default & quality, performance but what’sfor -...

Training Workshop on SARRA-H Crop Model for English Speaking Meteorological Services in West Africa24 - 28 FEBRUARY 2014,, Banjul (Gambie)

Default & quality, performance

But

What’s for ?

Présenté par Christian Baron

� Default & quality

� Short presentation

� Performance

� Scales & simulations’ objectives

� What’s for? For Whom?

� 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� Farmers survey

� Taking in account the hight plasticity of local varieties(photoperiodism)

� Diversity of simulations scenarios able to catch farmersstrategies

� Main part of parameters are measured or extract frompublished references

� Sarra_h based on robust and simple plant growthprocess representation:� All proces are links in a same daily loop

� Few parameters are used to caraterise the diversity of species/varietiesspecies/varieties

� Parameters values still along the simulation

� Development environment is highly versatile

� Sarra-h is a predictif crop model

� 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 definedwith a simple and global indiceswith a simple and global indices

� No mixed crops (mil/niébé)

� Other models are better adapted to technicalagricultural process management (better control of fertiliser, pesticid etc…)

Three main process in a same daily loop:

1) Water Balance: reservoir concept2) Carbon Balance: big leaf concept3) Phenology: process managment

SarraHSarraH

Dingkuhn, 2003

Climat (constraint)

(input data, Daily time step)

- Evapo-transpiration- Temperature- Global radiationor sunshine duration- Rainfall

Climat (constraint)

(input data, Daily time step)

- Evapo-transpiration- Temperature- Global radiationor sunshine duration- Rainfall

Plot (soil)

-Typology (Clay… Sandy) -Maximum depth- Surface tank depth

Plot (soil)

-Typology (Clay… Sandy) -Maximum depth- Surface tank depth

Practices (strategies)

- Species, Varieties- Sowing date or strategies- Sowing density- Irrigation- Global fertility level

Practices (strategies)

- Species, Varieties- Sowing date or strategies- Sowing density- Irrigation- Global fertility level

Maintenance respirationRain

Maintenancerespiration

ETo

T °

Sowing

ETo

Phenology(PPisme, termaltime...)

KC Dynamic(LAI beer law

fonction E &Tr séparation )

Carbon Assimilation (fonction of ℇa et ℇ b, water constraint…)

BiomassRepartition

Based onallometric law

TrPot = Kcp * EToEPot = Kce * ETo

Root Front

HumectationFront

Rainfall

Root zone

2 reservoirs

simulated

Tool box: Data Base, management of simulateddata, graphics…

Simulation effectuée avec SarraH v3.2 - Modèle SarrahV3.2 - http://ecotrop.cirad.fr

m²/m

²

3

2

kg/ha

10 500

10 000

9 500

9 000

8 500

8 000

7 500

7 000

6 500

6 000

5 500

LAI Above ground Biomass

Lai(DMR_S1_V3.2) Lai(DMR_S1_V3.2) BiomasseAerienne(DMR_S1_V3.2)BiomasseFeuil les(DMR_S1_V3.2) Rdt(DMR_S1_V3.2) BiomasseAerienne(DMR_S1_V3.2)BiomasseFeuil les(DMR_S1_V3.2) Rdt(DMR_S1_V3.2)

Date10/07/1225/06/1210/06/1226/05/1211/05/1226/04/12

m²/m

²

1

0

kg/ha5 000

4 500

4 000

3 500

3 000

2 500

2 000

1 500

1 000

500

0

Leaf Biomass Yield

9Thanks to Ulrich, Cirad PHD student, (Maïze experimentation in Benin, 2012)


Lai(B2BresIrrigV3.2)Lai(B2BresIrrigV3.2)BiomasseAerienne(B2BresIrrigV3.2)BiomasseFeuil les(B2BresIrrigV3.2)Rdt(B2BresIrrigV3.2)

Date24/01/0425/12/0325/11/0326/10/03

m²/

m²

6

5

4

3

2

1

0

kg/ha

26 000

24 000

22 000

20 000

18 000

16 000

14 000

12 000

10 000

8 000

6 000

4 000

2 000

0

Maize varieties inMali, Benin, Brazil, Tanzanie, USA, France


m²/

m²

4

3

2

1

kg/ha

8 000

7 000

6 000

5 000

4 000

3 000

2 000

1 000


5

4

14 000

12 000

10 000

Thanks to Seydou, Agali, Michel , Mamoutou, Bertrand, Fernando….

Rdt(B2BresIrrigV3.2)BiomasseAerienne(B2BresIrrigV3.2)BiomasseFeuil les(B2BresIrrigV3.2)Rdt(B2BresIrrigV3.2)

Tanzanie, USA, France

Lai(Souna96PluieV3.2)Lai(Souna96PluieV3.2)BiomasseAerienne(Souna96PluieV3.2)BiomasseFeuil les(Souna96PluieV3.2)Rdt(Souna96PluieV3.2)BiomasseAerienne(Souna96PluieV3.2)BiomasseFeuil les(Souna96PluieV3.2)Rdt(Souna96PluieV3.2)

Date02/10/9602/09/9603/08/96

0 0

Pearl Millet varieties inMali, Niger, Senegal, Burkina Faso…(Photoperiodic and non photoperiodic)

Lai(GuineaAmD104SarV3.2)Lai(GuineaAmD104SarV3.2)BiomasseAerienne(GuineaAmD104SarV3.2)BiomasseFeuilles(GuineaAmD104SarV3.2)Rdt(GuineaAmD104SarV3.2)BiomasseAerienne(GuineaAmD104SarV3.2)BiomasseFeuilles(GuineaAmD104SarV3.2)Rdt(GuineaAmD104SarV3.2)

Date19/11/0420/10/0420/09/0421/08/0422/07/04

m²/

m² 3

2

1

0

kg/ha

8 000

6 000

4 000

2 000

0

Sorghum varieties inMali, Kenya, Burkina Faso…(Photoperiodic and non photoperiodic)

Simulation effectuée avec SarraH v3.2 - Modèle SARRAHMil2 - http://ecotrop.cirad.fr

Lai(SarMil2AntsiE933)Lai(SarMil2AntsiE933)BiomasseAerienne(SarMil2AntsiE933)BiomasseFeuilles(SarMil2AntsiE933)

Date23/04/0423/02/0425/12/03

m²/

m²

3

2

1

0

kg/ha

12 000

11 000

10 000

9 000

8 000

7 000

6 000

5 000

4 000

3 000

2 000

1 000

0

Rainfed Rice variety in Madagascar

I�have�also�a�

test�on�

wheat�in�

France…..

Project AgMip Mil/Sorgho : Millet yieldsimulation , farmer’s survey in Senegal5 fertility level was determined for Sarra-h simulationsThanks to Agali &Myriam

Model 1

Model2

Projet AgMip Maïze: climate change impact scenarios on Maîze yield. Sarra-h the number 10. Series of analyzesshows that the model have coherentsresults face on sciences knowledges and other models.Thankw to CB & Simona

Process:Water Balance

AQUACROPDHC/SARRA… SARRA-H

Process: Water Balance Carbon Balance … SARRA-H Carbon Balance Physiology

Nitrogen Balance …STICSDSSAT APSIMWOFOST…

� 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 : � 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)

� Decision maker, administrateurs :� Administrativ level seasonal forecast short ou long term:

� Early warning system

� Breeding and adaptations

� Organisations (Services, NGO…) and farmersOrganisations (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

� 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? � 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 ?

MERCI

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