CIWARA: Climate change Impacts on West African agriculture: a Regional Assessment

CIWARA: Climate change Impacts on West African agriculture: a Regional Assessment

Data Source Data used for the calibration were obtained from Fechter et al., 1991 The cultivar used was Souna 3 (pearl millet) Study site: Nioro district, Senegal 13 43 N, 15 46 W Ceres-millet was used In absence of Souna3 coefficients in the models, CIVT cultivar which has a close life cycle to Souna3 was selected as the reference cultivar.

Management Experimental site: INRAN Research Sub-station at Tara, Niger (300 km southwest of Niamey). Sowing date: 25 June 1991 Fertilizer: 40 kgha -1 of N as calcium ammonium nitrate 20 and 40 days after sowing and 45 kgha -1 of P as P 2 O 5 before sowing CIVT P1 P2O P2R P5 G1 G4 PHINT Original 120 12.00 142.00 390.00 1.00 0.60 43.00 Modified 150 12.00 142.00 390.00 2.00 0.60 65.00

Model Unit of Sowing Parameter Parameter Measurement Date # value leaf_app_rate2 (TLA) oc day perleaf 1 49 2 50 3 62 4 66 0 0 tt_emerg_to_endjuva oc days All 753 pp_endjuv_to_init oc days h 1 All 112 tt_flower_to_maturity oc days All 380 tt_flag_to_flower oc days All 115 tt_flower_to_start_grain oc days All 112 Leaf area (main shoot)c 0 0 0 y0_const mm2 All -807 y0_slope mm2 per leaf All 1137 Grain yield head_grain_no_max grain head 1 All 5500 grain_gth_rate mg grain 1 day 1 All 0.7 a 100 + (n-1) x 10 oc day were subtracted for tiller number n to account for their earliness compared to the main shoot. b Maewa was considered as non-photosensitive for the last sowing c y0 = y0_const + y0_slope x TLN, where y0 and TLN are, respectively, the area of the largest leaf and the total leaf number on an axis. For any given cultivar, the same relation was used for tillers as for the main shoot.

Sarra-H Genetic parameters I Id Description Units of measurement Mil_CIVT Mil_Souna3 IdFamille Cereal Cereal SDJLevee Thermal time to emergence Degree day 70 70 SDJBVP Thermal time to emergence to panicle Degree day 620 470 initiation (BVP) SDJRPR Thermal time panicle initiation to Degree day 500 500 SDJMatu1 Thermal time flowering to milk stage Degree day 225 300 SDJMatu2 Thermal time milk stage to total Degree day 172 160 maturity (Matu2) KcMax Maximum Crop spieces coefficient 1,4 1,4 (Allen et al., 1998) SDJIni 200 200 SDJMid Not used 400 400 HautMin 0,15 0,15 HautMax 3 3 TxRuSurfGermi 0,6 0,6 PoidsSecGrain Mean weight of seeds corresponding 0,008 0,008 to number of plants per hill after thinning (on 1000 seed weight bases) TxResGrain Rate seed reserve converted into % 0,5 0,5 KRdtPotA Coefficient of potential yield 0,2 0,2 KRdtPotB Coefficient of potential yield 800 800 SeuilPP Photoperiod sensitivity lower limit 13,6 13,6 PPSens Photoperiod sensitivity coefficient 0,69 0,69 PPForme Type de courbe de réponse à la 0 0 photopériode (linéaire, parabole) PPCrit Seuil horaire ou la floraison est Hour 12 12 PcReallocFeuille Pourcentage de réallocation des 0,5 0,5 VRacLevee Root depth progess during emergence mm per day 25 25 VRacBVP Root depth progess during the basic Degree day 30 30 vegetative period - BVP VRacPSP Root depth progess during the photosensitive period - PSP Degree day 25 25

