Variable rate irrigation is usually based on prescription maps delineated according to a static approach. Irrigation rate and timing are optimized by sensor and/or models applied within homogenous zones whose spatial distribution is kept constant during the crop season. The objective of this study was to develop a procedure based on the combination of the crop-energy-water balance model FEST-EWB-SAFY with satellite data of vegetation variables and land surface temperature (LST) to generate dynamic irrigation prescription maps. The FEST-EWB-SAFY model couples the energy-water balance FEST-EWB, which allows computing continuously in time and distributed in space soil moisture and evapotranspiration, and the SAFY, a simple model for yield prediction and plant development. The model was tested in a 17.6 ha field cultivated with soybean in 2022 at Ceregnano (Italy). Irrigation was provided by a lateral move irrigation machine, equipped with a precision irrigation system with lateral resolution of 34 m. The model was pixelwise calibrated with satellite LST (RMSE 1.3 °C) and leaf area index (RMSE 0.45) as well as local measured soil moisture at 10 cm and 50 cm depth (RMSE 0.04). Four dynamic prescription maps were generated, calculating the pixel-by-pixel difference between the field retention capacity and the daily average of the 50-cm soil moisture profile computed by the FEST-EWB-SAFY. Dynamic variable rate irrigation was compared with a conventional irrigation system according to an experimental block design with three replicates and evaluated in terms of crop yield, irrigation volumes and irrigation water productivity (WP). The computed dynamic maps captured the crop water requirement variability originated by the interaction of ET, soil properties and field management. Compared with conventional system, there was a significant increase in WP, but not in crop yield. These results confirm that model-based dynamic prescription maps could be used to optimize variable irrigation in highly spatio-temporal dynamic cropping systems.
Optimizing variable rate irrigation using model and satellite-based dynamic prescription maps
Gabrieli, Davide;Furlanetto, Jacopo;Morari, Francesco
2024
Abstract
Variable rate irrigation is usually based on prescription maps delineated according to a static approach. Irrigation rate and timing are optimized by sensor and/or models applied within homogenous zones whose spatial distribution is kept constant during the crop season. The objective of this study was to develop a procedure based on the combination of the crop-energy-water balance model FEST-EWB-SAFY with satellite data of vegetation variables and land surface temperature (LST) to generate dynamic irrigation prescription maps. The FEST-EWB-SAFY model couples the energy-water balance FEST-EWB, which allows computing continuously in time and distributed in space soil moisture and evapotranspiration, and the SAFY, a simple model for yield prediction and plant development. The model was tested in a 17.6 ha field cultivated with soybean in 2022 at Ceregnano (Italy). Irrigation was provided by a lateral move irrigation machine, equipped with a precision irrigation system with lateral resolution of 34 m. The model was pixelwise calibrated with satellite LST (RMSE 1.3 °C) and leaf area index (RMSE 0.45) as well as local measured soil moisture at 10 cm and 50 cm depth (RMSE 0.04). Four dynamic prescription maps were generated, calculating the pixel-by-pixel difference between the field retention capacity and the daily average of the 50-cm soil moisture profile computed by the FEST-EWB-SAFY. Dynamic variable rate irrigation was compared with a conventional irrigation system according to an experimental block design with three replicates and evaluated in terms of crop yield, irrigation volumes and irrigation water productivity (WP). The computed dynamic maps captured the crop water requirement variability originated by the interaction of ET, soil properties and field management. Compared with conventional system, there was a significant increase in WP, but not in crop yield. These results confirm that model-based dynamic prescription maps could be used to optimize variable irrigation in highly spatio-temporal dynamic cropping systems.
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