Improved collective decision-making in action for irrigated rice farmers in the Senegal River Valley

AGRICULTURAL

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Agricultural Systems 89 (2006) 299–323

SYSTEMS

Improved collective decision-making inaction for irrigated rice farmers in the

Senegal River Valley

Jean-Christophe Poussin a,*, Youssouf Diallo b,Jean-Claude Legoupil c

a Institut de Recherche pour le Developpement (IRD), BP 64501 34394 Montpellier cedex 5, Franceb Centre National de Recherche Agronomique et de Developpement Agricole (CNRADA),

BP 22 Kaedi, Mauritaniac Centre International de Cooperation pour le Developpement (CIRAD), Avenue Agropolis,

34398 Montpellier cedex 5, France

Received 9 March 2004; received in revised form 7 September 2005; accepted 28 September 2005

Abstract

The gap between potential and actual yield levels reveals an opportunity for increasing pro-ductivity without technological innovation within irrigated rice systems. A participatoryapproach to promoting integrated crop management in rice cultivation has been tested as partof a village irrigation scheme on the Mauritanian side of the Senegal River during the wet sea-sons of 1998, 1999 and 2000. This approach was based on simulation-aided collective decision-making for the purpose of organizing a planned cropping calendar for the entire target irriga-tion zone prior to the beginning of the growing season. Moreover, a software called ‘‘CalCul’’(for ‘‘Calendrier Cultural’’ or ‘‘Cropping Schedule’’) was introduced; this application is basedon the knowledge acquired on irrigated rice cropping in the Sahel region stemming from twosources: agronomic understanding, thanks to the irrigated rice development simulation modelRIDEV; and management notions on the rice production system within the Senegal River Val-ley irrigation schemes. This participatory research-driven effort applied in the Senegal RiverValley has combined diagnostic monitoring, discussions with the farming community and

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* Corresponding author. Tel.: +33 4 6714 9031; fax: +33 4 6714 9068.E-mail address: [email protected] (J.-C. Poussin).

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300 J.-C. Poussin et al. / Agricultural Systems 89 (2006) 299–323

advisers about organizational and technical changes, cropping calendar planning at the levelof the irrigation scheme, and then a repeated monitoring exercise. This orientation has led toincreasing gross margin at the irrigation scheme level by over 80% without having to imple-ment any technological innovation.� 2005 Elsevier Ltd. All rights reserved.

Keywords: Irrigated rice; West Africa; Crop management; Input efficiency; Decision support tool

1. Introduction

In many countries, actual irrigated rice yields amount to only about 4–6 t ha�1, ascompared with a potential of 10–11 t ha�1 (Tran, 2004). For a wide array of studiesacross many countries (Cantrell and Hettel, 2004; Defoer et al., 2004; Tantawi,2004), this yield gap has revealed an opportunity to enhance rice output throughincreasing input efficiencies. Participatory approaches and the promotion of inte-grated crop management in rice cultivation are amongst the methods of reducing thisgap. These two methods have been tested in the Senegal River Valley, where poten-tial irrigated rice grain yields (limited solely by solar radiation and temperature)amount on average to approximately 9 t ha�1 during the wet growing season fromJuly to November (Dingkuhn and Sow, 1997), while actual average farming yieldsare ca. 5 t ha�1 (SAED, 1997; SONADER, 1998). Agronomic studies (Le Gal,1997; Wopereis et al., 1999; Haefele et al., 2001; Poussin et al., 2003) have ascribedthe sub-optimal timing of crop management intervention as a major cause of thisyield gap. Along these lines, Dingkuhn et al. (1995) developed a decision supporttool, RIDEV (RIce DEVelopment), which enables extension workers in the Sahelto advise farmers on the best timing for crop management intervention at the fieldlevel. Wopereis et al. (2001) used the RIDEV model to devise a set of integrated cropmanagement options for farmers within the Senegal River Valley. These optionswere then evaluated with both large-scale farms in Mauritania and small-scale farmsin Senegal whose farmers individually manage their own irrigation schemes (or irri-gated perimeters). Improved weed and fertilizer management raised yields by almost2 t ha�1 for both types of farms and increased net profits by between 40% and 85%.

Most of the irrigation schemes within the Senegal River Valley are utilized jointlyby many farmers. Since the structural adjustment programs of the 1990s in both Sen-egal and Mauritania, these schemes are collectively run by farmers� organizations.Decision-making on irrigation schedules, land preparation, purchase of inputs (fer-tilizers, herbicides), harvesting and marketing of produce are all performed collec-tively at the scheme level. Financing the production costs for the growing seasonrequires credit obtained through collective loans, which are then repaid after the har-vest. Decision-making at the field level (e.g. for weeding or fertilizer application) byindividual farmers is strongly influenced by decisions taken collectively at the schemelevel (Poussin, 1995; Le Gal and Papy, 1998). Farmers working with the sameirrigation scheme are technically and financially interdependent: improving individ-ual decision-making is therefore not of any great relevance over the medium- or

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long-term. We have thus opted to complement the approach developed by Wopereiset al. (2001) which was targeted at individual farmers. In order to improve rice-farm-ing productivity within collectively-managed irrigation schemes, best-bet croppingcalendars need to be determined both at the scheme and field levels. We hypothesizedthat setting up a crop calendar at the scheme level before the beginning of the grow-ing season can raise both input efficiency and rice output at field level.

The objectives of the present study have been to: (i) develop a rice managementtool for improved collective decision-making at the irrigation scheme level; (ii) applythis tool to suggest management changes in a village irrigation scheme of the SenegalRiver Valley where the determinants of rice yield had previously been investigated;and (iii) analyze the effects of changes in collective decision-making on rice yieldsand profitability.

2. Planning cropping calendars at the irrigation scheme level

The software package ‘‘CalCul’’ (a French acronym for ‘‘Cropping Schedule’’) isbased on the RIDEV irrigated rice development model; it provides an optimal crop-ping calendar at the scale of an irrigation scheme (Poussin, 2000) by taking intoaccount the organization of collective operations (i.e. collectively-managed opera-tions) in Senegal River Valley irrigation schemes.

2.1. The RIDEV irrigated rice development model

The rice phenology model RIDEV (Dingkuhn et al., 1995) simulates irrigated ricedevelopment in the Sahel region and provides crop management recommendationsbased on crop phenology considerations at the field level. For a given cultivar, sow-ing date and planting method (direct sowing or transplanting), RIDEV provides: (i)the percentage of spikelet sterility due to cold or hot weather during flowering; (ii)the dates of critical stages in rice development (panicle initiation, flowering andmaturity); and (iii) the optimal timing for transplanting, weeding, fertilizer applica-tion, final drainage before harvest, and harvesting. Input data consist of the site�sgeographical latitude, the daily minimum and maximum temperatures, and photo-thermal characteristics of the rice cultivar (Dingkuhn and Miezan, 1995).

2.2. Planning cropping calendars at the irrigation scheme level

In most irrigation schemes within the Senegal River Valley, the techniques ofmechanized land preparation, flooding before land preparation (pre-flooding) orbefore sowing (flooding), transplanting and mechanized harvesting are all performedcollectively (Poussin, 1997; Le Gal and Papy, 1998). Establishing a cropping calen-dar at this level therefore requires taking into account how these collective actionsget organized and their sequencing.

