opportunities and impediments for diversifi ation of
TRANSCRIPT
Opportunities and impediments for diversification of smallholders’ rice-based farming systems in the Senegal
River Valley
Master student: Antoine G.L. Brosseau
Supervisors: Jeroen C.J. Groot, Pepijn A.J. van Oort, Kazuki Saito Examiner: Walter A.H. Rossing
Master thesis to obtain the title of MSc degree in Organic Agriculture Farming System Ecology Group Droevendaalsesteeg 1 – 6708 PB Wageningen – The Netherlands
Wageningen – Netherlands
May 2018
Opportunities and impediments for diversification of smallholders’ rice-based farming systems in the Senegal
River Valley
Master student: Antoine G.L. Brosseau
Registration number: 950717132040
Course code: FSE-80436
Period: September 2017 – May 2018
Master thesis to obtain the title of MSc degree in Organic Agriculture
Abstract
Rice is the staple food for millions of people in Senegal and the most destitute smallholders depend on
rice farming to subsist. While the Senegalese government seeks to intensify rice production, policy-based or
technological interventions are unlikely to be effective if not aligned with farmers’ objectives, constraints and
decision-making processes. Previous studies in the Senegal River Valley (SRV) have mostly focused on
constraints for rice farming, with little to no attention for other crops, livestock and decision-making at the
farm household level. Here we (1) describe the main drivers influencing farm management, (2) explain current
farming systems functioning along with farmers’ strategies, and (3) investigate trade-offs and synergies
between productive, economic, social, and environmental performances of smallholder rice-based farming
systems in response to innovative rice cultivation activities. Farming systems were qualitatively analysed
through interviews conducted in the Delta and in the middle valley of the SRV. The FarmDESIGN model was
used to quantitatively evaluate farm performances. Several important commonalities and differences were
found. Common findings were that vegetables were more profitable, more time-consuming and had larger N
losses to the environment than rice. The main constraints to vegetable cultivation were related to household
rice self-sufficiency since rice and vegetable cropping calendars could overlap, lack of financial and technical
support, high labour requirements, and lack of knowledge on cultivation. Rice grown in the hot dry season
produced higher yields and was perceived to have lower risks than rice grown in the wet season. Soil K mining
was common. Finally, crop diversification was desired by all farmers. However, smallholder farmers had
limited room for their decision making due to institutional and financial service arrangements. Also,
differences were found between farmers. Cropping systems differed in terms of crop location, soil
preparations, fertilisers use, weeding intensity, pest control, and type of harvest resulting in differences in
terms of yields, labour requirements, cultivation costs and N losses. Rice double cropping was more common
in the Delta than in the middle valley although rice cultivation costs were higher due to increased
mechanisation. Farms with large areas had more options and room for improvement than small farms. In all
cases, the increase of farm profit occurred at the expense of household leisure time and low N losses. Finally,
the total area of rice could be increase cultivating rice in HDS in the fields currently dedicated to vegetables.
We concluded that technical, financial, and organisational supports to smallholder farmers would be needed
to develop the vegetable sector, to enhance the rice sector, and to diversify (crop) production in the SRV.
Key-words: Rice intensification; crop diversification; smallholder farmers; modelling; Senegal River Valley
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1. Introduction
Over 65% of the world’s population consumes rice on daily basis, and the most destitute people depend
on rice farming to subsist. The recent food crises, increasing agricultural input costs, climate change and
growing water scarcity stress the need for sustainable rice production methods (Djaman et al., 2017; Krupnik
et al., 2012a; Seck et al., 2013). This is true for Senegal, where rice is the staple food for millions of people
(Tanaka et al., 2015). However, local production is still insufficient to meet consumption needs, which are
expected to increase in the future (Saito et al., 2015b; Seck et al., 2013). As the national population increases,
the Senegalese government still seeks to increase rice production levels to achieve national rice self-
sufficiency, in particular promoting rice double cropping (MAER, 2014). Since rice is produced almost
exclusively by smallholder farmers, increasing rice production is crucial to ensure food security, but also to
enhance rural livelihood economic performances (Krupnik et al., 2012b; Tanaka et al., 2015).
In the main zone of rice production in the country, the Senegal River Valley (SRV), rice-based smallholder
farms face many challenges to intensify rice production. There is still a substantial yield gap between potential
and actual yield obtained by smallholders in lowlands irrigated rice production systems despite innovative
opportunities towards rice intensification (Tanaka et al., 2015; van Oort et al., 2016). In wet season, the main
cause of the yield gap was identified as a delay in sowing which increases the risk of spikelet sterility caused
by cold temperatures at the end of the season. The problems in scheduling of activities have been attributed
to delays in credits attribution, to limited availability of machinery, and to sub-optimal decisions on timing of
irrigation (Krupnik et al., 2012b; Poussin et al., 2006, 2005; Tanaka et al., 2015). Inappropriate fertiliser
applications and bird damage were also commonly identified as reducing factors (Tanaka et al., 2015). These
persistent issues highlight the existence of barriers in institutional arrangements and underlying social
interactions surrounding smallholder farms that directly affect farm functioning (Diagne et al., 2013; Poussin
et al., 2005; Tanaka et al., 2015). Concurrently, cultivation of vegetables, such as onion and tomato, during the
cold dry season is widely adopted in the valley due to their profitability (Tanaka et al., 2015). Therefore, some
farmers give priority to vegetable cultivation over timely sowing of rice (Krupnik et al., 2012b; van Oort et al.,
2016). Moreover, many farmers prefer short duration rice varieties (e.g. Sahel 108) over medium duration
varieties because the yield gain of a medium duration variety is insufficient to offset the extra cultivation costs
which crop profitability is very important to guide farmers’ decisions (van Oort et al., 2016).
Interventions, whether policies or technological innovations, are unlikely to be effective if not aligned with
farmers’ objectives, constraints and decision-making processes. Previous studies in the SRV have mostly
focused on constraints for rice farming, with little to no attention for other crops, livestock and decision-
making at the farm household level (Diagne et al., 2013; Haefele et al., 2002a; Krupnik et al., 2012b; Poussin
et al., 2005, 2006; Tanaka et al., 2015; van Oort et al., 2016). Multi-objective optimizations using models are
well-suited to explore trade-offs and synergies of farming systems in response to innovative farming options
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(Groot et al., 2012). Such studies at farm level have proven to be efficient in Ethiopia, Kenya, Madagascar, and
Mexico (Cortez-Arriola et al., 2014; Flores-Sanchez et al., 2011; Naudin et al., 2015, 2012; Paul et al., 2015). In
the SRV, multi-objective studies have been conducted to assess irrigation scheme performances but such
studies at farm-system level are lacking (García-Bolaños et al., 2011; van Oort et al., 2016). Hence, Pareto-
based multi-dimensional farm analyses including other crops than rice in the cropping system represent
opportunities for the exploration of strategic improvements of farming systems (Groot et al., 2012; van Oort
et al., 2016). Finally, supportive policy measures would be needed to facilitate farming systems improvement
and to enhance nutritional and economic aspects of rural livelihood in a sustainable way.
The present study investigated trade-offs and synergies between productive, economic, social, and
environmental performances of smallholder rice-based farming systems, as affected by implementation of
innovative rice cultivation activities. We used the DEED approach for farming systems research: Describe,
Explain, Explore, Design (Giller et al., 2011, 2008; Tittonell, 2008). This study described current farming systems
and the main drivers impacting farm management, explained current farmers’ decisions and their
consequences on farm functioning, and explored options for improvements according to a range of innovative
scenarios. The last part of the DEED approach (Design) was not part of this study because conception of
farming systems should be done in consultation with stakeholders after receiving feedbacks on the first three
phases. The objectives of this study were (1) to describe the main drivers impacting farm management, (2) to
understand farmers’ perspectives and analyse current farming systems, (3) to conduct model-exploration of
trade-offs and synergies between productive, economic, social, and environmental performances of farming
systems in response to innovative rice cultivation activities. To achieve this, farmers interviews were
conducted in the SRV and the data collected was analysed with the FarmDESIGN model.
2. Material and Methods
2.1. Case study areas
Two study areas were selected: Diama and Fanaye, both located in the SRV in the region of Saint-Louis,
Senegal (Figure 1). Diama, in the west, is a municipality of Ndiaye district in the department of Dagana. The
district is representative of the delta in the SRV (Tanaka et al., 2015). Fanaye, in the middle valley, is a
municipality of Thillé Boubacar which is a district in the department of Podor. The district is representative of
the middle valley in SRV (van Oort et al., 2016). In the SRV, the main vegetation has been classified as woody
steppe with abundance of Acacia and Commiphora (Keay, 1959). For both sites, the climate is of Sahelian type,
with three seasons: humid and hot (locally called the Wet Season, WS, about 200 mm rainfall) from July to
October, dry and warm (Cold Dry Season, CDS) from November to February, dry and hot (Hot Dry Season, HDS)
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from March to June (Haefele et al., 2002b). In Diama, temperatures range from 11°C to 44°C (De Vries et al.,
2011). The main soil type is Orthithionic Gleysol, with high salinity due to marine salt deposits in the subsoil
(Haefele et al., 2002b, 2004). In Fanaye, temperatures range from 8°C to 46°C (De Vries et al., 2011). The main
soil type is Eutric Vertisol with a low or absent natural soil salinity (Haefele et al., 2002b, 2004). Most irrigation
schemes have been developed on heavy clay and silty clay soils (Tanaka et al., 2015).
Figure 1. Location of the study areas on the map of the region of Saint-Louis, Senegal.
2.2. Selection of farms
Salinity can be a constraint for vegetable cropping and hence rice-rice systems are relatively more present
in the Diama region (Delta) while rice-vegetable systems are more common in the Fanaye region (middle
valley) (Haefele et al., 2002b; Tanaka et al., 2015). Two villages were selected in each study area: Pont-
Gendarme and Boundoum-Barrage in Diama, and Ndierba and Fanaye-Diéri in Fanaye. In each of the four
villages five random farmers were interviewed for the rapid system analysis (twenty farmers in total), followed
by detailed analyses for one farmer per village. The small number of farms allowed to describe farm
functioning in detail. The small number of farms and the fact that they were all (intentionally) different in
terms of location, household head age and level of education, size of the household, main source of income,
farm area, cultivation choices, type of financing, rice self-sufficiency meant we could not conduct any statistical
analysis.
2.3. Farming system characterization
A rapid system analysis was followed by a more detailed system analysis (Flores-Sanchez et al., 2011).
Figure 2 describes the methodological framework for a complete farm diagnosis. The first phase focused on
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understanding farm functioning and farmers situation and perspectives, and on describing major drivers
influencing farm management. The information collected in the first phase was used to select four farms (one
from the five in each village) and set up the detailed system analysis. This second phase aimed to provide
insights in agronomic and socio-economic variables at field and farm level, and to understand underlying
processes regulating farm management.
Figure 2. Methodological framework adapted from (Flores-Sanchez et al., 2011). A rapid system analysis was followed by a more
detailed system analysis. First, interviews and transect walks were used to describe farming systems and the main drivers influencing farm functioning. Then, interviews and model calculations were used to explain underlying processes regulating farm management.
2.3.1. Rapid system analysis
A total of twenty farmers were interviewed in November 2017 about five main components. Structured
interviews comprised questions about (1) household structure and situation: household capitals, household
diet; and (2) farming system functioning: cultivated crops, livestock husbandry. During semi-structured
interviews, farmers were asked to describe (3) perceived problems and key assets: major encountered
problems and key assets, significant effects, possible causes, and potential solutions; (4) socio-economic
environment and farmers’ opinions: opinion about subventions, national rice self-sufficiency, potential farm
expansion and/or diversification, and obstacles; and (5) farmers’ objectives: ideal of work, and future
prospects for the farm and the farmer. Transect walks were organised, accompanied by farmers and AfricaRice
staff to observe variations in landscape and soil type, and to get insights on access to infrastructure for
transport and communication. For each farm, the characteristics listed in Appendix A were noted, and
interviews were recorded with a phone. The objectives noted during interviews were derived into indicators
to measure farm performances in the second phase.
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2.3.2. Detailed system analysis
From the initial group of twenty farmers, one per village was invited to participate in the detailed system
analysis. Criteria to select these farmers were their location, ability to speak French to avoid translation bias,
and farm specificity (e.g. farm size, self-financing capacity, cropping system). Structured interviews were
conducted in December 2017 about four main components. Farmers were asked about (1) household labour
management: household member activities (on-farm and off-farm) for each season, and permanent hired
labour; (2) cropping systems and management: field area, ownership status, type of soil and water supply,
distance from home, crop successions, cultural practices and associated required labour, inputs, and crop
products future; (3) livestock production system and management: type of animals, herd size, whereabouts,
required labour, feeding, animal health, inputs, manure management, and animal products management; and
(4) farm economics: labour costs, input costs, farm products prices, subsidies, interest rate, and purchase and
life time of machinery/tools. Farmers’ fields were visited and visual soil assessments were conducted to
characterize soil quality (Shepherd, 2000). For each farm, the characteristics listed in Appendix 8 were noted,
and interviews were recorded with a phone. The FarmDESIGN model was used to quantitatively evaluate the
performances of the selected farms (Groot et al., 2012).
