# Emerging farms in Northern Cameroon: an economic and social change towards high agricultural productivity?

Hervé Guibert<sup>±1</sup>, Ibrahim Ngamié<sup>2</sup>, Henri Clavier<sup>2</sup>, Michel Havard<sup>3</sup> & Pinardel Kenne<sup>4</sup>

## 1 Introduction

Sub-Saharan family farms have often been described as structures with low production means explaining their weak economic performance (Mbétid-Bessane et al., 2006), and also their difficulties to innovate. In their diversity, some more efficient farms qualified as emerging were identified by the Cotton Development Company in Cameroon (SODECOTON) as being capable of cultivating more than five hectares of cotton, allowing individual marketing of their production and inputs. Fifteen years ago, these farms were rare (less than one per thousand farms). In 2014, over 4,000 emerging farms were counted (2% of all farms), growing 13% of the cotton area in North Cameroun. How these farms come to such results, and are they more likely to implement technical innovations, are questions whose answers may allow addressing in a targeted way the development of agriculture in northern Cameroon. A survey was conducted in 2014 on these farms, aiming to (i) characterize their structure, (ii) understand how their management could explain their results, and (iii) whether specific supports would improve the sustainability and efficiency of their production system.

## 2 Materials and Methods

Using the 2013 census (by SODECOTON) of 3,532 emerging farms in the administrative regions of North and Far North of Cameroon, a survey was conducted in 2014 with a single pass questionnaire among 45 of them located in the SODECOTON area of Ngong, in a SODECOTON area where they were particularly numerous. The questionnaire included closed and semi-open questions and had to be administered in less than one and a half hour. The results were compared with the average data of farms, using various reports and published work (Mbétid-Bessane & Havard, 2013; Bourou et al., 2006).

## 3 Results – Discussion

### 3.1 Structure of emerging farms

The number of families dependent was more important than in the average of farms in the region (table 1). Immediate family was large, but collateral and permanent hired labor was found as well. It should be noted that 91% of school-age children were actually attending school, girls and boys as well. The number of equipment in animal traction and cattle existing was also more important.

<table><caption>Table 1. Structure of emerging farms</caption><thead><tr><td></td><td>Number of family dependents</td><td>Number of working age people</td><td>Number of draught animal full equipments</td><td>Tropical Livestock Units existing</td></tr></thead><tbody><tr><td>Emerging farms</td><td>16.2</td><td>5.1</td><td>1.8</td><td>11.3</td></tr><tr><td>Regional average</td><td>5.1</td><td>2.9</td><td>0.26</td><td>1.5</td></tr></tbody></table>

### 3.2 Cropping Systems

Emerging farms have grown twice more surfaces per capita than the regional average for comparable crop rotation (Fig.1). They were not only the largest farms in the region but also functioned differently. To achieve this, they relied heavily on external hired labor: on average 522 working days for the 2014 crop year. The crops were also cultivated in an intensive way: the doses of fertilizers applied (in a localized manner) on cotton were close to the recommendations (while besides, the half dose is usually used, often deposited on the surface without being covered), and the recommended doses were exceeded in the cultivation of maize. All cultivated areas were treated with herbicide at least once, sometimes twice. Nearly all farms have used organic manure. Three-quarters had trees plantations. To raise their animals, almost all producers have made fodder reserves and resorted to buying food supplements. Half of the farms have made forage (forage sorghum).

<footnote n="1">¹ CIRAD, UR AÏDA</footnote>

<footnote n="2">² SODECOTON (Société de Développement du Coton au Cameroun)</footnote>

<footnote n="3">³ CIRAD, UR INNOVATION</footnote>

<footnote n="4">⁴ IRAD, Laboratoire des Sols, Plantes, Eau et Engrais</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: herve.guibert@cirad.fr</footnote>
<figure><img src="image_263.png" /><figcaption>**Fig.1. Emerging farms acreage per capita**</figcaption></figure>

## 3.3 Economic Approach

The survey does not establish a complete operating account, but some data can be calculated. While inputs for cotton production are subject to a seasonal credit campaign, inputs for food crops and hired labor payment are to be settled in cash. So, emerging farms spent an average of 854,000 FCFA (Table 2) during the crop year and before harvest. This high flow capacity, rare in African family farming, allowed them to set up their crop system. Note that the sale of cotton covered virtually all cropping costs, and its fixed price was an insurance to recover all of the expenses. The farms also produced food surpluses. These farms are similar to the entrepreneurial farms described by Boscet al., 2014.

<table><caption>**Table 2.** Some emerging farms costs and products data of crops in FCFA, for the crop year 2014</caption><thead><tr><td>st of food inputs (cash)</td><td>Cost of hired labor (cash)</td><td>Cost of cotton inputs (on credit)</td><td>Total cropping costs</td><td>Cotton Product, 2013 crop year (for 30 farms)</td></tr></thead><tbody><tr><td>427,000</td><td>427,000</td><td>1,045,000</td><td>1,898,000</td><td>1,797,000</td></tr></tbody></table>

## 3.4 Social environment of the production

In a region where land insecurity reigns, these farms reported being safe on 69% of their cultivated plots in average. They were confident that over half of their plots, they had the exclusive right of the profits on wood products. The heads of these farms often had traditional or economic titles (village or district chiefs, group delegates), but so far the ownership of all of these farms was in general in no way linked to traditional hierarchy.

## 4 Conclusions

These farms are emerging, firstly because they are becoming more and more numerous, and secondly because they put in place a system of intensive and efficient crops through a controlled economic management. The economic area and the social environment of these farms are conducive to the adoption of technical alternatives that have had little success elsewhere. Measures to limit the negative impact of the systematic use of herbicides, improving the quantity and quality of organic fertilizers produced on the farm, the establishment of an integrated fertility management for farms, are examples of targeted support that are likely to bring good results with these farms. This study also shows segregation of the farms with the poorest selling their labor and sometimes their inputs to the more efficient ones.

## References

* Bosc, P.M., Marzin, J., Bélières, J.-F., Sourisseau, J.-M., Bonnal, P., Losch, B., Pedelahore, P. & Parrot, L. (2014). Définir, caractériser et mesurer les agricultures familiales. In: *Agricultures familiales et mondes à venir*. - Versailles: Ed. Quae. 43-60

* Bourou, M., Wey, J., Havard, M. & Folefack, D., P. (2010) Adaptation des assolements des exploitations agricoles du Nord-Cameroun à la crise cotonnière. *Prasac, N'Djaména, Tchad* ; Cirad, Montpellier, Actes du colloque « Savanes africaines en développement : innover pour durer », 20-23 avril 2009, Garoua, Cameroun.

* Mbétid-Bessane, E. & Havard, M. (2013). Stratégie adaptatives et viabilité des exploitations agricoles familiales des savanes cotonnières d'Afrique Centrale. *Agronomie Africaine*, **25** (2), 171-185.

* Mbétid-Bessane, E., Havard, M. & Djondang, K. (2006). Évolution des pratiques de gestion dans les exploitations agricoles familiales des savanes cotonnières d'Afrique centrale. *Cahiers Agricultures*, **15** (6), 556-561.
# A GxExM approach to manage climate risks in rainfed maize cropping systems

Joseph Eyre *±1,3*, Hae Koo Kim <fnref n="2" />, Peter deVoil <fnref n="1" />, Amsal Tarekegne <fnref n="3" />, Zaman-Allah Mainassara <fnref n="3" /> & Daniel Rodriguez <fnref n="1" />

## 1 Introduction

Recent maize yield gains have been achieved by increasing plant population densities using crowding tolerant hybrids in high-input high-output cropping systems. However, the high cost of maize seed relative to alternative cereals such as sorghum limits the inclusion of maize in large-scale high-input commercial rainfed systems. The costs are also prohibitive for low-input and low-output rainfed smallholder farming of Africa.

Technologies that reduce maize establishment costs such as using prolific maize hybrids i.e. hybrids that set more than one cob per plant on the main culm, grown at lower plant populations, have the potential to reduce risk and provide incentives for the sustainable intensification of agriculture. Prolific hybrids that can yield up to 10 Mg ha⁻<fnref n="1" /> of grain under optimal conditions when sown at population densities lower than 3 plants m⁻<fnref n="2" />. These materials are currently available in both Australia and Sub Saharan Africa.

Here we used data sets from field trials in Africa and in Australia together with a cropping systems simulator (APSIM) to evaluate the influence of maize population densities and hybrid phenotypes (i.e. different degrees and types of prolificity) across recommendation domains both in eastern and southern Africa on yield and yield stability.

## 2 Materials and Methods

A total of 158 three-way-cross maize hybrids were evaluated in 48 locations across southern Africa in 2013-2014. Each trial consisted of 2-3 replicates sown at 2.7, 5.3 or 7.9 plants m⁻<fnref n="2" />. All trials were managed with best practice agronomy for conventional tillage at each site including optimal fertiliser application and weed control. The crop water supply demand ratio was simulated for check hybrids to characterise the environment using APSIM. This model was parameterised based on locally measured soil properties and cultivar specific inputs for widely grown early to mid duration hybrids. Data points for analysis of the plant growth rate and kernel number relationship were extracted from published literature with the WebPlotDigitizer (http://arohatgi.info/WebPlotDigitizer/).

## 3 Results – Discussion

Prolific hybrids were generally high yielding across all sites and population densities in the 2013-2014 cropping season (Fig. 1). Prolificity was not associated with yield gains in the more favourable maize cropping environments of Argentina (Echarte et al. 2013). However, the influence of prolificity on long-term grain yields across productivity gradients and contrasting rainfall environments is untested.

The prolific phenotype can be predicted from the plant growth rate during critical period bracketing silking (PGRs), but the degree of prolificity or propensity for prolificity varies between hybrids. Secondary cobs only develop when resource availability supports PGRs between 3.6 and 6.0 g plant⁻<fnref n="1" /> day⁻<fnref n="1" /> depending on the hybrid (Fig. 2; Lizaso et al. 2011). Different combinations of thermal time calculations, critical period durations or growth rate methodologies were used to obtain the data presented in Figure 2. The APSIM model also predicts kernel number based on the plant growth rate during a different critical period starting 130 and ending 260 growing degree days before and after anthesis, respectively. Despite the inconsistent methodologies, base PGRs for kernel development on the primary and secondary cob (hybrid dependant) are consistent across all studies. The critical period for prolificity and kernel number determination occurs during the linear growth phase of the maize crop, therefore slight changes in the critical period definition are unlikely to influence predictions.

Crop simulation can be used to identify the management by environment combinations that favour prolific hybrids as determined by the frequency distribution of PGRs that are greater than critical thresholds. Further simulations can then be used to compare long-term yields of non-prolific and prolific maize hybrids grown under optimal management across environmental gradients.

<footnote n="1">¹ Queensland Alliance for Agriculture & Food Innovations (QAAFI), The University of Queensland, Qld, 4072</footnote>

<footnote n="2">² The International Maize and Wheat Improvement Center (CIMMYT), CMC Road, P. O. Box 5689, Addis Ababa, Ethiopia</footnote>

<footnote n="3">³ CIMMYT, P.O. Box MP163, Mt Pleasant, Harare, Zimbabwe</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: j.eyre@uq.edu.au</footnote>
<figure><img src="image_264.png" /><figcaption>**Fig. 1.** Grain yield of the 5 highest yielding and highly prolific hybrids (y = 1.107x + 0.099, R² = 0.89, P< 0.001) and all other hybrids verses trial means. The 5 hybrids with the highest relative yield anomaly that also developed greater than 1.4 cobs per plant averaged across all sites were identified at a population density of 2.7 plants m⁻². Circle, triangle and square shaped symbols identify population densities of 2.7, 5.3 and 7.9 plants m⁻², respectively.</figcaption></figure>

<figure><img src="image_265.png" /><figcaption>**Fig. 2.** Primary cob (●) and total including secondary cob (○) kernels verses growth rate on an individual plant basis. Dotted line defines the base plant growth rate for kernel development on the primary cob and below which baron cobs occur. Dashed and solid vertical lines show the base growth rate for secondary cob development as defined by Lizaso *et al.* 2011 and Tollenaar *et al.* 1992, respectively.</figcaption></figure>

## 4 Conclusions

Results demonstrate that the high yielding prolific maize hybrids are potentially suitable for environments with yield potentials from 1-to-12 Mg ha⁻¹. The propensity for prolificity is hybrid dependant, but can be predicted from simulations of PGRs. Research continues to explore (i) the environment and management combinations that support expression of the prolificity phenotype, (ii) the degrees of prolificity available in current maize hybrids, and (iii) the influence of this technology on water and nitrogen demand patterns.

*Acknowledgements*. We acknowledge the support for this project by ACAIR, CRP MAIZE and the SIMLESA team.

## References

* Andrade, F.H., Vega, C., Uhart, S., Cirilo, A., Cantarero, M. & Valentinuz, O (1999) Kernel number determination in maize. *Crop Science* 39, 453-459.

* Echarte, L, Andrade, FH, Vega, CRC, Tollenaar, M (2004) Kernel number determination in Argentinean maize hybrids released between 1965 and 1993. *CropScience* 44, 1654-1661.

* Echarte, L, Della Maggiora, A, Di Matteo, J, Nagore, L, Robles, M, Cambareri, M (2013) Grain yield determination and resource use efficiency in maize hybrids released in different decades. INTECH Open Access Publisher

* Lizaso, JI, Boote, KJ, Jones, JW, Porter, CH, Echarte, L, Westgate, ME, Sonohat, G (2011) CSM-IXIM: A New Maize Simulation Model for DSSAT Version4.5. *Agronomy Journal* 103, 766-779.

* Tollenaar, M, Dwyer, LM, Stewart, DW (1992) Ear and kernel formation in maize hybrids representing 3 decades of grain-yield improvement in Ontario. *Crop Science* 32, 432-438.
# Combined and targeted application of crop residues and cattle manure increases maize productivity in a crop-livestock farming system on granitic sandy soils of Zimbabwe

Isaiah Nyagumbo * ±<fnref n="1" />, Siyabusa Mkuhlani <fnref n="1" />, Leonard Rusinamhodzi <fnref n="1" />, Sandra Madamombe <fnref n="1" /> & Walter Mupangwa <fnref n="1" />

## 1 Introduction

Granitic sandy soils of low pH and poor inherent fertility constitute about two thirds of Zimbabwe's arable land area (Anderson *et al.*, 1993). These soils are characterized by low clay, organic carbon and nutrient, content. They also are prone to degradation due to excessive soil loss and run-off caused by poor soil management practices (Vogel *et al.*, 1994). Various technologies including integrated soil fertility management (Zingore *et al.*, 2011) and conservation agriculture (CA) technologies (Thierfelder *et al.*, 2014) have been tested to rehabilitate these soils. Use of cattle manure in such systems has been found to significantly improve crop productivity (Rusinamhodzi *et al.*, 2013). Hence the dwindling cattle numbers due to poor pastures imply farmers need to use manure resources more efficiently. CA systems, recommended as a strategy for sustainable intensification involving reduced soil disturbance, crop rotations and provision of a permanent soil cover using crop residues, have also been the major focus of agricultural research in southern Africa in the last decade. One of the major challenges of practising CA has been the scarcity of mulch emanating from the conflict between use of maize crop residues as feed for cattle during the dry winter months and as mulch in crops (Rufino *et al.*, 2011). This study investigated the combined effects of maize residue and cattle manure application on maize yields under CA. It also sought to understand the potential impact of such management options on food security in Goromonzi and Murehwa districts, Zimbabwe.

## 2 Materials and Methods

The study was conducted on a sandy soil, 30 km north of Harare, Zimbabwe over four cropping seasons (2010/2011-2013/2014). CA treatments with 0, 3 and 5 t ha⁻<fnref n="1" /> surface applied maize residues, were tested with and without 5 t ha⁻<fnref n="1" /> cattle manure applied in basins (15 cm diameter × 15 cm deep) laid out in a split plot experimental design with residue application as the main treatments and fertility management regimes as sub-plots. All treatments received 250kg ha⁻<fnref n="1" /> basal fertilizer (7% N:14% P₂O₅:7% K₂O) and top dressing (200 kg ha⁻¹ammonium nitrate (34.5%N). Manure was applied annually as subtreatments from 2010/2011 at a rate of 5000 kg ha⁻<fnref n="1" /> at 225g per station (22 222 stations ha⁻<fnref n="1" /> and spacing of 0.9 × 0.5 m). A hybrid maize variety was planted annually at the onset of the rainy season. Results from this experiment were then used to assess the potential impact of the adoption of such technologies on food security in Goromonzi and Murehwa districts using local demographic data from baseline studies.

## 3 Results and Discussion

Maize yields generally increased with time (Fig. 1). Analysed over the four years, significant interactions on maize grain yields were observed between maize residue application rate and fertility management (p<0.007) as well as between fertility management and cropping year (p<0.001). Thus time (year) influenced the applied manure effectiveness and linear regression analysis of yield results, showed highly significant (p<0.001) difference in responses over time between manured and non-manured treatments, irrespective of maize residue rates applied. Manure application thus resulted in a steeper slope (3.36) of the yield-time linear relationship compared with non-manured systems (1.65)suggesting that manure application will be beneficial in the long-term (Fig. 2), an effect attributed to the observed increase in soil phosphorus content and micronutrients, as well as improved physical conditions of the soil. However, linear regression analysis showed no apparent differences in yields between the two fertility levels with increase in maize residue application rates. The margin of differences in yields with or without manure progressively widened over time (Fig. 1). Thus in 2013/2014, the application of 3 t ha⁻<fnref n="1" /> maize residues without manure, depressed yields by 1.0 t ha⁻<fnref n="1" /> (36% reduction) but increased yield by 1.6 t ha⁻<fnref n="1" /> (56%) when manure was applied. When the mulch rate was increased to 5 t ha⁻<fnref n="1" /> and combined with manure, a yield increase of 2.2 t ha⁻<fnref n="1" /> (77%) was observed relative to no residues without manure, whereas a marginal yield increase of 0.5 t ha⁻<fnref n="1" /> was observed for a similar increase in mulching rates without manure. Thus the most recent results from this on-going experiment suggest that the that application of cattle manure at 5 t ha⁻<fnref n="1" /> combined with modest residue applications of 3t/ha could potentially increase yields by at least 60% (Figure 1). By inference, the judicious use of manure at 5 t ha⁻<fnref n="1" /> combined with residue application rates of 3 t ha⁻<fnref n="1" />, and assuming a 60 % yield increase above current ones, the 44% cattle owning households in Goromonzi and Murehwa

<footnote n="1">¹ CIMMYT, P.O Box MP163, Mt. Pleasant, Harare, Zimbabwe</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: i.nyagumbo@cgiar.org</footnote>
districts, could potentially benefit immensely and reduce food insecurity with an estimated mean 27% increase in mean district annual maize output based on the analysed sample ward demographics (Table 1). These preliminary results however, also suggest that farmers applying about 3 t ha⁻¹ residues in CA without manure, may need to apply more fertilizer to offset the yield reduction attributed to nitrogen immobilization from maize residues. The need for integrated soil fertility management is apparent. Farmers often broadcast manure on small areas of e.g. 0.3 to 0.5 t ha⁻¹ yr⁻¹, thus can achieve the modest targeted spot application rates and the benefits suggested in this study.

<figure><img src="image_266.png" /><figcaption>**Fig. 1.** (a) Residue application rates and soil fertility management and (b) Effects of manure application on maize yields at Domboshawa Training Centre (DTC) (2010-14).</figcaption></figure>

N.B: error bars denote l.s.d(0.05), *Residue rate*\*fertility for comparing means within and across years.

<table><caption>**Table 2.** Estimated productivity changes with CA and manure application in Goromonzi and Murehwa districts of Zimbabwe.</caption><thead><tr><th>Disttict</th><th>% cattle<br>ownership per hhd</th><th>Cultivated<br>area (ha/hhd)</th><th>maize</th><th>Current<br>output (t/<br>hhd)</th><th>maize</th><th>Expected maize<br>output (t/hhd)</th><th>Expected mean<br>increase in maize<br>output per hhd</th></tr></thead><tbody><tr><td>Goromonzi</td><td>40</td><td>0.591</td><td></td><td>0.56</td><td></td><td>0.89</td><td></td></tr><tr><td>Murehwa</td><td>44</td><td>0.554</td><td></td><td>0.51</td><td></td><td>0.83</td><td rowspan="2">27%</td></tr><tr><td>Mean</td><td>42</td><td>0.573</td><td></td><td>0.53</td><td></td><td>0.86</td></tr></tbody></table>

Current maize yield estimates= 0.94 t/ha: N.B A household of six requires 1.2 tmaize/yr

## 4 Conclusions

A combination of surface applied maize residues, inorganic fertiliser plus spot application of organic cattle manures, improves crop productivity in both short and long term and maybe sustainable in mixed crop-livestock systems of smallholder farmers if conflicts between multiple uses of crop residues can be reduced. Results support the need for integrated soil fertility management even in conservation agriculture systems. Adoption of such crop management strategies in similar agro-ecologies could potentially increase household maize output by 27 % thereby reducing food insecurity.

*Acknowledgements.* We acknowledge funding received from the Australian Centre for International Agricultural Research for components of this study.

## References

Anderson, I.P., Brinn, P.J., Moyo, M., Nyamwanza, B., 1993. Physical resource inventory of the communal Lands of Zimbabwe - An Overview.

Natural Resource Institute, Chatham, UK.

Thierfelder, C., Rusinamhodzi, L., Ngwira, A.M., Mupangwa, W.T., Nyagumbo, I., Kassie, G.T., Cairns, J.E., 2014. Conservation agriculture in southern Africa: advances in knowledge. Renewable Agriculture and Food Systems 23, 224–246.

