Statistical modelling of crop yield in Central Europe using climate data and remote sensing vegetation indices

In the present study, multiple linear regression models were constructed to simulate the yield of winter wheat, rapeseed, maize and sunflower in Hungary for the 2000-2016 time period. We used meteorological data and soil water content from meteorological reanalysis as predictors of the models in monthly resolution. We included annual fertilizer amount in the analysis to remove trend from the census data. We also used remote sensing based vegetation index to extend the approach for early crop yield forecast purposes and to study the added value of proxy data on the predictive power of the statistical models. Using a stepwise linear regression-like method the most appropriate models were selected based on the statistical evaluation of the model fitting. We provided simple equations with well interpretable coefficients that can estimate crop yield with high accuracy. Cross-validated explained variance were 67% for winter wheat, 76% for rapeseed, 81% for maize and 68.5% for sunflower. The modelling exercise showed that positive anomaly of minimum temperature in May has a substantial negative effect on the final crop yield for all four crops. For winter wheat increasing maximum temperature in May has a beneficial effect, while higher-than-usual vapour pressure deficit in May decreases yield. For maize soil water content in July and August is crucial in terms of the final yield. Incorporation of the vegetation index improved the predictive power of the models at country scale, with 10%, 2% and 4% for winter wheat, rapeseed and maize, respectively. At the county level, remote sensing data improved the overall predictive power of the models only for winter wheat. The results provide simple yet robust models for spatially explicit yield forecast as well as yield projection for the near future.