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To what extend can satellite rainfall estimates be used for crop yield prediction
in West Africa?
Ramarohetra J.
Sultan B., Baron C., Gaiser T., Gosset M., Alcoba M.
95% Rainfed
Population growth
Agriculture
1/15
Introduction Context and challenges
Cereal production /hab. ↘
70% of the population
95% Rainfed
70% of the population
Crop yield increase =
a challenge for tomorrow
Agriculture
1/15
Introduction Context and challenges
Population growth
Cereal production /hab. ↘
Agriculture
Limitation
Adaptation
EWS
Translate climate variations in terms of agricultural relevant
variables
1/15
Introduction Context and challenges
Crop yield
Crop models
Climate inputs
Where can we find rainfall data?
2/15
Introduction How to make crop yield predictions?
Crop yield
Crop models
Climate inputs
Where can we find rainfall data?
Long historical time series Insufficient network density Missing data Delay in reporting time
2/15
Introduction How to make crop yield predictions?
Long historical time series Insufficient network density Missing data Delay in reporting time
2/15
Crop yield
Crop models
Climate inputs
Where can we find rainfall data?
Introduction How to make crop yield predictions?
Long historical time series Insufficient network density Missing data Delay in reporting time
Past, present and future climate Big biases
2/15
Crop yield
Crop models
Climate inputs
Where can we find rainfall data?
Introduction How to make crop yield predictions?
Long historical time series Insufficient network density Missing data Delay in reporting time
Past, present and future climate Big biases
2/15
… and ?
Crop yield
Crop models
Climate inputs
Where can we find rainfall data?
Introduction How to make crop yield predictions?
Long historical time series Insufficient network density Missing data Delay in reporting time
Past, present and future climate Quite bad spatial resolution
Quasi global coverage Better and better resolution Increasingly available Accessible in near real time
2/15
Crop yield
Crop models
Climate inputs
Where can we find rainfall data?
Introduction How to make crop yield predictions?
Long historical time series Insufficient network density Missing data Delay in reporting time
Past, present and future climate Quite bad spatial resolution
Quasi global coverage Better and better resolution Increasingly available Accessible in near real time
2/15
Crop yield
Crop models
Climate inputs
Where can we find rainfall data?
Introduction How to make crop yield predictions?
Crop yield
To what extend can satellite rainfall estimates be used for crop
prediction ? 3/15
?
Introduction How to make crop yield predictions?
Crop models
Yield to assess
Crop models
4/15
?
Material and Method Satellite rainfall products assessment.
Control yield
COMPARISON
To what extend can satellite rainfall estimates be used for crop
prediction ?
Material and Method Satellite products to assess
Satellite Product Type PERSIANN
Uncalibrated CMORPH
TRMM 3B42-RT GSMAP-MKV+
GPCP-1dd Calibrated TRMM 3B42v6
RFEv2
Daily, 1°, 2003-2009
5/15
Near real time Satellite data only
Satellite and in situ data
Niamey & Djougou AMMA-CATCH observing
system
Precipitation:
Block kriging performed from
- 54 to 56 raingauges in Niger
- 54 raingauges in Benin
6/15
Material and Methods Area of study and groundbased data
2003-2009 mean annual rainfall
Maize, low fertilization (5kg of N /year).
2 Mechanistic crop models Field scale
Daily time step
First aim: quantify soil erosion impacts on soil productivity Nutrient uptake taken into account
3 pearl millet: - Hainy Kire (early) - Somno (late) - Souna 3 (early)
7/15
Based on water balance No account for nutrient
Material and Methods Crop modelling
Niamey Djougou
8/15
Near real time Global non calibrated
Global calibrated Regional
calibrated
(Niger)
(Bénin)
(Niger)
(Niger)
Results The biases in crop yield prediction
8/15
Near real time Global non calibrated
Global calibrated Regional
calibrated
(Niger)
(Bénin)
(Niger)
(Niger)
Results The biases in crop yield prediction
+
-
Near real time Global non calibrated
Global calibrated Regional
calibrated
(Niger)
(Bénin)
(Niger)
(Niger)
Results The biases in crop yield prediction
8/15
+
-
Near real time Global non calibrated
Global calibrated Regional
calibrated
(Niger)
(Bénin)
(Niger)
(Niger)
Results The biases in crop yield prediction
8/15
+
-
Near real time Global non calibrated
Global calibrated Regional
calibrated
(Niger)
(Bénin)
(Niger)
(Niger)
Results The biases in crop yield prediction
8/15
+
-
What are these biases due to ?
Near real time Global non calibrated
Global calibrated Regional
calibrated
Results The biases in crop yield prediction
9/15
10/15
Results What drives maize yields in Benin?
Maize yield vs. Cycle cumulative rainfall
Maize yield vs. Rainy days number
10/15
Results What drives maize yields in Benin?
R=0.59
Maize yield vs. Cycle cumulative
rainfall < 800 mm
Maize yield vs. Rainy days number
All Cum. Rain. < 800 mm
10/15
Results What drives maize yields in Benin?
