improve Current Predictions. PLoS ONE 10(6): e0125814.
doi:10.1371/journal.pone.0125814.
Hengl T, et al. 2017. Soil nutrient maps of sub-Saharan Africa: assessment of soil nutrient content
at 250 m spatial resolution using machine learning. Nutr. Cycl. Agroecosystems 109: 77–
102.
Malone BP, Minasny B, McBratney AB, et al. 2017. Using R for digital soil mapping, Springer.
Vagen T-G et al. 2016. Mapping of soil properties and land degradation risk in Africa using
MODIS reflectance. Geoderma 263: 216–225.

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1st African Conference on Precision Agriculture | 8-10 December | 2020#7442 AFFORDABILITY OF MECHANISATION SERVICES FOR SMALLHOLDERSIN ZAMBIA BY AGRODEALER DEVELOPMENT
S. Peets, and S. Woods
Harper Adams University, Newport, Shropshire, TF10 8NB, United Kingdom, speets@harperadams.ac.uk
ABSTRACTThe objective of this work was to assess the affordability of mechanisation systems alongwith conservation agriculture for smallholders in Zambia by agrodealer development. Twosmallholder communities (60 km east of Lusaka, and 150 km southwest of Lusaka) with aconservation farming extension service and a suitable agrodealer available were the focus of thetrial. Each agrodealer received a mechanisation package to operate and offer the service to local
smallholder farms. The performance of agrodealers was monitored by a variety of methods andshowed agrodealers as mechanisation contractors can be profitable given sufficient business pumppriming and training. Conservation farming practices undertaken by trained agrodealers were also
shown to improve yields and the resilience of smallholder farms in difficult growing season.
INTRODUCTION
In Zambia, there are 1.46 million small and medium-scale farmers (Sitko et al. 2015) of
which only 17.9% (261,590) are medium-scale farmers farming 5–100 ha. 81.1% (1,196,720) areeither subsistence farmers farming 2 ha or less by hand and typically with a family of five to six
members, or slightly larger farms farming 2–5 ha. Smallholders have a restricted purchasing powerand cannot afford to invest in agricultural machinery.
The challenges hampering agricultural mechanization in sub-Saharan Africa (SSA) areaffordability, availability, lack of farmer skills and constraints within the private sector (Sims etal., 2016). Low capacity and lack of support for mechanisation contractors (agrodealers) to succeed
is therefore holding back the development.
There are activities to stimulate private investment in agriculture (Musika) and promoteconservation farming practices (Conservation Farming Unit) in Zambia. Thierfelder et al. (2015)
found that in 80% of cases studied, regardless of soil type or conservation system employed,conservation management increased maize yield. They also suggested that the continuing
improvements seen over time under conservation management were due both to recovery in soil
health and the skills of the producers themselves
There is a business opportunity for mechanisation service providers to be in the vanguardof conservation agriculture for African countries (Sims et al. 2014, Adu-Baffour et al 2019). Thisis a key strategy needed to improve farm resilience and improve crop yields and incomes forsmallholder farmers in Sub-Saharan Africa (SSA) (Rockström et al., 2009).
The objective of this work was to assess the affordability of mechanisation systems alongwith conservation agriculture for smallholders in Zambia by agrodealer development.

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MATERIALS AND METHODS
Two smallholder communities (60 km east of Lusaka, and 150 km southwest of Lusaka)with a conservation farming extension service and a suitable agrodealer available were the focusof the trial in Zambia (Peets et al 2019). Two agrodealers, one on each site, received mechanisation
packages to operate and offer the service to local smallholder community. The mechanisation
package consisted of: 82HP 2WD tractor, 3 tine ripper, off-set disc harrow, 3 row planter, 3 tonne
trailer, 12m boom sprayer, and a maize sheller (Table 1). Resale values and years owned were
estimated based on local knowledge of resale values and applying a straight-line depreciation
model. The performance of agrodealers was monitored by telemetry data (standard reports such as
daily engine hours, and advanced GPS tracking analysis to detect boundaries of worked areas),agrodealer sales invoices, agrodealer accounting data, and site visits during the period of June 2018
to August 2019.
Table 1. Mechanisation package items and cost (USD).
Item
TractorRipperDisc Harrow
Boom Sprayer
Planter
Sheller
Trailer
Total

Purchase Price ($)
33538
2250
4200
2800
4650
7000
4000
58438

Resale ($)
16000
500
2500
500
1000
2000
2000
24500

Years owned
6
10
10
5
10
5
10
–

The data obtained was modelled in Microsoft Excel using standard costing methodology(Landers, 2000). Multiple spreadsheets were produced which costed the individual machines on a
per hectare basis using theoretical work rates. The model accumulated hectares worked as morejobs were undertaken with the aim of spreading the fixed costs of ownership over the total hectares.
This allowed judgements and model adjustments to be made which reflected the actual workrates
achieved by the machinery according to the telemetry data. Maize yields were assessed in adjacent
fields for a range of customers where mechanisation was done on one field and standard local
practice applied to the other.
RESULTS AND DISCUSSION
The results of mechanisation contractor (agrodealer) performance analysis demonstrate thefollowing: fieldwork was conducted on 142 days out of available 393 calendar days; tractor
worked 980 engine hours during this period; a total of 219 jobs were done; invoiced area was 364
ha (258 ha of conservation farming); average worked area was 2.3 ha; seasonal work rate was 3.8
ha/day (0.5 ha/hr); revenue generated 43 USD/ha (18.63 USD/hr).
The actual quantity of work achieved fell short of the projected values for a variety ofreasons ranging from the timing of machine delivery missing a large part of the season (Table 2.)
to delayed advertisement of tractor service. This is highlighted by the planter, sprayer and shelling212

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machines in particular. However, once working, the operators produced better workrates thanprojected.
Table 2. Actual and projected work in a season (year).
Quantity of work
Type of work
ActualProjected
Ripping (ha)258300Discing (ha)35
70
Planting (ha)25
100Spraying (ha)46
100Shelling (bags)
11036
33000
Trailer (hrs)
65
200
Total
364 ha570 haTime to complete (hr)ActualProjected
512
595
63
50
46
124
24
46
221
165
65
200
931
1180

Work rate ha/hr
ActualProjected
0.50
0.50
0.56
1.40
0.54
0.81
1.92
2.16
50
200
–
–
–
–

Table 3 shows the projected financial statement for the Agrodealer business which could
have made a gross profit of over $15,000 with the enterprise breaking even in just under 4 years.
This is based on favourable and timely working conditions with minimal breakdowns but in noway unrealistic rates of work.
Table 3. Projected Agrodealer mechanisation services financial statement (USD).
Task
Ripping
Spraying
Discing
Planting
Shelling
Trailer
Total ($/yr)
Opex ($/yr)
Capex ($/yr)

Cost($/hr)
19.47
30.35
34.41
21.05
17.49
19.74
24,151.18
13,397.16
10,754.03

Cost ($/ha)
38.64
14.05
24.58
25.99
0.09

Retail ($/ha)
50.00
46.15
46.15
46.15
0.19
30.00
39,807.69
Breakeven (yr)Gross profit ($)
3407.863210.371510.172016.443460.472051.20
15,331.51
3.81

The actual financial statement shown in Table 4 shows a loss of just over $1,500. Twotasks were profitable, and it is envisaged that given a better season and a longer work window withfull machine availability that planting and shelling operations will also contribute to the enterpriseprofitability. Indeed, spraying which only became available part way through the season stillturned a profit.
The farm trial results demonstrated that maize crop established by ox ploughing wilted
mostly and performed poorly (mean yield 0.3 t/ha), crop established by tractor ripping
demonstrated more vigorous root development and better yield (mean yield 5.5 t/ha) (number offarms in comparison trial 21). The rainfall in the period of November 2018 to April 2019 was 277
mm as measured at five locations in the trial area.

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Table 4. Actual Agro dealer mechanisation services financial statement (USD).
Task
Ripping
Spraying
Discing
Planting
Shelling
Trailer
Total ($/yr)
Opex ($/yr)
Capex ($/yr)

Cost ($/hr)
22.51
48.42
33.20
35.61
18.37
29.92
22,404.58
11,650.55
10,754.03

Cost ($/ha)
44.66
25.22
59.29
65.94
0.37

Retail ($/ha)
50.00
46.15
46.15
46.15
0.15
26.70
21,225.65
Breakeven (yr)Gross profit ($)
1378.31963.01-459.59-494.61-2356.90-209.15-1,503.93-38.86

CONCLUSIONS
Contracting business (agrodealer) can be profitable, however, in early stages needs support
such as set up with financial aid, time to build customer base, and training. The actual financial
statement for the mechanisation contractor business showed a loss of just over $1,500. Rippingand spraying were profitable. The projected financial statement for the agrodealer mechanisation
contractor business shows a potential to make a gross profit of over $15,000 with the enterprise
breaking even in just under 4 years. This is based on favourable and timely working conditions
with minimal breakdowns but in no way unrealistic rates of work.
Conservation farming practices undertaken by trained agrodealers were also shown to
improve yields and the resilience of smallholder farms: a yield improvement of mechanised ripping
for maize establishment versus ox driven techniques of 5.2 t/ha (mean yield 5.5 t/ha and 0.3 t/ha
respectively) during difficult season was shown.
Further work is required to improve the efficiency of tractor field work by better planning,scheduling and training.
ACKNOWLEDGMENTS
This work has been funded by Innovate UK (grant No 103713), AGCO Ltd, Daniel Tembo
General Dealers, and ChoAgro.
REFERENCES
Adu-Baffour F, Daum T, Birner R. 2019. Can small farms benefit from big companies’ initiatives
to promote mechanization in Africa? A case study from Zambia. Food Policy 84: 133–145(https://doi.org/10.1016/j.foodpol.2019.03.007)
Landers A. 2000. Farm machinery: Selection, Investment and Management. 79-94. Farming Press,
Tonbridge. UK.
Peets S, Misiewicz PA, Woods S, Ngwene B, Burbidge J, Barlow G.R. 2019. Mechanization ofsmallholders in Sub-Saharan Africa by agrodealer development. Written for presentation
at the 2019 ASABE Annual International Meeting, Sponsored by ASABE Detroit,
Michigan July 7-10, 2019 (https://doi.org/10.13031/aim.201901285).

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Rockström J, Kaumbutho P, Mwalley J, Nzabi AW, Temesgen M, Mawenya L, Barron J, Mutua,
J, Damgaard-Larsen S. 2009. Conservation farming strategies in East and Southern Africa:
Yields and rain water productivity from on-farm action research. Soil and Tillage Research
103 (1): 23–32 (https://doi.org/10.1016/j.still.2008.09.013).
Sims B, Breen J, Luchen S. 2014. Mechanization of smallholder Conservation Agriculture inZambia: lessons learnt and future directions. Landwards e-Xtra, 69 (4): 1–12.
Sims BG, Kienzle J, Hilmi M. 2016. Agricultural mechanization: A key input for sub-SaharanAfrican smallholders. Integrated Crop Management Vol 23. FAO.
Sitko NJ, Chamberlin J, Mulenga B. 2015. Unpacking the Growth of Medium-scale Farms
Zambia: What are the Implications for the Future of Smallholder Agriculture? IAPRI,
Lusaka.
Thierfelder C, Rusinamhodzi L, Ngwira AR, Mupangwa W, Nyagumbo I, Kassie GT, Cairns JE.
2015. Conservation agriculture in Southern Africa: Advances in knowledge. Renewable
Agriculture and Food Systems 30(4): 328–48.

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#7515 SPATIAL VARIABILITY AND MAPPING OF SELECTED SOIL QUALITY
INDICATORS FOR PRECISION FARMING AT A SMALLHOLDING LEVEL INMINNA, NIGERIA*B.A. Lawal, M.K.A Adeboye, and P.A. Tsado
Department of Soil Science and Land Management, Federal University of Technology, PMB 65,
Minna, Nigeria, lawalba63@futminna.edu.ng, Mobile: +2348036207353
ABSTRACT
Smallholding farmers in Nigeria still practice blanket application of fertilizers, without
considering spatial variations in soil properties across their fields. Understanding of spatial
variability in soil properties is essential for precision farming, especially in this era of resource
scarcity and high cost of fertilizers. This study was carried out to assess and map the spatial
variability in selected soil quality indicators in a smallholder farm in Minna, North-central Nigeria,
for site-specific management. Four-hectare field was selected for the study. The field was divided
into subplots of 25 m x 25 m and serially numbered from 1 to 64. Each subplot was geo-referenced
and soil samples were collected from 0-15 cm depth in 10 randomly selected subplots and sent to
the laboratory for analysis. Field and laboratory data were analyzed using descriptive statistics.
The data were then transferred into GIS software, SURFER 11, for interpolation and productionof spatial maps of the selected soil quality indicators. Results indicated that the texture of the soils
was sandy loam, pH was neutral; soil organic carbon and total nitrogen were low while phosphorus
and potassium were medium. Spatial variability was low in sand, clay, bulk density and pH with
their coefficient of variation (CV) ≤ 10 %. The CV for silt (28 %) and nitrogen (25 %) weremoderate, while soil organic carbon (40 %), phosphorus (38 %) and potassium (36 %) were high.
Correlation analysis indicated a non-significant (> 0.05) relationship between the micro-relief andspatial distribution of the soil properties within the farm. Based on the results, management
practices related to soil organic carbon, nitrogen, phosphorus and potassium will require dividing
the farm into relatively smaller homogenous units. This would help the farmers significantly inmanaging the spatial variability through application of the right quantity of fertilizers appropriate
for each unit.
Keywords: Site-specific management, spatial variability, geospatial mapping
INTRODUCTION
Spatial variability in soil properties is a major global challenge and contributes todifferences observed in growth, yields and quality of field crops (Jabro et al., 2010). Hence,
knowledge of the spatial variability of soil properties is a prerequisite for site-specific management
as it permits appropriate application of field inputs, e.g. fertilizer, irrigation water, herbicides etc.
according to the spatial requirements of soil and crop.
Knowledge of spatial variability of soil properties in smallholder (peasant) fields for the
purpose of site-specific management practices has been a major concern, particularly in Nigeria.
Recent studies in a north-central state in Nigeria had revealed that farmers in this category are yet
to understand that such phenomenon exists in soils (Abdulrasak et al., 2019). This could haveaccounted for differences usually observed in the growth and yield of their field crops. The way
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forward is to quantify the spatial distribution pattern of soil properties and construct reliable spatial
variability map(s) of smallholding arable fields. Therefore, this study assessed and mapped thespatial variability of some soil quality indicators for site-specific management in a smallholding
farm.
MATERIALS AND METHODS
Description of the Study Site
The study site is located at Gidan-Kwano, a suburb of Minna, Niger State, Nigeria, on
latitudes 9º 30′ 27.150″ N and longitudes 6º 27′ 16.746″ E at 223-240 m above mean sea level.
Minna lies within sub-humid tropical zone with mean annual rainfall above 1200 mm and mean
daily temperature of 33 ºC which peaked at 40 ºC between February to March. Dominant soils are
Ferric Luvisols, Ferric Acrisols and Ferric Cambisols (Ojanuga, 2006). The vegetation of the study
area is southern Guinea wooded savanna of Nigeria. Maize, rice, sorghum, soybean, cowpea andyam are the major crops grown by smallholder farmers.
Soil Sample Collection and Laboratory Analysis
Four hectares field was selected for the study. The field was divided into subplots of 25 m
x 25 m and numbered 1 to 64. Ten subplots were selected for sampling using random samplingtechnique. Geo-referenced soil samples and core samples for determination of bulk density werecollected at the middle of the selected subplots at 0-15 cm depth, to allow for interpolation andproduction of spatial maps of some soil properties.
The air-dried samples for routine analysis were gently crushed and passed through a 2 mm
mesh. The processed soil samples were analyzed for bulk density, particle size distribution, soil
pH, soil organic carbon (SOC), total nitrogen (N), phosphorus (P) and exchangeable potassium(K), following the standard laboratory procedures outlined in the IITA’s Soil and Plant Analysis
Manual (IITA, 2015).
Data Processing and Digital Mapping
Data collected from the field and those determined in the laboratory were analyzed using
descriptive statistics to obtain minimum, maximum, mean, standard deviation (SD) and Pearson
correlation analysis (SAS Institute, 2015). Coefficient of variation (CV) was computed by dividing
SD with mean for each attribute measured. Spatial variability ranking followed the guidelines
outlined in Wilding and Drees (1983), in which CV values of 0-15, 16-35 and 36 % and above
were ranked as low, moderate and high. The relationship between micro-relief and some soil
properties were estimated using correlation coefficient whose significance was tested at 5 % level.
Spatial variability maps were generated using GIS-software; SURFER 11 (Golden Software Inc.,
2012).
RESULTS AND DISCUSSION
Soil Physical and Chemical Properties
The texture of the soils was sandy loam. Average bulk density was 1.47 Mg m-3. Soilreaction was neutral with mean pH value of 6.9, making the soil favorable for cultivation of many
field crops. SOC and N were low; P was medium while K content in the soil was high. Thus, SOC
and N are critical in soil quality management in the studied field.

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Spatial Variability of Soil and Land Attributes
The results summarizing the descriptive statistics of the soil and land attributes measured
are presented in Table 1 and spatial variability maps of some soil quality attributes represented in
Figures 1a-d. The micro-relief of the farm had low coefficient of variation (CV), implying low
spatial variability in land configuration. Except silt with moderate CV, other soil physicalproperties measured had CV values < 15 %, also indicating low spatial variability. Seasonal tillage
activities could have been responsible for such homogenization in those soil properties.
Table 1: Spatial variability of land and soil properties of a farmer’s field in Minna, Nigeria
Parameter

Min.

Max.

Mean

SD

Topography
Sand (g kg-1)
Silt (g kg-1)
Clay (g kg-1)
Bulk density (Mg m-1)
Ph
Organic carbon (g kg-1)
Total nitrogen (g kg-1)
Phosphorus
Potassium

223
662
60
158
1.19
6.5
2.16
0.06
5
0.30

240
772
140
198
1.67
7.4
7.30
0.12
18
1.08

228.90
719
102
179
1.47
6.9
4.94
0.08
10.90
0.64

5.80
38.89
28.60
18.53
0.13
0.28
1.98
0.02
4.12
0.23

CV
(%)
3
5
28
10
9
4
40
25
38
36

Spatial variability
Ranking
Low
Low
Moderate
Low
Low
Low
High
Moderate
High
High

Figure 1. Spatial distribution of (a) soil organic carbon, (b) total nitrogen, (c) available phosphorus
and (d) potassium in the surface soils of the studied site.

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Spatial variability in chemical properties varied from low in pH, moderate in N, high in
SOC, P and K. This variation could be attributed to differences in land use types (tuber and cereal
crops) requiring different soil fertility management practices. Maniyunda et al. (2013) reported
high spatial variability in SOC, N, P, K and S in some savanna soils which they attributed todifferences in land use types, management and cultural practices and the socio-economic status offarmers. Thus, the implication of the current findings is that while issues related to soil reaction
may be handled uniformly, management of SOC, N, P and K have to conform to their spatial
variations in the soil. In this regard, it is appropriate to partition the field into relatively
homogenous units for effective management.
Relationship Between Micro-Relief and Soil Properties
The Pearson correlation between the micro-relief of the farm showed non-significant(P>0.05) correlations with sand (r = 0.403), silt (r = -0.514), clay (r = -0.082), bulk density (r =0.419), pH (r = 0.310), SOC (r = -0.449), P (r = 0.009), N (r = -0.052) and K (r = 0.435). Thissuggests that other pedogenic factors overshadowed the effect of topography on spatial distributionof soil properties within the confine of the farm.
CONCLUSIONS
On the basis of the soil texture, bulk density and soil pH, management practices related tosoil compaction and soil reaction (pH) could be handled uniformly. Uniform/blanket soil fertility
management should be discouraged due to high spatial variability, particularly for the amendment
of SOC, N, P and K. Smallholder farmers in this part of Nigeria are encouraged to partition theirfarms into relatively homogenous management units to permit proportionate application of thesenutrients. This may eliminate the possibility of some parts of their farms receiving insufficient ofthese nutrients, while other parts received in excess.
REFERENCES
Abdurasak YL, Lawal BA, Raji BA, Yakubu, M, Panti A.A. 2019. An Assessment of Farmers
Awareness and Perception on use of Soil Information in Niger State. In: Chinma, C.E. et
al. Food Insecurity in Africa: Agricultural Diversification as a Panacea. Proceedings of theInternational Conference of Agriculture and Agricultural Technology, 23rd - 26th April,2019 held at the Federal University of Technology, Minna, School of Agriculture andAgricultural Technology. pp453-459.
Golden Software Inc. 2012. SURFER 11 for Windows. Surface mapping system package.
(www.goldensoftware.com)
IITA. 2015. Selected Methods for Soil and Plant Analysis. Manual Series N0.3, IITA Ibadan,
Nigeria. 126pp.
Jabro JD, Stevens WB, Evans RG, Iversen WM. 2010. Spatial variability and correlation of
selected soil properties in the AP horizon of a CRP grassland. Applied Engineering inAgriculture, 26 (3): 419–428.
Maniyunda LM, Raji BA, Gwari MG. 2013. Variability of Some Soil Physicochemical Properties
on Lithosequence in Funtua, North - Western Nigeria. International Journal of Science andResearch (IJSR) 2(9): 174-180.

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Ojanuga AG. 2006. Agroecological Zones of Nigeria Manual. FAO/NSPFS, Federal Ministry ofAgriculture and Rural Development, Abuja, Nigeria, 124pp.
SAS Institute. 2015. SAS systems for information delivery for Windows. Statistical analysis
software. SAS Institute, Cary, NC, USA.
Wilding LP, Drees LR. 1983. Spatial variability and pedology. I. Concepts and interactions. In
L.P. Wilding, N. Smeck and G.F. Hall (eds.). Pedogenesis and Soil Taxonomy.
Wageningen, Netherlands, 83-116.

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#7534 FARMER CHARLIE: PRECISION AGRICULTURE AT SMALLHOLDERFARMERS’ SERVICE
B. Bonnardel, G. Cursoli,
Farmer Charlie, AB5 Consulting, London, United Kingdom
betty@farmer-charlie.com +44 (0)7733 381 935
ABSTRACT
Preliminary research and an ongoing project in Nigeria showed that agriculture is a crucial
activity in the country. It is mainly carried out in small, family-owned farms, suffering from thelack of agricultural data and weather forecast information easily available, that could play anessential role in improving cultivation strategies, the more so in consideration of the impact ofclimate change.
Farmer Charlie was conceived to provide farmers, especially smallholders ones, withconnectivity and data to exploit weather information and to know their land conditions andfeatures. The aim of the project is to eventually double their income, subsequently allowing
farmers to access market opportunities. Vertically integrated, the service includes sensors, satellite
and IoT technology. It is cost-effective and modulable. Besides, it offers the advantage of
permitting a sustainable use of resources (water, fertilisers, pesticides).
Farmer Charlie is currently being tested on cassava crops in Nigeria. A grant awarded from
Innovate UK has allowed the identification of 20 farms in various regions of the country, where abespoke technology will be installed. The project kicked off at the beginning of June 2020 andcassava seeds are currently being planted.
INTRODUCTION
Current technology development represents opportunities which can benefit a range ofactivities. In agriculture, technology has been deployed to inform farmers and support them in their
daily tasks. Historically, and for economic reasons, most of the technology has been dedicated tolarge farmlands, and industrial-scale agriculture. An example is provided by The Netherlands,
where government policy is being applied to greenhouses complexes of large area, ‘some of themcovering 175 acres’i. However, this large-scale model cannot apply in the shorter term tosmallholder farms in emerging countries, as the level of investment and the associated return ofinvestment do not consider the eco-system in which farmers in emerging countries operate.
After reviewing the factors affecting farming in emerging countries, the study will review
the development and implementation of Farmer Charlie, a smart agritech hub, specificallydesigned for smallholder farmers. The first lessons learned from the research will also bepresented.
MATERIALS AND METHODSOn being awarded an honorary doctorate in January 2020, the President of the African
Development Bank Akinwumi Adesina stressed the crucial role of agriculture in fostering foodsecurity and global development in Africa as well as worldwide. ‘Agriculture is the most important221

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profession and business in the world,’ Adesina statedi. Because 65 per cent of the arable landremaining to feed the world is in Africa, investment in agriculture should be an essential priority
not only for the continent, but for people all over the world.
The 2019 Report by the Oxford Business Group highlighted, too, that ‘To date, agriculture
is still one of the most important economic sectors in Africa, accounting for over 15% of the region
GDP. Notably, family farming accounts for 90% of agricultural activities’ii.
More specifically, the Global Panel on Agriculture and Food System for Nutrition's
Foresight 2.0 report has pointed out that ‘Smallholder activities in agriculture still contribute animportant share of food production in sub-Saharan Africa [...]. Initiatives aimed at shifting relative
product prices, supporting technological innovations, investing in market infrastructure to reducetransactions costs, facilitating access to information and credit, [...] must all take the needs andconstraints of smallholders into accountiii.’
The devastating impact of COVID-19, unfortunately, contributed to worsen the situation,
increasing the need for ensuring farmers of emerging countries, the availability of nutritional andsustainable food, and reaffirming the importance of efficient food systems, which ‘do not provide
only food but also jobs, income, infrastructure, skills (socio-economic outcomes) and ecologicalservices (environmental outcomes)’iv.
Nigeria is considered one of the countries where the need for food security is most felt. It
is one of the largest African countries, with a population of 201M, rapidly increasing over the last
five years because of a very rapid birth rate. Due to the availability of vast arable land and the
involvement of 70 per cent of the population in agricultural activities, the Federal Government has
been trying to intensify the expansion of the agricultural sector during the past few years. However,observers remark that this effort has lacked to address fundamental issues of mechanization,
irrigation, seeds, extension service, insurance, research and development in the sectorv.
In Nigeria, 20% of the country GDP comes from agriculture and a conspicuous 88%
smallholder farmers cultivate their own land, first for their own subsistence, secondly to sell partof their production to the market. Most of them own less than 0.5 ha, and yield is often low.
Previous research on cassava and maize has shown that despite the huge potential for sustainableproduction, quality nutrition, waste recycling and export, opportunities are still unexploited. For
instance, Nigeria is the highest producer of cassava in the world (59M tons - 20.4% world share in2017), the second by consumption, but in 2017 it recorded a small total export value (USD 1.25M),
outshone by Thailand USD1.19billionvi. Nigeria’s average yield rate was 8.7 t/ha in 2017, while itis 23.07 t/ha in Thailand. Factors contributing to this gap include choice of stems and fields,
improper usage of fertilizer, high postharvest losses, small-scale farming without possibility tocommercialise the production, cyclical markets gluts, policy inconsistencies, low level credit
available, high transaction costs.
Information and information sharing have been therefore identified as a pivotal necessity
for smallholder farmers to achieve better quality and quantity production, to be aware of their own
soil conditions, manage the impact of weather and climate change and sensibly use productivity
tools (fertilisers, pesticides), in addition to dealing with distributors and agents—who are often
perceived as harmful to the smallholders’ interests. However, in a land where around 40 million
people have not had access to any form of telephony servicesvii and the rural communities are stillto enjoy the benefits of accessing 3G and 4Gviii, the challenge of providing information throughconnectivity and delivering useful data is huge.

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RESULTS AND DISCUSSION
Farmer Charlie was conceived after researching and assessing the emerging countries needfor an efficient and cost-effective agricultural management. Farmer Charlie is developing an
economical, smart agriculture hub, providing farmers with broadband internet connectivity,
information on weather and land conditions and an app store filled with apps relevant to farming.
Farmer Charlie works in partnership with academic researchers and farmers to conceive, test andimplement intelligent and precise farming technology. Farmer Charlie aims to increase the yield's
productivity and quality. Vertically integrated, the service includes sensors, satellite and IoTtechnologies. It is cost-effective and modulable, which means that it could be adapted to the needsof different crops, soil and countries, but also expanded to include transaction services, insurance
and market-linkage through a digital, vertically integrated, and cost-effective approach. Besides,
it permits for a sustainable use of resources, which in turn creates an impact towards environment
protection and decreased waste production.
Farmer Charlie leverages on advances in remote sensing, data and analytics and mobile
phone technology to connect farmers and provide them with information about weather and theirfields' conditions and features. The precise agronomy tips enable farmers to better manage
resources (water, fertilizer, pesticides) and maximize their yield potential at a dramatically lower
cost than current solutions. Farmer Charlie system is described in Figure 1 below.

Figure 1. Farmer Charlie system
Farmer Charlie is currently in the concept development phase and it is being tested in the
Nigeria thanks to the funding provided by the United Kingdom Research and Innovation (UKRI)
organization, further to winning the Agri-tech Catalyst Round 9 call. Collaboration with the local
partner Amolexis and with University College London produced a proposal inspired by the UN
SDG criteria. The aims are multiple:• improving the agricultural productivity and incomes of small-scale food producers, inparticular women, indigenous peoples, family farmers,• providing inputs, knowledge, financial services, markets and opportunities for value
addition• bringing connectivity, gathering information on field, to be transmitted via a locallydeveloped application.
Ultimately, Farmer Charlie is not only working towards improved food security andeconomic sustainability of cassava farmers, but also increasing the cassava yield. Last, but notleast, the project involves an increase in the number of farmers using improved connectivity withsmartphone applications.

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Using the principles of Theory of Change, Farmer Charlie has been designed for creating
an impact on food security. Key performance indicators of success are based on the sustainabilityof the solution and its adoption by the smallholder farmer community.
The project is on-going, and the first results are expected early 2021. However, somelessons are already being learned. When researching to create an impact, the use of technology bysmallholder farmers must take into consideration the current ecosystem in which Farmer Charlie’s
hubs are deployed.
Farmer Charlie needs to be adapted to the users and situation of the local communities.
Our strong links with the local partners is undoubtedly effective in considering features and pains
affecting local farmers. However, our project includes a subsection on Human Centered Design,where market research and activities will be performed to properly assess the needs of local
stakeholders, adapt the design to the existing infrastructure (for instance, the user interface will be
designed using a human-centered design technique to improve adoption), explain the integrationof Farmer Charlie and possibly provide some training to allow smallholder farmers to make themost of the service.
Consideration must be given to infrastructure; this includes the road access, as well as theavailability of power sources, irrigation and the availability of broadband internet. The roadinfrastructure is an important element which will affect the logistics and transport of the hub to thefarms, as well as the maintenance of the equipment. Early on, Farmer Charlie was designed to be
self-sustainable, and therefore is connected to batteries and solar panels, which make all theelectronic equipment fully autonomous. In terms of irrigation, only one percent of Nigerian
cropland is currently irrigatedix. While Farmer Charlie is not yet including an independent
irrigation system, the IoT sensors will measure soil humidity, and a weather station to inform
farmers about weather conditions, which will then support the farmers’ decision to adapt theirpractices according to the new information gathered. In terms of broadband internet connectivity,
Farmer Charlie’s hub will be available for uses beyond agriculture. Given the connection isavailable 24 hours, it is possible to use this connection for education, communication, and otherneeds.
CONCLUSIONS
While final research results will be available in summer 2021, the value of Farmer Charlie’s
smart hub has already been demonstrated. The challenges faced in the deployment of the solution
are being solved.
The trial is providing useful insight on the benefits of the smart agritech hub, and at thesame time, informs on logistics challenges. It is also an important opportunity to ensure theadoption of Farmer Charlie by farmers.
REFERENCES
I. Viviano. 2017. National Geographic, The Netherlands has become an agricultural giant byshowing what the future of farming could look like.
II. African Development Bank Group. 2020. ‘Agriculture is the most important business in theworld’.
https://www.afdb.org/en/news-and-events/press-releases/agriculture-mostimportant-business-world-african-development-bank-president-akinwumi-adesina-tellsstudents-33785, last accessed 13/11/2020.

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III. Oxford Business Group. 2019. The Report. Agriculture in Africa 2019. 3-4.
IV. Global Panel on Agriculture and Food System for Nutrition. 2020. Future Food Systems: for
V.
VI.

VII.
VIII.

IX.
X.

People, Our Planet, and Prosperity. Foresight 2.0. 80.
Dury S, et al. [eds.]. 2019. Food Systems at Risk. New Trends and Challenges. 17.
Business Day Nigeria. 2020. Prospects. Challenges of Nigeria’s Agriculture in 2020.
https://businessday.ng/agriculture/article/prospects-challenges-of-nigerias-agriculture-in2020/, last accessed 13/11/2020.
Otekunrin-Sawicka. 2019. Cassava, a 21st Century Staple Crop: How can Nigeria Harness Its
Enormous Trade Potentials? Acta Agriculturae Slovenika 3(8):198.
The Guardian. 2019. Telecoms operators tasked on rural connectivity in Nigeria.
https://guardian.ng/technology/telecoms-operators-tasked-on-rural-connectivity-innigeria/, last accessed 13/11/2020.
Ibidem.
You, Takeshima, Xie. 2018, International Food Policy Research Institute Blog, Cultivating
growth in Nigerian agriculture with small-scale irrigation.

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#7564 SCALING PRECISION AGRICULTURE IN WEST AFRICA SMALLHOLDERIRRIGATION AND WATER MANAGEMENT SYSTEMS
Adebayo OlubukolaOke1* and Vincent Aduramigba-Modupe1
Institute of Agricultural Research and Training, Obafemi Awolowo University, Moor Plantation,
Ibadan, Nigeria*Correspondence author: E-mail: bayooke1410@gmail.com; +234-8025245305
ABSTRACTThe advent of Precision Agriculture (PA) is changing the crop farming productivity aroundthe world. The principles underline PA ensure that the inputs required for the management of soil,
water and crop agronomy are supplied in a precise way such that what is needed across the fieldlandscape is actually supplied resulting in optimum yield. The benefits of precision agriculture
include improved food security through increases in water and nutrient use efficiency, and timely
management of activities such as weed control and harvesting. The practise of PA could be
beneficial across the commercial – large scale – as well as smallholder production systems
including especially in the water management and irrigation sector. Smallholder irrigated crop
production across west Africa accounts for significantly more areas in Ghana, Nigeria, Mali,
B/Faso and Senegal than the conventional large irrigation schemes. Scaling the practice of PA
specifically in large scale commercial and smallholder irrigation systems faces different challenges
thus requiring different approaches. Distinction is made between large scale irrigation (with large
land holdings) either government or private and smallholder irrigation with farm holdings between
0.1 – 1 .0ha. Farmers technical and financial capacity are among the major critical factors limiting
the scaling of irrigation technology. These factors further limit the deployment of PA in the west
Africa irrigation sector. Application of the conventional PA system requires machine controls forfield equipment guide with monitors, sensors and GPS systems to achieve variable rate application.
This is feasible in large scale private irrigation systems where PA equipment uses Centre Pivot,
Traveling Sprinkler Systems and drip technology water application systems. However, in small
holder irrigated farms, surface irrigation, bucket, hose sprinkling, conventional sprinklers and low
scale drip are predominant. Therefore, contrary to the use of fixed interval irrigation regime,
blanket fertilizer recommendation and overirrigation which characterises smallholder irrigation
practices, PA is adaptable to the smallholder irrigation as well. Scaling PA in West Africa
smallholder system is without automation or sophisticated equipment. Basic tools like TDR
moisture meter, Wetting Front Detector and simple soil test kits to are required to guide irrigation
scheduling and ensure adequacy of nutrient application. The Chameleon sensor is anotheremerging useful tool to implement PA in smallholder irrigation system. This enables efficientwater use, reduced nutrient leaching and improve crop yield. The use of these tools is still very
low across the West Africa with only few farmers in Ghana that have been trained in the use of
Wetting Front Detector and Chameleon sensor. There is need to scale down and digitalize soil
information from regional project like AfSIS and make it available to smallholder farmers via
mobile communication system to improve on fertilizer uses. Scaling these tools for efficient
deployment of PA in irrigation systems at smallholder manual scale will involve increased
awareness of PA benefits as a driving incentive, capacity building, bringing the tools closer to theend users and a business model that make such tools affordable for small holder irrigation farmers.

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INTRODUCTION
The need to ensure that inputs in crop production system are efficiently allocated, utilized
and deliver highest benefit necessitated the concept of precision agriculture (PA). The PA refers
to an integrated crop management system that attempts to match the kind and amount of inputs
with actual needs of crop for small area within a field (Davis et al., 1998). Variability in soil
characteristics specifically and the agricultural field generally are well known. Farmers had always
attempt to do some form of site-specific crop management although the idea of using electronic
information technology to automate that process is relatively recent. (James and Bruce, 2019). As
observed by Whelan and MacBratney (2000). PA is an attempt to match resource application andagronomic practices with soil and crop requirements as they vary in space and time within a field.
According to Ncube et al., (2018) a recent addition to the principles of PA is the precision
irrigation. Precision irrigation focuses on ‘differential irrigation’ treatment of field variation as
opposed to the ‘uniform irrigation’ treatment that underlies traditional irrigation management.
Precision irrigation saves water and reduces costs by applying only the optimum amount ofirrigation to individual plants or small areas within a field, while the traditional practice takes a‘whole-field’ approach (Smith and Baillie 2009) in which specific amount of water is given to theentire field irrespective of soil moisture variation at the time of irrigation. The major benefits of
PA is in substantial savings in agricultural inputs. Farming inputs are applied as needed in thecropping system (Ncube et al., 2018; Takacs-Gyorgy et al. 2013). Precision farming uses
technology to improve efficiency. It offers benefits for yields, profits and the environment (James
and Bruce 2019; Silva et al., 2007). The PA including precision irrigation comes with investment
in machinery particularly when deployed at the conventional commercial or at the scale beyond
the smallholders. Although, studies have shown that farmers may consider this cost too expensive(Reichardt and Jürgens 2009) nevertheless, PA can save up to 60% in agro-chemical and 30% inmineral fertilizers (Ncube et al., 2018). Another major advantage of PA is what Isioye (2013)
observed as the cyclic nature of the processes inputs and yield measurement resulting in variable
rate application. The operation of PA depends on the use of control systems of crop, soil andpositioning sensors, machine controls responsible for variable rate application, computer-based
systems. Conventional PA requires integration of three elements: (1) positioning capabilities(currently, global positioning system or GPS) to know where equipment is located; (2) real-timemechanisms for controlling nutrient, pesticide, seed, water or other crop Production inputs; and(3) databases or sensors that provide information needed to develop input response to site-specificconditions (Abdusalam, 2019; Evandro et al., 2007).
This paper examines the level of PA practices in the smallholder irrigation systems in West
Africa and suggest the levels at which PA could be deployed.
MATERIALS AND METHODSThis work present, firstly a review of literatures on PA practices in irrigation and water
management among the smallholder farmers in West Africa. A distinction is made between
conventional PA practices typically deployed in large farms with machine control equipment asagainst field equipment and tools used in smallholder systems to guide specific field operations.
Secondly, the interactions with smallholder farmers in Burkina Faso, Ghana, Mali and
Nigeria, in relation to their approach to irrigation and water management and how much ofprecision agriculture principles and practices have been integrated.

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Precision Irrigation Agricultural Practices
The deployment of precision irrigation in public large and commercial irrigation schemes wereconsidered. The following were considered in relation to application of variable rate technolgoies
and its components- Irrigation methods and extent of precision irrigation practice- Equipment types deployed- Knowledge and capacity to efficiently use the systems- Constraints and limitation to the scaling of the precision irrigation agricultural practices
and how those constraint could be overcome
The study identified technology access related issues to improve the the deployment of precision
irrigaiton practices in the West Africa farming systems.
PA Related Technologies in Smallholder Irrigation Systems
The observations and evaluation present were conducted using field survey, stakeholders’meetings, innovation platforms and personal interviews. This were part of different agricultural
water management and irrigation extension programmes at different times. The experiences of the
farmers in smallholder systems confirms the imperativeness of deployment of precision
agricultural practices to enhance productivity of the farming systems. The following are the key
issues focused on in the various meeting and evaluation of smallholder irrigation works- Size of farm holdings- Irrigation practices – types and methods- Irrigation equipment- Constraints to increased irrigation practices- Knowledge and capacity
Investigations include efforts of the farmers to ensure high productivity in water application suchas the irrigation schedule practices and nutrient application methods. The contribution of thesestrategies to the principles and practices of precision irrigation were identifiedb
RESULTS AND DISCUSSIONS
Precision Agriculture in West Africa
The conventional PA is gradually gaining recongnition in across the Africa agriculturallandscapes. Howeever, Olalekan (2013) while agreeing that the various components of aconventional PA system may be available, the practice is not yet feasible and indeed negligible(James and Bruce, 2010) within the smallholder systems.
The components required to ensure variable rate application are not yet available to farmers
at the smallholder scale either in the general agricultural practices or for irrigaiton in the countries
of west africa.(Abdusalam 2019; Olalekan 2013)
Agricultural Water Management and Irrigation System Across West Africa
Irrigation practices across west african countries falls under 3 major categories: Formal
Public Irrigation schemes, Commercial large scale schemes and Informal smallholder private
irrigation or farmer led irrigation practices. This structures is what is observed in Ghana, Nigeria,Mali, Burkina Faso as well as in other sub-Saharan countries (Namara et al., 2014).).

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Public Large Irrigation Schemes
The large schemes in Nigeria, Ghana, Mali and Burkina Faso are governement managed
by a specific agency of Government (Table 1). The main approach has been to share the large
schemes as small holdings among farmers who produced in the scheme crops of their choices.
With the exception of Mali, which has equipped over 40% of her irrigation potential, the actualarea under irrigaiton in most other west african countries is about 12% (Namara and Sally, 2014)
Table 1. Irrigation potential in selected West African countries (Namara and Sally, 2014)
Country Irrigation Development Agency

Estimated% of potential
Potential
area equipped for
irrigatedarea irrigation(1,000ha)
Burkina Ministry of Agriculture and Irrigation 165
15.2
Faso
Development Burkina Faso (Ministère del'Agriculture et des Aménagements
Hydrauliques)
Ghana
Ghana Irrigation Development Agency
1900
1.6
Mali
Office Du Niger
566
41.7
Nigeria River Basin Development Authorieties
2331
12.6
In the public large schemes, irrigation water management are difficult to be synchronized among
the water users therefore, most of the schemes uses the fixed interval irrigation scheduling,wherein, water is supplied at specific interval (e.g. 5 or 7 days) and the farmers have to irrigate
irrespective of their specific field. Achieving irrigation scheduling or precision water application
based on specific field need is difficult in the schemes. Also, there are no incentives for watersaving practices since many of these schemes across West Africa runs as public enterprises where
farmers hardly pay what is commensurate to the volume of water use. (Yanbo, et al., 2019) Thishas been observed in Ghana, Mali and Burkina Faso (World Bank 2007).
Commercial Irrigation Farms
Commercial private irrigation farms are becoming a major part of irrigation development
efforts in the West Africa. These have been observed in Burkina Faso, Ghana, Mali and Nigeria
where Rice, Wheat and Sugar cane cultivation are taking the centre stage. A number of these largecommercial farms use sprinkler and centre pivot systems. These irrigation methods of have goodpotential for the integration of PA infrastructure in their systems. The advantages in the use of PI
with these equipment have been identified by Best and Duke 2001. The integration of variable ratetechnology which could be possible with the centre pivot irrigation system (Figure 3), this is not
feasible with the basic equipment and irrigation methods common – basin, furrow, hose sprinkling- in the smallholder private irrigation systems. (James and Bruce, 2010)
Smallholder Irrigation Practices
Outside the large public irrigation schemes across the West Africa, there have a been along-standing practice of crop production off season in the inland valleys, flood plains and withinlandscapes with shallow groundwater. These are farm holdings between 0.2 – 1ha (Namara et al.,2014).

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Often, irrigation scheduling and efficient water appication methods are not quickly
adoptable in the smallholder farmings system. This is because, the reason for the extra cost ofequipment to better manage water use are not ‘convincing’ enough to farmers.

a) Map of application of water based oncrop requirements in the different parts of
the area under irrigation.

b) Example of aerial view and subdivision ofirrigation areas based on the variable rateapplication system installed.

Figure 1. Center pivot variable rate irrigation system (Best and Duke, 2001)
Plausible Precision Irrigation Practices in Smallholder Irrigation Systems
Inclusion of conventional PA principles, equipment and practices in smallholder irrigaiton
practices are very remote. Smallholder irrigation farmers are often concerned about supplying
water to meet the crop needs. This is done without measurement or control. Precision irrigation
may not be easily integrated in the smallholder irrigation except some practices that can ensure
precise and efficient water application are adopted. Few practices that are adaptable to smallholder
system with potential to improve productivity of irrigation. These include:• Irrigation scheduling: Contrary to what obtains in the public irrigation schemes, improving
irrigation scheduling will allow water to be applied when crops need it.
• Efficient irrigation methods: To improve on PI in smallholder system, the immediate approach
may include deploying more efficient irrigation methods. Deployment of more efficientirrigation methods such as sprinkler and drip irrigation systems, may increase precise
application of irrigation water• Soil moisture depletion management: Smallholder irrigators often depend on their personal
judgement, experience and observation of soil and plant stresses to determine when to irrigate.
The deployment of small field level moisture monitoring tools such as Time Domain
Reflectometry (TDR) Probes, Chameleon Sensor (Stirzaker, 2005) and Wetting front detector(Stirzaker, 2003) will go a long way in monitoring moisture depletion and ensuring precise
water application. (Ncube et al., 2018). This tool has great potential to help smallholder farmerin the guide against overirrigation and ensuring an application of appropriate depth of irrigationwater.
Soil Nutrient Monitoring, Application and Precision Agriculture Practices
The work of the African Information System (AfSIS) which set to provide Spatial
predictions of soil macro and micro-nutrient content across Sub-Saharan Africa at 250 m spatial
resolution and for 0–30 cm depth interval is presented (Hengi et al., 2017). This work has potential230

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to assist in making reliable and precise nutrient levels available for major macro and micronutrient
available to guide nutrient application within the smallholder fields. The AfSIS focuses on 15
targets nutrients: These types of information if made available at the farm scale level, such that
farmers can actually apply site specific nutrient as related to the crop and field will be a step higher
in the deployment of PA in Africa.
REFERENCES
Abdulsalam A. 2019. Benefits of Precision Agriculture in Nigeria. London Journal of Research in
Science: Natural and Formal 19(2):29-34.
AGCO. 2005. http://www.fieldstar.com/agco/FieldStar/FieldStarUK/System/DataCollection.html
Best SC, Duke HR. 2001. Spatial distribution of water and nitrogen application under center pivot
sprinklers. Proceedings of Central Plains Irrigation course and exposition. Kearney,
Nebraska, Central Plane Irrigation Association. p. 58-65.
Bruno B, Jean-Yves J, Hervé O, Abdoulaye D, Boubacar B. 2014. Irrigation Investment Trends
and Economic Performances in the Sahelian Countries of West Africa. In Namara RE,
Sally H. (Eds.). Proceedings of the Workshop on Irrigation in West Africa: Current Status
and a View to the Future, Ouagadougou, Burkina Faso, December 1-2, 2010. Colombo, Sri
Lanka: International Water Management Institute (IWMI). 373p. doi:10.5337/2014.218
Davis G, Casady WW, Massey RE. 1998. Precision agriculture: An introduction. Columbia:
Extension publications, (MU).
Evandro C, Francisco de AC, Daniel M, Emilio R. 2007. Precision Agriculture: New tools toimprove technology management in agricultural enterprises. Perspectives. Technology andInnovation
Hengl T, Leenaars JGB, Shepherd KD, et al. 2017. Soil nutrient maps of Sub-Saharan Africa:
assessment of soil nutrient content at 250 m spatial resolution using machine learning. Nutr
Cycl Agroecosyst. 109: 77-102. https://doi.org/10.1007/s10705-017-9870-x
Isioye OA. 2013. Precision Agriculture: Applicability and Opportunities for Nigerian Agriculture
MiddleEast
Journalof
Scientific
Research
13(9):1230-1237.
doi:
10.5829/idosi.mejsr.2013.13.9.1004
James L, Bruce E. 2019. Setting the Record Straight on Precision Agriculture Adoption. Agron. J.
111:1552-1569. doi:10.2134/agronj2018.12.0779
James LD, Bruce E. 2010. The Search for the Killer App: Precision Farming in Africa. Georgetown
Journal of International Affairs 11(2):107-116.
Ncube B, Mupangwa W, French A. 2018. Precision Agriculture for Food Security in Africa. In P
Mensah, et al., (eds). Systems Analysis Approach for Complex Global Challenges
https://doi.org/10.1007/978-3-319-71486-8_9
Namara RE, Sally H. (eds). 2014. Proceedings of the Workshop on Irrigation in West Africa:
Current Status and a View to the Future, Ouagadougou, Burkina Faso, December 1-2,
2010. Colombo, Sri Lanka: International Water Management Institute (IWMI). 373p.
doi:10.5337/2014.218
Reichardt M, Jürgens C. 2009. Adoption and future perspective of precision farming in Germany:
Results of several surveys among different agricultural target groups. Precision Agriculture10(1):73-94.

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Silva CB, do Vale SMLR, Pinto FA, Müller CA, Moura AD. 2007. The economic feasibility of
precision agriculture in Mato Grosso do Sul State, Brazil: A case study. Precision
Agriculture 8(6):255-265.
Smith R, Baillie J. 2009. Defining precision irrigation: A new approach to irrigation management.
In Irrigation Australia 2009: Irrigation Australia Irrigation and Drainage Conference:
Proceedings (pp. 1-6). Irrigation Australia Ltd.
Stirzaker R. 2005. Managing irrigation with a wetting front detector. UK Irrigation 33:22-24.
Stirzaker RJ. 2003. When to turn the water off: Scheduling micro-irrigation with a wetting front
detector. Irrigation Science 22:177-185. https://doi.org/10.1007/s00271-003-0083-5.
Takács-György K, Lencses E, Takács I. 2013. Economic benefits of precision weed control andwhy its uptake is so slow. Studies in Agricultural Economics 115(1).
World Bank. 2002. Investment in Agricultural Water for Poverty Reduction and Economic Growthin Sub-Saharan Africa. Synthesis Report
Whelan BM, McBratney AB. 2000. The “null hypothesis” of precision agriculture management.
Precision Agriculture 2(3):265-279.
Yanbo L, Jinming H, Weijiao D, Liyun Z, Daniel OS, Hong Z. 2019. Success Factors of Irrigation
Projects Based on A "Public–Private Partnership" Model in A Mountainous Area: A Case
Study in the Nujiang River Valley, China Sustainability 11: 6799. doi:10.3390/su11236799
Yara. 2005. The three stages of the N Sensor System. Available at http://fert.yara.co.uk/en/crop
fertilization/advice_tools_and_services/n_sensor/the_three_stages/index.html

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#7639 CLOSING THE YIELD GAP IN AFRICA THROUGH SOIL ATTRIBUTE
MANAGEMENT USING REMOTE SENSING AND PRECISION AGRICULTURE
APPROACHES AT THE FIELD SCALE
K. Khechba1*, A. Laamrani1, A. Chehbouni1,2, D. Dhiba1,3, K.Misbah1
Centre for Remote Sensing Applications (CRSA), Mohammed VI Polytechnic University(UM6P), Ben Guerir, Morocco; 2 Centre d’Etudes Spatiales de la Biosphère (CESBIO/IRD),
Université de Toulouse, Toulouse cedex 9, France; 3 International Water Research Institute(IWRI), UM6P, Ben Guerir, 43150, Morocco.
* keltoum.khechba@um6p.ma +212616215900
1

ABSTRACT
Intensive agriculture is a common practice to meet the high demand for food, leading to
optimal use of fertilizers, water and agrochemicals. Therefore, it is necessary to identify andunderstand the spatio-temporal distribution of soil attributes in order to optimize the use of
agricultural inputs (e.g. fertilizer, seed rate) at a specific site. For these purposes, the quantitative
assessment and mapping of important soil attributes such as soil texture, soil organic carbon(SOC), soil nitrogen (N), and soil moisture content (CM) on the ground are essential. Since fieldmeasurements by conventional techniques of this soil attributes (i.e., N, , P, K, SOC) are difficult
to perform and time-consuming, there is a need for an accurate and rapid approach for themeasurement of soil attribute levels at the farm scale. Remote sensing sensors placed on an
Unmanned Aerial Vehicle (UAV) is a rapid, cost-effective and a method that meet the requirementof spatial, spectral, and temporal resolution to quantify and monitor soil attributes (i.e., N, P, K,
SOC) yield-related over agricultural land. In this study, soil attributes ground measurements and
UAV imagery will be collected at agricultural fields from Morocco. The UAV spectral data will
be collected using multispectral, hyperspectral, and thermal sensors and datasets generated by thisproject will support algorithm development. In terms of data statistical analysis, a number oftypical machine and deep models will be explored to perform remote sensing image classification,
including: Partial Least Squares, Random Forest, Support Vector Machine. The results of thisstudy are expected to support the use of remote sensing derived soil attributes maps by farmers
and managers to make precision farming decisions about how to properly manage nutrients andwater in the soil for optimal yield.
Keywords: remote sensing, machine learning soil fertility, precision agriculture, yield gap,
UAVs
INTRODUCTION
In Africa, which has the most population growth in the world, the agricultural system ischaracterized by the predominance of smallholder farmers. In most countries, smallholder
agricultural systems are inefficient, and yields fall short of their potential; this phenomenon is
known as yield gaps. Yield gap is defined as the difference between potential yield and actual
yield (Lobell et al., 2009) and is an inevitable method to improve yields while decreasing theenvironmental impacts of agricultural systems. The variability of yields is strongly controlled by233

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fertilizer use, irrigation management, and climate impact. Consequently, quantitative assessmentand mapping of important soil attributes such as soil nitrogen (N), soil moisture content (MC),Phosphorus (P), Potassium (K), Soil organic Carbon (SOC), and soil texture (i.e., clay, sand, silt
contents) on the ground are essential to tackle the yield gap closing.
The use of traditional soil laboratory analysis techniques is known to be costly and time consuming
and requires efforts. To overcome these constraints, rapid, accurate and inexpensive methods ofsoil attributes measurement and monitoring are needed.
Laboratory Visible (VIS), infrared (IR), and Shortwave near infrared (SWIR) spectroscopy
are alternative methods to laboratory analysis of soil chemical parameters, including SOC (Jiang
and all, 2016), N, P, K, pH (Viscarra Rossel et al., 2006) and some physical parameters such as
soil structure, density apparent and texture (Virgawati et al., 2018). Although laboratory
spectroscopy has resulted in robust and accurate estimates of soil properties, this technique only
provides an estimate at the location of the sampling point and geostatistical techniques should be
used to derive continuous spatial information at large scale
The use of hyperspectral remote sensing sensors on board drone-based platforms (also known as
unmanned aerial vehicles, UAVs) has introduced new opportunities for providing spatially explicit
spectral information detailed over multiple soil properties, including SOC content (Laamrani etal., 2019). This study is to develop and test methods to quantitatively generate accurate soil cover
attributes needed for spatialized yield gap analysis, using laboratory spectroscopy and airborne
hyperspectral data.
MATERIALS AND METHODS
Study Area
The study will take place in the region of Sidi Rahal in Morocco. The region ischaracterized by a semi-arid Mediterranean climate, with an average annual precipitation of around
250 mm and an evaporative demand of around 1600 mm per year according to the FAO method(Jarlan et al., 2015). The land is flat with an elevation of 550 m above sea level, and the soil ischaracterized by a fine texture with 47% clay, 33% silt and 18.5% sand. Agricultural fields, mainly
irrigated and rainfed wheat crops, are dominant (Ezzahar et al., 2009).
Soil Sample Collection and Soil Attribute Measurements
For this study we have selected six soil attributes relevant for soil characterization: Total
N, SOM, texture, P, and K. The total N will be measured using the kjeldahl method. SOM wasdetermined using the Walkley-Black method (Walkley & Black, 1934) and used to determine SOC
content using formulas in the literature. Soil texture (i.e., particle size distribution) will be obtainedby the pipette method (ISO 11277:2009). Available P will be extracted with a combined solutionof 0.1 M HCl and 0.03 M NH4F. Available K will be determined using Ammonium acetate
NH4OAc (1 M pH=7)
Spectral Measurements and Analysis
In this study we will collect spectral data from soil samples in the field and Laboratory
using a FieldSpec 3 spectroradiometer sensor (Analytical Spectral Devices Inc.). FieldSpec 3 has
three detectors and covers a wider spectral range (350–2500 nm) with a band resolution (width) of
3 nm wide in VNIR and 10 nm in SWIR.

