Staff Publications

  • external user (warningwarning)
  • Log in as
  • language uk
Record number 531971
Title Probabilistic maize yield prediction over East Africa using dynamic ensemble seasonal climate forecasts
Author(s) Ogutu, Geoffrey E.O.; Franssen, Wietse H.P.; Supit, Iwan; Omondi, P.; Hutjes, Ronald W.A.
Source Agricultural and Forest Meteorology 250-251 (2018). - ISSN 0168-1923 - p. 243 - 261.
DOI http://dx.doi.org/10.1016/j.agrformet.2017.12.256
Department(s) Water Systems and Global Change
WIMEK
Water and Food
Earth System Science
Alterra - Climate change and adaptive land and water management
Publication type Refereed Article in a scientific journal
Publication year 2018
Keyword(s) Crop models - Dynamic crop forecasting - East Africa - Forecast lead-time - Probabilistic ensemble prediction - Rainfed agriculture
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.
Comments
There are no comments yet. You can post the first one!
Post a comment
 
Please log in to use this service. Login as Wageningen University & Research user or guest user in upper right hand corner of this page.