Vogel, H., Nyagumbo, I., Olsen, K., 1994. Effects of tied ridging and mulch ripping on water conservation in maize production on sandveld soils. Der Tropenlandwirt "Journal of Agriculture in the tropics and Subtropics" 3-4, 33–44.

Zingore, S., Tittonell, P., Corbeels, M., van Wijk, M.T., Giller, K.E., 2011. Managing soil fertility diversity to enhance resource use efficiencies in smallholder farming systems: A case from Murewa District, Zimbabwe. Nutrient Cycling in Agroecosystems 90, 87–103. doi:10.1007/s10705-010-9414-0

Rufino, M.C., Dury, J., Tittonell, P., van Wijk, M.T., Herrero, M., Zingore, S., Mapfumo, P., Giller, K.E., 2011. Competing use of organic resources, village-level interactions between farm types and climate variability in a communal area of NE Zimbabwe. Agr. Syst. 104, 175-190.

Rusinamhodzi, L., Corbeels, M., Zingore, S., Nyamangara, J., Giller, K.E., 2013. Pushing the envelope? Maize production intensification and the role of cattle manure in recovery of degraded soils in smallholder farming areas of Zimbabwe. Field Crop Res. 147, 40-53.
# Innovative participatory farming system design: combining on-farm crop/livestock trials with ex-ante trade-off analysis

Gatien N. Falconnier *±1*, Katrien Descheemaeker 1, Thomas A. Van Mourik 2 & Ken E. Giller 1

## 1 Introduction

In Southern Mali, farmers grow cotton for income generation, cereals for food self-sufficiency and keep livestock for draught power, milk, meat, manure, and buffer against risk. Due to increasing land and market pressure, farmers need to adapt to the decline of cotton sector (Coulibaly et al., 2015) and decreasing fodder availability for livestock. This study presents an innovative approach to design adaptive farming systems based on participatory on-farm crop/livestock trials and ex-ante analysis, using farmers' input at every stage of the process.

## 2 Materials and Methods

An iterative learning cycle of testing and refining seven options for sustainable intensification was applied in the Koutiala district with farmers belonging to four farm types: High Resource Endowed farms with Large Herds (HRE-LH), High Resource Endowed farms (HRE), Medium Resource Endowed farms (MRE), and Low Resource Endowed farms (LRE). The options were co-designed by farmers and researchers and each contained two to four treatments. For maize, sorghum and groundnut, improved varieties combined with fertilizer and manure were compared with farmer practice. For soybean and two improved varieties of cowpea, inoculation (soybean) and addition of P (cowpea and soybean) was compared with a control with no input. Two other options included cereal/legume intercropping and stall feeding of lactating cows during the dry hot season. This basket of options was tested by 12, 121, and 132 farmers in 2012, 2013 and 2014 respectively in a total of 451 on-farm trials. Farmer practice for maize, sorghum and groundnut, and soybean and cowpea were assessed based on yield and gross margin (revenue-variable costs). Treatments with extra input (e.g. inoculation, addition of mineral fertiliser, cotton seed cake) were assessed based on yield increase, return to investment and probability to generate profit (based on spatial variability in trials). Participatory Analysis of Variance (PANOVA) of yields in trials was carried out and farmers were asked to indicate possible reasons for the yield differences observed in contrasting trials. After the field visits, a productivity and profitability analysis was discussed with 30 farmers who were asked to indicate options and specific treatments they preferred. Scenarios integrating these preferred options were designed with 12 farmers (three per farm type) during individual sessions and assessed with a simple farm trade-off analysis linking crop area and yields to total production and total income from crops and lactating cows. The trade-off analysis was refined based on farmers' feedback, measured yields and results from soil analyses. Yields were averaged per soil type and previous crop when there was a significant effect or averaged across soil type and previous crop when there was no significant effect. Cowpea fodder was assumed to be fed to lactating cows in the stall (for HRE-LH and HRE farms) and surplus sold. Using the current cropping pattern of 37 farms, we performed ex-ante trade-off analysis of the farmer-designed scenarios and assessed the effect on average food self-sufficiency and average increase in net cash income per farm type.

## 3 Results – Discussion

Average grain yields with farmer practice were 1.83, 1.03 and 0.54 t ha⁻<fnref n="1" /> with an average gross margin of 191, 244, 527 USD ha⁻<fnref n="1" /> year⁻<fnref n="1" /> for maize, sorghum and groundnut respectively. Improved maize and sorghum varieties did not increase yields, while groundnut improved variety gave a 28% yield increase, a 1.46 return to investment and a 58% chance to generate profit. Soybean, cowpea grain variety and cowpea fodder variety with no inputs were more profitable (280, 311, 750 USD ha⁻<fnref n="1" /> year⁻<fnref n="1" /> respectively), but yielded less grain (0.41, 0.23 and 0 t ha⁻<fnref n="1" /> respectively) compared to maize and sorghum with farmer practise. Addition of P did not increase cowpea grain yield but gave a 126% yield increase for soybean, with a 1.7 return to investment and a 49% chance to generate profit. Mixing cowpea with maize gave a 4 and 13% decrease in maize yield, a 7.4 and 16.3 return to investment with a 60% and 76% chance to generate profit for the cowpea grain and fodder variety respectively. During the PANOVA, farmers indicated that soil type and previous crop could explain spatial yield variability. This perception, substantiated with a statistical analysis of trial results, allowed for identification of specific niches for intensification: (i) on clay soils after cotton or maize, soybean was two times more profitable than sorghum (ii) on gravelly and sandy soils, cowpea

<footnote n="1">¹ Plant Production System, Wageningen University</footnote>

<footnote n="2">² Hellen Keller International, Africa Regional Office</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: falconniergatien@yahoo.fr</footnote>
grain variety was 2 and 1.4 times more profitable than sorghum respectively, (iii) after cotton or maize, there was no maize grain yield penalty due to the intercropping with cowpea. HRE-LH farmers preferred the maize/cowpea option, HRE farmers the cowpea fodder variety option, MRE farmers the maize option and LRE farmers the cowpea grain variety option (Table.1).

<table><caption>**Table. 1.** Percent farmers who chose the option (all treatments taken together).</caption><thead><tr><th>farm type</th><th>n</th><th>maize</th><th>sorghum</th><th>maize/cowpea</th><th>sorghum/cowpea</th><th>cowpea grain</th><th>Cowpea fodder</th><th>Soybean</th></tr></thead><tbody><tr><td>HRE-LH</td><td>7</td><td>57</td><td>14</td><td>71</td><td>0</td><td>43</td><td>57</td><td>43</td></tr><tr><td>HRE</td><td>12</td><td>50</td><td>50</td><td>33</td><td>8</td><td>50</td><td>67</td><td>42</td></tr><tr><td>MRE</td><td>6</td><td>83</td><td>17</td><td>17</td><td>0</td><td>50</td><td>50</td><td>33</td></tr><tr><td>LRE</td><td>5</td><td>80</td><td>60</td><td>20</td><td>0</td><td>100</td><td>0</td><td>40</td></tr></tbody></table>

Stall feeding of lactating cows showed a four-fold increase in total milk yield and a doubling of total manure production compared to the farmer practice of free grazing, with a 0.4 return to investment. During participatory scenario design, HRE-LH and HRE farmers wanted to evaluate the combination of maize/cowpea intercropping with stall feeding of cows. MRE farmers wanted to assess substitution of sorghum by soybean, while LRE farmers were interested in substitution of sorghum by cowpea grain variety.

<figure><img src="image_267.png" /><figcaption>**Fig. 1.** Ex-ante trade-off analysis for HRE-LH farms (a), HRE farms (b), MRE farms (c) and LRE farms (d). FSS= household Food Self-Sufficiency, IW= net cash Income per Worker</figcaption></figure>

Ex-ante trade-off analysis showed that for HRE-LH farms, intercropping with cowpea on 60% of the maize area after cotton would allow to feed all the lactating cows in the stable during the dry hot period (results not shown) without compromising household food self-sufficiency (Fig.1a). For HRE farms who keep less cattle, this percentage would be reduced to 30%. In those two scenarios, income increase would be limited (Fig.1a&b) as the extra milk barely offsets the cost of cotton seed cake to feed the cow. However, 2 and 0.3 t of extra manure would be produced by HRE-LH and HRE farms respectively, and better reproductive performance of the herd would be achieved in the long term (de Ridder *et al.*, 2015). Intercropping 100% of maize with cowpea and selling the fodder produced beyond cows need would lead to a 20 and 42% income increase for HRE-LH and HRE respectively. MRE farms replacing 50% of sorghum by soybean (on clay soils after cotton) would increase their income by 30% while maintaining food self-sufficiency (Fig.1c). LRE farms replacing 30% of sorghum by cowpea grain (on gravelly and sandy soils) and selling cowpea fodder would increase their net IW by 28% without compromising food self-sufficiency (Fig.1d).

## 4 Conclusions

The trust built through regular interactions between farmers and researchers, combined with reflective on-farm testing, participatory appraisal and participatory scenario design and analysis led to the identification of farm type-specific promising pathways to agro-ecological intensification. Farmers were enthusiastic about results of the scenarios and some farmers who did not participate in the research buy improved grain and fodder variety cowpea seeds. Other farmers expressed their interest to expand the area allocated to soybean. Our work highlights the value of designing adaptive farming systems based on participatory on-farm crop/livestock trials and ex-ante analysis, using farmers' input at every stage of the process.

## References

* Coulibaly, J.Y., Sanders, J.H., Preckel, P.V. & Baker, T.G. (2015). Will cotton make a comeback in Mali? *Agric. Econ.* 46, 53–67.

* De Ridder, N., Sanogo, O.M., Rufino, M.C., van Keulen, H., Giller, K.E. (2015). Milk: the new white gold? Milk production options for smallholder farmers in Southern Mali. *Anim. Int. J. Anim. Biosci.* 1–9.
# Ecosystem services for West African farming families: the role of woody shrub mulch

Georges F. Félix<sup>*±1</sup>, Jean-Marie Douzet<sup>2</sup>, Marcel Ouédraogo<sup>3</sup>, Philippe Belliard<sup>1</sup>, Rabah Lahmar<sup>2</sup>, Cathy Clermont-Dauphin<sup>4</sup>, Johannes Scholberg<sup>1</sup>, Pablo Tittonell<sup>1,2</sup> & Laurent Cournac<sup>4</sup>

## 1 Introduction

Woody vegetation in semi-arid West Africa provides ecosystem services that benefit local livelihoods through provision and regulation of natural resources (Sinare and Gordon, 2015). Use of traditional fallows to restore soil productive capacity via organic matter accrualment has been compromised by population growth in West Africa and increased land degradation (Bonetti and Jouve, 1999). Continued crop cultivation and insufficient fallow periods have thus, led to severe soil organic matter depletion and subsequent soil degradation in semi-arid West Africa. This situation ultimately undermines food provision of local farm communities and may affect irreversibly ecosystem provision and regulation services within these landscapes. Improved use of local manure and compost as options to regenerate soils may be limited by availability (usually in homestead enclosures), transport constraints (dependent on donkey cart access and distance to fields), and labour requirements (harvest and application effort). As crop residues are vastly collected for livestock forage during the dry season, farming families in certain dryland areas are left with little sources of organic matter to regenerate soils. Innovative farmers in Burkina Faso have developed spatial and temporal shrub-crop arrangements by switching from a strategy of slash-and-burn to a strategy of slash-and-mulch to optimize services provided by *in situ* native woody shrub biomass (Félix, 2015). Increasingly, scientific evidence supports key roles that shrub vegetation types have in sustaining crop productivity and enhancing soil quality in the sub-region (Dossa *et al.*, 2013; Hernandez *et al.*, 2015; Lahmar *et al.*, 2012; Yélémou *et al.*, 2013). Event though woody shrub-based farming systems may have heterogeneous shrub densities, these provide soil water regulation services (Kizito *et al.*, 2007) and nutrient provision services through organic matter from branches and leaves (Ba *et al.*, 2014). In this context, if 100% available woody shrub biomass is the usual application by farmers as mulch, then two questions arise: (1) To what extent is crop productivity affected by *in situ* available mulch application? and (2) What is the effect of twice that dose of application on crop productivity?

## 2 Materials and Methods

Our study was located in Yilou, Burkina Faso (13°01' N, 01°32' W), where rainfall pattern is unimodal, with a rainy (and cropping) season usually occurring between June and September and total rainfall of 615 mm in 2013 and 653 mm in 2014. Eight on-farm plots of 300-900 m<fnref n="2" /> were established in areas with homogeneous distribution of *Piliostigma reticulatum* DC. Hoscht shrub types, following a randomized complete block experimental setup (n=4 in 2013; n=8 in 2014, two of which were on the same piece of land as in 2013). These plots were divided in three equivalent sections where standing woody shrub biomass was cleared, weighed, and applied as three fresh mulch treatments: no mulch (M0), 100% standing biomass (M1; one-third), and 200% standing biomass (M2; two thirds). Mulch application rates varied among plots since total standing biomass was heterogeneous between plots (Fig. 1). Planting dates varied from mid-June to mid-July during both years; sorghum (0.80 x 0.40 m) was intercropped with cowpea (0.80 x 0.40 m) using reduced tillage techniques and fertilizer application 21 days after sowing at 100 kg.ha⁻<fnref n="1" /> NPK (23-10-5). Sorghum (grain, straw) and cowpea (grain) yields were measured on three 8 m<fnref n="2" /> sized sub-plots per treatment at harvest (November 2013 and 2014). Two-sample inference Student paired *t*-test was conducted to analyse significance of treatment effects on crop yields.

## 3 Results - Discussion

Numerically, average results from 2013 and 2014 on-farm trials show that yields were highest when 200% biomass mulch (M2) was applied than with treatments of 100 (M1) and 0% (M0) available biomass application (Table 1). Highly significant statistical differences (p<0.005) were found for sorghum grain for treatment M2 compared to both M0 (73% yield increase) and M1 (35% yield increase). Significant statistical differences (p<0.05) were found for sorghum straw production for treatment M2 compared to M0 (50% increase) and M1 (24% increase). No statistical differences were perceived for cowpea production even though M2 presented 38% and 35% yield increases as compared to M0 and M1, respectively. Overall, these results evidence that woody mulch may contribute to increased crop yields (M2>M1>M0) as related to enhanced soil water use efficiency and reduced water losses (Yélémou *et al.*, 2014). Moreover, the contribution of biological activity (i.e. termites, fungi, bacteria) to enhance soil productive

<footnote n="1">1 Farming Systems Ecology Group, Wageningen University & Research Centre, The Netherlands</footnote>

<footnote n="2">2 UMR AIDA, Centre International de Recherche Agronomique pour le Développement, Montpellier, France</footnote>

<footnote n="3">3 Institut du Développement Rural, Université Polytechnique de Bobo-Dioulasso, Burkina Faso</footnote>

<footnote n="4">4 Eco&Sols, Institut de Recherche pour le Développement, Montpellier, France & Dakar, Senegal</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: georges.felix@wur.nl</footnote>
capacity and nutrient retention as drivers for chemical soil fertility increases may be evoked (Diedhiou-Sall *et al.*, 2013) and should be explored in further studies.

<table><caption>**Table 1.** Average yield results (kg.ha⁻¹) from 2013 and 2014 and yield differences amongst treatments for on-farm trials with mulch applications of 0, 100, and 200% available woody shrub (*Piliostigma*) biomass in Yilou, Burkina Faso</caption><thead><tr><th></th><th>Sorghum grain (n=12)</th><th>Sorghum straw (n=9)</th><th>Cowpea (n=10)</th></tr></thead><tbody><tr><td>Treatment M0 (0% biomass)</td><td>526.4 (222.7)<sup>a</sup></td><td>1124.9 (465.2)<sup>a</sup></td><td>376.1 (341.5)<sup>a</sup></td></tr><tr><td>Treatment M1 (100% biomass)</td><td>674.8 (321.7)<sup>a</sup></td><td>1353.7 (566.3)<sup>a</sup></td><td>384.0 (243.3)<sup>a</sup></td></tr><tr><td>Treatment M2 (200% biomass)</td><td>912.0 (361.3)<sup>b</sup></td><td>1688.9 (664.5)<sup>b</sup></td><td>519.8 (397.0)<sup>a</sup></td></tr><tr><td>Difference M1 - M0</td><td>148.4 (362.0)</td><td>228.8 (588.7)</td><td>7.9 (197.8)</td></tr><tr><td>Difference M2 - M1</td><td>** 237.2 (187.9)</td><td>* 335.2 (426.3)</td><td>135.7 (218.3)</td></tr><tr><td>Difference M2 - M0</td><td>** 385.6 (370.1)</td><td>* 564.0 (728.1)</td><td>143.7 (348.3)</td></tr></tbody></table>

Values are shown as Means (S.D.); Means within the same column followed by the same letter are not statistically different; (*) indicates differences are significant at p<0.05 and (**) indicates differences are significant at p<0.005, following two-samples paired Student t-test

Plotting sorghum grain yield increase (%) in function of actual *Piliostigma* dry matter application reveals increased yields with higher application of standing woody biomass availability and variability of crop response to mulching between fields (Fig. 1). These results suggest that there are no linear responses of sorghum to mulching with shrub branches and leaves and that increased crop productivity varies according to soil texture and landscape location (data not shown).

<figure><img src="image_268.png" /><figcaption>**Fig. 1.** Sorghum grain yield increase (%) as a function of *Piliostigma* shrub mulch application (n=8, 2014 yield data).</figcaption></figure>

## 4 Conclusions

Woody shrub mulch, based on above-ground *Piliostigma* biomass, at rates corresponding to *in situ* availability, had clear positive effects on crop productivity. Doubling mulch application rates revealed increased crop production as compared to no mulch and 100% mulch application. However, recommendations for optimal application rates to return highest benefits, in form of ecosystem provisioning services to farming families, requires further study and context adaptation, especially in regards to soil type. Agroecosystem services to dryland farming systems through shrub vegetation types should be further explored, both on farmer fields and experimental stations across West Africa.

*Acknowledgements.* This research is part of a collaborative effort for the Woody Amendments for Sudano-Sahelian Agroecosystems (WASSA) project, funded by the European Union (ERA-ARD-II). Authors are grateful to farming families of Yilou and field technicians, Oumarou and Zérôme.

## References

Ba, M.F., Samba, S.A.N., Bassene, E., 2014. Influence des bois rameaux fragmentés (BRF) de Guiera senegalensis J. F. Gmel et de Piliostigma reticulatum (Dc) Hochst sur la productivité du mil, Pennisetum glaucum (L.). Int. J. Biol. Chem. Stud. 8, 1039–1048.

Bonetti, C., Jouve, P., 1999. Jachères et systèmes agraires en Afrique subsaharienne. CORAF, Union Européenne, Dakar, Senegal.

Diedhiou-Sall, S., Dossa, E.L., Diedhiou, I., Badiane, A, N., Assigbetse, K.B., Samba, S. a N., Khouma, M., Sene, M., Dick, R.P., 2013. Microbiology and Macrofaunal Activity in Soil beneath Shrub Canopies during Residue Decomposition in Agroecosystems of the Sahel. Soil Sci. Soc. Am. J. 77, 501–511.

Dossa, E.L., Diedhiou, I., Khouma, M., Sene, M., Badiane, A.N., Ndiaye Samba, S. a., Assigbetse, K.B., Sall, S., Lufafa, A., Kizito, F., Dick, R.P., Saxena, J., 2013. Crop Productivity and Nutrient Dynamics in a Shrub-Based Farming System of the Sahel. Agron. J. 105, 1237.

Félix, G.F., 2015. From slash and burn to “slash and mulch.” Farming Matters 31.1, 14–17.

Hernandez, R.R., Debenport, S.J., Leewis, M.-C.C.E., Ndoye, F., Nkenmogne K., I.E., Soumare, A., Thuita, M., Gueye, M., Miambi, E., Chapuis-Lardy, L., Diedhiou, I., Dick, R.P., 2015. The native shrub, Piliostigma reticulatum, as an ecological “resource island” for mango trees in the Sahel. Agric. Ecosyst. Environ. 204, 51–61.

Kizito, F., Sène, M., Dragila, M.I., Lufafa, A., Diedhiou, I., Dossa, E., Cuenca, R., Selker, J., Dick, R.P., 2007. Soil water balance of annual crop-native shrub systems in Senegal’s Peanut Basin: The missing link. Agric. Water Manag. 90, 137–148.

Lahmar, R., Bationo, B.A., Dan Lamso, N., Guéro, Y., Tittonell, P., 2012. Tailoring conservation agriculture technologies to West Africa semi-arid zones: Building on traditional local practices for soil restoration. F. Crop. Res. 132, 158–167.

Sinare, H., Gordon, L.J., 2015. Ecosystem services from woody vegetation on agricultural lands in Sudano-Sahelian West Africa. Agric. Ecosyst. Environ. 200, 186–199.

Yélémou, B., Dayamba, S.D., Bambara, D., Yaméogo, G., Assimi, S., 2013. Soil carbon and nitrogen dynamics linked to Piliostigma species in ferugino-tropical soils in the Sudano-Sahelian zone of Burkina Faso, West Africa. J. For. Res. 24, 99–108.