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Field reflectance measurements will be collected an airborne unmanned aerial vehicle(UAV) with sensors developed for deployment on UAV platforms. The images will be captured
in sunny weather with clear skies over the soil samples and an average spectrum of eachhyperspectral image will be calculated. The UAV-derived data will be collected on the same day
as ground samples observations for optimal correspondence.
Statistical Analysis and Modeling
Identification of soil nutrients analysis data by several statistical parameters such as mean,
maximum, minimum and median used to describe the central tendency and distribution of thevalues of soil parameters. The standard deviation, range and variance used to measure thedispersion between soil parameters. The skewness and kurtosis to measure the asymmetry of soil
parameters. The use of a set of different models to quantify the relationtionship between soil
attributes and the corresponding spectral reflectance measured. This ensemble will consist ofdifferent methods such as multivariate regression techniques, random forest regression (Breiman,
2001), support vector machine regression (Karatzoglou et al., 2004), and partial least squares(Mevik et al., 2015). The models will be evaluated by the following indices: coefficient ofdetermination (R2) and root mean square error (RMSE)EXPECTED RESULTS
The results of this study are expected to demonstrate the ability of different precision
agriculture and modeling approaches to analyses and predict soil attributes in Morocco using UAV
spectral data. The study will also show the potential of remotely sensed data to build accurate mapsof soil attributes, making it easier for farm managers to make decisions about how to properly
manage nutrients and water in the soil and will ultimately lead to reducing the yield gap.
REFERENCES
Breiman L. 2001. Random forests. Mach. Learn. 45:5-32.
Crucil G, Castaldi F, Aldana-Jague E, van Wesemael B, Macdonald A, Van Oost K. 2019.
Assessing the Performance of UAS-Compatible Multispectral and Hyperspectral Sensors forSoil Organic Carbon P*rediction. Sustainability 11:1889.
Ezzahar J, Chehbouni A, Er-Raki S, Hanich L. 2009. Combining a Large Aperture Scintillometerand estimates of available energy to derive evapotranspiration over several agricultural fields
in semi-arid regions. Plant Biosyst. 143:209-221.
Jarlan L, Khabba S, Er-Raki S, Le Page M, Hanich L, Fakir Y, Merlin O, Mangiarotti S, Gascoin
S, Ezzahar J, et al. 2015. Remote sensing of water resources in semi-arid Mediterraneanbasins: The Joint International Laboratory TREMA. Int. J. Remote Sens. 36:4879–4917.
Jiang Q, Chen Y, Guo L, Fei T, Qi K. 2016. Estimating soil organic carbon of cropland soil at
different levels of soil moisture using VIS-NIR spectroscopy. Remote Sensing 8(9).
Karatzoglou A, Smola A, Hornik K, Zeileis A. 2004. kernlab-an S4 package for kernel methods
in R. J. Stat. Softw. 11:1-20.
Laamrani A, Berg AA, Voroney P, Feilhauer H, Blackburn L, March M. Dao PD, He Y, Martin
RC. 2019. Ensemble Identification of Spectral Bands Related to Soil Organic Carbon Levels
over an Agricultural Field in Southern Ontario, Canada.

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Lobell DB, Cassman KG, Field CB. 2009. Crop yield gaps: Their importance, magnitudes, andcauses. Annu. Rev. Env. Resour. 34: 179-204.
Mevik B-H, Wehrens, R. 2015. Introduction to the pls Package. In Help Section of the “Pls”
Package of R Studio Software; R Foundation for Statistical Computing: Vienna, Austria, pp.
1-23.
Virgawati S, Mawardi M, Sutiarso L, Shibusawa S, Segah H, Kodaira M. 2018. Mapping thevariability of soil texture based on VIS-NIR proximal sensing. Journal of Applied Geospatial
Information 2(1):108e116.
Walkley AJ, Black IA 1934. Estimation of soil organic carbon by the chromic acid titration
method. Soil Sci. 37: 29-38.

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#7646 POTENTIAL PRECISION AGRICULTURE PRACTICES FOR HIGH QUALITYAND EFFICIENT FRUITS AND VEGETABLES PRODUCTION IN WEST AFRICA:A MINI REVIEW
Nicodeme V. Fassinou Hotegni1, Leocade Azonhoumon, Enoch G. Achigan-Dako
Laboratory of Genetics, Horticulture and Seed Science (GBioS), Faculty of Agronomic Sciences,
Abomey-Calavi University, 03 BP 2819 Cotonou, Benin1
nicodemef@gmail.com
ABSTRACTDuring the last decades, efforts have been made to increase the yield and the quality ofmajor fruits and vegetables but still, farmers mainly those in West African countries are struggling
to close the yield gap. Precision agriculture has been reported in most developed countries as a setof tools integrating information and technologies for efficient crop production. Over the decades,
scholars have been skeptical about the development and implementation of precision agriculture
in West Africa mainly because of the type of agriculture, which is smallholder driven. The present
paper aims at grabbing existing precision agriculture technologies in the West Africa to adapt andscale them up in the fruit and vegetable sector. A systematic literature review approach used. Outof the 353 papers pre-selected, 71 scientific papers were finally considered. It came out that there
is a lack of research on precision agriculture related to fruits and vegetable crops. The existing
technologies have been widely documented for field and cash crops; some technologies wereregion-specific. Technologies such as seed priming and seed treatment (13% of selected papers)
and conservation agriculture (12% of selected papers) are more specific to the semi-arid West
Africa whereas site-specific fertilizer management (25% of selected papers) is mostly present inhumid West Africa. However, low-cost mechanization (6% of selected papers), fertilizer microdosing (21% of selected papers) and precision water management (23% of selected papers) areused throughout the West African region. A multi-steps model is proposed for a wide adoption ofthese technologies for high quality and efficient fruits and vegetables production in West Africa.
New research initiatives need to be undertaken to identify other low-cost technologies such as
hand-held sensors which could be used as a decision support tools by smallholders’ farmers.
Keywords: Precision horticulture, fruits and vegetables, fertilizer micro-dosing, precision water
management, site-specific fertilizer management, West Africa
INTRODUCTION
Fruits and vegetables are the main products highly demanded in West African horticulture(Bouët & Odjo, 2019). The trade of vegetables in West Africa was worth 27.3 US$ million between
2001 and 2005 and rose to 133.7 US$ million between 2011 and 2013 (Badiane et al., 2018).
Vegetables grown in West Africa are diverse across countries and are generally categorized basedon the harvested parts. Thus, three categories of vegetables are found: leafy vegetables, fruit
vegetables, and root vegetables. Most important leafy vegetables in West Africa include
Corchorus olitorusL., Solanum macrocarpumL., Sesamum radiatumS., Amaranthus hybridusL.,
Lactuca sativaL., Brassica oleraceaL.), fruit vegetables include Solanum Lycopersicum L.,

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Capsicum annumL., Solanum aethiopicumL., Phaseolus vulgarisL., Cucumis sativusL.and root
vegetables include Daucus carotaL., Allium cepaL., Allium ampeloprasumL. (Chagomoka et al.,2015; Houngla et al., 2019; James et al., 2010). Important exporters of vegetables within West
Africa are Niger (71.2%), Ghana (15.2%), and Burkina Faso (11.8%) (Badiane et al., 2018). The
West Africa fruits sector is made of commodities such as banana, pineapples, mango, coconut, etc.
Fruits and vegetables are not just profitable but they are key components of balanced diets andthus essential to the food security of population in the region. Fruits and vegetables are goodsources of dietary fibre which consumption is associated with a lower incidence of cardiovasculardiseases and obesity; they are also good sources of vitamins and minerals as well as
phytochemicals that serve as antioxidants, phytoestrogens, and anti-inflammatory agents (Alissa& Ferns, 2017; Catarino et al., 2019; Slavin & Lloyd, 2012). Despite efforts of many scientists toimprove quality including the yield of horticultural products, farmers still face some problems suchas the (i) decline of soil fertility over the years, (ii) high susceptibility of crops to pests and diseases,(iii) low yield along with a low harvest index of many horticultural crops in West Africa, (iv) not
efficient irrigation facilities, (v) poor inputs management technologies increasing labour andconsequently production cost, and (vi) poor storage facilities (Adebooye et al., 2018; James et al.,2010). These problems constitute a bottleneck to the development of a sustainable horticultural
sector in West Africa mainly in the current context characterized by the high interest of young
entrepreneurs to agribusiness and mainly to vegetables production. To tackle these problems andincite more people to invest in horticultural sector in West Africa, there is an urgent need to rethink
the whole horticultural value chains, mainly the fruits and vegetables farming systems. Sustainableagriculture intensification could be an option to revisit the fruit and vegetable production since itpromotes efficient use of inputs (fertilizer, pesticides, water, etc.) (Aune et al., 2017) which will
help fruits and vegetables farmers to reduce the huge yield gap. Such agricultural intensification
calls for precision agriculture which promotes the timely and efficient execution of farming
operations using the optimal rate of input and ensuring its application at the right location in thefield (Aune et al., 2017). Indeed, the main objective of precision agriculture is to use informationand science-based decision tools to make the best management decisions, achieve the highest
possible yield, and reduce the agricultural impact on the environment thereby making it moreproductive, profitable, and sustainable (Kent Shannon et al., 2018). During the last decade, many
scientists engaged some research fitting into the context of precision agriculture for fruits andvegetables production in West Africa and institutions such as the African Plant Nutrition Institute(APNI) based in Benguérir, Morocco has recently started promoting precision agriculture in Africa
including, West Africa. Based on the need to develop fruits and vegetables sector in West Africa
and tackle the different constraints encountered by stakeholders engaged in this sector, there is aneed to capitalise existing technologies, classify them based on the readability to be used.
Therefore, the present review aims at grabbing precision agriculture technologies for fruits andvegetables production in West Africa context and proposing a model to scale up these technologies
in order to boost fruits and vegetables production. The mini-review is divided into three main
sections: (i) definition of precision agriculture in African context, (ii) precision agriculture
technologies for fruits and vegetables production in West Africa, and a (iii) way forward for thepromotion of precision agriculture in horticultural sector in West Africa.

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MATERIALS AND METHODS
A systematic literature approach was used. Google scholar and Scopus were used to findscientific papers related to precision agriculture practices in West Africa. The following key words
were used in various combinations: ‘Precision agriculture’, ‘precision horticulture’, ‘vegetables’,‘fruits’, ‘horticultural crops’, ‘West Africa’, ‘micro-dosing’, ‘drip irrigation’, ‘deficit irrigation’,‘conservation agriculture’, ‘site-specific management’,‘farming mechanization’, ‘seed priming’and ‘seed treatment’. A total of 353 papers were found. Considering criteria such as the study
location (which should be within West Africa), the period when the research was conducted (from1990 -when precision agriculture emerged- to . 2020, and the adaptability of the technology to fruitand vegetable production in West Africa, a total of 71 scientific papers were retained and used inthis review.
RESULTS AND DISCUSSION
What is Precision Agriculture and How Can It be Defined in West African Agriculture
Context?
The International Society for Precision Agriculture (ISPA) define Precision Agriculture(PA) as: "a management strategy that gathers, processes and analyzes temporal, spatial andindividual data and combines it with other information to support management decisions
according to estimated variability for improved resource use efficiency, productivity, quality,
profitability and sustainability of agricultural production"(http://www.ispag.org). Althoughcomplete, this official definition may not always fit in African and mainly West African context
where the use of technology in agriculture is still lagging and the management approach is not
information-intensive like in developed countries. (Mondal & Basu, 2009) reported that soft
precision agriculture technologies are present in West Africa. Technologies such as mineral
fertilizer micro-dosing, mechanized sowing, fertilizer application, and weeding, bamboo drip
irrigation system based on the principles of precision farming have been used for long and wereproven useful for agriculture in West Africa (Agossou et al., 2019; Dubos et al., 2019). So, an
adapted definition fitting into the context of West African agriculture is proposed: "Precision
Agriculture is a tool or set of tools which allow farmers to reduce uncertainties by making theright management decisions through the use of the right variety, the right inputs, the right crop
management, the right fertilizers, the right application time and dose, the right pest management
strategies, to obtain the right product for the right market and at the right time". Based on suchdefinition, many technologies are available and can be classified as precision agriculture practices.
Seed Priming and Treatment
Seed priming is low-cost technology and physiological method to improve pre germinative
ability of seeds before sowing. Few scientific papers (13% of selected scientific papers) dealing
with seed priming in West Africa were found. (Waqas et al., 2019) reported many seed priming
treatments ranging from conventional methods (hydro-priming, osmo-priming, nutrient priming,chemical priming, bio-priming, and priming with plant growth regulators) to advanced methods(nano-priming and priming with physical agents). Priming increased yield by up to 67% in Sudan
and 40% in Mali (Aune et al., 2012; Coulibaly et al., 2019). (Harris, 2006) demonstrated thatpriming reduces time to germination and improved crop establishment, plant vigour, advances
flowering and increases yield in regions with very limited rainfall making it a good tool in dry

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areas in West Africa. A deep description and explanation of this technique can be found in thechapter of (Waqas et al., 2019) entitled “Advances in the Concept and Methods of Seed Priming’’.
It is important to stress that in seed priming technique, there is a safe limit for soaking time toensure the seed do not germinate before sowing. In fact, germination prior to sowing could resultin a big failure in dryland because very harsh conditions for the new seedling to thrive. Studies
have revealed that for many tropical crops the ‘safe limits’ for soaking seed is around 8h(overnight) (Harris, 2006). Primed seed should be surface dry prior to sowing and sown only whensoil is moist enough to allow seed to absorb additional water from the soil and offers conducive
soil condition for proper plant establishment (Harris, 2006). A study in Mali revealed that seedpriming did not improve yield in area with relatively high rainfall, probably because of high soil
moisture (Coulibaly et al., 2019). The same author reported the use of priming technique in areawith erratic rainfall. So, in fruit and vegetable production this technique can be used.
Conservation Agriculture
Few scientific papers (12% of selected papers) dealing with conservation agriculture (CA)were found). The No-till (NT) or minimum tillage system of CA is in line with the principles of
PA for it guarantees an efficient use of labour force at sowing. In addition, CA implementation
leads to the improvement of soil quality, creating good soil conditions for the success of PA
practices. In fact, mulching and crops diversification (legumes, cereals, etc.) have lots of different
benefits such as improved total soil nitrogen and soil organic carbon and other biophysical andnutritional qualities (Naab et al., 2017). CA is then a good practice to be considered in sustainableagriculture (Naab et al., 2017; Tittonell & Giller, 2013). Some challenges evolve from CA
implementation such as the dearth of crop residue for mulching due to limited yield and thecompetition for multiple uses and economic value of the crop residue (Giller et al., 2009).
Site-Specific Fertilizer Management, Microdosing, Precision Irrigation and Water
Management
Regarding soil fertility, several plant growth simulation models have been developed andproved efficient. In Benin, DSSAT (Decision Support System for Agrotechnology Transfert) has
been used to make fertilizer recommendation in different maize production system (Igue et al.,2018; Saïdou et al., 2018). In both studies, DSSAT has been used as decision-support tool which
integrated soil and agro-ecological conditions to propose the suitable fertilizer rates. Furthermore,
the fertilizer recommendations based on EPIC model (Erosion Productivity Impact Calculator) has
been used and authors found that Field Specific Nutrient Management (FSNM) gave higher yieldthan common local practices in cotton field in Benin (Gandonou & Dillon, 2017; Jones et al., 2003;
Williams et al., 1989). However, due to the cost of the technology adoption in Benin (mainly ofsoil analysis), FSNM is less cost effective than the conventional approach of sole fertilizer rate forevery soil unit. In this context, the efforts made by CORAF to avail D4Ag West African databasefor region-specific seed and fertilizer recommendation needs to be acknowledged and can beviewed at the following website: https://www.coraf.org/2020/09/24/feserwam-a-new-digital-foragriculture-d4ag-tool-launched/. Fertilizer microdosing has also been listed as proventechnologies. The combination of micro-dosing and seed priming greatly increased the crop yield(Aune et al., 2012; Aune et al., 2007). This clearly shows that combining micro-dosing with seedpriming result in an additional benefit in semi-arid West Africa. These two practices have highpotentiel if used in fruit and vegetables production in West Africa but still studies need to be
conducted to validate their effectiveness.

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Regarding water management, apart from drip irrigation techniques, an automatedirrigation system based on sensors has been developed. One of this type of technology is thechameleon sensor which is place under the soil surface and possesses a solar-powered chameleon
reader showing three different colour patterns: blue meaning excess of water, green meaningadequate moisture and red meaning lack of water (Ncube et al., 2018). If the sensing device isconnected with the irrigation system, the red colour pattern triggers the watering of the field andstops it as well when soil is humid enough (blue pattern). The use of this kind of technology could
significantly reduce need labour force, save water for other uses and spare times for farmer to go
for others business or farming practices. Based on information gathered, a model for PA in fruitand vegetable production in West Africa should be based on the crop (fruit and vegetables)
physiology, farmer constraints, cost effectiveness, and accessibility of the technologies. These PA
technologies should also be designed taking into account the most preferred quality attributes ofthe products and farmers should be trained to use them.
REFERENCES
Adebooye O, Akponikpe P, Oyedele D, Peak D, Aluko E. 2018. Synergizing fertilizer microdosing and indigenous vegetable production to enhance food and economic security ofWest African farmers. Global Affairs Canada, Ottawa, ON, Canada.
Agossou H, Bogardi J, Tischbein B. 2019. Soil-water management performance and layout
optimization of an innovative bamboo-drip system in West-Africa.
Alissa EM, Ferns GA. 2017. Dietary fruits and vegetables and cardiovascular diseases risk.
Critical reviews in food science and nutrition, 57(9): 1950-1962.
Aune J, Traoré C, Mamadou S. 2012. Low-cost technologies for improved productivity of dryland
farming in Mali. Outlook on Agriculture 41(2): 103-108.
Aune JB, Coulibaly A, Giller KE. 2017. Precision farming for increased land and labour
productivity in semi-arid West Africa. A review. Agronomy for sustainable development,
37(3): 16.
Aune JB, Doumbia M, Berthe A. 2007. Microfertilizing sorghum and pearl millet in Mali:
Agronomic, economic and social feasibility. Outlook on Agriculture, 36(3): 199-203.
Badiane O, Odjo SP, Collins J. 2018. Africa agriculture trade monitor 2018: CTA.
Bouët A, Odjo SP. 2019. Africa agriculture trade monitor 2019: Intl Food Policy Res Inst.
Catarino L, Romeiras MM, Bancessi Q, Duarte D, Faria D, Monteiro F, Moldão, M. 2019. Edible
Leafy Vegetables from West Africa (Guinea-Bissau): Consumption, Trade and Food
Potential. Foods 8(10): 493.
Chagomoka T, Drescher A, Glaser R, Marschner B, Schlesinger J, Nyandoro G. 2015. Vegetableproduction, consumption and its contribution to diets along the urban–rural continuum inNorthern Ghana. African Journal of Food, Agriculture, Nutrition and Development, 15(4):10352-10367.
Coulibaly A, Woumou K, Aune JB. 2019. Sustainable intensification of sorghum and pearl millet
prodction by seed priming, seed treatment and fertilizer microdosing under different
rainfall regimes in Mali. Agronomy 9(10) : 664.
Dubos B, Baron V, Bonneau X, Dassou O, Flori A, Impens R, . . . Pardon L. 2019. Precision
agriculture in oil palm plantations: diagnostic tools for sustainable N and K nutrient supply.

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Gandonou J-M, Dillon C. 2017. Field Specific Nutrient Management Profitability and Production
Risk Analysis: The Case of Cotton Production in Benin. International Journal of Businessand Social Science 8(5).
Giller KE, Witter E, Corbeels M, Tittonell P. 2009. Conservation agriculture and smallholder
farming in Africa: the heretics’ view. Field Crops Research 114(1): 23-34.
Harris D. 2006. Development and testing of “On-Farm” seed priming. Advances in Agronomy 90:
129-178.
Houngla EJH, Hinson CDT, Gbankoto A, Anani BLC 2019. Characteristics of urban market
gardening in Porto-Novo, Republic of Benin, West Africa. International Journal of
Agricultural Science 4.
Igue A, Balogoun I, Oga A, Saidou A, Ezui G. 2018. Recommendations of Fertilizer Formulas for
the Maize Production in Northern Benin. Adv Crop Sci Tech, 6(359): 2.
James B, Atcha-Ahowé C, Godonou I, Baimey H, Goergen H, Sikirou R, Toko M. 2010.
Integrated pest management in vegetable production: A guide for extension workers inWest Africa: IITA.
Jones JW, Hoogenboom G, Porter CH, Boote KJ, Batchelor WD, Hunt L, . . . Ritchie JT 2003.
The DSSAT cropping system model. European journal of agronomy 18(3-4): 235-265.
Kent Shannon D, Clay DE, Sudduth KA. 2018. An introduction to precision agriculture. Precision
agriculture basics, 1-12.
Mondal P, Basu M. 2009. Adoption of precision agriculture technologies in India and in somedeveloping countries: Scope, present status and strategies. Progress in Natural Science,
19(6): 659-666.
Naab JB, Mahama GY, Yahaya I, Prasad P. 2017. Conservation agriculture improves soil quality,
crop yield, and incomes of smallholder farmers in North Western Ghana. Frontiers in plantscience 8: 996.
Ncube B, Mupangwa W, French A. 2018. Precision agriculture and food security in Africa. In
Systems Analysis Approach for Complex Global Challenges (pp. 159-178): Springer.
Saïdou A, Balogoun I, Ahoton E, Igué A, Youl S, Ezui G, Mando A. 2018. Fertilizer
recommendations for maize production in the South Sudan and Sudano-Guinean zones ofBenin. In Improving the Profitability, Sustainability and Efficiency of Nutrients ThroughSite Specific Fertilizer Recommendations in West Africa Agro-Ecosystems (pp. 215-234):
Springer.
Slavin JL, Lloyd B. 2012. Health benefits of fruits and vegetables. Advances in nutrition 3(4): 506516.
Tittonell P, Giller KE. 2013. When yield gaps are poverty traps: The paradigm of ecologicalintensification in African smallholder agriculture. Field Crops Research 143: 76-90.
Waqas M, Korres NE, Khan MD, Nizami A-S, Deeba F, Ali I, Hussain H. 2019. Advances in theconcept and methods of seed priming. In Priming and Pretreatment of Seeds and Seedlings(pp. 11-41): Springer.
Williams J, Jones C, Kiniry J, Spanel DA. 1989. The EPIC crop growth model. Transactions of
the ASAE, 32(2): 497-0511.

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PRECISION NUTRIENT MANAGEMENT

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#7462 MAPPING SPATIAL VARIABILITY OF SOIL NUTRIENT DEFICIENCIES IN
SMALLHOLDER VILLAGES – A PREREQUISITE FOR IMPROVED CROPPRODUCTION IN AFRICA
Mats Söderström1,3, Kristin Piikki1,2, Job Kihara2, John Mutua2, Johanna Wetterlind1
1
Swedish University of Agricultural Sciences (SLU), Skara, Sweden
2
Alliance of Bioversity International and International Center for Tropical Agriculture(CIAT), Nairobi, Kenya, 3mats.soderstrom@slu.se; +4651167000
ABSTRACT
Various approaches to derive decision support for site-specific soil fertility management
were assessed in a smallholder farming system, a village in western Kenya. The results indicatethat at least a small number of soil analyses from the village is important. A collaborative approach
among farmers within an area or village can facilitate detailed and cost-effective soil properties
mapping, where parts of samples are analyzed in the laboratory, and most samples are scanned by
a combination of sensors. Maps from continental geodatabases were less useful.
INTRODUCTION
Precision agriculture in the context of smallholder farming, the dominating type of
agricultural production in many parts of Africa, can be regarded as adapting the management tomatch the specific needs of individual plots. Management of soil fertility through balanced crop
nutrition that takes account of site-specific deficiencies in macronutrients and micronutrients isneeded to close the yield gap to remedy hidden hunger (Kihara et al., 2016, 2020). A fundamental
prerequisite is the availability of basic information on soil properties at a detailed enough level.
Such detailed soil information is often missing, but there are different ways in which it can beacquired. Traditional soil mapping by collecting and analyzing soil samples may be regarded as
too expensive for small-scale farmers. However, recently, global or continental digital soil
property maps of high spatial resolution have become available, including maps of predictedconcentrations of many nutrients of interest (Hengl et al., 2017). Such data sources provide great
opportunity to enable spatially extensive recommendations with improved and sustainable nutrient
management advice (Rurinda et al., 2020). On another spatial scale, an alternative is to use
proximal soil sensors that rapidly can measure many soil samples (Viscarra Rossel et al., 2011).
So far, however, such sensors normally do not directly measure the soil property of interest, but
require some mathematical/statistical models in order to determine that soil property. As with datafrom general geodatabases, the accuracy of generated results can be difficult or impossible to judge
for the local farmer or advisor.
In this case study we have assessed various approaches to derive decision support for sitespecific soil fertility management in a smallholder farming system. Our study area is a village inwestern Kenya. The aim was to compare and discuss different methods to generate practicallyuseful soil information for farms in this village: i) a regional soil dataset covering the watershedwithin which the village is located, ii) a continental publicly available digital soil databaseproduced by machine learning methods, iii) soil maps derived by interpolation of local soil sample
data, iv) the continental map downscaled by local soil samples, and v) information derived by three244

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proximal soil sensors (near- and mid-infrared spectroscopy and portable x-ray fluorescence) fromlocal calibration models.
MATERIALS AND METHODSOne hundred sixty-six topsoil (0-20 cm) samples were collected in Mukuyu village(covering about 400 ha) in western Kenya following a random stratified sampling design, where
each sample consisted of four subsamples from a 16 m2 area. The most important crops in Mukuyu
are maize (Zea mays L.) and beans (Phaseolus vulgaris L.), primarily produced for subsistence bysmallholder farmers. The average farm size is 1.5 ha (Djurfeldt & Wambugu, 2011). From thesurrounding watershed covering 11000 ha, a regional topsoil dataset of 200 samples was available.
All soil samples were analyzed by the Crop Nutrition Laboratory Services. Among the analyzed
properties, we have in this study used soil clay content, pHH2O, cation exchange capacity (CEC),
total carbon content, and the contents of plant-available fractions (Mehlich-3) of phosphorous (P),potassium (K), magnesium (Mg), calcium (Ca), sulfur (S) and zinc (Zn). Further details on
sampling and laboratory analyses were reported in e.g. Piikki et al. (2016). The 166 soil samplesof Mukuyu were divided into validation and calibration samples sets: 56 validation samples(selected by a stratified random strategy) and three sets of calibration samples (30, 70 and 110
samples, respectively) randomly selected from the remaining samples. The ICRAF Soil-Plant
Spectral Diagnostics Laboratory (Nairobi, Kenya) analyzed all soil samples from Mukuyu by nearinfrared (NIR; Bruker FT-NIR MPA) and mid-infrared (MIR; Bruker FT-MIR Tensor27/HTs XT)
spectroscopy, and portable X-ray fluorescence (PXRF; Bruker Tracer Vi). To remove noise andavoid overlapping spectral regions, the 1000-2500 nm range was used for the NIR analysis and the2500-16300 nm range (4000-614 cm-1) for the MIR analysis. The NIR and MIR spectral data,
expressed as absorbance, were transformed and smoothed by first-order, 21- and 7-point SavitzkyGolay derivative, respectively (Savitzky & Golay, 1964). For PXRF data, we used 26 elementsthat had registered concentrations above the level of detection in all except three samples.
Calibration models for each soil property were constructed for each calibration set with partial
least squares regression (PLSR; for NIR and MIR data) and multivariate regression splines (forPXRF data). For each of the validation samples, data of all the above soil properties were extracted
from the online continental soil database iSDAsoil with 30-m spatial resolution (https://www.isdaafrica.com/isdasoil/; Hengl et al., 2020). Ordinary block kriging was used for interpolation ofregional and local soil data, and values were extracted for the validation sample locations. The
iSDA data were downscaled with regression kriging combined with the local calibration datasets(following the method described in Nijbroek et al., 2018). The Nash-Sutcliffe modelling efficiency(E) was used to compare observed soil analyses values and predicted values with the different
methods (1 = perfect match; 0 and below not better than or worse than using the average).
RESULTS AND DISCUSSION
Soil analyses from the validation farms in Mukuyu indicate that soils in general have a low
pH and are relatively low in organic matter (low TC) and have low CEC (Table 1). Among thenutrients presented in Table 1, especially plant-available contents of P, Ca, S and Zn are low in thearea. The 10th percentile (p10) values show that most nutrients are very low in some parts. Statistics
for these farms derived from regional maps covering the entire Murugusi watershed show that themedian value is captured relatively well, but that the range of data and the values in specific plots

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1st African Conference on Precision Agriculture | 8-10 December | 2020are difficult to assess. The iSDA database performs even worse in terms of capturing the spread ofdata, but the median is fairly accurate for some properties (clay content, pH, CEC and plantavailable contents of P, K and Ca are within about 20% of the observed median).
The modelling efficiencies for the tested methods can be examined in Table 2. Using
already available map products (regional map and continental database, respectively) was clearly
not successful. To have at least 30 local soil analyses was a major improvement. Just byinterpolating these soil data gave models that were better than using the village average.
Downscaling of the continental database by the local soil samples did improve the performance ofthat dataset, but it was not better than just interpolating the soil data. Models based on the spectral
methods were in most cases the best approach. Even a limited number of local soil analyses was
sufficient to construct calibration models for NIR, MIR and PXRF sensors useful for rapid andfunctioning assessment of clay content, TC content, pH, CEC, Ca and Mg. If only 30 calibration
samples were available, especially the MIR models performed well. However, for P and Zn, (andto some extent for K and S) none of the tested methods were useful, not even when as many as
110 local observations were used for model calibration. Note, however, that we did not assess theperformance of sensors calibrated on the national level, but earlier studies have shown that suchmodels can be improved by spiking with local data (e.g. Wetterlind & Stenberg, 2010; Nocita etal., 2015). Another option not tested is to combine different sensors in the modelling. In previous
research in an area in central Kenya, it was possible to generate well performing local models forall of these soil nutrients except for P when multiple sensors were combined (Piikki et al., 2016).
Digital soil mapping, where digital soil properties maps are created by empirical modellingusing spatial covariates, has become increasingly used at all spatial scales. This is of course
particularly interesting in areas where soil sampling and chemical analysis are sparse, since it may
provide easily accessible information at a seemingly high detail. As has been shown here and in
earlier research care must be taken before such data is applied for decision support at the farm orfield level (Nijbroek et al., 2019; Söderström et al., 2017). The best approach to use such products
is to combine the data with some local data to both assess the performance, and if needed use thelocal data for downscaling. Systems for this purpose are now available, e.g. GSDM online(https://gsdm.online; Nzuki, 2019).
To conclude, in order to produce detailed information of soil conditions for smallholder
farms for more accurate management decision, this study conducted in western Kenya suggeststhat at least a small number of soil analyses from the village is important. A collaborative approach
among farmers within an area or village can facilitate detailed and cost-effective soil properties
mapping, where parts of samples are analyzed in the laboratory, and most samples are scanned by
a combination of sensors.
ACKNOWLEDGEMENTS
Funding; VGR/SLU (contract: RUN 2018-00141), Formas/SIDA (contract: 220-19752013), Formas (contract: 220-2014-00646), BMZ (contract: 81206681), and GIZ (contract:81193729). Thanks to the farmers in Mukuyu and the surrounding watershed for goodcollaboration. The soil samples were collected by J. Mukulama, S. Munialo, J. Mutua, S. Nyawade,
R. Rogito, and G. Kavochi.

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Table 1. Summary statistics (10th, 50th and 90th percentiles) of soil analyses from samples collected
at 56 smallholder farms in Mukuyu village in western Kenya. Similar statistics are shown for theregional soil maps of the Murugusi watershed, as well as for the iSDA continental soil database.

Clay (%)
TC (%)pH-1

CEC (cmolc kg )P (ppm)K (ppm)Ca (ppm)
Mg (ppm)
S (ppm)Zn (ppm)

Village samples (n=56)p10Median
p90

p10

22
0.93
5.0

30
1.18
5.2

Regional map
Medianp90

Continental database
p10
Median
p90

32
1.42
5.7

33
1.78
6.2

38
2.01
5.7

40
2.22
5.7

42
2.45
5.8

36
1.31
5.4

42
1.82
6.0

5.7

8.5

13.3

7.8

10.6

15.2

9.0

10.0

11.0

5.5
55
342
73
4.5
1.0

13.9
130
787
117
10.8
2.0

26.3
218
1310
267
15.5
4.6

11.6
87
652
108
5.6
0.9

15.4
125
991
163
7.0
1.7

18.2
220
1800
239
10.9
2.8

10.0
122
812
134
4.5
3.3

11.0
134
945
164
5.0
3.7

12.2
148
1097
181
5.0
3.7

Table 2. Modelling efficiency (E) for predictions of soil properties of 56 smallholder farms in
Mukuyu village using various methods. Cells are coloured according to performance: darkest is
the best (>0.70) – to uncoloured (<0.10) indicating a model similar to the village average or worse.

Regional map
Continental map

Clay
(%)
0.11
-0.85

TC
(%)
-0.31
-5.54

pH

CEC
-1
(cmolc kg )
-0.15
-0.08
-0.16
0.01

P
(ppm)
-0.04
-0.19

K
(ppm)
0.18
-0.03

Ca
(ppm)
-0.64
-0.07

Mg
(ppm)
0.05
0.15

S
(ppm)
-0.73
-2.45

Zn
(ppm)
-0.23
-0.32

30 soil analyses
Interpolation
Downscaling
NIR
MIR
PXRF

0.29
0.24
0.42
0.79
0.80

0.36
0.15
0.08
0.76
0.14

0.09
0.05
0.31
0.52
-0.52

0.26
0.26
0.41
0.50
0.58

-0.21
-0.25
-0.07
-0.21
-0.06

-0.03
-0.02
0.20
0.11
-0.94

0.33
0.20
0.43
0.46
0.37

0.46
0.46
0.42
0.67
0.62

0.35
0.20
0.11
0.00
-0.09

-0.06
-0.11
0.03
0.03
0.14

70 soil analyses
Interpolation
Downscaling
NIR
MIR
PXRF

0.12
0.09
0.63
0.83
0.77

0.30
0.14
0.61
0.85
0.48

0.05
0.00
0.48
0.79
0.40

0.22
0.15
0.70
0.83
0.78

-0.34
-0.33
-0.07
-0.23
-0.17

0.12
0.12
0.30
0.12
-0.25

0.17
-0.26
0.71
0.94
0.74

0.23
0.24
0.66
0.83
0.71

0.28
0.21
0.18
0.08
-0.29

-0.58
-0.71
-0.02
-0.23
0.04

110 soil analyses
Interpolation
Downscaling
NIR
MIR
PXRF

0.29
0.21
0.62
0.84
0.84

0.28
0.21
0.41
0.86
0.56

0.03
0.04
0.71
0.67
0.44

0.27
0.26
0.78
0.81
0.62

-0.05
-0.05
-0.01
-0.11
-0.63

0.14
0.14
0.43
-2.95
0.14

0.20
0.12
0.79
0.80
0.65

0.41
0.42
0.74
0.88
0.83

0.27
0.25
0.17
0.35
0.09

-0.44
-0.49
-0.48
-11.200.13

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consumption in Kenya. J East Afr Studies 5: 447–464.
Hengl T, Leenaars JGB, Shepherd KD, et al. 2017. Soil nutrient maps of Sub-Saharan Africa:
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Hengl T, Miller M, Križan J, Kilibarda M. 2020. iSDAsoil (Version v0.13) [Data set]. Zenodo.
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Nijbroek R, Piikki K, Söderström M, et al. 2018. Soil Organic Carbon Baselines for Land
Degradation Neutrality: Map Accuracy and Cost Tradeoffs with Respect to Complexity inOtjozondjupa, Namibia. Sustainability 10: 1610.
Nocita M, Stevens A, van Wesemael B, et al. 2015. Chapter Four - Soil Spectroscopy: An
Alternative to Wet Chemistry for Soil Monitoring. Adv Agron. 132: 139–59.
Rurinda J, Zingore S, Jibrinc JM, et al. 2020. Science-based decision support for formulating crop
fertilizer recommendations in sub-Saharan Africa. Agr Syst. 180: 102790.
Nzuki R. 2019. Simplifying soil data management in sub-Saharan Africa. Blog of the Alliance ofBioversity International and International Center for Tropical Agriculture (CIAT).
https://blog.ciat.cgiar.org/simplyfying-soil-data-management-in-sub-saharan-africa/
Piikki K, Söderström M, Eriksson J , et al. 2016. Performance Evaluation of Proximal Sensors forSoil Assessment in Smallholder Farms in Embu County, Kenya. Sensors 16: 1950.
Piikki K, Söderström M, Sommer R, et al. 2019. A boundary plane approach to map hotspots forachievable soil carbon sequestration and soil fertility improvement. Sustainability 11:4038.
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Savitzky A, Golay M. 1964. Smoothing and differentiation of data by simplified least squares
procedures. Anal Chem 36: 1627–1639.
Viscarra Rossel RA, Adamchuch VI, Sudduth KA, et al. 2011. Chapter Five - Proximal Soil
Sensing: An Effective Approach for Soil Measurements in Space and Time. Adv Agron.
113: 243–291.
Wetterlind J, Stenberg B. 2010. Near-infrared spectroscopy for within-field soil characterization:
small local calibrations compared with national libraries spiked with local samples. Eur J
Soil Sci. 61: 823–843.
Söderström M, Piikki K, Cordingley J. 2017. Improved usefulness of continental soil databases
for agricultural management through local adaptation. South African Journal of Plant andSoil 34: 35–45. DOI: 10.1080/02571862.2016.1166400

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1st African Conference on Precision Agriculture | 8-10 December | 2020

#7489 PRECISION FARMING USING SPATIAL SOIL VARIABILITY MAPS FOR
IMPROVED BANANA NUTRIENT MANAGEMENT ON A FERRALSOL IN
CENTRAL UGANDA
Patrick Musinguzi, Hannah Kabasomi and Twaha A. Basamba
School of Agricultural Sciences, P.O. Box 7062, Kampala, Makerere University
musipato7@gmail.com
ABSTRACT
Banana is one of the most important crops for millions of farmers in Uganda. However, its
production has remained low due to limited understanding of the variability of soils for targeted
nutrient management. Measures that improve the understanding of soils are instrumental to guideprecision nutrient management in highly heterogeneous cropping systems. A study was conducted
on a Ferralsol in Wakiso District in Uganda to assess the spatial variability of Soil Organic Carbon(SOC), Total Nitrogen (TN) and soil pH in a mono-cultured banana production. Thirty-six (36)soil samples were collected from an area measuring 120 m x 30 m and sampling was done
systematically. The sampling design involved establishing grids of 10 m x 10 m where one
composite soil sample was collected from each grid. Four plant samples were randomly collectedfrom banana bunches on standing plants in the demarcated area. Laboratory soil data was geostatistically analyzed using ordinary kriging interpolation and semi variograms. Results showedthat pH, SOC and TN had weak and moderate spatial dependences with nugget: sill ratios of 92.3,
91.7 and 68.3% for OM, TN and pH, respectively. A section of soils (southern landscape) of thebanana plantation was slightly more acidic than the Northern landscape. Generally, lower SOC
values (1.53 -1.01%) were noticed in the southern landscape of the plantation, with the upper southeastern region having the least. The northern part had slightly higher organic matter levels (1.97 1.62%), with the upper North West region having the highest values. Soil N levels varied the same
way as organic matter. Lower N levels were observed in the southern part of the plantation, withthe upper South East region having the least nitrogen. The properties also exhibited moderatevariation with organic matter (CV=40.949% and total nitrogen CV=40.983%). Generally, selectedsoil properties in this plantation varied despite the soils being typically Ferralsols under the
cropping and management regimes that were investigated. The high soil variability in a banana
plantation reveals the need for precision application of inputs (spot application) within the soil
fertility zones with low SOC, TN and acidic conditions. There is need to promote use of soil
nutrient maps to guide farmers on the proper use of fertilizers under specific soil fertility zones.
INTRODUCTION
Soils are characterized by variation at multiple scales ranging from point measurements to
global ones (Minai, 2015; Njoroge et al., 2017). This gives rise to what is basically referred to as
spatial variability in soils which occurs when a soil parameter measured at different spatial
locations exhibits values that differ across the locations. Soil properties exhibit variability as aresult of dynamic interactions between natural environmental factors i.e. climate, parent material,
vegetation and topography and those significant differences in the soil nutrients from areas withuniform geology are known to be related to landscape position (Ebanyat, 2009). Uzielli et al.

249

1st African Conference on Precision Agriculture | 8-10 December | 2020(2006) points out that soils are naturally variable because of the way they are formed and thecontinuous proceses of the environment that alter them. In Sub-Saharan Africa (SSA), soil fertility
varies spatially and temporally from field to region scale and is influenced by both land use andsoil management practices of the smallholder farmers (Tittonell et al., 2005, Hartemink, 2006,
Ncube et al., 2009). A better understanding of spatial variation in soils has both practical andtheoretical ramifications. Understanding spatial variation is key in making precise quantificationof soil properties to influence management or planning processes (Garten et al., 2007; Rücker,
2005). Understanding variability of soil fertility, its distribution and the causes of the observed
variability are important in addressing sustainable land use strategies (Jing-wei et al., 2011.
Musinguzi et al., 2016, Ebanyat. 2009)). The precise quantification at different scales allows costeffective practices such as site-specific management or precision agriculture that would on theother hand help in resolving problems of pollution and land degradation (Uzielli et al., 2006). In
order to tailor effective site-specific corrective land management, analysis of spatial variability of
key soil quality parameters and the flow of nutrients in the system needs to be done.
In developing countries in SSA, effective use of fertilizer inputs is affected by soil
variability (Musinguzi et al., 2016, Smaling and Braun, 1996). Knowledge of major drivers ofvariability of soil properties such as SOM, pH and N has had limited research, especially in Banana
production, a commonly grown crop in Uganda (Lekasi, 1997, Taulya, 2013). Efforts to improve
nutrient use efficiency can be done through spatial nutrient variation analysis in specific fields toallow site specific soil fertility management. Knowledge about variability of soil properties within
a banana plantation can assist in generating effective site-specific designs; this can help to reduceon the misuse of inputs (organic and inorganic fertilizers). Therefore, there is need for spatial
variability analysis of soil properties for a more effective way to address soil fertility issues on a
farm, particularly at plot level. The objective of this study was to assess spatial variability oforganic carbon, total nitrogen and soil pH in a Ferralsol under banana production.
MATERIALS AND METHODS
Study Area Description
This study was carried out at Makerere University Agricultural Research Institute,Kabanyolo (MUARIK) which is located in the Lake Victoria basin in Wakiso district, KyadondoCounty, Central Uganda. It is located approximately 19kmsNorth east of Kampala along the
Gayaza-Namulonge road at latitude 0º28ºN and longitude 30º37oE (Figure 1). It has an elevationof between 1250 – 1320 meters above sea level (Yost and Eswaran, 1990). It receives a meanannual rainfall of 1100mm per annum, with a bimodal distribution in the months of March to Juneand short rains from September to December. Temperatures range between 21 and32oC. The soils
are predominantly Ferralitic, formed on residuum and colluviums enriched soil, with lateritic
gravel being common features among them (Radwanski, 1960; Yost and Eswaran, 1990,
Wortmann and Eledu, 1999).

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Figure 1. Map of Uganda showing location of the study area
Field Selection and Experimentation Process
The study was carried out on a Ferralsol in a banana plantation with an area of 2.2 hectares,a typical land size for most smallholder farmers in Uganda. The plantation was established in 2007
and had been under paddock for cattle for more than fifteen years. Thirty-six (36) soil samples
were systematically collected from an area measuring 120m×30m. The sampling design involved
establishing grids of 10mx10m where one composite sample was collected from each grid bypicking four (4) sub samples at 5cm from the centre point of the grid. The sub-samples were mixed
to form a composite sample and approximately 0.5kg of soil was packed from the composites.
Sampling was done for topsoil at 0-20cm depth. Coordinates from the centres of each grid wereobtained using a Global Positioning System (GPS).
Table 1. Grid sampling plan

A
B
C

10m
A1
B1
C1

10m
A2
B2
C2

10m
A3
B3
C3

10m 10m 10m 10m 10m 10m 10m
A4 A5 A6 A7 A8 A9 A10
B4
B5
B6
B7
B8
B9 B10
C4
C5
C6
C7
C8
C9 C10

10m
A11
B11
C11

10m
A12 10m
B12 10m
C12 10m

The samples were air-dried for three days, crushed and then analyzed for total nitrogen,total organic carbon and pH. Soil variability of selected chemical parameters in the banana
plantation was investigated by first assessing the variability of the selected soil properties acrossthe sampled part of the plantation by analysis of variance (ANOVA) which was used to investigate
the significance of the variance of the soil properties. The ANOVA was generated by use of
GENSTAT biostatistics software. Geo-statistics were applied to spatially characterize and251

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interpolate soil parameters. The total variability was analyzed by descriptive statistics based on thedataset of 36 samples. The results of these statistics were compared with the critical thresholdvalues of Okalebo et al. (2002). The selected soil chemical parameters were spatially analyzed
using variography and interpolation techniques. Variography characterizes and models spatial
variance of data using a semi-variogram. The semi-variogram determines the increase in variance
between samples collected at increasing separation distances from one point to another. A semivariogram was plotted to show how semi-variance changes as the distance between observations
changes (Karl and Maurer, 2010). It measures the spatial dependence between two observations
as a function of the distance between them. Semi-variograms are characterized by: (i) the nugget
which is the “variability at distances smaller than the shortest distance between sampled
points, including the measurement error”. (ii) the sill, which is the “total observed variation of thevariable” (iii) the range parameter, which is “the distance at which two observations could beconsidered independent” (Karl and Maurer, 2010) (Figure 2).

Figure 2. Features of an ideal semi-variogram
A stochastic simulation for interpolation was used to predict values in the field that had notbeen sampled. Ordinary Kriging was applied (Jafar et al., 2009, Zhang et al., 2011). Semi
variograms and Kriging operations were computed using ARCMAP10.2 and in order to identify
possible spatial structures of the different soil parameters, semi-variograms were calculated andthe best model that described these spatial structures was identified. Kriged and measured values
were mapped using ARCGIS ARCMAP10.2. Spatial dependence was defined using the nugget tosill ratio according to Cambardella et al. (1994) and the ranges of spatial variability were definedaccording to Lopez-Granadoz et al. (2002).

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Table 2. Critical ranges for spatial dependence
Percentage (Nugget:Sill)
Inference<25%
Strong spatial dependence
25-75%
Moderate dependence>75%
Weak spatial dependence
The performance of each of the interpolation techniques in terms of accuracy of estimates wasassessed by comparing the deviation of estimates from the measured data through the use of thecross-validation technique with ARCMAP 10.2.
RESULTS
Spatial Variability of Organic Carbon, Total Nitrogen and Soil pH
Overall, there was evidence of variability in the Banana production systems. Majority ofthe soil parameters analysed were moderately variable except soil pH. Soil organic matter
registered a CV=40.949%, while total nitrogen had a CV=40.983%. However, soil pH exhibitedvery low variation with a CV of 2.849 % (Table 3).
Table 3. Descriptive statistics for the soil parameters

Parameter MeanMedian Min

Max

Criticalvalues SD

TN (%)

2.7

2.8

0.3

4.5

0.2

1.1

41

pH

6.2

6.2

5.8

6.5

5.2

0.2

3

OM (%)

1.5

1.6

0.2

2.6

3

0.6

41

CV (%)

Spatial Dependence of Selected Soil Properties
Analysis of individual soil properties for spatial dependence in the banana field confirmed
spatial variability for all the parameters. The semi-variograms (Figures 3, 4 and 5) demonstrated
weak spatial dependence with Nugget: Sill ratios of 0.923, 0.917 and 0.683 for OM, TN and pH,
respectively and ranges of 29.0, 29.2 and 44.8m respectively (Table 4). Only soil pH showed someincrease with change in distance, but soil organic matter and nitrogen had a different pattern.

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Table 4. Variogram models for the parameters studied
Variable

Model

NuggetSill

Partial
Sill
0.03
0.03
0.01

Range
(m)
29.0
29.2
44.8

Nugget:
Sill
0.923
0.917
0.683

Nugget:Sill
(%)
92.3
91.7
68.3

OM (%) Spherical
0.35
0.37
TN (%)
Spherical
0.34
0.37
H
p
Stable0.02
0.034
Sill=nugget+partial sill****Critical values for the nugget:sill ratios are <25% -strong spatial dependence, 25-75%moderate dependence, and >75% -weak spatial dependence according to Lopez-Granadoz et al.
(2002).

Model: 0.023424*Nugget+0.01086*stable (44.8062)
Figure 3. Semi-variogram for soil pH in the banana plantation254

1st African Conference on Precision Agriculture | 8-10 December | 2020

Model: 0.34547*Nugget+0.028817*Spherical (29.029)
Figure 4. Semi-variogram for soil organic matter in the banana plantation255

1st African Conference on Precision Agriculture | 8-10 December | 2020

Model: 0.34171*Nugget+0.030991*Spherical (29.169)
Figure 5. Semi-variogram for soil total Nitrogen in the banana plantation
Spatial Interpolation Patterns of Selected Soil Properties - Soil pH
From the Ordinary Kriging method of spatial interpolation for pH, the southern part of thebanana plantation was slightly less acidic compared to the Northern one. The central region of theplantation was moderately acidic and the northern region of the banana plantation was slightly
more acidic with the upper North east part as the most acidic (Figure 6). Generally, soils in thisplantation are slightly acidic with pH ranging between 6.02 and 6.34.

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Figure 6. Spatial variation of soil pH in the banana plantation
Soil Organic Matter
From the ordinary kriging method of spatial interpolation for soil organic matter, lower
SOM values were noticed in the southern part of the plantation, with the upper south eastern region
having the least SOM. The northern part had higher organic matter levels with the upper North
Western region having the highest values. The interpolated soil organic matter map shows thatnone of the regions in the banana plantation have soil organic matter levels greater than 3.0%,
which is the critical threshold recommended by Okalebo et al. (2002) and Musinguzi et al. (2016).
The soil organic matter in the field typical of a Ferralsol ranges from 1.005 to 2.054%.

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Figure 7. Spatial variation of soil organic matter in the banana plantation
Total Nitrogen
Ordinary kriging results showed high soil N levels in the northern part of the plantation
with the upper North eastern region having the highest nitrogen levels. Lower N levels wereobtained in the southern part of the plantation, with the upper South Eastern region having the least
nitrogen.

