Climate impacts on agriculture in West Africa › aosta_old › aosta2009 › Lectures... ·...
Transcript of Climate impacts on agriculture in West Africa › aosta_old › aosta2009 › Lectures... ·...
Benjamin SULTANLOCEAN : Laboratoire d’Océanographie et de Climatologie par l’Expérimentation et l’Approche Numérique
Climate impacts on agriculture in West Africa
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The world’s largest rainfall deficit of the last century
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Impacts on…
Human activities
Water resources
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Health
An illustration of hydrological impacts
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Wet
DryI
I
Sahel region (last century)
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Niger at Malanville: 2.106 km²
I
I
I
IA decrease of 50 % of the Niger runoff
An illustration of health impacts: malaria and meningitis
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malaria and meningitis
Meningitis and climate in West Africa
� Every year Western African countries within the Sahelo-Sudanian band are suffering important meningococcal meningitis disease outbreaks
� Affect up to 200,000 people, from which mainly young children
� Interaction between different environmental parameters (e.g. immune receptivity of individuals, a poor socio-economical level, the transmission of a more virulent serotype, social habits like pilgrinages, tribe migrations and meetings, and some specific climatic conditions) may intervene in disease outbreaks and diffusion within local populations
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The role of climate:
� The timing of the epidemic year, which starts in February and ends late May
�the spatial distribution of disease cases throughout the “Meningitis Belt”
This Sahelo-Sudanian region is submitted to sequence of dry winter, dominated by Northern winds, called the Harmattan, and wet season starting at spring with the monsoon.
Meningitis and climate in West Africa
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The winter characteristics, through a weakening of human mucous membranes of the oral cavity due to air dryness and strong dust winds, make propicious conditions to the development of the meningoccus bacteria
Humidity during both the Spring and Summer seasons strongly reduce disease risk due to lower transmission capacity by the bacteria
Malaria in West Africa
Malaria is caused by a parasite called Plasmodium, which is transmitted via the bites of infected mosquitoes.
In West Africa, malaria is involved in 90% of the
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The vector anopheles
involved in 90% of the mortality of children younger than 5 years old.
A map of malaria based on climate factors
The three main climate factors that affect malaria are temperature, precipitation, and relative humidity (Pampana, 1969).
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Climate predicts, to a large degree, the natural distribution of malaria (Bouma and van der Kaay, 1996).
Climate suitability for malaria in South Africa
Climate and malaria
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Number of cases of malaria in South Africa
Climate impacts on agriculture
Climate has a strong influence on agricultural prod uction :
The most weather-dependent of all human activities (Oram 1989; Hansen 2002)
Socio-economical impacts whose severity varies from one region to another (Ogallo et al. 2000)
These impacts are particularly strong in developing countries in the tropics :
- High variability in climate like the monsoon system over West Africa and India and the ENSO influence over the American continent (Challinor et al. 2003)
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ENSO influence over the American continent (Challinor et al. 2003)
- Poverty increases the risk and the impact of natural disasters (UNDP 2004).
This is especially true in the Sahel :
- Rainfed crop production is the main source of food and income
- Means to control the crop environment are largely unavailable to farmers (no irrigation, low use of mechanization, fertilizers)
- Rapidly growing population
Climate impacts on agriculture
• The relationships between crop and climate
• The scale issues
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• The incertainties
• The potential benefits of climate forecasts
Agriculture in West Africa
Contribution of agriculture in the GDP (%)
Fraction of population in the agriculture sector (%)Population / 1000
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Two crop types in West Africa
The food crops (ex : millet, sorghum)
The cash crops (ex : maize, cotton)
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millet, sorghum) maize, cotton)
The temporal variations of climate play a role in agriculture:
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The seasonal scale
The seasonal cycle of rainfall in Senegal
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6
8
10
12
14
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Rai
nfal
l (m
m/m
onth
)
17
0
2
4
j f m a m j j a s o n d
Rai
nfal
l (m
m/m
onth
)
Time
Same quantity of rainfall in Senegal and in Paris but it follows a seasonal cycle
���� This seasonal cycle is very important for agriculture
A millet field throughout the rainy season
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May-June (sowing) June-July (early stages)
July (weeding) August-September (late stages)
The millet after the rainy season
The flowering structure (inflorescence) in pearl millet is called as panicle or head.