In order to prevent leakage, the flooding and land preparation steps are typicallynot carried out at the same time; the fields are usually flooded just before sowing or


transplanting, and the initial flooding can last two days for each field. The optimaltiming of transplanting, top-dressed fertilizer applications and harvesting is based onrice phenology. To obtain an optimal cropping calendar for each field, all operations(sowing, transplanting, top-dressed fertilizer applications, harvesting) should lastequally long at the scheme level, because without an equivalent duration (e.g. nurs-ery sowing lasts 2 days and transplanting 15 days at the scheme level), the timingwould be sub-optimal in some of the fields (i.e. seedlings would be 2 weeks olderin the last transplanted field than in the first). The slowest collective operation thusserves to set the duration common to all growing actions, in particular sowing.

The ‘‘CalCul’’ software input data comprise: (i) planned start date of the sowingperiod; (ii) the name(s) of the rice cultivar sown; (iii) the planting method (assumedcommon to all fields); and (iv) the duration of each collective operation at the schemelevel, i.e. land preparation, flooding and pre-flooding (both assumed to last the sameperiod), transplanting (should this planting method be chosen), and harvesting(manual harvesting is often performed individually, whereas mechanized harvestingis organized collectively). Moreover, in the case of pre-flooding, the average durationof soil drainage is also required.

Farmers often choose their rice cultivar individually (Poussin, 1995) and directsowing and transplanting may coexist within the same scheme (Poussin, 1997). Nev-ertheless, they may agree to select one or two common cultivars or cultivars withsimilar growth durations. The cultivar with the shortest duration would then besown at the end of the sowing period so as to minimize total crop duration forthe scheme as a whole. Direct sowing and transplanting lead to different crop devel-opment durations for the same cultivar. In the case where both planting methods areemployed, two cropping calendars (and two organizational modes) at the schemelevel should be established.

‘‘CalCul’’ utilizes the RIDEV phenology model in order to simulate rice develop-ment for the first cultivar sown at the beginning of sowing period (S1) as well as forthe second cultivar sown at the end of period (S2). The sowing period duration cor-responds with the time it takes to complete the slowest collective operation. Thesetwo simulations (S1 and S2) are performed using historical weather data recordednear the site. Each simulation provides average and extreme dates for several stages(Fig. 1): start of tillering, panicle initiation, heading, flowering and maturity. Thespikelet sterility percentages (average and maximum) for both S1 and S2 are also cal-culated by the software.

At the scheme level, the timing of each stage will be an average of the mean dates forS1 and S2 and may start as early as the minimum S1 date and end as late as the max-imum S2 date. The starting and end dates (averages and extremes) of crop manage-ment interventions (weed management, fertilizer applications, last drainage andharvest) are again based on rice phenology. The initial flooding can last up to 2 or 3days for each field and ends the day before sowing (or transplanting); land preparationfinishes just before the flooding starts (it can end as late as the day before the end offlooding); pre-flooding starts up depending on the duration of soil drainage.

The weeding and fertilizer management steps outputted by ‘‘CalCul’’ comply withWARDA recommendations (Wopereis et al., 2001). Initial weeding can be undertaken

NAKHLET - PLANNED CROPPING CALENDAR FOR THE 1999 WET SEASONCrop establishment: DIRECT SOWING with pre-flooding (soil draiange delay: 20 days)Land preparation: 2 daysFlooding: 12 daysHarvest: 10 daysSowing is stagered according to the duration of flooding (12 days)Cultivar (first sowing): Jaya (spikelet sterility mean: 1.2%. maxi: 11.5%)Cultivar (last sowing): Jaya (spikelet sterility mean: 1.0%. maxi:14.2%)

1 11 21 1 11 21 1 11 21

Start tilleringPre-flooding - - - - - - - - - - >Land preparation - - - - - - - - - - >FloodingWeeding pre-emergenceSowingWeeding post-emergence1st N topdressingManual weeding <

1 11 21 1 11 21 1 11 21

Panicle initiation <- - - - - >Heading <- - - - - >Flowering <- - - - - >Maturity <- - - - - - - - >Manual weeding - - - - - >2nd N topdressing <- - - - - >3st N topdressing <- - - - - >Drainage <- - - - - >Harvest <- - - - - - - - >

June July Agust

NovemberOctoberSeptember

Fig. 1. Planned cropping calendar at scheme level determined with the ’’CalCul’’ software for the 1999 wet season. Averages in rice phenology stages areindicated in light gray blocks; averages in crop management interventions in dark gray blocks. Earliest and latest dates are indicated by �<� and �>�,respectively, in the event they differ from average dates. Land preparation may be undertaken at the latest 1 day before flooding, and pre-flooding must becompleted at the latest 20 days (soil drainage delay) before land preparation.

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chemically using pre-emergence or post-emergence herbicides, and a second weedingstep can then be performed manually prior to the second N top-dressing. It is advisedto split the nitrogen fertilizer supply into 2 or 3 top-dressed applications at times whentillering, panicle initiation and possibly heading get underway. P and K fertilizer man-agement is not specified. Contrary to the recommendations issued by irrigation andextension authorities in both Mauritania (SONADER) and Senegal (SAED), farmersgenerally combine P fertilizer with the first top-dressed N application (Wopereis et al.,1999; Poussin et al., 2003). The basal application of K fertilizer before land prepara-tion depends on the soil-exchangeable K status (Haefele et al., 2001). Cropping calen-dars for each field should be synchronized with the calendar at the entire scheme level.The execution of each field operation must follow the order of the water allocationcycle.

3. Site description, surveys and diagnostic models

3.1. Site description

Studies have been conducted on the irrigation scheme of Nakhlet, a small villagelying on the northern bank of the Senegal River 60 km east of Rosso (16�29 0N,15�12 0W), in Mauritania. This type of irrigation scheme, with land areas spanningless than 50 ha and cultivated by farmers from individual villages, covers about25% of the irrigated area within the Senegal River Valley (SAED, 1997; SONADER,1998).

The Nakhlet irrigation scheme was devised in 1981 for the Mauritanian irrigationand extension authority ‘‘Societe Nationale de Developpement Rural’’ (SONA-DER); this layout encompasses 27.5 ha, with 119 fields cultivated by 29 farmers. Irri-gation is carried out through pumping from a tributary of the Senegal River. Theirrigated rice is cultivated only during the wet season, from mid-June until the endof November. Crop establishment in all fields is performed using a disk plow alongwith direct sowing of pre-germinated seeds. In 1998, 1999 and 2000, farmers usedthree indica cultivars with slender grain: Sahel 108 (IR13240-108-2-2-3, short dura-tion), Jaya (medium duration) and Sahel 202 (ITA 306, medium duration). Thepotential yields of these three cultivars are similar and average about 8–9 t ha�1 dur-ing the wet season (Dingkuhn and Sow, 1997; WARDA, 1999).

After some 10 years under semi-public management, the Nakhlet farmers� organi-zation (NFO) was assigned responsibility in 1992 for managing this irrigationscheme. The organization oversees credit provision, water pumping and irrigation,input supply (herbicides, fertilizers, fuel, etc.), and land preparation. All of thesetasks had previously been performed by SONADER. NFO also organizes generalmeetings to decide how to proceed for the next growing season and to draw upthe balance sheet for the previous growing season. Field irrigation is implementedby farmers on a rotational basis, beginning in fields nearest to the irrigation source.This means of operation is typical of village irrigation schemes on both sides of theSenegal River (Diemer and van der Laan, 1987).


3.2. Agronomic, hydraulic and economic surveys

All 119 fields lying within the Nakhlet village irrigation scheme were monitoredduring the wet seasons of 1998, 1999 and 2000 (1998WS, 1999WS, 2000WS). The1998WS survey was used for determining the initial situation in terms of crop andwater management, sources of yield gap and variability.

