ISSN 2185-159X

erd

International Journal ofEnvironmental and Rural Development

Volume 8 Number 1

June 2017

International Society of Environmental and Rural Development

IJERD – International Journal of Environmental and Rural Development

erdInternational Journal ofEnvironmental and Rural Development
Official Journal of the International Society of Environmental and Rural Development(Accessible from CABI International, EBSCO, Google Scholar, etc.)

Aims and Scope:
The International Journal of Environmental and Rural Development, IJERD, is an interdisciplinary journal
concerning environmental and rural development research in the education for sustainable development, ESD.
The IJERD is devoted to publishing peer-reviewed research article, review article and technical report in thefields of environmental and rural development, such as education for sustainable rural development, ruraldevelopment, environmental management or agricultural systems. Every article or report in IJERD must be
accepted by at least 2 reviewers and an editorial member.
The aims of IJERD are to publish the latest research development and achievements, to strengthen
education systems, to promote cooperation in environmental and rural development and to contribute to theprogress in sustainable development. The editorial board of this journal covers a variety of scientificdisciplines.
Thematic Areas:
The following areas are envisioned in IJERD.
Education for Sustainable Rural Development:
Environmental Education, Food and Agricultural Education, Participatory Approach, Capacity Building,Community Empowerment, Agricultural Extension, etc.
Rural Development:
Marketing, Partnership, Value Added Product, Community Development, Access to Technology,
Cultural Preservation, etc.
Environmental Management:
Bio-Diversity, Soil Degradation and Land Conservation, Water Quality Conservation, Deforestation andSustainable Forest Management, Environmental Management, etc.
Agricultural Systems:
Organic Farming, Conservation Tillage, Mechanization, Irrigation and Drainage, Nutrient and Pest
Management, Cattle Breeding, Agro-Forestry, Indigenous Technology, etc.
Infrastructural Systems:
Water Resource Development, Land Reclamation, Road Construction, etc.
Editorial Board:
Editor in Chief:
Prof. Dr. Machito Mihara, Tokyo University of Agriculture, Japan /Institute of Environmental Rehabilitation and Conservation, Japan
Managing Editors:
Dr. Jeeranuch Sakkhamduang, Institute of Environmental Rehabilitation and Conservation,
Southeast Asia Office / Association of Environmental and Rural Development, Thailand
Dr. Junaid Alam Memon, Pakistan Institute of Development Economics, Pakistan
Dr. Julian Torillo, Jr., Bohol Island State University, Philippines
Dr. Carlos Onan Tovar Mendoza, Honduras Embassy in Japan, Japan
Editorial Advisory Board:
Dr. Venkatachalam Anbumozhi, Asian Development Bank Institute, Japan
Dr. Robert J. Farquharson, The University of Melbourne, Australia
Prof. Dr. Keishiro Itagaki, Tokyo University of Agriculture, Japan
Dr. Chuleemas Boonthai Iwai, Khon Kaen University, Thailand
Prof. Dr. Arata Koga, University of Veterinary and Animal Science, Pakistan
Prof. Dr. Robert J. Martin, University of New England, Australia
Prof. Dr. Bunthan Ngo, Royal University of Agriculture, Cambodia
Dr. Hiromu Okazawa, Tokyo University of Agriculture, Japan
Prof. Dr. Salvador Catello, University of the Philippines, Philippines
Prof. Dr. Rajendra P. Shrestha, Asian Institute of Technology, Thailand
Prof. Dr. Murari Suvedi, Michigan State University, USA
Prof. Dr. Mario T. Tabucanon, United Nations University, IAS, Japan
Dr. Vidhaya Trelo-Ges, Khon Kaen University, Thailand
Editorial Secretariat:
Dr. Shinobu Terauchi, Institute of Environmental Rehabilitation and Conservation, Japan

IJERD – International Journal of Environmental and Rural Development (2017) 8-1

Contents
Effect of Drip-Fertigation Intervals and Hand-Watering on Tomato Growth and Yield
Nareth Nut, Sambath Seng and Machito Mihara

1

The Importance of Non-timber Forest Products in Rural Livelihoods and Ecosystem Services at
Phnom Prich Wildlife Sanctuary, Cambodia
Phanith Chou

7

Global Overfishing: A Last Call for Our World Natural Resource
Anucha Wittayakorn-Puripunpinyoo

14

Comparative Study of Methane Emission and Structural Development of Rice Plants from SRI and
non-SRI Methods in a Lysimeter ExperimentIshwar Pun and Eiji Yamaji

20

Development of Portable Artificial Rainfall Simulator for Evaluating Sustainable Farming in Kenya
Jacob Muriungi Maore and Machito Mihara

27

Event-Based Rainfall-Runoff Forecasting in Pampanga River Basin, Philippines
using Artificial Neural Networks (ANN)
Jcob C. Malaguit, Marriane F. Makahiya
and Mark Lexter D. De Lara33

The Effects of Land Restoration on Soil Fertility in Tsunami-inundated Farmlands ofMiyagi Prefecture in Japan
Kingshuk Roy, Satoki Iwata, and Yurika Kato

39

Development of a Device for Measuring the Load Bearing Capacity of a Farm Road
Koji Watanabe, Futoshi Kawana and Yasushi Takeuchi

49

Benchmarking Concepts of Community Wellbeing in an Area Vulnerable to Disasterdue to Volcanic Eruption
Merites M. Buot

56

Coral Transplantation Technology for Sustainable Fisheries and Underwater Tourism
Samuel J. Gulayan

63

Mobility Assessment for Sustainable Rural Development: Conversion of Conventional
Mobility Data and Historical Analysis
Satomi Kimijima and Masahiko Nagai

70

Reducing Cadmium and Copper Uptake of Soybeans by Controlling Groundwater Leveland its Impacts on Growth and Yield
Songtao Li, Choichi Sasaki, Chihiro Kato, NobuhikoMatsuyama, Takeyuki Annaka, Akira Endo and Kiichi Sasaki

77

Analysis on Surface Deformation and Cracks in Paddy Fields by 2016 Kumamoto Earthquake
using GNSS and Photogrammetry
Takahiko Kubodera, Takanobu Suzuki, Hiroshi Masaharu
and Eiji Matsuo

85

Spatial Assessment of Land Cover Change and Ecosystem Services from a Case Studyin Savannakhet Province, Laos
Thipphachanh Souphihalath, Kiichiro Hayashi, Makoto Ooba
and Wataru Kobayashi

93

Application of Soil and Water Assessment Tool (SWAT) Model to Predict Streamflow
and Sediment Yield in Wahig-Inabanga Watershed, Bohol, Philippines
Tomas D. Reyes, Jr.

104i

IJERD – International Journal of Environmental and Rural Development (2017) 8-1

Logging Effects on Soil Organic Carbon and Hydraulic Property in North Appalachian Regionof Ohio USA
Toru Nakajima and Rattan Lal

111

Impact of the Land Use Diversity on the River Water Quality in the Agricultural Area
Yuri Yamazaki, Toshimi Muneoka, Masato Kimura
and Osamu Tsuji

117

Comparison of Land and Water Improvement Projects and the Water Management System
between Japan and Nigeria
Dayyabu Muhammad Zaharaddeen, Katsuyuki Shimizu
and Yumi Yoshioka

124

Forest Ecosystem Services and Agricultural Production of Communities in Protected Areas:
A Case Study of Phu Kao - Phu Phan Kham National Park, Thailand
Wipada Janta, Adcharaporn Pagdee and Somkid Uttaranakorn

132

Estimation of Soil Erosion Based on USLE and GIS in Gardez Basin of Paktya Province,
Afghanistan
Abdul Malik Dawlatzai and Machito Mihara

139

Simplification of the Colorimetric Method to Detect Methanol Contaminationin the Cambodian Local Rice Liquor
Mizuki Matsukawa, Mitsuru Hamano, Kasumi Ito
and Toshiharu Tanaka

150

Pre and Postharvest Losses and Marketing of Grapes in Afghanistan: Case Study in
Mirbachakot, Shakardara and Kalakan districts of Kabul
Yusufi Mohammad Mirwais and Ryuichi Yamada

156

Determining Crop Insurance Participation in Laguna, Philippines Using Subsidy
and Asymmetric Information Incentives
Diane Carmeliza N. Cuaresma, Justine Angeli L. Saquing,
and Vincent Paulo M. Ty

163

Event-based Rainfall-runoff Simulations using GETFLOWS for Kourtimalei Catchment
in Djibouti
Fadoumo A. Malow, Sawahiko Shimada and Aurelien Hazart

169

Certification Schemes for Alternative Agriculture in Japan
Olga Tyunina and Eiji Yamaji

176

Current Agricultural Status and Problems faced in Paghman District of Kabul Province, Afghanistan
Rahmani Shafiqullah and Machito Mihara

183

A Comparative Study of Housing Conditions among living Sectors in Sri Lanka and among Residential
Areas in Tea Industry, Matara District, Southern Part of Sri Lanka
Yuko Fukuda and Eiji Yamaji

190

Evaluation of Microfinance Institutions in Ethiopia from the Perspective of Sustainability and Outreach
Fraol Lemma Balcha and Salehu Anteneh Tamare

197

Supply Chain Analysis for Determining the Requirements for Continuous Woody Biomass Energy
Utilization Systems: Comparison of the Actual Management Conditions in Japan
Kazuyoshi Nemoto, Hiromasa Inuzuka, Shogo Nakamura andYasuhumi Mori

203

Assessing Factors Influencing Farmers’ Perceptions on Adaptation to Climate Change:
A Case of Apple Farmers in Cheongsong, Korea
Seong Yoon Choi and Eiji Yamaji

210

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IJERD – International Journal of Environmental and Rural Development (2017) 8-1

Research article
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Effect of Drip-Fertigation Intervals and Hand-Watering on
Tomato Growth and Yield
NARETH NUT*
Faculty of Agricultural Engineering, Royal University of Agriculture, Phnom Penh, Cambodia
Email: nnareth@rua.edu.kh

SAMBATH SENG
Faculty of Agricultural Engineering, Royal University of Agriculture, Phnom Penh, Cambodia

MACHITO MIHARA
Faculty of Regional Environment Science, Tokyo University of Agriculture, Japan
Received 17 December 2016

Accepted 3 May 2017(*Corresponding Author)

Abstract Irrigation is vital to increase the crop yield or crop productivity. The research was
conducted in Royal University of Agriculture (RUA), Phnom Penh, Cambodia, in order tocompare the influence of different irrigation intervals and methods on plant development andyield of tomatoes. As replicated three times, the treatments designed in the experiment layout
included daily drip irrigation without fertilizer (T0), daily drip fertigation (T1), drip fertigationin every two days (T2), and (T3) daily hand-watering by applying the same amount of fertilizer
before planting. In the study, the quantity of water applied in each treatment was equal to 5.22
m3, or 20.88 m3 as a whole. The chemical fertilizers, 46-0-0 and 20-20-15, were only applied
for three treatments (T1, T2 and T3), and the total amount of fertilizers used was 7,662 g, or
2,554 g for each treatment, whereas T0 was not added with any fertilizer. The result illustrates
that T0, T1, T2 and T3 yielded 10.4 t/ha, 42.25 t/ha, 27.45 t/ha and 29.95 t/ha, respectively. The
average numbers of tomatoes in each treatment were 8, 22, 18 and 17 fruits per stem for T0, T1,
T2 and T3 respectively. Moreover, the stem growth rate and diameter growth rate was 63.36 cm
and 9.38 mm (T0), 84.81 cm and 12.03 mm (T1), 75.96 cm and 10.50 mm (T2) and 79.33 cm
and 11.10 mm (T3). Based on the experiment, it could be concluded that the application ofwater and nutrients to meet the crop needs without interrupting irrigation, as seen in T1, had
optimal effects on the growth and yield of tomatoes. Therefore, growers should irrigate crops
by focusing on the real crop needs for water and nutrient and should choose drip-fertigation
methods, which offers multiple benefits such as providing water effectively, reducing erosion
and loss of nutrients in the soil, making the ground slower in density, reducing grass, saving
time and water and increasing crop growth and yield.
Keywords drip-fertigation, hand-watering, effect, tomatoes, growth, yield, fertilizer

INTRODUCTION
Supplying an adequate amount of water is very important for plant growth; especially, when rainfall is
not sufficient, the plants must receive additional water from irrigation. There are various irrigation
methods that can be used to supply water to the plants. Each irrigation method has its own advantages
and disadvantages, so these should be taken into account when choosing the irrigation method which isbest suited to the local circumstances. A simple irrigation method is to bring water from the source of
supply, e.g. a well, to each plant with a bucket or a watering can (FAO, 2001). The drip irrigation
system is one of the most effective irrigation systems which can controll the water supply to theⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2017) 8-1

rootzone of the crops before the water is evaporated or run-off. This irrigation method is able to savewater and time, providing a certain amount of water to suit crop requirements. However, water content
in the soil must be taken into consideration before supplying the irrigation water as non-uniformity ofthe soil water content could lead to different crop productions (Hargreaves et al., 1998). Drip irrigation
is currently a suitable irrigation method for reducing the impact from drought to the agricultural
productivities. It can diversify and maximize crop productions through effective water management(GDA, 2016). Moreover, in the irrigation process, proper irrigation interval is very important tostrengthen the effective water use and crop productivities by providing a particular amount of water
based on the crop water need (Ismail et al., 2009).
Tomato is one of the most popular vegetable crop which is widely grown in the world. It belongs
to the genus Lycopersicon which is grown for its edible fruit (Jones, 1990). The fruit contains highlevels of vitamin A, B, C, E and nicotinic acid and is therefore an important source of vitamins. On the
average, the fruit contains 8% protein, 34% minerals (mainly K + Ca + and P), 48% total soluble
sugars, 9% citric acid and 0.5% vitamin. Tomato has a higher acreage than any vegetable crop in theworld and it requires a high water potential for both optimal vegetative and reproductive development
stages (Jones, 1990). The crop tolerates fairly acid soil and liming is unnecessary unless the soil pH isbelow 5. Well drained sandy loam is preferred by the crop. No horticultural crop has received more
attention and detailed study than tomato (Lycopersicon esculentum). Water deficit decreases tomato
growth, yield and quality (Byari and Al-Sayed, 1999). Therefore, proper water management is vital forsustainable crop production. In Cambodia, drip irrigation system is mainly used for fruit tree
plantations and some vegetable cultivation. Tomato is one of the crops which is suitable for drip
irrigation to produce a better yield (Sy, 2004).
OBJECTIVE
The research aims to compare the influence of different drip fertigation intervals and irrigation
methods with the diversed application of fertilizers on plant development and yield of tomatoes.
MATERIALS AND METHOD
Study area: The study was carried out at the Faculty of Agricultural Engineering, Royal University ofAgriculture (RUA), which is located at latitude of 11030’40.91” and longitude of 104054’01.24” fromJanuary 01, 2016 to April 30, 2016. Soil samples at the study area were taken to the laboratory to
determine pH = 7.10, Organic matter (OM) = 0.85%, Ec = 137.00 μS cm-1, Nitrogen (N) = 530.00
mg/kg, Phosphorus (P) = 109.60 mg/kg, and Potassium (K) = 180.00 mg/kg. Flow rate and operatingpressure before and after irrigation were measured with flow meters and a pressure gauge. The amountof water and fertilizers were applied equally in all treatments/plots. The total amount of irrigated water
was 20.88 m3, from which each treatment consumed 5.22 m3. The chemical fertilizers, 46-0-0 and 2020-15, were only applied for three treatments (T2, T3 and T4), and the total amount of fertilizers used
was 7,662 g, or 2,554 g for each treatment, whereas the control treatment (T 0) was not tested with any
fertilizer.
Experimental design: A Randomized Complete Block Design (RCBD) was applied. There were 12
plots and each plot size was 1 x 2.8 m. There were two rows in each plot with plant spacing of 0.4 x
0.8 m and plant population per plot was 16 plants. The experimental plots were divided into 4
treatments as the following:- Treatment T0 = Drip irrigation with no application of fertilizer (Control treatment)- Treatment T1 = Drip irrigation with daily application of fertilizers- Treatment T2 = Drip irrigation with every two days application of fertilizers- Treatment T3 = Daily hand-watering with application of fertilizer at the land preparationⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2017) 8-1

All treatments were irrigated in the morning and evening when the irrigation schedules wererequired.
Sample collection: Some parameters selected to analyze plant development/growth and yield werecollected during the experiment such as blooming period, stem diameter, stem height, and yield
including good and bad yields of the fruits.
Statistical analysis: Analysis of Variance (ANOVA) was conducted on the data using XLSTAT
statistical package. The means were compared by applying Least Significant Difference (LSD) at test
5% probability level.
RESULTS AND DISCUSSION
Blooming 50% and 100% of Tomatoes
The duration of 50% blooming was counted when the 6 plants per plot flowered and the period of
100% blooming was also counted when all plants reached full flowering..
Table 1 Effect of irrigation method on blooming period of tomatoes
Treatment
Treatment T0
Treatment T1
Treatment T2
Treatment T3
F-test probability
CV (%)

50% Blooming
56.33a47.33b53.66ab50.00ab
0.03
3.50

100% Blooming
63.66a50.66b
58.33ab54.33ab
0.03
2.70

Note: Different used alphabets indicate measurement is significantly different (p<0.05) from each other

Table 1 shows that the average blooming period of tomato plants in each treatment wassignificantly different. For the number of 50% blooming days, T 1 could produce the flowers within 47
days, which was faster compared to the other treatments. Meanwhile, T2 and T3 took 52 days, whereas
56-day flowering period was recorded in T0, and this period was the longest and differed significantlywith T1. Aside from these, the tomato plants took 51-64 days in order to produce 100% blooming
period. Based on the observation, the 100% blooming period were the same as the 50% blooming
period did. The different irrigation system could affect the blooming period of the tomatoes. T 1 hadquick flowering, but T2 had slower flowering, 6 days for 50% flowering and 7 days for 100%
flowering. Similarly, the research conducted by (Sionit, 1977) and (Kramer, 1983) on soybean foundthat if we retard the water to the plants, it might prolong the blooming period but also cause more
flower drop.
Stem Diameter and Height
Figures 1 and 2 illustrate the stem growth rate (stem height) and diameter growth rate (stem diameter)of all treatments: T0, T1, T2 and T3 which there were 63.36 cm and 9.38 mm, 84.81 cm and 12.03 mm,
75.96 cm and 10.50 mm and 79.33 cm and 11.10 mm, respectively. When the tomato plants were 3040 days old (5 days after applying fertilizer), each stem diameter and height in all treatments were notmuch significantly different. However, after 25 days of fertilizer application, the stem diameters andheight were steeply increased because it was the development stage of the crop, but the stem diametersand height increased slightly at the age of 70-90 days (flowering stage), which was the mid-season of
the crops. Moreover, irrigation systems highly affected the stem diameter and height of tomato crops,
as shown in figures below. After 45 days of planting, the stem diameter and height of tomatoes in T1Ⓒ ISERD

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IJERD – International Journal of Environmental and Rural Development (2017) 8-1

were the greatest in comparison to the other treatments. This illustrates that the development of plant
diameter is parallel with the increase in stem height if the adequate amount of water and fertilizers areapplied regularly. Moreover, the differences in average stem diameter measured in each treatment
remained significant until the end of the harvesting time. The stem diameter grew rapidly in thedevelopment stage, but from the flowering stage till the end of harvesting time, its growth was retarded.

Fig. 1 Effect of irrigation on stem diameter

Fig. 2 Effect of irrigation on stem height

Having compared the average stem heights in all treatments, the findings show that T2 had greater
stem height than T3, but from day 60, the plants grown in T 3 was 2 cm higher than in T2. Cevik et al.,(1996) and Yazar et al., (1991) showed that insufficient irrigation scheduling based on the crop waterrequirements can reduce partly the development of plant growth. Moreover, Srinivas et al., (1989)
carried out the research on plant water relations, canopy temperature, yield and water-use efficiency ofwater melon Citrullus lanatus indicates that while the plants cannot be able to intake enough water, themoisture at the root zone is reduced, resulting in retardation of the plant development.
Yield of Tomatoes
a. Number of fruits: The count of tomato fruits per stem was done at the harvesting time by counting
the numbers of fruits out of 6 tomato stems in each treatment. Each fruit was identified and totaled
considerately. The average numbers of tomatoes in each treatment were 8, 22, 18 and 17 fruits per stem
for T0, T1, T2 and T3 respectively. These tomato fruits per stem collected from each treatment wereaffected by different irrigation as it was indicated in the Fig. 3 that T 1 could yield 22 fruits/stem,compared to other treatments.

Number of fruit per stem40
P < 0.05

30
20
10
0
T0

T1

T2

T3

Treatment

Fig. 3 Effect of irrigation on number of fruits

Fig. 4 Effect of irrigation on fruit yieldsⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2017) 8-1

However, there was no significant difference in tomato fruits per stem between T 2 and T3, while
the T0 yielded the lowest numbers of fruits which was only 8 fruits/stem and it was significantly
different in comparison to the other three Treatments. Candido et al., (1999) studied the effect ofirrigation regime on yield and quality of processing tomato cultivars found that inadequate applicationof water could retard the plant development and fruit size. Moreover, low soil moisture can cause
irregularity to plant physics as a result of dropping flowers and producing less flowers. Furthermore,
Ponce et al., (1996) indicated that all crops with stress problems may encounter stunted growth,lessening flowers to produce the fruit set.
b. Total tomato yield: Total tomato yield (Fig. 4) included good and bad yields of fruits which werecollected from all treatments. The Table 2 below illustrates the total yield of tomatoes during theexperiment, and the result indicates that different irrigation methods and irrigation intervals could
dramatically affect the yield of tomatoes.
Table 2 Effect of irrigation method and interval on tomato yield (t/ha)
Irrigation method
Treatment T0
Treatment T1
Treatment T2
Treatment T3

Tomatoes yield (t/ha)
10.40c42.25a27.45b
29.95bF-test probability

74.59

CV (%)

1.79

SE
0.08
0.56
0.31
0.28

Note: Different used alphabets indicate measurement is significantly different (p<0.05) from each other

The results show that T0, T1, T2 and T3 yielded 10.4 t/ha, 42.25 t/ha, 27.45 t/ha and 29.95 t/ha,
respectively. The highest yield of tomatoes (42.25 t/ha) obtained from the experiment was the T 1,
whereas the lowest yield (10.40 t/ha) obtained was the T0, while the T2 could produce the same yieldas the T3. Zhai, et al., (2010) studied the effects of drip irrigation regimes on tomato fruit yield andwater use efficiency found that the more the irrigation interval is prolonged or not often irrigated, thelower the yield would be. Moreover, the other research findings of Pasternak, et al., (1995) also
indicated that drip irrigation systems with daily fertilizer application provide higher yields, comparedto the same drip irrigation method with the application of fertilizer for 2-3 days per time.
CONCLUSION
Based on the experiment, it could be concluded that the drip-fertigation system is an effective irrigation
method to promote tomato growth and yield and it is easy to control the amount of fertilizer and water
applied to the crops. The application of water and nutrients to the crop needs without interrupting
irrigation as in T1 had optimal effects on growth and yield of tomatoes while it highly affected not only
the stem diameter, but also the height of tomatoes which after 45 days of planting, were the greatest incomparison to the T0, T2 and T3. Moreover, the tomato fruits per stem collected from each treatment
were also affected by different irrigation methods as T 1 could yield 22 fruits/stem. Furthermore, thehighest yield, 45.30 t/ha, could be obtained, if compared to the other Treatments and the average yieldof bad fruit was only 3.05 t/ha as the yield of T 3 was 29.95 t/ha and the yield of its bad fruit was 6.9
t/ha. Therefore, growers should irrigate crops by focusing on the real needs of the crop requirement andshould choose drip-fertigation methods, which offers multiple benefits such as providing water
effectively, reducing erosion and loss of nutrients in the soil, making the ground slower in density,
reducing grass, saving time and water and increasing crop growth and yield. Thus, farmers shouldⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2017) 8-1

apply this irrigation technique for their own cultivations and stop using the conventional hand-watering
as it may waste water and obtain low yields.
ACKNOWLEDGEMENTS
The authors would like to be grateful to lecturers and staff at the Faculty of Agricultural Engineering,
Royal University of Agriculture (RUA), for providing the place to conduct the experiment by giving
relevant data and assisting in the research.
REFERENCES
Byari, S.H. and Al-Sayed, A.R. 1999. The influence of differential irrigation regimes on five greenhouse tomato
cultivars. 2. The influence of differential irrigation regimes on fruit yield. Egyptian J. Horticul. Sci., 26, 127-146.
Candido, V., Miccolis, V. and Pemiola, M. 1999. Effect of irrigation regime on yield and quality of processing
tomato cultivars. Acta Hortic., 537, 779-788.
Civik, B., Abak, K., Sari, N., Kirda, C. and Topaloglu, F. 1996. Effects of different irrigation levels on yield andquality of some vegetables irrigated with drip irrigation in Harran plain. GAP no: 105, 62.
Food and Agricultural Organization (FAO). 2001. Irrigation water management: Irrigation methods. Available fromhttp://www.fao.org/ docrep/s8684e/s8684e02.htm#chapter%201.%20introduction (accessed on 28/12/2015).
General Department of Agriculture (GDA). 2016. Techinical manual for farmers. Selection of suitable technics
for water management and agricultural fertilizer for sustainable crop production to adapt to Climate Change.
MAFF, Phnom Penh, Cambodia.
Hargreaves, G.H. and Merkley, G.P. 1998. Irrigation fundamentals. Water Resources Publications, Westview
Press, Boulder, Colorado, 12-17. USA.
Ismail, M.S. and Ozawa, K. 2009. Effect of irrigation interval on growth characteristics, plant water stress
tolerance and water use efficiency for Chile pepper. Proceeding of the 13th International Water Technology
Conference, March 11-13, Hurghada, Egypt, 545-556.
Jones, H.G. 1990. Plant water relations and implications for irrigation scheduling. Acta Hortic., 278, 67-76.
Kramer, P.J. 1983. Water relations of plant. Acadermic Press, London, 342-373.
Pasternak, D. and De-Malach, Y. 1995. Irrigation with brackish under desert conditions and irrigation management of
tomatoes (Lycopersicon esculentum, Mill.) on sand dunes. Agric. Water Management, 28, 121-132.
Ponce, M.t., Selles, S.G., Fneyra, E.R., Peralla, J.M., Moyan, A.S. and Ainrichsen, A.S. 1996. Metabolic
indicators of water deficit as a possible criterion for evaluation of irrigation management, The case of sweet
pepper (Capsicum annuum L.). Agric. Temcia, 56, 57-63.
Srinivas, K., Hedge, D.M. and Havanagi, G.V. 1989. Plant water relations, canopy termerature, yield and wateruse efficiency of water melon Citrullus lanatus (Thunb.). J. Hort. Sci, 115-124.
Soinit, N. and Kramer, P.J. 1977. Effect of water stress during different stages of growth of soybeans. Agronomy
Journal, 69, 274-277.
Sy, T. 2004. Adaptive ability of selected tomato seeds tolerant to drought in dry season. Bachelor Thesis. Royal
University of Agriculture, Phnom Penh, Cambodia.
Yazar, A., Oguzer, V., Tulucu, K., Anoglu, H., Gencoglan, C. and Diker, K. 1991. Determination ofirrigation
schelding of soybean in Harran plain using class-A-pan. GAP no: 45.
Zhai, Y.M., Shao, X.H., Xing, W.G., Wang, Y., Hung, T.T. and Xu, H.L. 2010. Effects of drip irrigation regimes
on tomato fruit yield and water use efficiency, 8, 709-713.

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Research article
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The Importance of Non-timber Forest Products in Rural
Livelihoods and Ecosystem Services at Phnom Prich Wildlife
Sanctuary, Cambodia
PHANITH CHOU*
Graduate School of International Development, Nagoya University, Nagoya, Japan /Faculty of Development Studies, Royal University of Phnom Penh, Cambodia
Email: chou.phanith@d.mbox.nagoya-u.ac.jp / c.phanith@gmail.com
Received 15 December 2016

Accepted 3 May 2017(*Corresponding Author)

Abstract This study aims to answer the research questions as follows: What is the current
context of Phnom Prich Wildlife Sanctuary (PPWS)? Where are the most accessible sites of
non-timber forest products (NTFPs) over the landscape of PPWS? Who are NTFPs-dependent
people? What is the importance of NTFPs in rural livelihoods? What is the contribution of
NTFPs in ecosystem services? Analyzed the NTFPs endowment, the open access simulation
model was applied. From the 310 sample households, NTFPs dependency and intensity wereanalyzed through descriptive statistics. Cross tabulation was applied to identify the main usersof NTFPs. The role of NTFPs in rural livelihoods and ecosystem services were discussed. The
simulation result clearly shows that NTFPs are rich over the landscape, which local people caneasily access. Among many types of NTFPs, eight of them are considered as the mostimportance for rural livelihoods including liquid resin, solid resin, bamboo shoot, bamboo poles,
wild honey, orchid flower, fuelwood, and Prich leaf (Melientha suavis Pierre). Around 93% ofsampled households collect NTFPs for foods, cash incomes, house construction, and farmequipment. NTFPs are the resources not only for the poor but also to all rural households at
PPWS. Some of NTFPs also contribute to ecosystem services.
Keywords NTFPs, rural livelihoods, ecosystem services, Phnom Prich Wildlife Sanctuary,
Cambodia

INTRODUCTION
Policy makers tend to forget the role of non-timber forest products (NTFPs) because they lackavailable information on contribution to the daily life of rural people and ecosystem services at national
or global levels (De Beer and McDermott, 1996). In Cambodia, about 84% of rural people heavily
depend on forest resources, especially on NTFPs for domestic consumption and complementary cash
income (MoE, 2011). NTFPs are also an intrinsic part of culture and traditions of forest-based andindigenous communities of Cambodia (EC-FAO, 2002). Local people have collected NTFPs
traditionally for various purposes without any statistical recording to the national economy, yet theimportance of NTFPs are not recognized well by the policy makers (Tola and McKenney, 2003). Royal
government of Cambodia has implemented the forest protection and management in various policies,
but they do not include NTFPs in their primary development agenda (FA, 2009). At present, theempirical evidence on the importance of NTFPs is not well documented in Cambodia. The role of
NTFPs has traditionally measured regarding direct benefits from only the amount sold. This
description does not reflect the real benefits of NTFPs to rural livelihood, national economy, or global
ecosystem services. To understand the real importance of NTFPs; first, the uniform category of
NTFPs-dependent people must be known because it gives insight into who are actually utilizing theseⒸ ISERD

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resources for livelihoods. Then, the role of NTFPs in ecosystem services should also be revealed toreflect the real importance.
OBJECTIVE
The objective of this study is to explore the importance of NTFPs in rural livelihoods and ecosystemservices at Phnom Prich Wildlife Sanctuary (PPWS). This study addresses the following questions:
What is the current context of rural households in PPWS? Where are the most accessible sites of
NTFPs over the landscape of PPWS? Who are NTFPs-dependent people? What is the importance of
NTFPs in rural livelihoods? What is the contribution of NTFPs in ecosystem services?
METHODOLOGY
Study Site Selection
Phnom Prich Wildlife Sanctuary (PPWS) is located in the west of Mondulkiri province, north-east
Cambodia. The whole areas of PPWS are 2,225km2. PPWS has a rich intricate mosaic of forest habitats,which consists of a mosaic of deciduous dipterocarp forest (1027 km2) and wetter semievergreen/mixed-deciduous forest (1070 km2) (WWF, 2016). Whilst PPWS regards as the wealth ofecosystems, and it is also of great importance to local communities who thrive to enhance livelihood
through extraction of NTFPs. Currently, there are eight community protected areas (CPAs) have been
established. However, this study selected six communities including Nglaoka, Sre Y, Chi Klab,
Poutong-Pouhoung, Toul, and Srae Khtong.
Data Collection and Analysis
Fieldworks took place in September 2015, March and April 2016. Questionnaire testing and adjusting
were conducted prior to the survey. Secondary data were mainly gathered to produce the NTFPs
accessibility map. The types of secondary data were the dataset of forest cover 2010, population
centers and road locations over the landscape. Participatory rural appraisals (PRA) were conducted infour focus group discussions from four communities protected areas (CPAs) in the different areas of
PPWS. Two primary tools were applied including seasonal calendar and NTFPs accessibility stocks.
Structured questionnaire interview were conducted with the 310 sampled households, which wererandomly selected from six communities.
Descriptive statistics were used to describe social characteristics of local people in PPWS, thefrequency of forest resources dependence, and frequency of people who involved with NTFPs. Thenthe people-forest relationships were indicated by the seasonal calendar and people’s dependence on theforestry resources. The simulation of “Open Access” model in Arc-GIS software (version 10.1) was
run to get the map of NTFPs accessibility stock over the landscape of PPWS. The inputs data forprocessing were current land use and land cover (LULC) 2010, NTFP harvest products stocks (uniquevalue 0, 0.3, 0.5, 0.7, 1), maximum travel distance to the product, population center, road locations andsize and legal accessibility for harvesting. The output by whether NTFP resources are abundant in thearea or not and how local people make use those resources for their daily life were interpreted.
Crosstabs tabulations were used to analyze the people’s dependence on the NTFPs, which varies indifferent categories of households. The importance of NTFPs to rural livelihoods through the types andutilization level of NTFPs was analyzed. Last, the role of NTFPs in ecosystem services by using
literature reviews was described.

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RESULTS AND DISCUSSION
Current Context of Rural Households in PPWS
Figure 1 shows the current livelihood activities of local people in PPWS. All agricultural activities,
including rainfed rice, cash crop, and vegetables are done, in the rainy season. Livestock, fishing, andsmall business are done in the year-round. People, who were landless or owned small farmland,
worked for other farmers especially in the sowing and harvesting periods of the rice crop. Also, local
people collected NTFPs in both seasons upon the types of NTFPs. For illustration, bamboo shoot canbe collected in the rainy season, but wild honey and Prich leaf (Melientha suavis Pierre) are available
only in the dry season. Liquid resin, solid resin, bamboo pole, orchid flower, and fuelwood can becollected in year-round. In the dry season, local people often traded liquid resin, solid resin, wild honey,
and orchid flower. However, this result indicates that NTFPs play the vital role in livelihood
diversification especially during the off-season, when local people are free from farming.
Livelihood
Activities
Rainfed Rice
Chamkar
Vegetables
Livestock
Fishing Activity
Small Business
Off-farm
Liquid Resin
Solid Resin
Wild Honey
Orchid Flower
Bamboo-Pole
Bamboo-ShootPrich Leaf
Fuelwood

Dry Season (Nov-April)
Jan
Feb
Mar
Apr

May

Rainy Season (May-October)
Jun
Jul
Aug
Sept

Oct

Nov

Dec

Fig. 1 Seasonal livelihood activities
Noted: Blank, light grey and pattern represent no activity, occasionally activity,and intensively activity respectively

NTFPs Resources Endowment in Phnom Prich Wildlife SanctuaryFig. 2 NTFPs resources endowment in PPWSⒸ ISERD

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Figure 2 shows the result of “Open access” model simulation. Through the NTFPs accessibility
map, it clearly shows that NTFPs are abundant over the landscape, according to the identified value ofhigh accessibility - light green (0.7) and dark green (1.0). The value of resources accessibility is likely
to be less in the areas nearby roads, village zones, and city, as indicated by the red, orange, and yellow
colors. Each NTFP has different characteristics of habitats and capacity to produce. Bamboo, fuelwood, and prich leaf (Melientha suavis Pierre) are very abundant, so local people easily access nearby
the village zones. The commercial NTFPs including liquid resin, solid resin, wild honey, and orchid
flower are located in further distance, mostly in evergreen and semi-evergreen forests where thedistance ranged from 9 km to 14 km from their villages.
NTFPs Dependent People in Phnom Prich Wildlife Sanctuary
Households’ characteristics make a crucial distinction between people who rely on NTFPs as the main
source or starting point of livelihoods. Fig. 3 shows that indigenous people (Bunong) tend to depend
more on NTFPs because it is the traditional activities to sustain their livelihoods. The study also findsthat households, who had moderate livelihood diversification (3 to 4 occupations per household), seemto collect more NTFPs. Also, low education people were more likely to rely on NTFPs. However, thisfinding agrees with other studies that native people and low education people are more likely to dependon NTFPs for livelihoods (Shackleton and Pandey, 2014). The claimed from Wunder (2001) that only
less income diversification households depend more on NTFPs is rejected because NTFPs are very
important for everyone living in the forest sanctuary. Regarding households’ production factors, thehousehold group having more members collected NTFPs more than the households who have less, as
be shown in Fig. 4. This result indicates that collection NTFPs is labor intensive. Also, households
owned a motorcycle for transportation enables to travel further to collect more NTFPs. Moreover, thehouseholds owned average agricultural land are likely to collect more NTFPs than the landless or
people owned large land. Despite the claimed from Cavendish (2002) that only local people who limit
the land ownership, limit capital, limit labor, and less income diversification tend to depend on NTFPs
heavily, is rejected.

Fig. 3 NTFPs dependency level by households’ characteristics

Fig. 4 NTFPs dependency level by households’ production factorsⒸ ISERD

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Importance of NTFPs in Rural Livelihoods
The results from focus group discussions indicated 14 NTFPs which local people have collected themost in PPWS. Among the 14 NTFPs, six NTFPs are considered as the most important NTFPs
including fuelwood, bamboo shoot, prich leaf, solid resin, bamboo poles, and liquid resin, which
represent 98%, 85%, 83%, 56%, 56%, and 50% respectively, based on 288 sampled households, whocollected NTFPs. Additionally, wild honey and orchid flower are considered as importance NTFPs
because they constitute as sources of households’ cash income. Table 1 shows the diversity of NTFPs
use and commercialization in PPWS. The most important NTFPs for subsistence use in PPWS were
fuelwood, bamboo shoot, bamboo poles, prich leaf, wild honey, and liquid resin which identified by
the sign + and +/- in Table 1. Bamboo shoot and prich leaf were consumed for food during the wet
season and the dry season respectively. Fuelwood ultimately serve the local communities for energy
sources for cooking. Local people used the bamboo poles for construction, fencing, and furniture. Wildhoney was used for traditional medicine and food ingredient. Liquid resin was used as the raw material
for small construction. Table 1 shows that liquid resin, solid resin, wild honey, and orchid flower were
widely collected for commercialization in PPWS. Wild honey, orchid flower, and solid resin wereseasonally collected and sold. Liquid resin was the only NTFP that local people extracted intensively
and sold in the year-round. In a few cases, bamboo poles and prich leaf were sold to the market. This
result is similarly to another study that many NTFPs are the critical subsistence in rural households’
economy. People traded NTFPs when only the markets were available at their locations (Cavendish,2002).
Table 1 Importance of NTFPs to local livelihoods
NTFPs
Liquid resin (n=143)
Solid resin (n=162)

Wild honey (n=89)
Orchid flower (n=91)
Bamboo poles (n=160)
Bamboo shoot (n=244)
Prich leaf (n=239)
Fuelwood (n=281)

Key species
1. Dipterocarpus alatus
2. Dipterocarps intricatus Dyer
1. Shorea guiso
2. Shorea siamensis
3. And some of Genera ofDipterocarpaceae (Vatica & Hopea)
1. Apis dorsata
2. Apis florae
3. Apis cerana
1. Vandopsis gigantea
1. Bambusa sp.
2. Bambusa bambos
1. Bambusa sp.
2. Bambusa bambos
1. Melientha suavis Pierre
Diverse long-lived tree species

Importance to rural
livelihoods
Use
Commercial
+/+
-

+/-

+/-

+/-

-

+/-

+/-

-

+/-

-

+/+

-

Noted: Frequency of use/sell: - not or little use/sell; +/- sometimes use/sell (moderate amount/seasonally); + often use/sell (regularly in year round)
Source: Author’s structured interviews (2016).

Contribution of NTFPs to Ecosystem Services
Although most of the NTFPs do not play a direct role in ecosystem services, they are ones among thefinal products provided by ecosystem services. However, trees/forests, which provides NTFPs, play a
crucial role in ecosystem services as follows.
First, many of the trees providing NTFPs are the long-lived trees, so all parts of the trees play arole in carbon sequestration, (Table 2). The carbon storage function has the social value which equals
to the social damage avoided by not releasing the CO2 into the atmosphere (Nordhaus, 2007). Second,Ⓒ ISERD

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the forest can regulate the runoffs the rainwater. It can reduce flood volumes during torrential rains,and in dry seasons, forest gradually releases the absorbed water that maintains river flow. The value ofwatershed protection is commonly equal to water treatment costs, water supply or investment costs ofreservoir construction (Gaodi et al., 2010). Third, the trees/forest helps to prevent soil erosion andminimize sedimentation in water reservoir or rivers. This service has a value that equal to the cost of
sediment removal from rivers and reservoirs (Keeler et al., 2012). Fourth, the honey bee not only
provides the honey products but also to crop plants pollinated. They are the pollinators that canincrease yield, quality, and stability of fruit and seed crops. Value equals to the investment costs tooptimize agriculture and conservation (Breeze et al., 2016). Last, some species, especially wild orchid
flower also contribute a role in aesthetic or recreation. The value of recreation equal to the travel costsof tourists to visit the particular site (Gaodi et al., 2010). This finding is consistent with other studies
that NTFPs play a direct or indirect role to maintain the value of ecosystem services because extraction
activities do not impact critically to the forest or trees (Arnold and Pe´rez, 2001; Neumann and Hirsch,
2000; Ros-Tonen and Wiersum, 2005).
Table 2 Contribution of the NTFPs to environmental services
Primary NTFPs
Long-lived trees: Liquid
resin, Solid resin,
Fuelwood, Bamboo, andPrich leaf (Melientha
suavis Pierre)

Ecosystem Services

Description of functioning contributed by NTFPs

Carbon storage

Leaves, branches, stems, barks, and roots of the longlived trees play role in carbon storage

Watershed protection

Water can be relocated to regulate availability of surface
water and runoff through the crown, trunk, undergrowth
vegetation and forest littler and soil.

Soil erosion prevention

Trees/forest help to prevent soil erosion and minimize
sedimentation in reservoirs and rivers.

Honey bee

Pollination

A honey bee is the key animal pollinator for croppollination

Ornament plant:
Orchid flower

Aesthetic

Slightly contribute to recreation and eco-tourism(physical wellbeing, learning, and quality of life)

Source: Author’s literature reviews (2016).

CONCLUSION
The contribution of NTFPs to rural livelihoods and forest conservation is regarded as very promising.
NTFPs are the common natural wealth for all residents, and their importance in rural livelihoods is
confirmed because of the strong dependence from most of the local people regardless their different
households’ characteristics. NTFPs make a significant contribution to the local economy such as food,house construction, fencing, energy, farm equipment, and cash income. Some primary types of NTFPs
also contribute simultaneously to ecosystem services through carbon storage, watershed protection, soil
erosion prevention, pollination, and aesthetic.
ACKNOWLEDGEMENTS
The author is extremely grateful to Professor USAMI Koichi for his enthusiasm for his helpfulcomments and suggestions.

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REFERENCES
Arnold, J.E.M. and Pe´rez, M.R. 2001. Can non-timber forest products match tropical forest conservation anddevelopment objectives? Ecological Economics, 39, 437-447.
Breeze, T., Santo, M.E., Fernandez, J.R., Worati, E.K., Salles, J.M., Sandhu, H. and Bond, J. 2016. Chapter 4:
Economic valuation of pollinator gains and losses The thematic assessment on pollinators, pollination andfood production. Bonn, Germany: Secretariat of the Intergovernmental Science-Policy Platform on
Biodiversity and Ecosystem Services.
Cavendish, W. 2002. Quantitative methods for estimating the economic value of resource use to rural households. In
Campbell, B.M. & Luckert, M.K. (Eds.), Uncovering the Hidden Harvest: Valuation Methods for Woodland andForest Resources, 17-65, Earthscan Publication Ltd., London, UK.
De Beer, J.H. and McDermott, M.J. 1996. The economic value of non-timber forest products in southeast Asia.
Second Revised Edition, Committee for IUCN, Amsterdam, Netherlands.
EC-FAO. 2002. Non-wood forest products in 15 countries of tropical Asia an overview. The Food andAgriculture Organization of the United Nations, Bangkok, Thailand.
FA. 2009. Cambodia's national forest programme. Background document, Forestry Adminstration, Ministry ofAgriculture, Forestry and Fisheries of Cambodia, Phnom Penh.
Gaodi, X., Wenhua, L., Yu, X., Biao, Z., Chunxia, L., Kai, A. and Jinzeng, W. 2010. Forest ecosystem services
and their values in Beijing. Geogra. Sci, 1, 0-51-058. doi: 10.1007/s11769-010-0051-y
Keeler, B.L., Polasky, S., Brauman, K.A., Johnson, K.A., Finlay, J.C., O’Neill, A. and Dalzell, B. 2012. Linking
water quality and well-being for improved assessment and valuation of ecosystem services. PNAS, 109,
18619-18624.
MoE. 2011. Cambodia human development report 2011, Climate change and forestry, Minisry of Environment of
Cambodia, Ministry of Economy and Finance of Cambodia and United Nations Development Programme. 9.
Neumann, R.P. and Hirsch, E. 2000. Commercialisation of non-timber forest products, Review and analysis ofresearch. Center for International Forestry Research. Indonesia.
Nordhaus, W. 2007. Critical assumptions in the Stern review on climate change science, 317, 201-202.
Ros-Tonen, M.A.F. and Wiersum, K.F. 2005. The scope of improving rural livelihoods through non-timber forest
products, An evolving research agenda. People, Trees and Livelihoods, 15 (2), 129-148.
Shackleton, C.M. and Pandey, A.K. 2014. Positioning non-timber forest products on the development agenda.
Forest Policy and Economics, 38, 1-7.
Tola, P. and McKenney, B. 2003. Trading forest products in Cambodia, Challenges, threats, and opportunities forresin. Cambodia Development Resource Institute, Cambodia.
Wunder, S. 2001. Poverty alleviation and tropical forest, What scope for synergies? World Development, 29,
1817-1833.
WWF. 2016. Phnom Prich Wildlife Sanctuary. World Wildlife Fund for Nature-Cambodia.

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Research article
erd

Global Overfishing: A Last Call for Our World Natural
Resource
ANUCHA WITTAYAKORN-PURIPUNPINYOO*
School of Agriculture and Co-operatives, Sukhothai Thammathirat Open University,
Nonthaburi, Thailand
Email: puanucha@windowslive.com
Received 2 December 2016

Accepted 5 May 2017(*Corresponding Author)

Abstract Global overfishing has been a severe problem for the last 37 years. The research
objectives were to 1) study the situation of global overfishing as our common property 2)
examine factors influencing global overfishing and 3) explain the global overfishing policy
implementation. Secondary data were collected from the database of Food and Agriculture of
the United Nations from 1979 to 2016 as well as additional countries. Descriptive statistics
were applied as a tool for data analysis. The inferential statistics were an applied econometricmodel to examine factors affecting global overfishing. Research results found that 1) for the last
37 years, the situation of global overfishing: according to the FAO database (2016), the world
has been faced with overfishing for 37 years, with 3 percent underexploited, 20 percent
moderately exploited, 52 percent fully exploited, 17 percent overexploited, and 7 percent
depleted; 2) factors affecting global overfishing were comprised of the world amount of fishcaught, the world quantity of consumption demand, the world fishery product price, the world
population and the fishery technology index; 3) for the global overfishing policy and its
implementations: in China, they launched a policy called legal regulations on the price ofaccess-rights to fisheries resources in China. Also, they have resource fishery taxes, which areabout 1 to 3 percent of the total production value. In Australia, they applied Individual
Transferable Quotas (ITQs) of southern bluefin tuna (SBT) as policy implementation resulting
in about 67 percent by average. ITQs reduced the amount of SBT caught. As common property,
global overfishing has been, and still is, one of our world problems that everyone in the world
should pay attention to. It is a last call for this world natural resource.
Keywords global overfishing, world natural resource

INTRODUCTION
Overfishing is a form of overexploitation where fish stocks are reduced to below acceptable levels.
Overfishing can occur in water bodies of any size, such as ponds, rivers, lakes or oceans, and can result
in resource depletion, reduced biological growth rates, and low biomass levels. Sustained overfishing
can lead to critical dispensation, where the fish population is no longer able to sustain itself. Someforms of overfishing, for example the overfishing of sharks, have led to the imbalance of entire marine
ecosystems (Scales, H.2007). The ability of a fishery to recover from overfishing depends on whether
the ecosystem's conditions are suitable for the recovery. Dramatic changes in species composition can
result in an ecosystem shift, where other equilibrium energy flows involve species compositions
different from those that had been present before the depletion of the original fish stock. For example,
once trout have been overfished, carp might take over in a way that makes it impossible for the trout tore-establish a breeding population (Gaia Vince, 2016).
Overfishing occurs when more fish are caught than the population can replace through natural
reproduction. Gathering as many fish as possible may seem like a profitable practice, but overfishinghas serious consequences. The results not only affect the balance of life in the oceans, but also theⒸ ISERD

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social and economic well-being of the coastal communities who depend on fish for their way of life
(Gaia Vince, 2016). Overfishing has significantly affected many fisheries around the world. As muchas 85% of the world's fisheries may be over-exploited, depleted, fully exploited or in recovery from
exploitation (Gaia Vince, 2016). Significant overfishing was also observed in pre-industrial times. In
particular, the overfishing of the western Atlantic Ocean from the earliest days of European
colonization of the Americas has been well documented (Bolster, W. Jeffery, 2012).
Following World War Two, industrial fishing rapidly expanded with rapid increases in worldwide
fishing catches. However, many fisheries have either collapsed or degraded to a point where increased
catches are no longer possible. Examples of overfishing exist in areas such as the North Sea, the Grand
Banks of Newfoundland, and the East China (Sea Lu Hui, ed, 2006).
According to a 2008 UN report, the world's fishing fleets are losing US$50 billion each year
through depleted stocks and poor fisheries management. The report, produced jointly by the WorldBank and the UN Food and Agriculture Organization (FAO), asserts that half the world's fishing fleets
could be scrapped with no change in catch. In addition, the biomass of global fish stocks have been
allowed to run down to the point where it is no longer possible to catch the amount of fish that used tobe caught (Black, R, 2008).
There are three recognized types of biological overfishing: growth overfishing, recruit overfishing,and ecosystem overfishing (Fish recruitment, 2013). Economic or bio-economic overfishingadditionally considers the cost of fishing when determining acceptable catches. Under this framework,
a fishery is considered to be overfished when catches exceed maximum economic yield where resource
rent is at its maximum. Fish are being removed from the fishery so quickly that the profitability of thefishery is sub-optimal. A more dynamic definition of economic overfishing also considers the present
value of the fishery using a relevant discount rate to maximize the flow of resource rent over all futurecatches. Overfishing occurs when more fish are caught than the population can replace through natural
reproduction. Gathering as many fish as possible may seem like a profitable practice, but overfishinghas serious consequences. The results not only affect the balance of life in the oceans, but also thesocial and economic well-being of the coastal communities who depend on fish for their way of life
(Pauly, D.1989).
Billions of people rely on fish for protein, and fishing is the principal livelihood for millions ofpeople around the world. For centuries, our seas and oceans have been considered a limitless bounty offood. However, increasing fishing efforts over the last 50 years, as well as unsustainable fishingpractices, are pushing many fish stocks to the point of collapse. More than 85 percent of the world's
fisheries have been pushed to or beyond their biological limits, and are in need of strict management
plans to restore them. Several important commercial fish populations such as Atlantic Bluefin tuna
have declined to the point where their survival as a species is threatened. Target fishing of top
predators, such as tuna and groupers, is changing marine communities, which leads to an abundance ofsmaller marine species, such as sardines and anchovies. Many fishermen are aware of the need tosafeguard fish populations and the marine environment; however, illegal fishing and other regulatory
problems still exist. The World Wildlife Fund works with stakeholders to reform fishery management
globally, focusing on sustainable practices that conserve ecosystems, but also sustain livelihoods andensure food security (https://www.worldwildlife.org/initiatives).
In this research, the researcher tries to explain the situation of global overfishing, examine factors
affecting global overfishing, and explain the global overfishing policy implementation in China andAustralia, which present both sides of the policy implementation—negative and positive impacts.
OBJECTIVESIn this research, the research objectives were to
1. study the situation of global overfishing as our common propertyⒸ ISERD

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2. examine factors influencing global overfishing
3. explain the global overfishing policy implementation
METHODOLOGY
Secondary data were collected from the database of Food and Agriculture (FAO) of the United Nations
from 1979 to 2016 as well as additional countries. Statistical methodology was applied multiple
regression analysis in order to construct a global overfishing model to estimate the factors affecting
global overfishing. Statistical methodology was applied as the tool for data analysis with T-test, F-test,
Coefficient of Determination (R2), Durbin Watson (DW).
RESULTS AND DISCUSSION
Following research objectives, the research results would be expressed into 3 parts as follows.
The Situation of Global Overfishing
According to the FAO database (2016), the world has faced with overfishing for 37 years, with 3
percent underexploited, 20 percent moderately exploited, 52 percent fully exploited, 17 percent
overexploited, and 7 percent depletion (Table 1, and Fig 1). Obviously, there are more than 50 percent
of global fisheries faced with the fully exploited, which potentially led to global overfishing.
Table1 World fishing situation
Global Fishing Situation

Percentage

Underexploited

3

Moderately exploited20

Fully exploited

52

Overfished17

Depleted
7
Source: Food and Agriculture Organization of the United Nations (FAO), 2016

Fig. 1 The global fishery situation
Source: Food and Agriculture Organization of the United Nations (FAO), 2016

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Factors Affecting Global Overfishing
The relation could be expressed as the econometric model below:
GOF = -227.750 + 0.025*(MFC) + 0.003*(QCD) + 0.0026*(NWP) + 0.124*(FTI) + error term
t-value (275.421)** ( 186.592)**
R2
.996
F
226.378**
D.W
1.96(127.643)***(89.643)**(212.146)**** Statistical Significance at 99 percent level, the estimated parameters were estimated by stepwise regression

Where GOP = Global Overfishing (%)
MFC = The World Amount of Fish Caught (%)
QCD = The World Quantity of Consumption Demand (%)
FPP = The World Fishery Product Price (%)
NWP = World Population (%)
FTI = Fishery Technology Index (%)
According to the above equation, it revealed that factors affecting global overfishing were theworld amount of fish caught, the world quantity consumption demand, the world fishery product price,
the world population, and the fishery technology index. The results expressed that when the world
amount of fish caught increased by 1 percent, it would increase global overfishing by 0.025 percent.
When the world quantity consumption demand increased by 1 percent, it made global overfishingincrease by 0.003 percent. And an increase in world population by 1 percent made global overfishingincrease by 0.0026 percent. Also, the fishery technology index was one of factors affecting global
overfishing, it obviously expressed that the increase in 1 percent of the fishery technology index raised
global overfishing by 0.124 percent. From the research results, it was clear that the fishery technology
index had the biggest impact on global overfishing.
The Global Overfishing Policy and Its Implementation
The Food and Agriculture Organization (FAO) of the United Nations encourages the member countries
to buy back fishing vessels older than 20 years. In this research, the researcher presented 2 different
countries, China and Australia.
China is one fishery that produces and trades in the world market. There is a Marine Fisherypolicy in China called the Fishing License System. By using this, fishermen can apply for a fishinglicense issued by the minister of agriculture of China. They also launched the policy called Legal
Regulations on the Price of Access-rights to Fisheries Resources in China. Moreover, they haveresource fishery taxes which are about 1 to 3 percent of the total production value. A fishing
moratorium on catches both inland and to a 200 nautical mile extent was initiated for fishing resourcesfrom late 1970. The impact of the resource tax or tax per value of fisheries in China led the Chinese
government to collect the taxes as revenues. In contrast, the number of new entrants in the fishingindustry increased more than 50 percent. The size of fleet grew along with advanced technology(compared with the old vessels.) The increased size of the fleet in the Chinese fishery industry forced
Chinese fishermen to operate beyond 200 nautical miles. Nowadays, China has the biggest size fleet in
the world and is the biggest producer.
Australia is one of the countries that applied Individual Transferable Quotas (ITQs) of southern
bluefin tuna as a policy implementation. By 1983, the southern bluefin tuna (SBT) fishery was in asevere state of decline. Catch rate and the recruitment to parent stock were going down. A group of
biologists from the major bluefin tuna fishing nations concluded that the total SBT catch should notexceed its estimated 1980 level, which was 21,000 tons. In 1983, the Australian government respondedⒸ ISERD

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to biologists’ recommendations by implementation of a total allowable catch limit of 21,000 tons in theAustralian SBT fishery. In 1984, the Australian government reduced the total allowable catch from
21,000 tons to 16,000 tons. In 1985, the ITQs were introduced with a catch quota of 14,500 tons. ITQs
caused a 50 percent reduction in fleet size because of the reduction in allowable catch limits. Australian
fishermen can sell their quotas and exit the industry, or buy quotas from others, which led to a majority(over 50 percent) of Australian fishermen selling their quotas and leaving the industry. Fishermen whobought up quotas from others improved their technology and fishing capacity by about 67 percent on
average. ITQs reduced the amount of SBT caught and the efficiency of the fishery increased as older
boats left, and also because fishermen increased the technology of the remaining boats. It required 10
years, from 1984 to 1994, for the parent stock to recover and stabilize. Nowadays, ITQs of 11,750 tonsof SBT are issued for Japanese, Australian, and New Zealand fleets. In sum, the impact of ITQs of
SBT in Australia brought about 1) reduction in the size of fleet and number of fishermen, 2) increased
and improved fishing technology (it was about 67 percent on average), 3) the total amount of ITQs
dropped the annual catch by almost 50 percent from the pre-quota level, and 4) it took almost 10 years
for the parental stock of southern bluefin tuna to recover.
CONCLUSION
Global overfishing is the now a severe problem for our world natural resource. Obviously, 17 percent
of our world fishery is overexploited with 7 percent already depleted. According to the data, 52 percent
is now fully exploited—this means that there is a high chance of increasing to overfishing. Over thelast 37 years, global fishing has increased year by year. Factors affecting global overfishing were theworld amount of fish caught, the world quantity consumption demand, the world fishery product price,
the world population, and the fishery technology index which had the biggest impact on global
overfishing. In order to relieve global overfishing, we should reduce all factors that affect all global
overfishing, especially the fishery technology index. Moreover, it should be our function to provide
relief for our world fishery natural resource to keep it with us. Nowadays, the FAO still encourages all
country members to apply a fishery policy to relieve global overfishing and protect us from its return.
Some countries have been successful with policy implementation, but others have not. Furthermore, it
has brought up other problems. Global overfishing could be declared as our world natural resource
problem, and we should save our world natural resource. Food Agriculture Organization of the United
Nations as a key global organization would have concrete policy and its implementation to restrain
global overfishing. Moreover; everyone in the world would have attention and cooperation with global
overfishing in order to restrain our would severe problem since it is our world common property.
ACKNOWLEDGEMENTS
I, personally, would like to thank my beloved family—Wittayakorn and Puripunpinyoo—for theirunlimited and unconditional love all my life. Thank to my beloved father and mother for their support:
without them I could not stand here.
REFERENCES
Benjamin, D.K. 2001. Fisheries are a classic example of the tragedy of the commons. PERC Reports. 19 (1).
Archived from the original on 2016-02-19.
Black, R. 2008. Fisheries waste costs billions. BBC News. Retrieved 2016-05-01.
Coad, B.W. and McAllister, D.E. 2008. Dictionary of ichthyology. Retrieved15 October 2013.
Costello, Gaines & Lynham. 2008. Fish recruitment. The Scottish Government. Retrieved 16 October 2016.
Gaia, V. 2016. Fture - how the world's oceans could be running out of fish. BBC.com. Growth overfishing.
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IJERD – International Journal of Environmental and Rural Development (2017) 8-1

Retrieved 2016-05-01.
Kirkley, J.E. and Squires, D. 1999. Capacity and capacity utilization in fishing industries. Discussion paper 99-16,
Department of Economics, University of California, San Diego, USA.
Lu Hui, ed. 2006. Pollution, overfishing destroy East China Sea fishery. Xinhua on GOV.cn. Retrieved 2016-0501.
Owen, J. 2016. Overfishing is emptying world's rivers, lakes, experts warn. National Geographic News.
Retrieved 16 October 2016.
Pauly, D., Silvestre, G. and Smith, I.R. 1989. On development, fisheries and dynamite, A brief review of tropicalfisheries management (PDF). Natural Resource Modeling, 3 (3), 307-329. Retrieved 15 October 2016.
Environment Database. 1999. Peruvian anchovy case, Anchovy depletion and trade. Retrieved 2016-01-05.
Vestergaard, N., Squires, D. and Kirkley, J.E. 2003. Measuring capacity and capacity utilization in fisheries, Thecase of the Danish gillnet fleet. Fisheries Research. 60 (2-3), 357-368. doi:10.1016/S0165-7836(02)00141-8Ⓒ ISERD

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Research article
erdComparative Study of Methane Emission and Structural
Development of Rice Plants from SRI and non-SRI Methods
in a Lysimeter Experiment
ISHWAR PUN*
Graduate School of Frontier Science, The University of Tokyo, Chiba, Japan
Email: punisr@gmail.com

EIJI YAMAJI
Graduate School of Frontier Science, The University of Tokyo, Chiba, Japan
Received 16 December 2016

Accepted 5 May 2017(*Corresponding Author)

Abstract Mitigating greenhouse gas (GHG) emission is a challenging issue in context ofcoping with the ongoing climate change. Rice farming is considered as the major emitter of
CH4 along with gaseous N2O. There are several methods of irrigation management that reduces
the amount of CH4 and N2O gases produced, such as intermittent irrigation and mid-season
drainage. SRI (System of Rice Intensification) is one of the new methods known to increase theyield while mitigating GHGs by applying one of key elements intermittent irrigation, and is amethod which is now disseminating in many tropical countries. This study was conducted tomeasure the effectiveness of SRI method by measuring rice plant development, yieldcomponent, and methane emission using a lysimeter facility. The transplanted nursery was
koshihikari, a Japanese rice variety. The study compared between two plots with different
water treatments. The results showed that a plant growth characteristics were better in aContinuous plot than in a Intermittent plot, while grain yield was not significantly different.
Methane gas was almost 50% less in the Intermittent plot than in the Continuous plot. Total
global warming potential from methane emission for Intermittent plot and Continuous plot
were 50.41 g CO2/m2 and 100.53 g CO2/m2, respectively. The results suggest that SRI
methodology could be an effective method for mitigating methane emission without reducing
the grain yield.
Keywords methane, water management, system of rice intensification, climatic variability

INTRODUCTION
Mitigating greenhouse gases is a challenging issue in the present world. Exponential growth in world
population against a linear growth of food supply, coupled with a decrease in agricultural land, aremajor problems for the world. To overcome the problems, several technologies are being applied toincrease food supply to support the growing population. The development of rice technology isbeneficial for both the environment and rice production. In rice production, technologies like SRI(System of Rice Intensification), and an irrigation management method called intermittent (Alternative
Wetting and Drying) irrigation, are being practiced in areas with limited resources to adapt to theongoing climate changes. SRI was originated in Madagascar in 1983, developed by Father Henri deLaulanı´e and later disseminated by Cornel University to more than 50 countries. SRI is broadly
defined as an agro-ecological methodology for increasing the productivity of irrigated rice by changing
the management of plants, soil, water, and nutrients (Cornell International Institute for Food,
Agriculture and Development, CIIFAD). Moreover, SRI method is a single transplanting method that
reduces seed requirement by 90%, increases yield by 20-100%, and saves water by 50%, according toⒸ ISERD

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the CIIFAD report. However, several issues are also known in implementation of SRI within local
farming practices from region to region, because the rice production largely depends on the topography,
climate conditions, and soil nutrients. Nonetheless, SRI method has shown positive synergies. Sato etal., (2005) reported that it is a method that saves water and costs, while also producing high yields inIndonesia. SRI method in India resulted in higher yields by almost 67% than a conventional method(Singh and Talati, 2006). Similarly, SRI method can save up to 25-50% water compared toconventional practices in Japanese rice farming (Chapagain and Yamaji, 2010). A study of the rice
plant development in China (Defeng et al., 2002) under a wider space transplanting method resulted ina higher root number than a close transplanting method. SRI method itself is a single transplanting
method of baby nursery with wider spacing. Chapagain et al., (2011) showed that the SRI method
results in a higher development of roots, where root number was greater by 30% and tillering and early
flowering increased by 25% during the paddy field experiment in Japan. Similarly, an experiment inIndia with the SRI method observed a significantly higher development in terms of physiological andplant development characteristics (Thakur et al., 2009).
Yet there are still a lack of reports for SRI adoption, particularly in Japan, since there arechallenges that needs to be overcome against local methods. Therefore, a comparative study of SRI and
non-SRI methods was conducted with a lysimeter facility of the University of Tokyo in Kashiwa,
Chiba, Japan. The objectives of the study were to examine the structural development of rice plant andmethane emission by two methods: the SRI method (intermittent irrigation) and non-SRI method(continuous flooding). The hypothesis was that the SRI method would perform better in comparison to
the non-SRI method. In particular, the SRI method was expected to result in higher grain yield,
biomass, and longer roots elongation, as reported in other countries. The experiment was conducted in
the lysimeter with transplantation of single seedling in two plots, where the intermittent plot was
irrigated by intermittent irrigation, and non-SRI method plot by continuous flooding. how high yields
are achieved through SRI’s key principles through a range of environmental factors and agronomic
management practices including variety selection.
METHODOLOGY
Study Area and Experimental Set Up
The experiment was conducted on the lysimeter situated in the University of Tokyo in Chiba
Prefecture during the rice growing season, from May to the end of October, in 2014. The lysimeter area
was 500 x 160 cm, soil depth 25 cm, was equipped with a drainage system on the right border andirrigation tap on the left border (Fig. 1). The soil was puddled homogeneously at the time of soil
preparation. 800 g of organic fertilizer was applied homogeneously. The composition of fertilizer was
1.3% nitrogen, 0.6% phosphorus, and 1.8% potassium, with C/N ratio of 22. The lysimeter was divided
into two plots by along the center by a plastic sheet. Plot A was named Intermittent plot and plot B wasnamed Continuous plot. Two water tubes of 25 cm length and 13.5 cm diameter were installed tomonitor the ponding depth of the plots. The water tubes featured a fixed measuring scale made ofaluminum to observe the water level in the lysimeter. Twelve days-old single nursery of Japanese rice
variety (koshihikari) was transplanted. The nurseries were prepared by the local farmers under ourrequest. Thirty-two nurseries were transplanted on May 23. The space between each hill was (30 x 30)
cm. The Soil Eh (ORP) sensors were set up for both plots at 5 cm and 10 cm depths. Soil Eh data wererecorded by EH-120 (Fujiwara Scientiﬁc Co. Ltd., Tokyo, Japan).
Management MethodⒸ ISERD

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Two types of water management were applied in the experiment. One was intermittent irrigation for
the Intermittent plot, and the other was continuous flooding for the Continuous plot. For the first two
weeks after the transplantation, shallow ponding condition was maintained in both plots until the
young rice nurseries became stronger. Intermittent plot was then managed by intermittent irrigation
method (Fig. 2). Water was drained by a pumping tube in case of excess rainfall. Continuous plot wasponded continuously during the vegetative phase. The first weed was removed from both plots three
weeks after the transplantation. The plots were maintained by periodical weeding thereafter. The weeds
removed from the plots were buried under the soil as practiced in the SRI method. The first floweringof the rice plant was observed on August 9th. The lysimeter was covered by a net on August 29th toprevent damage by birds. The rice was harvested on October 3, 2014. Lysimeter was kept dry for thelast two weeks to prepare for the harvest.

Fig. 1 Layout of lysimeter

Fig. 2 Ponding condition in lysimeter
RESULTS AND DISCUSSION
Rice Plant Development
The rice plant development in this study is divided into three phases. The day from the transplantation
to panicle formation is the vegetative phase. From panicle formation to the fertilization is the
reproductive phase. The final phase is the ripening phase, which includes maturity of rice grains.
During the vegetative phase, rice plant height was similar in growth (Fig. 3a). After the vegetative
phase, plant height grew significantly higher in the Continuous plot, suggesting that plant height growsⒸ ISERD

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more favorably without water stress. The development of rice tillers during the first three weeks after
the transplantation was similar. Afterwards, a significant increase in the number of tillers was observedin the Continuous plot (p<0.01). Higher number of tillers was observed in Continuous plot DAT (Days
After Transplant) 49 and 56. Intermittent plot showed a higher number of tillers in DAT 70 (Fig. 3c).
After DAT 70, tiller number decreased as the lower parts of small tillers decayed in both plots, which
was not included in counting. Higher number of leaves was observed in DAT 70 in both plots (Fig. 3b).
Average number of leaves for the Intermittent plot and Continuous plot were 51.90 and 66.06,
respectively. In the reproductive phase, similar cases of descending number of leaves were observed
after the dry leaves were excluded from the count. The first panicle was counted on DAT 78. Lower
number of panicles was recorded in Intermittent plot because of lower tiller number compared to theContinuous plot. The number of panicles and tillers were same at the time of harvest (Fig. 3d).

Fig. 3 Rice plant development: (a) plant height, (b) number of leaves,(c) number of tillers, and (d) number of flowerings
Rice Yield and Root Development
Table 1 Comparisons between number of grain, branch, rice root, and methane
Measurement indicators
Number of grains
Grain weight (gram)
Panicle branches (number)Panicle weight without grain (gram)
Root length (centimeter)
Dry root weight (gram)
Methane flux (mg/m2/hr.)

