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

The world’s largest rainfall deficit of the last century

Impacts on…

Human activities

Water resources

Health

An illustration of hydrological impacts

Sahel region (last century)

Niger at Malanville: 2.106 km²

IA decrease of 50 % of the Niger runoff

An illustration of health impacts: malaria and meningitis

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

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

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

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).

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

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)

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

• The relationships between crop and climate

• The scale issues

• 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

Two crop types in West Africa

The food crops (ex : millet, sorghum)

The cash crops (ex : maize, cotton)

millet, sorghum) maize, cotton)

The temporal variations of climate play a role in agriculture:

The seasonal scale

The seasonal cycle of rainfall in Senegal

j f m a m j j a s o n d

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

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

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:

The interannual scale

Rainfall and yield in Niger

Wet years

21We find the same tendancy in rainfall and yield�� Climate drives yields and food supply

1950 1955 1960 1965 1970 1975 1980 1985 1990

Dry years

Source : FAO, Agrhymet

The spatial variations of climate play a role in agriculture

Rainfall in West Africa: climatology and tendancies

Potential of biomass production in West Africa: climatology and tendancies

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

R² = 0,437

rved y

Linear regressions between yield and rainfall

Cotton in Mali Millet in Niger

1000 1500 2000 2500 3000

Observed yield (kg/ha)

200 300 400 500 600 700O

rved y

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

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

From a research station to a farm the main factors

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)

farm the main factors controlling the yield change

The drivers of the simulated yields

30Baron et al. (2005)

The drivers of the simulated yields

Baron et al. (2005)

SpaceFine scaleLarge

Disaggregation

Climate variability

Scale issues in modelling the impacts of climate

Fine scale

IMPACTS

Human activities

Aggregation

- Climate models-Long-term

drought- Climate change

CLIMATE VARIABILITY

SPACEFine scaleLarge

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

Yield at the plot level

IMPACTS

Fine scale

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

5P=0.25

It rains almost

0.0 0.2 0.4 0.6 0.8 1.0

Rain event frequency

nts almost

every day (P=0.9)

It never rains

Baron et al. (2005)

It rains every day

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

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

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

Disaggregation

Climate variability

Scale issues in modelling the impacts of climate

Fine scale

IMPACTS

Human activities

Aggregation

13.80AlkamaGardama KouaraKoyria

Wankama13.60

A multi -scale and multidisciplinary field survey in Niger

Rendement

0 500 1000 1500 2000

Rendement

0 500 1000 1500 2000

Yield x 2

The plot scale

1.80 2.00 2.20 2.40 2.60 2.80 3.0013.00

BanizoumbouBerkiawal

Sadore (IH Jachere)

Tanaberi

Torodi

1.80 2.00 2.20 2.40 2.60 2.80 3.0013.00

30 kmYield x 2

The village scale

� A multi-scale and multidisciplinary field survey in Niger

From the plot level to the village level

ieldR=0.67 R=0.88

Validation of the crop model at different aggregati on-levels

0 500 1000 1500 2000 2500 3000

RDTsimule[which(Cyclecomplet <= cycle)]

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

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)

Many uncertainties in such forecast and scenarios due both to climate predictability and to the vegetation response

Uncertainties in climate change impacts on agriculture

Positive

Negative

Cultures tropicales Cultures tempérées

Impacts of climate change on crop yields based on the analysis of 43 studies mentionned in the IPCC 2001

Tropical crops Temperate crops

Change in T°and rainfallReference

period:1980-1999

Dots: the difference in the multi-models mean is greater than the inter-model standard deviation

Source: GIEC 2007

Période de

référence:1980-1999

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

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

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!

The potential yield is almost never attained because of limiting factors and reducing factors

To anticipate the response of yield to

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

Effect of CO2 on the Leaf Photosynthesis

Combination of several factors

The different factors interact in a non-linear way

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?

Palmer et al. (2004)

The multi-models approach

GLAM with YGP calibrated using :

� The respective single-model yield ensemble mean

� The multi-model

� 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)

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

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

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

Forecast for July-September 2007 Forum PRESAO ACMAD May 2007

Probabilistic forecast

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…).

� 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

• 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

Mai Juin Juillet Août Septembre

Late onsetEarly

cessation� The role of intraseasonal variability

Existence of viable decision options sensitive to forecast information

Decision options sensitive to forecast informations (1)

Millet Sorghum

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

0 200 400 600 800 1000 1200

Rainfall during rainy season

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

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

Intraseasonal fluctuations of rainfall in Sahel

Dry spell at 15 days

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)

(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

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

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):

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 ????

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…

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?

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

• 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

GAMS (General Algebraic Modeling System)

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?

using seasonal forecasts?

Model’s optimal crop allocation according to the quality of the rainy season

a0 a1 a2 a3

sorgh,baf

mil ,dior

mil ,deck

mais2,deck

arac2,dior

arac1,dior

arac1,deck

CTRL DRY NORM WET

DRY forecasts WET forecasts

DRY years

Costs and benefits of seasonal forecasts

Benefits

DRY years

NORM years

WET years

Success Failure SuccessFailure

Impacts

100000

200000

300000

400000

500000

600000

arac1* arac2* mais1 mais2 mais3 mil sorgho

/ha) a1

Gross margins

The WET strategy is the riskier with the

arac1 arac2 mais1 mais2 mais3 mil sorgho

the riskier with the choice of Maize and Groundnut

Standard deviation

Which sensitivity of the benefits to the forecasts skills?

benefits to the forecasts skills?

Forecasts evaluation matrix

DRY NORM WET

DRY A B C

OBSERVATION

DRY A B C

NORM D E F

WET G H I

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

Use of dry years forecasts

Use of wet years forecasts

Do existing forecasts schemes (DEMETER, PRESAO) have

(DEMETER, PRESAO) have an economic value?

Assessing the economic value of 3 widely known prediction schemes (deterministic forecasts)

� 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

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

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

� 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.

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.

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.

climate impacts on agriculture in west africa › aosta_old › aosta2009 › lectures... ·...

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