The topsoil (0–0.2 m deep) from all fields was sampled in 1998 before the growingseason began (this entailed one composite sample from the four taken at random perfield). Soil analyses (clay content, C and N content, P-Olsen, pH, electrical conduc-tivity, CEC and exchangeable K) were conducted at the IRD laboratory in Dakar,Senegal, in accordance with standard methodologies. P and K availabilities werefound to lie above the critical levels for rice (Dobermann et al., 1995; Sanchez,1976). Electrical conductivity and pH do not constitute constraints for irrigated ricecropping, provided irrigation is well-managed (Asch and Wopereis, 2000). We havetherefore assumed that initial P and K soil status and soil salinity levels, when irri-gation is well managed, have not been hampering rice growth.

For each field, the following data were recorded: cultivars sown, type of seed (cer-tified or retained from the previous harvest), types and rates of applied fertilizers, typeof weed control (manual or chemical), and applied herbicide rates. The dates of eachmanagement intervention (i.e. land preparation, irrigation, sowing, weed control, fer-tilizer applications, harvesting) were also recorded. Moreover, visual scores (0–3) ofweed infestation at panicle initiation (PI) in each field were assigned, following themethod suggested by Poussin et al. (2003): 0 for no coverage; 1 for weak coverage (lessthan 10% of surface area covered by weeds); 2 for strong coverage (between 10% and30%); and 3 for very strong coverage (over 30%). The grain yield (corrected for 14%moisture) was determined at maturity in the center of each field using a 25 m2 sam-pling area. The rice grain yield of the entire field may be slightly overestimated,although comparisons among years at the scheme level remain relevant.

The volume of water pumped into the Nakhlet irrigation scheme in 1998 and 1999was monitored daily during the wet season. Discharge measurements at the head ofthe irrigation scheme were performed via a rectangular weir installed at the outflowpoint of the pump�s stilling basin. The characteristics of this flume (length, width,discharge coefficient) serve to calculate, using the Manning–Strickler formula, thedischarge from the water level upstream of the flume, as measured through a gaugeplaced inside the stilling basin (Bos et al., 1991): Q = Cdb(2g)0.5H1.5, where Q is thedischarge (in m3 s�1), b the breadth of the control section (m), Cd the discharge coef-ficient, g the gravitational acceleration (m s�2), and H the upstream head above thecrest (m). A counter was installed on the pump in order to measure the operatingtime period. The irrigation water volume was estimated at 70% of the pumped inflowto allow for conveyance losses (Tuzet and Perrier, 1998). We further assumed thatirrigation water was distributed homogeneously in each rice field. No measurementswere conducted during the 2000WS since similar water allocation rules were appliedas for the 1999WS.

Farmers financed their production costs through bank loans granted by NFO.NFO purchased inputs and provided farmers with certified seeds, fertilizers and


herbicides, in quantities proportionate with the surface area of their plots. Afterthe harvest, farmers reimbursed NFO in rice paddy, which was then sold byNFO to repay the bank. Rice paddy was sold to the State, which guaranteed apaddy price of between 38 and 45 uguiya (the Mauritanian currency [UM]) perkg (1 UM = 0.0032 euro – March 2004 exchange rate), depending on the rate ofmilling recovery (SONADER, 1998). A financial survey was also conducted atthe scheme level during the 1998, 1999 and 2000 wet seasons. Irrigation costsincluded fuel, motor oil, maintenance and pump attendant�s salary. Cropping costsincluded land preparation, use of certified seeds, and purchase of fertilizers andherbicides. These costs were all obtained from supplier invoices and service fees.The costs of seeds retained from the previous harvest were estimated at45 UM kg�1. The harvest was performed manually by each farmer during the1998WS and 1999WS and then by means of a combine harvester, organizedthrough a service provider, during the 2000WS. The comparison between yearsundertaken excluded harvesting costs. The gross margin of the scheme corre-sponded to the total rice paddy production value, with an average paddy sellingprice of 42 UM kg�1, minus the production costs. Gross margin was also calcu-lated for each individual field.

3.3. Using models for diagnostic purposes

Daily rainfall during the 1998WS, 1999WS and 2000WS was measured in Nakhletwith a pluviometer. The reference evapotranspiration using the Penman–Monteithequation (Smith et al., 1992; Allen et al., 1998) was calculated from weather datarecorded at the WARDA (West African Rice Development Association) experimen-tal station in Fanaye (16�33 0N, 15�46 0W), opposite Nakhlet, on the Senegalese sideof the Senegal River. The evapotranspiration of rice was calculated by multiplyingthe reference evapotranspiration by a varying crop factor (kc). The set of kc-valuesgiven by FAO (1986) and by Siddeek et al. (1988) were used: kc equals 1.15 duringthe vegetative growth phase, 1.3 during the reproductive phase, and 1.05 after thefirst week of maturation. The cumulative potential evapotranspiration values for riceduring the crop season were compared with the total of irrigation water supply plusrainfall.

The OryzaS rice growth model (Dingkuhn and Sow, 1997) simulates both energy-limited and temperature-limited potential rice yields in the Sahel using daily minimumand maximum temperatures, solar radiation and geographical latitude (to calculatethe photoperiod). This model has been applied to calculate potential yields (and thusthe yield gaps) for each field during the 1998WS, 1999WS and 2000WS on the basis ofclimatic data recorded in Fanaye over the same period.

The RIDEV rice phenology model was used for simulating rice developmentand determining spikelet sterility percentages in each field during the 1998WS,1999WS and 2000WS. These simulations then served to evaluate differencesbetween the optimal and actual cropping calendars for each field, by use of dailytemperatures recorded in Fanaye over the rice crop duration during these three wetseasons.


4. Background: Diagnostic assessment of the 1998 wet season

During the 1998WS, farmers sowed two cultivars: Jaya on 15.5 ha (65 fields) andSahel 108 on 12.5 ha (54 fields). Sahel 108 was cultivated in Nakhlet for the first timethat particular year. Sahel 108 seeds were certified, and the Jaya seeds had beenretained from the previous harvest. All fields were manually harvested, on averageat 17 days after maturity (simulated with RIDEV) for Jaya and 20 days for Sahel108 (Fig. 2). RIDEV simulations did not indicate any problems with spikelet sterilityin any of the fields (i.e. a simulated spikelet sterility ratio < 5%). The average poten-tial yield calculated with OryzaS equaled 8.7 t ha�1 for Jaya and 8.2 t ha�1 for Sahel108, while the actual rice grain yield measured at maturity ranged from 2.7 to7.1 t ha�1, with an average of 4.8 t ha�1. Low yields were mainly localized in thosefields furthest from the irrigation source. The average yield for Jaya (5.5 t ha�1)was significantly higher (Fisher�s test: p < 0.0001) than for Sahel 108 (4.0 t ha�1).Farmers thus sought to understand the origins of yield variability and the distinctdifferences from one cultivar to the other.

4.1. Irrigation management

Irrigation commenced on June 19, 1998 with flooding prior to direct sowing andended on September 30. The total pumped water volume was 265,700 m3, including79,000 m3 for the flooding (Table 1). The combination of irrigation water supply plusrainfall was ca. 160 mm higher than the evapotranspiration of rice; yet water lossesthrough percolation were estimated at some 2 mm per day (i.e. over 200 mm fromsowing to maturity) for this type of soil (Raes et al., 1997).