Pearl millet at maturity
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In Africa pearl millet is consumed as fermented and nonfermented flat breads, couscous, thick and thin porridges, boiled and steamed foods, and alcoholic and nonalcoholic beverages.
The temporal variations of climate play a role in agriculture:
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The interannual scale
Rainfall and yield in Niger
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2
3
4
Wet years
21We find the same tendancy in rainfall and yield���� Climate drives yields and food supply
-3
-2
-1
0
1950 1955 1960 1965 1970 1975 1980 1985 1990
Dry years
Source : FAO, Agrhymet
The spatial variations of climate play a role in agriculture
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Rainfall in West Africa: climatology and tendancies
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Potential of biomass production in West Africa: climatology and tendancies
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Quantifying the relationships betweenclimate and crop yield
Crop modelling
Simulate explicitly the growth of the plant, quantify water and other stresses that affect the development of the crop
Advantage: takes into account stresses occurring during sensitive stages of the crop
Disadvantage: requires many agronomic data to calibrate the crop model, requires precise climate data (daily time scale, plot scale)
Statistical analyses
For instance, linear regression between seasonal rainfall amount and yield
Advantage: does not require many agronomic data or fine scale climate data
Disadvantage: not sensitive to intraseasonal scales, needs large dataset to calibrate the relationships, stationnary
The statistical links between rainfall and crop yield
2000
2500
3000
Pre
dict
ed y
ield
(k
g/ha
)
R² = 0,437
400
450
500
550
600
Obse
rved y
ield
(kg
/ha)
Linear regressions between yield and rainfall
Cotton in Mali Millet in Niger
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1000
1500
2000
1000 1500 2000 2500 3000
Pre
dict
ed y
ield
Observed yield (kg/ha)
200
250
300
350
400
200 300 400 500 600 700O
bse
rved y
ield
(kg
/ha)
Rainfall (mm/year)
Rendement simulé =F (length of the rainy season, water budget)R²=0.86
Annual rainfall amount and crop yield in NigerR²=0.44
Données d’un essai de longue durée conduit par l’IER/SRCFJ (Section Recherche Cotonnière et Fibre Jutière de l’Institut d’Économie Rurale) au Mali de 1965 à 1990 dans la région de Koutiala (station de N'Tarla)
Données FAO en moyenne sur l’ensemble du Niger sur la période 1965-1995
Groundnut yields and climate in India
A large fraction of the world production of groundnut comes from India
Not irrigated and thus highly dependant on climate
27Challinor et al. (2003)
Fluctuations in the Indian monsoon explain half the variability of the yields (52%)
Groundnut yields and climate in India
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Challinor et al. (2003)
Validation of a crop model in Senegal
Validation over a research station
The model explains near 90% of the variability of yield
Sim
ulat
ed Y
ield
(kg
/ha)
From a research station to a farm the main factors
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Sultan et al. (2005)
On-farm validation
The model simulates the attainable yield and under-estimates the on-farm variability
Observed Yield (kg/ha)
Simulated Yield (kg/ha)
Obs
erve
d Y
ield
(kg
/ha)
farm the main factors controlling the yield change
The drivers of the simulated yields
30Baron et al. (2005)
The drivers of the simulated yields
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Baron et al. (2005)
Climate impacts on agriculture
• The relationships between crop and climate
• The scale issues
• The incertainties
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• The incertainties
• The potential benefits of climate forecasts
SpaceFine scaleLarge
scale
Disaggregation
Climate variability
Scale issues in modelling the impacts of climate
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Fine scale
Tim
e
IMPACTS
Human activities
Aggregation
- Climate models-Long-term
drought- Climate change
CLIMATE VARIABILITY
SPACEFine scaleLarge
scale
TIM
E
Downscaling
If the models do a credible job on the global scale they fail on the regional scale:
� Subgrid-scale processes (cloud formation, rainfall, infiltration, evaporation, runoff, etc.) are parameterized and badly simulated
Regional climate� local impact
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Yield at the plot level
IMPACTS
Fine scale
TIM
E
However, these subgrid processes are actually thosewith the greatest ecological or societal impact, since they strongly affect the local climate at the scales of the human and ecological environment.