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Figure 8. Spatial variation of soil total nitrogen in the banana plantation
DISCUSSION
Spatial Soil Variability in a Banana Farming System
Precision farming is an important soil and crop production practice that has not been
applied in most tropical soils. The evaluation of one of the banana farms on one soil type (aFerralsol) reveals the need for site specific mapping and precision for optimal nutrient
management. Total N was too high since it had a maximum of 4.45% which is above the criticalvalue of 0.2% (Okalebo, 2002) and a minimum of 0.28% which is slightly above the critical value.
This N could be sourced from the mulching materials that were applied in the banana field. Therewas generally high nitrogen variability in the soil which could have been due to past land-use(night paddocking) which could have resulted in variation in cattle manures (cow dung and urine).
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Soil organic matter also resulted in high variation (CV=40.949%) and this was confirmed with theOrdinary kriging interpolation results that showed high soil N levels in the parts that had high soilorganic matter.
Generally, Soil Organic Matter (SOM) was slightly below the critical level, with amaximum value of 2.58% which was below the critical value of 3% (Okalebo, 2002), with aminimum value of 0.16%. From the interpolation patterns, the lowest SOM values were noticed in
the southern part of the banana plantation and the northern part had higher organic matter. This
could have been due to the different mineralisation rates for the different materials used formulching since a mixture of organic mulch materials was used. These mulches were placed at
different densities in the entire plantation. So probably areas with quick-decaying mulch had
higher OM and N than those with slow decaying mulch. Despite the high variations discussed
above, TN and OM demonstrated weak spatial dependence (91.68 and 92.30%, respectively),
implying that there is no clear pattern in their distribution in this plantation. This phenomenon is
opposed to the usual strong spatial dependence of soil properties seen in homesteads where theamounts of OM and TN are seen to decrease as one moves away from the home (Tittonel et al.,2005). This could be due to nonuniform distribution of the mulches and also the previous land use
could have resulted into non-uniform dung and urine distribution; this could have caused different
decomposition rates of the mulches allocated at the different parts of the banana plantation.
Therefore, this banana plantation requires spot application of inputs in order to address the nonuniform distribution pattern of TN and SOM
Soil pH showed non- significant variation (CV=2.8%) and a moderate spatial dependence
in this banana plantation perhaps because the place was used for banana production without use of
agricultural inputs and also it could have been due to uniformity in the parent material which
determines soil pH (Brady, 1990). Soil pH values ranged from 5.8 to 6.5, which is within the criticalrange of 5.5-6.5; this could be because of reduced leaching of the bases in the topsoil layers. Thisimplies that management can be easier since pH distribution has a pattern (showed by the pHinterpolation map) and hence the plantation caretakers can apply lime in the affected areas.
CONCLUSIONS
Soil fertility variability is a major challenge to nutrient management efforts since the mostcritical parameters such as pH, SOM and nitrogen are highly variable and with poor spatial
dependency on a typically mono-cropped banana system under one soil type. The soil chemical
properties demonstrated weak spatial dependence, suggesting the need for precision farming
approaches and need for site specific management of nitrogen and organic matter. However,amendments for soil pH are not troublesome since they exhibit less variability and are within therecommended range for agricultural production. There is need for more application of GIS-enabledfield-based mapping to enable precision agricultural management of nutrients in tropical soils of
Uganda. This would guide smallholder farms to optimize fertilizer use and minimize wastage thathas been evident with generalized application of fertilizers with no regard to spatial variability.
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#7506 GESTION STRATEGIQUE DES NUTRIMENTS POUR L’AMELIORATION DU
RENDEMENT ET DE LA PROFITABILITE ECONOMIQUE DU GOMBO(Abelmoschus esculentus L.) SUR LES SOLS FERRALITIQUES AU SUD TOGO
Kossi Kotchadjo1, Jean. M. Sogbedji2*Laboratoire Interface Sciences du Sol, Climat et Production Végétale (LISSCPV), Ecole
Supérieure d’Agronomie, Université de Lomé, kkotchadj@gmail.com, mianikpo@yahoo.com
1,2

RESUMEDes stratégies efficientes de gestion des nutriments sont indispensables pour une
production agricole améliorée et durable. La performance du gombo (Abelmoschus esculentus L.)
a été évaluée sous cinq approches de fertilisation dans un cycle de deux cultures successives à la
Station d’expérimentations agronomiques de l’Ecole Supérieure d’Agronomie de l’Université deLomé (SEA/ESA/UL), sur sol ferralitique. Le but est d’identifier une approche efficiente defertilisation de la culture. Les approches de fertilisation ont été : S1 (sans engrais pendant les deux
cultures), S2 (200 kg du complexe N15P15K15 + 100 kg d’urée à 46% N ha-1 au cours de chaqueculture), S3 (6000 kg ha-1 de fumier au cours de chaque culture), S4 (6000 kg ha-1 de fumier à lapremière culture et sans engrais en deuxième culture) et S5 (6000 kg ha-1 de fumier à la première
culture et 200 kg de N15P15K15 + 100 kg d’urée ha-1 à la deuxième culture). Le dispositif
expérimental en bloc aléatoire complet à quatre répétitions a été adopté. Le rendement en fruit etla profitabilité économique ont été déterminés. L’approche de fertilisation n’a significativement
affecté le rendement en fruit qu’à la deuxième culture. Les meilleurs rendements ont été obtenus
sous les approches de fertilisation S3 et S4 (9,09 et 9,62 Mg ha-1) mais en moyenne 85,71, 26,32
et 19,85% plus élevés que ceux sous S1, S2 et S5 respectivement. Le rendement moyen des deux
cultures a été profitable sous toutes les approches avec des profits cumulés approximativement
similaires pour S3 et S4 (3359000 et 3604000 F CFA) mais en moyenne 67,95, 25,96 et 13,11%
plus élevés que ceux sous S1, S2 et S5 respectivement. L‘approche S4 parait prometteuse pour une
production améliorée rentable du gombo dans l’agroécosystème de l’étude.
Mots clés: fertilisation, fumier et engrais minéral, rendement, profitabilité, gombo, Sud Togo
ABSTRACT
Efficient nutrient management strategies are essential for improved and sustainableagricultural production. The performance of okra (Abelmoschus esculentus L.) was evaluatedunder five fertilization approaches in a cycle of two successive cultures at the Agronomic
Experiment Station of the Advanced School of Agronomy of the University of Lomé (SEA / ESA/ UL), on ferralitic soil. The aim is to identify an efficient fertilization approach of the crop. The
fertilization approaches were: S1 (without fertilizer during the two cultures), S2 (200 kg of the
N15P15K15 complex + 100 kg of urea at 46% N ha-1 during each culture), S3 (6000 kg ha- 1 ofmanure during each culture), S4 (6000 kg ha-1 of manure during the first culture and without
fertilizer during the second culture) and S5 (6000 kg ha-1 of manure during the first culture and
200 kg of N15P15K15 + 100 kg of urea ha-1 during the second culture). The complete randomized263

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block design with four replications was adopted. Fruit yield and economic profitability were
determined. The fertilization approach only significantly affected fruit yield during the second
culture. The best yields were obtained under the S3 and S4 fertilization approaches (9.09 and 9.62
Mg ha-1) but on average 85.71, 26.32 and 19.85% higher than those under S1, S2 and S5
respectively. The average yield of the two cultures was profitable under all approaches withcumulative profits roughly similar for S3 and S4 (3,359,000 and 3,604,000 F CFA) but on average
67.95, 25.96 and 13.11% higher than those under S1, S2 and S5 respectively. The S4 approachseems promising for sustaining enhanced okra crop productivity and profitability in the agroecosystem of the study.
Keywords: fertilization, manure and mineral fertilizer, yield, profitability, okra, South Togo
INTRODUCTION
Durant les trois dernières décennies, l’Afrique Subsaharienne a connu une croissance de sa
population de 3,1% contre 2,1% de sa production alimentaire (Henao et al., 2006). Cette situation
se matérialise par une pression sévère sur les ressources en terre, une disparition des pratiques
traditionnelles d’utilisation des terres et la prévalence des approches d’utilisation continue desterres. Pourtant selon Liniger et al., (2011), la production alimentaire devrait augmenter de 70%
d’ici l’horizon de 2050 pour répondre au besoin calorique nécessaire à la population. Dans ces
conditions, seule l’intensification agricole est reconnue comme la principale opportunité pour faire
face à ce besoin alimentaire sans cesse croissant (Kihara et al., 2012). Cette intensification doit
impérativement se baser sur une gestion efficiente et raisonnée des nutriments. Aussi dans lesconditions socioéconomiques de l'Afrique, il est prôné que la fertilisation des sols soit focalisée
sur la technologie de la matière organique où l'on recommande l'usage maximal des nutrimentsd'origine organique et la minimisation de l'usage d'engrais chimiques qui sont d'ailleurs trèscoûteux (Smalling et al., 1992 cité par Adden, 2008) en vue d'améliorer la santé du sol. Par ailleurs,étant donné que les besoins des plants en éléments nutritifs varient d’une culture à une autre et quel’efficience interne des fertilisants suit également la même dynamique, il devient urgent dedévelopper des technologies de fertilisation appropriées à chaque spéculation. C’est dans cettemême logique que Sogbedji (2001) insiste sur la compréhension de la dynamique des nutriments
dans le système sol - plante - atmosphère afin de prévenir la dégradation des sols en ressources debase et soutenir donc une production agricole efficiente avec une gestion propre des nutriments.
La présente étude s’intéresse donc particulièrement à la production du gombo vue son importancedans la lutte contre la faim et la pauvreté. En effet, le gombo joue un rôle remarquable et original
du fait de la valorisation de toutes ses parties tant sur le plan alimentaire que médicinal, sur le plan
artisanal qu’industriel (Marius et al., 1997). Cependant la production du gombo rencontre
malheureusement toute une panoplie de contraintes influençant négativement sa productivité.
Parmi ces contraintes, la pauvreté des sols occupe une place majeure. Conscient de cette situation,des efforts concrets de recherche ont été effectués par bon nombre de chercheurs dans la plupart
des pays de la sous – région pour accroître la production de ladite culture ; tandis qu’au Togo
aucune stratégie de fertilisation appropriée n’est disponible par rapport à la culture du gombo
jusqu’à ce jour. D’où la pertinence de cette étude qui consiste à étudier d’une part, l’effet continu
du fumier et des engrais minéraux, d’autre part l’effet alterné des deux fertilisants et enfin l’effet
résiduel du fumier sur la productivité et la profitabilité du gombo ainsi que sur la santé du sol.

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L’objectif principal attaché à cette étude est d’améliorer durablement la production dugombo au Togo méridional à travers des stratégies de gestion des nutriments techniquement,socialement, économiquement justifiées et respectueuse de l’environnement. Spécifiquementl’étude vise à : (i) tester différentes approches de fertilisation sur la productivité du gombo, (ii)évaluer la rentabilité économique des différentes approches et (iii) identifier une approche
efficiente de la fertilisation de la culture.
MATERIEL ET METHODE
Site Expérimental
L’essai a été réalisé à la Station d’Expérimentation Agronomique de Lomé (SEAL) del’Ecoles Supérieure d’Agronomie de l’Université de Lomé (06°17’N, 001°21’E) dans la région
maritime. La pluviométrie annuelle fluctue entre 800 à 1100 mm tandis que la températuremoyenne annuelle est de 27 °C. Cette station est caractérisée par un sol ferralitique communément
appelé « terre de barre ». Ce dernier est bien drainé et possède un faible taux de matière organique(< 10 g.kg-1). Sa teneur en potassium (K) est inférieure à 2 cmol kg-1 ; il a un contenu en phosphore
total (P total) variant de 250 à 300 mg kg-1, une capacité d’échange cationique de 3 à 4 méq.kg-1,
un pH de 5,2 à 6,8 (Raunet, 1973 ; Tossah, 2000). Son contenu sableux est approximativement de
800 g kg-1 dans l’horizon 0 – 20 cm et décroît à moins de 600 g.kg-1 à partir de 50 à 120 cm deprofondeur (Lamouroux, 1969). Signalons que le site a été sous culture continue du maïs (zea mays
L.) durant deux ans avant l’installation de cette expérimentation.
Dispositif expérimental et approches de fertilisation adoptées
Différentes approches de fertilisation ont été adoptées dans un cycle de deux cultures
successives (tableau 1). Ces dernières ont été entre autre: S1 (sans engrais pendant les deux
cultures), S2 (200 kg du complexe N15P15K15 + 100 kg d’urée à 46% N ha-1 au cours de chaqueculture), S3 (6000 kg ha-1 de fumier au cours de chaque culture), S4 (6000 kg ha-1 de fumier à lapremière culture et sans engrais en deuxième culture) et S5 (6000 kg ha-1 de fumier à la première
culture et 200 kg de N15P15K15 + 100 kg d’urée ha-1 à la deuxième culture). Les différentes
approches de fertilisation sont clairement explicitées dans le tableau 1 ci-dessous. Le dispositif
expérimental adopté a été celui en blocs aléatoires complets à 4 répétitions. La superficie de laparcelle expérimentale a été de 265m2 soit 25m x 10,6m. L’unité expérimentale a consisté en une
planche de 9,6 m2 soit 4,8m de long sur 2m de large. Au total 20 planches ont constitué la parcelle
expérimentale. Les planches ont été espacées de 0,5 m tandis que l’espacement entre les différents
blocs a été 1m.
Gestion du sol et de la culture
L’étude a été portée sur une variété locale de gombo (Abelmoschus esculentus L. Moench).
Le semis a été effectué le 19 septembre 2015 et le 15 Janvier 2016 respectivement pour la 1ère etla 2ème culture et est précédé d’un labour ainsi que la confection des planches. L’application dufumier de ferme (crottes de petits ruminants) et de la fumure minérale NPK : 15-15-15 a étéintervenue au 25è jour après semis (JAS) durant les deux cultures ; alors que l’Urée a été appliquée
dès le début de la floraison (soit 43 JAS) des plants de gombo. Rappelons que la densité de semisa été de 3grains/poquet suivi d’un démariage à un plant/poquet au stade 3 feuilles et que le schéma
cultural 60cm X 40cm a été adopté. Les travaux d’entretien ont consisté aux opérations suivantes:

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ü Un arrosage régulier jusqu’à la fin de la récolte;
ü Un sarclobinage;ü La protection phytosanitaire a été effectuée ici pour lutter en particulier contre les maladies
fongiques et les insectes vecteurs de maladies virales.
La première récolte a été faite le 51è et le 48è (JAS) respectivement pour la 1ère et la 2èmeculture tandis que la dernière a eu lieu le 97è et le 110è JAS respectivement pour la 1ère et 2èmeculture. La récolte a été faite tous les deux ou trois jours de telle sorte que le fruit ne soit pas
ligneux avant d’être récolté.
Méthode de collecte et d’analyse des données
Le rendement en fruit a été évalué sur les trois lignes centrales sur cinq de chaque unité
expérimentale en cumulant les productions de chaque traitement en fin de récolte. L’analyse desdonnées a été effectuée par le logiciel GenStat édition 4 suivant la méthode ANOVA au seuil de
5%. Les différentes moyennes ont été discriminées par le test de DUNCAN au seuil de 5%.
Tableau 1. Approches de fertilisation dans un cycle de deux cultures successives.
Approchedefertilisation
S1

Culture 1

Culture 2

Sans engrais

Sans engrais

S2

200 kg N15P15K15 + 100 kg urée ha-1

S3

200 kg N15P15K15 + 100 kg urée
ha-1
Fumier, 6 Mg ha-1

S4
S5

Fumier, 6 Mg ha-1
Fumier, 6 Mg ha-1

Sans engrais
200 kg N15P15K15 + 100 kg urée ha-1

Fumier, 6 Mg ha-1

Analyse économique
La profitabilité de chaque approche de fertilisation a été estimée à travers une analyse debilan partiel qui est la différence entre le gain brut (output) et la charge totale ou input (coût desintrants + coût des travaux manuels). Le gain brut a été constitué par le montant d’argent
correspondant au rendement moyen en fruit de chaque traitement, qui a été supposé être vendu à
300 F CFA kg-1. Les intrants ont été constitués par les coûts associés à chaque traitement incluant
ceux liés à l’achat des semences, des fertilisants et des pesticides. Tandis que les travaux manuels
ont consisté à la préparation du sol, au semis, au sarclage, à la récolte et travaux connexes. Le coûtde la main d’œuvre a été estimé à 2000 F CFA par personne-jour ; le montant de 11000 F CFA parsac de 50 kg d’engrais NPK : 15-15-15 et d’urée à 46% d’azote (prix subventionné par legouvernement) a été utilisé et le prix de la tonne de fumier de ferme a été estimé à 20 000 F CFA.
L’estimation de la main d’œuvre par approche de fertilisation et par culture est présentée dans lesTableaux 2 et 3.

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Tableau 2. Estimation du coût de la main d’œuvre à la 1ère culture (en F CFA) sous chaqueapproche de fertilisation.
Approche de fertilisation

S1

S2

S3

S4

S5

Préparation du sol
Semis
Sarclage
Application fertilisantsRécolteMain d’œuvre total

Nombre de personne par jour par ha
30
30
30
30
30
35
35
35
35
35
90
90
90
90
90
0
20
10
10
10
225
225
225
225
225
380
400
390
390
390

Coût main d’œuvre
760 000 800 000 780 000 780 000 780 000
Le coût total de la main d’œuvre a été calculé sur la base de 2000 F CFA par homme-jour.
Tableau 3. Estimation du coût de la main d’œuvre à la 2ème culture (en F CFA) sous chaqueapproche de fertilisation.
Approche de fertilisation

S1

S2

S3

S4

S5

Nombre de personne par jour par ha
Préparation du sol
30
30
30
30
30
Semis
35
35
35
35
35
Sarclage
90
90
90
90
90
Application fertilisants
0
20
10
0
20
Récolte
225
225
225
225
225
Main d’œuvre total
380
400
390
380
400
Coût main d’œuvre
760 000 800 000 780 000 760 000 800 000
Le coût total de la main d’œuvre a été calculé sur la base de 2000 F CFA par homme-jour.

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RESULTATS ET DISCUSSION
Rendement en fruit du gombo
Tableau 4. Effet des différentes approches de fertilisation sur le rendement en fruit du gombo (enMg/ha).
Approches

S1

S2

S3

S5

Culture 1

8,53±0,84 a 9,17±0,20 a

Culture 2

5,04±2,75 c 7,41±0,38 b 9,09±1,29 ab 9,62±1,83 a 7,81±0,01 ab

Moyenne6,79±1,79

8,29±0,29

9,7±0,33 aS49,39±0,81

9,52±0,15 a 9,93±0,56 a
9,57±0,99

8,87±0,29

CV Culture 1 = 9,8 % ; CV Culture 2 = 15,2 %. Les valeurs d’une même ligne affectées d’un même indicede lettre sont statistiquement identiques au seuil de 5%. S1 (sans engrais pendant les deux saisons), S2 (200kg du complexe N15P15K15 + 100 kg d’urée à 46% N ha-1 au cours des deux saisons), S3 (6000 kg ha-1 defumier pendant les deux saisons), S4 (6000 kg ha-1 de fumier à la première saison et sans engrais en
deuxième saison) et S5 (6000 kg ha-1 de fumier à la première saison et 200 kg de N15P15K15 + 100 kg
d’urée ha-1 à la deuxième saison).

L’Analyse de la variance au seuil de 5% du rendement en fruit du gombo n’a révélé aucune
différence significative pour les différentes approches de fertilisation à la 1ère culture ; par contre
elle s’est révélée significative à la 2è culture (tableau 4). La discrimination des moyennes desdifférentes approches à la 2è culture par le test de DUNCAN au seuil de 5% nous a permis d’avoir
trois classes (a, b et c) et une classe intermédiaire ab.
Ainsi les rendements obtenus aux niveaux des approches S4, S3, S5 et S2 ont été
respectivement supérieurs de 90,87, 80,36, 54,96 et 47,02% comparés à S1, les rendements sous
S4, S3 et S5 ont été supérieurs à S2 de 29,82, 22,67 et 5,40% respectivement, les rendements sous
S4 et S3 ont été de 23,18 et 16,39% respectivement plus élevés que S5 tandis que le rendement
sous S4 a été de 5,83% supérieur à S3.
Les rendements moyens des deux cultures sous S4, S3, S5 et S2 ont été plus élevés de40,94, 38,29, 30,63 et 22,09% respectivement par rapport à S1, les rendements moyens sous S4,S3 et S5 ont été de 15,44, 13,27 et 7,00% supérieurs comparés à S2 respectivement, les rendements
moyens sous S4 et S3 ont été caractérisés par une supériorité de 7,89 et 5,86% comparativement à
S5 respectivement pendant que le rendement moyen sous S4 a été de 1,92% plus élevé que celui
obtenu sous S3.
L’absence d’effet des différentes approches de fertilisation sur le rendement en fruit dugombo à la 1ère culture serait inhérente à la bonne fertilité initiale du sol due aux effets résiduels
des engrais minéraux (NPK, TSP, KCl et l’Urée USG) utilisés successivement pendants 2 ans sous
culture continue du maïs (précédent cultural) avant l’installation de notre culture du gombo. En
effet le maïs restitue assez de résidus de récolte riche en potassium et en azote (soit 1.2% et 0.7%
respectivement) ; de plus les engrais phosphatés à l’instar du TSP utilisé ont tendance à libérer
progressivement leur phosphore. « D’une manière générale, même s’ils sont apportés sous une
forme soluble, les engrais phosphatés et potassiques ne sont jamais utilisés en totalité par lescultures. Celles-ci ne prélèvent en moyenne que 0 à 30 % des apports. Le reste est fixé dans le sol
sous des formes plus ou moins disponibles. » (Asdrubal, 2006).

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La supériorité des rendements obtenus aux niveaux des approches S3 et S4 pendant ladeuxième culture soit en moyenne 85,71, 26,32 et 19,85% plus élevés que les rendements sous S1,S2 et S5 respectivement, serait due d’une part au fait que le fumier de ferme utilisé a amélioré les
propriétés physico-chimiques et biologiques du sol ; d’autre part à ses effets résiduels étant donné
qu’il libère graduellement ses nutriments (en particulier l’azote). Ces résultats sont en conformité
avec ceux obtenus par bon nombre d’auteurs notamment (Abd-Allah et al., 2001; Aly, 2002;
Bayoumi, 2005; Ehaliotis et al., 2005 cité par Omidire et al., 2015) montrant donc que l’utilisation
de fertilisants organiques accroît considérablement le rendement comparé aux engrais chimiques.
Les résultats de cette étude viennent confirmer également les efforts de recherches de (Allah et al.,2013 ; Awodun, 2007) indiquant que le fumier de ferme améliore le rendement en fruit du gombo.
Cependant la faiblesse du rendement au niveau de l’approche S2 au cours de cette secondeculture peut être liée d’une part à une lixiviation des engrais apportés ; d’autre part à l’intoxication
du sol. En effet l’utilisation continue de la fumure minérale sans combinaison ni rotation avec lamatière organique est préjudiciable pour les microorganismes telluriques et par conséquent pourla santé et la productivité des sols.
Par ailleurs en apportant la fumure minérale tout juste après un apport de la matièreorganique (fumier), on peut espérer un effet positif sur le rendement en fruit du gombo ; pourtantnous faisons face à une situation contraire au niveau de l’approche S5 où on assiste à une chutenumérique de rendement comparé aux approches S4 et S3 ; ceci peut être justifié par la loi desrendements moins que proportionnel. D’où la nécessité de tenir compte de l’arrière effet de lamatière organique dans la formulation de nos différents engrais chimiques. Ce résultat est en
adéquation avec celui obtenu par Maltas et al., (2012) signalant que « L’azote apporté par le fumierreprésente 16 % du N total apporté l’année de l’apport, 8 % un an plus tard et 5 % deux ans après.
Donc la non prise en compte de ces valeurs fertilisantes dans le calcul de la fertilisation azotée
accroît les stocks d’azote minéral du sol à la récolte et donc, les risques de lixiviation ».
Toutefois l’analyse de la production moyenne des deux cultures nous indique que lesdifférentes stratégies de fertilisation (apport de fumure organique et/ou minérale) ont
favorablement affecté le rendement en fruit du gombo ; ce qui se démarque par leurs supériorités
caractéristiques par rapport au témoin continu sans apport (S1).
Analyse économique
Les profits réalisés durant les deux cultures sous les différentes approches de fertilisation
sont consignés dans le tableau 5. Les outputs des différentes stratégies ont suivi la dynamique desrendements étant donné que le kilogramme du fruit du gombo a été supposé vendu à un prix uniquede 300 F CFA. L’analyse du tableau 6 nous montre que les profits engendrés par les 5 approchesà la première culture ont été d’une manière générale identiques. Par contre ces derniers ont
beaucoup fluctué à la deuxième culture ; c’est ainsi que les profits sous S4, S3, S5 et S2 ont été de267,84, 209,55, 141,33 et 117,93% respectivement supérieurs à S1, les profits sous S4, S3 et S5
ont été de 68,78, 42,04 et 10,73% plus élevés respectivement comparé à S2, les profits sous S4 et
S3 ont été de 52,42 et 28,27% respectivement supérieurs par rapport à S5 alors que le profit sous
S4 a été de 18,83% supérieur comparativement à S3.
On note également une forte dépression de profit aux niveaux des approches S1, S5 et S2 ;et une dépression moyenne au niveau de l’approche S3 à la deuxième culture comparée à lapremière, soit respectivement 67,12, 32,72, 32,08 et 10,33% tandis qu’un gain de 9,90% a été
enregistrée au niveau de la stratégie S4.

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Toutefois, en considérant le profit cumulé des deux cultures, on voit que toutes lesapproches de fertilisation ont été profitables. Cependant le meilleur profit se retrouve toujours au
niveau de l’approche S4 suivi respectivement des stratégies S3, S5, S2 et S1. D’une manière plus
claire, les profits cumulés sous les stratégies S4, S3, S5 et S2 ont été de 73,85, 62,04, 48,48 et
33,33% respectivement plus élevés comparés à S1, les profits cumulés sous S4, S3 et S5 ont étéde 30,39, 21,53 et 11,36% supérieurs par rapport à celui sous S2 respectivement, les profits
cumulés sous S4 et S3 ont été de 17,09 et 9,13% supérieurs respectivement en comparaison avec
celui sous S5 alors que le profit cumulé réalisé sous S4 a été de 7,29% plus élevé comparativement
à celui sous S3.
En définitive, on voit que les meilleurs profits ont été répertoriés aux niveaux des stratégies
de fertilisation S4 et S3 indiquant donc que la technologie de la matière organique seule est pluséconomiquement rentable pour la culture du gombo dans les conditions de notre étude.
Tableau 5. Bilan partiel pour chacune des cinq approches de fertilisation.
APPROCHES S1

S2

S3
S4
S5
-1
____F CFA ha ____PREMIERE CULTURE
Output Total+2 559 000 2 751 000 2 910 000 2 856 000 2 979 000
Input Total-999 000 1 105 000 1 139 000 1 139 000 1 139 000
Main d'œuvre
(760 000) (800 000) (780 000) (780 000) (780 000)
Semence(23 000) (23 000) (23 000) (23 000) (23 000)
Fertilisants(0) (66 000) (120 000) (120 000) (120 000)
Pesticides(216 000) (216 000) (216 000) (216 000) (216 000)
Bénéfice+1 560 000 1 646 000 1 771 000 1 717 000 1 840 000
DEUXIEME CULTURE
Output Total+1 512 000 2 223 000 2 727 000 2 886 000 2 343 000
Input Total-999 000 1 105 000 1 139 000 999 000 1 105 000
Main d'œuvre
(760 000) (800 000) (780 000) (760 000) (800 000)
Semence(23 000) (23 000) (23 000) (23 000) (23 000)
Fertilisants(0) (66 000) (120 000)(0) (66 000)
Pesticides(216 000) (216 000) (216 000) (216 000) (216 000)
Bénéfice+513 000 1 118 000 1 588 000 1 887 000 1 238 000
BENEFICE CUMULE DES DEUX CULTURES+2 073 000 2 764 000 3 359 000 3 604 000 3 078 000
CONCLUSION
Des résultats de cette étude, il ressort que la technologie de la matière organique seule
semble être l’approche à encourager sous la culture du gombo vu son impact en terme derendement et de profitabilité aux niveaux des stratégies S4 et S3. Toutefois notons que la stratégie
S4 (c’est-à-dire le fumier et son arrière effet) semble être plus prometteuse pour une production
améliorée et rentable du gombo dans l’agroécosystème de l’étude.

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1st African Conference on Precision Agriculture | 8-10 December | 2020RÉFÉRENCES
Abd-Allah AM, Safia MA, Abou-Hadid AF. 2001. Response of Some Tomato Hybrids to theOrganic Fertilizer under Newly Reclaimed Soil Conditions. Egypt Journal Horticulture 28(3): 341-353.
Adden K A. 2008. Effets de trois systèmes culturaux sur la durabilité de la production de maïs(Zea mays L.) sur sol ferralitique au Togo Méridional. Diplôme de DEA Sciences desAgroressources et Génie de l'Environnement. Université de Lomé – 87p.
Allah B G, Hafiz M S, Shazia J, Tooba M, Muhammad IA, Muhammad A. 2013. Effects of Animal
manure on the growth and development of Okra (Abelmoschus esculentus L.). ARPN
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Asdrubal M. 2006 Fertilisation et amendements, dossier d’autoformation. ENESAD, Domaine
technologique et professionnel, Educagri, 2006.
Awodun MA. 2007. Effect of Goat Manure and Urea Fertilizer on Soil, Growth and Yield of Okra
(Abelmoschus esculentus (L.) Moench). International Journal of Agricultural Research 2:632-636
Henao S, Baanante C. 2006. Agricultural production and soil nutrient mining in Africa:
Implication for resource conservation and policy development. IFDC, Muscle shoals, AL
35662, USA, 75 p.
Kihara J, Fatondji D, Jones JW, Hoogenboom G, Tabo R,Bationo A. 2012. Improving Soil Fertility
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Lamouroux M. 1969. Carte pédologique du Togo au 1/1000000. Office de la Recherche
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mondial des approches et technologies de conservation (WOCAT) et Organisation desNations Unies pour l’alimentation et l’agriculture (FAO). Rome, Italie, 243 pp.
Maltas A, Oberholzer H, Charles R, Bovet V, Sinaj S. 2012. Effet à long terme des engrais
organiques sur le rendement et la fertilisation azotée des cultures. Station de recherche
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Omidire NS, Shange R, Khan V, Bean R, Bean J. 2015. Assessing the Impacts of Inorganic andOrganic Fertilizer on Crop Performance Under a Microirrigation-Plastic Mulch Regime,
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Sogbedji JM. 2001. Sustaining maize yields in smallholders cropping systems on West Africa
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Tossah BK. 2000. Influence of soil properties and organic inputs on phosphorus cycling in
herbaceous legume-based cropping systems in the West African derived savanna. Ph.D.
Thesis No. 428, K.U. Leuven, Belgium.

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#7513 PRECISION NUTRIENT MANAGEMENT FOR CASSAVA PRODUCTION
Kodjovi S. Ezuia, Meklit Tariku Meklitb, Adeyemi Olojedec, Mutiu Busarid, Peter Deusdedit
Mlaye, Florence Olowokered, Joy Adielec,f, Rebecca Enesig,h, Ademola Adebiyih, Christine
Kreyeh, Stefan Hauserh, Frederick Baijukyai, Thompson Ogunsanmih, Theresa Ampadu-Boakyeb,
Shamie Zingorea, Bernard Vanlauweb, Pieter PypersbaAfrican Plant Nutrition Institute (APNI), Nairobi, Kenya; b International Institute of Tropical
Agriculture (IITA), Nairobi, Kenya; c National Root Crops Research Institute, Umudike, Nigeria;dFederal University of Agriculture Abeokuta (FUNAAB), Nigeria; e Tanzania Agricultural
Research Institute (TARI), Mwanza, Tanzania; f Wageningen University and Research Centre(WUR), Wageningen, The Netherlands; g Swiss Federal Institute of Technology, ETH-Zurich,
Switzerland; h International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria;iInternational Institute of Tropical Agriculture (IITA), Dar-es-Salaam, Tanzania, g.ezui@apni.net/ sezui@yahoo.com
ABSTRACT
Matching nutrient supplies with plant nutrient requirements is key to sustaining crop
production while preserving the environment. However, in cassava production systems in subSaharan Africa, fertilizer recommendations are either inexistent or provided as blanket rates. We
studied the effects on cassava yield and profitability of site-specific fertilizer rates against farmer’s
practices within the framework of the African Cassava Agronomy Initiative (ACAI) project inNigeria and Tanzania.
A fertilizer recommendation framework combining crop modelling, geospatial analysis,
machine learning techniques and price optimization was designed to estimate site-specifc fertilizer
rates. This framework uses data on temporal and spatial variabilities of soil and weather, fertilizer
and cassava root prices, and farmer’s budget to generate fertilizer recommendations optimized tomaximum return for investment. The resulting decision support tool for agronomy advice delivery
was used to estimate site-specific fertilizer rates per planting and harvest dates for more than 800
farms in Nigeria and Tanzania in 2018 and 2019. The fertilizer recommendations considered thetypes of fertilizer available in the regional market, and accompanied with advices on split dressingsand timing of fertilizer applications following 4R nutrient stewardship.
Results revealed that large variation in yield response was associated with variation inlocations. Recommended fertilizer nutrient rates ranged from 0-120 kg N, 0-23 kg P and 0-115 kg
K ha-1 in Nigeria against 0-129 kg N, 0-30 kg P and 0-46 kg K ha-1 in Tanzania for a maximum
fertilizer budget of 200 USD ha-1. Application of recommended fertilizer rates generated yield
increases in 91% of cases, and 9% negative responses compared with the control without fertilizer.
Average yield increase was about 7 t ha-1 in Nigeria and 5 t ha-1 in Tanzania. Revenue generated
through these site-specific recommendations were profitable for 72% of the farmers.
Applying site-specific fertilizer rates was beneficial for most farmers. However, therecommendations were highly susceptible to market price fluctuations of cassava roots.
Developing affordable fertilizer blends for cassava and linking farmers to markets with moreconsistent prices may sustain positive returns on investments for cassava producers.

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Keywords: African Cassava Agronomy Initiative, decision support tool, 4R nutrient
stewardship, site-specific fertilizer.
INTRODUCTION
The sustainable intensification of agricultural production is a key requirement for meetingthe predicted surge in food demand due to the expected increase of the global population
amounting 9.7 billion by 2050 (Worldometers, 2020). Within this context, cassava, Manihot
esculenta Crantz, appears to be a crop of hope in sub-Saharan Africa (SSA), given that it is not
only a source of energy with about 16.5 MJ kg-1 DM (Montagnac et al., 2009), but is also a robust
crop that can survive harsh growing conditions such as drought and low soil fertility. Cassava canalso perform well under optimal conditions by responding to fertilizer application (Howeler, 2002;
Byju et al., 2012, Ezui et al., 2016, Adiele et al., 2020). However, high and profitable responses tofertilizer application can be achieved only when nutrients are provided at adequate rates andcomposition and are matching the supply with the crop’s nutrient demands, in order to minimize
losses to the environment as recommended by the 4R nutrient stewardship principles.
Applying 4R nutrient stewardship principles in SSA comes along with the challenge of
deploying appropriate decision support tools. Unlike classical long term field experiments that are
cost intensive and time consuming, computer models can effectively predict location-specificnutrient requirements and the best time for applying them to achieve high and profitable yields.
Recent efforts have been reported on computer models adjusted for cassava production in West
Africa with QUEFTS (Ezui et al., 2016) and LINTUL models (Ezui et al., 2018, Adiele, 2020).
While the QUEFTS model covers soil spatial variabilities, LINTUL captures both spatial andtemporal dimensions of weather and soil in addition to cultivar characteristics. Thus, a fertilizer
recommendation system involving these models would be suitable to address both the issues ofright place, right time, right rate and the right source through location-specific advices. Anotherdecision support systems (DSS) has also been reported for cassava in SSA such as fertilizer
optimization tool (Senkoro et al., 2018) based on nutrient response curve optimization withfinancial constraints to provide fertilizer recommendations. Other DSS for cassava exist globally
but are yet to be adjusted for SSA growing conditions.
The current paper evaluates the effects on cassava root yield and profitability of sitespecific fertilizer rates developed based on a LINTUL-QUEFTS fertilizer recommendation systemcompared with farmer’s practices within the framework of the African Cassava Agronomy
Initiative (ACAI) project in Nigeria and Tanzania.
MATERIALS AND METHODS
The study covered 805 cassava fields across the cassava belts in Nigeria and Tanzania.
Cassava was planted from May to August in Nigeria in 2018 and 2019, and October to January in2018 and 2019 in Tanzania, and were harvested after 12 months after planting (MAP). In eachfield, two 7×8m plots were delimitated, with one receiving the site-specific fertilizer rate (SSR)
and the other one not receiving any fertilizer application (CON). Apart from fertilizer application,other crop management operations were the same for CON and SSR plots. For pratical reasons,fertilizer applications were made as one dressing at 2 to 6 weeks after planting (WAP) if therecommended rate is below 50 kg ha-1, and otherwise in two equal dressings at 2 to 6 WAP and at
8 to 12 WAP, however with regards to sufficient rain fall around the application dates.

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The fertilizer rate was obtained using a decision support tool in the form of an app named‘AKILIMO’ meaning ‘Smart Agriculture’ inferred from the fusion of two Swahili words ‘akili’
and ‘kilimo’ (www.akilimo.org). AKILIMO calculates fertilizer recommendations based on a
modular modelling framework involving Light Interception and Utilization (LINTUL) and
Quantitative Evaluation of the Fertility of Tropical Soils (QUEFTS), and machine learningtechniques. This framework was improved from season to seasons based on field data. The input
were daily weather data from NASA POWER (1° resolution) for solar radiation, temperature(minimum and maximum) and wind speed, from CHRIPS (0.05° resolution) for rainfall data, andfrom ISRIC (250m resolution) for soil grid data. Farmers provided information on their desired
planting and harvest dates and estimate of current yield achieved in their farm by selecting one outof five pictures of cassava root stocks corresponding to different yield classes. The calculated SSR
was also tailored to farmers investment capacity and market prices of fertilizers and produce (fresh
storage roots) using an optimization procedure to generate fertilizer rates optimized to maximum
return for investment.
The profitability of investments in fertilizers was evaluated using benefit-cost ratio (BCR),
estimated as the ratio of the difference in gross revenues between SSR and CON plots over thecost of fertilizers used in the SSR. Gross revenue was obtained by multiplying storage root yieldby its unit price. The price range per Megagram (Mg) of storage roots was 13.9-97.2 USD in
Nigeria and 20.9-159.1 USD in Tanzania. For simplicity of this analysis, we calculated BCR using
average values of 55.6 USD and 90.0 USD Mg-1 in Nigeria and Tanzania, respectively.
RESULTS AND DISCUSSION
Performance of the Recommendations
The results show wide yield ranges across country and season indicating a large variability
across locations and time (Fig.1A). This highlights the varying response of cassava to soil fertility
management decisions. Thus, time and location-specific recommendations are indispensable.
Recommended fertilizer nutrient rates ranged from 0-120 kg N, 0-23 kg P and 0-115 kg K ha-1 inNigeria against 0-129 kg N, 0-30 kg P and 0-46 kg K ha-1 in Tanzania. Predictions improved fromseason 1 to 2. In about 88 and 96% of the fields in seasons 1 and 2 in Nigeria, respectively, and 88
and 92% in Tanzania (Fig. 1A), cassava root yields were higher in the SSR plots (i.e. are above
the 1:1 line), indicating better performance of the recommended fertilizer rates (SSR) comparedwith the control plots without fertilizer (CON). This means that AKILIMO provided good advice
to farmers in 91% of the cases by recommending the application of the site-specific fertilizer rate
and predicting a root yield increase. However, this advice was not correct for about 9% of the
fields. In these cases, the DST provided sub-optimal estimates of the attainable yield and or the
initial soil supply of nutrients. The latter was obtained from soil grid data corrected with farmers
estimates of their current yields during the preceeding cropping season. Picking wrong yieldclasses led to imcomparable values of initial soil supply of nutrients. Sub-optimal results were aswell caused by the scale of predictions with differences in micro-climate conditions between
farms, because weather and soil conditions were obtained at a much larger scale, thus not being
able to consider small scale differences.

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Profitability of the Recommendations
Financial returns of fertilizer use have also improved from season 1 to 2. Values of BCR>1,
indicating that the net revenue generated from the use of fertilizer was larger than the investment,
were achieved in 69 and 74% of the fields, in season 1 and 2, respectively (Fig. 1B).
A

B

Figure 1. Fresh storage root yields achieved in the control (CON) and with recommended sitespecific fertilizer rates (SSR) in Nigeria and Tanzania in two seasons (A) and the related
distribution of Benefit-Cost Ratio and profit class (B). Seasons 1 and 2 are 2018 to 2019 and 2019
to 2020 growing periods, respectively. The plain line across the dots corresponds to 1:1 line (A).
The profit class comprises ‘loss’ for BCR < 1, ‘low profit’ for 1 ≥ BCR < 2, and ‘medium to highprofit’ for BCR ≥ 2 (B).
This implies that although on average 91% of the fields had yield increase due to fertilizer
application, this was profitable only for 72% of them. Financial considerations are important for
fertilizer recommendations. However, BCR < 2 may be risky due to price fluctuations at harvest.
A safer BCR ≥ 2 is strongly recommended. The improvement of predictions from season 1 to 2
helped achieve larger number of cases with BCR ≥ 2 in season 2 compared to season 1 (53 vs276

1st African Conference on Precision Agriculture | 8-10 December | 202047%). Larger profits can be attained with higher root price while lower price can also increase theprofit gap. Thus, ensuring reliable and stable root prices at harvest is of a paramount importancefor producers to sustainably intensify cassava production through the use of fertilizer. This can be
implemented through diverse strategies such as: i) scheduled planting and harvesting at periods
with favourable root prices, ii) policies enabling a reduction in root price fluctuations or iii) linking
producers with processing companies offering pre-negotiated convenable prices. The negative
BCR are associated with underestimation of the current and attainable yields.
CONCLUSIONS
Increased root yields and profits can be achieved through the use of site-specific fertilizer
recommendations. While efforts should be intensified to better capture the diversity of cassava
farming systems for enhanced production, mechanisms should also be in place to guarantee
relatively stable produce prices to ensure that profits are achieved from the use of decision support
tools.
REFERENCES
Adiele 2020. Growing out of hunger: Towards an improved understanding of the water andnutrient limited yield of cassava. PhD thesis, Wageningen University, The Netherlands,
138p.
Adiele JG, Schut AGT, van den Beuken RPM, Ezui KS, Pypers P, Ano AO, Egesi CE, Giller KE.
2020. Towards closing cassava yield gap in West Africa: Agronomic efficiency and storage
root yield responses to NPK fertilizers. Field Crops Res. Published online
https://doi.org/10.1016/j.fcr.2020.107820.
Ezui KS, Franke A, Mando A, Ahiabor B, Tetteh FM, Sogbedji J, Janssen B, Giller KE. 2016.
Fertiliser requirements for balanced nutrition of cassava across eight locations in West
Africa. Field Crops Res. 185: 69-78.
Ezui K, Leffelaar P, Franke AC, Mando A, Giller KE. 2018. Simulating drought impact andmitigation in cassava using the LINTUL model. Field Crops Res. 219: 256-272.
Howeler RH. 2002. Cassava mineral nutrition and fertilization. In: Hillocks RJ, Thresh JM,
Bellotti AC (eds) Cassava: biology, production and utilization. CABI, Wallingford
Montagnac JA, Davis CR, Tanumihardjo SA. 2009. Nutritional value of cassava for use as a staple
food and recent advances for improvement. Compr. Rev. Food Sci F. 8: 181-194.
Senkoro CJ, Tetteh FM, Kibunja CN, Ndungu-Magiroi KW, Quansah GW, Marandu AE, Ley GJ,
Mwangi TJ, Wortmann CS. 2018. Cassava Yield and Economic Response to Fertiliser in
Tanzania, Kenya and Ghana. Agron. J. doi: 10.2134/agronj2018.01.0019
Worldometers. 2020. Current world population. https://www.worldometers.info/worldpopulation/277

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#7563 MAPPING AND ASSESSING FERTILITY OF AFRICAN SOILS USING HIGHRESOLUTION REMOTE SENSING AND MACHINE LEARNING APPROACHES
Mohammed Hmimou1,2*, Ahmed Laamrani1, Said Khabba4, Faissal Sehbaoui2,
Abdelghani Chehbouni1,3, Driss Dhiba1,4
1
Centre For Remote Sensing Applications (CRSA), Mohammed VI Polytechnic University(UM6P), Ben Guerir, Morocco; 2 AgriEdge, Mohammed VI Polytechnic University (UM6P),
Ben Guerir, Morocco; 3 Centre d’Etudes Spatiales de la Biosphère (CESBIO/IRD), Université deToulouse, Toulouse cedex 9, France; 4 International Water Research Institute (IWRI),
Mohammed VI Polytechnic University (UM6P), Ben Guerir, Morocco.
*correspondent author email: Mohammed.HMIMOU@um6p.ma
ABSTRACT
Africa is far from exploiting its true agricultural potential. United Nations Food andAgriculture Organization (FAO) indicates that the continent has 60% of non-cultivated lands
worldwide. While it is well established that soil fertility is one of the major limiting factors, only
limited information is available on soil nutrient contents and nutrient availability in the African
soils. Soil fertility of agricultural fields is related to many physical and chemical properties, suchas the clay, sand, and organic matter (OM) contents; cation exchange capacity (CEC); pH; andavailable nutrients such as Nitrogen (N), Phosphorus (P), and Potassium (K). In agriculture,
characterising soil fertility is a key prerequisite to improve farms profitability through best
qualitative and quantitative crops harvesting. In this context, several studies have evaluated the
diagnosis of fertility attributes utilizing sampling grids with different spacings. Due to many
challenges associated with the spatiotemporal characterization of soil attributes and the high costof sampling grids methods, remote sensing technologies have been introduced as an efficient
alternative tool for the monitoring of agricultural soils. This alternative technology can minimize
the effort and time related to sample collection and the cost of laboratory analysis. In addition,these technologies are widely accepted as a cost-effective and non-destructive sensing tool forcharacterising soil attributes (i.e., N, P, K, OM). Another important feature of these remote sensing
technologies is the possibility of registering spectral data on images using remote sensing
platforms such as Unmanned Aerial Vehicle (UAV) equipped with multi-sensors (i.e.,
multispectral, hyperspectral, thermal). In this study, soil fertility ground measurements and UAV
imagery will be collected over representative regions across Morocco where different varieties ofcrops are planted. The UAV spectral data collected using hyperspectral sensors, and soil fertility
parameters derived from laboratory analysis will be used to calibrate machine learning models.
We anticipate that we can achieve much more accurate relationships among observed reflectanceand soil fertility parameters, thanks to the large range of reflectance of the hyperspectral images(Visible to Short Wave Infrared) and the capacity of machine learning techniques to model nonlinear correlations. Soil fertility is a key piece of information in agricultural lands asset
management. Adding to that the capacity of the mapping solution to be developed to map soil
fertility properties from local to regional scales, a broad range of stakeholders, with varying andoften unexpected levels of potential interest in the results of the current project. Thus, this is
expected to create a novel agricultural service for the African farming community contributing to
unlock the potential of African agricultural lands.

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Keywords: African farming community, digital soil mapping, hyperspectral remote sensing,
infrared, machine learning, precision agriculture, short-wave infrared, soil fertility, UAV, visible
INTRODUCTION
Information about soil nutrient contents is key for explaining measured crop responses tosoil fertility management practices and for updating and upscaling of soil fertility management
recommendations, especially in a continent like Africa, where according to Lebtahi (2017), 60%of the world’s potential for land cultivation. Yet, most of this land is in poor condition and unable
to satisfy the needs of agricultural production. As the population increases so too will the demand
for soil nutrient rich land to meet the needs of food production. This growing need for land
restoration is also paralleled by a need for agricultural and ecological data in Africa.
For the management of soil fertility of agricultural fields, physical and chemical properties,
such as the clay, sand, and organic matter (OM) content; cation exchange capacity (CEC); pH; andavailable nutrients, should be known at proper spatial resolution. The spatiotemporal variability ofthese attributes is dynamic, occurring with different amplitudes of variation and spatial patterns.
These variations occur according to the classical factors of soil formation (McBratney et al., 2003)
and owing to minor alterations caused by a combination of local factors such as relief andmanagement (Viscarra Rossel & Lobsey, 2016).
Digital Soil Mapping helps meet these needs with a gridded Soil Information System (SIS).
Besides, by integrating remote sensing data, the SIS can be updated so frequently.
LITERATURE SURVEY
Several local studies have characterized the spatial dependence of physical and chemical
attributes via geostatistical analysis (Nanni et al., 2011; Montanari et al., 2012). Results show thatsample grids greater than 100 × 100 m (1 sample/ha) are not efficient for characterizing thevariability of most soil fertility attributes (Wetterlind et al., 2010).
Generally, factors related to the soil class and its formation (e.g., texture) require a lower
sampling density. However, for pH and available P, K, Ca, Mg, and other chemical attributes, ahigher sample density is required to characterize the variability (Wetterlind et al., 2010).
Schirrmann and Domsch (2011) did not achieve good spatial models for the available K.
According to the authors, the microscale variation of available K, with a spatial dependence range
less than 25 m, limited the characterization of this nutrient.
Owing to the challenges associated with the spatiotemporal characterization of soil
attributes, sensing technologies have been introduced as an efficient tool for the monitoring ofagricultural soils. This alternative would minimize the effort related to sample collection and costof traditional laboratory analyses.
Soil sensors can be classified based on their design concept as follows: (i)optical/radiometric, (ii) electrical/electromagnetic, (iii) electrochemical, and (iv) mechanical(Adamchuk et al., 2004; Kuang et al., 2012). These allow the measurement of the soil capacity to(i) absorb, reflect, and/or emit electromagnetic energy; (ii) accumulate or conduct electricalcharge; (iii) release ions; and (iv) resist mechanical distortions (Viscarra Rossel & Lobsey, 2016),
respectively.

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Diffuse Reflectance Spectroscopy (DRS) is widely accepted tool for characterising soilfeatures because of low operating cost, non-destructive with little or no sample preparation(Stenberg et al., 2010). Another important feature of DRS is the possibility of registering spectral
data on points or images using different platforms, e.g., using sensors directly on the field, using
benchtop sensors in the laboratory with sampled material, or using Remote Sensing Platforms withmulti or hyperspectral cameras. DRS involves remote, proximal (in-field), or laboratory
measurements and is a promising technique for digital soil mapping (McBratney et al., 2003) andPrecision Agriculture (Adamchuk et al., 2004).
DRS has been used in Soil Science since the beginning of 1950. However, only in the last
three decades has it gained importance with the development of more practical applications(Viscarra Rossel et al., 2011).
Several scientific studies have successfully estimated soil physical and chemical properties
using DRS in the spectral regions of the visible (vis; 400–700 nm) and Near-Shortwave Infrared(NSIR; 700–2500 nm) (Viscarra Rossel et al., 2006). Moreover, DRS has been successfully
applied directly in the field using sensors embedded in mobile platforms (Mouazen et al., 2007;
Christy, 2008).
Worldwide, many attempts have been made to predict the physical and chemical attributesof soil using vis-NSIR spectra. In general, calibrations of organic and total C, total N, and clay
content are more likely to succeed because clay minerals and OM are the spectrally active soil
constituents, with well-known spectral features in the vis-NSIR region (Ben-Dor, 2002).
Other soil attributes (e.g., CEC, pH, and V %) do not present absorption features in thisspectral region and, hence, their correlations with vis-NSIR spectra are generally weak (Stenberg
et al., 2010). However, there may be exceptions, as observed by Demattê et al. (2017) for available
Mg and K in Brazilian tropical soils and by Mouazen and Kuang (2016) for available P in soils oftemperate regions.
These occasionally successful calibrations can be attributed to the covariance of soil
attributes with some spectrally active constituents (Kuang et al., 2012). This behavior has generally
been observed at the local level. In agricultural soils, this explanation is reasonable because
nutrients are depleted with plant production, which is related to productivity. Depending on the
degree to which the productivity is regulated by the clay and soil OM, the available nutrients will
be associated with these variables and, consequently, with the vis-NSIR spectrum (Stenberg et al.,2010; Iticha & Takele, 2019).
Regarding the African context, The Africa Soil Information Services project has developed
a gridded Soil Information System of Africa at 250 m resolution (pixel = 6.25 ha) showing the
spatial distribution of primary soil properties of relatively stable nature, such as depth to bedrock,soil particle size fractions (texture), pH, contents of coarse fragments, organic carbon andexchangeable cations such as Ca, Mg, Na, K and Al and the associated cation exchange capacity(Hengl et al., 2017).
These maps were derived from a compilation of soil profile data collected from current andprevious soil surveys. As a spatial prediction framework, they used an ensemble of random forestand gradient boosting machine-learning techniques. Furthermore, as inputs to train the model, theyused the most complete compilation of soil samples obtainable and a diversity of soil covariates(primarily based on remote sensing data) (Hengl et al., 2017).

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DISCUSSION OF RESULTS FROM LITERATURE•••

From the previous literature survey, the following points are worth highlighting:According to this literature survey, the only study that was done in the African context(Hengl et al., 2017) was limited to some sub-Saharan African countries. Thus, it does notinclude countries like Morocco. Furthermore, the use of Remote Sensing data was limited
to derive some covariates like Digital Elevation Model (DEM) and time-series vegetation
indices. Thus, the use of high-resolution hyperspectral images is not yet explored.
The soil fertility management requires high sampling density as reported by (Wetterlind etal., 2010). Thus, the use of geostatistical methods is not practical especially in Africancountries where farmers that choose to do chemical analysis of their farmland’s soil
generally limited the number of soil samples to 1 sample per farmland (independently of
the farmland area and soil characteristics variability). This results in a broader grid density
than what is recommended. Having said that, the development of remote sensing-based
mapping techniques is of major importance to overcome the cost and the effort related tosoil sampling.
As stated by Stenberg et al., 2010; Kuang et al., 2012; Iticha & Takele, 2019, successful
calibrations using vis-NSIR spectrum can be attributed to the covariance of soil attributeswith some spectrally active constituents (eg. clay and soil OM), which depends on the
degree to which the productivity is regulated by the clay and soil OM. This common
observation between those studies stressed the local character of calibrating successful
spectral-based models.

The use of high resolution hyperspectral (vis-NSIR) imaging in African countries, is
expected is to help developing new mapping techniques for soil fertility parameters estimation
and, thus, better agricultural soils management. High resolution (equivalent to a very high-density
soil sampling grid) and high accuracy soil fertility prediction data-driven based-models is expected
to be achieved. Hence, the interest in carrying out this study.
REFERENCES
Adamchuk VI, Hummel JW, Morgan MT, Upadhyaya SK. 2004. On-the-go soil sensors for
precision agriculture. Computers and electronics in agriculture 44(1):71-91.
https://doi.org/10.1016/j.compag.2004.03.002
Ben-Dor E. 2002. Quantitative remote sensing of soil properties. Advances in Agronomy 75:173244. https://doi.org/10.1016/S0065-2113(02)75005-0.
Christy CD. 2008. Real-time measurement of soil attributes using on-the-go near infrared
reflectance spectroscopy. Computers and electronics in agriculture 61(1):10-19.
https://doi.org/10.1016/j.compag.2007.02.010.
Demattê JA, Ramirez-Lopez L, Marques KPP, Rodella AA. 2017. Chemometric soil analysis on
the determination of specific bands for the detection of magnesium and potassium byspectroscopy. Geoderma 288:8-22. https://doi.org/10.1016/j.geoderma.2016.11.013.
Hengl T, Leenaars JGB, Shepherd KD, Walsh MG, Heuvelink GBM, Mamo T, … Kwabena N.A.
2017. Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250
m spatial resolution using machine learning. Nutrient Cycling in Agroecosystems
109(1):77-102. https://doi.org/10.1007/s10705-017-9870-x.

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Iticha B, Takele C. 2019. Digital soil mapping for site-specific management of soils. Geoderma
351:85-91. https://doi.org/10.1016/j.geoderma.2019.05.026.
Kuang B, Mahmood HS, Quraishi MZ, Hoogmoed WB, Mouazen AM, Henten, EJV. 2012.
Sensing soil properties in the laboratory, in situ, and on-line: a review. In: Advances inAgronomy 114:155-223p. Academic Press.
Lebtahi R. 2017. Africa has 60% of non-cultivated lands worldwide (FAO), ecoFin aGency, 28
February. Available at: https://www.ecofinagency.com/agriculture/2802-36529-africahas-60-of-non-cultivated-lands-worldwide-fao (Accessed: 14 November 2020).
Mc Bratney AB, Santos MM, Minasny B. 2003. On digital soil mapping. Geoderma 117(1-2):352. https://doi.org/10.1016/S0016-7061(03)00223-4.
Montanari R, Souza GSA, Pereira GT, Marques JJ, Siqueira DS, Siqueira GM. 2012. The use ofscaled semivariograms to plan soil sampling in sugarcane fields. Precision Agriculture,
13(5):542-552. https://doi.org/10.1007/s11119-012-9265-6.
Mouazen AM, Maleki MR, Baerdemaeker J, Ramon H. 2007. On-line measurement of someselected soil properties using a vis-NIR sensor. Soil Tillage Research 93(1):13-27.
https://doi.org/10.1016/j.still.2006.03.009.
Mouazen AM, Kuang B. 2016. On-line visible and near infrared spectroscopy for in-field
phosphorus
management.
Soil&
TillageResearch
155:471-477.
https://doi.org/10.1016/j.still.2015.04.003.
Nanni MR, Povh FP, Demattê JAM, Oliveira RBD, Chicati ML, Cezar E. 2011. Optimum size ingrid soil sampling for variable-rate application in site-specific management. Scientia
Agricola 68(3):386-392. http://dx.doi.org/10.1590/S0103-90162011000300017.
Schirrmann M, Domsch H. 2011. Sampling procedure simulating on-the-go sensing for soil
nutrients. Journal of Plant Nutrition and Soil Science 174(2):333-343.
https://doi.org/10.1002/jpln.200900367.
Stenberg B, Viscarra Rossel RA, Mouazen AM, Wetterlind J. 2010. Visible and near-infrared
spectroscopyinsoil
science.
AdvancesinAgronomy
107:163-215.
https://doi.org/10.1016/S0065-2113(10)07005-7.
Viscarra Rossel R, Walvoort DJJ, McBratney AB, Janik LJ, Skjemstad JO. 2006. Visible, nearinfrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneousassessmentofvarioussoil
properties.
Geoderma131(1-2):59-75.
https://doi.org/10.1016/j.geoderma.2005.03.007.
Viscarra Rossel RA, Adamchuk VI, Sudduth KA, McKenzie NJ, Lobsey C. 2011. Proximal soil
sensing: an effective approach for soil measurements in space and time. In: Advances inAgronomy 113:243-291. Academic Press.
Viscarra Rossel RA, Lobsey C. 2016. Scoping review of proximal soil sensors for grain growing.
CSIRO, Australia.
Wetterlind J, Stenberg B, Söderström M. 2010. Increased sample point density in farm soil
mapping by local calibration of visible and near infrared prediction models. Geoderma
156(3-4):152-160. https://doi.org/10.1016/j.geoderma.2010.02.012.