Significant
level
**
*
***
**

Intermittent plot
MeanSD†
1086.10395.49Continuous plot
MeanSD†
1198.30
383.15

21.03
95.50
1.09
19.20
5.07
0.82

21.97
117.30
1.34
20.60
7.62
1.77

6.82
36.08
0.41
2.53
2.21
0.73

5.95
39.19
0.49
2.63
3.23
1.70

* Significant level at 0.1, ** Significant level at 0.05, *** Significant level at 0.01, - Not Significant† Standard deviation

To study the yield component, ten rice plants were selected from both plots. The rice plants wereselected from both sides of the plot borders in order to make the sampling process uniform. Larger
panicles were observed in rice plants from borders than from the middle of the plot. The number ofgrains, panicle branches, and roots were measured from the same rice plant samples. Everymeasurement indicators are an average of ten plants from each plot. The average grains number andⒸ ISERD

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weight were higher in the Continuous plot, but without significant difference compared to theIntermittent plot. A significant difference was observed in panicle branches. Higher number of panicle
branches were found in the Continuous plot than in the Intermittent plot (Table 1). In terms of dry root
weight, Continuous plot had a higher weight of total root than the Intermittent plot, but there was nodifference between root lengths. Thakur et al., (2009) reported a higher elongation and better
distribution of root system by SRI method, and several other studies from outside Japan has also
reported of high yield and water savings by SRI. In this study, however, no significant difference wasobserved in terms of the yield and grain numbers.
Methane Emission
The methane gas emission from the Intermittent plot was typically lower than the Continuous plot. The
methane gas reduction process by intermittent irrigation is already known by many researchers (Hadi etal., 2010; Kudo et al., 2014). The pattern of methane emission in both plots were increasing during thevegetative phase and decreasing during the reproductive phase. The rapid development of rice plants
during the vegetative phase and longer application of irrigation caused the higher emission of methane.
Soil Eh was measured as an indicator of methane gas emission from both plots. A significantly positive
correlation between methane flux and soil water content was found in the lysimeter experiment in 2013
(Pun et al., 2013).

Fig. 4 Methane gas emission in Intermittent plot and Continuous plot

Fig. 5 Relation between methane flux and soil EhⒸ ISERD

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Moreover, to understand the mechanism of methane gas flux with soil layer condition, depth-wisemeasurements of soil Eh were taken. In both plots at 10 cm depth, positive correlation was observed
between methane flux and soil Eh in Figs. 5a and 5b. However, Intermittent plot showed a negative
correlation at 5 cm depth in Fig. 5a. In the Intermittent plot, the soil surface was alternatively kept wet
and dry by intermitttent irrigation, resulting in a soil Eh fluctuation and low level of methane formation.
The aerobic condition of the soils kept the emission low in the Intermittent plot. In case of theContinuous plot with continuous water ponding, soil Eh was lower at both 5 cm and 10 cm depths withhigher methane flux. During the continuous flooding, the soil was under a reducing condition and thus
emitted the methane gas. Cumulative methane flux during the rice growing season was reduced bynearly 50% in the Intermittent plot compared to the Continuous plot. Total methane flux emitted fromthe Intermittent plot and the Continuous plot were 50.41 g CO2/m2 and 100.53 g CO2/m2 per rice
growing season, respectively. Another anthropogenic gas, N2O emission, occurred when the flooding
water disappeared from paddy field and fertilizer was applied (Cai et al., 1997).
CONCLUSION
The experiment was conducted to measure methane emission and the structural development of rice
plant by SRI and non-SRI methods. The structural development of rice plant in the Continuous plot
was significantly greater than the Intermittent plot. Young single seedlings were used in both plots, andthere was no difference in grain yield. Dry root weight was greater in the Continuous plot but nodifference was observed for root length. The methane flux in Intermittent plot was significantly lower
than the Continuous plot, with almost 50% reduction in emission. The methane flux peak was mainly
observed during the vegetative and pre-reproductive phases. If the water could be managed without
stress to the plant, then SRI method can be the appropriate method for reducing global warming
potential. The higher correlation between soil Eh and methane flux were found at 10 cm depth in bothplots. The experiment was conducted on concrete made Lysimeter, and the heat from the concrete may
have affected the measurements for soil temperature and Eh. To clearly understand soil Eh in relation
to depth, tests in real paddy fields are needed. SRI method itself is a new environment-friendly method,
and while several researchers produced higher yields than a conventional rice cultivation method, theresults from this study was different. The dissemination of SRI method is still a work in progress,
where key basic elements are being identified by particular local areas, since different areas havedifferent climate conditions and resource availability. The difference in result was because the same
number of rice seedling was transplanted in both plots. In other to further understand the structuraldevelopment in SRI and non-SRI methods, an experiment is needed to test by seedling densities.
REFERENCES
Cai, Z., Xing, G., Yan, X., Xu, H., Tsuruta, H., Yagi, K. and Minami, K. 1997. Methane and nitrous oxide emissions
from rice paddy ﬁelds as affected by nitrogen fertilisers and water management. Plant and Soil, 196, 7-14.
Chapagain, T. and Yamaji, E. 2010. The effects of irrigation method, age of seedling and spacing on crop
performance, productivity and water-wise rice production in Japan. Paddy Water Environ, 8 (1), 81-90.
Chapagain, T., Yamji, E. and Riseman, A. 2011. Assessment of system of rice intensification (SRI) andconventional practices under organic and inorganic management in Japan. Rice Science, 18 (4), 311-320.
Defeng, Z., Shibua, C., Yuping, Z. and Xiaqing, L. 2002. Tillering patterns and the contribution of tillers to grain
yield with hybrid rice and wide spacing. In Assessments of the System of Rice Intensification: Proceedings ofInternational Conference, Sanya, China, 1-4 April 2002, 125-131. Eds N. Uphoff et al. Ithaca, New York:
Cornell International Institute for Food, Agriculture and Development. Retrieved from http://ciifad.cornell.edu/s
ri/proc1/sri_28.pdf.
Hadi, A., Inubushi, K. and Yagi, K. 2010. Effect of water management on greenhouse gas emissions andmicrobial properties of paddy soils in Japan and Indonesia. Paddy Water Environ, 8, 319-324.
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Kudo, Y., Noborio, K., Shimoozono, N. and Kurihara, R. 2014. Effects of water management practices for
mitigating gas emission and maintaining rice yield in central Japan. Agriculture, Ecosystem and Environment,
186, 77-85.
Pun, I, Yamaji, E. and Ches, S. 2015. Effect of INTERMITTENTI Practices on methane gas emission in a small
scale lysimeter. Paper presented at: The THA 2015 International Conference on “Climate Change and Water& Environment Management in Monsoon Asia”, Bangkok, Thailand.
R Core Team. 2014. R: A language and environment for statistical computing. R foundation for statisticalcomputing, Vienna, Australia. Retrieved from https://www.R-project.org/
Sato, S. 2005. 3 years experience of SRI (System of Rice Intensification) under SSIMP-DISIMP in eastern
Indonesia. Paper presented at: Integrated Citarum Water management Project, 4-5 July, 2005, Jakarta.
Sato, S., Yamaji, E. and Kuroda, T. 2010. Strategies and engineering adaptations to disseminate SRI methods inlarge-scale irrigation systems in Eastern Indonesia. Paddy water environ, 9, 79-88.
Singh, S.K. and Talati, J. 2006. Productivity impacts of the system of rice intensification (SRI), A case study inWest Bengal, India. Agricultural Water Management, 87, 55-60.
Thakur, A.K., Uphoff, N. and Antony, E. 2009. An assessment of physiological effects of system of riceintensification (SRI) practices compared with recommended rice cultivation practices in India. Expl. Agric.,
46 (1), 77-98. Doi: 10.1017/S0014479709990548Ⓒ ISERD

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Research article
erd

Development of Portable Artificial Rainfall Simulator for
Evaluating Sustainable Farming in Kenya
JACOB MURIUNGI MAORE
Graduate School of Agriculture, Tokyo University of Agriculture, Tokyo, Japan
Email: maorej@yahoo.com

MACHITO MIHARA*
Faculty of Regional Environment Science, Tokyo University of Agriculture, Tokyo, Japan
Email: m-mihara@nodai.ac.jp
Received 30 December 2016

Accepted 5 May 2017(*Corresponding Author)

Abstract Artificial rainfall simulation is an important aspect to investigate soil erosion process
and surface runoff through application of rainfall simulator. On the other hand portable artificial
rainfall simulator can be utilized in remote areas to solve some of the challenges that may beposed by heavy and expensive factory-made rainfall simulators at the same time maintaining
the effectiveness and efficiency of the equipment. Drop size, kinetic energy, surface runoff andsediment yield were obtained from experiments conducted using artificial portable rainfall
simulator developed in the lab of land and water use engineering for testing on actual fields
with different vegetation cover in Kenya. Average kinetic energy and drop size calculated from24 outlet needles of the rainfall simulator was found to be approximately 1.6 x 10-5 J and 3.42mm respectively. Using calibrated cylinder, intensity was measured randomly along thesimulator needle outlets and average intensity calculated as 40 mm hr-1. Water pressure was
generated through elevated Marriote bottle. Experiments were conducted in research site inMombasa Kenya. Drop size and kinetic energy for actual rainfall in Kenya were also
determined. Evaluation of soil erosion using portable artificial rainfall simulator is therefore
important in establishment of proper and effective soil erosion control strategy for achieving
sustainable farming in Kenya.
Keywords rainfall simulator, soil erosion, sustainable farming, portable, Mombasa Kenya

INTRODUCTION
Kenya is located in Eastern part of Africa and It lies between latitudes 5°N and 5°S, and longitudes 34°
and 42°E and its total area is 582,650 km2. It borders five countries (Tanzania, Uganda, Sudan,
Ethiopia and Somali) and Indian Ocean on south eastern part. Agriculture in Kenya faces various
challenges like water shortage, soil erosion, inadequate technical farming knowledge etc.
Research on soil erosion through application of portable rainfall simulator is among the basic
steps necessary in improving sustainable agriculture. Although water is considered as the most criticalresource for sustainable agricultural development worldwide (Konstantinos et al., 2015), soil erosion
control is also one of the factors considered in enhancing sustainable agriculture. Rainfall
aggressiveness has always been a prominent environmental threat in the course of human history,
especially affecting the agricultural sector (Nazzareno et al., 2010). Rainfall intensity, drop size, drop
fall velocity and kinetic energy are considered in determination of rainfall effect on the soil whereas
together with other factors like vegetation cover, land slope, soil erodibility and support practice can beapplied to estimate amount of soil loss.
Rainfall simulators have been developed and used extensively for runoff, infiltration and erosion
data collection for several years (Roberto et al., 2013). Approximately, for the last 75 years rainfallⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2017) 8-1

simulators have been developed and used throughout to conduct research on infiltration, surface waterrunoff and soil erosion (Humphry et al., 2002). Weight, efficiency, cost and portability are among the
major factors considered in development of rainfall simulators. They have produced raindrops either
through nozzles or tubing tips like hollow needles. In this research, more emphasis is put on simulatorsthat utilize hollow needles and require lower pressure as compared to those that use nozzles.
Development of portable rainfall simulator can be achieved through utilization of readily available
materials and a Marriott bottle for evenly distributed water pressure generation.
Many of the existing rainfall simulators are laboratory tools suited for working with disturbed soil
samples in shallow trays (Abudi, 2011). Data from the actual fields can yield better analytical results ascompared to prepared laboratory plots and provides more actual conditions for further research. Simple
design of portable rainfall simulator facilitates extensive research to be done in many fields. In thisstudy more emphasis is put on how to develop portable artificial rainfall simulator and evaluate soil
erosion through application of portable artificial rainfall in different fields and appropriate soil erosion
control measures to improve environmental factors and also promote economic factors leading toincreased farm output.
OBJECTIVE
To develop portable artificial rainfall simulator with Marriott bottle and to evaluate soil erosion on
agriculture field through observing soil loss in different fields condition under a portable artificial
rainfall simulator for sustainable farming practices in Kenya.
METHODOLOGY
Rainfall simulators are commonly used instruments to study sheet erosion, since they can be set topredefined values such as rainfall duration and quantity (Schindler et al., 2012). Development ofaffordable portable artificial rainfall simulator can therefore be utilized in solving some of thechallenges that may be posed by heavy and expensive factory made rainfall simulators at the same time
maintaining the effectiveness and efficiency of the equipment.
Materials: Locally available materials were used to develop portable artificial rainfall simulator which
includes; 2 pieces of PVC pipes 1 meter long each and 8 mm internal diameter, outlet needles 0.83 mm
internal diameter, connecting plastic pipes with control valves, Marriott bottle and metallic stand.
Twelve holes were drilled 8 cm apart on each pipe and outlet needles 0.83 mm internal diameter
fixed tightly to allow evenly distributed water drops. Marriott bottle was attached for constant pressure
head as shown in Fig. 1.

A

B

C

D

Fig. 1 Materials for artificial potable rainfall simulator Fig. 2 Needles numbering and grouping
Using simple metallic stands, developed rainfall simulator was suspended horizontally and firmly
at a height of 73 cm from the base and Marriott bottle connected approximately 83 cm from the base toⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2017) 8-1

generate equal and evenly distributed pressure and maintain a constant head of water. The experiments
were conducted in laboratory to determine intensity, drop size, drop velocity and kinetic energy of theraindrop simulated. Filter paper method was used to determine drop size, whereas camera method wasused to determine drop velocity. Kinetic energy is calculated by mathematical relationship of drop
mass and velocity. Drops from all 24 needles were collected and considered individually. Also, theintensity of the raindrops from simulator developed were also measured since it is crucial in therelationship between rainfall intensity and kinetic energy and its variations in time and space isimportant for erosion prediction (Van Dijk, 2002).
Raindrop size measurement: Using a filter paper, drops were collected individually and approximate
diameter of each circle formed by the rain drop measured and the average diameter calculated using
equation 1. To determine mass of the rain drop, its volume was calculated and multiplied with water
density.
d = 0.141d’+0.888 (m)

(1)

3

3

𝑉𝑜𝑙 = π/6𝑑 (m )(2)

𝑀 = 𝑉𝑜𝑙∗𝐷 = 𝑉𝑜𝑙∗1000 (𝑘𝑔)(3)

Where d is assumed diameter of spherical drop (m), d’ Diameter of the drop on the filter paper(m), M Mass of the drop (kg), and D Density of water (1000 kg/m3).
Raindrop velocity: Rain drop velocity was calculated using the camera method and applying equation
4. Distance travelled refers to height covered by a falling raindrop represented by image captured bycamera at a known camera shutter speed.
ms

Velocity ( ) =Distance (m)Time( s)(4)Time represents the camera shutter speed which is set as 1/100 seconds of camera shutter anddistance in meters represents the height captured by camera shutter covered by the falling drop.
Raindrop kinetic energy: Raindrop kinetic energy was determined from product of mass and raindrop
velocity.
𝐾𝐸𝐷 =1
𝑀𝐷 𝑉𝐷 2
2

(5)

Where KED is kinetic energy (J), MD raindrop mass (kg), and VD Drop velocity (m/s).
Pressure generated by Marriott bottle: For production of desired intensity over a period of time, it isimportant to supply equal water pressure which can be achieved by maintaining constant water head
Pressure = h x ᵨ x g (Pa)(6)
3Where h is head difference (m), ᵨ density of liquid (kg/m ), and g gravitational force (m/s2).
Actual rain drop observation in Kenya: Drop size determination by filter paper method and drop
velocity determination by camera method were also applied in actual rainfall in Kenya for comparison
with simulated drop characteristics. Several other methods which are more complicated and expensive
but more precise are applied to determine the size of the raindrop and drop velocity. The simple filter
paper and camera methods were applied for easy and quick comparison with simulated raindrop.
Evaluation of soil erosion through application of developed simulator in the field: Runoff plots
were designed by isolating an area in the field of 1.1 m long by 0.2 m wide and sheet metal inserted
around to a depth of approximately 0.2 m below the ground. After leaving the place wet overnight,
surface runoff was collected at an interval of five minutes. Six samples from each of the four different
field conditions were collected and each sample was measured and recorded. Soil lose was also
determined by oven drying surface runoff collected.

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RESULTS AND DISCUSSION
Raindrop Diameter and Kinetic Energy
Size of raindrop is defined by its diameter assuming that the raindrop is spherical. Size and kinetic
energy of each of the twenty four needles was calculated and graphically represented as shown in Fig.
3. Average kinetic energy was calculated as 1.6 x10-5 J and raindrop size approximately 3.42 mm.
Actual raindrops have different sizes but in average according to many researchers, they ranges
between 0.5 mm to 5 mm. Simulated raindrop is therefore within this range.
4.0
3.0

2.0

2.0
1.0

1.0

0.0

0.0
7 9 11 13 15 17 19 21 23
Simulator needle number
Kinetic energy (x10-5) J
Raindrop diameter (mm)13Raindrop diameter(mm)

Kinetic energy(x10-5) J

3.0

5

2.00

a*

a*

a*

a*

4.00

Mean drop diameter(mm)

Mean kinetic energy(x10-5) J

Fig. 3 Kinetic Energy and raindrop diameter
a*

a*

a*

a*

A

B
C
Needle groups

D

3.00
2.00

1.00

1.00

0.00
A

B
C
Needle groups

0.00

D

**p< 0.01, *p< 0.05**p< 0.01, *p< 0.05

Fig. 4 Mean kinetic energy and mean drop diameter statistical analysis
As shown in Fig. 4, according to needle grouping A, B, C and D, there was no statisticaldifference among the means for both drop size and kinetic energy. This indicates that pressure
calculated as 981 Pa was uniformly distributed among all the needles and the drops impact on the
ground is fairly uniform. Also using a measuring container, intensity was measured directly from four
sections of the rainfall simulator and average intensity was calculated as 40 mm/hour. Actual rainfall
intensity has a wide range depending on the amount of rain per specific time. Raindrop from thisdeveloped simulator utilizes gravity and its own weight to fall. Similarly homogeneous pressure
generated through Marriott bottle is due to gravity.

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IJERD – International Journal of Environmental and Rural Development (2017) 8-1

Actual Rainfall Drop in Kenya and Simulated Drop Characteristics

Simulator drop occurrences

Simulator drop occurrences
Natural rain drop occurrences

30

Cumulative(Occurrencies)

Cummulative(Occurrences)

Actual raindrops differ with the simulated drops since it’s hard to maintain same drop velocity, shapeand size throughout the experiment as compared to simulated ones. Therefore, application of camera
method and filter paper to determine drop velocity and drop size respectively for actual rainfall wasapplied purposely to obtain an estimate of these characteristics range and for comparison purposes.
After sampling 22 actual raindrops and determining their drop velocity, average velocity wascalculated as 1.38 m/s compared to average simulated raindrop velocity of 1.23 m/s. To determine
actual rainfall drop size, 58 raindrops were sampled and average drop size calculated as 3.94 mm
which is slightly higher than average of simulated raindrop which was calculated as 3.42 mm.

20
10
0

Natural rain drop occurrences

60
50
40
30
20
10
0

Drop size range (mm )Drop velocity range (m/s )Fig. 5 Natural rain drop and simulator drop relation on drop velocity and drop size
From Fig. 5, drop size, drop velocity and kinetic energy were found to be within the range
although actual rain drop size varied greatly. Portable artificial rainfall simulator can therefore beapplied in the field to simulate rainfall which is comparable to actual rain for evaluation of soil erosion.
Maize field
Grass land
Uncultivated land
Cowpeas field

3000

100
Soil loss (mg l-1)
(x 10 3')

Runoff discharge(mm min-1)

4000

2000

1000
0

Maize field
Grass land
Uncultivated land
Cowpeas field

80
60
40
20
0

0

5

10
15
20
Time (mins)

25

30

0

5

10
15
20
Time (mins)

25

30

Fig. 6 Runoff discharge and soil loss graphs for different soil field types
Evaluation of Soil Erosion through Application of Developed Simulator in the Field
Surface runoff was collected at an interval of 5 minutes on 1.2 m by 0.2 m plot on undisturbed soil.
According to Fig. 6, developed portable rainfall simulator was applied to evaluate soil erosion in four
different fields. Quantitative surface runoff and soil loss was calculated. Highest soil loss and highestⒸ ISERD

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surface runoff was observed at maize field since the soil was looser due to frequent cultivation ascompared to uncultivated field and probably due to less vegetation cover as compared to other fields.
This indicates that better soil conservation measures are necessary to reduce soil erosion for sustainable
farming practices in Kenya, depending on soil erosion rate, type of soil, water availability, different
fields’ vegetation cover and amount of rainfall among other related factors.
CONCLUSION
Portability of the rainfall simulator allows the researcher to work in remote areas in Mombasa County
and Simple installation can be an added advantage in performing timely, effective and efficientresearch. Development of affordable and simple but effective artificial rainfall simulator encourages
more research on soil erosion to be conducted in different fields which would otherwise been difficult
especially in developing countries.
Calibration through experiment indicated that portable artificial rainfall simulator connected toMarriott bottle for pressure generation produces simulated rain drop of approximately 3.42 mm andkinetic energy of approximately 1.6 x10-5 J for soil erosion impact study. Through statistical analysis
the simulated drops had no statistical difference both for the drop size and drop kinetic energy.
Moreover, characteristics of the simulated drops were within the range of the actual rain drops in
Kenya. This implies that the developed rainfall simulator can be effectively applied in field
experiments.
Evaluation of soil erosion using portable artificial rainfall simulator is important since soil erosion
is among the leading challenges faced by farmers. Soil with less cover and more cultivated are easily
eroded, therefore proper and effective soil erosion control strategy establishment is important forachieving sustainable farming in Kenya. Since terraces are common practice of soil erosion control but
less effective, better strategies like thick vegetation cover and trees are recommended to reduce directcontact of raindrops with the soil.
ACKNOWLEDGEMENTS
The authors acknowledge the support of members of Lab. of Land and Water Use Engineering and thefinancial support from Japan International Cooperation Agency and Tokyo University of Agriculture.
REFERENCES
Abudi, G. and Berliner, P. 2011. Rainfall simulators for field runoff studies, Journal of Hydrology, 76-8, 454-455.
Humphry, J., Daniel T., Edwards D. and Sharpley A. 2002. A Portable rainfall simulator for plot-scale runoff
studies, 18 (2), 199-204.
Konstantinos, C. and Maria, B. 2015. Sustainable water management in agriculture under climate change.
Proceedings of Agriculture and Agricultural Science, 4, 88-98.
Nazzareno, D. and Gianni, B. 2010. A rainfall erosivity model for the Mediterranean region. Journal of
Hydrology 387, 119-127.
Roberto, C., Tiffany, W. and Leo, P. 2013. On the estimation of surface runoff through a new plot scale rainfall
simulator in Sardinia, Italy. Proceedings of Environmental Sciences, 19, 875-884.
Schindler, Y., Wildhabera, D., Bänningera, K. and Burrib, C. 2012. Evaluation and application of a portable
rainfall simulator on subalpine grassland. CATENA, 91, 56-62.
Van, D., Bruijnzeel, L. and Rosewell, C. 2002. Rainfall intensity-kinetic energy relationships. Journal of
Hydrology, 261, 1-23.

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Research article
erd

Event-Based Rainfall-Runoff Forecasting in Pampanga River
Basin, Philippines using Artificial Neural Networks (ANN)JCOB C. MALAGUIT*
University of the Philippines Los Baños, Laguna, Philippines
Email: jcmalaguit@up.edu.ph

MARRIANE F. MAKAHIYA
University of the Philippines Los Baños, Laguna, Philippines

MARK LEXTER D. DE LARA
University of the Philippines Los Baños, Laguna, Philippines
Received 15 December 2016

Accepted 5 May 2017(*Corresponding Author)

Abstract This study developed a series of rainfall-runoff forecasting models that can be used indesigning the flood warning system around Pampanga River Basin. The data regarding rainfall
and water level of the river was obtained from the Hydrometeorological Division (HMD) of
Philippine Atmospheric Geophysical and Astronomical Services Administration (PAGASA).
Data were collected from 2014 and 2015 hourly water level reading and rainfall reading.
Feedforward Backpropagation Model, a variant of the Artificial Neural Network (ANN), wasused in the study along with Gradient Descent with Adaptive Learning Rate Algorithm as alearning technique for the network. MATLAB R2009b was used to train and design the
networks. A total of 45 networks were trained. Results of the training gave reasonablepredictions for most of the stations with a minimum accuracy of 96%. Inaccuracy of training insome stations were attributed to the inconsistency in data and other factors.
Keywords Artificial Neural Networks, river basin, event-based forecasting,Feedforward Backpropagation, rainfall-runoff model

INTRODUCTION
Flooding has been one of the most destructive phenomena in the Philippines. Flood causes loss of lives,
damage to personal assets and real properties, and adverse economic, environment and agricultural
impacts (Chen and Graham, 2005). In 2014, it was reported that 153 out of 324 natural disasters that
occurred in the country were hydrological disasters (Guha-Sapir et al., 2015) and in the last decade, thecountry experienced frequent flooding due to continuous raining and typhoons brought about by its
archipelagic characteristic.
In Central Luzon, Philippines, one of the regions that have been affected by recent disastrous
events including Typhoon Nona (Typhoon Melor) and Lando (Typhoon Koppu), there situates thesecond largest river in Luzon region, the Pampanga River. It traverses the provinces of Pampanga,
Nueva Ecija, Tarlac and Bulacan. Pampanga-Agno river basin serves as the catch basin for Tarlac,
Nueva Ecija, Pampanga and Bulacan provinces. Severe flooding of this river can swallow the wholeprovince of Pampanga and its neighboring provinces. Pampanga River Basin is continuously being
monitored by Philippine Atmospheric Geophysical, Astronomical Services Administration (PAGASA)
since it is one of the major land reservoir that affects the region.
The use of conventional deterministic models in this case requires extensive data and costs since itconsiders various physical factors that are not only tedious to collect but are also complex in nature.
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Hence, the use of Artificial Neural Networks (ANN) attracted many researchers because of its simple
yet powerful architecture to deal with complex nature of problems without sacrificing the results. The
black-box nature of this model needs no requirement to fully understand the system in the aggregate(Brath and Toth, 2002). The model, given sufficient data, can be used as a reliable forecasting tool
especially in forecasting the flood in Pampanga River Basin.
The use of ANN models as a rainfall-runoff model proves to produce qualitative forecasts (Lekkas
et al., 2005); and the rainfall level and actual stream flow (water level) can be actually used asparameters for the development of the model (Elsafi, 2014). This study focuses on the application of
ANN as a rainfall-runoff model in Pampanga River Basin with rainfall level and streamflow level(water level) as its parameters.
OBJECTIVE
The objective of this research is to forecast the flooding of the Pampanga River Basin. This will help
develop a warning signal that will inform the residents living near the area ahead when to evacuate. In
this way, a network will be trained to determine the model that will give the reasonable relationshipbetween the water levels and rainfall in Pampanga River Basin specifically on how the rainfall levelcan forecast stream flow (water level) downstream.
METHODOLOGY
Study Site

Fig. 1 Hydrological map of Pampanga River Basin, PhilippinesⒸ ISERD

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The Pampanga River Basin (Fig. 1), the 4th largest basin in the Philippines, has an aggregate area of
10,434 km2. About ninety five percent (95%) of the basin transcends the bounds of four provinces,namely Nueva Ecija, Tarlac, Pampanga and Bulacan. The remaining five percent is part of provinces
including Aurora, Zambales, Rizal, Quezon, Pangasinan, Bataan and Nueva Vizcaya.
Data Preparation
Rainfall and water level data of the Pampanga River Basin for the years 2014 and 2015 were gatheredfrom the Hydrometeorological Division (HMD) in Philippine Atmospheric Geophysical andAstronomical Services Administration (PAGASA). An hourly data from January 2014 to October 2015of the water level and rainfall reading was provided by the office. Since this study focuses on eventbased modelling, cumulative 24-hour rainfall level of the wet season of Philippines including June2014 – Dec 2014 and from June 2015 – October 2015 were considered.
In the data given, there were seventeen rainfall reading stations, but only thirteen stations wereconsidered since the data from other stations have problems in it including incomplete or erroneous
data. The rainfall data from these stations will serve as input data for the model. Nevertheless, for thewater level reading stations, there are ten of them but only nine stations were considered. These ninestations will represent the nine sets of models which will serve as basis for the clustering of the data set.
Data from these nine stations will represent the output/target data for the model. The input data is, then,normalized using the normalization formula intrinsic in the use of the MATLAB Neural Network
Toolbox given in Eq. (1).

p' 2

p  min( p)1max(p)  min( p)(1)

In Eq. (1), p represents the original data before normalization, p' represents the normalized data
and; min(p) and max(p) represents the minimum and the maximum data in the set, respectively. The
data was split into two computational components: training set and validation set which will be used as
calibration for the model developed. Using MATLAB Neural Network Toolbox, the input data can berandomly classified into seventy percent (70%) for the training set and the thirty percent (30%) for thevalidation set.
Model Development and Implementation

Fig. 2 Network visualization of the architecture of ANNⒸ ISERD

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ANN’s are designed to mimic the behavior of a neuron inside a human brain while it sends information(Sarkar and Kumar, 2012; Gershenson, 2003; Zhang and Govindaru, 2000; Chakraborty et al., 1992). It
requires inputs (like neuron synapses) which are multiplied by numerical weights (strength ofrespective signals), and mathematical functions that determine the activation of the neuron. In the end,a function computes for the total output of an artificial neuron. In this study, a Feedforward
Backpropagation ANN Model was used with fixed network structure consisting of two hidden layers
and ten nodes each as shown in Fig. 2.
A hyperbolic tangent sigmoid (tansig) function is used in the problem for all the transfer (oractivation) functions in the model. The tansig function is given by Eq. (2).

tansig(n)211  e 2 n

(2)

Before the training process starts, several parameters were set. These values will be used in theprocess and is important to achieve the best possible model using the Mean Square Error (MSE)
criterion. Parameters (Table 1) are set accordingly before training the model.
Table 1 Values of the model parameters before the model training
Parameters
Maximum number of epochs to train
Maximum validation failures
Initial learning rate
Learning rate increase factor
Learning rate decrease factor
Maximum performance increase
Momentum
Minimum performance gradient
Epochs between displays

Values assigned
1000.00
1000.00
0.01
1.05
0.70
1.04
0.90
10-10
25.00

Values shown were the pre-set of MATLAB Neural Network Toolbox.

RESULTS AND DISCUSSION
Network Model Assessment
Forty-five (45) models using the set architecture of ten neurons in the first layer and a single node in
the last layer for the output were developed after the training process. For each area cluster, there are
five models that were developed – each forecasting from three hours water level advance up to sevenhours water level advance. In general, most of the models showed good fit in its regression analysis interms of their forecasting capability and validity. Using the Mean Square Error (MSE) criterion, it
measured the average squares of errors or deviation from the predicted water level data to the actual
recorded water level data and the Pearson Correlation (R) that defines the strength of linear fit for the
ANN trained.
The other four to seven-hour models follow the same behavior as the initial three-hour model foreach area cluster. Overall the average square of deviation of all these models ranges from 0.0007 to
0.7624 which is relatively low compared to the actual predicted data. Most of the models showedsignificantly strong linear relationship (R between values 0.8 to 1) for the predicted data except for theset of Sasmuan area cluster with weak linear relationship (R between values 0.4 to 0.6).

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Table 2 MSE and R of the three-hour model for each of the nine (9) area clusters
Clustered Station
ArayatCandaba
MexicoPenaranda
San Isidro
Sapang Buho
Sasmuan
SulipanZaragoza

Mean Square Error (MSE)
0.58320.00600.00190.00900.02950.04730.02460.20000.0135Pearson Correlation (R)0.99160.9991
0.99220.99620.99790.99260.54480.98890.9948

Data Plots
The actual plot of area model with the highest value of R (three-hour model in Candaba station) showsa clear visual captivity of behavior of the actual recorded water level versus the predicted data in
Candaba station of water level as shown in Fig. 3.

Fig. 3 Plot of target data vs predicted data
for three-hour ahead forecastingin Candaba station

Fig. 4 Plot of target data vs predicted data
for five-hour ahead forecastingin Sasmuan station

On the contrary, the model with the lowest value of R (five-hour model in Sasmuan station)
showed weak captivity of behavior of the actual water level data (Fig.4). The model cannot linearly
capture the trend of the actual data and the actual data shows periodic up and down of plot. This may
be attributed to the nonlinear relationship of the rainfall and water level in the area and to the otherphysical phenomena or factors not considered in this study.
CONCLUSION
The application of Artificial Neural Networks (ANN) to the hydrology in Philippine setting has been
explored by creating forty-five (45) forecasting models under nine (9) area clusters in Pampanga river
basin. The training of the model resulted in good-fitted models except for the case of the Sasmuan
station. Inaccuracy of the model in Sasmuan station may be attributed to the other factors notconsidered in this study and hence can be further investigated and studied so that the model can beⒸ ISERD

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recalibrated. Furthermore, the use of ANN is not governed by theoretical rules nor any rule-of-thumbsuggestions that will result in a global optimal model. This side of the ANN can be further explored in
its application to hydrology. In addition to this, ANN is a data-dependent method and reasonableamount of data must be used in order to build a much reliable network model.
ACKNOWLEDGEMENTS
The data provided by the Hydrometeorological Division (HMD) of PAGASA thru Engr. Dominic
Joshua Lopez is hereby gratefully acknowledged.
REFERENCES
Brath A. and Toth, E. 2002. Flood forecasting using artificial neural networks in black-box and conceptual
rainfall-runoff modelling. Proceeding of the 1st Biennial meeting of the International Environmental and
Software Society, 166-171.
Chakraborty, K., Mehrota, K., Mohan, C.K. and Ranka, S. 1992. Neural networks and their applications. Review
of Scientific Instruments, 65, 1803-1832.
Chen, Y. and Graham, G. 2005. Flood forecasting in real time. Waterworld. [Online]. Available: http://www.
waterworld.com/articles/wwi/print/volume-21/issue-4/features/flood-forecasting-in-real-time.html.
Elsafi, S. 2014. Artificial neural networks (ANN’s) for flood forecasting at Dongola Station in the River Nile,
Sudan. Alexandria Engineering Journal, 53, 655-662.
Gerhenson, C. 2003. Artificial neural networks for beginners (August 2003) [Online]. Available: http://arxiv.org/
pdf/cs/0308031
Guha-Sapir, D., Hoyois, P. and Below, R. 2015. Annual disaster statistical review 2014, The numbers and trends.
Brussels: CRED [Online]. Available: http://ww.cred.be/sites/default/files/ADSR_2014
Lekkas, D., Onof, C., Lee, M. and Baltas, E. 2004. Application of artificial neural networks for flood forecasting.
Global Nest: The International, 6 (3), 205-211.
Sarkar, A. and Kumar, R. 2012. Artificial neural networks for event based rainfall-runoff modeling. Journal of
Water Resource and Protection, 4, 891-897.
Zhang B. and Govindaru S. 2000. Prediction of watershed runoff using bayesian concepts and modular neuralnetworks. Water Resources Research, 36 (3), 753-762.

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Research article
erd

The Effects of Land Restoration on Soil Fertility in Tsunamiinundated Farmlands of Miyagi Prefecture in JapanKINGSHUK ROY*
College of Bioresource Sciences, Nihon University, Kanagawa, Japan
Email: roy.kingshuk@nihon-u.ac.jp

SATOKI IWATA
Graduate School of Agricultural Science, Tohoku University, Sendai, Miyagi, JapanYURIKA KATO
Geosciences Department, University of Missouri-Kansas City, Missouri, USA
Received 3 February 2017

Accepted 5 May 2017(*Corresponding Author)

Abstract In previous studies, a series of samples were analyzed to measure the salinity status ofagricultural lands in Miyagi prefecture, where the extent of the seawater damage was the most
severe among the tsunami-affected area in Northeast Japan following the Great East Japan
earthquake in 2011. Since then, various restoration projects have been implemented byconcerned agencies of the central and local governments. In the agricultural lands damaged by
the earthquake and flooded with tsunami water, most of the technical tasks, such as the removalof debris and salts and the repair of irrigation and drainage infrastructures, were in the final
stages of completion in 2016, and the efforts seems to have achieved their primary objectives.
However, soil productivity in the restored farmlands remains a big issue for the local farmers.
The present study was carried out in different farmlands in two adjacent towns in Miyagi
Prefecture, Watari-cho and Yamamoto-cho. The major physicochemical and biologicalproperties of soil were investigated, and the results show that the soil dressing used as topsoil in
the restored farmlands had a low average cation exchange capacity (CEC) of <6 cmol(+)kg-1,
which ultimately contributed to lower average CEC values overall (<9 cmol(+)kg-1). Moreover,carbon-to-nitrogen (C:N) ratios in the soil dressings and the restored lands showed a wider
range (5 to 19), making the soil inconsistent in nutrient supply i.e., less fertile, compared tothose in the farmlands where there was no top-dressing and in the farmlands at nearby inlandsites. The study also suggests that issues concerned with rebuilding soil fertility and improving
soil productivity in tsunami-inundated agricultural lands should be resolved through sustainable,soil-friendly methods and practices.
Keywords tsunami, farmland, soil salinity, soil fertility, soil dressing, land restoration

INTRODUCTION
The Great East Japan earthquake and tsunami disaster of 2011 caused massive damages to the coastal
area. The earthquake damaged irrigation and drainage infrastructures severely, while the tsunami
waves inundated large areas of agricultural land, causing excessive saline in the soil. Since theoccurance, a lot of restoration efforts and studies have been carried out in the disaster-affected areas.
Most notably, the central government of Japan announced a comprehensive recovery plan in thedevastated area (Government of Japan, 2012), and accordingly, all concerned ministries, agencies,
prefecture administrations, and non-governmental organizations (NGOs) started restoration projects indifferent sectors. In agricultural lands, salinity was a very big issue soon after the tsunami, especially in
Miyagi prefecture, as this region accounted for nearly two-thirds of the tsunami-inundated agriculturalⒸ ISERD

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lands (23,600ha) in northeast Japan (MAFF, 2011). The different stages and the order of execution
related to the restoration, remedial, and reclamation works in agricultural lands consisted of mostly
removing debris, puddling and leaching with freshwater and flushing away salts from the topsoil, andcutting away the top layer and dressing it with new soil carried from a nearby mountain area along withthe rejuvenation of irrigation/drainage infrastructures. Roy et al. (2014, 2015) monitored thespatiotemporal changes in soil salinity in the tsunami-inundated agricultural lands in Miyagi prefecturefrom 2013 to 2015 and concluded that the salinity varied because of several physical factors, such as
topography, soil texture, and water stagnation due to damage to drainage infrastructure. Researchstudies carried out by Tomomasa et al. (2013) and Toyama and Tajima (2014) specified the damage tounderground infrastructures (i.e., buried drainage pipes) as an important factor responsible for waterlogging, poor infiltration, and high salinity. As the restoration works progressed and reached the final
stages of completion in 2016, salinity levels in most areas had returned to pre-tsunami levels (electricalconductivity<1 dS m-1). In addition, the area had enough precipitation (approximately 1,200mm/year) tohelp to mitigate the salinity level even in non-restored lands (NARO, 2013; Roy et al., 2015). However,when crops were cultivated, farmers started to face new problems related to soil productivity in therestored lands.
Soil productivity refers to the capacity of the soil in its normal environment to support plant
growth, while soil fertility refers to the ability of the soil to provide nutrients in proper quantities forthe growth of plants (Gruhn et al., 2000). Therefore, to better understand the potential productivity ofsoil in the restored area, it is important to examine key soil characteristics and indicators—such as soil
texture, depth, pH, organic matter content, and fertility—that might be affected through humanactivities, such as the use of a soil dressing in restoration works, or by natural processes, such as theaccumulation of salt due to seawater intrusion. A report published by the Miyagi Prefecture Furukawa
Agriculture Resaerch Station (2013) showed that the parts of restored paddy fields with a soil dressing
had a reduced average production (361 kg/10a) compared to that of non-restored lands (563 kg/10a).
During prior field surveys from 2013 to 2015, restoration works and farming activities were observed,
and local farmers were interviewed at random about these issues. It is not difficult to understand that,in the case of any sudden replacement of the topsoil with a new soil (soil dressing), the fertility of the
new soil is a big factor that ultimately affects soil productivity. No reports were published to dateindicating that any prior investigations or analyses were completed regarding the fertility status of the
new soil used as the soil dressing in the restored lands in the studied area. This promted further study todetermine the existing soil productivity in terms of the soil fertility in the area. In particular, fieldsamples were collected from different farmlands in two adjacent towns, Watari-cho and Yamamotocho, in Miyagi prefecture, where samples were also collected from 2012 to 2014. Soil samples werecollected from restored farmlands with topsoil dressing (“dressed land”); farmlands with no soil
dressing (“undressed land”); new/mountain soil used in topsoil dressing (“soil dressing”); and inland
farmlands (“inland”) and analyzed for soil fertility.
The aim of this study is to determine the soil fertility in tsunami-inundated farmlands in Miyagi
prefecture, where restoration works have already been completed, as well as to investigate sustainable,soil-friendly methods for agricultural lands where salinity and soil recovery are big issues.
METHODOLOGY
Study Sites and Methods of Sampling
Two adjacent towns, Watari-cho and Yamamoto-cho, both of which accounted for the largest
inundated areas in Miyagi prefecture, were selected as study areas for sampling, although the postdisaster statuses and recovery plans were different in these two sites (Watari-cho, 2015; Yamamotocho, 2015). Field samples were collected and interview surveys were conducted five times fromⒸ ISERD

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December 2014 to December 2015. Fig. 1 presents the sampling sites and sampling points (farmlands)
in the study area. The location of each sampling point were traced by a portable GPS receiver (Garmin
eTrex). It is notable that, in some cases, the land use of the surveyed farmlands (paddy fields/uplands)
as shown in Table 1, differed before and after the tsunami. The sampling points of dressed andundressed lands were randomly selected based on the information gathered during interviews with thelocal farmers. Samples of soil dressing were collected with permission from construction workers at
the study sites. In addition, soil samples were collected from inland farmlands within a distance of 1km from the inundated zone (sampling point) to represent the pre-tsunami soil characteristics of theinundated farmlands. A total of 48 samples were collected from different farmlands in the two
sampling sites: 17 from dressed lands (paddy fields:14, upland:3), 13 from undressed lands (paddy
fields:4, upland:9), 6 from soil dressing, and 12 from inlands (paddy fields:6, upland:6). All soil
samples were collected from the top layer of disturbed soil (upto a depth of 10 cm) by a diagonal
sampling method, packed in polyethylene sacks, and brought back to the laboratory for analysis. Other
related data about land use (paddy, upland, or mountain), land condition (dressed or undressed lands),
plant growth, fertilizer uses, and yields were recorded during interviews with farmers.
A visual outlook of two adjacent farmlands, one a restored land with soil dressing and the otherwith no soil dressing, as presented in Fig. 2, points a clear difference in growth between the two
farmlands.

(Picture taken on June 22, 2015)

Fig. 1 Study sites with sampling locations
Miyagi Prefecture, Japan

Fig. 2 Plant growth in two adjacent paddy fields inat Yamamoto-cho in Miyagi Prefecture

Analysis of Soil Samples
All of the soil samples were processed and analyzed in the laboratories of the College of Bioresource
Sciences at Nihon University. The collected soil samples were air-dried and sieved through a 2 mm mesh,and then select parameters representing key indicators related to soil quality and productivity were
measured. The parameters selected to represent the chemical properties were pH, electrical conductivity(EC), cation exchange capacity (CEC), and exchangeable cations (Ca2+，Mg2+，K+, Na+). The pH (H2O)
and the EC of the soil were measured with a pH meter (Twin pH B-212, Horiba, Japan) and an EC meter(B-173, Horiba, Japan), respectively. The ratio of soil to deionized water was 1:2.5 for pH, and 1:5 for
EC. The CEC, estimated from soil cations (Ca2+ ，Mg2+ ，K+, Na+), was determined by ammonium
acetate method (soil extracted in 1 mol L-1 ammonium acetate (NH4OAc) at a pH 7) and measured with anⒸ ISERD

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atomic absorption spectrophotometer (AA-6680, Shimadzu, Japan). The concentrations of exchangeable
cations, as well as the total exchangeable bases, were then expressed in terms of centi-mol per kg(cmol(+)kg-1). Moreover, basic cation ratios—e.g., calcium-to-magnesium ratio (Ca:Mg), magnesium-topotassium ratio (Mg:K)—and the percent base saturation were also calculated from the amounts of
exchangeable cations analyzed. On the other hand, the selected physical properties were mainly particle
size distribution and soil texture. In addition, gravel content (weight percentage) was also determinedsince the local farmers were worried about an excess of gravel in the soil dressing. The particle size
distribution of the soil was measured using with the pipette method based on Stoke's theorem, and thesoil textural classes were determined according to the IUSS (International Union of Soil Sciences)method. Gravel contents were calculated based on the remnants after sieving the air-dried soil through a
2 mm mesh. For biological properties, total carbon (TC), total nitrogen (TN), carbon-to-nitrogen (C:N)
ratio, and organic matter content (OMC) were measured. The C:N ratio, TC, and TN of the soil were
determined using an NC analyzer (Sumigraph NC-220F, Sumika Chemical Analysis, Japan), while the
OMC of the soil was estimated using with the loss-on-ignition method and a muffle furnace (KBF-748,
Koyo Thermos, Japan). All measured and calculated data regarding soil properties are listed in Table 1.
Statistical Analyses
Descriptive statistics, correlation analyses, and one-way analyses of variance (ANOVA) among thephysicochemical and biological parameters in this study were executed using a Microsoft Office Excel
2013 add-in application.
RESULTS AND DISCUSSION
Chemical Properties of Soil
As presented in Table 1, the average soil CEC values in dressed lands were 8.7 cmol(+)kg-1 and 7.8
cmol(+)kg-1 in Watari-cho and Yamamoto-cho, respectively, whereas those values in undressed lands
were 17.5 cmol(+)kg-1 and 14.9 cmol(+)kg-1, respectively. The inland soil at these two sites had similar
average CEC values at 18.4 cmol(+)kg-1 in Watari-cho and 16.7 cmol(+)kg-1 in Yamamoto-cho. However,average CEC values were much lower for soil dressings at these two sites—5.9 cmol(+)kg-1 and 5.5
cmol(+)kg-1 in Watari-cho and Yamamoto-cho, respectively. Soil from several dressed lands at both sites(sample numbers 14 to 18 and 34 to 36 in Table 1) showed comparatively lower pH values (4.5 to 5.6)
and percent base saturation (17% to 55.4%). However, EC1:5 values in the respective lands werecomparatively higher than in other farmlands. Although CEC and percent base saturation are essential
indicators of soil fertility, the overall balance in exchangeable ions that can be estimated from thenutrient ratios is more important for the nutrient supply to the plants. Fig. 3 and Fig. 4 present the basic
cation ratios (Ca:Mg, Mg:K) in the studied farmlands. The suitable values as recommended by MAFF
(2007) are 2 to 6 for Ca:Mg and 2 to 4 for Mg:K. The nutrient ratios from different farmlands, aspresented in Table 1 and graphed in Fig. 3 and Fig. 4, indicate that, while most of the farmlands’ soils
were within the MAFF-recommended limits, some soil samples collected from the dressed lands(sample 8 and 13 through 18 for Watari-cho, and samples 35, 36, and 39 for Yamamoto-cho in Table
1) had either higher or lower values than the recommended ranges. Such a situation indicates animbalance in nutrition supply in the dressed lands at both Watari-cho and Yamamoto-cho.
Physical Properties of Soil
As presented in Table 1, most of the soil samples (42 out of 48 samples), including each type of lands
in both of the studied areas, were sandy (sandy loam (SL), sand (S), or loamy sand (LS)). The texturalⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2017) 8-1

class of 31 soil samples (of 48) was sandy loam (SL), according to the IUSS classification system.
Therefore, the texture of the soil in the study area was generally sandy by nature, regardless of thepresence or absence of top dressing. However, the average particle size distribution showed a tendency
toward fewer fine fractions (silt and clay) in dressed lands than that in undressed and inland lands
because of the abundant gravel contents in the soil dressing—averages of 14.6% and 20.8% in Wataricho and Yamamoto-cho, respectively (Fig. 5).
Biological Properties of Soil
Fig. 6 presents the total C:N ratio of soil, a determinant of organic matter content and an important
indicator for plants’ nutrient availability, at different farmlands in the studied sites. Dividing the C:Nratios into four groups (Fig. 6) shows that most of the soil samples were within the range of 10<C:N<20.
10

10
8

MAFF recommended
range: (2.0 to 6.0)

6

Mg:K ratio

Ca:Mg ratio

8

4

6

MAFF recommended
range : (2.0 to 4.0)

4
2

2

0

0

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46

Sample number

Sample number

Notes: 1) MAFF: Ministry of Agriculture, Forestry and Fisheries, 2007
2) Sample number: 1 to 27 denote Watari-cho, 28 to 48 denote Yamamoto-cho3) Land use and condition: As presented in Table 1

Fig. 3 Soil Ca:Mg ratios at different farmlands
in Miyagi Prefecture

Fig. 4 Soil Mg:K ratios at different farmlands
in Miyagi Prefecture

0.30

25

5＜C:N ＜10

0.25

20

10＜C:N＜20

C:N＜5

0.20

15

TN (%)

Gravel content (%)

30

10

0.15

5

0.10

0

0.05

C:N＞20(N deficiency)

0.00
0.00
Watari-cho

Yamamoto-cho

Fig. 5 Gravel contents in soils from different
farmlands in Miyagi Prefecture

1.00

2.00

Watari- cho Undressed
DressedSoil dressing
InlandYamamoto- Undressedcho
DressedSoil dressing
Inland3.00

TC (%)

Fig. 6 Total C:N ratios of soils from different
farmlands in Miyagi PrefectureⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2017) 8-1

The C:N ratio in most agriculture soils remains more or less constant; Cleveland (2007) found a
global tendency for the C:N ratio of overall soil to be 14.3±0.5. A soil with a C:N ratio greater than 20
indicates N-deficiency because of microbial growth (JSSSPN, 1998).Although none of the soil samples
in this study had poor C:N values, however, the individual C:N values from the soil dressing and the
dressed lands were highly irregular and scattered across a wider range (5.7 to 18.4) compared to thosein the undressed (10.5 to 15.7) and inland soils (10.1 to 15.8). Moreover, the TC and TN values werealso lower in soil dressings and dressed lands than that in undressed and inland soils. The averagevalues for organic matter content (ignition loss) in the samples of the field soils (dressed, undressed
and inland) ranged within 3.9% to 4.9%, whether the same in the soil dressings showed lower values of
2% and 2.1% in the two study sites.
Soil Properties and Their Correlations
Table 2 presents the correlations between the measured properties of the soil samples collected fromdifferent farmlands in Miyagi prefecture. The correlation matrix also includes the grain-yield factor as
recorded during interviews with farmers at 15 paddy fields with different land conditions. From thetable, the grain yield was found to have strongly correlated (positively and negatively) with soil CEC(r=0.794), clay content (r=0.813), and gravel content (r=-0.87). Moreover, significant correlations
existed between the pH and the base saturation (r=0.889) and EC (r=-0.743); between the EC1:5 andthe base saturation (r=0.739); and between the CEC and the TN(r=0.884), clay (r=0.882), and gravel
contents (r=-0.970). Except for the C:N ratio, most of the measured parameters of the soil samples
that represent the productivity and fertility of the agricultural lands were found to have significantly
correlated to one another (p<0.001, p<0.01, or p<0.05), as presented in the correlation matrix table.
Status of Soil Fertility and Productivity in Restored Lands
While the data and analyses presented above give an idea of soil properties, Fig. 7 shows the yield in2015 at different farmlands in our study sites. As seen in the soil properties, the lands that were
restored with soil dressing (dressed lands) are relatively imbalanced nutritionally and, therefore, lessfertile compared to the undressed and inland fields, which ultimately negatively affects grain yields
at paddy fields with soil dressing in both Watari-cho and Yamamoto-cho, as shown in Fig. 7.
Although fertility does not always guarantee the productivity of soil, it (fertility) represents the
productivity of agricultural lands. Data from Table 1 show that the soils’ CEC and percent basesaturation values were lower overall in dressed lands than in undressed lands, and the extent ofvariation widens when comparing the values from dressed lands with the inlands. The use of soil
Table 2 Correlation matrix between soil properties in Miyagi prefecture
Grain yield
pH(H2O)(Paddy)Grain yield
pH(H2O)
EC1:5

EC1:5

CEC

Exchangeable cations
CaO

MgO

K2O

Base
saturation

TC

TN

C:N ratio

Sand

Silt

Clay

Silt+Clay

Gravelcontent

1.0
0.275-0.660 ***0.794 ***
CEC
0.790 ***
CaO
0.853 ***
MgO
-0.157
K2O
Base saturation 0.310 *
0.749 ***
TC
0.697 ***TN
0.187
C:N ratio-0.705 ***Sand
0.583 ***
Silt
0.813 ***
Clay
0.706 ***
Silt+Clay
Gravel content -0.870 ***1.0-0.743 ***
0.037

1.0
-0.293 *1.0

0.484 *** -0.564 ***0.837 ***

1.0

0.175

-0.484 ***0.916 ***

0.868 ***

1.0

0.116

-0.2510.620 ***

0.576 ***

0.649 ***

1.0

0.889 *** -0.739 ***0.077

0.560 ***

0.321 *

0.264

-0.093-0.1250.902 ***

0.734 ***

0.806 ***

0.485 ***

-0.0481.0-0.043-0.2570.884 ***

0.751 ***

0.817 ***

0.549 ***

0.042

0.904 ***

-0.113

0.325

0.011

-0.069-0.102-0.226-0.2350.201-0.0680.334 *

-0.792 ***-0.659 ***-0.797 ***-0.450 **-0.133-0.704 *** -0.693 *** -0.002

0.079-0.365 *0.685 ***

0.580 ***

0.734 ***

0.382 **

0.165

0.606 ***

0.609 *** -0.047-0.970 ***

1.0

0.052

-0.2170.882 ***

0.720 ***

0.791 ***

0.517 ***

0.050

0.789 ***

0.754 ***

0.102

-0.855 ***0.707 ***

1.0

0.076

-0.340 *0.801 ***

0.669 ***

0.805 ***

0.456 **

0.137

0.712 ***

0.702 ***

0.001

-0.999 ***0.970 ***

0.857 ***

1.0

-0.0490.254-0.734 ***-0.660 ***-0.725 ***-0.302 *-0.072 *-0.757 *** -0.647 *** -0.320 *0.535 *** -0.454 **-0.625 ***-0.545 ***1.0
1.0-0.1931.0
1.0

Significance of correlations indicated by *, **, and ***, are equivalent to p=0.05, p=0.01, and p=0.001 (n=48; only except grain yield, where n=15)Ⓒ ISERD

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IJERD – International Journal of Environmental and Rural Development (2017) 8-1

dressings with a low CEC (4.02 cmol(+)kg-1 to 8.35 cmol(+)kg-1) in the restored lands can be said tobe responsible for that. Because pH and base saturation are generally closely related to soil CEC(Turner and Clark, 1966; Huvlin et al., 2011), even a soil with a high CEC does not guarantee
productivity if the pH is low. In the current study, comparatively lower pH values (4.5 to 5.3) andpercent base saturations (15.67% to 55.49%) were observed in parts of the dressed lands at bothstudy sites (5 lands out of 11 in Watari-cho, and 2 lands out of 6 in Yamamoto-cho), although the EC
were comparatively higher (0.32 dS m-1 to 0.59 dS m-1) in the respective areas. In such case, whilesoil pH needs to be rectified to increase the percent base saturation and thus to enhance the fertility
and favor productivity, farmers were observed using overdoses of fertilizers in these fields, whichmight affect the C:N ratio and increase the EC1:5 of the soil. As a result, while the C:N ratio in mostof the agricultural soil is constant, as found in the inland soil (10 to 12), dressed lands showed
inconstant and wider-ranged values (5 to 19) within the same type of land condition in both study
areas. Lower cation ratios also indicate the inferiority of soil fertility levels in these dressed lands.
All of these chemical properties were found to have strong correlations with the proportion of finer
and the coarser particles from different soils in the study sites. While none of the inland fields had
more than 3.4% gravel contents, the undressed fields contained 6.4% (although there is somepossibility that undressed fields might have some gravel parts carried by the tsunami water), and soil
dressings contained between 14.1% and 25.8%, resulting in maximum percentages of 21.1% gravel
content in dressed lands. During the survey, many volunteers were observed periodically sorting out
gravel at the restored farmlands. All of these factors and activities individually and jointly
deteriorated the nutritional balance and fertility of the soil in these specific lands.

Grain yield (kg m -2)

6

5

4

3
Undressed ｜ Dressed ｜Inland｜Undressed｜Dressed ｜Inland
Watari-cho
Yamamoto-cho
Sampling site

Fig. 7 Yields from paddy fields at different farmlands
in Miyagi Prefecture (2015)
CONCLUSIONS
Several years have passed since the Great East Japan earthquake and tsunami disaster of 2011, and thetsunami-inundated farmlands have been influenced by many natural and anthropological activities.
Therefore, to figure out the absolute status of fertility and productivity in the fields before and after thetsunami is very difficult. Soil samples from nearby inland fields were collected and analyzed of toprovide an idea of the pre-tsunami soil quality in the area.
The analytical results indicate that soil in the restored (dressed) lands is inferior from theperspective of agricultural production compared to the non-restored (undressed) land. The extent ofthat inferiority becomes more distinct when compared with the soil inland, and the reason lies in thequality (or inferiority) of new soil used as a top dressing during the restoration. The findings of thisstudy conclude that, in the tsunami-inundated agricultural fields, the presence of a soil dressing (i.e.,Ⓒ ISERD

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IJERD – International Journal of Environmental and Rural Development (2017) 8-1

mountain soil) with inferior physical properties (i.e., fewer fine fractions) contained poor andimbalanced chemical and biological constituents, making it less fertile, which ultimately affected soil
productivity even after the restoration. It may take several years or even decades for the topsoil toregain its productivity, and the process can be accelerated only by proper fertility management after
figuring out the specific cause(s) and extent(s) in the specific field(s).
This study also serves to remind that, in the case of agricultural practices with long-term histories andprocesses, rapid land restoration does not necessarily ensure land productivity, which is the most important tofarmers and agriculturists. ‘Slow but steady’ mitigation processes, such as phytoremediation by using
halophytes (Yaneshita, 2011), would be much more practical because the latter does not damage theinherent soil fertility in agricultural fields. Instead, over-fertilization will not only worsen the soil quality
but also have drastic side-effects on soil and natural environment in the long run.
More detailed analyses considering more spatiotemporal data on soil humus, isotopes, and buried
geology could give a clearer picture of the overall fertility and productivity in the area after the tsunami
disaster. Future studies related to salinity mitigation in agricultural fields should include research using
different inexpensive and locally originated materials, such as cotton waste, charcoal, fly ash, andhalophyte species, all of which have salt removing potentials and are harmless to agricultural soils
which is a precondition to sustainable agriculture.
ACKNOWLEDGEMENTS
The authors would like to express gratefulness to the local farmers in Miyagi prefecture, who werevery kind and cooperative throughout the study period. Prof. Yukihiro Hayashi at the College of
Bioresource Sciences at Nihon University in Japan provided valuable suggestions. Thanks are due tothe students of the International Environment Conservation Lab at the College of Bioresource Sciencesat Nihon University for their support in carrying out the experiments and sample analyses.
REFERENCES
Cleveland, C.C. and Liptzin, D. 2007. C:N:P stochiometry in soil: Is there a “Redfield ratio” for the microbial
biomass? Biogeochemistry, 85 (3), 235-252.
Government of Japan. 2012. Road to recovery. Retrieved from http://japan.kantei.go.jp/policy/documents/
2012/__icsFiles/afieldfile/2012/03/07/road_to_recovery.pdf, access date: August 11, 2016.
Gruhn, P., Goletti, F. and Yudelman, M. 2000. Integrated nutrient management, soil fertility, and sustainableagriculture: current issues and future challenges. Food, Agriculture, and the Environment Discussion Paper
32, International Food Policy Research Institute, Washington, DC, 1-31.
Huvlin, J.L., Tisdale, S.L., Nelson, W.L. and Beaton, J.D. 2014. Soil fertility and fertilizers (8th ed.). Pearson
Prentice Hall, Upper Saddle River, N.J. , 550p.
JSSSPN (Japanese Society of Soil Science and Plant Nutrition). 1998. Glossary of soil science and plant nutrition(Fujiwara, S. et al. Ed.). Rural Culture Association Japan, Tokyo, 338p (in Japanese).
MAFF (Ministry of Agriculture, Forestry and Fisheries, Japan). 2007. Improvement of chemical properties.
Retrieved from http://www.maff.go.jp/j/seisan/kankyo/hozen_type/h_sehi_kizyun/pdf/ntuti16.pdf, accessdate: September 1, 2015 (in Japanese).
MAFF (Ministry of Agriculture, Forestry and Fisheries, Japan). 2011. Estimated area of agricultural land by theGreat East Japan Earthquake (tsunami). Department of Statistics, MAFF. Retrieved from http://www.
maff.go.jp / j/tokei/saigai/pdf/shinsai.pdf, access date: September 1, 2015 (in Japanese).
Miyagi Prefecture Furukawa Agriculture Research Station. 2013. Characteristics of nitrogen mineralization at therestored paddy fields after tsunami disaster in Miyagi prefecture. Retrieved from http://www.naro.affrc.go.jp/org/tarc/seika/jyouhou/H25/kankyou/H25kankyou010.html, access date: November 1, 2016 (in Japanese).
NARO (National Agriculture and Food Research Organization). 2013. The EC transition by natural rainfall in thetsunami affected paddy fields (no cropping). Retrieved from http://www.naro.affrc.go.jp/org/tarc/seika/
jyouhou/H23/kyoutuu/H23kyoutuu001.html, access date: August 29, 2015 (in Japanese).
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Roy, K., Kato, Y., Sato, N. and Nagasaka, S. 2015. Post-tsunami salinity status of reclaimed and nonreclaimed
farmlands in Miyagi Prefecture. J. Environmental Information Science, 43, 19-24.
Roy, K., Sasada, K. and Kohno, E. 2014. Salinity status of the 2011 Tohoku-oki tsunami affected agriculturallands in northeast Japan. International Soil and Water Conservation Research, 2 (2), 40-50.
Tomomasa, T., Kitagawa, I., Mineta, T. and Haraguchi, N. 2013. Restoration and desalinization of tsunamidamaged agricultural land. J. Japanese Geotechnical Soc., 61, 20-23 (in Japanese).
Toyama, K. and Tajima, T. 2014. Measures and issues for restoration and rehabilitation of farm lands andagricultural facilities destroyed by the Great East Japan Earthquake. Water, Land and Environmental
Engineering, 82 (3), 201-204 (in Japanese).
Turner, R.C. and Clark, J.S. 1966. Lime potential in acid clayand soil suspensions.Trans. Comm. II & IV Int. Soc.
Soil Science, 208-215.
Watari-cho (Maintenance team: Agriculture, Forestry and Fishery Division). 2015. Rice planting and reclamationin restored farmlands of Watari cho in 2015, Retreived from http://www.town. watari. miyagi.jp /index.cfm/
9,30060,c,html/30060/20141128-091304.pdf, access date: September 10, 2016 (in Japanese).
Yamamoto-cho (East Infrastructure Promotion Office). 2015. Master plan for restoration of agricultural lands inYamamoto cho. Retrieved from http://www.town.yamamoto.miyagi.jp/uploaded/attachment/5704.pdf, accessdate: November 2, 2016 (in Japanese).
Yaneshita, M. 2011. Restoration of agricultural land flooded by seawater from the Tohoku tsunami: salt-removalusing seawater-tolerant plants. Report of Taisei Technology Center, 44 (20),1-4 (in Japanese).

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Research article
erd

Development of a Device for Measuring the Load Bearing
Capacity of a Farm Road
KOJI WATANABE*
Graduated School of Agriculture, Tokyo University of Agriculture, Tokyo, Japan
Email: 45515008@nodai.ac.jp

FUTOSHI KAWANA
Faculty of Regional Environmental Science, Tokyo University of Agriculture, Tokyo, Japan

YASUSHI TAKEUCHI
Faculty of Regional Environmental Science, Tokyo University of Agriculture, Tokyo, Japan
Received 14 December 2016

Accepted 5 May 2017(*Corresponding Author)

Abstract The maintenance of infrastructure for agricultural production is essential tosustainable agricultural productivity. Farm roads are particularly important infrastructure fortransporting agricultural products. Load-bearing capacity is a performance index for evaluating
the soundness of a road, and recently, falling weight deflectometer (FWD) tests have been used
to measure such capacity. However, an FWD is a stationary device, so its use is time intensive.
This study aims to develop a moving wheel deflectometer (MWD) device that determines theload-bearing capacity continuously and economically, for the efficient management of farmroads. For that reason, the development of the MWD involved fabrication, testing and theverification of its practicality on a public road. The MWD was used to estimate the load-bearing
capacity of a public road at travel speeds of 10 to 60 km/h. The soundness evaluated based on
the MWD measurements were shown to be comparable to that as evaluated by the FWD at all
travel speeds. The results demonstrate that the MWD has the potential to be used for thecomprehensive measurement of load-bearing capacity.
Keywords pavement management, soundness, load-bearing capacity,
falling-weight deflectometer

INTRODUCTION
The maintenance of agricultural infrastructure is essential for sustainable agricultural productivity, andmaintenance and repair should be implemented at times and scales that extend the service life.
Therefore, a sustainable, recycling-oriented society can’t be realized without preventive management
system for infrastructure. To apply that system to farm road management, the soundness of
infrastructure must be quantitatively evaluated. In general, a road’s soundness is evaluated in terms of
its surface condition and load-bearing capacity. Road surface measuring vehicles are able to obtain
continuous data on road surface conditions, such as cracking, rutting and roughness, along a survey
road, so such devices enables the efficient determination of road conditions. In contrast, load-bearing
capacity is evaluated in terms of road surface deflection. The falling weight deflectometer (FWD) test
has been used to obtain road surface deflection data. This non-destructive testing device accurately
measures road surface deflection, however, its use is too time intensive for comprehensive roadmeasurement. Also, the FWD is a stationary device, which means that there is the risk of overlooking
localized damage between measurement points. Therefore, a method needs to be developed for
determining the load-bearing capacity continuously at high travel speed.
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Devices that continuously measure road surface deflection have been developed in other countries
since 1990 (Kano, 2008). The rolling wheel deflectometer (USA), road deflection tester (Sweden) andhigh-speed deflectometer (Denmark) have been put into practical use (Maruyama, 2012; Flintsch et al.,2012). These devices determine load-bearing capacity by road surface deflection measured by Laser
displacement sensors or assumed by deflection velocity measured by Doppler vibrometer (Van, 2009;
Elseifi 2012; Diefenderfer et al., 2008; Ferne et al., 2009). The Laser displacement sensor measuresdirectly the distance between the sensor and the road surface. This direct measurement enables toobtain road surface deflection easily without numerical calculation. Also, the deflection measurement
results are independent of running speed. However, the measurement accuracy is closely related toroad surface conditions, and measurement at curved sections is difficult. In contrast, the measurement
results by Doppler vibrometer are not affected by road surface conditions because the device measuresthe deformation velocity of the road surface. In addition, deflection can be assumed by integration ofdeflection velocity. However, the deflection velocity is zero in the stationary condition. This means
evaluation becomes difficult at slow running speed.
In general, Farm road has important roles of not only transporting products but also carrying inand out agricultural machines. Measurement at high travel speed makes ensuring safety difficult duringrunning test. In addition, there are many curved sections and narrow sections in farm roads. Currently
available moving devices for measuring load bearing capacity are used by heavy trailer vehicles thatgenerate large deflection, for example RWD consisting of a 16.3 m long semitrailer. It means thesedevices are not necessarily suitable for farm road management. Therefore, a device to be used bysmaller vehicles and which is capable of measuring load bearing capacity without effect on travel
speed should be developed. For that reason we developed a moving wheel deflectometer (MWD) to
determine which points of a deteriorating road sections require more detailed survey, such as FWD test
(Terada, 2012; Matui, 2013). The MWD performes measurements by using a laser displacement sensor
in combination with a Doppler vibrometer, and consists of 8.25 m vehicle. The Doppler vibrometer
have been used to measure deflection velocity at high travel speed, and laser displacement sensors havebeen used at low travel speeds. In the present work, we propose a method for evaluating the pavementload-bearing capacity on farm roads by using an MWD equipped with a laser displacement sensor, andgive the results of measurements on a public road.
METHODOLOGY
Outline of the MWD

Bearing
Deck for sensor
Odometer
450

Fig. 1 Installation state of deck for sensor

450

Laser displacement sensor

Unit: mmFig. 2 Sensor configuration

In this study, load-bearing capacity is evaluated in terms of the deflection under the rear wheel asshown in Fig. 1. The deflection is measured by laser displacement sensor. Because of the vehicle’s
suspension, the axle and the body frame do not move in unison. Hence, as shown in Fig. 1, in order toⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2017) 8-1

make the sensor and axle move in unison, a sensor deck was attached to the wheel axle by the axle
bearing. The sensor arrangement is shown in Fig. 2. One sensor is installed at the wheel axle, and othersensors are installed ±450 mm from the wheel axle. Additionally, running distance is measured byodometer installed in the rear body
Evaluation Method
The MWD test determines the load-bearing capacity based on road surface deflection generated by rear
wheel load. Fig. 3 shows the concept of the determination method proposed in this study. In Fig. 3, thex-axis indicates the vehicle travel direction and the y-axis indicates the deflection direction. The originof the x-axis is set at the wheel axle. Pavement deflection at the rear wheel is calculated by thefollowing equation in the case of the ideal condition without vibration or torsion of the vehicle body.

w0 =

0

(1)

d

Where,
𝑤0 : road deflection at the rear wheel (𝑥 = 0)
∆0 : distance between the wheel axle and the road surface at the rear wheel (𝑥 = 0)
∆𝑑 : distance between the wheel axle and the road surface without deflection (𝑥 = 𝑑)
The wheel radius and deformation are R and δ, respectively. If δ does not depend on pavement
rigidity, then the difference between R and δ is always constant.
0=R= Const(2)

As mentioned above, the deflection immediately below the rear wheel can be calculated byequation (1) under ideal conditions. However, measurement at the position 𝑥 = 𝑑 is difficult, because
measurement value in that position is influenced by vibration of sensor and the tilt of vehicle body inpractical measurement. For that reason, as shown in Fig. 4, a deck for the sensors was attached to thewheel axle, and three sensors were attached so as to measure maximum deflection at the position 𝑥 = 0
and deflection at the position 𝑥 = 𝑎, −𝑎; also, the deck is designed to tilt around the wheel axle.
Therefore, in this study, the load bearing capacity was evaluated by the “deflection difference,” i.e., thedifference between deflection generated just under the rear wheel and deflection generated at any otherpoint (𝑥 = 𝑎). Assuming that the deflection curve is generated symmetrically with respect to the yaxis, the error of deflection measurement at the position 𝑥 = 𝑎 due to the tilt of the sensor deck can beeliminated by averaging the measurement value at the position 𝑥 = 𝑎 and measurement value at theposition 𝑥 = −𝑎. Deflection difference is defined by following equation.

w=
a

=

0

1
2

(3a)

a

(

f

+

b

)

(3b)

Where,
𝑤 : deflection difference between deflection just under rear wheel and that at any other∆𝑓 : distance between the sensor and the road surface at the position 𝑥 = 𝑎∆𝑏 : distance between the sensor and the road surface at the position 𝑥 = −𝑎

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Deck for sensor

Wheel axle

x = －a

R

x=d

Δ0

Δd
w0

x=a

x

Δb
w

y

x

Δ0
y

Fig. 3 Concept of deflectionΔfΔa: Laser displacement sensor

Fig. 4 Correction of displacement
for the sensor deck tilt

MWD Testing
The MWD test was carried out at the testing course of the Public Works Research Institute in Tukuba,
Japan (Fig. 5). The length of the testing section was approximately 1400 m. To assess repeatability andthe effects of running speed, measurement was implemented three times for each with six runningspeed (10, 20, 30, 40, 50 and 60 km/h). Also, FWD test was conducted at the same test course todemonstrate accuracy of MWD. The pavement consists of an asphalt layer (14 cm) and a granulated
subbase course (31 cm), and there are many cracks at several points. The sampling frequency was 2000
Hz. The wheel load in a static state was 46.5 kN.