The fields were irrigated 4–9 times after flooding. The estimated irrigation watersupply (including flooding) was between 528 and 828 mm, depending on irrigationfrequency (Table 2). Both irrigation frequency and water supply were lower in fieldsfurther from the irrigation source, which happened to be sown primarily with Sahel108. Water allocation rules, based on reinitializing the water allocation cycle follow-ing rainfall, contributed to this irrigation frequency heterogeneity (water allocationcycle restarted in those fields nearer the irrigation source).

The effects of irrigation frequency (through irrigation water supply and risk ofwater shortage) on rice yield during the 1998WS were highly significant (ANOVA,Student�s test: p < 0.0001). Fields irrigated less than 7 times after flooding were sownmainly with Sahel 108, whereas the other fields were sown mainly with Jaya (Table2). This distinction could explain the yield gap between the cultivars. Nevertheless,the average yield obtained using Jaya was still significantly higher (according tothe Student�s test) in fields irrigated 6 or 7 times after flooding, in cases where bothcultivars were sown.

4.2. Weeding and fertilizer management

A total of 110 l of propanil and 55 l of 2,4-D amine were bought by NFO beforethe 1998WS and distributed to farmers in proportion with the surface area of their

1 11 21 1 11 21 1 11 21

Land preparationFloodingSowingWeeding: actual

optimal1st N topdressing: actual

optimal2nd N topdressing: actual

optimal Jayaoptimal Sahel 108

1 11 21 1 11 21 1 11 21

2nd N topdressing: actualMaturity JayaMaturity Sahel 108Harvest

Weed infestation score at panicle initiation: average(SD) 1.5 (1.2) Sowing 15.0 ha Jaya (retained seeds)12.5 ha Sahel 108 (certified seeds)

Weeding post-emergence 2 l ha-1 2-4 D + 4 l ha-1 propanil1st N top dressing 100 kg ha-1 TSP + 125 kg ha-1 urea2nd N topdressing 125 kg ha-1 urea

June July Agust

September October November

g. 2. Actual and optimal cropping calendars at the scheme level during the 1998 wet season. The actual timing in crop management intervention is indicateddark gray blocks; optimal timing indicated in light gray blocks. Weed infestation scores at panicle initiation, cultivar and type of seeds, types and rates ofrbicides and fertilizers are also indicated.

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Table 1Pumped water volumes and estimated water supply for the entire irrigation scheme of Nakhlet (averages and standard deviations) for pre-flooding, flooding,irrigation during the 1998 and 1999 wet seasons

1998 wet season 1999 wet season

Pumped watervolume (103 m3)

Estimated watersupply in thefields (mm)average (SD)

Number of irrigationsequences after flooding(no.) average (min–max)

Pumped watervolume (103 m3)

Estimated watersupply in thefields (mm)average (SD)

Number of irrigationsequences afterflooding (no.)average (min–max)

Pre-flooding None 70.1 171 (14)Flooding 79.0 202 (18) 70.3 181 (35)Irrigation 216.3 554 (105) 6.5 (4–9) 233.0 589 (67) 8.2 (8–9)

Total 295.3 756 (110) 373.4 939 (72)

Rainfall 207 183

Evapotranspiration of rice 802 774

The evapotranspiration of rice, number of irrigation sequences and rainfall contributions are also indicated. Standard deviations (SD) are shown inparentheses.

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Table 2Irrigation water supply, number of fields sown with Jaya and Sahel 108, and rice grain yield during the1998 wet season as a function of the number of irrigation sequences after flooding

Irrigation sequences after flooding (no.) 4 5 6 7 8 9Average irrigation water supply (mm) 528 602 666 720 774 828

Fields (no.) 11 21 26 33 14 14Yield average (mg ha�1) 4.07

(0.24)4.03(0.18)

4.50(0.21)

5.04(0.14)

5.37(0.17)

5.92(0.18)

Fields sown with Jaya (no.) 1 6 11 20 13 14Yield average (mg ha�1) 5.47a(0.90) 5.41a (0.79)

Fields sown with Sahel 108 (no.) 10 15 15 13 1 0Yield average (mg ha�1) 3.79a(0.47) 4.48a (0.55)

Standard deviations are shown in parentheses.a Simple comparison between cultivars with the Student�s test and p < 0.001.


fields. The first weeding was performed using herbicides in all fields, with a combinedaverage of 4 l ha�1 of propanil and 2 l ha�1 of 2,4-D amine (i.e. close to the recom-mendations). This first weeding occured on average at 27 days after sowing (DAS);the lag between optimal (as simulated with RIDEV) and actual weeding time rangedfrom �2 to 17 days. Despite heavy weed infestation observed at panicle initiation (anaverage visual score >1, Fig. 2), a second manual weeding was only pursued on aminority number of fields. Late weeding with low herbicide content and a virtualabsence of manual weeding was also observed by Haefele et al. (2001) as a typicalpractice in Mauritania and by Poussin et al. (2003) in Senegal. Moreover, the visualscores of weed infestation at panicle initiation were greater in fields further from theirrigation source (at low irrigation frequency and therefore high risk of water short-age), with Sahel 108 being sown in most instances.

NFO also bought 7 tons of urea (46% N) and 2.75 tons of TSP, triple superphos-phate (20% P). These fertilizers were distributed to farmers proportionally to theircultivated land area at a rate of 250 kg ha�1 of urea (115 kg N ha�1) and 100 kg ha�1

of TSP (20 kg P ha�1), i.e. quite close to the recommended quantities of120 kg N ha�1 and 20 kg P ha�1. On average, a total of 117 kg N ha�1 and19 kg P ha�1 were applied in two top-dressed applications. Farmers encounteredproblems when it came to dividing the fertilizer bags. This explains the differencebetween these averages and the distributed rates, and also the variability in appliedN fertilizer, with a range observed between 90 kg N ha�1 and 150 kg N ha�1. Never-theless, no correlation was detected between the applied N rate and rice yield. Thisfinding corresponded with the strong variability in N fertilizer efficiency previouslyobserved by Wopereis et al. (1999) and Haefele et al. (2001) in similar situations.

All farmers applied fertilizer in two top dressings (Fig. 2). As a first split, occur-ring on average at 36 DAS, all TSP and half the urea were applied. The remainder ofthe urea was then applied at about 60 DAS. The optimal dates simulated usingRIDEV for these two splits were, on average, 23 DAS and 60 DAS for Jaya and23 DAS and 50 DAS for Sahel 108. The time lag between actual and optimal datesranged from 2 to 23 days late for the first split and from �9 to 21 days late for the


second split. Increasing the delay in N application led to considerable declines inmaximum yield (Fig. 5), i.e. a decrease in applied N efficiency. The lag in the secondsplit for Sahel 108 (on average 11 days) was greater than for Jaya (on average just 1day). Farmers respected quite closely the agricultural advice that recommended asecond split at 60 DAS (SONADER, 1998). This recommendation however didnot match with the Sahel 108 growth duration, which on average is 10 days shorterthan the Jaya growth duration during the wet season (according to RIDEV simula-tions with daily temperatures recorded at Rosso, 1970–1984).

Low irrigation water supply combined with sub-optimal timing of weeding andnitrogen fertilizer applications resulted in rice yield losses. A lower irrigation fre-quency, combined with greater weed infestation and greater delays for N fertilizerapplication in fields sown with Sahel 108 than fields sown with Jaya, served toexplain the yield differences observed between the two cultivars.