Yélémou, B., Yaméogo, G., Koala, J., Bationo, B.A., Hien, V., 2014. Influence of the Leaf Biomass of Piliostigma reticulatum on Sorghum Production in North Sudanian Region of Burkina Faso. J. Plant Stud. 3, 80–90.
# Climate Change Impacts and Food Production in Sub Saharan Africa

Adedugbe Adebola*±1

## 1 Introduction

The agricultural sector (in the broad sense, including forestry, animal production, aquaculture, etc.) represents the dominant part of the economy in most countries and provides the majority of employments and livelihoods. Agriculture has and according to most observers, will continue to have a central role to play in the development process of the African continent. As the global population continues to grow, diet changes associated with rising incomes drive greater demand for food and other agricultural products, while global food systems are increasingly threatened by land degradation, climate change and other stressors. Ultimately, climate change is about human acting in socio-ecological settings in which biophysical, socio-cultural, economic, institutional, political and legal mechanism operate. Agriculture must change to meet both rising demand and become ecologically sustainable. Approaches with the potential for informing and guiding policy and practices are imperative. One of these approaches is Ecosystem-based adaptation (EbA), which provides flexible, cost effective, and broadly applicable alternatives for building robust food systems on less inputs and reducing the impacts of climate change. Practices such as agro-forestry, buffer strips, on-site water conservation, use of native species, etc., have demonstrated that ecological based approaches can provide just one right framework for catalyzing transformative change on a larger scale. If the critical impacts of climate change are not addressed, the impact on ecosystem will be numerous, placing added pressure on already limited land space and natural resources. This paper provides a short description of the potential influence of climate change, variability on food systems and local adaptation strategies as it affects food production.

## 2 Materials and Methods

Adaptation strategies that can be implemented during droughts serve as a foundation for planning response to future climate change. Climate change is already affecting the livelihoods of West African smallholder farmers who rely on rain-fed agricultural techniques, and it is expected to make food shortages more acute as the region's Population continues to grow. Farmers in the region are trying to cope with irregular rainfall, flooding and degraded soil and we must recognize the potential to reduce some of the effects of climate variability and change. With the shift towards sustainable development goals (SDG) to replace the Millennium Development Goals (MDGs) after 2015, approaches that severe multiple purpose and provide cross-cutting benefits are highly needed in Africa and elsewhere. For example, achieving food security is unmanageable without adaptation to climate change measures and practices that not only support farmers in producing enough food to meet people's nutritional needs, but that also preserve ecosystems from degradation. The infusion of the indigenous knowledge and the scientific views and cross-cutting initiatives at the local and national levels has led to restoration of both terrestrial and marine/aquatic species. A range of specific techniques were adopted to enhance climate change adaptation, among these Payment for Ecosystem services (PES), preservation and promotion of indigenous species and sustainable harvesting practices, afforestation and mangrove rehabilitation, water system rehabilitation (including reservoirs, wastewater reuse, and early maturing and drought resistant crop adaptation were among the most successful projects implemented.

## 3 Discussion

As the global Population climbs steadily towards 9billion, natural systems that support us all may not be able to withstand the pressure that this growth exerts. Water scarcity, land degradation and the loss of natural (ecosystem) services we all depend on, point to fundamental problems caused by unsustainable development. The direct causes of inadequate food access are poverty, environmental stressors and conflict. Catastrophes like floods, earthquakes, drought and conflict in vulnerable countries force the poor to abandon their homes and livelihoods, creating even more victims of hunger. It is in this complex system that disasters emerge and that society has to cope with. Human and food security within the context of climate change remains relatively under explored. It has now been widely established that the pervasive societal emphasis on the modes and volume of food production in developing countries has been detrimental to resolving problems relating to food distribution, affordability and accessibility. The singular focus on production has consequently amplified food insecurity in many parts of the world. Agriculture must change to meet both rising demand and become ecologically sustainable. Agriculture has significant linkages to poverty and hunger, nutrition and health, peace and security and preserving the world's natural resources. Variability in climatic conditions has been argued to be a stumbling block to food security in most developing countries and especially in Sub-Saharan Africa. This is because, Sub-Saharan Africa already experiences high temperatures and low (and highly variable) precipitation; second, because

<footnote n="1">¹ Farm Ideas Nigeria (FIN), 10 Umme street off Gonder street wuse zone 1 Abuja – Nigeria</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: bolaa2001@yahoo.com</footnote>
the economies are high dependent on agriculture and; third, because there is low adoption of modern technology.
Extreme poverty, hunger and undernourishment can be eradicated by 2030 while protecting and even reversing harm to
natural resources, despite the challenges of climate change and weather extremes.

## 4 Conclusions

Further, the search for synergies should intensify as governments at different levels (International, national and local) engage private and citizen groups to identify opportunities for resource optimization through efficient use of environmental and budgetary resources. How equipped is the scientific community to capture changes in human behaviour (such as cropping practices), analyze their impact on bio-physical processes and build capacity of governments to predict and respond to environmental shocks and stresses (examples: decline in soil fertility and air quality. We must also realize that smallholder farmers play a key role when it comes to ensuring food for all and climate change and hence need our help. A more integrated approach is needed which recognizes the impacts of; and relationship between agriculture and other development activities. By neglecting the management of natural resources, unsustainable pro-poor land and water allocations, which increase resource efficiency, our ability to as a global community to meet future food needs and address climate change may be compromised. With the anticipated impacts on coastal ecosystem, from climate change, it is vital that measures be put in place to ensure measures for resilience, to allow a system to absorb and recover from the effects of a hazardous event and maintain its essential functions and structures.

## References

* Amthor, JS; 2001 Effects of atmospheric CO₂ concentration on wheat yield. Brandshaw, B; Dolan,H; 2002. Farm level adaptation to climatic variability and change. Clover,J; 2003. Food security in Sub-Saharan Africa.Conference Report; First food Security and Adaptation conference, 2013.

* Food and Agricultural Organization, 2001; State of food insecurity in the world, Rome.

* Food and Agricultural Organization (2010) Climate Change implications for food security and natural resources management in Africa. Twenty- sixth regional conferences for Africa.

* Intergovernmental Panel on climate change (2001) climate change 2001: impacts, adaptation and vulnerability. IPCC Working group 11, Third assessment Report. McCarthy JJ,Canzaianiof.

* Jones PG, Thornton PK (2003.). The potential impacts of climate change on maize production in Africa and Latin America in 2055. Global Environ Change 13:51-59.

* Kurukulasariya P et al (2006) Will Africa agriculture survive climate change? United States Agency for International development (1992) Policy Determination: Definition of food security, PD-19. USAID, Washington DC, 4 PP.
# Assessing soil water trajectories and WUE: A multi-year modeling approach to design resilient cereal-legume rotations in the dry areas

Hélène Marrou *±1*, Michel Edmond Ghanem 2, Hatem Belhouchette 3, Carina Moeller 4 & Thomas R. Sinclair
5

## 1 Introduction

Cropping system simulation can address the complex and interactive nature of resilience and allows for biological (crop growth and development), physical (soil-water dynamics), chemical (soil carbon and N turnover), and management-related (e.g. crop choice, applications of nitrogen fertilizer, timing of sowing) aspects of resilience to be quantified. Soltani & Sinclair (2012) recently presented a non-calibrated, mechanistic, simple model, called Simple Simulation Model (SSM), which uses a generic approach for simulating the growth and yield of cereal and legume crops. The robustness of the legume and cereal versions of SSM has been demonstrated for a wide range of environments and various species including wheat (Soltani *et al.*, 2013) and chickpea (Vadez *et al.* 2013). In cropping systems of the semi-arid Mediterranean region, variable and deficient rainfall and drought episodes are primary constraints to productivity. Under these conditions, maximizing water-use efficiency (WUE, defined as the ratio of yield per unit evapotranspiration) is critical. Hence the accurate prediction of soil-water dynamics and crop-water relations is required to identify management strategies that increase the use of scarce rainfall and its conversion into grain yield. We applied SSM to examine rotations including wheat and chickpea that are representative of rainfed environments of the southern and eastern Mediterranean.

## 2 Materials and Methods

Simulations were carried out for Tel Hadya, Syria. The site (36°01′N, 36°56′E; elevation 284 m) has a semi-arid Mediterranean climate. Firstly, SSM was parameterized to simulate the growth and development wheat and chickpea crops and the soil water dynamics as observed in experiments conducted in 1998/99 and 1999/2000 at Tel Hadya (Moeller *et al.* 2007). Secondly, data from a longer term two-course rotation experiment established in 1983 at Tel Hadya (Harris, 1990) were used to assess the ability of the model to simulate long-term soil water dynamics and crop productivity as observed under field conditions. In the rotation experiment, wheat (*Triticum turgidum* ssp. *durum*, cv. Cham3) was rotated with wheat, annual fallow, and chickpea (*Cicer arietinum*, cv. Ghab2), and crop productivity and the soil-water dynamics were quantified under different N fertilizer regimes from 1986 to 1998 (12 crop cycles). For the purpose of simulating successive rotational cycles, a new version of the model (SSM-Rotation) was designed. In SSM, the soil water status is calculated daily as the amount of transpirable soil water (ATSW) using the water balance equation for an expanding volume of soil, corresponding to the addition of the soil layer explored by roots on that given day. Finally, SSM-Rotation was applied to simulate a continuous 30-year period and compare three long-term rotations of Wheat-Fallow (used as reference), Wheat-Chickpea and Wheat-Wheat-Chickpea. Simulations were run with no nitrogen stress effect on wheat; chickpea is supposed to be self-sufficient in Nitrogen. The performance of rotations is discussed regarding yield, in-crop WUE (WUE1), and the WUE calculated over all cycles of the rotation (WUE2). For the sake of uniformity, the wheat yield equivalent (WYE) of chickpea was calculated as: WYE = (Yield of Chickpea x (Price of Chickpea/Price of Wheat)) (Chetty & Reddy, 1987).

## 3 Results - Discussion

Overall, the model was able to simulate wheat and chickpea yields, total biomass, and the soil water dynamics as reported for contrasting conditions in Moeller *et al.* (2007) (Fig.1 A-D). In addition, SSM-Rotation simulated the long-term soil-water dynamics in rotations with reasonable accuracy (Fig.1E). The predictive abilities of SSM-Rotation demonstrated here showed that the model is robust enough to be applied to explore indicators of cropping systems resilience such as WUE.

<footnote n="1">1 UMR SYSTEM, Montpellier SupAgro 2, place Viala - 34060 Montpellier Cedex 2, France</footnote>

<footnote n="2">2 Crop Phsyiology Laboratory, North-Africa Platform, International Center for Agricultural Research in the Dry Areas (ICARDA), Morocco</footnote>

<footnote n="3">3 Institut Agronomique Mediterranéen de Montpellier (IAMM), France</footnote>

<footnote n="4">4 Tasmanian Institute for Agriculture, University of Tasmania, Australia</footnote>

<footnote n="5">5 Department of Crop Sciences, North Carolina State University (NCSU), USA</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: helene.marrou@supagro.fr</footnote>
<figure><img src="image_269.png" /><figcaption>**Fig. 1.** (A-D) Simulated (curves) and measured (closed circles) of biomass chickpea and wheat grown at Tel Hadya: (A) rainfed chickpea, 1998-99, (B) irrigated chickpea, 1999-00, (C) rainfed wheat, 1998-99, with a nitrogen (N) fertilizer rate of 60 kg N.ha⁻¹, and (D) irrigated wheat, 1999-00, with a N fertilizer rate of 100 kg N.ha⁻¹. (E) Simulated (curves) and measured (closed blue circles) of actual transpirable soil water in the rootzone (ATSW-RZ) in a rainfed wheat-chickpea rotation without N fertilizer applications.</figcaption></figure>

Although wheat WUE was increased in wheat/fallow rotation compared to diversified rotations, the total WUE of the rotation (WUE2, Table 1) was lower in the fallow rotation due to important evaporative losses during the fallow period. The 3 years wheat/wheat/chickpea rotation was the most productive and the most efficient regarding total water use (WUE2) as it combined i) a high proportion of high economic value wheat, ii) reduced evaporative losses due to ground cover every year.

<table><caption>**Table 1.** Simulated mean yield and WUE in three rotation schemes</caption><thead><tr><th>Rotation scheme</th><th>Wheat yield (t.ha<sup>-1</sup>)</th><th>Chickpea yield (t.ha<sup>-1</sup>)</th><th>Wheat WUE</th><th>Chickpea WUE</th><th>WUE1 (in-crop)</th><th>WUE2 (over entire rotation)</th></tr></thead><tbody><tr><td>Wheat/Chickpea</td><td>1.42</td><td>0.89</td><td>0.53</td><td>0.39</td><td>0.42</td><td>0.30</td></tr><tr><td>Wheat/Fallow</td><td>1.80</td><td>-</td><td>0.60</td><td>-</td><td>0.60</td><td>0.26</td></tr><tr><td>Wheat/Wheat/Chickpea</td><td>1.62</td><td>0.75</td><td>0.62</td><td>0.32</td><td>0.50</td><td>0.37</td></tr></tbody></table>

## 4 Conclusions

The overall performance of the model was sensible, providing sufficient confidence in the simulation capabilities of SSM-Rotation for subsequent scenario analyses. The analyses will be expanded to different crops (lentil, barley), sowing dates, N rates, and crop maturity types (short and long-season).

*Acknowledgements.* This work was supported by the CGIAR Research Program on Grain Legumes.

## References

* Chetty, C.K. & Reddy, M.N. (1987). A general proposal for ranking intercrop treatments. *Indian Journal of Agricultural Sciences*, **57**, 64–65.

* Harris H.C. (1990). Productivity of crop rotations. In 'Farm Resource Management Program: Annual Report for 1989'. pp. 137–157. (ICARDA: Aleppo, Syria).

* Moeller, C., Pala, M., Manschadi, A.M., Meinke, H., & Sauerborn, J. (2007). Assessing the sustainability of wheat-based cropping systems using APSIM: model parameterization and evaluation. *Australian Journal of Agricultural Research*, **58**, 75–86.

* Soltani, A. & Sinclair, T.R. (2012). Modeling Physiology of Crop Development, Growth and Yield. CAB International, Wallingford, UK

* Soltani, A., Maddah, V. & Sinclair, T.R. (2013). SSM-wheat: a simulation model for wheat development, growth and yield. *International Journal of Plant Production*, **7**, 711-740.

* Vadez, V., Soltani, A., & Sinclair, T.R. (2013). Crop simulation analysis of phenological adaptation of chickpea to different latitudes of India. *Field Crops Research*, **146**, 1-9.
# Improving resource allocation in nitrogen constrained systems: acknowledging within and cross-farm variability effect on yield and NUE

Nascimento Nhantumbo *±1,2, John Dimes <fnref n="1" />, Miranda Mortlock <fnref n="3" />, Isaiah Nyagumbo ⁴ & Daniel Rodriguez <fnref n="1" />

## 1 Introduction

Sowing multiple fields is a common risk management strategy, implemented by poor resourced smallholder farmers of Central Mozambique. This is done in an attempt to take advantage of the growing climatic and cross-field biophysical variability, i.e., soil types, soil water holding capacity and also natural and management induced soil fertility patterns. However, engaging in this extensive farming practice comes at a cost, mainly because of the poor resource allocation strategies and limited supportive services to assist smallholder farmer's decision making process. Nowadays with the increasing promotion of conservation agriculture among poor resourced smallholder farmers in Sub-Saharan Africa (Giller et al., 2011) the resource allocation burden has increased specially with fertilizer use being increasingly advocated alongside, residues retention and legume incorporation. Therefore, finding best fit allocation strategies for crop residues and the limited amounts of nitrogen fertilisers across crops and fields within agro-ecologies, remains a question of research especially in the maize dominated cropping systems of central Mozambique. In this study we hypothesize that, there are site specific management practices and resource allocation strategies that are more likely to improve yields and increase resource utilization while helping farmers make an easy shift into more productive and labour saving farming systems.

## 2 Materials and Methods

To evaluate the effect of cross-farm variability on yields and NUE in rain fed N-constrained systems, maize and cowpea were grown as sole crops across three contrasting farming environments. The environments were represented by three soils of contrasting soil water holding capacities and starting soil-N, i.e. a fine sand (fSa) – with low starting N and low initial soil water, a sandy loam (SaL)- low starting N and high initial soil water, and a coarse sandy clay loamy (cSaCL) – with high starting N but low initial soil water. The crops were grown across two residue levels, 0 and 6t/ha, using giant thatching grass as an alternative to maize residues. Three levels of nitrogen application were used i.e. 0, 23 and 92 kg N/ha representing an unfertilized N-deficient (ND), limited N-application (LNA) and a non-limiting N-application (NLA) system. The soils fort he trials were selected from resource allocation maps developed by farmers during a two day participatory crop modelling workshop held in Macate (low rainfall) and Rotanda (High rainfall) during the 2013-14 cropping system.

## 3 Discussion

The response of maize and cowpea to high C/N ratio residues differed across, fields and levels of N-applied. In unfertilized N-deficient systems (ND), residue application significantly increased cowpea yields by 31.2% and 29.4% in the cSaCL and SaL in detriment of maize yield (Fig. 1). Although expected, no positive responses to residues were measured across all N-levels in the drier fSa. In maize, positive responses to high C/N residues were only measured under LNA and NLA in the wetter SaL and N-rich cSaCL. The poor maize response to residue application in ND systems suggests that, under this circumstances, the high C/N ratio crop residues would be better off if invested to improve the performance of the legume where short term benefits can be obtained. Here, residues a part from improving legume yields, they would also help build up soil organic matter and generate additional income for the households.

In terms of N-productivity, negative responses to N-application were measure in cowpea mainly resulting from poor N-translocation into grain above 23 kg N/ha. For maize however, contrarily to residues, significant responses to N-application were measured with increased N-application but responses differed across sites (figure 1). NUE have differed across sites and crops (Fig. 2). In maize, N-uptake and partitioning into grain yield was higher in the N-rich cSaCL and considerable wetter SaL where the good moisture regime appeared to compensate for the lower initial N content especially at high N application levels. In the N-rich cSaCL the low starting moisture content have undermined N-uptake and partitioning into grain specially when no residues where applied. Residue application was detrimental to increase N-responses in the N-rich cSaCL. Despite the measured differences in N-response, the best nitrogen use efficiency (NUE) for all tested fields, was measured under limited N-applications (Fig. 2). The poor response to high

<footnote n="1">¹ Queensland Alliance for Agriculture and Food Innovation (QAAFI), the University of Queensland PO Box 102 Toowoomba Qld (4350) Australia</footnote>

<footnote n="2">² Divisão de Agricultura, Instituto Superior Politecnico de Manica;</footnote>

<footnote n="3">³ School of Agriculture and Food Science (SAFS), the University of Queensland; ⁴CIMMYT-Harare</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: n.nhantumbo@uqconnect.edu.au</footnote>
N-applications can demonstrate that for this unreliable rainfall environments high inorganic N-application are agronomically risky (Sadras and Rodriguez, 2010) and unprofitable (Dimes, 2011) as no enough water is available for the crop to make a more effective use of the available N.

<figure><img src="image_270.png" /><figcaption>Fig. 1. Crop response to residue and fertilizer application across fields of contrasting water and starting nitrogen.</figcaption></figure>

<figure><img src="image_271.png" /><figcaption>Fig. 2. Relation between N-application levels with maize NUpE and NUE across tested environments</figcaption></figure>

## 4 Conclusions

Cowpea, positive response to high C/N ratio residues application in unfertilized ND systems, call for a shift in practice especially under N-deprived conservation agriculture, where this alternative resource allocation is more likely to help improve not only legume productivity but also the availability of nitrogen rich legume residues and consequently improve soil-N availability in the system. With regards to responses to N-application, despite significant crop yield increases being measured with increased N-application across tested farming environments, for the unreliable rainfall environments of Central Mozambique the best returns form N-application (NUE) are achieved with limited N-applications (LNA), except for wetter fields were despite decreasing NUpE if good soil moisture for N-translocation (NUtE) into grain is secured good NUE can be achieved at higher N-application rates. This results mean systems design and resource allocation in the region need to be tailored across-fields of contrasting moisture and N-levels within and across agro-ecologies so that farmers can improve their resource allocation and get the best returns out of their already limited resource pool.

## References

* Dimes, J. (2011). Formulating Crop Management Options for Africa's Drought-Prone Regions: Taking Account of Rainfall Risk Using Modeling Innovations as Key to the Green Revolution in Africa. (A. Bationo, B. Waswa, J. M. M. Okeyo, F. Maina and J. M. Kihara, eds.), pp. 785-794. Springer Netherlands.

* Giller, K. E., Corbeels, M., Nyamangara, J., Triomphe, B., Affholder, F., Scopel, E., and Tittonell, P. (2011). A research agenda to explore the role of conservation agriculture in African smallholder farming systems. *Field Crops Research* **124**, 468-472.

* Sadras, V. O., and Rodriguez, D. (2010). Modelling the nitrogen-driven trade-off between nitrogen utilisation efficiency and water use efficiency of wheat in eastern Australia. *Field Crops Research* **118**, 297-305.
# Application of farm typology to explore soil fertility variability and farm-specific nutrient management recommendations in smallholder farming systems in sub-Saharan Africa

Shamie Zingore *±<fnref n="1" />, Regis Chikowo <fnref n="2" /> & Mirasole Pampolino <fnref n="3" />

## 1 Introduction

Smallholder farms in sub-Saharan Africa (SSA) exhibit a high degree of soil fertility heterogeneity, and as consequence, crop productivity and crop yield response to applied nutrients varies considerably across fields (Zingore et al., 2007; Titonell et al., 2008). Sustainable crop production intensification in SSA will largely depend on the development of farm and field-specific nutrient management practices, rather than blanket recommendations across highly diverse farms. Farm typology is a useful tool to explore the wide diversity among smallholder farms to improve targeting of crop production intensification strategies (Chikowo et al., 2014). Key studies on smallholder farm typologies have shown consistent patterns of differences in soil fertility between farm categories, mainly associated with access and use of nutrient resources. Despite advances in the understanding of farm-level soil fertility and socio-economic heterogeneity, there remain major knowledge gaps in integrating available information to develop simple decision support tools that can be used to evaluate the agronomic and economic impacts of nutrient management practices at farm scale. Improved understanding of the interaction between farmers’ access to resources, soil types, management history and seasonal rainfall variability on maize productivity and economic viability of various soil fertility management options is an important step to develop meaningful and flexible recommendations that allow farmers to use scarce fertilizer and organic resources effectively.