MWD test section(About 1400 m)

Fig. 5 Section for the MWD test
RESULTS AND DISCUSSION
Fig. 6 illustrates the measured pavement surface deflection difference, i.e., the difference between
deflection just under rear wheel ( 𝑥 = 0 ) and deflection at an arbitrary point ( 𝑒. 𝑔. , 𝑥 = 45cm )(hereinafter referred to as (D0−D45)) calculated by equation (3a). The MWD test was implemented
three times for each running speed. As shown in Fig. 6, the results at each running speed were similar,
which means that the measurements are repeatable. In addition, it is shown that the measurement
accuracy is not closely dependent on the running speed. The results of FWD testing for a given
measurement section is also presented in Fig. 6. In general, road surface deflection is affected bypavement temperature. Hence, temperature correction is necessary to get rid of effect of temperature on
the measured deflection, and the MWD and FWD can measure pavement temperature during operation.
The MWD and FWD tests were conducted in November and December respectively. As a result of
temperature measurement, there was not almost difference of the temperature. For that reason, it wasⒸ ISERD

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judged that there was no need for temperature correction. Also, the loads of MWD and FWD are
different. Hence, the results of FWD testing are those after linear correction of load. As shown in Fig.6,
it is showed that the results of MWD testing agree substantially with that of FWD. For example, thepart and number of the biggest (D0−D45) obtained by MWD test are consistent with that of FWD test.
It is possible that sensor deck designed to tilt around the wheel axle cleared measurement errors causedby tilt of sensor deck. In contrast, the results showed the scattering and uniformity of FWD and MWD
data in approximately 350 m. It is possible that this is attributed to greater vibration and bouncing
caused by road roughness during measurement. Also, these measurement results include effects onload change and impact load during measurement. Accordingly, the value of (D0−D45) does not always
need to be accurate. However, in comparison with RWD that have been put into practical use, theseresults indicate that the MWD have enough accuracy as the device to specify deteriorating roadsections which need detailed survey. Finally, the study determined from these results that the MWD
has applicability as moving device for measuring load bearing capacity.
2000

10kmph-1st
10kmph-2nd
10kmph-3rd
FWD(D0-D45)

D0-D45 [μm]

1500
1000
500
0

0

200

400

600
800
Disance [m]

1000

1200

1400

400

600
800
Disance [m]

1000

1200

1400

400

600
800
Disance [m]

1000

1200

1400

400

600
800
Disance [m]

1000

1200

1400

(a) Running Speed: 10 km/h
D0-D45 [μm]2000

20kmph-1st
20kmph-2nd
20kmph-3rd
FWD(D0-D45)

1500
1000
500
0
0

200

(b) Running Speed: 20 km/h
2000

30kmph-1st
30kmph-2nd
30kmph-3rd
FWD(D0-D45)

D0-D45 [μm]

1500
1000

500
0
0

200

(c) Running Speed: 30 km/h
D0-D45 [μm]2000

40kmph-1st
40kmph-2nd
40kmph-3rd
FWD(D0-D45)

1500
1000

500
0

0

200

(d) Running Speed: 40 km/h
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D0-D45 [μm]2000

50kmph-1st
50kmph-2nd
50kmph-3rd
FWD(D0-D45)

1500
1000
500
0

0

200

400

600
800
Disance [m]

1000

1200

1400

400

600
800
Disance [m]

1000

1200

1400

(e) Running Speed: 50 km/h

D0-D45 [μm]2000

60kmph-1st
60kmph-2nd
60kmph-3rd
FWD(D0-D45)

1500

1000
500
0

0

200

(f) Running Speed: 60 km/h
Fig. 6 The deflection difference (D0-D45)
CONCLUSION
This study aims at developing a device for measuring the load bearing capacity and at establishing anefficient method for evaluating the soundness of farm roads. In this paper, we proposed adetermination method that uses (D0−D45) obtained by MWD test. The results of MWD measurement
showed that MWD deflection measurements are repeatable. Also, MWD measurement is not closely
dependent on the running speed at travel speeds of 10 to 60 km/h. In addition, the results of MWD
testing were agreement with that of FWD. Therefore, it is showed that the applicability of the MWD
was demonstrated. A further direction of this study will be to accumulate test data under various
conditions in order to improve the proposed method.
ACKNOWLEDGEMENTS
This study was done in collaboration with the National Institute for Land and InfrastructureManagement. We are grateful to all those who provided assistance.
REFERENCES
Kano, T. 2008. Study on a moving deflectometer, The development of a moving deflectometer for pavement,
Asphalt. 51, No. 224, 43-54.
Maruyama, T. 2012. High-speed moving deflectometer, Journal of Civil Engineering, 53 (5), 58-59．
Flintsch, G., Ferne, B., Diefenderfer, B., Katicha, S., Bryce, J., Nell, S. and Clark. T. 2012. Assessment ofcontinuous pavement deflection measuring technologies, SHRP 2 Renewal Project R06F, Transportation
Research Board.
Van, T. Rolling wheel deflectometer, Network-level pavement structural evaluation, Measuring Deflection at
Highway Speeds, Asset Management, USA.
ERES Consultants. Rolling wheel deflectometer (RWD), Demonstration and comparison to other devices in
Texas, Asset Management, US Department of Transportation, FHWA 2004.2Ⓒ ISERD

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Brian, K. 2010. Diefenderfer, Investigation of the rolling wheel deflectometer, Virginia Transportation ResearchCouncil, Final Report, VTRC 10-R5.
Elseifi, M.A., Abdel-Khalek, A.M., Gaspard, K., Zhang, Z. and Ismail, S. 2012. Evaluation of continuous
deflection testing using the rolling wheel deflectometer in Louisiana, Journal of Transportation Engineering,
ASCE, 138 (4), 414-422.
Rasmussen, S., et al. 2008. A comparison of two years of network level measurements with the traffic speed
deflectometer, Transport Research Arena Europe.
Ferne, B., Langdale, P. and Round, N. 2009. Development of the UK highways agency traffic speed
deflectometer, Bearing capacity of roads, railways and airfields, Tutumluer & AI-Qadi, 409-418.
Terada, T., Kawana, H., Kubo, K., Takeshi, Y. and Matsui, K. 2012. A study on evaluation of pavementsoundness using a mobile deflection measuring device. Journal of JSCE, 68 (3), 13-20.
Matui, H., Kubo, K., Terada, T. and Kawana, H. 2013. Development of moving weight deflectometer andapplication to pavement structural evaluation, Civil Engineering Journal, 55 (12), 10-13．

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Research article
erd

Benchmarking Concepts of Community Wellbeing in an Area
Vulnerable to Disaster due to Volcanic Eruption
MERITES M. BUOT*
College of Arts and Science, University of the Philippines Los Baños, Laguna, Philippines /Faculty of Education, UP Open University, Laguna, Philippines
Email: mmbuot@up.edu.phReceived 16 December 2016

Accepted 5 May 2017(*Corresponding Author)

Abstract Victims of natural disasters have countless challenges to face on right after the event.
Immediate local government unit and other community leaders are the first line of help toaffected communities. The wellbeing of everyone in a vulnerable community is greatly affectedby how the local sectors response. A deeper understanding of the concepts of community
wellbeing is hereby realized through a qualitative research approach in the field. Several focus
group discussions are done with five local sectors in the selected sites. The narratives of thevictims are analyzed through thematic analysis in identification of sectoral concerns regarding
concepts of community wellbeing.
Keywords community wellbeing, leaders, stakeholders, trust

INTRODUCTION
The recently heightening interest in local community wellbeing (CWB) indicators reflects a growing
awareness of their importance in harnessing citizen engagement, strengthening community planning,and encouraging evidence-based policy making (Buot et al., 2016, Lee et al., 2015). Community
Wellbeing (CWB) concepts are defined and measured for stakeholders and policymakers to be able torethink the ways in which governmental priorities are debated and progress is scaled. Earlier studies
related to CWB began in the 1960s, but only a limited area had been tackled (Cummins, 2001; Baum,1999). It is undeniable that natural disasters are indeed real phenomena, and their impacts are
becoming all the more pressing especially so with the current climate change scenario. This is why
there has been a recent surge in risk reduction planning, capacity building, and community organizing
activities.
The Philippines has always been prone to natural disasters. Filipinos are no stranger to volcanic
eruptions, earthquakes, huge landslides, and typhoons, which are the most frequently occurring
disasters in the country. These unfortunate events have always led to significant casualties and losses.
From 2000 to 2014, approximately 17,232 died due to natural disasters, in addition to theinsurmountable destructions of property reported (CRED, 2015). The goal of these activities is toprepare communities so that they can withstand the impacts of natural disasters and be able to adapt tothe difficult situation ahead of them.
This study, which assessed the different concepts of wellbeing from the perspective of the five
local sectors, contributes to a better understanding of how people survive the destruction and gradually
make progress toward starting again.
METHODOLOGY
Albay, Philippines was purposively chosen as the province with the closest proximity to Mt.
Mayon. Camalig was then chosen randomly from the list of communities (Fig. 1). A qualitativeⒸ ISERD

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approach to determine the concepts of community wellbeing (CWB) in the aftermath of the disasters
was used. Focus group discussions (FGD) were conducted as a form of group interview using
discussion guides developed around the topic of CWB concepts. Probing questions were added if
needed in order to encourage more personal responses. Among the sectors represented during the focus
group discussion (FGD) conducted were the local government, education sector, business sector,
people’s organizations, and socio-civic sectors (Table 1). All participants were survivors of previous
disasters. They either lost properties or jobs owing to terrifying and fatal events like the volcanic
eruption in Albay. Group sizes varied between 6 to 10 participants representing different The sessions
lasted between 1.5 and 2 hours. Pseudonyms were used in order to protect the identity of theparticipants.

Fig. 1 The study site, Albay, Philippines
Table 1 Focus group participants
SITE
ALBAY

SECTOR

AGE
MEAN AGE
GENDER
Youngest Oldest
Male
FemaleEDUCATION
26
65
47
2
4
BUSINESS3270
53
6SOCIO CIVIC
23
34
27
3
3
LGU
34
61
49
5
4
PO
34
58
45
6
3
TOTAL
21
15

TOTAL
6
6
6
9
9
36

RESULTS AND DISCUSSION
Salient Insights from Albay
On September 14, 2014, the provincial government of Albay declared a preemptive evacuation aroundthe 8km extended danger zone (EDZ) of Mayon Volcano (PHIVOLCS, 2014). Several barangays of
Camalig, Albay were affected like Anoling, Sua, Tumpa, Quirangay, Salugan and Cabangan. Mt.
Mayon did not erupt but the affected families stayed in the evacuation centers (EC) for almost 3
months.
Education sector: Six teachers from the public school represented the education sector (Table 1). Allof them were married and had children. This sector felt most of the pressures in the wake of a disaster.
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Teachers, for example, might not be affected personally by the event but their workplace was usuallytransformed into an evacuation center. The 2014 volcanic activity did not bring lahar or volcanic debris
to their locality, but there were many evacuees who stayed for almost 3 months in the local school.
Doing duty even with disaster at hand (Table 2). Although classes were suspended for 2 weeks,
the teachers were required to report to duty to help in the evacuation organization. When classes
resumed after a while, they had to conduct classes while the evacuees were around. They knew that
their students were disturbed physically and emotionally. The tents serving as their new classrooms
were hot and the schoolchildren saw the discomfort of the evacuees, too.
“Naiyak ako kase ang mga bata nakabitbit ng pamaypay, nakahubad na. Naiyak ako. Ang init sa
tent.” – Mia (I cried because the children were all bringing fans, they even took off their shirts. I cried.
It was too hot in the tent.)
Table 2 Thematic analysis of the narratives in focus group discussion of participants
Sectors
Education
Business
Socio Civic
Local Government Unit (LGU)
People’s Organization

ThemesDoing duty even with disaster at hand
Acceptance of the inevitable
Better basic services means comfort in life
Disaster creates situation for Samaritan to emerge
Welfare for all
Dagus dagos (sustainability)
Common sentiments
Desires and wishes at disaster time

The education sector willingly performed these duties even in the face of danger because they
consider it as their moral obligation. They were hopeful that performing their role may bring a bright
future to these small kids under their care. The education sector expressed that morality and values
serve as the standard and foundation of a community. Respect and concern for the neighbors came out
as real values that the community must do collectively in order to engage all towards one community
goal. While they were helping the evacuees housed in their workplace, they were expecting that theevacuees themselves and other agencies which were trying to reach out to the affected families must
not forget the basics of respect and concern.
Acceptance of the Inevitable. While maintaining their duty at the EC, the teachers knew and haveaccepted the fact that the promises given to them might never come. Glenn shared that they were all
working to provide at least an enabling environment for the affected families. They tried to help withthe most they could extend. Oftentimes they had to solve with the help of the principal, complaints
about the comfort room, non-functional electric fans, and other basic necessities. In 2006, some ofthem, were themselves victims of the mudflow from Mayon volcano. Those past experiences hadtaught them countless lessons.
“Kami man ay experience sa bagyo at lahar. Naku nahulog ako sa may tubig. Pero ako talaga
survive lang kase kami lang asawa ko. Lahar ay apekted na din. Kalahati na ang tubig. Muntik na abot
ang bahay.” – Glenn (We have our own experience with typhoon and the lahar flow. I fell into thewaters. But I had to survive because my husband and I were alone. Lahar was a threat. Water was
everywhere. The flood water was almost in our house.)
Ruth wanted to say that the volcanic eruption may not happen too soon. With this in mind, theywanted to stay put where they were at the moment. A stronger faith in difficult times may have given
them more hope to stay (Nelson et al., 2011). But Mia, having experienced almost being killed by the
impacts of Mt. Mayon, was ready to move out anytime.
Business sector: Six participated from the business sector (Table 1). The participants were all
successful in their own field. Pete retired from his work in the US and decided to come back to theⒸ ISERD

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Philippines. Noli, an engineer, started his business by opening a welding shop in the 80s. From there,
he created jobs for his neighbors, relatives and other people. An economist, Manuel, made businesses
out of the natural resources of Albay. He was into adventure tourism. Both Noli and Manuel somehow
benefitted from the occurrence of natural disasters. Alan and Rey were into farming and the youngest
businessman, Dani at age 32, was into retail.
Better Basic Services Means Comfort in Life (Table 2). As good citizens, the participants said thatthey paid their taxes religiously and on time. In return, therefore, they expect the government toprovide them better basic services. Pete mentioned that he had paid high taxes in the US but nevercomplaint because the services were at par with his expectations. Manuel strongly invoked his idea that
community wellbeing could be attained only with improved basic services for all. Noli summed it up:“Yun pong obligations ay nagagawa. Ang government ay hindi mabibigay sa atin yung services
kung hindi natin din ginagawa ang obligations. Ang pera ay dapat maibalik in the form of services.
Government should do also their tasks/responsibility very well.” (Obligations should be fulfilled. The
government cannot give us the basic services if we do not fulfill our obligations. The resources we haveshared should be given back to the community in different forms of basic services.)
Insurance was also considered vital for their personal security and for the family in times of
emergencies. Everybody in the business sector agreed that insurance was important for emergency
cases and acknowledged it as a “social responsibility” of the government. Because both Alan and Rey
were in agriculture, they strongly believed that farms should be insured as well. When natural disasters
occur, the agricultural farms suffered a lot, even the agricultural animals, too. Alan categoricallyclassified insurance as a “contingency fund and a savings” as well. Rey considered insurance as a“security” for people in the agriculture sector. Even with the destruction of the agricultural products
during natural disaster, in this case, volcanic eruption, at least a contingency fund is available to thefarmers for recovery purposes. Rey called it a “fall back that will be a source for us to stand again.”
Disaster creates situation for Good Samaritans to emerge (Table 2). Disaster to somebusinessmen meant business. For example, Noli, being in the construction business benefitted in theaftermath of any disasters. But this kindhearted man, never thought about the benefit. He was thinkingof his employees every time a suspension of work happened. Although he was not personally affectedby the preemptive evacuation in Albay last 2014 he had experienced the after effect of Mt. Mayon’s
activity in 2006 and other effects caused by strong typhoons. If not for their neighbors, whom they had
not really known before, his family might not have survived. Noli described the disaster as “an eyeopener. And he added that “the best person who could help you would be your neighbor.” He realizedthat no one hadtaken time to know more about his neighbors and that disasters broughtneighborhood together.
“ Naging blessing ang trahedya. Ipaparealize nya talaga sa iyo ang maraming bagay na minsan
yung kapit bahay mo na hindi mo pinapansin yun pa pala ang tutulong sa iyo.” – Noli (During
disasters, the goodness in the heart was revealed. Tragedy becomes a blessing. One can say thatdisaster is an eye opener. A tragedy would make one realize that the neighbor you wouldn’t evennotice in ordinary days, became the source of help.)
Whatever the social status, everyone is on equal footing whenever in a disaster. Manuel describeddisasters as a “great leveler.” Jokingly, he added that social status does not matter if “pare-pareho
kayong masasalanta at kakain ka din ng sardinas.” (when everyone was affected by disaster and one
had to eat canned sardines.) No one can be choosy about what to eat. On the other hand, occurrence ofdisasters became a “wake up call” for the government to be always ready. Bicol region, specifically
Albay, had a long history of disaster incidence like typhoons, floods, or volcanic eruptions. Therefore,
the effort of preparation and mitigation for the worst disaster ever since was at the top on each local
government units’ agenda.
Socio civic sector: The socio civic participants were young leaders who joined the volunteer group to
help the community in times of disaster. Three males and 3 females joined the FGD (Table 1). Theirmean age was 27 years old. All of them had their own jobs and they were all singles. A sense ofⒸ ISERD

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fulfillment in doing volunteer works and the acquired value in each unique experience were consideredessential by each of them.
Karahayan san Gabos (Welfare for all) (Table 2). When one gives his/her time in order to extend
help to the community, it means sacrifices too. Even though the socio-civic sector participants
expressed that they received invaluable lessons by giving their time and they felt fulfilled and happy, acertain part of themselves were also given away. Commitment to help without expecting in return wasembedded in each story shared by the participants. Great concern for the affected communities can beshown in all the community works they had participated in. In addition to concern for the welfare ofothers, it was a strong grass root participation that would help boost one’s wellbeing. Jessica showedthat real concern for the welfare of the community can be shown even by simply listening to the storiesof the afflicted families. Sharing time with the victims was necessary and was expected of them. To beproactive was necessary for the welfare of everyone as shown by “precaution and warnings given.”
Since the participants were church-based, the spirit of faith and hope were strongly evident in the
narratives.
Dace highlighted how participation contributed for the good of the community, and for him,
everything was a result of a collective effort. All of the participants strongly expressed that community
effort was only successful with concerted personal efforts.
The local government sector: The FGD sessions were participated in by members of the barangay
council (6), municipal office (1), and one each from the provincial DRRMO, and from the Civil
Defense Office (Table 1).
Dagus-dagos (Sustainability) (Table 2). Sustainability of the effort from the LGU leadership
down to the lowest bracket of the organization was always basic in order to see the seriousness of
whoever was in command. Albay is very vulnerable to any Mt. Mayon’s activity. In Camalig alone, 6barangays are situated along the 8km EDZ. A small tremor from the volcano would mean thousandsof families would have to be evacuated. Education through continual training and workshops, serve asa constant reminder to the community members that the municipal leadership was really sincere inattaining a zero casualty objective. The health workers present were focused on giving what they
commit to do. Just like the political leaders, the health workers, showed consistency in the continuous
effort to bring the community to zero sickness. Lou, Lil and Beth shared that they would not stop
imparting the right knowledge to the residents. If the people would not come to the health clinics forcertain reasons, then they themselves visited them. Specifically, for Lou, she related that “house tohouse” was an option to take because she considered it as her “social responsibility” to increase thepeople’s knowledge about health.
Jo, a 61 year old kagawad, continued to serve the community even if he lacked the financial
capability. He believed that serving meant finding other sources of help by going to the right persons.
He always projected a positive attitude to tap other linkages in order to bring help to the constituents.
Sense of “pagkukusa” (initiativeness) was cited by Lou and Jo. Indeed, the barangay local leadershipdid not wait lethargically but was more proactive. Security or people’s safety for their lives andpossessions dominantly echoed in Efren’s shared stories. As Jo and Efren narrated, community
wellbeing meant a “visible leadership.” Not only during extreme events but more so during theplanning stage to prevent a disastrous effect of a volcanic eruption (Atkinson and Sapat, 2014).
Sustenance of the effort towards “karahayan” (welfare) of the entire community started with theindividual’s initiative and reaching its climax with initiative and dynamic leadership.
People’s organization: The Mayon’s Farmer Association was under the oversight of the municipal
Department of Agriculture (DA). Forty-five years was the mean age of the participants (Table 4.1). Six
males and 3 females joined the FGD. These farmers obviously were risk takers because they continued
to farm even under the constant threat of Mt. Mayon. Their farms were located in Sua, a barangay
within the 6-8 km EDZ. During the last preemptive evacuation in 2014, these farmers were personallyaffected.

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The common experiences and sentiments (Table 2). Their proximity to the volcano sometimes
gave them an edge over other policy makers, national or local. They knew when to stay or flee. But
because of the zero-casualty program of the provincial government, these farmers could not refuse toflee during a volcanic activity. During the prolonged preemptive evacuation of 3 months, most of themoney saved by the farmers were gone. Going to an evacuation center was not a welcome news but
they could not do otherwise. Sonia, being a mother, was always worried about the safety of thechildren, thus she openly declared that she wanted the needs of the family to be provided while staying
at the EC. Elvie and the rest of the participants concurred that they could not just depend on thegovernment for all their needs and therefore they had to work. Because they were farmers and theirsource of living was from their farms, they wanted to go home as soon as possible. Elvie was vocal
about their need to go home. Jona was talking about the sustainability of their source of income whichmight be destroyed if they stayed away from their farms far too long. Yes, relief goods were
distributed but Jona believed that it “was not enough to accommodate the needs of the family” forseveral months.
Personal desires and wishes at disaster time (Table 2). The provision of basic needs was foremostin every parent’s mind in the aftermath of a disaster. During this uncomfortable situation, everyone
wanted to have “ extra money” for emergencies. Odie and the rest of the participants felt this financial
need. Security against crime was also essential in this most troubled times. Thus the visibility of thepolicemen was a welcome sight for them.
A public leader they could easily approach during extreme need was the plea of Ebie. It was also asymbol of hope for her to see leaders who “were available and willing to help.” In addition to that,
Ebie hoped to have a safe neighborhood so that the feeling of security would be enhanced. Emil, thechair of the organization desired to have a safe community for all. For him it meant, safe for him “tomove around, and to continue working without mental worries.” Having a safe neighborhood improved
the feeling of trust in other people of the community.
A community that experienced intermittent natural disasters have members who are risks takers.
In addition, Odie included the people’s “strong faith in God who would not put them into more risks
they cannot handle.” Odie shared that the more one takes risks, the more one “becomes strong anddetermined to survive for the family and the community as well.”
CONCLUSION
Concepts of community wellbeing can clearly be visible at the wake of disasters, when all the victims
were trying to use rationality to understand the damage and the loss. Having intact value system madeone to be kind and patient. All the business representatives agreed that a “positive mindset” isnecessary when disaster strikes, it will be a temporary state of affairs. Having a positive mind wasnecessary so that the victims might be able to put everything back, after the experience. Trust in theleadership and unity perhaps can erode but every sector might regain in due time. Faith was what
people always arm themselves as fall back during times of fatal disasters.
The decision of what a community needs should be shared by all sectors so that conflict can beeliminated immediately. If everyone works for the good of the community, problems will be solved
and the level of community wellbeing can be enhanced.
ACKNOWLEDGEMENTSoutheast Asian Regional Center for Graduate Study and Research in Agriculture (SEARCA) andAgricultural Training Institute through the Expanded Human Resource Development Program(EHRDP) of ATI-Department of Agriculture.

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Buot, M.M., Cardenas, V.R., Nelson, G.L.M., Quimbo, M.A.T. and Dizon, J.T. 2016. Meanings of community
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Lee, S.J., Kim, Y. and Phillips, R. 2015. Exploring the intersection of community well-Being and community
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Research article
erdCoral Transplantation Technology for Sustainable Fisheries
and Underwater Tourism
SAMUEL J. GULAYAN*
Bohol Island State University, Candijay Campus
Email: samgulayan@yahoo.com
Received 3 January 2017

Accepted 5 May 2017(*Corresponding Author)

Abstract Application of coral transplantation technology was the project funded by Philippine
Council for Agriculture, Aquatic and Natural Resources Research and Development of the
Department of Science and Technology (PCAARRD DOST). Implemented by Bohol Island
State University – Candijay Campus in partnership with Provincial Government of Bohol,
Municipality of Anda, and AMUN Ini Beach Resort. 30,000 coral fragments from dislodged
coral colonies were transplanted in damaged reef areas in Anda, Bohol, Philippines. Coralfragments were attached in the nailed concrete nails, fixed with plastic cable ties, and tightenedwith aqua epoxy clay from 5 m to 12 m depth. After year of transplanting corals, it was foundthat the total mean average of survival = 37.1 percent, however, in term of significant differenceof survived coral it was revealed that there is a significant different due to “p value = 0.367”
which is higher than the p = 0.05”. With regards to the influence of transplanted corals to fish, it
was observed that there were 38 families with 52 species of fish, and thousands of individuals
attracted and settled in the rehabilitated area. The dominant species of fish in both areas werePomacentridae and Apogonidae with the occurrence of other attractive animals such as sea
slugs, sea turtles, leopard anemone shrimp, whale shark and eagle ray.
Keywords coral, coral fragment, rehabilitation, reef fishes, transplantation

INTRODUCTION
Coral reefs are underwater structures formed as a result of the deposition of calcium carbonate byliving organisms, predominantly by corals. Coral reefs are home to thousands of coral polyps which
feed on planktonic organisms or photosynthesize by means of their symbiotic algae calledzooxanthellae. Corals can only grow in shallow waters where light is not limited by depth. Corals are
only found along the shallow coastlines of more than 100 countries worldwide (Moberg and Folke,1999). Coincidentally, coral reefs attract wide range of organisms. In fact, there are up to 9 million ofreef living animals and plants described up to date (Knowlton, 2001a) making it appropriate to callcoral reef as the rainforest of the sea (Mulhall, 2008; Knowlton, 2001b). Coral reefs have also been
considered as one of the most productive ecosystem on earth. In the Philippines, it provides livelihood
for more than a million of fishers who contribute almost US$ 1 billion annually to the country’s
economy (White et al. 1998). Unfortunately, coral reefs are in serious deterioration-suffering massive,
long term declines in abundance, diversity and habitat structure due to overfishing, pollution, tourism,
and other anthropogenic and natural disturbances (Abelson, 2006).
Anda is considered one of the prime tourist destinations, next to Panglao, in Bohol and is now
starting to experience surge of beachfronts developments to accommodate growing number of local
and foreign tourists. Recreational services provided by these resorts are diving and island tours, thusincreasing the number of boats anchoring directly on the shallow reef flats causing destruction within
the area. To make matter worse, the sheer number of tourist divers with poor buoyancy control andlack of spatial awareness adds to the destruction of reefs in popular dive sites.
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The result of the participatory coastal resource assessment (PCRA) conducted by Bohol
Environment Management Office (BEMO) and Department of Environment and Natural Resources(DENR) last 2011 revealed that in all eight coastal barangays in Anda (Badiang, Linawan, Talisay,
Bacong, Virgen, Candabong, Poblacion, and Suba), only Virgen and Bacong was at a good condition
having 57.5% and 51.5% live hard coral cover (LHC), respectively, while the rests are found to be atpoor condition with LHC ranging from 7.3-23.5%. Coral rubbles and dead corals covered with algae
were predominantly dominated in the reef areas leading them to suggest previous dynamite fishing,
and the use of poisonous and obnoxious substances were to blame. However, with the increased ofboat use in the area, coral breakage due to anchors was also observed.
Transplanting coral fragments is one of the many ways of restoring damaged portions of reef. It
has been observed that the use of coral fragments from donor colonies or from dislodged fragments(Monty et al. 2006), have the capacity to restart new colonies elsewhere when artificially attached tostable substrates (Thongtham and Chansang, 2008) as compared to waiting for new recruits to regrow(Fox et al. 2005). Several studies have shown the importance of coral nurseries in this endeavor (Shafir
et al. 2006, Herlan and Lirman, 2008; Shaish et al. 2008). Nurseries provide a way to determine coral
growth rates empirically as well as giving them a chance to heal before transplanting.
These techniques were pilot tested in the different parts of the Philippines, including Panglao,
Bohol, through the national coral reef restoration program last 2012 and were found out to be effective.
Amper J.A. et al. 2015 observed that the fragments attached to a nursery can grow up to 2.5 cm. inthree months while according to the unpublished report by the University of San Carlos and Bohol
Island State University (2013), same fragments transplanted in the area can grow up to 0.5 – 0.7 cm permonth.
This coral transplantation was done in Anda, Bohol with funding support from Philippine Council
for Agriculture, Aquatic and Natural Resources Research and Development of the Department of
Science and Technology (PCAARRD DOST), implemented by Bohol Island State University –Candijay Campus in partnership with the Province of Bohol, Municipality of Anda, Bohol, and AMUN
Ini Beach Resort. The town is located in the eastern part of Bohol with coordinates 9˚45 N, 124˚34’ E
and a distance of 99 km from the City of Tagbilaran. The specific sites for coral reef restoration are
strategically located in three barangays of Anda, Bohol, Philippines (Fig. 1). It was chosen because it
passes the criteria of site selection.

PHILLIPINES

Badiang

ANDA

BOHOL

Suba
Candabong

2 mi
2 km

Fig. 1 Project sites
OBJECTIVES
The objective of the project was to transplant 30,000 dislodged coral fragments from colonies, monitor
the survival rate of transplanted corals after year of transplanting in the damaged reef areas, count theⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2017) 8-1

species of fish in the rehabilitated areas, and compare the population of fish before and after year ofcoral transplantation, and observe the environmental parameter on the aspect of temperature, andsalinity. And, test the significant difference on the performance of coral transplantation after year of
project implementation, and the fish abundance in the rehabilitated coral reef areas in the municipality.
MATERIALS AND METHODS
Dislodged corals from colonies were utilized as the planting materials. Damaged reef areas with live
hard coral cover from 40-60 percent were chosen as the area for the coral rehabilitation. The method
applied was the following.
Collection of planting materials: Detached corals from colonies were collected, placed in plastic
crates, and transported to the project areas. Crate filled with coral fragments were carried out by thetwo divers to the rehabilitation areas where the other divers waiting the planting materials.
Rehabilitation of damaged reef areas: Coral rehabilitation was done in areas with water depth from 5
m to 12 m. Coral fragments were attached in the nailed concrete nails number 4, reinforced with cable
plastic ties (36 mm X 6 mm), and tightened with aqua epoxy clay. One hundred coral fragments weretransplanted in one quadrat. Estimated size per quadrat was 5 m X 5 m or 25 m2. The distance between
coral fragments was one foot to one meter, depending on the topography of the reefs.
Monitoring on the performance of transplanted corals: Transplanted coral fragments weremonitored after year of coral transplanting. Ten sampled quadrats were monitored per project area.
Transplanted live corals inside the quadrat were counted to determine the survival rate, to include
counting of fishes by using fish visual census (FVC). Reef fishes were counted inside the 100 mtransect line with 2.5 m side by side, at two replicate transects per project area, and aided by video.
Statistical treatment used: Simple mathematical computation was applied of understanding survival
rate. Formula where: survive corals over 100 then times 10 to get the total mean average of thesurvived corals after year of transplanting. Kruskal Wallis Test was used of getting the significant
difference of survival rate of transplanted corals, and Kolmogorov Smirnov Test was used to determine
the fish abundance from before and after year of project implementation.
RESULTS AND DISCUSSION
Branching corals were used in the rehabilitation of damaged reef areas. The coral transplantation wasstarted from February 2015 to January 2016. From one year period, the project team was able totransplant 30,000 corals in three areas (Table 1), from Badiang in the north, Suba in the east, andCandabong in the southern part of the municipality. 10,000 coral fragments were transplanted in per
project area. Planting materials were taken from the dislodged coral fragments from colonies. Thismethod differ on the method applied by Thongtham and Chansang, 2008, wherein they were utilized
planting materials from the coral nursery. According to them, grown corals from the coral nursery havethe capacity to start new colonies elsewhere when permanently attached in the stable substrates.
Table 1 Coral Fragments Transplanted in the Damaged Reef Areas
Reef Areas Rehabilitated
Barangay Badiang
Barangay Suba
Barangay Candabong
TOTAL

Number of Coral Fragments
10,00010,00010,00030,000Ⓒ ISERD

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StatusTransplanted coral fragments
able to enhance the damaged
reef areas in the project areas

IJERD – International Journal of Environmental and Rural Development (2017) 8-1

The following table describes the survival rate of transplanted corals after year of transplanting in
the degraded reefs. It was found that the three project areas have great difference on the survival rate.
Candabong was an excellent result of 87 percent, while the other two project areas were very low
survival rates with 13.1 and 11.9 percent respectively. In term of the significant difference on thesurvival rate of the transplanted corals in three areas, it was disclosed after subjected to Kruskal-Wallis
Test that p value = (0.368) which is higher than the p = 0.05 level of significance. Therefore, there is asignificant difference on the survival rate among the three project areas. The result of Candabong area
corroborate of the study conducted by Alasdair Edwards, 2010, that coral transplantation havedelivered reasonable survival over a few years, just a first towards a trajectory of improving ecosystem
structure and function.
Table 2 Survival of Transplanted Corals
Location
Q1

Q2

Q3

Number of Quadrats (100 corals/quadrat)
Q4
Q5
Q6
Q7
Q8

Q9

Q10

Suba
Badiang
Candabong

6
18
98

12
1
78

8
7
73

9
29
89

6
11
87

23
16
92

10
14
85

14
5
93

11
7
95

20
23
80

Total
Survival
Rate (%)
11.9
13.1
87.0

Total Ave.
Mean

40.67

30.33

29.33

42.33

34.67

43.67

36.33

37.3

37.7

41

37.33

There were 38 families with 52 species of fish found in the rehabilitated areas after year oftransplanting corals. Pomacentridae and Apogonidae were dominated in the rehabilitated areas withpresent of some commercial fishes, species of sea slugs, sea turtle, seahorses and its relatives, leopard
anemone shrimp, and the occurrence of whale shark and eagle ray. According to Feary et al. 2007 fishabundance, biomass and number of species are closely correlated with the condition of the coral
community. A potential positive effect of coral transplantation on the fish community has been widelystated, only few studies have been published that included observations on this connection (Alfeche,2003). This study describes that there is a positive connection of coral transplantation and attraction offish. From very poor to very high fish condition was observed in barangay Candabong and Suba, if it isbased of fish condition index published by Hilomen et al. 2000, however, barangay Badiang remains
very poor condition.
It was perceived that one of the factors that contribute in the increased of fish individuals in two
rehabilitated areas was the strict implementation of no-take zone policy. The significant difference offish abundance in coral transplantation areas, revealed after subjected to Kolmogorov Smirnov Test
that there is a significant difference from before with p = 0.910 and p = 0.998 after year oftransplanting corals in degraded reef in three areas, which is higher than the significance level of p <0.05.
Table 4 shows the surface temperature and salinity, and perceived cause of mortality oftransplanted corals in project areas. It was observed that water salinity start to change from the monthof September to December of same year. It was happened due to continuous rains and flooding. The
perceived cause of high mortality rate of the two project areas (Badiang and Suba) was due to thechanged of water salinity and weak of protection of transplanted corals. Fishermen sometimes engage
fishing in the rehabilitated reef areas.

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Table 3 Number of Fish Individuals Found After Year of Coral Transplantation(1,000 m2/project area)
Project Area

Name of Families

Barangay Suba

Acanthuridae
Apogonidae
Blennidae
Caesionidae
Chaetodontidae
GobiidaeLabridae
Mullidae
Nemipteridae
Ostraciidae
Pomacanthidae
Pomacentridae
Serranidae
Siganidae
Scorpaenidae
Synanceiidae
Tetradontidae
17
Acanthuridae
Amphiprionidea
Apogonidae
Blennidae
Batrachoididae
Caesionidae
Callionymidae
Chaetodontidae
Epinephelidae
Haemulidae
Labridae
Lutjanidae
Mullidae
Muraenidae
Ophichthidae
Pomacentridae
Pinguipedidae
Scaridae
Serranidae
Siganidae
Sphyraenidae
Syngnathidae
Tetraodontidae
22
Acanthuridae
Amphiprionidae
Apogonidae
Balistidae
Caesionidae
Chaetodontidae
Centriscidae
Haemulidae
Holocentridae
Labridae
Lethrinidae
Lutjanidae
Monacanthidae

TOTAL
Barangay
Candabong

TOTAL
Barangay
Badiang

No. of Ind./Family
Before (Feb 2015)
T1
T2
Total
0
0
0
7
51
58
0
0
0
0
0
0
1
1
2
0
0
0
2
3
5
1
2
3
0
0
0
0
0
0
0
0
0
9
63
72
1
0
1
0
1
1
1
0
1
0
0
0
1
1
2
23
122
145
2
2
4
3
3
6
3
4
7
0
0
0
0
0
0
0
2
2
1
1
2
1
3
4
1
2
3
0
2
2
5
9
14
0
1
1
1
2
3
1
1
2
1
1
2
5
5
10
1
1
2
1
1
2
0
0
0
1
2
3
0
1
1
2
2
4
1
1
2
30
46
76
1
1
2
2
2
4
7
7
14
1
1
2
1
1
2
1
2
3
1
1
2
0
2
2
1
1
2
4
8
12
1
1
2
1
3
4
1
1
2

Ⓒ ISERD67

No. of Ind./Family
After (Jun 2016)
T1
T2
Total
1
0
1
880
930
1,810
1
1
2
8
8
16
5
2
7
3
2
5
31
27
58
0
0
0
2
1
3
1
0
1
1
0
1
415
259
674
1
0
1
2
0
2
1
0
1
1
0
1
2
0
2
1,355
1,230
2,585
2
7
9
0
0
0
0
0
0
9
0
9
0
1
1
0
0
0
0
0
0
5
2
7
0
0
0
0
0
0
5
0
5
4
0
4
0
0
0
0
0
0
0
0
0
4,311
1,530
5,841
0
0
0
0
0
0
324
0
324
3
1
4
0
0
0
0
0
0
0
0
0
4,663
1,539
6,204
0
0
0
2
4
6
24
15
39
0
0
0
1
1
2
1
1
2
0
0
0
2
2
4
1
1
2
5
11
16
1
1
2
2
2
4
0
0
0

IJERD – International Journal of Environmental and Rural Development (2017) 8-1

Mullidae
1
1
2
2
2
Ostracidae
1
1
2
1
1
Ogcocephalidae
0
1
1
0
0
Nemipteridae
1
0
1
0
0
Pempheridae
1
1
2
1
0
Plotosidae
0
1
1
1
0
Pomacanthidae
1
1
2
2
1
Pomacentridae
5
6
11
40
103
Scaridae11
2
00
Scorpaenidae
1
1
2
0
0
Serranidae
1
1
2
0
0
Siganidae
02224Synodontidae
1
1
2
0
0
Tetraodontidae
1
1
2
0
0
Zanclidae
0
1
1
0
0
TOTAL
28
37
51
88
88
149
Legend: 0 – 200 very poor; 201 – 676 poor; 677 – 2,267 moderate; 2,268 – 7,592 high; 7,593 above very high

4
2
0
0
1
1
3
143
0
0
0
6
0
0
0
237

Table 4 Environmental parameters
PeriodFeb 6, 2015
Mar 6, 2015
Apr 3, 2015
May 8, 2015
Jun 5, 2015
Jul 10, 2015
Aug 7, 2015
Sep 4, 2015
Oct 9, 2015
Nov 6, 2015
Dec 4, 2015
Jan 8, 2016

Badiang
Temp ˚C &SalinityTime(Ppt)
24 (08:00AM)
33
23 (08:00AM)
33
26 (08:00AM)
34
28 (08:00AM)35
29 (08:00AM)35
26 (08:00AM)35
32 (11:00AM)35
29 (08:00AM)30
31 (11:00AM)30
32 (11:00AM)30
28 (08:00AM)29
26 (08:00AM)
30

Project Areas
Suba
Temp ˚C & TimeSalinity(Ppt)
24 (08:20AM)35
23 (09:00AM)35
26 (08:40AM)
35
28 (08:20AM)35
29 (08:30AM)35
26 (08:30AM)35
32 (11:30AM)35
29 (08:20AM)
31
31 (11:30AM)
32
32 (11:20AM)30
28 (08:40AM)30
26 (08:30AM)
31

Candabong
Temp ˚C & TimeSalinity (Ppt)
24 (08:40AM)
23 (09:20AM)
26 (09:00AM)
28 (08:40AM)
29 (08:50AM)
26 (08:50AM)
32 (11:50AM)
29 (08:40AM)
31 (12:00NN)
32 (11:40AM)
28 (09:00AM)
26 (08:50AM)

35
35
35
35
35
35
35
35
35
35
35
35

CONCLUSION
After year of transplanting corals in the degraded reef areas, it was concluded that the corals
transplanted in the cliff/wall area with water depth from 5 to 15 m has excellent survival rate than thecoral fragments transplanted in rolling substrates with water depth from 4 to 5 m during lowest tide.
Another was, the rehabilitated areas with strict enforcement of no-take zone policy was able to improve
the fish population from very poor to high fish condition if based on the standardized fish conditionindex, and with the occurrence of different species like sea slugs, seahorse, sea turtle, leopard anemone
shrimp, eagle ray, and whale shark.
REFERENCES
Abelson, A. 2006. Artificial reefs vs coral transplantation as restoration tools for mitigating coral reef
deterioration: benefits, concerns, and proposed guidelines. Bulletin of Marine Science, 78 (1), 151-159.
Alfeche, L.R. 2003. Coral reef restoration through coral transplantation: the case of Duka Bay, Medina, Misamis
Oriental. Second International Tropical Marine Ecosystems Management Symposium (ITMEMS), March 2427, 2003, Manila.

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Amper, J.A, Gulayan, J.M., Olavides, C. and Gulayan, S.J. 2015. Growth rate of acropora nobilis attached to table
type framework. International Journal of Environmental and Rural Development, 6 (1), 108-113 .
Edwards, A.J. (ed) 2010. Reef rehabilitation manual. Coral Reef Targeted Research & Capacity Building forManagement Program: St Lucia, ii + 166. Australia.
Feary, D.A., Almany, G.R., Jones, G.P. and McCormick, M.I. 2007. Coral degradation and the structure oftropical reef fish communities. Marine Ecology Progress Series, 333, 243-248.
Fox, H.E., Mous, P.J., Pet, J.S., Muljadi, A.H. and Caldwell, R.L. 2005. Experimental assessment of coral reef
rehabilitation following blast fishing. Conservation Biology, 19 (1), 98-107.
Herlan, J. and Lirman, D. 2008. Development of a coral nursery program for the threatened coral Acropora
cervicornis in Florida. Proceedings of the 11th International Coral Reef Symposium, 1244-1247.
Knowlton, N. 2001a. The future of coral reefs. PNAS. 98 (10), 5419-5425.
Knowlton, N. 2001b. Ecology, Coral reef biodiversity-habitat size matters. Science, 292 (5521), 1493-1495.
Moberg, F. and Folke. C. 1999. Ecological goods and services of coral reef ecosystems. Ecological Economics.
29 (2), 215-233.
Monty, J.A., Gilliam, D.S., Banks, K.W., Stout, D.K. and Dodge, R.E. 2006. Coral of opportunity survivorshipand the use coral nurseries in coral reef restoration. Proceedings of the 10 th International Coral Reef
Restoration, 1665-1673.
Mulhall, M. 2008. Saving the rainforest of the sea: An analysis of international efforts to conserve coral reefs.
Duke Environmental Law and Policy Forum, 19, 321.
Shafir, S., Rijn, J.V. and Rinkevich, B. 2006. Steps in the construction of underwater coral nursery, An essential
component in reef restoration acts. Marine Biology, 149, 679-687.
Shaish, L., Levy, G., Gomez, E. and Rinkevich, B. 2008. Fixed and suspended coral nurseries in the Philippines,
Establishing the first step in the “gardening concept” of reef restoration. Journal of Experimental Marine
Biology and Ecology, 358, 86-97.
Thongtham, N. and Chansang, H. 2008. Transplantation of Porites lutea to rehabilitate degraded coral reef at
Maiton Island, Phuket, Thailand. Proceedings of the 11 th International Coral Reef Symposium, 1271-1274.
University of San Carlos – Cebu City and Bohol Island State University – Candijay Campus. 2013. Project 6:
Pilot Technology Demonstration on Coral Reef Restoration in Panglao, Bohol (Unpublished report).
White, A.T. and Trinidad, A.C. 1998. The values of Philippine coastal resources, Why protection andmanagement are critical. Coastal Resource Management Project, Cebu City, Philippines, 96.

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Research article
erd

Mobility Assessment for Sustainable Rural Development:
Conversion of Conventional Mobility Data and Historical
Analysis
SATOMI KIMIJIMA*
School of Engineering and Technology, Asian Institute of Technology, Phatumthani, Thailand
Email: st114307@ait.asia

MASAHIKO NAGAI
Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Japan
Received 6 January 2017

Accepted 5 May 2017(*Corresponding Author)

Abstract Dawei Special Economic Zone (DSEZ) Project is one of the largest petrochemicalindustrial estates in South East Asia which aims to transform the country into a pivotal hub forregional connectivity and logistics. Socio-economic factors determine local lifestyles, including
mobility pattern, and directly impact on rural sustainability. Changes in mobility may represent
not only generating alternative opportunities associated with socio-economic development, butalso the vulnerability of rural people which can be a significant challenge for social
sustainability in a rural area. Consequently, mobility can be one of the powerful indexes toassess impacts resulted in socio-economic changes in a long-term. The objectives of this paper
are to: convert conventional mobility data to spatiotemporal data and visualize them; and toassess the change of mobility in 2005, 2010 and 2015 with respect to social parameters such assex and age. A total of 345 individual respondents were stratified-randomly selected forassessing one-day mobility. Conversion of conventional mobility data was conducted using
online maps. Historical analysis of mobility data was conducted after performing mobility data
validation. The result shows that different mobility was observed by sex and age group.
Average increases of males’ mobile distance between 2005/2010 and 2010/2015 show 2.6 and1.7 times increase, while that of females shows 1.6 and 2.6 times, respectively. Especially,working age females show a high increase in 2010/2015. The study concluded that mobility
data obtained in different formats in different time periods can be integrated and visualized forlong-term mobility assessment. This contributes to better understand how local people is
responding to such socio-economic development. Mobility can be an important parameter andfurther provides significant perspectives to policy development for sustainable ruraldevelopment.
Keywords mobility, GPS-Logger, Myanmar, rural development, socio-economic changes

INTRODUCTION
The economic transition from an isolated country to opening up to the global economy is creating
opportunities to develop a high potential for economic growth for Myanmar. Dawei Special Economic
Zone (DSEZ) is one of the newly emerging largest petrochemical industrial estates in South East Asia.
This aims to transform the country into a pivotal hub for regional connectivity and logistics. This
project includes the development of the Dawei deep seaport, an industrial estate and highway road andrail links to Thailand with a total investment of 8.6 billion US$ (Mahesuan Kruewan, 2014).
In developing countries, socio-economic factors determine local life styles, including mobility
pattern, and directly impact on rural sustainability. Changes in mobility may represent not only
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IJERD – International Journal of Environmental and Rural Development (2017) 8-1

generation of alternative opportunities associated with socio-economic development, but also thevulnerability of rural people. This vulnerability is a significant challenge for social sustainability in arural area. As sociological diversity such as age, sex, and economic status generates different mobility
pattern, it is needed to understand heterogeneous diverse local impacts from socio-economic
development for aiming sustainable rural development.
The increased flow of foreign investment in the telecommunication sector in Myanmar has
facilitated to the mobile phone penetration (World Bank, 2015); therefore, ways of obtaining mobility
data have been largely shifted from conventional methods such as questionnaire survey and diaries to
Global Positioning System (GPS) survey by loggers and mobile phones. However, conventional
mobility data can be mainly displayed as static (Sheller and Urry, 2006), and is visualized only at theindividual level (Yu and Shaw, 2004). On the other hand, comparison of two different mobility data set
obtained in different format was mainly studied only in the developed countries including Australia,
Canada, Sweden, Switzerland, the UK, and the USA where geographic information has been welldeveloped (Kelly, Krenn, Titze, Stopher, and Foster, 2013; Stopher, FitzGerald, and Xu, 2007).
Furthermore, disaggregated mobility analysis by sociological components has not been focused. In thisregard, integrating two different mobility data obtained in different formats in different time periods isneeded for a long-term mobility assessment.
Therefore, the objectives of this study are to: (1) convert conventional mobility data tospatiotemporal data and visualize mobility data, and (2) assess mobility changes in 2005, 2010 and2015 with respect to social parameters such as sex and age. This research is expected to contribute tointegrate different mobility data set and assess a long-term mobility for rural sustainability associatedwith the socio-economic development.
METHODOLOGY
Study Area and Data Set
The study area is located in Dawei, Tanuntharyi Region, Myanmar, 132 km from the border ofThailand. The DSEZ consists of five zones such as heavy, medium, light industry and a combination ofthese, with a total area of 250 km2 (Min & Kudo, 2012). Villages in the DSEZ mainly depend on
agricultural activities such as plantation and paddy cultivation; however, the project development has
led to the reconstruction of villagers’ mobility patterns due to the creation of employment opportunities
and development of roads. The study purposively selected rural villages dependent on rural agriculturalactivities (Fig. 1).

Fig. 1 SEZ in DaweiⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2017) 8-1

A field survey was conducted in 2015 to collect mobility data and personal profiles. Stratified
random sampling by sex and age was employed to understand characteristics of mobility. Non-spatial
personal attributes such as age, sex, marital status, education level, household status, occupation,monthly household income and mobile mode were collected through a pre-tested questionnaire
conducted earlier in 2014. Spatial information such as origin, destination, direction, distance, andduration in 2005, 2010 and 2015 was also collected. The study also employed formal and informalinterviews with key informants such as village heads, using checklists.
Wearable GPS components such as “ i-gotU USB Travel & Sports Logger – GT-600” were used
to log mobility and validate the questionnaire mobility data. This device recorded 24-hour mobility
with a 5-second interval using the motion detection mode. A maximum of 38 devices was distributed at
one time to a total of 345 respondents aged over 16 years old. Both questionnaire and GPS log data are
available from the 345 samples.
Research Flow
The following Fig. 2 is the overall methodology consists of four major steps. First, mobility data fromthe questionnaires was indexed and converted to spatiotemporal information such as time, speed/hr.,direction, and geographical coordinate by utilizing online mapping service. Those converted mobility
data was visualized in an animation format. All indexed spatiotemporal data are listed in a timeline andsaved in a comma-separated values (csv) format. Simultaneously, non-spatial attributes such as age,sex, marital status, education level, household status, occupation and household income, were also
integrated with the file. Visualization of the mobility was done using the visualization tool such as
Mobmap (Center for Spatial Information Science, n.d.). Details of the moving segment such as thetotal number of mobility, mobile distance, and mobile duration were manually calculated from thequestionnaires and listed. In this study, a single mobility is defined from a staring from a location to a
destination, such as from home to a workplace.
Second, stay point and moving point extraction from GPS log data was performed. The
spatiotemporal data such as time, latitude and longitude were extracted from the devices. The split ofsegments was conducted to extract stay points with outlier detection and removal technique utilized by
Witayangkurn et al., (2013) Eq. (1):Distance (pstart, pend) < D threh and TimeDiff (pstart, pend) > Tthreh(1)

Where the parameters Dthreh, considerable maximum distance as a stay point, and Tthreh, minimum timespending at the same place, are adjustable. In this study, a stay point is detected if T threh > 20 minutes
and Dthreh <= 300 meters. Extracted stay points are listed by start-time, end-time, duration, distance inmeters, average speed in km/hr and the total number of stay points. Additionally, outlier detection andnoise removal technique were applied by using standard deviation (σ).
Third, validation of two data sets was performed with respect to the selected parameters. The
following Eq. (2) was used to calculate differences in the number of one-day mobility, mobile distance,and mobile duration.
Relative Change (x, y) = Absolute difference/ Max (x, y)*100 = |△|/ Max (x, y)*100(2)

Where x is the data from the questionnaire and y is the data from the GPS loggers.
Fourth, changes in these parameters in 2005, 2010 and 2015 were further analyzed based on social
parameters such as sex and age. In this process, mobility made out of villages such as in Yangon and
Thailand, and unfixed mobility such as daily employment at various places within or outside thevillages, were excluded.

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Fig. 2 Overall methodology
RESULTS AND DISCUSSION
Conversion of Questionnaire Mobility Data to Spatiotemporal Data and its Visualization
Mobility data obtained from the questionnaire survey was indexed and converted to the spatiotemporal
data (Fig. 3). All mobility data is simultaneously visualized and shown in an animation format (Fig. 4).
Those mobility data can be also displayed according to the attributes obtained from questionnaires. As
traditional mobility data such as questionnaires and diary can be only displayed at the individual level(Yu and Shaw, 2004), visualization of a large volume of mobility data is difficult to display
simultaneously and compare historical mobility. However, the study result makes mobility datavisualize simultaneously, as a result, more useful mobile characteristics in different time periods can beextracted. Illustrative visualization is easy to understand and more information can be obtained for abetter understanding of the underlying tendency behind the data (Andrienko, Andrienko, & Augustin,2007).

Fig. 3 Conversion of questionnaires data Fig. 4 Visualization of questionnaire mobility data
Comparison of Two Data Sets
To validate the questionnaire survey data, the average differences between two data sets were
calculated. This reveals a total of 543 mobility, with a total 1,936.3 km of mobile distance and 7,457.0
minutes of mobile duration from the questionnaires while data from the GPS log shows a total of 403
mobility, 2,302.0 km of mobile distance and 7,403.6 minutes of mobile duration. The result shows theⒸ ISERD73

IJERD – International Journal of Environmental and Rural Development (2017) 8-1

average differences between the two data sets as 25.1%, 34.9% and 38.0% in the number of mobility,
mobile distance and mobile duration, respectively (Table 1). Those differences between the two data
sets can be generated from different setting during data processing or understanding of individual
mobility among local people. Several studies conducted in the developed countries such as Australia,
United State of America and the United Kingdom show much larger differences in mobile duration
between GPS and self-reported data, 75.4%, 62.4%, 46.7%, respectively (Kelly et al., 2013). In thisline, the level of error allows us to utilize the mobility data in 2015, 2010 and 2005 obtained from thequestionnaire survey.
Table 1 Average difference of two data sets
Average differences (%)
25.1
34.9
38.0

Number of Mobility
Mobile Distance
Mobile Duration

Change in Mobility
Mobile distance in 2005, 2010 and 2015 was compared and the results of yearly change are describedin Table 2. The results show that the number of mobility does not show much difference over a periodof 10 years. Mobile distance increases yearly and the changes are 1.9 (2005-2010), 2.1 (2010-2015)
and 4.1 (2005-2015) times. Mobile duration also increased in 2010; however, in 2015, it again
decreased to a duration similar to 2005. Based on these trends, it can be said that the main change inmobile behavior is the increase of the mobile distance and this can result from the change in modes
such as walking, traveling by motorbike, car or other modes. It can be confirmed that as the mobile
distance increases, the mode also changes. Indeed, the motorbike mode increased 5.9 times from 2005
(9.2%) to 2015 (54.0%). Furthermore, project-induced employment opportunities as local project staff,
surveyors, and road construction workers have largely contributed to this mobility increase.
Table 2 Average number of mobility, mobile distance and mobile duration by year
Number of Mobility
Mobile Distance (km)Mobile Duration (min)

2005
2.3
2.3
40.6

2010
2.1
4.1
58.4

2015
3.0
9.2
46.0

Change in Mobile Behavior by Sex and Age
Sex and age are among the important parameters for assessing mobility change. The mobile distancesin 2005, 2010 and 2015 were compared with respect to sex and age (Table 3 & 4). The averageincrease rate of mobile distance during 2005-2010 and 2010-2015 is 2.6 and 1.7, and 1.6 and 2.6 formales and females, respectively.
The notable increase in the female age group 16-20 years during 2010-2015 (2.9 times) is due tooccupational changes from being a student to working in agriculture, non-agriculture and otheractivities. Furthermore, some females travel to a city university, so making the search for higher
education is one of the main driving factors for this age group. The higher increase rate for the female
age group 21-30, and 31-40 years during 2010-2015 (4.9 and 3.7 times, respectively) is also foundresulted in their engagement in project-related employment, including as office staff and as laboratory
workers at the project camp, and business work in the city. This large increase was generated with the
combination of a significant increase in the use of motorbike mode of travel to work. On the other hand,Ⓒ ISERD

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for the male age group 31-40, and 41-50 years, the rate during 2010-2015 decreases by 1.3 and 1.2
times, respectively. This is probably due to non-significant changes in occupation for males.
Furthermore, for the male age group 51-60 years, the rate during 2010-2015 increases by 3.0 times.
This notable increase is the result of increased engagement in non-agricultural activities, such as
driving car taxis.
Based on the findings of this study, it can be said that surrounding socio-economic development
largely affects local activities. Furthermore, occupation and mobility modes are significant factors
influencing the mobility of the villages.
Table 3 Average mobile distances by sex and age
group
Age

2005
M

2010
F

M

Table 4 Increase rate of mobile distance

2015
F

Age

M

F

(A) 16-20
1.2
1.2
4.3
4.0
7.3
(B) 21-30
4.8
1.1
5.9
1.7
11.1
(C) 31-40
3.0
2.2
8.9
1.1
11.9
(D) 41-50
3.4
2.2
11.1
2.7
13.4
(E) 51-60
4.0
3.1
6.1
4.1
18.3
(F) 61<
1.4
0.9
4.2
1.5
5.7
Average
2.8
1.8
6.5
2.8
11.6
Note: M stands for male and F stands for female
Unit is km

11.6
8.3
4.1
7.5
1.7
1.6
6.8

(A) 16-20(B) 21-30(C) 31-40(D) 41-50(E) 51-60(F) 61<
Average2005-2010
M
F
3.6
3.3
1.2
1.5
3.0
0.5
3.3
1.2
1.5
1.3
3.0
1.7
2.6
1.6

2010-2015
M
F
1.7
2.9
1.9
4.9
1.3
3.7
1.2
2.8
3.0
0.4
1.4
1.1
1.7
2.6

Note: M stands for male and F stands for female

CONCLUSION
Socio-economic developments reconstruct local livelihood. Mobility largely represents changes ofactivities associated with the development. Thus, this study conducted mobility assessment in a longterm by integrating two different data sets in different formats in different time period, and visualized
them. Mobility data from questionnaire survey was converted to spatiotemporal data and visualized
together with GPS data in an animation format. Those mobility data from questionnaires was further
validated by GPS log data for assessing historical mobility pattern. Mobility trend during 2005-2015
with respect to social parameters was further analyzed. Large increases in motorbike-based mobile
distance for working age females employed either with the project or in non-agricultural activities were
found. This mobility assessment and its visualization contribute to better understand activity changes
and it diversity associated with project development. Mobility can be a powerful parameter to assess
both opportunity and vulnerability along with the huge and accelerated project development. Thisfurther provides significant perspectives to policy development for sustainable rural development.
REFERENCES
Andrienko, G., Andrienko, N. and Augustin, S. 2007. Visual analytics tools for analysis of movement data. ACM
SIGKDD Explorations, 38-46. https://doi.org/10.1145/1345488.1345455
Center for Spatial Information Science. n.d.. Mobmap for chrome. Retrieved September 17, 2015, from http://
shiba.iis.u-tokyo.ac.jp/member/ueyama/mm/
Kelly, P., Krenn, P., Titze, S., Stopher, P. and Foster, C. 2013. Quantifying the difference between self-reported
and global positioning systems-measured journey durations, A systematic review. Transport reviews: A
Transnational Transdisciplinary Journal, 33 (4), 443-459. https://doi.org/10.1080/01441647.2013.815288
Mahesuan Kruewan. 2014. Thailand development policy for neighboring countries: DAWEI DEVELOPMENT
PROJECT CASE STUDY.
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Min, A. and Kudo, T. 2012. Newly emerging industrial development nodes in Myanmar, Ports, roads, industrial
zones along economic corridors. Emerging Economic Corridors in the Mekong Region. Bangkok.
Mobile Action. n.d.. Mobile action technology - i-gotU Official Site. Retrieved September 17, 2015, fromhttp://www.i-gotu.com/
Sheller, M. and Urry, J. 2006. The new mobilities paradigm. Environment and Planning A, 38 (2), 207-226.
https://doi.org/10.1068/a37268
Stopher, P., FitzGerald, C. and Xu, M. 2007. Assessing the accuracy of the Sydney household travel survey with
GPS. Transportation, 34 (6), 723-741. https://doi.org/10.1007/s11116-007-9126-8
Witayangkurn, A., Horanont, T., Ono, N., Yoshihide, S. and Shibasaki, R. 2013. Trip reconstruction andtransportation mode extraction on low data rate GPS data from mobile phone. IGU 2013 Kyoto Regional
Conference.
World Bank. 2015. Myanmar, Empowering People for Inclusive Growth.
Yu, H. and Shaw, S. 2004. Representing and visualizing travel diary data, A spatio-temporal GIS approach. 2004
ESRI International User Conference, (April 2016), 1-13. Retrieved from http://citeseerx.ist.psu.edu/
viewdoc/download?doi=10.1.1.83.6247&rep=rep1&type=pdfⒸ ISERD

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Research article
erd

Reducing Cadmium and Copper Uptake of Soybeans byControlling Groundwater Level and its Impacts on Growthand Yield
SONGTAO LI*The United Graduate School of Agricultural Sciences, Iwate University, Morioka, JapanCHOICHI SASAKI
Faculty of Agriculture and Life Science, Hirosaki University, Hirosaki, Japan
Email: chsasaki@hirosaki-u.ac.jp

CHIHIRO KATO
Faculty of Agriculture and Life Science, Hirosaki University, Hirosaki, JapanNOBUHIKO MATSUYAMA
Faculty of Agriculture and Life Science, Hirosaki University, Hirosaki, Japan

TAKEYUKI ANNAKA
Faculty of Agriculture and Life Science, Hirosaki University, Hirosaki, JapanAKIRA ENDO
Faculty of Agriculture and Life Science, Hirosaki University, Hirosaki, Japan

KIICHI SASAKI
Aomori Prefectural Improvement Projects Federation, Aomori, Aomori, Japan
Received 15 November 2016

Accepted 5 May 2017(*Corresponding Author)

Abstract In Japan, cadmium (Cd), copper (Cu) and arsenic have been designated as specificharmful substances in the Agricultural Land Soil Pollution Act. Though the safety standards ofthese substances have not been provided for upland field crops in Japan, it is important tominimize the concentration of these contaminants in agricultural products for both international
trading and our health. The objective of this study is to clarify the effects of controlling
groundwater levels on Cd and Cu uptake and growth and yield of soybeans. For this experiment,
three plastic containers were prepared, and then 14cm-thick of non-contaminated gravel, 15cmthick of non-contaminated soil (0.12 mg Cd kg-1; 2.4 mg Cu kg-1) and 25cm-thick ofcontaminated soil (2.24 mg Cd kg-1; 43.4 mg Cu kg-1) were placed in this order from the bottomof each container. Then the groundwater level of each container was maintained as 5cm (GL5),10cm (GL10) and 40cm (GL40) during the growing period. As a result, Cd and Cu
concentration of soybean seeds of GL5, GL10 and GL40 were 0.25, 0.52 and 1.07 mg Cd kg-1,
respectively, and 5.08, 5.82 and 9.96 mg Cu kg-1, respectively. Significant difference in both Cd
and Cu concentration of soybean seeds were found among three models at 5% level. On theother hand, growth and yield of soybeans tended to decrease with the rise of the groundwaterlevel. Noticeably, plant heights and weights of a hundred grains were significantly different
among these three models. From the above, it can be concluded that controlling groundwaterlevels can reduce Cd and Cu uptake and affect growth and yield of soybeans.
Keywords soybeans, uptake of cadmium and copper, control of groundwater level,
growth and yieldⒸ ISERD

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INTRODUCTION
Soil contamination in arable lands is a threat to human health through the intake of agricultural produce.
In Japan, since there has been some serious problems as seen in such grave impacts of heavy metals in
the cases of itai-itai disease induced by cadmium (Cd) and a decrease of rice yield affected by copper(Cu) (Yamane et al., 1997), “Agricultural Land Soil Pollution Act” was passed into law in 1970.
According to this law, Cd and Cu, and arsenic were designated as specifically harmful substances(Kobayashi, 1978). In 2010, the safety standard for Cd in brown rice was lowered to 0.4 mg kg -1 in
Japan, while other countries such as countries in the EU, China, Australia, etc., have stricter standards(MAFF, 2015). The safety standards for upland crops have not been provided yet in Japan. However,studies for reconsideration of Cd standards have been conducted as consumers’ interests in safety andsecurity of food (MAFF, 2007).
In Japan, an expansion of growing areas of soybeans is one of the important counter- measures forincreasing calorie based food self-sufficiency rate. Some countries provide the safety standard of Cd insoybean seeds as 0.2 mg kg-1, while in Japan, Cd concentration in soybean seeds which have been
produced even in non-contaminated field often exceeds this value (MAFF，2004 ). It is inferred,
therefore, that soybeans produced in Cd-contaminated and semi contaminated fields may contain much
more Cd. Thus there is real concern that Japanese domestic soybeans will not meet the standards ofexport-partner countries such as United States and Hong Kong etc. (Ishitsuka and Jindai, 2013) ininternational trading. As for Cu in soybeans, it is a future task for Japan to provide a safety standard
value, while some other countries already have such, e.g., 20 mg kg-1 in China (MAPRC, 2005).
As similar to the case of paddy fields, some techniques for reducing Cd uptake of soybeans e.g.,soil dressing, chemical methods such as controlling pH, and phyto -remediation etc. have been
evaluated recently (Akahane et al., 2013; Dong et al., 2007). These measures, however, cost muchmoney and take a long time, and so they have not been implemented widely yet (Haque et al., 2014a,b). Furthermore, soil dressing needs a large amount of soil and so it causes a heavy environmental
burden (MEGJ, 2010).
As a cost-saving and easy technique, Murakami et al. (2011) and Haque et al. (2014a, b) reported
that the Cd uptake of soybeans decreased by controlling oxidation reduction potential of the plowed
layer by changing the groundwater level since Cd becomes insoluble under a reduction condition.
Cu also becomes insoluble under reduction condition (Matsui and Okazaki, 1993) and thus it isexpected that controlling the groundwater level is also effective in reducing the Cu concentration incrops. The effects of a shallow groundwater level, especially higher than 10cm, on the Cu uptake ofcrops have not been evaluated yet and neither the distribution of Cu in soybean plant bodies.
Heretofore groundwater level was controlled by relief well and insoluble condition of Cu by pH
control amendment was made (Matsui and Okazaki et al., 1993; Yamane et al., 1997).
Against this background, the objective of this study is to clarify the effects of the controlling
groundwater level on Cd and Cu uptake and growth and yield of soybeans. To achieve the purpose,
growing tests of soybeans were conducted by using Cd- and Cu- contaminated soil samples. During theexperiments, the groundwater level was maintained as 5, 10 and 40cm to control the thickness of soil
layer, in which Cd and Cu was soluble.
This study will help to clarify the effects of rice-soybean crop rotation in contaminated ill- and/or
well-drained paddy fields on Cd and Cu uptake of soybeans and to develop techniques for reducing Cd
and Cu uptake with a low cost.
MATERIALS AND METHODS
Soil PropertiesⒸ ISERD

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In this experiment, we used soil sampled from the plow layer of a paddy field which had been
contaminated with agricultural water from mine drainage. Non-contaminated soil had been sampledfrom the plow layer of the paddy field at Kanagi farm of Hirosaki University, Aomori prefecture, thenortheastern part of Japan. Gravels had been collected from a nearby mountain, Mt.Iwaki, Aomori
prefecture.
Chemical and physical properties of the soil samples are shown in Table1. Soil textures ofcontaminated and non-contaminated soil were both clay loam. Cd concentration in contaminated soil,
non-contaminated soil and gravels were 2.27, 0.12 and 0.13 mg kg-1, respectively while Haque et al.
(2014a, b) had used contaminated soils of 3.39 and 1.57 mg kg-1. Cu concentration in the contaminatedand non-contaminated soil in this study was 43.4 and 2.4 mg kg-1, respectively. The contaminated soils
in this study contained seven times and twice as much Cd and Cu, respectively, as those in the averagenon-contaminated agricultural lands in Japan, the figures of which are 0.33 mg Cd kg-1 and 24.8 mg Cu
kg-1 (Asami, 2010).
Table 1 Physical and chemical properties of the soil samples
SampleDensity(g cm-3)

Soil
Texture

MgO
*

Na2O
*

CaO
*

K2O
*

Cu
*

Cd
*

T-N
(%)

T-C
(%)

C/N

OM
(%)

Polluted soil

2.57

CL

224

96

2032

232

43.4

2.27

0.28

3.39

12.2

5.86

Non-polluted
2.54
CL
120
64
400
120
2.4
0.12 0.16 2.07 13.3 3.62
soil
Gravel
2.68
147
18
539
58
1.5
0.13
0
0.05
Note: Soil texture is based on the International Soil Society classification. CL: Clay loam. Gravel diameter size 2-4mm.
*mg kg-1

Experimental Design
In this study, a growing test of soybeans was conducted with stratified field models. Three plastic
containers (41×61×63 cm) were prepared and then they were packed with 14cm-thick of gravels (drybulk density ρd=1.40 g cm-3), 15cm-thick of non-contaminated soil (ρd=0.89 g cm-3) and 25cm-thick ofcontaminated soil (ρd=0.80 g cm-3) in this order from the bottom of each container. After packing thesamples, oxidation reduction potential sensors were inserted at depths of 2.5, 7, 9, 12.5, 14.5, 22, 32,
37, 42cm. Soil temperature sensors were also buried at depths of 5, 15, 25 and 35cm in each container.
During the experiment period, the groundwater level of each container was maintained as 5, 10, and40cm; hereafter we call the models GL5, GL10 and GL40, respectively.
Cultivation Procedure
We used Ryuho soy bean (Glycine max (L.) Merr.cv.Ryuho) as the breed variety of our cultivation
experiment. The holes of 3 to 5 cm diameter at the four positions in each container were made, andthen 5 to 6 grains were seeded in June 16, 2014. Thinning out was done in 14 days after seeding andleft two of normal growth. Harvesting was conducted between late September and early October,which is the grain filling period in Japan. The groundwater level after seeding was 15cm, the groundwater levels after germination were 5, 10 and 40cm. The authors used Marriott bottles to control thegroundwater levels and checking of each groundwater level was carried out by using three groundwater
pipes attached to the containers. The recommended amount of chemical fertilizer for Ryuho soy bean
was added. 2L of irrigation water was supplied in 4-days intervals (equivalent to 2.0 mm d-1).
Protection treatment was done when it was necessary.