���� We need downscaling
���� Find large scale patterns with an impact at the local scale
Regional climate� local impact
35Source: GIEC 2007
Statistics of annual mean responses to the SRES A1B scenario, for 2080 to 2099 relative to 1980 to 1999, calculated from the 21-member AR4 multi-model ensemble using the methodology of Räisänen (2001).
Effect of aggregation on simulating yield
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5P=0.25
It rains almost
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0.0 0.2 0.4 0.6 0.8 1.0
01
2
Rain event frequency
Cou
nts almost
every day (P=0.9)
It never rains
Baron et al. (2005)
It rains every day
Effect of aggregation on simulating yield
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Effect of aggregation on simulating yield
38Baron et al. (2005)
Regional specificities
Global climate (GCM, NCEP…)
Topography, land use, land-
sea distribution…
To combine large-scale information with regional specificities to simulate the local
climate.
Downscaling
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Local climate
���� Dynamical and statistical approaches
3 types of statistical methods
Dynamical methods
� LAMs or RCMs are sophisticated atmospheric (or oceanic) models of a limited geographical area with a resolution of the order of 20–50 km, that use the largescale fields simulated by the GCMs as boundary conditions, but that take the regional characteristics, such as topography, into account.
Downscaling
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3 types of statistical methods
� Weather generatorsstochastic models generating virtual climate series with the same statistical
properties than observed ones
� Regression-type methodscanonical analyses, multiple regression, neural networks
� Weather typesAnalogues, clustering
SpaceFine scaleLarge
scale
Disaggregation
Climate variability
Scale issues in modelling the impacts of climate
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Fine scale
Tim
e
IMPACTS
Human activities
Aggregation
13.60
13.80AlkamaGardama KouaraKoyria
Wankama13.60
13.80
1600
1800
2000
A multi -scale and multidisciplinary field survey in Niger
Rendement
Nb
de p
arce
lles
0 500 1000 1500 2000
02
46
810
Rendement
Nb
de p
arce
lles
0 500 1000 1500 2000
05
1015
Yield x 2
The plot scale
1.80 2.00 2.20 2.40 2.60 2.80 3.0013.00
13.20
13.40
13.60
BanizoumbouBerkiawal
Kare
Sadore (IH Jachere)
Tanaberi
Torodi
1.80 2.00 2.20 2.40 2.60 2.80 3.0013.00
13.20
13.40
13.60
0
200
400
600
800
1000
1200
1400
1600
30 kmYield x 2
The village scale
� A multi-scale and multidisciplinary field survey in Niger
From the plot level to the village level
500
1000
1500
2000
2500
3000
rdt.n
ew[w
hich
(Cyc
leco
mpl
et <
= cy
cle)
]
050
015
0025
00
obs.
mea
n
Obs
erve
d Y
ield
Obs
erve
d Y
ieldR=0.67 R=0.88
Validation of the crop model at different aggregati on-levels
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0 500 1000 1500 2000 2500 3000
050
0
RDTsimule[which(Cyclecomplet <= cycle)]
rdt.n
ew[w
hich
(Cyc
leco
mpl
et <
= cy
cle)
]
0 500 1500 2500
sim.mean
Simulated YieldSimulated Yield
At the village level, the mean yield is better pred icted by the crop model
���� We need upscaling
Plot levelVillage level
Aggregation
Climate impacts on agriculture
• The relationships between crop and climate
• The scale issues
• The incertainties
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• The incertainties
• The potential benefits of climate forecasts
Predicting Climate impacts on agriculture
The prediction and scenarios of climate impacts:
- Seasonal prediction (prediction of yield from one year to another)
- long-term scenarios (climate change)
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Many uncertainties in such forecast and scenarios due both to climate predictability and to the vegetation response
Uncertainties in climate change impacts on agriculture
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Uncertainties in climate change impacts on agriculture
30%
40%
50%
60%
70%
80%
90%
perc
enta
ge o
f stu
dies
Positive
Null
Negative
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0%
10%
20%
Cultures tropicales Cultures tempérées
perc
enta
ge o
f stu
dies
Impacts of climate change on crop yields based on the analysis of 43 studies mentionned in the IPCC 2001
Tropical crops Temperate crops
Uncertainties in climate change impacts on agriculture
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Change in T°and rainfallReference
period:1980-1999
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Dots: the difference in the multi-models mean is greater than the inter-model standard deviation
Source: GIEC 2007
Période de
référence:1980-1999
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Figure 10.12
Dots: More than 80% of the models agree in the sign of the changeSource: GIEC 2007
Performance of the models in simulating the seasonal cycle of rainfall in West Africa
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D’Orgeval et al. (2005)
A selection of the best three models in the simulation of present climate in West Africa:
The uncertainty remains!
