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Probabilistic maize yield prediction over East Africa using dynamic ensemble seasonal climate forecasts
- Source :
- Agricultural and Forest Meteorology, 250-251, 243-261, Agricultural and Forest Meteorology 250-251 (2018)
- Publication Year :
- 2018
-
Abstract
- We tested the usefulness of seasonal climate predictions for impacts prediction in eastern Africa. In regions where these seasonal predictions showed skill we tested if the skill also translated into maize yield forecasting skills. Using European Centre for Medium-Range Weather Forecasts (ECMWF) system-4 ensemble seasonal climate hindcasts for the period 1981–2010 at different initialization dates before sowing, we generated a 15-member ensemble of yield predictions using the World Food Studies (WOFOST) crop model implemented for water-limited maize production and single season simulation. Maize yield predictions are validated against reference yield simulations using the WATCH Forcing Data ERA-Interim (WFDEI), focussing on the dominant sowing dates in the northern region (July), equatorial region (March-April) and in the southern region (December). These reference yields show good anomaly correlations compared to the official FAO and national reported statistics, but the average reference yield values are lower than those reported in Kenya and Ethiopia, but slightly higher in Tanzania. We use the ensemble mean, interannual variability, mean errors, Ranked Probability Skill Score (RPSS) and Relative Operating Curve skill Score (ROCSS) to assess regions of useful probabilistic prediction. Annual yield anomalies are predictable 2-months before sowing in most of the regions. Difference in interannual variability between the reference and predicted yields range from ±40%, but higher interannual variability in predicted yield dominates. Anomaly correlations between the reference and predicted yields are largely positive and range from +0.3 to +0.6. The ROCSS illustrate good pre-season probabilistic prediction of above-normal and below-normal yields with at least 2-months lead time. From the sample sowing dates considered, we concluded that, there is potential to use dynamical seasonal climate forecasts with a process based crop simulation model WOFOST to predict anomalous water-limited maize yields.
- Subjects :
- Atmospheric Science
010504 meteorology & atmospheric sciences
Yield (finance)
Probabilistic ensemble prediction
Forecast skill
Water en Voedsel
Forcing (mathematics)
01 natural sciences
Dynamic crop forecasting
Earth System Science
Range (statistics)
0105 earth and related environmental sciences
Annual percentage yield
Global and Planetary Change
WIMEK
Water and Food
Anomaly (natural sciences)
Sowing
Forestry
04 agricultural and veterinary sciences
East Africa
Climate Resilience
Klimaatbestendigheid
Climatology
040103 agronomy & agriculture
0401 agriculture, forestry, and fisheries
Environmental science
Leerstoelgroep Aardsysteemkunde
Water Systems and Global Change
Crop models
Crop simulation model
Forecast lead-time
Agronomy and Crop Science
Rainfed agriculture
Subjects
Details
- Language :
- English
- ISSN :
- 01681923
- Database :
- OpenAIRE
- Journal :
- Agricultural and Forest Meteorology, 250-251, 243-261, Agricultural and Forest Meteorology 250-251 (2018)
- Accession number :
- edsair.doi.dedup.....787e1b359b20717a8c6ec3d75fe7239b