Sarra-H Genetic parameters II VRacRPR Root depth progess during panicle Degree day 25 25 initiation to flowering phase - RPR VRacMatu1 Root depth progess during the phase Degree day 0 0 VRacMatu2 Root depth progess during the phase Degree day 0 0 Kdf Coefficient moyen d'angle des feuilles 0,5 0,5 TxConversion Coefficient de conversion de l'énergie G MJ -1 J -1 5 4,2 lumineuse en biomasse TxAssimBVP Coefficient d'efficience d'assimilation % 1 1 des feuilles, Phase BVP TxAssimMatu1 Coefficient d'efficience d'assimilation % 0,8 0,8 des feuilles, Phase Matu1 TxAssimMatu2 Coefficient d'efficience d'assimilation % 0,7 0,7 des feuilles, Phase Matu2 TxRespMaint Température de Référence de 0,012 0,012 Respiration de Maintenance TBase Température de base C 11 11 TOpt1 Température Optimale1 C 30 30 TOpt2 Température Optimale2 C 36 36 TLim Température limite C 44 44 PFactor Coefficient de l'espèce pour le calcul 0,55 0,55 du taux de transpiration SlaMin Surface massique des feuilles m² kg -1 0,0015 0,0013 minimum (specific leaves area) SlaMax Surface massique des feuilles m² kg -1 0,0069 0,0059 SlaPente Pente Surface massique des feuilles 0,43 0,13 AeroTotBase Base de la relation de répartition % 0,65 0,65 BiomFoliaire-BiomAérienne AeroTotPente Pente de la relation de répartition 3,00E-05 3,00E-05 BiomAérienne-BiomTotale FeuilAeroBase Base de la relation de répartition % 0,65 0,65 BiomAérienne-BiomTotale FeuilAeroPente Pente de la relation de répartition -0,0002-0,00012 BiomFoliaire-BiomAérienne TxRealloc Taux de réallocation des assimilats % 0,5 0,5 TempMaint Température de maintenance C 25 25 StressResistance Coéfficient de resistance au stress 5 5 SeuilCstrMortality Seuil de mortalité des plants selon la 11 11 constante de stress (Cstr) PPExp 0,3 0,3

For the validation, data source; Akponikpe (2008) Experimental site: Sadoré, Niger (13 15 N, 2 17 E and 240 m of altitude) Sowing dates: 19 April, 19 May, 18 June and 19 July 60 kg N ha 1, 12 kg P ha 1 and 9 kg K ha 1 were applied combining a basal dressing of Single Superphosphate (SSP) before sowing, hill placed NPK (15-15-15) at sowing and split application of urea approximately at tillering and flowering

Integrated Assessment Socio-economic survey data of Nioro: from the World Bank Ruralstruc surveys conducted by Initiative Prospective Agricole et Rurale (IPAR), Senegal The data was collected in Jan-Feb 2008 In total we have 6 communities -238 farms

Missing inputs data from survey Sowing date: base on experts data Plant population and row spacing: 3plant/m2 and 90 cm row spacing Quantity of fertilizer used per field; inorganic & organic (cost was translated into quantity of manure and fertilizer).

Observed vs simulated - DSSAT

Observed vs simulated - APSIM

Observed vs simulated Sarra-H

Cumulative probability distribution

Challenges APSIM: 1. The ACMO had to be disabled 2. Output variables provided by the translator had to be changed 3. Tillers aggregation was absent 4. A number of soil input variables (finert, SWCON, CN2bare ) for APSIM were not present in the DOME file hence it the model made assumptions which had to be modified 5. APSIM does not recognize Pearl millet it has to be changed to millet

APSIM: 1. 226 farms times 6 tillers = too large batch file for APSIM and needs to be split in sub-batches. 2. Fertilizer codes not define ex NPK 3. The APSIM model in QUADUI need to be reviewed and improved 4. Frequency of reporting had to be changed from daily to harvesting DSSAT 1. NPK (15,15,15) code is not define DSSAT Overall: There is a need to collect reliable socioeconomic data for sites in West Africa

Way forward (MAM) 1. Troubleshoot the germination problem in APSIM including: 1. Redo the randomization for more realistic distribution of planting dates 2. Troubleshoot initial soil water conditions 2. Fine-tune calibration for sorghum varieties and include other calibrated millet maize and peanut varieties 3. Finalize and publish the Nioro fast track for the three models 4. Start the same process for the 4 other integrated assessment sites (BF, Ml, Gh, Ne)