The performance of the models in simulating the WAM in the present is not the only factor to explain uncertainties in the future projections
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Sahelian JJAS precipitation differences (mm/day) from the 1949–2000 mean in various GCM simulations with A2 scenario forcing after 2000.
Source: Cook and Vizy 2006
The uncertainty remains!
Uncertainties in climate change impacts on agriculture
The potential yield is almost never attained because of limiting factors and reducing factors
To anticipate the response of yield to
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Crop production levels depending on defining, limiting or reducing factors (see [Goudriaan and Zadoks, 1995]).
response of yield to climate change, we need to know the evolution of each factor, their interaction and their impact on the crop
Furher. (2003)
Predicted effect of CC 2050 on crop yield (Parry et al. 2004)
The fertilizer role of CO2 for the crops
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Effect of CO2 on the Leaf Photosynthesis
Combination of several factors
The different factors interact in a non-linear way
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Effects of elevated CO2 and increased temperature, singly and in combination, on yield of wheat. The data represent the ratio of yield relative to current ambient CO2 and temperature (relative yield change). Data are taken from the review by [Amthor, 2001], Table 7). Plots show median and standard percentiles (n=17). Furher (2003)
How to represent uncertainty using GCMs?
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Palmer et al. (2004)
The multi-models approach
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Palmer et al. (2004)
The multi-models approach
GLAM with YGP calibrated using :
� The respective single-model yield ensemble mean
� The multi-model
ERA40
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� The multi-model ensemble mean
� For a given GCM, the forecast error is weaker by using an ensemble of simulations (true for most of the used models)
� By using the ensemble multi-model mean, we reduce the forecast error (only one model has a weaker forecast error than the MMEM)
Challinor et al. (2005)
The relative contributions to uncertainties
Maize yields projection under future climate in China
Measuring the uncertainties linked to biophysical processes (sensitivity with 60 parameters in crop modelling) and to climate scenarios (sensitivity with 10 scenarios)
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Palmer et al. (2004)
10 scenarios)
Comparison between probability density functions of projected yield changes during 2050s based on different number of climate change scenarios and sets of parameters across the maize cultivation grids in Shandong
Climate impacts on agriculture
• The relationships between crop and climate
• The scale issues
• The incertainties
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• The incertainties
• The potential benefits of climate forecasts
The potential benefits of climate forecasts to agriculture
Climate predictability
Human vulnerability
The opportunity of a beneficial use of climate forecasts falls within the intersection of:
- Human vulnerability
- Climate predictability
- Decision capacity
Potential to
benefit
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5 Prerequisites to beneficial forecast use:
� Forecast information must address a need real and perceived� Existence of viable decision options sensitive to forecast information� Prediction in relevant periods, at an appropriate scale, with sufficient accuracy and lead-time for relevant decisions� Effective communication of relevant information� Institutional commitment and favorable policies
Decision capacity
Hansen (2002)
Forecast information must address a need real and perceived
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Forecast for July-September 2007 Forum PRESAO ACMAD May 2007
Probabilistic forecast
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The needs of farmers (1)
A questionnaire addressed to commercial farmers in South-Africa :
� Among the factors influencing decisions (such as market prices and tendancies…), climate forecast is an important factor(seasonal rainfall, start of the rainy season, risk of drought…).