## 2 Materials and Methods

We used the Nutrient Expert for Hybrid Maize (NE) model to explore the potential impact of various options for maize production intensification for various categories of farmers in Siaya, Western Kenya, and Wedza, Eastern Zimbabwe. Farm types generated from past studies were used as a basis for development of farm-specific maize production intensification nutrient management strategies. NE applies the site-specific nutrient management concepts to develop field- and farm-specific strategies to manage fertilizer N, P, and K. Determination of N, P, and K fertilizer requirement for a given field by NE is based on the relationship between the soil nutrient supply potential and the balanced uptake of nutrients at harvest and grain yield, which are predicted using the quantitative evaluation of the fertility of tropical soils (QUEFTS) model (Smaling and Janssen, 1993).

## 3 Results – Discussion

Resource-endowed farmers have access to large quantities of manure and mineral fertilizers, which contribute to higher soil fertility and crop productivity on their farms. Resource-constrained households use little or no manure and mineral fertilizers, and have limited capacity to invest in labour-demanding soil fertility management technologies. These farmers often have to rely on off-farm opportunities for income that are largely limited to selling unskilled labour to their resource-endowed neighbours. The variability in management practices by farmers has resulted in three main soil fertility classes that can be used for targeting soil fertility management technologies, characterized by potential response to fertilizer application as: (i) low-responsive fertile fields; (ii) high-responsive infertile fields; (iii) poorly responsive degraded soils cultivated for many years with little or no nutrient additions.

Analysis of nutrient management options using NE at the two study sites showed that current maize productivity was strongly related to farm resources endowment and soil fertility, with high resource endowment (HRE) category farmers achieving >3 t ha⁻<fnref n="1" /> compared to about 2 tha⁻<fnref n="1" /> and 1 tha⁻<fnref n="1" /> for medium resource endowment (MRE) and low resource endowment farmers, respectively. Despite water limited yields of >6 t/ha, farm-specific attainable yields were highly variable, depending on soil constraints. In Wedza, NE analysis showed that MRE and HRE farms can at least double maize productivity and net profits by increasing fertilizer use by about 100 and 50% respectively.

<footnote n="1">¹ Sub-Saharan Africa Program, International Plant Nutrition Institute</footnote>

<footnote n="2">² Crop Science Department, Faculty of Agriculture, University of Zimbabwe</footnote>

<footnote n="3">³ South-East Asia Program, International Plant Nutrition Institute</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: szingore@ipni.net</footnote>
<table><caption>Table 1. Analysis of the current maize production situation across farm types in Zimbabwe and Kenya</caption><thead><tr><th>Site</th><th>Resource endowment</th><th>Soil fertility status</th><th>Farm size</th><th>N-P-K applied</th><th>Manure applied</th><th>Maize productivity</th><th>Gross margin</th><th>Net profit</th></tr><tr><td></td><td></td><td></td><td>ha</td><td>Kg ha<sup>-1</sup></td><td>t ha<sup>-1</sup></td><td>t ha<sup>-1</sup></td><td>US$ farm<sup>-1</sup></td><td>US$ farm<sup>-1</sup></td></tr></thead><tbody><tr><td rowspan="3">Wedza, Zimbabwe</td><td>Low</td><td>Low</td><td>1</td><td>27-7-4</td><td>0</td><td>0.8</td><td>68</td><td>-213</td></tr><tr><td>Medium</td><td>Medium</td><td>2</td><td>54-14-7</td><td>2</td><td>1.9</td><td>512</td><td>-183</td></tr><tr><td>High</td><td>High</td><td>3</td><td>80-20-10</td><td>7</td><td>4.0</td><td>1,674</td><td>1,299</td></tr><tr><td rowspan="3">Siaya, Kenya</td><td>Low</td><td>Low</td><td>0.2</td><td>21-3-0</td><td>0</td><td>1.4</td><td>59</td><td>47</td></tr><tr><td>Medium</td><td>Medium</td><td>0.8</td><td>32-9-0</td><td>5</td><td>2.2</td><td>364</td><td>219</td></tr><tr><td>High</td><td>High</td><td>1.6</td><td>80-58-0</td><td>6</td><td>3.4</td><td>1,172</td><td>448</td></tr></tbody></table>

In Siaya, there was scope for MRE and HRE to increase maize yields and net profits with optimal N and P fertilizer rates, and including application of K. However, NE results suggested the need to increase fertilizer use under low and moderate soil fertility conditions, but reduce fertilizer application in the fertile soils on HRE farms in Siaya. At the two study sites, the poor soil fertility conditions on the LRE led to very low attainable yields of <2.5 t/ha. Due to small farms sizes and poor soil fertility conditions, the capacity for self-sufficiency in maize production was low for the LRE, even with optimal nutrient management. The need for managing drought risk in Wedza necessitated options for reducing target yields and reducing fertilizer application rates. Application of manure, or retention of maize residues reduced fertilizer P and K requirements, but had no effects on N requirements. The analysis conducted using NE showed that socio-economic and soil fertility diversity between smallholder farms had profound effects on crop productivity and nutrient management recommendations to optimize yields and profits. Simple approaches that integrate scientific data for refinement of nutrient management practices to suit different types of farmers are essential for developing practical options for sustainable maize production intensification in heterogeneous smallholder farming systems. Additional efforts to build the capacity of extension to apply the NE guidelines and test nutrient management options in an iterative process with farmers is required for wide-scale validation and dissemination of site-specific nutrient management recommendations.

<table><thead><tr><th>Site</th><th>Resource endowment</th><th>Soil fertility status</th><th>N-P-K applied</th><th>Maize productivity</th><th>Gross margin</th><th>Net profit</th></tr><tr><td></td><td></td><td></td><td>Kg ha<sup>-1</sup></td><td>t ha<sup>-1</sup></td><td>US$ farm<sup>-1</sup></td><td>US$ farm<sup>-1</sup></td></tr></thead><tbody><tr><td rowspan="3">Wedza, Zimbabwe</td><td>Low</td><td>Low</td><td>40-20-10</td><td>2.0</td><td>224</td><td>-61</td></tr><tr><td>Medium</td><td>Medium</td><td>100-38-22</td><td>4.0</td><td>864</td><td>88</td></tr><tr><td>High</td><td>High</td><td>120-63-32</td><td>7.0</td><td>4,743</td><td>3,172</td></tr><tr><td rowspan="3">Siaya, Kenya</td><td>Low</td><td>Low</td><td>58-25-20</td><td>2.5</td><td>96</td><td>82</td></tr><tr><td>Medium</td><td>Medium</td><td>100-40-32</td><td>4.5</td><td>712</td><td>435</td></tr><tr><td>High</td><td>High</td><td>50-32-23</td><td>6.5</td><td>2,467</td><td>1,652</td></tr></tbody></table>

## 4 Conclusions

Combining farm typology and the Nutrient Expert of Hybrid Maize model provided a practical framework for analysis of nutrient management options for maize production intensification under variable soil fertility conditions in smallholder farming systems in Zimbabwe and Kenya. The evaluation of current farmers management practices captured the opportunities and constraints that exit for farmers in different resources groups to improve crop productivity and income from maize production. Soil fertility conditions and current maize productivity was strongly influenced by access to land, manure and mineral fertilizers; resources that should be considered when developing strategies for maize production intensification.

*Acknowledgements.* The authors acknowledge the financial support from the CGIAR MAIZE Coordinated Research Program coordinated by the International Maize and Wheat Improvement Center (CIMMYT).

## References

Tiltonell, P., Vanlauwe, B., Corbeels, M. and Giller, K.E. (2008) Yield gaps, nutrient use efficiencies and response to fertilizers by maize across heterogeneous smallholder farms of western Kenya. Plant and Soil 313:19-37.

Zingore, S., Murwira, H.K., Delve, R.J., and Giller, K.E. (2007) Influence of nutrient management strategies on variability of soil fertility, crop yields and nutrient balances on smallholder farms in Zimbabwe. Agriculture Ecosystems and Environment 119: 112-126.

Chikowo, R., Zingore, S., Snapp, S., Johnston, A. (2014) Farm typologies, soil fertility variability and nutrient management in smallholder farming in Sub-Saharan Africa. Nutrient Cycling in Agroecosystems.100:1-18.

Smaling, E.M.A., Janssen, B.H., 1993. Calibration of QUEFTS: a model predicting nutrient uptake and yields from chemical soil fertility indices. Geoderma 59: 21-44.
# Bio-physical and Socio-economic Factors Influencing Farmers’ Decisions on Whether to Intercrop or Sole Crop Maize in Ethiopia

Abeya Temesgen *±1, Shu Fukai 2, Moti Jaleta 3 & Daniel Rodriguez 1

## 1 Introduction

Intercropping is a practice traditionally used by low input (low investment) low output (low productivity) small-scale farmers. For example maize intercropped with legume is commonly practiced by small scale farmers in both semi-arid and sub-humid agro-ecologies in Ethiopia. However, whether increases in the use of inputs would also require the replacement of intercropping systems by more sole cropping systems is not clear. As sustainable intensification practices and on-farm investment increases, driven by agriculture development programs, improved understanding of biophysical and socio-economic factors influencing farmers’ preference of cropping system is required. The main aim of this study was therefore to answer what are the key drivers for farmers’ choice of cropping in two environments of contrasting agro-ecological potential (i.e. rainfall) in Ethiopia.

## 2 Materials and methods

A total of 120 households were randomly selected from semi-arid and sub-humid agro-ecologies of Ethiopia in 2012. Sixty household heads were interviewed from each agro-ecology. Survey questions included questions related to available household resources, soil fertility, cropping systems and practices as well as farmer performance e.g. yields and incomes, and farmers’ perception on intercropping and sole cropping. Soil samples were collected at 0-0.15m depth from three representative sites at each agro-ecology to understand the soil properties of the study sites. Descriptive analysis was used to compare household’s characteristics within each agro-ecology. Independence sample t-tests were used to assess the difference between the two agro-ecologies. Household typologies were developed after identifying relevant variables from a principal component analysis that were used to cluster households into homogeneous groups. Similarity between each pair of individual households in cluster analysis was measured according to Euclidean distances and Ward’s method (Hair *et al.*, 1998). One-way ANOVA was used to test the significant difference between the identified clusters of households. The decision on farmers’ preference for each cropping system was further analysis using a Tobit regression model. All statistical analyses were carried out using SPSS and STATA.

## 3 Results – Discussion

**Bio-physical (climate and soil) factors:** The average in crop rainfall, the amount of fertilizer used for maize production and total soil N was significantly higher in sub-humid than in semi-arid environment while air and soil temperature, SOC, exchangeable K, exchangeable Ca and EC were significantly higher in semi-arid than in sub-humid of the survey areas. The average soil pH was found to be 5.9 in sub-humid while it was 7.2 in semi-arid. In contrast, the average tropical livestock unit and amount of manure used for haricot bean production as well as soil properties such as available soil P, CEC, exchangeable Mg and exchangeable Na were not significantly different between the study sites of semi-arid and sub-humid agro-ecologies.

**Socio-economic factors:** Average family size of sub-humid was significantly bigger than that of semi-arid. Older farmers and larger farm labour were reported in sub-humid than in semi-arid environment. Average household expenses in sub-humid was twice as high as in semi-arid environment.

In general, adopters of intercropping were 34% in semi-arid and 27% in sub-humid environments. Unlike to 60% of farmers were reported practicing intercropping in the semi-arid environment of Ethiopia (Katungi *et al.*, 2010), our result indicated that greater than 60% of farmers practice sole cropping in both semi-arid and sub-humid agro-ecologies.

**Cluster analysis:** Cluster analysis was separately computed for semi-arid and sub-humid environments. The analysis was performed using the loadings of principal component analysis. Farm types in general were clustered into three groups and the average values of biophysical and socio-economics indicators were used to describe each cluster type.

**Cluster analysis for semi-arid:** Households of cluster 1 use high amount of fertilizer to maximize their income from maize production. This farm type was made up of 22% of the total sample households and 38% of the cluster households were adopters of an intercropping system. In contrast, intercropping adopters of cluster 2 were 44% and this cluster comprises 42% of the total sample households. Households of cluster 2 use small amount of fertilizer for maize

<footnote n="1">¹ The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI) - QLD 4343, Queensland, Gatton, Australia</footnote>

<footnote n="2">² The University of Queensland, School of Agriculture and Food Sciences (SAFS) - QLD 4072, Queensland, St Lucia, Australia</footnote>

<footnote n="3">³ International Maize and Wheat Improvement Centre (CIMMYT) - P. O. Box 5689, Addis Ababa, Ethiopia</footnote>

<footnote>*Speaker</footnote>

<footnote>‡Corresponding author: abeya.tefera@uqconnect.edu.au</footnote>
production. In addition their annual average income is very low ($ 1,641, for example in the year 2012). They satisfy their subsistence food requirement from the amount of maize produced during crop growing season as compared to cluster 1 and cluster 3. Farmers grouped under cluster 2 are regarded as resource poor small scale farmers and they usually practice intercropping system. The main distinguishing features of cluster 3 are large household size and large crop land. The household survey data showed that cluster 3 comprises 36% of the total sample households and only 18% of their total households in cluster 3 adopted intercropping. In addition, households of cluster 3 had larger livestock holdings. Farmers of cluster 3 use higher amount of DAP for maize production. Additionally, they produce haricot bean as a cash crop to generate better income. Households of this farm appeared to be richer and they prefer sole cropping system than clusters 1 and 2.

**Cluster analysis for sub-humid:** In sub-humid, 42% of cluster 1 households were adopters of intercropping systems. This farm was made up of 32% of the sample households. Cluster 1 represents households with low input low output small scale farming system. Framers of this cluster appeared to be very poor. For example the average crop land size of this cluster was found to be smaller (1.52 ha) as compared to average farm size of cluster 2 (3.44 ha) and cluster 3 (3.62 ha), indicating farmers with smaller farm size are more likely to be adopters of intercropping systems. This is consistent with findings of Iqbal *et al*. (2006) who found association between farm size and intercropping adopters. Similar to their counterpart cluster 2 of semi-arid environment, the households of cluster 1 use low fertilizer for crop production. In addition, household heads of cluster 1 were less educated and their average household income and expenses were less as compared to cluster 2 and cluster 3. Households of cluster 2 of sub-humid were older, mature and well experienced in farming. Their family size is larger and they have large farm size. This cluster represents 30% of the total sample households and only 22% were adopters of intercropping systems. Cluster 3 households are educated and owners of large farms and several livestock. Their land is very fertile and they also apply more fertilizer (urea, diammonium phosphate and manure) for crop production. This cluster was made up of 38% of the sample households and few farmers of this cluster adopted (17%) intercropping systems.

In general, cluster 1 of sub-humid and cluster 2 of semi-arid were low input small scale farmers and relatively many of their households were adopters of intercropping system than clusters 1 and 3 of semi-arid and 2 and 3 of sub-humid.

**Factors affecting adoption of intercropping:** Further analysis was undertaken using Tobit regression model to understand farmers' decisions to adopt intercropping system over the sole cropping. In general, adopters of intercropping in sub-humid were older and matured household heads. They also had more livestock population, whereas non-adopters of intercropping had larger families and more availability of labour in sub-humid environment. In semi-arid environment, non-adopters of intercropping had more availability of labour, and larger farms. This result appears to indicate that, irrespective of the agro-ecology, larger and richer farmers prefer to sole crop compared to poorer smaller farmers. Similar to our findings, survey results from India (Rajasekharan and Veeraputhran, 2002) and Kenya (Hassen, 1996) showed a negative relationship between farm size and adoption of intercropping practices. Households with larger crop land were less likely to adopt intercropping systems, which was in agreement with farmer's perception.

**Farmer's perception on intercropping:** Farmers in semi-arid were more optimistic than in sub-humid on the benefit of intercropping in terms of improving soil fertility, better use of resource, reducing unstable grain price, animal fodder, reducing crop failure due to climate risk, contributing to cash sale, improving crop yield, contributing to food security, reducing cost of fertilizer application, increasing soil organic carbon, reducing soil loss due to erosion, reducing weed competition, providing lodging resistant, and reducing insect pests and crop diseases.

## 4 Conclusion

The result of this study showed that farmer's choice to adopt intercropping system had an association with bio-physical and socio-economic factors. It has been shown that resource poor small scale farmers were more likely to adopt intercropping than resource rich farmers. In addition, farmers cropping in low soil fertility under low rainfall and high temperature conditions tend to intercrop than farmers in potential environment. In both agro-ecologies, households with larger farm size and more availability of labour were less likely to adopt intercropping system.

*Acknowledgements:* This study was funded by the AusAID- African Development Project. We greatly appreciate the support from Australian government and staffs of Bako and Melkassa Agricultural Research Centres. We also thank development agents and farmers of Bako Tibe, Sibu Sire, Boset and Adami Tulu districts for their support and valuable information they shared with us during the household interview.

## References

Hair, J.F., Andrson, R.E., Tatham, R. L. and Black, W.C. (1998). Multivariate Data Analysis, Fifth Edition. Blake, W. and Larkin, J. (eds). Prentice-Hall PTR, USA, pp 730.

Hassen, R.M. (1996). Planting strategies of maize farmers in Kenya: a simultaneous equation analysis in the presence of discrete dependent variables. *Agricultural Economics* **15**:137-149.

Iqbal, S.M.M., Ireland, C.R. and Rodrigo, V.H.L. (2006). A logistic analysis of factors determining the decision of smallholder farmers to intercrop: A case study involving rubber-tea intercropping in Sri Lanka. *Agricultural System* **87**:296-312.

Katungi, E., Farrow, A., Mutuoki, T., Gebeyehu, Karanja, D., Alamayehu, F., Sperling, L., Beebe, S., Rubyogo, J.C. and Buruchara, R. (2010). Improving common bean productivity: An Analysis of socio-economic factors in Ethiopia and Eastern Kenya. Baseline Report Tropical legumes II. Centro Internacional de Agricultura Tropical - CIAT. Cali, pp 1-126, Colombia.

Rajasekharan, P. and Veeraputhran, S. (2002). Adoption of intercropping in rubber smallholding in Kerala, India. *Agroforestry System* **56**:1-11.
# Institutionalizing Systems Approaches for Improving Agricultural Livelihoods in an Arid Ecoregion of South Asia

Shalander Kumar *±1*, Anthony Whitbread ¹ & Thiagrahaj Ramilan ¹

1 Resilient Dryland Systems, International Crop Research Institute for Semi-Arid Tropics (ICRISAT), Patancheru 502 324, INDIA

## 1 Introduction

The arid agro-ecosystems of South Asia are affected by severe resource degradation, low and unstable farm-based livelihoods and persistent poverty. In these environments, a major research program, the CGIAR Research Program (CRP) Dryland Systems, is underway that utilises multi-disciplinary and systems approaches that build on the indigenous coping and adaptation strategies. The objectives of this study were: to identify relatively homogeneous farm typologies among dryland farmers in the extensive to intensive agricultural systems of Indian Thar desert to target context specific technologies for increased impacts, and prioritize interventions across different farm typologies and facilitate appropriate institutional machanism for future trajectory development for resilience building and or intensification.

## 2 Materials and Methods

In a study based in Western Rajasthan in India, 250 farm households were randomly selected along the rainfall gradient (Jodhpur, Barmer, Jaisalmer districts) and surveyed using survey techniques and focussed group discussions (FGDs). Built farm-system typologies based on key livelihood assets that helps to explicitly understand the potential, expectation and the limitations of farms and thus develop a “recommendation domain”, which can be defined as: “a group of farm-systems, relatively homogenous, with similar circumstances, and for which we can make more or less the same recommendation (Giller 2013). A multivariate approach was used to exploit the large number of recorded variables in the most efficient way. Statistical analysis was carried out by using Principal Component Analysis (PCA) and Cluster Analysis (CA) (Usai et al., 2006; Rufino et al., 2013; Riveriro et al., 2013). Prior to building the farm typologies the major constraints to the farming systems for the each selected village were identified based on FGDs with farmers and stakeholders consultations. Corresponding possible interventions based on the available resources and technologies were identified during the multistakeholder Innovation Platform workshop. In the next stage, the major factors constraining agricultural production and farmers’ livelihoods were prioritized for each farm typologies by using pairwise comparisons of different constraints with farmers (men and women) group. The corresponding interventions identified by the multistakeholders innovation platform and the farmers groups were also prioritized using the same method. Thereafter ex-ante assessment of priority options was carried out on farm typologies. Based on the above analysis the best fit options were assessed on-farm as components of the integrated agro-ecosystem, targeting resilience and intensification at different scales: field, farm and landscape. Enabling institutional mechanism, enhance stakeholders’ capacity to innovate and strengthening value chains were the key components of the systems approach.