2.4. Multi-objective optimization and model calibration
2.4.1. Multi-objective optimization and differential evolution
The aim of a multi-objective optimization is to create alternative farm configurations with respect to a
selected set of farm parameters and objectives. The trade-offs between productive, socio-economic, and
environmental performances were explored via a multi-objective Pareto-based Differential Evolution
algorithm (Groot et al., 2012, 2010). The multi-objective equation was built as follow:
𝑀𝑎𝑥 𝑂(𝑋) = (𝑂1(𝑋), 𝑂2(𝑋), 𝑂3(𝑋), 𝑂4(𝑋)) (1)
𝑋 = (𝑥1, 𝑥2, … , 𝑥𝑖) (2)
Subject to a set of constraints 𝑔(𝐶):
𝑓(𝑋) ← 𝑔(𝐶) (3)
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Where 𝑂1(𝑋), 𝑂2(𝑋), 𝑂3(𝑋) and 04(𝑋) were the objective functions (farm performances) to simultaneously
maximize or minimize, and (𝑥1, 𝑥2, … , 𝑥𝑖) were the decision variables (farm parameters) to adjust to generate
alternative farm configurations (model outputs; 𝑓(𝑋)). Decision variables can take values within a range
defined by the user. Constraints limited the undesirable outputs induced by specific combinations of decision
variables (e.g. unacceptable nutrient mining) (Groot et al., 2012).
In this study, four objectives were defined based on interviews with farmers and expert knowledge of the
three co-authors:
O1: maximise area of rice
O2: maximise farm operating profit
O3: maximise farmer leisure time
O4: minimise N balance.
Appendixes 2, 3 and 4 present the full list of farm parameters, decision variables, and constraints, respectively.
2.4.2. Cropping systems and livestock
Maximization of the area of rice (ArRice; ha) tended to increase the area of rice through rice single
cropping and/or crop successions comprising at least one rice cultivation per year. It was chosen to meet: (1)
the goal of the Senegalese government towards national rice self-sufficiency, and (2) the smallholder farmers’
goal towards household rice self-sufficiency (MAER, 2014).
Cropping patterns analysed in this study consisted of either a single crop or a succession of two crops. A
single crop was defined as the only crop grown in a field within a year. A succession was defined as two
consecutive crops (one same crop or two different ones) grown on the same field (one crop per season) within
a single year. To create one succession rice-vegetable, one single vegetable was added to the rice grown in
hot dry season. Five single crops and four successions were built in this study:
- Rice WS (single crop; WS): Jul/Aug to Nov/Dec.
- Rice HDS (single crop; HDS): Feb/Mar to Jun/Jul.
- Onion (single crop; CDS): Oct/Nov to Mar/Apr.
- Tomato (single crop; CDS): Oct/Nov to Mar/Apr.
- Gombo (single crop; WS): Jun/Jul/Aug to Oct/Nov.
- Rice HDS – Rice WS (succession): Feb/Mar to Jun/Jul – Jul/Aug to Nov/Dec.
- Rice HDS - Onion (succession): Feb/Mar to Jun/Jul – Oct/Nov to Mar/Apr.
- Rice HDS - Tomato (succession): Feb/Mar to Jun/Jul – Oct/Nov to Mar/Apr.
- Rice HDS - Gombo (succession): Feb/Mar to Jun/Jul – Jun/Jul/Aug to Oct/Nov
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Each cropping pattern was characterized by a set of attributes. If a crop was not originally grown by a
farmer, estimated values were calculated for this crop based on information collected from other farmers.
In FarmDESIGN, rotations represented the areas of cropping patterns as allocated within a year. Two crop
rotations were created: the first rotation gathered the cropping patterns with single vegetables, whereas the
second gathered single rice and successions comprising rice. In this way, it became possible to maximize the
area of the second rotation, which maximized the area of rice.
For each farmer, fresh yields of rice, tomato, gombo and onion were asked from interviews and converted
to yield per hectare. With farmers renting assigned land in irrigation schemes developed by the government,
farmers knew their crop area quite accurately from official documents. They also knew their production quite
accurately, because all vegetables were sold and for rice, local millers were paid on a product basis. For rice,
five crop products (𝑐𝑝𝑖) were created according to their percentage dry matter (𝑃) in paddy rice and their dry
matter fraction (𝐷𝑀%): milled rice (𝑃 = 60%; 𝐷𝑀% = 86%), broken rice (𝑃 = 4%; 𝐷𝑀% = 86%), rice
bran (𝑃 = 14%; 𝐷𝑀% = 90%), rice husk (𝑃 = 22%; 𝐷𝑀% = 92%), and rice straw (𝐷𝑀% = 90%) (INRA
et al., 2017). Percentages were based on author’s knowledge and were cross-checked with Dr S.A. Ndindeng,
grain quality specialist at AfricaRice. We assumed equals DM yields for rice straw and paddy rice considering
an harvest index of 0.5 (De Vries et al., 2011). Calculations in FarmDESIGN were all with Fresh Matter Yield
(𝐹𝑀𝑌). 𝐹𝑀𝑌 of the different products was calculated assuming a dry matter fraction of 86% for paddy rice.
𝐹𝑀𝑌𝑐𝑝𝑖=
𝐹𝑀𝑌𝑝𝑎𝑑𝑑𝑦 𝑟𝑖𝑐𝑒×𝐷𝑀%𝑝𝑎𝑑𝑑𝑦 𝑟𝑖𝑐𝑒×𝑃𝑐𝑝𝑖
𝐷𝑀%𝑐𝑝𝑖
(4)
Paddy rice was either sold or self-consumed. Rice brans and husks from the paddy rice saved for self-
consumption were fed to animal. The whole broken rice was self-consumed. Household level of rice self-
sufficiency could increase, but not decrease compared with the current household situation.
Tomato fruits, gombo fruits, and onion bulbs were the only vegetable crop products, and were assumed
to be totally exported.
Animal production was also considered in the model. The feed evaluation system was based on animal
dry matter intake capacity (DMI), and requirements of metabolizable energy (ME), crude protein (CP), and
structure for ruminants (STR). The feed balance was calculated for the whole year without distinction between
seasons, since no major difference in feeding strategies were observed during dry and rainy seasons. Animals
were either located in open yard, or off-farm grazing. Animal physical features were based on Tourrand (2000).
Animal ME and CP requirements were calculated on the basis of needs for body maintenance, growth and
production (e.g. meat production) and were derived from Šebek & Gosselink (2006). Meat products were
assumed to be exported.
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2.4.3. Labour management
At the farm level, labour requirement (F’) was calculated as the sum of crop labour (FC), animal labour
(FA), and farm maintenance labour (FM). F’ should be provided by household (HhF) or hired (HiF) labour,
household labour input being smaller or equal to their total available work force (HhWf).
𝐹′ = 𝐹𝐶 + 𝐹𝐴 + 𝐹𝑀 (5)
𝐹′ = 𝐻ℎ𝐹 + 𝐻𝑖𝐹 (6)
𝐻ℎ𝑊𝑓 ≥ 𝐻ℎ𝐹 (7)
Where FA and FM were assumed to be fixed. Since farmers possessed several small fields quite far apart, travel
times by scooter or by horse/donkey, were also accounted in FM.
Most interviewed farmers expressed the desire and ambition to: (1) find or keep off-farm jobs, (2) raise more
animals, (3) spend more time with their family (wife(s) and children) and for their hobbies. Therefore, the
maximization of Household Leisure Time (HhLT; h year-1) was retained as objective, and calculated as:
𝐻ℎ𝐿𝑇 = 𝐻ℎ𝑊𝑓 − 𝐻ℎ𝐹 − 𝐻ℎ𝑂 (8)
Where HhWf was a fixed parameter per household, HhF depended on farmer’s decisions on crop
management, and HhO was off-farm labour which was assumed to be fixed. In this way, the leisure time could
be spent with the family or to any other profitable activity.
FarmDESIGN distinguished regular and casual labour (Groot et al., 2012). Regular labour referred to family
members working on-farm all year around and to hired skilled labour. Regular labour was permanently needed
for weeding, fertilizers and pesticides applications, sowing, irrigation, and other soil preparation works.
Regular labour was also required for animal husbandry (e.g. feeding, medical care), herd management (e.g.
keeping), and farm maintenance (Groot et al., 2012). Casual labour was provided by family members
occasionally helping in the fields (e.g. women, children) and by temporally hired workers. Casual labour was
needed during labour peaks, as vegetable transplanting and harvests Grass collection for the livestock also
required casual labour. Regular labour had a higher cost than casual labour. Contract work was not accounted
in any labour (Groot et al., 2012).
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2.4.4. Farm economics
Economic performance was expressed through Farm Operating Profit (FOP; FCFA year-1): the maximization
of FOP tended to enhance smallholders’ household economic prosperity. It was calculated as the difference
between the gross margins of crops and animals and the costs of manures, crop protection products, farm
equipment, and regular and casual labour (Groot et al., 2012).
Crop gross margin was affected by crop products fresh yields, price, cultivated area, cultivation costs, and
contract work costs. Cultivation costs were the sum of seed costs, irrigation costs, union fees, and other costs
(e.g. contribution CGER, fine, etc.). Bank fees and interests were included for farmers with access to credit.
For rice, contract work costs were the sum of soil preparation, harvest and post-harvest operations, and
transportation costs. For vegetables, only soil preparation costs were accounted in contract work costs (and
transportation costs for onion); harvest costs were included in labour costs and transportation was free for
tomato and gombo.
In the present study, manures referred to fertilizers and crop protection products referred to pesticides.
Since those inputs were subsidised up to 50% of their price, subsidies were deduced from the initial product
costs.
Farm equipment referred to all equipment owned by farmers (e.g. motor pump, scooter, cart, hand
spray).
Regular and casual labour costs were calculated by weighting hired regular and hired casual labour costs,
respectively.
2.4.5. Nitrogen balance
Environmental performance was expressed through Nitrogen Balance (NiB; kg ha-1): the minimization of
NiB tended to lower the nitrogen losses in the environment. It was calculated as:
𝑁𝑖𝐵 = 𝑁 𝐼𝑛𝑝𝑢𝑡𝑠 − 𝑁 𝑂𝑢𝑡𝑝𝑢𝑡𝑠 (9)
Where N inputs comprised crop product imports, animal product imports for household, fertilizer imports,
symbiotic and non-symbiotic fixation, and deposition. N outputs comprised crop product exports, animal
product exports, manure exports, household manure exports.
A NiB larger than zero implied a surplus supply of nitrogen that could be lost in the environment. A NiB lower
than zero implied soil nutrient mining. Thus, a minimum value of 0 was set up to NiB to avoid unsustainable
soil mining.
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3. Results
Following the DEED cycle (Giller et al., 2008), we describe key drivers influencing farming systems (Section
3.1), the case study farms (Section 3.2), underlying processes of farmers’ choices case study farms (Section
3.3) and we explore trade-offs at the farm systems level (Section 3.4). Together, this provides a deeper
understanding of trade-offs and synergies between productive, economic, social, and environmental
performances of smallholder’s rice-based systems in the SRV.
3.1. Description of key drivers influencing farming systems
3.1.1. Government efforts towards national rice self-sufficiency
Since the 1980s, the Senegalese government has put considerable efforts into increasing rice production
to support the national rice self-sufficiency programme (Demont and Rizzotto, 2012). Multiple irrigation
systems and infrastructures for production were built all along the SRV through large investments. Small fields
with access to irrigation were allocated to farmers by the government on the condition that rice would be
grown at least once a year (Bonnefond, 1982). In this way, lands remained government property and only very
few farmers were actual land owners. The Senegalese government also invested in agricultural finance and
agronomic research for rice (MAER, 2014). The implementation of credits from the bank CNCAS (Caisse
nationale du Crédit Agricole du Sénégal) and government subsidies for fertilizers, pesticides, and machinery
have fostered rice production and continue to be significant incentives. The agricultural extension agency
SAED (Société d’Aménagement et d’Exploitation des terres du Delta et des vallées du fleuve Sénégal et de la
Falémé) played a considerable role in developing irrigated agriculture through research projects carried out
with various partners, technical support to farmers, and supply chain arrangements (Bonnefond, 1982). Rice
double cropping was promoted by SAED but was not common practice. Because the national population
increases, the Senegalese government still sought to increase rice production levels to achieve national rice
self-sufficiency (MAER, 2014). Therefore, the government objective towards rice intensification remained a
strong driver for smallholder farms functioning.
3.1.2. Socio-economic environment shaping farming activities
Complex socio-economic and institutional structures existed within a village that shaped farming activities.
Usually, several farmers were gathered in an Economic Interest Group (EIG; in French: Groupement d’intérêt
économique), and several EIG constituted a farmers’ Union (in French: Union d’agriculteurs) (Bonnefond,
1982). There were generally several farmers’ Unions in one same village, and a single farmer could be part of
several Unions. Important decisions regarding agricultural activities were taken by farmers’ Unions
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representatives who collaborated with the government (Bonnefond, 1982). Farmers’ Unions bought farm
machinery and arranged their usage schedule (Gay and Dancette, 1995; MAER, 2014). In this way, farmers
within a Union shared the few available machines or relied on the services of a few private investors, which
were much more expensive. In this way, a lucky farmer could benefit from the availability of Union machinery
at the right moment to start and end the growing season on time, whereas an unlucky farmer could suffer
yield penalties caused by delayed planting. In addition, farmers’ Unions managed credit distribution and
reimbursement since that most farmers relied on bank credits to afford crop inputs, and that individual credits
did not exist (Gay and Dancette, 1995). Hence, farmers had limited room for their decision making due to
institutional and financial service arrangements.