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� To deliver a relevant service climate forecast should consider the critical times for farming activities (i.e. planting) and for plant growth (i.e. grain-filling period)
Klopper et al. (2006)
The needs of farmers (2)
In Burkina Faso, most farmers expressed strong interest in receiving seasonal rainfall forecast
The most salient rainfall parameters farmers in Burkina Faso want in a forecast (in order of declining priority):
• Onset and end of the rainy season
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• Onset and end of the rainy season
• Rainfall distribution within the rainy season
• Total amount of rainfall
At the moment, only the total amount of rainfall is forecasted in West Africa
Ingram et al. (2002)
Rainfall and millet yields in Niger
The total rainfall amount does not explain the variability of the yields
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0
50
100
150
200
250
Mai Juin Juillet Août Septembre
2004
2005
2006
2007
Late onsetEarly
cessation� The role of intraseasonal variability
Existence of viable decision options sensitive to forecast information
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Decision options sensitive to forecast informations (1)
1500
2000
2500
3000
3500
4000
4500
5000
Gra
in Y
ield
(kg
/ha)
Millet Sorghum
Maize
Farmers' Fields
Research Stations A yield gapbetween research stations yield (and simulated yield) and on farm yield
This yield gap increases with rainfall
Yield gap
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0
500
1000
1500
0 200 400 600 800 1000 1200
Rainfall during rainy season
Gra
in Y
ield
(kg
/ha)
Relationship between rainfall during the rainy season and yield of maize , sorghum and millet at 15 dryland locations in India (after Shivakumar et al. 1983)
rainfall
� Fertilizers are more useful in good rainfall years
� Prediction of good rainfall years is thus useful for farmers
Yield gap
Decision options sensitive to forecast informations (2)
Clear upland areas for planting Order less herbicide Jan
Order less insecticide Sell livestock or go in transhumance Jan
Plant longer duration crops/varieties Plant shorter duration crops/varieties Feb
Plant more cash crops Plant more cereal crops May
Above Normal Below normal Month requir.
Potential response strategies in response to rainfa ll forecast in Burkina Faso
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Plant more cash crops Plant more cereal crops May
Apply more fertilizer or manure Apply less fertilizer or manure Jun
Sell grain stocks during rainy season Store grain stocks Jul
Acquire capital to purchase inputs Ration food Jan
Increase income-generating enterprises Jan
Migrate Mar
Purchase or borrow food grain Apr
Send younger men abroad to work Jun
(after Ingram et al. 2002)Non-agricultural responses
Agricultural responses
Prediction in relevant periods, at an appropriate scale, with sufficient accuracy
and lead-time for relevant decisions
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and lead-time for relevant decisions
Intraseasonal fluctuations of rainfall in Sahel
Dry spell at 15 days
Dry spell at 40 days
Dry spell at 15 days
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Intraseasonal fluctuations of rainfall at two different timescales:
� Around15 days
� Around40 days
The role of intraseasonal variability of rainfall
The link between yield and rainfall during the critical stages of the crop growth
An important role of rainfall during the critical stages (R=0.51)
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(R=0.51)
Importance of the forecast timing
Farmers from US (Mjeld et al. 1988) and from Burkina Faso (Ingram et al. 2002) agree :
A less accurate forecast with a sufficient lead-tim e is more valuable than a highly accurate forecast that arrives after farmers have made irrevocable decisions
Lead-time Strategic decisions
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3-4 months before
1-2 months before
onset of the rains
Clearing new fields, applying more and more manure, ordering inputs
Optimize labor and land allocation, obtain seed of different varieties, prepare fields in different locations
Though a less value, forecasts could still contribute to revisions of minor farm decisions
(Ingram et al. 2002)
Advanced information in the form of seasonal climate forecasts has the potential to improve farmers’ decision making, leading to increases in farm profits.
Because seasonal climate forecasts may have an impact on farmers’ welfare, both qualitative and quantitative assessments are important to fully exploit the potential benefits associated with them (value) and to understand the limitations of their application (use).
Quantifying the potential benefits of climate forecasts
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Two methods: Ex-ante evaluation and Ex-post evaluation
Ex-ante valuation seeks to assess the potential benefits of an innovation in advance of its adoption, while ex-post valuation seeks to assess actual outcomes following adoption.
Although seasonal forecasts have been issued routinely for more than two decades in parts of the world, their effective dissemination and systematic use to manage climate risk in agriculture represent a new innovation relative to most other agricultural technologies— in most cases too new for reliable ex-post assessment of value.