## 3 Discussion

The values of coefficient of variation (CV) in the crop yields and net returns per standard animal unit indicated very high variability across farm households during the same agricultural year; which was not related to landholding size. It indicates that there might be a number of livelihood assets other than landholding-size which could differentiate farm households in terms of their capacity to make proper use of resources to produce and to adopt new interventions and technologies. As part of characterization we looked at both the farm structure and function. The socially diverse and spatially heterogeneous households were grouped into four broad farm typologies based on multiple livelihood assets using multivariate analysis: 1) Rainfed extensive crop-livestock medium farms; 2) Semi-irrigated intensive diversified medium; 3) Rainfed extensive livestock off-farm income based small; 4) Irrigated semi-intensive off-farm income based small. For each typology, the common structures and functions were developed (Table 1) and constraints prioritized using participatory tools. The magnitude of most of the 32 livelihood assets was significantly different across the farm typologies underlining the need for such clustering. Based on ex-ante assessment and farmers' preferences for promising options/system components, potential interventions were prioritized and implemented in participatory mode engaging innovation platform and community. Farmers’ perceptions of constraints and priorities for potential interventions differed across typologies. For example an ex-ante analysis of agro-silvi-horticulture systems demonstrated higher net-returns by 1.5 to 2 times in typology 2 and 4 and 2 to 4.5 times in typology 1 and 3.
Besides the typology specific-technical interventions which were implemented through >250 on-farm trials, other important system interventions are underway including: institutional mechanisms for managing natural resources base

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: k.shalander@cgiar.org</footnote>
(e.g. common property resources- pastures and water); value chain approaches for improving market access (fruits and medicinal plants) and information; the establishment of long term multi-institutional partnerships to influence policy and up-scaling. A village development committee facilitated to be evolved in each action village was involved in planning and implementing interventions as part of systems approach. At districts/region level, a multiple stakeholders Innovation platform contributed in planning of need based interventions and enhancing linkages and convergence for upscaling. All relevant actors; farmers, government departments, researchers, NGOs, industry and development institutions were appropriately involved for enhancing economic viability and resilience of the farming systems.

<table><caption>**Table 1.** Structural and functional characteristics of households under different farm typologies</caption><thead><tr><th>Characteristics</th><th>Typology 1</th><th>Typology 2</th><th>Typology 3</th><th>Typology 4</th><th>Probability value</th></tr></thead><tbody><tr><td><strong>Structural characteristics</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>-Landholding size cultivated, ha</td><td>6.2</td><td>6.0</td><td>3.3</td><td>3.2</td><td>0.0696<sup>ns</sup></td></tr><tr><td>-Land labour ratio, ha per adult person</td><td>1.7</td><td>1.4</td><td>0.7</td><td>1.0</td><td>0.0417*</td></tr><tr><td>-Standard animal Units (SLU), No.</td><td>5.0</td><td>6.2</td><td>5.1</td><td>5.1</td><td>0.0112*</td></tr><tr><td>-Number of months own produce support farm family</td><td>5.4</td><td>10.4</td><td>3.8</td><td>5.9</td><td>&lt;.0001**</td></tr><tr><td>-Number of crops grown</td><td>2</td><td>5</td><td>4</td><td>2</td><td>&lt;.0001**</td></tr><tr><td>-% income from off/non-farm earnings</td><td>56</td><td>25</td><td>52</td><td>46</td><td>&lt;.0001**</td></tr><tr><td>-Number of livelihood strategies</td><td>3.1</td><td>2.9</td><td>3.3</td><td>3.5</td><td>0.0423*</td></tr><tr><td>-Status of feed availability (months of sufficiency)</td><td>6</td><td>10</td><td>5</td><td>8</td><td>&lt;.0001**</td></tr><tr><td>-Amount borrowed from bank/financial institutions, US$</td><td>313</td><td>3196</td><td>2999</td><td>853</td><td>&lt;.0001**</td></tr><tr><td>-Average distance of input market, km</td><td>15</td><td>17</td><td>7</td><td>3</td><td>&lt;.0001**</td></tr><tr><td>-Total investment in past 5 years, US$</td><td>319</td><td>7876</td><td>758</td><td>1324</td><td>&lt;.0001**</td></tr><tr><td>-No. of times the farmers visit the extension officials/office</td><td>0.3</td><td>0.2</td><td>0.0</td><td>0.4</td><td>0.4623<sup>ns</sup></td></tr><tr><td>-Women headed households (%)</td><td>14</td><td>0.0</td><td>17</td><td>0.0</td><td>0.0211*</td></tr><tr><td>-Households opt for out migration (%)</td><td>24</td><td>3</td><td>33</td><td>13</td><td>0.0121*</td></tr><tr><td><strong>Functional characteristics</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>-Manure Applied, Kg/ha</td><td>71</td><td>2035</td><td>297</td><td>122</td><td>&lt;.0001**</td></tr><tr><td>-Quantity of Fertilizer-Urea used kg/ha</td><td>1</td><td>82</td><td>11</td><td>16</td><td>&lt;.0001**</td></tr><tr><td>-Quantity of Fertilizer-DAP used kg/ha</td><td>1</td><td>65</td><td>7</td><td>17</td><td>&lt;.0001**</td></tr><tr><td>-Access to bore-well for irrigation (% households)</td><td>14</td><td>94</td><td>15</td><td>0</td><td>&lt;.0001**</td></tr><tr><td>-Access to khadins (% households)</td><td>28</td><td>0</td><td>0</td><td>0</td><td>&lt;.0001**</td></tr><tr><td>-Access to canal for irrigation (% households)</td><td>4</td><td>0</td><td>0</td><td>100</td><td>&lt;.0001**</td></tr></tbody></table>

Note: NS- Not significant at 0.05; * significant at 0.05 level; ** significant at 0.01 level

## 4 Conclusions

This paper aims to share the methods and processes of designing resilient farming systems to improve livelihoods under the drylands in South-Asia. Our analysis proved that the dryland smallholder farming systems in south Asia occur within diverse agro-ecological and socio-economic environments and develop different livelihood strategies driven by opportunities and constraints encountered. Multiple livelihood assets determine different land use patterns and agricultural management practices. Well-designed household survey on socio-economic and agro-ecological variables and statistical approach helped capture the diversity of livelihood assets to categorize households into homogenous farm typologies. The follow up FGDs with farmers were equally important to validate the farm typologies and prioritizing the constraints in each typology. The analysis makes a strong case for revisiting the method/ criteria for grouping the farmers for targeting technological and livelihood interventions in arid and semi-arid ecoregions of South Asia. Engaging the innovation platform for identification of possible options and their prioritization at district level; farmers for each farm typology, and ex-ante assessment of promising options led to the on-farm assessment of farm typology specific most appropriate interventions in the action villages. The institutional mechanism being experimented at village to regional level has strengthened the capacity of the community/stakeholders to improve the farming systems resilience and economic viability. An ex-post assessment will be undertaken in these communities to assess the impact. This study contributes to the understanding of how research for development through technology targeting for trajectory development can contribute towards stabilizing farm incomes, sustainable intensification and smoothening livelihood of resource poor farmers in vulnerable dry regions.

*Acknowledgements.* The authors are grateful to the CGIAR research program - Dryland Systems for financial support

## References

Giller, K.E. (2013). Can we define the term ‘farming systems’ A question of scale. *Outlook on agriculture*, **42**, doi 10.5367/oa.2013.0139

Riveiro, J.A., Mantecón, A.R., Álvarez C.J. & Lavine, P. (2013). A typological characterization of dairy Assaf breed sheep farms at NW of Spain based on structural factors. *Agricultural systems* **120**, 27-37

Rufino, M.C., Thornton, P.K., Ng'ang'a, S.K., Muitie, I., Jones, P.G., Van Wijk, M.T. & Herrero, M. (2013). Transition in agro-pastoralist systems of East Africa: impacts on food security and poverty. *Agriculture ecosystems and Environment* **197**, 215-230

Usai, M.,G., Casu., S., Mille, G., Decandia, M., Ligioos, S., Carta, A. (2006). Using cluster analysis to characterize the goat farming system in Sardinaia. *Livestock science*, **104**, 63-76
# What level of detail in input data and crop models is required for food production studies in West Africa?

Katharina Waha<sup>±1,2</sup>, Neil Huth<sup>3</sup>, Peter Carberry<sup>3</sup> & Enli Wang<sup>4</sup>

## 1 Introduction

Modeling agro-ecosystems aims at describing and understanding relevant plant processes and their interactions with abiotic and biotic factors and future behavior of the system. Crop models are common tools for this task but various uncertainties exist; in the model design and model parameters, and maybe even more important in input data. While one might argue that the higher the resolution of the model input the better and the less uncertain the model output, this might not be true as models are developed for different scales and the aggregation level of the model output differs.

## 2 Materials and Methods

We analyze the performance of the point-scale model APSIM and the global scale model LPJmL in the maize-growing areas of Burkina Faso. We test the models' response to different levels of input information from little to detailed information on soil, climate and agricultural management and compare the models' ability to represent the observed spatial and temporal variability in crop yields. We simulate grid-cell and national maize yields between 1961 and 2000 with APSIM and LPJmL. We compare simulated maize yields with different input data for climate, soil and sowing dates (Table 1). Up to eight combinations of different input settings are possible for the two crop models. Even though some settings might not be very practicable for an actual model application they represent the upper and lower level of information / resolution of input data available for the study area.

<table><caption>Table 1. The level of information in soil, climate and management data used in APSIM and LPJmL simulations.</caption><thead><tr><th>Level of information</th><th>Climate</th><th>Soil</th><th>Management - Sowing Date</th></tr></thead><tbody><tr><td>low</td><td>CRU TS3.0;<br>Simple, grid-cell specific monthly climate data<br>(Mitchell & Jones, 2005)</td><td>FAO/IIASA-v1.2;<br>Multiple, grid-cell specific soils from global soil map<br>(Nachtergaele et al., 2012)</td><td>MIRCA2000;<br>Single national sowing date from global crop calendar (Portmann et al., 2010)</td></tr><tr><td>high</td><td>WFD;<br>Grid-cell specific daily climate data (Weedon et al., 2011)</td><td>AfSIS;<br>Multiple, grid-cell specific soils from African soil database<br>(Leenaars, 2012)</td><td>Variable;<br>Multiple, grid-cell specific sowing date from a climatic rule based on rainfall (Dodd & Jolliffe, 2001)</td></tr></tbody></table>

## 3 Results – Discussion

We found that the level of information of different soil, climate and management data sets influences the simulated crop yields in both models. The uncertainty in input data propagates to uncertainty in simulated maize yields and production. The country's annual maize production of about 500,000 tonnes is underestimated by 1-17% in LPJmL simulations, underestimated in most APSIM simulations by 3-46% but overestimated by 15% in APSIM simulations with daily climate, local soil information and variable sowing dates.

However, the difference between models can be larger than between input data in particular when assessing the spatial variability of crop yields (see how the points and triangles in Figure 1 group together). Further, the agreement between simulated and observed spatial variability is higher than between simulated and observed temporal variability (not shown) due to abrupt changes in national mean yields from 1987 to 1991 in Burkina Faso which cannot be explained by rainfall variability like in the previous decades and therefore cannot be simulated from the two crop models. The most accurate estimation of spatial variability in maize yields with APSIM is possible with daily climate information and uniform sowing dates i.e. with detailed information on climate data but little information on sowing dates (R=0.65) (WFD-MIRCA, Fig. 1). In contrast the most accurate estimation of spatial variability in maize yields with LPJmL is possible with monthly climate information and uniform sowing dates i.e. with little information on both, climate and

<footnote n="1">¹ CSIRO Agriculture, 306 Carmody Road, St. Lucia, QLD 4067, Australia</footnote>

<footnote n="2">² Potsdam Institute for Climate Impact Research, Telegraphenberg A 31, 14412 Potsdam, Germany</footnote>

<footnote n="3">³ CSIRO Agriculture, 203 Tor Street, Toowoomba, Qld 4350, Australia</footnote>

<footnote n="4">⁴ CSIRO Agriculture, Clunies Ross Street, Black Mountain, ACT 2601, Australia</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: katharina.waha@csiro.au</footnote>
sowing dates (R=0.80) (CRU-MIRCA, Fig. 1). APSIM and LPJmL tend to overestimate and underestimate, respectively the spatial variability of maize yields. Soil data that determines water holding capacities is less important for the skill of the two crop models to reproduce the observed spatial variability (see how simulations with different soils group together in Fig. 1) even though the spatial variation in the two soil data sets differs to a larger extent than the spatial variation in the two climate data sets and is of similar magnitude as in the two sowing date settings. However soil fertility levels and soil processes in the crop models such as NO3 leaching from the root zone are important and partly explain the deviations between both models and between simulations with monthly and climate data.

<figure><img src="image_272.png" /><figcaption>**Fig. 1.** Taylor diagram displaying a statistical comparison with observations of 16 model estimates (two crop models by eight input data sets) of grid-cell yields in 2000 (A). The closer the coloured symbols to the unfilled circle on the x-axes the higher the correlation and the smaller the root mean square error. The solid green and dotted blue contours indicate the centred root-mean-square (RMS) difference and the standard deviation. The three maps show the spatial pattern of observed and simulated grid-cell maize yields for one example input setting (please note that units in the maps are different from the units of the data used to plot the diagram).</figcaption></figure>

Our results and conclusions are valid for the low-input agricultural systems in Burkina Faso and other parts of West Africa with low yield levels compared to other world regions and they depend on the limitations to crop growth specific to this study area. We expect changes in spatial and temporal variability with increasing yield levels which might lead to different conclusions on the ability of the two crop models to simulate observed yield levels.

## 4 Conclusions

The findings of our study highlight the importance of scale and model choice and show that the most detailed input data does not necessarily improve model performance. Further we inform about the magnitude of uncertainty in simulated maize yields and production arising from different input data and crop models which will assist identifying the level of detail in input data and interpretation of results in future modeling studies in West Africa.

## References

* Dodd, D. E. S., & Jolliffe, I. T. (2001). Early detection of the start of the wet season in semiarid tropical climates of western Africa. *International Journal of Climatology*, 21(10), 1251–1262.

* Leenaars, J. G. B. (2012). *Africa Soil Profiles Database, Version 1.0*. (Africa Soil Information Service (AfSIS) project and ISRIC - World Soil Information, Ed.) *ISRIC Report 2012/03* (p. 148). Wageningen, the Netherlands: ISRIC. Retrieved from http://www.isric.org/content/isric-reports-2000

* Mitchell, T. D., & Jones, P. D. (2005). An improved method of constructing a database of monthly climate observations and associated high-resolution grids, 712, 693–712. doi:10.1002/joc.1181

* Nachtergaele, F., Velthuizen, H. van, Verelst, L., Batjes, N., Engelen, V. van, Petri, M., & Shi4, X. (2012). *Harmonized World Soil Database*. (FAO/IIASA/ISRIC/ISS-CAS/JRC, Ed.). Rome, Italy and Laxenburg, Austria: FAO/IIASA/ISRIC/ISS-CAS/JRC.

* Portmann, F. T., Siebert, S., & Döll, P. (2010). MIRCA2000 - Global monthly irrigated and rainfed crop areas around the year 2000: A new high-resolution data set for agricultural and hydrological modeling. *Global Biogeochemical Cycles*, 24(GB1011), 1–24.

* Weedon, G. P., Gomes, S., Viterbo, P., Österle, H., Adam, J. C., Bellouin, N., ... Best, M. (2011). Creation of the WATCH forcing data and its use to assess global and regional reference crop evaporation over land during the twentieth century. *Journal of Hydrometeorology*, 12, 823–848.
# Participatory modelling of the trajectories of agro-sylvo-pastoral systems at landscape and community levels in West Africa – the case of Senegalese groundnut basin

Myriam Grillot*±<fnref n="1" />, Jonathan Vayssières<fnref n="1" />, Jérémy Bourgoin<fnref n="2" />, Alassane Bah<fnref n="3" />, Frédérique Jankowski<fnref n="4" />,
Richard Lalou<fnref n="5" /> & Dominique Masse<fnref n="6" />

## 1 Introduction

West African populations are rapidly increasing and are becoming more urban (Ouedraogo, 2007). Due to improvement of living standards, consumption habits in this region are also evolving. The fast growing demand for livestock products creates an important gap between the supply and demand (Kamuanga et al., 2008). Competition over resources increases due to rural population growth and the resulting cropland expansion (van Asselen & Verburg, 2013). Furthermore, the climate stays highly unpredictable with very high inter-annual variability. To predict the future of agrosystems in this context, it is important to identify the ecological and social drivers of their trajectories. The objective of this research is to explore the trajectories of agro-sylvo-pastoral systems at landscape and community levels through participatory modelling. The study area is located nearby Niakhar, in the Senegalese basin, which is an area that has been well-documented since 1960 (Lericollais, 1999; Audouin, 2014). The observed environmental changes are representative of the West African trends described above. Three villages in the area have been chosen for the analysis. These villages are exposed to similar constraints and pedo-climatic conditions but have different landscape structures and diverse combinations of household types. The hypothesis is that despite the contrasts and diversity of household's strategies certain ecological properties are conserved at the landscape level. These properties (plant and animal biodiversity, crop-livestock integration and spatial heterogeneity) are thought to increase the resilience of the whole socio-agro-ecosystem to climatic, pest and market hazards.

## 2 A role-playing game to define past and future village trajectories with farmers

Three different decision levels are classically distinguished in agronomy: the strategic, tactical and operational decision levels (Sebillote & Solers, 1988). Strategic decisions are defined as those that guide the production system for numerous years by choosing production activities and productive resource allocation (human migrations, crop expansion, change in crop or livestock activities, etc.). Tactical decisions aim at managing the production system over seasons. Operational decisions use the resources allocated on a daily basis. Tactical and operational decisions can be observed on the basis of farm survey or on-farm immersions (Vayssières et al., 2007). Strategic decisions are more difficult to apprehend due to their long term dimension and the fact that they depend on household and community levels. We consequently plan to use a role-playing game to capture farmer's strategic decisions and the resulting past and future village trajectories. The role-playing game combines a game-board and a simulation model, responding to each-other (Le Bars et al., 2011; Matthews, 2006). Both represent a hypothetical village (Fig. 1). Farmers take strategic decisions during the game. Each playing round is a year. Farmers' strategic decisions are inputs for the computerized model which simulates the consequences of these strategic decisions on the performances of the different farm types and at the village level. Computerized model outputs are information provided to farmers at the end of each playing round. This information is used by farmers to adjust (or not) their strategies during the next playing round.

<figure><img src="image_273.png" /><figcaption>Fig. 1. Interactions between farmers and MAS during the role-playing game (arrows are information flows)</figcaption></figure>

<footnote n="1">¹ Umr SELMET, CIRAD / DP PPZS</footnote>

<footnote n="2">² Umr TETIS, CIRAD / BAME, ISRA</footnote>

<footnote n="3">³ Ummisco, UCAD</footnote>

<footnote n="4">⁴ Ur Green, CIRAD</footnote>

<footnote n="5">⁵ Umr LPED, IRD</footnote>

<footnote n="6">⁶ Lmi IESOL Umr Eco&Sols, IRD</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: myriam.grillot@cirad.fr</footnote>
This sequence of role-playing and simulation is implemented for 10 to 50 rounds, i.e. 10 to 50 years. Played scenarios are based on real demographic, climatic and market series (from 1960 to 2015) or projections (2015 to 2050). Both strategic decisions taken by farmers while playing the game and resulting simulation are recorded, and used to describe and analyse village trajectories.

## 3 A multi-agent system to assess the resilience of socio-agro-ecosystems along their trajectories

The computerized model used for simulations in the role-playing game is a multi-agent-system (MAS). It represents the functioning of the socio-agro-ecosystem at landscape and community levels. Model inputs are of two types: i) parameters directly impacted by farmers' type and strategies (the landscape structure, the action plan and corresponding decision rules) and ii) environmental parameters (resource availability, unreliable weather and variations of demand for livestock products). The MAS integrates two systems in interaction: a decision system and a biophysical system (Fig. 2). The decision system represents farmers' tactical decisions, i.e. their action plan (one per farmer type) and its implementation. It defines the technical actions performed by farmers on plots and cattle over seasons. The biophysical system is essentially a stock-flow model. It represents the biomass productions and the organization of biomass flows through livestock, soil, rangelands, crops, and feed, forage and manure storage structures. The majority of these flows are defined by the technical actions decided by the decision system (e.g. crop manuring). Biomass flows are translated into nutrient, working-time and money flows to calculate different sustainability indicators at farm and landscape levels.

<figure><img src="image_274.png" /><figcaption>Fig. 2. Conceptual model of the MAS (adapted from Vayssières et al., 2007)</figcaption></figure>

Two types of output indicators are provided by the model: property and performance indicators. Property indicators are used by researchers to verify if some properties (biodiversity, integration and heterogeneity) are conserved over time to increase the resilience of the performances of the whole socio-agro-ecosystem. Three types of performances will be analyzed at landscape level: technical viability (crop and livestock production, workload), socio-economic livability (food self-sufficiency, gross margin) and environmental compliance (nitrogen balance, nitrogen use efficiency). Some of these performance indicators (productions, workload, food self-sufficiency and gross margin) are also the indicators that are provided to farmers at farm level during role-playing games.

## 4 Conclusions

In West Africa, the demographic, economic and environmental context is changing at a rapid pace, reorganizing rural territories. By using a participatory simulation, farmers are involved in exploring collectively past and future trajectories of agro-sylvo-pastoral systems at landscape and community levels. The proposed game links a board game and a multi-agent system. The role-playing game puts farmers in a position to choose activities and allocate resources on a yearly basis. The board is used to materialize these choices and the MAS provides information on their consequences on the year performances of farms and the whole territory. After N iterations, i.e. an N-years exploration, the dynamic of choices and their consequences can be recorded and considered as probable past or future trajectories.