4.3. Technical/organizational changes in 1999 and 2000, based on 1998 wet season

The assessment of the 1998WS was explained and discussed with farmers during ageneral meeting held at Nakhlet in April 1999. Farmers were asked to implementseveral changes in their technical organization; these changes were intended to: (i)reduce the variability in irrigation frequency between fields, and (ii) narrow thegap between optimal and actual cropping calendars.

It was thereby suggested to continue with the water allocation cycle followingrainfall (i.e. the water allocation schedule is not to be reinitialized and, after a rainfallevent, is to start in the next sequential field). In order to install a field irrigation infor-mation system, a map of the scheme (based on a cadastral survey, from which allfarmers could identify their fields) was posted; this map showed week-by-week whichfields were to be irrigated (based on the irrigation schedule). Farmers assignedresponsibility for field irrigation were required to indicate on this map the fields thathad already been irrigated in order to inform the other farmers (field irrigation per-formed by farmers on a rotating basis). It was also suggested to plan the croppingcalendar at the scheme level for the next wet season and to post a map of the entirescheme (based on the cadastral survey) showing which operations were to be exe-cuted weekly and which fields were to be treated (based on both the planned crop-ping calendar and water allocation cycle). SONADER agricultural advisers, whoattended the general meeting, suggested to pre-flood during the 1999WS one monthbefore land preparation so as to reduce early weed infestation, in addition to usingthe certified Sahel 202 seed during the 2000WS.

Farmers accepted these suggestions; furthermore, they opted to grow just one cul-tivar (Jaya during the 1999WS and Sahel 202 during the 2000WS) and to delay thesowing period to ensure that harvest would occur after the rainy period.

Planned cropping calendars were established for both the 1999WS and 2000WSusing ‘‘CalCul’’. Flooding constituted the slowest collective operation and thusserved to set the common duration of crop management interventions at the schemelevel. After this planning step, the farmers were left to manage the cropping seasonwithout any external interference. The surveys carried out on the 1999WS and


2000WS were then compared with 1998WS for the purpose of evaluating the effectsof these changes on yields and profitability.

5. Effect of changes: Diagnostic assessment of the 1999 and 2000 wet seasons

During the 1999WS and 2000WS, the potential rice grain yields (simulated withOryzaS) ranged between 8.8 and 9.2 t ha�1, while the average actual yield increasedgreatly compared with 1998WS, reaching 7.2 t ha�1 in 1999 and 8.2 t ha�1 in 2000.Nevertheless, the standard deviation in yield did increase slightly (1.4 t ha�1 in1999 and 1.8 t ha�1 in 2000), indicating a continued heterogeneity among fields.

5.1. Irrigation management

During the 1999WS, the total pumped water volume was 78,100 m3 higher thanduring the 1998WS (Table 1). This increase was mainly due to pre-flooding irriga-tion. The first flooding used 8700 m3 less than in 1998, and irrigation after floodingamounted to 16,700 m3 more than in 1998. The total water supply after pre-floodingwas estimated at 961 mm, compared to 951 mm in 1998, while the evapotranspira-tion of rice was estimated at 774 mm, down from 802 mm in 1998. The irrigationwater supply variability between fields was lower in 1999 than in 1998 (72 mm vs.110 mm, Table 1). Farmers respected the new water allocation rules and irrigationplanning approach, and all fields were irrigated 8 or 9 times after flooding. Theincrease in irrigation frequency compared with 1998 did not lead to a strong increasein the total pumped water volume. During the 2000WS, farmers did not pre-floodand all fields were irrigated 8 or 9 times after flooding, according to the water allo-cation rules and irrigation plan.

The increase in irrigation frequency reduced the risk of water shortage. Based on1998WS results (Table 2), this increase in irrigation frequency cannot on its ownexplain the increase in rice yield with Jaya cultivar (by 2.4 t ha�1 in 1999 and by3.4 t ha�1 in 2000). Irrigation management was not the only factor therefore contrib-uting to yield increase.

5.2. Land preparation, flooding and sowing

During both 1999WS and 2000WS, farmers chose to delay the sowing period inorder to ensure that harvesting would not occur during the rainy period, while atthe same time maintaining a maximum spikelet sterility percentage below 15% (assimulated with ‘‘CalCul’’ using daily temperatures recorded at Rosso, 1970–1984).During the 1999WS, most farmers were farming the Jaya cultivar, using seedsretained from the previous harvest. With a sowing period extending from July 7–22, the crop had matured in early November (Fig. 3). During the 2000WS, they usedthe Sahel 202 cultivar with certified seeds. By sowing between July 19 and August 4,the crop matured by the end of November (Fig. 4). The spikelet sterility simulatedwith RIDEV for both wet seasons was less than 10% in all fields.

11 21 1 11 21 1 11 21 1 11

Pre-flooding: actualplanned

Land preparation: actualplanned

Flooding: actualplanned

Sowing: actualplanned

Weeding (manual): actualplanned (herbicide)

1st N topdressing: actualplanned

21 1 11 21 1 11 21 1 11 21

Weeding (manual): actual1st N topdressing: actual2nd N topdressing: actual

plannedMaturity: actual

plannedHarvest: actual

planned

Weed infestation score at panicle initiation: average(SD) 1.1 (1.6) Planned ActualSowing 27.5 ha Jaya (retained seeds) 26.6 ha Jaya

0.9 ha Sahel 108Weeding 2 l ha-1 2-4 D + 4 l ha-1 propanil manual1st N topdressing 100 kg ha-1 DAP + 50 kg ha-1 urea 150 kg ha-1 urea2nd N topdressing 150 kg ha-1 urea 150 kg ha-1 urea

August September October November

May June July August

Fig. 3. Actual and planned cropping calendars at the scheme level during the 1999 wet season. The actual timing in crop management intervention is indicatedin dark gray blocks; planned timing indicated in light gray blocks. Weed infestation scores at panicle initiation, cultivar and type of seeds, types and rates ofherbicides and fertilizers are also indicated.

J.-C

.P

ou

ssinet

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/A

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ltura

lS

ystem

s8

9(

20

06

)2

99

–3

23

313

July August September1 11 21 1 11 21 1 11 21

Land preparation: actualplanned

Flooding: actualplanned

Sowing: actualplanned

Weeding: actualplanned

1st N topdressing: actualplanned

2nd N topdressing: actualplanned

October November December1 11 21 1 11 21 1 11 21

2nd N topdressing: actualMaturity: actual

plannedHarvest: actual

planned

Weed infestation score at panicle initiation: average(SD) 0.8 (1.2) Planned ActualSowing 27.5 ha Sahel 202 (certified seeds) 22.5 ha Sahel 202

5.0 ha Jaya (retained seeds)Weeding 2 l ha-1 2-4 D + 4 l ha-1 propanil 2 l ha-1 2-4 D + 4 l ha-1 propanil1st N topdressing 100 kg ha-1 DAP + 50 kg ha-1 urea 150 kg ha-1 urea2nd N topdressing 150 kg ha-1 urea 150 kg ha-1 urea

Fig. 4. Actual and planned cropping calendars at the scheme level during the 2000 wet season. The actual timing in crop management intervention is indicatedin dark gray blocks; planned timing indicated in light gray blocks. Weed infestation scores at panicle initiation, cultivar and type of seeds, types and rates ofherbicides and fertilizers are also indicated.