Ex-ante assessment of the value of seasonal forecasts serves two related roles (Thornton 2006):
Quantifying the potential benefits of climate forecasts
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related roles (Thornton 2006):
� it provides the evidence needed to mobilize funds and influence the agendas of institutional partners in the face of competing priorities.
� it provides insights that inform targeting of effort (e.g., farming systems, locations, forecast characteristics, and decision support tools) where the net benefits are likely to be greatest.
Evaluating the potential benefits of climate prediction
For nine test years, seasonal rainfall forecasts are provided to 7 commercial farmers.
They had then to indicate the impacts on yield either positive or negative of reacting to the forecasts.
In 1994, more than 50% of the
Skillful climate forecasts : « the next green revol ution » or inability to use the climate informations ????
760
20
40
60
80
100
1991 1992 1993 1994 1995 1996 1997 1998 1999
No benefits (60%)
Benefits (33%)
Negative impacts (6%)
In 1994, more than 50% of the farmers would have benefited from having seasonal forecasts
After Klopper et al. (2006)
In West Africa: very few studies despite the importance of agriculture in Sahelian countries
This quantification is difficult because of the interactions of several factors: dessimination and effective use of the forecasts, available decisions options sensitive to the forecasts…
Quantifying the potential benefits of climate forecasts
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An ex-ante study to give some quantitative elements on the potential benefits of climate forecasts for agriculture in West Africa
Methodology
Bioeconomic modelA very simple model to simulate farmers’ decision with a priori information on the quality of the rainy season
Sensitivity to the forecasts skillIs the economicalvalue of the forecastsvery sensitive to the forecastsskill?
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Is the economicalvalue of the forecastsvery sensitive to the forecastsskill?
Evaluation of existing forecasts schemesDo existing forecasts schemes (DEMETER, PRESAO) have an economic value?
Rainfall (Hulme)DEMETER (7 x 9 runs)
JAS 1970-2000
SST (Reynolds)
Farm characteristic
• 3 workers• 6 persons• Family consumption 200 kilos of grains• Land type: lowland, deck and dior
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• Land type: lowland, deck and dior• 100000 FCFA of capital
• 4 crops (millet, sorghum, mais, peanuts), • 3 intensification levels for maize• 1 hectare manured with animal dung
Model
GAMS (General Algebraic Modeling System)
Maximize the expected income according to:
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Maximize the expected income according to:
• Prices, yields, inputs prices • Three types of rainy seasons• land, labor, capital and food security
constraints
What is the economic value of using seasonal forecasts?
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using seasonal forecasts?
Model’s optimal crop allocation according to the quality of the rainy season
3,5
4
4,5
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0
0,5
1
1,5
2
2,5
3
a0 a1 a2 a3
sorgh,baf
mil ,dior
mil ,deck
mais2,deck
arac2,dior
arac1,dior
arac1,deck
CTRL DRY NORM WET
Hec
tare
s
20
40
60
80
100
DRY forecasts WET forecasts
Ben
efits
(%)
DRY years
Costs and benefits of seasonal forecasts
Benefits
84
-80
-60
-40
-20
0
20
Ben
efits
(%)
DRY years
NORM years
WET years
Success Failure SuccessFailure
Impacts
0
100000
200000
300000
400000
500000
600000
arac1* arac2* mais1 mais2 mais3 mil sorgho
Gro
ss m
argi
ns (F
CFA
/ha) a1
a2
a3
Gross margins
The WET strategy is the riskier with the
85
0
100
200
300
400
500
600
700
800
arac1 arac2 mais1 mais2 mais3 mil sorgho
Sta
ndar
d de
viat
ion
(kg/
ha)
the riskier with the choice of Maize and Groundnut
Standard deviation
Which sensitivity of the benefits to the forecasts skills?
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benefits to the forecasts skills?
Forecasts evaluation matrix
DRY NORM WET
DRY A B C
OBSERVATION
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DRY A B C
NORM D E F
WET G H I
PR
ED
ICT
ION
HR dry = A / (A+D+G) HR wet = I / (C+F+I)
Forecast = Rainfall + Noise * k
If k = 0, COR=1, HR=1 : perfect forecast
COR and HR decrease with an increase of k
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COR and HR decrease with an increase of k
Use of dry years forecasts
Use of wet years forecasts
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Use of dry years forecasts
Use of wet years forecasts
90
Do existing forecasts schemes (DEMETER, PRESAO) have
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(DEMETER, PRESAO) have an economic value?