## References

* Audouin, E., 2014. Terroirs comparison in terms of biomass flows and nitrogen balances: study case of Diohine and Barry Sine in the former groundnut bassin (Mémoire de master 2). Isara Lyon, Ecole d'ingénieurs de purpan, Norwegian University of Life Sciences.

* Kamuanga, M., Somda, J., Sanon, Y., Kagoné, H., 2008. Élevage et marché régional au Sahel et en Afrique de l'Ouest: Potentialités et défis. Club du Sahel et de l'Afrique de l'Ouest/OCDE.

* Le Bars, M., Le Grusse, P., Albouchi, L., Poussin, J.-C., 2011. Un jeu de simulation pour préparer une gouvernance de l'eau: une expérience en Tunisie centrale. Cah. Agric. 20, 105-111.

* Lericollais, A., 1999. Paysans sereer: dynamiques agraires et mobilités au Sénégal. Editions d'IRD (ex-Orstom), Paris.

* Matthews, R., 2006. The People and Landscape Model (PALM): Towards full integration of human decision-making and biophysical simulation models. Ecol. Model. 194, 329-343. doi:10.1016/j.ecolmodel.2005.10.032

* Ouedraogo, D., 2007. Les dynamiques démographiques (Atlas de l'intégration régionale en Afrique de l'Ouest), Série Population. CEDEAO-CSAO/OCDE.

* Sébillotte M., Soler L.G., 1988. Le concept de modèle général et la compréhension du comportement de l'agriculteur. C.R.Acad.Agric.Fr.74 n°4 (15/06/1988), p 59-70.

* Van Asselen, S., Verburg, P.H., 2013. Land cover change or land-use intensification: simulating land system change with a global-scale land change model. Glob. Change Biol. 19, 3648-3667.

* Vayssières J., Lecomte P., Guerrin F., & Nidumolu U.B. (2007) Modelling farmers' action: decision rules capture methodology and formalisation structure: a case of biomass flow operations in dairy farms of a tropical island. animal, 1, 716-733.
# A more integrated approach for a diversity of intensification approaches and pathways to cope with the necessity of sustainable intensification of African agri-food systems: The IntensAfrica initiative: Position paper.

Etienne Hainzelin *<fnref n="1" />, Philippe Petithuguenin <fnref n="1" /> & Florent Maraux ±<fnref n="1" />

Agriculture faces some unprecedented challenges at global level and in Europe but, for Africa, these challenges are particularly acute on several fronts. The agriculture sector (in the broad sense, including crops, animal production, forestry, aquaculture, etc.) represents the dominant part of the economy in most African countries and provides the majority of employments and livelihoods and hence will play a central role in the development of the continent. At the same time, African agriculture and its associated value chains are expected to contribute to local food and nutrition security, to preserve biodiversity, to provide work opportunities in rural areas, and to have a catalytic effect on the development of related economic sectors. As the African population will continue, in the midterm, to grow both in urban areas and in rural areas, African agriculture will be required to grow and evolve quickly, in particular to adapt to changes in demand. Beyond the expected surge in productivity, African producers will have to engage in a process of intensification in a sustainable way, which means increase of yield in a context of scarcity of natural resources and threats against fragile livelihoods while, at the same time, facing new constraints linked to climate change, competing energy chain values and dwindling natural resources.

In Africa (and in Europe as well), agricultural and food systems will face growing constraints: scarcity of natural resources, environmental degradation, increased energy and input costs, markets more opened to competition, higher vulnerability to various risks, price volatility, demographic changes and migration, etc. Furthermore, European agriculture is confronted with a “plateauing” of yields. Doubts are raised on the ability of such systems to further increase yield, while avoiding increased energy and input costs and negative environmental impacts. European agricultural practices are also often criticized for their consequences on water contamination and their significant contribution to greenhouse gas (GHG) emissions.

It is clear that current agricultural practices, with or without high levels of external inputs, often have negative impacts on the environment and the natural resource base. Effects may vary, from soil fertility degradation, loss of biodiversity and ecosystem functions, pollution of water sources, to emission of GHG, for instance. There is a growing consensus that the sustainability of agriculture needs to increase and that “business as usual” can no longer be considered as a sustainable option. However, there is still much debate about what should be done instead.

New approaches will be required since sustainable intensification is not only about higher outputs, but also about prudent and efficient use of resources, eco-system services, social and economic impacts, induced technological dependency, limits of natural and energetic resources, etc., all at different scales of time and space. New exciting avenues are offered by agro-ecological approaches based on the understanding and mobilization of agroecological processes like the optimization of available water and nutrients and the control of pests with limited use of fertilizers, pesticides and energy. These avenues need to be analyzed and compared, with the appropriate tools and metrics, in order to evaluate their performance and resource use efficiency as well as their sustainability. Comparative research is needed to fully unlock the potential and the limitations of this approach.

The socio-economic and biophysical environments are extremely diverse across Africa, resulting in very diverse farming systems and diets. As a consequence, the solutions aimed for need to be built and adapted to each local context, which means that no magical solution exists, and different pathways for sustainable intensification need to be developed. Despite the great importance of the local contexts, there is also evidence that improving some generic soil or ecophysiological functions<fnref n="1" />, either by practices or by crop breeding, could significantly alleviate the burden generated by the impact of intensified agriculture on natural resources, and would dramatically modify the long term natural resources balances.

Economic development has demonstrated the capacity of African farming systems to respond to emerging markets, nationally and internationally, but this often comes with considerable environmental or social costs, which appear poorly self-regulating. This highlights the importance of replacing the “business as usual” approach by tailor-made local adaptions – able to create and manage markets.

It is clear that current agricultural practices, with or without high levels of external inputs, often have negative impacts on the environment and the natural resource base. Effects may vary, from soil fertility degradation, loss of biodiversity

<footnote n="1">¹ Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD) Direction régionale Méditerranée. Moyen Orient, Avenue Agropolis, TA 178/04, 34398 Montpellier Cedex 5, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: maraux@cirad.fr</footnote>
and ecosystem functions, pollution of water sources, to emission of GHG, for instance. There is a growing consensus that the sustainability of agriculture needs to increase and that “business as usual” can no longer be considered as a sustainable option. However, there is still much debate about what should be done instead.

Because of the diversity and the complexity of the situations, local innovation systems will play a crucial role to bring up new solutions, mingling all kinds of knowledge and achieving the desired impacts. The role of scientific research in these systems will be important but should be revisited, as S3A states, in multi-stakeholders partnerships. At the same time, scientific research must also be active in combining policies, science, market organization, etc. and involving not only producers but all the actors along the value chain, thus attracting expertise and capital into the agricultural sector.

Though the situation has improved in some countries in recent years, National Agricultural Research systems in Africa often have limited capacity, in human resources, research infrastructure and funding, to address all the challenges involved in sustainable intensification of the agro-food systems. International and European research organizations and research funding agencies are increasingly trying to invest in research for development programs in concerted action with their African partners. However, this external support often remains scattered, which hampers the impact of these efforts on the agricultural led development of Africa. There is a need to increase the African capacity in research for development and to harmonize the support from other partners to create a critical mass for addressing the challenge of sustainable agricultural intensification.

The overall objective of this initiative is only achievable through a concerted and persistent effort and IntensAfrica will be developed as a long term partnership between the two continents. It will contribute to the sustainable intensification for food and nutrition security and economic development by concentrating means on strategic issues of common interest and by creating a critical mass to address the complex issues related to the sustainable intensification pathways development. It will aim at bringing together and streamlining the wealth and diversity of existing partnerships and projects in different agroecological situations, socioeconomic contexts, and policy environments working on sustainable intensification already.
# Ex-ante analysis of opportunities for the sustainable intensification of maize production in Mozambique

Caspar Roxburgh *±1 & Daniel Rodriguez 1

## 1 Introduction

Farming systems of central Mozambique are dominated by small-scale maize (Zea mays) production (Cavane 2007). Despite high in-crop rainfall, production and N fertilizer use in the country are among the lowest in sub-Saharan Africa (FAOSTAT, 2015). A fundamental roadblock to increasing inputs and productivity is poor agronomic management (Xu et al. 2009). Without addressing this, increases in nitrogen fertilization are less likely to be profitable and may pose a risk to the resilience of smallholders. We refer to these improvements as ‘feasible pathways’ to sustainable intensification as they account for the capacity of farming communities to successfully adopt new management and technologies. This study aimed to determine how agronomic management may be improved, so that nitrogen use efficiency (NUE) and maize productivity are increased.

## 2 Materials and Methods

This study took place in Manica province of central-western Mozambique. Farmers (n=52) from three communities were surveyed to establish their input use, productivity and income levels (18 in Marera, and 17 in both Chinhamdombwe and Rotanda). These data were analysed to identify the poorest performing location and farms. The cropping systems model APSIM (Holzworth et al., 2014) was then used in two exercises: 1) A sensitivity analysis to identify agronomic management contributing towards reported production variability; and 2) An estimation of the potential for three simple agronomic changes to increase productivity for the lowest performing farms (defined as the lowest yielding with no N input use from all three sites).

The APSIM model was parameterised for simulations using farm management described by the communities during two group workshops. Soil data from the Estação Agraria de Sussundenga was used along with farmer descriptions and data from the literature to parameterise APSIM's soil module. Meteorological data from Watching Force Data (Weedon et al., 2011) was used for the sensitivity analysis, while observed data was used in the subsequent modelling activity. The sensitivity analysis explored the effect of soil fertility (initial N and organic carbon), agronomic practices (sowing density, sowing date and residue levels) and weed burden (weed density) on yield variability at all sites. The second modelling exercise ran simulations to identify the highest yielding agronomic management of sowing density, sowing date and row spacing at the poorest performing location to establish the potential for low performers to benefit from improved agronomy.

## 3 Results – Discussion

Maize production was low (mean yield across all three sites was 1033kg/ha⁻<fnref n="1" />), as was N input use. Productivity, N input use and yield gaps between average and maximum yields varied with site (Table 1). Marera proved to be the lowest-yielding location as well as that with the lowest level of N use. Average maize yields in Marera were significantly lower than the other two sites. Farm income was lowest at Chinhamdombwe, and significantly higher in Rotanda compared with Marera.

<table><caption>**Table 1.** N input use and productivity of maize fields at each site; values shown ± standard error; lower case letters next to values indicate significant difference between sites (P < 0.05).</caption><thead><tr><td>Site</td><td>N input (kg/ha<sup>-1</sup>)</td><td>Mean yield (kg/ha<sup>-1</sup>)</td><td>Maximum yield (kg/ha<sup>-1</sup>)</td><td>Farm income (US$)</td></tr></thead><tbody><tr><td>Marera</td><td>0.79 ± 0.1</td><td>625<sup>a</sup> ± 12</td><td>2700</td><td>175<sup>a</sup> ± 49</td></tr><tr><td>Chinhamdombwe</td><td>16.1 ± 3.3</td><td>1537<sup>b</sup> ± 27</td><td>3500</td><td>696<sup>b</sup> ± 131</td></tr><tr><td>Rotanda</td><td>16.0 ± 5.9</td><td>1085<sup>b</sup> ± 30</td><td>5400</td><td>26 ± 18</td></tr></tbody></table>

Results from the sensitivity analysis indicated that maize sowing density was highly influential in determining yield variability at all sites and particularly in Marera (Figure 1). Other agronomic factors affecting yields were residue levels and to a much lesser extent sowing date. Aside from agronomic management, varying soil fertility (represented by initial nitrogen and soil organic carbon) was able to simulate high levels of yield variability.

<footnote n="1">¹ Centre for Plant Science, Queensland Alliance for Agriculture & Food Innovation, The University of Queensland</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: c.roxburgh@uq.edu.au</footnote>
<figure><img src="image_275.png" /><figcaption>Fig. 1. CV of mean maize yield simulated by adjusting six model input parameters; values from fields with no N input use; Chnh = Chinhamdombwe, Marr = Marera, Rtnd = Rotanda.</figcaption></figure>

The second modelling exercise found that by optimising agronomy, the bottom 25% of surveyed farms (all from Marera) could increase productivity by 120% and profit by US$49.66 (Table 2) – a 28% increase in mean annual income. These results suggest that such farms stand to gain from efforts to address simple knowledge gaps (Schreinemachers 2006) in agronomy. These farms require extension efforts to improve their management prior to investing in costly and risky fertilisers or other inputs advocated by some as the pathway to development of African farming systems (Bationo and Waswa 2011; Jeng 2011).

<table><caption>**Table 2.** Simulated production and profit benefits from simple agronomic changes to the bottom quantile of maize fields not using N inputs (Marera only); simulations used meteorological data from 1951-2013 with median value provided; values shown ± standard error.</caption><thead><tr><th>Mean reported maize production (kg/ha)</th><th>Optimal agronomic management</th><th>Improved Maize production (kg/ha)</th><th>Percentage increase (%)</th><th>Increased profit (USD)</th></tr></thead><tbody><tr><td>309.72 ± 39.15</td><td>Sowing: 1 December<br>Row Spacing: 0.5 m<br>Sowing rate: 2.5 pl./m²</td><td>682.15 ± 19.23</td><td>120</td><td>49.66</td></tr></tbody></table>

## 4 Conclusions

Through studying farm-to-farm differences in maize systems of Mozambique, we were able to identify a feasible pathway to intensifying the lowest performers. Our study shows that understanding local productivity and profit levels can help to develop practicable interventions to improve farm performance. Improving basic agronomic management remains a valuable tool to improving maize production systems in Mozambique and should be pursued prior to more costly interventions.

*Acknowledgements.* The authors would like to acknowledge the help from colleagues in administering the survey and the help of Dr John Dimes in modelling exercises and study feedback. This project received financial support from the Australian Centre for International Agricultural Research (ACIAR).

## References

* Bationo, A., & Waswa, B. S. (2011). New Challenges and Opportunities for Integrated Soil Fertility Management in Africa. In A. Bationo, B. Waswa, J. M. Okeyo, F. Maina, & J. M. Kihara (Eds.), *Innovations as Key to the Green Revolution in Africa* (pp. 3-17): Springer Netherlands.

* Cavane, E. P. A. (2007). *Farmers' attitudes and adoption of improved maize varieties and chemical fertilizers in the Manica District, Mozambique*. Ph.D., Michigan State University, Ann Arbor.

* FAOSTAT (2015). FAOSTAT. Food & Agricultural Organization of the United Nations, Rome.

* Holzworth, D., Huth, N., deVoil, P., Zurcher, E., Herrman, N., McClean, G., et al. (2014). APSIM - Evolution towards a new generation of agricultural systems simulation. *Environmental Modelling & Software, In Press*, 1-24.

* Jeng, A. S. (2011). African Green Revolution Requires a Secure Source of Phosphorus: A Review of Alternative Sources and Improved Management Options of Phosphorus. In A. Bationo, B. Waswa, J. M. Okeyo, F. Maina, & J. M. Kihara (Eds.), *Innovations as Key to the Green Revolution in Africa* (pp. 123-129): Springer Netherlands.

* Schreinemachers, P. (2006). *The (ir) relevance of the crop yield gap concept to food security in developing countries*. Doctoral, University of Bonn, Weert.

* Weedon, G. P., Gomes, S., Viterbo, P., Shuttleworth, W. J., Blyth, E., Österle, H., et al. (2011). Creation of the WATCH Forcing Data and Its Use to Assess Global and Regional Reference Crop Evaporation over Land during the Twentieth Century. *Journal of Hydrometeorology*, 12(5), 823-848, doi:10.1175/2011JHM1369.1.

* Xu, Z., Guan, Z., Jayne, T. S., & Black, R. (2009). Factors influencing the profitability of fertilizer use on maize in Zambia. *Agricultural Economics*, 40(4), 437-446, doi:10.1111/j.1574-0862.2009.00384.x.
# Possible *ex-ante* assessment of rice-vegetable systems performances when facing data scarcity: use of the PERSYST model in West Africa

Théo Furlan *<fnref n="1" />,<fnref n="2" />*, Rémy Ballot <fnref n="3" />,<fnref n="4" />, Laurence Guichard <fnref n="3" />,<fnref n="4" /> & Joël Huat ±<fnref n="2" />,<fnref n="5" />

## 1 Introduction

Inland valleys (also called *bas-fonds*) are considered to be promising ecosystems for increasing food security in West Africa due to their relatively high and secure water availability and soil fertility compared to the surrounding uplands (Andriesse et al., 1994). These areas amount to more than 22 million ha in West Africa and are of particular interest for the intensification of agricultural production, in order to feed the constantly increasing population (Rodenburg et al., 2014). To achieve well-adapted inland valleys development, public and private decision-makers need accurate and reliable data on system productivity. In West Africa, agricultural yields are difficult to estimate on a large scale due to a lack of agricultural production records and crop management variability. Under such conditions, agricultural models could be a relevant option for viable yield estimation. In this study, we set out to adapt the PERSYST model (Guichard et al. 2013), developed by INRA in France for *ex-ante* assessment of cropping systems based on annual crops, for crop yield estimations in inland valley rice-vegetable systems under West African conditions.

## 2 Materials and Methods

The study was carried out in 5 *bas-fonds* in the departments of Mono and Couffo in southwestern Benin, and 2 *bas-fonds* in the Circle of Sikasso in southern Mali. In 2013 and 2014, we interviewed 49 farmers in Benin and 45 farmers in Mali, covering 160 and 94 fields, respectively, ranging from several acres to 1 ha. Data on household structure and functioning, as well as land use, crop rotation and cropping practices were recorded by technicians from extension services (public and NGO) and from research centers (Institute of Rural Economy in Mali). The field sizes were calculated by GPS tracking in both countries. In Mali, crop damage, weed pressure and harvested production were recorded on two or three observation plots per field to calculate yield. In Benin, harvesting was finished when the interviews began. Harvested yields were therefore estimated with farmers in local units. As these units varied in terms of weights, volumes and containers, we all measured them on the local markets to increase dataset reliability. Soil samples were taken at plot level (in Mali) or *bas-fonds* level (in Benin) for chemical and texture analysis, while taking into account farmers' knowledge about soil fertility. Focus groups were organized at village level to estimate inter-annual yield variability and identify the main factors affecting yields according to local farmers' knowledge. A literature review and interviews of scientific experts completed the datasets. Data collection, data checking, and analysis were performed by the scientific team using descriptive statistics and the PERSYST and DEXI models.

PERSYST is based on a participatory parameterization approach, integrating local expert knowledge. Crop yield calculations take into account crop rotation and crop management effects, allowing simulations at cropping system level. Crop management effects on yields were estimated by DEXi (Bohanec, 2008), a qualitative multi-criteria assessment tool integrated into the model.

## 3 Results – Discussions

The identified cropping systems mainly consisted of lowland rice during the rainy season followed by one or two vegetable crops during the dry season. In Benin, the main vegetable crops were two leafy vegetables ‘crincrin’ (*Corchorus olitorius*) and ‘gboma’ (*Solanum macrocarpon*), hot pepper (*Capsicum annuum*), and okra (*Abelmoschus esculentus*), while in Mali, potato (*Solanum tuberosum*) and sweet potato (*Ipomoea batatas*) remained the most cultivated crops.

We observed a great range between maximum and minimum yields within a cropping season (Table 1). Inter-annual yield variability was also high, with sometimes a ten-fold increase between bad and good years. According to the farmers, rainfall variability explained most of these differences. The availability of water was recorded as a main constraint. Farmers' knowledge about soil fertility matched the results of the laboratory analysis fairly well.

The GPS tool appeared to be very useful for accurate field size estimation. The field size sometimes changed between two rainy seasons. Indeed, after a rice crop, the field was split into several smaller plots for vegetable growing (Fig 1.)

<footnote n="1">¹ Montpellier SupAgro, F-34060, Montpellier</footnote>

<footnote n="2">² CIRAD, UPR HortSys, F-34398, Montpellier</footnote>

<footnote n="3">³ INRA, UMR 211 agronomie, F-78850, Thiverval-Grignon</footnote>

<footnote n="4">⁴ AgroParisTech, UMR 211 agronomie, F-78850, Thiverval-Grignon</footnote>

<footnote n="5">⁵ Africa Rice Center, 01 BP 2031, Cotonou, Benin</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: j.huat@cgiar.org</footnote>
and not in the same manner, thus making it difficult to clearly discern the effect of the previous crops on the following crops. Moreover, the person cultivating a field could also change from one year to the next. This practice casts doubt on the method of assessing the cropping system effect when the boundaries of the field change considerably from one cropping season to another. We also observed complex crop associations, mixing maize, cowpea and cassava, for example, and sowed on different dates.