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Measurement Method
Oxidation-reduction potential (ORP) electrodes were installed for measuring oxidation-reduction
potential at arbitrary depths of the soil boxes. Measuring with the ORP meters (model UC-23, Central
Kagaku co. Ltd) was conducted during the entire period of cultivation experiment. The growth andyield assessments such as plant height, leaf age, branching, pod number and 100-grains weight, werecarried out based on the cultivation guideline of soybean in Akita Prefecture (Akita Prefecture, 2015).
After harvesting, Cd and Cu were extracted with HCl and HNO3 and their concentration in stem andleaf, root, grain and soil, was quantified by analyzing their specimens with the atomic absorption
spectrophotometry (MAFF, 1979). Other physical and chemical properties of the soil was alsomeasured with the standard methods in Japan. We found a significant difference in the results ofgrowth and yield assessments by using the Tukey-Kramer method.
RESULTS AND DISCUSSION
Oxidation-Reduction Potential (Eh)Eh(mV)For a long time, the reduction condition of soil has been known to cause insolubility of Cd and Cu, thishas been utilized to Cd uptake suppression (Yamane et al., 1997). That is, by increasing the solubilityof Cd and Cu in the soil in the oxidation condition, the uptake of these heavy metals from plant roots is
promoted. On the other hand, it is known that the uptake of these heavy metals is inhibited when theirsolubility is reduced in the reduction condition. In the oxidation-reduction potential (Eh), the oxidation
layer and the reduction layer are defined as Eh≧300mV and Eh＜300mV, respectively (Iimura, 1981).
Measured Eh values on each of the groundwater levels are shown in Figs.1, 2 and 3. In the GL5
container (Fig.1), Eh values measured at the 2.5cm depth was an oxidation layer of about 600mV;
however, below the 7cm depth Eh values indicated reduction condition. In the GL10 container (Fig.2),
Eh values at 2.5cm and the 7cm depths indicated more than 500mV; however, Eh values measured
below the 12cm depth was a reduction layer of Eh ＜ -100mV. In the GL40 container (Fig.3), except
for Eh values measured at the 42cm depth, all of the observation depths became oxidation layers of Eh≧ 500mV.

800
700
600
500
400
300
200
100
0
-100
-200
-300

Oxidation state
Reduction state

0

Soil depth fromthe surface
2.5cm
7cm
12cm
14.5cm
22cm
32cm
42cm

10 20 30 40 50 60 70 80 90 100
Measurement day
Fig.1 Temporal changes of Eh with GL5Ⓒ ISERD80

Eh(mV)

IJERD – International Journal of Environmental and Rural Development (2017) 8-1

Soil depth fromthe surface
2.5cm
7cm
Oxidation state
12cm
Reduction state
14.5cm
22cm
32cm
42cm

800
700
600
500
400
300
200
100
0
-100
-200
-300

Eh(mV)

0 10 20 30 40 50 60 70 80 90 100 110
Measurement day
Fig.2 Temporal changes of Eh with GL10
Soil depth fromthe surface
2.5cm
7cm
Oxidation state
12cm
Reduction state
22cm
32cm
37cm
42cm

800
700
600
500
400
300
200
100
0
-100
-200
-300

0 10 20 30 40 50 60 70 80 90 100110120
Measurement day
Fig.3 Temporal changes of Eh with GL40
From these results, it became apparent that controlling the upper part of the groundwater table tobe an oxidation layer and the part below the groundwater table to be a reduction layer can be possible.
It is inferred from this that thickness of supply layer of solubilized Cd and Cu are estimated as about
5cm for GL5, about 10cm for GL10, and about 40cm for GL40. From the temporal changes of Ehvalues measured at each level of the groundwater table, after clarifying the oxidation-reduction
environment of the root zone, preparation for comparing values of the uptake behavior of Cd and Cu
can be said to have been established. Although the values of heavy metal concentration in thecontaminated soil were different, the vertical directions of Eh distribution controlled in the GL10 and
the GL40 models were almost the same as the result of Haquet al. (2014a, b). In addition, it isestimated that the Eh values in the canonical paddy field depend on the soil crashing rate, the amountof organic matter, and soil pH.
Cd and Cu Concentration in Soybean Plants
Cd concentration in soybean seeds was GL5 (0.25mg kg-1) < GL10 (0.52mg kg-1) < GL40 (1.07mg kg -1)
and there were significant differences among the three treatments (p < 0.05) (Table 2). Those values
were three to ten times greater compared with Cd concentration in soybeans in non-polluted soil
(Murakami et al., 2011). Cd concentration in stems was GL5 (0.28mg kg-1) < GL10 (0.45mg kg-1) <GL40 (1.48mg kg-1) and there were little significant difference between GL5 and GL10. However,Ⓒ ISERD

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there was significant difference between GL5 and GL40, and also between GL10 and GL40 (p < 0.05).
Cd concentration in roots was GL10 (3.54mg kg-1) < GL5 (4.92mg kg-1) < GL40 (5.80mg kg-1) andthere was a significant difference between GL10 and GL40 (p < 0.05). The trend of Cd concentration
in soybean plants were seed < stem < root as found by Haque et al. (2014a, b). Cd concentration inseeds was about 1/10 compared with the one in roots in the case of GL5 and GL10. Cd concentration inseeds was about 1/5 compared with that of roots in the case of GL40. It is thought that Cd
concentration in soybean plants is affected by the groundwater level. Cd concentration in seeds was
0.25mg kg-1 in the GL5 treatment and it was close to the standard Cd content in non-polluted soil
(MAFF, 2004). There is a high possibility that Cd concentration in soybean seeds can be suppressed
when soybeans are cultivated in poorly drained Cd-contaminated paddy field. From these results, it
was considered that the control of the groundwater level had the effect of reducing the Cd
concentration in soybean seeds.
Table 2 shows Cu concentration in soybean plants. Cu also change the solubility by oxidationreduction potential as well as Cd. Cu concentration in soybean seeds was GL5 (5.08mg kg-1) < GL10
(5.82mg kg-1) < GL40 (9.96mg kg-1) and there were significant differences among the three treatments
at p < 0.05. The average Cu concentration in edible soybeans in Japan, the United State and China is
0.97-1.07 mg kg-1 (MEXT, 2016). The Cu concentration found in our current study was about fivetimes higher than the above figure. One reason may have been that the Cu concentration in the soil weused this time was twice higher than the average Cu concentration in typical Japanese soil. Cu
concentration in stems was GL5 (2.45mg kg-1) < GL10 (2.76mg kg-1) < GL40 (5.58mg kg-1) and there
was little significant difference between GL5 and GL10. However, there were significant differences
between GL5 and GL40, and also between GL10 and GL40 (p < 0.05). Cu concentration in roots was
GL40 (14.06mg kg-1) < GL10 (39.48mg kg-1) < GL5 (50.04mg kg-1) and there were little significant
difference between GL5 and GL10. However, there was significant difference between GL5 and GL40,
and also between GL10 and GL40 (p < 0.05). The trend of Cu concentration in soybean plants wasstem < seed < root. Cu concentration in seeds, however, was higher than that in stems unlike Cdconcentration in the plant. It is thought to be due to transfer characteristics of Cu in soybean plants. Cu
concentration in roots in GL5 and GL10 was twice higher than that of GL40. The morphologicalcharacteristic of roots is that there are many thick roots in the GL40 treatment and many fine root mat
in the GL5 and GL10 treatments. The different root morphology may have affected the uptake ofnutrient by soybeans. Cu concentration in soybeans at low groundwater levels was relatively high. Cu
concentration in soybeans was higher than that in brown rice (Paul et al., 2011). Soybean seeds tend toaccumulate more Cu than brown rice. Cu concentration in soybeans is defined as less than 20mg kg-1 in
China (MAPRC, 2005). On the other hands, there is no regulation value of soybean Cu content inJapan. However, those results of ours are valuable data because soybeans are important food and Cu in
this food is detrimental for human health. From the above results, the control of the groundwater levelis considered to be effective in reducing Cd concentration and Cu concentration in soybean seeds.
Table 2 Cd and Cu concentration in soybean of three different groundwater levels
Model
GL 5cmGL 10cm

Seed-Cd

Stem-Cd

0.25±0.04
0.52±0.06

ab

Root-Cd

0.28±0.02a

0.45±0.09a4.92±1.54

Seed-Cuab

3.54±0.56a5.08±0.27a5.82±0.33b

Stem-Cu

Root-Cu

2.45±0.19

a50.04±13.93a

2.76±0.98

a39.48±7.51a

GL 40cm
1.07±0.17c
1.48±0.41b
5.80±1.03b
9.96±0.62c
5.58±0.51b
14.11±3.66b
Note: Small letter indicates significant difference at 5% level according to Turkey-Kramer test; ± shows standard
deviation. For Seed Cd analysis (n=8); for other cases (n=5). Unit: mg kg-1

Soybean Yield and Its Components
Soybean yield and its components are indicated in Table 3. Average stem heights (n=8) were GL5 (59.7Ⓒ ISERD

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cm) < GL10 (66.8 cm) < GL40 (78.8 cm). Significant differences were recognized among the threemodels. Murakami et al. (2011) and Haque et al. (2014a, b) reported that in excess soil moisture
condition, soybean plants do not grow well after the germination and result in low yield. A similar
result was obtained in this study that the high groundwater level conditions suppress the growth of thestem.
The averages of the stem diameter and the number of seeds per pod (seed/pod) did not show any
significant difference. The average of the branch number showed little significant difference between
GL10 and GL40. However, there were significant differences between GL5 and GL10, and also
between GL5 and GL40. The averages of good seed weight per plant were GL5 (20.5 g) < GL10 (36.2g) < GL40 (56.3 g), showing significant differences among them. Averages of 100 seeds weight also
showed a similar trend as the good seed weight. The good seed weights of GL5 and GL10 models
lowered by about 36 % and 64 %, respectively, compared with that of GL40 model, which suggested
that even a slight difference in the groundwater level can bring a critical effect on the soybean yieldunder a high groundwater level condition. According to Shimada et al. (1995), it is important to control
the groundwater level according to the rain condition in actual paddy fields. Arihara (2000) reported
that the soybean yield was highest in the fields with a 40-50 cm groundwater level. His finding was
based on the investigation on the relationship between groundwater level and soybean yield.
The soybean yield in this experiment using Cd contaminant soil was higher under the low
groundwater level condition and lower under the high groundwater level condition.
Table 3 Soybean yield components of three different groundwater levels
Model
GL 5cmStem height(cm)
59.7±6.3a

GL 10cm

66.8±3.8bStem diameter(cm)
7.1±0.9a
7.6±0.8abBranch No.

Seed/Pod

3.6±0.9ab

5.1±0.8

1.6±0.2a

100 seed wt.
(g)28.3±2.3aGood seed wt.
(g)20.5±8.9a

1.6±0.2a

37.2±2.1b

36.2±6.3b

GL 40cm
78.8±5.2c
8.5±0.7b5.5±1.1b
1.6±0.1a
40.0±1.8c
56.3±4.7c
Note: Small letter indicates significant difference at 5% level according to Turkey-Kramer test; ± shows standard
deviation. Seed weight at 15% moisture. In all case (n=8).

CONCLUSION
Soybean was cultivated in containers using Cd contaminant soil (concentration:2.27 mg kg-1, thickness:
25 cm) under groundwater levels of 5 cm, 10 cm, and 40 cm with measuring the oxidation-reduction
potential in the soil. Soil was in oxidation condition above the groundwater level and in reduction
condition below it. Cd and Cu concentrations of stems and seeds were found to be lowered under ahigher groundwater level condition. Under a higher groundwater level condition, Cd concentration inroots showed a low value but that of Cu showed the opposite result. It was confirmed, as earlier studies
had reported, that growth and yield of soybeans were good when the groundwater level was low.
Controlling groundwater level can be one of the simplest and most economical methods for reducing
the Cd and Cu uptake by soybean plants. When we apply this method on site, relief well will beavailable for controlling ground water level. This can help minimizing toxic metals uptake from
polluted soils of soybean cultivated fields with high groundwater level in developing countries.
ACKNOWLEDGEMENT
The authors would like to thank Hiroaki Hasegawa, Jinhun Fan, Hideki Takahashi for their cooperation
throughout this research. In addition, we appreciate the support of this research by grants from theⒸ ISERD

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Faculty of Agriculture and Life Science, Hirosaki University and JSPS KAKENHI Grant Number
JP15K18756.
REFERENCES
Akahane, I., Makino, T., Katou, H., Nakamura, K., Sekiya, N., Kamiya, T. and Takano, H. 2013. Remediation of
cadmium-contaminated arable soils by washing with iron chloride, J. Jpn. Soc. Soil Phys., 123, 55-63.
Akita Prefecture, 2015. http://www.pref.akita.jp/www/contents/1427889836131/index.html
Asami, T. 2010. Toxic heavy metal pollution in soils of Japan, Agune-gijutsu center, Tokyo. Japan, 3-8.
Arihara J. 2000. Soybean. Nobunkyo, Tokyo, Japan, 101-103.
Dong, J., Mao, W.H., Zang, G.P., Wu, F.B. and Cai, Y. 2007. Root excretion and plant tolerance to cadmium
toxicity, A review. Plant Soil Environ., 53 (5), 193-200.
Haque, M.Z., Sasaki, C., Matsuyama, N., Annaka, T. and Sasaki, K. 2014a. Effect of groundwater level on
cadmium uptake and yield of soybean from cadmium polluted soils. IJERD, 5, 107-113.
Haque, M.Z., Sasaki, C., Matsuyama, N., Annaka, T. and Kato, C. 2014b. Groundwater level effect on redox
potential on cadmium uptake and yield of soybean. Am. J. Plant Sci., 5, 3022-3031.
Ishitsuka, S. and Jindai, H. (Ed.). 2013. The current state and the prospect of export strategies for agricultural
products in Japan. Tsukuba Shobo, Tokyo, Japan, 93-109.
Kobayashi, J. 1978. Pollution by cadmium and the Itai-itai disease in Japan. In: Oehme, F.W. (Ed.), Toxicity ofheavy metals in the environment, part 1, Marcel Dekker, New York, 29-35.
MAFF (The ministry of Agriculture, Forestry and Fisheries of Japan). 1979. 1. The foundation of environmental
paddy field and investigation of paddy field, water quality and crop analysis method, In National conferenceof paddy field preservation. Tokyo, Japan, 113-115.
MAFF (The ministry of Agriculture, Forestry and Fisheries of Japan). 2004. http://www.niaes. affrc.go.jp/
techdoc/soya_cd.pdf.
MAFF (The ministry of Agriculture, Forestry and Fisheries of Japan). 2007. http://www. maff.go.jp/j/syouan/nouan/kome/k_cd/taisetu/pdf/04.pdf.
MAFF (The ministry of Agriculture, Forestry and Fisheries of Japan). 2015. http://www. maff.go.jp/j/syouan/seisaku/risk_analysis/priority/pdf/15083cd.pdf
MEGJ (The Ministry of Environment Government of Japan) 2010. http://www.env.go.jp/council/10dojo/y10701/mat07 -1.pdf.
Matsui, T. and Okazaki, M. 1993. Environmental soil science. Asakura-syoten, Tokyo, Japan, 134-142.
MEXT (The Ministry of Education, Culture, Sports, Science and Technology). 2016. http://fooddb.mext.go.jp/
MAPRC (The ministry of Agriculture of the People’s Republic of China). 2005. http://dombalib.com/view/6f0ec85c13b4cf04178dd1848cee3895.html.
Murakami, S., Sasaki, C., Nakagawa, S. and Ota, N. 2011. The influence of oxidation-reduction state in soil bygroundwater level control on the growth and the yields of soybeans and the uptake of cadmium. J. Jpn. Soc.
Soil Phys., 119, 29-38.
Paul, S.K., Sasaki, C., Matsuyama, N. and Kato, K. 2011. Effect of percolation pattern on yields andaccumulation of copper and cadmium in the rice plants with soil dressing models. Environ. Sci. Eng., 5, 14641473.
Shimada, S., Kokubun, M. and Matsui, S. 1995. Effect of water table on physiological treits and yield of soybean.
Jpn. J. Crop Sci., 64 (2). 294-303.
Yamane, I., Hamada, R., Yoshinaga, N. et al. 1997. Soil science. Bunneidou, Tokyo, Japan, 196-204.

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Research article
erdAnalysis on Surface Deformation and Cracks in Paddy Fields
by 2016 Kumamoto Earthquake using GNSS andPhotogrammetry
TAKAHIKO KUBODERA*
Faculty of Science and Engineering, Toyo University, Saitama, Japan
Email: kubodera@toyo.jp

TAKANOBU SUZUKI
Faculty of Science and Engineering, Toyo University, Saitama, JapanHIROSHI MASAHARU
Faculty of Science and Engineering, Toyo University, Saitama, Japan

EIJI MATSUO
Faculty of Engineering, Kyushu Sangyo University, Fukuoka, Japan
Received 10 January 2017

Accepted 5 May 2017(*Corresponding Author)

Abstract Paddy fields in Kumamoto Prefecture were struck seriously by the 2016 Kumamoto
Earthquake. The surface deformation and cracks damaged water distribution system for paddy
fields. In order to clarify damages with rapidity, the surveying combining the aerial-based
surveying and ground-based surveying was necessary. The aerial photogrammetry was
conducted for the surface deformation and crack area, to clarify them quantitatively. The aerial
photos were taken by an airplanes after the earthquakes. The aerial photogrammetry by airplane
is suited to compare the topographic model after earthquakes to the topographic model before
earthquakes, because the topographic model before earthquakes was surveyed by airplane. The
Global Navigation Satellite System (GNSS) surveying was also conducted at the ground points
in the area of aerial photos. The orthophoto mosaic and the Digital Elevation Model (DEM)
after the earthquakes were made from these surveying. Further, the orthophoto mosaic and theDEM after the earthquakes were input into the Geographic Information System (GIS), to
analyze the surface deformation and cracks in paddy fields. The accurate positions for cracks
were found by overlaying the translucent orthophoto mosaic on the existing map. The
differences between the DEMs before and after the earthquakes were analyzed by overlaying
the DEM after the earthquakes on the DEM before the earthquakes. As the results, it was foundthat the overall upheaval and the local cave-in arose in the focused paddy field. The overall
upheaval was 0.0 m - 0.4 m. The local cave-in was about 2.0 m. In order to check the
differences between the DEMs before and after the earthquakes, the DEM after the earthquakes
was compared to the elevation value by the GNSS surveying at the verification points. As theresult, it was verified that the difference was 0.118 m as standard deviation.
Keywords aerial photogrammetry, GNSS, DEM, GIS, paddy field

INTRODUCTION
The Kumamoto Earthquake was a series of earthquakes that struck Kumamoto Prefecture in the southof Japan on and after April 14, 2016. A foreshock with a magnitude on the Japan Meteorological
Agency seismic intensity scale of Mj 6.5 and with its hypocenter at a depth of 11 km struck theⒸ ISERD

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Kumamoto area at 9:16 p.m. on April 14, 2016. At 1:25 a.m. on April 16, 28 h after the foreshock
occurred, the main shock with a magnitude of Mj 7.3 and with its hypocenter at a depth of 12 km
struck the same Kumamoto area that had experienced the foreshock (Kato el al., 2016). Kumamoto
Prefecture is highly productive in terms of agriculture, as evidenced by the fact that, before theearthquakes occurred, it was ranked the fifth most productive prefecture out of all the 47 prefectures inJapan in terms of the value of agricultural production. However, after the earthquakes, many paddy
fields were seriously damaged because the vertical deformation and cracks made it impossible toirrigate them.
In this study, we conducted aerial photogrammetry and a Global Navigation Satellite System(GNSS) survey, because a survey consisting of both aerial- and ground-based studies were necessary
for a prompt understanding of the complete picture of the damaged area. Aerial photogrammetry is asurveying technology utilizing ground-based three dimension (3D) models, which are constructed fromphotos taken from the sky, camera coordinate values, and coordinate values of ground control points(GCPs). In this study, we also built digital elevation models (DEMs) and orthophoto mosaics, because
these were obtainable through the aerial photogrammetry. DEMs are models of the elevations of theland surface, excluding the heights of buildings and vegetation. An orthophoto mosaic is a single aerial
photograph on which several orthographically projected aerial photographs are assembled. We
conducted the GNSS surveying of the devastated areas to obtain the coordinate values of the GCPs. A
GNSS surveying is a survey method to determine the positions using signals from positioning satellites
such as the GPS, GLONASS and Galileo. The accuracy ranges from several meters to several
millimeters, depending on the method used. In this research, we conducted a network-based real time
kinematic (RTK) GNSS surveying to determine the coordinate values of the GCPs with high accuracyof several millimeters. We used the Geographic Information System (GIS) to determine the difference
between the DEMs before and after the earthquakes in order to clarify the vertical deformation of theland surface in the paddy fields quantitatively.
The satellites have been used for surveying in Kumamoto (Research Group for High-resolution
Satellite Remote Sensing, 2016; Obata and Iwao, 2016). The drones have been used for surveying inKumamoto (Ishiguro el al., 2016). In this research, we considered that aerial photogrammetry byairplane would be preferable to satellite or drone for the comparison of the DEMs before and after theearthquakes, because an airplane survey had already been conducted to obtain a DEM before theearthquakes. For this reason, we used aerial photos taken from an airplane after the earthquakes. In
Japan, the Geospatial Information Authority of Japan (GSI) has built a detailed database of thecountry’s land surface with airplanes, and it released the DEM before the earthquakes free of charge.
We used aerial photogrammetry to build the DEM after the earthquakes, and analyzed the difference
between the DEMs before and after the earthquakes. At the same time, we overlaid orthophoto mosaics
we built from the aerial photogrammetry onto the existing maps to determine cracks on the land
surface in the paddy fields.
METHODOLOGY
Figure 1 shows the target area of the survey and analysis. Kumamoto Prefecture is located in thesouthern part of Japan. Of the large area damaged by the earthquakes, we selected Aso City in order tofocus on the damage to paddy fields. We used the photogrammetry software PhotoScan Professional,
which is a structure-from-motion (SfM) software product. PhotoScan Professional allows for aerial
triangulation with the help of multiple aerial photos and multiple GCPs. It creates DEMs throughimage matching and filtering treatments, and also creates orthophoto mosaics automatically. Fig. 2
shows the 3D model that we created. The blue rectangles in the sky show the 3D position and attitudeof the aerial photographs. The circles on the land surface indicate the 3D positions of the GCPs where
we conducted the GNSS surveying. The coordinate system we used is the Japanese Geodetic DatumⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2017) 8-1(JGD) 2011 Plane-rectangular Coordinate System Area 2 (Matsumura el al., 2004; Tsuji andMatsuzaka, 2004; Imakiire and Hakoiwa, 2004; Geospatial Information Authority of Japan, 2011). We
successfully created a group of 3D points on the land surface through aerial photogrammetry. On thenear side of the Fig. 2, the site of the landside disaster triggered by the earthquakes can be seen. The
aerial photographs were taken on July 5, 2016. The digital aerial camera provides its orientation
accurately because it was calibrated. Using the RTK GNSS surveying and Inertial Measurement Unit
(IMU) technology, it was possible to provide the coordinate axes and attitudes of the digital aerial
camera with high accuracy of several tens of millimeters.

Aerial photos
Aso City
Paddy fields

LandsideFig. 1 Target area in KumamotoFig. 2 The 3D model by aerial photogrammetry

Table 1 SD values of residual errors of the mostprobable values of nine GCPs

SD

X [m]

Y [m]

H [m]

0.088

0.149

0.149

Fig. 3 GNSS surveying
As the photograph in Fig. 3 shows, we conducted a network-based RTK GNSS surveying inKumamoto on the 23rd and 24th of July 2016. We used the Trimble R10 for the GNSS surveying, andthree types of satellite: GPS, GLONASS, and the Quasi-Zenith Satellite System (QZSS). In addition,
we used two frequency bands: L1 and L2. The geoid model we used was “GSIGEO2011” (Miyahara elal., 2014; Kubodera el al., 2016). For conducting the GNSS surveying on the ground, we selected thefour corners of the course, sites accessible by car, and pavements so that the aerial camera could takeclear photographs. Based on the surveying calculations from the GNSS surveying, we found that thestandard deviation (SD) of the most probable value was less than 0.01 m. We conducted aerialⒸ ISERD

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triangulation based on the 3D coordinate values of the camera and the GCPs. Table 1 shows the SD
values of residual errors of the most probable values of nine GCPs. The SD values for the X and Y
coordinates and elevation H were lower than 0.15 m, indicating a high accuracy. We created a DEM
after the earthquakes with 1 m mesh, in order to determine the differences between the DEMs before
and after the earthquakes as well as the elevation values. In addition, we created orthophoto mosaics togain a clearer picture of the damage.
RESULTS AND DISCUSSION
Specifying the Position of Cracks with Orthophoto Mosaic
Figure 4 shows the orthophoto mosaic developed on the GIS. For the aerial photogrammetry, we used
the JGD2011 Plane-rectangular Coordinate System Area 2. The GIS software used was ArcGIS 10.4.1.
We used the existing map as a background for confirmation of the positions. By doing this, we foundthat the created mosaic was developed on the designated positions correctly. The orthophoto mosaic
indicates that this area consists of paddy fields. Subsequently, we set the transmission of theorthophoto mosaic at 50% and overlaid it onto the existing map to specify the positions of cave-ins. As
shown in Fig. 5, we successfully specified the exact positions of the cracks. We were able to confirm
cracks along the fault that occurred in the paddy fields. Fig. 6 shows a photograph taken by the second
author of this study immediately after the earthquakes occurred in the area surrounding the paddy fields.
It shows several cracks created along the fault. This visually confirms that there was a cave-in about 2
m deep.

Fig. 5 Cracks in paddy fields

Fig. 4 Orthophoto mosaic developed on the GIS

Fig. 6 Actual cracksⒸ ISERD

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Differences between the DEMs before and after the Earthquakes
For the DEM before the earthquakes, we used the results (5 m mesh, elevation value in units of 0.01 m)
obtained from the GSI. For the analysis of the difference between the DEMs before and after theearthquakes, we set the mesh sizes of the DEM before and after the earthquakes to 1 m. Fig. 7 shows
the results of the analysis. We deducted the elevation values of the DEM before the earthquakes fromthose of the DEM after the earthquakes to obtain the differences. We blacked out the objects with aheight difference greater than 3 m because these were assumed to be vegetation or buildings. The
analysis results indicate that the area on which we focused underwent upheaval as a whole. Whenfocusing on the agricultural roads, it was found that they underwent upheaval of 0.0–0.4 m. The paddy
fields apparently underwent greater upheaval than the agricultural roads, but ambiguous places such as
vegetation showed a low accuracy in matching. The rectangular part in the center of Fig. 7 is thecracked part shown in Fig. 5. Based on the analysis of the differences between the DEMs before andafter the earthquakes, we successfully clarified the vertical deformation both dimensionally andquantitatively.

Fig. 7 Differences between the DEMs before and after the earthquakes
Accuracy Verification of Elevation Values
As Table 2 shows, we compared the elevation values of the DEM after the earthquakes and those of the
GNSS after the earthquakes at verification points to verify the accuracy of the constructed DEM after
the earthquakes. At these verification points, we did not adopt the GCPs by aerial triangulation, butrather conducted a GNSS surveying on the points. The GNSS showed a higher accuracy than the DEM.
Based on the above results, we found that the elevation values of the DEM and the GNSS after theearthquakes closely matched each other with the discrepancies between them ranging from +0.031 m
to +0.196 m. The SD of the elevation values of the DEM after the earthquakes was 0.118 m, assumingⒸ ISERD

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that the elevation values of the GNSS after the earthquakes were true values. That is, it is possible tostate that the DEMs after the earthquakes was accurate with an SD of 0.118 m.
Table 2 Accuracy verification of elevation values
Elevation value [m]Name of point

DEM after earthquake GNSS after earthquake

Discrepancy

1482.608

482.577+0.031

2

479.938479.888+0.0503

478.280478.084+0.196SD

0.118

Comparison of the DEMs before and after the Earthquakes with the Lateral Profile
We found several points where a cave-in could be confirmed by the orthophoto mosaic of Fig. 5, eventhough it was not possible to detect these when comparing the differences between the DEMs before
and after the earthquakes, such as in the rectangle in Fig. 7. The reason for this may be that, although
the point caved-in, it underwent upheaval as a whole. For this reason, we compared the DEMs before
and after the earthquakes using a lateral profile. Fig. 8 shows the transverse line A–A’ used to compare
the DEMs before and after the earthquakes. We designated Points B, C, D, E, and F at distances of 4,
20, 30, 60, and 70 m from Point A. This rectangular area is the same as that shown in Fig. 5. Although
the earthquakes caused a cave-in, a large difference could not be detected when comparing the DEMs
before and after the earthquakes. This is presumably because the overall area underwent upheaval, thus
causing a relative cave-in. Fig. 9 shows a cross section along the line A–A’. The section from Point A to
Point B is a paved agricultural road, and it underwent upheaval of about 0.9 m as a result of theearthquakes. This upheaval can be observed from Points A to C. However, a sharp cave-in from thelevel before the earthquakes can be detected from Points C to E. At Point D, where the sharpest cave-in
was observed, the height difference was about 2 m from the land surface. It could also be determinedthat upheaval resumed from Points F to A’. When comparing the DEMs before and after theearthquakes, it was found that the paddy fields had a more irregular elevation profile on the DEM after
the earthquakes. This is presumably because the image mapping has a low accuracy in areas withvegetation. As a whole, we successfully clarified the vertical deformation of the cracks on the fault
dimensionally and quantitatively.

Fig. 8 Differences between the DEMs before and after the earthquakes at the cracksⒸ ISERD

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Fig. 9 Cross section at the cracks
CONCLUSIONAs the conclusions, the following points are summarized in this study.
(1) The exact positions of the cracks in paddy fields were specified by the orthophoto mosaic.
(2) Based on the analysis of the differences between the DEMs before and after the earthquakes,
the vertical deformation was clarified dimensionally and quantitatively.
(3) The SD of the elevation values of the DEM after the earthquakes was 0.118 m, assuming thatthe elevation values of the GNSS after the earthquakes were true values. That is, it is possible
to state that the DEMs after the earthquakes was accurate with an SD of 0.118 m.
ACKNOWLEDGEMENTS
The Information Provision Support Team of the Major Disaster Management Headquarters of GSI
provided the aerial photos used in this research in order to support the recovery project implemented by
Kumamoto Prefecture. We gratefully acknowledge the kindness and support they extended to our study.
REFERENCES
Geospatial Information Authority of Japan. 2011. (Website: http://www.gsi.go.jp/ENGLISH/page_e30030.html,
accessed; 15 Dec. 2016).
Imakiire, T. and Hakoiwa, E. 2004. JGD2000 (vertical) -The New Height System of Japan-. Bulletin of the GSI,
51, 31-51.
Ishiguro, S., Matsuta, N., Inoue, H., Nakata, T., Tanaka, K., Ishiyama, T., Mita, T., Takenami, D., Moriki, H.
and Hirouchi, D. 2016. High-resolution digital surface models of surface rupture associated with the 2016
Kumamoto earthquake, by using unmanned aerial vehicle photography. Journal of The Remote Sensing
Society of Japan, 36 (3), 214-217 (in Japanese).
Kato, A., Nakamura, K. and Hiyama, Y. 2016. The 2016 Kumamoto earthquake sequence. Proceedings of theJapan Academy, Series B, 92 (8), 358-371.
Kubodera, T., Okazawa, H., Hosokawa, Y., Kawana, F., Matsuo, E. and Mihara, M. 2016. Effects of surveying
methods between GNSS and direct leveling on elevation values over long distance in mountainous Area.
International Journal of Environmental and Rural Development, 7 (1), 62-69.
Matsumura, S., Murakami, M. and Imakiire, T. 2004. Concept of the new Japanese Geodetic System. Bulletin of
the GSI, 51, 1-9.

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Miyahara, B., Kodama, T. and Kuroishi, Y. 2014. Development of new hybrid geoid model for Japan,“GSIGEO2011”. Bulletin of the GSI, 62, 11-20.
Obata, K. and Iwao, K. 2016. Relation between slope failures caused by Kumamoto earthquake in 2016 and
NDVI changes in ASTER data. Journal of The Remote Sensing Society of Japan, 36 (4), 417-420 (inJapanese).
Research Group for High-resolution Satellite Remote Sensing. 2016. High resolution satellite remote sensing forthe disaster of the 2016 Kumamoto earthquake. Journal of The Remote Sensing Society of Japan, 36 (3), 211213 (in Japanese).
Tsuji, H. and Matsuzaka, S. 2004. Realization of horizontal geodetic coordinates 2000. Bulletin of the GSI, 51,
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Research article
erd

Spatial Assessment of Land Cover Change and Ecosystem
Services from a Case Study in Savannakhet Province, Laos
THIPPHACHANH SOUPHIHALATH*
Nagoya University, Nagoya, Japan
Email: poumpui@gmail.com

KIICHIRO HAYASHI
Nagoya University, Nagoya, Japan

MAKOTO OOBANational Institute for Environmental Studies, Miharu Japan

WATARU KOBAYASHI
Nagoya University, Nagoya, Japan
Received 15 December 2016

Accepted 5 May 2017(*Corresponding Author)

Abstract Land use without proper land management may cause deforestation and othernegative impacts on ecosystem services (ESs), as defined in the Millennium Ecosystem
Assessment of 2005. In Laos, natural resource sectors have received many investments in thepast decades, during which a loss of ESs occurred. The aims of this study were to assess
impacts on the potential provisions of ESs caused by land cover changes. The Savannakhet
province in Laos was selected for the case study. Land cover changes were investigated using
Landsat satellite images from 1988. A land cover map for 2010 was obtained from thegovernment. After classifying the image into five land cover types, spatial analyses of the ESs
were conducted by utilizing the primary unit values of six ESs. The results indicated that forests
were converted to agricultural areas. Most of the potential provisions of ESs decreased due tothese conversions in the past two decades.
Keywords ecosystem services, land cover change, spatial analysis, Laos

INTRODUCTION
Land use without proper land management may cause deforestation and other negative impacts on
ecosystem services (ESs) as defined in the Millennium Ecosystem Assessment (MA, 2005).
Appropriate land use management practices are required to provide adequate ESs. Goldewijk andRamankutty (2004) and Chase et al. (2000) determined that land cover change was a key driver ofenvironmental and ES changes. Numerous studies have focused on the relationship between land coverchanges and the loss of ESs (Kay Khaing Lwin et al., 2016). An understanding of the spatial patterns
associated with ESs could be applied for developing effective environmental policies and for decisionmaking (Izquierdo and Clark, 2012). In Lao People's Democratic Republic, land use changes
associated with potential development might cause negative impacts on ES provisions (USAID, 2015;
Yoshida et al., 2010) and natural resource sectors such as agriculture and forestry have received many
investments in the past decades (Saunders et al., 2014). In Laos, Savannakhet province located in thesouth of the country received eight percent of the total foreign direct investments from 2004 to 2010,
two-thirds of which were provided to the agriculture and forestry sectors (IUCN and NERI, 2011).
This may cause agricultural expansion and forest degradation in this province. There are fewⒸ ISERD

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researches conducted in this province. Therefore, a study of the spatial impact assessment by land
cover changes is needed, with a focus on the changes of ES potential provisions in Laos, especially inthe province. The results of the ES study could be applicable to land use planning and other decisionmaking processes related to ES conservation in this area.
OBJECTIVE
The objective of the study is to assess impacts on the potential provisions of ESs caused by land coverchanges in the past two decades in Savannakhet province, Laos. A spatial analysis of ESs was
conducted based on comparing a 1988 land cover map with those of a 2010 map.
MATERIALS AND METHODS
Study Area
Savannakhet province is the largest province in area in Laos and is located in the southern part of thecountry (the Administration Office of the province: 16°34′15.2″ N, 104°45′48.3″ E) (Fig. 1).
The average annual temperature was 26.2 ℃, and the average annual rainfall was 1,672.3 mm (Lao
Statistics Bureau, 2013). The total population was 969,700 (Lao Statistics Bureau, 2015), and the area
covers 21,774 km2, 90 % of which is flat (IUCN and NERI, 2011). The elevation of this study area
ranges from 62 m to 255 m, based on the ASTER GDEM (ASTER Global Digital Elevation Model:
http://www.jspacesystems.or.jp/ersdac/GDEM/E/index.html)

Fig. 1 Maps of Laos (on the left) and Savannakhet province (on the right)
Note: Black and red lines showed the country and the study area boundaries.

Research Process
The study consisted of three steps. The first step was the acquisition of satellite images and thedevelopment of a 1988 land cover map. Then we performed a reclassification of a 2010 land cover map
provided from the Forest Inventory and Planning Center (FIPC), Government of Laos. The second step
was an estimation of ES supply potentials by utilizing each ES unit value based on a literature survey,
a land surface temperature calculation using band 6 (thermal band) of a Landsat 5 Thematic Mapper(TM) images, and the Universal Soil Loss Equation (USLE) analysis. Finally, after the normalizationof the ES values by utilizing equal weights for each ES unit, a comprehensive analysis was conducted
to understand the impacts on the ESs caused by land cover changes in the past two decades. ArcGIS
10.4.1 (ESRI) was used for the analysis.
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1988 Land Cover Map Development and Reclassification of 2010 Land Cover Map
The following process was conducted for the development of a 1988 land cover map. Landsat 5 images
for 1988 at 30 m x 30 m resolution were downloaded from the United States Geological Survey
website (http://landsat.usgs.gov). Four satellite images (WRS path/row 127049 and 127048 dated 16th
March and 16th Feb. 1988, 126049 and 126048 dated 6th Feb. 1988) covered the study area. The images
were subset to the study area located in the western part of the province, shown as a red line in Fig. 1.
The images were classified into five types of land cover classes: forest, dry-forest, water, urban andbare land, and agriculture area. The normalized difference vegetation index (NDVI) and the normalized
difference water index (NDWI) (McFeeters, 1996) were used to separate forest and water, respectively.
For the classification of the remaining classes, namely, mixed-forest, dry-forest and grassland, urban
and bare land, and agriculture area, supervised methods were used. Subsequently, the mixed-forestclass was integrated into the forest class. Swamp areas were difficult to identify in the 1988 images;
therefore, swamps were classified as “Other” together with clouds which were excluded from the ES
analysis.
An accuracy assessment was conducted for the 1988 classification using ancillary data by creating
approximately 100 random points for each land class, except urban and bare land class, which only 35
points were set due to the small ratio of urban area against the total area. Then the random points of the1988 map were compared with 1988 Google Earth image and original satellite images as references.
The result of the accuracy assessment was 0.853 by the kappa coefficient (Table 1). However, theresolution of the reference is not high. But we could not find any other reference for the old map.
We used a land cover map of 2010 provided by the FIPC, and reclassified the existing 22 land
cover types into the same five land cover types used for the 1988 map (Table 2).
Table 1 Accuracy assessment of the 1988 land cover map
ForestClassified
Forest1988
Dry-Forest
land
Water
cover
Urban and Bare land
map
Agriculture area
Total
Kappa coefficient
Overall accuracy

90
7
0
3
0
100

1988 Google Earth image
DryUrban and
Water
ForestBare land
10
0
0
90
0
0
4
88
0
3
0
26
9
0
0
116
88
26
0.853
86.7 %

Agricultur
e area
0
3
8
3
92
106

Total
100
100
100
35
101
436

Table 2 Reclassification of the 2010 land cover map
National Level Classification System (NLCS) for Laos 2010 by FIPC
11Evergreen Forest, 12Mixed Deciduous Forest, 14Coniferous Forest, 15Mixed Coniferous andBroadleaved Forest, 16Forest Plantation, 21Bamboo, 22Regenerating Vegetation
13Dry Dipterocarp Forest, 31Savannah, 32Scrub, 41Grassland
81Water
71Urban, 72Barren Land and Rock, 80Other Land
51Upland Crop, 61Rice Paddy, 62Other Agriculture, 63Agriculture Plantation
42Swamp, 82Cloud, 83Cloud Shadow
Note: Land cover numbers were defined under NLCS by FIPCⒸ ISERD95

New category for thisstudy by authors
ForestDry-forest
Water
Urban and bare land
Agriculture area
Other

IJERD – International Journal of Environmental and Rural Development (2017) 8-1

Table 3 shows the ES unit values based on a literature survey conducted by Kay Khaing Lwin etal. (2016) with several adjustments for this study, except temperature regulation and USLE estimation.
The ES categories from the MA (2005) were used, including provisioning, regulating, and supporting
services, except for cultural services. For supporting services, gross primary production (GPP) andcarbon stock were selected. For regulating services, temperature regulation (land surface temperature
estimation: LST), USLE and water infiltration were chosen. For provisioning services, only
agricultural products (averaging per ha production of local products, namely, rice, vegetables, maize,
soil bean, peanut, starchy roots from Lao Statistic Bureau (2013)) were used.
Table 3 Primary unit values of ESs
Types of ESs
Supporting Services:
GPP (t/ha/yr)1
Carbon stock (t/ha)2
Regulating Services:
Water infiltration (mm/h)3
Temperature regulation (°C)
Provisioning Services:
Agriculture Products (t /ha)

Agriculture Urban and
Water
area
bare land

ForestDry-Forest

30.7

4.585

12.09300131.61

41.19

0

0

0

100
2.93

280
1.06

38.5
0.26

2
0

-

4.00

-

0
Chaplot et al. (2002)
5.23 by authors*
Lao Statistic Bureau(2013)-

SourceChen et al. (2013) and
Hirata et al. (2013)
Forest Carbon (2015)

Source: Kay Khaing Lwin et al. (2016) revised by the authors,*: developed by using Landsat 5 as explained in the land surface temperature estimation section below.
1: Data were attempted to collect in the study area or nearest location. GPP for forest was obtained from Hirata et al. (2013)’
Thailand data which was close to this area, and Chen et al (2013) estimated in GPP of Asian region that were used for dryforest and agriculture area.
2: Carbon stock unit values in Laos were presented in Forest Carbon (2015) which used biomass equations originally from
Chave et al. 2005 and 2014. Then the unit values for forest and dry-forest in this paper were revised by taking into
consideration the ratio of evergreen forest and deciduous forest areas in Savannakhet comparing 2000 and 2010 values(NLCS for Laos 2000 and 2010 by FIPC).
3: Water infiltration unit value of tree plantation from Chaplot et al. (2002) was used for those of forest unit value in this study,
and the fallow and pastures unit values in the same paper were used for dry-forest in this study. For agriculture area, theaveraged figures of cultivated (corn, vegetables) and cultivated (with conservation) in Chaplot et al. (2002) were used.

Land Surface Temperature (LST) estimation: LST for 1988 and 2010 were calculated by using theband 6 images of Landsat 5 TM. Four image scenes were used for making the 1988 land cover map,
but only two scenes of Mekong River side where majority of urban areas situated, were utilized in thisLST estimation for each year, which included enough urban area with other land cover types. Then, thefollowing 16 images in separate eight years were collected: for 1988 LST estimation (Path/Row of
127/049: 16 Feb. 1988, 11 March 1989, 18 Feb. 1990, 10 April 1991; and those of 127/048: 16 March
1988, 11 March 1989, 19 Dec. 1990, 10 April 1991) and for 2010 LST estimation (Path/Row of
127/049: 5 March 2007, 24 April 2008, 25 Feb. 2010, 28 Feb. 2011; and those of 127/048: 1 Feb. 2007,
24 April 2008, 25 Feb. 2010, 28 Feb. 2011). These images were used to calculate LST as a shot of LST
in different time in each year. Most of the images were captured around 10a.m. in Laos local time. The
images were acquired in dry season from January to April or beginning of May to avoid the effect ofclouds. The following processes as described by Kumar et al. (2012) originally from NASA (2004)
were used to estimate LST. The first is Eq. (1).
LJ­½
LMAX  LMIN®
¾ u DN  1  LMIN
¯ QCALMAX  QCALMIN ¿(1)

Note: LMAX=the spectral radiance that is scaled to QCALMAX in W/(m2 u sr u 𝜇m), LMIN=the spectral radiance that isscaled to QCALMIN in W/(m2 u sr u 𝜇𝑚 ), QCALMAX=the maximum quantized calibrated pixel valueⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2017) 8-1(corresponding to LMAX) in DN=255, QCALMIN=the minimum quantized calibrated pixel value (corresponding toLMIN) in DN=1, DN=Digital Number

Table 4 Parameter values of Landsat 5 TM
Satellite/Sensor
Landsat 5 TM
Source: the Landsat 5 TM metadata fileLMAX
15.303

LMIN
1.238

K1
607.76

K2
1260.56

The values for LMAX, LMIN, QCALMAX, and QCALMIN were obtained from the Landsat 5 TM
metadata in Table 4.
After converting DN to Lγ, the black body temperature TB was calculated using Eq. (2) which iscalled Plank's inverse function (Kumar et al., 2012).
TB

(2)

K2
§ K ·
ln ¨1  1 ¸
¨
LJ ¸¹
©

The values of K1 and K2 were obtained from the Landsat 5 TM metadata file in Table 4. TB wasconverted to LST using Eq. (3) (Yue et al., 2007; Kumar et al., 2012).
LST

TB
§ T ·
1  ¨¨ J B ¸¸ ln H
© U ¹

(3)

Note1: 𝛾=the wavelength of emitted radiance (𝛾 = 11.5 𝜇𝑚), 𝜌 = ℎ u 𝑐/𝜎 p=1.438 u 10-2 (mK), h=Plank`s constant (6.626
u 10-34 Js), c=the velocity of light (2.998 u 108 ms-1), 𝜎=Boltzmann`s constant (1.38 u 10-23 JK-1), 𝜀=spectral emissivity(vegetation=0.95, non-vegetation=0.9, water=1.0 (Nichol 1996))
Note2: LST results for 1988, 2007 and 2008 contained error points because of including little cloud part, the results weremodified by excluding those parts out before adjustment by DEM data.

After converting the sea level elevation value for each pixel of the LST value by assuming as aconstant of 6.5 /km lapse rate (a surface temperature decreases with an increase in altitude) (Minderet al.,2010), the LST values were averaged in each land cover type separately. Then the LST value
differences were calculated based on the difference between the averaged LST value of urban areas andthose of other land cover types in each year separately. Finally, the ES unit values for temperature
regulation were estimated by averaging the LST value differences for eight years by each land covercategory (Table 3).
USLE: USLE is based on a formula to estimate the quantity of soil erosion per unit area caused bysurface water (precipitation) (USDA, United States Department of Agriculture: http://www.usda.gov/wps/portal/usda/usdahome). It was calculated using Eq. (4). The R value was calculated using Eq. (5)(Kobayashi et al., 2002). A in Eq. (5) used rainfall data from Savannakhet Meteorology Station by the
years of 1990 and 2010. The K value was calculated by Eq. (6) to Eq. (10) (Wawer et al., 2005
originally by Williams, 1995) based on the soil map of Laos obtained from the FAO (Food andAgriculture Organization of the United Nations: http://www.fao.org/soils-portal/en/). The L and S
values are topographical factors and were calculated in ArcGIS using the ASTER GDEM by a simple
method, LS calculated just from the slope value in % using Eq. (11) (Mediavilla et al., 2017 originallyby Ededo et al., 1995). The C value was obtained from Dubber and Hedbom (2008). The P factor was
not considered. Hence, assuming that P = 1 (constant) when no soil conservation measures werepracticed.
E Ru K u Lu S uC u P(4)Ⓒ ISERD

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IJERD – International Journal of Environmental and Rural Development (2017) 8-1

R

§ A ·
0.32 u ¨¸© 100 ¹

K

KW

2.5

(5)
(6)

f csand  f cl si  f orgc  f hisandf csand§ªm º·¨ 0.2  0.3  exp « 0.256  ms  §¨1  silt ·¸» ¸¨© 100 ¹¼ ¸¹¬©

f cl si§ msilt ·¨¨¸¸© mc  msilt ¹

f orgc§·0.25  orgC
¨¨1 
¸¸
>
@
orgC

exp
3
.
72

2
.
95

orgC
©
¹

f hisand

§
·
m ·
§
¨
¸
0.7  ¨1  s ¸
¨
¸
100 ¹
©
¨1 
¸
ª
º
m
m
§
·
§
·
¨ ¨1  s ¸  exp « 5.51  22.9  ¨1  s ¸» ¸
¨ © 100 ¹© 100 ¹¼ ¸¹
¬
©

LS

(7)

0.3

­0.009 u p 2  0.0798 u p,®
¯0.2558 u p  3.248,

(8)
(9)

(10)

p d 30%(11)

p ! 30%Note: E: mean annual soil loss (t/ha/year), R: rainfall erosivity factor (MJ・mm/ha/h/year), K: soil erodibility factor (t・h/MJ/mm), L: slope length factor, S:slope steepness factor, C: crop management factor, P: erosion control practicefactor, A: Annual rainfall (Savannakhet Meteorology station by statistic yearbook 2010), ms: the sand fraction content(0.05-2.00 mm diameter) (%), msilt: the sand fraction content (0.002-0.05 mm diameter) (%), mc: the sand fraction
content (<0.002 mm diameter) (%), p: slope (%), orgC: the organic carbon (SOC) content (%).

ESs Mapping
The land cover maps and the ES unit values were used to create maps for the potential provisions ofESs. A grid with a mesh size of 500 m × 500 m was created. A comprehensive analysis was conducted
for the 1988 and 2010 maps by averaging each normalized ES score (0 to 1 scale). However, the USLE
was converted to be a minus value before normalization because the original USLE value was high ES
value in low USLE figure.
Table 5 Percent of land cover in 1988 and 2010Land cover type
ForestDry forest
Water
Urban and bare land
Agriculture area

1988 (%)
51.18
36.60
1.33
0.05
9.83

Ⓒ ISERD982010 (%)
42.31
28.00
2.06
0.42
27.21

IJERD – International Journal of Environmental and Rural Development (2017) 8-1

RESULTS AND DISCUSSION
Table 5 and Fig. 2 show the results of the land cover classifications. Forest and dry-forest areas
decreased considerably and in contrast, agricultural area increased markedly from 1988 to 2010. In
order to compare both maps, the areas of cloud and swamp parts which were classified as “Other”
category in the 2010 map were excluded from the 1988 map as well. The results of the spatial analysis
are shown in Fig. 3. Some ES supply potentials have decreased especially for carbon stock and GPP
because of the decrease of the forest areas in general. As agricultural area increased, the provision offood production has also increased. Fig. 3c) and 3d) defined the change of soil erosion quantities, thered color of the USLE maps was identified the lowest annual soil loss which means soil loss of the2010 map was smaller than that of the1988. Water area had the highest value with regard totemperature regulation service, followed by forests (Table 3). Surface temperatures changed
considerably between 1988 and 2010 mainly because of the impact from land cover change especiallyfor the center and southwest parts where the transformation from forest and dry-forest to agriculturalarea have happened, which had the higher potential provisions of its service in forest and dry-forest
than agriculture area. As consequent, as shown in the Fig. 3g) and 3h) the temperature regulation
service was decreased in general. Water Infiltration service was identified as water infiltration capacity
which also affected from land cover changes. As shown in the Fig. 3i) and 3j), water infiltration service
in the southwest part of the study area was declined due to the change from dry-forest to agriculturalareas, followed by forest transformation to agricultural areas.

Fig. 2 Land cover classification (a) 1988 and (b) 2010
The results of comprehensive analysis were presented in Fig. 4(a) and 4(b) to prioritize ES
conservation needs. The overall values of ES supply potentials were higher for 1988 than for 2010.
Entirely, forest area had the highest importance of ES potential provisions in both maps. Therefore, theperspective of nature conservation, a land use policy sector should consider it properly for their future
land use development policy, for example, land concession for industrial plantations which need thehuge area of their cultivation. It will be one of the policy options to limit land use types, for example,
introducing land use zoning policy, to protect forest area in order to avoid reducing the potential
provisions of ESs.

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a) food production 1988b) food production 2010c) USLE 1988d) USLE 2010e) GPP 1988f) GPP 2010g) temperature regulation 1988h) temperature regulation 2010

Fig. 3 Maps of ESs in the province for 1988 and 2010 [a) - h)]Note: Map of USLE Fig. 3c) and 4d) were higher values in the blue color which means higher area of soil erosion quantities(this means low ES provision)Ⓒ ISERD

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i) water infiltration 1988j) water infiltration 2010k) carbon stock 1988l) carbon stock 2010

Fig. 3 cont. Maps of ESs in the province for 1988 and 2010

(a)

(b)

Fig. 4 Maps of comprehensive assessments for (a) 1988 and (b) 2010: 0 to 1 scale
CONCLUSION
A spatial analysis of potential provisions of ESs was conducted to understand the distributions of theESs and to determine priority areas for conservation of the ESs. Forest area decreased considerablyfrom 1988 to 2010 in contrast to agricultural area. Most of the changes in land cover type came fromthe transformation of forest and dry-forest to agricultural areas, and this might be the main cause forthe changes in the potential provisions of ESs over two decades. Most of the potential provisions of
ESs decreased due to these changes. The present study could be a useful tool for land use planning
based on prioritizing conservation areas for ESs in this region. Our results illustrate the importance ofconsidering ESs into land-use planning and land management policy. This approach could apply not
only for this study area but also throughout nationwide and could probably be applicable for otherⒸ ISERD

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developing countries. Future issues that require further study include the increased number of ESs, thecollection of ES unit values for Laos, and the increase in the number of land cover classes. Culture
services are also important in terms of social environment to understand the ES influence on their local
livelihood activities.
ACKNOWLEDGEMENTS
The authors thank the Forest Inventory and Planning Center (FIPC), Government of Laos for providing the2010 land cover map.
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Yue, W., Xu, J., Tan, W. and Xu, L. 2007. The relationship between land surface temperature and NDVI withremote sensing: application to Shanghai Landsat 7 ETM+ data. International Journal of Remote Sensing, 28(15), 3205-3226.

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Research article
erdApplication of Soil and Water Assessment Tool (SWAT) Modelto Predict Streamflow and Sediment Yield in Wahig-Inabanga
Watershed, Bohol, Philippines
TOMAS D. REYES, JR.*
Bohol Island State University, Philippines
Email: tomseyer@gmail.com
Received 18 January 2017

Accepted 5 May 2017(*Corresponding Author)

Abstract The study applied the Soil and Water Assessment Tool (SWAT) model -- to predict
streamflow and sedimentation in Wahig-Inabanga Watershed, Bohol, Philippines. The
applicability of the SWAT model was evaluated and its output was integrated to GIS togenerate sedimentation hazard map. The result of the SWAT model performance evaluation on
stream flows was satisfactory given prediction efficiency values: NSE = 0.6578; R2 = 0.7038;
PBIAS = 14.94%; and RSR = 0.5850. Satisfactory result was also achieved in model validation
with model accuracy values on NSE, R2, PBIAS and RSR of 0.41, 0.57, 25.09%, and 0.71,
respectively. However, the model did not provide precise estimates of sediment yield in subbasins and hydrological response units (HRU) especially with corn as single land use or one of
the landuses even on flat to gently rolling terrain. Inaccuracy issue on sediment yield prediction
deferred further analysis including thesedimentation risk valuation which supposed to provide
baseline information for watershed management and land use planning.
Keywords Soil and Water Assessment Tool, SWAT, streamflow, sediment yield

INTRODUCTION
The Philippines is very vulnerable to natural disasters because of its natural setting (Palao et al., 2013;
Principe, 2012; Luna, 2001), its socioeconomic, political, and environmental context, and especially its
widespread poverty (DepEd et al., 2008; Lasco et al. [undated]). Moreover, the country is prone togeologic and natural hazards mainly because of its geographic, geologic, and tectonic setting. Disasters,
whether natural or human-induced, have caused tremendous losses in the country’s national economy.
Storms, landslides, floods, and sometimes the combination of all three at once, displace as many as 8
million people every year. The National Disaster Risk Reduction and Management Council(NDRRMC) under the Office of the Civil Defense, Department of National Defense (OCD-DND) has
recorded more than 15 major disasters to have devastated different parts of the country in the last five
years alone, including typhoon Haiyan (“Yolanda”), said to be the greatest typhoon ever recorded in
Philippine history. Being mountainous and rugged in topography, the Philippines is no doubt prone tomassive erosions and landslides. These two, together with floods, typhoons, and fire, account forbillions of pesos of government spending annually for disaster rehabilitation programs.
Soil erosion alone has been considered as a major threat to sustainable agriculture and to theenvironment (Paningbatan et al., 1995) especially in the developing world particularly the Philippines(Genson, 2006; Bantayan, 2006; Bantayan, 1997). According to a report from PCARRD (1999), some74 to 81 million tons of soil is lost annually, affecting 63% to 77% of the country’s total land area.
Using the Global Assessment of Soil Degradation (GLASOD) database, FAO (2000) as cited by
Genson (2006) confirmed the severity of land degradation giving an estimate of about 79%. Indeed,
land degradation is a major environmental and development issue in the Philippines. Its process isⒸ ISERD

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advancing at an alarming rate due to deforestation and inappropriate agricultural activities in fragileand highly sensitive mountainous environments. This is further aggravated by frequent cyclones andthunderstorms due to climate change and weather extremes which somehow explain the very highsediment yield rates throughout the country (Palao et al., 2013; Principe, 2012; PCARRD, 2008).
The integration of SWAT and GIS in streamflow and sediment yield modeling in Wahig-Inabanga
Watershed, Bohol, Philippines, is perceived to be of great advantage in terms of improved procedures
and analyses, and yield outputs both for sub-basin and hydrological response unit (HRU) scale.
Evidence of GIS and SWAT integration are the availability of several readily downloadable SWAT
add-ins compatible with selected GIS software packages. One particular SWAT add-in used in thisstudy was ArcSWAT, the SWAT geo-spatial interface in ArcGIS.
OBJECTIVESThe study aimed to apply and evaluate the performance of the Soil and Water Assessment Tool
(SWAT) model in streamflow and sedimentation modeling in the Wahig-Inabanga Watershed using
ArcGIS as the platform of geo-spatial analysis.
METHODOLOGY
Study Site
The Wahig-Inabanga Watershed, also known as Wahig-Inabanga River Watershed Forest Reserve(WIRWFR), is the biggest watershed in the province of Bohol, Philippines. From the Northwest Bay of
Inabanga, the river dissects the central part of the island embracing a total land area of more than 610
km2 (Figure 1). This watershed is about 15.20% of the total land area of the province.It is
geographically located between 124o3’36” and 124o23’24” East longitude, and between 9o43’48” to10o4’48” North latitude (GCS Luzon-Philippines 1911 datum).

(a)
(c)

(b)

Fig.1 Location of the study area: (a) map of the Philippines showing the Central Visayas;(b) map of the Central Visayas showing Bohol; and (c) map of Bohol showing the study area
SWAT Modeling
The SWAT model, being physical-based, claims for more complex types of input data in order tobecome operationally functional. In the study, there were several types of data (database input files,
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obtained from different reliable sources. The database input files which were used included: 2000-2012
climatic data from Bohol Experiment Station (BES); soil physical and chemical data from the Bureauof Soil and Water Management (BSWM); and daily rainfall datafrom two rainfall stations within thewatershed, all of which need to be preprocessed and organized into a format readable by the SWAT
model. In addition, the required GIS input layers were land cover (LULC) and soil, including a digital
elevation model of the watershed in which the manual definition of slope was based. Other optional
input layers like watershed mask and stream network were also used to reduce the processing time
during watershed and sub-basins delineation. Unluckily, in the study area only streamflow dataset wasavailable and this was applied for sensitivity analysis, calibration and validation of the model.
ArcGIS was used to preprocess some of the various SWAT model parameters observed andcollected from the real watershed condition into a format that is acceptable by the model. Only the
manual calibration method was applied in this study after parameterization. It was done by manually
adjusting values of identified significant parameters. This was followed by model validation which
aided in demonstrating whether a given site-specific model was capable of making sufficiently accurate
simulations or otherwise. Results of the model simulation included the estimates on streamflow andsediment yield in sub-basin and HRU levels. Acceptance of the model was based on the satisfactory
values of the model efficiency indicators such as Nash-Sutcliffe efficiency, coefficient of determination,root mean square error–observations standard deviation ratio, and percent bias. Computation, however,
was done outside the GIS interface. The ArcSWAT2009 version 488, unfortunately, does not directly
provide spatio-temporal erosion maps. However, since the SWAT simulation was run within the GIS
setting, visualization of the outputs was obtained by adding the sediment yield estimates in theattributes of sub-basin and HRU layers.
RESULTS AND DISCUSSION
Sensitivity Analysis
Auto-sensitivity analysis was performed on 20 relevant stream flow parameters for the period 19861991. The absence of sediment yield data limited the sensitivity analysis only to streamflow-relatedparameters. Based on parameter sensitivity scale rating of Lenhart et al. (2002), out of the 20
parameters evaluated, half were found significant on simulated vs. observed output.
Table 1 Ranking of significant flow parameters after sensitivity analysis with observed data
Parameter

Description

Rank

Sensitivity Index

CN2

Curve number for moisture condition II

1

0.9960**

CH_K2

Effective hydraulic conductivity in main channel (mm/hr)

20.8000**

ESCO

Soil evaporation compensation factor

30.7720**

ALPHA_BF

Baseflow alpha factor (days)

4

0.1920*

CH_N2

Manning’s N value for the main channel

50.1660*BLAI

Maximum potential leaf area index

60.0693*GWQMN

Shallow aquifer threshold water depth (mm H2O)

7

0.0665*CANMX

Maximum canopy storage (mm H2O)

8

0.0542*

SOL_AWC

Available water capacity of the soil layer (mm H2O/mm soil)

90.0512*

Note: Sensitivity Rating (Lenhart et al., 2002): **= high; * = medium (moderate)Ⓒ ISERD

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Table 1 shows the result of the analysis ranked according to sensitivity index values. The first
three parameters were highly sensitive based on Lenhart et al. (2002) sensitivity rating while the rests
were medium sensitive.Those parameters with less than 0.05 sensitivity index values were considerednegligible based on Lenhart et al. (2002) sensitivity rating and, thus, were not included in model
calibration.
Stream Flow Model Calibration and Validation
SWAT stream flow model calibration was performed using the 9 significant parameters during thesame simulation period (1986-1991). Model validation, on the other hand, was done for the period
1991-1996. Each run was evaluated using model efficiency indices such as NSE (Nash-Sutcliffe
efficiency coefficient), R2 (coefficient of determination), PBIAS (percent bias), and RSR (root meansquare error – observations standard deviation ratio). The results of the simulation passed the model
efficiency criteria as required by each of the pre-identified indices. Summary results of the stream flow(runoff) initial run, calibration and validation evaluation are presented in Table 2.
Table 2 Summary result of model prediction efficiency evaluation of the stream flow modelfor Wahig-Inabanga Watershed
SIMULATIONPERIOD

NSE

R2

PBIAS

RSR

Initial Run

1986-1991

0.50350.683125.27960.7046

Calibration

1986-1991

0.65780.703814.9373

0.5850

Validation

1991-1996

0.4106

0.569925.0883

0.7083Several SWAT users (Principe, 2012; Duan et al., 2009) suggested that models with NSE of morethan 0.4 and R2 greater than 0.5 for both calibration and validation can still realistically simulatebasin’s hydrological processes. Thus, based on the calibration and validation results, it was assumed
that the model captures the actual stream flow condition in the study area. According to the SWAT
user group [https://swat.tamu.edu/media/82492/swat-user-group], it is a requirement that a SWAT
model intended for soil erosion prediction through sediment yield must fulfill this condition.
Basin-Level Water Balance Tradeoff and Sediment Yield
The water balance tradeoffs and the resulting changes in sediment yield of Wahig-Inabanga Watershedas outputs of model calibration for streamflow are presented in Table 3. Results show a considerable
decreasing pattern on sediment yield as values of significant parameters were adjusted from theirdefault or assigned values. Out of the 121 sub-basins of Wahig-Inabanga Watershed, 47 exceeded thesoil tolerable loss of 11.2 tons/ha/yr (Figure 3). The tolerable soil loss is based from Hudson (1995)
and Lal (1994). It was noticed that all 47 sub-basins had CORN as a single land use or in combination
with some other land uses. This only revealed that the model may have over predicted the SYLD for
CORN, and perhaps on some other LULCs, irrespective of slope class. In this regard, a comprehensive
assessment is required in the HRU-level output of the model. Sub-basins with CORN as its single
LULC had annual averages ranging from 31.38 tons/ha to 55.21 tons/ha, while those sub-basins withseveral other LULCs in addition to CORN had annual averages of about 11.81 tons/ha to 138.58
tons/ha.

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Table 3 Basin-level water balance trade-off of Wahig-Inabanga Watershed during streamflow
model calibration

HRU-Level (LULC) Water Balance and Sediment Yield
Another important output of each simulation in SWAT is the HRU-level water balance and sedimentyield. Rearrangement of the output was purposely done to determine which among the LULC types
have been mis-predicted by the model in terms of sediment yield prediction. Table 4 presents the waterbalance and sediment yield after having adjusted all flow sensitive parameters.
Table 4 HRU-level water balance and sediment yield (ton/ha) of calaibrated flow model using
flow sensitive paramerets summarized by landuse and land cover

The spatial distribution of the predicted average annual soil erosion rates in terms of sedimentyield (tons/ha) in sub-basin and HRU-levels are presented in Figures 2 and 3. The soil erosion(sediment yield) values for the whole watershed were divided into six levels: slight (< 2 tons/ha/yr);
acceptable (2-11 tons/ha/yr); moderate (12-25 tons/ha/yr); high (26-50 tons/ha/yr); severe (51-80
tons/ha/yr); and very severe (> 80 tons/ha/yr). This classification, accordingly, is based from theⒸ ISERD108

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tolerable erosion rate of 10 to 12.5 tons/ha/yr adopted from Paningbatan (1987) as cited by PCARRD(1991) and was also used by several local researchers in the Philippines.
The overall output of the model and given the limited available input data used in the simulations,
it can still be assumed that the model is able to predict stream flow. However, it is not accurate inestimating sediment yield for some of the sub-basins and HRUs, particularly those with corn as alanduse. The inaccuracy in sediment yield estimation using the SWAT model deferred its risk
quantification and valuation.