Assessing the economic value of 3 widely known prediction schemes (deterministic forecasts)
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� Persistence based predictions� Statistical SST-based predictions (PRESAO-like)� DEMETER dynamical forecasts
HR dry COR Benefits
SST June 0.6 0.66 15.0
SST May 0.5 0.62 8.5
SST April 0.5 0.41 13.7
Forecast skills and benefits
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Persistence 0.3 0.30 -5.0
HR dry COR Benefits
DEMETER 0.2 -0.20 -15.9
DEMETER corr
0.6 0.42 9.6
Use of dry years forecasts
Use of wet years forecasts
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Use of dry years forecasts
Use of wet years forecasts
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Conclusions
Studying the impacts of climate on agriculture is a very important issue in West Africa (populations highly vulnerable to fluctuations in the agricultural sector)
But it raises many issues
� Scale incompatibility between climate and agriculture� Bad representation of crucial variables
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� Bad representation of crucial variables� Uncertainty in climate and yield projections
Trying to reduce this uncertainty is a key point to produce useful information for stakeholders
RéférencesBaron C., B. Sultan, M. Balme, B. Sarr, T. Lebel, S. Janicot and M. Dingkuhn, 2005.From GCM grid cell to agricultural plot: scale issues affecting modelling of climateimpact, Phil. Trans. Roy. Soc. B, 360 (1463), 2095-2108.
Bazzaz, F. and W. Sombroek, 1996 : Global climate change and agricultural production.Direct and indirect effects of changing hydrological, pedological and plant physiologicalprocesses. John Wiley, FAO, Rome, Italy.
Challinor, A.J., J.M. Slingo, T.R. Wheeler, P.Q. Craufurd and D.I.F. Grimes, 2003. Towarda combined seasonal weather and crop productivity forecasting system : determination ofthe working spatial scale. J. Appl. Meteorol., 42, 175-192.
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Challinor, A.J., T.R. Wheeler, P.Q. Craufurd, J.M. Slingo, and D.I.F. Grimes, 2004. Designand optimisation of a large-area process-based model for annual crops. Agric. For.Meteorol., in press.
De Rouw, A., 2004. Improving yields and reducing risks in pearl millet farming in theAfrican Sahel. Agricultural Systems, 81, 73-93.
Dingkuhn M., B.B. Singh, B. Clerget, J. Chantereau and B. Sultan, 2006. Past, presentand future criteria to breed crops for water-limited environments in West Africa,Agricultural Water Management, 80 (1-3), 241-261.
Hansen, J.W., 2002. Realizing the potential benefits of climate prediction to agriculture :issues, approaches, challenges. Agricultural Systems, 74, 309-330.
Références
Ingram, K.T., M.C. Roncoli, and P.H. Kirshen, 2002. Opportunities and constraints forfarmers of West Africa to use seasonal precipitation forecasts with Burkina Faso as acase study. Agricultural Systems, 74, 331-349.
Ogallo, L.A., M.S. Boulahya, and T. Keane, 2000. Applications of seasonal to interannualclimate prediction in agricultural planning and operations. Agric. For. Meteorol., 103, 159-166.
Sivakumar, M.V.K., 1988 : Predicting rainy season potential from the onset of rains inSouthern Sahelian and Sudanian climatic zones of West Africa, Agricult. And Forest.Meteorol., 42, 295-305.
98
Meteorol., 42, 295-305.
Sultan, B. and S. Janicot, 2003. The West African monsoon dynamics. Part II: The pre-onset andthe onset of the summer monsoon.J. Climate, 16, 3407-3427.
Sultan, B., C. Baron, M. Dingkuhn and S. Janicot, 2005. Agricultural impacts of large-scale variability of the West African monsoon. Agric. For. Meteorol. 128, 93-110.
UNDP, 2004. Reducing disaster risk : a challenge for development, UNDP global report,Pelling M. (Ed.), 146 pp.
Zorita, E. and H. von Storch, 1999. The analog method - a simple statistical downscalingtechnique: comparison with more complicated methods. J. Climate, 12, 2474-2489.