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Coupling remote sensing data with crop models for

crop monitoring and yield forecasting in West Africa

Training course on the use of Satellite based data and crop monitoring and forecasting tools for drought monitoring and agro-m eteorological applications

06-10 May 2019, Nairobi, Kenya

Christian Baron, Agnès Bégué, Mathieu Castets, Camille Jahel, Danny Lo Seen

CIRAD – TETIS Research UnitMontpellier, France

Seydou B. Traoré, Alhasane Agali, Henri Songoti,

AGRHYMETNiamey, Niger

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2

Agricultural campaign monitoring

• Use of the DHC crop simulation model

• Identification of zones where rainfall at the start of the season allowed sowing

• Later verification of successful sowing

• At the end of July, analysis of the sowing situation across CILSS member countries

– Focus on zones with late sowing relatively to the average and the previous year

Crop Yield Forecasting at AGRHYMET

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3

Agricultural campaign monitoring

• Water requirement satisfaction indices

• Soil water reserves at the end of the dekad

• Water requirements for remaining crop cycle

• Issuing alerts in case of prolonged water deficit (2 successive dekads)

• First estimation of potential crop yields at the end of August

• Update every dekad thereafter

Crop Yield Forecasting at AGRHYMET

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4

Possible Sowing dates

Potential crop yields anomalies

Crop water requirementsatisfaction indices

Outputs of the DHC model

Crop Yield Forecasting at AGRHYMET

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Some deficiencies of the DHC model

– Valid only for millet in the sahelian zone

– Underestimation of yields under optimal wateringconditions (< 1000 kg/ha all the time)

– Does not account for photoperiod sensitivity of local cropvarieties still predominantly used by farmers

Crop Yield Forecasting at AGRHYMET

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• The SARRA -H model

– Outcome of researches conducted at CIRAD incollaboration with African partners in the framework ofinternational projects (CERAAS, PROMISE, AMMA)

– Simulate both water and carbon balances (morephysiologically oriented)

– Can be used for several crop types and agroclimatic zones

– Good results with on station experimental data

The Advent of SARRA -H

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Maintenance respirationRain

ETo

T °

Sarra-H: a determinist model, simple & robust

Maintenancerespiration

7

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Impact of water stress on biomass

b a m b e y P lu ie

B i o m A e ro (b a m b e y P l u i e ) N u m P h a se (b a m b e y P l u i e ) B i o m A e ro (B a m b e y E t m ) N u m P h a se (B a m b e y E t m )

N b J a s8 07 57 06 56 05 55 04 54 03 53 02 52 01 5

1 3 0 0 0

1 2 0 0 0

1 1 0 0 0

1 0 0 0 0

9 0 0 0

8 0 0 0

7 0 0 0

6 0 0 0

5 0 0 0

4 0 0 0

3 0 0 0

2 0 0 0

1 0 0 0

0

7

6

5

4

3

2

1

0

Above ground biomass and grain yield (kg/ha)

PanicleInitiation

Anthesis

Water Stress

PotentialYield

Day after sowing8

Water Stress

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• On station trials to characterize the most used local or about to be released millet, sorghum and maize varieties for the parameterization of the SARRA-H model

• On farm agronomic surveys to evaluate the model

• Several sites with contrasted agroclimatic conditions and cropping systems

Adaptation and Evaluation of SARRA -H

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10

• Two millet varieties

• Two sowing dates,

• Two nitrogen fertilization levels (N0, N1)

0500

100015002000250030003500400045005000550060006500700075008000

Dry

wei

ght (

kg h

a-1 )

Observation date

HKP x N1

0500

100015002000250030003500400045005000550060006500700075008000

Observation date

Total abovegroundLeavesGrains

MTDO x N1

0500

100015002000250030003500400045005000550060006500700075008000

Dry

wei

ght (

kgha

-1)