<table><caption>Table 1. Estimated crop yields in Benin and Mali in 2013/2014</caption><thead><tr><th>Country</th><th>Crop</th><th>Mean (t.ha<sup>-1</sup>)</th><th>Minimum (t.ha<sup>-1</sup>)</th><th>Maximum (t.ha<sup>-1</sup>)</th><th>Nb answers</th></tr></thead><tbody><tr><td rowspan="6">Benin</td><td>Rice</td><td>2.6</td><td>1.1</td><td>4.6</td><td>21</td></tr><tr><td>Crincrin</td><td>12.0</td><td>2.9</td><td>24.9</td><td>13</td></tr><tr><td>Gboma</td><td>1.1</td><td>0.4</td><td>2.0</td><td>10</td></tr><tr><td>Maize</td><td>2.3</td><td>0.5</td><td>6.4</td><td>14</td></tr><tr><td>Cassava</td><td>4.7</td><td>3.8</td><td>5.5</td><td>2</td></tr><tr><td>Hot pepper</td><td>1.6</td><td>0.4</td><td>3.3</td><td>14</td></tr><tr><td rowspan="5">Mali</td><td>Rice</td><td>1.3</td><td>0.2</td><td>4.5</td><td>Focus group</td></tr><tr><td>Potato</td><td>19.5</td><td>5.3</td><td>29.9</td><td>71</td></tr><tr><td>Sweet potato</td><td>12.5</td><td>10</td><td>15</td><td>Focus group</td></tr><tr><td>Egg plant</td><td>2</td><td>0.7</td><td>4.0</td><td>Focus group</td></tr><tr><td>Maize</td><td>0.9</td><td>0.5</td><td>1.5</td><td>Focus group</td></tr></tbody></table>

<figure><img src="image_276.png" /><figcaption>Fig. 1. Changes in land use at field level from one rainy season to the next. On the left: rice in the rainy season (year 1); in the middle: field divided for 5 vegetable crops; on the right: rice in the rainy season (year 2)</figcaption></figure>

Use of the PERSYST model on African family farming systems was limited due to a lack of references and data, and a lack of knowledge and local expertise among farmers as well as among research and extension service technicians. To improve co-operation with farmers and the data collection process in terms of time saving and data accuracy and profitability, there is a need to effectively teach technicians and researchers about farming system complexity, the different reference frameworks used by farmers and scientists, and about erroneous results due to a choice of poor methods (Van Asten et al., 2009). Poor quantitative information was found in the scientific literature concerning the effects of the nitrogen cycle, and of cropping systems and crop management operations on yields under local or similar soil and climate conditions. However, the credibility of the simulation model can be improved if the relevant agronomic improvements that can be promoted for smallholder farmers in West Africa can be precisely addressed (Whitbread et al., 2010). By combining the research and farmer knowledge provided by the literature review and the different interviews (individual and collective), respectively, we were able to successfully parameterize the DEXi model for assessing the effect of different crop management scenarios on yield.

## 4 Conclusions

Although we could not fully use the PERSYST model due to a lack of scientific knowledge in the African environment, DEXi was used to estimate crop yields at field level. This study highlights key knowledge that remains to be acquired to capture the effect of cropping systems on the observed yield gaps in the context of African farming systems and land use management. Moreover, substantial investment in the training of observers and technicians from research centers, as well as extension agencies, is needed to really benefit more from smallholder farmers' knowledge as a way of increasing the use and relevance of model simulation.

## References

* Andriesse, W., Fresco, L.O., Van Duivenbooven, N. & Windmeijer, P.N. (1994). Multi-scale characterization of inland valley agro-ecosystems in West Africa. *Netherlands Journal of Agricultural Science*, **42**, 159-179.

* Bohanec, M. (2008). DEXi: A Program for Multi-Attribute Decision Making. Version 3.02. http://wwwai.ijs.si/MarkoBohanec/dexi.html.

* Guichard, L., Ballot, R., Glachant, C. & Aubert, C. (2013). PERSYST, un outil d'estimation des PERformances agronomiques de SYSTèmes de culture - Adaptation à l'agriculture biologique en Île-de-France. *Innovations Agronomiques*, **32**, 123-138.

* Rodenburg, J., Zwart, S.J., Kiepe, P., Narteh, L.T., Dogbe, W. & Wopereis, M.C.S. (2014). Sustainable rice production in African inland valleys: seizing regional potentials through local approaches. *Agricultural Systems*, **123**, 1-11.

* Van Asten, P., Kariaa, S., Fermont, A.M. & Delve, R.J. (2009). Challenges and lessons when using farmer knowledge in agricultural research and development projects in Africa. *Experimental Agriculture*, **45**, 1-14.

* Whitbread, A.M., Robertson, M.J., Carberry, P.S. & Dimes, J.P. (2010). How farming systems simulation can aid the development of more sustainable smallholder farming systems in southern Africa. *European Journal of Agronomy*, **32**, 51-58.
# Participatory Management of Farming Systems in the Western Highlands of Cameroon for Poverty alleviation

Henri Grisseur Djoukeng ±1

## 1 Introduction

Small farmers produce much of the developing world's food (Dixon *et al.*, 2001). Cameroon is an agricultural economy country whose rural sector provides 30% of gross domestic product (ECAM3, 2008). The western highlands agro-ecological zone, gathering the Western and Northwest regions is the main center of production of vegetable crops in the country, as well as the Central Africa sub-region. This production is completely ensured by small family units (Tankou, 2013). As an area of high human density, the steepest sloping hills (> 25%) are colonized and farmers face the soil erosion by runoff that significantly reduces their income (Djoukeng *et al.*, 2015). The smooth operation of any farming system is based on several factors including farmers, land, rural environment, production techniques, policies, institutions, market and communication. The results of the field investigations have allowed us to identify the threat on soil and rural environment factors; this threat is a harmful consequence of production techniques factor. In order to increase and maintain sustainable farmer's incomes, we participatory introduced the tied ridging seedbed preparation method in mountain's agriculture for the production of vegetable crops. The overall objective of this study is to evolve technically feasible and economically viable farming system models by integrating cropping with new technics in hilly areas with a view to generate income and employment from the farm. The Specific Objectives are:

1. To identify existing farming systems in hilly areas and access their relative viability;

2. To ensure optional utilization and conservation of available resources and effective recycling of farm residues within system, and to maintain sustainable production systems without damaging natural resources/environment.

## 2 Materials and Methods

The experiment was set up in 2013 with the participation of farmers with potatoes (*Solanum tuberosum* L.) at Méloh village, Fongo-Tongo subdivision. The variety "Spunta" that farmers consider as the most resistant to various attacks was used in the experiment. The depth of plowing was 0.30m in all treatments; the soil was tilled with the hoe. Demarcation was done with cut twines and branches of *Eucalyptus saligna*. The ridges had a trapezoidal shaped of 0.80m large base and 0.70m small base and were separated by a groove of 0.20m. Inter knolls were placed at intervals of 1m with a width of 0.30m that formed micro-dams between the ridges. A thermometer and a rain gauge were installed at the experimental site to collect climatic data (temperature and precipitation). These data were recorded every day during the period from January 1st to December 31st. During the crop year 2013, we tested the effectiveness of this technique compared to local techniques (flatbed cultivation and ridging along the steepest slope) on 8 blocks of Wischmeier plots in two slopes named 11% and 29%. Each slope has received 4 blocks of 3 plots with a total area of 480 m². Harvesting of all 8 blocks was done with the hoe on the same day. Data analysis was done using MINITAB 16. With this statistical software, we calculated the probabilities by analysis of variance (ANOVA) of several factors in the generalized linear model (GLM) at the probability level of 5%.

## 3 Results - Discussion

Ridging along the steepest slope caused more soil loss than the other two methods of land preparation. Cumulated soil losses in 11% slope were lower than in 29% slope (Fig.1). Considering any slope equal, cultivation on flatbed and ridging along the steepest slope caused the highest soil loss, while tied ridging caused the lowest soil loss; it retained five times more sediment than the other two soil preparation practices.

<footnote n="1">¹ Department of Biosystems Engineering, Gembloux Agro-Bio Tech, University of Liège, Belgium</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: djhgrisseur@yahoo.fr</footnote>
<figure><img src="image_277.png" /><figcaption>**Fig. 1.** Chart of average sediment and yields per hectare, per cultivation method and per slope</figcaption></figure>

Analysis of variance at the threshold α = 0.05 shows that there was a significant difference in performance between the three modes of tillage in the same slope (p = 0.000) and between the two slopes (p = 0.000); on the interaction slope and modes of land preparation and no significant difference (p = 0.130), amongst the land preparation in different slopes. All things otherwise being equal, yields were lower in the slope of 29% than in 11%; flatbed cultivation method and ridging along the steepest slope had lower yields, while tied ridging had higher yields (Fig. 1). In addition to the fact that tied ridging land preparation keeps the plot fertilizer for plants, this increase in performance (30% gain) is also a result of the high planting density (3% gain).

**4 Conclusions**

For the cultivation of potato in the Bamiléké's mountains, three modes of soil preparation were tested on two different slopes. Regardless of the value of the slope, it could be concluded that despite overall soil losses in ridging along the steepest slope the yield was almost identical to that of the flatbed; only tied ridging presented a largely positive effects on plants density (3% gain), runoff (lowers by a factor six the runoff), soil loss (lowers by a factor five the soil loss) and yield (increase by 30%). Furthermore soil losses which result in loss of soil organic matter, low yields in the flatbed and ridging along the steepest slope could be a result of leaching of fertilizers. In addition to its benefits in soil and water conservation, tied ridging leads to 10% extra work; creating employment on the one hand and increases the income of farmers on the other hand, demonstration a great socioeconomic interest. Tied ridging technique proved to be the best in terms of creation of employment opportunities, increase yields, soil and water conservation. This study is a first track to fight against rural poverty in the Western Highlands of Cameroon; the study of other production factors (human behavior, institutions, policies, market and communication) will enable us to reduce it considerably.

***Acknowledgements.*** We would like to thank MrsValdea Mansutti (Codroipo, Italy) for his moral and financial support, and also all interested farmers who helped in data collection and popularization of tied ridging method of land cultivation.

**References**

Dixon, J., Gulliver, A. & Gibbon, D. (2001). Farming Systems and Poverty. IMPROVING FARMERS’ LIVELIHOODS IN A CHANGING WORLD. Djoukeng, H.G., Tankou, C.M. & Degré, A. (2015). Siltation and Pollution of Rivers in the Western Highlands of Cameroon: a Consequence of Farmland Erosion and Runoff. *International Journal of Agricultural Research and Reviews*, **3**(3), 206-212.

ECAM3. (2008). Enquête camerounaise sur les ménages. Rapport du Ministère de l'Agriculture et du Développement Rural. John Dixon and Aidan Gulliver with David Gibbon

Tankou, C. M. (2013). The Interactions of Human Mobility and Farming Systems and Impacts on Biodiversity and Soil Quality in the Western Highlands of Cameroon. (PhD thesis), Leiden University, The Netherlands.
# FIGHTING FOOD INSECURITY AND ALLEVIATING POVERTY IN THE FACE OF CLIMATE CHANGE THROUGH RICE-GROWING IN TONGA (WEST-CAMEROON)

Christelle Tchieudjo *±1 & Moïse Moupou <fnref n="1" />

## 1 Introduction

The uncontrolled and rapid urbanization and population growth have impacted on food resources and creates negative effects on the environment. One of the main risks related to population growth that threatens the world today is food insecurity which is closely linked to poverty.

The "disengagement of the state" in the agricultural sector has already been studied by several authors who have identified the causes of this disengagement resulting to the fall of coffee and cocoa prices, and finally the economic crisis and structural adjustment policies that followed.

This context has led us to focus on "rice growing in Tonga, an activity to fight against food insecurity and poverty alleviation". This study aimed at assessing rice cultivation potential in Tonga, as the locality manages to have a food self-sufficiency while making efforts to preserve the environment. Tonga is a rural locality situated in the western region of Cameroon, with a small population size of 10.000 inhabitants, of which 78% are mainly active in the agricultural sector (Moupou et al., 2008).

## 2 Materials and Methods

This study was investigated through sampling techniques and survey methods, data collection and processing (Thiétart, 1999 and Quivy Campenhoudt and Van, 2006); the analysis of secondary data derived from literature, field investigation (enquiries to relevant stakeholders, on-the-spot assessment). Questionnaires survey was also conducted in order to determine the benefit and problem of rice growing. Around 200 people were questioned during our investigation.

## 3 Results – Discussion

In this study, we checked whether rice growing in Tonga really helps to fight food insecurity and poverty alleviation. We also considered the gender and youth issue regarding land access. Both men (60%) and women (40%), mostly less than 59 years old are implicated at all level of the production. Therefore, our investigation have allowed us to demonstrate that through the implementation of the Special Food Security Program (FSP) and the Agricultural Sector Development Program (PADFA), rice growing production in Tonga has increased to 85% instead of 50%, the total cultivated area for rice is approximately 780 ha over 354 km2 of the surface area of the locality. The average land area per family or group of individual according to their workforce and financial constraints is 1.5 ha, which equates approximately to 500kg of rice yielded in good conditions of cultivation per growing season. These programs are strategies developed throughout the country and specifically in the locality. Rice production rose up to 70 000 tons these couple years because almost 40 000 hectares of lowland were serviced, which shifted the yield from 0.5 t/ha to 1.5t/ha.

<figure><img src="image_278.png" /><figcaption>**Fig. 1.** Proportion of men and women working in the rice-growing sector in Tonga.</figcaption></figure>

<figure><img src="image_279.png" /><figcaption>**Fig. 2.** Precipitations and number of rainy days in Tonga</figcaption></figure>

<footnote n="1">¹ Department of Geography, The Faculty of Arts, Letters and Social Sciences, The University of Yaoundé I, P.O Box 755 Yaoundé (Cameroon)</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author:</footnote>
<table><caption>**Table 1.** The composition of the population according to the annual report N° 83/42/RA/DAA/TGA.</caption><thead><tr><th rowspan="2">Total area</th><th rowspan="2">Total Population</th><th rowspan="2">Density</th><th rowspan="2">Urban Population</th><th rowspan="2">Rural Population</th><th colspan="3">Labour force</th></tr><tr><th>Men</th><th>Women</th><th>Total</th></tr></thead><tbody><tr><td>354 km²</td><td>10 000</td><td>28</td><td>8 420</td><td>1 580</td><td>2 100</td><td>5 677</td><td>7 777</td></tr></tbody></table>

Because of its natural environmental conditions, Tonga is suitable for rice growing. This has led to the introduction of the New Rice of Africa, type 3 (NERICA 3) that fit well with climate. The scent and special taste of its cultivated rice have contributed to the fame of the locality in terms of rice production in Cameroon. Tonga farmers are well organised into cooperatives, and farmer's organisations share their knowledge in order to make this agriculture a real profession, even if this is still a family agriculture facing many structural and organisational difficulties.

<figure><img src="image_280.png" /><figcaption>**Photo 1:**
Reproductive phase (heading tage)</figcaption></figure>

<figure><img src="image_281.png" /><figcaption>**Photo 2:**
Maturation phase</figcaption></figure>

<figure><img src="image_282.png" /><figcaption>**Photo 3:**
Harvesting of «NERICA 3» type</figcaption></figure>

## 4 Conclusions

Food security has become a major priority of humanity, especially in the fight against poverty alleviation and hunger concerns, which is one of the Millennium Development Goals. Rice has become a strategic food in Cameroon; it is therefore important for the government to go further in establishing policies and strategies. The rehabilitation and reopening of Tonga rice industry, could make that that ambition became a reality. More supports to farmers to eradicate hunger and poverty, and finally ensure a brighter future for rice growing, new challenges and technological opportunities for rice-based production systems for food security and poverty alleviation are then needed in Tonga.

*Acknowledgements.* I am very grateful to the organising committee of the 5th International Symposium for Farming Systems Design for granting me a travel assistance to participate to this international event. I would like to thank Professor Moupou Moïse for his supervision.

## References

* Achancho, V. (2004). Les organisations paysannes au Cameroun: acteurs de mise en œuvre de la stratégie de réduction de la pauvreté. Yaoundé, Projet MINAGRI, MINEPIA, FAO, Cameroun.

* Moupou, M. et Akei Mbanga, L. (2008). Désengagement de l'Etat et réponses paysannes au Cameroun. Les cahiers d'outre-mer, 241-242/2008, 163-183.

* Quivy, R. et Van Campenhoudt L. (2006). Manuel de recherche en sciences sociales, Paris, Dunod (1re éd. 1988). Thietart, R.-A. (1999). Méthodes de recherche en management. Paris, Dunod. 7.

* Wannehain, H. et Janin, P. (2012). Commercialisation des céréales et incidence sur la disponibilité céréalières des producteurs agricoles: étude de cas dans les zones greniers au Mali. Cahiers Agricultures 21 ird-00734440.
# W7. Aquaculture systems

Chair: Patrick Dugan, WorldFish Center

Co-chair: Lionel Dabbadie, CIRAD
# Effectiveness of a participatory approach for collection of economic data in aquaculture systems at farm level in Brazil

Manoel Xavier Filho Pedroza<sup>*±1</sup>, Andrea Elena Pizarro Munoz<sup>1</sup> & Roberto Manolio Valladão Flores<sup>1</sup>

## 1 Introduction

The lack of economic information at farm level is one the most important bottlenecks in aquaculture sector, especially in developing and emerging countries (Flores & Pedroza Filho, 2014). This kind of information is crucial for decision making process at producer level and also concerning public institutions related to issues like insurance, credit, support policies, research, technology transfer and extension actions. However, gathering economic data in agricultural systems at farm level requires a substantial methodology in order to ensure reliability of results. This paper aims to evaluate the effectiveness of a participatory approach which is currently being applied by the Brazilian Agricultural Research Corporation (Embrapa) and Brazilian Confederation of Agriculture and Livestock (CNA) in aquaculture sector in Brazil.

## 2 Materials and Methods

Methodology consists in the collection and analysis of technical parameters, production costs and other economic data by applying panel method with producers of one specific region. Variables are obtained through consensus among participants of the panel (generally between 10 and 20) by using the criteria of the most frequent features of the fish farms ("modal or typical producer"). At the end of the panel, all collected data are analyzed and presented to producers in order to correct errors and to improve participants' comprehension about their economic performance in aquaculture business. One panel is promoted in each production zone selected and later data is updated in a regular basis by calling input suppliers, fish farmers and wholesalers in order to monitor prices and inputs costs variation. The information is disseminated via newsletters on website periodically. Besides, participants receive the spreadsheet filled in panel for their own use in the farm as feedback. Data released consist of: costs of production, analysis of economic viability (e.g. net margins, net present value, effective operative cost), inputs and fish price index (Matsunaga et al, 1976).

<figure><img src="image_283.png" /><figcaption>Fig. 1. Panels with fish farmers in Brazil</figcaption></figure>

This methodology is being used by several institutions in Brazil for collecting economic data at farm level in many different sectors as poultry, pork, cattle, soybean, cotton, sugar cane.

## 3 Discussion

The collection of economic data at farm level is critical because it is very dependent of producer's capacity in providing reliable information. This capacity in offering reliable information is directly related to producers' knowledge about all costs concerned to fish farming production. Often, producers have difficulty to describe all items because some costs are indirect (e.g. energy) or it depends on the assessment of other technical parameters (e.g. feed conversion ratio-FCR). Furthermore, sometimes these information are related to data which is strategic and, consequently, confidential. Therefore, producers may provide this sensible information in biased way.

<footnote n="1"><sup>1</sup> Embrapa Fisheries and Aquaculture, Brazilian Agricultural Research Corporation, Ministry of Agriculture</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: manoel.pedroza@embrapa.br</footnote>
<table><caption>**Table 1.** Zootechnical information and indicators used in the panels – Modal fish farmer of Tilápia in cage culture / São Francisco Valley, Bahia state.</caption><thead><tr><td>Indicators</td><td>Unit</td><td>Quantity</td></tr></thead><tbody><tr><td>Size of land for support area</td><td>hectares</td><td>3</td></tr><tr><td>Number of cage culture (6m³)</td><td>Unit</td><td>250</td></tr><tr><td>Duration of cultivation cycle of fish</td><td>days</td><td>180</td></tr><tr><td>Final feed conversion ratio</td><td>Kg of feed/kg of fish</td><td>1,61</td></tr><tr><td>Final density</td><td>Kg of fish/m³</td><td>144</td></tr><tr><td>Initial weight of fingerlings</td><td>g</td><td>25</td></tr><tr><td>Final weight of fish (harvest)</td><td>g</td><td>1,100</td></tr></tbody></table>

Thus, one of the challenges is to find a methodology able to overcome these barriers and assure reliable data. The panel methodology was chosen because it offers the possibility of solving this problem by using a participatory approach. In this methodology, the consistency of collected data is assured by a triangulation process in which each information must be confirmed by the majority of participants in a consensus process. Furthermore, the presence of agents from different segments of the productive chain (i.e. feed and fingerlings suppliers, fish farmers, processors, wholesalers, policy makers) reinforces the validation of the information.

<table><caption>**Table 2.** Economic indicators used in the panels – Modal fish farmer of Tilápia in São Francisco Valley/Bahia state.</caption><thead><tr><td>Indicators</td><td>Unit</td><td>Quantity</td></tr></thead><tbody><tr><td>Price of tilapia (gross profit)</td><td>R$/kg</td><td>R$ 5,50</td></tr><tr><td>Effective Operational Cost</td><td>R$/kg</td><td>R$ 3,50</td></tr><tr><td>Total Operational Cost</td><td>R$/kg</td><td>R$ 3,77</td></tr><tr><td>Gross profit margin</td><td>R$/kg</td><td>R$ 1,95</td></tr><tr><td>Net profit margin</td><td>R$/kg</td><td>R$ 1,73</td></tr></tbody></table>

Additionally, since the methodology is applied in several production regions, it is possible to compare the economic viability of different species in several geographical zones.