3.1.3. Smallholder farmers ambitions towards a decent livelihood
Rice-based smallholder farmers lived in precarious conditions and were very dependent on external
services to subsist. However, they sought to enhance the economic situation of their household. Most farmers
earned low income that they should wisely spend to feed their household throughout the year. Many farmers
relied on bank credits to afford rice and tomato inputs (seeds, fertilizers, pesticides, irrigation costs) and soil
preparation costs (only tillage, ploughing and levelling being self-financed). All other inputs and crops should
be self-financed.
As rural household size increased, rice production intensification was crucial to ensure food security of
smallholder farmers. Farmers preferred short duration rice varieties such as Sahel 108 in response to
biophysical risks (e.g. risks of early rains at the end of the HDS, risks of early cold at the end of the WS), and/or
to alleviate the impact of delayed sowing. Most farmers used certified seeds due to their purity, germination
rate and speed of germination, and productivity. Farmers told us that they mainly grew rice in WS but the
restorations of irrigation systems (from 1995 to 2000), the higher reachable yields in HDS, and the various
problems experienced in WS (e.g. cold, difficulty to access fields due to heavy rains, birds, and insects) led
them to favour rice cultivation in HDS over WS. Rice was mainly grown for household self-consumption but a
significant portion of the production was used to reimburse cultivation costs, and part of the production
should be sold to cover household daily expenses. In other words, high production levels enabled farmers to
reimburse the bank while ensuring household food security, whereas low production levels did not provide
enough to meet household needs.
Crop profitability was an essential driver impacting farm functioning. In that respect, vegetables were
increasingly popular and represented an alternative to the low-profit rice production. The most common
vegetables were onion (Allium cepa) and tomato (Solanum lycopersicum), but gombo (Abelmoschus
esculentus), watermelon (Citrullus lanatus), or aubergine (Solanum melongena) could also be found. All
farmers used certified seeds supplied by nearby agro-industries. Tomatoes were generally sold to nearby agro-
12
industries through sale contracts, which was very convenient for farmers, even though the payment of the
harvest is delayed. Onions were immediately sold on local markets after harvest, which provided a direct
income to farmers but brought onion price down at harvest peak (David-Benz and Seck, 2018). In response,
some farmers harvested prematurely to be firsts on the market, while others preferred to wait for price
increase. Long-term storage onion varieties were preferred to avoid post-harvest losses which were still very
substantial due to the few storage facilities and transport infrastructures. Overall, vegetable production
enabled farmers to increase their total income and to spread that income throughout the year to finance daily
expenses. In this way, farmers could ensure food security to their household all year around. Nevertheless,
salinity in the Delta could prohibit vegetables growth, but if salinity was absent, lighter soils could be an
advantage for onion compared to the heavy clays soils near Fanaye. Vegetable cultivation was also constrained
by the limited farmers’ self-financing capacity, which explained the relatively small cultivated areas. Proximity
to buyers (e.g. agro-industries, merchants) was necessary to ensure the sale of the harvest while avoiding
post-harvest losses as much as possible. Finally, market price fluctuations regulated selling prices and thus
crop profitability. We observed that crop diversification through the integration of (more) vegetables was
considered as desirable by many farmers, but only few of them integrated new crops in their system. Farmers
told that they did not dare to grow new crops since it was an experimental process which required self-
financing. Farm area per household being small, testing a new crop on this scarce land could be a costly
exercise if that crop does not produce or sell. Many farmers could not afford this risk. In summary words,
farmers lacked technical, financial, and organisational support to integrate (more) vegetables in their cropping
systems.
Animals were very important but resource-demanding farm components. Animal production represented
a growing business even though it was usually not considered as major farming activity. Donkeys and horses
were the key pillar of the farm, allowing any type of transportation to the field or the city. Sheep were very
valuable cultural assets, notably due to the religious context of the SRV. Goats were sometimes preferred over
sheep due to their autonomy, but were less valuable. Small ruminants and equines were generally raised on-
farm, fed with rice residues (bran and husks) from the rice kept for household consumption, with
supplementary feeding of concentrates and harvested native vegetation. The cattle were kept outside the
village by ranchers, and mainly used as financial backup to enable smallholders to survive if harvests were bad.
In exchange of a small income, ranchers kept a herd gathering cattle of multiple farmers in the village. The
cattle should utilize the available natural resources, and were fed with the surrounding vegetation and crop
residues left on the fields. Farmers told that when feed and water became scarce at the end of the HDS, it was
common to see cattle entering the thriving rice fields, destroying part of the production. Hence, a solution was
to employ a field keeper to make sure that animals did not enter the cultivated fields; meanwhile, the keeper
13
could ward off wild animals such as birds (De Mey et al., 2012). Animal manure was abandoned, although
occasionally applied on vegetable nurseries.
Off-farm activities, and personal time (for family, religious events) were also drivers impacting household
and farm functioning. Smallholder farmers should wisely divide their available time. It was common that
smallholder farmers had other sources of income than crop production. Occupying a function such as farmers’
representative, SAED agent, or water pump station attendant provided a significant second source of income
but was time-demanding.
3.2. Description of the case study farms
In this section, we describe the four case study farms selected for the detailed system analysis. Some key
figure on the four farms are listed in table 2. Below, the four farming systems are described along with their
major problems and key assets, and farmers’ strategies and objectives.
Table 1. Household characteristics of the four case study farms. Data based on interviews conducted in November and December 2017.
Farmer Location Household size 1
Age of
household
head
Level of
education
of
household
head
Family
members
working
on the
farm
Household
workforce
First
source
of
income
Main
type of
financing
Rice self-
sufficiency
[name] [name] #♂ #♀ #☺ (year) [education
level] # (h year-1) [name] [name] (months)
Farmer_MP Pont du
Gendarme 1 1 1 26 University 1 2190 Onion Credit 1 to 4
Farmer_DB Boundoum
Barrage 1 1 5 45 Primary 1 (+1)2 2080
(+470)2 Rice Credit 8 to 10
Farmer_DN Ndierba 10 10 5 42 University 4 4137 Tomato Self-
financing 10 to 12
Farmer_AF Fanaye
Diéri 2 6 10 39 Primary 2 (+4)
4380
(+1220) Rice Credit 4 to 6
1 ♂ stands for adult men, ♀ stands for adult women, and ☺ stands for children. 2 () represents household casual labour.
Farmer_MP was a young farmer in Pont du Gendarme (Diama, Delta region) who recently inherited from
his father. His household comprised three members. Farmer_MP owned 0.5 ha and he was the only family
member working on-farm, but he got some help from his neighbours. His main source of income was onion
production. Farmer_MP implemented a two years rotation (rice HDS, Mar/April-Jul – rice WS, Aug-Dec – rice
HDS, Feb-Jul – onion/tomato CDS, Oct-Feb/Mar). Considering credit reimbursement and household daily
expenses, Farmer_MP could not save enough rice to feed his household all year around, and should buy extra
rice once his stock has been depleted. Between 2 and 4 sheep were raised for household consumption and
sales during the Tabaski festival. The main problems encountered by Farmer_MP were the lack of machinery
(especially for land preparation) inducing delays in the cropping calendar, the lack of surface area for crop
cultivation and the high interest rate that limited farm profit, and the difficulty to afford extra labour for casual
14
works since it was not included in credits. However, Farmer_MP was very proud of his sheep which provided
extra income to buy food and/or extra fertilizers. Farmer_MP’s objectives were to become wealthy through
agriculture, to raise more sheep, and to occupy a higher position within his farmers’ Union to develop
agriculture in his village, or even in the country. He also desired to diversify his cropping system through the
adoption of new crops such as gombo, aubergines, potato (Solanum tuberosum), and turnip (Brassica rapa),
and wished to raise poultry.
Farmer_DB was a plain rice grower in Boundoum Barrage (Diama, Delta region). His household comprised
seven members. Farmer_DB was the only family member working on-farm and he hired a worker almost full
time to assist him. His wife was mostly busy with children although she helped for the harvest, threshing and
sales. Farmer_DB owned 1 ha of land to grow rice in HDS and WS, and he additionally rented 2 ha in HDS.
Thus, Farmer_DB’s main source of income was rice production. Each season, Farmer_DB reimbursed his credit,
kept enough rice to feed his household until the next harvest, and sold the remaining part. Farmer_DB raised
3 sheep for household consumption, and 2 zebus for his own pleasure. In addition to his farming activities,
Farmer_DB worked to the village water pump station 48 hours per week. Recently, five extra hectares have
been attributed to Farmer_DB as part of a new project. To successfully cultivate the additional area, he should
manage his time very carefully. The main problems encountered by Farmer_DB were the lack of machinery
inducing delays in sowing and harvest, the presence of insects and wild animals destroying crops, the lack of
infrastructure to access field, and the lack of storage facilities which exposed rice to bad weather and reduced
its quality. However, Farmer_DB was very proud of his job which provided him an extra income to cover
household daily expenses, and health insurance. Farmer_DB’s objectives were to keep his job at the water
pump station, to successfully cultivate his new fields, and to increase his profit to live a decent life. He also
wished to diversify his cropping system through the integration of crops such as gombo and chili (Capsicum
chinense), and desired to raise more cattle, sheep and poultry.
Farmer_DN was a self-financed farmer in Ndierba (Fanaye, middle valley). His household comprised 25
members. He was the only family member working full time on-farm, but his three brothers also significantly
participated in farming activities. Farmer_DN owned 4.35 ha and each field hosted a specific crop to avoid any
cropping calendar overlaps. When fields were not used for cropping, they were left in fallow. Farmer_DN grew
rice on 2.3 ha in HDS and occasionally used a part of this area to grow rice in WS. In addition, he rented 1 ha
extra in HDS to grow rice. In CDS, tomato and onion were grown on 0.5 and 0.75 ha, respectively. Finally,
gombo was grown on 0.8 ha in WS. Farmer_DN’s main source of income was tomato production. Without
credits to repay, Farmer_DN’s household was self-sufficient in rice. Due to the large cropped area, Farmer_DN
relied on a substantial amount of external labour. Around 15 sheep were raised for household consumption
and for sales, and 20 zebus were kept as savings; the milk was kept by the rancher. Farmer_DN also owned a
thresher which he rented out to nearby farmers in exchange of 10% of their harvest, which provided him an
15
extra income. The main problems encountered by Farmer_DN were the disease pressure for rice and tomato
and birds reducing yields. Soil fertility decline and poor maintenance of water channels also affected crop
yields. Then, Farmer_DN mentioned that rice was difficult to sell for good price due to the competitiveness
among farmers. However, Farmer_DN was very proud of his crop management (efficient weeding through
regular spraying, intensive use of fertilisers) and the tomato profitability. He was also very happy to self-
finance his activities which allowed him to avoid paying expensive interests to the bank. Farmer_DN’s
objectives were to increase his current production level, to extend his cropped area and diversify his cropping
system through the adoption of crops such as cucumber (Cucumis sativus) and squash (Cucurbita).
Farmer_AF was a farmer in Fanaye Diéri (Fanaye, middle valley). His household comprised 18 members.
Farmer_AF and his younger brother were both working full-time on-farm. The four women were mostly busy
with children but they participated in vegetable harvests. Farmer_AF owned 1.3 ha and rented 1.15 ha. In the
past, he grew crops such as corn (Zea mays), bell pepper (Capsicum annuum), and aubergine but he refocused
on growing tomatoes and onions. Nowadays, rice was grown on 1.85 ha in HDS, tomato and onion were
respectively grown on 0.35 and 0.14 ha in CDS, and gombo was grown on 0.11 ha in WS. Due to the multiple
problems occurring in WS, Farmer_AF’s Union chose to stop growing rice in that season. Thus, rice fields were
in fallow in WS, and the growing grass was eaten by the free-ranging cattle. Farmer_AF’s main source of
income was rice production. After credit reimbursement at harvest time, most of the rice production was sold
to provide an income. As a result, the household could be fed with his own production up to six months after
the harvest, and purchasing of extra rice was required for the rest of the year. Farmer_AF only hired external
labour during the harvest period. Three sheep were raised to give birth to lambs to consume in the household
and/or to sell, and 4 zebus were kept as saving. Raising poultry was a hobby. The main problems encountered
by Farmer_AF were the impact of birds on rice production, and the difficulty to sell rice and tomato products
for a good price. He also complained about the limited farm surface area, which did not enable him to feed
his growing family. Farmer_AF’s objectives were to increase his income and leisure time to live a more
comfortable life and spend more time with his family. He also desired to diversify his cropping system through
the adoption of new crops such as cucumber, potato and corn, and wished to increase herd size.
3.3. Explanation of underlying processes of farmers choices
In the following two sections, we present a quantitative analysis made using FarmDESIGN.