Fig. 2. Sub-basin-level sediment yield class
ratings of Wahig-Inabanga Watershed

Fig. 3. HRU-level sediment yield class
ratings of Wahig-Inabanga Watershed

CONCLUSION
The study validated the applicability of the SWAT model only in simulating flow discharge dynamics
of the watershed based on the satisfactory values of the statistical measures of model efficiency.
However, it does not provide accurate estimates of the annual sediment yield in some LULCs such as
grassland, rice, perennial cultivated crops, and especially corn, on flat to gently sloping areas in thewatershed. The model, on the other hand, somehow precisely predicted annual sediment yield for builtup, open forest, mangroves, and inland water. The absence of sediment yield data restricts thecalibration and validation only to streamflow-related parameters. The sediment yield input data,
supposedly, should have been included in the sensitivity analysis with measured data for more efficientmodel calibration in case sediment yield prediction is the intention of the study.
RECOMMENDATIONSIn order to fully appreciate the utility of the SWAT model, its application must be employed only inwatersheds with at least minimum required input data. Assumptions on the values of importantparameters, if not provided quantitatively, will just complicate the interpretation of the results.
Acquired, measured or observed data on discharges (stream flows) and sediment yields are needed
especially if the intention of using the model is for sediment yield prediction. The absence of acquired
data on sediment yield in the present study which is supposed to be included in sensitivity analysis andⒸ ISERD

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used in calibration may have affected the results of sediment yield estimation. Therefore, subsequent or
related studies, using the same model, are only suggested if these data are all available.
Parameters in the crop database must be studied well. The over-predicted values of sediment yieldin corn and some other LULCs demand further examination of the crop database to determine whichparameters need iteration. In addition, it is possible in the current and latest SWAT versions to delete,edit, update and add a specific crop type or LULC (e.g. agroforestry) which is not available in the crop
database as long as matching parameter values taken in the field will also be provided. This issuggested primarily to address difficulty in model parameterization.
Fine-tuning of parameters in the SWAT model is generally a stumbling block to new users of thesoftware. The task would be more tedious especially when some critical parameters need to beprecisely adjusted to minimize error and increase accuracy of the model. Therefore, auto-calibration
which was not performed in the study is suggested only for the identified sensitive parameters.
REFERENCES
Alibuyog, N.R., Ella, V.B., Reyes, M.R., Srinivasan, R., Heatwole, C. and Dillaha, T. 2009. Predicting the effects ofland use on runoff and sediment yields in selected sub-watersheds of the Manupali River using the ArcSWAT
Model. Soil and Water Assessment Tool (SWAT), Global Application, 253-266.
Bantayan, N.C. 1997. GIS-based estimation of soil loss. Pterocarpus, 9 (1), 25-36.
Bantayan, N.C. 2006. GIS in the Philippines: principles and applications in forestry and natural resources. 1 st edition,
Los Baños, PARRFI and AKECU, 173.
DEPED, NDCC-OCD, UNDP, ADPC and European Commission Humanitarian Aid Department. 2008.
Mainstreaming disaster risk reduction in the education sector in the Philippines.
Duan, Z., Xianfeng, S. and Liu, J. 2009. Application of SWAT for sediment yield estimation in a mountainous
agricultural basin. In International Conference on Geoinformatics, 1-5.
Genson, I.C. 2006. Erosion and water resources assessment in the Upper Inabanga Watershed, Philippines,
Application of WEPP and GIS tools. Masteral Thesis. University of Western Sydney, Hawkesbury Campus,
Richmond, New South Wales, Australia.
Hudson, N. 1995. Soil conservation. BT Batsford Limited, London.
Lal, R. 1994. Soil erosion research methods. Soil and Water Conservation Society, Ankeny.
Lasco, R.D., Rangasa, M., Pulhin, F.B. and Delfino, R.J. (undated). The role of local government units inmainstreaming climate change adaptation in the Philippines. Centre for Initiatives and Research on Climate
Adaptation (CIRCA). Legazpi City, Albay, Philippines
Lenhart, T., Eckhardt, K., Fohrer, N. and Frede, H.G. 2002. Comparison of the two different approaches ofsensitivity analysis. Physics and Chemistry of the Earth, Elsevier Science Ltd, 27, 645-654.
Luna, E. 2001. Disaster mitigation and preparedness, The case of the NGOs in the Philippines, Disasters, 25 (3),216-226.
Palao, L.K.M., Dorado, M.M., Anit, K.P.A. and Lasco, R.D. 2013. Using the Soil and Water Assessment Tool
(SWAT) to assess material transfer in the Layawan Watershed, Mindanao, Philippines and its implications on
payment for ecosystem services. Journal of Sustainable Development, 6 (6).
Paningbatan, E.P., Ciesiolka, C.A., Coughlan, K.J. and Rose, C.W. 1995. Alley cropping for managing soil erosionof hilly lands in the Philippines. Soil Technology, 8, 193-204.
PCARRD. 1999. Guidelines for watershed management and development in the Philippines. PCARRD/DOSTDENR-DA-UPLB, Los Baños, Laguna, Philippines.
PCAARRD. 2008. Annual Report. PCAARRD-DOST.

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Research article
erd

Logging Effects on Soil Organic Carbon and Hydraulic
Property in North Appalachian Region of Ohio USA
TORU NAKAJIMA*
Faculty of Regional Environmen Science, Tokyo University of Agriculture, Tokyo, Japan
Email: tn206473@nodai.ac.jp

RATTAN LAL
School of Environmental Natural Resources, Carbon Management and Sequestration Center,
The Ohio State University, Ohio, USA
Received 7 December 2016

Accepted 5 May 2017(*Corresponding Author)

Abstract Forest soils play an important role in the global carbon cycle. Thus, documentingchanges in carbon stocks and hydraulic property by logging are essential to sustainable
management. However, information on logging in relation to soil organic carbon (SOC) stock is
scarce and it can be site specific. Thus, the effects of logging on the SOC stock and hydraulicproperty were analyzed after logging at a site in the North Appalachian Experimental
Watershed (NAEW) near Coshocton, Ohio, USA. The objectives of the study were to quantify
the impacts of logging on SOC stock and hydraulic property. Results show that for the 0-30 cm
soil depth, SOC stock after logging (61.5 Mg ha-1) was 54.5% lower than that before logging(135.3 Mg ha-1). Further, soil water retention at different potential was consistently higher
before logging than that after logging. A plot of the hydraulic capacity vs. suction under naturalforest differed significantly than that after logging at 0-10 cm soil depth. Thus, logging reduced
SOC stock, and degraded hydraulic property of the surface layer.
Keywords forest logging, soil organic carbon, soil water retention

INTRODUCTION
Soils in forest ecosystems are a major sink of global carbon, in part due to the large area involved at a
global scale. World soils contain an estimated 1550 Pg of carbon to 1 m depth, an amount that is nearly
twice that contained in the atmosphere (Lal, 2004). Within the forest biomes, forest soils constitute
31% of the total carbon stock (Kimble, 2003). Thus, forest soils play an important role in the global
carbon cycle. However, land use change causes disturbance of the forest ecosystem and can influence
the soil organic carbon (SOC) stock and soil hydraulic property (Lal, 2005). There have been
numerous studies on forest resources and their management (Lorenz and Lal, 2010). However, fieldstudies on forest management practices in relation to soil is scarce (Kimble, 2003). In addition, theeffect of SOC stock and hydraulic property can be soil and site specific. Therefore, there is a need forsignificant advances in measurement of SOC stock and hydraulic property in the forest ecosystem. In
the United States, the Renewable Resources Planning Act Assessment Report showed that in 2003,
about 33% of the total land area was under forest compared with 46% in 1630. About 120 Mha offorestlands have been logged for the forest resources and converted to other uses (e.g., agriculture).
Logging creates soil disturbance and compaction by machinery such as bulldozers, tractors etc. During
logging, the use of heavy machinery is common in the United States and many parts of the world,which alters soil hydraulic and mechanical properties and adversely affect plant growth (Soane, 1990).
Disturbed and compacted soil is highly prone to accelerated erosion and increased surface runoff (Sidle
and Drlica, 1981). Displacement of surface soil can also occur during the logging operations. A soil
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under undisturbed native forest has a high macro porosity and low bulk density, and it is prone tocompaction by heavy machinery (Huang et al., 1996, Lacey and Ryan, 2000). These factors adversely
affect plant growth due to restricted root growth, and reduced air and water availability.
OBJECTIVE
The goal of this study was to assess differences in SOC stock and hydraulic properties for before andafter logging sites in the surface layer. The hypothesis tested in this study was that logging reduces the
SOC stocks, degrades hydraulic properties.
METHODOLOGY
Study Site and Soil Sampling
The experimental site was located at the North Appalachian Experimental Watershed (NAEW) near
Coshocton, Ohio, USA (40°22’N, 81°48’W). The NAEW was established in the late 1930s to study theeffect of soils, land management, geology and climate on water flow characteristics from agriculturaland forest lands (Kelley et al., 1975). The NAEW is located within the mixed oak region of theunglaciated Allegheny portion of the Appalachian Plateau in east central Ohio (Kelley et al., 1975).
The mean annual precipitation at this watershed is 950 mm and the mean annual temperature is 10.3 °C(Lorenz and Lal, 2010). Soils at NAEW are classified as Berks silt loam (loamy-skeletal, mixed, active,
mesic, Typic Dystrudepts) (Dick et al., 1998; Kelley et al., 1975). The wooded areas were logged inJuly 2012. Soil sampling was carried out during September 2012 at after logging at shoulder position
with triplicate, and before logging with triplicate.
Soil Organic Carbon and Total Nitrogen
SOC and total nitrogen (TN) concentrations were determined by the dry combustion method (960 ˚C)using a Vario TOC analyzer (Elementar Inc., Hanau, Germany). The SOC stocks were calculated basedon an equivalent soil mass (ESM) basis to correct for differences in compaction among the sites (Lee etal., 2009; Ellert and Bettany, 1995). The SOC stock (Mg ha-1) was computed by multiplying the SOC
concentration by the bulk density and the equivalent soil depth using following Eq. 1:
𝑆𝑂𝐶𝐸𝑆𝑀 𝑠𝑡𝑜𝑐𝑘 = 𝜌𝑏 × 𝑑 × 𝑆𝑂𝐶𝑐𝑜𝑛 × 104 (𝑚2 ℎ𝑎−1 )(1)where, SOCESM stock is the SOC stock associated with ESM (Mg ha-1), ρb is the soil bulk density (BD)
(Mg m-3), d is the equivalent thickness of soil depth (m), and SOCcon is the SOC concentration (g kg-1).
Soil Water Retention Curve
Soil water retention curve (SWRC) was assessed by using a combination of tension table and pressure
plate extractors (Dane and Hopmans, 2002). The SWRC was determined at 0, –0.4, –1, –2.5, and –5
kPa with tension table and at –10, –20, –30, and –1500 kPa with pressure plate methods. All available
soil hydraulic models, the van Genuchten-Mualem model is the most widely used in simulation of
SWRC (van Genuchten et al., 1991). The function in Eq. 2 describes the SWRC:𝜃 = 𝜃𝑟 −𝜃𝑠 − 𝜃𝑟(2)[1 + (𝛼|𝜓𝑚 |)𝑛 ]1−1⁄𝑛Ⓒ ISERD112

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where, 𝜃 is the volumetric water content (m3 m-3), 𝜃𝑟 is the residual volumetric water content (m3 m-3),
𝜃𝑠 is the saturated volumetric water content (m3 m-3), and 𝜓𝑚 is the matric potential (kPa). Parameters𝛼 (cm-1) and 𝑛 are the empirical fitting parameters characterizing the shape of the retention curve byusing Eq. 2. Change in soil structure, land use, or plotting the differential SWRC indicates soil
management practices. A plot of the slope of SWRC 𝑑𝜃⁄𝑑𝜓𝑚 vs. 𝜓𝑚 can be used as an indicator of
the change in soil structure and hydraulic properties, due to changes in land use (Radcliffe andŠimůnek, 2010; Lal and Shukla, 2004).
𝐶𝜃 = |𝑑𝜃⁄𝑑𝜓𝑚 |(3)-1

where, Cθ is hydraulic capacity (kPa )Statistical Analysis
Statistical analysis was carried out for each soil depth separately, with different management before
and after logging sites. The data were statistically analyzed using SAS code PROC UNIVARIATE
GLMM (Generalized Linear Mixed Model) procedure(SAS 2007). Statistical significance wascomputed at p ≤ 0.05, unless otherwise stated.
RESULTS AND DISCUSSION
Soil Bulk Density, Texture, pH and EC
Results for soil BD, texture, pH, and electrical conductivity (EC) at 0-10, 10-20, and 20-30 cm soil
depths at before and after logging sites are shown in Table 1. For before logging, soil BD increased
with increase in depth. However after logging, soil BD slightly decreased from 1.64 to 1.43 Mg m-3
going from 0-10 and 10-20 cm depths then increased to 1.56 Mg m-3 at 20-30 cm. For the 0-10 cm soil
depth, soil BD under after logging (1.64 Mg m-3) was 49.0% higher than that before logging (1.10 Mgm-3). For the 10-20 cm soil depth, soil BD under after logging was slightly higher than that before
logging, but not statistically different at P ≤ 0.05. Before logging soil had 36.3 and 8.2% clay content
at 0-10 and 10-20 cm soil depth, respectively, but not statistically different those after logging soil. The
pH and EC did not differ among before and after logging at any soil depths.
Table 1 Effects of logging on soil texture, pH, electrical conductivity (EC), and bulk density(N=6 for each depth)
Depth
Sand
Silt
Clay
TexturepH
EC
Bulk Density
(cm)
%
%
%
Class
µS cm-1
Mg m-3After Logging
0-10
28.5a*
55.0a
16.5a
Silt Loam
4.99a
243a
1.64a
10-20
27.5a
54.3a
18.2a
Silt Loam
5.05a
152a
1.43b
20-30
28.7a
52.7a
18.6a
Silt Loam
5.23a
114a
1.56b
Before Logging
0-10
30.0a
47.5a
22.5a
Loam
5.34a
244a
1.10c
10-20
27.6a
52.7a
19.7a
Silt Loam
5.50a
155a
1.32b
20-30
28.3a
55.5a
16.2a
Silt Loam
5.47a
129a
1.60a
*Means with different letters (a, b, and c) among before vs. after logging soil for each depth are not significantly different at
p ≤ 0.05.
Site

Soil Organic Carbon and Total Nitrogen
Results for SOC and TN stock associated with ESM (Mg ha-1) for 0-10, 10-20, 20-30, and 0-30 cm soil
depths for before and after logging are shown in Fig. 1. The logging event significantly influenced theⒸ ISERD

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SOC stock in 0-10, 10-20, and 20-30 cm soil depths (P<0.01, P<0.01, and P<0.01, respectively). For
0-30 cm depth, SOC stock after logging soil (61.5 Mg ha-1) was 54.5% lower than that before logging
soil (135.3 Mg ha-1). For 0-10, 10-20, and 20-30 cm soil depths, SOC stock was 58.3, 48.5, and 53.8 %,respectively, higher before logging soil (62.8, 37.7, and 34.7 Mg ha-1, respectively) than after logging
soil (26.2, 19.4, and 16.0 Mg ha-1, respectively). For both before and after logging soils, SOC stocks
decreased with increase in depth. Logging effects were more predominant in the surface layer. The TN
stocks were significantly affected by logging at 0-10 and 10-20 cm soil depths (P<0.01, and P<0.01).
Lower TN stocks were observed after logging soil (2.39, 1.85, and 1.62 g kg-1, at 0-10, 10-20, 20-30
soil depths, respectively) than before logging soil (4.19, 2.69, and 2.67 g kg-1, at 0-10, 10-20, 20-30 cm
soil depths, respectively). For 0-30 cm soil profile, TN stocks after logging soil (5.87 Mg ha-1) were
38.5 % lower than those before logging soil (9.55 Mg ha-1).
Soil Water Retention Curve
The data in Fig. 2 (top) show the SWRC for 0-10, 10-20, and 20-30 cm soil depths for before and after
logging, and fitted SWRC by van Genuchten-Mualem model using RETC code (van Genuchten et al.
1991). The hydraulic capacity vs. soil moisture potential are shown in Fig. 2 (bottom).
a

a

b
a

b

a

b

b

a

b
a
b

b

a

a
a

1

Fig. 1 Effects of logging on soil organic carbon (SOC) and total nitrogen (TN) stocks associatedwith equivalent soil mass (EMS) at the 0-10, 10-20, 20-30, and 0-30 cm soil depths. Meanswith different letters among after vs. before logging for each depth are not significantly
different at p ≤ 0.05. Error bars indicate standard error (N=3)Ⓒ ISERD

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The SWRC were well described by the van Genuchten relationship (R2 > 0.94). Overall the
SWRC before logging soils were consistently higher than those after logging soils at all depths.
Between saturation and approximately 10 kPa, hydraulic capacity before logging soil was consistently
higher than that after logging soil at 0-10 cm soil depth. For 10-20 and 20-30 cm soil depths, thehydraulic capacity across the suction did not differ among before and after logging soils.
Discussion
Logging significantly affected SOC stocks at all depths (Fig. 1). For the 0-30 cm soil depth, SOC
stocks after logging soils were 54.5% lower than those before logging soils. Decline in SOC stocks twomonths after logging may be due to logging practices such as harvesting, which involve heavymachinery for cutting and transporting of trees, causing severe soil compaction, drastic soil disturbanceand a mixing of the forest floor into the mineral soils (Kimble, 2003). Furthermore, logging can cause
altered soil water content and temperature regimes, which can accelerate decomposition and decrease
net primary production (NPP) (Lal, 2005). The exposure of the soil also exacerbates losses due to soil
erosion and leaching of dissolved organic carbon (Kimble, 2003). Further, SOC sequestration could bealso decreased due to the reduction of biotic activities and decrease in soil moisture content (Lal, 2005).
Effects of logging on the reduction of SOC stocks were more predominant in surface soil layer, due tothe smaller inputs of fresh litter, decrease in decomposition with increase in depth. The SWRC is an
important indicator of soil structure, and relative distribution of micro and macro pores (Nakajima andLal, 2014). The SWRC at each potential was consistently higher before logging soil than that after
logging soil (Fig. 2 top), primarily due to having greater hydraulic capacity between matric potential 0
to 10 kPa at the 0-10 cm soil depth (Fig. 2 bottom). At 0 kPa matric potential, (or saturation point),
before logging soils had greater water holding capacity than after logging soils, indicating greater
occurrence of macro pores. This trend persisted to 10 kPa, also an indication of greater occurrence ofintermediate and micro sized pores and clay content. A lower hydraulic capacity over time or change inmanagement practice indicates progressive declines in soil structure and degradation of soil physicalproperties (Lal and Shukla, 2004).

Fig. 2 Effects of logging on soil water retention curve (SWRC) (top) and hydraulic capacity vs.
suction (bottom) for the 0-10, 10-20, and 20-30 cm soil depths. Lines (top) show the fit ofvan Genuchten equation (VG) SWRC using RECT code

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CONCLUSION
The hypotheses that logging in this study decrease SOC stocks and degrade soil hydraulic property
were supported by the results. Results also supported the following conclusions (1) the SOC stocks
after logging soil were 54.5% lower than those before logging soil for the 0-30 cm soil depth. The SOC
stocks declined sharply by logging within 2-month period (2) logging adversely affected SWRC,
especially in the 0-10 cm soil depth, probably due to decrease in macro pores and meso pores.
ACKNOWLEDGEMENTS
The authors gratefully acknowledge the support of Mr. James Buxton for sampling and valuable
discussions. The authors would also like to acknowledge the North Appalachian Experimental
Watershed, the Ohio Agricultural Research and Development Center of The Ohio State University forsupporting and providing this research opportunity.
REFERENCES
Dane, J.H. and Hopmans, J.W. 2002: 3.3.2 water retention and storage, laboratory. In: A.W. D (ed) Methods ofSoil Analysis: Part 4 Physical Methods, 675-719. Soil Science Society of America Book Series, Madison,
Wisconsin, USA.
Dick, W.A., Blevins, R.L. and Frye, W.W. 1998. Impacts of agricultural management practices on carbon
sequestration in forest-derived soils of the eastern corn belt. Soil and Tillage Research, 47, 235-244.
Ellert, B.H. and Bettany, J.R. 1995. Calculation of organic matter and nutrients stored in soils under contrasting
management regimes. Can. J. Soil Sci., 75, 529-538.
Huang, J., Lacey, S.T. and Ryan, P.J. 1996. Impact of forest harvesting on the hydraulic properties of surface soil.
Soil Science, 161, 79-86.
Kelley, G.E., Edwards, W.M., Harrold, L.L. and McGuiness, J.L. 1975. Soils of the north Appalachianexperimental watershed. US Government Printing Office, Washington, DC.
Kimble, J.M. 2003. The potential of U.S. forest soils to sequester carbon and mitigate the greenhouse effect. CRC
Press, Boca Raton.
Lacey, S.T. and Ryan, P.J. 2000. Cumulative management impacts on soil physical properties and early growthof Pinus radiata. Forest Ecology and Management, 138, 321-333.
Lal, R. 2004. Soil carbon sequestration impacts on global climate change and food security. Science, 304, 16231627.
Lal, R. 2005. Forest soils and carbon sequestration. Forest Ecology and Management, 220, 242-258.
Lal, R. and Shukla, K. Manoj. 2004. Principles of soil physics. Marcel Dekker, New York, NY.
Lee, J., Hopmans, J.W., Rolston, D.E., Baer, S.G. and Six, J. 2009. Determining soil carbon stock changes,
Simple bulk density corrections fail. Agric. Ecosyst. Environ., 134, 251-256.
Lorenz, K. and Lal, R. 2010. The importance of carbon sequestration in forest ecosystems. Carbon Sequestrationin Forest Ecosystems, Springer, 241-270.
Nakajima, T. and Lal, R. 2014. Tillage and drainage management effect on soil gas diffusivity. Soil and Tillage
Research, 135, 71-78.
Radcliffe, D.E. and Šimůnek, J. 2010. Soil physics with hydrus modeling and applications, CRC Press, Boca
Raton, FL, USA.
SAS. 2007. SAS user’s guide, Statistics. SAS Inst, Cary, NC.
Sidle, R.C. and Drlica, D.M. 1981. Soil compaction from logging with a low-ground pressure skidder in theoregon coast ranges. Soil Science Society of America Journal, 45, 1219-1224.
Soane, B.D. 1990. The role of organic matter in soil compatibility, A review of some practical aspects. Soil and
Tillage Research, 16, 179-201.
van Genuchten, M.T., Leij, F.J. and Yates, S.R. 1991. The RETC code for quantifying the hydraulic functions of
unsaturated soils. EPA Report 600/2–91/065. US Salinity Laboratory U, ARS, Riverside, CA.

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Research article
erd

Impact of the Land Use Diversity on the River Water Quality
in the Agricultural Area
YURI YAMAZAKI*The United Graduate School of Agricultural Sciences, Iwate University, Iwate, JapanTOSHIMI MUNEOKA
Obihiro University of Agriculture and Veterinary Medicine, Hokkaido, Japan
Email: muneoka@obihiro.ac.jp

MASATO KIMURA
Obihiro University of Agriculture and Veterinary Medicine, Hokkaido, JapanOSAMU TSUJI
Obihiro University of Agriculture and Veterinary Medicine, Hokkaido, Japan
Received 15 December 2016

Accepted 5 May 2017(*Corresponding Author)

Abstract Water pollution due to agriculture has become a serious global problem. Also,
agricultural development has had major impacts on biodiversity. Impacts on the ecology of awatershed should be considered when proposing changes to land use and management for water
quality conservation. Habitat diversity is one of the most important factors for conserving
biodiversity in an agricultural landscape. In recent years, several fields of research have showna positive correlation between a mosaic of land types and species richness. Herein, we
evaluated the land use diversity of the Tokachi River basin and examined an appropriate spatial
unit to calculate a land cover heterogeneity index for river water quality. The Tokachi Riverbasin is located in the northern part of Japan and plays an important role in a food production
area. The main land use types are forest, cropland, pasture, and dairy farming. Here we applied
the Satoyama Index (SI) as a land cover heterogeneity index to examine land use diversity. SI
evaluates a diverse mosaic of agricultural and non-agricultural land as an index from 0.0 to 1.0
based on Simpson’s diversity index. High SI values are an indicator of high habitat diversity.
The mean SI value of the Tokachi River basin (No.17) was 0.6, and SI tended to be high in thecentral basin and low in the headwater of mainstream and tributaries. SI values had negative
correlations with total nitrogen, nitrate, and electrical conductivity (EC) of river water. Unified
land uses for extensive agricultural land results in increasing nitrogen and EC of the river waterand leads to monotonic habitat conditions in the Tokachi River basin.
Keywords Satoyama Index (SI), land use diversity, river water quality, biodiversity,
spatial unit size

INTRODUCTION
Ecologically sustainable agriculture is regarded as a strategy to respond to global population growthand food issues, environmental degradation, climate change, and degrading natural resources (Kassam
and Friedrich, 2012; Robertson et al., 2014; Bedoussac et al., 2015). The concept of an “ecologicalfarm” is an approach adopted to implementing ecologically sustainable agriculture that is in harmony
with nature; it involves the use of cultivation techniques and breeding programs that do not rely on
soluble chemical fertilizers, pesticides, herbicides, or artificial genetic modifications. The Food and
Health Organisation (FAO) established the Globally Important Agricultural Heritage System (GIAHS)Ⓒ ISERD

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in 2002 to promote traditional agricultural systems and landscapes; these have been created, shaped,
and maintained based on diverse natural resources, using locally adapted management practices.
However, the agricultural production of eco-friendly agriculture still requires improvement.
In the Tokachi area, which is an important food production area in Japan, nitrogen pollution
caused by agriculture has been reported (Yamazaki et al., 2013, 2014, 2015, 2016(a)). Yamazaki et al.,2016(b) showed that it is necessary to control the restructuring of land use to alleviate nitrogen
pollution of the Tokachi River basin. Local ecosystems have to be considered when designing aneffective layout of land use in an agricultural land to facilitate water quality conservation.
In recent years, several studies have demonstrated a positive correlation between land use
diversity and species richness (biodiversity) in an agricultural area (Robinson and Sutherland, 2002;
Firbank et al., 2008). Kadoya and Washitani (2011) proposed the Satoyama Index (SI), a biodiversity
indicator which is a simple composite index of agricultural landscape heterogeneity and thecontribution of nonagricultural land use. They showed that a high SI value is an indicator of highhabitat diversity, whereas a low SI indicates a monotonic habitat condition, which is typical ofextensive monoculture landscapes.
In this study, we analyzed land use diversity of the Tokachi River basin by SI and examined therelationship between the SI score and the river water quality to design an eco-friendly measure of water
quality conservation in the Tokachi region, a large-scale agricultural landscape in Japan.
METHODOLOGYThe Tokachi River basin
This study was conducted in the Tokachi River basin located in the eastern part of Hokkaido, Japan(142.68–144.02 N, 42.55–43.65 E, 0–2,077 m altitude) (Fig. 1 and Table 1). The basin has a total areaof 9,010 km2 and a total stream length of 156 km. According to the Köppen–Geiger climateclassification, the Tokachi River basin is characterized as a warm summer continental climate type(Dfb) with an annual mean air temperature of 6.8 °C and an annual precipitation of 887.8 mm/y; thesemeasurements are the means of the recorded values at Obihiro City from 1981 to 2010. The soil types
in the Tokachi River basin are volcanic soil, lowland soil, upland soil, and peat soil. In particular,
volcanic soil (andosols) is widely distributed in this basin. The main land uses in this river basin are for
agriculture and forestry, with 60% of the agricultural land used as cropland and 40% used as pasture.
Both chemical fertilizers and livestock manure are applied to the agricultural land.
Water Quality Investigation and Analyses
The river water quality was monitored at 21 sampling points located on the main stream (No.17) and ateach tributary (A–T) of the Tokachi River basin in June, in either August or September, and in Octoberfrom 2007 to 2011 under base flow conditions. Water samples were analyzed for total nitrogen (T-N),
nitrate (NO3-N), total phosphorus (T-P), pH, biological oxygen demand (BOD), suspended solids (SS),and electrical conductivity (EC).
Satoyama Index (SI; Kadoya and Washitani, 2011)
We calculated the SIs of 21 watersheds in the Tokachi River basin. Calculation for SI is shown in Eq. 1
based on the agricultural landscape heterogeneity index (ALHI) using Simpson’s diversity index(Lande, 1996; Eq. 2) by ArcGIS Desktop (ver.10, ESRI).

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SI = ALHI ×

𝑝𝑜𝑡ℎ𝑒𝑟
𝑘
×
𝑘
𝑘 −1

(1)

ALHI = 1 − ∑𝑠𝑖 =1 𝑝𝑖2(2)

pother is the number of grid squares of the other land uses, and k is the total number of the gridsquares in a spatial unit in Eq. 1. S is the number of different land cover items in a spatial unit, and pi isthe proportion of agricultural land to the total number of grid squares in Eq. 2.

Fig.1 Locations of the 21 sampling points in the Tokachi River basin
In this study, the following were the values
used: “land use raster data” (100 m grid size;
publication in 2009) published by the National
Digital Information and “JAXA High Resolution
Land-Use and Land-Cover Map (Ver. 16.02)”(10 m grid size; publication in 2016) publishedby ©JAXA. The proportion of agricultural land
and forest land in Table 1 was calculated by“land use raster data”. Also, Kadoya andWashitani (2011) set a spatial unit size of 6 km;
however, we set four different spatial unit sizes(500×500 m2, 1×1 km2, 5×5 km2, and 10×10km2) to calculate SI of the Tokachi River basin.

Table 1 Watershed and land use informationfor each sampling point
Sampling
point

RESULTS AND DISCUSSION
Figure 2a, b shows the spatial distribution of SI
calculated by a combination of 500 × 500 m2
spatial unit size and 10 m or 100 m resolution(500 m unit size and 10 m resolution or 500 m
unit size and 100 m resolution) in sampling
point No.17. SI tended to be low in the center(lowland) of the watershed and high in the
headwater of mainstream and tributaries of thewatershed. In the Tokachi River basin, theⒸ ISERD

119

Watershed
area

River
lengthProportion (%)

km

2

km

Agricultural
land

Forest
land

A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T

23
48
72
337
210
26
35
180
164
33
667
693
197
704
316
127
449
2850
173
66

47
27
29
42
25
16
18
38
36
22
67
94
80
43
41
31
52
150
25
13

0
2
2
25
40
73
79
16
73
52
47
18
71
30
65
79
70
19
23
44

98
96
97
68
53
11
18
74
17
44
44
61
70
12
28
12
25
76
73
51

17

8982

146

31

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IJERD – International Journal of Environmental and Rural Development (2017) 8-1

center area is dominated by extensive agricultural land, resulting in monotonic habitat conditions;however, the headwater tributary areas were covered by forests and had rich habitat diversities. The
spatial distribution of SI of combinations with other spatial unit sizes (1 × 1 km2, 5 × 5 km2, and10 × 10 km2) showed the same trend. SI of the 10 m resolution varied between the center area (low SI)
and the headwater of mainstream and tributaries (high SI).

Fig.2a, b Spatial distribution of the Satoyama Index in the sampling point No.17
Fig. 3a, b shows the percentage of each SI value (0.0–1.0) calculated by a combination of four
spatial unit sizes (500 × 500 m2, 1 × 1 km2, 5 × 5 km2, and 10 × 10 km2) and two resolutions (10 m,
Fig. 3a; 100 m, Fig. 3b) in the sampling point No.17. SI‒1.0 and SI‒0.1 were high percentages of 10 m
resolution, whereas SI‒1.0 and SI‒0.0 were high percentages of 100 m resolution. Also, SI‒0.2 to SI‒
0.9 increased with increasing unit size of both resolution types. From this, the land use of the TokachiRiver basin is polarized between either agricultural land area or forest area.
We calculated the mean SI of the each watershed of the 21 sampling point of No.17 and A–T.
Fig. 4 shows the mean SIs of 500 m unit size‒10 m resolution and 500 m unit size‒100 m resolution in
the sampling point No.17 and A–T as one example. Where the sampling points have less than 0.5 of SI
value, Mean SI of the 500m unit size-10 m resolution and 500m unit size -100m resolution showednearly the same value. However, the mean SI of the 500 m unit size-100m resolution was found to behigher than that of the 500 m unit size-10 resolution at the sampling points has more than 0.5 of SI
value. It was considered that minor land use type recognized at 50 m resolution was excluded at 100 m
resolution. Habitat diversity of the watershed at the sampling points A–D, H, L, R, and S werecomparatively rich since mean SI of these sampling points was higher than SI‒0.6. Conversely, thepoints on the watershed located at F, G, I, M, O, P, and Q were dominated by extensive agriculturalland since mean SI of these sampling points was lower than SI‒0.2. The same trend was confirmed of a
combination with another spatial unit size (1 × 1 km2, 5 × 5 km2, and 10 × 10 km2). However, SI
tended to decrease with increasing unit size.
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IJERD – International Journal of Environmental and Rural Development (2017) 8-1(a) Percentage of SI of 10 m grid size
0.980Percentage of SI (%)

100

1.0

0.8
0.7

60

0.6
0.5

40

0.4

0.3

20

0.2

(b) Percentage of SI of 100 m grid size
1.0
0.9

80
Percentage of SI (%)

100

0.8
0.7

60

0.6
0.5

40

0.4

0.3

20

0.2

0.1
0

500_101000_105000_10

10000_10

0.0

0.1
0

500_100

Unit size_grid size (m)1000_100

5000_100 10000_100

0.0

Unit size_grid size (m)Fig.3 Percentage of Satoyama Index (SI)‒0.0 to SI‒1.0
1.0

0.6
SI

500_10SI

0.8

500_100

0.4
0.2

0.0
A B C D E F G H I

J K L M N O P Q R S T

17

Sampling point (No.17; A-T of tributaries)

Fig.4 Mean Satoyama Index (SI) of the watershedin the 21 sampling point of No.17 and A–T
Table 2 shows the correlation coefficients of the relationships between seven river water quality
variables (T-N, NO3-N, T-P, pH, BOD, SS, and EC) and mean SI. The mean SI of 21 sampling points
had a negative correlation with T-N, NO3-N, and EC. This implies that nitrogen concentration anddissolved matter tended to increase with low SI (unified to agricultural land). When we compared thecorrelation coefficients of T-N, NO3-N, and EC with each combination of unit size and resolution, thecorrelation coefficients tended to be higher in 10 m resolution than in 100 m resolution, and alsotended to be higher in the small unit size.

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Table 2 Correlation coefficients of the relationships between water quality
and mean Satoyama Index (SI). ** shows significance level (p < 0.01).
Water quality
index

500 m

SI_ 10 m grid size
Unit size
1 km
5 km

10 km

500 m

SI_ 100 m grid size
Unit size
1 km
5 km

-0.81**-0.79**-0.78**-0.76**-0.69 **
-0.68 **

-0.82**-0.80**-0.80**-0.79**-0.75 **
-0.73 **

-0.69**-0.68**

10 km

T-N

(mg/L)

NO3-N
T-P(mg/L)-0.82 **
-0.81 **

(mg/L)

-0.32

-0.37

-0.34

-0.36

-0.36

-0.34

-0.33

-0.34

0.12
-0.42

0.10
-0.42

0.01
-0.42

0.14
-0.41

0.15
-0.43

0.13
-0.38

0.03
-0.35
-0.03
-0.69**pH
BOD(mg/L)

0.18
-0.41

SS

(mg/L)

-0.06

-0.09

-0.07

-0.06

-0.05

-0.08

-0.06

EC

(mS/m)-0.83 **

-0.84**-0.79**-0.73 **

-0.82**-0.81**-0.75 **

CONCLUSION
Herein, we analyzed the habitat diversity of 21 sites in the Tokachi River basin using the Satoyama
Index (SI) and examined the relationships between the river water quality and SI. In the Tokachi Riverbasin, the center (lowland) area had low SI and was dominated by extensive agricultural land, whereas
the headwater of mainstream and tributaries had high SI and had rich habitat diversity. Mean SI of the
21 sampling points showed a negative correlation with T-N, NO3-N, and EC. The nitrogenconcentration and dissolved matter of the river water tended to increase in the sampling points
dominated by extensive agricultural land. Also, it was suggested that the combination of 500 m unit
size and 10 m resolution was optimal for explaining the relationships between SI and the river water
quality.
ACKNOWLEDGEMENTS
This research was supported by JSPS KAKENHI, Grant Number 15J04743, 2015. We would like toexpress our gratitude to the research assistance provided by the students and the Obihiro University ofAgriculture and Veterinary Medicine.
REFERENCES
Bedoussac, L., Journet, E.P., HauggardhabitatNielsen, H., Naudin, C., Corre-Hellou, G., Jensen, E.S., et al. 2015.
Ecological principles underlying the increase of productivity achieved by cereal-grain legume intercrops inorganic farming. A review. Agronomy for Sustainable Development, 35-3, 911-935.
Robinson, R.A. and Sutherland, W.J. 2002. Post-war changes in arable farming and biodiversity in Great Britain.
Journal of Applied Ecology, 39, 157-176.
Firbank, L.G., Petit, S., Smart, S., Blain, A. and Fuller, R.J. 2008. Assessing the impacts of agriculturalintensification on biodiversity, A British perspective. Philosophical Transaction of the Royal Society B, 363,
777-787.
Kadoya, T. and Washitani, I. 2011. The satoyama index, A biodiversity indicator for agricultural landscapes.
Agriculture, Ecosystems and Environment, 140, 20-26.
Kassam, A. and Friedrich, T. 2012. An ecological sustainable approach to agricultural production intensification,
Global perspectives and developments. Field Actions Science Reports, 6.
Land, R. 1996. Statistics and partitioning of species diversity, and similarity among multiple communities. Oikos,
76, 5-13.
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Robertson, G.P., Gross, K.L., Hamilton, S.K., Landis, D.A., Schmidt, T.M., Snapp, S.S., et al. 2014. Farming forecosystem services, An ecological approach to production agriculture. BioScience, biu 037.
Yamazaki, Y., Muneoka, T., Wakou, S,. Shimura, M., Yoshino, K., Tsuji, O. and Tabuchi, T. 2013. The
difference of agricultural land use in watersheds and long term fluctuation on the river water quality.
International Journal of Environmental and Rural Development, 4 (1),152-157.
Yamazaki, Y., Muneoka, T., Wakou, S,. Shimura, M., Yoshino, K., Tsuji, O. and Tabuchi, T. 2014. Evaluation of
the ion components for the estimation of total nitrogen concentration in river water based on electricalconductivity. International Journal of Environmental and Rural Development, 5 (1), 160-164.
Yamazaki, Y., Muneoka, T., Wakou, S., Kimura, M. and Tsuji, O. 2015. Characteristics of ion components inriver water with multivariate analysis and piper diagram in agricultural area. International Journal ofEnvironmental and Rural Development, 6 (1), 80-85.
Yamazaki, Y., Muneoka, T., Okazawa, H., Kimura, M. and Tsuji, O. 2016 (a). Characteristics of the river water
quality under base flow condition in the Tokachi River Basin, Japan. International Journal of Environmental
and Rural Development, 7 (1), 154-159.
Yamazaki, Y., Muneoka, T., Okazawa, H., Kimura, M. and Tsuji, O. 2016(b). Influence of the agricultural land
agglomeration to the nutrients of the river water in the Tokachi River Basin. Paddy and Water Environment,
DOI:10.1007/s10333-016-0546-y.

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Research article
erdComparison of Land and Water Improvement Projects andthe Water Management System between Japan and Nigeria
DAYYABU MUHAMMAD ZAHARADDEEN
Graduate School of Agriculture, Tottori University, Tottori, JapanKATSUYUKI SHIMIZU*
Faculty of Agriculture, Tottori University, Tottori, Japan
Email: shimizu@muses.tottori-u.ac.jp

YUMI YOSHIOKA
Faculty of Agriculture, Tottori University, Tottori, Japan
Received 19 December 2016

Accepted 11 May 2017(*Corresponding Author)

Abstract Food security is strongly linked to water security. Irrigation is recognized as a means
to substantially increase agricultural productivity. Consequently, the Food and Agriculture
Organization indicated that 75% of agricultural growth required in Nigeria by 2025 would haveto result from intensification, with the remaining 25% resulting from arable land expansion.
However, the total water demand in Nigeria was estimated to be 5.93 billion cubic meters(BCM)/year in 2010, which is expected to increase to 16.58 BCM/year by 2030. Irrigationwater demands will increase from 30% to 40% under minimal utilization. Accordingly, as
irrigated agriculture is likely to be promoted and expanded, there is a need for appropriate onfarm water management of the available water resources to avoid the potentially alarming
problem of water shortage. Water improvement projects and the management system were
described through a comparative study to investigate the major differences in water
management systems. The aim was to formulate equitable and effective water management
practices that can improve water use and increase food production in Nigeria. In this study,some major differences identified between Japan and Nigeria were the procedure for thedevelopment of water improvement projects; system of operation, management, andmaintenance; and the relationship between water managers and farmers, which were found to
be bottom-up and top-down processes, dependent and independent, and mutual relationship andindividuality, respectively. A realistic solution to improve the Nigerian water management
system has been proposed through an in-depth analysis based on a questionnaire and interview
survey.
Keywords on-farm water management, water user association, water scarcity, Nigeria

INTRODUCTION
Food security is strongly linked to water security. Irrigation is recognized as a means to substantially
increase agricultural productivity. Consequently, the Food and Agriculture Organization (FAO)
indicated that 75% of agricultural growth required in Nigeria by 2025 would have to result from
intensification, with the remaining 25% resulting from arable land expansion (FAO, 2005).
This will probably have to be achieved with reduced water use; this is particularly because
agriculture is the major user of water, accounting for approximately 40% of a country’s water
resources. Accordingly, if irrigated agriculture is aggressively promoted and expanded, there is a needfor appropriate water management of available water resources to avoid the potentially alarming
problem of water shortage.
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However, the total water demand in Nigeria was estimated to be 5.93 billion cubic meters(BCM)/year in 2010, which is expected to increase to 16.58 BCM/year by 2030; irrigation water
demands will increase from 30% to 40% under minimal utilization (Sanyu Consultants Inc., 2013).
The irrigation potential estimated in Nigeria varies from 1.5 to 3.2 million ha. The latest estimate
provides a total irrigation potential of approximately 2.1 million ha, of which approximately 1.6
million ha can be realized from surface water resources and 0.5 million ha from groundwater resources.
During the oil boom in the 1970s, an investment program in support of public irrigation was launched.
Public irrigation in the Nigerian context translates to schemes run by River Basin Development
Authorities.
The schemes that were developed have not yet been fully operationalized, or they have been
implemented with inappropriate infrastructure and management processes. By 2004, only
approximately 20% of the area planned for public sector irrigation had been developed and only 32%of the developed area was being irrigated.
The poor utilization of the developed irrigation area in the public irrigation sector in Nigeria canbe attributed to a number of factors. These include 1) the lack of a coherent irrigation subsector
development policy and strategy; 2) insufficient attention to management systems; 3) inadequate
funding (including poor cost recovery); 4) high capital and operating costs; 5) inadequate farm support
services; 6) poor operation, repair, and maintenance of irrigation facilities; 7) a low level of project
ownership acceptance by direct beneficiaries; and 8) uncertain financial and economic viability. These
factors result in inequity and inefficient water management practices, consequently resulting in a
number of these schemes deteriorating considerably; these schemes are now in urgent need of majorrenovation and repair, less than 20 years after their inception (FAO, 2005).
Under the system of Land Improvement Districts (LIDs) in Japan, which are farmers’ autonomous
irrigation associations with total responsibility for irrigation system management, Japanese water
management is one of the successful forms of participatory irrigation management (PIM) (Ishii and
Satoh 2003). Therefore, the introduction of the basic ideas of LIDs to the irrigation management oflarge-scale projects in Asia, Africa, and Central and South America may be effective to improve their
irrigation management (Minami, 2002).
Hence, successful knowledge transfer is important in anticipating future failures or difficulties inland and water improvement projects and the management system in Nigeria. This will assist in
discussing how to adopt improved options for better agricultural production and sustainability. The
objective of the present study is to investigate problems, causes, and solutions associated with thewater improvement projects and the management system in Nigeria through a comparative study
between Japan and Nigeria.
METHODOLOGY
Study Site
The Hadejia Valley Irrigation Project (HVIP) is one of the biggest public irrigation projects in Nigeria,
and it considerably contributes to agricultural food production. This project is located in the Hadejia
Local Government Area of Jigawa State, between the Hadejia River and its tributaries in the northernpart of Nigeria. The average annual precipitation in Hadejia (project location) is 595 mm. The initialarea proposed for development under the HVIP in the late 1970s was 12,500 ha under Phase I (Fig. 1),
which consisted of stages I and II; however, work has not yet started on Stage II. Stage I wasconstructed in different steps; however, this stage remains incomplete. Stage I has a total commandarea of 5,300 ha, divided into 19 sectors (15 constructed, 4 unfinished) with 6,000 farmers cultivating
approximately 90% rice and other crops such as maize and vegetables.

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The major water source for the project is the Hadejia River, on which two dams (Tiga andChallawa dams) are situated at the upstream site, and the Hadejia Barrage is situated at the downstream
site, which supplies water to the northern main canal through the feeder canal by gravity.
Although, land consolidation is yet to be done in HVIP unlike the Japanese land which undergoes
a land consolidation to improve performance and productivity of the farmlands (through Land
Improvement act 1949), making it into a regular shape for easy use of machineries and paddy ricecultivation. However, the standard agricultural lot in HVIP is 4 ha while it is 0.3 ha in Japan.
LGA

N

Fig. 1 Layout of Hadejia Valley Irrigation Project (HVIP) phase I (stages I and II)

For the purpose of comparison, the Toyogawa Irrigation Project in Aichi Prefecture, Japan, whichis part of the Toyogawa Water Resource Development Project that was established in 1968, wasselected as the study site. This project is one of the most successful modern water resource
development projects in Japan. The study project covers newly developed irrigation areas of 16,000 ha(paddy: 5,000 ha, upland: 11,000 ha). This command area includes traditional paddy irrigation areas.
The average annual rainfall is approximately 1,700 mm/year (Japan Meteorological Agency, 2016).
However, dry spells in the region sometimes extend to 1 month; therefore, it is impossible for farmers
to realize stable agricultural production without irrigation. The major water source is the Toyo River,and supplemental water originates from the adjacent Tenryu River basin. The Toyogawa Water
Resource Development Project also encompasses the Ure and Ohshima dams in the upstream part of
the Toyo River (Kono et al., 2012).
Data Collection and Analysis
A comparative analysis of land and water management systems between Japan and Nigeria wasconducted through 1) a questionnaire and interview survey, 2) a literature review, and 3) field
investigations in both Japan and Nigeria. A total of 36 farmers, consisting 12 farmers each from the
upstream, mid-stream, and downstream sectors of the HVIP in Nigeria were randomly selected toanswer the questionnaire, which contained 37 open ended and 10 close ended questions both related towater availability, the irrigation system, organization for water management, and the method of
operation and maintenance cost of the scheme. The questionnaire was structured for easy
understanding of water user activities for water management and to determine the opinions of farmers
regarding the equity of water distribution and operation of irrigation facilities. Interviews wereconducted with four project supervisors from Hadejia Jamaare River Basin Development Authority(HJRBDA) staff and the three zonal office staff, and 15 officials of Water User Associations (WUAs)
from all sectors, to obtain detailed information regarding the performance and structure of WUAs,
organizations for water management, water allocation planning, operation and maintenance system ofⒸ ISERD

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the facilities, and PIM in the HVIP (Nigeria). In addition, field visits to the study projects wereconducted to visualize the scheme and to gain access to farmers to obtain information and answers toquestionnaire structured for the study. However, after the analysis on irrigation water allocation, theproject should be operated to achieve the purpose of fair water distribution among the entire
beneficiary farmers. Thus, the WUAs and farmers will manage the irrigation facilities, as a result, thestruggled and complained over inadequate water at the downstream will be resolved and farmers
become more satisfied with the irrigation system.
In Japan, an interview with the head of Japan Water Agency (JWA) office in Kagawa Prefecture
was conducted, where we discussed the operation of water resource systems (reservoirs, diversion
dams, regulating reservoirs, and main canals) and their operation and maintenance arrangement. Thiswas followed by a field visit to observe and understand what had been discussed. However, literature
reviews containing detailed information concerning the Toyogawa Irrigation Project in Japan were also
assessed for the comparison.
RESULTS AND DISCUSSION
Organizations for Water Management
According to the questionnaire and interview discussion, the organization currently responsible forwater management in the HVIP is the HJRBDA, which is a public unit established to manage public
irrigation schemes within state boundaries in Nigeria, and its zonal office, which assists the HJRBDA
in the management of the HVIP. The HJRBDA is the agency responsible for the overall operation andmaintenance of the main irrigation facilities (main and lateral canal and the drainage system including
the Tiga Dam, Challawa Dam, and Hadejia Barrage). The zonal office manages on-farm facilities andalso coordinates with farmers for the collection of water fees. A community of WUAs was formed,
which assist the zonal office in collecting water charges and control certain secondary and tertiary
canals. Although some members of the WUAs assist the zonal office, 60% of farmers interviewed at
the downstream sector complained of inadequate water distribution during the maturity stage of theirrice crop, whereas 90% of farmers at upstream and mid-stream sector were satisfied with the water
distribution, although they were not involved in the management process. This shows that theparticipation of WUAs in partnership with the zonal office is poor, which leads to inequitable
distribution of water among the farmers.
In the Toyogawa Irrigation Project in Japan, five types of organizations are involved in the water
management of the project, which have different functions to ensure equity and fair management: 1)
the JWA, which is a public entity that manages the construction project; 2) Toyogawa LID, which is anautonomous irrigation association of farmers established for the Toyogawa Irrigation Project; 3) local
LIDs, which are established based on the administrative boundaries of cities or towns; 4) Management
Districts, each of which corresponds to a traditional local community; and 5) Management Groups,
which are established by beneficiary farmers of an on-farm irrigation facility (all LIDs are establishedafter agreement by 2/3 of the beneficiary farmers). All these organizations are assigned roles in water
management according to the level of a facility, from main to on-farm level. The JWA manages themain infrastructure of an irrigation facility (reservoirs, diversion dams, and regulating reservoirs) andmain canals and is also responsible for delivering water to lateral canals. The Toyogawa LID manages
lateral canals and diverts water to farm ponds. The Toyogawa LID staff patrol farm ponds daily toassess stored water, and they adjust the allocation of water to lateral canals as required. Local LIDs
assemble requests from Management Districts and request water from the Toyogawa LID according to
the need. Local LIDs, Management Districts, and Management Groups manage farm ponds and onfarm facilities in a group. Management Group leaders in a Management District adjust the water
distribution among themselves (Kozuki, 2000).
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Therefore, in the Toyogawa Irrigation Project, it is evident that farmers are responsible for the
management of their irrigation; most farmers are categorized into different groups (Toyogawa LID,
local LIDs, Management Districts, and Management Groups) and are assigned a certain responsibility
to ensure equity distribution and fair management of the irrigation project. In contrast, in the HVIP,
farmers are not responsible for the management of irrigation, and only certain selected WUAs assist
the zonal office. To solve the problem of inequitable water distribution among farmers, water managers(the HJRBDA and zonal office) should be assigned to manage the construction of the project and thefarmers should be divided into different groups, with each group assigned the responsibility ofmanaging a different level of irrigation facilities. This will assist in resolving the inequitable and unfair
distribution of water among farmers in the HVIP as was achieved in the Toyogawa Irrigation Project.
Water Allocation Planning
The result of the questionnaire and interviews shows that in the HVIP, the zonal office of the HJRBDA
controls water gates from the dam (Hadejia Barrage) to main canals by constant releasing water at a
fixed amount of 10 m3/s (this is now the maximum carrying capacity of the main canal), which is adecreased amount from the actual designed capacity of 15 m3/s. However, water allocation planning iscompletely absent from the zonal office, and only farmers (WUAs) arrange their water planning for
distribution to the secondary and tertiary canals based on their experience, without any investigation on
the water demand of each sector. This causes improper water planning and inequitable water
distribution, resulting in the upstream water users using more water and in dissatisfaction by
downstream water users, as stated by 60% of farmers at the downstream sector during interviews. The
current water supply method in the HVIP is shown in Fig. 2.
Leader ofManagement
Group

Leader of Management
District (at 10:00 a.m. 2
days before)

Local LID

Branch office of the
Toyogawa LID (at 4:00
p.m. 2 days before)

Apex WUA (control water
diversion to the secondary
and tertiary canals)

Toyogawa LID(water rate)

JWA(at 10:00 a.m. theprevious day)

Farmers (release water totheir field)

JWA: Ono diversion
dam(at 2:00 p.m. theprevious day)(operation)

JWA: Distribution
to lateral canals(at 9:00 a.m.)

Zonal office of the HJRBDA
Constant release of water(control water gates from thedam to the main canal)

Farmers(irrigationarea)

Original operation

Fig. 2 Water supply method in the
Hadejia Valley Irrigation Project(HVIP)

Modified

Fig. 3 The original and modified water
allocation method and distribution process
in Toyokawa irrigation projectIn the Toyogawa Irrigation Project, some in-depth studies have been conducted to determine thewater demand of each sector and a simplified process was established to meet the water demands offarmers following the process depicted in Fig. 3, which reflects the original water distribution method
applied to the project. The water demand was calculated based on the declared daily irrigation area by
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a flow rate, and forwarded to the JWA. Because of the long procedure and resulting complaints by
farmers, the water allocation and distribution system was improved to avoid this complicatedprocedure and the problems it induced. In the new system, the first four steps in the previous system
are disregarded, and the Toyogawa LID branch offices take the first step to request water on behalf of
farmers.
Therefore, water demand studies in each sector (upstream, mid-stream, and downstream) of the
HVIP are important to avoid water conflict among upstream and downstream water users and also to
determine whether the amount of water released is sufficient to irrigate crops or if there is a need toreconstruct or rehabilitate the main canal to meet the capacity of the water requirement identified.
Again, after investigating the water demand, the water managers (the HJRBDA and zonal office staff)
should simplify guidelines and support their procedures by providing proof of logical and technicalinformation and also explain the effect of over-irrigation on crops and agricultural lands such as water
logging and salinization problems to avoid the overconsumption of water by farmers. Likewise, water
allocation plans in the HVIP should be decided in meetings attended by all delegates from different
interest groups (beneficiary farmers, the HJRBDA, zonal office authority, and Chad basin management
office). This will assist in achieving equitable distribution and improved understanding of theimportance of appropriate water allocation planning among farmers which will increase the amount ofwater utilized effectively for irrigation and ensured water availability to all farmers.
System of Operation and Management Cost of Irrigation Facilities
From the result of the interviews and the field visit to the HVIP, farmers and water managers said
government is responsible for the construction, operation, maintenance, and management costs ofirrigation facilities; however, it was found that proper system operation is currently not possible at alllevels of the systems. The main findings are that 1) operation and management (O&M) of the facilities
has long since fallen to a level below what is required; 2) the water fee (0.4 $/ha) collected isinsufficient to maintain the irrigation facilities, even when considering the amount provided by thegovernment to cover the O&M cost of the irrigation facilities; 3) the systems are currently in such atechnically poor condition that rendering proper system operation at most levels is impossible; 4) there
has been a substantial loss of command area; 5) a weakened organization responsible for O&M has notbeen able to rectify the situation; and 6) farmers have developed mistrust in the HJRBDA’s capacity
and capability to perform its maintenance duties for which they are supposed to pay.
All costs for water management in the Toyogawa Irrigation Project are covered by beneficiary
farmers. The Toyogawa LID assists the JWA in the collection of money from farmers. The
membership fees are based on acreage as well as on the land use of paddy or upland fields. The JWA,
Toyogawa LID, local LIDs, Management Districts, and Management Groups form a hierarchicalsystem corresponding to facility levels and operate in an environment of functional role sharing in the
Toyogawa Irrigation Project. In this environment, a directly interested organization is not allowed tooperate independently, and instead, a higher level of organization is assigned responsibility. In this way,
it is easy to distribute water in a fair and neutral way following the decisions made. It is difficult forfarmers or farmers’ organizations to operate modern, large-scale, complicated irrigation facilities as
specialized knowledge and skills are required. Therefore, role sharing in the facility operation of alarge-scale irrigation project is inevitable. In addition, a bulletin is periodically distributed by the JWA
to every member farmer, thereby supporting the understanding of the state and functions of facilities
and the processes of water management by every farmer, which includes the decisions, operation,
monitoring, and feedback (Satoh, 2003).
Therefore, for the sustainability and proper performance of the operation and management of the
HVIP, the water fee to be collected from farmers should be adjusted and added to the budget provided
by the government for the operational and management costs of the project to enable the budget to
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supervision of a higher organization (HJRBDA). In addition, the government should disseminate
information regarding how the fee collected was used for the operational, management, andmaintenance costs of the facilities and also the status of irrigation facilities to local farmers. This willencourage farmers to contribute to management and operational costs. All related organizations should
be assigned specific roles within operation and management at different levels of the facilities. By thispractice, costs will be reduced and a physical transparency system of the operation and management offacilities will be implemented that will encourage beneficiaries to contribute to the operational andmanagement costs for achieving the sustainability of the project.
CONCLUSION
The main points analyzed for the HVIP to develop an appropriate system to achieve water
improvement and management practices include the following;1) Involvement of farmers in management organization in such a way that farmers are categorized
into different groups (e.g., HVIP WUAs, local WUAs, Management Districts, and Management
Groups) and assigned a certain responsibility to ensure equitable distribution and fair management of
the irrigation project.
2) Water demand studies for water allocation are important in the HVIP to determine the demandin each sector to avoid water conflicts among water users in the upstream and downstream sectors. The
water demands should be decided in meetings attended by all delegates from different interest groups.
3) The water fee to be collected from farmers should be adjusted and added to the budget provided
by the government for the operational and management costs of the project to enable the budget tocover the operation, management, and maintenance costs of the irrigation facilities, under thesupervision of a higher organization (HJRBDA).
Therefore, the adoption and implementation of the identified processes of land and water
improvement projects and the management system in the HVIP will play an important role inenhancing the equitable distribution of irrigation water. Similarly, this will enable a system of physicaltransparency of operation and management of irrigation facilities for better water use in Nigeria.
ACKNOWLEDGEMENTS
This study was conducted under the project titled “Development of crop husbandry technology inmarginal rain fed environment using dryland plant resources” by Tottori University.
REFERENCES
FAO. 2005. Irrigation in Africa in figures. AQUASTAT Survey, Nigeria.
Ishii, A. and Satoh, M. 2003. PIM, Journal of Rural Planning Association, 22 (3), 239-240.
Japan Meteorological Agency. 2016. Meteorological statistics, http://www.data.jma.go.jp/obd/stats/etrn/index.
php
Kono, S., Ounvichit, T., Ishii, A. and Satoh, M. 2012. Participatory system for water management in the
Toyogawa Irrigation Project, Japan. Paddy Water Environment, 10 (1), 75-81.
Kozuki, R. 2000. Japanese case. In Kuroda (ed) Management of Irrigation Systems, The Japan Association for
Irrigation and Drainage Mechanization, Tokyo, 199-202
Minami, I. 2002. Public irrigation scheme development and management in Japan, Country case studies on
irrigation institutions. The 6th International Seminar on Participatory Irrigation Management, Beijing.
Sanyu Consultants Inc. 2013. The study on the “National Water Resources Master Plan of Nigeria 2014”. Federal
Ministry of Water Resources, 4, 4-18.

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Satoh, M. 2003. Principles for participatory irrigation management by organized farmers. Keynote Speech at the
3rd World Water Forum JBIC-JICA Session Report “Sustainable and Efficient Use of Irrigation System &Water, and Farmers’ Participation”, 1-9.

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Research article
erd

Forest Ecosystem Services and Agricultural Production ofCommunities in Protected Areas: A Case Study of Phu Kao Phu Phan Kham National Park, Thailand
WIPADA JANTA*
Khon Kaen University, Khon Kaen, Thailand
Email: j.wipada19@gmail.com

ADCHARAPORN PAGDEE
Khon Kaen University, Khon Kaen, Thailand

SOMKID UTTARANAKORNHead of Phu Kao–Phu Phan Kham National Park
Department of National Parks, Wildlife and Plant Conservation, Thailand
Received 15 December 2016

Accepted 8 June 2017(*Corresponding Author)

Abstract Forest ecosystems provide goods and services that support our livelihoods. However,forestlands are often cleared for agriculture due to nutrient availability for crop growth. This
study examined forest ecosystem services and agricultural practices of three villages at Phu Kao– Phu Phan Kham National Park, northeastern Thailand. A survey on agricultural production
and socioeconomics was conducted in June 2016, together with GIS spatial analysis to examine
correlations between agricultural productivity and forest-to-farmland distances (FD) andvegetative cover (VC) surrounding farmlands within a 100m radius. We hypothesized thatfarmlands closer to the forests and/or surrounded by greater vegetative structure would receive
more benefits from forest ecosystem services than those farther away. In total, 100 household
representatives answered the questionnaire. Cassava was a major cash crop planted withapproximately 8.94 tons/ha. Production costs of cassava plantation (i.e., labor and fertilizer)
were estimated 24,67.95 Baht/ha, mainly from harvesting costs and chemical fertilizers. Forestto-farmland distances and VC did not result in significant yields (tons/ha). However, the total
production costs of cassava plantations closer to the forest (<1 km) were significantly smaller
than those farther away (mean difference = -7,053 Baht/ha, p = 0.007), while VC showed amarginal difference. Farmlands with less VC (<1 ha) resulted in greater total production costs
than those with larger VC (mean difference = 2,540.00 Baht/ha, p = 0.073). These findings
illustrate that adjacent forests provide ecosystem services to cassava production, at least to
some degree. Farmers incur smaller production costs, thereby receiving greater economic
returns when their farmlands were closer to the forest and surrounded by larger amounts ofvegetation.
Keywords forest ecosystem services, agricultural production, protected areas, Phu Kao – Phu
Phan Kham National Park, Thailand

INTRODUCTION
Forest ecosystems provide goods and services such as food, fuel, and other basic necessities of life that
support at least half of the world’s population (World Resource Institue, 2008). The Millennium
Ecosystem Assessment (2005) categorized ecosystem services into four types: supporting, provisioning,
cultural and regulating services. Supporting services include biodiversity, soil fertility, nutrient cyclingⒸ ISERD

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and the provision of water, while pollination, natural pest control and water purification are examplesof regulating services and traditional ecological knowledge represents example of cultural benefits.
Consequently, forestlands are often cleared for agricultural use because of high nutrient availability,
thus making agriculture the main force of deforestation (Lawson, 2014). Reducing the amount of
deforestation caused by agriculture is not as simple as asking farmers to stop clearing the land,
although it is the cheapest option in the short run. Furthermore, household livelihoods are a higher
priority than forest protection. Several studies have shown the benefits of forests over agricultural
production (Olschewski et al., 2006; Ricketts, 2004), so farmers can see what has been gained in theconversion process, especially supporting and regulating services e.g., pollination, soil fertility and pest
control, in addition to direct provisioning services such as non-timber forest products and potential
reserves for agricultural expansion.
Instead of simply asking villagers to stop land encroachment and/or agricultural expansion, thisstudy showed the benefits of forest ecosystem services. It investigated farming practices and householdsocioeconomic conditions since these factors affect agricultural production. The study was conducted
at Phu Kao – Phu Phan Kham National Park (PKNP) in northeastern Thailand. The area attracted someattention because of community settlement inside the park, which is not allowed in protected areas.
However, this case was an exception since the villages claimed that they were established before thepark was designated. Land conflict between villagers and park authorities continued until the Cabinet
Solution in 1998 in which usufruct rights were granted to villagers who were able to prove existence
before park designation. Following the 1998 Cabinet Solution, approximately 1,600 ha of protected
lands were set aside for agricultural and residential use purposes, together with land use rules andregulations. Moreover, management authority was transferred from the Department of National Parks,
Wildlife, and Plants Conservation to local authorities, complicating law enforcement efforts. Ruleviolations, including land encroachments outside the permitted area, agricultural expansion andplantations of prohibited crops/trees (i.e., para-rubber), can be observed. The PKNP authority recorded
at least 21 lawsuits of land encroachments inside the park during 2015. Furthermore, the community
has expanded substantially, from small villages of seven to 10 households at the beginning to 528
households in 2015 (Office of Civil Registration, 2015).
OBJECTIVESThis study examined correlations between agricultural productivity, FD and VC surrounding the
farmlands. We hypothesized that farmlands closer to the forests and/or surrounded by greater VC
would receive more ecosystem benefits than those more isolated (Olschewski et al., 2006; Ricketts,
2004). Therefore, farmers should obtain greater yields and/or incur less costs.
METHODOLOGY
Study Area
The boundary of PKNP, Thailand’s 50th national park, crosses three provinces in northeast Thailand(Khon Kaen, Nong Bua Lamphu, and Udon Thani) covering approximately 32,200 ha. The park
consists of two main areas, including Phu Kao and Phu Phan Kham. This study took place at Phu Kao(PK), located between latitudes 16° 51’ - 17° 1’ N and longitudes 102° 24’- 102° 31’E in Nong Bua
Lamphu. Phu Kao consists of diverse landscapes, including sandstone mountains, undulating
topography, and a vast floodplain of Pong River, creating the Ubonrattana Reservior – the largest damin the northeast. The major vegetation is dry Dipterocarp forest, covering approximately 80% of PK,
followed by mixed deciduous forest and dry evergreen forest. Intermixed within the park are Phu Kao
National Forest Reserves, making forest access less restrictive when compared to the national park
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lands because they are managed by a different agency, the Royal Forest Department. Moreover,approximately 1,600 ha of park lands were set aside for community agricultural and residential use
purposes as part of the 1998 Cabinet Solution. These communities include Dongbak, Wangmon andChaimongkol village. Management authority over this designated area was transferred to local
administrative organizations of Nonsang District, Nong Bua Lamphu Province.
Data Collection on Agricultural Production and Household Socioeconomic Conditions
A questionnaire was used to collect data on agricultural production, household socioeconomic
conditions, and other relevant information e.g., forest use and villager adaptation to environmental andsocioeconomic change. The survey was semi-administered by household representatives (i.e., head of
the family, spouse and/or main labors) who were personally interviewed by the researchers. Theinterview was conducted in the following order: household socioeconomic information, agriculturalactivities and production, boundary locations of farmlands, crop yields, and production costs. The
village leaders were contacted prior to the actual visits, which occurred in June 2016, to inform them of
the study and seeking their permission to participate. In total 100 household representatives from all
three villages were selected randomly. In addition, personal interviews with the village leaders andonsite observations were performed for confirmation and clarification.
Spatial Data Analysis
In addition to surveyed data, FD and VC surrounding the farmlands were estimated using ArcGIS 10.1.
First, household representatives were asked to identify their farm location and boundaries using
GoogleEarth images. Midpoint coordinates for each of the identified farmlands were specified for
calculation of FD and VC surrounding the farmlands. Calculation of FD were performed using Ruler
Tool to measure a distance from each farmland midpoint to the nearest forest. Furthermore, calculationof VC was done using farmland coordinates and images downloaded from GoogleEarth. The
coordinate system: WGS1984 UTM Zone 48N was used as referenced data for farmland coordinatesand sizes of the actual areas before the dereferencing pointer adjusted the processed images to get
accurate coordinates. The farmland midpoint coordinates from Excel were imported into ArcGIS,
creating an entire data set of farmland coordinates. Afterwards, shapefiles were created to digitize VC
within a 100m radius before reclassification and specification of areas and values of cells. The laststage was to buffer each sampled area within a 100m radius using the Calculate Geometry tool tocompute size of VC until the set of farmland coordinates was completed.
Statistical Analysis
Data from the survey and spatial analysis were entered into SPSS version 20 for statistical analysis.
Descriptive statistics were used to describe household socioeconomic conditions and their agricultural
production activities. Pearson correlations were used to examine socioeconomic factors relating toagricultural production. Finally, independent t-tests were used to compare yields, farm inputs andproduction costs between farmlands with different FD and surrounding VC. Mean values i.e., 1,000 m
for FD and 10,000 m2 (1 ha) for VC, were used to classify the variables (i.e., yields and production
costs) into two groups.
RESULTS AND DISCUSSION
Household’s Socioeconomic ConditionsⒸ ISERD

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In total, 100 household representatives (36 from Wangmon, 30 from Chaimongkol, and 34 from
Dongbak), including village leaders, participated in the survey. Their socioeconomic conditions areshown in Table 1.
Table 1 Socioeconomic information of farmers participated in the study
Socioeconomic conditions
Wangmon(n=36)
GenderFemaleMaleMain occupation
FarmerMerchant
Government service
Hired labor
Household income (Baht/year)
Averaged size of agricultural lands (ha)% of respondents
Chaimongkol
Dongbak(n=30)(n=34)

Summed(n=100)

14
22

11
19

21
13

46
54

35
1
141,083
2.81

25
1
1
2
111,340
3.77

34
1
183,894
4.00

94
2
2
2
135,538
3.52

The majority of villagers (98%) earned most of their income from selling agricultural products,
essentially cassava; and a small number of them made additional income from selling non-timber forest
products (approximately 100 Baht/month). About 57% of participants reported growing rice. This
number is small compared to other agricultural-based communities in the northeast, which is nearly
100%. Perhaps this is due to topographic unsuitability for rain-fed rice cultivation, which usuallyoccurs in the lowlands, but the three villages are located on hilly terrain. All villagers indicatedgrowing rice for personal consumption which helped to reduce household spending. However, almost
three-fourths (71%) of the farmers faced rice shortage, so they needed to purchase extra amounts forhousehold consumption. The estimated amounts of rice purchased were 392.21 kg/household or
approximately 12,855 Baht/household per year.
Land Use and Agricultural Production
Two common cash crops were planted i.e., cassava and sugarcane for income generation. Sincesugarcane prices have dropped in recent years while production costs remain high, many farmers haveswitched to cassava. Only five per cent of the villagers reported planting sugarcane during 2014-2015
crop year, while 98% grew cassava. Rice is cultivated for household consumption rather than income
generation. Three farmers (one from each village) reported planting para-rubber trees even though thistree is prohibited to preserve natural forests in the park. High market demands, especially in the past 10
years when the government promoted para-rubber plantations in the northeast, gave villagers economic
incentives to plant this tree, despite rules and regulations against it. Although this incident is
considered illegal, park officers often compromise to avoid conflict by asking villagers not to expand
their plantations.
Average sized farmlands, about 3.52 ha, are mainly used for cash crop plantations i.e., cassava.
Average yields of cassava was 8.94 tons/ha, which is much lower than the average provincial yield(21.18 tons/ha) in 2015 (Office of Agricultural Economics, 2015). Total production costs were
estimated from explicit costs of labor used in each of the plantation stages, together with costs offertilizer application, including manure and chemical fertilizers. The average production costs were
24,676.95 Baht/ha. Harvesting accounted for the highest proportion of total production costs (53%),
followed by farm maintenance (34%), planting processes (8%) and land preparation (5%) (Table 2).
Farming is becoming more market-driven. Cash returns from agricultural activities are thought to beⒸ ISERD

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more valuable than subsistence benefits. Therefore, farmers increase crop productivity in an attempt toserve increasing market demands by using more chemical fertilizers and relying on hired labor.
Subsequently, production becomes expensive with relatively smaller profits (Bowman and Zilberman,2013).
Table 2 Production costs of cassava plantation
Process of cassava production
Total costs of cassava production (Baht/ha)
1) Land preparation
2) Cassava planting
3) Farm maintenance3.1) Chemical fertilizer application
3.2) Manure application
3.3) Weeding4) Harvesting costs

Households samples (n = 94)
24,676.95
1,335.15 (5%)
1,853.52 (8%)
8,391.33 (34%)
5,884.59 (70%)
147.90 (2%)
2,358.84 (28%)
13,096.95 (53%)

Factors Affecting Farmer Agricultural Production
Farm production depends on both ecological and socioeconomic factors. Ecological conditions e.g.,
nutrient contents, soil pH and moisture, and amounts of rainfall, directly influence crop growth.
Nonetheless, quality of farmlands absolutely relies on farming practices, especially farm maintenanceand land conservation. In this study, total production costs, amounts and costs of chemical fertilizers
used in cassava plantations showed significant correlations with size of farmlands (r = 0.363, -0.247
and -0.241, p<0.05, respectively). All farmers reported using chemical fertilizers to help improve crop
productivity. The amount and cost of chemical fertilizers varied according to the size of farmlands,
which subsequently influenced the total production costs. The larger the planting areas, the smaller theamounts and costs of chemical fertilizers used per hectare. Size of agricultural lands also affected theamount of farm input since farmers can only provide so much labor, fertilizer, and other inputs tomaximize crop yields. Nonetheless, average yields did not show significant correlations with total costs,
amounts and costs of fertilizers and labor costs. Although farm input directly affected crop productivity,
ecological conditions, including climatic and soil factors, also played an important role in determining
productivity.
Farm Production Based on Forest-to-farmland Distance and Vegetative CoverThe analysis used to examine if FD influenced agricultural production illustrated that total costs infarm maintenance and total production costs (i.e., labor, manure and chemical fertilizers) of farmlands
located closer to the forest were significantly smaller than those farther away (Table 3). Costs ofchemical fertilizers showed a marginal difference; cassava plantations closer to the forest resulted inlower costs. Although average yields were not significantly different, they were smaller for cassava
plantations with smaller VC, while total production cost and total labor cost were marginally higher for
cassava plantations having smaller vegetative cover (Table 3).
To some extent, these findings imply positive contributions or services from the forest to cassava
plantations, including the influence of FD on total costs in farm maintenance, total production costs
and costs of chemical fertilizers used in planting cassava, and the impact of VC on total production
costs and labor costs. However, average yields did not reveal connections with FD and VC because
they do not solely depend on farm input, but also from climatic factors and soil conditions. History of
landuse, crop rotation and farm maintenance practices directly influence agricultural land conditions
and productivity. Longevity of land use, especially continuous use of chemical fertilizers, leads to adecrease in soil nutrients and higher erosion (FAO, 2016; Virto et al., 2015), which in turn, reducesⒸ ISERD

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crop yields. Moreover, some studies reveal that VC can hinder crop growth since it could reduce
sunlight and other necessary growth factors, causing low productivity of crops (Yasmin et al., 2016;
Wang et al., 2012; Cerdan et al., 2012).
Table 3 Comparisons of agricultural production (t-tests) between farmlands with different
forest-to-farmland distances and vegetative coverGroupvariables

Forest-tofarm
Distances

Vegetative
coversTest Variablesxˉ ±SD

Mean
Differencet1) Costs of chemical fertilizer application (Baht/ha)
Zone 1 (0-1,000 m)
5,212±4,216-1,466.61 -1.726
Zone 2 (>1,000 m)
6,679±3,966
2) Total costs in farm maintenance (Baht/ha)
Zone 1 (0-1,000 m)
9,805±6,149-7,063-2.99
Zone 2 (>1,000 m)
16,868±14,379
3) Total production costs of cassava plantation (Baht/ha)
Zone 1 (0-1,000 m)
15,215±7,631-7,053-2.77
Zone 2 (>1,000 m)
22,269±15,115
1) Total production costs of cassava plantation (Baht/ha)
Zone A (0-10,000 m2)
19,273±16,411
2,540.00
0.898
Zone B (>10,000 m2)
16,733±9,489
2) Total labor costs (Baht/ha)
Zone A (0-10,000 m2)
13,712±15,998
3,616.77
1.323
Zone B (>10,000 m2)
10,095±9,332

t-testN

pvalue

51
43

0.088

51
43

0.004*

51
43

0.007*

49
45

0.073

49
45

0.092

Note: * significantly different p<0.05, underlined numbers represent marginal difference

CONCLUSION
There is a strong interdepency between forests and farmers, although it can be difficult to quantify. The
forest provides benefits and services that support farmer livelihoods, while its existence depends onfarming practices. Increasing demands for food and other types of agricultural products e.g., energy
crops, force farmers to expand their production, which in turn, leads to deforestation. Unfortunately,
authorities cannot simply ask farmers to stop agricultural practices just to halt deforestation. Therefore,
one way to motivate farmers not to expand their agricultural lands into nearby forests is to show how
the forests can contribute to their production. This study illustrates that FD and VC influence farm
production, at least to some extent. Farmlands close to the forest and/or surrounded by large patches ofvegetation benefit from the forest, resulting in lower costs of chemical fertilizers, total costs in farm
maintenance and total production costs. Finally, the protected forest in Phu Kao, Nong Bua Lamphu
Province is an example to reiterate that it helps provide ecosystem services that improve farmer’s
agricultural production. Typically, forest protection and agriculture are in opposition, but should beviewed as complementary. Therefore, effective forest protection is beneficial for agricultural
production rather than hindering it – a new paradigm to better understand their interconnectivity.
ACKNOWLEDGEMENTS
We would like to thank Khon Kaen University research group and center: 1) Research Group on
Problem Soils in Northeast Thailand and 2) Research and Development Center for Integrated Water
Resources Management, for useful input on the study. Most importantly, this project would not be
possible without the collaboration and support from Phu Kao – Phu Phan Kham National Park and allcommunity leaders and villagers at Dongbak, Wangmon and Chaimongkon Village.
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REFERENCES
Bowman, S.M. and Zilberman, D. 2013. Economic factors affecting diversified farming systems. Ecology andSociety, 18 (1), 33. DOI.org/10.5751/ES-05574-180133
Cerdan, R.C., Rebolledo, C.M., Soto, G., Rapidel, B. and Sinclair, L.F. 2012. Local knowledge of impacts of tree
cover on ecosystem services in smallholder coffee production systems. Agricultural Systems, 110, 119-130.
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Food and Agriculture Organization. 2016. Agriculture and soil biodiversity. [Online]. Available at:
http://www.fao.org/agriculture/crops/thematic-sitemap/theme/spi/soil-biodiversity/agriculture-and-soilbiodiversity/en. Access date: November 28, 2015.
Lawson, Sam. 2014. Consumer goods and deforestation, An analysis of the extent and nature of illegality inforest conversion for agriculture and timber plantations. Forest Trends Report Series Forest Trade andFinance [Online]. Available at: http://www.forest-trends.org/documents/files/doc_4718.pdf. Access date:
October 29, 2016.
Millennium Ecosystem Assessment. 2005. Ecosystems and human well-being, A framework for assessment.
Island Press: Washington, DC. [Online]. Available at: http://www.millenniumassessment.org /documents/document.356.aspx.pdf. Access date: October 29, 2015.
Office of Civil Registration. 2015. Demographic records (in Thai). Non Sang District.
Office of Agricultural Economics. 2015. Agricultural production data, Cassava (in Thai) [Online]. Available at:
http://www.oae.go.th/download/prcai/DryCrop/amphoe/casava-amphoe58.pdf. Access date: December 1,
2016.
Olschewski, R., Tscharntke, T., Benítez, P.C., Schwarze, S. and Klein, A. 2006. Economic evaluation ofpollination services comparing coffee landscapes in Ecuador and Indonesia. Ecology and Society, 11 (1), 7.
URL: http://www.ecologyandsociety.org/vol11/iss1/art7/.
Ricketts, H. Taylor. 2004. Tropical forest fragments enhance pollinator activity in nearby coffee crops.
Conservation Biology, 18 (5), 1262-1271. Doi:10.1111/j.1523-1739.2004.00227.x.
Virto, I., Imaz, J.M., Fernandez-Ugalde, O., Gartzia-Bengoetxea, N., Enrique, A. and Bescansa, P. 2015. Soil
degradation and soil quality in western Europe, Current situation and future perspectives. Sustainability, 7,313-365. Doi:10.3390/su7010313.
Wang, S., Fu, B.J., Gao, G.Y., Yao, X.L. and Zhou, J. 2012. Soil moisture and evapotranspiration of different
land cover types in the Loess Plateau, China. Hydrology and Earth System Sciences, Doi: 10.5194/hess-162883-2012.
World Resource Institute. 2008. Roots of resilience, Growing the wealth of the poor. Washington, DC: WRI.
Yasmin, A.R., Leuschner, C., Barus, H., Tjoa, A. and Herte, D. 2016. Cacao cultivation under diverse shade tree
cover allows high carbon storage and sequestration without yield losses. Plos one, 11 (2). DOI:
10.1371/journal.pone.0149949.