<table><caption>**Table 3.** Comparison between economic indicators of three production zones in Brazil</caption><thead><tr><td>Production zone</td><td>Specie</td><td>Net profit margin (R$/kilo)</td></tr></thead><tbody><tr><td>São Francisco Valley/Bahia State</td><td>Tilapia</td><td>1,73</td></tr><tr><td>Northern region of Mato Grosso State</td><td>Amazonian catfish</td><td>1,13</td></tr><tr><td>Central region of Tocantins State</td><td>Tambaqui (Colossoma macropomum)</td><td>0,18</td></tr></tbody></table>

## 4 Conclusions

As positive aspects, the methodology shows a strong reliability of data because information is directly provided by a representative sample of producers. Moreover, data collection presents a low cost compared to individual visits to producers or to surveys method. Continuous updating of database and the high level of participation of producers are other assets of this methodology. Despite its effectiveness, the method also presents some challenges as: (a) Heterogeneity of producers' profile and consequent difficulty in standardizing data; (b) Logistic requirements related to team travel and organization of panels; (c) Producers' mobilization in order to assure their presence in panels. One important advantage is the possibility to cover a large number of production zones, which is crucial in a large country like Brazil. For example, in large states like Mato Grosso (903.366,192 km²) it is possible to collect data in 3 fish farming regions in one week. Other advantage of this methodology refers to its low cost compared to other methods as census research, for example. Basically, the main cost for data collection consists in one travel into the production zone for a team of 3 people. This travel includes one panel with duration of about 4 to 5 hours and field visits in 1 to 2 fish farmers. In order to ensure the updating of the economic data, a trainee is in charge of monitoring inputs costs and fish prices by calling suppliers and producers by phone.

## References

* Flores R. M. V. & Pedroza Filho, M. X. (2014). Is the internal market able to accommodate the strong growth projected for Brazilian aquaculture? Journal of Agricultural Science and Technology. **4**, p. 407/5-417.

* Matsunaga, M.; Bemelmas P.F.; Toledo P.E.N.; Dulley R.D.; Okawa H. & Pedroso I.A. (1976). Metodologia de custo utilizada pelo IEA. Agricultura em São Paulo. **23**, t. 1, p.123-139.
# The role of fish farming in the farming system in the Betafo areas of Madagascar : approach by agronomic analysis and socio-economic inquiries.

Marc Oswald<sup>±1,2</sup>, Sophie Moreau<sup>2</sup> & Aurélie Metay<sup>3</sup>

## 1 Introduction

The reasons for the diversification of a smallholder's farming system are intricate, in particular when the diversification concerns fish farming: it can be justified by economic, environmental (water management) or agronomic reasons.

Various sources indicate that rice-fish farming has maintained itself in the Betafo areas of the Vakinakarach (Madagascar) and even more so, it may have considerably expanded. If economic reasons seem obvious, the role of fish farming among the soil fertility of rice plots is to investigate, especially in a context where soils have a low (poor) organic matter level and where it is crucial to maintain the rice plot fertility, an essential element of these smallholders farming system (Rabeharisoa, 2004). This issue arises as cattle manure resources are decreasing, linked to the increase of demographic pressure (Blanc-Pamard, 2000). To face this main fertility constraint, diversification with vegetable during the off-rice season, especially tomato cropping, is often presented as a good lever to answer it; it is also supposed to finance the supply of organic and mineral fertilisation. On an other hand, to be more profitable, rice fish farming needs ponds to stock fish during the off-rice season which is also the colder season.

## 2 Materials and Methods

In order to answer this questioning, a pilot study was conducted in 2007 in this area during the off-rice season (June to September) where rice plots are used for vegetable cropping (tomato) or fish farming. This exploratory study induces the sampling of plots in order to identify basic hypothesis about fertility management in order to enable further analysis. Inside plots, a balance of organic matter, of nitrogen, of phosphorus (Olsen) and other soil major indicators (pH, CEC) was conducted as the same time as an inquiry on the economic results of these crops among smallholders. 11 plots were studied (5 with tomatoes and 6 with fish farming cycle). Each amount of organic and mineral fertilisers was collected as well as crop exports during the off-rice season. Soil was sampled down to 20 cm inside the hard stratum under the mud which was also collected. Differences in C and N contents between the two sampling dates were tested (Student T-test) to assess the effect of each farming system on soil fertility.

## 3 Results and discussion

Although vegetable plots received higher quantities of organic matter, the results (cf. figure 1) show a significant increase (p<0.05) of N content during fish farming cycle and a stagnation during tomato cycle. Similar evolutions are noticed on Carbon content (p <0.1, figure 2).

On the economic aspect, tomato cropping systems bring a positive gross margin profit and fish farming a negative one, especially because all the fish are kept for stocking the following cycle (average was respectively 11 191 Ariari/are and -1 012 Ariari/are). Tomatoes require a higher cash-flow (6 286 Ariari/are and 1 012 Ariari/are respectively). Fish farming during this season provides a tool to restore the fertility with limited financial means.

<figure><img src="image_284.png" /><figcaption>**Fig. 1.** Soil nitrogen in % in the collected soil samples. 1 and 2 refer to the soil samples before and after the off-rice cycle respectively. P and T refer to fish and tomato farming systems respectively. Error bars represent standard deviation.</figcaption></figure>

<footnote n="1">¹ ISTOM, Cergy, 32 Bd. du port., 95094 Cergy-Pontoise cedex, France.</footnote>

<footnote n="2">² APDRA Pisciculture paysanne, Massy, 9 avenue de France, 91300 Massy, France</footnote>

<footnote n="3">³ Montpellier Supagro, UMR System, bât. 27, 2 place Viala, 34090 Montpellier, FRANCE</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: m.oswald@istom.net</footnote>
<figure><img src="image_285.png" /><figcaption>**Fig. 2.** Soil carbon in % in the collected soil samples. 1 and 2 refer to the soil samples before and after the off-rice cycle respectively. P and T refer to fish and tomato farming systems respectively. Error bars represent standard deviation.</figcaption></figure>

These results confirm that under water, a soil stocks higher organic matter than in a dry environment (Shibu *et al.*, 2006) especially in low trophic environment. Compared to N supply, the balance shows a strong decrease for tomato cycles. (Rochette, 2008)

During the off-rice season, this study shows that, for a smallholder facing cash-flow constrains, fish farming can offer an efficient alternative known as “Masaka” in the traditional knowledge (Bouayad-Agha, 1995). From an economic point of view, it enables him to get positive environmental impacts for the next rice cycle and to improve his further gross margin on the following rice cycle by combining fish-farming and rice. At farm level, plots which are not valorised by vegetables due to lack of financial means and organic fertilisation supply, can still be valorised by fish-farming.

## 4 Conclusions

Despite classical restrictions of this methodology, results show that the strategy to maintain soil fertility has to be taken into account to explain how fish farming is integrated, in particular by the observed nitrogen and carbon increases it provides. This kind of analysis should be extended to the whole cropping and breeding systems to correctly analyse the rationality of the integration of fish farming inside irrigated rice systems in the area studied.

*Acknowledgements.* Authors want to thank the APDRA team in Madagascar for their welcome and their availability, special thanks to Tsirihasina Randriampeno, Barbara Benz, Fabien Cousseau, Vola Ratiarivelo and Bénédicte Lorgeroux. They also want to thank Michel Bertrand (UMR Agronomie INRA-AgroParisTech) for his precious advises.

## References

Blanc-Pamard C, Rakoto Ramiaantsoa H.(2000). Le terroir et son double, Tsarahonenana 1966-1992, Madagascar. Collection à travers champ; Editions de l'IRD, 254 p.

Bouayad-Agha M, Clément J, Guillaume J. (1995). Gestion sociale de l'eau sur les réseaux d'irrigation traditionnels – Région de Bétafo – Vakinankaratra – Madagascar. Mémoire de fin d'étude, ENS/CNEARC, 165 p.

Rabeharisoa L. (2004) Gestion de la fertilité et de la fertilisation phosphatée des sols ferralitiques des hautes terres de Madagascar. Thèse de Doctorat d'Etat es Sciences Naturelles de l'Université d'Antananarivo, Madagascar 196p.

Rochette P. (2008). Le respect de l'environnement : tout simplement essentiel. Colloque en agroenvironnement, 27 novembre 2008, Drummondville, Canada.

Shibu ME, Leffelaar PA, Van Keulen H, Aggarwal PK. (2006). Quantitative description of soil organic matter dynamics—A review of approaches with reference to rice-based cropping systems. Geoderma 2006; 137: 1-18.
# Aquaculture systems & farming systems: inside, outside or side-by-side?

Lionel Dabbadie ±<fnref n="1" />,<fnref n="2" /> & Olivier Mikolasek <fnref n="1" />

## 1 Introduction

Two main approaches have been used as regard to aquaculture systems design. The first one considers aquaculture as an integral part of the farming system, as it is the case for most traditional aquaculture systems that have been designed over the centuries in a deep integration with the surrounding crop and animal production (Edwards, Little, & Demaine, 2002). However, not all aquaculture systems fit within this category. Over the centuries, aquaculture ponds have also sometimes been perceived in isolation, or even as a competitor to agriculture development, occasionally leading to their draining and destruction (Billard, 2010). Many modern technologies have also been promoted in a relative isolation from their agricultural surroundings, either because they were soilless or implemented in natural environments (lakes or marine areas, mangroves etc.). For this kind of technology, the system design has been more towards understanding the interactions with their socio-ecological system. This presentation and paper will review several case studies in order to contribute to its formulation.

## 2 Aquaculture, a component of the farming systems or a component of the socio-ecological systems?

Aquaculture is a millenary activity and over the centuries, highly efficient and integrated aquaculture systems emerged globally (Edwards et al., 2002; FAO, 2003; D. Nhan et al., 2007). They have been advocated to increase land & water use efficiency as well as nutrient recycling (D. K. Nhan, Milstein, Verdegem, & Verreth, 2006). One of the most famous is probably the Vietnamese VAC (Vuon, Ao & Chuong meaning garden/pond/livestock pen in Vietnamese) that combines a multi-fish-species pond with a garden producing vegetables or fruits, and livestock supplying organic fertilizers (Luu, 2003). Its widespread promotion started in the 1980s and two models developed: the upland VAC system, which is generally larger (garden: 1000-15000 m<fnref n="2" />) and more extensive than the lowland model (garden: 200-300 m<fnref n="2" />). Edwards (1998) developed a FSD framework comprising three interrelated aspects: production technology, social and economic aspects, and environmental aspects to describe such integrated aquaculture-agriculture systems. Studies have however pointed out their complexity of management, as the pond sub-system not only requires good management practices to maximize benefits to farmers while minimizing environmental impacts, but also implies to be integrated as much as possible with existing farming activities to maximize production while minimizing nutrient discharges (D. K. Nhan et al., 2006).

The need for a system approach describing and understanding this kind of aquaculture also emerged among development practitioners, as a result of the necessity to better understand and propose technologies to fit to farmer's needs. In Africa, FSD approach including an aquaculture component was proposed in Ivory Coast, within the framework of a development project conducted in the Midwestern region (Dabbadie et al., 1994). In such cases, the aquaculture system is just a sub-component of the farming system, similar to the livestock or crop systems but with its own technical and economical specificities. Considering the socio-economical conditions prevailing in the country at the end of the 1990s (Léonard & Oswald, 1995), aquaculture was a good candidate for agriculture diversification and two models emerged, based on FS diagnostics: one semi-intensive close to urban centers based on fish polyculture combining species with supplementary feeding habits (tilapia, catfish, *Heterotis niloticus*, *Hemichromis fasciatus*) and rice bran feeding + fertilization, and another one located in rural area where access to inputs was almost impossible (Dabbadie, 1996). The production was extensive but quantitatively important, by making use of very large ponds obtained by building drainable dams across valleys and stocking them at a very low fish density (Dabbadie, 1996).

But aquaculture has not always emerged as a component of farming systems, particularly in recent decades. It has also colonized new ecosystems, such as mangroves (Primavera, 2005), lakes (De Silva & Davy, 2010), coastal areas (GESAMP, 2001) and even, the open-sea (Troell et al., 2009). In such areas, the systemic studies have rather considered the ecological (habitat destruction, eutrophication etc.) and social system (conflicts for resources among users, collective action and decision making) but not the farming system.

## 3 Conclusion

In reality, modern globalized aquaculture can never be considered in complete isolation. Even the fish produced offshore are for example largely fed with agricultural products, originating from existing farming systems sometimes on a global scale. Feed used in Vietnamese pangasius aquaculture often contains American soybeans, for example. On the other side, society demands for a better consideration of the social and environmental impacts of aquaculture. A new

<footnote n="1">¹ CIRAD, UMR ISEM, F-34398 Montpellier, France</footnote>

<footnote n="2">² Projet AMPIANA (CIRAD, APDRA, FOFIFA, MPE), Antananarivo, Madagascar</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: lionel.dabbadie@cirad.fr</footnote>
FSD for aquaculture, capable of dealing with all dimensions of modern aquaculture on both the local and global scale, is thus required and the framework proposed by Ostrom (2009) could serve as a basis for this purpose. In the aquaculture-dominated Pampanga delta (Philippines), a territorial approach has been developed in a systemic manner by combining remote sensing, GIS and field analysis (Mialhe, Gunnell, Mering, & Dabbadie, 2011). By showing the spatial organization of subsystems, it has evidenced the main drivers of changes. It could serve as a basis for further FSD research and development on aquaculture systems.

## References

Billard, R. (2010). *Derrière chez moi, y'a un étang. Les étangs, textes d'hier, regards d'aujourd'hui et de demain*. Versailles, France: Editions Quae.

Dabbadie, L. (1996). *Etude de la viabilité d'une pisciculture rurale à faible niveau d'intrant dans le Centre-Ouest de la Côte d'Ivoire : Approche du réseau trophique*. Université Pierre et Marie Curie (Paris 6). Retrieved from http://tel.archives-ouvertes.fr/tel-00321419/fr/

Dabbadie, L., Oswald, M., Morissens, P., Sanchez, F., Koffi, C., & Sevrin-Reyssac, J. (1994). Un exemple de coopération directe entre scientifiques et acteurs de terrain: le développement de la pisciculture en Côte d'Ivoire. In *Table ronde CNRS Environnement, recherche et société* (mars 1994) (pp. 200–202). Montpellier, France: CNRS.

De Silva, S. S., & Davy, F. B. (2010). *Success Stories in Asian Aquaculture*. (S. S. Silva & F. B. Davy, Eds.). Dordrecht, The Netherlands: Springer. doi:10.1007/978-90-481-3087-0

Edwards, P. (1998). A systems approach for the promotion of integrated aquaculture. *Aquaculture Economics & Management, 2*(1), 1-12. doi:10.1080/13657309809380209

Edwards, P., Little, D., & Demaine, H. (2002). *Rural aquaculture*. Wallingford, UK: CABI.

FAO. (2003). *Intégration agriculture-aquaculture. Principes de base et exemples*. Rome, Italy: Institut International pour la Reconstruction Rurale, Worldfish Center, FAO.

GESAMP. (2001). *Planning and management for sustainable coastal aquaculture development. Reports and Studies* (Vol. 68). Rome, Italy: IMO/FAO/UNESCO-IOC/WMO/IAEA/UN/UNEP Joint Group of Experts on the Scientific Aspects of Marine Environmental Protection.

Léonard, E., & Oswald, M. (1995). Cocoa smallholders facing a double structural adjustment in Côte d'Ivoire: responses to a predicted crisis. In F. Ruf & P. S. Siswoputranto (Eds.), *Cocoa cycles: the economics of cocoa supply* (pp. 125–150). Great Abington, UK: Woodhead Publishing Limited.

Luu, L. (2003). Le système de culture VAC dans le nord du Vietnam. In *Intégration agriculture-aquaculture. Principes de base et exemples* (pp. 29–32). Rome, Italy: Institut International pour la reconstruction Rurale, Worldfish Center, FAO.

Mialhe, F., Gunnell, Y., Mering, C., & Dabbadie, L. (2011). A systemic approach to Integrated Coastal Management in the aquaculture- dominated Pampanga delta, Philippines. *Aquaculture Research, 42*, 151–152. doi:10.1111/j.1365-2109.2010.02764.x

Nhan, D. K., Milstein, A., Verdegem, M. C. J., & Verreth, J. A. V. (2006). Food inputs, water quality and nutrient accumulation in integrated pond systems: A multivariate approach. *Aquaculture, 261*(1), 160–173. doi:10.1016/j.aquaculture.2006.07.015

Nhan, D., Phong, L. T., Verdegem, M. C. J., Duong, L., Bosma, R., & Little, D. (2007). Integrated freshwater aquaculture, crop and livestock production in the Mekong delta, Vietnam: Determinants and the role of the pond. *Agricultural Systems, 94*(2), 445–458.

Ostrom, E. (2009). A General Framework for Analyzing Sustainability of Social-Ecological Systems. *Science, 325*(5939), 419–422. doi:10.1126/science.1172133

Primavera, J. (2005). Mangroves, fishponds, and the quest for sustainability. *Science, 310*(October), 57–59. Retrieved from http://www.sciencemag.org/content/310/5745/57.1.short

Troell, M., Joyce, A., Chopin, T., Neori, A., Buschmann, A. H., & Fang, J.-G. (2009). Ecological engineering in aquaculture — Potential for integrated multi-trophic aquaculture (IMTA) in marine offshore systems. *Aquaculture, 297*(1-4), 1–9. doi:10.1016/j.aquaculture.2009.09.010
# Market Access and fish farms' density in a sub-Saharan rural country side: a case study of the village of Gbotoye in the forested areas of Guinea.

Charline Rangé<fnref n="1" />,<fnref n="2" />, Augustin Pallière*<fnref n="1" />,<fnref n="2" />, Alpha Ly<fnref n="1" />, Moïse Théa<fnref n="1" /> & Marc Oswald ±<fnref n="3" />,<fnref n="4" />

## 1 Introduction

The agro-environmental benefits of a fish-farm integrated into a farming system, mostly self-sufficient for supply of stocking fish and fish-food, are broadly admitted. But its contribution to rural household economy is sometimes qualified as insignificant, even non-profitable or even contemplative, sometimes it is only a self-sufficiency crop. In their review of African aquaculture, Brummett et al. (2007) identify the « local and regional marketing infrastructure for the sale of food fish produced » and the « lack of access to wealthier markets » as key constraints that are a main explanation of the failure of the promotion of a sustainable aquaculture integrated into farming systems across Africa. As a conclusion, they recommend promotion of small and medium-scale enterprises. More recently, Cai et al. (2012) point out that « However, the profitability of farming low-value species is usually low because of limited market potential ». So, the narrowness of the effective demand accessible to the rural fish producers would build up an impassable obstacle for the integrated fish farming development of the integrated fish farming. In these conditions, the economic benefits of this fish farming type at the level of the farming system would decrease as the number of rural fish farmers competing on the same fish market increases. Does the saturating of the so narrow local market doom the ability to densify the networking of the rural producers? This is an essential question in order to appreciate the potential contribution of this type of fish production to face the food security of the rural population in Sub-Saharan Africa. Although in Guinea, rural fish farming development schemes including the purpose of producing fish for the rural consumers have induced strong dynamics for about 15 years. Other choices targeted the quality of the integration of the fish production into the agricultural smallholding and the self-sufficiency for the supply of the fingerlings and for the fish-food. The strong development of this kind of fish farming in the region of N'Zérékoré for a decade justifies the support that the Government is providing to its promotion today. The village of Gbotoye is an interesting case study to answer this question. In May 2015, in this village of about 2000 inhabitants, 41 fish farming farms have been estimated to produce 11 t of fish, that means approximately 5,5 kg/inhabitant/y if all the fish is locally consumed. At the beginning of the years 2000, official estimations evaluate to 1kg the yearly fish consumption of a person living in forested Guinea. Although deepened studies show this figure was under-evaluated, the today global production seems significant before the local demand and shows a probably saturated market or becoming saturated.

## 2 Materials and Methods

Although economy and agricultural policies have focused on export crops, the rise of the food-producing sector for commercial purposes has supported strong dynamics of agricultural development (Chaléard and al. 2002 for Guinea and Chaléard 1994 for the Côte d'Ivoire). By mobilizing a geographic approach, this study seeks to explain how the fish farming sector slots into the village area and how this village area is itself connected to greater networks, particularly commercial ones. At this stage the connection of the village with N'zérékoré, a secondary town of 300 000 inhabitants (RGPH of 2014) is essential. This approach is completed by a description and an analysis of the fishfarming practices and of their evolution in order to point out how Gbotoye fish farmers adapt themselves to the market's opportunities and constraints. The systemic approach proposed by Cochet (2012), enables us to go over the fish-farm level in order to fit in those evolutions inside the global agricultural transformations all over the N'Zérékoré Region marked by a strong politicies instability (Marchal and al 2002, Bangoura et al 2006). This study relies on a deep understanding of those farmers and of their agriculture smallholdings. Data are issued from field surveys dated ten years between them, at the beginning of 2000 up to 2013 – 2015. Three types of inquiries have been conducted : individual inquiries to understand the course of the individual development, inquiries on the fish-farming practices and their evolution, to analyse the integration of the fish farming unit inside the familial farming system and last, historical and group inquiries to record the evolutions of the village, global evolution of the fish farming sector and processors' perceptions.

## 3 Results - Discussion

<footnote n="1">1 Projet de Développement de la Rizi-Pisciculture en Guinée Forestière APDRA Pisciculture paysanne, N'Zérékoré, Guinea</footnote>

<footnote n="2">2 UMR Prodig, Agroparistech, Paris, 16 rue Claude Bernard, 75005 Paris, France.</footnote>

<footnote n="3">3 APDRA Pisciculture paysanne, Massy, 9 avenue de France, 91300 Massy, France.</footnote>

<footnote n="4">4 ISTOM, Cergy, 32 Bd. du port,, 95094 Cergy-Pontoise cedex, France.</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: m.oswald@apdra.org</footnote>