Defining irrigation thresholds in remote sensing-based decision support systems: a review of crop models mechanistic descriptions of crop water stress

Irrigation management decision support systems based on remote sensing and hydrological models need to find a balance between simplicity and accuracy in the definition of crop water stress thresholds when irrigation should be triggered. Among the most widely used crop models, which synthesize current mechanistic knowledge of crop water stress processes, there is a wide range of complexity that is worth exploring in order to improve the formalisms of current hydrological models.

In the present work, some of the most widely used crop models (chosen among those freely available and well documented) were examined in their description of crop water stress processes and irrigation thresholds definition. They are: APSIM, AQUACROP, CROPSYST, CROPWAT, DAISY, DSSAT, EPIC, STICS and WOFOST. Model manuals and scientific papers were reviewed to identify differences and similarities in the water stress functions related to crop growth.

A strict categorization of the model features is inappropriate, since the functions utilized are always at least slightly different and the models may focus on different features of the agroecosystem. Nevertheless, major similarities and differences among the models were found:

The models were then calibrated for the maize and tomato crops using field and remote sensing data on crop yield, soil moisture, evapotranspiration (ET) and leaf area index (LAI), for two locations, respectively in Northern and Southern Italy (Calcinato and Capitanata). Simulations were then carried out and compared in terms of the optimal irrigation amounts calculated by the different models and predicted yields.