WHCrop: A novel water-heat driven crop model for estimating the spatiotemporal dynamics of crop growth for arid region

Abstract

Crop models are widely used to assist in agricultural management decision-making and water productivity optimization. However, traditional crop models often depend on artificial and specific field management inputs, posing challenges in maintaining crops within a desired stress range. Consequently, the derived optimization schemes from these models become highly uncertain. Moreover, the complexity of the mechanisms involved and the multitude of parameters make it challenging to apply traditional crop models uniformly across various crops and regions. In this study, we have developed a novel crop model called WHCrop (Water-Heat Driven Crop model) that effectively captures, reflects, and controls the impact of various environmental factors (meteorology, topography, soil, and management) on crop growth process. The WHCrop model combines the simulation principles of biomass and yield from the CERES module in the DSSAT model, along with the soil water balance from the AquaCrop model, to estimate the dynamics of crop growth and production processes. Results indicated that WHCrop-based simulations, including canopy cover (CC), daily evapotranspiration (ET), and yield, matched well with ground-based measurements, and were better than the traditional crop model (DSSAT and AquaCrop) at both field and regional scales, especially under deficient irrigation conditions. Besides capturing the key variables associated with crop growth, WHCrop model could reproduce the adaptive response of these various to regional-scale temperature changes. Notably, the WHCrop model could effectively minimize uncertainties resulting from individual environmental change, thanks its incorporation of dynamic response mechanisms for crop growth under stress factors. Overall, the novel and informative WHCrop model offers some advantages over traditional crop models since it allows for optimal decision making to be derived from the randomly different inputs. As a result, the WHCrop model proves instrumental in assisting decision-makers in formulating critical water allocation strategies and developing effective management recommendations to enhance regional agricultural water productivity.