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#7579 SOIL FERTILITY MAPPING OF DRY SAVANNAH ZONE OF TOGO
Ganyo Komla*1, Ablede Komlan1, Koudjega Kossi1, Ani Sékou1, Afawoubo Koffi1, Anoumou
Dosseh1, Mensah Achité1, Assih-Faram Essodésibè1, Tchalla-Kpondi M’klwa1, Bonfoh
Bédibètè1, Kpemoua Kossi1, Lombo Yao1
Institut Togolais de Recherche Agronomique, BP : 1163, Lomé-Togo, desireganyo@gmail.com,
00228 90 87 51 38
ABSTRACT
Increasing agricultural productivity, and therefore the production, requires a goodknowledge of the soil fertility status and a sustainable nutrients management. The objective of thisstudy is to map the spatial distribution of selected soil fertility parameters in the dry savannahagro-ecological zone that covers the regions of Savanes and Kara in Togo. Soil pH, available
phosphorus (P), exchangeable potassium (K) and organic matter were determined in soil samples
collected at 1000 m grid intervals (1/100 000). Based on the soil samples analysis data, GIS maps
were designed using Arc GIS v10.5 software. The results showed that the soils in the area wereslightly acidic (5.5 <pH <6.5) while 62.5% of soils had low organic carbon levels (<2%). In theregions, the available phosphorus was low (<15 mg P/kg) in 94.3% of the agricultural soils.
Exchangeable potassium was also low (<131 mg K/kg) in 97.5% of agricultural soils in this area.
Results from soil samples tests showed a deficiency in the macro-nutrients (P and K) and a loworganic matter content of the soils of the dry Savannah. Based on the soil fertility statusdetermined, the crop needs and the environment conditions there is a need of reviewing the currents
fertilizer application rates recommended.
Keywords: Soil fertility maps, Dry Savannah, Togo
INTRODUCTION
Togo is mainly an agricultural country where agriculture employs about 70% of the active
population. Although the agriculture, dependent on rainfall, it constitutes the main sector thatdrives the economy and the social development of the country. It is generally practiced by small
farmers with low productivity due to various biophysical and low-level farming management
constraints. Inadequate or poor rainfall distribution, and low levels agricultural soils in nitrogen(N) and phosphorus (P) greatly affect crop productivity (Bationo et al., 2003). Crop yields are
generally limited as result of nutrients mining and soil fertility depleting due to inadequate or no
fertilizer application, (Prabhavati et al., 2015). Soils are not only thirsty but also hungry (Wani,
2008).
Increasing agricultural productivity, and therefore production, requires a good knowledge of soil
fertility status and land degradation indices at the national level. Unfortunately, Togo does not yet
have maps on soil fertility status and the lack of a national soil information system limits land use
planning for sustainable agricultural development.
With regard of the above-described situation of lack of soil fertility status, Togo haveundertaken a study in 2017 with the objective of mapping the fertility of agriculture soils. This283

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paper presents the results of the regions of Savanes and Kara located in the dry savannahagroecological zone of Togo.
MATERIALS AND METHODSThe Zone of Study
The agro-ecological zone of dry savannah in Togo comprising the regions of Savanes andKara is located within 9 and 11° North and 0 and 1° East. The climate is of the Sudanian typecharacterized by two seasons, a rainy one from May to October and a dry one from November toApril. The annual rainfall ranges from 600 to 1200 mm. Vegetation consists of vast grassy
savannas.
Soil Sampling, Analysis and Mapping
Composite soil samples were collected on grid points of 1000 m interval (1/100,000) at 20
cm depth. A total of 10,849 samples were collected: 4,283 the region of Savanes and 6,566 in theregion of Kara. Soil samples were air-dried and sieved using a 2 mm mesh sieve. pH and electricalconductivity were determined by Jackson, (1959) method , available phosphorus (P), exchangeable
potassium (K) and organic matter (OM) were determined by the methods described by Olsen andSommers (1982), Helmke and Sparks (1996) et Nelson and Sommers (1996) respectively.
Fertility maps were designed using ArcGIS 10th ed software and the inverse distance method forextrapolation.
Table 1. Soil fertility parameters classification
Nutrients
Phosphorus (mg/kg)
Potassium (mg/kg)
Organic matter (%)
Soil reaction
pH (H20)

Very low
<8
< 90
< 0,7

Low
8 - 1590 - 131
0,7 - 2< 5,5Acidic5,5 – 6,5
Slightly
acidicFertility ratings
MediumHigh
15 - 23
23 - 30
131 - 170
170 – 200
2 - 3,53,5 – 4,5

Very high
> 30
> 200
> 4,5

6,5 – 7,5
Neutral> 8,5Alkaline

7,5 – 8,5
Slightly
Alkaline

RESULTS AND DISCUSSION
Soil pHDry savannah soils are slightly acidic (85% of the soils, Figure 1) with an average pH valueof 6.1 ± 0.5 (mean ± standard deviation). This can be explained by the fact that most of the soils
in the area are formed on acid parent materials (granite, gneiss, quartzite, etc.) (Lamouroux, 1969).

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Figure 1. Soil pH distribution in dry savannah in Togo.
Organic Matter
The results revealed that soils in this area of Togo are poor in organic matter. About 63%
has an organic carbon content less than 2% (Figure 2). This low soil organic matter content is dueto the overuse of the soils for agricultural production without any adequate fertility management.

Figure 2. Soil organic matter distribution in dry savannah in Togo.
Available Phosphorus
The average available phosphorus content of dry savannah soils is 5.9 ± 6.3 mg P/kg.
Figure 3 shows that 94.3% of the soils in the zone have a P content below 15 mg/kg. This P285

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deficiency of the soils can be attributed to the native parent rock materials and to the overuse of
the lands for agricultural production.

Figure 3. Map of the soil available phosphorus distribution in dry savannah in Togo.
Exchangeable Potassium
In general, the soils (97.5%) have a very low level of exchangeable potassium (< 131 mg/kg
soil) with an average of 62.2 ± 50 mg K/kg soil. This low K content can be explained by a very
low and partial release of potassium related to a relatively long and very marked dry season.

Figure 4. Map of the soil exchangeable potassium distribution in dry savannah in Togo.

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CONCLUSIONS
The results of this study revealed a deficiency in the macro nutrients analyzed (P and K) inthe dry savannah soils of Togo and a low organic matter content. With to this overall low level offertility of the agricultural soils in the studied areas, there is a need to review the current
recommendations of fertilizer rates to be applied on crops. The new fertilization formula shouldtake into consideration the soil fertility status, crop needs and the environment conditions.
ACKNOWLEDGEMENTS
We thank the Foundation OCP and FAO for providing funds and facilities without which
this study would not have been possible.
REFERENCES
Bationo A, Mokwunye U, Vlek PLG, Koala S, Shapiro BI. 2003. Soil fertility management forsustainable land use in the West African Sudano-Sahelian Zone, In: Gichuri MP, Bationo
A, Bekunda MA, Goma HC, Mafongoya PL, Mugendi DN, Murwuira HK, Nandwa SM,
Nyathi P, Swift M. (Eds.), Soil Fertility Management in Africa: A Regional Perspective.
African Academy of Sciences; Centro Internacional de Agricultura Tropical (CIAT);
Tropical Soil Biology and Fertility (TSBF), Nairobi, Kenya, pp. 253–292.
Helmke PA, Sparks DL. 1996. Lithium, sodium, potassium, rubidium and cesium, in: Sparks, D.L.
(Ed.), Methods of Soil Analysis, Part 3, Chemical Methods. American Society ofAgronomy and Soil Science Society of America., Madison, WI, pp. 551–574.
Jackson ML. 1959. Soil Chemical Analysis. J. Agric. Food Chem. 7: 138.
https://doi.org/10.1021/jf60096a605
Lamouroux M. 1969. Notice Explicative - Carte Pédologique du Togo au 1/1.000.000, Office de.
ed, OSTROM. Paris, France.
Nelson DW, Sommers LE. 1996. Total carbon, organic carbon and organic matter, in: Sparks, D.L.
(Ed.), Methods of Soil Analysis, Part 3. Chemical Methods. American Society ofAgronomy and Soil Science Society of America., Madison, WI, pp. 961–1010.
Olsen SR, Sommers LE. 1982. Phosphorus, in: Page, A.L. et al. (Ed.), Methods of Soil Analysis,
Part 2. American Society of Agronomy and Soil Science Society of America., Madison,
WI, pp. 403–430.
Prabhavati K, Dasog GS, Patil PL, Sahrawat KL, Wani SP. 2015. Soil fertility mapping using gis
in three agro-climatic zones of belgaum district, karnataka. J. Indian Soc. Soil Sci. 63: 173–
180. https://doi.org/10.5958/0974-0228.2015.00022.5
Wani SP. 2008. Taking Soil Science to Farmers’ Doorsteps through Community Watershed
Management. J. Indian Soc. Soil Sci. 56, 367–377.

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#7581 NUTRIENT MANAGEMENT TAILORED TO SMALLHOLDER
AGRICULTURE ENHANCES PRODUCTIVITY AND SUSTAINABILITY
Pauline Chivenge1, Kazuki Saito2, Michelle Anne Bunquin1, Sheetal Sharma3, Achim
Dobermann4
1
International Rice Research Institute (IRRI), DAPO Box 7777, Metro Manila 1301, Philippines;2
Africa Rice Center (AfricaRice), 01 B.P. 2551, Bouaké 01, Côte d'Ivoire; 3IRRI, NASC
Complex, Pusa, New Delhi-110012, India; 4International Fertilizer Association (IFA), 49 avenue
d’Iena, 75116 Paris, France
ABSTRACT
Plant nutrition plays a central role in the global challenge to produce sufficient andnutritious food, lessen rural poverty, and reduce the environmental footprint of crop production.
Efficient fertilizer use requires tailored solutions that are scientifically sound, practical andscalable especially for smallholder farmers, such as the crop-led site-specific nutrient management(SSNM) approach developed in the 1990s for cereal production systems in Asia to address
variability among farms. Originating from a simple model to calculate crop nutrient requirements,
this unique approach has evolved over 25 years, covering a growing number of crops and countries
in Asia and Africa with development of digital tools and dissemination approaches. We performed
a meta-analysis using 61 published papers across 11 countries to compare SSNM with the farmer
fertilizer practice (FFP), for rice, wheat and maize. Overall, relative to the FFP, grain yield was
0.7 Mg ha-1 (12%) greater with SSNM, and this was achieved using about 20 kg N ha-1 less nitrogen(N) fertilizer; associated with greater agronomic N use efficiency under SSNM than the FFP (17vs 12 kg grain kg-1 N applied). This was likely because SSNM had more splits of N fertilizer than
FFP, which was applied in better congruence with key periods of crop growth and N demand,
thereby reducing N pollution to the environment and sustaining soil health. Moreover, the benefits
with SSNM were achieved through balanced nutrition, with application of the same amount ofphosphorus (P) but higher potassium (K) rates than FFP. In countries where grain yield with the
FFP was high, a substantial reduction in N application rate with SSNM resulted in greater
improvement of N use efficiency and reduced N loss and this was especially the case for China
across the three crops. In contrast, larger yield gains were observed for farmers who typically attainlow yields than farmers that already have high yields. Such cases were mainly observed in Africa
and South Asia. We know no other agronomic intervention that has increased crop yield,
profitability, and N use efficiency across three cereal crops and geographies. This approach
represents a win-win situation from which millions of smallholder farmers could benefit.
INTRODUCTION
Nutrient management is an important component for sustainable crop production to meetthe challenges of rising food demand driven by population growth, while protecting theenvironment. Production of cereal crops increased significantly in the developing world since the1960’s, largely due to increased use of fertilizer and agrochemicals, coupled with adoption of
improved, high yielding varieties, and increased access to irrigation (Pingali, 2012). However, theorientation of producing more food has been associated with overuse of fertilizers, although288

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fertilizer use has been widely variable across regions and crops (FAO, 2006; Lu and Tian, 2017).
More than 50% of the global nitrogen (N) is used in Asia while less than 5% is used in Africa(Heffer et al., 2017). While low fertilizer us in Africa has been associated with low crop
productivity, in Asia fertilizers have been overused, with losses to the environment; both cases
affect sustainability of cropping systems. Scientifically sound, tailored nutrient management
solutions that are scalable are needed especially in smallholder farming systems.
The site specific nutrient management (SSNM) approach, developed in the 1990’s tocalculate field specific requirements for fertilizer N, P and K for cereal crops (Dobermann et al.,2004) can potentially contribute to the attainment of sustainable cropping systems. This approach
was initially conceptualized for rice cropping systems in Asia, based on principles from the
Quantitative Evaluation of the Fertility of Tropical Soils (QUEFTS) model (Janssen et al., 1990)
to estimate fertilizer nutrient requirements as the difference between the total amount of nutrient
required by the crop to achieve a specific target yield and the indigenous supply of the nutrient(Witt and Dobermann, 2004). SSNM was shown to improve crop yields, nutrient use efficiency
and profit versus the farmer fertilizer practice (FFP), which is often based on blanket
recommendations (Dobermann et al., 2002; Pampolino et al., 2007; Peng et al., 2010; Wang et al.,2001), sometimes while reducing fertilizer application (Peng et al., 2010). The timing of fertilizer
application is adjusted to meet peak crop demand to enhance nutrient use efficiency.
This unique approach has evolved over 25 years, covering a growing number of crops(Khurana et al., 2008; Witt et al., 2006) and extended to other geographies in Asia and Africa(Saito et al., 2015). The advancements in information and communication technology, the SSNM
approach have led to the development of digital tools (Buresh et al., 2019; Pampolino et al., 2012;
Saito et al., 2015) for wider dissemination of SSNM recommendations to smallholder farmers. In
this study we conducted a meta-analysis using 61 published papers to provide a comprehensive
and systematic evaluation of the performance of SSNM in terms of grain yield, N use efficiency
and profit compared to the FFP for three cereal crops; maize, rice and wheat.
MATERIALS AND METHODS
We conducted a meta-analysis using data collected from 61 published studies conductedin 11 countries in Asia and Africa. A literature search was conducted on major search engines
using search terms: site-specific nutrient management (SSNM), SSNM rice, SSNM maize, SSNM
wheat, SSNM cereals, SSNM vs farmers’ fertilizer practice (FFP). We focused on rice, wheat andmaize, which account for an estimated 43% of the world's food calorie supply and consume about
half of the world’s NPK fertilizers annually. To be included in this meta-analysis, studies shouldhave reported grain yield under SSNM and FFP, and other agronomic management practices weresimilar between SSNM and FFP. Of the reviewed studies, 65, 21, and 14% were on rice, wheat
and maize, respectively; all conducted in Asia, except three papers on rice in Africa.
Means for grain yield, agronomic N use efficiency (AEN) under SSNM and FFP wereretrieved from each study. Site characteristics including location, study duration, soil properties,
climatic conditions were recorded. Management practices were also recorded. We calculatedpartial factor productivity of N (PFP N), another index for N use efficiency, based on grain yieldand total N fertilizer applied, using the following equation:
PFP N = GYN (kg ha-1)/ N rate (kg N ha-1)289

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Where GYN is the grain yield in a treatment with N application and GY0 is the grain yield in atreatment without N application.
We also calculated economic performance of SSNM compared to FFP: total fertilizer cost
(TFC), gross return, and gross return above fertilizer cost (GRF) using the following equations:
TFC (US$ ha-1) = (pN × Nrate) + (pP × Prate) + (pK × Krate)
Gross return (US$ ha-1) = FGP × GY
GRF (US$ ha-1) = Gross return – TFC
Where pN, pP, pK = prices of N, P and K fertilizers, respectively (US $ kg-1); Nrate, Prate, Krate =amount of N, P and K applied (kg ha-1); FGP = farmgate price of paddy rice, maize or wheat (US$kg-1); GY = grain yield of paddy rice, maize and wheat (kg ha-1)
We estimated fertilizer prices from the 10-year average across countries listed in the
database (indexmundi, 2020) and reported as per unit of nutrient; US$ 0.642 kg-1 N, US$ 2.151
kg-1 P, and US$ 0.633 kg-1 K. We used grain prices of US$ 0.25 kg-1 paddy rice, US$ 0.15 kg-1
maize and US$ 0.20 kg-1 wheat based on the trend of the market prices over the last 25 years.
Grain yield, N fertilizer rates, number of N splits, AEN, PFPN and economic parameters
were analyzed using MetaWin 2.1 software (Rosenburg et al., 2000).
RESULTS AND DISCUSSION
Overall, relative to the FFP, SSNM increased grain yield, gross return on fertilizer,
agronomic efficiency of N (i.e., additional grain yield per kg of fertilizer N applied), by 12, 15,
and 46% respectively (Fig. 1). While mean grain yield increases were 0.7 Mg ha-1 (12%) acrossthe three crops, greater increases were for wheat (20%), followed by maize (15%), with lowest forrice (9%). Gross return was 12% greater, while gross return above fertilizer cost was 15% higher
under SSNM than FFP. This resulted in an average profit of USD 140 ha-1 greater under SSNM
than FFP. Importantly, grain yield and N use efficiencies were greater with SSNM, but with 20 kg
N ha-1 (11%) lower N fertilizer rates than FFP. This was because of more N splits under SSNM
(3.1) compared to 2.6 times under FFP, and N fertilizer was applied in better congruence with key
periods of crop growth and N demand (Cassman et al., 2002), thereby reducing N pollution to theenvironment and sustaining soil health. This resulted in greater N use efficiency with SSNM than
FFP where AEN was 17 kg grain kg-1 N applied for SSNM compared to 12 kg grain kg-1 N applied
for FFP. Similarly, PFP N was 54 and 43 kg grain kg-1 N applied for SSNM and FFP, respectively.
Moreover, the benefits with SSNM compared to FFP were achieved through balanced nutrition,with application of the same amount of P but higher K rates than FFP. Larger yield gains wereobserved for farmers who typically attain low yields than farmers that already have high yields.
Such cases were mainly observed in Africa and South Asia (data not shown). In contrast, where
grain yield in FFP was high, a substantial reduction in N application rate in SSNM resulted ingreater improvement of N use efficiency and reduced N loss. This was especially the case for
China.

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398
59
84
255

all crops
maize
wheat
rice

295
50
71
174

all crops
maize
wheat
rice

398
59
84
255

all crops
maize
wheat
rice

-20

-10

0

10

20

Gross return

all crops
maize
wheat
rice

TF

295
50
71
174

GRF

# data points

Grain

a)

30

% difference in yield and profit between SSNM and FFP

all crops
maize
wheat
rice

184
25
34
125

all crops
maize
wheat
rice

AEN

128
17
30
81

all crops
maize
wheat
rice

366
56
79
231

all crops
maize
wheat
rice

-40

-20

0

20

40

60

PFP

366
56
79
231

No. N splits# data points

N rateb)80% difference in N-management and N-use efficiency between
SSNM and FFP

Figure 1. a) Grain yield, total fertilizer cost (TFC), gross return and gross return above fertilizer
cost (GRF), and b) N use efficiency (agronomic N use efficiency; AEN, partial factor of
productivity; PFP N) responses to site-specific nutrient management (SSNM) compared to thefarmer fertilizer practice (FFP) for rice, wheat and maize. Responses are expressed as mean
response percentage with 95% confidence intervals represented by error bars. Numbers of effectsize comparisons are given as # of data points.

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We know no other agronomic intervention that has achieved increase in crop yield,
profitability, and N use efficiency across three cereal crops and geographies. SSNM represents awin-win situation from which millions of smallholder farmers could benefit and is an essential
component of agronomic solutions for improving crop production. Several digital decision support
tools and platforms were recently developed in collaboration with national agricultural extension
agencies and other partners to scale out SSNM adoption. Examples include Nutrient Expert(Pampolino et al., 2012; Xu et al., 2017), Rice Crop Manager (Buresh et al., 2019; Sharma et al.,2019), and RiceAdvice (Saito et al., 2015). Evaluation of these tools with smallholder farmers has
shown improved yields, profitability and N use efficiency under irrigated and rainfed conditions
that are comparable to our meta-analysis, indicating a very robust performance in widely varying
environments. Reaching millions of farmers can be achieved through integration of policy
incentives, financial and input supply services, and improved knowledge exchange among
extension, public and private partners.
REFERENCES
Buresh RJ, Castillo RL, Torre JCD, Laureles EV, Samson MI, Sinohin PJ, Guerra M. 2019. Sitespecific nutrient management for rice in the Philippines: Calculation of field-specificfertilizer requirements by Rice Crop Manager. Field Crops Research 239: 56-70.
Cassman KG, Dobermann A, Walters DT. 2002. Agroecosystems, nitrogen-use efficiency, andnitrogen management. AMBIO 31: 132-140.
Dobermann A, Witt C, Dawe D. 2004. "Increasing productivity of intensive rice systems throughsite-specific nutrient management. Science Publishers, Inc., Enfield, NH, USA and Int.
Rice Res. Inst., Los Baños, Philippines," Science Publishers.
Dobermann A, Witt C, Dawe D, Abdulrachman S, Gines H, Nagarajan R, Satawathananont S, Son
T, Tan P, Wang G. 2002. Site-specific nutrient management for intensive rice cropping
systems in Asia. Field Crops Research 74: 37-66.
FAO. 2006. Fertilizer use by crop. FAO Fertilizer and Plant Nutrition Bulletin 17, Rome, 109 pp.
Heffer P, Gruère A, Roberts T. 2017. Assessment of fertilizer use by crop at the global level 20142014/15. International Fertilizer Association and International Plant Nutrition Institute,
Paris.
Janssen BH, Guiking F, Van der Eijk D, Smaling E, Wolf J, Van Reuler H. 1990. A system forquantitative evaluation of the fertility of tropical soils (QUEFTS). Geoderma 46: 299-318.
Khurana HS, Phillips SB, Alley MM, Dobermann A, Sidhu AS, Peng S. 2008. Agronomic andeconomic evaluation of site-specific nutrient management for irrigated wheat in northwest
India. Nutrient Cycling in Agroecosystems 82: 15-31.
Lu CC, Tian H. 2017. Global nitrogen and phosphorus fertilizer use for agriculture production in
the past half century: shifted hot spots and nutrient imbalance. Earth System Science Data
9: 181.
Pampolino MF, Manguiat IJ, Ramanathan S, Gines H, Tan P, Chi T, Rajendran R, Buresh R. 2007.
Environmental impact and economic benefits of site-specific nutrient management(SSNM) in irrigated rice systems. Agricultural Systems 93: 1-24.
Pampolino MF, Witt C, Pasuquin JM, Johnston A, Fisher MJ. 2012. Development approach andevaluation of the Nutrient Expert software for nutrient management in cereal crops.
Computers and electronics in agriculture 88:103-110.

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Peng S, Buresh RJ, Huang J, Zhong X, Zou Y, Yang J, Wang G, Liu Y, Hu R, Tang Q. 2010.
Improving nitrogen fertilization in rice by site-specific N management. A review.
Agronomy for Sustainable Development 30: 649-656.
Pingali PL. 2012. Green revolution: impacts, limits, and the path ahead. Proceedings of the
National Academy of Sciences 109: 12302-12308.
Rosenburg MS, Adams DC, Gurevitch J. 2000. Metawin. Statistical software for meta-analysis,
version 2. Sinauer Associates Inc, Sunderland, Massachusetts, USA.
Saito K, Diack S, Dieng I, N’Diaye MK. 2015. On-farm testing of a nutrient management decisionsupport tool for rice in the Senegal River valley. Computers and Electronics in Agriculture
116: 36-44.
Sharma S, Panneerselvam P, Castillo R, Manohar S, Raj R, Ravi V, Buresh RJ. 2019. Web-based
tool for calculating field-specific nutrient management for rice in India. Nutrient Cyclingin Agroecosystems 113: 21-33.
Wang G, Dobermann A, Witt C, Sun Q, Fu R. 2001. Performance of site-specific nutrient
management for irrigated rice in southeast China. Agronomy Journal 93: 869-878.
Witt C, Dobermann A. 2004. Toward a decision support system for site-specific nutrient
management. Increasing Productivity of Intensive Rice Systems Through Site Rice
Systems Through Site-Specific Nutrient Management 359.
Witt C, Pasuquin J, Dobermann A. 2006. Towards a site-specific nutrient management approachfor maize in Asia. Better Crops 90: 28-31.
Xu X, He P, Yang F, Ma J, Pampolino MF, Johnston AM, Zhou W. 2017. Methodology of fertilizer
recommendation based on yield response and agronomic efficiency for rice in China. Field
crops research 206: 33-42.

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#7599 IMPACT OF SENSOR-BASED PRECISION NITROGEN MANAGEMENT ON
WHEAT YIELD AND NITROGEN USE EFFICIENCY
Abdou A. Soaud1. El-Metwally A. El-Metwally2, Ali M. Ali3 and Radwa K. Sayed1
1
Department of Soil Science, Faculty of Agriculture, Cairo University, Egypt. 2Department ofAgronomy, Faculty of Agriculture, Cairo University, Egypt. 3Department of Soil Fertility andMicrobiology, Desert Research Center, Egypt, a2soaud@yahoo.com
ABSTRACT
Optical sensors are promising new technology for precision nitrogen management in crops.
Fertilizer N management for wheat (Cultivar: Giza 171) using optical sensor (GreenSeeker®) was
evaluated at the Experimental Farm of Faculty of Agriculture, Cairo University, Giza Governorate,
Egypt. The experiment was laid out in randomized block design with three replications during two
successive winter seasons (2017/2018 and 2018/2019) to quantify the relationship between N
uptake at jointing growth stage with GreenSeeker measurements and to formulate a strategy to
optimize N fertilizer use efficiency. An increasing rate of N fertilizer was applied in the experimentconducted in the first season to create variability in GreenSeeker readings (Normalized Difference
Vegetation Index, NDVI) determined at jointing growth stage of wheat. The data revealed that
relationship between total N uptake and sufficiency index (SI, SI=VDVI of the measured
treatment/NDVI of the reference treatment)) of NDVI measured by GreenSeeker at Feekes 6
growth stage of wheat fitted to power function (y= 291.47x-1.686). The suggested exponential model
based on the GreenSeeker could explain about 78% of the variation in N uptake. Accordingly, astrategy to refine N application dose applied at jointing growth stage of wheat was suggested as
guided by the sensor in the second season. The suggested strategy was applying 0, 10, 61, 77, 85
or 109 kg N ha-1 corresponding to sufficiency index of NDVI values of 0.80, 0.74, 0.72, 071 and
0.68, respectively. When appropriate prescriptive N fertilizer was applied (100 kg N ha-1in two
splits, 40 and 60 kg N ha-1) followed by corrective dose (161 kg N ha-1) as guided by theGreenSeeker, the achieved N recovery efficiency was 74.1% compared with 51.5% in the generalrecommendation. The grain yield of this treatment has no statistically significant effect comparedwith general recommendation treatment. This study indicated that N fertilizer could be managed
more efficiently in wheat using GreenSeeker sensor compared with the current generalrecommendation.
Keywords: nitrogen use efficiency, GreenSeeker, sufficiency index, NDVI, wheat
MATERIALS AND METHODSThe Experimental Site
In two successive winter seasons (2017/2018 and 2018/2019), field experiments werecarried out on wheat ( Triticum aestivum L.) variety Giza 171 at the Experimental Farm of theFaculty of Agriculture, Cairo University, Giza Governorate , Egypt. Initial soil samples were takenfrom the experimental site and analyzed using the procedures outlined by Page et al. (1982) for
physical and chemical characteristics as recorded in Table 1.

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Table 1. Some physical and chemical properties of the topsoil (0-30 cm) of the experimental site.
EC** Organic matter Available N Available P Available K
dS m-1%
mg kg-1
mg kg-1
mg kg-1
Clay Loam 7.91 4.53
2.30
100.9018.50
354.00*pH in saturated soil paste.
**
Electrical conductivity in saturated soil paste extract.
TexturepH*

Experimental Design and Treatments
The soil has been ploughed and levelled prior to sowing. In both seasons, in midNovember, wheat (Triticum aestivum L.) of the variety Giza 171 grains was mechanically sownin rows 15 cm apart and divided into 15 m2 parcels. N fertilizer levels of 0, 40, 80, 120, 160, 200,
240, 280 and 320 kg N ha-1 were added in three equal split doses in the first season as ammonium
sulphate. This range was used to determine plots with great variability in the wheat uptake andyield of N. The second season was developed to validate the effectiveness of the GreenSeeker
Sensor for the application of N fine-tuning fertilizer. The treatment consisted of setting various
prescriptive N application scenarios at the early growth stage, followed by a corrective dose at thejoint growth stage, as directed by GreenSeeker. The experiments were performed with three
replications in a randomized complete block design. Following the general recommendation,
phosphorus (as a single superphosphate) was applied for sowing. Potassium fertilizer was avoided
because sufficient quantities of available K (354 mg kg-1) were present in the soil.
Plant Sampling and Analysis
At the joint growth stage, over ground plant samples from an area of 1 m2 were collectedfrom each plot straight after the GreenSeeker readings were obtained. The wheat production was
manually collected from a net area of 6 m2 at maturity from the centre of each plot. Grain andstraw samples are collected from each plot were left to dry to constant weight and soil in the hot
air oven at 70°C. Samples were digested in a mixture of H2SO4-H2O2, and total N was determinedusing the micro-Kjeldahl method (Kalra, 1997).
Calculations and Statistical Analysis
Using Microsoft excel program (a component in Microsoft Office 2016), regression
models were mounted. Variance analysis (ANOVA) has been used to evaluate the effect of N
treatments on the data collected. As described by Gomez and Gomez (1984), Duncan's multiple
range test (DMRT) at probability value < 0.05 was used to examine the difference between
means. As described by Cassman et al. (1998), the recovery efficiency of N (REN) was computedas:𝑇𝑜𝑡𝑎𝑙 𝑁 𝑢𝑝𝑡𝑎𝑘𝑒 𝑖𝑛 𝑓𝑒𝑟𝑡𝑖𝑙𝑖𝑧𝑒𝑑 𝑝𝑙𝑜𝑡 − 𝑇𝑜𝑡𝑎𝑙 𝑁 𝑢𝑝𝑡𝑎𝑘𝑒 𝑖𝑛 𝑧𝑒𝑟𝑜 𝑁 𝑝𝑙𝑜𝑡
𝑅𝐸. (%) =𝑄𝑢𝑎𝑛𝑡𝑖𝑡𝑦 𝑜𝑓 𝑎𝑝𝑝𝑙𝑖𝑒𝑑 𝑁 𝑓𝑒𝑟𝑡𝑖𝑙𝑖𝑧𝑒𝑟
RESULTS AND DISCUSSION
Effect of N Fertilizer Application Rate on Grain Yield of Wheat
In contrast to the increasing N fertilizer rate, grain yields of wheat collected from the firstseason study were plotted (Fig.1). The relationship exhibited a second-degree response function,
as is shown in the curve. Function derivation analysis show that the highest grain yield of 8881 kg

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ha-1 can be achieved by applying an N fertilizer rate of 215.8 kg N ha-1. Approximately 155 kg N
ha-1 was calculated as the N fertilizer rate required for economic grain yield (8437 kg ha-1, 95
percent of maximum yield). The widely adopted general N fertilizer recommendation for wheat in
the area is 180-240 kg N ha-1. In addition, N fertilizer levels are usually applied by farmers evenhigher than the general recommendation, which means that unnecessary amounts of N fertilizer
are applied. In addition to the susceptibility to loss of excess N fertilizer from the soil-plant system,
it could also lead to soil health deterioration (Bijay-Singh, 2018). These results suggest that there
is a need to establish site-specific management strategies in the season that have the ability toadjust the rate of application of N fertilizer according to the actual need for the crop.

Grain yield (kg ha-1)

10000
8000
6000
4000
2000

y = -0.1064x2 + 45.922x + 3926.2
R² = 0.94

0
0

50

100

150

200

250

300

350

N fertilizer rate (kg ha-1)
Figure 1. Response of wheat to increasing rate of N fertilizer fitted to quadratic function.
Prediction of N Uptake at Jointing Growth Stage using GreenSeeker
Rapid acquisition of N uptake information where plants can respond to N inputs prior toharvesting is essential for the development of a successful N fertilizer management plan for
precision. Variation in N uptake at the joint growth stage of wheat was created by the increasingrate of N fertilizer applicable in the first season experiment. This variability has been reflected ingrain yield increases. This data was derived from the relationship among grain yield and N wheat
uptake:Estimated maximum uptake = 373 kg N ha-1
Estimated maximum yield = 7981 kg grain ha-1
95% of the maximum grain yield = 7582 kg grain ha-1
Optimum N uptake = 275 kg N ha-1

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9000

Grain yield (kg ha-1)

7500
6000
4500
3000
1500

y = -0.0634x2 + 47.367x - 865.69
R² = 0.8969

0
0

50

100

150

200

250

300

Total N uptake (kg ha-1)

Figure 2. Relationship between grain yield and N uptake in wheat.
For the development of strategies to optimize N fertilization and reduce theenvironmental dangers associated with the application of high amounts of N fertilizer,
monitoring of N uptake during the season is crucial. Inaccurate N uptake prediction may result in
N fertilizer over- or under-applications as compared to the actual demands (Yao et al., 2012).
Many other studies have also shown that in-season spectral measurements of leaf can estimate
the N status and grain production of many crops (Varvel, 1997; Raun et al., 2001; Ali et al.,2014). In fact, portable hand - held sensors such as GreenSeeker have opened a new approach toquickly make precise choices in the season.
Sufficiency Index Approach for Managing N Fertilizer using GreenSeeker
By many varietal groups, seasons or regions, leaves greenness may vary. Consequently,
one GreenSeeker fixed threshold value may not work effectively. The strategy to the sufficiency
index (calculated as the ratio of NDVI reading of the evaluated plot and that of a reference N-richplot) allows dynamic values instead of a fixed threshold value to be used for precision N
management. According the variability of soil properties and seasons, this strategy has thepotential to be self-calibrating.

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Total N uptake (kg ha -1)

350
300
250
200
150
100

y = 291.47x1.686
R² = 0.7752

50
0

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

Sufficiency index of NDVI

Figure 3. Relationship between total N uptake and sufficiency index of NDVI measured by
GreenSeeker at Feekes 6 growth stage of wheat fitted to power function.
In keeping with these findings, it was recommended a strategy to modify N applicationdose be added in the second season at jointing stage of wheat, as steered by the GreenSeeker. From
this algorithm N fertilizer dose (kg N ha-1) was calculated as:
The proposed algorithm
𝟐𝟕𝟓d 𝟐𝟗𝟏.𝟒𝟕× 𝑺𝑰 𝑵𝑫𝑽𝑰𝟏.𝟔𝟖𝟔
𝟎.𝟔𝟓

9000

350

7500

300

Total N uptake (kg ha -1)

Grain yield (kg ha -1)

N fertilizer dose (kg N ha-1) =

6000
4500
3000
1500

y = -0.0634x2 + 47.367x - 865.69
R² = 0.8969

0

250
200
150
100

y = 291.47x1.686
R² = 0.7752

50
0

0

50

100

150

200

250

300

0.4

0.5

0.6

0.7

0.8

0.9

Sufficiency index of NDVI

Total N uptake (kg ha-1)

In this study, the GreenSeeker values at jointing wheat growth stage matched Feekes 6
growth stage (approximately 50 days after sowing) and this is considered to be the suitable stage
for obtaining information and making decisions on in-season N fertilizer management. For
example, Raun et al. (2001) found that the relationship between both the readings of opticalsensors and wheat grain yield was the highest among Feekes 4 and 6 stages. Zhang et al. (2019)
also noted that leaf dry matter in wheat is more varied than other stages during Feekes stages 4 to7, and that agricultural information can be obtained accurately.
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Validation of GreenSeeker in Managing N Fertilizer
The experiment performed during the second season has been used to assess theGreenSeeker sensor performance as proposed in this study. Various doses and timings of
N fertilizer were added prior to applying the corrective dose as steered by the GreenSeeker tomake growth variance in biomass and N uptake in wheat.
The data mentioned in table (2) show that the grain yield was obtained in Treatment # 3(applying 40 and 60 kg N ha-1 at 0 and 30 DAS, respectively, followed by a corrective dose of 60.9
kg N ha-1 as guided by the GreenSeeker for a total of 160.9 kg N ha-1) is approximately equal to
the yield was obtained in the general recommendation, but with 79 kg N ha-1 less. Other treatments
demonstrated the GreenSeeker's effectiveness in increasing or decreasing the N fertilizer levels at
jointing growth stage, depending on the plant's need. The N management based on GreenSeeker
successfully overcame the variability in wheat growth caused by various prescriptive N
management and with less N fertilizer quantities had been used.
REN data indicate that GreenSeeker-guided N treatments have resulted in greater efficiency
of use compared with the general recommendation. For example, when suitable prescriptive
N fertilizer (Treatment # 3) was applied, accompanied by a corrective dose as guided by theGreenSeeker, a 22.6 percent increase in REN compared to the general recommendation. Therefore,
by using GreenSeeker in guidance N management could efficiently control N fertilizer to achieve
higher yield along with less N fertilizer being applied.
Table 2. Wheat grain yields, total N uptake, and N use efficiencies as influenced by different N
fertilizer treatments as guided by GreenSeeker sensor.
N fertilizer
NDVI Corrective
Total
Grain Total N REN+
dose
amount of yield uptake%
At 30 days Feekes 6-1-1-1
kg N ha N fertilizer kg ha
kg ha
sowing age
kg N ha-1
T1 (zero-N)00003118 d 109.6 c
T2 (gen. rec.)
80
80
0.75
80 (fixed) 240
8023 a 233.4 a 51.5 c
T3
40
60
0.74
60.9
160.9
7989 a 228.9 a 74.1 a
T4
100
0
0.72
77.3
177.3
7373 b 238.7 a 72.8 a
T5
0
100
0.71
85.3
185.3
7742 a 243.2 a 72.1 a
T6
0
0
0.68
109.1
109.1
6114 c 183.5 b 67.7 b
T7
100
100
0.80
10.1
210.1
7871 a 224.6 a 54.7 c+
REN = Recovery efficiency of nitrogen fertilizer.
Means followed by the same letter within the same column are not significantly different at the
0.05 level of probability by Duncan’s multiple range test (DMRT).
Treatment

CONCLUSIONSThe GreenSeeker sensor is proved to be an effective tool to predict N uptake in wheat fromdata measured at jointing growth stage. This hand-held GreenSeeker sensor can be used reliably
for the management of N fertilizer in wheat. Accordingly, the application of corrective doses of 0,80, 60.9, 77.3, 85.3, 109.1 or 10.1 kg N ha-1 corresponding to the sensor values of GreenSeeker
has suggested a strategy. Compared to the general recommendation, the suggested strategy used299

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effectively in the management of N fertilizer led to an increasing in N recovery efficiency level of
22.6 percent with statistically similar yield.
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Singh B, Singh Y. 2003. Environmental implications of nutrient use and crop management in ricebased ecosystems. In International Rice Research Conf. Beijing, China, 16-19 September
2002. International Rice Research Institute.
Singh B. 2018. Are nitrogen fertilizers deleterious to soil health? Agron. J. 8(4): 48.
Singh B, Gupta RK, Singh Y, Gupta SK, Singh J, Bains JS, Vashishta M. 2006. Need-based
nitrogen management using leaf color chart in wet direct-seeded rice in northwestern
India. J. of New Seeds 8(1): 35-47.
Singh B, Sharma RK, Kaur J, Jat ML, Martin KL, Singh Y, Singh V, Chandna P, Choudhary OP,
Gupta RK, Thind HS, Singh J, Uppal HS, Khurana HS, Kumar A, Uppal RK, Vashistha
M, Raun WR, Gupta R. 2011. Assessment of the nitrogen management strategy using an
optical sensor for irrigated wheat. Agronomy for Sustainable Development 31(3): 589-603.
Singh B, Singh Y, Ladha JK, Bronson KF, Balasubramanian V, Singh J, Khind CS. 2002.
Chlorophyll meter–and leaf color chart–based nitrogen management for rice and wheat inNorthwestern India. Agron. J. 94(4): 821-829.
Singh V, Singh B, Singh Y, Thind HS, Buttar GS, Kaur S, Singh M, Kaur S, Bhowmik A. 2017.
Site-specific fertilizer nitrogen management for timely sown irrigated wheat (Triticum
aestivum L. and Triticum turgidum L. spp. durum) genotypes. Nutrient Cycling in Agro
Ecosystems 109(1): 1-16.
Varvel GE, Schepers JS, Francis DD. 1997. Ability for in-season correction of nitrogen deficiency
in corn using chlorophyll meters. Soil Sci. Soc. Am. J. 61(4): 233-1239.
Yao Y, Miao Y, Huang S, Gao L, Ma X, Zhao G, Jiang R, Chen X, Zhang F, Yu K, Gnyp ML.
2012. Active canopy sensor-based precision N management strategy for rice. Agronomy
for Sustainable Development 32(4): 925-933.
Zhang J, Liu X, Liang Y, Cao Q, Tian Y, Zhu Y, Cao W, Liu X. 2019. Using a portable active
sensor to monitor growth parameters and predict grain yield of winterwheat. Sensors 19(5), p.1108.

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#7617 A GEOSTATISTICAL APPROACH TO DEFINE A SOIL FERTILITY INDEXBASED ON THE MAIN SOIL MACRONUTRIENTS
H.S. Aboelkier1, A. Nasrallah1, S. Shaddad1, and G. Buttafuoco2
Soil Science department, Faculty of Agriculture, Zagazig University, Egypt
shaddadsm@gmail.com +201016007959; 2National Research Council of Italy - Institute for
Agricultural and Forest Systems in the Mediterranean (ISAFOM), Rende (CS), Italy
1

ABSTRACT
Soil fertility is greatly affected by main soil macronutrients such as nitrogen (N),phosphorus (P), and potassium (K). These macronutrients can be used to define a synthetic fertility
index to support soil fertilization. The study was aimed to propose a geostatistical approach todefine a synthetic fertility index based on factorial cokriging. It consists in quantifying andreducing the spatial variability of multivariate data to only a few factors, related to different spatial
scales. Such factors summarize the variability of multivariate data and can be used to divide thefield in areas of similar levels of the three macronutrients. Hundred 100 soil samples were collectedaccording to a quite regular grid (20 m x 20 m) from a field of 3.6 ha located in Bilbies district(Egypt). The joint variation of N, P, and K was modelled by a linear model of coregionalization
including a nugget effect and two spherical models at short range (42.4 m) and long range (86 m).
The joint multivariate variability of N, P, and K in the study area was synthetized by using the first
two factor at short and long ranges. The first factor at long range allowed more effectively todelineate the field into different management zones than at short range.
INTRODUCTION
Nutrient supply to plants is one of the main soil factors constraining crop growth andconsequently, the yield. The addition of fertilizers has become the main practice to overcome thisconstraint, often developing a meaning of soil fertility limited to the potential nutrient supply forcrop growth (Stockdale et al., 2013). However, fertilization, together with irrigation, is essential
to obtain profitable crop yields. In Egypt farmers add fertilizers to soil considering that fields are
uniform without taking spatial variability into account. Although this is simpler in application,some areas may receive fertilizers that do not meet their needs, while others may be in excess withnegative environmental consequences. Site-specific fertilization may result in maximize soil
productivity and minimize the environmental impacts by adding fertilizer where and when theyneed, and with the precise quantity. Variable rate application of nutrients allows taking into
account the field spatial variability of nutrients in soil and meeting the requirements of crops.
Variable rate application may result in a more effective use of inputs enhancing crop yield to ensure
food security promoting environmental sustainability. Variable rate application is based on thedelineation of management zones which are defined as homogeneous subfield regions that have
similar yield-limiting factors or similar attributes affecting yield (Doerge, 1999; Khosla andShaver, 2001). Management zones being used to avoid over- or under application of agricultural
inputs in some parts of the field and then wasting of natural and financial resources (Mzuku et al.,2005).

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Geostatistics provides the tools to quantify the spatial variation of soil properties and to
produce continuous maps using interpolation techniques, generally known as kriging (Chilès andDelfiner, 2012; Matheron, 1971). Differently from classic statistical interpolators, geostatistics
provides a term of error (kriging variances) which can guide to the reliability of the estimate(Oliver, 2013). Cokriging is a multivariate generalization of kriging to deal with two or more soil
properties which have been measured at the same sampling locations (Chilès and Delfiner, 2012).
Geostatistics may help to solve different aspects of precision agriculture such as delineating
management zones representing subfield regions with homogeneous characteristics within whicha single rate of a specific crop input is appropriate (Buttafuoco et al., 2010). Generally, theidentification of subfield areas is difficult because of the complex combination of factors which
could influence the effectiveness of a specific input (i.e. fertilization, irrigation, pesticide) that
affects variation in response variables, such as requested quality and quantity of crop yield.
Factorial cokriging (FK) allows to summarize the variation of attributes or limiting factors
affecting agricultural production (Buttafuoco et al., 2010). FK allow to quantify and reduce spatial
variability of multivariate data to only a few factors, related to different spatial scales. Such factors,
can be used to divide the field in areas of size manageable by farmers.
The objective of this study is defining a synthetic fertility index to support soil fertilization.
The index is based on three soil macronutrients such as nitrogen, phosphorus, and potassium.
MATERIALS AND METHODS
Study Area and Data
The study area (3.6 ha) is located in Bilbies district, Sharkia Governorate, Egypt. The
coordinates of its centroid are: 31° 39ʹ 24.70ʺ E, 30° 25ʹ 47.45ʺ N. The study area was cultivatedby two different crops: sesame in the southern half field and pepper in the remaining area. Its
climate is characterized by hot dry summers and mild winters with very low annual precipitation(90-125 mm). Mean air temperature is 13.0 °C in January and 29.3 °C in August (El-Marsafawy
et al., 2019).

Figure 1. Study area and sampling locations
Topsoil samples were collected at 100 locations at the nodes of a quite regular grid (20 m x20 m) and analyzed for available N, P, and K. Available nitrogen (NH4-N and NO3-N) was
extracted by KCl 2 N and the extracted nitrogen was determined with steam - distillation procedure303

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using MgO - Devarda alloy (it is an alloy of aluminum (44%–46%), copper (49%–51%) and zinc
(4%–6%) according to Bremner and Keency methods as described by Black et al. (1965); available
phosphorus content (P) (mg kg−1) extracted by Olsen et al. (1954); the extracted phosphorus wasmeasured calorimetrically using the ascorbic acid method (Watanabe and Olsen, 1965) with UV–
vis-NIR spectrophotometer; available potassium (K) (mg kg−1) extracted using 1.0 N ammonium
acetate at pH 7.0 and determined using flame photometer method, (Jackson, 1973).
Geostatistical Methods
Each datum z(x ) at different location x (x is the location coordinates vector and thesampling points = 1, ..., N) of the three soil nutrients was interpreted as a particular realization ofa random variable Z(x ) and analyzed by ordinary cokriging (Wackernagel, 2003) and Factorial
kriging analysis (FKA) (Chilès and Delfiner, 2012; Matheron, 1982).Ordinary cokriging (OCK)
is one of the most basic geostatistical interpolation methods under the assumption of intrinsic
stationarity for all variables. OCK requires modelling the Linear Model of Coregionalization(LMC) (Journel and Huijbregts, 1978), which considers all the n study variables (the threenutrients in this case) as the result of the same independent physical processes, acting over different
spatial scales u. The n(n+1)/2 simple and cross variograms of the three variables are modelled bya linear combination of NS standardized variograms of unit sill, gu(h), each one corresponding to a
spatial scale (u). The goodness of LMC fit was evaluated by the Mean Error (ME) and the Mean
Squared Deviation Ratio (MSDR) (Webster and Oliver, 2007). Ordinary cokriging estimates theunknown soil properties values at the unsampled location as a linear combination of neighboring
observations of all variables ordinary cokriging (Wackernagel, 2003). Factorial kriging analysis
includes three basic steps: (1) modelling the coregionalization of the set of variables using thelinear model of coregionalization, (2) analyzing the correlation structure between the variables, byapplying PCA at each spatial scale, to obtain independent regionalized factors which synthesize
the multivariate information and (3) estimating by cokriging the values of these specific factors at
each characteristic scale and mapping them. In the geostatistical approach, even though it is not
required the data to follow a normal distribution, variogram modelling is sensitive to strong
departures from normality, because a few exceptionally large values may contribute to many verylarge squared differences. All data were transformed into Gaussian-shaped variables with zeromean and unit variance using a Gaussian anamorphosis (Wackernagel, 2003), which is amathematical function that transforms a variable with a Gaussian distribution into a new variablewith any distribution. All statistical and geostatistical analyses were performed by using thesoftware package ISATIS®, release 2018.4 (www.geovariances.com).
RESULTS AND DISCUSSION
All the nutrient values were normalized before applying the multivariate geostatistical
approach using the Gaussian anamorphosis and the variographic analysis allowed to compute theexperimental simple and cross-variograms of all variables. No relevant anisotropy was observedin the variogram maps (not shown) and the experimental simple and cross variograms looked upperbounded. Then, the joint variation of the Gaussian values of N, P, and K was modelled by a LMC
including a nugget effect and two spherical models at short range (42.4 m) and long range (86 m).
Therefore, the LMC showed that the levels of N, P, and K occur to two different spatial scales. To
synthesize the joint multivariate variability of N, P, and K in the study area in a restricted numberof zones to be submitted to differential management, the first two regionalized factors at short and304

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long ranges (Fig. 2) were retained and the ones corresponding to nugget effect were omitted,
because mostly affected by measurement error and variation at a scale smaller than the sampling
distance.
The most influencing soil variables on the first factor at short range were P and K, whereas
K was the most influencing soil variable on the first factor at long range. The resultant maps,
depicting the potential MZ are shown in Fig. 2. It is worth to mention that mapping the first factor
at long range allowed more effectively to delineate the field into different management zones thanat short range. In fact, the values of the first regionalized at long range would allow to split thefield into larger and manageable zones than those for first regionalized at shorter range.

Figure 2. First two regionalized factors at short (a) and long (b) ranges
These results encourage the use of this approach for precise fertilization considering thetype of cultivated crops.
REFERENCES
Black CA, Evans DD, Ensminger LE, White JL, Clarck FE. 1965. Methods of Soil Analysis. Amer.
Soc. of Agron, Madison, Wisconsin, U.S.A.
Buttafuoco G, Castrignanò A, Colecchia AS, Ricca N. 2010. Delineation of management zones
using soil properties and a multivariate geostatistical approach. Ital. J. Agron. 5:323-332.
Chilès, J-P, Delfiner P. 2012. Geostatistics: Modeling Spatial Uncertainty. John Wiley & Sons,
Inc., Hoboken, NJ, USA.
Doerge TA. 1999. Defining management Zone Concepts. Crop Insights 8:1-5.
El-Marsafawy S. Bakr N. El-Bana T. El-Ramady H. 2019. Climate. In: El-Ramady, et al. (eds.),
The Soils of Egypt, World Soils Book Series.
Jackson ML. 1973. Soil Chemical Analysis. Prentice Hall, Inc, Englewood California, New Jersey.
Khosla R. Shaver T. 2001. Zoning in on nitrogen needs. Colorado State University Agronomy
Newsletter 21:24-26.
Matheron G. 1971. The theory of regionalized variables and its applications, Les Cahiers du Centre
de Morphologie Mathematique. Ecole Nationale Superieure des Mines de Paris,
Fontainebleau.
Matheron G. 1982. Pour une analyse krigeante des données regionalisées in Report 732, Centre de305

1st African Conference on Precision Agriculture | 8-10 December | 2020

Geostatistique, Fontainebleau. Fontainebleau.
Mzuku M, Khosla R, Reich R, Inman D, Smith F, MacDonald L. 2005. Spatial variability ofmeasured soil properties across site-specific management zones. Soil Sci. Soc. Am. J.
Oliver MA. 2013. An overview of precision agriculture. In: Oliver MA, Bishop TFA, Marchant,
BP. (eds.). Precision Agriculture for Sustainability and Environmental Protection.
Routledge, New York, pp. 3-19.
Olsen SR, Cole FS, Dean LA. 1954. Estimation of Available Phosphorus in Soils by Extraction
with Sodium Bicarbonate. 939. U.S. Dept. Agri. Cir, pp. 1-9.
Stockdale EA, Goulding KWT, George TS, Murphy DV. 2013. Soil fertility. In: Nortcliff S,
Gregory PJ. (eds.), Soil Conditions and Plant Growth. Blackwell Publishing Ltd, Oxford,
pp. 49-85.
Wackernagel H. 2003. Multivariate geostatistics: an introduction with applications. Springer.
Webster R, Oliver MA. 2007. Geostatistics for Environmental Scientists. John Wiley & Sons, Ltd,
Chichester, UK.

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#7619 QUANTIFICATION OF OPTIMAL FERTILIZERS DEMAND FOR WHEAT
AND CORN FIELDS IN MOROCCO USING VERY HIGH RESOLUTION REMOTE
SENSED IMAGERY AND HYBRID COMPUTATIONAL APPROACHES
K. Misbah1,* A. Laamrani1, A. Chehbouni1,2, D. Dhiba1,3, J.Ezzahar1,4, K.Khechba1
Mohammed VI Polytechnic University (UM6P), Center for Remote Sensing Applications(CRSA), Ben Guerir,43150, Morocco; 2 Centre d’Études Spatiales de la Biosphère, Université deToulouse, Toulouse cedex 9, France; 3 Mohammed VI Polytechnic University (UM6P),
International Water Research Institute (IWRI), Ben Guerir,43150, Morocco; 4 MISCOM, École
Nationale des Sciences Appliquées de Safi, Université Cadi Ayyad, Maroc* khalil.misbah@um6p.ma
1

ABSTRACT
Demand on agricultural products is increasing as population continues to grow. Data driven
management of macronutrients (i.e., nitrogen (N), phosphorus (P) and potassium (K)) and crops
are of critical prominence to get the most out of soil in terms of crop yield while preserving
environment. This study aims to establish a quantitative framework for macronutrient (i.e.,
nitrogen, phosphorus, and potassium) status (i.e., excess, deficiency) for winter wheat (Triticum
aestivum L.) and corn (Zea mays L.) crops, in order to establish variable rate and optimal
fertilization at a field scale. To do so, we will collect soil inputs over experimental plots of wheat
and corn with a fertility gradient variability, located in two Moroccan regions with different
agroclimatic conditions (Mediterranean to semi-arid areas). We will proceed in situ soil and crop
sampling at significant growing stages and dates for lab chemical analysis. The collected field data(soil and crops samples) will be analyzed against remote sensing multi sensors onboard dronebased platforms datasets (i.e., hyperspectral, multispectral, and thermal wavelengths). In parallel,
the collected datasets will serve for training, validation and testing of computational models thatwill be developed in this study. For instance, this study will investigate the performance of a hybrid
approach that incorporates empirical methods of regression (e.g., random forest, principal
component analysis, support vector regression least squares, partial and artificial neural networks)
during the major growth stages for wheat assimilated N, P, and K content estimation, with physicalmethods based on radiative transfer models. If successful, this study is expected to showcorrelations between major nutrients nitrogen (N), phosphorus (P) and potassium (K) content insoil obtained in laboratory and corresponding reflectance generated from processed hyperspectral
and thermal remote sensing datasets. To this end, we will evaluate the development of a generalpurpose model for estimating the respective macronutrients content from these images. The
performance of the model will be assessed using descriptive statistical indices and analysis ofvariance. We expect therefore that this project will demonstrate the potential of precision
agriculture suitability, especially in sub-Saharan African countries.
Keywords: Airborne remote sensing, Drone-based platforms (also known as Unmanned Aerial
Vehicle (UAV)); Agricultural soils and crops; variable rate fertilization, soil fertility, precision
agriculture, machine learning, multi-modeling approaches

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INTRODUCTION
Demand on agricultural products is increasing as population continues to grow (Godfray
et al., 2010). Data driven management of macronutrients (i.e., nitrogen (N), phosphorus (P) andpotassium (K)) and crops are of critical prominence to get the most out of soil in terms of crop
yield while preserving environment. Thus, it is important to know the quantity of soil uptake-readymacronutrients to bring deficient rate through manure or inorganic fertilizers, avoiding therefore
yield loss and potential environmental consequences, usually due to extra dosage of it. Nitrogen(N) presence is critical at physiological scale and constitutes just about 0.2 percent of a plant’s dry
weight. It increases the leaf area index (LAI), chlorophyll content and the activity some enzymes(e.g. phosphoenolpyruvate carboxylase) (Boussadia et al., 2010; Zhu et al., 2014). Nitrogen cycle
in soil and air is quite complex and seems to rapidly enter into volatilization process, and crops
remove an excessive amount of it from soil which turns the reactive part extremely scarce (Naeemet al., 2017). Phosphorus (P) is also ubiquitous in major crops biochemical processes such as
photosynthesis and takes part in some biomolecules such as nucleic acids, adenosine di- and triphosphates (ADP and ATP, respectively) (Wang et al., 2020). Similarly to N and P, Potassium (K)is involved notably in photosynthesis and enzyme activation, which are eventual activities tosustain crop development (Wang et al., 2020).
Soil fertility is a proxy for crop yield, hence laboratory-based soil and yield mapping
techniques can help into getting information about fertility status of soil and to come out with theappropriate recommendation (Patel et al., 2020). However, these methods prove to be costly andvery restricted in time and space, especially if we plan to apply variable rate fertilization over large
fields. During the last decade, multispectral and hyperspectral remote sensing has been employed
for the identification and determination of plant uptake as well as soil nutrient properties throughprocessing of spectral absorption features (Diacono et al., 2014). Various studies on the estimationof important macronutrients of soil through the application of remote sensing were conducted. For
instance, in a recent study, Patel et al. (2020) used Derivative Analysis for Spectral Unmixing(DASU) approach on hyperspectral signatures of different types of soils and found that endmemberfeatures of NPK compost and soil have respectively diagnostic spectral absorption bands
around 989.3 nm and 2195.1 nm , respectively. Reda et al. (2020) conducted a research in Morocco
to assess performance of empirical regression models combined with variable selection algorithms
to predict total P and Olsen P -the one in soil solution available for crops- under soil texture
variation. The study showed that Olsen P prediction with Support Vector Machine regression
coupled with Genetic Algorithm exhibited the best performance, with an R2 = 0.77 and RMSE =20.09 mg kg−1, and noticed a wide variation in regression accuracy between the different
combinations of algorithms and soil textures under investigation. Nonetheless, variable rateapplication technology relies basically on soil and yield mapping to achieve decent fertilizers
recommendation not only for soil calibration but also for in-season correction whenever a kind ofmacronutrients deficiency occurs. For cereal crops, Fu et al. (2020) exhibited that crop reflectancevariability along different growing stages is explained up to 68% by the variation in LAI and
Chlorophyll a and b contents, which are eventual proxies of crop Nitrogen status. Liang (2005)
mentioned that we can assess crop N uptake either through data driven empirical methods or leaf
and canopy radiative transfer models or both. Other studies in fact have tried to estimate leaf N
content through hybrid methods (i.e. the inversion of PROSAIL model with fitting algorithms)
based on aerial hyperspectral data (Li et al., 2019; Liang et al., 2015).