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The planned sowing dates were well respected in both 1999 and 2000 (Figs. 3 and4). The cropping calendar indicated the start of the first flooding 3 days before sow-ing, but it turned out that farmers sowed on the same day as flooding. Land prepa-ration was performed using disk plows 2 days before flooding in 1999 and one weekbefore flooding in 2000; this operation lasted 3 or 4 days in both seasons. The dis-crepancy between actual and planned dates for both land preparation and floodingexerted no impact on the subsequent crop management interventions.

During the 1999WS, farmers pre-flooded their fields about 2 weeks before theplanned period in order to ensure that weeds would sprout prior to land preparation.Weeds were then eliminated by land preparation about 1 month after pre-flooding.Early weed infestation was low and farmers refrained from any herbicide use. Inspite of the efficiency this technique offers on early weed control and its low cost (fuelconsumption of ca. 1000 l for pre-flooding, corresponding to 66,000 UM – Table 3),the farmers did not repeat pre-flooding during the 2000WS. Pre-flooding requires thefarmers to be present at Nakhlet one month before the beginning of the crop season,which turns out to be difficult since farmers exercise other activities in addition toirrigated agriculture (Diemer and van der Laan, 1987).

5.3. Weed and fertilizer management

During the 1999WS, early weed infestation levels were low. Most farmers pre-ferred to proceed by manual weeding for economic reasons (about 150,000 UM insavings at the scheme level, Table 3). Manual weeding was undertaken at 25 DAS(Fig. 3). It lasted however until September 13 in some fields, which delayed the firstN application. Visual scores of weed infestation at PI were lower on average than in1998, yet standard deviations (1.6 in 1999 vs. 1.2 in 1998) indicated strong variabilityfrom one field to the next.

During the 2000WS, farmers applied a combination of propanil (4 l ha�1) and2,4-D amine (24 l ha�1) as in 1998 (Fig. 4). Herbicides were applied 6 days later thanscheduled, with this delay due in part to the delivery date of fertilizer and herbicides(August 19, 2000). Weeding on those fields sown before July 29 was inevitablydelayed (the optimal date for weeding was about 20 DAS). This delay in herbicidetreatment directly induced a delay in the first N application. Weed infestation atPI was not very extensive in most fields (with a visual score average <1) but washigher in fields sown before July 29 (a visual score average of 1.8).

In both the 1999 and 2000 seasons, farmers ordered 3 tons of DAP, diammonium-phosphate (18% N, 20% P), and 5.5 tons of urea with planned application rates of100 kg ha�1 of DAP and 200 kg ha�1 of urea, i.e. 20 kg P ha�1 and 110 kg N ha�1.The DAP fertilizer however was not available; 8.5 tons of urea were thus deliveredand distributed to farmers at a rate of 300 kg urea ha�1. The total applied N aver-aged 152 kg ha�1 in 1999 and 2000, with a standard deviation of about 20 kg ha�1.The increase in applied N compared with 1998 could not explain the increase in riceyield, since no correlation was found between total applied N and rice yield.

As was the case in 1998, N fertilizer was applied in two equal splits. The timingplanned for the first split was 23 DAS, and that for the second split was 56 DAS

Table 3Scheme-level financial results from the 1998, 1999 and 2000 wet seasons TSP: triple superphosphate (20% P); DAP: di-ammonium phosphate (18% N, 20% P)

1998 wet season 1999 wet season 2000 wet season

Amount(l or kg)

Unit price(UM)

Value(103 UM)

Amount(l or kg)

Unit price(UM)

Value(103 UM)

Amount(l or kg)

Unit price(UM)

Value(103 UM)

Fuel 4100 65 266.5 5100 66 336.6 4200 88 369.6Motor oil 18.2 24.2 23.8Pump attendant�s salary 64.0 64.0 64.0Maintenance 55.2 66.2 78.7

Total irrigation costs 403.9 491.0 536.1

Land preparation 132.4 132.9 134.5Retained seeds 2000 45 90.0 3000 45 135.0 600 45 27.0Certified seeds 1500 90 135.0 2280 90 205.2Herbicides 252.0 74.2 233.4Urea 7000 42 294.0 8500 44 374.0 8500 45 382.5Other fertilizers

(TSP or DAP)2500 38 95.0

Total crop intervention costs 998.4 716.1 982.6

Miscellaneousexpenses (transport, etc.)

170.5 194.2 226.6

Total costs 1652.8 1481.3 1825.3

Gross product 128,500 42 5397.0 197,000 42 8274.0 223,000 42 9366.0Gross margin 3744.2 6792.7 7540.7

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in 1999 and 58 DAS in 2000 (Figs. 3 and 4). The delays between optimal and actualtiming were, on average, 9 days in 1999 and 6 days in 2000 for the first split, and 2days in 1999 and 4 days in 2000 for the second split. The delay in the first split alwaysremained greater than that of the second. In 1999, manual weeding partiallyexplained the delay in the first N application, whereas in 2000 the delivery datefor herbicides and fertilizers partially explains the delay in sowing prior to July 29.Nevertheless, these delays were significantly less (Fischer�s test: p < 0.05) in 1999and 2000 compared to 1998. Like in the 1998 season, the N fertilizer applicationdelay in both 1999 and 2000 induced a great drop in maximum yield (Fig. 5). In addi-tion, the yield measured on samples overestimated the yield of individual fields and

2

4

6

8

10

12

-7 0 7 14 21 28

first N fertilizer application delay (days)

yiel

d (M

g ha

-1)

2

4

6

8

10

12

-7 0 7 14 21 28

second N fertilizer application delay (days)

yiel

d (M

g ha

-1)

Fig. 5. Rice grain yield (in mg ha�1) measured in all fields during the 1998, 1999 and 2000 wet seasons(1998WS [o], 1999WS [+], 2000WS [x]) as a function of gap (in days) between actual and optimal Nfertilizer application timing (optimal date for the first split: 23 days after sowing; optimal date for thesecond split: panicle initiation; optimal dates determined for each field using the RIDEV model).

0

5

10

15

20

25

perc

enta

ge o

f fie

lds

(%)

0

1998WS

100 200 300 400

gross margin per hectare (103 UM ha-1)

0

5

10

15

20

25

perc

enta

ge o

f fie

lds

(%)

0

1999WS

100 200 300 400


0

5

10

15

20

25

perc

enta

ge o

f fie

lds

(%)

0 100 200 300 400


2000WS

Fig. 6. Frequency distribution of gross margins per hectare for the 1998, 1999 and 1999 wet seasons(1998WS, 1999WS, 2000WS).


even exceeded the potential yields calculated using OryzaS. Still, the maximum yieldobserved during the 1999 and 2000 seasons indicated that the potential yield for bothJaya and Sahel 202 was reached in many fields.


5.4. Harvest

Farmers were able to harvest their fields on time, at close to maturity of the riceplants in 1999, with a rice yield of 7.2 t ha�1. In 2000, combine harvesters were usedapproximately 13 days after maturity; the average grain yield was 8.2 t ha�1.

5.5. Financial benefits

During the 1998WS, total costs for the entire scheme (excluding harvesting andlabor costs) amounted to 1.7 millions UM. The rice paddy production value was5.4 millions UM and the gross margin 3.7 millions UM (Table 3). Gross marginat the scheme level increased to 6.9 millions UM in 1999 and to 7.5 millions UMin 2000, which means that gross margins roughly doubled, with respect to 1998.Moreover, except for pre-flooding, organizational changes did not result in increasedlabor input: the timing of crop interventions was modified, yet the length of eachwork task at the field level remained unchanged.