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Research article
erd

Estimation of Soil Erosion Based on USLE and GIS
in Gardez Basin of Paktya Province, Afghanistan
ABDUL MALIK DAWLATZAI
Graduate School of Agriculture, Tokyo University of Agriculture, Tokyo, Japan
Email: abduldawlatzai@gmail.com

MACHITO MIHARA*
Faculty of Regional Environmental Science, Tokyo University of Agriculture, Tokyo, Japan
Email: m-mihara@nodai.ac.jp
Received 13 December 2016

Accepted 26 June 2017(*Corresponding Author)

Abstract Soil erosion is a serious problem in Afghanistan, which has been accelerated by
improper land management day by day and a growing problem especially in agricultural land.
The land in the country is facing continuous soil loss and sediment accumulation due to theirregular topography, deforestation and desertification. It does not only reduce agricultural andlivestock production, but also decrease the water availability for irrigation purpose. This study
focused on the estimation of the rate of soil loss and soil erosion risk using Universal Soil Loss
Equation (USLE) and ArcGIS for the Gardez basin Paktya Province, Afghanistan. All factors
used in USLE (R, K, L, S and C) were calculated for the study area using local data. The bestequation for estimating the R value from the annual rainfall was discussed on the basis of theobserved annual rainfall with the installed rain gauge. Also, all factors were presented by rasterlayers in ArcGIS platform then multiplied together to predict soil loss (A). The results indicatedthat the annual soil loss within Gardez Basin ranges from 0 to greater than 100 t ha-1 y-1. The
value was divided into five (5) risk classes. The result showed that slight class of soil loss
having a range of soil loss between 0 to 5 t ha-1 y-1, moderate class having rates between 5 to 10
t ha-1 y-1, high class having rates between 10 to 50 t ha-1 y-1, severe class rates between 50 to 100t ha-1 y-1 and very severe class rates greater than 100 t ha-1 y-1, covering 64.31%, 13.95%,
19.76%, 1.76% and 0.22% of the Gardez Basin area, respectively. Most of the agricultural lands
are slight to high soil loss categories. However, high soil erosion is found in the barren land,
rangeland and rainfed agricultural land. The soil erosion risk is extremely higher on steep slope
and foothills. Based on the mean soil erosion value of different land use classes, target land use
for conserving strategies was discussed for planning soil conservation practices.
Keywords USLE, ArcGIS, Gardez Basin, soil loss, Afghanistan

INTRODUCTION
Erosion is one of the major ecological problems which threaten our national reserves as well as thewhole world. It also reduces soil fertility significantly and crop yields. Afghanistan is located in thesouth and central Asia which is under high soil erosion effect mostly due to deforestation, arid andsemi-arid climates and irregular topography. Soil erosion has resulted in prolonged and great impact onsocial and economic development in the region, in fact, recent environmental assessments indicatedthat decades of war and continuous drought have caused widespread environmental degradation
throughout the country.
The Universal Soil Loss Equation (USLE) was developed by Wischmeier and Smith in 1978. It
has been the most commonly used model for predicting soil erosion loss. The USLE and its modified
versions such as RUSLE (Renard et al., 1997) and MUSLE (Williams, 1975) have been widely used inⒸ ISERD

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various scales and regions.
Soil erosion has received scanty attention in Afghanistan. However, the study conducted by
Sahaar, (2013) using combined Revised Universal Soil Loss Equation (RUSLE) model and Geographic
Information System (GIS), the annual soil loss of Kabul Rivers was estimated 19 t acr -1 y-1, (4748 t km2 -1
y ). The excessive sedimentation clogs stream channels and increase costs for maintaining water
passage structures. Similarly, the annual soil loss of the Lower Harirud watershed in Heart province
used RUSLE model and GIS ranges from 0.025 to 778 Mg ha-1 y-1, which is 3.6 times greater thanmaximum tolerable soil erosion (Ehsan, 2015). Field study conducted by US Military Agricultural
Development Team, (2011) in the Dawlatzai village of Paktya Province, soil erosion depended on area
ranges between 500 t/ha to 1200 t/ha.
Estimation of soil erosion is economically and environmentally very important in Paktya province,
Afghanistan. Soil erosion does not only reduced soil fertility and water quality but also severeinterrupts irrigation network. Poor vegetation cover, steep slopes, deforestation and high intensity
rainfall in short time are the main factors influencing soil erosion in Paktya province. Therefore, toevaluate the impact of these factors on sustainable agriculture and environment, to quantify the extent
of soil erosion it needs for appropriate and applicable erosion model. For this reason, the USLE model
has been widely used worldwide approximately more than four decades to predict soil erosion.
Recently, there are many types of research conducted regarding the USLE model in conjunction with
GIS technology which has been used to predict the annual soil loss. The objective of this study is toevaluate soil erosion risk using ArcGIS technique and empirical Universal Soil Loss Equation in
Gardez basin of Paktya Province, Afghanistan.
MATERIALS AND METHODS
Study Area
The study was conducted in Gardez Basin located east part of the country and also it is the capital of
Paktya Province, Afghanistan. The basin covers approximately 48,104 ha (481.04 km2) as shown in
Fig 1. It is geographically positioned between latitude N 33° 46′ 0″- N 33° 28′ 0″ and longitude E
69° 26′ 30″- E 69° 26′ 30″. It topographically ranges in slope between 0 to 65 degrees with an elevationof approximately 3663 m above sea level. The rainfall data was obtained from the daily automatic rain
gauge installed in the study area for one-year duration (July 15, 2015 to July 14, 2016), the annual
rainfall is 354.6 mm y-1 and exhibits a dry climatic condition with a minimum and maximum
temperature of -11 °C and 41 °C, respectively.

Fig. 1 Map of study area in Paktya Province, AfghanistanⒸ ISERD

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Methods
The USLE model was developed by Wischmeier and Smith (1978), as an equation representing themain factors controlling soil erosion, namely climate, soil characteristics, topography and land covermanagement. The expression is shown in Eq. 1.
A= R×K×L×S×C×P(1)

Where A is computed annual soil loss per unit area (t ha-1 y-1), R is runoff erosivity factor (MJ mm
ha y-1), K is soil erodibility factor (t ha hr ha-1 MJ-1 mm-1), L is slope length factor, S is slope
steepness factor, C is cover management factor and P is supported practice factor.
In the present study, annual soil loss rates and scale were computed based on USLE in GIS
platform and different data sources were referred to analyze the estimation of soil loss in the study
area. A digital elevation model (DEM) with 30 m resolution was obtained from Aster Global Digital
Elevation Model (Available online: https://asterweb.jpl.nasa.gov/gdem.asp), the elevation range is from2205 m to 3663 m as shown in Fig. 2. The DEM was used to estimate slope gradient, flow direction,basin area, flow length and flow accumulation for the study area using ArcGIS 10.3.1. The slope lengthand slope steepness (LS) factor required by USLE was calculated. The land cover classification map
developed by Food Agriculture Organization (FAO, 2016), was used for the analysis of crop
management factor (C-value). Soil classification map developed by United States Department ofAgriculture, Soil Conservation Services (USDA-SCS, 2001) was used for analyzing the soil erodibility
factor (K-value). Analysis of rainfall erosivity factor (R-value) was derived from area automatic rain
gauge data in Gardez city and mean annual rainfall data in surrounding areas.
-1

Fig. 2 DEM map of Gardez BasinRESULTS AND DISCUSSION
Rainfall Erosivity Factor (R)Rainfall erosivity is defined as the aggressiveness of the rain to cause erosion. Rain has a direct impact
on the surface of the soil. The kinetic energy of the raindrops destroys the soil aggregates, making
them susceptible to transfer by runoff water. The R factor was computed using the Eqs. 2 and 3
developed by Wischmeier and Smith (1978).
KE = 11.87 + 8.73 log I

(2)
-1

-2

-1

Where I is the rainfall intensity (mm h ) and KE is the kinetic energy (Jm mm ).
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R

¦ EI

(3)

max

1000

R is rainfall erosivity factor in MJ m km-2 h-1 y-1 and EI is the total storm energy in Jm-2 mm-1. The
R value was calculated from energy-intensity relationships. Daily rainfall data was recorded using
automatic rain gauge located in the study area for the duration of one year, (July 15, 2015 to July 14,2016). Using Eq. 2 and 3, the data in Table 1 was obtained.
Table 1 Calculation of the erosivity factor
Max. intensity(mm hr-1)
3.95
3.72
3.57
3.98
21.27
8.20
11.11
15.88
Total

Total energy(J m-2)402.06343.16
272.62
270.63292.55294.06341.36277.45Rainfall erosivity(MJ m km-2 hr-1 y-1)
1.59
1.28
0.97
1.08
6.22
2.41
3.79
4.41
21.75

Rainfall erosivity(MJ mm ha-1 hr-1 y-1)
15.9
12.8
9.7
10.8
62.2
24.1
37.9
44.1
217.5

Using the data obtained from the automatic rain gauge installed in the study area for one-year
duration, the R-factor value was calculated as 21.75 MJ m km-2 hr-1 y-1 (217.5 MJ mm ha-1 hr-1 y-1). The
original method for calculating the R value for a storm event requires rainfall amount in mm, intensity
in mm h-1 and the maximum 30 minutes intensity in mm h-1. Due to lack of adequate metrological data
and long-term rainfall intensity data in some countries such as Afghanistan, it is hard to apply Eq. 2
and 3. It is therefore necessary to interpolate between available data hence, the attention should be paid
to investigate new methods and equations to calculate the erosivity factor using annual rainfall. R
factor based on annual precipitation were computed using various equations shown in Table 2.
Table 2 List of equations used to investigate correlation
No

Reference

1
2
3
4
5
6
7
8
9
10
11
12

Morgan, 1974
Foster et al., 1981
Cooper, 2011
Eltaif et al., 2010
Deumlich et al., 2006
Renard and Fremund, 1994
Yu and Roswell, 1996
Parveen and Kumar, 2012
Singh et al., 1981
Arnoldus, 1980
Renard and Freimund, 1993
Roose, 1975

EquationⒸ ISERD142

R = 2.28P - 8,838
R = (0.27P75)/100
R = 9.17P0.20
R = 23.61e(0.0048P)
R = 12.98 + 0.0783P
R = 0.04830P1.510
R = 0.0438P1.61
R = 29 + 0.363P
R = 79 + 0.363P
R = 0.03P1.9
R= 0.07397F1.847/17.02
R = 0.5P

IJERD – International Journal of Environmental and Rural Development (2017) 8-1

However, the erosivity index calculated using Eq. 4 by Singh et al., 1981 showed the best fit wasachieved between R value calculated with USLE and the mean annual rainfall for the Gardez Basin.
The results based on Eq. 4 was summarized in Table 3.
R = 79 + 0.363P(4)

Where P is the mean annual precipitation (mm) and R is the erosivity factor (MJ mm ha-1 hr-1 y-1).
In terms of ArcGIS layers, each weather station was represented by a point. The Inverse Distance
Weighted (IWD) interpolation method in ArcGIS was used to create a raster map for R factor.
However, rainfall erosivity (R) was calculated using rainfall data from six rainfall stations across the
Gardez Basin. The high erosivity was found in the northeast and low erosivity was found in thesouthwest part of Basin. The R-factor varied from 157.58 to 199.72 MJ mm ha-1 hr-1 y-1 as shown in
Fig. 3.
Table 3 Rainfall erosivity, (R value)
No

StationRainfall (mm y-1)

R value (MJ mm ha-1 hr-1 y-1)

1Tera Garden (Gardez city)

333

199.882

Rhoni Baba farm (Zarmat District)

216

157.41

3

Khost (Province)

330

198.79

4

Sharana (Paktika Province)219

158.505

Urgoon (Paktika Province)

252

170.48

6

Logar (Province)

294

185.72

Fig. 3 Rainfall erosivity map of Gardez Basin

Fig. 4 Soil erodibility map of Gardez Basin

Soil Erodibility Factor (K)The soil erodibility factor indicates susceptibility of soil particles or surface materials to be detachedand transported by rainfall and runoff (Renard et al, 1997). Soil erodibility factor was obtained fromsoil classification map of the country which is presented by USDA-SCS, 2001 as shown in Fig. 4.
Based on the classification of soil and soil texture classes, the K factors (t ha hr ha-1 MJ-1 mm-1) are
shown in Table 4.

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Table 4 Soil classification and erodibility values
No

Soil classification

Soil texture

Order

K value1

Xerochrepts with Xerorthents

Silt loam

Xeric

0.048

2

Haplocambids with Torriorthents

Silt loam

Aridic0.040

3Torriorthents with Torrifluvents

Silt clay loam with cobbly loam

Aridic0.038

4Haplocambids with Torriorthents

Silt loam with fine sandAridic0.063

Fig. 5 Slope map of the Gardez Basin

Fig. 6 LS-factor map of Gardez Basin

Slope Length and Slope Steepness Factor (LS)The LS factor has been used in a single index, which expresses the ratio of soil loss as defined by
Wischmeier and Smith, 1978.
LS = (X/22.1)m (65.41 sin2 θ + 4.56 sin θ + 0.065)(5)

Where, X is slope length (m); θ is the angle of slope in degrees; and m is a constant dependent on
the value of the slope gradient: 0.5 if the slope angle is greater than 2.86 degree, 0.4 on slope of 1.72 to
2.85 degrees, 0.3 on slope of 0.57 to 1.72 degrees, and 0.2 on slopes less than 0.57 degrees. Erosion
increases as slope length and slope steepness increases. The slope length (L) and slope steepness (S)are combined in a single topographic index termed LS-factor was computed for the Gardez Basin byusing spatial analyst extension in ArcGIS software has used to generate raster layers of the slope. The
slope of Gardez Basin range values between 0 to 65 degree and was derived from the DEM as shownin Fig. 5.
First step, the elevation value was modified by filling the sinks in the grid. Second step Flow
direction was generated from the fill grid. Third step the flow accumulation was calculated andgenerated from the flow direction. Flow accumulation tool identifies how much surface flow
accumulates in each cell; cells with high accumulation values are usually stream or river channels andalso recognizes local topographic feature such as mountain peaks and ridgelines. Finally, raster
calculator function under Spatial Analyst tool was used to input the modified Eq. 5. To compute LS
factor. The values between 0 to 175.64 as shown in Fig. 6.
Crop Management Factor (C)Ⓒ ISERD

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The crop management factor (C factor) is the ratio of soil loss of a specific crop to the soil loss under
the condition of continuous fallow (Renard et al., 1997). It measures the effect of canopy and groundcover on the hydraulics of raindrop impact and runoff.
C factor is a relation between erosion on bare soil and erosion observed under a cropping system.
It varies from 1 on bare soil to 1/1000 under dense forest, 1/100 under grasslands and plants and 1 to
4/10 under root and tuber crops (Morgan, 2005). Based on the national land cover map published by
FAO-UN, 2016, the land cover classification of the Gardez basin has 11 classes. Therefore a crop
management factor (C factor) was assigned for each land use type from the literature reviewed asshown in Fig. 7.

Fig. 7 C factor map of Gardez Basin

Fig. 8 Soil loss map of Gardez Basin

Conservation Practice Factor (P)Factor P in USLE model expresses the effect of conservation practices that reduce the amount and rateof water runoff, which decrease erosion. It is the ratio of soil loss with specific support practice tocorresponding soil loss with upslope and downslope parallel tillage (Renard et al, 1997 andWischmeier and Smith, 1978). Currently, there are no support practices in the study area, hence P is
assigned value of 1 in the calculation.
Estimated Soil Loss
The data layers (maps) extracted for R, K, LS and C factors of the USLE model were multiplied within
the raster calculator of ArcGIS spatial analyst in order to generate the map of soil loss for Gardez Basin.
The final map presents the annual soil loss t ha-1 y-1 a pixel level. The soil loss values estimated for
Gardez basin ranges from 0 to > 100 t ha-1 y-1 which is shown in Fig. 8.
The annual soil loss map obtained was classified into 5 classes. The results presented in Table 5shows that about 64.31% of the study area is classified as slight erosion risk (0 - 5 t ha-1 y-1), 13.95% of
the area is classified as moderate soil erosion risk (5 - 10 t ha-1 y-1), 19.76% of the area is classified ashigh soil erosion risk (10 - 50 t ha-1 y-1), 1.76% of the area is classified as severe soil erosion risk (50 100 t ha-1 y-1) and 0.22% of the area is classified as very severe soil erosion risk (greater than 100 t ha-1y-1). The higher soil loss is due to high slope steepness, very poor vegetation and no conservation
practices which are the most prominent causes of soil erosion, severe and very severe soil erosion risk
classes mainly located in mountains/foothills.

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Table 5 Annual soil loss rate and risk categories
Soil loss (t ha-1 y-1)
0-5
5 - 10
10 - 50
50 - 100
> 100
Total

Risk categories
SlightModerateHigh
SevereVery severe

Area (ha)30,934
6,713
9,503
847
107
48,104

Area (%)
64.31
13.95
19.76
1.76
0.22
100.00(Morgan et al., 2004)

The land use map of country was developed by FAO-UN 1993 and subsequently updated in 2016
(FAO-UN). Land use classification map of Gardez basin consists of 11 classes as shown in Fig. 9, thatare explained as follows; irrigated agricultural land, rainfed agricultural land, rangeland,
rangeland/barren land, barren land, barren land/rangeland, forest and shrubs, fruit trees, vineyards, built
up and water bodies and marshland. Rangeland is the most scattered land covering over 59% of thetotal area, irrigated agricultural land covers 24.5%, forest and shrubs covers 2.1% and built up areas
covers 3.2% of the Gardez basin as shown in Table 6.

Fig. 9 land use map of Gardez Basin (FAO, 2016)
Table 6 Dominate land use/land cover in different mean annual soil loss rate
No
1
2
3
4
5
6
7
8
9
10
11

LULC
Rangeland
Rangeland/barren land
Forest and shrubs
Built-up
Irrigated agriculture land
Water bodies and marshland
Fruit trees
Vineyards
Rainfed agriculture land
Barren land
Barren land/rangeland

Area (ha)
28529
3731
1045
1687
11806
427
273
4
409
2
191

Area (%)
59.31
7.76
2.17
3.51
24.54
0.89
0.57
0.01
0.85
0.00
0.40

Mean soil erosion (t ha-1 y-1)
7.18
11.26
2.21
2.67
3.48
2.77
2.28
2.86
6.39
11.54
74.81

In order to identify average soil erosion rates for different land use classes of Gardez Basin, land
use/land cover map of the study area was intersected with classified soil erosion map. From Table 6, it
is clear that high levels of soil erosion classes are found on the fallow land, barren land/rangeland,barren land, rangeland/barren land, rangeland and rainfed agricultural land. The annual average soil
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erosion is lower in the forest/shrubs and fruit trees. Moreover, slight, moderate, high, severe and very
severe soil loss area which was obtained based on land use map approximately, 30,933.96, 6,713.24,
9,503.04, 846.89 and 106.87 ha shown at Table 7, respectively. In addition, irrigated and rainfed
agricultural lands area is about 12,215 ha of the total area and also most parts soil losses occur in slightto high soil loss categories. Since most of the agricultural lands are slight to high soil loss classes,
immediate attention of soil conservation practices is required. To suggest site specific sustainable land
use practices for controlling slight to high soil erosion risks, this result allows assessment of soil loss
quantitatively, identify the risk zones and draw appropriate planning measure for implementing
optimal land use management practices.
Table 7 Risk categories of Gardez Basin area (ha) based on land use/land cover
Risk categories
No
1
2
3
4
5
6
7
8
9
10
11

LULC
Rangeland
Rangeland/barren
land
Forest and shrubs
Built-up
Irrigated
agriculture land
Water bodies andmarshland
Fruit trees
Vineyards
Rainfed agriculture
land
Barren land
Barrenland/rangeland
Total

Slight0–5
t ha-1 y-1
16804.82

Moderate
5 – 10t ha-1 y-1
3920.66

High
10 – 50t ha-1 y-1
7215.25

Severe50 – 100t ha-1 y-1
570.58

Very severe> 100t ha-1 y-1
17.73

28529.04

2089.36

511.15893.20210.8026.49

3731.00

909.15

46.29

89.35

0.16

0.00

1044.95

1365.96215.94102.062.36

0.67

1687.00

8813.18

1888.96

1074.35

27.15

2.36

11806.00

384.00

35.48

7.00

0.30

0.21

427.00259.00

12.99

0.79

0.22

0.00

273.00

3.80

0.20

0.00

0.00

0.00

4.00

252.60

74.07

77.10

5.24

0.00

409.00

0.00

0.00

2.00

0.00

0.00

2.00

52.09

7.49

41.94

30.08

59.40

191.0030933.96

6713.249503.04

846.89106.8748103.99

Total

CONCLUSION
The present study indicates that using GIS technologies for soil loss mapping, based on the USLE
model provided satisfactory results. Different components of USLE model were used withmathematical equations; the rainfall erosivity R-factor calculated, using USLE method (rainfall in mm,
intensity in mm hr-1, maximum 30 minutes intensity in mm hr-1) from daily automatic rain gauge is217.5 MJ mm ha-1 hr-1 y-1 and the R-factor calculated based on annual rainfall amount using various
equations, range between 157.58 to 199.72 MJ mm ha-1 hr-1 y-1. However, the best fit was achieved
between the R value from USLE method and annual rainfall for Gardez Basin. Soil erodibility factor(K) which was obtained from soil classification map range between 0.038 to 0.063 t ha hr ha-1 MJ-1
mm-1. Slope length and slope steepness factor (LS) values obtained from DEM between 0 to 175.64.
Crop management factor (C) values were obtained from land cover classification map range between 0 to 1.
The final map presents the annual soil loss, the values range from 0 to greater than 100 t ha-1 y-1, andclassified into five (5) classes; includes that about 64.31% of the study area is slight erosion risk (0 - 5 t
ha-1 y-1), 13.95% of the area is moderate soil erosion risk (5 - 10 t ha-1 y-1), 19.76% of the area is high soil
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erosion risk (10 – 50 t ha-1 y-1), 1.76% of the area is classified as severe soil erosion risk (50 – 100 t ha-1y-1) and 0.22% of the area is classified as very severe soil erosion risk (greater than 100 t ha-1 y-1).
Most of the agricultural lands are classified slight to high soil loss. However, high soil erosion isfound in the barren land, rangeland and rainfed agricultural land. The soil erosion risk is extremely
higher on the steep slope and mountains/foothills. The land use map of the study area was prepared andthe average annual soil loss for different land use will be highly useful in recognizing the priority areas
for application of land use practices and soil conservation measures in Gardez Basin. The rainfed andirrigated agricultural lands require immediate attention for soil conservation practices. Based on the
result of this study, the estimated soil loss and proposed land use map could be an effective input for
the future planning and implementing soil conservation strategy in the eastern part of Afghanistan.
ACKNOWLEDGEMENTS
The authors are profoundly grateful to Department of Agriculture Engineering, Tokyo University ofAgriculture and Japan International Cooperation Agency (JICA).
REFERENCES
Arnoldus, H.M.J. 1980. An approximation of the rainfall factor in the universal soil loss equation. In De Boodt M,
Gabriels D (eds) Assessment of Erosion. Wiley, Chichester, 127-132.
Cooper, K. 2011. Evaluation of the relationship between the RSULE R-factor and mean annual precipitation.
http://www.engr.colostate.edu/~pierre/ce_old/Projects/linkfiles.
Deumlich, D., Funk, R., Frielinghaus, M.O., Schmidt, W.A. and Nitzsche, O. 2006. Basics of effective erosion
control in German agriculture. Journal of Plant Nutrition and Soil Science, 169, 370-381.
Ehsan, M. 2015. Estimation of soil erosion potential in Herat Province, Afghanistan. Graduate School of Life andEnvironmental science, Shimane University, Japan.
Eltaif, N.I., Gharaibeh, M.A., Al-Zaitawi, F. and Alhamad, M.N. 2010. Approximation of rainfall erosivity
factors in north Jordan. Pedosphere, 20 (6), 711-717.
FAO. 1993. Land cover database of Afghanistan. www.fao.or/publications.
FAO. 2016. Land cover database of Afghanistan. www.fao.or/publications.
Foster, G.R., Lane, L.J., Nowlin, J.D., Laflen, J.M. and Young, R.A. 1981. Estimating erosion and sediment yieldon field sized areas. Transactions of the American Society of Agricultural Engineers, 24, 1253-1263.
Morgan, R.P.C. 1974. Estimating regional variations in soil erosion hazard in Peninsular Malaysia, Malayan
Nature Journal, 28, 94-106.
Morgan, R.P.C. 2005. Soil erosion and conservation, (3rd edition). National Soil Resource Institute Cranfield University.
Morgan, R.P.C., Hann, M.J., Shilston, D., Lee, E.M., Mirt Skhoulava, Ts.E., Nadirashvili, V., Topuria, L., Clarke,
J. and Sweeney, M. 2004. Use of terrain analysis as basis for erosion risk assessment, A case study frompipeline rights of way in Georgia.
Parveen, R. and Kumar, U. 2012. Integrated approach of universal soil loss equation (USLE) and geographicalinformation system (GIS) for soil loss risk assessment in Upper South Koel basin, Jharkhand. Journal of
Geographic Information, 4 (6), 588-596.
Renard, K., Foster, G., Weesies, G., McDool, D. and Yoder, D. 1997. Predicting soil erosion by water, A guide toconservation planning with the revised universal soil loss equation (RUSLE). Agriculture Handbook, 703, USDAARS, USA.
Renard, K.G. and Freimund, J.R. 1993. Using monthly precipitation data to estimate the R-factor in the revised
USLE. Journal of Hydrology, 157, 287-306.
Roose, E.J. 1975. Erosion et ruisellement en Afrique de l’ouest: vingt annies de measures enpetites parcelles
experimentales. Cyclo. ORSTOM, Adiopodoume, Ivory Coast.
Sahaar, A.S. 2013. Erosion mapping and sediment yield of Kabul River Basin, Afghanistan. M.Sc. thesispresented at the Department of Civil and Environmental Engineering. Colorado State University, USA.
Singh, G., Babu, R., Narain, P., Bhushan, L.S. and Abrol, I.P. 1981. Soil loss and prediction research in India.
Bulletin, No. T, 12/D9. Central Soil and Water Conservation Research Training Institute, Dehra Dun, India.
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US. Agriculture Development Team (US-ADT). 2011. Dawlatzai watershed report and project proposal.
USDA-SCS. 2001. Soil map and soil climate map. Soil Science Division, World soil resources. Washington D.C.
USA. http://soils.usda.gov/use/worldsoils/mapindex/afghanistan-soil.html.
Williams, J.R. 1975. Sediment yield prediction with universal equation using runoff energy factor. In Present andProspective Technology for Predicting Sediment Yield and Sources ARS, s-40, USDA, WA, USA.
Wischmeier, W.H. and Smith, D.D. 1978. Predicting rainfall erosion losses, A guide to conservation planning.
U.S. Department of Agriculture, Agriculture Handbook No. 537, USA.
Yu, B. and Rosewell, C.J. 1996. Technical notes, A robust estimator of the R-factor for the universal soil loss equation.
Transactions of American Society of Agricultural Engineers, 39 (2), 559-561.

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Research article
erd

Simplification of the Colorimetric Method to Detect Methanol
Contamination in the Cambodian Local Rice Liquor
MIZUKI MATSUKAWA*
Graduate School of Bioagricultural Science, Nagoya University, Nagoya, Japan
Email: matsukawa.mizuki@gmail.com

MITSURU HAMANO
International Center for Agricultural Education and Research, Faculty of Agriculture,
Shinshu University, Minami-minowa, JapanKASUMI ITOInternational Cooperation Center for Agricultural Education, Nagoya University, Nagoya , JapanTOSHIHARU TANAKAInternational Cooperation Center for Agricultural Education, Nagoya University, Nagoya , Japan
Received 16 January 2016

Accepted 26 June 2017(*Corresponding Author)

Abstract Methanol, an alcohol, is known to be hazardous for human consumption. Methanol
contamination in traditional rice liquor caused many deaths in local areas in Cambodia.
Contamination happened in every step of liquor manufacturing, distribution, and consumption.
To avoid this problem, monitoring the quality of alcohol is important. However, only a few
government institutes in the capital can detect the methanol contamination at an institutional
level by colorimetric methods. To detect methanol contamination easily at the local level, asimplified method is urgently required. We tested the original colorimetric methods todetermine the influence of the amount of chemical solutions, the time and the alcohol
percentage to the color change. Further we checked the shelf life of the chemical solutions. The
results showed that methanol was detectable at one-twenty of original volume after treatment 25 hours, and the alcohol percentage was not influence of the color changes. In addition, we
tested 21 liquor samples collected from markets in Phnom Penh and 6 provinces with thesimplified method, resulting that methanol was not detected in all samples.
Keywords local liquor, methanol, simplified methods

INTRODUCTION
Methanol, an alcohol, is known to be hazardous for human consumption. In the worst-case scenario,
symptoms of methanol poisoning caused by inhalation or intake are blindness and death (Bennet et al.
1953). Recently, newspapers have reported that the contamination of methanol in traditional rice liquor
caused many deaths in local areas of Cambodia (Gee & Martin 2012, The Cambodia Daily 2011).
People bought cheap industrial alcohol at a high concentration, and mixed it as a loading fluid in every
step of liquor manufacturing, distribution, and consumption.
Usually, when the equipment is available, the detection of methanol contamination is performed
by gas chromatography, which is a quick and accurate method. However, due to high cost and shortageof human resources, this might be difficult to perform in developing countries.
In Cambodia, the Industrial Laboratory Center of Cambodia and CAMCONTROL in Phnom Penh
have the resources to monitor the quality of alcohol at an institutional level using colorimetric methods.
However, government agencies for the control of the quality of alcohol have not been established inⒸ ISERD

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other provinces. Therefore, the establishment of a simple method to detect methanol contamination
based on the existing protocol is required promptly to decrease the number of fatal accidents occurringwith traditional rice liquor in Cambodia or other developing countries.
OBJECTIVESIn this study, we improved some aspects of the conventional colorimetric method for detecting
methanol by visual analysis. In order to simplify the colorimetric method, we aimed to modify thefollowing 4 aspects: (1) detection sensitivity depending on reduced amount of chemical solutions, (2)optimum time to check the solution color after the final treatment, (3) detection sensitivity dependingon the alcohol percentage, and (4) retention period of the chemical solutions. In addition, we conducted(5) methanol detection in rice liquors available in the local markets from 6 provinces.
METHODOLOGY
Original Method
We followed the colorimetric method reported by the Japan National Tax Agency in 1961(National
Tax Agency of Japan 2007). A brief description of the method is reported below.
Preparation of chemical solutions: Solution A (500 mL): 15 g of potassium permanganate (VII)(KMnO4) and 75 mL of phosphoric acid (H3PO4) were dissolved in distilled water and up to 500 mL,
Solution B (500 mL): 25 g of oxalic acid dihydrate [(COOH)2・2H2O] was dissolved in 500 mL 50%(v/v) sulfuric acid (H2SO4), Solution C (500 mL): 0.5 g of basic fuchsin was dissolved in about 300 mLof distilled water boiled at 100°C, and 5 g of sodium sulfite (Na2SO3) was dissolved in about 50 mL of
distilled water. Fuchsin solution was mixed with Na2SO3 solution and 5 mL of hydrochloric acid (HCl)was added. Then, the final solution was diluted to 500 mL with distilled water. Finally, 40 mL of 10N
H2SO4 was added, and the solution was kept at room temperature for more than 5 h. The color often
became light brown after adding HCl. Although it should be used after the color of fuchsin disappear,
it could be a pale yellow color solution. The all chemicals were purchased from Wako Pure Chemical
Industries.
Preparation of standard solutions: The standard solutions were prepared with 99.5% ethanol andmethanol (Wako Pure Chemical Industries, Ltd). Ethanol was adjusted to 40% (v/v) with distilledwater, and 0, 5, and 15µL methanol was added into 10 mL of 40% ethanol to make the 0, 0.05, and
0.15% (v/v) methanol artificial contaminated solutions since the Cambodian government set 0.15% is
the highest standard level of methanol contamination in the original liquor. Finally these mixtures were
8 times diluted to prepare the 5% alcohol percentage for testing.
Test procedures according to original method: The sample solution (5 mL) was mixed in a test tube
with 2 mL of solution A and left for 10 min. Then, 2 mL of solution B and 5 mL of solution C wereadded in this order and mixed well. After 30 min, the color of the solution was checked and theabsorbance was determined at 590 nm with a spectrophotometer (U-2000A, HITACHI) (Hayashibe1955) using low volume cell. Checking the absorption wavelength of standard solution determined thepeak at 580-600 nm, hence we adopted 590 nm as the wavelength for the measurement. The colors of
mixture turned from clear and colorless (0%), weak purple (0.15%) to purple (over 0.8%) according to
the trial experiment.
Simplification of the Original MethodⒸ ISERD

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The 40% ethanol with methanol 0, 0.05 and 0.15% (v/v) was diluted into 5% as the standard solutions.
All steps were conducted under the room temperature (about 22-25°C).
Dose reduction of the chemicals: Various total amounts of standard and chemical solutions (i.e., 1,
1/2, 1/5, 1/10 and 1/20 of the amount indicated in the original protocol) were used to detect themethanol contamination in standard solutions. The color of the solution was assessed by visual analysis
and spectrophotometer (590 nm) in following analyses.
Optimum time to check the color of the solution: The change of color was assessed 0.5, 1, 2, 3, 5,
and 24 h after adding the final solution C. The amounts of solution were as follows: standard 0.25 mL,
solution A 0.1 mL, solution B 0.1 mL, and solution C 0.25 mL.
Difference in the sensitivity depending on the percentage of alcohol in the sample solution: We
compared the sensitivity of the colorimetric method between ethanol standard solutions (5%) andoriginal solutions (40%) respectively. Further, rice liquor samples produced in Cambodia (Sraa Takeo
40% alcohol/volume, Royal University of Agriculture, Phnom Penh, Cambodia) were used; original
percentage of the liquors (40%) with 0, 0.05 and 0.15% methanol artificial contamination, and its 5%
diluted solutions. The amounts of solution were as follows: ethanol/liquor solutions (40% or 5%) 0.25
mL, solution A 0.1 mL, solution B 0.1 mL, solution C 0.25 mL. The color was checked 3 h after
treatment.
Expiration period of chemical solutions: Test solutions were kept for 2 and 4 weeks at room
temperature (about 22–25°C) and, then, were tested in comparison with fresh solutions. The
combination of the solutions is shown in Table 1; N: fresh solution and O: 2 or 4 weeks old solutions.
The amounts of the solutions were as follows: standard solutions 0.25 mL, solution A 0.1 mL, solution
B 0.1 mL, and solution C 0.25 mL. The color was checked 3 h after treatment.
Table 1 The combinations between fresh and old solutions
Test
solution
A
B
C

1
N
N
N

2
O
N
N

3
N
O
N

Combinations*
4
5
N
O
N
O
O
N

6
O
N
O

7
N
O
O

8
O
O
O

* N: fresh solutions, O: 2 or 4 weeks old solutions

Detection of methanol from the local rice liquors: Rice liquors collected from the local market in
Phnom Penh and 6 provinces (Battambong, Kompong Chhunang, Prey Veng, Pursat, Svay Rieng,
Takeo) were used as liquor samples; 3 samples/province and 21 samples in total. Rice liquors wereclear and colorless and the original liquors were diluted into 5% for testing. The amounts of thesolutions were as follows: liquor 0.25 mL, solution A 0.1 mL, solution B 0.1 mL, and solution C 0.25
mL. The color was checked 3 h after treatment.
Data analysis: The data was analyzed using with Excel statistical analysis add-in software (Excel
statistic 2012; Social Survey Research Information Co., Ltd., Tokyo, Japan).
RESULTS AND DISCUSSION
The methanol percentage in the all figures indicated the methanol percentage in the original
concentration (0, 0.05 and 0.15%), and the absorbance values of solutions (original/5% diluted) wereshown without change.
Scale-down of the amount of chemical solutions: The reduction of the waste chemical solutions isrequired because the disposal of the toxic waste in Cambodia is not yet well-implemented and heavy
metal such as KMnO4 pollutes the environment when discarded without treatment. We could
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and the absorbance among 0, 0.05, and 0. 15% methanol were significantly different in 1/20 of theamount in the original protocol (Fig. 1). It shows that methanol contaminations in the smallest amountof chemical solutions (ethanol standard: 0.25 mL, A: 0.1 mL, B: 0.1 mL and C: 0.25 mL) could be
detected as same as original amount.
cbaFig. 1 Scale-down of the amount of chemical solutions
Significant differences among each methanol concentration of 1/20 of the initial solution were
determined by one way-ANOVA. The same letters (a-c) mean no significant difference (p<0.01).

Optimum time for detecting the color of the solution: The optimum time for comparing the color byvisual judgment was determined. 0.15% methanol, the highest allowed level of methanol
contamination decided by the Cambodian government, was visually detected 0.5 h after adding thefinal solution. Two hours later, the differences of the color between 0% and 0.15% methanol could beobserved easier, while no difference could be detected visually between 0% and 0.05% samples.
However, after 24 h, although the difference of absorbance was detected by spectrophotometer, nodiscernible difference could be observed. Therefore, we can check the color of the solutions 2–5 h after
treatment (Fig. 2).

Fig. 2 Optimum time for detecting the color of the solution
Detection sensitivity for methanol contamination depending on the alcohol percentage: The
detection sensitivity of the 40% ethanol/rice liquor were compared with 5% diluted ethanol/ rice liquor
to know whether methanol contamination can be detected in the original percentage. We could notrecognize visually the difference between 0 and 0.05% methanol in both samples, however between
0% and 0.15%, it was detectable the difference by naked eyes though the absorbance was lower inoriginal percentage than in 5% diluted one of both ethanol and liquor solutions (Fig. 3). To detect theaccurate amount of methanol in alcoholic beverage, though gas chromatography is so convenienceⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2017) 8-1(Wang et al. 2004), the method shown in this study makes us possible to quick detection and visual
discrimination if concentration of methanol included in the beverage is higher than specified safetylevel without using special equipments.

Fig. 3 Difference of detection sensitivity for methanol contamination
depending on the alcohol percentage
Concentration-dependent change of solution color of (a) ethanol standard (5%) and (b) rice liquor (40%) with 0, 0.05, and
0.15% methanol contaminations from left to right, (c) concentration-dependent change of absorbance among 0, 0.05, and
0.15% methanol contaminations in 5% and 40% ethanol/rice liquor. The same letters (a-c) mean no significant difference(p<0.01) of the 40% rice liquor among each methanol concentrations as determined by one-way ANOVA.

Expiration period of the chemical solutions: To save time for the preparation of the chemicals, theretention period of chemical solutions was determined. The combination of fresh chemicals with oldsolutions was tested at 2 and 4 weeks after preparation. The detection sensitivity of the chemicalsolutions was not changed after 2 weeks. However, after 4 weeks, a difference could be observed andthe detection sensitivity became lower in conditions 2, 4, 5, 6, 7, and 8, which include old A and C
solutions. On the other hand, in condition 3, which includes old B solution, the color was the same asthat of the normal fresh condition (Table 1, Fig. 4).

Fig. 4 Expiration period of each chemical solution affecting detection sensitivity
Dot, diagonal line, and black bars represent the absorbance of the 5% ethanol solution containing 0, 0.05, and 0.15%
methanol, respectively. Conditions 1-8 show different combinations with fresh and old chemicals of A, B, and C (Table 1).
Significant differences among conditions in (a) fresh and 2-week-old solutions, (b) fresh and 4-week-old solutions were
calculated with one-way ANOVA respectively. The same letters mean no significant difference (p<0.05): 0.05% (a-e) and
0.15% methanol (s-v) among condition 1-8.

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Detection of methanol from the local rice liquors: The 21 local rice liquor samples were tested by
the simplified method. No methanol contamination was detected from those samples.
Table 2 Detection of methanol contamination from local rice liquors
Province
Battambong
PursatKompong Chhunang
Phnom Penh
Prey Veng
Svay Rieng
Takeo
Total

Number ofsample
3
3
3
3
3
3
3
21

Methanolcontamination
Not detected
Not detected
Not detected
Not detected
Not detected
Not detected
Not detected

CONCLUSION
Based on our observations, we conclude that: (1) the amount of solutions can be reduced to a total
volume of 0.7 mL (standard 0.25 mL, solution A 0.1 mL, solution B 0.1 mL, and solution C 0.25 mL);(2) the difference of color can be detected easily 2-5 h after the final treatment; (3) Moreover, themethanol contamination was detected without dilutions, and all the degrees of methanol contamination
could be identified by comparison with 0% methanol standard solution though the detection ability was
higher in 5% diluted solutions; (4) The degradation of the solutions increased with time, especially forsolution C; therefore, the results indicated that solutions A and C could be used for 2 weeks, andsolution B for 4 weeks. It is preferable to prepare fresh solutions within 2 weeks considering that thevolatile substances in the solutions are hazardous; (5) Furthermore, this method can be used for rice
liquor from local markets. Our results suggested that the simplified methods could identify themethanol contamination over the highest standard level by visual though it couldn’t be measured
accurate percentage of methanol contamination. Once the contamination is detected, the rice liquor isrequired to be analyzed in detail by laboratory experiments.
ACKNOWLEDGEMENTS
We thank the staff of “JICA Grass-Root Project on Development of Agro-processing Technology
through Rehabilitation of Traditional Industry in Cambodia”.
REFERENCES
Bennett, I.L. et al. 1953 Acute methyl alcohol poisoning, A review based on experiences in an outbreak of
323cases. Medicine, 32, 431-63.
Gee, P. and Martin, E. 2012. Toxic cocktail, Methanol poisoning in a tourist to Indonesia. Emergency Medicine
Australasia, 24, 451-453.
Hayashibe, R.1955. Quantitative determination of methylalcohol (First report). Study of condition of photometric analysis
according US methods. Nihon jozo kyokai (Journal of the brewing Society of Japan), 50, 59-61 (in Japanese).
Wang, M.L. et al. 2004. A rapid and accurate method for determination of methanol in alcoholic beverage bydirect injection capillary gas chromatography. Journal of Food Composition and Analysis, 17, 187-196.
National Tax Agency of Japan. 2007. Kokuzeicho shotei bunsekiho (National Tax Administration Agency
prescribed analytical methods), Tokyo, Japan, 36 (in Japanese).
The Cambodia Daily. http://www.cambodiadaily.com/archive/rice-wine-poisoning-leaves-a-village-reeling-2885/Ⓒ ISERD

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Research article
erd

Pre and Postharvest Losses and Marketing of Grapes in
Afghanistan: Case Study in Mirbachakot, Shakardara andKalakan Districts of Kabul
YUSUFI MOHAMMAD MIRWAIS*
Graduate School of Agriculture, Tokyo University of Agriculture, Japan
Email: mirwaisyusufi@gmail.com

RYUICHI YAMADA
Faculty of International Agriculture and Food Studies, Tokyo University of Agriculture, Japan
Received 22 December 2016

Accepted 26 June 2017(*Corresponding Author)

Abstract Grape is one of the most commonly produced and globally well-known fruit crops.
And also grape is the major fruit species grown in Afghanistan that accounts 48% of the total
fruit growing area. Reduction of post-harvest losses is the indispensable challenge in the
country, hence to increase the availability of fruits and vegetables. However, the purpose of thisstudy is to determine the pre and post-harvest losses of grape at stages of marketing anddistribution. A questionnaire survey was conducted in Mirbachakot, Shakardara and Kalakan
districts of Kabul province, Afghanistan. A total 60 farmers, including contractors, wholesalers
and retailers were randomly selected and interviewed across in the study areas, using structured
and semi-structured questionnaire sheet during the months of August-September in 2016. Basedon the result of questionnaire, pre-harvest loss of grape from large, medium and small by 14%,
13 and 12%, respectively. Similarly, total post-harvest losses of grapes; Contractor loss, 9.0%,
Wholesaler loss, 10.3% and Retailer loss 12.0%. In addition, there are many other causes suchas, insect attack, diseases, dropping, mummification, water berries, improper packing andtransportation. All these factors contribute and significantly declining the grape production. It
can be suggested that grape growers would be trained on the subject of pre and post-harvest
management and marketing practices.
Keywords pre and post-harvest loss, marketing, grape, Afghanistan

INTRODUCTION
It is acknowledged that grape is one of the most important fruit crops of the world and it contains manyof the most valuable elements necessary for life. In Afghanistan 48% of the total fruit growing area isunder grape vine equivalent to 82,450 hectares with estimated 874,500 tons production (CSO,
2016).The outputs of all agricultural commodities produced in the field have to undergo a series ofoperations such as harvesting, packing, transportation, processing, storage and exchange before theyreach the consumer, and there are substantial losses occur during pre-harvest and post-harvest stages(Kader et al., 2004). The information on the extent of losses at these stages are important not only for
agricultural scientists, but it would be useful for the policy makers as well. The agricultural scientist
and technologist will be guided by these findings in improvements in the crops production and postharvest technologies aimed at minimizing these losses. The sum quantity of outputs loss in these
operations at all of these stages is referred to as post-harvest losses (FAO, 1980). One of the main
reasons attributed to lower availability food is plenty of post-harvest losses that occur at various stagesof marketing which ranges from 15% to 50% (FAO, 1981 and Roy, 1989).
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Pre-harvest factors greatly influence the crop conditions on the stage of harvest, storage, andnutritive potential. There are many pre-harvest factors have known that affect storage quality, including
genotype, cultivar selection, and stage of maturity at harvest has a very important influence on
subsequent storage life, soil texture and fertility, fertilizer application, climate conditions such astemperature, light intensity, and rainfall amount (Watada et al., 1984).
Evaluating pre-harvest conditions indicated the most influence on the postharvest fruit quality (Delong et al., 2003; Stanle et al., 1999; Robert, et al., 1996). The importance of post-harvest losses has
been discussed at several national and international levels. Many studies and researches have been
conducted in developed and developing countries for estimating the post-harvest losses, but theimportance of post-harvest losses in agricultural commodities have not fully recognized yet. However,studied by USAID, 2012 and ROP, 2009 the agricultural production in Afghanistan has not fully linked
to marketing. The number of scientists involved in production research in these countries issignificantly higher than those concerned with post-harvest losses in agricultural commodities.
Unfortunately, much time and money are being spent to cultivate crops, irrigation, fertilization andprotection. Nevertheless, has received little attention and resources devoted to the issues related to preand post-harvest losses resulting in failure, to meet food requirement, thereby millions of people now
suffer from hunger throughout the nation (FAO, 1980 and FAO, 2011).
In developing countries, it is not only the problem of production, but it is equally important tosave whatever is produced. Therefore, knowledge regarding to the magnitude of losses at various
stages of handling and storage is considered to be very important to introduce necessary improvement
for saving and preserving production (FAO, 1980). The purpose of this study is to determine the preand post-harvest losses of grape at various stages and marketing practices in the Mirbachakot,
Shakardara and Kalakan districts of Kabul province, Afghanistan.
METHODOLOGY

Fig. 1 Map of the study area
In order to identify the pre and post-harvest losses and marketing of grape a survey was conducted in
Mirbachakot, Kalakan and Shakardara districts of Kabul province, Afghanistan as shown in Fig.1. A
total 60 farmers were randomly selected across the entire study area and interviewed using structured
and semi-structured questionnaire in the survey. Farmers were categorized into six groups; large,
medium, small grape growers, pre-harvest contractor, wholesaler and retailer based on their trade andcharacteristics (production, farm size of vineyard). In addition, two kinds of data were collected as
primary and secondary during the grape harvesting and marketing season. Primary data included
information, face to face interview with grape growers, local authorities of villages and NGO.
Secondary data were assigned from scientific papers, journals, and books. Data were calculated forⒸ ISERD

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average and percentages of pre and post-harvest losses and marketing channels. Likewise, primary data
were recorded for various parameters were subjected to statistical analysis, Critical Difference (CD) atthe 5% level of probability and used Statistic Package for Social Science (SPSS) software.
RESULTS AND DISCUSSION
Pre-harvest Losses
The pre-harvest losses defines from production to the farm gate, which includes growing, harvestingand on-farm handling (COMCEC, 2016). Although, estimates of losses in developing countries are
hard to judge, evaluating pre-harvest conditions exert the most influence on post-harvest quality.
However, based on the survey conducted, the pre-harvest losses of fresh grapes are due to several
common factors like insect attack, diseases, birds, droppings, water berries, short berries,
mummification, poor water management and drought, and cultural practices (pruning, fertilizing, andpesticide spraying). Insect attack and diseases were contributed the maximum loss, 3.2% and 3% inlarge and medium farmers respectively. Similarly, in the small grape farmers maximum loss was
reduced by 2.7%. The total pre-harvest of grape loss was 14% from large farmers, 13% from medium
farmers and 12% from small farmers. The result showed that the severe pre-harvest loss was by insect
attack, diseases and dropping among three categories of farmers as shown in Table 1.
Reported by COMCCE, 2016 loss of the agricultural production ranges 23% to 39% in Africa,
Asia and the Middle East, high percentage of loss is recorded for fruits and vegetables. In Developing
countries the agriculture production losses and waste are mostly, financial, managerial and technicallimitations, but developed countries, mainly related to consumer attitude (FAO, 2011). Thus preharvest losses could be significant impacts on production and post-harvest process leading to lost
revenue, lower yields and waste of resources.
Table 1 Pre-harvest losses of grapes at large, medium and small level of farmers
Large farmers
Stages of losses
Quantity harvested
Dropping
Water berries
Shot berries
Mummification
Insect and diseases
Total
Quantity Remaining(USD 1 = 66.5 Afghani)

Qty
(kg)
1,000
28
26
24
26
32
136
864

Packing

Loss%
100
2.8
2.6
2.4
2.6
3.2
14
86

Value
(AFN)
20,000
560
520
480
520
640
2,720
17,280Medium farmers
Qty
Value
Loss%
(kg)
(AFN)
1,000
100
20,000
27
2.7
540
25
2.5
500
23
2.3
460
24
2.4
480
30
3.0
600
129
13
2,580
871
87
17,420Transportation

Small farmers
Qty
(kg)
1000
27
22
24
22
26
121
879

Loss%
100
2.7
2.2
2.4
2.2
2.6
12
88

Wholesaler

Fig. 2 Packing, transportation and marketing grapes system in AfghanistanⒸ ISERD

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Value
(AFN)
20000
540
440
480
440
520
2,420
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Most of the fruit losses occur between leaving the farm and reaching the consumer. The grapes
normally harvests in the farm. After that removing the damaged bunches and berries, packed in plasticbags or cartons, then transporting to market by vehicles as shown in Fig. 2. It was observed that poor
packing and faulty transporting were the main factors for loss of agricultural production, particularly
fruits in the study site.
Postharvest Losses
Over the past decades, 95% of the research investments were reported to have focused on increasing
productivity and only 5% directed towards reducing losses (Kader, 2005; Kader and Roller, 2004;
WFLO, 2010). Food production is currently being challenged by limited land, water and increased
weather variability due to climate change. To sustainably achieve the goals of food security, foodavailability needs to be also increased through reductions in the post-harvest process at farm, retail andconsumer levels. Based on Table 2, the loss at contractor, wholesaler and retailer level were 9%, 10.3%
and 12% respectively, due to handling and packing, loading and unloading, poor transportation, andfaulty storage. The maximum loss during the transportation and storage were 2.3% and 2%,
respectively.
Other researchers found that the high losses occur during on-farm and postharvest stages indeveloping countries (COMCEC, 2016). In Pakistan about 21% the grapes grown (9.8 thousand tons
amounting $3.25 million) lost and wasted, due to gaps in the cold chain, unavailability of cold storage
and poor transportation (Khalid et al., 2011). On the other hand field management practices; genotype,cultivar selection, soil texture and fertility, fertilizer application, pruning, irrigation, pest control, andstage of maturity play a very important role in determining quality attributes size, color, flavor, texture,and nutritional values. Understanding the effect of pre-harvest factors would contribute minimize
losses and maintain the quality of the grape.
Table 2 Post-harvest losses of grapes at different level
Stages of losses
Quantity purchased
Transportation
Handling and packing
Loading and unloading
Storage
OthersTotal losses
Quantity Remaining

Contractor loss (%)
100
2.3
1.9
1.8
2.0
1.0
9.0
91.5

Wholesaler loss (%)
100
2.5
1.5
2.3
3.0
1.0
10.3
89.7

Retailer loss (%)
100
2.5
2.3
2.2
3.0
2.0
12.0
88.0

It observed from Table 1, that there was negligible different grape loss between large, medium
and small farmers, But at the field level, the dropping, insect attack and diseases accounted for thehighest percentage of loss. Based on Table 2, the total postharvest loss in grapes in pre-harvest
contractor was found to be 9% .The wholesaler level loss was found to be 10.3% and loss at the retailer
level was slightly higher at 12%. At the pre-harvest, wholesaler and retailer level, the storage andtransportation were found to contribute more toward the loss. Due to, insufficient storage, poor roads
and no specialized transportation vehicles exclusively for fruits were used in the study site.
According to the Fig. 3 post-harvest losses from point of harvesting to consumption worked out tobe 43.3% comprising of 13% at the farm, 9% in pre-harvest contract, 10.3% in wholesaler, and 12% at
the retailer level respectively. Based on table 4, findings indicated, that the maximum grape loss
occurred was due to insect attack, diseases and dropping at farm level.

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100%

13

9

10.3

12

87

91

87

88

Farm level

pre-harvest contract
level

Wholesaler level

Retailer level

50%

44.3
55.7

0%
Quantity of Grapes

Total

Quantity Losses

Total

Fig. 3 Pre and post-harvest losses percentage of grapes in different stages
Marketing Channels
The marketing of grapes begins when the produce leaves the farm and ends when it reaches to the final
consumers. It is rather a series of important business activities that transform a farm producer`s productinto a number of finished products desired by the consumer. The results indicated that 60% of thegrape growers sell their yield at a lower price to channel 1, due to non-availability of cold storage,
followed by 25% farmers market through direct sale of the products to the channel 2, because direct
sales benefit the producers more than contract sales. While remaining 15% farmers’ sale their productto commission agents. It is found that grape growers in the study area follow several marketing
channels.
Channel 1

Producer

Pre-harvest contractor

Channel 2

Producer

Wholesaler

Channel 3

ProducerCommission agent

Wholesaler
Wholesaler

Retailer

Consumer

Retailer

Consumer

Retailer

ConsumerTable 3 Coefficient of Variation = 5.16
Degrees of
freedom24814Source of variation
Replications
Treatments
Error
Total

Sum ofsquares
22.53
74.26
14.13
-

Mean sum ofsquares
11.26
18.56
1.766
-

F calculated

F probability

6.373
10.50
-

0.020
0.008
-

F calculated

F probability

Table 4 Statistical analysis (ANOVA)
Source of variation

Degrees of freedom

Sum of squares

Replications
Treatments
Error
Total

2
4
8
14

41.2
495.0
74.13
-

Treatment No.
Treatment Average
Critical Difference (CD) Compared

T4
26.6
a

T1
24.3
ab

T3
21.0
ab

Mean sum ofsquares
20.6
123.7
9.2
-

T2
19.0
b

T5
10.0
c

2.22
13.351P5 P129.3 27.3
a
a

P2
24.3
b

0.177
0.002
P4
P3
24.0 23.6bb(T1-T5 show critical difference at large, medium, small farmers and P1-P5 critical difference at pre-harvest
contract, wholesaler and retailer level). Significant difference at p<0.05Ⓒ ISERD

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CONCLUSION
Despite the global progress in the food loss reduction there remain many gaps in the knowledge
regarding the quantity of losses at pre and postharvest stages and marketing practices are consideredvery useful to introduce necessary improvement to enhance quality and quantity of production. Pre andpostharvest losses are a complex problem in Afghanistan, it is one of the big challenges for the farmers
particularly for fruit growers. Due to improper management and poor knowledge of growers about
30% to 40% losses of agriculture production. However, grape is the most important fruit in
Afghanistan, it covers 48% of total fruit growing areas and annual production is about 874,500 tons.
The questionnaire survey was conducted in Mirbachakot, Shakardara and Kalakan districts of Kabul
province. To determine the pre and post-harvest losses of grape at stages of marketing and distribution.
A total of 60 (farmers, contractors, wholesalers and retailers) was randomly selected across the entire
study area and interviewed in the survey. Based on the result of questionnaire, pre-harvest loss of grape
from large, medium and small by 14%, 13% and 12%, respectively. Similarly, total post-harvest lossesof grapes; Contractor loss, 9.0%, Wholesaler loss, 10.3% and Retailer loss 12.0%. In addition, there aremany other causes identified such as, insect attack, diseases, dropping, mummification, water berries,
improper packing and transportation. Meanwhile, the majority of the farmers live on the margin offood insecurity, so reduction grape losses could have an immediate and significant impact on theirsustainable livelihoods. It can be suggested that grape growers would be trained on the subject of preand post-harvest management and marketing practices. Thereby, using better agricultural practices andadequate technologies can significantly reduce the losses and help in strengthening food security andpoverty.
ACKNOWLEDGEMENTS
The authors are grateful for financially supported by Tokyo University of Agriculture (TUA) and theJapan International Cooperation Agency (JICA). Thanks my friends for comments that greatly
improved the research paper.
REFERENCES
Bayani, M.A. 2011. Pre and post-harvest factors influencing quality of table grapes in Bagram district, Parwan,
Afghanistan. University of Applied Science Wegeningen, Netherlands.
COMCEC Cooperation Office. 2016. Reducing On-Farm Food Losses in the OIC Member Countries.
CSO. 2016. Central Statistical Organization Afghanistan.
De Long, J. and Prange, R. 2003. Storage Thomas B, Murray BG Encyclopedia of applied plant science.
FAO. 1980. Assessment and collection data on post-harvest food grain Losses, Economic and Social
Development Paper 13, Rome, Italy. http://www.fao.org/fileadmin/templates/ess/ess_test_folder/
FAO. 1981. Food loss prevention in perishable crops. Agricultural service Bulletin, 43, Rome.
FAO. 2011. Global food losses and food waste , Extent, causes and prevention. Rome
FAO. 2011. Global food losses and food waste. Indian J. Agric. Econ. 59 (4), 772-786. http://www.fao.org/
docrep/014/mb060e/mb060e00.pdf., http://eafghanag.ucdavis.edu/other-topic/postharvest.
Kader, A.A. 2005. Increasing food availability by reducing postharvest losses of fresh produce. Acta Horticulture,682, 2169-2176.
Kader, A.A. and Rolle, R.S. 2004. The role of post-harvest management in assuring the quality and safety
horticultural crops. Food and Agriculture Organization. Agricultural Services Bulletin, 152, 52.
Khalid, M.A., Nisar, A., Muhammad, I. and Arshad, F. 2011. Post-harvest losses and marketing of grapes inPakistan. Technolochgy Transfer Institute, Taarnab Peshawer- Pakistan Sarhad, J. Agric. 27.
Louw, T. 2010. Afghan grape post-harvest handling manual.
Murthy, D.S., Gajanana, T.M. and Sudha, M. 2004. Post-harvest loss and its impact on marketing cost, marginand efficiency.
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Robert, K. Prange and Jennifer, R. 1996. Pre-harvesting factors affecting postharvest quality of berry crops.
ROP. 2009. Grape value chain operations manual case study, improving the Mirbachakot grape value chain.
Roy, S.K. 1989. Role of post-harvest technology horticultural crops in India - trends of food science andTechnology. Proceeding of the second international food convention.
Sajad, A.M. and Abid, H. 2015. Post-harvest deterioration of tropical root crops, Losses and their control.
International Journal of Science and Research (IJSR), https://www.ijsr.net.
Stanle, J. and Kays. 1999. Pre-harvest factors affecting the appearance. Postharvest Biology and Technology, The
University of Georgia, USA.
USAID. 2012. Commercial horticulture and agricultural marketing program (CHAMP), Final Report, USA.
Watada, A.E., Herner, R.C. and Kader, A.A. 1984. Terminoloav for the description of developmental stages forhorticultural crops, HorScience, 19.