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The choice made by each of the previous studies have shown a potential of remote sensing datasets
to hold information about crop yield and nutrition status, nevertheless these studies featured that
this information is actually hidden under layers governed most of the time by eventual spatial andgenotypical variability. To our knowledge, no research had previously assessed high resolution
remote sensing-based models that fall within optimization of fertilizers in arid and semi-arid regionin Africa on any kind of crops whatsoever. This study aims to tack this question by establish aquantitative framework for optimal macronutrient application for winter wheat and corn crops,
which will ultimately lead to the development of regional framework and a crop specific variable
rate nutrition model at a field scale.
MATERIALS AND METHODS
Study Area and Field Data Collection
This study will be conducted initially in Morocco, with a focus on regions with variability
in both agroclimatic conditions (Mediterranean, semi-arid) and soil types (clay-marl, reddish
siliceous, humus). To do so, we will establish an experimental design with an artificial fertility
gradient experiment using experimental plots that have variability in terms of N, P and K
concentration in soil. Similar protocol was used in previous studies (i.e., Mahajan et al., 2014) toassess nutrients uptake in wheat crops. In terms of the investigate crops, this study will focus on
winter wheat and corn crops as there are the dominant annual cereal crops that exists in Morocco
and in other countries in Africa.
To acquire a holistic overview regarding our prospective experimental plots, it will benecessary to get the most possible amount of data from soil and crops across our experimental
plots, according to a tight schedule of sampling extended over the crop cycle. For soil testing, data
corresponding to soil status before fertility gradient being established to assess the initial amountof solution NPK and crop uptake ready. We will also sample our plots a short period next to fertility
gradient design and sowing, but the most important will be required subsequently after harvest to
be able to establish our balance sheet in respect to crop yield, soil NPK uptake and total removal
by crops. Soil and crop samples will be sent to laboratory for chemical testing to get eventual
responses regarding to NPK in soil solution, crop fresh and dry weight, NPK uptake andconcentration in leaves, stalk and grains.
Remote Sensing Data Acquisition and Processing
Multi sensors UAV (Hyperspectral, multispectral, and thermal) data will be collected overthe investigated the area. This study will gather spectral information over many high-resolution
bands over our experimental fields. The UAV-derived data will be collected on the same day as
ground samples observations (e.g., of soil and crop samples) which will, among other reasons,highlight growth stages in which distinct spectral responses occur. This decision will help us
identify in which physiological stage and spectral wavelength we have significant response related
to the quantity of macronutrients absorbed by our respective crops. In fact, several studies tried tofind out for some crop species, notably for wheat the most effective physiological stages to be
remotely sensed exploited for the estimation of crop yield and nutrition. For instance, Barzin et al.
(2020) tried to develop a yield prediction model for corn using UAV remote sensing and exhibitedthat during vegetative stages of the plant (i.e. V3-4-5), Soil Adjusted Vegetation Index and Canopy
Chlorophyll Content Index were the head estimators in yield predicting models, while Green Leaf
Index and Visible Atmospherically Resistant Index were dominating at tasselling stage (VT) in309

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assessing accurately corn grain yield (R2=0.93). Li et al. (2010) also found that Red edge and NIR
bands turn to be good estimator for wheat N concentration at Feekes 4–7 growth stages (F. Li etal., 2010). Raw data will be geometrically and atmospherically calibrated and converted toreflectance images using ENVI Software (L3Harris Geospatial, USA).
Statistical Analysis and Modeling
We expect to develop and investigate the performance of a hybrid multi-approach that will
incorporate empirical methods of regression (e.g., random forest, principal component analysis,
support vector machine, regression, partial least squares, and artificial neural networks) during the
major growth stages for wheat assimilated N, P, and K content estimation. This method will be
combined with other physical methods based on inversion of radiative transfer models (e.g.,
PROSPECT, LIBERTY.) and regression based on distinct published Red-Edge, IR wavelengths,
as well as hyperspectral vegetation indices. For this, we will use Python based libraries (Pandas,
NumPy, Scikit-Learn, TensorFlow) for training our models, as being commonly used in a wide
community of scientists operating in machine learning and data science. The various modelling
methods will be applied using different packages in R as well as the assessment of the performanceof models.
EXPECTED RESULTS AND PERSPECTIVES
We hypothesize that this study will reveal significance between solution soil nutrient
availability, crops nutrients uptake rate, photosynthesis, physiological events (seedling...), growthstages and spectral responses, mostly around distinguished Red-Edge and IR wavelengths.
Furthermore, we expect to identify diagnostic spectral bands or/and regions that could remotelytranslate nutrient deficiencies which may occur at different growing stages. This would
accordingly reduce yield loss caused by in-season deficiency through eventual crop nutrientscalibration in a preventive manner. Although it has been mentioned in anterior studies that, theempirical relationships are “growth stage site-specific”, and may consequently give rise to some
sort of outliers or inaccurate predictions, we believe that their application in other sites withdifferent conditions (i.e., radiation incidence angle, soil texture and moisture, crop health status
and respective canopy and leaf distribution) can lead to different outcomes (Hatfield et al., 2008).
To avoid such constraints, we have considered incorporating hybrid computational approaches bytrying to fit our models in respect to canopy and leaf radiative transfer models, through which we
expect to determine crops physical and physiological features associated with crop nutrition status.
This study will take advantage of the increased availability of lightweight hyperspectral
sensors onboard UAVs to acquire high-spatial-resolution imagery (i.e., hyperspectral). Hence,using cost-effective remote sensing tools to optimize the process of fertilization and water
consumption will enhance the extension of precision agriculture efficiency. It will also help tohave efficiency controlling farming inputs with higher accuracy regarding the specific need innutriments in Morocco and Africa. Moreover, the temporal and spectral resolutions that airborne
imagery is offering in this research, will drastically reduce the cost of soil analysis and fieldresearch, and will ultimately contribute to maximize crop yields with remaining sustainable, anaccessible matter to all size of farms, especially smallholder farms. This will strongly commit not
only to Moroccan practices of agriculture and food security but will also impact the African
environmental and social development.

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Naeem M, Ansari AA, Gill SS. 2017. Essential plant nutrients: Uptake, use efficiency, andmanagement. In Essential Plant Nutrients: Uptake, Use Efficiency, and Management.
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EM. 2020. Predicting soil phosphorus and studying the effect of texture on the prediction

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accuracy using machine learning combined with near-infrared spectroscopy. Spectrochimica
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#7708 ENHANCING THE USE OF APPROPRIATE FERTILIZERS FOR IMPROVINGRICE AND MAIZE PRODUCTION IN TANZANIA
C.J. Senkoro1, K.A. Mtua1, C. Komba1, S.A. Kazyoba1, J.D. Mbogoni1 and M.M. Msolla2
1
Tanzania Agricultural Research Institute, TARI Mlingano, P. O. Box 5088, Tanga, Tanzania
2
OCP Tanzania Limited, P. O. Box 10049, Dar es Salaam, Tanzania
catherine.senkoro@tari.go.tz; cathysenkoro@gmail.com +255 754 649188
ABSTRACTIn years 2017 to 2019, site specific fertilizer trials were conducted in Tanzania to enhance
the precision in using appropriate fertilizer in rice and maize production. Two new fertilizers
namely: NPS (19- 38-0+7S) and NPSZn (12- 45-0+5S+1Zn) were tested in comparison with DAP,
the commonly used fertilizer by farmers. Control treatment was also added to assess crop yields
under farmers practices. The trial design was randomized complete block designed. Treatments
were replicated 4 to 16 times in each village depending on soil variations and willingness offarmers to set trial. The amount applied for NPS and NPSZn fertilizers were based on therecommended rates of P for rice and maize in the studied Agricultural Zones. Nitrogen fertilizer
was applied in 2 splits as urea in all treatment except control. The amount of urea applied was
adjusted to take into consideration the N contained in each fertilizer. Results indicates that there isa significant variation (P<0.05) of soil fertility status and crop yields among agro-ecological zones.
All study sites are characterized by low levels of N and OC for maize and rice production. Mostof the study areas have medium to low levels of P, K, Zn, Mg, Ca, and S. In Southern highlands
zones, application of NPSZn and NPS gave significantly higher rice grain yields up to 8.39 t ha-1
as compared to DAP fertilizer (6.84 t ha-1). For the other 7 zones, fertilizers tested (DAP, NPSZn,
NPS) had comparable effectiveness in improving rice and maize grain yields. The tested fertilizers
increased up to 3 times more grain yields than control practices. Economically, NPSZn and NPS
fertilizers gave more profit in maize and rice production than DAP and farmer’s practices. It is
recommended that, fertilizers NP+S and NP+SZn be adopted by farmers in the study areas insteadof DAP. Further research is needed to determine appropriate S, K, Zn, Mg, and Ca nutrients rates
which can be used to formulate balanced fertilizer recommendations for improving maize and riceproduction in Tanzania.
INTRODUCTION
Maize and rice are important staple food in Tanzania. Maize ranks the first staple food
followed by rice. These crops are also used to generate income when there is surplus. Most soils
under maize and rice production in the country are characterized by low soil fertility. Nitrogen (N)and phosphorus (P) are the major limiting nutrients. For this reason, nutrient management efforts
have been concentrating on these two nutrients. The available fertilizer recommendations for mostcrops by Samki & Harrop (1984), Mowo et al. (1993) and Marandu et al. (2016) do not cover
micronutrients because of insufficient information on micronutrient status of the soils as well as
response of crops to micronutrients. However, some studies have indicated deficiencies of
micronutrients particularly Zn, Cu and S in some parts of Tanzania (Amur and Semu 2006;
Massawe and Amur, 2012; Kamasho and Singh 2012; Mhoro et al., 2015; Senkoro et al., 2017)).

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Fertilizer blends which contain macro nutrients in combination with secondary and/or
micronutrients for rice and maize production are currently very few in Tanzania. Among major
reasons include inadequate information on soil fertility status of most maize and rice producing
areas, and few scattered experiments, which did not generate adequate information that can beused to develop fertilizer formulations for entire country. Most of the established recommendations
are also blankets recommendation. They did not consider the variations in soil fertility acrossfarmers’ fields. Site specific soil fertility assessment is therefore important to formulate
appropriate fertilizer blends and increase precision and efficiency in fertilizer use by farmers.
The main Objective of this study was to generate information which will be used toformulate appropriate fertilizer blends for improving rice and maize production in Tanzania. The
specific objectives were to: i) assess current soil fertility status in selected major rice and maize
growing areas in Tanzania, ii) determine the limiting nutrients which contribute to low rice andmaize production, iii) assess the effects of OCP blended fertilisers namely NPS and NPSZn tomaize and rice yields iii) determine the economic returns of using OCP blended fertilisers on maize
and rice production.
MATERIALS AND METHODSIn years 2017 to 2019, soil fertility assessment was conducted in 769 farmers’ field in theSouthern Highland Zone (SHZ), Western Zone (WZ), Lake Zone (LZ), Northern Zone (NZ),
Eastern Zone (EZ) and Central Zone (Figure 1). The sites were purposively selected to evaluate
performance of (NPS (19-38-0+7S) and NPSZn (12-45-0+5S+1Zn) on rice and maize yields. Sites
selection was based on the production potential for both crops, climatic conditions, fertilizers
use and accessibility. In each selected field, a geo-referenced composite sample of topsoils (0 –20 cm) and sub soil (20 – 40 cm) were collected and brought to laboratory for analysis. Soilsamples were air dried and sieved through a 2 mm sieve ready for laboratory analysis.

Figure 1. Location of the study villages in Tanzania where site specific OCP fertilizer trials wereconducted.

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Nutrient availability was determined using mid-infrared (MIR) spectral analysis following
validation using data from wet chemistry analysis which were 10% of the total samples (Shepherd
and Walsh, 2007; Terhoeven-Urselmans et al. 2010; and Towett et al. 2015). Soil pH wasdetermined with a 1:2.5 soil: water slurry. The Mehlich 3 extraction was used for available P,
exchangeable bases and available micronutrients (Mehlich 1984).
Soils at the study areas are characterized by low levels of N and OC for maize and riceproduction. Most of the study areas have medium to low levels of P, K, Zn, Mg, Ca, and S ((Landon
1991; Horneck et al. 2011; Howeler 2002).
The experimental design was randomized complete block designed with 4 treatments
replicated 4 to 16 times in one village depending on soil variations. Treatments tested were: (1)absolute control (2) NP at recommended rates for maize and rice, (3) NPS (19-38-0+7S) and (4)NPSZn (12-45-0+5S+1Zn). The amount applied for NPS and NPSZn fertilizers were based onrecommended rate of P for rice and maize (Senkoro et. al., 2017). Nitrogen fertilizer was appliedin 2 splits as urea in all treatments except control. The amount of urea applied was adjusted to take
into consideration the N contained in each fertilizer. The amount of fertilizer nutrient rates applied
are as indicated in Table 1. Maize and rice grain yield data were subjected to Analysis of Varianceusing Statistix statistical programme and where significant difference existed, means wereseparated using Duncan’s New Multiple Range Test. The profitability of maize and riceproduction in the study areas from each of the four fertilizer types was estimated through a partial
budget analysis, and Value-Cost Ratio (VCR).
Table 1. Nutrients from NPSZn and NPS fertilizers applied to rice and maize in the studied
zones.
Agricultural Zone

Rice
N*
P*
S
Zn
Nutrients applied (kg ha-1) from NPSZn (12-45-0+5S+1Zn)
Eastern Zone
150
20
5.0
1
Northern Zone
100
20
5.0
1
Southern Highland Zones
116
40 10.02Western Zone
66
23
8.1
1.2
Lake Zone
80
31
7.8
1.6
Nutrients applied (kg ha-1) from NPS (19-38-0+7S)
Eastern Zone150
20
7Northern Zone
100
20
7Southern Highland Zones
116
40
14
Western Zone
66
23
8.1
Lake Zone80
31 10.9*Fertilizer recommendations for the Zones

0
0
0
0
0

N

Maize
P
S

Zn

65
74
60
43
53

40
20
20
25
30

10
5
5
6.3
7.5

2
1
1
1.3
1.5

65
74
60
43
53

40
20
20
25
30

14
7
7
8.8
10.5

0
0
0
0
0

RESULTS AND DISCUSSION
The results indicate that most smallholder famers (52 – 95%) in Central, Western, Eastern,
Northern and Lake Zones do not use fertilizers in maize and rice production; 70% of farmers inSouthern Highland Zone use fertilizers. There is a significant variation (P<0.05) of soil fertility

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status and crop yields among agro-ecological zones indicating that site specific fertilizer
recommendation is important to increase fertilizer use efficiency and crop yield. All study sites
are characterized by low levels of N and OC for maize and rice production. Most of the study areas
have medium to low levels of P, K, Zn, Mg, Ca, and S. In Southern highlands zones, applicationof NPSZn and NPS gave significantly higher rice grain yields up to 8.39 t ha-1 as compared to
DAP fertilizer (6.84 t ha-1). For the other 7 zones, fertilizers tested (DAP, NPSZn, NPS) had
comparable effectiveness in improving rice and maize grain yields (Table 2). The tested fertilizers
increased up to 3 times more grain yields than control practices (Table 2).
Economically, NPSZn and NPS fertilizers gave more profit in maize and rice production
than DAP and farmer’s practices. Rice yield from NPSZn fertilizer in Nzega district in the lake Zone
had the highest net benefit of TZS. 7,370,597 equivalents to US $ 3,258.40.
Table 2. Rice response to tested fertilizers in the studied Agricultural Zones in Tanzania.
Grain yield per fertilizer type (t ha-1)

Crop and Zone

Level ofsignifica
nce

CV
(%)

DAP

5.35a
8.81a
7.82a
6.32c
5.1
4.5

NPSZ
n
5.29a
9.76a
8.39a
7.56a
6.4
4.8

5.72a
8.29a
6.35b
6.84b
5.9
4.8

***
***
***
***
ns
ns

32.0
32.1
25.0
14.0
31.8
18.3

4.24
5.21
6.55a
3.61

4.27
5.83
6.69a
3.95

3.62
5.99
6.46a
3.26

ns
ns
***
ns

55.0
78.0
39.0
42.0

Control

NPS

Rice
Lake Zone
Western Zone
Southern Highland Zone 1
Southern Highland Zone 2
Northern Zone
Eastern Zone

2.26b
4.54b
3.55c
5.37d
5.1
3.6

Maize
Lake Zone
Western zone
Southern highlands1
Southern highlands2

1.83
3.42
3.36b
1.60

Northern Zone
2.057b
3.173b
3.745b
3.7b**
21.6
Eastern Zone
1.15
1.73
1.99
1.91
ns
26.7
Numbers within the same row bearing the same letter are not significantly different using
Duncan New Multiple Range Test (DNMRT)CONCLUSIONS AND RECOMMENDATIONSThere is significant variation of soil fertility status among sites in areas under rice andmaize production in Tanzania emphasizing that more assessment is needed to establish site specificnutrient recommendations. Due to nutrient mining, application of N and P alone will not lead tosustainable production. It is recommended that, fertilizers NP+S and NP+SZn be adopted by
farmers in the study areas instead of DAP. Further research is needed to determine appropriate S,
K, Zn, Mg, and Ca nutrients rates which can be used to formulate balanced fertilizer
recommendations for improving maize and rice production in Tanzania.

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ACKNOWLEDGEMENTS
TARI Mlingano is grateful to OCP Africa for providing funds to conduct this study. The
author acknowledges the Ministry of Agriculture and the District Councils for enabling the fieldresearch. Many thanks to researchers, OCP Africa staff, farmers and extension officers who
participated to this study.
REFERENCES
Black CA. 1982. Methods of Soil Analysis. Part 2. Chemical and Microbiological Properties,
Agronomy Monograph 9.2, 1982 Published by: American Society of Agronomy, Soil
Science Society of America
Blaesing D. 2006. in http://www.agvita.com.au/pdf/expresssoil/Soil_P_tests.pdf
Dewis J, Freitas F. 1970. Physical Methods of Soil and Water Analyses. FAO Soil Bull. No.10,
pp. 39-51.
Grobler L, Bloem AA, Claassens AS. 1999. A critical soil sulphur level for maize (Zea mays L.)grown in a glasshouse, South African Journal of Plant and Soil 16(4): 204-206, DOI:
10.1080/02571862.1999.10635012
Horneck DA, Sullivan DM, Owen J, Hart JM. 2011. Soil test interpretation guide
https://catalog.extension.oregonstate.edu/ec1478, accessed on 20th December 2017
Landon JR. 1991. Booker tropical soil manual. Longman Group UK Ltd., Harlow. 474 pp
Marandu AET, Mbogoni JDJ and Ley GJ. 2014 (Eds.). Revised Fertilizer Recommendations for
Maize and Rice in the Eastern, Southern Highlands and Lake Zones of Tanzania. Ministryof Agriculture, Food Security and Cooperatives, Department of Research andDevelopment, Dar-es-Salaam, Tanzania. 40 pp.
Mehlich A. 1984. Mehlich 3 Soil Test Extractant: A Modification of Mehlich 2 Extractant.
Comm. Soil Sci. Plant Anal. 15 (12): 1409–1416. DOI 10.1080/00103628409367568
Mowo JG, Floor J, Kaihura FBS, Magogo JP. 1993. Review of Fertilizer Recommendations in
Tanzania. Part 2: Revised Fertilizer Recommendations for Tanzania. National Soil
Service, ARI Mlingano, Ministry of Agriculture, Tanga, Tanzania.
Ruffo M, Olson R, D averede I. 2016. Maize Yield Response to Zinc Sources andEffectiveness of Diagnostic Indicators. Communications in Soil Science and Plant Analysis
47: 137-141.
Sawyer JE, Mallarino AP, Killorn R. 1999. Differentiating and Understanding the Mehlich 3, Brayand Olsen soil P tests. In General guide for crop nutrient recommendation in Iowa. Publ.
Pm-1688 (Rev.). Iowa State Univ. Ext., Ames.
Senkoro CJ, Ley GJ, Marandu AE, Wortmann C, Mzimbiri M, Msaky J, Umbwe R and Lyimo SD.
2017. Optimizing fertilizer use within the context of integrated soil fertility management
in Tanzania. In Fertilizer Use Optimization in sub-Saharan Africa. Wortmann CS andSones K (Ed). Published by CABI, UK pp 176-192
Senkoro CJ, Kalumuna M, M t u a K , Wickama J. 2017. Site Selection and Characterization–Report 1. OCP Africa and ARI Mlingano, Tanga. 30 pp
Sillampaa N. 1982. Micronutrients and nutrients status of soils. A global study, FAO soil
bulletin, FAO. Rome, pp. 17 – 96.
Wendt JW. 1995. Evaluation of the Mehlich 3 soil extractant for upland Malawi soils.
Communications in Soil Science and Plant Analysis 26(5-6).

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#7785 COMPARATIVE FERTILIZATION EFFECTS ON MAIZE PRODUCTIVITYUNDER CONSERVATION AND CONVENTIONAL TILLAGE ON SANDY SOILSIN A SMALLHOLDER CROPPING SYSTEM IN ZIMBABWE
Nyasha Kafesua, Regis Chikowoa and Shamie ZingorebaUniversity of Zimbabwe, Harare, ZimbabwebInternational Plant Nutrition Institute, Nairobi, Kenya, nyashakafesu@yahoo.com
ABSTRACTLow crop yields, food insecurity and abject rural poverty continue to be rampant in muchof Southern Africa. Components of conservation agriculture (CA) are being widely promoted insouthern Africa as one of the strategies to increase food security and mitigate rural poverty, despite
there being scarce empirical evidence on their efficacy on degraded soils. This research aimed toassess the effects of tillage systems on maize grain yields under rain-fed conditions across a soilorganic matter gradient using on-farm trials set-up in Eastern Zimbabwe. The effects of threetillage systems were compared, that is a) conventional tillage (CT), b) basins-based CA (B-CA),and c) furrow-based CA (F-CA) on sandy soils with soil organic carbon (SOC) ranging from 0.180.89% and clay content from 60 -150 g kg-1. Fields were tagged using a Geography Positioning
System (GPS) and mapped to improve nutrient targeting across seasons. An on-farm study wasestablished with thirty farms, each with two fields previously selected as either rich or poor byhost farmers as a nutrient omission trial using nitrogen (N), phosphorus (P), potassium (K), cattle
manure (M) and their combinations. Host farmers’ local soil fertility rating of poor and rich fields
was validated by lab-based results which showed that poor fields had SOC <0.4%, were more
acidic, had lower amounts of exchangeable bases (Mg, Ca, K), available P and total N. Whilst no
significant tillage effects were observed in the first year, nutrient management significantly
increased maize yields across the three years (P<0.001). Maize grain yields increased from 0.3 Mg
ha-1 for unfertilized control to 4.1 Mg ha-1 for the NPKSM treatment. Maize grain yields weresignificantly higher under B-CA compared to both F-CA and CT in the second year (P <0.01),
responding to improved targeting of fertilizers using basins. Maize grain yields were consistently
larger for SOC rich fields. Response to N increased with increase in soil fertility, suggesting higher
N use efficiency for soils with higher SOC. An amalgamated approach to nutrient management
using both organic and inorganic nutrient sources is vital to ensure maize productivity on poor
soils in agro-ecologies receiving unreliable rainfall.
Keywords: conservation agriculture, soil organic carbon (SOC), nutrient targeting, tillage practice
INTRODUCTION
Hunger and poverty that continues to ravish sub-Saharan Africa (SSA) is mainly a resultof poor maize grain yields by farmers averaging less than 1 Mg ha-1. Agricultural crop production
is primarily rain-fed, characterised by mid-season droughts and flash floods. There is need torethink strategies to improve productivity (Tittonell & Giller, 2013), as approaches to curb foodinsecurity in the region have largely failed to capture the individual needs of farmers and therefore
gains continue to be abortive. Blanket fertiliser recommendations on heterogenous soils are often318

1st African Conference on Precision Agriculture | 8-10 December | 2020

promoted without consideration of farm specific needs. Farmers constantly use little to no fertiliseruse, with resource constrained farmers failing to invest in organic fertilisers (Mtambanengwe &Mapfumo, 2005). Strategies to preserve moisture and maximise the little additional fertilisers
continue to be relevant in soils with low soil organic carbon (SOC), areas experiencing rainfall
variability and farmers who are resource constrained. Solutions that are often presented to work in
the present environment have often worked elsewhere but with little empirical evidence on how
they will perform in the local context. Governmental effort in Zimbabwe is concentrated on
promoting CA but there is lack of knowledge on how the technology will perform in varied farming
systems with little fertiliser use and dominated by maize monocropping. There is increased needto generate data on how the technology will perform in comparison to balanced CT systems. The
study therefore aimed to: i) assess the influence of inherent soil fertility on the performance of CA
and CT technologies under similar production environments in the smallholder farming systems,
and ii) determine the interaction of soil fertility and tillage system on maize yield response to
application of macronutrients. In this context, CA is used but is deficient in meeting all thenecessary CA pillars. In its strictest sense they are tillage systems compared as a result.
MATERIALS AND METHODS
The study was established in Zimbabwe, Murewa district (17°49′S, 31°34′E; 1400 masl)
spanning over three seasons till 2016. Murewa recorded daily cumulative rainfall of 1087 mm anda minimum cumulative daily rainfall of 587mm with varied distribution from November to Marchacross the seasons. Characteristic sandy-lixisols with poor SOC content are dominant. Murewa has
a strong crop-livestock interaction where livestock graze on crop residues and manure is used tofertilise fields. For this study, to help understand how CA best fits in the local farmer context, crop
residues were retained in the field after each harvest and were partially grazed by livestock.
Farmers were tasked to identify the most fertile (rich) field and the least (poor) field in an
exploratory survey involving 70 farms. Farmer’s soil fertility rating was amongst other factors afunction of preceding nutrient management and response to fertilizer amendments, historical crop
productivity and indicator weed species. Composite soil samples consisting of five subsamples
were collected along the field’s diagonal line from the plough layer (0–20 cm depth) and bulked.
The soil samples were air dried and prepared, pH and soil texture determined (Gee & Bauder,1986) and tested for SOC (Okalebo, et al., 2002), total N determined, as well as available P(Anderson & Ingram, 1993) and extractable bases. Soils had clay content that ranged between 60– 150 g kg-1 and SOC ranged between 0.18 – 0.89% C. Fields were later were grouped into threesoil fertility classes (Field Types 1–3) as defined by Kurwakumire et al. (2014). The majority(48%) had SOC of less than 0.4% C – type 1. Experiments were set on these rich and poor fields
as split-plot designs with tillage as the main plot (B-CA, F-CA and CT), 6 nutrient omission
treatments as sub-plots in 6 x 5m plots and farmers used as replicates. Nutrient omission treatments
were as follows: i) Control (no nutrients added), ii) PKS (single super phosphate, (18 P2O5 + 9%S) + muriate of potash (60% K). iii) NK (ammonium nitrate (34.5% N) + muriate of potash. iv)
NPS (ammonium nitrate + single super phosphate). v) NPKS (ammonium nitrate + muriate ofpotash + single super phosphate). vi) NPKSM (ammonium nitrate + muriate of potash + single
super phosphate, +cattle manure −M). Fertilizer treatments were designed to reflect amendmentsthat are normally accessible and used by farmers which are usually constrained in availability assole S fertilizers. Nitrogen was applied as a rainfall response strategy (targeting 0-140 kg N ha−1),
at 110 kg N ha−1 in years 1 and 2, and 90 kg N ha−1 in year 3. Other fertilizer rates were at 30 kg

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P ha−1, 30 kg K ha−1, and 5 Mg ha−1 manure. Manure used in the study contained an average of
1.1% N, 0.15% P, 0.18% Ca, 0.09% Mg, 0.7% K, 20 mg kg−1 Cu, 285 mg kg−1 Mn, 810 mg kg−1
Fe and 115 mg kg−1 Zn, which translate to annual nutrient investments of 55 kg N, 7.5 kg P, 35 kg
K, 4.5 kg Mg, 9 kg Ca, 0.1 kg Cu, 1.425 kg Mn, 4.05 kg Fe and 0.575 kg Zn ha−1. Rain gauges
were used to monitor rainfall in all study sites, and planting was done to achieve a plant populationof 44400. Harvesting of maize plants was done at physiological maturity from central net plots of
3.6 m2 (2 rows × 2 m long). Yields were computed and reported at 12.5% moisture content, in linewith moisture level at which maize grain is reported and marketed in the region. Nutrient
limitations/responses were determined by calculating the difference in the attainable yield and thenutrient-limited yield.
All the data violated the ANOVA assumptions, hence were transformed before ANOVA
analysis. Soils were grouped into three classes based on SOC content according to Kurwakumire
et al. (2014). Data was clustered into three field Types based on SOC content. Data was subjected
to ANOVA analysis using a generalized linear model to test tillage effects (main plot factor),fertilization (sub-plot factor), and their interactions on grain yields across the three years. [Field
Type 1, SOC < 0.4; Type 2, 0.4% < SOC < 0.6%; and Type 3, SOC > 0.6%]. Separation of means
was done using the Fischer’s protected least significance test (LSD) at 5% significance level. All
statistical analyses were done using GENSTAT version 14 statistical package.
RESULTS
The season (year), field type and fertilization showed significant maize yield effects(P<0.005). Maize productivity constantly increased as we moved from type 1 (SOC< 0.4%) totype 3 (SOC > 0.6%) fields across all seasons and treatments. Full NPKS + manure treatment
showed consistently larger grain yields across all field types, whilst the control (0.27–0.38 Mgha−1) and PKS (0.36–0.48 Mg ha−1) treatments were consistently lower for all field types.
Whilst no tillage effects were observed in the first year, significant tillage effects were only
observed from the second season (P = 0.005). The B-CA outperformed both F-CA and CT (by up
to 1 Mg ha-1), even in poor soils (with SOC < 0.4%), which could be as a result of increased soil
water availability and precise fertilizer application. Grain yield differences (deviation from the
NPKS treatments) showed largest negative differences under B-CA and the largest positive
differences (0.8 Mg ha-1) for NPKSM treatment under B-CA as well, suggesting additional benefitsof using manure. Nutrient omission experiments showed a constant grain yield penalty following
N omission across all sites. Nutrient N and P response was highest in soils with > 0.6% SOC at 37kg grain kg−1 N and 63 kg grain kg−1 P applied.
DISCUSSION
In year 2, B-CA showed significantly higher yields than both F-CA and CT. Improved
nutrient targeting, cumulative fertilizer effects and increased efficiency of rainwater are
highlighted as the major reasons surrounding successes in CA systems, especially in seasons withlimited seasonal rainfall. Thierfelder and Wall (2012)’ s studies have proved the benefits of CA
over CT after at least one cropping season, while Kihara et al. (2011), sited gains after 3 cropping
seasons. Concerns have been raised over the increased costs on labor with CA, but Twomlow etal. (2008a, 2008b) established that CA gave returns that are twice those of CT. Whilst residue
retention helps improve soil quality (Govaerts, et al., 2009), and retain P in soils, it is proving to320

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be extremely difficult to retain all crop residues as livestock are left to graze openly (Zingore, etal., 2011). Using both organic and inorganic fertlizers resulted in increased crop productivity
which was synonymous with results from Mtambanengwe et al., (2006) and Kurwakumire et al.,(2014). Mtambanengwe and Mapfumo, (2005); Zingore et al., (2008) attributed these increases
partly to pH amelioration, increased water infiltration, reduced run-off and increased SOC andimproved micronutrient uptake. Strategic fertilizer targeting was identified as one of the strategies
for viable fertilizer use (Giller, et al., 2006).
Inherent soil fertility affects maize response to applied fertilizers (Vanlauwe et al., 2006)
and integrating N2- fixing is suggested as the most viable option in many cases (Chikowo et al.,
2004; Rusinamhodzi et al., 2012). Farmers’ ability to rate fields as poor and rich could be exploited
to allow them to focus limited nutrient amendments on fields that give better returns to nutrient
amendment. These results were in line with results by Zingore et al. (2007) where home fields that
had higher SOC content, available P and exchangeable bases, had higher yields than the outfields
with poor SOC content. Rehabilitating depleted soils therefore proves to be necessary to getmeaningful returns to input investments.
CONCLUSIONSThe study investigated the effect of tillage practices on productivity as affected by nutrient
management on predominantly poor fertility soils. The positive effect of B-CA was observed fromthe second year, and was probably a function of both season type and accumulation of nutrients.
Basins-based CA concentrates nutrients as nutrient application is physically localized near plantroots hence superior yields observed. The highest yields were achieved with the application of
NPKSM irrespective of tillage system. As expected on soils with such low SOC, N was the mostlimiting nutrient for maize crop productivity. Prioritization is therefore essential due to poor
residual effects of N fertilizer, particularly on sandy soils. Co-application of N and P was the ideal
fertilizer investment strategy in Year 1. This fertilization strategy is beneficial irrespective oftillage system, when acutely poor soils that are non-responsive to fertilizers are avoided. Thishighlights the challenges to sustainable crop production intensification faced by smallholder
farmers in SSA. Maize grain yields were consistently larger for SOC rich fields. Response to N
increased with increase in soil fertility, suggesting higher N use efficiency for soils with higher
SOC. Combining both organic and inorganic nutrient sources therefore proves to be a viableapproach to nutrient management to ensure maize productivity on poor soils, which were the mostwidespread soils in agro-ecologies receiving unreliable rainfall.
REFERENCES
Anderson JM, Ingram JS. 1993. Tropical Soil Biology and Fertility: A Handbook of Methods.
Wallington, Oxfordshire: CAB International.
Chikowo R, Mapfumo P, Nyamugafata P, Giller KE. 2004. Maize productivity and mineral N
dynamics following different soil fertility management practices on a depleted sandy soil
in Zimbabwe. Agric. Ecosyst. Environ. 100: 119-131.
Gee GW, Bauder JW. 1986. Particle size analysis. In: A. Klute, ed. Methods of soil analysis.
Madison, WI: Am. Soc.Agron. pp.383-411.
Giller KE, Rowe EC, de Ridder N. van Keulen H. 2006. Resource use dynamics and interactionsin the tropics: scaling up in space and time. Agric. Syst. 88: 8-27.

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Govaerts B et al. 2009. Conservation agriculture and soil carbon sequestration: between myth andfarmer reality. Crit. Rev. Plant Sci. 28: 97-122.
Kihara J, et al. 2011. Conservation tillage, local organic resources and nitrogen fertilizer
combinations affect maize productivity: soil structure and nutrient balances in semi-arid
Kenya. Nutr. Cycl. Agroecosyst. 90: 213-225.
Kurwakumire N, et al. 2014. Maize productivity and nutrient and water use efficiencies across soil
fertility gradients on smallholder farms in Zimbabwe. Field Crops Res. 164: 136-147.
Mtambanengwe F, Mapfumo P. 2005. Organic matter management as an underlying cause for soil
fertility gradients on smallholder farms in Zimbabwe. Nutri. Cycl. Agroecosyst.
73: 227-243.
Mtambanengwe F, Mapfumo P, Vanlauwe B. 2006. Comparative short-term effects of different
quality of organic resources on maize productivity under different environments in
Zimbabwe. Nutr. Cycl. Agrocosyst. 76: 271-284.
Nikoli T, Matsi T. 2011. Influence of liquid cattle manure on micronutrients content and uptake
by corn and their availability in a calcareous soil. Agron. J. 103: 113-118.
Okalebo JR, Gathua KW, Woomer PL. 2002. Laboratory Methods of Plant and Soil Analysis: A
Working Manual. Nirobi, Kenya: TSBF-UNESCO.
Rusinamhodzi L, Corbeels M, Nyamangara J, Giller KE. 2012. Maize–grain legume intercropping
is an attractive option for ecological intensification that reduces climatic risk forsmallholder farmers in central Mozambique. Field Crops Res. 136: 12-22.
Thierfelder C, Wall PC. 2009. Effects of conservation agriculture techniques on infiltration andsoil water content in Zambia and Zimbabwe. Soil Tillage Res. 105: 217-227.
Thierfelder C, Wall PC. 2012. Effects of conservation agriculture on soil quality and productivity
in contrasting agro-ecological environments in Zimbabwe. Soil Use Manage. 28: 209-220.
Tittonell P, Giller KE. 2013. When yield gaps are poverty traps: the paradigm of ecologicalintensification in African smallholder agriculture. Field Crops Res. 143: 76-90.
Twomlow SJ, Urolov JC, Jenrich M, Oldrieve B. 2008b. Lessons from the field–Zimbabwe’s
conservation agriculture task force. J. SAT. Agric. Res. 6:1-11.
Twomlow S, et al. 2008a. Building adaptive capacity to cope with increasing vulnerability due toclimatic change in Africa - a new approach. Phys. Chem. Earth. 3: 780-787.
Vanlauwe B, et al. 2010. Integrated soil fertility management operational definition andconsequences for implementation and dissemination. Outlook Agric. 39: 17-24.
Vanlauwe B, Tittonell P, Mukalama J. 2006. Within-farm soil fertility gradients affect response ofmaize to fertilizer application in western Kenya. Nutr. Cycl. Agroecosyst. 76: 171-182.
Zingore S, Delve RJ, Nyamangara J, Giller KE. 2008. Multiple benefits of manure: the key tomaintenance of soil fertility and restorationof depleted sandy soils on African smallholder
farms. Nutr. Cycl. Agroecosyst. 80: 267-282.
Zingore S, Murwira HK, Delve RJ, Giller KE. 2007. Soil type, management history and current
resource allocation: three dimensions regulating variability in crop prouctivity on African
smallholder farms. Field Crops Res. 101: 296-305.
Zingore S, et al. 2011. Managing soil fertility diversity to enhance resource use efficiencies insmallholder farming systems: a case from Murewa district, Zimbabwe. Nutr. Cycl.
Agroecosyst. 90: 87-103.

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#7835 SPATIAL VARIABILITY OF SOIL AND PLANT NUTRIENT STATUS IN
RELATION TO THE OCCURRENCE OF BITTER PIT IN APPLE ORCHARDS IN THE
SAIS PLATEAU, MOROCCO
R. Bouabid*, H. Labazi and H. Bahri
National School of Agriculture of Meknes, Morocco, rbouabid@enameknes.ac.ma
ABSTRACT
Bitter-pit is a nutritional disorder that affects apple fruit quality and post-harvest cold
storage aptitude. In many Moroccan orchards, this disorder causes substantial yield loss on thefield and upon cold storage. It is attributed to several factors, mainly the Ca-K-Mg nutrition
equilibrium. Soil, leaf and fruit analyses can help assess the nutritional status and vulnerability tosuch a disorder. However, conventional methods relying on few samples cannot inform about the
spatial variability of soil and tree conditions across the field, especially in large farms. The present
study aims at assessing the spatial variability of soil, leaf and fruit K-Ca-Mg in an apple orchard
using a high-density grid sampling in order to get a better understanding of the incidence of bitter
bit and propose site-specific nutrient management. The study was conducted on a 30-ha field andtargeted the Golden Smoothee variety. Composite samples of soil and leaf were taken at 130
regularly spaced points (50 m). Soil analyses concerned mineral N, exchangeable K, Mg and Ca.
Leaf analyses included N, K, Mg and Ca. Spatial variability maps were elaborated for each nutrientand for the (K+Mg/Ca) ratio using spatial interpolation to identify the most vulnerable areas tobitter-pit. The results revealed important spatial variability for all nutrients, with K buildup on amajor part of the field, thus causing a high (K+Mg)/Ca ratio incriminated for bitter-pit incidence.
Leaf K content was normal to high with a N-S gradient, while leaf Ca was low on a large part of
the field despite the high levels of exchangeable Ca in the soil. Leaf (K+Mg)/Ca ratio showed alsosimilar trends to that of soil with values greater than 1.2 considered as a threshold for bitter pit
development. Based on cut-off levels of 1.25 and 1.5, the map shows that about 35% of the field
is of moderate risk and about 25% is of high risk to bitter-pit incidence. The spatial variability
maps can serve as a decision support for site-specific management of fertilizers application to thesoil, mainly K and N, as well as foliar applications of Ca in order to balance tree nutrition, avoid
bitter pit occurrence and ensure better fruit quality, and profitability.
Keywords: bitter-pit, apples, Golden Smoothee, soil test, foliar analysis, potassium, calcium
magnesium, spatial variability
INTRODUCTION
Bitter pit is a nutritional disorder, related directly or indirectly to calcium nutrition, andaffects apple fruits. It manifests as brown spots on the apple skin with a bitter taste of theunderneath tissue. It can develop at fruit maturity, but develops even more after periods of cold
storage (Rui-Luiz, 1985; Lotze, 2005), therefore, affecting fruit quality and market value. Its
occurence is attributed to various factors that impacts calcium (Ca) levels in the fruits (Ferguson
et Watkins 1989). Moreover, potassium (K) and magnesium (Mg) are particularly involved in the
Ca-K-Mg equilibrium, as both are considered antagonists to Ca (Faust and Shear, 1968; Ferguson323

1st African Conference on Precision Agriculture | 8-10 December | 2020and Watkins, 1989). Fruits with bitter pit expression are of low Ca contents. However, a high(K+Mg)/Ca ratio of leaf or fruit have been reported as a better indicator of bitter pit occurrencecompared to Ca alone (Boon, 1980). The incidence can exceed 30% when leaf (K+Mg)/Ca ration
is greater than 1.15 (Boon, 1980). Soils with high exchangeable K concentrations may lead to bitter
pit even when soil exchangeable Ca is adequate or high. Excess nitrogen (N) absorption in the fruit
can cause an increase in fruit size, and therefore a consequent Ca flesh dilution (Rui Luiz, 1985;
Saure, 2005). Some varieties have been reported to be more susceptible than others (Ferguson andWatkins, 1989; Cheng and Sazo, 2018). Bitter-pit has been observed on many apple orchards in
the Sais and Mid Atlas mountain regions of Morocco (Faraj, 2006), with fruit loss exceeding 50%of the yield after cold storage. Soil testing and foliar analysis are often used to assess soil fertility
and tree nutrition. However, few composite samples are insufficient to reveal orchard nutrient
spatial variability and depict the vulnerable areas to bitter-pit. The objective of this study was toassess the spatial variability of K, Ca and Mg in the soil and leaf in an apple orchard in the Sais
plateau in NW Morocco in order to gain a better understanding of the occurrence of bitter-pit andpropose site-specific nutrient management as a precision farming practice.
MATERIALS AND METHODS
The study was conducted on an apple orchard farm near the city of Meknes, in northwestern
Morocco. The farm experienced repeated important losses of fruit quality due to bitter pit, mainly
after cold storage periods. A 30-ha apple orchard (12 years old) with a density of 1250 tree/ha, wasselected. It houses 3 varieties (Lyse Golden, Golden Smoothee and Ozark Gold), but the study
focused on the Golden Smoothee only. The fertilizer program varies from one year to another andconsists of 100-120 kg/ha N, 80-100 kg/ha P2O5 and 180-240 kg/ha of K2O, in addition to K, Ca
and boron (B) foliar sprays.
Composite samples (consisting of 9 subsamples) were collected at 130 geopositioned grid
points (50x50m) for soil (3 weeks before bud) and leaf (80 days after full flowering). At eachpoint, soil samples were taken at 2 depths (0-30cm and 30-60cm) and leaf samples were takenfrom 4 nonboring trees and comprised 160 leaves. Soil samples were prepared and analyzed formineral N, available P and exchangeable K, Ca and Mg. Leaves were analyzed for N, P, K, Ca andMg contents. Analysis were done according to Estephan et al. (2013).
Spatial variability maps of individual nutrients and (K+Mg)/Ca ratios in the soil and theleaves were derived by surface interpolation using Inverse Distance Weight (IDW) method undergeostatistical analyst of ArcGIS software. In the case of soil data, only the maps of the surface
layer will be presented.
RESULTS AND DISCUSSION
The orchard field shows a moderate NW-SE gradient in terms of soil depth (60 to 80 cm),
soil texture (clayey to clay loam), organic matter content (1.8 to 2.7%), active lime (15.6% to 10.0)
and pH (8.3 to 7.9).
Soil Exchangeable K, Ca and Mg
Exchangeable potassium showed an important spatial variability, with values ranging from122 to 780 ppm in the surface layer and 100 to 452 ppm in the sub-surface layer, with means of
452 and 237 ppm respectively. Surface and subsurface K were significantly correlated (R2=0.58).

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The map for the surface layer (Figure 1) shows that more than 2/3 of the orchard has high K
content, considered rich to excessive for apples orchards, and may cause antagonistic effect on
calcium nutrition for apple trees.
For exchangeable Ca, values ranged from 6372 and 12084 ppm in the surface layer (cv=10%) and from 6482 to 10094 ppm in the subsurface (cv = 9.2%). These values are typical of
calcareous soils containing important levels of active CaCO3. A small, but significant, correlation
exists between surface and subsurface Ca (R2= 0,32). The surface layer Ca map (Figure 1) showsa clear N-S gradient, but variations are due mainly to inherent soil conditions than to management
practices. Despite high Ca levels, Ca deficiencies can be observed as a result of K and Mg
antagonisms and/or imbalance. Excess Ca absorption by plants does not occur and is believed tobe physiologically controlled compared to other nutrients such as N and K.
Soil exchangeable Mg showed values ranging from 508 and 2156 ppm with a mean of 1588
(cv= %) in the surface and from 628 et 2608 ppm with a mean of 1800 ppm (CV= %) in thesubsurface. Magnesium content in the subsurface were higher than those at the surface comparedto calcium. Surface and sub-surface Mg were highly correlated (R2=0.84). The surface layer Mg
map (Figure 1) shows less variability compared to K and Ca. The high Mg levels add up topotassium and therefore impact Ca absorption by the apple trees, making them more vulnerable tobitter pit development.
The soil (K+Mg)/Ca ratio (eq/eq) varied from 0.1 to 0.5 with an average of 0.31
(CV=25.8%). The corresponding map (Figure 1) shows a net N-S gradient that reveals an
important imbalance of Ca-K-Mg across the field. It is suggested that a cut-off value of 0.3 beconsidered for this ratio as a threshold for Bitter pit incidence.

Figure 1. Spatial variability maps of exchangeable K, Ca, Mg and (K+Mg)/Ca ration in thesurface (0-30cm) layer of the apple orchard field studied.
Leaf K, Ca and Mg
Leaf K contents varied from 1.45% to 3.17%, with an average of 2.3% (CV=17%). Theselevels are considered satisfactory to moderate high. The leaf-K map (Figure 2) shows a net N-S
gradient, with about a third of the trees being over-fertilized with K. A leaf K content greater than
1.5% can be favourable to Bitter pit if leaf Ca content are lower than 1.6% (Van Der Boon, 1980).
A highly significant correlation (R2=0.62) was observed among soil-K and leaf-K contents.
Leaf Ca contents ranged from 0.89% to 1.45%, with an average of 1.16% (CV=11.2%). In
general, these values are considered relatively low for apple trees (the optimum being about 1.5 to325

1st African Conference on Precision Agriculture | 8-10 December | 20201.8%), especially in the southern part of the field (Figure 2) which is more vulnerable to bitter-pit.
Terblanche et al. (1988) reported that bitter pit occurred over 30.7% of an apple orchard field (var.
Golden Delicious) with an average of 0.92% leaf Ca content.
Leaf Mg contents ranged from 0.28% to 0.46%, with an average of 0.35% (CV=11,4%).
These levels are considered optimum. The leaf Mg map (Figure 2) shows a significant
heterogeneity despite the lower heterogeneity in the case of soil exchangeable Mg. This could be
attributed to the antagonistic effect of other nutrients such as K and NH4, which is often assessedby looking at the K/Mg ratio.
The leaf (K+Mg)/Ca ratio (eq/eq) varied from 0.98 to 2.16 with an average of 1.55
(CV=18,7%). Despite the important heterogeneity, the corresponding map (Figure 2) shows an
overall N-S gradient that depicts the K-Mg-Ca leaf nutrient imbalance across the field. Van derBoon (1966) reported that apple trees with a (K+Mg)/Ca >1.2 (eq/eq) presented a high risk ofbitter-pit development. Based on cut-off levels of 1.25 and 1.5, the map shows that about 35% of
the field is of moderate risk and about 25% is of high risk to bitter-pit incidence, respectively. Leaf
(K+Mg)/Ca ratio was significantly correlated to that in the soil (R2=0.65).

Figure 2. Spatial variability maps of leaf K, Ca, Mg and (K+Mg)/Ca ratio in the apple orchard
field studied.
Leaf N content can also inform about the risk of bitter-pit vulnerability. High nitrogen canlead to increased fruit size, and therefore cause Ca dilution which results in lower tissue cohesion
and bitter-pit development (Wilson and Cline, 2000, Saure, 2005). Measured leaf N ranged from2.05 to 3.34% with average of 2.66 (CV=14,7%). The corresponding map (not shown) revealed
also that a large part of the field had high N content and could contribute to the aggravation ofbitter-pit.
Field observations, showed that apple fruits expressed symptoms of bitter-pit in thesouthern part of the field in early maturity stage. These observations confirm the results revealedby the maps and call for a rigorous site-specific management of the duo K-Ca, with a drawdown
of soil K fertilization ratio and an increased Ca-foliar fertilisation in the high (K+Mg)/Ca. In
addition, N should be reduced to avoid high-size fruits that causes fruit Ca dilution. Val et al.
(2000) reported that when apples (Golden Smoothee) show symptoms of bitter-pit in the field,
about 80% of the fruits are likely to develop bitter-pit after 2 months of cold storage. Furthermore,
it is recommended to sort apples harvested from the moderate to high-risk areas and subject them326

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to soaking in a 1% CaCl2 or Ca(NO3)2 solution (for about 2 minutes) prior to cold storage (Boon,
1968).
CONCLUSIONSThe K, Ca and Mg nutrient levels in the soil and in the leaves of apple orchard wereassessed to determine its vulnerability to bitter-pit. Spatial variability was approached using a 130
grid sampling points over a 30-ha field. Maps of K, Ca as well as that of the (K+Mg)/Ca ratio
showed that the apple field presented a high risk of bitter-pit development that explains thesubstantial yield loss observed after apples undergo a period of cold storage exceeding four weeks.
About 60% of the field is at risk, with 35% of moderate risk and 25% of high risk. These findings
help explaining the high incidence of bitter-bit. The spatial variability maps allowed andidentifying the most contributing areas would orient site-specific nutrient management including
both soil and foliar fertilization. Appropriate cost-benefit soil and foliar testing tools can be used
for soil fertility and three nutrient status assessment as a precision agriculture practice to avoid
post-harvest loss of yield due to bitter-pit.
REFERENCES
Cheng L, Sazo MM. 2018. Why Is ‘Honeycrisp’ so Susceptible to Bitter Pit? Fruit Quarterly 26(1):19-23.
De Freitas ST, do Amarante CVT, Mitcham EJ. 2015. Mechanisms regulating apple cultivar
susceptibility to bitter pit. Scientia Horticulturae 186: 54–60.
Estephan G, Sommer R, Ryan J. 2013. Methods of Soil, Plant, and Water Analysis: A manual for
the West Asia and North Africa Region: Third Edition. Beirut, Lebanon: International
Center for Agricultural Research in the Dry Areas (ICARDA).
Faraj A. 2006. Vulnérabilité du pommier au bitter pit et essais d’apport du calcium en pré et postrécolte. Mémoire de 3éme cycle, Ecole Nationale d’Agriculture de Meknès, Maroc.
Faust M. Shear CB. 1968. Corking disorders of apples: A physiological and biochemical review.
Bot. Rev. 34: 441–469.
Ferguson IB, Watkins CB. 1989. Bitter pit in apple fruit. Hort. Rev. 11:2 89–355.
Lotze E. 2005. Pre-harvest determination of bitter pit potential in apples. Dissertation presented
for the Degree of Doctor of Philosophy (Agric). University of Stellenbosch. December
2005.
Rui-Luiz V. 1985. Preharvest fruit analysis as a predictor of d'Anjou pear and Yellow Newtown
apple physiological disorders, storage quality and ripening behavior. Thèse d’obtention dedoctorat dans la philosophie d’horticulture. Oregon State University. 363 p.
Saure MC. 2005. Calcium translocation to fleshy fruit: its mechanism and endogenous control.
Sci. Hortic. 105: 65–89.
Terblanche JH, Gurgen KH, Hesebeck I. 1988. An integrated approach to orchard nutrition andbitter pit control. In: Mineral nutrition of fruit trees, D. Atkinson, J.E. Jackson, R.O.
Sharples et W.M. Waller Eds. Studies in the Agricultural and Food Sciences,
Butterworths, London-Boston, 11-16.
Val J, Gil A, Aznar Y, Monge E, Blanco A. 2000. Nutritional Study of an Apple Orchard as
endemically affected by bitter-pit. Estación Experimental de Aula Dei (CSIC). Zaragoza,
Spain.