The increase in rice yield without significant changes in amount of input consti-tuted the main factor contributing to this result. In comparison with the 1998 season,production costs decreased slightly in 1999 given that less herbicide was used despitethe introduction of pre-flooding, which served to increase fuel consumption by about1000 l. Production costs further rose in 2000 due to the use of certified seeds, coupledwith a fuel price increase. Similar results were obtained in Mauritania by WARDA,in working with large-scale farmers cultivating their own irrigated schemes (Haefeleet al., 2001).

At the field level, gross margins ranged between 50,000 and 250,000 UM ha�1 in1998, 50,000–375,000 UM ha�1 in 1999, and 100,000–400,000 UM ha�1 in 2000(Fig. 6). The increase in gross margin per hectare was highly significant (from multi-ple comparisons using Sheffe�s test: p < 0.0001). Moreover, the gross margin perhectare for fields sown after July 29 in 2000 (the late arrival of fertilizers and herbi-cides did not affect these fields) varied from about 250,000–400,000 UM ha�1, i.e.above the gross margin range for 1998. Combine harvesting in 2000 costed around40,000 UM ha�1, a supplementary expense that each farmer could easily cover.

6. Conclusion

Despite roughly similar growth conditions for rice cropping across the irrigationscheme, the gap between potential and actual yields ranged from 1.6 to 5.8 t ha�1 dur-ing the 1998 wet season. Low irrigation water supply combined with sub-optimal tim-ing for weeding and nitrogen fertilizer application could explain these yield losses.Moreover, farmers used two cultivars (Jaya and Sahel 108), which featured similarpotential yields, yet actual yields obtained with certified seeds of Sahel 108 were lowerthan with Jaya seeds kept from previuous harvest. Higher weed infestation (partiallydue to lower irrigation frequency), combined with a greater delay for the second Napplication on fields sown with Sahel 108, explained this yield gap compared with Jaya.


Based on this diagnosis, farmers were asked to change their water allocation rulesin order to homogenize field irrigation frequency and establish a planned croppingcalendar at the scheme-wide level for the next season in order to improve the timingof crop management interventions in all fields. During both the 1999 and 2000WS,all fields were irrigated 8–9 times after flooding and delays in timing between optimaland actual N fertilizer applications were reduced. The average rice grain yieldreached 7.2 t ha�1 in 1999 and 8.2 t ha�1 in 2000, whereas production costs at thescheme level remained stable. Gross margins at the overall scheme level increasedby more than 80% as compared to 1998. This increase in irrigated rice output wasdue mainly to better collective management in improving the efficiency of input use.

These results confirm the importance of sound crop management in general withinirrigated systems across the Sahel region (Wopereis et al., 2001). Current crop man-agement techniques allow achieving potential yields of the rice cultivars being used.Nonetheless, farmers� collective organization at the scheme level and individual prac-tices at the field level often remain sub-optimal. While herbicide and fertilizer ratesstay fairly close to recommended levels, the timing of crop management operationsoften gets delayed. The creation of a planned cropping calendar aids in assisting withthe tasks of farm management.

Soil fertility and crop management on farms in the Senegal River Valley (as wellas in other countries) may also depend on other external actors, such as suppliersand motorized service providers. In our study, soil P fertility managementdepended on the availability of P fertilizer from suppliers, and late input deliveryled to delayed herbicide and fertilizer applications. Le Gal (1997) demonstrated thereliance on combine harvester availability for double rice crop farmers in the Sen-egal River Delta. In order to preserve these delivery schedules or the availability ofinputs and agricultural machinery, the task of planning cropping calendars canhelp farmer organizations establish contractual relations with suppliers and provid-ers early on.

This approach corresponds with that set forth in Keating and McCown (2001),who recognized two key components in farming systems: the biophysical ‘‘produc-tion system’’ and the ‘‘management system’’. Along the lines of this ‘‘decomposi-tion’’, the ‘‘CalCul’’ software combines agronomic knowledge gained from theRIDEV rice phenology model, as adapted to Sahelian climatic conditions, with deci-sion-making knowledge gained from farmer management of irrigation schemes inSahelian countries. The aim of the ‘‘CalCul’’ application is not to simulate the effectsof crop management on rice production, like with APSIM (Keating et al., 2003) orDecible (Chatelin et al., 2005); its operations are therefore much simpler than suchdecision-support systems, given that management practices do not interact withthe crop model. Nonetheless, the use of ‘‘CalCul’’ complies with the ‘‘FARM-SCAPE’’ approach (Carberry et al., 2002), i.e.: participatory research with farmersand their advisers, combining monitoring, diagnosis and simulation-aided discus-sions on the collective management of the irrigation scheme. According to thisapproach, the ‘‘simplicity’’ of the simulation model (simplicity as to how the solutionis derived) may be an asset by virtue of facilitating the ability of farmers and advisersto master the decision-support tool.


Acknowledgements

The authors would like to express their gratitude to the farmers of Nakhlet. Spe-cial thanks also go to Mr. Marco Wopereis for his comments and corrections. Thisstudy was financed by the ‘‘Pole Regional de Recherche sur les Systemes Irrigues’’(PSI, i.e. the Regional Research Initiative on Irrigated Systems) of the WECARD/CORAF organization.

References

Allen, R.G., Pereira, L.S., Raes, D., Smith, M. 1998. Crop evapotranspiration Guidelines for computingcrop water requirements. FAO Irrig. and Drain. Paper 56. Food and Agriculture Organization of theUnited Nations, Rome, pp. 300.

Asch, F., Wopereis, M.C.S., 2000. Responses of field-grown irrigated rice cultivars to varying levels offloowater salinity in a semi-arid environment. Field Crops Research 70, 127–137.

Bos, M.G., Replogle, J.A., Clemmens, A.J., 1991. Flow Measuring Flumes for Open Channel Systems.American Society of Agricultural Engineers, New York, USA, ISBN 0929355156, 321 pp.

Cantrell, R.P., Hettel, G.P., 2004. New challenges and technological opportunities for rice-basedproduction systems for food security and poverty alleviation in Asia and the Pacific. In: Rice in GlobalMarkets and Sustainable Production Systems, International Year of Rice, FAO Rice Conference, 12–13 February 2004, Rome, Italy.

Carberry, P.S., Hochman, Z., McCown, R.L., Dalgliesh, N.P., Faele, M.A., Poulton, P.L., Hargreaves,J.N.G., Hargreaves, D.M.G., Cawthray, S., Hillcoat, N., Robertson, M.J., 2002. The FARMSCAPEapproach to decision support: farmers�, advisers�, reserchers� monitoring, simulation, communicationand performance evaluation. Agricultural Systems 74, 141–177.

Chatelin, M.H., Aubry, C., Poussin, J.C., Meynard, J.M., Masse, J., Verjux, N., Gate, P., Le Bris, X.,2005. DeciBle, a software package for wheat crop mangement simulation. Agricultural Systems 83, 77–99.

Defoer, T., Wopereis, M.C.S., Jones, M.P., Lancon, F., Erenstein, O., Guei, R.G., 2004. Challenges andtechnical opportunities for rice-based production systems for food security and poverty alleviation insub-Saharan Africa. In: Rice in Global Markets and Sustainable Production Systems, InternationalYear of Rice, FAO Rice Conference, 12–13 February 2004, Rome, Italy.

Diemer, G., van der Laan, E., 1987. L�irrigation au Sahel: la crise des perimetres irrigues et la voiehaalpulaar. Karthala/CTA, Paris/Wageningen, France/Nederlands, ISBN 2 86537 193 X, 226 pp.

