Optimizing yield with agricultural climate and weather forecasts

Abstract

Weather affects agriculture more than any other variable. For centuries, growers had to depend upon small bits and pieces of local climatological data collected and passed down in almanacs. Over the last 100 years, however, scientists have developed complex Numerical Weather Prediction (NWP) models that are able to forecast weather with increasing accuracy. The objective of this work was to use a probabilistic NWP model (the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS)) as a component to couple with agricultural decision-making tools and models. First, customized ECMWF EPS forecasts were used as an irrigation scheduling aid for a field trial. Next, the CROPGRO Cotton Model was used to simulate the field experiment as well as an additional irrigation scheduling strategy. Finally, a cotton canopy temperature model was developed and coupled with customized ECMWF EPS forecasts to generate hourly canopy temperature forecasts. These forecasts were used to create a heat stress warning system. Results from the field trial indicate that using precipitation forecasts to schedule irrigation could provide a convenient alternative relative to a standard method. Results from the simulated field trial suggest using precipitation forecasts issued on the day of irrigation could be more efficient than using forecasts issued one to two days prior. Last, results from the heat stress project indicate forecasts were skillful to 10 days, allowing enough time for growers to protect crops if needed. In light of the above, implications for the agricultural community could be significant. Coupled atmospheric-agricultural models have the ability to put weather forecasts in terms producers can understand and can quickly use to make strategic on-farm decisions, therefore, possessing the potential to make a large positive global impact.