Pan-legume simulation modelling: An approach to guide breeding and management targets

Size: px

Start display at page:

Download "Pan-legume simulation modelling: An approach to guide breeding and management targets"

Cody Cannon
6 years ago
Views:

1 Pan-legume simulation modelling: An approach to guide breeding and management targets V Vadez O Halilou H Hissene - M Ghanem H Marrou J Clavijo TR Sinclair A Soltani ICRISAT CERAAS ICARDA Supagro NCSU Gorgan Univ. Pan-African Grain Legume Conference 29 th Feb 2016

2 Today s presentation SSM: Simple Simulation Model Scenario analysis Testing genetic effects on yield Testing Ag. Management effects

3 Yield is an interaction G YIELD E M Crop models help understand these interactions

4 Transparency and accessibility Climate (4) of the model algorithm Crop T o min T o max Rainfall Radiation Crop (48) The SSM crop model Dynamic of accumulation and allocation of dry matter Dynamic of N uptake 48 MEASURABLE parameters No optimisation Soil (11) Depth Hydrolic properties Initiation of water content Management (2) Process driven algorithm Soil Dynamic of soil water content Sowing date Sowing density Marrou et al, 2014 IFLRC VI & ICLGGV II 4

5 The SSM crop model A simple (large) and accessible Excel macro

6 CROP: Bean Culivar LINGOT phyl = 68.9 PLACON = 1 PLAPOW = 3.34 SLA = TBRUE = 10 TP1RUE = 24 TP2RUE = 24 TCRUE = 30 KPAR = 0.6 IRUE1 = 2 IRUE1 = 2 CROP: Bean Culivar LINGOT SLNG = 2 SLNS = 0.8 SNCG = SNCS = 0.01 GNC = MXNUP = 0.57 WSSN = 0.35 TBD = 10 TP1D = 24 TP2D = 24 TCD = 30 The SSM crop model Model Inputs FLF1A = 0.65 FLF1B = 0.45 WTOPL = 160 FLF2 = 0.34 FRTRL = 0.22 GCF = 1.0 PDHI = WDHI1 = 0 WDHI2 = 0 WDHI3 = 9999 WDHI4 = 9999 DEPORT = 200 EED = 1000 GRTDP = 35 TEC = 5 WSSG = 0.3 WSSL = 0.4 WSSD = 0 CPP = ppsen = 0 ttswem = 3.9 ttemr1 = 28.1 ttr1r3 = 4.0 ttr3r5 = 4.0 ttr5r7 = 26.0 ttr7r8 = 4.0 ttbrp = 0 tttrp = 0.0 ttwsd = 40.0 ttr1tlm = 0 ttr1tlp = 8 ttrue = 28.1 CROP: Bean Culivar LINGOT ttbsg = 36.0 tttsg = 66.0 ttbrg = 3.9 tttrg = 40.0 ttbnf = 12 TRESH = 0.78 ttdkill = 36 LtFtsw = 0 LtWdDur = 2 vpd_resp = 1 vpd_cr = 20 6

7 Same architecture across 8 crop species

8 Validation Step: Comparing observed and simulated groundnut grain yield (16 trials carried out in India and Niger) Predicted Yield (g m -2 ) RMSE = 16% India Niger Measured Yield (g m -2 ) Extreme robustness of the simulation model

9 SSM: Simple Simulation Model Scenario analysis Testing genetic effects on yield Testing Ag. Management effects

10 Baseline yields (g m -2 ) Groundnut Chickpea Yield (g m -2 ) Yield (g m -2 )

11 Yield losses due to drought (g m -2 ) (Groundnut) Major yield losses No loss to drought Yield loss (g m -2 ) Vadez et al., in prep.

12 SSM: Simple Simulation Model Scenario analysis Testing genetic effects on yield Testing Ag. Management effects

13 1 st example: Transpiration restriction under high vapor pressure deficit (VPD) Transpiration (g cm -2 h -1 ) H77/ PRLT-2/89-33 Water saving VPD (kpa) What could be the effects on yield?? Kholova et al 2010 J. Exp. Bot

14 Soybean Yield (g m -2 ) increases Due to Limited-Transpiration Yield increase (g m -2 ) < > 15 Sinclair et al., Global Food Security 2014

15 Probability of yield increase With Tr sensitivity to VPD in soybean 75% (wet) Median 25% (dry) Sinclair et al., 2010 Agron. J. 102:

Grain yield increase (%) with VPD response trait - Peanut -1 0

16 Grain yield increase (%) with VPD response trait - Peanut % yield decrease, especially at high latitudes (12-15ºN)

short type Alternate scenario : long type Grain

17 2 nd example: Grain yield increase from longer duration lentils in East Africa Baseline scenario ; short type Alternate scenario : long type Grain Probability yield of increase success Ghanem, Marrou et al., 2014

18 SSM: Simple Simulation Model Scenario analysis Testing genetic effects on yield Testing Ag. Management effects

19 Yield change from 20 to 40 plant m -2 Groundnut Major yield benefits Yield increase (g m -2 ) Vadez et al. in prep.

20 What is the optimum sowing window for lentils? Ghanem, Marrou et al., Agric. Systems, 2015

21 Combining both longer phenology and optimal sowing date in lentils Ghanem, Marrou et al., Agric. Systems, 2015 Major yield benefits

22 Comparing genetic and agronomic interventions Faster root growth Deeper water extraction 30mm Irrigation at R5 M G Models are a tool for making decisions Vadez et al. FCR

23 In summary Modelling helps solving GxExM interactions Provides scenarios and probability of success Predictions give a representation of reality, but don t re-create the truth A powerful tool to guide breeding & Ag. Management decisions Simple is beautiful Donors: GCP / CRP / BMGF Thank you

Linking research disciplines for a more. Production challenges

Linking research disciplines for a more targeted Understanding, efficient Connecting, breeding Scaling: of legume cultivars Using for harsh biology farming to solve environments food Production challenges