3.3.1. Farming system analysis
Large differences were observed between farmers in terms of yields, labour requirements, cultivation
costs, and fertilizer use (Table 3). Onion yields variations could be explained by the lighter soils observed in
the Delta compared with the middle valley, crop management, and/or timing of harvest. In fact, Farmer_MP
16
applied large quantities of urea to maximize yields, and harvested the bulbs prematurely to benefit from
higher market prices (David-Benz and Seck, 2018). This strategy seemed to work at the expense of onion
quality, or rice yield; the rice grown in WS being the most impacted by delayed sowing (David-Benz and Seck,
2018; Tanaka et al., 2015). Variations in labour requirements for rice, tomato, and gombo and onion could be
explained by differences in terms of bird control strategy, manual weeding intensity, and produced yields,
respectively. Rice cultivation costs were higher for Farmer_MP and Farmer_DB than for Farmer_DN and
Farmer_AF due to differences in crop management strategy (e.g. soil preparation, type of harvest and
threshing). The type and amount of fertilizer applied on tomato, onion, and gombo were different from a
farmer to another. All farmers used the recommended amount of fertilizers on rice, apart from Farmer_DB
applying 50 kg ha-1 of urea extra. The consistently slightly higher cultivation costs for Farmer_MP, Farmer_DB,
and Farmer_AF compared to Farmer_DN could be due to the bank fees. The exact cause(s) did not become
fully clear because interviews did not go into that level of detail.
Usually, vegetables and rice were grown in distinct fields to avoid growing season overlaps. However,
some farmers preferred to harvest vegetables prematurely, or to delay rice sowing. The purchase of extra
fertilizers compared to the recommendations, and the considerable amounts of fertilisers applied on
vegetables brought us to retain farm nitrogen balance as indicator of environmental performance.
Table 2. Farm characteristics of the four case study farms. Data based on interviews conducted in December 2017.
Farmer
Crops Animals
Type Season Area Yield Cultivation
costs Margins
Labour
needed
Fertilisers
Type Number Urea DAP 9-23-
30
[name] [name] [name] (ha) (t FM
ha-1) (K FCFA ha-1) (K FCFA ha-1) (h ha-1)
(kg
ha-1)
(kg
ha-1)
(kg
ha-1) [name] #
Farmer_MP
Rice WS Aug/Sep - Nov/Dec 0.50 5.6 404 348 684 300 100 - Donkey 1
Rice HDS Mar/Apr - Jul/Aug 0.50 8.0 472 642 756 300 100 - Sheep 3
Onion CDS Oct - Feb/Mar 0.30 32.3 545 3,487 1,416 833 333 -
Tomato CDS Oct - Mar 0.20 37.5 238 1,712 883 250 - 850
Total 0.50
Farmer_DB
Rice WS Jul/Aug - Nov/Dec 1.00 6.8 474 473 580 350 100 - Donkey 1
Rice HDS Feb/Mar - Jun/Jul 3.00 8.0 522 593 580 350 100 - Sheep 3
Zebu 2
Total 3.00 Poultry 3
Farmer_DN
Rice WS Aug – Nov/Dec 1.00 6.4 313 578 672 300 100 - Donkey 2
Rice HDS Mar - Jul 3.30 7.3 304 714 468 300 100 - Horse 1
Onion CDS Oct - Mar/Apr 0.75 14.7 505 1,335 923 200 - 667 Lamb 5
Tomato CDS Oct - Mar 0.50 30.9 230 1,403 1,293 200 250 650 Sheep 10
Gombo WS Jun - Aug/Sep 0.80 2.3 211 351 829 750 250 - Zebu 15
Total 5.35
Farmer_AF
Rice HDS Mar - Jul 1.85 6.3 336 570 450 292 95 - Horse 1
Onion CDS Oct - Mar/Apr 0.14 16.7 540 1,549 1,229 536 714 - Lamb 5
Tomato CDS Oct - Mar 0.35 38.7 251 1,762 1,409 214 171 571 Sheep 3
Gombo WS Jun - Aug/Sep 0.11 7.2 191 1,419 3,806 1,636 455 - Zebu 4
Poultry 41
Total 2.45 Goat 16
At the time of writing, 16 May 2018, FCFA (XOF) 1000 = EUR 1.52 = USD = 1.79.
17
Onion and tomato were the most profitable crops. Tomato seemed to be slightly more profitable than
onion with respect to the biophysical conditions in the SRV. Tomato had low cultivation costs. However, it was
very time-consuming due to (daily) crop monitoring, manual weeding, multiple fertilizers and pesticides
applications, and labour peaks at transplanting and harvest. Onion could outperform tomato if grown in
appropriate conditions. This crop had the highest cultivation costs due to its expensive seeds. Onion was very
time-consuming with some labour peaks at transplanting and harvest. Significant quantities of fertilizer were
applied on both vegetables.
Rice was the less profitable crop. Rice HDS produces higher yield than rice WS resulting in higher margin
per hectare. This could explain the ongoing shift of rice cultivation during WS towards the HDS, with
Farmer_AF having altogether stopped rice cultivation in WS. Manual harvest, threshing, and harvest with
combine harvester costed 10%, 10% and 18% of the total rice production, respectively. Therefore, rice HDS
had higher cultivation costs than rice WS due to the higher yields inducing higher harvest costs. Bird control
was the most time-consuming activity related to rice cultivation. Gombo yields (and profitability) were very
variable according to crop management strategy. Gombo had low cultivation costs but it was very time-
consuming due to the tedious harvest. Nevertheless, great caution was necessary when studying gombo due
to imprecise farmers’ estimations.
3.3.2. Cropping systems analysis
In this section we analyse farmers’ decisions in perspective of the four objectives of maximising area of
rice, maximising farm operating profit, maximising farmer leisure time and minimising N balance. Trade-offs
between these objectives are shown in Figure 3, with symbols in the 3 charts showing rice and non-rice based
cropping patterns.
18
Figure 3. Analysis of nitrogen balance, crop margin, and household leisure time at cropping-system level for the four case study farms: FarmDESIGN outputs before multi-objective optimization. For each cropping system, household leisure time was calculated as the difference between total household available time minus the labour required to grow one hectare of the related crop(s). Negative leisure time occurred for time-intensive crops which required to hire extra labour. Each colour represents a farm and each marker represents a single crop or a crop succession.
Low N balances were reached when crops with low N inputs, high N outputs, and low N losses are grown.
In this way, single crops were more suitable than successions (Figures 3a & 3c). Less N fertilizer was applied
on rice than vegetables, therefore rice is suited to reduce N balance. Fertilizers with low N content were usually
applied on tomato, resulting in low N balance for the latter too (Table 3). For onion, N balance differed from
a farm to another; fertilizer use being very much dependant on farmer financial capacity and experience with
the crop. Some farmers could not afford much fertilizers since their costs were not covered by credits, and
other farmers observed a direct correlation between the amount of N fertilizer applied and onion yield. As a
result, some farmers applied much more fertilizers than others on this crop (Table 3). In the selected farms,
large amount of N fertilizer was applied on gombo, resulting in a high N balance. Limited research has been
done on the gombo crop in the SRV, and the few available recommendations are quite old and currently not
promoted by the extension services.
Single crops were always less time consuming than crop successions (Figures 3a & 3b). Depending on
farmers’ management practices, tomato or onion were the second most labour-consuming crops. Rice as
single crop offered the largest leisure time for the household. Since labour costs were not accounted in credits,
it made sense for a farmer to prioritize the less time-consuming crops and to favour own labour.
0
1
2
3
4
5
0 250 500 750 1 000
Cro
p M
argi
n (
FCFA
ha-1
) Mill
ion
s
N Balance (kg ha-1)
c)
0
250
500
750
1 000
-2 000 -1 000 0 1 000 2 000 3 000 4 000
N b
alan
ce (
kg h
a-1)
Household Leisure Time (h ha-1)
a)
0
1
2
3
4
5
-2 000 -1 000 0 1 000 2 000 3 000 4 000
Cro
p M
argi
n (
FCFA
ha-1
) Mill
ions
Household Leisure Time (h ha-1)
b)
19
Crop successions were always more profitable than single crops. The succession rice-rice was still less
profitable than a single onion or tomato, which could justify farmers’ choice towards single vegetables rather
than double rice cultivation.
Overall, crops ensuring high margins tended to be the most time-consuming (e.g. onion, tomato), and
showed the highest N balance (e.g. onion, gombo). Crops showing low N balances tended to be the less
profitable and the less labour intensive (e.g. rice_WS, rice_HDS).
3.4. Exploration of trade-offs at farm-system level
In the two following sections, we investigated alternative farm configurations according the multi-
objective optimization using FarmDESIGN.
3.4.1. Exploration of trade-offs between productive, economic, social and environmental performances
For all farmers, household leisure time (HhLT) decreased when farm profit (FOP) increased (Figure 4a;
Figure 3). Due to his larger farm area (Table 3), Farmer_DN could reach the higher profits, followed by
Farmer_AF and Farmer_DB, and finally Farmer_MP. In that respect, farms with large areas had more room for
improvement than small farms. Considering the total household workforce (Table 3), it was understandable
to observe Farmer_AF reaching higher HhLT than the other farmers.
For Farmer_DB, a larger rice area (ArRice) meant lower FOP. For the other three farmers, no clear relation
was found between ArRice and FOP (Figure 4b). For Farmer_DB, shifting from single rice and rice-rice to single
vegetables and rice-vegetable successions could increase FOP considering the consistently lower gross margin
of rice compared with other crops (Table 3; Figure 3). For Farmer_DB and Farmer DN, the simultaneous
increase of ArRice and FOP could be possible shifting from single vegetables and low profit single rice to more
profitable rice-vegetable successions (Table 3; Figure 3).
For Farmer_MP, Farmer_DB, and Farmer_DN, nitrogen balance (NiB) increased linearly when FOP
increased (Figure 4c). Farmer_MP had the steepest slope and Farmer_DN had the lowest increase rate.
Farmer_AF responded differently with a slow increase followed by a prononced increase of NiB with increasing
FOP. The causes did not became clear but we suspected total farm area to limit FOP increase. Farmer_MP’s
farm had higher NiB than the other farms in the initial situation due to the important part occupied by
vegetables in the initial cropping system.
There was no clear relation between ArRice and HhLT (Figure 4d). For Farmer_DB, Farmer_DN, and
Farme_AF, the extension of ArRice either decreased or increased HhLT. This coul be possible adopting labour-
intensive rice-vegetable successions or a single rice which was less time-consuming for the same area of rice
(Figure 3).
In line with our results (3.3.2.), NiB decreased with increasing HhLT for all farmers (Figure 4e).
20
For Farmer_DB, larger ArRice meant lower NiB since the single rice or rice-rice had consistently lower N
losses than single vegetables or rice-vegetable successions (Figure 4f; Figure 3). For Farmer_DB and Farmer
DN, a larger ArRice could decrease NiB through the integration of a single rice instead of a single vegetable, or
could increase NiB through the adoption of rice-vegetable successions (Table 3; Figure 3).
The optimization results for Farmer_MP’s farm showed that the options for improvements in the various
indicators were quite limited as is seen from the initial farm configuration (red dot) and the pareto frontier in
each chart (Figure 4). This is because Farmer_MP was already growing the successions performing the best in
perspective of the selected objectves, which gives little room for improvement. The options for further
improvement were also limited because of the small crop area (Table 3); this resulted in the narrow blue
Pareto-frontier in each chart. The optimization results for the other farms showed that performances could
be enhanced; consistently a gap existed between performances of initial farm configurations (red dots) and
attainable performances as reflected by Farmer_DB, Farmer_DN, and Farmer_AF farm optimization. In the
next section, we look further into what changes are needed to achieve better performances.
Figure 4. Relation between FOP, HhLT, NiB, and ArRice at farm-system level for the four case study farms as represented by Pareto frontiers after multi-objective optimization with FarmDESIGN. Each colour represents a farm. Each dot represents a new farming system configuration. The dots circled in red represent the original farming system.
3.4.2. Exploration of alternative farm configurations
For Farmer_MP, the best crops to match the four objectives were rice-tomato, rice-onion, rice-rice, and
rice_HDS. To increase FOP, Farmer_MP would need to increase his area of rice-onion (Figure 5a). Total labour
0
500
1 000
1 500
2 000
2 500
3 000
3 500
4 000
4 500
5 000
0 2 500 000 5 000 000 7 500 000
Hh
Lei
sure
Tim
e (h
.yea
r-1)
a)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
0 2 500 000 5 000 000 7 500 000
Are
a o
f ri
ce (
ha)
b)
0
50
100
150
200
250
300
350
400
450
0 2 500 000 5 000 000 7 500 000
N b
alan
ce (
kg N
.ha-1
)
Farm Operating Profit (FCFA.year-1)
c)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
0 1 000 2 000 3 000 4 000 5 000
d)
0
50
100
150
200
250
300
350
400
450
0 1 000 2 000 3 000 4 000 5 000
Hh Leisure Time (h.year-1)
e)
0
50
100
150
200
250
300
350
400
450
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0
Area of rice (ha)
f)
21
and total farm expenses would increase due to the labour-intensive and costly onion cultivation (Table 3).
