Agrometeorological analysis and prediction of wheat yield at the departmental level in France

Predicting annual crop yields is of interest for many agricultural applications. We present a prediction scheme at the departmental level, circa 100 km by 100 km, of winter wheat yields in France, applied for 23 departments, using official yield statistics from 1986 to 2010. Each model is a linear combination of 5-7 variables, selected from an initial pool of over 250 candidates. Candidate variables were generated using a phenological model and a crop water balance model, applied to a representative cropping situation for the department. Variable selection was carried out with forward stepwise regression methods. The variable selection process was cross-validated, so as to select robust variables. Model prediction performance was also evaluated by cross-validation. Satisfactory models were created for 20 departments, with root mean square error of prediction ranging from 0.25 t/ha to 0.39 t/ha. During use, whole season weather data is not available: this is complemented by frequential calculation over the past 20 years of historical weather data. We assessed the impact of time of prediction on model error by hindcasting yields for all 25 years of the dataset. We estimate that predictions can start 20 days after heading on average. We analysed predictive performance in an independent dataset and propose recommendations for use of these models outside their training dataset. The models give new insight as to the climatic factors that are key in determining yield in France.