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Van Der Boon J. 1980. Prediction and control of bitter pit in apples. I. Prediction based on mineralleaf composition, cropping levels and summer temperatures. Journal of Horticultural
Science 55 (3): 307-312.
Van Der Boon J, Das A, Van Schreven AC. 1968. Control of bitter pit and breakdown by calciumin the apples Cox's Orange Pippin and Jonathan. Agricultural Research Report, 711, Centre
for Agricultural Publishing and Documentation, Wageningen.

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PRECISION WATER MANAGEMENT

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#7475 CROP WATER REQUIREMENTS, BIOMASS AND GRAIN YIELDSESTIMATION FOR SULLA (HEDYSARUM CORONARIUM L.) USING CROP WAT
IN SEMI-ARID REGION OF TUNISIA
R. Hajri, M. Rezghui, M. Mechri, and M. Ben Younes
Pôle Régional de Recherche Développement Agricoles du Nord Ouest semi-aride, Tunisia,
rimhajri@yahoo.fr
ABSTRACT
Dwindling water resources and increasing food requirements require greater efficiency inwater use, both in rainfed and in irrigated agriculture. Regulated deficit irrigation provides a meansof reducing water consumption while minimizing adverse effects on yield. With the current water
shortage in Africa improving crop water use is vital especially in the arid and semi-arid regions.
Models can play a useful role in developing practical recommendations for optimizing crop
production under conditions of scarce water supply. To determine the optimal irrigation levels for
maximum Hedysarum coronarium L. production and to assess the effect of limited water supply
on field grown Hedysarum coronarium L. yield under different irrigation regimes, we tested the
FAO CROPWAT model version 8.0 during the cropping seasons of 2017 and 2018 at the stationof Centre de Formation Professionnelle Agricole de Sidi Bourouis in northern Tunisia. Threeirrigation scheduling levels such as 70 % of crop water requirement (ETc), 50% ETc, 30% ETc
and finally rainfed regime were replicated three times. We found that the crop water requirement(CWR) were estimated for the seedling emergence stage (5.4 mm), vegetative growth stage toflowering stage (118.3 mm), flowering stage (170.1 mm) and reproductive growth stage (48.2mm). The results of data analysis showed that use of various irrigation regimes brought asignificant effect (P<0.01) effect on the grain yield and biomass parameters of Sulla. The rainfed
regime produced the lowest fresh and dry forage biomass with 18.45 t / ha of fresh matter comparedto the three irrigated regimes (70% ETc, 50% ETc and 30% ETc), which had respectively, 28 t /ha and 30.6 t / ha and 31.97 t/ ha. CROPWAT performed excellently and could be used toefficiently estimate water requirements and reference evapotranspiration.
Keywords: CROPWAT, ETO, irrigation regime, yield response
INTRODUCTION
Global climate change has led to irreversible phenomena that have significantly affected
agriculture of many countries of the world (CCUNCC, 2018), especially developing countries.
With an average of 410 m³ of water per inhabitant per year, Tunisia is clearly below the water
scarcity threshold. This situation is all the more worrying as climate change will lead to a 28%
drop in conventional water resources by 2030 and decrease in surface water resources will also berecorded (CCUNCC, 2018). Tunisia is classified among 17 countries least endowed with fresh
water on the planet.
In Tunisia, agriculture is the largest (80%) consumer of water and hence more efficient use
of water in agriculture needs to be top most priority (ONAGRI, 2011). Thus, efficient water use
and management are currently major concerns (FAO, 2015). There appears to be a consensus that330

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irrigated agriculture, in general, is up against a future with less water. This consequently, calls forincreased efficiency in the utilization of scarce water resources. A better understanding of the
intricate interactions between climate, water and crop growth needs to be a priority area in Tunisia.
Crop simulation models such as DSSAT, CROPWAT 8 and FASSET can play a usefulrole in developing practical recommendations for optimizing crop production under conditions ofscarce water supply (De Wit et al., 2019; Halimi et al., 2019). Crop water requirement simulation
models compute effective rainfall, reference evapotranspiration (ETo), crop evapotranspiration(ETc), net irrigation water requirement (NIWR), gross irrigation water requirement (GIWR),
irrigation scheduling and crop growth. Several methods are used for ETo calculation such as masstransferbased methods, radiation-based methods and temperature-based methods, etc. (Adarsh etal., 2018). The Penman–Monteith method (Allen et al., 1998) is proved as one of the most reliable
and comprehensive methods for estimation of evapotranspiration and crop water requirements, and
it is widely used. CROPWAT 8 model developed by FAO is used for the calculation of crop waterrequirements and irrigation requirements based on soil, climate and crop data. In addition, theprogram allows the development of irrigation schedules for different management conditions andthe calculation of scheme water supply for varying crop patterns (FAO, 2020).
Forage Fabaceae, such as Sulla (Hedysarum coronarium L.) play a fundamental floristic
and agronomic role. The specie is commonly used to enhance the productivity and sustainabilityof cereal-based systems because of its excellent adaptability to marginal and drought-prone
environments (Amato et al., 2016). Annicchiarico et al. (2008) highlighted the particular value ofgenetic resources of Sulla for severely drought-prone environments, whose extent in the
Mediterranean Basin is expected to rise as a consequence of climate change and foreseeable
reduction of water available for irrigation (CCUNCC, 2018). Forage of Sulla is a versatile cropwhich can be used for silage production, grazing and hay making. It has a high biomass yield andis one of the more productive legume crops with high protein content in rainfed environments. It
is a perennial species that require less energy to plant and fertilize, reduce the risk of soil erosion,and increase soil carbon (Schwartz et al., 2013). Moreover, it has several nonagricultural uses; forexample, it can be planted to protect soil (Amato et al., 2016) and revegetate disturbed land or
used for honey production and landscape architecture (Annicchiarico et al. 2008).
There is a lack of information with respect to Sulla on crop water requirements in generaland in Tunisia. Hence, in this study an attempt has been made to compute the crop waterrequirements of Sulla in semi arid regions at the station of Centre de Formation Professionnelle
Agricole de Sidi Bourouis in northern Tunisia using FAO CROPWAT model version 8.0. Also,
FAO CROPWAT 8.0 was used to determine of optimal irrigation levels for higher Sulla(Hedysarum coronarium L.) forage production under four irrigation regimes (Three irrigation
regimes of 70 % of crop water requirement (ETc), 50% Etc–, 30% ETc and rainfed treatments).
MATERIALS AND METHODS
Experimental Location, Design, Treatments and Cultural Practices
Trials were conducted during the cropping seasons of 2017 and 2018 at the station of Centrede Formation Professionnelle Agricole of Sidi Bourouis in northern Tunisia. The research station
is located about seven kilometers north of Siliana. It is in the east of the State between northlatitudes 36.10 ′ and east longitudes 9.07, at an elevation of 406 m above sea level. The climate of
the area is semi-arid Mediterranean climate, with a dry period that can reach 5 months. The rainfall
in the order of 350 to 400 mm per year. Soils at the station are coarse textured sandy clay loam

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type. The design of the experiment was randomized plot with three replicates. The subplot size
was 3 ×6 m. The main plots comprised three irrigation four irrigation regimes (Three irrigation
regimes of 70 % of crop water requirement (ETc), 50% Etc, 30% ETc and rainfed treatment). The
subplots were cultivated with Hedysarum coronarium cv. Bikra 21. The seeds were sown on 28
October 2017.
Soil, Crop and Meteorological Data
The Data of soil physical parameters, such as soil texture, depth, infiltration rate, theavailable soil moisture, and bulk density, are required to estimate the total available water in thearea of root zone and the irrigation plan. The parameters derived from this search are presented inTable 1.
Table 1. Soil parameters.
Parameter
Total plant available moisture (mm/m)
Maximum rain infiltration rate (mm/day)
Maximum rooting depth (cm)
Initial soil moisture depletion (as % TAM)
Initial available soil moisture (mm/m)

Value
120
36
80
90
12

Crop coefficient (Kc) curves of variation of the seasonal/annual for the studied crops
were taken according to Allen et al, (1998). Maximum and minimum temperature (oC), humidity(%), wind speed (m/S) and sunshine (hours) were collected from the Meteoblue website(Meteoblue, 2018). Daily rainfall (mm) was collected from Sidi Bourouis station.
RESULTS AND DISCUSSION
Reference Evapotranspiration, Water Requirement
The estimate of the Reference Evapotranspiration (ET0) for the study area is presented in
Figure 1. The results show that the highest average daily evapotranspiration ET0 values are
attained in May (5.13 mm/day) and in April (4.76 mm / day), it is a critical phase with regardof Sulla. If we multiply these values by the number of days of each month, you can have theamount of water that has been evapotranspired, respectively during the month of May (156.03mm) and April (142.8 mm). Consequently and in the absence of rains during this period must
be applied to net irrigation dose during March of 1560 m 3 /ha and during the month of April
1428 m3/ha.
The total water requirements of Sulla (CWR) are equal to 343 mm. We found that the cropwater requirement were estimated for the seedling emergence stage (5.4 mm), vegetative growthstage to flowering stage (118.3 mm), flowering stage (170.1 mm) and reproductive growth stage(48.2 mm) (Fig. 2).

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1st African Conference on Precision Agriculture | 8-10 December | 2020

Months

etc

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Figure 1. Reference Evapotranspiration (ET0).

Stage

Figure 2. Crop water requirements of Sulla.
The results of data analysis showed that use of various irrigation regimes brought asignificant effect (P<0.01) effect on the grain yield and biomass parameters of Sulla. The rainfed
regime produced the lowest fresh and dry forage biomass with 18.45 t / ha of fresh matter comparedto the three irrigated regimes (70% ETc, 50% ETc and 30% ETc), which had respectively, 28 t/ha,
30.6 t/ha and 31.97 t/ha (Table 2).
Table 2. Effects of water supply on fresh and dry forage and grain yields (t ha-1) of Sulla underfour water regimes.
Parameters
Fresh forage yield (t ha-1)
Dry forage yield (t ha-1)
Grain yield (Kg)

Rainfed Regime
18.45c6.40c706.33c

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70% ETc
28b
10.74b935c

50% ETc
30.6a
12.22a1148.67b

30% ETc
31.97a13.22a2024.67a1st African Conference on Precision Agriculture | 8-10 December | 2020

CONCLUSIONSThe Sustainable Water Management helps ensure a more stable production. Improving
irrigation efficiency is very important for farmers to have a more correct use of waterand for this reason, before thinking of irrigation as a water source, they must establish whether
irrigation is really necessary or not in their specific environmental conditions. For this purpose,a preliminary analysis is very useful. CROPWAT performed excellently and could be used toefficiently estimate water requirements and reference evapotranspiration.
REFERENCES
Adarsh S, Sulaiman Sanah KK, Murshida PN. 2018. Scale dependent prediction of reference
evapotranspiration based on Multi-Variate Empirical mode decomposition. Ain Shams Eng
J. 9: 1839–48.
Allen R, Pereira L, Raes D, Smith M. Crop evapotranspiration guidelines for computing crop waterrequirements. 1998. FAO irrigation and drainage paper 56. FAO, Rome.
Amato G, Giambalvo D, Frenda AS, et al. 2016. Sulla (Hedysarum coronarium L.) as Potential
Feedstock for Biofuel and Protein. Bioenerg. Res. 9: 711–719.
Annicchiarico P, Ruisi P, Di Miceli G, Pecetti L. 2014. Morpho-physiological and adaptive
variation of Italian germplasm of Sulla (Hedysarum coronarium L.), Crop and Pasture
Science 65: 206-213.
CCNUCC. 2018. Koronivia Joint Work on Agriculture: Analysis of Submissions. Environment
and Natural Resources Management Series, Working Paper 71, Rome. 52 pages.
de Wit A, Boogaard H, Fumagalli D, Janssen S, Knapen R, van Kraalingen D, Supit I, van der
Wijngaart R, van Diepen K. 2019. 25 years of the WOFOST cropping systems model.
Agric Syst. 168: 154-167.
FAO. 2020. CROPWAT software. http:// www. Fao. org/land-water/databases-andsoftware/Cropwat/en/.
FAO. 2015. État des ressources en sols du monde Résumé technique 92: 13-14.
Halimi A, Ashebir HT. 2019. Application of CROPWAT model for estimation of irrigation
scheduling of Tomato in changing climate of Eastern Europe: the case study of Godollo,
Hungary. Int J Agric Environ Sci. 6:1–11.
ONAGRI. 2011. Enquête sur les périmètres irrigués 2010-2011. Observatoire National del’Agriculture. Ministère de l’Agriculture et des Ressources Hydrauliques, Tunis.
Schwartz C, Amasino R. 2013. Nitrogen recycling and flowering time in perennial bioenergy
crops. Front Plant Sci 4. 76:1–7.

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#7595 A PRECISION IRRIGATION APP FOR SMART WATER MANAGEMENT BYFARMERS: “IRRISMART”
A. Abouabdillah*, and R Bouabid
National School of Agriculture of Meknès, aabouabdillah@enameknes.ac.ma
ABSTRACTIn a context of climate change and water scarcity which is globally recognized, Moroccois one of the countries that are facing already insufficient water supply for irrigation in order tosustain productivity and food security. Therefore, there is a strong need for adapting agricultural
practices and developing new technologies for efficient and smart irrigation management to makebest use of available water and maximize productivity per unit of consumed water. Recent studies
have shown that water use by farmers in drip irrigation systems exceed in many cases therecommended crop requirements. This is due mainly to the limited knowledge of farmers for theirrigation parameters in relation to crop requirements, climatic demand and soils conditions. This
work aimed to develop a Smartphone application that integrates all components of the soil-watercrop-atmosphere continuum to provide precision irrigation to farmers, managers and otherpotential users in order to help them better manage irrigation water and improve profitability perunit water used. The application allows the farmer to enter specific climate information or use
information provided from nearby weather stations or satellite data. The application uses suitable
crop coefficients (Kc) adjusted to the climatic context and gives the user the possibility of finetuning them according to crop cycle in order to obtain appropriate ETc values. Daily water needs
are calculated using soil conditions, crop planting and drip irrigation system characteristics. The
user receives then a daily water irrigation scheduling program for the chosen crop (amount andfrequencies). A wide list of crops is available. The application keeps track of past waterrequirement and can provide a forecast of the needs for upcoming days.
Keywords: precision irrigation, drip irrigation, smartphone app
INTRODUCTION
In arid regions, water is a major constraint for crop production, given the dramatic increasein demand for this resource on one hand and the consequences of climate change on the other hand.
Irrigation has known a considerable development in Morocco in recent years. However, thequestion of rational use of irrigation water is still of concern, even in drip irrigation systems.
It’s well known that there is a limited knowledge on irrigation parameter determination (ie:frequency, timing, quantity). In fact, studies carried out on irrigation within Fes-Meknes region(Abouabdillah et al., 2019; Assouli et al., 2019) have shown that the applied irrigation water by
the farmers, under the drip system, in many cases exceed the recommended crops waterrequirements. Another study conducted in the Ain Taoujdate region (Benouniche et al., 2014)
showed that 100% of onion producers in the region over irrigate their crops and apply quantities
of water that far exceed the crop water needs recommended by FAO (Allen et al. al., 1998).
Rational water management is an essential component of agricultural development. It
necessarily involves the adoption and improvement of localized irrigation techniques (in particular335

1st African Conference on Precision Agriculture | 8-10 December | 2020

the drip irrigation system) and the development of new methods of irrigation management. In order
to be effective, irrigation must be conducted in an adequate and smart manner. There are threemain aspects related to smart irrigation management. First of all, knowledge on crop waterrequirements, secondly, irrigation frequency (number of irrigations) and then the irrigation
duration. However, it should be clearly state that having this information available for farmer intime is even more crucial for better decision making.
This work aimed to develop a Smartphone application that integrates all components of thesoil-water-crop-atmosphere continuum to provide farmers, managers and other potential users
with precision irrigation parameters, such as daily irrigation calendar, in order to well manage
irrigation and as consequence save water, and improve profitability.
MATERIALS AND METHODS
IrriSmart app was developed based on proven scientific algorithm combined with IT
technology. The four components of the continuum were considered. The climate is considered toestimate the reference evapotranspiration “ET0” by either of the two suggested methods: the firstone is based on Penman Monteith formula using the climatic parameters measured bymeteorological stations available in the area of Fes-Meknes, (three stations). While the second
method based on satellite data using climatic parameters generated from an open source satellite
server. The generated climatic parameters cover the whole country with 1698 different locations
assigned mainly to the administrative districts as well as all significant different altitudes within
the same district. As a result, daily climatic demand is attributed to each registered field fromnearby location data based on its geographic coordinates.
Regarding the crops, a large list of vegetable crops practiced in the country was includedin the app. Once the crop is chosen, a crop coefficient “Kc” model is assigned using data from
FAO 66 or using new crop coefficients (for other crops) that have been developed in the area
(Abouabdillah et al., 2019; Bergui et al., 2020; El Jaouhari el al., 2018). Furthermore, theassignment of the Kc depends on the phenological stage of the crop determined by the date ofsowing which is identified by the app user. The modification of the crop coefficient as well as theduration of the phenological stage is available on a professional version of the application, formore knowledgeable users.
The third component of the continuum consists on the soil role as a water reservoir for theplant as little attention is being paid to irrigation scheduling based on soil monitoring. The
availability of water depends on soil water holding capacity; It is crucial to consider the available
water content as well as the water holding capacity within the soil to be readily for the plant. As
consequence, two options were considered in the app. The first one considers the soil proprieties
indicating the percentage of clay, and sand, and then using the Saxston formula (saxstan et al.,2006) to estimate the soil water availability. While the second option, is based on the selection ofa soil texture from a proposed list of various soil texture classes, where an average of soil water
available is attributed (Keller et kameli, 1974)
The fourth component defining the irrigation duration is the system characteristics such as
the dripper flow, and the line and dripper spacings, in order to determine the flow rate. Other
parameters are estimated using a combination of other inputs such as the percentage of humidified
soil, the efficiency and the uniformity of the system.
All these inputs are thereby organized in 5 steps, field identification, climatic simulation,
crop choice and sowing date, irrigation system characteristics and finally soil characteristics.

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RESULTS
The irrigation program, which is of most importance to the app user, is generated on daily
basis based on the approach and algorithms used. All the data are saved on a cloud server, andthen sent directly to the user. Output data are summarized in five items: (i) daily crop waterrequirement “ETc”; (ii) water quantity to be applied for each irrigation; (iii) irrigations
frequency; (iv) duration of one irrigation; and (iv) the total duration of irrigation per day. In
simple terms, scheduling irrigation parameters are generated on daily basis and can be found by
the user on a specific section assigned to each saved farm field providing the actual irrigation
parameter and also the previously recorded ones. It should be also point it out, that in case of
non-access to internet network, the app user can request to get the outputs via SMS. Up to thispoint, the IrriSmart app is under validation by users, and the results obtained raised great interest
among farmers and other water management decision makers.
The new developed irrigation app IrriSmart, will contribute to a precision irrigation anda smart management of irrigation water leading to a significant water saving, mainly in thecontext of climate change and water scarcity that Morocco is facing.
REFERENCES
Abouabdillah A, Bouabid R, Bourioug M, Mrahli D, El Jaouhari N. 2019. Réponse de l’oignon(Allium Cepa) à l’irrigation déficitaire continue avec différentes fréquences dans larégion du Saïs. Communication orale. Conférence: Exploitation des eaux souterraines etdéfis de la durabilité de l’agriculture irriguée Mercredi 10 juillet 2019 au Qualipole
Alimentation de Meknès.
Allen RG, Pereira LS, Raes D, Smith M, et al. 1998. Crop evapotranspiration-Guidelines forcomputing crop water requirements-FAO Irrigation and drainage paper 56. FAO, Rome,
300(9), D05109.
Assouli O, El Bilali H, Abouabdillah A, Harbouze R, El Jaouhari N, Chaoui M, Bouabid R.
2019. Transition from surface to drip irrigation in morocco: analysis through the multilevel perspective. AGROFOR 3(3) https://doi.org/10.7251/AGRENG1803142A
Benouniche M, Kuper M, Hammani A, Boesveld H. 2014. Making the user visible: Analysing
irrigation practices and farmers’ logic to explain actual drip irrigation performance.
Irrigation Science 32(6): 405-420. https://doi.org/10.1007/s00271-014-0438-0
El Bergui O, Abouabdillah A, Bouabid R, El Jaouhari N, Brouziyne Y, Bourioug M. 2020.
Agro-physiological response of potato to “sustainable” deficit irrigation in the plain ofSaïs, Morocco; Published online: 03 August 2020; E3S Web of Conferences 183, 03001,
3S Web Conf. Vol. 183, DOI: https://doi.org/10.1051/e3sconf/202018303001
El Jaouhari N, Abouabdillah A, Bouabid R, Bourioug M, Aleya L, Chaoui M. 2018. Assessmentof sustainable deficit irrigation in a Moroccan apple orchard as a climate change
adaptation strategy. Science of the Total Environment 642: 574–581.
Keller J, Karmeli D. 1974. Trickle irrigation design parameters. ASAE Transactions 17(4): 678684.
Saxton KE, Rawls WJ. 2006. Soil Water Characteristics estimates by texture and organic matterfor hydrologic solutions. Soil Sci. Soc. Am. J. 70: 1569-1578.

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#7645 MONITORING IRRIGATION WATER USE AT LARGE SCALE IRRIGATEDAREAS USING REMOTE SENSING IN WATER SCARCE ENVIRONMENT
M.H. Kharrou1, V. Simonneaux2, M. Le Page2, S. Er-Raki4,6, G. Boulet2, J. Ezzahar5,6, S.
Khabba3,6, A. Chehbouni1,2,6
1International Water Research Institute, Mohammed VI Polytechnic University, Benguerir,
Morocco. 2 CESBIO – Centre des Etudes Spatiales de la BIOsphère, Toulouse, France. 3 LMME,
Department of Physics, Faculty of Sciences Semlalia, Marrakesh, Morocco. 4 LP2M2E,
Department of Physics, Faculty of Sciences and Technology, Marrakesh, Morocco. 5 Ecole
Nationale des Sciences Appliquées, Cadi Ayyad University, Safi, Morocco. 6 Center for Remote
Sensing Application, Mohammed VI Polytechnic University, Benguerir, Moroccomohamed.kharrou@um6p.ma
ABSTRACT
Increasing pressure on available water resources in semi-arid region will affect theavailability of water for irrigated agriculture. In this context, adoption of innovative and costeffective tools for water management and analysis of water use patterns in irrigated areas isrequired for an efficient and sustainable use of water resources. This study aims to evaluate aremote sensing-based approach which allows estimation of the temporal and spatial distribution ofcrop evapotranspiration and irrigation water requirements over large irrigated areas. The method
consists of an adaptation of the daily step FAO-56 Soil Water Balance model combined with timeseries of basal crop coefficient (Kcb) and the fractional vegetation cover (fc) derived from highresolution satellite NDVI imagery (Normalized Difference Vegetation Index). The model was firstcalibrated and validated at plot scale using evapotranspiration measured by eddy-covariancesystems over wheat fields and olive orchards which represents the main crops grown in the study
area of Haouz plain located around Marrakesh city. Then, the model was used to compare remotelysensed estimates of irrigation water requirements and observations of irrigation water use at plotscale over an irrigation district in Haouz plain along three agricultural seasons. At plot scale, theresults showed that the model provides good estimates of evapotranspiration for wheat and olive
trees, given specific calibration of crop and soil related parameters that control transpiration andevaporation processes. At the irrigation district scale, the comparison of spatialized irrigation waterrequirements and irrigation water use showed great discrepancies indicating a temporal and spatial
varying demand and supply of irrigation water over the seasons. In addition to the model and theobserved data uncertainties, the variability observed could be influenced by different biophysicalfactors and the farmer’s behaviour and management practices. Also, some differences between
observations and estimates were spatially correlated at some extent with the distribution of wellsin the area, which shows a potential use of the method for monitoring groundwater withdrawals.
These results suggested that the water supplied within the studied irrigation district needs to be
improved for a better performance of irrigation. The findings demonstrate the potential interests
for irrigation managers of using remote sensing-based models to assess irrigation waterrequirements and monitor irrigation water use for efficient and sustainable use of water resources.

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INTRODUCTION
Irrigated agriculture is the main water consumer worldwide, accounting for about 70% of
all available fresh water (FAO, 2016). However, increasing pressure on available water resources,
particularly in semi-arid region, due to population growth, climate change and competition fromother economic sectors will affect the availability of water for irrigated agriculture in the future.
In this context, assessing irrigation performance and improving irrigation water management using
innovative and cost-effective tools is necessary for an efficient and sustainable use of water
resources.
One key information useful for irrigation managers to evaluate irrigation performance isthe temporal and spatial distribution of crop water use or evapotranspiration (ET) over large
irrigated areas. Recently, research has demonstrated the potential for remote sensing to monitor
crop development and to assess the spatial and temporal variability in crop water use (Tasumi andAllen, 2007; Zhang et al., 2016).
In this study, we evaluated the reliability of an approach based on a soil water balance
model assisted by remote sensing data for assessing crop irrigation requirement and monitoring
spatialized irrigation water use. This approach was used to compare remotely sensed estimates ofcrop irrigation requirements and in situ observations of irrigation water use in the Haouz irrigatedplain (Morocco). Our goal is to demonstrate the operational application of a remote sensing-based
water balance model to monitor irrigation water use at field level over large irrigated areas.
MATERIALS AND METHODS
The irrigation district evaluated in this study, during three agricultural seasons (2002-03,
2005-06 and 2008-09), is located in the Haouz plain around Marrakesh city (Morocco) which is apart of the Tensift watershed characterized by a semi-arid climate (Figure 1).
The crop irrigation water requirements over the irrigated area were obtained by a remotesensing-based Soil Water Balance model which consists in coupling the FAO-56 dual crop
coefficient model with time series of high resolution NDVI imagery (Normalized Difference
Vegetation Index) providing estimates of the actual basal crop coefficient (Kcb) and the fractional
vegetation cover (fc). The FAO-56 model was modified by adding a deep soil layer below the root
compartment to account for deep water storage and capillary rise (Zhang and Wegehenkel, 2006).
A series of Landsat TM (in 2002-03, 2008-09) and Formosat (in 2005-06) images of the studied
area were acquired and used to provide a NDVI time series and a land cover map (Simonneaux etal., 2008; Duchemin et al., 2008). The latter shows a predominance of annual crops (mainly
cereals), and trees (mainly olive trees) over the irrigated areas (Figure 1).
Calibration and validation of the model were performed at plot scale using
evapotranspiration (ET) measured by eddy-covariance systems over wheat fields and olive
orchards during experiments conducted in the studied area (Er-Raki et al., 2007, 2010). Then, themodel was used to compare spatialized estimates of irrigation water requirements withobservations of irrigation water use at plot scale over the R3 irrigation district in Haouz plain along
the three agricultural seasons.

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Tensift watershed

Figure 1. Location of the studied area and land cover map (2008-09). Land cover: trees in green,
cereals in orange and bare soil in brown. Bold line: plot boundaries in R3 irrigation district.
RESULTS AND DISCUSSION
The calibration was performed for the parameters controlling the processes of transpiration(Kcb mid-season, root zone depth max, diffusion coefficient between deep and root zone layers
and maximum soil depth) and evaporation (diffusion coefficient between root zone andevaporation layers, Readily evaporable water, soil surface layer depth). The Kcb and fc profiles
were estimated from satellite data using linear Kcb-NDVI and fc-NDVI relationships and the otherparameters (soil moistures at field capacity and wilting point, fraction of soil surface wetted byirrigation, initial soil moisture) were obtained from ground observations.
The parameters values obtained allowed a good adjustment between the ET estimated by
the model and the measured ET. The validation of the model was performed using the same set ofcalibrated parameters over other wheat and olive fields. The results showed a good performanceof the model in estimating actual ET with average Nash-sutcliff efficiency (NSE) and Root Mean
Square Error (RMSE) respective values of 0.67, 0.56 mm/day for wheat and 0.64, 0.52 mm/day
for olive tree. The dynamic of actual ET estimated by the model followed reasonably well the timecourse of observed actual ET (Figure 2).
At the irrigation district scale, the model was used to estimate the spatial and temporal
distribution of ET and irrigation water requirement. The cumulated seasonal ET values were 470
mm, 420 and 400 mm on average respectively for the 2002-03, 2005-06 and 2008-09 seasons witha great heterogeneity inside the R3 district. The same was true for irrigation requirement withaverage values of 163, 156 and 103 mm respectively for the 2002-03, 2005-06 and 2008-09
seasons. This seasonal variability could be explained by the differences in climatic conditions
characterized by high climatic demand (reference evapotranspiration, ETo) and low rainfall in
2002-03 compared with the other seasons.

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ET_obsET_simactual ET (mm)

7
6
5
4

3
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0

Figure 2. Time course of observed and simulated actual ET over a validation wheat field in
2002-03 season (left), and map of difference between simulated and observed irrigation at plotscale for 2002-03 season over R3 irrigation district (right).
The differences between irrigation water needs and irrigation water supplied at farm level
showed great spatial variations in each season and across seasons (Figure 2). This suggest thatlarge proportion of farms were either over-irrigating or under-irrigating regardless the seasonal
water availability. The spatial distribution of farms whose irrigation water needs exceeded
observed supply were located to some extent close to the wells suggesting groundwater extractions
to meet crop water needs (Figure 2). In addition to the model and the observed data uncertainties,
the variability observed could be attributed to different biophysical factors, irrigation supply
conditions which depends on water availability, the farmer’s behaviour (inadequate irrigation
scheduling, financial considerations, …) and management practices (cultivar, sowing date,fertilizers, weed control…etc) as previously reported (Duchemin et al., 2008; Kharrou et al. 2013).
These results revealed that this approach could provide an interesting framework for irrigation
managers to monitor irrigation water use for efficient and sustainable use of agricultural water.
REFERENCES
Duchemin B, Hagolle O, Mougenot B, Simonneaux V, Benhadj I, Hadria R, Ezzahar J, Hoedges
J, Khabba S, Kharrou MH, Boulet G, Dedieu G, Er-Raki S, Escadafal R, Olioso A,
Chehbouni A. 2008. Agrometerological study of semi-arid areas: an experiment foranalysing the potential of FORMOSAT-2 time series of images in the Marrakech plain.
Int. J. Remote Sens. 29 : 5291-5299.
Er-Raki S, Chehbouni G, Guemouria N, Duchemin B, Ezzahar J, Hadria R. 2007. Combining
FAO-56 model and ground-based remote sensing to estimate water consumptions of wheat
crops in a semi-arid region. Agric. Water Manage. 87: 41–54.
Er-Raki S, Chehbouni G, Boulet G, Williams D. 2010. Using the dual approach of FAO-56 forpartitioning ET into soil and plant components for olive orchards in a semi-arid region.
Agr. Water Manage. 97: 1769–1778.
FAO. 2016. AQUASTAT website. Food and Agriculture Organization of the United Nations(FAO). http://www.fao.org/aquastat/en/.
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Kharrou MH, Le Page M, Chehbouni A, Simonneaux V, Er-Raki S, Jarlan L, Ouzine L, Khabba
S, Chehbouni G. 2013. Assessment of equity and adequacy of water delivery in irrigation
systems using remote sensing-based indicators in semi-arid region, Morocco. Water
Resour. Manage. 27: 4697-4714.
Simonneaux V, Duchemin B, Helson D, Er-Raki S, Olioso A, Chehbouni A. 2008. The use ofhigh-resolution image time series for crop classification and evapotranspiration estimate
over an irrigated area in central Morocco. Int. J. Remote Sens. 29(1): 95-116.
Tasumi M, Allen RG. 2007. Satellite-based ET mapping to assess variation in ET with timing ofcrop development. Agric. Water Manag. 88: 54–62.
Zhang K, Kimball JS, Running SW. 2016. A review of remote sensing based actual
evapotranspiration estimation. Wiley Interdiscip. Rev. Water 3834–53.
Zhang Y, Wegehenkel M. 2006. Integration of MODIS data into a simple model for the spatial
distributed simulation of soil water content and evapotranspiration. Remote Sens. Environ.
104 : 393–408.

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#7660 A REINFORCEMENT LEARNING BASED APPROACH FOR EFFICIENTIRRIGATION WATER MANAGEMENTEL HACHIMI Chouaib1, BELAQZIZ Salwa1,2, KHABBA Said1,3 and CHEHBOUNI
Abdelghani1,41
Mohammed VI Polytechnic University (UM6P), Center for Remote Sensing Applications(CRSA), Benguerir, Morocco; chouaib.elhachimi@um6p.ma; 2 Faculty of Sciences d’Agadir, Ibn
Zohr University, Agadir, Morocco; salwa.belaqziz@um6p.ma; 3 LMFE, Faculty of Sciences
Semlalia, Cadi Ayyad University, Marrakech, Morocco; khabba@uca.ac.ma; 4 Centre d’Etudes
Spatiales de la BIOsphère (CESBIO), Toulouse, France; ghani.chehbouni@ird.fr
ABSTRACTDue to population growth and the effects of climate change, most of the world's regions
are threatened by water scarcity, especially in Africa and Mediterranean region. In Morocco, theagriculture consumes more than 85% of available water. Thus, to preserve water resources, therational management of irrigation water is necessary. In this context, recent technological progress
and the emergence of artificial intelligence could provide an effective decision support tool for therational and sustainable use of this resource. In this paper, we propose an approach based on
reinforcement learning, a type of machine learning that uses trial-and-error principle to learn how
to best fit situation to action in a highly dynamic, stochastic environment. In this proposed
approach, a Farmer Agent learns to choose the optimal cropping pattern defined by the type ofcrop, area to cultivate, sowing date and irrigation plan depending on the water availability at the
beginning of the agricultural season. Each agent interacts with the environment which is composedof environmental and socio-economic modules containing different processes to provide theFarmer Agent with the information he needs to learn. The use of reinforcement learning in thiscomplex system will certainly change the traditional irrigation water management mode and bringmore intelligence into the system. This approach will be generalized in future work to cover theentire agricultural sector and study the behavior of many Farmer Agents. This will be used then,for the design and implementation of a decision support system platform that can be used at the
beginning of the agricultural season to make informed decisions.
Keywords: water resources management, irrigation water management, reinforcement learning,
precision agriculture, machine learning, cropping pattern optimization.
INTRODUCTION
Water resources are already under pressure [1]. Most regions of the world are experiencing
water stress. This is due to several factors such as population growth, rising living standards, andclimate change which intensifies dangerous phenomena such as heavy rains in some areas anddrought in others due to the disruption of the normal water cycle. The latter represents the supplyof water to the environment. Water resources problems arise at demand levels. In Morocco, 15%of water resources are used by industry for the cooling of machines and for production, or tourism
which uses it for swimming pools, and irrigation of golf fields and for domestic use. The remaining343

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85% is used by the agricultural sector mainly for irrigation activities [2]. In addition, over 76% of
irrigation in Morocco is gravity-fed and uses non-optimal methods and techniques.
Having a better combination of crop choice, optimal area to cultivate, sowing dates, andprofitability is a real problem for farmers. It is currently done in a non-optimal way. Thus, we
propose in this paper an approach that uses reinforcement learning to address this optimization
problem.
LITERATURE REVIEW
Several studies have tried to solve the problem of efficient irrigation water managementwith different approaches. Roque et al. [3] used supervised learning with the objective ofreproducing the behavior of an expert agronomist by regressing the amount of water to be pumped
into the local basin at the following week. They used different types of data collected from sensors
installed in orchards and meteorological data. Meanwhile, Evangelos et al. [4] have proposed amethod for optimal management of irrigation water in the city of Athena. This method is divided
into two phases. The phase of training during which a neural network learns based on historical
weather data and the constraints associated with each reservoir of the city. And an application
phase during which the system will be able to perform optimal operations for each new state. With
such approaches, the performance of the model will be linked directly to the data annotation
process which depends on annotator reasoning. It can therefore never exceed the level of expertiseof the agronomist who annotated the data for the first case and the environmental characteristics
for the second case. In recent years, reinforcement learning has shown promising results as an
optimization method. Most of their applications are only in games such as Elhadji et al. [5] where
Deep Reinforcement Learning was applied in the Pong game, testing whether two agents candynamically learn to divide their area of responsibility and avoid collisions in front of a hard-coded
adversary. Elhadji et al used several architectures, namely DQN, DQN with double Q-learning,and Dueling network architectures.
We propose in this paper to apply the Reinforcement Learning to a real-world problem,
where an agent learns to maximize his objective function independently of a supervisor.
PROPOSED METHOD
Our approach is based on reinforcement learning where a Farmer Agent learns from its
environment through actions and feedbacks. Each time he observes the situation, selects an action
and gets feedback (reward/punishment) (Figure 1).
The environment is characterized by a state belonging to S, the set of states. This
environment changes depending on the interaction with the Farmer Agent. It provides him withobservations (Ot) at each instant t. These observations represent the current state of theenvironment.
Then, the Farmer Agent decides based on these observations. And the environment will beaffected by that decision and provides him with a reward (Rt) in case of a positive results(increasing the profit), or a punishment otherwise (exceed the amount of allocated water, deficit).
This repetitive process will enable the Farmer Agent to evaluate his actions according to his final
objective.

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Figure 1. The proposed model architecture
The environment is composed of two modules: the environmental module which is
responsible for managing meteorological data, water availability and soil information, and thesocio-economic module that provides the agent with all information needed such as crops waterneeds, growth phases durations, seed prices, and fertilizers prices. It also allows the agent tocalculate his incomes at the end of the agricultural season. After the training phase, the Farmer
Agent will be able to choose an optimal cropping pattern defined by the type of crop, the area tobe cultivated, the sowing date, and the irrigation plan depending on the amount of available waterand initial constraints (Figure 2).

Figure 2. Inputs and outputs of the final system345

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The mathematical modeling of our problem uses the Markov Decision Process [6], which
represents the basis of reinforcement learning. The goal of solving MDP is to find an optimal
policy that will maximize the sum of the expected rewards (Equation 1). The Bellman equation(Equation 1) is called the value function. It is decomposed into two parts: an immediate rewardR(s, a), and a discounted value of the successor state.
𝑉 (𝑠) = 𝑚𝑎𝑥s t𝑅(𝑠, 𝑎) + 𝛾 A 𝑃(𝑠, 𝑎, 𝑠′)𝑉(𝑠′){yzEquation 1. Bellman equation

Where:
S: the set of states which is water availability and environment’s constraints (Figure 2),
A: the set of actions (a combination of a crop type, area to cultivate, sowing date and irrigationplan),
P: the set of transition’s probabilities,
R: A reward in case of a positive income, and a punishment in case of exceeding water
availability or initial constraints.
λ: discount factor.
CONCLUSIONSIn this paper, we have proposed an approach based on reinforcement learning for the
agricultural sector at the crop field level. It will provide farmers with the best cropping pattern,
defined by the type of crop, area to cultivate, sowing date, and irrigation plan depending on thewater availability at the beginning of the agricultural season and initial constraints. This work will
form the basis for developing a decision support platform to help farmers make informed decisions
about their agricultural practices. The next phase will be at the level of the entire agricultural sector
where we will generalize and study the interactions between many agents with different objectives.
REFERENCES[1] Richey AS, Thomas BF, Lo M-H, Reager JT, Famiglietti JS, Voss K, Swenson S, Rodell
M. 2015. “Quantifying renewable groundwater stress with GRACE”, Water Resour.
Res. 51:5217- 5238, doi:10.1002/2015WR017349.
[2] Hssaisoune M, Bouchaou L, Sifeddine A, Bouimetarhan I, Chehbouni A. 2020. “Moroccan
groundwater resources and evolution with global climate changes,” Geosci. doi:
10.3390/geosciences10020081.
[3] Torres-Sanchez R, et al. 2020. “A decision support system for irrigation management: Analysis
and implementation of different learning techniques” Water (Switzerland), doi:
10.3390/w12020548.
[4] Rozos E. 2019. “Machine Learning, Urban Water Resources Management and Operating
Policy,” Resources, doi: 10.3390/resources8040173.
[5] Diallo EAO, Sugiyama A, Sugawara T. 2020. “Coordinated behavior of cooperative agents
using
deep
reinforcement
learning,”
Neurocomputing,doi:
10.1016/j.neucom.2018.08.094.
[6] Bellman R. 1957. “A Markovian Decision Process,” Indiana Univ. Math. J. 6(4):679-684.

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PROXIMAL AND REMOTE SENSING

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#7451 ESTIMATING SOIL ORGANIC MATTER FROM CELL PHONE IMAGES
A. Biswas1, H. Vasava1, Y. Fu2, P. Taneja3, S. Lin1, and P. Daggupati3
1
School of Environmental Sciences, University of Guelph, 50 Stone Road East, Guelph,
Ontario, N1G 2W1, Canada. 2National Engineering Research Center for Information
Technology and Agriculture, Beijing, China. 3School of Engineering, University of Guelph,
50 Stone Road East, Guelph, Ontario, N1G 2W1, Canada, biswas@uoguelph.ca
ABSTRACT
Soil organic matter (SOM) is considered as the backbone of soil health and soil quality.
Thus, its’ estimation is critical to support the development of management decision including
precision agriculture. To overcome challenges of laborious, rather expensive, and time-consuming
laboratory measurements, recent advances in image acquisition systems provided a new dimensionof image-based SOM prediction. However, challenges remain in using soil images taken directlyin the field due to variable soil surface conditions including vegetation cover, illumination, andsoil moisture. Soil moisture can significantly influence soil color and thus confounds therelationship between SOM and soil color. This study quantifies the effects of soil moisture on therelationship between SOM and color parameters derived from cell phone images and establishes
suitable SOM prediction models under varying conditions of soil moisture contents (SMCs). To
simulate the continuous variation of soil moisture in the field, air-dried ground soil samples weresaturated and allowed to dry naturally. Images were captured with a cellular phone over time
representing various SMCs (set of images). Final set of images were captured on oven-driedsamples. Images were preprocessed using illumination normalization to avoid illumination
inconsistencies and segmentation technique to remove non-soil parts of the images including black
cracks, leaf residues and specular reflection before modelling. Five color space models including
RGB, HIS, CIELa*b*, CIELc*h* and CIELu*v* were used to quantify soil color parameters.
Univariate linear regression models were developed between SOM and color parameters and an
optimal set of color parameters that are capable of resisting variation in SMC was determined. It
was observed that SMC exerted a considerable influence on SOM prediction accuracy when its
value reached >10%. The threshold of 10% SMC was considered as the critical SMC.
Consequently, stepwise multiple linear regression (SMLR) models were developed for soil
samples with SMC below and above the critical SMC. For the soil samples at below the critical
SMC, the color parameter R based model produced satisfactory prediction accuracy for SOM with
R2cv, RMSEcv, and RPDcv values of 0.936, 4.44% and 3.926, respectively. For the soil samples atabove the critical SMC, the SOM predictive model including SMC as a predictor variable showed
better accuracy (R2cv=0.819, RMSEcv=7.747%, RPDcv =2.328) than that without including SMC(R2cv=0.741, RMSEcv=9.382%, RPDcv =1.922). This study showed potential of cellular phone to
be used as a proximal soil sensor fast, accurate and non-destructive estimation of SOM both in thelaboratory and field conditions.
Keywords: Proximal soil sensor; Soil moisture content; Cell phone images; Color space models;
Stepwise linear regression348

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INTRODUCTION
Soil organic matter (SOM), the organic matter component of soil, is considered as the
backbone of soil health and regulates various physical, chemical, and biological processes andproperties. However, like other properties, SOM is highly spatially variable within a field which
contributes to the development of variable SOM pool in soil. Therefore, the information on spatial
variability of SOM can help decide site-specific management of agricultural resources including
application of nitrogen fertilizer and achieve the tradeoff between crop production increase andenvironment pollution reduction [1], the critical component of precision agriculture.
Traditional procedures for estimating SOM are laborious, costly and require time intensive
spatially dense soil sampling (10 m or less) and laboratory analysis [2]. This often restricts thedetailed characterization of its spatial variability in field. Furthermore, larger field sizes makedetailed characterization unaffordable for many growers. Recently, with the development oftechnology, various soil sensors have been used to characterize SOM. For example, soil
spectroscopy has shown potential to characterize SOM both in-situ and in laboratory conditions[3-11]. Although vis-NIR-MIR spectroscopy has shown great potential in predicting SOM, therelated complex processing techniques and expensive equipment restrict their widespread usage inpractical agricultural production scenarios. In addition, SOM prediction accuracy is limited using
vis-NIR-MIR spectroscopy when uncontrolled soil conditions, like variable soil moisture andsurface roughness are confronted [12-14].
Recently, with technological progression and the advancement of image acquisition
systems, image-based soil characterization techniques have garnered significant attention from theresearchers in soil science. Unlike soil diffuse reflectance spectroscopy, image acquisition devices
like digital cameras or even cameras in cellular phone are easily accessible. In the existing imagebased SOM or soil organic carbon (SOC) prediction studies [8, 15-17], soil color was used as aproxy to link SOM or SOC with images. Soils with darker color are generally associated withhigher OM contents and are regarded fertile and suitable for plant growth [18]. The existence(prevalence) of strong relationship amid soil’s color and its organic matter was also confirmed byresearchers [19, 20] who came up with a cell-phone application named SOCIT (only pertinent tomineral soils in Scotland) which utilizes this connection. The app provides an approximation of
topsoil organic matter content using a photograph of the soil of interest and user’s positional
information to access location-specific factors [21]. A recent study also reported the developmentof an algorithm to quantify soil organic matter and soil texture from image parameters using
geostatistical and regression-based methods [22]. However, due to the limited scope of the study
in terms of soil moisture conditions, the authors pointed out that their algorithm needs further
testing.
The existing image-based SOM or SOC prediction studies [8, 10, 15, 17, 22] directly usedsoil color parameters to develop prediction models without considering the contribution of otherfactors like soil moisture, surface residue, surface roughness and light [23]. These factors are
known to influence spectral response in the visible range of the electromagnetic spectrum (400 to
700 nm). Among these factors, soil moisture is the most important one that restricts practical insitu measurement of SOM. Usually, dry soils are lighter in color than wet soils [24, 25]. As soil
moisture content (SMC) increases, soil micro and macro pores are gradually filled with water andalter the physical structure of soil. Consequently, the relative refractivity at the soil particle surface
also changes causing the change in soil color [12]. The soil moisture, thus, makes the relationshipbetween SOM and soil color complicated and becomes a key determinant factor for the practical349

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use of image-based SOM prediction. SMC was implicitly involved in the models developed in theabove-mentioned studies since the SMCs of soil samples were variable in these studies, but it’s
influence on SOM prediction was not considered and hence, the given study was planned in which
SMC was explicitly considered for SOM prediction.
Following up on the missing links, the research questions addressed in this study were to(1) evaluate the ability of cell phone images to predict SOM using color parameters; (2) quantify
the effect of soil moisture on the accuracy of SOM prediction models based on color parameters;and (3) determine the critical moisture content at which it begins to influence SOM prediction
accuracy based on color parameters and establish suitable SOM prediction models accordingly.
MATERIALS AND METHODS
Twenty-five soil samples with a wide variation in SOM (3.3-62.7%) were selected for thisstudy, for whom SOM was measured using loss on ignition (LOI) method (Schulte and Hopkins,
1996). Digital images (2322 ×4128 pixels) were captured with a cellular phone 10-megapixel
camera set to a holder 32 cm above the sample and the images were saved as Joint Photographic
Experts Group (JPEG) standard compression. A total of six sets of images were collected
corresponding to six different levels of SMC (“group 1”, “group 2”, “group 3”, “group 4”, “group5” and “group 6” with increasing SMC). The images of oven-dried soil samples formed thecomponents/constituents/parts of “group 1”, images of air-dried soil samples of “group 2”, threesets of images collected during the natural drying process of “group 3”, “group 4” and “group 5”
and those of saturated soil samples of “group 6”. There were 146 images in all, with 25 images foreach group except 24 images for “group 2”and “group 6”, and 23 images for “group 1”. Before
the images were analyzed, preprocessing was carried out with four components: 1) region ofinterest (ROIs) selection, 2) illumination normalization, 3) image segmentation and 4) color spaceconversions.
RESULTS AND DISCUSSION
The scatter plot between SOM and color parameter R for all the groups is shown in Fig.
1. It can be witnessed that there exists a negative correlation between the color parameter R and
SOM for the first three groups. However, for the latter three groups, the scatter plot demonstrates
that an increase in SOM content occurred, without a considerable decrease in the R values.
Similar trends were observed in the scatter plots between SOM and other color parameters G and
B. The change in the behavior observed (the pattern of distribution of scatter plots) supported
that the negative correlation between SOM and color parameters cannot be held with increasing
variation in SMC, and that SMC influences color parameter-based SOM prediction in a different
manner below and above a specific level and hence, it was crucial to identify that level of SMC
after which it exerted significant influence.
Since the decrease in SOM prediction accuracy was not obvious for the first three groups,
the SMC frequency distribution for soil samples belonging to these groups was analyzed and theresults showed that 95% soil samples had a SMC of less than 10%. Similar patterns were also
observed in other studies; for instance, Nocita et al. (2013) grouped together soil samples with agravimetric SMC >=15% and developed a single SOC model with good prediction accuracy
using a PLSR of soil diffuse reflectance in the vis-NIR region. Rienzi et al. (2014) demonstrated
that predicting SOC over a range of 10% soil moisture variability did not substantially change

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prediction quality. Because of the behavior exhibited by our data as well as resemblance tosimilar studies, a SMC value of 10% was, therefore, determined to be as regarded as the critical
SMC in this study (for further exploration).

Figure 1. Scatter plots between SOM and color parameter R under varying soil moisture.
REFERENCES
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.

McBratney AB, Pringle MJ 1999. Precision Agriculture 1: 125-52.
O’Halloran IP, et al. 2004. Canadian Journal of Soil Science 84: 307-16.
Baumgardner MF, et al. 1986. Advances in Agronomy, pp. 1-44.
Ben-Dor E. 2002. Advances in Agronomy, pp. 173-243.
Ishida T, Ando H. 1999. International Journal of Remote Sensing 20: 1549-65.
Hummel JW, et al. 2001. Computers and Electronics in Agriculture 32: 149-65.
Barnes EM, et al. 2003. Photogrammetric Engineering and Remote Sensing 69: 619-30.
Gregory SDL, et al. 2006. Canadian Journal of Soil Science 86: 573-84.
Shi Z, et al. 2015. European Journal of Soil Science 66: 679-87.
Viscarra Rossel RA, et al. 2006. Geoderma 131: 59-75.
Dhawale NW, et al. 2015. European Journal of Soil Science 66: 661-9.
Nocita M, et al. 2013. Geoderma 199: 37-42.
Rienzi EA, et al. 2014. Soil Science Society of America Journal 78: 958-67.
Rodionov A, et al. 2014. Soil Science Society of America Journal 78: 949-57.
Chen F, et al. 2000. Soil Science Society of America Journal 64: 746-53.
Viscarra Rossel RA, et al. 2006. Geoderma 133: 320-37.
Viscarra Rossel RA, et al. 2008. Biosystems Engineering 100: 149-59.
Schulze DG, et al. 1993. Soil color, pp. 71–90.
Aitkenhead MJ, et al. 2012. Computers and Electronics in Agriculture 82: 108-16.
Aitkenhead MJ, et al. 2015. European Journal of Soil Science 66: 112-20.
Aitkenhead MJ, et al. 2013. E-SMART: Environmental Sensing for Monitoring andAdvising in Real-Time.
Sudarsan B, et al. 2016. Biosystems Engineering 152: 41-50.
Ben-Dor E, et al. 2008. Advances in Agronomy pp. 321-92.
Al-Abbas AH, et al. 1972. Soil Science 114: 477-85.
Stevens A, et al. 2008. Geoderma 144: 395-404.
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ROBOTICS, AUTOMATION, AND SMALL FARM MECHANIZATION

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#7493 WILLINGNESS TO PAY FOR DRONE TECHNOLOGY IN THE APPLICATION
OF PESTICIDE FOR THE CONTROL OF FALL ARMYWORM
S. Omega, F. Annor-Frimpong, S. Akaba, W. Ghartey, J. Ocran, and I. Asante
University of Cape Coast, Cape Coast, Ghana
Selorm.omega@stu.ucc.edu.gh; Phone: +233545022814
ABSTRACTIn Ghana, maize is one of the major staple food crops. Since 2016, it has been plagued byfall armyworm, leaving production capacity below the national average. The introduction of drone
technology is to assist farmers to reduce havoc caused by fall armyworm. The majority of researchdone in the area of drone technology has focused on the technical and mechanical aspects. Thisgap gives rise to this study, as the study seeks to ascertain the willingness to pay for drone
technology in the application of pesticides for fall armyworm control. The study adopted aquantitative research approach and cross-sectional survey design to ascertain the interplay ofvariables, using a sample of 152 maize farmers in the Northern and North East Region of Ghana.
The research used a questionnaire as the data collection tool with binary logistic regression foranalysis. The majority of respondents were willing to pay for drone technology but did not havethe resource to purchase. Their decision was influence by their source of income, access to credit
and input. The study recommends that the cost of drone technology be reduced to make itaffordable for all categories of farmers.
INTRODUCTION
The government has done little in the fight against fall armyworm (FAW) since its
discovery in 2017 in Ghana (Nunda, 2018). This limitation is due to the absence of empirical data
to guide the government in its policy formulation for effective control of FAW. Africa wasestimated to have lost 70% of its total maize, sorghum, rice and sugarcane yield in 2016 mainly
due to the activities of FAW (Prasanna et al., 2018). Day et al. (2017) estimated that the impact of
FAW in Ghana was 22% of yield in Ghana, translating to millions of dollars in losses. However,
the estimates were based solely on socio-economic studies that focused on the perceptions offarmers. Despite the large-scale use of drones in agriculture in other parts of the globe, literature
on Africa's agricultural use of drones for pesticide application is less to be desired. In Ghana, forinstance, there have been few reports of areas of drone technology application in the control of fallarmyworm, therefore this research was conducted to ascertain farmers’ willingness to pay fordrone technology for the control of FAW.
MATERIALS AND METHODS
A cross-sectional survey design was used for the study to ascertain the status of thevariables of the study and their inter-relationships. The study was conducted in the Northern
Region (Tolon and Mion) and the North East (West Mamprusi) Region of Ghana. A multistage
sampling technique was used to select communities in the Tolon (2) and Mion (3) district of theNorthern Region and two (2) communities from West Mamprusi in the North-East Region with353

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the assistance of Agricultural Extension Agents in charge of the districts. The first stage used thesimple random sampling technique to select the three (3) districts (Tolon, Mion, and West
Mamprusi) from the two Regions (Northern Region and North-East Region). Secondly, threecommunities from Mion, two from Tolon, and two from West Mamprusi were purposively selectedbased on knowledge on FAW infestation, sex of respondents, whether the individual farmer
controls FAW or not, size of farmland cultivated in the previous year. A sample size of 152
respondents (Tolon- 55, Mion- 48, and West Mamprusi- 49) out of a population of 301 (Tolon110, Mion- 80, and West Mamprusi- 111) was used for the study. A questionnaire was used as thedata collection tool. Farmers’ willingness to pay for drone technology for the control of FAW was
analysed using the contingent valuation method. The maximum and minimum prices that theconsumer was willing to pay was also generated and analysed. The binary logistic model was used
to estimate factors that influence willingness to pay for drone technology. Data collected wasanalysed in StataSE 13.0.
RESULTS AND DISCUSSION
Table 1 revealed the amount maize farmers were willing to pay to obtain drone services. It
was revealed that maize farmers in Tolon were more willing to pay as much as GH₡ 33.00-GH₡
43.00 per acre for drone services. It was also revealed that farmers in Tolon were willing to pay amuch higher price for drone services per acre because that’s what they felt they could afford. While
farmers in West Mamprusi were willing to pay the minimum of GH₡ 0.00 - GH₡10.00 per acre,with Mion farmers willing to pay GH₡ 11.00 - GH₡21.00 per acre. Mion, and West Mamprusi
farmers said it was due to limited resources available to them. This is supported by theOrganisation for Economic Co-operation and Development (OECD) (2001) and Gerpacio et al.
(2004) that technology and services are not evenly distributed across communities due to factors
like income constraints and changing demand of consumers.
Table 1. Amount maize farmers are willing to pay for drone services.
Maximum
Amount (Acres)(GH₡)
0.00-10.00
11.00-21.00
22.00-32.00
33.00-43.00
44.00-54.00
55.00-65.00
Reasons
Effectiveness
FasterSafe
Less Labourious
Limited Resource
That’s what I canafford

Tolon
F
4
2
17
18
8
5

%
7.5
3.8
30.9
32.7
15.1
9.4

7
4
2
5
16
21

13.2
7.5
3.6
9.4
30.2
39.6

Mion
mean
3.79

West Mamprusi

F
3
26
15
0
4
0

%
6.3
54.2
31.3
0.0
8.3
0.0

9
8
4
6
20
1

18.8
16.7
8.3
12.5
41.7
2.1

354

mean
2.50

F
22
10
10
6
1
0

%
44.9
20.4
20.4
12.2
2.0
0.0

6
12
2
2
24
3

12.2
24.5
4.1
4.1
49.0
6.1

mean
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Again, from Table 2, income source was found to be statistically significant in influencing
willingness to pay but the relationship was negative. This implies that as the income of farmers
increases, they stand a -17.461 chance of not being willing to pay for drone service and thisdirectionality denies the stated hypothesis. This is consistent with Alimi et al. (2016) who foundout that income was a very vital factor that influence farmers’ willingness to pay for a service.
Furthermore, access to credit was positively related to willingness to pay. Showing that if farmers
have access to credit, they will stand an 18.942 chance of being willing to pay for drone services.
Also, the relationship between willingness to pay and access to credit were statistically significant.
This agrees with Mersha (2018) that access to credit was a significant determinant of willingness
to pay. Lastly, from Table 2, access to input was inversely related to willingness to pay. As farmers
gain access to input, there is -3.049 chance of them not being willing to pay for drone service, thiswas significant. This is consistent with Chai et al. (2020) that consumer willingness to pay is not
influenced by access to obtain input.
Table 2. Binary logistic regression for willingness to pay for drone services.
Variables

Coef.