Observation date

HKP x N0

0500

100015002000250030003500400045005000550060006500700075008000

Observation date

Total abovegroundLeavesGrains

MTDO x N0

Adaptation and Evaluation of SARRA -H

AGRHYMET SiteAgali’s PhD thesis

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Sowing June 17 Sowing July 17 Sowing August 17

M. Kouressy & al., 2008, Adaptation to diverse semi-arid environments of sorghum genotypes having different plant type and sensitivity to photoperiod

a

V1

Calendar date

1/7 1/8 1/9 1/10 1/11 1/12 1/1

Dry

wei

ght (

Mg.

ha-1

)

0

5

10

15

20c

V3

1/7 1/8 1/9 1/10 1/11 1/12 1/1

Date 1, abovegroundDate 2, abovegroundDate 3, abovegroundDate 1, grainDate 2, grainDate 3, grain

b

V2

1/7 1/8 1/9 1/10 1/11 1/12 1/1

Same Variety

Same Guy DifferentSowing dates

Parameterizing photosensitive varieties

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Adaptation and Evaluation of SARRA -H

SENEGAL

Diourbel (450 mm)

Tambacounda (800 mm)

MALI

Cinzana (550 mm)

Koutiala (700 mm)

BURKINA FASO

Tougou (600 mm)

Dano (900 mm)

NIGERNiamey (500 mm)Bengou (700 mm)

On-farm surveys & Experimental trials

Millet Varieties

Sorghum Sowing dates

Maize Planting densities

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0,00

0,10

0,20

0,30

0,40

0,50

0,60

0,70

0,80

0 5000 10000 15000 20000

ratio

leav

es /

(leav

es+s

tem

s)

leaves + stems biomass (kg ha-1)

Souna

Thialack

Sanio

HKP

MTDO

Zatib

Choho

Toroniou

SNTC

Adaptation and Evaluation of SARRA -H

Duration of the sowing to flag leaf stages of different local sorghum varieties in Mali

Allometric relationships of different local millet varieties in Senegal, Mali, and Niger

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

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

3

2

1

0

kg/ha

10 500

10 000

9 500

9 000

8 500

8 000

7 500

7 000

6 500

6 000

5 500

5 000

4 500

4 000

3 500

3 000

2 500

2 000

1 500

1 000

500

0

LAI Above ground Biomass

Leaf Biomass Yield

14

Sarra-H performances 1

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

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

Lai(B2BresIrrigV3.2)Lai(B2BresIrrigV3.2)BiomasseAerienne(B2BresIrrigV3.2)BiomasseFeuil les(B2BresIrrigV3.2)Rdt(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, Tanzania, USA, France

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

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

m²/

m²

4

3

2

1

0

kg/ha

8 000

7 000

6 000

5 000

4 000

3 000

2 000

1 000

0

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

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

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

5

4

3

2

1

0

kg/ha

14 000

12 000

10 000

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

0kg/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

SARRA-Hcatches the variability

There is also wheat & Soy bean

(France)… and

last scoop Coton (Cameroun)

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

15

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Predictive Capacity of the SARRA -H model Maize in Burkina Faso

16

0 1000 3000 5000

010

0020

0030

0040

0050

0060

00

observed yield (kg/ha)

Sim

ulat

ed y

ield

(kg/

ha)

R²= 0.8054

P<0,05

(2014)

0 1000 3000 5000

010

0020

0030

0040

0050

0060

00

Observed yield (kg/ha)

R²=0,8561

P<0,05

(2016)

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Adaptation to crop monitoring needs

• User interface allowing easy execution of routine tasks (coupling with R ),

• Execution at the dekadal time step of simulations on crop status and yield prediction

• Combined use of historical and current climate data for the prediction

• Combination of several scenarios (crop species and varieties, sol types and depth)

• Mapping of results

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Adaptation to crop monitoring needs

Crop water requirement satisfaction indices

Soil water reserves

Past dekad

Average since sowing

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Adaptation to crop monitoring needs

Expected crop yields relatively to average

8 years out of 10

5 years out of 10

2 years out of 10

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Need for a Spatialized crop model

