Default & quality, performance But What s for?

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1 Training Workshop on SARRA-H Crop Model for English Speaking Meteorological Services in West Africa FEBRUARY 214,, Banjul (Gambie) Default & quality, performance But What s for? Présenté par Christian Baron

2 Default & quality Short presentation Performance Scales & simulations objectives What s for? For Whom?

3 A model particularly suited to analysing how the climate impacts the growth and potential yield of dry cereals in the Tropics: Millet, Sorghum, Maize and Upland Rice (project, partnership, publications ) Farmers survey Taking in account the hight plasticity of local varieties (photoperiodism) Diversity of simulations scenarios able to catch farmers strategies Main part of parameters are measured or extract from published references

4 Sarra_h based on robust and simple plant growth process representation: All proces are links in a same daily loop Few parameters are used to caraterise the diversity of species/varieties Parameters values still along the simulation Development environment is highly versatile Sarra-h is a predictif crop model

5 Sarra-h is little or no adapted to : Phytosanitary problems are not taken into account Density still to be more detailled (low density) No nitrogen balance, impact of fertility level is defined with a simple and global indices No mixed crops (mil/niébé) Other models are better adapted to technical agricultural process management (better control of fertiliser, pesticid etc )

6 SarraH Three main process in a same daily loop: 1) Water Balance: reservoir concept 2) Carbon Balance: big leaf concept 3) Phenology: process managment Climat (constraint) (input data, Daily time step) - Evapo-transpiration - Temperature - Global radiation or sunshine duration - Rainfall Dingkuhn, 23 Plot (soil) Practices (strategies) -Typology (Clay Sandy) -Maximum depth - Surface tank depth - Species, Varieties - Sowing date or strategies - Sowing density - Irrigation - Global fertility level

7 Maintenance respiration Rain ETo T Maintenance respiration

8 KC Dynamic Phenology (PPisme, termal time...) (LAI beer law fonction E &Tr séparation ) Carbon Assimilation (fonction of ℇa et ℇ b, water constraint ) Biomass Repartition Based on allometric law ETo Sowing TrPot = Kcp * ETo EPot = Kce * ETo Root zone Rainfall Tool box: Data Base, management of simulated data, graphics 2 reservoirs simulated Root Front Humectation Front

9 LAI Above ground Biomass Simulation effectuée avec SarraH v3.2 - Modèle SarrahV m²/m² 5 kg/ha /4/12 11/5/12 26/5/12 1/6/12 25/6/12 1/7/12 Date Lai (DMR_S1_V3.2) BiomasseFeui l les(dmr_s1_v3.2) BiomasseFeui l les(dmr_s1_v3.2) Leaf Biomass Lai (DMR_S1_V3.2) Rdt(DMR_S1_V3.2) Rdt(DMR_S1_V3.2) BiomasseAeri enne(dmr_s1_v3.2) BiomasseAeri enne(dmr_s1_v3.2) Yield Thanks to Ulrich, Cirad PHD student, (Maïze experimentation in Benin, 212) 9

10 kg/ha 14 Simulation effectuée avec SarraH v3.2 - Modèle SarrahV /1/3 25/11/3 25/12/3 24/1/4 Date 5 4 effectuée avec SarraH v3.2 - Modèle SarrahV Simulation m²/m² 5 kg/ha 6 2/9/96 Date BiomasseFeuilles(Souna96PluieV3.2) (Photoperiodic and non Rdt(Souna96PluieV3.2) photoperiodic) /12/3 23/2/4 Date 23/4/4 Rainfed Rice variety in Lai(SarMil2AntsiE933) Lai(SarMil2AntsiE933) BiomasseAerienne(SarMil2AntsiE933) Madagascar BiomasseFeuilles(SarMil2AntsiE933) 1 2/1/ Lai(Souna96PluieV3.2) Pearl Millet varieties in Lai(Souna96PluieV3.2) BiomasseAerienne(Souna96PluieV3.2) Mali, Niger, Senegal, BiomasseFeuilles(Souna96PluieV3.2) Rdt(Souna96PluieV3.2) Burkina Faso BiomasseAerienne(Souna96PluieV3.2) I have also a test on wheat in France /7/4 21/8/4 2/9/4 2/1/4 19/11/4 Date Lai(GuineaAmD14SarV3.2) Sorghum varieties in Lai(GuineaAmD14SarV3.2) BiomasseAerienne(GuineaAmD14SarV3.2) Mali, Kenya, Burkina BiomasseFeuilles(GuineaAmD14SarV3.2) Rdt(GuineaAmD14SarV3.2) Faso BiomasseAerienne(GuineaAmD14SarV3.2) BiomasseFeuilles(GuineaAmD14SarV3.2) (Photoperiodic and non Rdt(GuineaAmD14SarV3.2) photoperiodic) kg/ha /8/96 BiomasseFeuilles(B2BresIrrigV3.2) Rdt(B2BresIrrigV3.2) Simulation effectuée avec SarraH v3.2 - Modèle SARRAHMil Maize Lai(B2BresIrrigV3.2) varieties in Lai(B2BresIrrigV3.2) BiomasseAerienne(B2BresIrrigV3.2) Mali, Benin, Brazil, BiomasseFeuilles(B2BresIrrigV3.2) Rdt(B2BresIrrigV3.2) Tanzanie, USA, France BiomasseAerienne(B2BresIrrigV3.2) 3 Thanks to Seydou, Agali, Michel, Mamoutou, Bertrand, Fernando. kg/ha m²/m² 3 m²/m² m²/m² Simulation effectuée avec SarraH v3.2 - Modèle SarrahV

11 Model 1 Model2 Projet AgMip Maïze: climate change impact scenarios on Maîze yield. Sarra-h the number 1. Series of analyzes shows that the model have coherents results face on sciences knowledges and other models. Thankw to CB & Simona Project AgMip Mil/Sorgho : Millet yield simulation, farmer s survey in Senegal 5 fertility level was determined for Sarra-h simulations Thanks to Agali &Myriam

12 Process: Water Balance AQUACROP DHC/SARRA Process: Water Balance SARRA-H Carbon Balance Physiology Nitrogen Balance STICS DSSAT APSIM WOFOST

13 Processes simulated evaluate local situations: trials are performed at field level (calibration) and predictive capacity of the model is performed at village level (verification). The aim of the studies are based on ground network : Climate variability impact and risk Sarra-h is a predictive model of crops dynamics (biomass, yield) focus on climatic risk analysis taking in account farmers strategies (simulations scenarios)

14 Decision maker, administrateurs : Administrativ level seasonal forecast short ou long term: Early warning system Breeding and adaptations Organisations (Services, NGO ) and farmers Local monitoring vs forecast : Fields managment strategies: species/varieties choice, intensification level (mostly in case of dry forecast) Sowing conditions (early/late, re-sowing ) Crop conditions, soil water storage Potential yield forecast

15 Advices & spatial and temporal uncertainties? How may we estimate and display uncertainties? Face on uncertainties which relevance of advices, which advice may we diffuse? Weekly forecast and monitoring: which type of advice (ie sowing)? Seasonal forecast: which type of simulations with the crop model? Perspectives and actions Ground network and remote sensing: complementarity? Participative survey with farmers/villages: raingauges, cell phone, data bases.. But what efficient feedback for them? What s about contol and filling data methods? Complementary projects between agonomist and meteorologists?

16 MERCI De Votre Attention

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