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Research article
erd

Determining Crop Insurance Participation in Laguna,Philippines Using Subsidy and Asymmetric Information
Incentives
DIANE CARMELIZA N. CUARESMA*
University of the Philippines Los Banos, Philippines
Email: dncuaresma@up.edu.ph

JUSTINE ANGELI L. SAQUING
University of the Philippines Los Banos, Philippines

VINCENT PAULO M. TY
University of the Philippines Los Banos, Philippines
Received 15 December 2016

Accepted 27 June 2017(*Corresponding Author)

Abstract The Philippines is susceptible to tropical cyclones which threaten the production ofagricultural crops. To help farmers cover their losses, financial protection through crop
insurance can be employed. This paper studied the effects of the Philippine multi-peril crop
insurance on mean returns per acre. Data on palay (unhusked rice) yields in the province of
Laguna were gathered from 1978 to 2015. Using classical decomposition, the time series data
was decomposed into trend-cycle, seasonality and irregularity components to compute for the
Actual Production History (APH) yield and to forecast the APH yield for 2016. Using the crop
insurance incentives model of Just et. al., the data were used to explore the subsidy andasymmetric information incentives for insurance participation for both wet and dry seasons.
Three risk classes were considered: low, medium and high, while four insurance guarantee
levels were considered: 100%, 90%, 10% and 0%. It was found that as yield guarantee
increases, the effect of insurance on mean returns also increases. Additionally, as insurance
premium increases, the effect of insurance on mean returns per acre decreases. Results also
indicate that effects of crop insurance on mean returns per acre are highest when risk class is
low and yield guarantee is high.
Keywords crop insurance, subsidy incentive, asymmetric information incentive

INTRODUCTION
The Philippine government has been helping farmers, who are amongst the poorest citizen in the
country, cover their production losses by offering financial protection through crop insurance. These
insurance products pay the farmer the cost of production input when a partial or total loss is
experienced. A loss is considered partial if the loss is between 10% and 90%; otherwise, if the loss isabove 90%, it is considered a total loss. While some insurance products from the government are free,
farmers are required to pay a premium for these insurance products. The insurance premium varies
according to the season and risk classification. There are two main planting seasons in the country, thewet and the dry season. While there are three risk classes: low-risk, medium-risk or high-risk, which isclassified according to the riskiness of planting rice in that area. The farmer can choose the amount ofcover or guarantee level.
The government heavily subsidizes the premium to help the farmers. This is a great aid to many
low-income farmers, who still finds it difficult to purchase the insurance despite the subsidy. However,Ⓒ ISERD

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this may also attract opportunistic agricultural producers who use information advantages to obtain
more profits. Though extensive studies are conducted by the insurance providers, farmers are often
more knowledgeable about the risk in planting. Some studies conducted by Just, Calvin and Quiggin(1999), Walters, Shumway, Chouinard and Wandschneider (2014), and Wang, Hanson and Black(2003) suggest the presence of adverse selection and moral hazard in insurance. Profit-maximizing inadverse selection occurs when there is information asymmetry between two parties before theagreement, whereas profit-maximizing in moral hazard occurs when there is a change in behavior after
the agreement (Nickolas, 2016). If premiums paid are not able to cover the indemnities, this may
render the insurance product unsustainable.
Many studies identifying factors affecting farmer participation in crop insurance have been
conducted. According to some literatures, a set of factors that impact farmer participation decisions
include farmer-specific attributes, region-specific attributes and economic factors. Farmer-specificattributes include age, experience, education and income. Region-specific attributes involve farm size,
location and soil quality. Economic factors consist of the insurance premiums and indemnity payments.
Sai, Yulian and Xiaofeng (2010) found that in Chinese household, agricultural land, farmer’s
education, production capacity and transaction costs are the significant factors for families in rural
areas to enroll in crop insurance programs. A study performed in the United States verified the ideathat decreasing transaction cost improves crop insurance participation. According to Ker and Ergun(2003), when transaction cost is reduced through efficient delivery channels, greater insurance
participation can be elicited. A research by Cabas, Leiva and Weersink (2008) found that that farmer
participation in insurance is largely determined by price variables.
In this study, it will be determined if adverse selection is present in the crop insurance program in
the Philippines to aid the insurers in safeguarding the sustainability of their insurance products. This
study can also be useful for farmers who are on a tight budget and are picking only the likely situation
where insurance can help them in safeguarding their income.
OBJECTIVE
The primary aim of this study is to estimate the effects of insurance, particularly the multi-peril rice
insurance, on mean returns (bushels per acre) to the farmer and to look into the influence of subsidy
and asymmetric information in the decision of farmers to participate in crop insurance programs.
METHODOLOGY
In this study, an analysis is made by applying the crop insurance incentives model built by Just, Calvin
and Quiggin (1999). Data are collected from two sources: Regional Office of the Department ofAgriculture (DA), and the Philippine Statistics Authority. The data on rice production are collectedfrom the Regional Office of the Department of Agriculture (DA) in Calauan, Laguna. These included
harvest area, average yield (in metric tons per hectare) and yield production (in metric tons) in 2014
and 2015 during wet and dry seasons. There data collected from the Philippine Statistics Authority Bureau of Agricultural Statistics are downloaded from the website. These data are statistics on thequarterly area and volume of palay, and monthly farm-gate prices of palay in Laguna from the years
2006 to 2015.
The analysis uses data on rice production of Filipino farmers in Laguna to identify crop insurance
effects on mean returns per acre. The insurance effects on mean returns are then decomposed into
asymmetric information and subsidy incentives. Subsequently, the crop insurance effects are forecasted
for the following year using forecasted values of the Actual Production History (APH) yield and using
forecasted values of the palay price. Forecasting of APH yield and palay price for 2016 is employed toⒸ ISERD

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predict insurance effects on mean returns per acre and to predict the changes in the subsidy and theasymmetric information effects for 2016.
RESULTS AND DISCUSSION
Decomposition of Data Collected
Upon applying classical decomposition, the data collected on production shows that palay productionin the Philippines has an increasing trend (Fig. 1), except for the drop experienced in the first quarter of2010, which can be attributed to the palay damaged due to spread of tungro, rice blasts, black bug, as
well as the onset of El Niño. Another observation from the seasonality component (Fig. 2) of the datais that yields are highest during the wet season and lowest during the dry season. This observation isnecessary in determining the periods when insurance participation are most effective.
Computing the effect of insurance on mean returns per acre shows that as the premium increases,
the effect of insurance to the farmer’s mean return decreases. However, the effect of insurance
increases as the loss increases. This implies that adverse selection can happen for farms in high-riskarea. These results are summarized Table 1, Figs. 3 and 4. Figure 3 shows that as insurance premiumincreases, the effect of insurance on mean return decreases. Figure 4 shows that the effect of insurance
on mean returns increase for higher than 70% of loss.

Fig. 1 Trend-cycle component of Palay yield on a quarterly basis from 2006 to 2015

Fig. 2 Seasonality component of Palay yield on a quarterly basis from 2006 to 2015
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Fig. 3 Relationship between insurance premium and effect of insurance on mean returns
per acre during the wet season when there is total loss
Table 1 Effect of insurance on mean returns per acre
MultiRisk
Cover

Total Loss
PartialLoss
No LossLow
390.02 to790.74-14.25 to
390.02-14.31 to-14.25

Effect of Insurance on Mean Returns per Acre
Wet Season
Dry Season
MediumHigh
Low
Medium387.43 to384.83 to-10.40 to-12.73 to788.15785.55-2.55-4.88-16.84 to-19.44 to-12.87 to-15.20 to
387.43384.83-10.40-12.73-16.90 to-19.50 to
12.87-15.20-16.84-19.44High-15.06 to-7.21-17.53 to-15.06-17.53Fig. 4 Relationship between amount of loss and effect of insurance on mean returns
per acre during wet season (original and forecasted price)Ⓒ ISERD

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Analysis of the Subsidy and Asymmetric Information Incentives
This effect is decomposed into subsidy and asymmetric information incentives. Subsidy incentive
refers to the financial aid given by the government to increase farmer participation in crop insurance
programs, whereas the asymmetric information incentive is defined to be the incentive associated with
the inaccuracies in insurance underwriting because of incomplete information for the side of theinsurer.
Table 2 Subsidy effect of insurance
Yield
Guarantee> 90%
10% to 90%< 10%

Subsidy Effect of InsuranceLow-13.55 to585.63-14.33 to-13.55-14.33

Wet Season
Medium-16.14 to583.04-16.92 to-16.14-16.92

High-18.74 to580.44-19.52 to-18.74-19.52Low-12.09 to587.09-12.87 to-12.09-12.87

Dry Season
Medium
-14.42 to584.76-15.20 to-14.42-15.20High-16.75 to582.43-17.53 to-16.75-17.53The subsidy effect of insurance on mean net income of farmers is presented in Table 2. Results
show that for both cropping seasons, the subsidy effect on mean net income is very high in low-riskarea and at > 90% loss, while the least effect appears in high-risk area and at < 10% loss. This suggeststhat the largest increase of mean net income due to subsidy occurs when a farmer from a low-risk area
experiences a total loss. This implies that a farmer will benefit from the insurance if he is from a lowrisk area and he sees that for that cropping season losses are likely to occur. However, it is also seenthat adverse selection is greater during the dry season than during the wet season. As for the insurer,
this means that he should be more careful in underwriting the insurance product during the dry season.
By comparing subsidy effects across yield guarantees for both wet and dry seasons, high positive
mean subsidy effects of crop insurance are perceived for some yield guarantees between 90% to 100%.
This means that for these yield guarantees, the subsidy greatly increases mean net income when
agricultural loss is at least 90%. It can also be perceived that low negative mean subsidy effects are atyield guarantees from 0% to 90%. This means that insurance subsidy causes a slight decrease on mean
net income for yield guarantees 0% to 90%.
By comparing the values obtained by cropping season, it can be identified that the values for thedry season are greater than that of the wet season. Higher mean net income effects are expected during
the dry season because there are less storms and typhoons that hinder the growth of palay. It is seenthat adverse selection is greater during the dry season than during the wet season.
Another significant observation is that when the yield guarantee is low, the subsidy effect of
insurance on mean returns per acre is also low. This implies that there is a positive association between
these two. It is determined that as the premium increases, the effect of insurance on mean returns peracre decreases linearly.
As for asymmetric information effects (Table 3), calculations show that for dry season the meaneffect on income is negative when farmer, in any area, chooses the highest guarantee level. This meansthat farmers cannot exploit the insurer for insufficient information, or that adverse selection is greater
during the wet season than during the dry season. This implies that farmers are less likely to getinsurance during dry season, and are less likely to choose maximum coverage during this season.
By comparing asymmetric information effects across yield guarantees for both cropping seasons,it can be observed that the mean net income effect is very high when yield guarantee is 90%, while it isvery low when guarantee is 10%. When yield guarantee is 100%, negative asymmetric information
effects are also very high. However, for other yield guarantees 10% and 90%, positive asymmetricⒸ ISERD

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information effects are very low. It can be deduced here that for dry season, farmer cannot fully exploit
the insurers for insufficient information.
Table 3 Asymmetric information effect of insurance
Yield
Guarantee
100%
90%
10%

Low
205. 11403.57
0.07

Asymmetric Information Effect of Insurance
Wet Season
Dry Season
MediumHigh
Low
Medium205.11205.11-589.64-589.64403.57403.57
1.70
1.70
0.07
0.07
0.00
0.00

High
-589.641.70
0.00

By comparing the asymmetric information across cropping seasons, it can be identified that thevalues for the dry season are less than that of the wet season. This means that higher mean net income
effects due to asymmetric information are expected during the wet season. It is seen that adverse
selection due to asymmetry of information is greater during the wet season than during the dry season.
CONCLUSION
In conclusion, this study finds that adverse selection due to subsidy and asymmetric information
incentives is existent in the multi-risk crop insurance product. The results support that the adverse
selection is highest when yield guarantee is high. In most cases, adverse selection is highest when risk
is low and the yield guarantee is high. For the farmer, he will benefit if he is from a low-risk area andinsures when he expects that there will be losses for a certain cropping season.
REFERENCES
Alave, K.L. 2011. Philippines is running out of farmers. Philippine Daily Inquirer. Retrieved May 4, 2016 fromhttp://business.inquirer.net
Philippine Statistics Authority. 2004. A Review of the agriculture sector in CALABARZON. Retrieved May 9,2016 from https://psa.gov.ph/Cabas, J., Leiva, A. and Weersink, A. 2008. Modeling exit and entry of farmers in a crop insurance program.
Agricultural and Resource Economics Review, 37 (1), 92-105.
Philippine Statistics Authority. 2016. Cereals: Farmgate prices by geolocation, commodity, year and period.
Retrieved April 20, 2016 from http://countrystat.psa.gov.ph/Goodwin, B. and Smith, V. 2012. What harm is done by subsidizing crop insurance? American Journal of
Agricultural Economics, 95 (2), 489-497.
Just, R., Calvin, L. and Quiggin, J. 1999. Adverse selection in crop insurance, Actuarial and asymmetric
information incentives. American Journal of Agricultural Economics, 81 (4), 834-849.
Ker, A. and Ergun, A.T. 2003. On the revelation of asymmetric information of the private insurance companies in
the US crop insurance program. Paper prepared for presentation at the American Agricultural Economics
Association Annual Meeting; 2003 July 27-30; Montreal, Canada.
Marenya, P., Smith, V. and Nkonya, E. 2014. Relative preferences for soil conservation incentives amongsmallholder farmers: Evidence from Malawi. American Journal of Agriculture Economics, 96 (3), 690-710.
Nickolas, S. 2016. What is the difference between moral hazard and adverse selection? In Investopedia.com.
Retrieved May 19, 2016 from http://www.investopedia.com/
National Statistics Office. 2014. Philippines in figures. Retrieved April 28, 2016 from http://www.census.
gov.phIn Rain and Hail Insurance Society. 2015. Crop Insurance Update: Securing America’s Farmers.
Retrieved May 26, 2016 from www.rainhail.com/pdf_files/MKTG/MKTG_0123.pdf
Sai, T., Yulian, W. and Xiaofeng, H. 2010. An empirical study of agricultural insurance, Evidence from China.
Paper prepared for the International Conference on Agricultural Risk and Food Security 2010; Agricultureand Agricultural Science Procedia, 1, 62-66.
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Research article
erd

Event-based Rainfall-runoff Simulations using GETFLOWS
for Kourtimalei Catchment in Djibouti
FADOUMO A. MALOW*
Graduate School of Agriculture, Tokyo University of Agriculture, Tokyo, Japan
Email: fatoumaalimalow@gmail.com

SAWAHIKO SHIMADA
Faculty of Regional Environment Science, Tokyo University of Agriculture, Tokyo, Japan
Email: shima123@nodai.ac.jp

AURELIEN HAZART
Geosphere Environmental Technology Corp., Tokyo, Japan
Email: auhazart@getc.co.jp
Received 1 August 2017

Accepted 27 June 2017(*Corresponding Author)

Abstract This work was focused on the simulation of a surface terrestrial water flow processmodel in the area of Kourtimalei in Djibouti using GIS, RS and GETFLOWS, a physics-based
surface and subsurface fully coupled fluids flow code. A trial and error were used to calibrate
the model using observed surface water level of the pond. The manual calibration was
performed until the surface water level of the pond RMSE to be 0.40 m with K>0.8.
Furthermore kappa coefficient was used to evaluate the agreement between the pond areas
extents derived from available LANDSAT-8 images during the simulation period with the pond
area extents results of GETFLOWS simulation. The analysis showed that GETFLOWS
successfully simulated the surface water flow process. We conclude that the use satellite
derived datasets can help calibrate and evaluate GEFTLOWS hydrologic model for an
ungauged watershed like in the present case..
Keywords Djibouti, GETFLOWS, LANDSAT-8, simulation, surface runoff

INTRODUCTION
Djibouti is a typical arid country located in the horn of Africa with fluctuating and low rainfall (lessthan 200 mm yr-1) and high temperature in summer (June to August). However, intensive rainfall has
sometimes caused abrupt runoff on bare land surface, resulting in heavy flood damages along the
alluvial fan. When such rainfall occurs in the closed watershed, rainfall water accumulates at the lowest
point in the watershed and the ponding area can stay up for several months sometimes. Usually thatponding water is lost through evaporation and seepage in a three to six months period. Kourtimalei
catchment at Grand Bara desert is a good example of a closed watershed where ponding water appears
during intensive rainfall. For a period of two years (May 2012-August 2014) the local government put
in place a pilot farm project in the area investigating suitable irrigation techniques and cultivation
techniques. In this project, source of the irrigation water was set to be a pond collected from surface
runoff of the catchment water.
The purpose of this work is to characterize the runoff process of the watershed by applying
observed precipitation (and other meteorological variables), at scale of hourly interval. Therefore wecreated a 3D model simulating the surface flow process on the basis of spatial data (DEM, land cover,Ⓒ ISERD

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geology) along with hydrogeological and hydrological parameters and integrating into the flow
computer code GETFLOWS (General purpose terrestrial fluid-flow simulator) (Tosaka et al., 2000).
METHODOLOGY
Study Area
Located near the Grand Bara desert in the southern part of Djibouti, Kourtimalei watershed records anannual precipitation that ranges approximately from 150 to 200 mm yr-1. The land cover distribution
covers mostly bare land with sparse shrubs (Acacias), rocks, and alluvial fan surfaces on the steep hillarea. The geology of the area is comprised of alluvium sediment group along the alluvial fan or in the
Petit-Grand Bara desert and of basaltic rocks like Dalha basalts (3.4-9 Myr) Stratoid basalts (1.5-3.4
Myr) (Jalludin et al., 2003) (Fig. 1). The alluvium was found to be comprised of loam, sandy loam andgravel deposit which has various grain sizes. The topography influences best explains the observabledifferences of the soil texture and in the infiltration capacity seen tin the catchment (Toyoda et al.,2015).

(a)

(b)

(c)
Fig. 1 Location of the study area; (a) pilot farm near Kourtimalei pond;(b) Kourtimalei pond; (c) pumping engine
As in Djibouti, precipitation is sometimes intense and causes floods therefore the government of
Djibouti has decided to promote the active mobilization of surface water for agriculture. Therefore,around Kourtimalei, a pond reservoir was created (volume capacity 5,400 m3) (Fig. 1.b) and irrigatedcrops have started on a small scale (pilot farm of 0.65 ha) (Fig. 1.a) with a small pumping engine (Fig.
1.c). The major problem of this source of irrigation remains the instability of the reservoir volumes.
Most of the water is lost either through evaporation or seepage (JICA, 2014).
Data Collection
The key datasets enabling the implementation and testing of the numerical model are based on the
widely published information but also site investigation data in the Kourtimalei basin. It includes thefollowings:Ⓒ ISERD

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1) In this work, we used 4 LANDSAT-8 images (July 28, September 14 and 30, and December 03)
for the pond extent mapping and the area measurements. The images represent also a crucial partof the validation phase of the simulation. It offers a good combination of two months coveragewith an acceptable spatial resolution (30 m pixels).
2) ASTER GDEM data (30 m resolution DEM) published by NASA and The Ministry of Economy,
Trade, and Industry (METI) of Japan was used as elevation data for the watershed area. The
vertical accuracy was improved by modifying with point elevation data from ground survey in thereservoir area.
3) Observed precipitation records at different location in the watershed indicating an average of less
100 mm month-1. Although the small scale of the area, it was found that the amount of rainfall isn’t
homogenously distributed (Toyoda et al., 2015). Evapotranspiration was estimated from potential
evapotranspiration and represents 84% of the annual average precipitation.
4) Hourly record of the water level of pond collected from sensors within Kourtimalei reservoir.
5) The surface geology is from the national geology map (ORSTOM, 1986)
6) The land cover map with 3 classes (rock, soil and vegetation) was derived from LANDSAT-8
images by unsupervised classification (Fig. 1).
7) The fluid properties of water and air are represented by the typical value in the standard conditions.
The soil is mainly comprised of loamy and sandy loam. The loam soil hydraulic conductivity
ranges from 50 to 100 mm h-1, while for the sandy loam soil texture, larger ranges (160-190 mm h1) were recorded (Toyoda et al., 2015). The permeability of basaltic rock, weathered rock on thesurface was estimated by pumping test and considered to be small as shown in Table 1 (Jalludin
and Razack, 1994)
Table 1 Hydraulic parameters used
Geology
Loamy soil
Alluvium deposit
Weathered rock
Basalts

Intrinsic
Permeability (cm/s)
5.63 x 10-2
1.00 x 10-2
1.00 x 10-6
1.00 x 10-5

Porosity (%)
40
40
10
10

GETFLOWS Modeling and Verifications
The General purpose terrestrial fluid-flow simulator (GETFLOWS), developed by Tosaka et al. (2000),
was used to simulate rainfall runoff events at the study site. It is a physically based model that solves
both the surface and subsurface systems simultaneously by employing an integral finite difference
method approach for solving the governing equations. The natural system is discretized into small cells
in order to obtain exact solutions (Magnus et al., 2011). The governing equation for air/water twophase fluid flow in GETFLOWS is expressed by the following equation of mass conservation (Eq. 1).
𝜕−∇ (𝜌𝑝 𝑢𝑝 ) − 𝜌𝑝 𝑞𝑝 = 𝜕 (𝜌𝑝 ∅𝑆𝑝 )𝑡(𝑝 = 𝑤𝑎𝑡𝑒𝑟, 𝑎𝑖𝑟)

(1)

Where 𝑢𝑝 is the fluid flow velocity (m s-1), 𝜌𝑝 is the fluid density (kg m-3), 𝑞𝑝 is the production
and/or injection rate (m3 m-3 s-1), ∅ is effective porosity (-), 𝑆𝑝 is saturation degree (m3 m-3) and p is a
subscripts that indicate quantities on each fluids phases. In the surface GETFLOWS solves theManning’s equation for the horizontal surface water flow by using the linearized-diffusion-wave
approximation equations (Tosaka et al., 2000). The flow in the subsurface and the vertical flow in thesurface are expressed with the Darcy’s equation .GETFLOWS solves the saturated, unsaturatedⒸ ISERD

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subsurface flow and surface flow in a continuum approach within a single matrix. Nonlinear
interactions between all components of the system are simulated without a priori specification of thecoupling between surface and subsurface flow. Therefore streams formation are purely based onhydrodynamic principles governed by recharge, topography, hydraulic conductivity and flow
parameters (when water is ponded due to surfaces flux exceeding the infiltration capacity of the soil ordue to excess from subsurface soil saturation).
The study area was discretized spatially in 2D plane by grid blocks of approximately 30 m. The
secondary data (i.e. elevation, geology. land cover) were assigned into the corner-point coordinate of
the 2D grid blocks. Then the 2D plane grid-block system was enlarged into depth direction anddiscretized again along Z axis to generate the 3D grid-block system. The total number of grid cells
recorded to be 100,890 cells (Fig. 2).

Fig.2 Three dimensional model (topography)
Our aim is to analyze the runoff characteristic of the watershed that creates the Kourtimalei pond
during intense precipitation events. Thus the model was used for a short-term reproduction of therainfall events of August 01 to December 03, 2013. Recorded daily fluctuations of meteorologicalconditions were constrained onto the model. Then, GETFLOWS was calibrated using the available
daily pond water elevations observations for the period between August 01 and December 03, 2013 inKourtimalei. RMSE indicator is used to assess model accuracy in matching the model-simulated runoff
with observations of the differences in water level records of the pond. The criterion is used as an
objective functions for the calibration. Furthermore the Kappa coefficient is used to assess the accuracyof the model by comparing the pond area extent derived from LANDSAT-8 satellite images during thesimulated period with GETFLOWS simulation result at the 4 times when clear LANDSAT-8 images
were available (i.e., 10 am on August 29, September 14 and 30, and December 12, 2013) (Fig. 3).
RESULTS AND DISCUSSION
In this section, we present the results of the calibration process of GETFLOWS. The Root meansquared error (RMSE) has been used for comparison of simulated pond water levels with observed
water levels for the period of August to December.
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Fig. 3 LANDSAT-8 in false color (5:4:3) pond extents in August 29th,
September 14, September 30 and December 12th 2013
The trial and error method yielded for an optimized parameter combination that had a RMSE of
0.40 m (Fig. 4). The Comparison of GETFLOWS-simulated spatial extent of the pond with satellitebased observations images (LANDSAT-8) (Fig3) was to provide a further evaluation of the
GETFLOWS performance in simulating the spatiotemporal evolution of the pond extent during thecalibration period. Fig. 5 shows the statistical comparison between GETFLOWS and LANDSAT-8
which indicates that a rather good agreement between the obtained pond spatial extents of
GETFLOWS and LANDSAT-8.

Water Level (m)

509
508
507
506
505
504
503
502
2013 1-August

Observed water level
Simulated water level2013 13-August

2013 26-AugustFig. 4 Comparison between the observed and simulated water levels
during the month of August, 2013

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Pond area simulated from
GETFLOWS (km2)

1.2
1.0
0.8
0.6
0.4
0.2
0.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2
Pond area from Landsat
imagery (km2)

Fig. 5 Comparison of the surface
pond area extent between
LANDSAT vs GETFLOWS

Fig. 6 Simulated water depths of Kourtimalei pondat four time step with pond profile on Dec 03
For further verification of GETFLOWS simulations results, the objective function selected to
guide the agreement fit between satellite-based pond extent and GETFLOWS modeled extents was the
Kappa coefficient as shown in Table 2. Overall, GETFLOWS could recreate the rainfall-runoff process
successfully for each of the date. More 80% of the simulated pond extent matches the spatial extent
detected by LANDSAT-8 at the given date. Less than 20% of the simulated spatial extent of the pond
was either misclassified as flooded area (false) or either classified as non-flooded not all (missed).
Table 2 Comparison between GETFLOWS and LANDSAT simulated area vs derived area
Date ofevents
29/AUG
14/SEP
30/SEP
12/DECLANDSATdetected area
(km2)
1.00
0.86
0.77
0.22

Simulated area
(km2)
0.90
0.73
0.76
0.21

GETFLOWSCorrect
Missed
(%)
(%)
85.64
12.16
80.00
13.16
78.86
11.03
71.22
6.80

False
(%)
2.20
6.83
10.10
21.99

Kappa
Statistic
K
0.88
0.84
0.83
0.82

CONCLUSION
In this work, we have run a rainfall-runoff simulation with GETFLOWS of the Kourtimalei watershed,
and calibrated the model by using both water levels observations data, and then validated with satellite
derived datasets of the spatial extents of Kourtimalei pond. GETFLOWS model was able to reproduce
the rainfall-runoff process of the watershed fairly with RMSE of 0.40 m and statistics of Kappa largerⒸ ISERD

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than 0.8. This approach is in contrast with the conventional method of runoff modelling technique. The
proposed approach implements a distributed hydrologic model and further calibrates the model
parameters through satellite derived datasets that are freely available in the public domain. The impact
of such a demonstrated technique is to provide a cost-effective tool in poorly gauged or ungauged
watershed like in this case study.
REFERENCES
Magnus, U.I. and Achi, N.J. 2011. Finite difference method of modelling groundwater flow. Journal of Water
Ressource and Protection, 3,192-198.
Jalludin, M. 1997. Hydrogeological features of the Afar Stratod basalts aquifer on the Djiboutian part.
International Symposium on Flood Basalts, Rifting and Paleoclimates in the Ethiopian Rift and Afar
Depression, Addis Ababa, Djibouti, 3-14/02/1997.
Jalludin, M. and Razack, M. 1994. Analysis of pumping tests in fractured Dalha and stratoid basalts with regard
to tectonics, hydrothermal effects and weathering, Republic of Djibouti. J. Hydrol, 155, 237-250.
JICA/NTC International, 2014. The master plan study for sustainable irrigation and farming in southern Djibouti,
Final report. Republic of Djiboouuti Ministry of Agriculture, Water, Fisheries, Livestock and Marine
Resources, Deccember 2014, Japan International Cooperation Agency/ NTC International Co., Ltd.
ORSTOM. 1986. Carte geologique de la Republique de Djibouti a 1:100,000. Institut Francais de Recherche
scientifique pour le Development en Cooperation.
Sadiq, I.K., Hong, Y., Wang, J. and Koray. 2011. Satellite remote sensing and hydrologic modeling for floodinundation mapping in Lake Victoria Basin, Implications for hydrologic prediction in ungauged basins.
Seemanta, S.B. and Kawasaki, A. 2017. Consideration of the rainfall-runoff-inundation (RRI) model for flood
mapping in a deltaic area of Myanmar.
Tosaka, H., Itho, K. and Furuno, T. 2000. Fully coupled formation of surface flow with 2-phase subsurface flowfor hydrological simulation. Hydrological Process, 14, 449- 464.
Tosaka, H., Mori, K., Tada, K., Tawara, Y. and Yamashita, K. 2010. A general-purpose terrestrial fluids/heatflow simulator for watershed system management. In IAHR International Groundwater Symposium.
Toyoda, H., Shimada, S., Hirokane, T., Ismael, T.M., Hotta, T. and Menjo, M. 2014. A study on effective use of
surface runoff for irrigation in Djibouti, Watershed analysis using GIS and trial on runoff parameters
estimations. Journal of Arid Land Studies, 24, 253-256.

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Research article
erd

Certification Schemes for Alternative Agriculture in Japan
OLGA TYUNINA*
Graduate Program in Sustainability Science - Global Leadership Initiative (GPSS-GLI), Division ofEnvironmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa,
Chiba, Japan
Email: tyunina@s.k.u-tokyo.ac.jp

EIJI YAMAJI
Department of International Studies, Division of Environmental Studies, Graduate School of
Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan
Received 15 December 2016

Accepted 11 October 2017(*Corresponding Author)

Abstract Conventional agriculture, also known as modern or industrial agriculture, which main
aim is to maximize production, is currently prevalent in the world. Negative consequences ofsuch approach include environmental impacts such as soil degradation, groundwater pollution
and GHG emissions. Along with environmental problems, the use of chemical pesticides andfertilizers in conventional agriculture may cause problems to human health, both of producers
and consumers. To alleviate negative impacts of conventional agriculture, the Japanese
government has undertaken attempts to promote alternative agriculture. Laws, guidelines andcertification schemes for promotion of alternative agriculture have been introduced in late
1990s and early 2000s. Today there are three national level certification schemes: “JAS(Japanese Agricultural Standard) Organic”, “Eco-Farmer” certifications and “Specially
Cultivated Products” certification. This review takes a look at the history of developing thesecertification schemes in Japan as well as the differences in the requirements for obtaining thecertifications. The literature suggests that the role of national, regional and municipal
governments vary regarding the procedure of introducing and issuing the certifications of each
type. The possibility of negative economic impact on uncertified farmers practicing alternative
agriculture is discussed. Authors argue that a simplified labeling system is preferred to increasecustomers’ awareness and understanding of alternative agriculture certification schemes.
Keywords alternative agriculture, certification schemes, organic farming, eco-farmer,
specially cultivated agricultural products

INTRODUCTION
The discussion on conventional versus alternative agricultural paradigm was underpinned by Beus andDunlap in 1990. Conventional agriculture, which is also referred to as modern, industrial or intensive,is characterized by large-scale single crop production dependent on input of synthetic fertilizers andpesticides (Beus & Dunlap, 1990). Conventional agriculture, also known as modern or industrial
agriculture, which main aim is to maximize production, is currently prevalent in the world. Negative
consequences of such approach include environmental impacts such as soil degradation, groundwaterpollution, and GHG emissions. Along with environmental problems, the use of chemical pesticides andfertilizers in conventional agriculture may cause problems to human health, both of producers andconsumers. Organic farming is an example most often used as an agricultural practice opposite toconventional farming. However, a large variety of agricultural practices having a potential to alleviate
negative impacts of conventional agriculture on natural environment and human health exist. This
paper refers to these practices as alternative agriculture and is investigating its state in Japan.
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To alleviate negative impacts of conventional agriculture, the Japanese Government has
undertaken attempts to promote alternative agriculture. Laws, guidelines and certification schemes forpromotion of alternative agriculture have been introduced in late 1990s and early 2000s. An umbrellaterm that is often used in policy discourse in Japanese is kankyo hozengata nogyo, which can betranslated as environmentally friendly agriculture. However, the authors are using a broader term
“alternative agriculture” and the initiatives introduced in this paper will be viewed as a part of it. The
term ‘alternative agriculture’ is used here as an umbrella definition for different types of agricultural
practices that put effort into limiting its impact on natural environment. In this paper, authors include“JAS Organic” certification, “Specially Cultivated Agricultural Products” certification and “Ecofarmer” certification under the common definition of alternative agriculture. Authors intentionally
avoid using term “sustainable agriculture” (although this term is used in official English translation of
relevant legislative acts), as the paper does not put focus on evaluating long-term sustainability of eachpractice and certification scheme. The focus of this paper is on the agricultural practices and products
and excludes animal husbandry and livestock products.
In 2010, the tenth meeting of the Conference of the Parties (COP 10) was held in Nagoya, Aichi
Prefecture, Japan. In the light of this event, the concept of biodiversity conservation became widely
known among policymakers, citizens, and industry. In March of the same year, the Prime Minister
Cabinet Decision approved the new “The National Biodiversity Strategy of Japan 2010”. One of therecognized methods for protecting biodiversity is Payment for Ecosystem Services (PES). It is also
applicable to agricultural products, and eco-labels are playing important role in communicatingnecessary information from producers to consumers (Watanabe, 2012).
Importance of taking environmental aspects into consideration during agricultural production was
introduced in Food, Agriculture and Rural Basic Act of 1999 and later elaborated in Environmental
Norms for Agricultural Activities of 2005. Later additional laws and guidelines were issued to
introduce certification schemes and eco-labels to promote various types of alternative agriculture. Main
legislation acts for alternative agriculture in Japan are summarized in Table 1. Currently, there are threenational level certification schemes: “JAS (Japanese Agricultural Standard) Organic”, “Eco-Farmer”
certification and “Specially Cultivated Products” certification. Certification schemes and eco-labels are
known to be an effective instrument for providing information to consumers and thus have potential tocontribute to protection of environment, biodiversity and help promoting local products throughmarketing them (McCluskey & Loureiro, 2003). Thus, main focus of this paper is on threecertification schemes introduced by Japanese government.
With the trend for urban migration and shrinking and aging of rural population, the number of
population involved in farming activities in Japan is on decline, which is also influencing the degree ofalternative farming penetration.
OBJECTIVE
The objective of the paper is to summarize available information on national schemes for alternative
agriculture certifications, namely “JAS Organic”, “Eco-Farmer” certification and “Specially CultivatedAgricultural Products” certification. Historical background, related legislation, level of penetration andlevel of recognition by consumers are reviewed. Obstacles towards further dispersion of each practicewill be identified.
METHODOLOGY
The review of three national certification schemes for alternative agriculture is based on secondary data.
Research articles, book chapters, documents issued by the government and national statistical
databases served as main source of information.
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Table 1 Main legislation for alternative agriculture in Japan
Year
1999
2001
2005
2006
2011

Act/Law/Guideline
Food, Agriculture and Rural Basic Act
Law for Promoting the Introduction of Sustainable Agricultural Practices (“EcoFarmer”certification)
Specially Cultivated Agricultural Products Guideline
Environmental Norms for Agricultural Activities
Act on Promotion of Organic Agriculture
Direct Support Measures for Alternative Agriculture

RESULTS AND DISCUSSION
Overview
The application of synthetic chemicals in agriculture has sharply increased after the Second World War
and brought about benefits of less labor demand and higher yields. On the other hand, the cases of
farmers’ poisoning by the chemicals also became widespread (Nishigaki et. al., 2002). It was during
and soon after the period of Rapid Economic Growth (1955-1973), when industrial pollution wasproved to cause harm to human health (e.g. Minamata disease and Itai-Itai disease). In the result, publicconcerns towards the use of chemicals and environmental issues linked to its use started to grow.
Although different types of alternative agriculture, such as natural farming, were developed by
Mokichi Okada and Masanobu Fukuoka as early as 1930-1940s (Kristiansen et al, 2006), it was not
until 1970s that a solid social movement supporting alternative agriculture was formed.
During the period of Rapid Economic Growth in Japan, a lot of environmental pollution anddegradation was happening and causing harm to human health (e.g., Minamata disease, Itai-Itai disease,
Yokkaichi Asthma). In this regard, the public concern over environmental issues started to grow.
Social movements advocating environmentally friendly lifestyle and safe consumption also started toemerge. Green consumerism movement and the organic farming movement can be raised as examples.
The organic farming movement in Japan emerged in the 1960s. Main concerns during that time wereenvironmental degradation and health risks caused by synthetic pesticides and chemical fertilizers usedin conventional agriculture (Funato, 2010). The movement was lead by the Japan Organic Agriculture
Association (JOAA) that recognized the necessity of spreading awareness about food safety and eating
habits among urban consumers (Minamida, 1995). In 1978, JOAA introduced the document “Ten
Principles of Co-partnership”, which later evolved into so-called TEIKEI (Minamida, 1995). TEIKEI
is an alternative distribution system of agricultural products directly from a farmer to a consumer basedon mutual understanding and trust. Another outcome of the movement was a change in policy that led
to the introduction of labeling systems. Japanese Consumers’ Co-operative Union (JCCU, often referred
to as COOP) also played important role as a distribution channel for alternative agriculture products.
The three national labeling schemes are introduced below in detail and summarized in Table 2.
“JAS Organic” Certification
The first law introducing organic agriculture labeling system was issued in 2000 and the certification
scheme established is referred to as “JAS (Japanese Agricultural Standard) Organic” (“JAS Yūki” inJapanese). Currently “JAS Organic” certification is divided into four types: organic plants, processedfood (e.g., drinks, spices, flour etc.), feed and organic livestock products (the later is excluded from thescope of this paper for the sake of making comparison with other certification types). Prefectural
governments and municipal governments are in charge of establishing promotion strategy and plan for
their areas.
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The requirements for receiving certification are summarized in the text of “Japanese AgriculturalStandard for Organic Plants”. The Standard presents a list of prohibited substances that include
chemical pesticides and fertilizers. The listed substances are not allowed on the agricultural land for atleast two years before sowing or planting as well as during production.
Table 2 Alternative Agriculture Labels and Certification Requirements
Certification name“JAS Organic”

Label“Specially
CultivatedAgricultural
Products”

Certification Requirements
No chemical fertilizers andpesticides
No GMOsUse of compost

Certification authority
Authorized private
certification centers(certified by land)

50% decrease in use of chemicalfertilizers (by amount) andpesticides (by frequency) based onconventional level of eachprefecture

Authorized private
certification centers(certified by crop type)

Submission of a 5-year plan for
reduction of chemical fertilizers andpesticides use, and use of compost

Prefectural Governor(certified by crop type)

Design can be modified
by each prefecture“Eco-Farmer”

Design can be modified
by each prefecture
Image Sources: 1) “JAS Organic” Label – picture taken by author;
2) “Specially Cultivated Agricultural Products” Label – created by author
3) “Eco-farmer” Label – Chiba Prefecture Website. https://www.pref.chiba.lg.jp/annou/chibaeco/newlogomark.html

The Standard regulations also do not allow the use of Genetically Modified Organisms andencourage the use of compost. Moreover, necessary measures are required to prevent prohibited
substances from drifting or flowing to the land. There are rules that need to be followed at all stages of
production process. They include regulations regarding: cultivation sites, collection area, seeds orseedlings to be used in fields, fungus spawn, manure practice on the land, cultivation management incultivation sites, control of noxious animals and plants in fields or cultivation sites, generalmanagement, management concerning harvest, transportation, selection, processing, cleaning, storage,
packaging and other post-harvest processes.
In order to receive a certification, the producers are required to apply to private certification
centers authorized by Ministry of Agriculture Forestry and Fishery (MAFF). The certification
inspection is conducted by land units, without regard to the number of crops grown on the land. Thus
unit of certification is a plot, not crop type, which means that one certification can cover several crop
types harvested from the same plot. As of July 2016, there are fifty-six registered certifying bodies
within Japan. Sixteen certification centers among them certify not only domestic producers but
producers from overseas as well. After the inspection is conducted, certified producers are granted
permission to use “JAS Organic” label (Tab. 2). Registration number of the certification center has tobe mentioned on the label. Despite the efforts to promote “JAS Organic” certification, the number ofcertified households has been on the decline since 2012. Nevertheless, the size of farming area keeps
on increasing steadily (Fig. 1). This implies that the size of farming area per certified household isⒸ ISERD

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increasing, and that a farming household once certified as “JAS Organic” in the past can be converting
additional plots into organic practices in subsequent years.
In 2016, a number of farming households certified as “JAS Organic” reached 3,660 households.
At the same time, the number of the farmers practicing organic agriculture without obtaining “JASOrganic” certification is double of that, around 8,000 households (MAFF, 2016a). Uncertified
produces are not allowed to place word “organic” on their products, which might negatively influence
their sales.
According to the customers’ survey conducted by JOAA, more than 90% are familiar with theterm “organic farming”. Nevertheless, 54% of respondents were not familiar with “JAS Organic” labelthat is placed on the products (see Tab. 2) and only 5% knew the details of “JAS Organic” certification
process (JOAA, 2011).

Fig. 1. Number of farming households and farming area size certified as“JAS Organic” (2010-2016) based on data published on MAFF website“Eco-Farmer” Certification“Eco-Farmer” certification was established in 1999 with enacting of the Law for Promoting theIntroduction of Sustainable Agricultural Practices. This certification scheme is encouraging to decrease
the use of chemical pesticides and synthetic fertilizers on farms and promote the use of compost. In
order to receive certification, the applicant has to submit a 5-year plan for reducing chemicals input.
Unlike “JAS Organic”, “Eco-Farmer” certification is issued by crop type, and not by agricultural plot.
The certification process is organized via prefectural government and governor approves applications.
The certification is valid for 5 years and has to be renewed after that. With the average age of
farmers rising (66.4 years old in 2015) and the share of farmers over 65 years old growing (63.5%)(MAFF, 2015), it is often the case that once certified farmers do not renew the certification after 5
years since retirement is approaching.
After the certification process if successfully completed, certified farmers are allowed to place an“Eco-farmer” label (see Tab. 2) on their products. This label used to be universal for all prefectures
until 2011. After that only eleven out of forty-seven prefectures have continued to use the same label,
whereas the rest of prefectures have designed their own unique labels. Although it might help in
promoting local brands of each prefecture, it might also have negative impact on the level ofrecognition of the label by consumers. According to a consumers’ survey conducted in 2005, only 42%
of consumers interested in purchasing alternative agricultural products replied that they are familiar (orhave seen before) “Eco-Famer” label (Mibu & Okubo, 2005).

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Fig. 2 Number of issued “Eco-farmer” certifications (2010-2016)
based on data published on MAFF website
Although the number of “Eco-Farmer” certifications issued increased sharply from 19 cases in1999 to 216,341 cases in 2011 when it reached its peak, it then has fallen to 154,669 cases in 2015
(MAFF, 2016b). One of the reasons is that the financial return is not balanced with the cost oftransition and labor demand (MAFF, 2015). To overcome these obstacles, it was made possible for“Eco-Farmer” certified farming units to receive support in a form of Direct Payments and employ
extended periods for the return of loans granted for the improvement of agricultural practices.
“Specially Cultivated Agricultural Products” Certification
In terms of requirements towards the producers, “Specially Cultivated Agricultural Products”(Tokubetsu Saibai Nosanbutsu in Japanese) certification scheme lies somewhere in between “JASOrganic” and “Eco-farmer” Certifications. The guideline that is establishing the rules for the properlabeling of such products was introduced in 2001 and then amended in 2003. To use this label, theproducers are required to reduce the use of chemical fertilizers (by amount) and pesticides (byfrequency of application) by 50% towards the conventional level of the region.
Similar to “Eco-farmer” scheme, the design of the label can be changed by each prefecture, which
potentially decreases the level of its recognition by the consumers. Mibu and Okubo (2005) argue thatin comparison with “JAS Organic”, “Specially Cultivated Agricultural Products” labels lack credibility
and thus the market price of such products is lower than those labeled “JAS Organic” (Mibu andOkubo, 2005). This is one of potential reasons why the number of certified farmers is stagnant (around45,000 households in 2015), as the financial benefits do not fully cover the cost of transition (MAFF,
2015).
DISCUSSION
This paper looked into three national level schemes for certification of alternative agricultural products,
namely “JAS Organic”, “Eco-farmer” and “Specially Cultivated Agricultural Products” schemes.
Although the number of certified farmers and size of certified agricultural land is monitored annually,
the studies that analyze the reasons behind low penetration of alternative agriculture in Japan. A
thorough study surveying certified producers about the benefits and demerits they experienced on
obtaining certification, distribution and information channels they use as well as their opinion on thecertification schemes will help to shed the light on potential reasons for farmers’ lack of interest in
obtaining the certification. At the same time, additional consumers’ survey about their knowledge ofⒸ ISERD

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labels and willingness to purchase certified agricultural products would help in analyzing whether
demand for such products exists on Japanese market.
CONCLUSION
After the introduction of alternative agriculture certification schemes in late 1990s early 2000s, thenumber of farming households certified under each scheme increased sharply during the first years.
However, in 2011-2012 after reaching a tipping point, both the number of “Eco-Farmer” and “JASOrganic” certified farmers started to decline. The number of households certified under “Specially
Cultivated Agricultural Products” scheme is also stagnant lately. High cost of transition andinsufficient financial return as well as aging and shrinking of the farming population are among thefactors that are hindering further dispersion of alternative agriculture certifications. The recognitionlevel of the labels among consumers is low and simplification of current system might help in
improving consumers’ awareness and increasing their willingness to purchase such products.
Educating consumers about food safety can also be a useful method for promoting more understanding
towards alternative agricultural products. Lastly, a more inclusive certification system is recommended
for alleviating negative economic impacts experienced by non-certified farmers practicing alternative
agriculture, as they are currently not allowed to use term ‘organic’ for their products.
REFERENCES
Beus, C.E. and Dunlap, R.E. 1990. Conventional versus alternative agriculture, The paradigmatic roots of thedebate. Rural Sociology, 55, 590-616. doi:10.1111/j.1549-0831.1990.tb00699.x
Funato, S. 2010. The present status of the organic agricultural movement viewed from the group purchase, A casestudy of a consumer group, Anzen-na Tabemono wo Tsukutte Taberu Kai. Sustainability Research, Hosei
University, 1 (in Japanese).
JOAA. 2011. Yuki nogyo ni taisuru shohisha no ishi to rikai soukshin ni kan suru chosa hokoku. Report of thesurvey for promotion of customers’ awareness and understanding of organic farming (in Japanese).
Kondoh, K. 2015. The alternative food movement in Japan, Challenges, limits, and resilience of the teikei system.
Agric Hum Values, 32, 143-153. doi:10.1007/s10460-014-9539-x
Kristanen, P. Taji, A. Reganold, J. 2006. Organic agriculture, A global perspective. CSIRO Publishing. Australia. 1-480.
MAFF. 2015. Statistics of agriculture, forestry and fisheries.
MAFF. 2016a. Kankyo hozen gata nogyo no suishin ni tsuite. About promotion of alternative agriculture.
MAFF. 2016b. Number of issued “Eco-farmer” certifications.
McCluskey, J., Loureiro, J. and Maria, L. 2003. Consumer preferences and willingness to pay for food labeling:
A discussion of empirical studies. Journal of Food Distribution Research, 34 (3), 95-102.
Mibu, H. and Okubo, K. 2005. An analysis of consumer consciousness for labeling and purchasing factor oforganic agricultural products. Journal of Society of Practical Integrated Agricultural Sciences, 2005 (2), 72-81(in Japanese).
Minamida, S. 1995. Teikei or co-partnership. A characteristic type of the producer- consumer relationship in theorganic agriculture movement in Japan. The Technical Bulletin of Faculty of Horticulture, Chiba University,49, 189-199 (in Japanese).
Miyachi, T. 2007. Regional deployment of organic agriculture in Japan and regional agriculture promotion(Special Issue. Conditions of food production by agriculture and fishery in Japan and view of policy forregional development). Annals of the Association of Economic Geographers, 53 (1), 41-60 (in Japanese).
Nishigaki, Y., Matsushima, S., Kinebuchi, H., Nagami, H., Asanuma, S. and Usuda, M. 2002. Nationwide surveyof clinical cases of pesticide poisoning (Disorders) in Japan, 1998-2000 FY. Journal of the Japanese
Association of Rural Medicine, 51 (2), 95-104 (in Japanese).
Watanabe, T. 2012. Payment to ecosystem services and eco-labels. Seitaikei e no shiharai to kankyo raberu.
Aichi Gakuin Univeristy Bulletin. The Chiiki Bunseki. 51 (1),1-7 (in Japanese).

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Research article
erdCurrent Agricultural Status and Problems faced in Paghman
District of Kabul Province, Afghanistan
RAHMANI SHAFIQULLAH
Graduate School of Agriculture, Tokyo University of Agriculture, Japan
Email: salarhaleem@gmail.com

MACHITO MIHARA*
Faculty of Regional Environment Science, Tokyo University of Agriculture, Japan
Email: m-mihara@nodai.ac.jp
Received 15 December 2016

Accepted 27 June 2017(*Corresponding Author)

Abstract Afghanistan, an agricultural and land locked country, located in the heart of Asia.
More than 75% of country’s population is living in rural areas. Paghman District located in the
Kabul Province of Afghanistan was selected for conducting this research. In Paghman District,there are some severe problems regarding agricultural land use, water resource conservation,and utilization. The objective of this study is to identify current agriculture status and problems
faced in Paghman District for conserving water environment of the Qargha Reservoir
qualitatively and quantitatively. To achieve the objectives, a questionnaire survey was
conducted to find out the current agricultural status and problems faced in Paghman District.
One or more local farmers (representatives) were selected from each of the 15 different villages
in Qargha Reservoir watershed of Paghman District. The results indicated that water shortage,soil erosion, and low fertility were the major factors causing low agricultural productivity.
Majority of the local farmers perceived water shortage magnitude as severe and very severe at
33%, respectively. Local farmers’ awareness about soil erosion effects on their cultivated lands
were minor. About 54% of the local farmers were not aware of the soil erosion effects on theirlands. The correlation analysis showed that water shortage and soil erosion negatively affected
the yields. The development of proper conservation strategies as well as farmers education onproper soil and water resource management is needed to achieve sustainable agriculture in
Paghman District of Kabul Province, Afghanistan.
Keywords Paghman, Afghanistan, sustainable agriculture, low fertility, land use,water resource management

INTRODUCTION
Afghanistan is a country with majority of the population involved in agricultural activities and islocated in the center of Asia. Afghanistan is bordered by Pakistan in the south and the east, Iran in thewest, Turkmenistan, Uzbekistan and Tajikistan in the north, and China in the far northeast. More than
75% of country’s population is living in rural areas, and 50% or more of the GDP is generated byagriculture and related sectors in rural areas. By far, the greatest part of the land surface of Afghanistan
is extensive grazing land, desert, and semi-desert or high or Steep Mountain, only about 40% is said tobe suitable for winter grazing (Thieme, 2006).
Afghanistan faces problems of effective use of water resource, an increase of irrigation area, andimprovement of irrigation method (Kawasaki et al., 2012). Rainfall varies from a low 75 mm in Farah
Province to 1,170 mm in south Salang, occurs mostly in the winter months and particularly in the
February/April period. The wet season is concentrated in winter and spring when the vegetative coveris low. In higher elevation, precipitation falls in the form of snow that is highly critical for river flow
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and irrigation in summer. From June to October, Afghanistan receives hardly any precipitation. The
Afghan climate is continental with temperatures ranging from above 30° C in summer to below -20° Cin winter. In spring, late frost can affect fruit production (Favre and Kamal, 2004). According to Saba
et al., (2001), only 6% of the 15% of land in Afghanistan is usable. Due to the nature of the topography
and the arid climate, vast areas are subject to soil erosion. The annual average soil loss rate of Kabul
River Basin was estimated to be 19 tons/acre/year (4748 tons/km2/year) (Sahaar, 2013). Agriculture
production has been considered as a key sector for the revival of the economy and well-being of thepeople in the country, but it is not enough level to achieve the food self-sufficiency and to export ofagricultural products (Kawasaki et al., 2012). The objective of this study is to identify current
agricultural status and problems faced in Paghman District of Kabul Province, Afghanistan forconserving Qargha Reservoir water environment qualitatively and quantitatively by reducing sedimentyield, nutrient loss, and irrigation water shortage.
METHODOLOGY
Paghman District is located in west part of Kabul Province of Afghanistan, total of the area is 361 km2,
and average annual rainfall is 473 mm. Qargha Reservoir is located in Paghman District. Qargha
Reservoir is used for irrigation and recreational purposes. Furthermore, Qargha Reservoir provides
irrigation water for more than 2,000 hectares of farmland. Only seven extension workers are
responsible for managing agriculture in the whole study area with more than 150,000 population.

Fig. 1 Location of Qargha Reservoir Paghman District, Kabul, Afghanistan
Table 1 Land cover and land use classification in Paghman District (FAO, 2016)
Irrigated
land (ha)

Fruit
trees
(ha)

Vineland(ha)

Barrenland (ha)Forestshrub (ha)Rangeland

Build up

Water body

Total

(ha)

(ha)

(ha)

(ha)

8,080. 63

1,419.50

31.5

456,5999.93

24,587.40

907.9

537.4

36,12022.37%

3.93%

0.09%

1.26%

0.28%

68.07%

2.76%

1.49%

100%

In order to identify the current agricultural status and problems faced in Paghman District ofKabul Province, Afghanistan, a questionnaire survey was conducted in Paghman District of Kabul
Province, Afghanistan. Paghman District is located in the west of Kabul Province with 361 km2 area.
Qargha Reservoir is located in the Paghman District and used for irrigation and recreational purposes.
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In total, 24 local farmers were interviewed. One or more local farmers (representatives) wereselected from each of the 15 different villages in Qargha Reservoir watershed of Paghman District,
Kabul Province, Afghanistan. The questionnaire sheets contain different type of questions; thequestions were on basic information of the local farmers, cultivated crop and yield, soil erosion effects,
irrigation water shortage and irrigation water sources, future farming plan, fertilizer application,
agricultural extension services, assistance and technical training as shown in Table 2.
Table 2 Type of questions in the questionnaire sheet
Category

Related question

Basic information

Farmer’s information

Name, gender, household, address, education,
age and cooperative

Future farming

Continue farming

Yes, no and not decided

Fertilizer application

Type of fertilizer

MAP/DAP, Urea, farmyard manure, green
manure, compost

Soil erosion effects

Soil erosion effect on
fertility and crop field
Extension service
provision

Destroy crop, decrease productivity and notaware
Extension worker visit to the field

Assistance and technicaltraining

Any kind of agricultural
aid to the farmers by
Govt./NGO

Improved seed and nursery stock, machinery,
fertilizer and medicine

Irrigation water shortageand irrigation water source

Water shortage severity
and source of irrigation

Water source and water shortage severity

Agriculture Extension
service

Details

RESULTS AND DISCUSSION
Pre-harvest Agricultural Problems
Afghanistan is a country in which local farmers face a number of pre-harvest problems, which causes
their farmlands to yield low and direct them to agricultural insufficiency. The common problems local
farmers face are diseases and pests, low fertility, water shortage, and unwanted weed. In Afghanistan,
local farmers face agricultural problems during a complete growing session starting from land
preparation and sowing until harvesting. According to the results diseases/pests, irrigation water
shortage, soil erosion, and low fertility were the major problems in the study area with 63%, 83%, 50%,
and 29%, respectively as shown in Fig. 3. Former studies have indicated that water shortage, nutrient
loss, and soil erosion are the dominant problem in Afghanistan (Kawasaki et al., 2012; Sahaar, 2013;
Safi et al., 2016). Besides other factors, lack of agricultural technical knowledge is the source of low
productivity.
Extension workers as shown in Fig. 2 did not visit the majority (88%) of the farmers. Thus,
Agriculture in many parts of the country remains starkly underdeveloped. Hence, to solve these
problems faced in the study area, proper conservation strategies as well as farmers’ education onproper soil and water resource management.

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80%
60%
40%
20%

63%
60%

50%
29%

30%
13%

8%

4%

0%

83%

90%

88%
Farmers affected

Farmers visited

100%

0%
Not visitedOnce a year

Twice a year

Disease/pest

WeedDrought

Low fertility Soil erosion

Problems faced during crop production

Extension workers visit

Fig. 2 Extension workers visit

Fig. 3 Problems faced during crop production

Water Shortage
The availability of water resources is vital to the social and economic well-being and rebuilding ofAfghanistan. In the Kabul Basin, 10% reduction in recharge was simulated to assess the hydrologic
effect of potential climate change on groundwater resources (USAID, 2010). Size of cultivated land in
Afghanistan is 3.9 million ha of which 1.3 ha is rain-fed and 2.6 million ha is irrigated land. Almost
85% of all agricultural production is produced in irrigated areas. The rainfall begins in October,reaches its peak in March and ends in May. It hardly rains during the periods from June to October,especially in summer when the temperature is high.
33%

Farmers perception

33%
30%
18%

20%
8%

10%

8%

Average mothly rainfall (mm)from 2004 to 2015

120

40%

0%
Very
slightSlightModerate

Severe

96

100
80
60

87
75

59
38

40

34
12

20

16
6

9

27

14

0

Very
severePerceived water shortage severity

Vegetable yield (tons/ha)

700

Perceived vegetable yieldFig. 4 Farmers perception on water shortage Fig. 5 Average monthly rainfall (MAIL, 2016)
y = -45.781x + 411.41

600
500
400
300
200
100
0
1

2

3

4

5

5
4
3
2
1
1

Perceived water shortage severity

2

3

4

Perceived water shortage severity

Fig. 6 Trends between vegetable yield and perceived water shortageⒸ ISERD

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Majority of the farmers perceived water shortage as severe and very severe at 33% and 33%,
respectively. As a result, the problem of water shortages frequently occurs in the latter part of thegrowing season between April and October, causing a major deficit in crop yield. Agriculture in
Afghanistan faces a problem of water shortages during the latter half (summer) of planting period. The
issue is the wastage of water due to the aging of irrigation facilities and unorganized irrigation
practices. Therefore, the efficient use of limited water resources is an issue of paramount importance(Kawasaki et al., 2012). Correlation analysis indicated crop yield was affected by water shortage in the
study area. Hence, water shortage would cause low water inflow into Qargha reservoir. Therefore,
irrigation potential of Qargha reservoir is negatively affected. Karezes (Karez is a tunnel system used
to extract shallow groundwater) and tube wells were the major irrigation water sources in Paghman
District.
Soil Fertility Management
Afghanistan’s soils are mainly alkaline, soil pH ranges from 7 to 8.5, rich in calcium and potassium
minerals. Soil organic matter content ranges from 0.2 to 2.5% (Alemi, 2010). According to the results
farmyard manure, chemical fertilizers and crop rotation were the common soil fertility management
practices in the study area, with 100%, 96%, and 71%, respectively. Local farmers usually usedfarmyard manure for soil fertility management, mainly made of cow, goat and sheep dung alongsidehuman excreta. Chemical fertilizers were usually applied to the field by broadcasting on the soil andbanding around plant roots. The liquid form of fertilizer application is not common practice inAfghanistan, only used in governmental and non-governmental owned farms. Hence, local farmers use
intercropping and crop rotation in the agricultural field by growing alfalfa and clover to manage soil
fertility. Urea (96%) and DAP (92%) were the major chemical fertilizers used for soil fertility
enhancement in the study area as shown in Fig. 7. Potassium fertilizers are rarely used in Afghanistan.
Nitrogen, phosphorous, zinc and iron, are the major elements deficient in Afghanistan soils. Organic
fertilizers or Chelates (combination of an organic compound with a metallic ion) are rarely used by
farmers.
96%

92%

100%

100%

96%

80%

80%
Farmers applying

Farmers applying

100%

60%
40%
20%

4%

71%

60%
40%
20%

4%

0%

0%
Urea

DAP/MAP

Chemicalfertilizer

Potassium

Type of fertilizer

Farmyard
manureCrop rotation

CompostSoil fertility management methods

Fig. 7 Extension workers field visit

Fig. 8 Soil fertility management practice

Soil Erosion
Soil erosion is one of the issues influencing soil fertility all over the country. Due to irregular
topography and low land cover, Afghanistan soils are subjected to soil erosion risk. The annual averagesoil loss rate of the Kabul River Basin was estimated to be 47.4 tons/ha/year and the gross mean annual
soil loss rate was approximately 47.4 million tons/year. By producing 57% of the total annual averagesoil loss, rangelands were the primary contributor to the basin. Barren lands by producing about 38%Ⓒ ISERD

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were the second largest contributor to the overall soil loss rate in the basin (Sahaar, 2013). Paghman
District land use map shows that rangeland made 68.07% of the total land with low land cover andsteep slopes, which increases the risk of soil erosion. Half of the total local farmers responded that they
faced soil erosion during crop production. Thus, the majority (54%) of the responded local farmers
were not aware of soil erosion effects on their cultivated lands. In case, if soil erosion occurs in the
study area, Qargha reservoir would be at the receiving end by receiving a high amount of sedimentyield, which could decrease water storage capacity. Safi et al., (2016) reported that surface runoff
causes a significant amount of nitrogen, phosphorous, potassium and carbon losses from agricultural
fields. In case, in the study area, local farmers did not realize the significance of soil erosion. It is the
responsibility of governmental and non-governmental organizations to increase local farmer’sawareness about soil erosion effects by offering them technical training courses and workshops in the
study area.
554%
Perceived vegetable yield

Farmers affected

60%
50%
40%

32%

30%
20%
10%

4%

4%

4%

0%

4
3
2
1

Not aware Disturb soil Water
Increase Decease
texture absorption fertility soil fertility
Soil erosion effects

1

2

3

4

5

Perceived soil erosion severity

Fig. 9 Soil erosion effects

Fig. 10 Trends between perceived vegetableyield and soil erosion

CONCLUSION
This study was to identify current agricultural status and problems faced in Paghman District of Kabul
Province, Afghanistan for conserving water environment of Qargha Reservoir qualitatively andquantitatively. Water shortage was one of the issues local farmers seriously faced during cropping
season in the study area. About 83% of the farmers responded that they faced water shortage duringcrop production. Wells and karezes were the major water sources used for irrigation. Half of the local
farmers faced soil erosion during crop production. However, local farmers’ awareness about soil
erosion effects was at very low level. The majority (54%) of the local farmers were not aware of soil
erosion effects on their cultivated lands. Farmyard manure, chemical fertilizers, and crop rotation werethe common soil fertility management practices in the study area with 100%, 96%, and 71%,
respectively. Correlation analysis showed that soil erosion and irrigation water shortage negatively
affected productivity. Soil erosion, low fertility, and irrigation water shortage were identified as major
factors causing low agricultural productivity. Therefore, proper conservation strategies, as well as
farmers education on proper soil and water resource management is needed to achieve sustainableagriculture in Paghman District of Kabul Province, Afghanistan.
REFERENCES
Alemi, M.A. 2010. Soil fertility and plant nutrition. Lecture note, Kabul University, Afghanistan.
Favre, R. and Kamal, G.M. 2004. Watershed atlas of Afghanistan. Ministry of Energy and Water. funded by
Swedish Agency for Development and Cooperation and FAO.
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Kawasaki, S., Watanabe, F., Suzuki, S., Nishimaki, R. and Takahashi, S. 2012. Current situation and issues on
agriculture of Afghanistan. Journal of Arid Land Studies, 22, 345 -348.
Mack, T.J., Chornack, M.P., Choplen, T.B., Plummer, L.N., Rezai, M.T. and Verstraeten, I.M. 2010. Availabilityof water in the Kabul Basin, Afghanistan. U.S. Geological Survey. Prepared in cooperation with Afghanistan
Geological Survey under the U.S. Agency for International Development (USAID). Fact Sheet 3037, U.S.
Geological Survey (USGS) Afghanistan Project Product No. 177.
Saba, D.S. 2001. Afghanistan: Environmental degradation in a fragile ecological setting. International Journal of
Sustainable Development and World Ecology, 8, 279-289.
Sahar, A.S. 2013. Erosion mapping and sediment yield of the Kabul River Basin, Afghanistan. Master’s thesis.
Colorado State University Fort Collins, USA.
Safi, Z., Bahram, G.M., Ahmadzai, K.M. and Alemi, M.A. 2016. Organic matter and nutrient (NPKC) losses via
surface run-off in urban agriculture (UA) of Kabul, Afghanistan. International Journal of Agricultural
Research and Review, 4, 440-447.
Thieme, O. 2006. Country pasture/forage resource profiles of Afghanistan. Food and Agriculture Organization of
the United Nations (FAO).

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Research article
erd

A Comparative Study of Housing Conditions among living
Sectors in Sri Lanka and among Residential Areas in Tea
Industry, Matara District, Southern Part of Sri Lanka
YUKO FUKUDA*
Graduate School of Frontier Science, The University of Tokyo, Kashiwa, Japan
Email: yukotsu70@yahoo.co.jp

EIJI YAMAJI
Graduate School of Frontier Science, The University of Tokyo, Kashiwa, Japan
Received 16 December 2016

Accepted 28 June 2017(*Corresponding Author)

Abstract Sri Lanka is well-known as one of the most successful cases of human development
among South Asian countries, despite widening income disparities among sectors (urban, rural,
estate), especially in the estate sector. Tea industry is an important industry in the estate sector,
however, working and living conditions which affect the quality of life are generally worse thanother. This resulted in a decrease in well-being of the workers’ capabilities. This study aims toidentify the disparities in housing conditions among living four sectors in Sri Lanka and also
residential areas in tea industry. Qualitative and quantitative methods are applied for analysis.
To consider the well-being of people, capability approach is also applied. Data in the study
relied on the secondary data conducted in 2008/2009, and 302 households’ primary dataobtained between 2014 and 2015. Three indexes of housing facilities used in this paper areaccess to drinking water, exclusive toilet facilities, and electricity lighting. Results incomparison between housing conditions and living sectors indicate that people in estates areunder the low quality of housing facilities and face the risks under the unsanitary conditions.
Statistically, all three indexes showed there are disparities among living sectors. Comparing
indexes among sectors, accessibility to safe drinking water in estate has wider disparity than theothers two index. Exploring the tea industry based on the management style (RPC, Private
estate, Farmer) deeply found private estates are more likely to face disadvantages than others.
Accessibility to safe drinking water is lower than in others variables. Among residential areas
access to safe drinking water is not statistically significant, but access to toilet facility andelectricity is statistically significant at five percent level.
Keywords living conditions, capability, tea industry, rural development

INTRODUCTION
This study focuses on housing conditions of estate as whole country and inside tea industry community,and highlights differences among management styles, whiles providing suggestions to narrow the
disparities. Sri Lanka has developed its status as middle income country with achievement of most of
the MDG`s, even though income and social disparity among citizens and regions has being widening,
especially in estate sectors where tea, coconuts and rubber has been cultivated. Tea is one of mainindustries and financial resources for government. Tea industry is promoted by two sectors; large-scale
estate sector and smallholders. Export revenue in 2010 reached 1.37 billion US dollars, which
accounted for 16 percent of the total export of the country. However, the poverty rate of estate workers
was concerned for this community, accounted for 11.4 percent, compared to the urban average of 5.3Ⓒ ISERD

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percent and to 9.4 percent of rural in 2009/10 (DCS 2011). The GINI indices increased from 0.43 in
1980 to 0.49 in 2009/10 (Central Bank of Sri Lanka 2012).
Estate sector is classified by cultivation land elevation and area. Cultivation elevation is classified
into three; high-grown tea is over 1200 m, mid-gown tea is between 600 and less 1200, and low-grown
tea is less than 600m. Cultivation area larger than 20 hectares is categorized as estate sector andsmallholder is characterized as smaller than 20 hectares including farmers. Estate workers are protectedby the estate labour laws. The quality of life as individual well-being of life is actually valuable for thisperson (Sen 1992). Working and living conditions in estate, however, are generally harder than othersectors, and the majority of workers have no job security (Wal, 2008). Their economic and social
conditions are not enough to enhance their capabilities.
The literature reviews and interviews conducted in August 2012 at up-country found that people
in tea estates have been confronted with low income, poor housing and sanitary facilities as well asother basic needs (Kumari 1984, WB 2007 and Chandrabose 2011). Recent studies reveal that numberof tea cultivation managed by small holdings has increased, and living conditions in estates have been
improved (DCS2005, 2012). Firstly, this study delineates disparity of housing conditions among living
sector. Secondly, difference of residential areas based on the management categories will be described,
and factors will be described, and factors accounting for these differences will be explored.
METHODOLOGY
Descriptive manner will be applied for this study. The results presented in this paper are based on
qualitative and quantitative methods of secondary and primary data collection. Capability approach isalso applied for this analysis.
Characteristics of the Secondary Data
Secondary data was from the Child Activity Survey (CAS) which was conducted by the Department ofCensus and Statistic with the International Labor Organization (ILO) in 2008/09. The objective of CAS
was to identify the characteristics of Sri Lanka children. It, however, collected information abouthousing and household characteristics composed of 16,000 housing units that represent the wholecountry. Stratification was classified into three sectors of residence: urban, rural, and estate.
Differences in housing environments among living sectors, the areas where three sectors existed werechosen from the CAS. Recent management styles of agriculture vary, and the number of small holders
and farmers has been increasing. Thus, in this study, data were extracted and sorted from the rural
housing variable into farmer and the rural itself. Characteristics of farmer are to own a single house andland, at least one parent is working, ownership of livestock been less than 10 cows and goats, and owns
less than 100 chickens.
To deepen the multidimensional understanding of livelihood, the use of income index is notenough. One purpose of this study is to describe difference of dwelling conditions among three areas
based on the management styles in tea industry. Therefore, the data on access to safe drinking water,household exclusive toilet, and electricity lighting were adapted for this analysis. Definition of safe
drinking water in CAS is “Protected well within and outside premises” “Tube well” “Tap withinpremises and outside” and “Stream water collected & distributed by pipe lines”. Definition of unsafe
water is “Unprotected well” and “River/Tank/Steam/Spring and other”. Stream water collected &distributed by pipe lines, however, is categorized as unsafe drinking water since this survey did notinclude the scientific evidence. Limitation of this analysis depends on the circumstantial, but is not tobe able to show the scientific data.

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Target Area and Primary Data
Sri Lanka is composed of 25 districts organized into 9 provinces. The 25 districts are divided into 331
divisions which are further divided into 14021 GN divisions. There are 36822 villages in Sri Lana
(DCS 2013). Ecologically Sri Lanka is divided into two zones based on the availability of rainwater.
The southern part of the country where tea cultivation has been planted receives ample rainfall whilenorthern parts of country called the dry zone receive scarce rain.
Primary data were collected at Kotapola Division of Matara District in Southern part of Sri Lanka,
famous as one of five tea cultivation areas, called Ruhuna as Low-grown tea. Kotapola division iscomposed of 110 villages in 37 GN divisions. Where villages and estates existed are evaluated aslower housing conditions in Martara district (DCS 2013). Tea industry in this area has a different story
as oppose to upcountry where no villages were at the time of estates establishment. British government
during colonial area established huge tea estates on the hillside around low-country, which weresurrounded by villages even before the states were introduced (Bronkhorst 2008).

Fig. 1 Location of Deniyaya GN division
Management style of Sri Lanka tea industry are classified into three; RPC (Regional Plantation
Companies=semi-governmental estate), PE (private estate), and Farmer. Survey was conducted in thevillages and estates around Deniyaya GN division. Kiriweldola is one of near Deniyaya is the second
lowest GN division in Matara District. Primary sample size is 302 housing units which composed of
103 households at 5 RPC estates, 100 households at 22 private estates, and 99 households at individual
farms.
RESULTS AND DISCUSSION
Quality of housing affects quality of life. Water affects food and water contamination within the home.
Toilet and sanitation conditions not only have influences on public health, but also on individual health.
Inadequate lighting leads to eyesight problems, and sometimes sources of lighting causes other issues
such as fire or skin problems.
Housing Conditions in Estate Sector
Estate sector is composed of mainly tea, coconuts, rubber and cinnamon in Sri Lanka. Poverty andsocial disparity in estate are still remained. Housing conditions among four residential sectors can be
described through the CAS 2008/09.