• SarraH– Dry cereal crops (millet , maize, sorghum)– Water, carbon budgets, phenelogy– Daily rainfall, meteorological input data from stat ions

• Crop model spatialization– Ocelet spatial dynamics modelling environment– Same SarraH processes– Gridded and shape GIS input data– Rainfall estimates from satellite (e.g. TAMSAT, CHIRPS)

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Simulation protocol

• Input maps– Soil (FAO, 13 soil types) for estimating available wate r

capacity– Daily TAMSAT rainfall estimates– 10 days ECMWF data (PET, Rg, Tmin, Tmax)– Onset and End of rainy season (map kriged from histo rical

time series)

• Parameters– 4 fertility levels: very low to potential – 2 soil depths: shallow (60 cm), deep (2 m) – 8 Crop varieties (2 photoperiodic, 3 fixed cycle x 2 cycle

length)– 3 sowing date search methods (first rains, early, from end)

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Input images and maps

22

Rainfall Radiation Temp Min Temp Max Evapotranspiration

Soil Map

Onset dates

Cessation dates

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Yield anomaly evolution in Mali

-50

-40

-30

-20

-10

0

10

202007 2009 2011 2013 2015

Ano

mal

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de R

ende

men

t %

Années

2 months before

At harvest

Potential Yield at Anthesis

Potential Yield at Harvest

SARRA-O, UN MODELE ET LOGICIELDU CIRAD, UMR TETIS

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-10

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202007 2009 2011 2013 2015

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SARRA-O, UN MODELE ET LOGICIELDU CIRAD, UMR TETIS

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Yield anomaly evolution in Mali

2 months before

At harvest

Potential Yield at Anthesis

Potential Yield at Harvest

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-40

-30

-20

-10

0

10

202007 2009 2011 2013 2015

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Années

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Yield anomaly evolution in Mali

2 months before

At harvest

Potential Yield at Anthesis

Potential Yield at Harvest

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-50

-40

-30

-20

-10

0

10

202007 2009 2011 2013 2015

Ano

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de R

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Années

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Yield anomaly evolution in Mali

2 months before

At harvest

Potential Yield at Anthesis

Potential Yield at Harvest

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-30

-20

-10

0

10

202007 2009 2011 2013 2015

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Années

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Yield anomaly evolution in Mali

2 months before

At harvest

Potential Yield at Anthesis

Potential Yield at Harvest

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-50

-40

-30

-20

-10

0

10

202007 2009 2011 2013 2015

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Années

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Yield anomaly evolution in Mali

2 months before

At harvest

Potential Yield at Anthesis

Potential Yield at Harvest

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-40

-30

-20

-10

0

10

202007 2009 2011 2013 2015

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Années

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2 months before

At harvest

Yield anomaly evolution in Mali

Potential Yield at Anthesis

Potential Yield at Harvest

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-40

-30

-20

-10

0

10

202007 2009 2011 2013 2015

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2 months before

At harvest

Yield anomaly evolution in Mali

Potential Yield at Anthesis

Potential Yield at Harvest

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-50

-40

-30

-20

-10

0

10

202007 2009 2011 2013 2015

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Années

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Yield anomaly evolution in Mali

2 months before

At harvest

Potential Yield at Anthesis

Potential Yield at Harvest

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Evolution of above ground biomass in 2013 (Top) and 2015(Bottom)

Daily time step, 3.5 km resolution

2015 had a delayed onset and a below average cumulative rainfall

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Ongoing and future works

• Prototype testing– Tested at AGRHYMET in 2015– Outputs used in bulletins since 2016– Mid-season simulation with different rainfall scena rios

• Enhancements– Improved rainfall estimates (TAMSAT + SMOS + Mergin g

with Station data)– Start and cessations dates calculation with remote sensing

data

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Flash info November 2016

Monitoring the 2016 season at AGRHYMET

34

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Thank you for your attention

http://agrhymet.cilss.int/seydou.traore@cilss.int