Improving irrigation scheduling at farm level by using high quality weather forecasts

This study focuses on the use of limited area ensemble prediction system (LEPS) outputs, specifically COSMO-LEPS outputs, for improving irrigation scheduling at farm level in open field for precision agriculture. Weather forecasts are used in combination with Sentinel-2 satellite imagery for obtaining estimates of crop water requirement and irrigation schedules along with their predictive uncertainty. The analyses are performed for a farm located in Tarquinia (Central Italy), for which some field campaigns were completed in 2016 for validating the remote sensed crop parameter estimates, for the target crop processing tomato. For soil-vegetation-atmosphere modeling, according to the available input data, a bucket model is implemented, using as input the crop evapotranspiration computed with the leaf area index (LAI) and weather data. The results show the irrigation scheduling for two reference scenarios that regard two different probabilities of occurrence of crop water stress as forecasted by COSMO-LEPS. The forecasted crop evapotranspiration and water requirement are in perfect accordance with the best estimates obtained with weather observations when the median value of the ensemble is considered (i.e., probability of water stress equal to 0.5). A slightly overestimation of irrigation volumes is found when the 95
^th
percentile of the ensemble forecasts is chosen to set the water stress, as expected.