Household on-farm labour should increase to avoid hiring too much onerous external labour. Soil K mining
was observed, but reduced when FOP increased. N and P soil losses would increase due to the large amount
of urea and DAP applied on onion (Figure 6; Table 3). Almost the exact opposite pattern could be observed to
minimize NiB, namely the increase of rice-rice and rice_HDS areas reducing total labour and total farm
expenses (Figure 5b). Rice-tomato could also be grown since it had a lower N balance than rice-onion and
relatively high margin per hectare (Figure 3). NiB was positively and linearly correlated to N and P soil losses
(Figure 6). To maximize HhLT, Farmer_MP should favour rice-rice and rice_HDS over rice-onion (Figure 5c).
Hired casual labour and its associated costs should increase too. As a result, total labour would decrease and
total farm expenses would slightly increase; the reduction of crop cultivation costs being balanced by the
increase of labour costs. Soil N and P losses would be increased and then decreased, which coincides with the
evolution of rice-onion area (Figure 6; Figure 5c). Finally, all crop combinations including rice enabled
maximization of the ArRice (Figure 5d). However, the expansion of the succession rice-rice would decrease
NiB and increase HhLT, but significantly decrease FOP. No clear relation was found between ArRice and
nutrient losses (Figure 6).
Figure 5. Farmer_MP: Alternative farming system configurations according to FOP, NiB, HhLT, and ArRice after multi-objective optimization with FarmDESIGN. The fat black lines represent the original farming system performances. Each of the 1,000 coloured bars in a chart represents a new farming system configuration. a Total farm area = 0.5 ha
0%
20%
40%
60%
80%
100%
Cro
pp
ed a
rea
(ha)
a
Gombo
Rice-Gombo
Tomato
Rice-Tomato
Onion
Rice-Onion
Rice_Both seasons
Rice_DS
Rice_WS
0
200
400
600
800
1 000
1 200
1 400
Lab
ou
r (h
yea
r-1)
Hired Casual Labor
Hired Regular Labor
Hh Off-farm Labor
Hh On-farm Labor
0
200 000
400 000
600 000
800 000
1 000 000
37
5 3
09
49
2 3
40
57
6 6
66
65
1 3
82
73
0 5
36
82
1 3
48
91
2 0
08
1 0
19
71
9
1 1
28
11
2
1 2
32
98
0
1 3
66
73
4
Farm
Exp
ense
s (F
CFA
yea
r-1)
Farm Operating Profit (FCFA year-1)
13
1
14
9
16
5
18
1
20
0
22
3
24
9
27
4
30
3
32
8
35
1
Nitrogen Balance (kg ha-1)
1 2
85
1 4
04
1 4
68
1 5
18
1 5
78
1 6
27
1 6
76
1 7
20
1 7
68
1 8
24
1 8
74
Household Leisure Time (h year-1)
0.4
43
0.4
50
0.4
57
0.4
63
0.4
68
0.4
74
0.4
79
0.4
84
0.4
88
0.4
91
0.4
93
0.4
96
Area of Rice (ha)
Feed Import Costs
Hired Labor Costs
Crop Inputs Costs
Crop Production Costs
a) b) c) d)
22
Figure 6. Farmer_MP: Evolution of NPK soil losses in relation with FOP, NiB, HhLT, and ArRice at farm-system level as represented by Pareto frontiers after multi-objective optimization with FarmDESIGN. Each colour represents a nutrient. Each dot represents a new farming system configuration. The fat black lines represent the original farming system performances.
For Farmer_DB, the most suitable crops to match the four objectives were onion, rice-onion, and rice HDS;
rice-rice, rice-tomato and tomato were considered by the model but not as the main crops (Appendix 5). To
maximize FOP, the farmer should favour onion and/or rice-onion. Total labour and total farm expenses would
rise due to the increase of household on-farm labour and cultivation costs. Almost the opposite pattern could
be observed to minimize NiB, namely the increase of rice HDS area reducing total labour and total farm
expenses. Tomato was prioritized over onion since tomato has lower N balance (Figure 3). To maximize HhLT,
the farmer should increase the area of rice_HDS and hired labour. As a result, total labour and total farm
expenses would be decreased. Simultaneously, hired casual labour should slightly increase, raising labour
costs. A similar pattern could be observed to maximize ArRice.
For Farmer_DN, the most suitable crops to match all four objecties were rice HDS, onion, rice-onion, and
tomato. To maximize FOP, the farmer should prioritize rice-onion (Appendix 6). Even though rice-tomato had
higher margins, the extra labour needed when compared with rice-onion was proportionally greater than the
corresponding extra returns (Figure 3). Total labour and total farm expenses would rise due to the increase of
househosld on-farm labour and cultivation costs. Almost the opposite pattern could be observed to minimize
NiB, namely the increase of rice_HDS area at the expense of rice-onion area, which would reduce total labour
and total farm expenses. The single onion crop could be grown too to allow low N balance while providing
high returns (Figure 3). To maximize HhLT the farmer should prioritize rice_HDS and hired labour. As a result,
total labour and total farm expenses would decrease. Simultaneously, hired casual labour slightly increased,
raising labour costs. To maximize ArRice, the farmers should decrease the area of single tomato and favour
rice-onion and rice_HDS. Total labour and total farm expenses should rise due to the increase of household
on-farm labour and cultivation costs.
For Farmer_AF, the most suitable crops were rice_HDS, tomato, and rice-onion. To maximize FOP, the
farmer should grow rice_HDS, and rice-onion (Appendix 7). To reduce labour costs, hired casual labour should
decrease, but household on-farm labour should considerably increase. As a result, total labour would increase
and total farm expenses would slightly rise: the high cultivation costs being balanced by the low labour costs.
Almost the opposite pattern could be observed to minimize NiB. The single tomato could be grown too to
allow low N balance while providing high returns (Figure 3). To maximize HhLT, the farmer should prioritize
-100
-50
0
50
100
150
200
250
300
350
400
0.42 0.44 0.46 0.48 0.50 0.52
Area of rice (ha)
N soil losses
P soil losses
K soil losses
-100
-50
0
50
100
150
200
250
300
350
400
0 500000 1000000 1500000 2000000
Soil
nu
trie
nt
loss
es (
kg h
a-1)
Farm Operating Profit (FCFA year-1)-100
-50
0
50
100
150
200
250
300
350
400
0 100 200 300 400
N balance (kg ha-1)-100
-50
0
50
100
150
200
250
300
350
400
0 500 1000 1500 2000 2500
Household leisure time (h year-1)
23
rice_HDS and hired labour. Household on-farm labour should be reduced and labour costs should increase but
cultivation costs should decrease. As a result, total labour would decrease and total farm expenses would be
stable. Similar patterns were observed to maximize ArRice and FOP.
4. Discussion
4.1. Main findings
Several important commonalities and differences were found. First, we noted that the current analysis
was not meant to give a representative overview of farmers in the SRV. Our objective was to capture the
diversity in farming systems. The four farmers differed in many aspects: household size, household head age,
location, cultivation choices, main source of income, type of financing, rice self-sufficiency, etc. Since they
differ in so many aspects at a time, it was better not to make one-to-one comparison between these farms.
Common findings for all farmers were that (1) vegetables were more profitable than rice, (2) vegetables were
more time-consuming than rice, (3) vegetables had larger N losses to the environment than rice, (4) rice grown
in the HDS produced higher yields and was perceived to have lower risks than rice grown in the wet season,
(5) soil K mining was very common, (6) crop diversification was desired by all farmers, but (7) farmers have
limited room for their decision making due to institutional and financial service arrangements. In order of
importance, the main constraints to vegetable cultivation were related to household rice self-sufficiency since
rice and vegetable cropping calendars could overlap, lack of financial and technical support, high labour
requirements, and lack of knowledge on cultivation. Also, differences were found between farmers. Cropping
systems differed in terms of crop location, soil preparations, fertilisers use, weeding intensity, pest control,
and type of harvest resulting in difference in terms of yields, labour requirements, cultivation costs and N
losses. Rice double cropping was more common in the Delta than in the middle valley although rice cultivation
costs were higher due to increased mechanisation. Farms with large areas had more options and room for
improvement than small farms. In all cases, the increase of farm profit occurred at the expense of household
leisure time and low N losses. Finally, the total area of rice could be increase cultivating rice in HDS in the fields
currently dedicated to vegetables.
4.2. Farming systems functioning and production trends
We observed that successful rice cultivation, and especially rice double cropping, is very much dependent
on farmers’ Unions functioning, especially regarding machinery availability. This is in line with the results of
Tanaka et al. (2015) and Diagne et al. (2013) showing that farmers blamed machinery unavailability as one of
the main causes of delayed sowing. In 2008, ISRA (Institut Sénégalais de Recherches Agricoles) and SAED
24
showed that a considerable number of agricultural machines were broken down or unsuitable to biophysical
conditions in the SRV. Hence, agricultural machinery covered less than 50% of the needs in machinery in the
SRV. Therefore, rice production intensification, and support to rice double cropping, required further
mechanization (Diagne et al., 2013; MAER, 2014).
We also showed that rice HDS performed better than rice WS in most of the cases. Moreover, it has been
noticed that farmers prioritize the rice cultivation in HDS over WS. These results were in line with the findings
of Busetto et al. (2018) who observed a massive shift of rice cultivation from the WS to the HDS due to more
favourable weather conditions leading to higher yields (Busetto et al., 2018; Djaman et al., 2017; Saito et al.,
2015b). In addition to the higher rice yields, disease pressure appeared to be lower in HDS (Djaman et al.,
2017; Tanaka et al., 2015).
We observed that crop diversification through the integration of (more) vegetables was considered by
many farmers; vegetable representing the most profitable option for farmers. Despite various constraints,
crop diversification through vegetable production could enable to use the full production potential of lowland
systems in the SRV (Bado et al., 2018; Gay and Dancette, 1995; Haefele and Saito, 2013). Therefore, the
diversification of crop production could increase and spread farmers income, spread risks, and increase rural
food security (Bonnefond, 1982; Gay and Dancette, 1995; PNUE, 2005).
4.3. Farming system analysis and multi-objective optimization
The Senegalese government strongly promoted farmers to grow rice twice per year (MAER, 2014). None
of our multi-objective optimization showed an evolution of the area of rice double cropping, apart from
Farmer_MP’s farm. Farmer_MP’s farm was an exception because rice-rice was originally grown on half of the
area, which obliged the model to consider solutions comprising rice-rice. Other simulations were run
maximizing the area of rice-rice instead of ArRice to increase rice double cropping as much as possible. In that
case, FarmDESIGN prioritized single vegetables which performed better than rice-rice. This was due to model
functioning which used non-weighing Pareto-based methods to optimize the objectives (Groot et al., 2012).
Since the crop rice-rice conflicted with the three other objectives, it was understandable that its area did not
increase. This insight showed that in the current situation rice double cropping conflicted with farmers’
objectives (in terms of leisure time and profit), and that government policy of promoting rice-rice would not
be effective for farmers aiming for high profit.
The present study showed significant N and P soil losses, and soil K mining in all studied farms. We
observed significant surpluses on the N balance particularly for vegetables. In fact, farmers frequently bought
more fertilisers than recommended, hoping for high yields at the harvest. During interviews, we noticed that
farmers did not know the recommendations about tomato sowing densities, and consequently the purchase
of extra seeds was common. Huat et al. (2000) illustrated that tomato farming practices in the SRV were highly
25
variable compared to the recommendations. In addition, by contrast with rice cultivation, we noted in the
interviews that farmers did not benefit from any technical neither financial support for vegetables cultivation
(apart from tomato). David-Benz et al. (2018) confirmed this observation for onion, and pointed out the very
low quality of local onion despite the growing production levels. Van Oort et al. (2016) noticed the asymmetry
of available data between rice and vegetables in the SRV. In fact, the difficulties encountered in the vegetable
sector were mainly due to the limited technical, financial, and organisational support to producers, and
improper dissemination of agricultural information (David-Benz and Seck, 2018; Gay and Dancette, 1995;
MAER, 2014). These results stress the need for agronomic research, and for assistance of producers in the
vegetable sector (MAER, 2014). Tools to improve nutrient management would be needed for vegetable crops,
where both farmers and extension services could increase their expertise. Improvement of transport
infrastructures and post-harvest facilities, and dissemination of agricultural information, especially about
market fluctuations would also be needed to develop the sector (David-Benz and Seck, 2018).
For rice, long-term fertility experiments showed the sustainability of intensive cropping in irrigated
lowland conditions, when inorganic NPK fertilizers were applied following the recommendations (120 kg N ha-
1, 26 kg P ha-1, 50 kg K ha-1) (Bado et al., 2010; Haefele et al., 2002b). At the same time, rice cultivation could
not maintain soil fertility when K was not applied, resulting in considerable K depletion (Haefele et al., 2004).
In the present study, the calculated over-extraction of K would result in important K mining. Nevertheless,
Haefele et al. (2004) suggested that the high soil K reserves in the SRV region could buffer even large negative
K balances for decades. Simultaneously, Haefele et al. (2013) showed that fertiliser N losses ranged from 50%
to 82% of the applied amount. That was confirmed in the present study since the interviewed farmers applied
more N and P fertilisers than the recommended doses (156 kg N ha-1, 46 kg P ha-1) resulting in N and P soil
losses. Therefore, we could conclude that (intensive) irrigated rice cultivation could maintains soil fertility if N
and medium P doses were applied, but led to soil losses for the two nutrients (Haefele et al., 2004). Thus, the
concepts of Site-specific nutrient management (SSNM) and Integrated crop management (ICM), or nutrient
management decision-support for rice (NMR) could be suggested to improve fertilisers recommendations for
rice-based systems in lowland irrigated rice systems (Bado et al., 2018; Haefele and Saito, 2013; Saito et al.,
2015a).
