Seasonal Climate Forecasts in West Africa : Application to Crop Yield Forecasts in CILSS Member Countries
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1 Seasonal Climate Forecasts in West Africa : Application to Crop Yield Forecasts in CILSS Member Countries By Seydou B. TRAORE, agrometeorologist, AGRHYMET Regional Center, Niamey, Niger s.traore@agrhymet.ne
2 Outline Introduction Materials and Methods The DHC crop yield forecasting model The PRESAO seasonal climate outlooks Application to crop yield forecasting The Year 2002 case The Year 2004 case The Year 2005 case Conclusions and recommendations
3 Introduction The AGRHYMET food security early warning system (GTP) Agricultural campaign monitoring (rainfall and hydrological situations, crops and pastures status, market prices) Crop yield forecasting Crop production assessment and establishment of cereal balances Crop yield forecasting Use of a crop water balance simulation model (DHC ) Generally done at the end of August and updated at the end of September However, in case of delays in the beginning of the season, farmers and policymakers wonder about the possible outcome of the season because The end of season is generally stable A late beginning does not imply a late ending Late sowing usually results in lower yield potential How to use the PRESAO rainfall forecasts to come up with a most likely crop yield forecast scenario?
4 Materials and Methods The DHC-CP model : simulates the crop water balance using: Dekadal rainfall data Average dekadal potential evapotranspiration (PET) Assumptions / data on Sowing dates, crop cycle duration and water requirement Soil water holding capacity Many output variables, among which IRESP : index for expected yield IRESP(%)= (AET/MET)cycle x (AET/MET)critical phase Rdt : expected yield Rdt (kg/ha) = 11,3 x IRESP with R² = 0,66
5 Materials and Methods (cont.) The PRESAO process Use of dekadal rainfall data from the first dekad of May till the current dekad Use of historical data ( percentage points) from the current dekad till the end of season (3 rd dekad of September) Good year (above normal) : 25 percentage point Average year (normal) : 50 percentage point Bad year (below normal) : 75 percentage point Maps of expected yields compared with the average Updates every 10 day using actual rainfall data
6 Application to Yield Forecasting
7 Application to Yield Forecasting
8 Application to Yield Forecasting
9 Application to Yield Forecasting
10 Application to Yield Forecasting
11 Application to Yield Forecasting
12 Summary In 2002 as well as in 2004, the beginning of the season was late and made people worry about the outcome of the season The choice of the normal scenario for yield forecast, as predicted by the PRESAO outlook, was finally justified in both cases, as opposed to the optimistic scenario (what people hoped) So the warnings made should have allowed decision makers take adequate measures relative to food security The prospects for the 2005 campaign were good from the start, with above average yield in the sahelian zones and average yields in the sudanian zones This was confirmed by the Nouakchott meeting in November, where the general cereal production was estimated at more than 15 millions tons, about 34% increase, compared to last year and 31% compared to the average of the last 5 years ( ).
13 What to do better The current PREASO outlooks are qualitative and do not give any indication on temporal distribution Because of that, we have only one hypothesis for the whole season Need to have forecasts at the scale of the dekad, or at least of the month, including the months of May and June Better if quantitative (dekadal rainfall values) Need to have forecast of starting dates The methodology gives better results when applied starting from the end of July, when actual sowing dates are better assessed (known).
14 Thank You For Your Attention
15 Presao
16 Delays in the beginning of the season Year 2002 Year 2004
17 PRESAO 2002 PREVISION SAISONNIERE DES PLUIES EN AFRIQUE DE L' OUEST, LE TCHAD ET LE CAMEROUN JUILLET- AOUT- SEPTEMBRE ZONE I ZONE III ZONE II
18 Presao
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