GenderMarital Status
Access to Credit
Access to Input
Access to Information
StatusPrimary Occupation
Farm size
Income source
Contact with Extension AgentsHousehold size
Landholding
Age
Farming Experience
Education
Constant
Cut1

.308
.980
18.942-3.049
.060
.956
1.659
.907
-17.461.540
1.446-1.170
.220
.021
-.803
-23.873
51.774

RobustStd. Err.
1.635
1.652
2.099
1.560
1.868
.813
1.446
1.252
1.059
1.594
1.039
.920
1.240
.017
1.436
6.962
17.422

Z

P>ǀZǀ

.19
.59
9.03
-1.95
.03
1.18
1.15
.72
-16.49
.34
1.39
-1.27
.18
1.20
-.56
-3.43

.851
.553
.000***
.051*
.974
.240
.251
.469
.000***
.735
.164
.203
.859
.231
.576
.001***

Number of observations
150
Wald Chi2(17)1083.47
Prob> Chi2.0000
Pseudo R2.3295
Log Pseudo Likelihood-20.942
Source: Field Data, 2019, n= 152, *** significant at 1%, ** at 5%. * at 10%.

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REFERENCES
Alimi BA, Oyeyinka AT, Olohungbebe LO. 2016. Socio-economic characteristics and willingnessof consumers to pay for the safety of fura de nunu in Ilorin, Nigeria. Quality Assurance
and Safety of Crops & Foods 8(1): 81-86.
Chai L, Han Z, Liang Y, Su Y, Huang G. 2020. Understanding the blue water footprint ofhouseholds in China from a perspective of consumption expenditure. Journal of Cleaner
Production 262 (121321): 121-321.
Day R, Abrahams P, Bateman M, Beale T, Clottey V, Cock M, Gomez J. 2017. Fall armyworm:
impacts and implications for Africa. Outlooks on Pest Management 28(5): 196-201.
Gerpacio R, Labios J, Labios R, Diangkinay E. 2004. Maize in the Philippines: Production
systems, constraints and research priorities. Texcoco, Mexico: International Maize andWheat Improvement Centre (CIMMYT).
Mersha D. 2018. Farmers’ perception and willingness to pay for weather index based insurance incentral rift valley of Ethiopia: The case of Adamitulu Jidokombolcha and Bora Districts.
(Doctoral thesis, Haramaya University).
Nunda W. 2018. Fall armyworm: impacts and implications for Africa (Evidence Note Update).
Oxfordshire, England: CABI.
OECD. 2001. Oslo manual. Guidelines for collecting and interpreting innoviation data (3rd Ed.)
Paris, Fance: OECD and European Commission.
Prasanna BM, Huesing JE, Eddy R, Peschke VM. (Eds). 2018. Fall Armyworm in Africa:A
Guide for Integrated Pest Management, First Edition. Mexico, CDMX: CIMMYT.

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#7606 A REVIEW ON SENSOR BASED ROBOTIC AGRICULTURE: IMPROVINGTRADITIONAL AGRICULTURE PRACTICES
S.C. Karad, Prabhat Kumar and G.U Shinde
National Agriculture Higher Education Project, ICAR, New Delhi
Centre of Excellence for Digital Farming Solutions Enhancing
Productivity by Robots, Drones, and AGVs (DFSRDA)
Vasantrao Naik Marathwada Krishi Vidyapeeth, (431402) Parbhani
srfic.nahep.vnmkv@gmail.com, +919370728496
ABSTRACTAgribot is advanced mechatronic applicant machinery that serves precision agricultural
practices and works independently with logical programs duly coded with several set ofoperational task in the field. This is automated device that expedites accuracy and speed of every
task of field operations associated with the farming. The most important characteristics of sensors
in Agribot applications are such that it must be Robust, Smart, Low-cost , with strong signal
interpretation. The issues of Sensor Fusion, Robust algorithms and overall quick response toactivate the mechanism are important quality parameters. The operational task like properties andcontains sensing, paste detection and paste management, plant properties sensing and climate
monitoring issues are very important while designing a hardware and software deigning in Agribot.
The weed detection in which cameras, machine vision and image processing like methods andtools are developed and need to be very précises and specific as traditional practices arechallenging with an expected output such as operation cost and time must be saving with highquality agricultural production capacity and economic for Indian farming system. So sensors are
the core components of Agribot where in the cost of the device can be minimised so that there will
be a digital farming practices by smart farm machinery. The present paper introduces a overall
review about sensors used in weeding, insect and disease detection, spraying and harvesting like
operations.
Keywords: Precision Agriculture, Digital Farming, Field Operation, Autonomous vehicles &Sensors, DOF, Machine Vision
INTRODUCTION
Agriculture is the most dominant sector which affects the GDP of every nation in the worldmap. To soothe hunger and bridge demand and supply gap surely there is a need for precision
agriculture. Hi Tech agriculture technology outstandingly transformed almost every field
operations procedure both in crop and livestock systems in today's time. Use of these technologies
with sensor development is a need of an hour. Due to revolution in agriculture robotics technology,
minimum investment required in terms of time and efforts related with operation and production
cost. Operations involved in agriculture enhanced because of, evolution in software development,machine vision and multivariate data processing. Additionally, there is improvement in equipment
and machinery associated with field operations optimized real time scenarios faced by farmers. In
today’s time there is a dearth of human labours extensively required in field operations.

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Management of weed in intra row and harvesting is very tedious when it is done with traditionalfarm equipment and machines. As a consequence, labour arability crop field operations accelerated
with the help of robots [Marinoudi et. al. (2019)]. Some of complexities associated with operationand performance of advanced robotics used in the field operation. These complexities should be
addressed to transform the applicability of robotics in agriculture. While building a perfect robotic
solution for complex operations for the field, cost operation analysis, advantages anddisadvantages should be given priority [Pedersen et.al. (2006)]. Other factors are also anxiously
needed to execute any prominent task to suffice the need of requirement which includes adaption
capacity, smartness, networking and capability of communication, less length and weight[Blackmore et. al. (2008)]. Smaller self-dependent autonomous machines are preferred to perform
soil erosion and associated problems rather use of big farm machineries [Fountas et. al. (2010)].
Divide any big operation into small steps of operation before fully atomizing any task related withfield operations. To cope up complex conditions involved in the field, there is a need to optimize
all small operations [Fountas et. al. (2020)]. There are some frequently observed troubles
commonly faced by robots while performing field operations which include assessment of terrain[Reina et.al. (2017), Fernandes & Garcia et.al. (2018)], plan and path [Bochtis et.al.
(2009),Bochtis et.al. (2015),Yang & Noguchi et.al. (2012)] human observation - detection
problem [Yang & Noguchi (2012)], and light-footed robots [Yang et al. (2004)]. Troubles in tasks
related field operations are mainly associated with Inputs of utility and crop specific specifications
related with their physiology, anatomy and architecture and pest - disease detection. To implement
full autonomous application to any field operations, difficulties are always part and parcel. While
implementing any robotic solution common difficulties are frequently with actuation, intelligence,navigation and vision of robotic systems. Some multifaceted robotics responsibilities and tasks
associated with field operations which agribots won’t perform well includes harvesting, seeding,management of weeds, interaction, purning, navigation and assessment of systems [Aravind, et al.
(2017)]. Some arable land farming operation set has been examined [R Shamshiri (2018)]. Proper
attitude review has been a very integral part of commercially available agribots [Fountas et al.
2020]. Examination is essential for agribots, for study of farm environment and to see exact
technique of operation [Tsolaki et al. (2019)]. Most advanced agribots have seen for weed
management agribot in the field which is autonomously operated for weed related operation[Slaughter et al., (2008)]. Similarly, straw berry related study and operation has been done by these
agribots [Defterli (2016)]. However, holistic study of agribots architecture and operation isessential, because there are different types of field operations related to various types of crops andcontrolled environment. Primary challenges of agribots are associated with weather adaptablestructures which are evolved to fulfil various needs of crops and their respective field operations.
Weather agribots structures are accurate to cope up the challenges concerned with autonomy ofactuation. This paper will try to give review about the traditional agriculture practice
improvements based on sensors used in agribots.
MATERIALS AND METHODSCorrelational survey has been done for sensors used for weeding, insect and disease
detection, spraying and for harvesting applications has been studied. For this review paper several
research articles were downloaded from renowned peer reviewed journals and then papers as per
the sensor’s application category divided into four applications. While reading papers focus has
been given to sensors and types of crops these strategies used for each application. Afterwards, in358

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the (Table 1) weed detection types are presented such as chemical and mechanical. Additionally,
crop disease and insect detection are categorized in the column number three of (Table 2). (Table3) mentions about spraying mechanism two modes such as present and absent. At the end in (Table4) is depicting harvesting application rate of picking fruit in column number three, categorized into
two modes such as present or absent with their speed. These studies conducted while writing thisresearch paper will be a torchbearer to shade light to see how sensors revolutionized
every traditional practice in conjunction with field operation. The role of sensors in various
applications is discussed as follows.
WeedingAmongst all field operation related tasks, wedding tasks are quite repetitive and time
consuming in nature. More than 40% hard work required by farmers is to collect weed manually[Labrada et. al. (2006)]. There are certain crops that are quite disturbing to farmers and labours
and so it took lots of money to do that manually. This kind of field operation will have some ofthe very bad effect over the health of farmers because of manual herbicide sprayed over crops. As
a result yield will be less because of spraying without knowing the difference between the crops
and weed. There is a huge loss up to 61.5% in the yield of wheat and maize and 33.7% actual yield
loss because of heavy use of pest [orke et. al.(2006)]. So to avoid such huge losses weeding robots
is the solution [Utstumo et. al (2018)]. These weed robots are usually classified into two types:
chemical and mechanical. These robots 100% efficiently and effectively can detect the weed in thecrop field row and can spray the exact amount of herbicide required [Utstumo et. al (2018)].
Weeding robots can [Asterix. (2020)]. spot the weed very precisely in the range of 98% [Van
Evert et. al (2006)]. Some of the commercial robots can spot and destroy the weed very precisely
with the accuracy of 85%. Commercial chemical weeding robots [EcoRobotix (2020)] are less inperformance compared with mechanical types [Klose et. al. (2008)].
Cameras which are internet enabled are widely used. Listed cameras such as RGB and IR
are widely used. Sensors like optical and acoustic distance sensors, laser, gyroscope and IMU
mentioned in the literature. Sensors integrated with robotics systems can increase the performanceof weed detection in both chemical and mechanical types. Herbicide with weed refers to weed
extraction by chemical method which is famous than mechanical type because of less work done
required by it. But spraying more will have some bad toxic impact over the health of individuals
involved in these operations. So, precision spraying is the best solution for spraying chemicals inan accurate amount and in precise quantity over the weed. This is attained by integrating sensors
with robots with machine vision applications which are highly capable to detect weeds. In the end
even though there are good solutions available in the market, still there is a lot to do to increase
efficiency. That includes correct navigation guidance and simulation systems with the help of deeplearning, so as to make the exact decision at the correct time.
There is only one point which comes in between greater accuracy of robotic systems.
Accurate identification of most accurate weed from field is a challenge for such system. Sensors
and cameras integrated with robotics systems like vision cameras and sensors measuring distancecould be a very great help for precision detection and spraying on weeds. There is huge scope foradvancement of weed detects in their early stage like sprouting using soil EC sensors.

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Table 1. Sensor-based weed detection application.
Sensors Used

Crops

Weed Detection& TypeYes, Chemical

Cited
Work
Cameras, optical and acoustic distance
Maize
Klose, et al
sensors(2008)
RGB infrared camera
CarrotPartly, Chemical Utstumo, etal (2018)
Webcam, solid-state gyroscope
Potato, corn Partly, Chemical
Van et al(2006)
Color camera
Sugar beet Yes, Mechanical Bakker,et al(2006)
Color camera, artificial vision, compass
Beetroot
Yes, Chemical EcoRobotix
Color camera, Sensor Watch
TomatoPartly, Chemical Lee et al.,(1999)
Insect and Disease Detection
Disease and Insect detection recently gained great momentum because of scope in thesensors based robotic machine vision system. Traditional practices took lots of time, money, labour
and again fewer yields. If we can predict any disease of any crop early and advance surely thatwould avoid the economic burden of farmers. Monitoring with the help of these advanced system,
insects can be detected which are usually below leaf, underground and which are extremely
difficult to locate by human eye. In Table 2 we have categorized sensors, crops and then crop
disease identification with accuracy would be a great help for future study. From study we cansee that all colour cameras like multi spectral, hyperspectral and some of digital cameras which
are less costly also be used in some of research papers. Those are of high cost and would require
high GPU computing power to process images and train models to give precise results in less time.
Digital shade cameras detected viruses like wilt in pepper plants and mildew powdery in tomato
with high accuracy. Multispectral also been given great accuracy for such disease detection[Fountas et. al. (2020), Scho et. al. (2017)]. In olive trees, Xylela fastidious detected promptlywith the help of sensors [Rey et. al. (2019)].
RGB camera sensors usually used in strawberry to detect Powdery mildew [Mahmud, et al(2019)]. Rice RGB camera used to detect Pyralidae insects in Tomato [Liu et al. (2019)]. Two
DSLR cameras (one in BNDVI mode), a multispectral camera, a hyperspectral system in visible
and NIR range, a thermal camera, LiDAR, an IMU sensor used to detect Xylella fastidiosabacterium in Olive tree [ Rey et al 2019)]. The groundnut RGB camera Cotton (Bacterial blight,
magnesium deficiency) can be used to detect (leaf spot & anthracnose) groundnut in cotton [Pilli
et al (2015)]. The RGB camera, multispectral camera, laser sensor can be used to detect tomato
spotted wilt virus & Powdery mildew virus in Bell pepper [ Fountas et al (2020), Schor et al(2020)]. There are some of troubles faced while detecting disease and insects on different crops
those include: first is lack of image based on detection of models as per specified in the datasets;second is processing time from large sets of image datasets of multispectral, hyperspectral, thermal
and RGB camera; and third is, uneven light conditions present in various crop fields. [Zheng, etal., (2019)] to cope with these real time difficulties we should use sensor vision based modern
technology [Barth et al., (2018)].

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Table 2. Sensor based disease and insect detection application.
Sensor Used
RGB camera, multispectral
camera, laser sensor

Crop
Bell
pepperCrop Disease
tomato spotted wilt virus& Powdery mildew

groundnut RGB camera Cotton(Bacterial blight, magnesium
deficiency),
Two DSLR cameras (one in
BNDVI mode), a multispectral
camera, a hyperspectral system
in visible and NIR range, athermal camera, LiDAR, an
IMU sensor( * )rice RGB camera

Cotton(leaf spot & anthracnose)
groundnut

Cited Work[ Fountas et al(2020), Schoret al (2020)]
Pilli et al(2015)

Olive tree

Xylella fastidiosabacterium[ Rey et al
2019)

TomatoPyralidae insect

Strawberry

Powdery mildew

Liu et al.
(2019)
Mahmud, etal (2019)

RGB camera

One of the difficulties is uneven light conditions and that can be reduced using some of the novel
imaging modalities about light to detect some of the insects and diseases on crops [Mahmud, etal. (2019)]. There other difficulties too apart from lightning conditions which are some of insect
morphology related with imaging constraints such as shadow etc. For detection of bugs under theplant on the crop beneath the soil requires some of the advance mechanism to detect thatprecisely and that is the challenge.
Spraying
Even though we manage to control the toxic effect of active substances like herbicide andliquid fertilizer which we use for spraying application over pests and insects in the field. There isa risk associated with the health of farmers even if we use some advanced robotic for applications.
Precautionary measures should be taken. Spraying agri drones and agribots can avoid such risks.
Traditional spraying accuracy has been replaced by sensor integrated machine vision intelligence
nowadays. Using these practices with the help of drones and agriculture robots, we can attainprecise spraying over rightly spotted part of crops in the field operations. So as a result ofhomogenous spraying, we will get proportionate yield in less time. Research papers have beenreviewed for sensor applications in spraying applications which are shown in Table 3. Some ofthe processes were corrected which are used in the greenhouse in association with robots[Sammons et al. (2005)], robots which are working in very alignment of crop rows [Singh, et al(2005)]. Sensors which detect the correct spot used with robots always increase the accuracy of
precision spraying [Oberti et al. (2013), Underwood, et al (2015)]. Nozzles are used with spraying
devices associated with Agri drones [Sammons et al. (2005, Sogaard, & Lund (2007)], also that
nozzles could be used with the end effector with manipulator other types of agribots to attainvariety DOF applications ranging from 3 DOF [Slaughter et al. (2008)], [Underwood, et al.
(2015)] to 9 DOF [Oberti et al. (2013)].

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Table 3. Sensor-based spraying application.
Sensors Used

Crop

Cited Work

Vegetable crops

Presence or absent ofreal time Detection
Present

Thermal IR camera
Hyperspectral camera, stereo
vision, monocular color
cameraRobot controller

CantaloupeAbsentCucumberAbsentGrapevine

AbsentGrapevine

Present

Mahmud et al(2019)
ammons et al.
(2005)
Ogawa,et al(2003)[Oberti et al(2013),]

infra-red sensors , Bump
sensors, induction sensors
Ultrasonic sensor, color TV
cameraRGB camera, R-G-NIR
multispectral camera

Underwood,et al (2015)

Also, in some of the applications, spraying time and machine effectiveness plays animportant role.These suggestions should be taken in positive mode to optimize existing systems[Mahmud et al (2019)]. Other parameters should also be taken considerations to optimize existingmechanism such as machine error [Sánchez-Hermosilla et al. (2010), Singh et al. (2008)],
parameter of performance metrics [Oberti et al (2013)], actual [Sammons et al. (2005, SánchezHermosilla et al. (2010) Ogawa, et al. (2013)] and real time detection and spraying capabilities[Underwood, et al (2015)].
Harvesting
Harvesting is one of the most repetitive field operations out of all the other applications
mentioned in this paper. Some of the research universities and companies are taking efforts toautomate these repetitive applications. Based on literature review found, two types of robotics
harvesting applications which are Bulk type and second is selective type. Selective type application
is a need of the hour which is point of attraction to everyone because of its fastest and precise
operational results. Performance of these selective kinds of harvesting robots can be measured
based on the objects effective picking speed and picking charge [Hayashi, et al. (2014)].These
applications of harvesting with the help of sensor machine vision-based robotics should be donein precise given type without affecting crops and plant. Cash crops like strawberries suffer lotsof manufacturing and labour cost in some stage of harvesting [Qingchun et al., (2012), Feng et al.,(2012)]. So, to overcome that, strawberries harvesting robots is a solution [Hayashi et al. (2014)
Hayashi et al. (2014), Xiong et al. (2019]. Strawberries selection speed of harvester robots is 7.5
to 8.6 seconds per strawberries and claimed speed is about 8 second per this fruit in line of crop [Xiong et al (2019)], 5 second per fruit strawberries picking speed mentioned in [Arima, et al.,(2004)]. Only speed is immature, what matters is accuracy of picking fruit. Traditional harvesting
practices over acers of acres of land cost more to growers, so to avoid cost and exertion of robotics
harvesting is a solution. Performance metrics of harvesting robots is also an important point to beconsidered for harvesting [Shiigi, et al., (2008)].

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Table 4. Sensor-based harvesting application.
Sensors UsedCrop

CCD cameras, vacuum sensor

watermelon

CCD camera, photoelectric
sensor 62.5%
black and white CCD cameras,
proximity sensor, far and nearvision sensors
Pressure sensor, 2 convergent IR
sensors, telemeter, cameras
synchronized CCD cameras

eggplantmelonvarious fruits

Rate and picking
speed (present/
Absent)66.7%, Absent
64.1 sec/eggplant,
Present
15 sec/fruit, Present

Camera, laser sensor

cherry tomato

Binocular stereo vision system,
laser sensor
Stereo camera, playstation
cameratomato2 sec/fruit (only grasp& detach), Absent
45 sec/cucumber,
Absent8 sec/tomato bunch,
Present
15 sec/tomato, Present

tomato23 sec/tomato, Present

Color CCD cameras, reflectiontype photoelectric sensor
Sonar camera sensor, binocularcamera3D vision sensor with two setsof slit laser projectors & a TV
cameraLaser sensor, vision

strawberry

8.6 sec/fruit, Present

strawberry

31.3 sec/fruit, Present

asparagus

13.7 sec/asparagus,
Absentmushroomsensor 6.7
sec/mushroom, Present

3D vision sensor with red, IR
laser diodes, pressure sensor
High-frequency light, camera

cherry14 sec/fruit, Absent

apple tree

9 sec/fruit, Present

Color camera, gyroscope

alfalfa, sudan

2 ha/h (alfalfa), Absent

cucumber

Cited Work
Pilarski, et al.
(2002)
Hayashi,et al(2002)
Umeda et al(1999[Ceres et al.
(1998)][Van Henten,
et al (2002)][Feng et al.
(2018)][Lili, et al.,(2017)][Yaguchi etal (2016)]
Defterli,(2016)
Defterli,(2016)
Cerescon[Siciliano &Khatib(2016)]
Tanigaki etal. (2008)
Baeten,et al(2008)
Rowley(2009)]

Some examples are dogtooth [Dogtooth], Independent harvester selection strawberry[Sammons et al. (2005)] end effector based [Agrobot E-Series.] and harvesting robotics[Octinion.]. Harvesters are used for other plants, fruits and crops such as apples and tomatoes. For
instance, apple harvesters are very easy to pluck apples by recognizing apples by their color withthe help of robotics vision based grippers. Fastest of such harvester has speed of 7.5sec steps perapple [Silwal,et al., (2016)] for keeping it requires 9 sec per apple [Baeten, et al (2008)] such363

1st African Conference on Precision Agriculture | 8-10 December | 2020

machine has 90% around accuracy in dense orchids [FR Robotics] and apple [Bulanon & Kataoka
et. al. (2010)]. Vegetable crops such as tomato and potato, tomato harvester is used for plucking it
for a quickest speed of around 24 seconds [Yaguchi et al. (2016)], with 87% of picking price [Liliet al. (2017)]. Without moving a tomato bunch 8seconds per tomato speed also achieved by tomato
harvester [Feng et al (2018)]. Commercial tomato harvesters are also good such as [Metomotion.]and Root-AI [Root-AI.]. For citrus family fruit like oranges, citrus harvester is also used with the
speed of 3 seconds per orange [Energid]. For cherry orchid the speed is like 14 seconds per cherryorchards [Tanigaki, et al. (2008)]. For manually plucking fruit requires some more time [Ceres etal. (1998)]. Cucumber harvester claimed speed of around 45 seconds per it with 80% accuracy[Van Henten, et al (2002)]. For eggplant harvester it took 64 seconds per it with accuracy of 62%[Hayashi, et al (2002)]. Size and weight of object has affect over accuracy and precision ofplucking them. Harvester of commercial plucking of pumpkin and cabbage [Edan, et al. (2002)]
is also used and robotic system is also designed. For melon and watermelon, melon robotic
harvester has around 86 % accuracy [Umeda et al (1999)], with 67% of selection rate [Pilarski, etal. (2002)]. Harvester machine, designed for Sorghum showed 2 hector acer fastest speed of
harvesting it [Rowley et. al. (2009)]. Mushroom harvester shown 70% accuracy [Siciliano, &Khatib (2016)] damages were avoided and cost loss was made up with the help of these robotics
applications.
In summary, Harvester robots are of two types one which is mounted on tractor used forapple [Baeten, et al. (2008)] and other type is strawberries manual harvester [Xiong et al., (2019)]
and remaining type is independent one. Two types of picking structures such as suction vacuum
type and other is gripping gripper type. Gripper type is with a casual joints and links used to pluck
item by the force enabled mechanism of end effector with manipulator [Abundant Robotics].
Whereas suction vacuum type can able to pull and twist and then pluck the item. The gripper’s
arms is one of the advance structure helpful for plucking fruit in harvesting application [Agrobot
E-Series], peduncle type arms [Hayashi et al. (2014)] and fruit is plucked off with gripper or
vacuum suction [Yaguchi et al. (2016), Zapotezny & Lehnert (2019), Agrobot E-Series.]. For
localization of fruit is very important in machine vision using sensors such as RGB cameras, timeof flight sensors, infrared sensors [Xiong et al. (2019), Agrobot E-Series.] or laser sensors [Feng,et al. (2018)] and other robots uses [Cerescon] Proximity sensors instead of cameras. Manipulators
were commonly used in harvesting applications which has degree of freedom movements ranging
from 2 DOF to 7 DOF.
CONCLUSIONS
Sensors and cameras integrated with robotics systems like vision cameras would be very
great help for precision detection and spraying on weeds. There is huge scope for advancement ofweed detects in their early stage like sprouting using soil EC sensors. There other difficulties tooapart from lightning imaging constraints such as shadow etc. some of the advance mechanism todetect bugs under the plant on the crop beneath the soil requires that precisely and that is thechallenge. Some of the applications, spraying time and machine effectiveness play an importantrole and that suggestions should be taken in positive mode to optimize existing systems. Other
parameters should also be taken into like machine error parameter of performance metrics actualand real time detection and spraying capabilities. Manipulators were commonly used in harvesting
applications which works in degree of freedom between 2 DOF to 7 DOF. From this paper we cansay that sensors useful in agribots applications on Weed Detection, Spraying, Disease and Insect364

1st African Conference on Precision Agriculture | 8-10 December | 2020

Detection and harvesting. Out of these four applications harvesting application has much more
ahead in sensor development associated with vision-based agriculture robots. Whereas less sensors
used in the rest of applications. Specifically, for weed detection application we found there is huge
scope for full autonomous sensor-based weed detection and for its effective efficiency. However,weed control done by mechanical type than chemical one. Even though for insect and disease
detection application has good result accuracy but the work done on limited crop is very less.
Image processing should be immediately linked with processing strategies, communication way,vision structures and the extent of the photographs. A key task related to the robotic imaginativeand prescient structures is to offer uniform lighting situations via synthetic illumination methods.
In future we need to increase decision support system of these applications and there is need of
new algorithm development in relation with sensor based robotic system.
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Van Evert FK, Van Der Heijden GW, Lotz LA, Polder G, Lamaker A, De Jon A., Van Der Zalm
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#7641 LOCALLY ADAPTED MACHINERY SOLUTIONS FOR SUSTAINABLE
INTENSIFICATION OF CROP-LIVESTOCK SYSTEMS IN TUNISIAUdo Rudiger1*, Zied Idoudi1, Aymen Frija1, Mourad Rekik1, Monia El Ayed, Hatem Cheikh,and Anis Zaim
1
Resilient Agricultural Livelihood Systems Program (RALSP), International Center for
Agricultural Research in the Dry Areas (ICARDA), Tunis, Tunisia, u.rudiger@cgiar.org
ABSTRACT
Small crop-livestock systems in Low- and Middle-Income Countries (LMIC) such as North
African ones are characterized by low mechanization levels, thus undermining their productivity
and sustainability. Machinery being promoted in local markets are made and imported mostly fromindustrial Countries where farm systems are larger in terms of size. Prices of these machines areunaffordable for small to medium sized land-holding farmers who remain incapable of upgrading
and modernizing their farming operations. Through its work on crop livestock integration underconservation agriculture (CLCA) and the CRP livestock (feed and forage flagship), ICRADA andits national partners in Tunisia have been working on developing small machineries well adapted
to small farming systems, and contributing to crops rotations through inclusion of forage crops,
enhance the quality of animal feed, and allow to reduce the impact of livestock grazing on soil
covers. These machines include “small mobile seed cleaning and treatment unit” and “small
mobile feed-grinder machine”, etc. and were all locally manufactured at low cost. Adaptedbusiness models have been developed to further deploy these machines to small and medium
farmers’ cooperatives and other potential machinery service delivery enterprises, thus contributing
to diversify and enhance their respective incomes.
INTRODUCTION
The conventional national seed system in Tunisia is not providing enough quality forage
seeds. Forage seed production like barley, faba beans, vetch or alfalfa is mainly undertaken bylarge seed producing cooperatives through subcontracting with individual farmers. One private
seed enterprise COTUGRAIN and the national Office of Livestock & Pasture OEP are equallyengaged in forage seed production.
Due to insufficient forage seed supply, but also to save costs, many small-scale, mixed
farmers prefer using their own farm seed. The quality of these farm seeds is generally low as they
are normally cleaned manually, so the final product still contains some unproductive seeds (broken
seeds or small sized seeds). In addition, these seeds are sometimes attacked by pests and diseases
as they are not treated. The results of using these poor-quality farm seeds are low forage yields
and quality and low income.
To tackle this constraint the CRP livestock (feed and forage flagship) in collaboration with
the IFAD funded CLCA-II Project, promoted the use of innovative locally produced seed cleaning
and treatment units to develop business for lead farmers and farmer cooperatives around forage
seed production. After discussing with national partners (INRAT, OEP, INGC), the business idea
was found more suitable for small or medium SMSA (Mutual Association of Agricultural
Services/Societé Mutuelle des Services Agricoles) as the machine would benefit more farmers.

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SMSA are a kind of farmers’ cooperatives providing services to their members. The cooperatives
can provide seed cleaning and treatment services for their members. The business can help toprovide additional income for the cooperative and forage seed production of their members. The
seeds are used by the members themselves.

Figure 1. Traditional and manual seed cleaning by woman farmer (Photo: Zied Idoudi, ICARDA).
Low cost feed supply is a major constraint for small scale livestock farmers in Northern,
Central and Southern Tunisia, in particular during summer. Through grinding of locally available
feed the intake will be increased, digestibility is improved and productivity gained. In 2019, the
CRP livestock “Feed and Forages flagship” in collaboration with the IFAD funded CLCA-II
Project has introduced in Tunisia the technology of locally produced mobile grinders which canserve for feed mash production as well as simple grinding of bulky feed like straw and hay and arange of other agro-industrial feed resources to reduce feed wastage and provide alternative diets
for the summer feeding of flocks.
MATERIALS AND METHODSThis paper is providing a technical description of two-locally manufactured machines codeveloped to support small-scale, mixed farmers in the crop-livestock systems of North West
Tunisia in improving the efficiency of on-farm forage seed production and the feeding of theirlivestock. An illustration of the benefits issued from these machines to farmers and farmers
cooperatives will be provided in the results section. It is important to mention that the developmentof these machines is undertaken through different steps including:• Field investigation and characterization of small farming systems components. This
leads to the identification of technical gaps which can enable transformative change
in the farm system we are working on. We mainly focused on crop-livestock
system, as the focus of our program is mainly on “feed and forages.”• Identification of any available and affordable technical solutions currently existingin the market. If this is not the case, we then go for:370

1st African Conference on Precision Agriculture | 8-10 December | 2020o Co-design and co-development of an affordable technical solution which
can be relevant and accessible to small farmers. Small farmers are usually
involved in this design stage.
o Sub-contracting machinery manufacturer (who also contribute to thedesign) given the budget thresholds,
o Testing and piloting the developed machines at the farm level, through fieldand demonstration days while monitoring its robustness in addition to any
feedbacks from farmers and other technical partners,• Once the previous step is validated, we then proceed with the distribution of a small
number of machines to a network of farmers and farmer’s cooperatives. A businessplan is further developed to provide more evidence about the usefulness andprofitability of the machines. Only farmers and cooperatives who are willing topartly (financially) contribute to the price of the machine are considered.
The remainder of this section provides some technical characteristics of the “mobile seedcleaning and treatment unit”, and well as the “mobile feed grinder machine.”
The Mobile Seed Cleaning and Treatment Unit: Technical Characteristics andPerformance
One-unit costs 12,500 TND (about 4,350 US$) and has a capacity of about 0.8 t / hourdepending on the kind of seeds treated. It is produced by a local Tunisian blacksmith and can be
operated by electricity (220 V). The unit comes with four different sized sieves which allow thecleaning of different sized seeds (barley, faba beans, vetch, berseem, etc.). It also contains anadditional part to treat seeds against fungal diseases. The two wheels give flexibility to the use of
the unit as it can be attached and moved by a car to facilitate its movement from one site to another.

Figure 2. Mobile seed cleaning and treatment unit (Photo: Zied Idoudi, ICARDA).
Small-Scale Feed Grinder to Improve the Quality of Roughage Feed
The grinder can chop and grind material like cactus cladodes and fruits, small olive
branches and leaves, straw, hay, date kernels, cereals, faba beans etc. and works with both,electricity (380) V or PTO powered by a tractor. Just like the seed treatment unit, it can be moved
easily by a tractor, hence giving the farmer the opportunity to chop the materiel next to the fieldor the flock yard where he has no electricity. Production capacity per day varies between 1.5 and371

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10 t mainly depending on the type of material to be chopped. The price per unit is 1,000 US$ andIt was produced locally by SFEMI society with the support of ICARDA.
The machine has been introduced to farmers in June 2019 whereas the seed treatment units
have been distributed only end of 2019. Since then, ICARDA with its national partner OEP areclosely monitoring the use of the machines.
OEP and ICARDA developed monitoring sheets for both technologies to collect data
concerning different indicators like costs, return and benefit. We also looked at the different
management systems by the different beneficiaries. The final objective is to identify different
business models around the technologies and see to which extend they can be beneficial forusers.
RESULTS AND DISCUSSION
The Mobile Seed Cleaning and Treatment Unit
Four mobile seed cleaning and treatment units were delivered and distributed to farmers’
associations having between 150 and 350 members each and are located in different CLCA target
areas (North Western and Central regions of Tunisia) – globally, over 1,000 small-scale farmers
will benefit directly from these units during the upcoming years. Young farmers and women werealso considered among the beneficiaries.
With the help of the mobile seed cleaning and treatment unit, members of these four
cooperatives can significantly increase their seed quality and consequently their fodder production.
In addition, the unit can serve as an income generating activity for the cooperative as farmers haveto pay renting fees to use the machine. Beneficiaries, who have been carefully selected based on
their interest and need for the machine, contributed with 10% of the total price of the machine(1,250 TND/435 US$), which is used to train them on the machines and on other good practices
for seeds production and cleaning in general. The 10% contribution was also considered as
essential for farmers participation and engagement.

Figure 3. Training in use of mobile feed grinder (Photo: Udo Rudiger, ICARDA).
The distribution of the machines served to enhance small businesses of the recipient farmers’
cooperatives. Cooperatives started to rent them at a negotiated cost to their member farmers andgenerated additional income for the cooperatives. Table 1 shows the potential of this technology
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and the engagement of all four cooperatives in using the units. The treated seeds were to a large
extent forage cereal like barley; legume forage seeds like vetch and faba beans were also recorded.
During this short period of use, a total of 66 tons of seeds were cleaned only and 173.6 tcleaned and treated. A total of 138 farmers benefited from the four units. The total benefit for thesefour cooperatives with about 2,000 TND (682 US$) is not significant as the intention of somecooperatives during this first experience was rather to attract members using this service thanmaking a profit. This explains the different service prices varying between 10 TND and 35 TND
for cleaning 1 t of seeds and 50 – 80 TND for treating seeds. Service prices will be adjusted oncethe demand and market are created.
Table 1. Use of seed cleaning and treatment unit by four farmer cooperatives (SMSA) in
November and December 2019
SMSA

Qtt
Seeds
Cleane
d (t)

Qtt
Seeds
Treated (t)Cleaning Price(TND/t)Treatment Price(TND/t)

Retur
n

Total
Benefit(TND)

Numberof Users(Farmers)

El AmenEl FelahEttaouen
Melyen
Total

24.2
4.7
14.6
22.5
66

0
42.6
131.1
0
173.6

35
10
20
20
N/R

N/A
80
70
50
N/R

847
3,455
9,469
450
14,221

315
-13
1467
225
1,994

12
20
95
11
138

NumberofPotential SMSA
Users
320
200
350
150
1,020

All four cooperatives employed one person on a temporary basis to make the unit
functioning. Some cooperatives assigned the unit to be stationed at the cooperatives’ base; others
allowed the farmers to take it and use it at the farmers’ site. In all cases, it was the cooperatives’
employee who was responsible for manipulating the unit. The SMSA Ettaouen which used themachine for almost 150 t of seeds is already considering the purchase of a second machine as thedemand is high and the processing period limited. A hybrid system will be adopted whereby one
unit will be placed permanently at the cooperatives site and the other will be allowed to move fromfarmer to farmer.
Mobile Feed-Grinder Machine
Besides the seed cleaning and treatment units, ICARDA donated twenty mobile grinders
to 12 young entrepreneurs and eight farmers associations in Northern, Central and Southern
Tunisia. The beneficiaries contributed with 10% (300 TND / 110 $) and used the machine todevelop their feed and / or compost business.
More than 3,000 beneficiaries including young farmers and women are now benefitingfrom this equipment. Recipient farmers’ associations were carefully selected based on their interest
and need for the use of the machine to develop their feed and / or compost business. These grinders
can lead to reducing costs and thus increasing income. It is an ideal tool for smallholder farmers
to improve their incomes which represents an opportunity for improved livelihoods in traditional
small-scale farming. The use of these tools reduces the labor time spent on feed-farming operations
especially for women farmers.

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Each cooperative developed its own management strategy for the use of the grinding
machine. For example, the cooperative SMSA “Ettaouen” in Siliana, North-West Tunisia, with
350 members, uses three different management models:
1. If the farmer is a member without a tractor he can ask the cooperative to come and chophis feed at his farm using the cooperative’s tractor and driver. In such a case he pays 30
TND / hour (approx. 10 US$). This includes tractor rent, tractor drivers wage, and petrol.
2. If the farmer is a member with a tractor he can use the grinder with it at his farm but hehas to pay 25 TND / day ( 8.3 US$) for the cooperative’s grinder technician (operating thegrinder with his tractor) and 15 TND / day (5 US$) for the cooperative as renting fees forthe grinder which is used for maintenance of the machine. Petrol charges are at farmer’scost.
3. The farmer can also use the cooperatives tractor and grind his materiel at thecooperatives warehouse, bringing along his feed to chop. In such a case he pays 3 TND(1 US$) per 100 kg of feed irrespective of its nature (barley, hay, straw, etc.).
The SMSA “Ettaouen” has so far provided service to 40 members of their cooperative andemployed one person on a part time basis, depending on the demand. The objective of thecooperative is rather to provide services to their members and attract new farmers to join, thanmaking benefit with the machine. So far, the model (a) has been mostly requested.
The farmer organization SMSA “Serj-Weslet” in Ouslatia, Central-West, Tunisia has only
46 members but focuses also on service provision to non-members in order to generate revenue
for the cooperative. They are operating this small side business for six months and estimate theirmonthly net benefice at 150 TND (50 US$).
CONCLUSIONSThe efforts undertaken by ICARDA and its national partners in identifying the demand,co-designing, and co-developing locally adapted machinery solutions appropriate for small
farming systems is proving to be highly effective and beneficial. Similar self-sustained andinstitutionalized innovation processes are unfortunately rare in Tunisia. Most of the technologies
available to farmers are rather imported or developed without strong concertation with the different
stakeholders and mainly the end users.
ACKNOWLEDGEMENTS
This work was partly supported by the CGIAR Research Program on Livestock, and the CLCA
project, Phase II (Use of conservation agriculture in crop-livestock systems in the dry lands forenhanced water use efficiency, soil fertility and productivity in NENA and LAC countries) which
is funded by the International Fund for Agricultural Development (IFAD).
REFERENCES
Idoudi Z. https://hdl.handle.net/20.500.11766/11102
ReliefWeb. 2020. ICARDA develops mobile seed treatment unit prototype with Tunisian partners.
Sulc RM. 1998. Factors effecting forage stand establishment.

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Zhou K. 2018. Feed Processing Technology-Effects of grinding particle size on feed quality; VIV
online.

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#7648 LIDAR-BASED SOYBEAN CROP SEGMENTATION FOR AUTONOMOUS
NAVIGATION
V.A.H. Higuti, A.E.B. Velasquez, M.V. Gasparino, D.V. Magalhães, R.V. Aroca, D.M.B.P.
Milori, M. Becker
University of São Paulo, São Paulo, Brazil
becker@sc.usp.br +55 16 3373 8646
ABSTRACTThis study describes how a 2D LiDAR (Light Detection and Ranging) can be combinedwith ground mobile robot’s odometry to generate a 3D point cloud to represent the surroundings,
e.g. a soybean crop. Additionally, it also shows how the robot interprets the point cloud to allow
autonomous row following without the most common positioning source: GNSS (Global
Navigation Satellite System). Experiments were carried out with a 4WSD (four-wheel steering anddrive) robot in a soybean research crop to verify the behavior of the proposed system.
INTRODUCTION
The technological advances in the last few decades have greatly changed agriculture.
Modern farms have adopted sophisticated technologies, such as sensors, aerial images, and GNSS
(Global Navigation Satellite System) to become safer, more profitable, efficient, and sustainable.
These technologies not only increase the crop productivity, but also reduce the wide use of water,
fertilizers, and pesticides. Due to this, they reduce costs and negative environmental impacts, suchas the contamination of water sources. In special, the use of mobile robots has allowed morereliable monitoring and management of natural resources (NIFA, n.d.).
However, the application of robotics in agriculture is still challenging and researchers areseeking smarter autonomous vehicles that can safely operate in semi-structured or unstructured
dynamic environments, where humans, animals, and agricultural machinery may be present. The
most common sensor types used are cameras, LiDAR (Light Detection and Ranging), GNSS,
inertial sensors, and encoders. Real Time Kinematics GNSS has allowed the autonomous operationof many machinery due to its highly accurate positional information when a clean view of satellites
is possible. Nevertheless, such systems cannot handle dynamic obstacles, and their performance
may degrade due to uncontrollable sources that affect the satellites signals. To guarantee safety in
scenarios where GNSS may fail, cameras and/or LiDAR can be used (Reina et al., 2016).
Although vision systems are commonly used in mobile robot navigation, they suffer fromhigh variation of lighting conditions in outdoor environments and the large amount of data fromcamera is still a severe compromise between robustness and available processing power in mobile
robots. Other sensors (e.g. LiDAR, ultrasound, and infrared) are used to measure absolute
distances. Among them, LiDAR have greatly benefited from cost reductions while maintaining afast, high range, and millimeter-level measurement (Bechar & Vigneault, 2016). Some works,
including others in our laboratory, use a 2D LiDAR parallel to ground. This assumes that rows aretaller than the sensor and that horizontal reading planes provide similar information. However,soybean grows in branches and main stem may not always be visible. This confers a bushy shapethat may be misleading if sensed only in a single horizontal plane. Moreover, the sensor would376

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need to be positioned too close to ground to read plants at earlier stages, which is either dangerous
for device safety or it requires additional structural solutions.
In this paper, we describe the deployment of a perception system based on two 2D LiDAR
sensors for the navigation of an agricultural robot in soybean crops. The proposed system takes
advantage of the robot’s movement, which is tracked by odometry, to create a 3D point cloud by
concatenating consecutive 2D LiDAR readings. This setup can be referred as a push-broom(Baldwin and Newman, 2012) and it has the cost advantage when compared to an off-the-shelf 3D
LiDAR and dismisses additional hardware to obtain more degrees of freedom to the sensor, e.g.
rotation/translation around one axis to have multiple reading planes over time.
MATERIALS AND METHODSThis study follows a similar process as presented by Gasparino et al., 2020: a laser
reconstruction, data filtering, and determination of navigable space. In the first step, we change
from an active method to create the multilayered point cloud (custom hardware to rotate the sensor)
to a passive one (push broom). In data filtering, the Gaussian multiplication step is replaced by athreshold. Finally, the determination of navigable space changes from Under Canopy LiDARbased Perception Subsystem (Higuti et al., 2018) to a histogram.
The locomotion system is composed of four modules linked by a passive suspension. Each
one has a motor for traction and another for steering. All motors are electric, and they are controlled
by ESCON 50/5 (traction) or EPOS 24/5 (steering). An industrial computer Advantech ARK3510 running Ubuntu 16.04 with Robotic Operating System (ROS) Kinetic performs allcomputational tasks. Further information may be found in Higuti et al., 2017.
Two types of sensor provide the information to perform the environment recognition. First,
UTM30-LX Hokuyo is a 2D LiDAR that returns 1081 distance readings over a 270º range with 40
Hz update rate. One LiDAR was placed on each frontal locomotion module, hence one to the left
and another to the right, in a way that wheels and the sensor are aligned on the same longitudinalline and at a height of around 0.65 m from ground (see red circles in Fig. 1a). They point
downwards such that sensors’ field of view forms an angle of 60º with ground plane. Second,
MILE encoders provide the traction motors’ rotational speed and steering motors’ position.
The proposed system starts with the generation of a 3D point cloud from sensor data. A
single 2D laser scan provides distance measurements only on the sensor’s reading plane (seeorange features in Fig. 1b). Therefore, when the robot moves, the scans are assembled considering
the robot movement, creating a local 3D crop reconstruction. In this study, the targeted forward
speed is small (maximum of 0.3 m/s) and the terrain is regular, without bumps and/or weed
infestation. Furthermore, Mirã 2 passive suspension keeps all wheels with contact to ground. These
reasons allow us to conclude that slippage and dynamic interaction between wheels and soil havenegligible interference to estimate the instantaneous robot’s position with an odometry based on
robot kinematics (Jazar, 2014; Rajamani, 2006; Khristamto et al., 2015).
The odometry estimates the center of mass position, a point that represents the robot, which
was determined using the method presented in Velasquez, 2015 and Jazzar, 2014. The position ofcenter of mass is given on a cartesian reference frame (XY coordinates) whose origin is set as the
position where the robot starts moving autonomously, usually the begin of the lane. Angular andlinear velocities are integrated over time to estimate the current position of center of mass. The
velocities are calculated using Ackermann geometry (Jazar, 2014), a set of kinematic equations
that relates the wheels direction and speed into robot’s linear and angular velocities. The steering377

1st African Conference on Precision Agriculture | 8-10 December | 2020(a)(b)
Figure 1. (a) Mirã 2 in the soybean crop; (b) The axes show some reference frames: base_link isplaced on the robot’s center of mass; right_lidar and left_lidar represent how LiDAR sensors areplaced with respect to center of mass – note that the reading plane is given by red and green axes,
therefore an angled plane towards ground; and grid’s red and green axes represent the groundplane. The last LiDAR scan from the left sensor is shown by orange points, the red set is theresulting point cloud for right side, the grids are the white rectangles with gray/black elements,
and the navigable space is bounded by the green/blue or purple/brown boxes.
motor encoders inform the wheel direction and those from traction motors provide the wheel speed.
After center of mass position is known, a static spatial transformation from center of mass
to each LiDAR sensor allows us to know where each laser scan happened and two-point clouds(one from right and another for the left LiDAR sensor) are obtained by concatenating the scans(see the red point cloud in Fig. 1b). Each point cloud keeps 100 scans before throwing the old ones
and it is downsampled using a voxel, i.e. a box, of 0.02 m size in all axes. This leaves enoughdetail while reducing subsequent processing time. These operations use Point Cloud Library.
Subsequently, the 3D point cloud is converted to a 2D occupancy grid, a discrete
representation of navigable space on the plane parallel to ground. In this study, the grids have 100
rows, 90 columns and 0.02 m resolution. Their center point (between rows 49-50 and cols 44-45
with initial index 0) projects from the reading origin of LiDAR sensors down to the ground. Thiseffectively removes the point cloud z-dimension as each grid cell contains the number of points
from the point cloud that are within the cell XY-boundaries.
After grid is obtained, a mask enhances the cells that are in the column direction as they
are mostly linked to the crop rows (most populated region in the point cloud). After this operation,
the grid is normalized to have values between 0 and 100, with 100 given to the most populated
cell. Subsequently, two thresholds are applied to the grid and those cells under 40% are set to 0
and those above 60% are set to 100, respectively white and black cells on the grid rectangle in Fig.
1b. The gray cells are in-between values. Finally, a histogram on row axis highlights two densest
rows, one to the right and another to the left of sensor. These rows reflect the boundaries of thenavigable space (represented by the long-colored boxes in Fig. 1b). Going back from grid units to
XY coordinates, the distance to lateral rows (𝑑| for right and 𝑑} for left) become known. For acontrol system designed to follow row, a cross track error can be calculated for each LiDAR as𝑐𝑡𝑒 = 0.5(𝑑| − 𝑑} ) and such value shows the error from the desired position, i.e. middle of row.
In this study, the sum of lateral distances (lane width) will be used to assess the system performance378

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because it can be compared to a nominal value and variations along run and between runs on thesame rows show how stable is the determination of navigable space.
RESULTS AND DISCUSSION
Field experiments were conducted at the National Reference Laboratory in Precision
Agriculture (Lanapre) located in São Carlos, SP. Two plots with four soybean (Intacta RR2 Pro)rows each were planted. Distance between rows is 0.45 m within plot, total length is 32 m andplant spacing is about 12 plants/m. Tests happened 55 days after seeding.
A first set of tests evaluated a system like the one shown by Gasparino et al., 2020. Due toplatform difference, it already differed in the laser reconstruction, which was done as explained in
the previous section. It still used a multiplication of the grid by a sum of two Gaussian functions
to highlight the most probable row position. Although that had effect for their study with corn, thisfailed to keep a constant boost to only the densest part of the row: different regions were enhanced
in consecutive scans because of multiple branches. This led to navigable space changing more thana few centimeters between consecutive estimates. Such instability prejudiced control actions as therobot constantly thought it had a different navigable space than before. Additionally, the algorithm
transformed from grid to a laser scan parallel to ground. This would enable the usage of the Under
Canopy LiDAR-based Perception Subsystem (Higuti et al., 2018), which was assessed for corn,sorghum and dry soybean. But that method was designed for crops with well-defined stems, even
if sometimes occluded. This is not the case for early-mid soybean because of the high number ofbranches, and a similar behavior as from the Gaussian use happened: unstable definition of a lateral
distance. With such system, the robot could barely move few meters without hitting.
After changes described in this study, the robot completed 22 of 28 runs (78% successrate). The six failures occurred due to mechanical restrictions or an oscillatory behavior caused by
the control system. The following reported values reflect an average value for a run, if not
specified. Left LiDAR calculations gave lane width in the range of 0.323-0.406 m and standard
deviation between 0.063-0.156 m. Analogously, right LiDAR had lane width in the range 0.3630.423 m and standard deviation of 0.041-0.157 m. As these runs occurred on the same soybean
rows, these variations show that perceived navigable space is still not continuous enough, which
may have led the control to believe the robot had more/less room to operate than it truly had andmade oscillating commands. Indeed, with respect to each run’s average value for successful runs,
left LiDAR had only 37-61% and right LiDAR had 42-62% of the instantaneous lane width within
0.05 m error (a comparable range expected from RTK GNSS). However, this increases to 75-83%(left) and 77-92% (right) for a 0.1 m error. Coupled with the 78% success rate to traverse the 32
m plot, this 0.1 m error seems reasonable to allow autonomous navigation. Hence, we concluded
that the proposed perception system is a promising tool to detect the position of soybean crop rows.
ACKNOWLEDGEMENTS
Supported by Embrapa Instrumentation, University of São Paulo, Brazilian National Council
for Scientific and Technological Development (CNPq), Coordination for the Improvement ofHigher Education Personnel (CAPES) and São Paulo Research Foundation (FAPESP) with grants#2018/10894-2 and #2013/07276-1. The opinions, hypotheses, conclusions or recommendations
expressed herein do not necessarily reflect those of FAPESP.

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REFERENCES
Baldwin I, Newman P. 2012. Road vehicle localization with 2D push-broom LIDAR and 3Dpriors," 2012 IEEE International Conference on Robotics and Automation 2611-2617.
Bechar A, Vigneault C. 2016. Agricultural robots for field operations: Concepts and components.
Biosystems Engineering 149: 94–111.
Gasparino M, Higuti V, Velasquez A, Becker M. 2020. Improved localization in a corn crop row
using a rotated laser rangefinder for three-dimensional data acquisition. Journal of theBrazilian Society of Mechanical Sciences and Engineering 45: 592.
Higuti V, Velasquez A, Gasparino M, Oliveira G, Carrasco C, Risardi J, Magalhães D, Becker M,
Milori D. 2017. Robotic Platform with Passive Suspension to Be Applied on Soybean
Crops: Initial Results. Proceedings of the 24th ABCM International Congress of
Mechanical Engineering.
Higuti V, Velasquez A, Magalhaes D, Becker M, Chowdhary G. 2019. Under canopy lightdetection and ranging-based autonomous navigation. Field Robotics J. 36: 547–567.
Jazzar R, 2014. Vehicle Dynamics – Theory and Application. Springer. 2nd edition. 579-580.
Khristamto M, Praptijanto A, Kaleg S, 2015. Measuring geometric and kinematic properties todesign steering axis to angle turn of the electric golf car. Energy Procedia 68: 463–470.
National Institute of Food and Agriculture (NIFA), n.d. Agriculture Technology. Available on
https://nifa.usda.gov/topic/agriculture-technology
Rajamani R. 2006. Vehicle dynamics and control. Springer.
Reina G, Milella A, Rouveure R, Nielsen M, Worst R, Blas MR. 2016. Ambient awareness for
agricultural robotic vehicles. Biosystems Engineering 146: 114-132.
Velasquez A. 2015. ℏELVIS III – Desenvolvimento e caracterização da plataforma robótica.
Master dissertation, University of São Paulo.