4.4. Limitations
A limitation of our study was the small sample of interviewed farmers (20) and analysed farms (4). These
small samples did not allow us to describe farming systems in the SRV representatively. In that respect, we
could not extrapolate our results to the entire region. However, selecting farmers that strongly contrasted in
26
farm size, cultivation choices and farm limitations and objectives, we aimed to provide a broad overview of
opportunities and constraints of farmers.
For the simulations, we allowed crop cultivation independently from biophysical soil conditions. However,
it might not be possible to grow vegetables for all farmers, for instance because of saltiness. FarmDESIGN did
not simulate dynamic response of crop yield to soil nutrient availability or other management operations
(Groot et al., 2012). Linking with dynamic crop simulation models (e.g. APSIM, CropSyst) or technical
coefficient generators (e.g. TechnoGIN) could provide interesting options to capture variability in crop
production in relation to climate, soils, and crop management, e.g., use of inputs (Keating et al., 2003;
Ponsioen et al., 2006; Stöckle et al., 2003).
In the present study, double cropping left a very short window between the two crops, which sometimes
had overlapping growing periods. To cope with this situation, some farmers preferred to harvest vegetables
prematurely, or to give these vegetables priority over timely sowing of rice (David-Benz and Seck, 2018; van
Oort et al., 2016). However, those practices reduced vegetable quality, or induced large rice yield gaps (David-
Benz and Seck, 2018; Tanaka et al., 2015). Those insights stress the need for new cropping options. The
integration of more different crops (e.g. corn, sorghum, cowpea, sweet potato, chili, aubergine, watermelon)
would provide more cropping options. Since rice cultivation is shifting from WS to HDS, crops adapted to the
weather conditions in WS represent opportunities of diversification. Considering crop rotations of two or three
years instead of focusing on annual cropping(s) would multiply cropping calendar options (Gay and Dancette,
1995). Information would be needed about the various crops currently grown in the SRV. Subsequently further
model explorations including those crops could be done. Cropping calendar construction (CCC) model could
be used to investigate new calendar options prior to further multi-objective optimizations using FarmDESIGN
(Groot et al., 2012; van Oort et al., 2016).
In the model-based explorations of the windows of opportunities, farming options beyond the changes in
cropping patterns, including adjustments in animal husbandry, were not investigated in this study. However,
during the past years, sheep farming has developed and has showed to be a very fruitful business activity
which was highly considered by farmers. Since all the interviewed farmers owned some sheep and some
wished to increase the herd size, future studies could take animal production in account to enhance
smallholder farmers livelihood. Nevertheless, we could point out the divagation of livestock constraining rice
production (MAER, 2014). A common solution is to employ a field keeper to make sure that animals do not
enter the cultivated fields. The setting-up of fences around fields could be considered despite its high costs.
However, the integration of fodder crops and/or legumes in farmers’ cropping systems could enable to feed
the cattle, and to enhance soil quality. This idea was supported by Haefele et al. (2013) claiming that the
introduction of post-rice grain legumes in zones where they were not commonly grown could raise farm
27
productivity and profitability. Carsky and Ajayi (1992) also reported efforts to integrate legumes in rice-based
farming systems in the SRV.
5. Conclusions
In the SRV, smallholder farmers were very dependent on rice cultivation and external services to subsist.
They had limited room for their decision making due to institutional and financial service arrangements but all
had two common objectives: household food security and farm profitability. Biophysical, organisational, and
technical constraints as well as market prices fluctuations highly influenced farmers’ choices and farm
functioning. Shared between farm activities, family, religious events, and off-farm job opportunities, farmers
must manage their time carefully to meet their objectives. Higher rice yields, vegetable cultivation, sheep
production, and off-farm jobs represent viable alternatives to increase and spread farm income, and to
improve household food security. However, rice cultivation conflicts with farmers’ aspirations in terms of
profit, and vegetable cropping is time-consuming and generates significant nutrient losses. Tools to improve
nutrient management are needed for vegetable crops, where both farmers and extension services could
increase their expertise. In all case, technical, financial, and organisational supports to producers would be
needed to develop the vegetable sector, to enhance the rice sector, and to diversify (crop) production in the
SRV.
28
29
6. Bibliographic references
Abdissa, Y., Tekalign, T., Pant, L.M., 2011. Growth , bulb yield and quality of onion ( Allium cepa L .) as influenced by nitrogen and phosphorus fertilization on vertisol I . growth attributes , biomass production and bulb yield. African J. Agricutlural Res. 6, 3252–3258. https://doi.org/10.5897/AJAR10.1024
Akanbi, W.B., Togun, A.O., Adediran, J.A., Ilupeju, E.A.O., 2010. Growth, dry matter and fruit yields components of okra under organic and inorganic sources of nutrients. Am. J. Sustain. Agric. 4, 1–13. https://doi.org/10.1017/CBO9781107415324.004
Bado, V.B., Aw, A., Ndiaye, M., 2010. Long-term effect of continuous cropping of irrigated rice on soil and yield trends in the Sahel of West Africa. Nutr. Cycl. Agroecosystems 88, 133–141. https://doi.org/10.1007/s10705-010-9355-7
Bado, V.B., Djaman, K., Valère, M.C., 2018. Managing fertilizer recommendations in rice-based cropping systems challenges and strategic approaches 24–50. https://doi.org/10.1007/978-3-319-58789-9
Bonnefond, P., 1982. L’introduction de la culture irriguée sur les rives sénégalaises du bassin du fleuve Sénégal. Econ. Rural. 147–148, 72–78. https://doi.org/https://doi.org/10.3406/ecoru.1982.2842
Busetto, L., Zwart, S.J., Boschetti, M., 2018. Analysing spatiotemporal changes in rice cultivation practices in the Senegal River Valley using MODIS time-series and the PhenoRice algorithm. Int. J. Geoinf. Earth Obs. (in Prep.
Cortez-Arriola, J., Groot, J.C.J., Améndola Massiotti, R.D., Scholberg, J.M.S., Valentina Mariscal Aguayo, D., Tittonell, P., Rossing, W.A.H., 2014. Resource use efficiency and farm productivity gaps of smallholder dairy farming in North-west Michoacán, Mexico. Agric. Syst. 126, 15–24. https://doi.org/10.1016/j.agsy.2013.11.001
David-Benz, H., Seck, A., 2018. Améliorer la qualité de l’oignon au Sénégal: promouvoir la contractualisation et autres mesures transversales. Rapport d'analyse de politique, SAPAA (projet de Suivi et Analyses des Politiques Agricoles et Alimentaires). Rome, FAO. Available at http://www.fao.org/3/i8488fr/I8488FR.pdf [last accessed 01 June 2018] De Mey, Y., Demont, M., Diagne, M., 2012. Estimating Bird Damage to Rice in Africa: Evidence from the
Senegal River Valley. J. Agric. Econ. 63, 175–200. https://doi.org/10.1111/j.1477-9552.2011.00323.x De Vries, M.E., Leffelaar, P.A., Sakané, N., Bado, V.B., Giller, K.E., 2011. Adaptability of irrigated rice to
temperature change in Sahelian environments. Exp. Agric. 47, 69–87. https://doi.org/10.1017/S0014479710001328
Demont, M., Rizzotto, A.C., 2012. Policy Sequencing and the Development of Rice Value Chains in Senegal. Dev. Policy Rev. 30, 451–472. https://doi.org/10.1111/j.1467-7679.2012.00584.x
Diagne, M., Demont, M., Seck, P.A., Diaw, A., 2013. Self-sufficiency policy and irrigated rice productivity in the Senegal River Valley. Food Secur. 5, 55–68. https://doi.org/10.1007/s12571-012-0229-5
Djaman, K., Balde, A.B., Rudnick, D.R., Ndiaye, O., Irmak, S., 2017. Long-term trend analysis in climate variables and agricultural adaptation strategies to climate change in the Senegal River Basin. Int. J. Climatol. 37, 2873–2888. https://doi.org/10.1002/joc.4885
Flores-Sanchez, D., Koerkamp-Rabelista, J.K., Navarro-Garza, H., Lantinga, E.A., Groot, J.C.J., Kropff, M.J., Rossing, W.A.H., 2011. Diagnosis for ecological intensification of maize-based smallholder farming systems in the Costa Chica, Mexico. Nutr. Cycl. Agroecosystems 91, 185–205. https://doi.org/10.1007/s10705-011-9455-z
García-Bolaños, M., Borgia, C., Poblador, N., Dia, M., Seyid, O.M.V., Mateos, L., 2011. Performance assessment of small irrigation schemes along the Mauritanian banks of the Senegal River. Agric. Water Manag. 98, 1141–1152. https://doi.org/10.1016/j.agwat.2011.02.008
Gay, J.P., Dancette, C., 1995. La diversification des cultures, in: Nianga, Laboratoire de l’agriculture Irriguée En Moyenne Vallée Du Sénégal. Boivin Pascal (Ed.), Dia Ibrahim (Ed.), Lericollais André (Ed.), Poussin Jean Christophe (Ed.), Santoir Christian (Ed.), Seck Sidi Mohamed (Ed.). ORSTOM, ISRA. Paris : ORSTOM, 281. pp. 281–300. Available at http://horizon.documentation.ird.fr/exl-doc/pleins_textes/pleins_textes_6/colloques2/010006489.pdf [last accessed 01 June 2018]
30
Gemede, H.F., Haki, G.D., Beyene, F., Woldegiorgis, A.Z., Rakshit, S.K., 2016. Proximate, mineral, and antinutrient compositions of indigenous Okra (Abelmoschus esculentus) pod accessions: implications for mineral bioavailability. Food Sci. Nutr. 4, 223–233. https://doi.org/10.1002/fsn3.282
Giller, K.E., Leeuwis, C., Andersson, J.A., Andriesse, W., Brouwer, A., Frost, P., Hebinck, P., Heitkönig, I., Van Ittersum, M.K., Koning, N., Ruben, R., Slingerland, M., Udo, H., Veldkamp, T., Van de Vijver, C., Van Wijk, M.T., Windmeijer, P., 2008. Competing claims on natural resources: What role for science? Ecol. Soc. 13. https://doi.org/10.1016/j.biocon.2005.10.047
Giller, K.E., Tittonell, P., Rufino, M.C., van Wijk, M.T., Zingore, S., Mapfumo, P., Adjei-Nsiah, S., Herrero, M., Chikowo, R., Corbeels, M., Rowe, E.C., Baijukya, F.P., Mwijage, A., Smith, J., Yeboah, E., van der Burg, W.J., Sanogo, O.M., Misiko, M., de Ridder, N., Karanja, S., Kaizzi, C., K’ungu, J., Mwale, M., Nwaga, D., Pacini, C., Vanlauwe, B., 2011. Communicating complexity: Integrated assessment of trade-offs concerning soil fertility management within African farming systems to support innovation and development. Agric. Syst. 104, 191–203. https://doi.org/10.1016/j.agsy.2010.07.002
Groot, J.C.J., Jellema, A., Rossing, W.A.H., 2010. Designing a hedgerow network in a multifunctional agricultural landscape: Balancing trade-offs among ecological quality, landscape character and implementation costs. Eur. J. Agron. 32, 112–119. https://doi.org/10.1016/j.eja.2009.07.002
Groot, J.C.J., Oomen, G.J.M., Rossing, W.A.H., 2012. Multi-objective optimization and design of farming systems. Agric. Syst. 110, 63–77. https://doi.org/10.1016/j.agsy.2012.03.012
Haefele, S.M., Saito, K., 2013. Increasing Rice Productivity through Improved Nutrient Use in Africa, in: Realizing Africa’s Rice Promise. Wopereis Marco (Ed.), Johnson David E. (Ed.), Ahmadi Nourollah (Ed.), Tollens Eric (Ed.), Jalloh Abdulai (Ed.). Centre Du Riz Pour l’Afrique. Wallingford : CABI, 250-264. ISBN 978-1-84593-812-3. Available at https://pdfs.semanticscholar.org/447d/f066e592b8b7b75267797cf0e74d5204f1e6.pdf [last accessed 01 June 2018]
Haefele, S.M., Wopereis, M.C.S., Donovan, C., 2002a. Farmers’ perceptions, practices and performance in a Sahelian irrigated rice scheme. Exp. Agric. 38, 197–210. https://doi.org/10.1017/S001447970200025X
Haefele, S.M., Wopereis, M.C.S., Schloebohm, A.M., Wiechmann, H., 2004. Long-term fertility experiments for irrigated rice in the West African Sahel: Effect on soil characteristics. F. Crop. Res. 85, 61–77. https://doi.org/10.1016/S0378-4290(03)00153-9
Haefele, S.M., Wopereis, M.C.S., Wiechmann, H., 2002b. Long-term fertility experiments for irrigates rice in the West African Sahel: agronomic results. F. Crop. Res. 78, 119–131. https://doi.org/https://doi.org/10.1016/S0378-4290(02)00117-X
Heuvelink, E., 1996. Tomato growth and yield : quantitative analysis and synthesis. Available at http://edepot.wur.nl/206832 [last accessed 01 June 2018]
Huat, J., David-Benz, H., 2000. La tomate d’industrie au Sénégal : performance de la production et enjeux pour la filière, in: Pour Un Développement Durable de l’agriculture Irriguée Dans La Zone Soudano-Sahélienne. Synthèse des résultats du pôle régional de recherche sur les systèmes irrigués
(PSI/CORAF) : actes du séminaire, Dakar (Sénégal) du 30 novembre au 3 décembre 1999.