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Accessibility

100%
Safe
Water

80%

Exclusive
Toilet60%
40%

Electricity
Lightning

20%
Urban

Rural

EstateFarmer

Sector
Source: CS 2008/09.Ministry of Finance and Planning, Sri Lanka, 2009

Fig. 2 Living condition among residential sectors
Fig. 2 represents the rate of access to three indexes of housing conditions among Urban, Rural,
Estate and Farmer. Comparing access to safe drinking water among urban, rural and individual famer
sectors, the percentage of safe drinking water was 38.7. In contrast, in urban, it accounted for 98.9
percent, among rural and farmer, it accounted for 80.4 percent and 75.8 percent, respectively. Crosssectional analysis was statistically significant at the one percent level between residential sectors andaccessibility of safe drinking water, indicating inadequate water facilities at estate sector than othersectors.
Having an exclusive toilet for households is one of important factors to avoid the spread of
disease and prevent daily sicknesses. Comparing the access of the exclusive toilet facility to threesectors, the percentage of independent household’s toilet was 63.0. In contrast, in urban, it counted for94.1 percent, among rural and farmer, it accounted for 91.4 percent and 96.2 percent, respectively.
Cross-sectional analysis was statistically significant at the one percent level between residential sectors
and accessibility of exclusive toilet for household, indicating less possession of family independent
toilet at estate sector than other sectors
Appropriate light at night or in darkness enables people to see things properly, while inappropriate
degree of light causes eye problems, and sometimes affect the brain or causes mental disorder.
Kerosene or candle is used for lighting in house and on the roads. On the other hand, these have been acause of fire or burns. Couples in estates during the survey explained about the incidence of burn on
their face and body by fallen Kerosene lamp. Using inappropriate resources for light are not only risky
for health but also not environmentally friendly. Fig. 2 shows access of electricity in estate, thepercentage of electricity among other light resources was 67.34. In contrast, in urban was counted for98.52 percent, in rural and farmer were counted for 87.16 percent and 88.01percent. Cross-sectional
analysis was statistically significant at the one percent level between residential sectors andaccessibility of electricity for household, indicating higher possibility to have health problems andaccidents than other three sectors.
Comparative results among sectors are not different to the past literature reviews. People living inestate sector are under the low quality of housing facilities and more likely face the risks under theunsanitary conditions. Especially, accessibility to safe drinking water is lower than access to exclusive
toilet and electricity. Estates owned by RPCs or companies are generally large and equipping themwith adequate infrastructure costs the lot. In wet zones, however, people are more easily have access tonatural water than in dry zones without huge equipment. For electricity system in estate, it is difficult
for companies to maintain a proper system in such a large area even though electricity system in urban
and rural is more likely equipped by government. Structure of housing in estate has an effect on
housing facilities. Family living in the attached housing in a row in estate has shared kitchen and toilets
rather than family in urban and rural who have lived in a single house with household kitchen andtoilets.
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Housing Conditions in Tea industry
Comparted to other industry sectors in estate, working at tea industry are labor intensive, involvement
in working for long hours under harsh environment, and living conditions of estate workers have been
pointed out as unsanitary and insufficient. Recent trend in the tea industry is the increase in the numberof smallholders for promoting tea production. Conventional management system has been outdated
under the current globalization of economics and society. For improving the efficiency in production
and management, the government reprivatized the management of state owned estate sector in 1992
(Wenzlhuemer, 2007 and Ute 2004). Compared to the period before reprivatized management system
was implemented in 1992, the tea industry management can be categorized into three styles; semigovernmental companies which were privatized called RPC, private estates (PE) which include
smallholders, and farmer.
100%
Safe Water

80%
60%

Exclusive
Toilet40%

Electricity
Lightning

20%
RPC

PE

FarmerResearch Area: Kotapola Division, Matara district, Sothern part of Sri Lanka

Fig. 3 Living Conditions among Management Style
To critically explore the tea industry, three indexes are examined among three management styles
in low-country. Fig. 3 describes the access to three indexes among residential areas based on the
management styles. Except electricity light, the trend is similar, PE faces more disadvantages than
RPC or Farmer. Workers’ houses are called “Line-house”, where between 2 and 5 houses are attached,
and were built during the colonial area to accommodate seasonal temporary workers for coffee
production. Kitchen and Toilet outside premises used to be shared with neighbors. Knowledge of
public health and sanitary were not enough (Oxfam 2002, Williges 2004). Tea as replacement of coffee
in 1867 made the seasonal migrants become permanent residents (Kumari 1984, and Wenzlhuemer,2007).
Compared among three indexes, accessibility of safe drinking water is lower than other two
indexes. The percentage of safe drinking water in RPC was 33 percent, in PE and Famer was counted
for 26 percent and for 31 percent, respectively. Cross-sectional analysis was not statistically significant
at the one percent level between residential areas among three management styles, indicating
accessibility to water facilities of divisions of Kotapola district are under similar circumstances.
In comparison of the access of the exclusive toilet facility among residential areas, the percentage
of independent household’s toilet was 88 percent in RPC, in PE, it accounted for 85 percent, in Farmer,
it counted for 98 percent. Almost all families in Farmer own at least one household toilet within or
outside premises, but 7 percent of PE must share it with others, and 8 percent of them have no toilet athome. Cross-sectional analysis was statistically significant at the one percent level between residential
areas and accessibility of household’s exclusive toilet, indicating there is a difference of possession of
exclusive toilet.
Access of electricity light among residential areas, the percentage of electricity among other lightresources was 89 percent in RPC, 92 percent was in PE, and Farmer counted for 98 percent. CrossⒸ ISERD194

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sectional analysis was statistically significant at the five percent level. Lighting system in large estates
more likely depends on the traditional way rather than other two.
Accessibility to safe drinking water is not different among people at the target area, but toilet
facility and accessibility to electricity light is different among residential areas. There are many smallor large river surrounding Deniyaya areas and easy access to natural water in stream or rivers.
Regardless of self-employee or employed by companies, water environment around target area is under
the similar conditions while it contributes to the lower late of access to safe drinking water. Introducing
the individual toilet has been promoted by international or local organizations for public and individual
health. One of reasons is due to the fall that there are differences in toilet facility among residential
regions. Farmers live in a single house rather than sharing the house or rooms, while workers in large
or medium estate are more likely to live in line-houses where small houses are attached to each other in
one long line row house. As discussed above, electricity is more likely to be provided by government,
small private estates are located near the roads and more easily can access electricity. Large estates arestrictly regulated by laws and required to provide enough support to meet the basic needs, while small
estates infrastructures are dependent on owners’ decisions.
CONCLUSION
Soil contamination with pesticide or artificial fertilizer for tea cultivation has affected the quality ofdrinking water, unsanitary or shared toilet causes illness or disease on livings things including human,
improper lighting has influences not only on health, but also on people’s capability. In this paper, threeindexes of housing conditions in residential sector level and industry level were examined. Differencesin housing conditions among residential sectors are described through secondary data analysis, andtendency of housing condition in tea industry based on the management styles is explored throughprimary data analysis.
Result from secondary data analysis showed that disparities among living sectors in Sri Lanka
existed in all three indexes. It indicates that people living in estates are under the low quality of
housing facilities and face the risks of the unsanitary conditions. Especially, accessibility to safe
drinking water is lower than in other two indexes. Result of the second data analysis is not different
from the past literature reviews and researches.
To deepen our understanding of difference in living conditions of tea estates based on
management style, primary data was applied. Secondary data analysis indicates that famer housing
environments were better than in estates, while primary data analysis in tea industry showed different
result. Among residential areas in tea industry, there is no difference in access to safe drinking water at
ten percent level and to electricity lightning at one percent level. Access to electricity at five percent
level, however, is changed from not significant to statistically significant. There is difference in accessto toilet among residential areas. Access to toilet in estate sector (RPC and PE) is different fromfarmers although not statistically difference at 10 percent level between RPC and PE.
Limitation of this analysis is not to be able to show scientific evidence on safety of drinking waterand on sanitary conditions of toilet.
ACKNOWLEDGEMENTS
Many people gave me their support, assistance, and advice during the study. I would like to express myappreciations to people who positively participated in the long interviews which I conducted during thesurvey. I am sincerely grateful to all of these people and to those people who assist and help me. I also
express my sincere gratitude to academic supervisor and advisors for their guidance on this analysis,
and considerable encouragement. This research is supported by the Grant-in-Aid for Scientific
Research from the Ministry of Education, Culture, Sports, Science and Technology, in Japan.
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REFERENCES
Bronkhorst, J. 2008. From coffee to tea cultivation in Ceylon, 1880-1900, An economic and social history. Brill’s
Indological Library, Netherlands.
Central Bank of Sri Lanka. 2012. Economic and social statistics of Sri Lanka 2012. Statistics Department,
Central Bank of Sri Lanka, Sri Lanka.
Chandrabose, A.S. and Sivapragasam, P.P. 2011. Red colour of tea-central issues that impact the tea plantation
community in Sri Lanka. Human Development Organization, Sri Lanka.
Department of Census and Statistics (DCS), Government of Sri Lanka. 2011. Poverty in Sri Lanka (Based onhousehold income and expenditure survey 2009/10). DCS, Government of Sri Lanka, Sri Lanka.
DCS, Government of Sri Lanka. 2003. Census of agriculture 2002, Small holding sector. Agriculture andEnvironmental Statistics Division, DCS, Government of Sri Lanka, Sri Lanka.
DCS, Government of Sri Lanka. 2005 and 2012. Census of tea small holdings in Sri Lanka. Tea Small Holdings
Development Authority, Asian Development Bank.
DCS, Government of Sri Lanka. 2013. Classification of administrative divisions by quality of housing. Sri Lanka.
Kurihara, S. 2011. Tea estate plantation community in Nuwara Eliya District of Sri Lanka, An introductory
overview of social issues and poverty among residents living under the conventional plantation system.
Yokohama Journal of Social Sciences, 15 (6), 123-135.
Rachel, K. and Kumari, J. 2013. Plantation patriarchy and structural violence, Women workers in Sri Lanka.
Conference on Bonded Labour, Migration, Diaspora and Identity Formation in Historical and Contemporary
Context, June 6th - 10th, Paramaribo, Suriname.
Oxfam. 2002. The tea market: A background study. Oxfam Publications, Oxford, UK.
Sen, A. 1992. Inequality reexamined, Oxford University Press, UK.
Wal, S.V.D. 2008. Sustainability issues in the tea sector, A comparative analysis of six leading producing
countries. Centre for Research on Multinational Corporations (SOMO), Netherlands.
Wenzlhuemer, R. 2007. Indian labour immigration and british labour policy in nineteenth-century Ceylon.
Cambridge University Press, Modern Asian Studies, 41 (3), 575-602.
Wenzlhuemer, R. 2007. The shinhalese contribution to estate labour in Ceylon, 1881-1891. Journal of the
Economic and Social History of the Orient, 41 (3), 442-458.
Williges, U. 2004. Status of organic agriculture in Sri Lanka with special emphasis on tea production system.
Ph.D. thesis, University of Giessen, Gernany.

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Research article
erd

Evaluation of Microfinance Institutions in Ethiopia from the
Perspective of Sustainability and Outreach
FRAOL LEMMA BALCHA*Tokyo University of Agriculture, Tokyo, Japan
Email: fraolgel@gmail.com

SALEHU ANTENEH TAMARE
Addis Ababa University, Addis Ababa, Ethiopia
Received 11 December 2016

Accepted 28 2017(*Corresponding Author)

Abstract Ethiopia is a developing country, the second most populous in Africa with anestimated population of more than 94 million. The Ethiopian economy is based on agriculture,and poverty reduction has been the overriding development agenda. The coverage of formal
financial services estimated to be less than 10 percent, nevertheless banks consider the poor
“un-bankable”. It has been assumed that Microfinance Institutions (MFIs) have reached thepoor on a sustainable basis. Considering the situation, this study aims at evaluating the outreachlevel of Ethiopian MFIs and its sustainability. In order to achieve the planned research
objectives, quantitative as well as qualitative research methods are used. The target of thisresearch is MFIs currently operating in the country. The 14 samples of the study were selectedfrom operating 34 MFIs by using purposive sampling technique to include all categories. Data
were collected through questionnaires, whereas, secondary data from annual reports and otherdocuments. The analysis indicated that the breadth and the depth of outreach of Ethiopian MFIs
are in an increasing trend. The increase in the amount of voluntary savings was remarkable, andits ratio to the compulsory savings was 482.51 percent from 2005 to 2014. With regards todepth of outreach, 54.73 percent of women and relatively insignificant number of crop
producing farmers have been covered by microfinance services. The Operational Selfsufficiency Ratio and Financial Self-sufficiency Ratio of Ethiopian Microfinance Institutions’are in increasing trends because of the enhancements in generating more revenue. Outreachtrend is promising, though they have challenges to address the disadvantaged group of thesociety. The sustainability indicators suggested that the increase of operational sustainability
and financial sustainability ratios are due to the increase in revenue and the contribution ofdonors and/subsidization. It is also advisable to improve its sustainability by increasing theefficiency.
Keywords outreach, sustainability, microfinance, savings, subsidization

INTRODUCTION
The emergence of Microfinance institutions (MFIs) is a late phenomenon in Ethiopia compared toother developing countries. It only counts back to the second half of the 1990s as a pilot in one region,
and as a result of its success, the microfinance service was gradually replicated in other regions. The
MFIs in Ethiopia focus on group-based lending and promote compulsory and voluntary savings. Theyuse joint liability, social pressure, and compulsory savings as alternatives to conventional forms ofcollateral (SIDA, 2003). These institutions provide financial services, mainly credit and saving and, insome cases, loan insurance. The objectives of MFIs are quite similar across organizations. Almost all
MFIs in the country have poverty alleviation as an objective. They focus on reducing poverty andvulnerability of poor households by increasing agricultural productivity and incomes, diversifying offⒸ ISERD

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farm sources of income, and building household assets. They seek to achieve these objectives by
expanding access to financial services through sustainable microfinance institutions.
There is a big challenge in accessing financial resources from formal financial institutions/ banksby the poor in Ethiopia due to various reasons, and particularly, collateral. The experiences of some
Asian countries like, Bangladesh, were considered as a success stories in tackling the problem ofaccess to finance, and the Ethiopian government issued proclamation no. 40/1996 in 1996 that allowed
the establishment of MFIs. The government takes financial inclusion as a policy objective and assumed
MFIs can fill this gap. A number of studies indicated that sustainability is the basic question andchallenges of MFIs in developing countries, which hinders their ability to outreach the poor. This
problem has attracted the attention of many researchers and as a result many strategies have been put toensure that MFIs are sustainable (Yaron, 1992). Therefore, one of the objectives of this study is toclearly understand the factors affecting the outreach and sustainability.
The main objective of this study is to evaluate the current performance of Ethiopian MFIs to pointout the influencing factors of the sustainability and level of outreach for the better progress inperformance and as well to contribute to policy formulation.
METHODOLOGY
Outreach and sustainability: Meyer (2002) noted that the poor needed to have access to financial
service on long-term basis (sustainability) rather than just a onetime financial support. This indicates
that, outreach and sustainability are inseparable although there is a tradeoff between them (Izumida,
2003). Due to this fact, this study employed both outreach and sustainability to evaluate the position of
Ethiopian MFIs. This study used both primary and secondary data. Primary data were taken fromquestionnaire followed by interviews. On the other hand, secondary data were taken from time series
data (2005-2014) collected from reports, journals, and other financial statement sources. The study
employs both quantitative and qualitative methods of analysis. Thus, multiple regression models havebeen used with IBM SPSS statistical software. The sampling was done on the basis of purposive
sampling technique to incorporate from all categories (large, medium to small). Accordingly, 14 MFIs
were selected as a study sample from the targeted population of about 34 MFIs operating in the country.
RESULTS AND DISCUSSIONA) Outreach level of Ethiopian MFIs: Various studies have used the number of borrowers as ameasure of microfinance breadth of outreach (Nyamsogoro, 2010; Mersland and Strom, 2009; Harmes
et al., 2008). It is generally assumed that the increasing trend/ the larger the number of borrowers thebetter the outreach. The number of borrowers of Ethiopian MFIs were about 68, 000 in 2005 while thisnumber increased to about 2 million in 2014. In this case we are able to say that, the Ethiopian MFIs
have been expanding its outreach to the poor. The average balances of outstanding loans are proxy
indicators used to indicate a client’s socioeconomic level or depth of outreach. The lower the loan sizeis the indication of the quality of depth of outreach in which the industry average low end depth ofoutreach is loan size less than USD 150. The Ethiopian MFIs average loan size was about USD 121,
which qualifies good quality of depth of outreach. But there was also some MFIs tending to exceed thethreshold of the industry, which needs further investigation whether they are lean towards mission drift
or not. Provision of different kind of product by MFIs is also noted as performance indicators.
As depicted on Table 1, the types of financial services were concentrated on saving and credit
services, it has limitations on addressing the various scopes of needs of the poor. The loan programmes
actually performing better thought the geographically remote area, where poor farmers have very small
access to the financial services of the MFIs. Crop producing farmers have limited access to thesefinancial services. The other indicator of outreach is the percentage of women. With regards to thisⒸ ISERD198

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indicator the Ethiopian MFIs covered only about 55%, compared to the average coverage of South
Asia 86% and Africa 61%. With respect to saving characteristics, the ratio of voluntary savings tocompulsory savings stretched to 482.51 percent in ten years’ period of time from 2005 to 2014 which
is a well-off progress.
Table 1 Types of services, loan programmes, and clients of Ethiopian MFIs
Type of financial services by MFIs

Percentage

Pension
Saving and Credit
Insurance
Money Transfer
Type of loan programme

28.6%
100%
21.4%
21.4%

Loan for Geographically Remote Area
Loan for SME
Loan for Women
Loan for Farmer

28.6%
100%
54.4%
85.%

Percentage of MFIs who
have rural farmer clients
14.3%
21.4%
64.3%
MFIs serving
10%
40%
50%
Average

Percentage

< 10%
< 50%
> 50%
Crop producer
farmers
7.1%
22.9%
40%
35%

B) Sustainability: The definition of sustainability pointedly varies due to the big divides of thought(Institutionalist Vs Welfarist) in the industry. The institutionalist basically assumes MFIs as a purely
business entity. They argued that, the Dollar invested as a loan in microcredit should have to work in
the free market and poverty can be reduced by commercialization, democratization of capital andfinancializing of development (Roy, 2010). But the Walfarist argued, MFIs can achieve sustainability
without achieving financial sustainability. Sustainability basically appraised by two levels ofsustainability. These are Operational Self Sufficiency (OSS) and Financial Self Sufficiency (FSS). OSS
requires MFIs to meet all administrative costs and loan losses from operating income of any kind;
while FSS measures the extent to which an MFI’s business revenue; mainly interest received covers
the MFIs adjusted costs. Both OSS and FSS ratios are expected to be equal and/or more than 100% toprove sustainability of a particular MFI. Based on this assumption the analysis of the trends of OSS
and FSS ratios are indicated in Figure 1 below. Ethiopian MFIs were in increasing trend both in OSS
and FSS during the study period of time, except it tends to decrease of FSS ratio from 2013. The basic
question here is, what are the influencing factors behind this trend.

Fig. 1 Trends of OSS and FSS ratios of Ethiopian MFIs

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Multiple regression models have been used to assess the significant determinants of sustainability.
OSS and FSS are the dependent variables, where about eight independent variables have been
identified based on the Microfinance Information Exchange platform (MIX market), and from thedetailed reviews of the related literatures. These variables are YIELD, Cost Per Borrower (CPB),
Operating Expense Ratio (OER), Active Loan Balance Per Borrower (ALBPB), SIZE, Debt to Equity
Ratio (DER), Number of Active Borrowers (NAB) and Age of MFIs. The IBM SPSS statisticalsoftware have been used for empirical analysis. To apply this model most of the assumption of theregression model has been implemented; and the validation of the data used have been done.
Accordingly, the regression diagnostic test of the null hypothesis “normally distributed errors” hasbeen done and the Shapiro-Wilk’s tests for normality showed, there is insufficient evidence to suggestthat the data set is not normally distributed. Test of skewness and kurtosis also indicated the value of‘zero’ is within the 95% confidence interval. So in both tests we fail to reject the null hypothesis. An
admittedly arbitrary rule of thumb is established to constrain simple correlations between explanatory
variables to be smaller than 0.8 to 0.9. The null hypothesis to check for “No Multicollinearity” hasbeen tested using person correlation and reject the null hypothesis. To solve this problem, number ofactive borrower is removed according to its relative importance to size and the second test proves nomulticollinearity problem. The Durbin-Watson (DW) test for autocorrelation has been done and failed
to reject the null hypothesis, “no autocorrelation”. The disturbance of the error was also checked byscatterplot and proves no autocorrelation. After the consecutive test of validity and reliability of thedataset, the OSS and FSS determinants are identified along their significance level.
Determinants of Operating Self Sufficiency
Table 2 Econometric Results of OSS’s Determinants

Variables(Constant)
SIZE
YIELD
CPB
OER
ALBPB
DER
Age

Std. error
1.099
0.046
0.022
0.003
0.129
0.127
0.101
0.114

Standardized
coefficients
Beta
0.249
0.738
-0.951-0.698
0.052
0.775
0.221

t

Sig.

0.170
0.768
0.636
-4.129-0.456
3.486
0.786
0.184

0.245
0.228
0.267
0.000***
0.327
0.002**
0.222
0.428

Dependent Variable: OSS, *** Significant at 1%; ** Significant at 5%; * Significant at 10%
Source: Author’s Computation using IBM SPSS (2016)

As it can be seen from Table 2 above, from the estimated seven variables only about two variables
have a significant impact on OSS. The null hypothesis for these factors were as follows.
Ho: There is a negative significant relationship between cost per borrower ofmicrofinance institutions and operational self-sustainability.
H0: There is no significant positive relationship between average loan balance
per borrower and operational self-sustainability.
The result revealed that CPB have a negative significant impact on OSS at 1% significant level.
Therefore, we failed to reject the null hypothesis. The findings by (Woller and Schreiner, 2002;
Christen et al, 1995) also indicated the CPB negatively and significantly affect operationalsustainability. With regards to ALBPB, the statistics result indicated that it has positive impact on OSS
at a significance level of 5%. The ALBPB for Ethiopian MFIs is about USD 121. Thus, the EthiopianⒸ ISERD

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MFIs average loan size evidences high cost from this analysis. The smaller the loan size, the better inoutreach, but the higher the cost; here is where the tradeoff between the two triggered. The averageloan size for African countries was 307 USD during the study period of time. Other studies, forinstance (Adongo and Stork, 2006) similarly found that profitability is related to selling bigger loans.
Determinants of Financial Self Sufficiency (FSS)
The result of the econometric analysis indicated that, about three factors, namely, CPB, YIEDER, andDER have significant effect on financial sustainability. The proposed hypothesis for the above
mentioned influencing factors were as follows.
Ho: There is negative significant relationship between cost per borrowerof microfinance institutions and financial self-sustainability
H0: There is a significant positive relationship on the yield on gross loan
portfolio of microfinance institutions with financial self- sustainability.
H0: There is no positive significant relationship between debt to equity ratioof microfinance institutions and financial self-sustainability.
Table 3 Econometric Results of FSS’s Determinants

Variables(Constant)
SIZE
YIELD
CPB
OER
ALBPB
DER
Age

Std. error
0.706
0.030
0.144
0.085
0.083
0.082
0.065
0.073

Standardized
coefficients
Beta
0.371
0.879
-0.951-0.048
0.118
0.792
0.235

t

Sig.

0.652
0.163
3.532
-3.976-0.376
0.320
3.141
0.780

0.262
0.436
0.001**
0.000***
0.356
0.104
0.003*
0.224

Dependent Variable: FSS, *** Significant at 1%; ** Significant at 5%; * Significant at 10%
Source: Author’s Computation using IBM SPSS (2016)

As one can depict from Table 3 above, CPB have a negative significant effect on FSS at 99%
confidence interval, we failed to reject the null hypothesis. To the contrary, the study made by(Nyamsogoro, 2010), indicates that CPB has no significant impact on FSS of Tanzanian MFIs. The
YIELD from gross loan portfolio influenced FSS positively at 95% confidence interval, fail to reject
the null hypothesis. Correspondingly, the research finding by (Cull and Morduch, 2007) indicates thatthe coefficient for gross portfolio yield is positive and significant on financial self-sufficiency. The
result also exhibited DER have positive effect on FSS, rejects the null hypothesis. In the same way,
(Coleman, 2007) for example, found that highly leveraged microfinance institutions have higher ability
to deal with moral hazards and adverse selection than their counterparts with lower leveraged ratio.
This states that high leverage and profitability are positively correlated
CONCLUSIONS AND RECOMMENDATIONS
Access to financial resources in Ethiopia, particularly by poor is very limited, and the poor are
regarded as unbearable by formal banks. MFIs are playing vital roles to fill these gaps. In this study the
MFIs are evaluated on two main indicators, outreach and sustainability. Concerning the outreach, it isfound that the outreach level of Ethiopian MFIs has been improving in width and depth, although thereⒸ ISERD

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is still a need to diversify the services and programs to address the uncovered need of the disadvantagegroups of the society, such as people in the geographically remote location, gender, crop farmers. We
recommend the MFIs have to work against achieving this goal to fight poverty as per their major goals.
The government is also expected to set appropriate regulations, and policy framework which motivateand enable the MFIs to address these unmet needs, for instance supporting the MFIs to access cheaploans. The intervention of the government in the operations of MFIs, particularly in a loan recruitment
is vital because the MFIs cannot fully operate in self-sustainable manner.
Sustainability was measured on two levels, operationally, and financially. The study revealed that
Ethiopian MFIs are operationally self-sufficient while financially subsidy dependent. The increasing
trend of both financial and operational self-sufficiency is mainly due to the increase in revenue, and theexistence of subsidy; we recommend to keep the trend. The determinant factors on operationalsustainability were the cost per borrower, and loan size. Therefore, the MFIs are expected to reduce
cost per borrower by innovation, for instance, by introducing village banking systems, and mobile
phone in microfinance services. In case of loan size, the current loan size of the MFIs is desirable,
qualifying depth of outreach but risks sustainability. Hence, we recommend the donors andgovernment should give priorities for those MFIs addressing the need of the very poor providing smallloans. The influencing factors for financial sustainability were the cost per borrower, and yield fromgross loan portfolio and debt equity ratio. The MFIs have to work against these negative factors byincreasing their yield innovatively, increasing the volume of revenue, and/or the profit margin and by
reducing the cost to the optimal level. The debt equity ratio basically refers to cheap fund. It may bedesirable to increase this ratio by attracting the interest of donors/lenders using inclusive plan toaddress the need of the poor, particularly to reach the female beneficiaries. The study also recommendthat the government of Ethiopia should encourage involvement of private sector and donors in building
capacity of MFIs.
REFERENCES
Adongo, J. and Stork, C. 2006. Factors influencing the financial sustainability of selected microfinanceinstitutions in Namibia, NEPRU Research Report, 38, ISSN 1026-9258. Available: SSRN: http:// ssrn.com/abstract (October 27, 2011).
Christen, R., Rhyne, E., Vogel, R. and McKean, C. 1995. Maximizing the outreach of microenterprise finance,
An analysis of successful microfinance programs, USAID.
Cull, R., Demirguc-Kunt, A. and Morduch, J. 2007. Financial performance and outreach, A global analysis ofleading micro banks, The Economic Journal, 117, 107-133.
Nyamsgoro, D. 2010. Financial sustainability of rural microfinance institutions in Tanzania.
Woller, G. and Schreiner, M. 2002. Poverty lending, financial self-sufficiency, and the six aspects of outreach.
Harmes, N., Lensink, R. and Meesters, A. 2008. Outreach and efficiency of microfinance Institutions.
Izumida, Y. 2003. Programmed lending for social policies, challenges for Vietnam Bank for social policies.
Department of Agricultural and Resource Economics, Working Paper Series. The University of Tokyo.
Coleman, A. 2007. The impact of capital structure on the performance of microfinance institutions.
Mersland, R. and Strom, A. 2009. Performance and governance in microfinance institutions. Journal of Banking
and Finance, Elsevier, 33 (4).
Meyer, R. 2002. Track record of financial institutions in assisting the poor in Asia. ADB Institute Research Paper,
No 49, December 2002.
Roy, A. 2010. Poverty capital, Microfinance and the making of development. Routledge, New York.
SIDA, 2003. Transforming microfinance institutions. Providing Full Financial Services to the Poor.
Woller, G. and Schreiner, M. 2002. Poverty lending, financial self-sufficiency, and the six aspects of outreach.
Working paper, Washington DC.
Yaron, J. 1992. Successful rural finance institutions. World Bank discussion paper, 150, The World Bank,
Washington DC.

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Research article
erd

Supply Chain Analysis for Determining the Requirements forContinuous Woody Biomass Energy Utilization Systems:
Comparison of the Actual Management Conditions in JapanKAZUYOSHI NEMOTO*
National Institute for Environmental Studies, Tsukuba, Japan
Email: nemoto.kazuyoshi@nies.go.jp

HIROMASA INUZUKA
Sensyu University, Kawasaki, JapanSHOGO NAKAMURA
National Institute for Environmental Studies, Tsukuba, JapanYASUHUMI MORI
National Institute for Environmental Studies, Tsukuba, Japan
Received 16 January 2017

Accepted 28 June 2017(*Corresponding Author)

Abstract In Japan, the number of newly constructed woody biomass power plants are expected
to increase with the introduction of the Feed in Tariff mechanism. However, many woody
biomass energy plants, including heat supply systems, face difficulties because of issues such as
high fuel costs and find it difficult to maintaining a stable supply. This study aims to reveal therequirements for continuous business regarding woody biomass energy, focusing on four
factors: wood supply, fuel manufacturing, energy supply, and energy demand. Specifically, we
selected multiple Japanese cases that focus on the types of woody fuels such as cordwood,
wood chips, and wood pellets, conducted semi-structured interviews, and compared andexamined how their supply chains successfully developed. We found that integral cooperation
among the four factors was essential, and support regarding each factor, economic viability, as
well as balanced supply and demand for wood were necessary for ensuring continuous woody
biomass energy utilization systems.
Keywords renewable energy, forest industry, regional revitalization, wood fuels

INTRODUCTION
In regions with abundant forests, woody biomass is a potentially promising renewable energy resource
and is expected to develop into a regional industry. In Japan, the utilization of woody biomass togenerate electric power expanded with the introduction of the Feed in Tariff (FIT) in 2012, which
resulted in the construction of woody biomass power plants in various parts of the country. In addition,energy supply systems using woody biomass heat were being introduced as government subsidized
projects. After a biomass energy system has been created, trees must be cut down and transported fromthe forests to produce fuel. Fuel has to be fed to heat boilers; its combustion must also be controlled.
Therefore, to ensure the continuity of a biomass energy system, it is vital to establish a stable system tosupply woody fuel from the upstream to the downstream end of the system.
This study was based on the idea that it was important to build a supply chain that ensuresbusiness feasibility for organizations that stabilize supply, and maintain cooperative relationships
between these organizations. For a biomass supply chain, of course it was important to balance
expenditures and income to ensure continuous operation. Further, because of the large number ofⒸ ISERD

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organizations characteristically involved, business feasibility and inter-organizational relationships
were selected as the themes of this study. Regarding woody biomass energy utilization systems,
Raychaudhuri (2015) surveyed the literature on biomass supply chains in some countries in Asia andEurope, but Japan was not included. Becker (2011) suggested supply chain analysis framework forassessing policies in the United States. Tahvanainen (2011) and Yoshida (2016) analyzed costs ofwood chip supply chains in Europe and the US respectively. In Japan Sato (2015) designed a model forwood biomass supply chains in Tohoku area. However, there was no research related to biomassenergy utilization systems in Japan that evaluated actual supply chains from the perspective of businessfeasibility. Further, there was very little research on the supply chains of the systems in Japan from theperspective of inter-organizational relationships and the balance between biomass supply and demand
among the players. Therefore, this study analyzed supply chains from three perspectives: businessfeasibility, the related inter-organizational relationships and the balance between biomass supply anddemand.
OBJECTIVE
This study examined Japan’s woody biomass energy utilization systems that utilize forest resources inorder to identify the entire supply chain formed by a series of linked business organizations, and thestate of operation of each constituent player in this supply chain based on actual cases. Next, based on
the information obtained, the supply chain and its constituent players were analyzed from theperspectives of their business feasibility and inter-organizational relationships and the balance between
biomass supply and demand among the players in order to state the conditions required to ensure thecontinuity of the system.
METHODOLOGYThis study proposed the framework shown in Fig. 1, which divided the supply chain for a woody
biomass energy utilization system into four constituents: raw material supply, fuel production, energy
supply, and energy demand. For a system to be sustainable each player in the system should havebusiness feasibility and all players should be interconnected. Raw material supply referred to the
cutting of trees in the forest, the transportation, and sale of timber. Fuel production referred to the
production and/or sale of woody fuel (including firewood chips or pellets). Energy supply included
burning woody fuel in the equipment and supplying the energy to users as heat or electric power.
Energy demand referred to consumption of electric power or heat for air conditioning, hot water inhouseholds, businesses, public baths, and similar facilities.

EnergydemandRawmaterial
supplyEnergysupplyFuel
production

Fig. 1 Analysis framework of the woody biomass energy system
This study evaluated the four constituents of the supply chain of a woody biomass energy systemfrom the following three perspectives. The first was the business feasibility of each constituent player,Ⓒ ISERD

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whether each could be established alone without the support of external financial subsidy. The second
perspective was the relationship between the constituent players. The third was the balance between
biomass supply and demand among the players. To build a supply chain, the links between its players
were indispensable. It was also important for them to establish business relationships to ensure smoothbusiness. Moreover, it was equally important that they balance biomass supply and demand through thelinks between them.
This study conducted a survey to analyze a supply chain consisting of the four constituents
explained above by selecting six cases as typical energy utilization systems from among energy
systems such as electric power systems, heat systems. The six cases selected were shown in Table 1.
The supply chain and constituent players of the energy systems in each of these cases were shown in
Table 2. In Cases D, E, and F, local governments played major roles in building the overall supply
chains; therefore, these local governments as supporter were also included in our study.
Semi-structured interviews were conducted. The questions dealt with the business which theplayer conducts, stakeholders such as its suppliers and customers, quantities of biomass-related
products it acquired and sold, business costs such as the cost of production equipment, total sales,business prospects, and business challenges. The interviews were conducted from August 2014 toSeptember 2016.
Table 1 Cases analyzed
Case

Form of fuel

Energy equipment

Energy demand

A
B
C
D
E
F

Firewood
Firewood
Chips
Chips
Pellets
Pellets

StovesBoilers
Generator
Boilers
Stoves/boilers
Stoves/boilers

Heating
Hot water
Electric power generation
Cooling/Heating/Hot water
Heating/Hot water
Heating/Hot water

Amount of biomass
fuel (wet-t/y)
1,900
300
20,000
1,600
500
1,300

Table 2 The supply chain and constituent players in each case
CaseA

Raw material supply
Forestry association,
Citizens association,
Independent forester

Fuel production

Energy supply

Energy demand

Supporter

Company sellingwood stoves

Households

Households―

B

Independent forester

Community group

Public baths

Public baths

Citygovernment

C

Lumber company,
Orchard operator

Private enterprises

Electric power
utility

Households―

D

Lumber company

Sawmill operator

Third-sector

Public facility

E

Sawmill operator

Third-sector

F

Forestry association

Cooperative
association

Public facility
Households
Public facility,
Business officePublic facility
Households
Public facility,
Business officeTown
governmentCity
governmentCity
government

RESULTS AND DISCUSSION
1) Case A (wood stoves)
The system was principally supplying firewood for use in wood stoves used to heat households. With
regards to raw material supply, suppliers sorted out raw timber from managed forests for lumber andfirewood. With regard to fuel production, a wood stove company purchased the raw material and partⒸ ISERD205

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time workers used wood-splitting machines to process and naturally dried the material for producing
firewood. The processing was partly contracted to the raw material suppliers. Households equippedwith wood stoves purchased the firewood and used the energy to heat up their households.
The wood stove company established the supply chain. It had developed its firewood supply
system and home-delivery service, which helped to expand its wood stove market. Concurrently, thedemand for firewood rapidly surpassed forecasts, and with the production unable to keep up, stocks
became inadequate. This in turn led to deliveries being made after artificially drying the firewood for a
temporary duration. While the firewood fuel production system had balanced incomes andexpenditures, profits from new wood stove sales were higher; therefore, firewood fuel production was
not the principal business of the organization.
2) Case B (firewood boiler)
The system comprised a community group processing raw timber, which was supplied by independent
foresters, into firewood, which was subsequently supplied for use in a firewood boiler used to meet thedemand for hot water in public baths. In this case, the community group led the establishment of the
supply chain by forming an organization that purchased raw timber and made firewood. It purchased
the timber, with half of the necessary funds contributed by a local government. It built three yards(places to collect thinned wood), and in addition to firewood, handled the timber to sell to paper
makers. It also encouraged independent foresters to receive forestry training. A firewood boiler was
installed with the support of subsidies that covered two-thirds of the cost.
3) Case C (generating electricity with chips)
The system was the supply of woody chips for fuel to a woody biomass power plant. In this case, anelectric power utility built the supply chain. Fuel production was performed by a company affiliatedwith the power utility. The fuel production company signed raw material contracts with a group of
lumber companies in order to stabilize supplies of raw material. The raw material consisted of about
80% thinned wood, and woody waste was 20%. Energy supply was achieved by burning wood chips togenerate electric power for households. The purchase price of the electric power was supported by FIT
system, such that the power could be bought for a good price in relation to the overall production costs
set according to the type of raw material used as the woody fuel.
4) Case D (chip-powered boilers)
In this system, wood chips were supplied to a biomass boiler that supplied heat to a group of public
facilities. The town built the supply chain. At the suggestion of the town, a wood chip company
financed by a lumber company and a sawmill operator was established. Energy supply was the role ofthe third-sector public company that operated and maintained chip boilers owned by the town. The costof installing the three boilers was incurred by the town, prefecture and national government subsidy. It
cut the total value of fossil fuel costs in 2013 by 38.6 million yen per year. On the other hand, the town
paid 38 million yen for chip supply and equipment management. The town not only bore the cost of thechip production facility, it also signed supply contracts to help stabilize the income earned by thesystem for stable supply of the fuel.
5) Case E (pellet stoves and pellet boilers)
In this system, a city built a supply chain as a pellet production system to utilize forest resources. The
raw material (chip dust) for pellets was provided by chipmakers. In fuel production, a third-sector
company was formed with funds invested by the city, a forestry association, and small-scale
shareholders. Total funding for the fuel production equipment was provided by the city and nationalⒸ ISERD

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government subsidy. Initially, it was predicted that it would supply pellets for stoves in households.
However, because stoves had not gained as much ground as was expected, most of the pellet was
consumed by the city hall, schools, and welfare facilities.
6) Case F (pellet stoves and pellet boilers)
In this system, pellets were supplied to stoves in business offices and to pellet boilers installed inpublic baths in a city. Regarding raw material supply, raw timber was supplied through a market. In
fuel production, a cooperative association produced chips for paper makers in addition to those used to
produce pellets. Energy supply and demand were primarily regarding pellet boilers and pellet stoves
owned by the city along with demand for hot water supply and heating. The city led the developmentof the supply chain. It supported the formation of a cooperative association to manufacture pellets andworked to create demand for these pellets in order to revitalize the forestry industry. The city installedpellet boilers in public facilities, and pellet stoves in schools.
Analysis of the Supply Chain from Three Perspectives
1) Business Feasibility of Constituent Players
As seen from the six cases, in all systems, each constituent system could be operated stably. However,it could not be concluded that their systems were established economically, as many of these
constituent players covered costs of equipment for fuel production and energy supply with subsidies. In
Case C, it was clear that the system could continue as long as the FIT system was maintained.
Regarding Case A which acquired no subsidy, the system was established in combination with otherbusiness. The system was achieved by combining the production of firewood with the sale of stoves. In
addition, there were also cases where a player established its own systems in combination with otherbusiness. Regarding fuel production in Case B and F, biomass was a source of secondary income forthe players. They manufactured and sold materials to paper makers separately from biomass energy
utilization systems. Previous studies state that business feasibility requires an increase in efficiency
through measures such as increasing the scale of fuel production or changing the location of thechipper, or financial assistance to initial investments in each system. However, these studies do notdiscuss combining different businesses together.
2) Interrelationships between Constituent Players
An analysis of the six cases confirmed that private companies, administrative bodies, or community
groups acted as the builders of supply chains by creating business relationships between the various
players. Moreover, even after a supply chain had been constructed, the local government was seen toplay the role of overall manager of the chain in Case D, E and F. There were also cases where thebuilders also took part in the formation of constituent players or fuel production system of the supply
chain in Case A, B and C. For example, in Case B, a community group created a purchasing systemand provided independent foresters with practical forestry training. It also assisted with preparations ofsubsidy requests to introduce firewood boilers that would be later supplied with firewood through thesystem. Previous studies point to the need of coordinating with the stake holders and present casestudies where timber owners manage their businesses by outsourcing of fuel production. The role ofthe supply chain builders in our study incorporates all these activities.
3) Balanced Biomass Supply and Demand between OrganizationsⒸ ISERD

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In many cases each constituent ensured multiple suppliers and purchasers in order to prepare forfluctuations in demand for their energy systems. For example, in Case A, the firewood producer
associated with multiple suppliers, and also entrusted to the suppliers some part of processes infirewood production. While in Cases D, E and F, the local governments created a large demand byincreasing the number of boilers in public facilities. Even after their supply chains were completed andthe systems were in operation, there were cases where it was important to watch the flow of biomass
throughout the supply chain and overcome problems of quality control in the fuel and the supply
system. In both ways, the builders of the supply chains played important roles. Previous studies
mention the need for securing storage space, temporary workers for fuel production or the importanceof communication between upstream and downstream stakeholders. The multiple suppliers andpurchasers in our research extend these aspects and add management of energy demand.
DISCUSSION
Conditions of economic support for the supply chains were different between the electric power
generation system and the heat system in which stoves or boilers operated. The FIT system wasavailable for electric power generation systems, and the system economically ensured the continuity ofsales. In the case of heat system, although subsidies were available to cover cost such as introducing
boiler for the heat supply systems, there were no systems designed to ensure continuous operation of
the heat supply system along the overall supply chain. The economic difficulty in establishing a heat
system could be analyzed through the following two aspects. Firstly, the heat system competed with
low-priced energy systems, such as fossil fuel. Secondly, some regions would have small or no markets
for the heat system. These regions faced challenges such as few sellers of woody fuel or materials andfluctuations in supply and demand.
In the light of the above underlying circumstances, followings steps were being taken. With
regards to the first point, players were believed to have established systems by combining the biomassenergy business with other businesses. But, it relied on capabilities and size of the business
management of the players. With regards to the second point, it was necessary for a local government
or community group to build the supply chain. They should encourage the players to cooperate, help
the players to obtain public fund for fuel production equipment and create heat demands in regional
and public facilities. In addition to a balanced wood fuel supply and demand, the builders of the supply
chain needed to encourage players to have multiple suppliers and purchasers in regions where there
were no markets to trade inventory shortfalls or surpluses. Besides this, supply chain builders could notexist in all regions because it depended on the capability of the builders and their financial conditions.
Therefore, just like an electric power generating system, a heat supply system also would require asystem to support the overall supply chain. It was thought to be important to create, for example, asystem to correct the price gap between fossil and woody fuel, or a system to subsidize the purchaseprice of the biomass fuel or the heat supplied.
In terms of future challenge, this study couldn’t include wood biomass combined heat and powersystems because of no such systems operating stably in Japan at the time of the survey. It waspredicted that the future construction of many biomass power plants would rapidly increase thedemand for wood and raise its price. Therefore, even in the cases surveyed for this study, changing the
supply constituents would be unavoidable. These issues will have to be taken up in a future survey.
CONCLUSION
This study was undertaken by conducting interviews concerning six cases in order to clarify therequirements for continuous stable operation of biomass energy utilization systems using forest
resources in Japan. The supply chains in each case were divided into four constituents and analyzed
from three perspectives, that is business feasibility, relationship among constituents, and balance of theⒸ ISERD

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biomass supply and demand. The results showed that the following conditions were required tocontinuously operate a woody biomass energy utilization system.
1) For the constituent players, combining the biomass energy utilization system with anotherbusiness was necessary in order to ensure business feasibility.
2) In order to link the four constituents, it was important that there was a supply chain builder toestablish the necessary constituent players where none existed and to interconnect these players.
3) It was necessary to ensure multiple suppliers and purchasers for balancing the flow of materials
in demand and supply between the constituents.
4) A system such as FIT for electric power systems was also necessary for heat supply systems inorder to ensure the business continuity of the entire supply chain.
ACKNOWLEDGEMENTS
This work was supported by the Ministry of the Environment, Low Carbon Technology Assessment
Program. We wish to thank everyone who spared their valuable time and granted us interviews.
REFERENCES
Raychaudhuri, A. and Ghosh, S.K. 2015. Biomass supply chain in Asian and European countries. Waste
Management for Resource Utilisation, 35, 914-924.
Becker, D.R., Moseley, C. and Lee, C. 2011. A supply chain analysis framework for assessing state-level forest
biomass utilization policies in the United States, Biomass & Bioenergy, 35 (4), 1429-1439.
Tahvanainen, T. and Anttila, P. 2011. Supply chain cost analysis of long-distance transportation of energy woodin Finland. Biomass & Bioenergy, 35 (8), 3360-3375.
Yoshida, M. and Sakai, H. 2016. Form of supply chain management of fuel use wood chips and roles ofconcerned parties. Journal of Forestry, Japan, 4, 27-34 (in Japanese).
Sato,Y., Frubayashi, T., Morikami, K. and Nakata, T. 2015. Design of sustainable woody biomass supply chain
applying mixed integer linear programming. Proceedings of Conference on Biomass Science, The Japan
Institute of Energy, 119-120 (in Japanese with English summary).

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Research article
erd

Assessing Factors Influencing Farmers’ Perceptions on
Adaptation to Climate Change: A Case of Apple Farmers in
Cheongsong, Korea
SEONG YOON CHOI*
Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
Email: k147629@int.k.u-tokyo.ac.jp

EIJI YAMAJI
Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
Received 14 December 2016

Accepted 29 June 2017(*Corresponding Author)

Abstract One of the most important consequences of climate change in Korea is the moving of
the adequate cultivation area for apple. To respond adequately to such consequences, thefarmers require performing on the adaptation measures. Although Korean government puts
great efforts to develop adaptation measures, the livelihoods of agriculture and ruralcommunities are still posing great threats from climate variability and change. It is because themost of adaptation studies and policies fail to address the perception of farmers who decide andperform the adaptation measures. Without an understanding of farmers’ perceptions, private
adaptation strategies are unlikely to be effective. This paper, therefore, aims to investigate andanalyze factors influencing farmers’ perception on adaptation behaviors. To meet suchobjectives, this study based on theory, a Model of Private Proactive Adaptation to Climate
Change (MPPACC), explaining individual’s intention of adaptive behavior is based on sociocognitive aspects including perceived adaptation measure efficacy, self-efficacy and adaptation
costs. To analyze the factors influencing farmers’ perception and behavior of climate change
adaptation, 170 apple farmers in Cheongsong County is selected for farm household survey. By
analyzing through multiple linear regressions, the results were found that the farmers’
perceptions of adaptive efficacy are significantly associated with farm household demographic
and socioeconomic factors including investment in crop insurance, and contents and sources of
information. This implies that to enhance the farmers’ motivation to adaptation, the local
government should pay further attention to improve credibility of crop insurance efficacy andthe quality and source information to increase farmers’ adaptive capacity through increased
farmers’ perception on adaptation efficacy.
Keywords climate change, adaptation behaviors, perceived adaptation efficacy,
apple farming

INTRODUCTION
Agricultural communities in Korea have already experienced the impact of climate change, including
crop and livestock losses from severe drought and flooding, large-scale losses from weather-related
disasters, shifts in planning and harvesting times and cultivation lands (MoE, 2015). According toMinistry of Environment (2015), the assessment of the climate change vulnerability and its impact onapple cultivation in Korea indicated that adequate apple cultivation area, which has been in thesouthern, eastern parts of Korea, is moving to the northern parts of the Korean peninsula. Moreover, in
the Northern Provinces in Korea, it is already increasing apple production in the area. Although
Cheongsong County, one of the top apple-producing counties in southern parts, is increasing its appleⒸ ISERD

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production until recent years, the County is also projected to become inadequate to produce the apple
crops from the 2030s (RDA, 2015). Not only the projected climate is shown to change apple
cultivation in the County, the current climate variability already has been increasing farmers’
awareness on the climate variability and adaptation behaviors. To increase farmers’ resilience toclimate change, enhancing the adaptation capacity is vital. The farmers have been conducting
adaptation behaviors to prevent and lessen the damage of climate variability and change. The private
adaptation behaviors can be influenced by farmers’ perceptions and assessments on the specificadaptation measures. However, only limited studies has examined the factors influencing farmers’
perceptions on the adaptation behaviors.
The Model of Private Proactive Adaptation to Climate Change (MPPACC) is one of the limited
research to examine the socio-cognitive perspective of adaptation behaviors derived from discourses in
psychology and behavioral economics (Grothmann and Patt, 2005). According to this model,
individuals’ adaptation behavior to climate change can be influenced by some socio-cognitive factors
including perceived adaptation efficacy which is measured by individuals’ perceptions of 1) adaptation
measure efficacy (PME), 2) self-capacity to perform the adaptation measures (PSE), and 3) the costassociated with such performance (PAC). In this study, the factors hypothesized to influence such
perceptions are farm households’ characteristics (socioeconomic factors), previous experience withclimate change, and information (climate change, adaptation) from various sources. To enhance theapple farmers’ resilience to increasing climate risks, it is important to not only to develop adequate
adaptation measures but also to understand what factors may motivate the farmers to perform theadaptation measures.
OBJECTIVE
The main objective of this paper is to suggest some policy implications for enhancing farmers’
adaptation capacity by investigating and analyzing farmers’ perceptions on the effectiveness of private
climate change adaptation behaviors and the factors influencing such perceptions.
METHODOLOGYAs shown in Fig. (1), North Gyeongsang Province is located in the southeastern part of Korea andCheongsong County is located in the eastern part of the Province. The Province produced 372,627 tonsof apples in 2015 which is about 64 percent of total apple produced in Korea (RDA, 2015).
Cheongsong County is evaluated as one of the top County to produce the highest quality apples in
Korea (Cheongsong County, 2016). Because of its location, altitude and temerature, the County has
been well-suited for producing high-quality apples. The County is surrounded by mountains thatprovide a high diurnal range which is an advantage climate for apple cultivation. Moreover, the County
usually has less rainfall compared to neighbor Counties which are also an advantage climate factor forapple cultivation. The County’s annual average temperature, although increased from 12.5 in 2014, isrecorded as 12.9 in 2015 (KMA, 2016). Since the County has been known to have the suitableenvironment and climatic for apple cultivation, about 80% of farmers are engaged in apple cultivationin the County (Cheongsong County, 2016). The County, not only produces the high-quality apples, but
it also organizes several major events including the biggest apple festival in Korea. Apple cultivation is
not only an extremely important economic source, but it also has become a part of the lives of thepeople living in the County.
The farmers’ adaptation behaviors to climate change are collected through intensive review ofprevious studies and government reports. After listing all the adaptation measures that are applicable tothe apple farmers, the authors conducted interviews with the farmers and the local agricultural officials
for final selection. Finally, 9 adaptation measures were selected and analyzed in this study includes:Ⓒ ISERD

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adjusting farming dates, adjusting use of pesticides, switching to different crop, paying attention toclimate information, diversifying crops, buying crop insurance, improving soil condition, changing toother apple variety and searching for non-farming job.

Fig. 1 Conceptual framework
From April to June 2016, a farm household survey with 170 apple farmers in Cheongsong County
was conducted with structured questionnaires, also, in-depth interviews and focus group discussions
were conducted to collect the data on farmers’ characteristics, experiences with climate change andclimate change and adaptation information from various sources including neighbors, community
leaders, local agricultural extension services, public media and cooperatives. Farmers were also asked
about their perceptions of the adaptation effectiveness including their perception of adaptation measureeffectiveness, self-ability to perform measures, and the costs performing the 9 specific adaptation
measures. For perceptions of measure effectiveness and self-capacity, farmers were asked to scale 1(not effective at all) to 4 (extremely effective) while on the costs were scaled as 1(extremely expensive)
to 4 (not expensive at all). Four linear regressions were conducted for analyzing the factors affecting
farmers’ perceptions on adaptation efficacies. For the regression, farmers’ adaptation assessments wererepresented by four dependent variables: perceived measure efficacy (PME), perceived self-efficacy(PSE), perceived adaptation cost (PAC) and overall perception of adaptation efficacy. The overall
perception of adaptation efficacy is calculated by adding three adaptation perceptions and divided bythree to weigh all three perceptions equally.
Overall Perceived Adaptation Efficacy = (PAE + PSE + PAC) / 3
The explanatory variables considered in the regression are farm household characteristics,
socioeconomic factors such as income, sales mechanisms, crop insurance status, previous experience inclimate risk and information from various sources. Previous studies on individuals’ adaptation
behavior found that some demographic and socioeconomic factors such as age, experience, gender,
income, education and sales mechanisms influence farmers’ adaptation behaviors (Bryan et al., 2013;
Deressa et al., 2011; Fujisawa and Kobayashi, 2011; Grothmann and Patt, 2005). Personal experience
with climate risk can also motivate private adaptation by influencing one’s cognitive factors(Grothmann and Patt, 2005). Moreover, information can play a vital role in the farmers’ perception on
the adaptation efficacies (Grothmann and Patt, 2005). Not only the contents itself but how farmers
receive such information can have a diverse effect on them. Therefore, in this study, both climate andadaptation information are considered to be disseminated by public media, neighbor farmers, village
leaders, agriculture extension service centers, and agricultural cooperation. This study assumes thatclimate change and adaptation information from different sources can have a different influence on the
farmers’ perception of three adaptation efficacies. The models were tested for the R-squares,
multicollinearity, normality, linearity and homoscedasticity of the residuals
Perceived Adaptation Efficacy = f (farm household characteristics, climate risk experiences,climate change information, adaptation information)Ⓒ ISERD

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RESULTS AND DISCUSSION
To analyze the factors influencing farmers’ cognitive adaptation capacity, particularly on the cognitive
evaluation of adaptation behaviors are found from the four regression models. Table 1 shows the resultsof the regressions, and it was found that farmers’ perceptions of adaptation measure efficacy, selfefficacy, adaptation costs and overall adaptation behaviors are influenced by some farm household
characteristics, socioeconomic factors, and climate and adaptation information from various sources.
Moreover to all the regression coefficients presented in Table 1, Table 2 demonstrates the R 2, adjusted
R2, F-test (p-value) and VIF. The R2 for all models indicated that the statistically significant
explanatory variables could explain 46 to 57% of the variation of farmers’ adaptation perceptions. The
variance inflation factor (VIF) for all explanatory variables were less than 3and this indicates that there
is no multicollinearity problem found for the variables.
Farm Household Characteristics
Farm household characteristics found to have the significant influence on farmers’ assessment ofadaptation behaviors. According to the regression results, farmers’ age is statistically significant inrelation to farmers’ perception on the measure efficacy. The coefficient indicates that as the farmer isyounger, they perceive adaptation measures as more effective. Gender is also found to be a significant
factor. The female farmers than male farmers perceive overall adaptation behavior as effective. In
addition, years of education seems to be negatively related to the farmers’ perceptions, particularly
significant with the overall adaptation behaviors. Farm area is positively related to how farmers
perceive adaptation measure effectiveness while negatively correlated with adaptation cost. With
increasing farming area, farmers perceive adaptation measures as effective while the costs associatedwith the adaptation measures expensive. Income can play an important role in farmers’ assessment of
the cost of the measures. According to the results, a farmer with higher income can perceive theadaptation cost as more expensive. In general, farming is a family business that sons usually take overthe farm from their parents. Having successor can influence farmers’ perception on adaptation,
especially related to the perception on self-capacity to carrying on the measures on their farms.
Moreover, the number of participation in the agricultural training programs does show the significant
relation with how farmers assess their own adaptation capacity.
The apple farmers sell their apple through direct or indirect markets. Direct sellers can receive
feedbacks and new information. According to the regression model, the farmers with such market
channels can have a higher perception of the adaptation measure and overall adaptation behavior aseffective. Crop insurance plays an important role not only to recover the damages from the natural
disasters but to prevent and remedy farmers from future disasters. Farmers who bought the crop
insurance are more likely to perceive adaptation cost as less expensive and have a higher perception of
the overall adaptation behaviors. However, simply buying crop insurance and how many years thefarmers with crop insurance can have a different influence on their perceptions. Interestingly, as the
cumulative years of buying crop insurance increases, farmers’ perception on the adaptation measure,self –capacity and overall adaptation behaviors can diminish. This indicates that as the years offarmers’ buying crop insurance increases, they find the fraud in the system and feel that they do not get
any benefit of buying the insurance. This induces the necessity of improving the current insurance
system.
Climate Risk Experiences
Climate risk experiences with increasing temperature, changing patterns and intensity of precipitationand extreme weather events including typhoon can influence farmers’ motivation to perform adaptation
measures; however, according to the regression results in this study, climate risk experiences did notⒸ ISERD

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show any statistically significant influence on the farmers’ perceptions, particularly related to theadaptation efficacies. However, it could be worth of mentioning that according to previous studies, it
was found that previous climate-related risk experiences can have influence on farmers’ motivation or
intention to adaptation. Therefore, although not directly influencing the farmers’ perceptions related toadaptation measures, it could have an influence on other cognitive factors that influence farmers’
motivation to climate change adaptation behaviors.
Table 1 Multiple linear regression models on farmers’ perceptions of adaptation behaviors
Explanatory variables

Perceived
Measure Efficacy

Perceived
Self-efficacy

Perceived
Adaptation Costs

Overall
Perception

Farm household characteristics
Age (continuous)-0.0085***-0.0018***-0.0021****-0.0124***
Gender (1=male, 0= female)-0.1663***-0.1197***-0.0866***-0.3725***
Education level (continuous)-0.0224***-0.0113***-0.0259***-0.0597***
Farming area (continuous)-0.0966***-0.0664***-0.0960***-0.0671***
Farming Experience (continuous)-0.0032***-0.0001***-0.0017***-0.0013***
Income (continuous)-0.0047***-0.0398***-0.0822***-0.0471***%of income from apple (continuous)-0.0001***-0.0026***-0.0004***-0.0029***
Successor (1=yes, 0=no )-0.1136***-0.2233***-0.1030***-0.4399***
Agricultural training (continuous)-0.0040***-0.0230***-0.0119***-0.0151***
Smart-phone use (1=yes, 0=no)-0.1632***-0.0712***-0.2509***-0.4854***
Sales channels (1=indirect, 0=direct)-0.1084***-0.0658***-0.0181***-0.1562***
Land ownership (1=yes, 0=no)-0.0121***-0.0183***-0.0337***-0.0033***
Buying crop insurance (1=yes, 0=no)-0.1830***-0.0781***-0.4582***-0.7193***
Yrs. crop insurance (continuous)-0.0472***-0.0250***-0.0087***-0.0636***
Other crops (1=yes, 0=no)-0.0559***-0.0195***-0.0216***-0.0972***
Climate Risk Experiences (1=not at all, 2=barely, 3=have experience, 4=extremely)
Risk Experience (Temperature)-0.0207***-0.0085***-0.1027***-0.1149***
Risk Experience (Precipitation)-0.0605***-0.0998***-0.0224***-0.1827***
Risk Experience (Extreme weather)-0.0329***-0.0319***-0.0395***-0.0253***
Information (1=not at all, 2=sometimes, 3=often, 4=always)
Climate change info. (Public media)-0.1195***-0.1043***-0.0015***-0.2253***
Climate change info. (Neighbor farmers-0.0405***-0.0032***-0.0270***-0.0618***
Climate change info. (Village leader)-0.0837***-0.0220***-0.1639***-0.2256***
Climate change info. (Agri. Ext. center)-0.1138***-0.0943***-0.1130***-0.3210***
Climate change info. (Agri. Cooperative)-0.1414***-0.1479***-0.0208***-0.2686***
Adaptation Info. (Public media)-0.0428***-0.0403***-0.0387***-0.0444***
Adaptation Info. (Neighbor farmers)-0.0792***-0.2847***-0.0224***-0.3414***Adaptation Info. (Village leaders)-0.2003***-0.1358***-0.0635***-0.1281***
Adaptation Info. (Agri. Ext. center)-0.1734***-0.0134***-0.0952***-0.0916***Adaptation Info. (Agri. Cooperative)-0.1095***-0.0173***-0.0786***-0.0136****Significant at 10% level (p<0.1), ** significant at 5% level (p<0.05), *** significant at 1% level (p<0.001)

Information
As explained in the previous section, the contents and the sources of information can influence
farmers’ perceptions. According to the regression results, information on climate change received frompublic media, agricultural extension center and cooperatives can positively influence farmers’
perceptions on measure efficacy, self-efficacy, and overall adaptation efficacy. However, theinformation from neighbor farmers and village leaders can negatively influence farmers’ perceptions,particularly on self-efficacy, costs and overall adaptation efficacy. From these results, it can beⒸ ISERD214

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analyzed that the farmers perceive adaptation efficacies positively associated with climate change
information from objective sources. Interestingly, unlike the climates change information, the farmers’
perception of adaptation efficacies is positively and significantly related with adaptation informationfrom neighbors and village leaders. It can be analyzed that the farmers’ perception on adaptation
efficacies, which would eventually increase farmers’ motivation to perform on the adaptation can beenhanced if adaptation information could be disseminated by neighbors and village leaders, who thefarmers are personally connected with. The inclusion of subjective opinion or the personal experiencein disseminating the information related to adaptation measures could bring greater success indelivering adaptation information to farmers.
Table 2 Assessing the fit of regression models and multicollinearity
Dependent variables in the models
Perceived measure efficacy
Perceived self-efficacy
Perceived adaptation costs
Overall perception

R2
0.53
0.49
0.46
0.57

Adjusted R2
0.38
0.33
0.28
0.43

F test, p-value
0.00
0.00
0.00
0.00

VIF
Max: 2.92
Min: 1.14

CONCLUSION
To enhance the adaptive capacity of farmers, this study aims to investigate and analyze the factors
affecting the cognitive capacity of the apple farmers, in Cheongsong, Korea. According to the
MPPACC, the cognitive capacity to motivate adaptive behavior includes individuals’ perception ofadaptation behaviors and adaptation perception can be measured through perceived measure efficacy,
perceived self-efficacy, and perceived adaptation costs. This study conducted multiple regressions, andas a result, demographic and socioeconomic factors and information from different sources can have an
influence on the farmers’ perceptions differently. In sum, among the demographic and socioeconomic
factors, crop insurance and years of buying the insurance seem to influence farmers’ perceptions on
adaptation assessment. Moreover, climate information from objective sources, such as public media
and public centers can have higher influence than neighbors and village leaders, however, with regard
to adaptation information, the farmers’ perception of adaptation behaviors can be influenced more by
information from neighbors and village leaders. There was no statistically significant correlation was
found with previous climate risk.
The results of this study suggest some directions for how to achieve successful dissemination ofadaptation policies in the agricultural sector. Not only the contents but sources and quality of
information should be considered as important due to the potential influences on farmers’ perceptions
and their adaptation evaluations. Moreover, an improvement on the system of crop insurance inaddition to increasing credibility is deemed a necessity for successful adaptation strategies in
Cheongsong County. By understanding different elements that induce farmers’ perception on
adaptation appraisal, apple farming communities can increase its adaptive capacity and lessen thedamage from the impacts. However, there is the tendency of ignoring the importance of cognitive
factors. Enhancing broader application of measuring cognitive capacity should have a clearer
understanding of climate change adaptation capacity in apple farming communities. Moreover, thisstudy can be more developed to be applied to other regions and other sectors to be referred in
integrated climate change vulnerability assessment of rural agricultural communities.
ACKNOWLEDGEMENTS
The authors are grateful to the apple farmers and Agricultural Government officers of Cheongsong
County for their active participations and supports for this study.
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REFERENCES
Bryan, E., Ringler, C., Oloba, B., Roncoli, C., Silverstri, S. and Herrero, M. 2013. Adapting agriculture to climatechange in Kenya, Household strategies and determinants. J. Environ. Manage., 114, 26-35.
Choi, S.Y. and Yamaji, E. 2015. Local level climate change vulnerability assessment using three indices, A caseof four municipals of Gyeonggi Province in Korea. Journal of Rural Planning Association, 34, Special Issue,
261-266.
Cheongsong County. 2016. The statistical report for apple production [data file]. Retrieved from http://www.
cs.go.kr/countyOffice/Deressa, T.T., Hassan, R.M. and Ringler, C. 2011. Perception of and adaptation to climate change by farmers in
the Nile basin of Ethiopia. The Journal of Agricultural Science, 149, 23-31.
Fujisawa, M. and Kobayashi, K. 2011. Climate change adaptation practices of apple growers in Nagano, Japan.
Mitig Adapt Strateg Glob Change, 16, 865-877.
Grothmann, T. and Patt, A. 2005. Adaptive capacity and human cognition, The process ofindividual adaptation
to climate change. Global Environmental Change, 15 (3), 199-213.
Ministry of Environment. 2015. Korea climate change assessment report 2014. Climate change impact andadaptation. Ministry of Environment, Korea.
Korea Meteorological Administration. 2016. National climate data service system. Climate Information [datafile] http://sts.kma.go.kr.
Rural Development Administration. 2015. Manual of organic apple. National Institute of Horticultural & Herbal
Science.

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erdInternational Society ofEnvironmental and Rural Development

Philosophy of ISERD:
Recently, in developing countries, subsistence agriculture is being converted to export-oriented mono-culture,and the amounts of agricultural chemicals applied to the farmland are increasing every year. The appliedchemicals in farmland cause serious environmental problems downstream such as eutrophication, unusual
growth of aquatic plants, decrease in dissolved oxygen and accumulation of bottom mud in water resources.
Also, there seem to be many cases in which people apply agricultural chemicals without understanding its
impact to health and food safety. Therefore, it is necessary to promote and enhance understanding ofsustainable rural development among local stakeholders including farmers.
Sustainable rural development aims to meet human needs while preserving the natural environment. As
it should cover not only social and economic development but also natural environment conservation, no
single organization can achieve sufficiently the aspirations of sustainable rural development. Collaboration
among international, governmental and non-governmental organizations, together with the academe andscientific sector, is indispensable.
The knowledge and intelligence accumulated in universities and research institutions are also expected
to make the programs facilitated by the international, governmental and non-governmental organizations
more adequately implemented and meaningful to societal development. However, these cases especiallythose implemented locally have been scattered without having been summarized well or recorded in annals
academic or scientific societies.
So, the International Society of Environmental and Rural Development founded in 2010, aims to discussand develop suitable and effective processes or strategies on sustainable rural development focusing on
agricultural and environmental aspects in developing countries. The ultimate goals of the society are tocontribute to sustainable rural development through social and economic development in harmony with thenatural environment, and to support the potential or capacity building of local institutions and stakeholders in
the rural area with academic background.
Purposes of ISERD:
The primary purposes of ISERD are to contribute to sustainable rural development through social andeconomic development in harmony with the natural environment and to support the potential or capacity
building of local institutions and stakeholders in the rural area with academic background.
In order to enhance the realization of the primary purposes of ISERD, the secondary purposes are;- to facilitate interaction among international, governmental, non-governmental organizations and local
communities,- to hold conferences or symposia on environmental and rural development,- to publish the International Journal of Environmental and Rural Development, and- to encourage and develop local awareness concerning sustainable rural development.
Membership:
There shall be two categories of membership.
(a) Individual(b) Organizational
An application for membership of ISERD shall be submitted to the secretariat of ISERD, where is located in
the Research Center at Institute of Environmental Rehabilitation and Conservation (Japan) by writing or byother appropriate means.
Also every presenter who attends the International Conference on Environmental and Rural
Development (ICERD) is registered as an Individual Member of ISERD.
Council of ISERD:
The affairs of ISERD shall be governed and managed by the ISERD Council. The councilors are as follows.
President
Prof. Dr. Mario T. Tabucanon, United Nations University, Institute for the Advanced Study of
Sustainability, Japan

IJERD – International Journal of Environmental and Rural Development

Deputy Presidents
Prof. Dr. Bunthan Ngo, Royal University of Agriculture, Cambodia
Prof. Dr. Anan Polthanee, Khon Kaen University, Thailand
Prof. Dr. Eiji Yamaji, The University of Tokyo, Japan
Executive Secretary / Editor-in-Chief of IJERD
Prof. Dr. Machito Mihara, Tokyo University of Agriculture, Japan /Institute of Environmental Rehabilitation and Conservation, Japan
Managing Editors
Dr. Jeeranuch Sakkhamduang, Institute of Environmental Rehabilitation and Conservation,
Southeast Asia Office / Association of Environmental and Rural Development, Thailand
Dr. Junaid Alam Memon, Pakistan Institute of Development Economics, Pakistan
Dr. Julian Torillo, Jr., Bohol Island State University, Philippines
Dr. Carlos Onan Tovar Mendoza, Honduras Embassy in Japan, Japan
Treasurer
Dr. Lalita Siriwattananon, Rajamangala University of Technology Thanyaburi, Thailand
Regional Vice Presidents
Prof. Dr. Robert J. Martin, University of New England, Australia
Dr. Sinisa Berjan, University of East Sarajevo, Bosnia and Herzegovina
Dr. Buntong Borarin, Royal University of Agriculture, Cambodia
Prof. Dr. Dieter Trautz, University of Applied Sciences, Germany
Dr. Siaw Onwona-Agyeman, African Program on Field Oriented Leaders in Environ. Sectors, Ghana
Dr. Sri Wahyuni, Islamic University of Riau, Indonesia
Dr. Hiromu Okazawa, Tokyo University of Agriculture, Japan
Dr. Junaid Alam Memon, Pakistan Institute of Development Economics, Pakistan
Prof. Dr. Regucivilla A. Pobar, Bohol Island State University, Philippines
Dr. Chuleemas Boonthai Iwai, Khon Kaen University, Thailand
Prof. Dr. Murari Suvedi, Michigan State University, USA
Dr. Vo Quang Minh, Cantho University, Vietnam
ISERD Secretariat:
Institute of Environmental Rehabilitation and Conservation (ERECON)
2987-1 Onoji Machida-Shi, Tokyo 195-0064, Japan
Tel/Fax: +81-42736-8972
E-mail: iserd.secretariat@gmail.com
Webpage: www.iserd.net
Collaborated with

Association of Environmental and Rural Development (AERD)
93/64 Moo.3, Sinsab Village 2, Bungyeetho Sub-district, Thanyaburi District,Pathum Thani 12130, Thailand
Tel/Fax: +66-2957-8064
E-mail: iserd.secretariat@gmail.com
Webpage: www.iserd.net

Institute for the Advanced Studyof Sustainability
All articles and reports published in this journal were accepted through a peer-review process. However,most articles and reports published in this journal were presented at the International Conference on
Environmental and Rural Development that was co-organized by United Nations University, Institute for
the Advanced Study of Sustainability.