Limitant rainfall
70% 30%
R=0.59
Maize yield vs. Cycle cumulative
rainfall < 800 mm
Maize yield vs. Rainy days number
All Cum. Rain. < 800 mm
All Cum. Rain. < 800 mm
All Cum. Rain. > 800 mm
10/15
Results What drives maize yields in Benin?
The number of rainy days is crucial for crop yields in Benin
Maize yield vs. Cycle cumulative
rainfall < 800 mm
Maize yield vs. Rainy days number (cum.
rainfall > 800 mm)
For non limitant rainfall Limitant
rainfall R=0.59
70% 30%
11/15
Cumulative rainfall bias Rainy days number bias Maize yield bias
Bia
s %
Near real time Global non calibrated Global calibrated
Regional calibrated
Results The rainfall biases in Benin
Maize yield bias
→ Systematic bias
11/15
Cumulative rainfall bias Rainy days number bias Maize yield bias
Bia
s %
Near real time Global non calibrated Global calibrated
Regional calibrated
Results The rainfall biases in Benin
Maize yield bias and cumulative rainfall bias
→ No systematic bias
11/15
Cumulative rainfall bias Rainy days number bias Maize yield bias
Bia
s %
Near real time Global non calibrated Global calibrated
Regional calibrated
An underestimation of the number of rainy days in Benin
Results The rainfall biases in Benin
Maize yield bias and rainy days number bias
12/15
Results What drives millet (souna) yields?
The total rainfall amount is crucial for crop yields in Niger
Millet Souna yield vs. Cycle cumulative rainfall < 600 mm
83% 17%
Limitant rainfall
12/15
Results What drives millet (souna) yields?
An underestimation of the total rainfall amount leads to an underestimation of millet yields
Millet Souna yield bias vs. Cycle cumulative rainfall bias
83% 17%
Limitant rainfall
Millet Souna yield vs. Cycle cumulative rainfall < 600 mm
12/15
Calibrated Uncalibrated
Results What drives millet (souna) yields?
An underestimation of the total rainfall amount leads to an underestimation of millet yields
Millet Souna yield bias vs. Cycle cumulative rainfall bias
83% 17%
Limitant rainfall
Millet Souna yield vs. Cycle cumulative rainfall < 600 mm
13/15
Near real time Global non calibrated
Global calibrated Regional
calibrated
Results Biases attribution
Millet in Niger
Maize in
Benin
Raw SRFE
13/15
Near real time Global non calibrated
Global calibrated
Results The biases in Niger are reduced by correcting the total rainfall
Regional calibrated
Millet in Niger
Maize in Benin
Cumulative rainfall bias corrected SRFE
13/15
Near real time Global non calibrated
Global calibrated Regional
calibrated
Results The biases in Benin are reduced by correcting the rainfall distribution
Millet in Niger
Maize in Benin
Distribution bias corrected SRFE
Conclusion
• Satellite rainfall estimates may introduce large biases in crop yield prediction.
• Rainfall estimates bias propagation to yield depends on the crop and region considered.
• Crop yield biases are not simple extrapolation of rainfall cumulative amount biases, rainfall distribution is crucial as well.
• Uncalibrated products give the highest biases.
Prospects
• Compare simulated yield variations with yield observation
• What happens if radiation is taken from satellites as well ?
• Would a statistical bias correction improve near real time products performance for an Early Warning System application?
Material and Method: Bias attribution What are crop yield biases due to ? What are crop yield biases due to ?
Bias = +50% R = 0.83
113 rainy days
Bias = 0 R = 1
121 rainy days
Material and Method: Bias attribution What are crop yield biases due to ?
1. Adjusted cumulative rainfall
Bias = +50% R = 0.83
113 rainy days
Bias = 0 R = 1
121 rainy days
Bias = 0 R = 0.83
113 rainy days
Material and Method: Bias attribution What are crop yield biases due to ?
1. Adjusted cumulative rainfall
2. Adjusted distribution
Bias = +50% R=1
121 rainy days
Bias = +50% R = 0.83
113 rainy days
Bias = 0 R = 1
121 rainy days
Bias = 0 R = 0.83
113 rainy days
Maize, low fertilization (5kg of N /year).
Material and Method: Crop models
Based on water balance
First aim: quantify erosion effect on soil productivity
3 pearl millet: - Hainy Kire (local, early) - Somno (MTDO) (local, late) - Souna 3 (improved, early)
Satellite Product
Type Temporal coverage
Spat. Res. Temp. Res.
PERSIANN Near real time 03/2000 → 0.25° 3h
CMORPH Near real time 12/2002 → 0.072° (0.25°)
30min
TRMM 3B42-RT Near real time 2002 → 0.25° 3h
GSMAP-MKV+ Global
uncalibrated 01/2003-12/2008 0.1° 1h
GPCP-1dd Global calibrated 1997 → 1° daily TRMM 3B42v6 Global calibrated 01/1998 → 0.25° 3h
RFEv2 Régional
calibrated 01/2001 → 0.1° daily