Legoupil Jean-Claude (ed.), Dancette Claude (ed.), Maïga Ilias Mossi (ed.), NDiaye K.M. (ed.).
CORAF-PSI, CIRAD, CNRADA, ISRA, INRAN, IER, IRD. Dakar : PSI-CORAF, 167-187. Available at
http://agritrop.cirad.fr/476061/ [last accessed 01 June 2018] INRA, CIRAD, AFZ, FAO, 2017. Feedipedia - Animal Feed Resources Information System [WWW Document].
URL https://www.feedipedia.org/ Keating, B.A., Carberry, P.S., Hammer, G.L., Probert, M.E., Robertson, M.J., Holzworth, D., Huth, N.I.,
Hargreaves, J.N.G., Meinke, H., Hochman, Z., McLean, G., Verburg, K., Snow, V., Dimes, J.P., Silburn, M., Wang, E., Brown, S., Bristow, K.L., Asseng, S., Chapman, S., McCown, R.L., Freebairn, D.M., Smith, C.J., 2003. An overview of APSIM, a model designed for farming systems simulation. Eur. J. Agron. 18, 267–288. https://doi.org/10.1016/S0223-5234(03)00100-4
Keay, R., 1959. Vegetation map of Africa South of the Tropic of Cancer. Oxford (Great Britain) Oxford Univ. Press 24. https://doi.org/Doi 10.2307/212291
Khouma, M., 2000. Les grands types de sols du Sénégal, in: Quatorzième Réunion Du Sous-Comité Ouest et Centre African de Corrélation Des Sols. p. 268. Available at
31
http://www.fao.org/tempref/docrep/fao/005/y3948f/y3948f03.pdf [last accessed 01 June 2018] Krupnik, T.J., Shennan, C., Rodenburg, J., 2012a. Yield, water productivity and nutrient balances under the
System of Rice Intensification and Recommended Management Practices in the Sahel. F. Crop. Res. 130, 155–167. https://doi.org/10.1016/j.fcr.2012.02.003
Krupnik, T.J., Shennan, C., Settle, W.H., Demont, M., Ndiaye, A.B., Rodenburg, J., 2012b. Improving irrigated rice production in the Senegal River Valley through experiential learning and innovation. Agric. Syst. 109, 101–112. https://doi.org/10.1016/j.agsy.2012.01.008
MAER, 2014. Programme d’accélération de la cadence de l’agriculture sénégalaise (PRACAS). Available at https://www.ipar.sn/IMG/pdf/pracas_version_finale_officiele.pdf [last accessed 01 June 2018]
MARNDR, 2014. Réponse de trois variétés de riz (CAP, TCS-10 et L1) à différentes doses d’azote en termes de rendement-grain et de prodction de biomasse 1–28. Available at http://agriculture.gouv.ht/view/01/IMG/pdf/rapport_essai.pdf [last accessed 01 June 2018]
Naudin, K., Bruelle, G., Salgado, P., Penot, E., Scopel, E., Lubbers, M., de Ridder, N., Giller, K.E., 2015. Trade-offs around the use of biomass for livestock feed and soil cover in dairy farms in the Alaotra lake region of Madagascar. Agric. Syst. 134, 36–47. https://doi.org/10.1016/j.agsy.2014.03.003
Naudin, K., Scopel, E., Andriamandroso, A.L.H., Rakotosolofo, M., Andriamarosoa Ratsimbazafy, N.R.S., Rakotozandriny, J.N., Salgado, P., Giller, K.E., 2012. Trade-Offs Between Biomass Use and Soil Cover. the Case of Rice-Based Cropping Systems in the Lake Alaotra Region of Madagascar. Exp. Agric. 48, 194–209. https://doi.org/10.1017/S001447971100113X
Paul, B.K., Birnholz, C., Timler, C., Michalscheck, M., Koge, J., Groot, J., Sommer, R., 2015. Assessing and improving organic matter, nutrient dynamics and profitability of smallholder farms in Ethiopia and Kenya: Proof of concept of using the whole farm model FarmDESIGN for trade-off analysis and prioritization of GIZ development interventions 20. https://doi.org/10.13140/RG.2.1.4877.1929
PNUE, 2005. Evaluation intégrée de l ’ impact de la libéralisation du commerce: une étude de cas sur la filière du riz au Sénégal. Available at https://unep.ch/etb/publications/intAssessment/Senegal.pdf [last accessed 01 June 2018]
Ponsioen, T.C., Hengsdijk, H., Wolf, J., Van Ittersum, M.K., Rötter, R.P., Son, T.T., Laborte, A.G., 2006. TechnoGIN, a tool for exploring and evaluating resource use efficiency of cropping systems in East and Southeast Asia. Agric. Syst. 87, 80–100. https://doi.org/10.1016/j.agsy.2004.11.006
Poussin, J.C., Diallo, Y., Legoupil, J.C., 2006. Improved collective decision-making in action for irrigated rice farmers in the Senegal River Valley. Agric. Syst. 89, 299–323. https://doi.org/10.1016/j.agsy.2005.09.006
Poussin, J.C., Diallo, Y., Legoupil, J.C., Sow, A., 2005. Increase in rice productivity in the Senegal River valley due to improved collective management of irrigation schemes. Agron. Sustain. Dev. 25, 225–236. https://doi.org/10.1051/agro:2005021
Saito, K., Diack, S., Dieng, I., N’Diaye, M.K., 2015a. On-farm testing of a nutrient management decision-support tool for rice in the Senegal River valley. Comput. Electron. Agric. 116, 36–44. https://doi.org/10.1016/j.compag.2015.06.008
Saito, K., Dieng, I., Toure, A.A., Somado, E.A., Wopereis, M.C.S., 2015b. Rice yield growth analysis for 24 African countries over 1960-2012. Glob. Food Sec. 5, 62–69. https://doi.org/10.1016/j.gfs.2014.10.006
Šebek, L.B.J., Gosselink, J.M.J., 2006. Energie- en eiwitbehoefte van schapen 42. Available at http://library.wur.nl/WebQuery/wurpubs/fulltext/15665 [last accessed 01 June 2018] Seck, P.A., Touré, A., Coulibaly, J.., Diagne, A., Wopereis, M.C.S., 2013. Africa ’ s Rice Economy Before and
After the 2008 Rice Crisis. Realiz. Africa’s Rice Promise 24–34. https://doi.org/10.1079/9781845938123.0024
Shepherd, T.G., 2000. Visual Soil Assessment. Volume 1. Field guide for cropping and pastoral grazing on flat to rolling country. horizons.mw & Landcare Research, Palmerston North. Available at http://orgprints.org/30582/1/VSA_Volume1_smaller.pdf [last accessed 01 June 2018]
Stöckle, C.O., Donatelli, M., Nelson, R., 2003. CropSyst, a cropping systems simulation model. Eur. J. Agron. 18, 289–307. https://doi.org/Pii S1161-0301(02)00109-0
Tanaka, A., Diagne, M., Saito, K., 2015. Causes of yield stagnation in irrigated lowland rice systems in the
32
Senegal River Valley: Application of dichotomous decision tree analysis. F. Crop. Res. 176, 99–107. https://doi.org/10.1016/j.fcr.2015.02.020
Tittonell, P.A., 2008. Msimu wa Kupanda: targeting resources within diverse, heterogeneous and dynamic farming systems of East Africa. Available at http://library.wur.nl/WebQuery/wurpubs/fulltext/121949 [last accessed 01 June 2018]
USDA, 2018. USDA Food Composition Databases [WWW Document]. Available at https://ndb.nal.usda.gov/ndb/ [last accessed 01 June 2018]
van Oort, P.A.J., Balde, A.B., Diagne, M., Dingkuhn, M., Manneh, B., Muller, B., Sow, A., Stuerz, S., 2016. Intensification of an irrigated rice system in Senegal: Crop rotations, climate risks, sowing dates and varietal adaptation options. Eur. J. Agron. 80, 168–181. https://doi.org/10.1016/j.eja.2016.06.012
Yahaya, Y., 2010. Study of Nutrient Content Variation in Bulb And Stalk of Onions ( Allium Sepa ) Cultivated in Aliero , Aliero , Kebbi State , Nigeria. https://doi.org/10.4314/njbas.v18i1.56847
33
Appendixes
Appendix 1. Information on household collected during the rapid system characterization (interviews conducted in November 2017)
Variable Unit Notes
General
information
Location
Region [name]
Department [name]
Municipality [name]
Village [name]
Farm ID
Union [name]
Farmer organisation [name]
Name of household head [name]
Phone number [number]
Ethnic group Pular/Sérere/Wolof/Diola/Ot
her
Help from translator Y/N
Household
Natural
capital
Access to electricity Y/N
Access to drinkable water Y/N
Access to irrigation Y/N
Human
capital
Hh head age Year
Hh head gender M/F
Hh head farming experience Year
Time as hh head Year
Education level No/Primary/Secondary/Univ
ersity/Other
Position in the village [name]
Position outside the village [name]
People living on the farm #
Number of men #
Number of women #
Number of children #
Family members working on the
farm #
Relation with household
head
Physical
capital
Owned land area Ha
Cultivated land area Ha
Private garden area Ha
Number of plot #
Ownership status Owned/Rented/Shared/Othe
r
Distance to main city Km
Storage building Y/N
Machinery Y/N Type
Financial
capital
Main source of income Rice/Tomato/Onion/Other
Off-farm income Y/N If yes, how much (%)? And
since how long?
Access to credit (current year) Y/N If no, why?
Access to credit (past years) Y/N If no or if stopped, why?
Accounted products in credit [name]
Self-financing Y/N If yes, why?
Access to subventions Y/N
Accounted products for
subventions [name]
Access to sales contract Y/N If yes, with who?
Product sold through contract [name]
Social capital Participation to workshop Y/N If yes, who organizes?
Visits from extension agents Y/N If yes, #/year
Health and
nutrition
Access to farming insurance Y/N
Access to health insurance Y/N
Rice self-sufficiency Month/year
Number of meal per day #/day
Diet See table
Expenses Food FCFA/day
34
Living expenses FCFA/week
Fuel FCFA/week
Food group Examples Y/N
Cereals Rice, maize, wheat, millet, couscous, bread
Vitamin A rich vegetables and tubers Pumpkin, carrot, squash, sweet potato, sweet pepper
White tubers and roots Potato, yam, cassava, food from roots
Dark green leafy vegetables Cassava leaf, salad
Other vegetables Tomato, onion, eggplant, zucchini, cucumber
Vitamin A rich fruits Water melon, mango, cantaloup, « bouye », tangerine, papaya
Other fruits Roselle (“Bissap”), ginger, banana, sweet detar (“ditakh”), avocado
Organ meat (iron rich) Liver, kidney, heart, blood-based foods
Flesh meats Beef, lamb, goat, rabbit, chicken, duck, other(?)
Eggs Any eggs
Fish Fish and shellfish
Legumes, nuts, and seeds Beans, peas, lentils, nuts, seeds
Milk and milk products Milk, cheese, curd
Oil and fats Oil, fats, butter, coconut
Sweets Sugar, honey, sweetened soda, candies, chocolates
Spices and condiments Black pepper, salt, hot pepper, mustard
Beverages Coffee, tea
Insects Any insects
Pre-made food Any food (fast-food, hamburger, fries, pizzas, soup, etc.)
Variable Unit Notes
Crop
Type [name]
Growing season hDS/WS/cDS
Cultivated area Ha
Quantity produced Bag Kg/bag
Motivation for this crop [open]
Satisfaction level 1/2/3/4/5 Why?
Variety [name]
Motivation for this variety [open]
Satisfaction level 1/2/3/4/5 Why?
Seed provenance On-farm/Neighbour/Certified
Motivation for this type of seeds [open]
Satisfaction level 1/2/3/4/5 Why?
Time growing the crop Year
Trials during other seasons Y/N If yes, which
season?
Main use Sale/Hh consumption/Reimbursement
Animal
Type [name]
Amount #
Time raising this type of animal Year
Satisfaction level 1/2/3/4/5 Why?
Main feed (to order)
Rice
straw/Husks/Bran/Fodder/Concentrates/Ot
her
Satisfaction level 1/2/3/4/5 Why?
Whereabouts Barn/Pasture/Yard/Free-ranging
Main uses
Hh
consumption/Sale/Saving/Reimbursement/T
ransportation/Other
35
Main encountered problems
(and/or main constraints?)
Major effects
(why is it a problem? What can
you observe?)
Potential causes
(what is it due to?) Possible solutions
Key advantages
(what works the best?)
Major effects
(why is it an advantage? what does it
enable?)
Potential causes
(what makes it an advantage?)
Other questions Answers
Wat is your opinion about the implemented subsidies to favour rice
cultivation? Why?
Are those subventions a motivation to your eyes? Are they
fostering rice cultivation in your farm? Why?
