Predicting Heat Stress in Cotton Using Probabilistic Canopy Temperature Forecasts.

Heat stress can reduce crop yield or even cause total crop failure.  The ability to predict heat stress in advance would allow growers time to implement protective measures, helping to avoid such losses.  This study presents a strategy for producing probabilistic heat stress forecasts for well-watered cotton (Gossypium hirsutum L.) in the Camilla, Georgia region.  Multiple linear regression was used to develop a cotton canopy temperature model based on predicted air temperature, humidity, solar radiation and wind speed.  The European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS) was used to predict the meteorological variables used in the canopy temperature model, which produced 10-day probabilistic canopy temperature forecasts for each day of observations during 2014.  A statistical mean bias correction was applied to improve upon the raw model forecasts.  The forecasts were found to be skillful, with Relative Operating Characteristic (ROC) areas greater than 0.5.  The bias-corrected forecasts were found to increase skill.  A heat stress warning system was then created utilizing the forecasts.  Additionally, an economic analysis was performed as an example of how probabilistic forecasts can be utilized to aid producers with financial decisions pertaining to weather-related risks.