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SATELLITE IMAGERY

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#7479 THE VISION OF FUTURE EARTH OBSERVATION FOR AGRICULTURE
Charvat K, Kubickova H, Safar V
Wirelessinfo, Cholinska 1048/19, 784 01 Litovel, Czech Republiccharvat@wirelessinfo.cz
ABSTRACTThe main objective of EO4AGRI was to catalyze the evolution of the European capacity
for improving operational agriculture monitoring from local to global levels based on information
derived from Copernicus satellite observation data and through exploitation of associatedgeospatial and socio-economic information services.
EO4AGRI assists the implementation of the EU Common Agricultural Policy (CAP) withspecial attention to the CAP2020 reform, to requirements of Paying Agencies, and for the
Integrated Administration and Control System (IACS) processes. EO4AGRI works with farmers,
farmer associations and agro-food industry on specifications of data-driven farming services withfocus on increasing the utilization of EC investments into Copernicus Data and Information
Services (DIAS).
During project period we analysed current situation and user needs. Finally, an important
aspect, which influenced the content and ideas of this report update, was the publication by theEuropean Commission during this period the Green Deal (GD) Strategy and European Data
Strategy (EDS). These two strategies will strongly influence also all Earth Observation sectors andwill bring new possibilities.
INTRODUCTION
Precision agriculture is an important component of the third wave of agricultural revolution
and has enhanced hopes of battling food crises by increasing global food production with the helpof new technological advancements Agriculture comprises vital economic sectors producing food,agro-industrial feedstock, and energy and provides environmental services through managing soil,
water, air, and biodiversity holistically. Agriculture including forestry also contributes tomanaging and reducing risks from natural disasters such as floods, droughts, landslides, andavalanches. Farming with its close contact to nature provides the socio-economic infrastructure to
maintain cultural heritage. Farmers are also conservers of forests, pastures, fallow lands, and theirnatural resources and, in turn, of the environment. Agriculture today is a composite activity
involving many actors and stakeholders in agri-food chains that produce and provide food andagricultural commodities to consumers. In addition to farmers, there are farm input suppliers,
processors, transporters, and market intermediaries each playing their roles to make these chains
efficient.
More than 10 years ago the FutureFarm project [1] recognized that the agriculture sector isunder a strong influence on several different external drivers. The following factors wererecognized as the main drivers for changes in the agriculture sector - climate change,
Demographics (growing population, urbanization, and land abandonment), energy cost, newdemands on the quality of food (food quality and safety, aging population, and health problems,
ethical and cultural changes), innovative drivers (knowledge-based bio-economy, research, and382

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development, information and communication, education, investment), policies (subsidies,
standardization, and regulation, national strategies for rural development), economy and financing(economical and financial instruments, partnerships, cooperation, and integration and voluntaryagreements), sustainability and environmental issues (valuation of ecological performances,development of sustainable agriculture), public opinion (press, international organization,
politicians).
The common and future position of each driver can be different in reality. In many cases,
two drivers can stand against each other and their future influence on the agri-production and foodmarket depends on regulations and common policy. For example, focus on food quality and safety
could be contradictory with requests for increasing production due to the growing population.
Similarly, it is with increasing production and request on the production of bioenergy. These
drivers will lead to decreasing agricultural land, but request to increase production. We needincreased quality and safety, and it will be connected with a decreasing number of chemical inputs.
Also, these drivers will bring requirements for decreasing the consumption of energy and water.
So, we can tell that future agriculture production needs to be globally increased, with higher quality
and using less land and fewer inputs at the same time.
Due to the complexity of this problem we need to better understand all processes involved
and build for each agriculture sector a new knowledge management system. Knowledge
management is usually described as transferring data into information, knowledge, and finally to
Wisdom [2] [3] [4].

Figure 1. Schematic of knowledge management
MATERIALS AND METHODSEO4Agri performed an analysis of the needs of different groups of stakeholders from fourareas including agri-food, public, financial, and food security. These areas are connected with
agriculture, not only on the level of production of agricultural products and food but also on thelevel of developing policies for agriculture and financing. The four groups of stakeholders include:• Agricultural producers, service providers, advisers, machinery, and food sectors that use dataintensive services to improve their productivity in both agricultural production and businessadministration. The agri-food sector is composed of different players with different interests.
These users can be subdivided into:383

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○ A subgroup connected directly or indirectly with precision agriculture. This
subgroup can include agricultural producers, service providers, advisers,
machinery, and also the food sector.
○ A subgroup related to the analysis and prediction of the food market. Currently,
customers of this information are mainly in the food industry. However, theimportance of this information will grow in the future also for the primary sector
concerning planning production.
● Public sector organizations (and in particular national paying agencies responsible for the
management of the agriculture subsidies) that use EO data as an input to agricultural policy
formulation and for implementation of new farm subsidies payment systems based onmonitoring (instead of inspection) and performance (instead of compliance).
● Agricultural finance institutions that provide the agri-food industry with credit andinsurance services, as well as related services such as re-insurance and decision support
services to commodities and derivatives traders.
● Organizations that support global food security, in particular donors involved ininfrastructure and capacity building in third countries with security in food and nutrition.
The activities of the donors can be related to other issues such as climate and theenvironment, and the management of scarce resources such as water, soil, and nitrate-based
fertilizers. This group includes also local farm organizations, researchers, and the public
sector in developing countries. The main incentive of this group should be to combine topdown and bottom-up approaches to solve the problem of food security.
EO4Agri identified additional groups to be considered in the analysis:● The growing industry of data service providers that transform raw data and basic services
provided by Copernicus into services adapted to the needs of the four main stakeholder
groups mentioned above.
● The range of services that can be provided is in constant evolution thanks to the efforts ofresearchers and data entrepreneurs leveraging the latest knowledge and know-how in plant
and animal sciences, environment and climate sciences, economic, social, and geophysicalsciences as well as new and emerging domains of ICT based on the application of artificial
intelligence, machine learning or data learning in big data analytics.
● During the analysis, a set of data themes needed for each stakeholder group were identified
and these data themes are listed in Table 1.

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Main requirementsof the Agri-Foodgroup

Data on the weather forecast

Main requirementsof the Financial
sectorData for identification parcels for potential land for biomass
production
Data for creating flood maps (for Q5,25,50,100years)
Data for annual soil erosion risk maps
Data to produce maps of the occurrence of diseases
Data for the production of actual calamities map (droughts, flood,fires, earthquakes, ...)
Data for production maps of relevant information for biofuel
production
Data for determination productivity of grassland and pastures.

Main requirementsof the Public sector

Data for support of the Common Agricultural Policy new ‘greening’rules, crop, ecologically sensitive areas
Data for the identification of crops to control subsidies
Data for water protection against nitrates
Data for monitoring of the implementation of natural water retention
measures
Data for mapping parcels and validation of acreage parcels < 0,5ha
Data for updating the Land Parcel Identification System (LPIS)
Data for monitoring phenology of grassland (number of cuts/grazing
events per season)
Data for the production of crop growing calendar for agriculturalmonitoring

Main requirementsof Food andNutrition Security

Data for yield modelling for food security
Data for food security information
Data for cross-border land monitoring, given the interconnection ofenvironmental problems and food security
Data for near real-time vegetation biomass measurements for
agriculture and food security during the cropping season
Data for early warning information for food security

Data for agricultural yields forecasting
Data for soil water index
Data for providing a drought early warning system.
Data for producing maps of basic fertilizers
Data for production maps of fertilizer in the phenophase 30-34
Data for determining heights of crops
Data for estimating the extent of diseases or damages (losses)
Data for monitoring hydrological stresses
Data for producing exact information about climatic changes

Table 1. Data themes needed for each stakeholder group385

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Satellite data is an important source of information for future agriculture. There is a clear
need for new data, better spatial resolution, new bands, and more dense data. However, thewillingness and possibilities of farmers to pay for this are limited. The need for in situ data is
another important issue, helping users to use remote sensing data optimally. The process ofderiving useful information from satellite data that can help farmers to make precise decisions
must be supported. Integration with aerial data and in situ data is necessary.
With the growing availability of free EO data (Copernicus satellite imagery, etc.), thenumber of users that can efficiently use such data becomes more important. Different delivery
platforms for EO data integration, access, and analysis are needed to enable interpretation of data
as information. To access EO data, we need to have delivery platforms where stakeholders will
be able to access selected data and perform specific services. Satellite data, such as Copernicus
and Landsat data are available for free via several delivery platforms. The availability of other thansatellite data is rather limited. A lot of data is delivered on a commercial basis. The idea is that
European Data and Information Access Services (DIAS) will be self-financed, which could leadto the fact that some of them will be not operational after the financing period.
There are many platforms for delivering satellite data (open or commercial). At the sametime, there are several approaches to delivering end products created from this data. Satellite data
are usually processed by specialized companies and supplied to users for a fee (ministries, paying
agencies, insurance companies, and large farmers). In addition to platforms directly delivering
services related to satellite data, there exist many platforms, commercial or coming from different
projects, which offer services on the top of DIAS or other platforms. On the one hand, there arelarge investments from the public to private to build new solutions and delivery platforms. On theother hand, agriculture is highly fragmented with enormous amounts of players in different sectors(e.g. machinery, insurance, fertiliser producers). Access to knowledge is limited and the current
investments are not efficiently utilised. There is an urgent need to verify the investments for all
public and private partners and get a deep understanding of the return of investment for allparticipants as well as verification of climate change and/or environmental positive or negative
effects.
RESULTS AND DISCUSSION
Based on the previous analysis, a set of recommendations is proposed to:
1. Organize regular workshops and conferences of all interested stakeholders. These
workshops and conferences’ have to lead to the exchange of information, but they needalso educate all stakeholders about new methods
2. Discuss in the short period this white paper with a large community and based on thatdefine a new Strategic Research Agenda.
3. Support cooperation of all players from the public and private sectors to fulfill theEuropean Green Deal and SDG goals. It will also invite the food industry, machinery,
chemical industry, IT industry, financing organizations to build a common environment.
4. Support the farming sector with open data, including Copernicus and other EO data. This
will require additional investments.
5. Support new common research involving both EO and agriculture/agronomy experts todevelop new methods that guarantee food security and agriculture sustainability.
6. On the one side, continue with the development of new technologies and EO methods tobuild future Digital Twins. On the other side, there exists a large potential of existing386

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technologies recently developed, which potential is not fully exploited. It’s necessary to
prepare an overview of existing technologies and discussion among the teams on how tomake solutions interoperable and how to reuse existing solutions.
7. Finance a large number of smaller independent projects for technical development. Thiscan bring new ideas in the short term.
8. Support standardization efforts and use of existing standards. This needs to be done incooperation with existing standardization bodies including OGC, ISO, and W3C.
9. Support large scale coordination actions, which will improve cooperation among different
projects, initiatives, and standardization organizations.
10. There exist several technical problems, but the biggest problem will be at the level oflegislation and financing. It will require a reform of the Common Agriculture Policy andalso build effective strategies. This cannot be done only on a political level, but it will
require communication of politicians with technical experts and researchers to define a
successful strategy. For this purpose, it is necessary to establish a forum, where all theseplayers will meet. A new strategy has to be prepared based on expert opinions and scientificresults.
REFERENCES[1] Charvat K, Gnip P. 2009. Analysis of external drivers for farm management and theirinfluences on farm management information systems. Precision agriculture’ 09, 9, 915.
[2] Picture adopted from https://www.linkedin.com/pulse/we-have-knowledge-use-well-howardthomas/[3] Bellinger G, Castro D, Mills A. 2018. Data, Information, Knowledge and Wisdom, on-line:
http://www.systems-thinking.org/dikw/dikw.htm (cit. 9. 4. 2018)[4] Charvát K, Horáková Š, Mildorf T, Křivánek Z. 2013. Strategic Research Agenda for
introduction of innovative approaches on data exchange in agri-food industry, IST-Africa
2013 Conference Proceedings, Paul Cunningham and Miriam Cunningham (Eds), IIMCInternational Information Management Corporation, 2013, ISBN: 978-1-905824-38-0.
[5] EO4Agri D5.6 White Paper on Earth Observation Data in Agriculture387

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#7531 ORCHARD YIELD ASSESSMENT IN NORTH-EAST OF MOROCCO USINGSATELLITE IMAGERY
A. Matese1, K. Aberkani2, S. Samri2, M. Said2, S.F. Di Gennaro1 and P. Toscano1
1
Institute of Bioeconomy – IBE, National Research Council – CNR, Florence, Italy;
2
Polydisciplinary Faculty of Nador, University Mohammed First, Selouane, Moroccokamalaberkani@gmail.com +212 613 591 745
ABSTRACTAgricultural sector represents one of the pillars of the Moroccan economy. The Green
Morocco Plan (GMP) established in 2008 present the main engine of development of this sector
and for local economy. One of the objectives of Pillar I and Pillar II of GMP was to maximize
production with less use of water resources. Currently, Morocco is experiencing a strong
variability in spatial and temporal variability of precipitation with a detrimental effect on yield andquality of crop production. The aim of this work is to assess the potential of Sentinel-2 imagery in
precision agriculture to assess interannual production variability in two of the most valuable
orchard products in Morocco (Table grapes and Oranges). In this current study, commercial farms
of 34 ha located in Oriental region of Morocco with citrus and table grapes were chosen: Oranges(Citrus sinensis L. cv. Navel Washington) planted in 1999 (8ha) and 2013 (8ha); 2ha of clementine(Citrus reticulata cv. Marisol) and 8ha of table grapes (Vitis vinifera cv. Mousca Italy) planted in2008. The harvest date starts in October, January, and September for navel, clementine and tablegrapes, respectively. Yield data were collected during three years 2017, 2018 and 2019. The
comparison of the interannual NDVI at monthly time scale with the yield monitoring values
reveals (i) different patterns for young age orchard; (ii) different monthly trend as effect of climate
pattern and consequently in terms of vegetation growth and vigor; (iii) different rate of change interms of magnitude and sign of NDVI monthly series from blossoming to maturity are strictly
correlated with final production, for both orchards. Marisol and Navel orchards showed the highest
interannual variability in terms of production both for young and old plantation while table grapes
exhibit a reduced range. For Navel orchard the rate of increase in the time series curve of NDVI,
from July to December, is positively correlated with the final production and it is able toreconstruct most of the yearly variability (R2=0.9). Instead, for tables grapes strongly NDVI ratevariation (June-August) is negatively correlated with the final production (R2= 0.98).
INTRODUCTION
Agricultural sector represents one of the pillars of the Moroccan economy. It generates
around 14% of gross domestic product (GDP) (MAFRDWF, 2014), and this depending on the year
and climatic conditions. Agriculture also remains the country's leading source of jobs, with 40%of the working population living in this sector. The useful agricultural area (UAA) is estimated at
8 700 000 ha. However, this figure should be qualified by emphasizing the importance ofrangelands (over 20 million ha), which are not very productive, but which play a significant rolein pastoral zones for feeding livestock. Moroccan agriculture has been developed, recently, thanks
to the foundation of “Green Morocco Plan” (MAFRDWF, 2017). This project, launched in 2008,
aims to make agriculture one of the leading sectors of the country, promote agricultural388

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investments, ensure food security, stimulate exports and enhance local products. This programaims to support agriculture under two pillars. The first concerns modern agriculture with added
value and high productivity. The second pillar, aim to improve the living conditions of small
farmers and fight against poverty in rural areas by increasing agricultural income in the mostvulnerable areas. The issue of water is essential for the development of agriculture. Morocco very
early on turned towards the creation of large dams supplying irrigated areas which represent 1.1
million ha. Currently, small hydraulic structures and localized irrigation are favored anddeveloped, which is more beneficial and therefore more suited especially during the situation of
global warming. In recent years, nearly 400 000 ha irrigated by gravity have been converted to
localized irrigation. Rained agriculture zones, present 83% of the UAA (MAFRDWF, 2014), butmuch less productive because they are severely affected during dry years. According to the
Economic Social and Environmental Council, the water scarcity situation in Morocco is alarming
since its water resources are estimated at less than 650 m3 /inhabitant/year, against 2,500 m3 in
1960, and should drop below 500 m3 by 2030 (Puchot, 2020). As an effect of climate change,
Morocco is experiencing a strong variability in spatial and temporal variability of precipitation
with a detrimental effect on yield and quality of crop production. An increase in water demand by
field irrigation and competition over conventional water resources were recorded during the last
decade. Moreover, Moroccan agricultural policy is geared towards export rather than local
consumption, which generates strong pressure on water resources for field irrigation. Thus, theGreen Morocco Plan and the National Water Sector have adopted a national irrigation water saving
program to face this issue. However, despite these efforts, the water constraint stills a problem thatthe country faces.
Many technologies and strategies of irrigation were investigated and developed to optimize
water management for agriculture and face to the global warming. Using precision agriculture
tools and especially satellite imagery and smart sensors is considered as one of the current topics
of research in field of water optimization for irrigation. Aerial and satellite imagery have beenrecognized as excellent tools to obtain a large amount of spatial information (Herrero-Huerta et al.
2016). This is due to their ability to cover large areas and control crop water uses (Calera Belmonteet al. 2005). Hence, an open access to geo-referenced Landsat and Sentinel images in near realtime (Skakun et al. 2016) are allowed. Several decision support systems based on web-GIS tools
have been developed with different purposes (Gkatzoflias et al. 2013). Different geospatial
applications have been used. For example, SPIDER software that used Landsat-5 imagery could
estimate water requirements based on normalized difference vegetation index (NDVI) (Calera etal. 2017). The aim of this work is to assess the potential of Sentinel-2 imagery in precision
agriculture to assess inter-annual production variability in two of the most valuable orchard
products in Morocco.
MATERIALS AND METHODSIn this current study, commercial farms of 34 ha located in Oriental region of Morocco.
The field experiments were carried out on an agricultural production farm located in Sector No2,El Garet, Al Aaroui, Perimeter of Moulouya, Morocco (Latitude: 34 ° 56'10.8 “N and longitude:
3° 00 '19 “O). This region is characterized by a Mediterranean climate, with an annual precipitation
rate that does not exceed 400-500mm. Citrus and table grapes were chosen for our trials: Oranges(Citrus sinensis L. cv. Navel Washington) planted in 1999 (8ha) and 2013 (8ha); 8ha of tablegrapes (Vitis vinifera cv. Mousca Italy) planted in 2008. Plant density for table grapes and oranges

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were 2000 and 500 plants/ha, respectively. The production was irrigated and fertilized with a drip
irrigation system. Production management (from pruning to harvest) was done according to the
production methods used by commercial growers of table grapes and citrus in Morocco. Yield data
were measured for each treatment. Yield data were collected during three years 2017, 2018 and2019.
The satellite solution chosen is the Sentinel-2 platforms (European Space Agency
Copernicus program https://sentinel.esa.int/web/sentinel/missions/sentinel-2) to monitor the crop
status at weekly frequency (Toscano et al. 2019). Those platforms provide free images with 13
spectral bands covering the visible, NIR and SWIR at 10, 20 and 60m spatial resolution (images
available on ESA Sentinel Scientific Data Hub https://copernicus.eu/). Sentinel 2 platforms
represent a valuable solution for crop monitoring, due to their high revisit time of about 2-3 daysin mid-latitudes under cloud-free conditions. Polygons corresponding to the study areas will be
extracted from each of the Sentinel-2 images. Reflectance values from bands 4 (red) and 8 (nearinfrared) will be used to calculate NDVI.
RESULTS AND DISCUSSION
For how concern the first Navel orchard, planted in 2013 (Figure 1), during the first two
years of observation (2017 and 2018) NDVI shows substantially low values typical of a plantsystem that has not yet reached the optimum in both vegetative and reproductive stages.
NAVEL YOUNG
0.6

18

16

0.5

NDVI

12
10

0.3

8

0.2

6

Yield (t ha-1)

14

0.4

4

0.1

2

0

0
01_20
12_19
11_19
10_19
09_19
08_19
07_19
06_19
01_19
12_18
11_18
10_18
09_18
08_18
07_18
06_18
01_18
12_17
11_17
10_17
09_17
08_17
07_17
06_17

Date
NDVI

Yield

Figure 1. NDVI and yield comparison for Navel planted in 2013
Indeed, the NDVI average for the period June 2017 - January 2018 was of 0.28±0.03 andwithout showing a clear peak. In 2018 the orchard showed a slightly more pronounced peak withan average NDVI (June 2018 - January 2019) of 0.33±0.05. When a yield performance close to
optimal conditions was finally expected in the third year of observation, the orchard was affectedby unfavorable weather and water shortages (USDA 2020 annual report(https://apps.fas.usda.gov/newgainapi/api/Report/DownloadReportByFileName?fileName=Citru
s%20Annual_Rabat_Morocco_12-15-2019), especially in the first part of the growing season. The390

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NDVI (June 2019-January 2020) indeed presents a very low initial pattern and then reaches a peakin January (0.36±0.06).
For the second NAVEL orchard planted in 1999 (Figure 2), an under optimal yield
performance was experienced for the whole study period (expected 40 t/ha, harvested max 24.1t/ha), due to non-optimal conditions (biotic and abiotic stresses) in part highlighted by NDVI
during the reference periods June-January for each year. During the first year, although amonotonous growth in NDVI trend can be observed up to the peak of January, the absolute values
are low and are the lowest for the entire period in conjunction with the lowest yield. The second
year presents the higher yield and a monotonous trend of vigor with peak values that are notexcessively high when compared to citrus NDVI values reported in other regions (Vanella et al.
2020). The third year on the other hand, although it shows the highest peak values at the end of
the agricultural year, clearly shows a not monotonous trend with fluctuations in terms of NDVI
values as an effect of water shortages.
NAVEL OLD
0.9

30

0.8

NDVI

0.6

20

0.5

15

0.4
0.3

10

0.2

Yield (t ha-1)

25

0.7

5

0.1
0

0
01_20
12_19
11_19
10_19
09_19
08_19
07_19
06_19
01_19
12_18
11_18
10_18
09_18
08_18
07_18
06_18
01_18
12_17
11_17
10_17
09_17
08_17
07_17
06_17

Date
NDVI

Yield

Figure 2. NDVI and yield comparison for Navel planted in 1999
For table grape (Figure 3) the NDVI values for 3 months (June-August) preceding the
beginning of the harvest (September) were taken into consideration for the whole study period.
Although the yield performance over the 3 years is substantially equal (30.5, 29.1 and 31.1 t / hafor 2017, 2018 and 2019, respectively), a strong correlation was identified between NDVI andyield. All 3 years show a similar trend with higher NDVI values in June and August and the lowest
values in July (pruning effect), while the yield is strongly anticorrelated to ΔNDVI (delta NDVI)
between June-July and August-July (R2 = 0.99). i.e. agricultural years characterized by greater
regularity in the phases of vegetative development have higher yields. Therefore, the optimal yield
is obtained when the vegetative development does not suffer from accentuated fluctuations, butfollows regular patterns of growth and trend for NDVI. These results confirmed the hypothesized
correlation between VIs acquired during orchard vegetative stage and production. This
information, when coupled with weather observations and seasonal forecast, is valuable forforecasting yield, planning field activities and generating prescription for specific management
practices to contrast the significant yield losses due to drought stress.

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TABLE GRAPE
31.5
2019
31

Yield (t ha-1)

30.5

2017

30
29.5

y = -49.651x + 32.561
R² = 0.9964

2018

29

28.5
0

0.01

0.02

0.03

0.04
ΔNDVI

0.05

0.06

0.07

0.08

Figure 3. NDVI and yield comparison for Table Grape
REFERENCES
Calera A, Campos I, Osann A, D’Urso G, Menenti M. 2017. Remote Sensing for Crop Water
Management: From ET Modelling to Services for the End Users. Sensors 17:1104p.
Calera BA, Jochum AM, Cuesta García A, Montoro Rodríguez A, López Fuster, P. 2005. Irrigation
management from space: Towards user-friendly products. Irrig. Drain. 19:337-353.
Gkatzoflias D, Mellios G, Samaras Z. 2013. Development of a web GIS application for emissions
inventory spatial allocation based on open source software tools. Comput. Geosci. 52:2133.
Herrero-Huerta M, Felipe-García B, Belmar-Lizarán S, Hernández-López D, RodríguezGonzálvez P, González-Aguilera D. 2016. Dense Canopy Height Model from a low-cost
photogrammetric platform and LiDAR data. Trees 30:1287-1301.
Ministry of Agriculture, Fisheries, Rural Development, Water and Forest (MAFRDWF), 2014.
L’agriculture Marocaine en Chiffre. 17p.
Ministry of Agriculture, Fisheries, Rural Development, Water and Forest (MAFRDWF), 2017.
Green Morocco Plan. In: http://www.agriculture.gov.ma/en/pages/strategy
Puchot P. 2020. La sécheresse aggrave les difficultés du royaume marocain. Le Monde
Diplomatique. In : https://www.monde-diplomatique.fr/2020/04/PUCHOT/61631
Skakun S, Kussul N, Shelestov AY, Lavreniuk M, Kussul O. 2016. Efficiency assessment ofmultitemporal C-band Radarsat-2 intensity and Landsat-8 surface reflectance satellite
imagery for crop classification in Ukraine. IEEE J. Sel. Top. Appl. Earth Obs. Remote
Sens. 9: 3712-3719.
Toscano P, Castrignanò A, Di Gennaro SF, Vonella AV, Ventrella D, Matese A. 2019. A recision
Agriculture Approach for Durum Wheat Yield Assessment Using Remote Sensing Data
and Yield Mapping. Agronomy 9:437p.
Vanella D, Consoli S, Ramírez-Cuesta JM, Tessitori M, 2020. Suitability of the MODIS-NDVITime-Series for a Posteriori Evaluation of the Citrus Tristeza Virus Epidemic. Remote
Sens. 12:1965p.

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#7629 MONITORING CORN (ZEA MAYS) YIELD USING SENTINEL-2 SATELLITEIMAGES FOR PRECISION AGRICULTURE APPLICATIONS
A. Kayad1,2, M. Sozzi1, S. Gatto3, L. Sartori1, F. Pirotti1 and F. Marinello1
1
Department TESAF, University of Padova, Italy
2
Agricultural Engineering Research Institute, Agricultural Researh Center, Giza, Egypt
3
Porto Felloni srl, Lagosanto (FE), Italy
ahmed.kayad@phd.unipd.it 0039 3475828230
ABSTRACT
Currently, there is a growing demand to apply precision agriculture (PA) management
practices at agricultural fields expecting more efficient and more profitable management. One of
PA principal components for site-specific management is crop yield monitoring which varies
temporally between seasons and spatially within-field. In this study, we investigated the possibilityof monitoring within-field variability of corn grain yield through satellite images in a 22-ha field
located in Ferarra, North Italy. Archived yield data for 2016, 2017 and 2018 seasons werecorrelated with different vegetation indices derived from Sentinel-2 satellite images at different
crop growth stages. Yield data were filtered to remove field boundaries and other outliers tomaintain yield map accuracy. A total of 34 cloud-free satellite images (6 images for 2016, 14 for2017 and 14 for 2018 season) were analysed and vegetation indices such as Green Normalized
Difference Vegetation Index (GNDVI) were calculated. Vegetation indices of each season werecompared with the actual corn yield map for the same season and model accuracy metrics were
calculated for each index and image date. Results of this work are as follows: Firstly, GNDVI was
the most accurate vegetation index to monitor within-field variability of corn yield with an R2
value of 0.48 and showed the same trend for all studied seasons. Secondly, crop age of 120 days
after sowing (R4-R6) showed the best results for corn yield prediction which is during summer in
Italy (July to August) with less cloud probability. This study provides a tool for monitoring withinfield variability that could be applied for archived satellite images to provide farmers with their
historical yield spatial variability.
INTRODUCTION
Yield monitoring for within-field variability is a fundamental component for precision
agriculture (PA) system. Crop yield varies spatially within the same field and temporally between
seasons due to several reasons such as soil physiochemical variability, management practices andenvironmental impact. Crop yield maps are used for the delineation of management zones andsupport farmers for their management decisions.
Traditional yield measurement practices are laborious, destructive and time consuming.
Moreover, it is not suitable for in season measurements especially from large fields. Currently,
remote sensing from satellite images provides continues observations with high spatial resolutions
and temporal frequency. For instance, Sentinel-2 satellite images from the European Space Agency
provides images every 5 days with a spatial resolution of 10m with 12 different spectral bands.
Satellite images are used to derive vegetation indices, which could describe crop vigor andsubsequently predict crop yield through empirical models. Corn yield prediction through393

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vegetation indices such as normalized deference vegetation index (NDVI), enhanced vegetationindex (EVI) and green NDVI (GNDVI) was investigated in several studies while most of themwere at the country and county scale or used just limited field observations. Furthermore, thecurrent availability of yield monitors mounted on combine harvesters provides an accurate anddetailed yield data from agricultural fields. Yield monitors could provide thousands of yieldobservations from each field according to the field size, harvester width and the yield monitoring
sensor specifications. Both Sentinel-2 and yield monitors provided huge amount of data fromprevious seasons, which could be fused using machine-learning techniques to provide more robust
yield prediction models. Therefore, the main objective for this study was to predict corn yield
variability within field scale using Sentinel-2 images through vegetation indices.
MATERIALS AND METHODSThis study conducted in a 22-ha field located in North Italy and cultivated by corn in 2016,2017 and 2018 growing seasons. The sowing date was by the beginning of April and harvesting
after 160 days in average. Archived yield data was collected at harvesting time from the threestudied seasons using a calibrated grain yield monitor mounted on a combine harvester. The
harvester working width was 6m and the yield monitor could record yield observations every one
second, which is equivalent to 1.5m long in average. A total of 20,000 ground yield data werecollected from the study field every season. This data was filtered by removing outliers over ±3
standard deviation from each season. Then yield data was interpolated to 10m pixels to match with
Sentinel-2 spatial resolution.
Sentinel-2 satellite images level 2A were acquired from the Copernicus Open Access Hub.
A total of 34 cloud free images covering the study period where 6, 14 and 14 images acquired fromthe 2016, 2017 and 2018 seasons, respectively. All sentinel-2 images bands were resampled to
10m and several vegetation indices were calculated such as NDVI and GNDVI. Vegetation indiceswere correlated with corresponding yield at different crop growing stages corresponding to each
image date. Equations 1 and 2 describes the calculation of NDVI and GNDVI from Sentinel-2
images as an example.
Equation 1.
Equation 2.

NDVI = (B8-B4)/(B8+B4)
GNDVI = (B8-B3)/(B8+B3)

Where B8 is the NIR band, B4 is the red band and B3 is the green band of Sentinel-2 satellite
images.
RESULTS AND DISCUSSION
Results showed that GNDVI was the most accurate vegetation index to predict corn grain
yield with an R2 value of 0.48 while the most suitable period to predict corn grain yield was
between R4-R6 growing stages. In general, all studied vegetation indices showed the same trend
across studied seasons where the R2 values were < 0.3 until crop age of 90 days after sowing (DAS)
and increased to reach the beak around 120 DAS then decreased significantly before harvestingtime. This trend repeated for the three studied seasons and this result in agreement with previous
studies (Peralta et al., 2016 and Schwalbert et al., 2018). Figure 1 shows an example from the 2018season ground yield data and the best correlated GNDVI map from Sentinel-2 acquired on 7394

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August 2018. Gitelson et al. 1996, highlighted the sensitivity of GNDVI to the chlorophyll
concentration where the reflectance range between 520 and 630 nm could describe chlorophyll
content variability. Also, GNDVI is the most correlated index to FPAR for corn and subsequently
to corn yield (Tan et al., 2013). Much detailed descriptions about this study methodology, analysis
and results are available on a recently published article in the remote sensing by Kayad et al., 2019.

Figure 1. GNDVI map calculated from the Sentinel-2 image acquired on 7 August 2018 and theground corn yield map for 2018 season.
REFERENCES
Gitelson et al, 1996. Use of a green channel in remote sensing of global vegetation from EOSMODIS. Remote Sens. Environ. 58: 289–298.
Kayad et al, 2019. Monitoring Within-Field variability of Corn Yield using Sentinel-2 andMachine Learning Techniques. Remote Sens. 11: 2873.
Peralta et al, 2016. Mid-season high-resolution satellite imagery for forecasting site-specific cornyield. Remote Sens. 8: 848.
Schwalbert et al, 2018. Forecasting maize yield at field scale based on high-resolution satellite
imagery. Biosyst. Eng. 171: 179–192.
Tan et al, 2013. Using hyperspectral vegetation indices to estimate the fraction ofphotosynthetically active radiation absorbed by corn canopies. Int. J. Remote Sens. 34:
8789–8802.

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#7681 PREDICTING IN-SEASON SORGHUM YIELD POTENTIAL USING REMOTESENSING APPROACH: A CASE STUDY OF KANO IN SUDAN SAVANNAH
AGRO- ECOLOGICAL ZONE, NIGERIA*A. Tukur1, 2, H. A. Ajeigbe1, F. M. Akinseye1, 3, I. B. Mohammed2, M.M. Badamasi4, 5International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Kano, Nigeria
2
Department of Agronomy, Bayero University Kano, Nigeria; 3Department of Meteorology andClimate Science, Federal University of Technology, PMB 704, Akure, Ondo State; 4 Centre for
Dryland Agriculture, Bayero University Kano, Nigeria; 5Department of Environmental
Management, Bayero University Kano, PMB 3011 Kano, Nigeria; a.tukur@cgiar.org
1

ABSTRACT
Estimating crop yield prior to harvest using remote sensing techniques has proven to be
successful. However, accuracy of estimation still varies across crops and landscapes. This study
was conducted to examine the applicability of Sentinel-2B for estimating sorghum yield during
the 2018 rainy season in three locations (Bebeji, Dawakin Kudu and Rano) within the Sudan
Savannah agro-ecological zone of Nigeria. SAMSORG 45 (an early maturing improved sorghum
variety) was established in five (5) randomly selected farmer plots in each of the three LGAs. The
relationship among different vegetation indices, Normalized Difference Vegetation Index (NDVI),
Green Normalized Difference Vegetation Index (GNDVI), Ratio Vegetation Index (RVI) andgrain yield were determined using linear regression analysis. Models at different growth stages
were then compared using root mean square error (RMSE), coefficient of variations (CV) andcoefficient of determination (R2) respectively. The results from the statistical analysis showed thatNDVI was superior to GNDVI and RVI for grain yield estimation, indicating low RMSE, high R2
and low CV values at early vegetative (40 days after sowing, DAS), reproductive stage, and entire
crop-life cycle. The estimate at 40DAS, reproductive stage, and entire crop-life cycle showed
RMSE of 0.04, 0.03, 0.02, R2 (0.75, 0.77 0.93), CV (13.7%, 27.3%, 39.2%) respectively. In
addition, RVI had the best fit for stalk yield estimates, having RMSE (0.06, 0.04, 0.01), R2 (0.5,
0.83, 0.98) and CV (15.7%, 19.9% 38.5%) at 70DAS, reproductive stage, and entire crop-life cycle
respectively. This study therefore concludes that sorghum yield could be accurately predicted inseason with NDVI and RVI for grain and stalk yields using Sentinel-2B.
Keywords: Sorghum, Normalized Difference Vegetation Index (NDVI), green Normalized
Difference Vegetation Index (GNDVI), Ratio Vegetation Index (RVI), in-season yield estimate,
Sudan Savanna
INTRODUCTION
Sorghum [Sorghum bicolor (L.) Moench] is the most important cereal in the Guinea (800–1100 mm rainfall) to Sudan savanna (600–800 mm) zones of West Africa (Akinseye et al. 2020)
and in the drier Sahel (300-600mm) environments. Its productivity has an important influence onfood security, contributing directly to household food availability and as well as influencing
incomes due to its industrial demand (Ajeigbe et al. 2017). The recent advances in sensors
technology and availability of free high-resolution (spatial and temporal) multispectral satellite396

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images afford an opportunity to predict crop yields as well as mapping the spatial distribution innear real-time (Chivasa et al. 2017). In particular, crop yield estimation may play a fundamentalrole in supporting policy formulation and decision-making in agriculture (e.g. management of foodshortage) especially in the Savanna region of West Africa that is characterized by high climate
variability and food price volatility. Among the possible approaches that may be adopted for yield
estimation at large spatial scales include the integration of crop simulation model (Akinseye et al.
2020) and satellite data that seems to be one of the most appropriate quantitative analysis
methodologies (Moriondo et al. 2007). Yield estimation plays an important role in stabilizing
prices and can have a direct influence in marketing and logistical issues and determination ofpricing policies of food (Lobell et al. 2003).
In Nigeria, crop surveys, seed purchase records, land area under cultivation, field visits fromextension officers, visual assessment of the crop, etc., are mostly used in estimating yield. Thesemethods are either costly, time consuming, not accurately representing the overall production
picture or prone to large errors due to incomplete ground observations, leading to poor crop yield
estimation and often not available in good time for early warning purposes. As such, there is theneed to develop faster models for early crop yield estimation that can contribute to minimizing
yield gap (Printer et al. 2003). Satellite data has a wide range of applications in the field ofagriculture, which include yield estimation (Claverie et al. 2012). In this study, we examine thesuitability and applicability of Sentinel-2B for estimating sorghum yield using vegetation indicessuch as Normalized Difference Vegetation Index (NDVI), Green Normalized Difference
Vegetation Index (GNDVI) and Ratio Vegetation Index (RVI).
MATERIALS AND METHODS
The study was carried out during the 2018 cropping season in three selected sites within the
Sudan Savanna ecological zone of Nigeria: Bebeji (11.537°N 8.31°E), Dawakin Kudu (11.797°N
8.706°E) and Rano (11.485°N 8.514°E) Local Government Areas of Kano State.. The long-term
daily rainfall (1981–2016) for all sites was obtained to establish comparison with the cropping
year (2018). The record showed that 2018 total rainfall from May- October (852mm at Bebeji, 757
mm at Dawakin-kudu and 748 mm at Bunkure) was higher in Bebeji and a little below for
Dawakin- kudu and Bunkure compared to seasonal (1980 -2015) average of 784 mm for Kano as
the reference site. The analysis of monthly rainfall of both stations indicate a distinct mono-modal
pattern with the peak amount in August and varied between May and October. Over 50% of thetotal rainfall was received in the month of July and August, while both minimum and maximum
temperatures decrease uniformly throughout the growing season. Furthermore, the Sentinel-2B,level-1C time series images for the year 2018 were sourced and downloaded from Copernicus
Open Access Hub (COAH) using the link (https://scihub.copernicus.eu/dhus/#/home). The images
used were captured between 25 May and 11 November, 2018 at 10-day interval. The variety ofSorghum used was SAMSORG 45, which is an improved early maturing variety that reaches 50%
flowering in 67 days after sowing (DAS) and has a yield potential of 2.4 to 2.8 tons ha-1. Sen2Cor
version 2.4 processor was used to generate Level 2A (Bottom-of-atmosphere), while Sentinel
application platform SNAP version 5.0 was used to obtain NDVI, GNDVI and RVI values derived
for the sorghum plants. The vegetation indices tested were calculated using the formulae presented
in Table 1.

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Table 1. Vegetation Indices (VIs), their mathematical formulae, the scale of development andparameters estimated (Cammarano et al. 2011).
Index
NDVI (Normalized
Difference
Vegetation Index)
GNDVI (Green
Normalized
Difference
Vegetation Index)
RVI (Ratio
Vegetation Index)

FormulaScale

Parameter(NIRRed)/(NIR+Red)

CanopyBiomass; Vegetation Fraction(NIRGreen)/(NIR+Green)

CanopyChlorophyll; Vegetation Fraction

NIR/RedLeaf

Biomass

In-season estimated yield (INSEY) was determined using the equation described by Teal et al.
(2006):INSEY= VI / CGDD
where VI is the vegetation index and CGDD is the cumulative growing degree days from the
beginning of the season to the day of sensing.
Growing degree days (GDD) were calculated using the equation:
GDD = (Tmax + Tmin) / 2) - Tb
where Tmax - maximum daily temperature, Tmin - minimum daily temperature and Tb -basetemperature.
In addition, regression analysis was used in determining the relationship between VIs as
independent variables and final grain yield as a dependent variable. Finally, coefficient ofdetermination (R2), adjusted R2, root mean square error (RMSE) and the variability of thevegetation index measurements expressed as coefficient of variation (CV) in percentage (%) wereused as the criteria in selecting the best fit model.
RESULTS AND DISCUSSION
Table 2 shows the multiple regression analysis for the entire crop cycle using INSEY values
generated for both grain and stalk yield. The estimates of VIs (NDVI, GNDVI and RVI) for grain
and stalk yield varied due to the parameters the VIs measures on the crop. Among the three VIs
for grain yield, NDVI indicates the lowest RMSE of 0.019, highest R2 value of 0.93 and strong R
value of 0.96, and CV estimate was 39.2% respectively. Meanwhile for stalk yield, the INSEY
estimated revealed that RVI had the lowest RMSE (0.011), highest R2 value of 0.98 and CV valueof 38.5%. The analysis for the entire crop cycle showed that NDVI had the best model fit for grain
yield with 93% coefficient of determination, while RVI was found to have the best model fit forstalk yield, estimated 98% accuracy.

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Table 2. Multiple regression models for estimating sorghum grain and stalk yields for the entire
crop’s life cycle.
VI
CV
RMSE
R2
Multiple R
NDVI
39.23
0.019
0.93
0.96
Grain Yield
GNDVI
51.59
0.024
0.89
0.94
RVI
38.49
0.026
0.87
0.93
NDVI
39.23
0.025
0.92
0.96
Stalk Yield
GNDVI
51.59
0.034
0.84
0.92
RVI
38.49
0.011
0.98
0.99
VI= Vegetation Index, CV= coefficient of variation (%), RMSE= Root Mean Square Error and
R2= Coefficient of determination
Yield

The results agreed with similar findings reported by Morel et al. (2014) that found NDVI has mostappropriate estimative measure to crop productivity during entire growing season for wheat crop.
However, Table 3 reveals the estimates of sorghum grain yield at different stages, and theresults showed vegetative stage as most suitable model fit for grain yield estimates compared toreproductive and grain filling and physiological maturity stages. NDVI had the lowest RMSE andCV value of 0.03 and 27.3%, highest R2 of 0.77 and multiple R value of 0.88 respectively. The
vegetative stage suggests as the critical growing point differentiation (GPD) of any crop indicating
as best fit with 77% yield prediction accuracy. At this stage, the plant is entering into a phase of
rapid nutrients and water uptake, and has little tolerance to stress. This can significantly affect thegrain yield. This result agreed with findings by Shambel et al. (2017) who reported that grain yieldprediction in sorghum using spectral measurements should be carried out at a stage of criticalnutrient demand.
Table 3. Multiple regression models for estimating sorghum grain yield at different stages of thecrop’s development.
VI
CV
RMSE
R2
Multiple R
NDVI
27.26
0.03
0.77
0.88
Vegetative
GNDVI
33.36
0.04
0.76
0.87
RVI
27.66
0.04
0.63
0.80
NDVI
16.89
0.04
0.74
0.86
Reproductive
GNDVI
23.57
0.05
0.50
0.70
RVI
19.91
0.04
0.74
0.86
NDVI
23.36
0.06
0.40
0.63
Grain Filling andPhysiological
GNDVI
24.12
0.06
0.35
0.59
Maturity
RVI
33.30
0.06
0.26
0.51
VI= Vegetation Index, CV= coefficient of variation (%), RMSE= Root Mean Square Error and
R2= Coefficient of determination
STAGE

CONCLUSIONSThis study concludes that sorghum yield could be accurately predicted in-season with NDVI
and RVI for grain and stalk yields using Sentinel-2B.

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REFERENCES
Ajeigbe HA, Akinseye FM, Angarawai II, Umma SA, Inuwa AH, Adinoyi A, Abdulazeez T. 2017.
Enhancing Farmer’s Access to Technology and Market for Increased Sorghum
Productivity in the Selected Staple Crop Processing Zones. Proceedings of the 51st Annual
Conference of the Agricultural Society of Nigeria, Abuja, Nigeria. 1068-1072.
Akinseye FM, Ajeigbe HA, Kamara AY, Adefisan EA, Whitbread AM. 2020. Understanding the
response of sorghum cultivars to nitrogen applications in the semi-arid Nigeria using the
agricultural production systems simulator. Journal of Plant Nutrition 43(6): 834-850
Cammaran D, Fitzgerald G, Basso B, O’Leary G, Chen D, Grace P, et al. 2011. Use of the canopy
chlorophyll content index (CCCI) for remote estimation of wheat nitrogen content in
rainfed environments. Agronomy Journal 103: 1597–1603.
Chivasa W, Onisimo M, Chandrashekhar B. 2017. Application of remote sensing in estimating
maize grain yield in heterogeneous African agricultural landscapes: a review, International
Journal of Remote Sensing 38(23): 6816-6845.
Claverie M, Demarez V, Duchemin B, Hagolle O, Ducrot D, Marais-Sicre C, Dedieu G. 2012.
Maize and Sunflower Biomass Estimation in Southwest France using High Spatial andTemporal Resolution Remote Sensing Data. Remote Sensing of Environment 124: 844–
857.
Lobell DB, Asner GP, Ortiz Monasterio JI, Benning TL. 2003. Remote Sensing of Regional Crop
Production in the Yaqui Valley, Mexico: Estimates and Uncertainties. Agricultural
Ecosystem and Environment 94: 205–220.
Morel J, Todorof P, Bégué A, Bury A, Martiné JF, Petit M. 2014. Toward a Satellite-based System
of Sugarcane Yield Estimation and Forecasting in Smallholder Farming Conditions: A
Case Study on Reunion Island. Remote Sensing 6: 6620-6635.
Moriondo M, Maselli F, Bindi M. 2007. A simple model of regional wheat yield based on NDVI
data. Europ. J. Agronomy 26: 266–274.
Printer PJ Jr, Ritchie JC, Hatfield JL, Hart GF. 2003. The Agricultural Research Service’s Remote
Sensing Program: An Example of Interagency Collaboration, Photogramm. Engineering
Remote Sensing 69 (6): 615-618.
Rouse JW, Haas JAS, Deering DW. 1974. Monitoring vegetation systems in the Great Plains with
ERTS. Third ERTS-1 Symbosium. Washington, DC: 301-317.
Shambel MM, Kefyalew G, Roger KT, Kyle W, Hailin Z, Daryl BA, Starr LH, Brenda ST, Olga
W, Byungkyun C, William RR. 2017. In-season Estimation of Grain Sorghum Yield
Potential using a Hand-held Optical Sensor. Archives of Agronomy and Soil Science 53 (6):617 – 628.
Teal RK, Tubana BS, Girma K, Freeman KW, Arnall DB, Walsh O, Raun WR. 2006. In-season
Prediction of Corn Grain Yield Potential using Normalized Difference Vegetation Index.
Agronomy Journal 98: 1488–1494.

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SOFTWARE AND MOBILE APPLICATIONS

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#7368 SOCIO-DEMOGRAPHIC FACTORS CONTRIBUTING TO ADOPTION OF ECOMMERCE BY AGRIBUSINESS SMES IN THE UPPER EAST REGION OF GHANA
S. Omega and S. Akaba
University of Cape Coast, Cape Coast, Ghana
sakaba@ucc.edu.gh; Phone: +233505401918
ABSTRACTThe study’s objective focused on examining some socio-demographic indicators
influencing Agribusiness small and medium scale enterprises electronic commerce adoption andthe preferred Agribusiness small and medium scale enterprises electronic commerce applicationsin the Upper East region of Ghana. A cross-sectional survey design was adopted for the study todetermine the status of the variables and their interplay. The research used a purposive samplingtechnique for the study. Out of the 120 questionnaires administered for the study, only 100 (83.3%)
respondents filled it. Data were analysed using both descriptive and inferential statistics. Resultsof the objectives showed that educational level, the scale of the enterprise, age, years in businessand type of Agribusiness small and medium scale enterprise were significant to influencing the
adoption decision of Agribusiness small and medium scale enterprises in the study area. The results
also revealed that Agribusiness small and medium scale enterprises in the Upper East region use
electronic commerce applications mostly for customer relation management and inter-organisation
system.
INTRODUCTION
For businesses, E-commerce has become a playground as it offers a tremendous
opportunity for growth and development of businesses (Ya-ping, 2012). Description of Ecommerce includes the process of purchasing and selling goods over the Internet (Chong, 2008).
Rainer and Cegielski (2011) defined it as “the process of buying, selling, transferring or
exchanging of products, services and information via computer network including the internet”.
E-commerce is redefining the relationship between suppliers and customers as market-sensitive
commodities are being released to need-based customers at the right time and ensuring efficient
allocation of the resource by suppliers. Javalgi and Ramsey (2001) hold that E-commerce has
caused a paradigm shift in the concept of market competition by making smaller businesses
compete with more prominent and large market holders for market shares for quality andaccessibility. Early researchers classified E-commerce into business and consumer interaction
flow, while current researchers have acknowledged the increasing role of government as a market
player.
On the other hand, Agribusiness SMEs around the globe have served as a reliable option
for international economies development. In Ghana, the commonly used criterion for defining
Agribusiness SME is the employees’ number in the enterprise, which follows the Ghana statisticalservice (Ghana Statistical Service, 2007) framework of small-scale enterprises being enterprisewith less than ten (10) employees with above ten (10) employees as a medium and large-sizedenterprise. Bank Negara Malaysia (2005) reported that for SMEs to stay relevant continuously,
they have to enhance their capacity and efficiency to produce a high-quality product at a402

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competitive price, bearing in mind the role of E-commerce. Although Ghana is still trying to meetup with the swift progression of E-commerce, so far there has been little progress made. However,this paper was limited solely to the socio-demographic characteristics which influence the Ecommerce adoption of Agribusiness SMEs and usage of E-commerce applications Upper East
region of Ghana. This study is essential as it aims to analysis various ways Agribusiness SMEs
can stand out in the Agribusiness industry through E-commerce usage.
MATERIALS AND METHODS
The study adopted a cross-sectional survey design. The survey design was to help
generalise the study to a larger population from a sample to make room for inferences about the
population characteristics of the study. The population for the study was made of Agribusiness
SME owners in the Upper East Region of Ghana. Using Slovin’s formula (1960), the sample sizefor the study was 120 respondents. However, only 100 (83.33% response rate) respondents
participated in the study. A purposive sampling technique was employed to select the various
Agribusiness SMEs in the study area. The qualification for being part of this study was as follows:(a) the respondent must own a business that qualified to be an Agribusiness SME. (b) The locationof the Agribusiness SME must be in the Upper East Region of Ghana. Primary data was collected
through the use of questionnaires. Face validity and content validity of the research instrument was
ensured through a pilot study before the data collection to help correct ambiguous questions, andan expert in the field also checked it. The data collected was analysed using SPSS version 21.0.
RESULTS AND DISCUSSION
Adoption of E-commerce Among Agribusiness SMEs
Table 1 reveals that as the age of respondents increases, their chance of adopting Ecommerce for their operation falls by -3.656, and this was revealed to be significant (.058) toinfluencing adoption of E-commerce. The analysed result showed that age plays a significant rolein the adoption of E-commerce by Agribusiness SMEs. The finding disagrees with Wu and Wang
(2005) who reported that age is not a decisive factor (not significant) in the starting and theadoption of technologies. Instead attributed the decision to adopt E-commerce to enough training
and preparation. The educational level of the respondents was significant (.017) to influencing
adoption of E-commerce with the likelihood of the respondent adopting E-commerce falling by 2.793 as educational level increases. Educational level showed a significant relationship indicatingthat the level of educational attainment by an individual plays a vital role to influence adoption. It
implies that Agribusiness owners with less or no formal education are less attracted to Ecommerce. The findings agree with Quaye (2011) and Wadhwa et al. (2009) that ‘the businessowners with higher educational level are more likely to take the risk of adopting new technologies,
hence greater chances of succeeding’. As the years in business increases, the chance ofAgribusiness SME owner adopting E-commerce increases by 5.056 and this was significant. Yearsin business and the source of funding for the business also showed a statistically significant
relationship with E-commerce adoption. Thus, the age of business does determine if an
Agribusiness SME is going to adopt an E-commerce application or not. Interestingly, most of thebusinesses in the study area were less than 4 years implying that SME development and operation
were on the rise in the study area. The finding of this research was consistent with Autio (2005)
that ‘the longer a business stays in operation, it gives the business an advantage to the acquisition403

1st African Conference on Precision Agriculture | 8-10 December | 2020of research over time, and hence the tendency to adopt new technologies like E-commerce
application’. Again, it was revealed from Table 1 that the type of Agribusiness SME tends toincrease the likelihood of the business adopting E-commerce by 3.120, and this was significance(.054) to influencing adoption of E-commerce. Lastly, the scale of the enterprise decreases the
chance of Agribusiness SME adopting E-commerce by -4.885. It was a significant influence on
the adoption of E-commerce. The scale of the enterprise was revealed to be statistically significant
showing that the smaller the size of an Agribusiness SME, the higher the tendency to the adoptionof an E-commerce, this agrees with Stokes and Wilson (2010) that small Agribusiness SMEs are
regarded as being innovative and willing to try new approaches to production. Agribusiness SMEs
which are small in size, are more determined to increase market share. Hence the high likelihood
to adopt an E-commerce technology to increase its market position.
Table 1. Binary Logistic Regression of socio-demographic characteristics on adoption of Ecommerce.
Socio-demographic characteristics
ß
Std. Error
Sig.
Constant
12.6636.204.041 ***
Age-3.6561.930.058**
Gender
-2.103
1.429.141
Marital Status
1.062
1.415
.453
Educational Level-2.793
1.166.017***
Years in Business
5.056
2.586
.051**
Business Location
-.735
1.632
.203
Type of Agribusiness SME
3.120
1.618
.054**
Source of Funding
1.298
1.687
.442
Scale of Enterprise-4.8852.270.031***
Annual Revenue
.136
1.332
.919
Model Summary
Test
Value
Sig.
Omnibus Test of model coefficients
19.492.021
Hosmer & Lemeshow Test
1.092
.998
Cox & Snell R square.284
Nagelkerke R square.626-2 log Likelihood
27.055
Note: ***represent significant level at 5%, ** represent significant level at 10%
Preferred E-commerce Applications Used by Agribusiness SMEs
The results of Table 2, using the mean score showed that E-commerce application used for
Customer relation management (CRM) was the most used (1.7733), with Payment System being
the least used E-commerce application (1.4650). This assertion is supported by Turban et al. (2002)
and Ainin and Noorismawati (2003) that most Agribusiness SMEs prefer E-commerce application
for customer relation management. This is because the business wants to stay in touch with theircustomers, which create trust, and assure the costumers of quality. It is also a way of helping thebusiness in forecasting. On the other head, fewer respondents used E-commerce applications as ameans of payment system. Agribusiness SME owners preferred physical cash payment toelectronic payment. This method of payment was also revealed by Fatimah et al. (2000), that most404

1st African Conference on Precision Agriculture | 8-10 December | 2020

Agribusiness SMEs in developing countries are yet to benefit fully from the use of E-commercein payment. Although the introduction of mobile money payment has been introduced acrossdeveloping countries, its adoption remains a challenge for Agribusiness SMEs due to service
charges and the unwillingness of both parties to pay for the charges.
Table 2. The rank of E-commerce application used by Agribusiness SMEs.
E-commerce application
Mean Standard Deviation Mean Rank Median Value
Market research
1.4967.336672.59
1.33
Advertisement
1.5850.33375
3.20
1.50
Customer relation management 1.7733.279814.40
2.00
Inter-organisational system
1.7550.243554.45
1.75
Business performance
1.6900.33919
3.81
1.67
Payment System
1.4650.303642.55
1.50
2n= 100= 131.923 Asymp. Sig = .000 Overall Median =1.625
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