What is your opinion about rice self-sufficiency in Senegal? Why?
Which factors could increase your rice production (yield and/or
area)? Why?
Did you ever hear about double cropping of rice? What is your
opinion about it?
Would you like to grow more crops? If yes, which ones? Why?
Currently, why are you not doing so? If not, why?
Would you like to raise more animals? If yes, which ones? Why?
Currently, why are you not doing so? If not, why?
Do you have enough land? If no, why? Currently, why are you not
expending our cultivated area?
Think back through your career. Locate a moment that was a high
point, when you felt most effective and prosperous. Describe how
you felt, and what made this situation possible.
What do you prefer in your farm? What are you most proud of?
Without being humble, describe what you value most about
yourself.
In the same way, describe what you value the most in your work.
Why are you waking up every morning? What is your motivation to
get out of bed?
How do you recognize a good day when the latter is over?
Who do you imagine as household head after you?
Describe three wishes about the future of your farm/work
36
Appendix 2. FarmDESIGN parameters use and source
FarmDESIGN tab
FarmDESIGN parameter Value Source
[name] [name] [use] FCFA kg-1
Animal Whereabouts (h) Calculation nutrient balance Interviews Duration grazing period
(days) Calculation feed balance Interviews
Costs per animal (FCFA/animal)
Calculation of FOP Interviews
Regular labour (h/animal/year)
Calculation of FOP & HhLT Interviews
Casual labour (h/animal/year)
Calculation of FOP & HhLT Interviews
Livestock unit (-) Calculation feed balance (Šebek and Gosselink, 2006) Body weight (kg) Calculation feed balance Interviews; (Šebek and Gosselink, 2006) Carcass percentage (%) Calculation of FOP (Šebek and Gosselink, 2006) Carcass price (FCFA kg-1) Calculation of FOP Interviews Bedding material (kgDM
animal day-1) Calculation nutrient balance
Feed value Calculation feed balance (Šebek and Gosselink, 2006)
Animal product
Destination Calculation of FOP; Calculation nutrient balance
Interviews
Price fresh matter (FCFA kg-1)
Calculation of FOP Beef meat 1800 Interviews
Sheep meat
3500
Lamb meat 4000 Goat meat 1400 Poultry 240 Production (kg day-1) Calculation of FOP Interviews Marketable fraction Calculation of FOP Composition Human nutrition indicator (USDA, 2018)
Crop Humification coefficient Calculation of effective organic matter (EOM) from crop residues
Residue to product ration (R/P)
Calculation of EOM (Abdissa et al., 2011; Akanbi et al., 2010; Heuvelink, 1996; MARNDR, 2014); Weighted averages based on DM yield
Cultivation costs (FCFA ha-1)
Calculation of FOP Interviews
Contract work costs (FCFA ha-1)
Calculation of FOP Interviews
Regular labour needed (h ha year-1)
Calculation of FOP & HhLT Interviews
Casual labour needed (h ha year-1)
Calculation of FOP & HhLT Interviews
Fertiliser application (kg ha-1)
Calculation of FOP & nutrient flows Interviews
Pesticide application (kg ha-1)
Calculation of FOP Interviews
Crop product Destination Calculation of FOP; Calculation nutrient balance; Calculation OM balance
Interviews
Price fresh matter (FCFA kg-1)
Calculation of FOP Paddy rice 125 Interviews
Rice straw 25 Tomato
fruit 52-55
Onion bulb 125 Gombo
fruit 225
Fresh yield (kg ha-1) Calculation of FOP Interviews Composition Calculation of EOM; Calculation
nutrient balance; Calculation feed balances
(Gemede et al., 2016; INRA et al., 2017; USDA, 2018; Yahaya, 2010)
37
Economic Currency (FCFA) Calculation of FOP Interviews Other animal costs
(FCFA) Herd related costs; Calculation of FOP
Interviews
Casual labour price (FCFA h-1)
Calculation of FOP Interviews; weighted average of casual operations costs
Regular labour price (FCFA h-1)
Calculation of FOP Interviews; weighted average of regular operations costs
Off-farm labour price (FCFA/h)
Calculation of FOP Interviews
Farm labour (h year-1) Calculation of HhLT Interviews; estimation Herd labour (h year-1) Calculation of HhLT Interviews Hired regular labour (h
year-1) Calculation of FOP & HhLT Interviews
Hired casual labour (h year-1)
Calculation of FOP & HhLT Interviews
Environment Soil characteristics Calculation nutrient balance; OM balance
(Khouma, 2000)
Climate characteristics Calculation nutrient balance; OM balance
(De Vries et al., 2011; Djaman et al., 2017)
Fertilisers Price (FCFA kg-1) Calculation of FOP Interviews Composition Calculation nutrient balance
Household Regular labour (h year-1) Calculation of HhLT Interviews Casual labour (h year-1) Calculation of HhLT Interviews Off-farm labour (h) Calculation of HhLT Interviews
Machines Price of purchase (FCFA) Calculation of FOP Interviews Depreciation costs (%
year-1) Calculation of FOP Interviews
Costs for maintenance (% year-1)
Calculation of FOP Interviews
Manure Destination Calculation nutrient balance; OM balance
Interviews
Pesticides Price (FCFA kg-1) Calculation of FOP Interviews Composition Calculation AI
1 At the time of writing, 16 May 2018, FCFA (XOF) 1000 = EUR 1.52 = USD = 1.79
38
Appendix 3. Decision variables used for farming system optimization in FarmDESIGN
Category Variables Minimum Maximum
Area of crops in rotation (ha)
Rice_WS 0
Total farm area
Rice_HDS 0
Rice_Both seasons 0
Onion 0
Tomato 0
Gombo 0
Rice-Onion 0
Rice-Tomato 0
Rice-Gombo 0
Rice products destination
Rice grain to household (kg year-1) 0 Required amount to match with current hh
needs
Rice straw to soil (%) 0.45 1
Rice straw to animal (%) 0 0.55
Feed import (kg/year)
Concentrates 0 99
Peanut fodder 0 4,999
Rice bran 0 999
Rice husk 0 1,999
Rice straw 0 4,999
Manure destination (%) Fraction to soil 0 100
Fertilizer inputs (kg ha-1)
9-23-30 0 Required amount to grow the most
demanding crop on whole farm area DAP 0
Urea 0
Pesticide inputs (kg ha-1 or bag ha-1)
Herbicide (Melange) 0
Required amount to grow the most
demanding crop on whole farm area
Herbicide (Londax 10) 0
Herbicide (Weedone 638) 0
Herbicide (Propanil) 0
Insecticide (K optimal) 0
Insecticide (Furadan 5g) 0
Insecticide (Super glant) 0
Fungicide (Tomex 430) 0
Labour (h/year) Hired regular labour 0 Required amount to grow the most
demanding crop on whole farm area Hired casual labour 0
Appendix 4. Constraints used for farming system optimization in FarmDESIGN
Category Variables Minimum Maximum
Farm area Farm area (ha) Original value – 10% Original value
Profit Farm operating profit (FCFA year-1) 0 +
Crop products self-sufficiency (%) 100% +
Labour Household leisure time (h year-1) 0 Total hh available time
Balance casual labour (h year-1) 0 +
Balance regular labour (h year-1) 0 +
Nutrient N balance 0 +
N soil losses (kg ha-1) +15 +
P soil losses (kg ha-1) 0 +
K soil losses (kg ha-1) Original value +
Feed balance Deviation STRucture (kg year-1) 0 +
Deviation Dry Matter Intake (kg year-1) - +10
Deviation Metabolizable Energy (ME year-1) -15 +15
Deviation Crude Protein (kg year-1) -10 +40
Nutrition Deviation cereals Original value +10
39
Appendix 5. Farmer_DB: Alternative farming system configurations according to FOP, NiB, HhLT, and ArRice after multi-objective optimization with FarmDESIGN. The fat black lines represent the original farming system performances. Each of the 1,000 coloured bars in a chart represents a new farming system configuration.
a Total farm area = 3.0 ha
0%
20%
40%
60%
80%
100%
Cro
pp
ed a
rea
(ha)
a
Gombo
Rice-Gombo
Tomato
Rice-Tomato
Onion
Rice-Onion
Rice_Both seasons
Rice_DS
Rice_WS
0
2 000
4 000
6 000
8 000
10 000
Lab
ou
r (h
.yea
r-1)
Hired Casual Labor
Hired Regular Labor
Hh Off-Farm Labor
Hh On-Farm Labor
0
1 000 000
2 000 000
3 000 000
4 000 000
5 000 000
58
3 9
97
99
3 8
53
1 3
32
16
8
1 6
93
39
3
2 0
43
70
9
2 3
71
57
9
2 7
13
47
3
3 0
43
66
0
3 3
60
66
2
3 6
65
02
9
3 9
52
62
6
4 2
60
37
5
Farm
Exp
ense
s (F
CFA
.yea
r-1)
Farm Operating Profit (FCFA.year-1)
10
1
11
6
13
4
15
6
17
8
19
8
21
9
23
8
26
0
28
3
30
2
Nitrogen Balance (kg.ha-1)
0
27
6
53
2
78
3
98
3
1 1
71
1 4
18
1 6
85
1 9
55
2 2
06
2 5
00
Household Leisure time (h.year-1)
1.3
3
1.5
4
1.6
8
1.8
2
1.9
6
2.0
9
2.2
3
2.3
6
2.4
9
2.6
2
2.7
6
2.8
7
Area of Rice (ha)
Feed Imports Costs
Hired Labor Costs
Crop Inputs Costs
Crop Production Costs
40
Appendix 6. Farmer_DN: Alternative farming system configurations according to FOP, NiB, HhLT, and ArRice after multi-objective optimization with FarmDESIGN. The fat black lines represent the original farming system performances. Each of the 1,000 coloured bars in a chart represents a new farming system configuration.
a Total farm area = 5.35 ha
0%
20%
40%
60%
80%
100%
Cro
pp
ed a
rea
(ha)
a
Gombo
Rice-Gombo
Tomato
Rice-Tomato
Onion
Rice-Onion
Rice_Both seasons
Rice_DS
Rice_WS
0
2000
4000
6000
8000
10000
Lab
ou
r (h
.yea
r-1)
Hired Casual Labor
Hired Regular Labor
Hh Off-Farm Labor
Hh On-Farm Labor
-
1 000 000
2 000 000
3 000 000
4 000 000
5 000 000
6 000 000
3 9
80
98
2
4 4
43
83
0
4 7
83
64
8
5 0
62
71
8
5 3
36
32
9
5 6
06
41
3
5 8
13
81
5
6 0
07
19
6
6 2
98
85
0
6 6
80
82
4
7 0
88
53
5
Farm
exp
ense
s (F
CFA
.yea
r-1)
Farm Operating Profit
88
92
97
10
2
10
9
11
6
12
1
12
9
14
2
15
3
16
5
Nitrogen Balance (kg.ha-1)
1
20
7
42
1
62
6
80
3
1 0
07
1 3
07
1 6
95
2 0
54
2 4
71
2 9
28
Household Leisure Time (h.year-1)
3.1
7
3.5
6
3.8
0
4.0
1
4.1
4
4.2
2
4.2
9
4.3
5
4.4
0
4.4
4
4.4
9
Area of Rice (ha)
Feed imports costs
Hired labour costs
Crop inputs costs
Crop production costs
41
Appendix 7. Farmer_AF: Alternative farming system configurations according to FOP, NiB, HhLT, and ArRice after multi-objective optimization with FarmDESIGN. The fat black lines represent the original farming system performances. Each of the 1,000 coloured bars in a chart represents a new farming system configuration.
Annex 1. Farmer_AF: a Total farm area = 2.45 ha
0%
20%
40%
60%
80%
100%
Cro
pp
ed a
rea
(ha)
a
Gombo
Rice HDS - Gombo
Tomato
Rice HDS - Tomato
Onion
Rice HDS - Onion
Rice HDS - Rice WS
Rice HDS
Rice WS
0
1 000
2 000
3 000
4 000
5 000
6 000
Lab
ou
r (h
.yea
r-1)
Hired Casual Labor
Hired Regular Labor
Household Off-farmLabor
Household On-farmLabor
0
1 000 000
2 000 000
3 000 000
4 000 000
1 4
03
20
5
1 8
81
97
5
2 3
03
61
6
2 6
36
73
0
2 9
47
61
3
3 1
98
30
8
3 3
83
51
4
3 5
78
33
3
3 7
52
53
0
3 9
42
37
4
4 2
08
40
5
Farm
Exp
ense
s (F
CFA
.yea
r-1)
Farm Operating Profit (FCFA.year-1)
86
10
9
12
5
13
9
15
5
17
4
20
1
23
0
26
0
30
7
35
3
Nitrogen Balance (kg.ha-1)
30
48
8
86
0
1 1
85
1 5
43
1 9
13
2 2
39
2 5
46
2 9
00
3 2
61
3 7
70
Household Leisure Time (h.year-1)
0.5
1
0.8
8
1.1
6
1.3
5
1.5
3
1.7
1
1.8
6
1.9
9
2.1
0
2.2
0
2.2
7
2.3
5
Area of Rice (ha)
Feed Imports Costs
Hired Labor Costs
Crop Inputs Costs
Crop Production Costs