What does the agricultural research-fordevelopment community need from climate and weather data? Philip Thornton Workshop on Uncertainty in Climate Prediction: Models, Methods and Decision Support 9 December 2010, Cambridge
Outline The developing country context Climate & weather data needs: past, present, future Challenges
The developing country context Global population to 9.2 billion in 2050 Sub-Saharan Africa: 0.8 billion to 1.8 billion Population is urbanising: <30% in 1980 to >50% in 2009 Income per capita growing (slowly in places) Food demand increases SSA: livestock products from 200 kcal / person / day (2000) to 400 kcal (2050) still only 20% of OECD consumption levels SSA: cereal demand will more than double
In South Asia, cereal yields are up and poverty down but not in Sub-Saharan Africa World Development Report 2008 (WB)
1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 percentage Why is climate change so important in developing countries? 80 60 40 20 0-20 -40-60 -80 Ethiopia: Rainfall Variability and Growth in Gross Domestic Product (GDP) rainfall variation around the mean GDP growth year 25 20 15 10 5 0-5 -10-15 -20-25 -30 de Jong (2005), World Bank (2005)
How to achieve food security and maintain/ enhance livelihoods in the face of global change? How to protect and maintain ecosystems services, and foster economic growth?
Need for climate information: Past Example: Assessing sustainable alternatives in mixed farming systems in East Africa Field data collection to calibrate crop, livestock models Given farmers objectives, are there better ways to allocate land (food, cash crops, fodder crops, fuelwood)? Participatory evaluation and field testing of alternatives
Agricultural impacts models Crop models Detailed semi-mechanistic simulation of growth and development of crops (many cereals, grain legumes, roots & tubers, grasses) Livestock models Dynamic simulation model of digestion in ruminants, predicts intake, production (milk, meat), excretion (faeces and urine), metabolism end products (methane, etc)
Agricultural impacts models Key inputs: historical daily weather data Daily max temperature RUMINANT: Prediction of intake Daily min temperature Rainfall Solar radiation (Wind speed, relative humidity) predicted intakes (g/kg BW 0.75 ) 120 100 80 60 40 40 60 80 100 120 observed intakes (g/kg BW 0.75 ) 140 soto pred l and m pred shem pred kaitho pred manyuchi pred Kariuki pred Euclides pred j and h pred l and f pred fall pred
Options assessed High quality manure ready for use Cattle urine pits made, and used in Napier plots Tea + Napier + kale plots evaluated
Kilifi Domain Mixed rainfed humid-subhumid crop-livestock system Medium human population density
Need for climate information: Present Example: Seasonal forecasting Regions of sub- Saharan Africa where seasonal precipitation can be simulated with reasonable skill IRI (2005)
Coping strategies used by farmers in semi-arid W Africa Scale Time frame Pre-season Mid-season Post-season Plant Crop, variety selection for stress tolerance Plot Stagger planting dates Plant low densities Intercrop Manage run-off Delay fertilizer use Farm Diversify cropping, land types Fragment plots Household Village Region Stock up on cereals, other assets Utilise social, off-farm networks Replant earlier maturing varieties Replant different crops Change sowing density Thinnings Shift crops between land types Match labour inputs to season expectations Graze failed plots Plant late for forage Sell assets Utilise networks Migrate to find work Matlon and Kristjanson (1988)
Need for climate information: Future Example: What will agriculture in sub-saharan Africa look like in a four-plus degree world? Generated characteristic daily weather data using MarkSim as a GCM downscaler (difference interpolation + stochastic downscaling + weather typing) Estimated growing days and growing seasons using daily weather data and a simple water balance model Modelled the growth and development of maize, Phaseolus bean, Brachiaria decumbens, using DSSAT v4 crop models
Ensemble mean of LGP change estimates to the 2090s Substantial losses away from equator, some small gains in parts of E Africa Ensemble means of 14 GCMs and 3 emission scenarios Thornton et al. (2010)
Simulated yields (30 reps) in SSA under current conditions and in the 2090s Thornton et al. (2010)
Simulated yields (30 reps) in SSA under current conditions and in the 2090s Low CVs of yield changes in E Africa: quite a robust result Thornton et al. (2010)
Simulated yields (30 reps) in SSA under current conditions and in the 2090s High CVs of yield changes elsewhere: results depend on choice of GCM & emissions scenario Thornton et al. (2010)
The prognosis for a +5 C agriculture in SSA Appalling: rainfed agriculture in many places may cease to viable (especially south of the Zambezi) Croppers and livestock keepers have been highly adaptable to short- and long-term variations in climate: but the changes in a plus five-degree world would be way beyond experience
What are some of the challenges? Constraints to climate research in the region (GHA) Lack of climate data Lack of training of climate modelling and its applications Lack of funding Poor access to literature search/sources: little, slow or no access to institutional internet capacity Kinyangi et al. (2009), Scoping study on vulnerability to climate change and climate variability in the Greater Horn of Africa (ILRI & IDRC-CCAA)
Gaps in regional climate data Knowledge of basic climatology, including gaps in reliable data for establishing baselines and trends Providing reliable data for climate monitoring & spatial coverage of highly variable areas Lack of observational climate data a major constraint to understanding current & future climate variability Kinyangi et al. (2009)
Gaps: downscaled GCM / RCM data Data at appropriate temporal and spatial scales Availability, accessibility (variables, formats) Evaluation and comparison of methods: how much uncertainty does downscaling method contribute to the overall uncertainty associated with agricultural impact model outputs? How can uncertainty be appropriately communicated to decision makers?
Climate information is crucial for understanding impacts, assessing responses Enormous spatial heterogeneity in impacts, responses (local-level effects) Impacts and responses dynamic, non-linear Climate and other shocks affect power, governance and equity relations that disadvantage the vulnerable Need climate information to quantify impacts and responses that may arise from both changing means and changing variances Historical data critical to understanding how households have dealt with risk in the past (work with the highly exposed for adaptation work)
CGIAR (Consultative Group on International Agricultural Research) ESSP (Earth System Science Partnership) Objectives To provide diagnosis and analysis to ensure the inclusion of agriculture in climate change policies To identify and develop pro-poor adaptation and mitigation practices, technologies and policies
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