In the face of a changing climate and a growing global population, the need for efficient water use and sustainable crop production has never been more urgent. This is where the work of Sneha Chevuru, a researcher at the Department of Physical Geography, Utrecht University, the Netherlands, comes into play. Chevuru’s recent study, published in ‘Agricultural Water Management’ (which translates to ‘Agricultural Water Management’), delves into the intricacies of crop growth models, comparing two prominent models—AquaCrop-OS and PCR-GLOBWB 2-WOFOST—to understand their accuracy in predicting crop yield and water consumption.
The study, which spans from 2001 to 2019, focuses on irrigated maize, soybean, winter wheat, and spring wheat across the contiguous United States. Chevuru and her team applied the coupled hydrological-crop growth model PCR-GLOBWB 2-WOFOST using both its original settings and those harmonized with AquaCrop-OS. This dual approach allowed for a comprehensive analysis of differences in both model structure and parametrizations.
The findings reveal significant insights. PCR-GLOBWB 2-WOFOST, in its original form, closely aligns with reported yields for all considered crops. However, the harmonized version shows underestimates for soybeans and winter wheat, while AquaCrop-OS tends to overestimate yields. Chevuru notes, “The differences between the models highlight the importance of using local- and up-to-date information on crop-specific parametrization in crop growth modeling.”
One of the most striking findings is the varying sensitivity of the models to higher air temperatures. AquaCrop-OS is found to be much less sensitive to heat stress compared to PCR-GLOBWB 2-WOFOST. This discrepancy is crucial for the energy sector, as it directly impacts irrigation practices and water management strategies. As Chevuru explains, “These findings underscore the importance of model selection and parameterization in accurately simulating crop yield and crop water consumption under climate extremes, which is essential for improving agricultural practices under climate change.”
The implications of this research are far-reaching. For the energy sector, understanding the nuances of crop growth models can lead to more efficient water use and better irrigation practices. This, in turn, can reduce the energy required for water pumping and treatment, contributing to a more sustainable and resilient agricultural system. As climate change continues to bring about more frequent and severe hydroclimatic extremes, the ability to accurately predict crop yields and water consumption will be vital for ensuring both food and water security.
The study also emphasizes the need for continuous improvement in crop growth modeling. As Chevuru points out, “The differences between PCR-GLOBWB 2-WOFOST original and its harmonized settings emphasize the importance of using local- and up-to-date information on crop-specific parametrization in crop growth modeling.” This insight could shape future developments in the field, driving researchers and practitioners to refine their models and incorporate more precise, location-specific data.
In an era where every drop of water and every unit of energy counts, Chevuru’s work offers a roadmap for more accurate and sustainable agricultural practices. By understanding the strengths and weaknesses of different crop growth models, we can better prepare for the challenges posed by climate change and ensure a more secure future for food and water.