Dingkuhn, M., Miezan, K.M., 1995. Climatic determinants of irrigated rice performance in the Sahel. II.Validation of photothermal constants and characterization of genotypes. Agricultural Systems 48,411–434.

Dingkuhn, M., Sow, A., 1997. Potential yields of irrigated rice in the Sahel. In: Miezan, K.M.,Wopereis, M.C.S., Dingkuhn, M., Deckers, J., Randolph, T.F. (Eds.), Irrigated Rice in the Sahel:Prospects for Sustanaible Development. WARDA, Bouake, Cote d�Ivoire, ISBN 92 9113 1091, pp.361–379.

Dingkuhn, M., Le Gal, P.Y., Poussin, J.C., 1995. RIDEV, un modele de developpement du riz pour lechoix des varietes et des calendriers. In: Boivin, P., Dia, I., Lericollais, A., Poussin, J.C., Santoir, C.,Seck, S.M. (Eds.), Nianga, laboratoire de l�agriculture irriguee en moyenne vallee du Senegal, Editionsde l�ORSTOM, Coll. Colloques et Seminaires, Paris, ISBN 2 7099 1272 4, pp. 205–222.

Dobermann, A., Cassman, K.G., Sta. Cruz, P.C., Neue, H.U., Skogley, E.O., Pampolino, M.F., Adviento,M.A.A., 1995. Dynamic soil tests for rice. In: Fragile Lives in Fragile EcosystemsProceedings of theInternational Rice Research Conference, 13–17 February 1995. International Rice Research Institute,Manila, Philippines, pp. 343–365.

FAO, 1986. Yield response to water. FAO Irrigation and Drainage Paper 33. UN-FAO, Rome, Italy, 193pp.


Haefele, S.M., Wopereis, M.C.S., Donovan, C., Maubuisson, J., 2001. Improving the productivity andprofitability of irrigated rice production in Mauritania. European Journal of Agronomy 14, 181–196.

Keating, B.A., McCown, R.L., 2001. Advances in farming systems analysis and intervention. AgriculturalSystems 70, 555–579.

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., Verbug, 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, J.C., 2003. An overview of APSIM, a model designed for farming system simulation.Agricultural Systems 18, 267–288.

Le Gal, P.Y., 1997. Le developpement de la double culture irriguee au Sahel: contraintes et perspectivespour les agriculteurs. In: Miezan, K.M., Wopereis, M.C.S., Dingkuhn, M., Deckers, J., Randolph,T.F. (Eds.), Irrigated Rice in the Sahel: Prospects for Sustanaible Development. WARDA, Bouake,Cote d�Ivoire, ISBN 92 9113 1091, pp. 161–189.

Le Gal, P.Y., Papy, F., 1998. Co-ordination processes in a collectively managed cropping system: Doublecropping of irrigated rice in Senegal. Agricultural Systems 57, 135–159.

Poussin, J.C., 1995. Gestion technique de la riziculture irriguee. In: Boivin, P., Dia, I., Lericollais, A,Poussin, J.C., Santoir, C., Seck, S.M. (Eds.), ‘‘Nianga, laboratoire de l�agriculture irriguee en moyennevallee du Senegal’’, Editions de l�ORSTOM, coll. ‘‘Colloques et Seminaires’’, Paris, France, ISBN 27099 1272 4. pp. 153–170.

Poussin, J.C., 1997. Direct seeding or transplanting ? An example from the Senegal middle valley. In:Miezan, K.M., Wopereis, M.C.S., Dingkuhn, M., Deckers, J., Randolph, T.F. (Eds.), Irrigated Rice inthe Sahel: Prospects for Sustainable Development. WARDA, Bouake, Cote d�Ivoire, ISBN 92 91131091, pp. 191–200.

Poussin, J.C., 2000. CalCul: un outil pour construire un calendrier cultural previsionnel du riz irrigue auSahel. In: Legoupil, J.C., Dancette, C., Godon, P., Maıga, I.M., N�Diaye, M.K. (Eds.), Pour undeveloppement durable de l�agriculture irriguee dans la zone Soudano-Sahelienne. Synthese desresultats du Pole Regional de Recherche sur les Systemes Irrigues (PSI/CORAF). WECARD-CORAF,Dakar, Senegal, ISSN 0851 0296, pp. 87–99.

Poussin, J.C., Wopereis, M.C.S, Debouzie, D., Maeght, J.L., 2003. Determinants of irrigated rice yield inthe Senegal River valley. European Journal of Agronomy 19, 341–356.

Raes, D., Sy, B., Deckers, J., 1997. Simulation of irrigation water requirements of rice schemes in theSenegal river delta and valley. In: Miezan, K.M., Wopereis, M.C.S., Dingkuhn, M., Deckers, J.,Randolph, T.F. (Eds.), Irrigated Rice in the Sahel: Prospects for Sustainable development. WARDA,Bouake, Cote d�Ivoire, ISBN 92 9113 1091, pp. 411–419.

SAED, 1997. Recueil des statistiques de la vallee du fleuve Senegal, annuaire 1995/1996, version detaillee.Ministere de l�Agriculture - SAED, St. Louis, Senegal, 142 pp.

Sanchez, P.A., 1976. Properties and management of soils in the tropics. New York, USA, ISBN 0 47175200 2, 618 pp.

Siddeek, F.Z., Dillaha, T.A., Loganathan, G.V., 1988. Water management for lowland rice irrigation.Journal of Irrigation and Drainage Division, ASCE 114 (3), 407–423.

Smith, M., Allen, R.G., Monteith, J.L., Perrier, A., Pereira, L., Segeren, A. 1992. Report of the expertconsultation on procedures for revision of FAO guidelines for prediction of crop water requirements.UN-FAO, Rome, Italy, 54 p.

SONADER, 1998. Etude de la filiere riz en Mauritanie. Rapport definitif. SONADER, RepubliqueIslamique de Mauritanie, Nouakchott et CLG Consultants, Paris, France, 132 pp.

Tantawi, B.A., 2004. Rice-based production systems for food security and poverty alleviation in the Near-East and North Africa: New challenges and technological opportunities. In: Rice in Global Marketsand Sustainable Production Systems, International Year of Rice, FAO Rice Conference, 12–13February 2004, Rome, Italy.

Tran, D., 2004. Rice and narrowing the yield gap. International Year of Rice, Fact Sheets, FAO, Rome, 2 pp.Tuzet, A., Perrier, A., 1998. Les besoins en eau des cultures: analyse et applications. In: Tiercelin, J.R.

(Ed.), Traite d�irrigation. Lavoisier, Technique et documentation, Paris, France, ISBN 2 7430 0244 1,pp. 147–256.


WARDA, 1999. Returns to irrigated cultivar in Senegal. West Africa Rice Research Brief No. 1,WARDA, Bouake, Cote d�Ivoire, 2 pp.

Wopereis, M.C.S., Donovan, C., Nebie, B., Guindo, D., N�Diaye, M.K., 1999. Soil fertility managementin irrigated rice systems in the Sahel and Savanna regions of West Africa. Part I. Field Crop Research61, 125–145.

Wopereis, M.C.S., Haefele, S.M., Kebbeh, M., Miezan, K.M., Diack, B.S., 2001. Improving theproductivity and profitability of irrigated rice production in Sahelian West Africa. In: Proceedings ofthe Expert Consultation Meeting on Yield Gap and Productivity Decline in Rice Production at FAO,Rome, Italy, 5–7 September 2000, pp. 117–142.

Improved collective decision-making in action for irrigated rice farmers in the Senegal River Valley

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