Advances in regional-scale crop growth and associated process models

In an era of growing environmental uncertainties, ensuring food security while maintaining sustainable agricultural practices is a major global challenge. Regional-scale crop growth and associated process (CROP-AP) models have emerged as crucial tools for simulating agricultural productivity at watershed, national, and global scales. A new review, published in Science China Earth Sciences, provides a systematic analysis of these models, their classifications, functions, and future development directions.

The study categorizes CROP-AP models into four key types:

• Statistical Models: These models are based on statistical methods and focus on establishing relationships between input and output variables. They are primarily application-oriented, requiring fewer input parameters and making them suitable for large-scale agricultural yield forecasting. However, they do not reveal the underlying mechanisms of how different processes affect crop growth.
• Crop Growth Models: These models dynamically simulate crop growth, development, and yield formation, providing a more accurate representation of the relationship between crop growth and climate factors. They also allow the adjustment of growth processes through controlled factors such as fertilization, irrigation, and pesticide use. However, they require a large number of parameters and are computationally complex, limiting their extrapolation potential.
• Hydrology-Crop Coupling Models: These models integrate hydrological and crop growth processes by coupling crop models with hydrological models, adding crop modules to hydrological models, or adding runoff and routing modules to crop models. They can capture interactions between hydrological and crop growth processes. However, the coupling of hydrological and crop growth modules faces challenges related to temporal and spatial scales.
• Ecosystem Models: These models integrate large-scale biophysical, vegetation physiological, and ecological processes and can deeply depict crop phenology and dynamics. However, due to their larger spatial scale, they tend to simplify the dynamic factors related to crops.

The review highlights five major applications of these models:

• Crop yield prediction: Crop growth models are valuable tools for predicting yield variations across different regions and time scales. These models not only estimate current-year crop yields but also project medium- and long-term yield trends by coupling with regional and global climate models, with various yield prediction methods and models at regional scales offering yield forecast information to government agencies.
• Prediction of crop water requirements: Regional-scale CROP-AP models can accurately simulate crop water needs and provide insights into efficient water management. These models play a significant role in the development of water-saving agricultural practices, regional water planning, and the optimization of irrigation systems, optimizing irrigation strategies and improving resource efficiency.
• Agricultural non-point source pollution: These models are essential tools for quantifying and managing non-point source pollution. They can simulate the effects of different agricultural practices on water quality at various spatial and temporal scales.
• Greenhouse gas emissions: CROP-AP models can simulate greenhouse gas emissions and help identify mitigation strategies that reduce emissions while maintaining or increasing agricultural productivity.
• Climate change impacts: By projecting how global warming will affect food production and resilience strategies, these models provide a scientific foundation for understanding and mitigating the effects of climate change.

Despite their advancements, CROP-AP models still face challenges, including uncertainties in model validation, limitations in simulating multi-scale interactions, and constraints in data accessibility. The study emphasizes future research priorities:

• Conducting more extensive and rigorous model calibration and validation across broader areas.
• Coupling multi-process simulations of hydrological processes, ecological processes, crop physiology, and human water use activities.
• Strengthening research on scale transformation methods and their scalability.
• Integrating multi-model simulations (coupling multiple crop models and coupling crop models with environmental models).
• Sharing model code, input data, and verified data.
• Combining Artificial Intelligence (AI) with crop models to improve simulation accuracy and efficiency.

These efforts will further strengthen the ability of regional-scale CROP-AP models to address complex issues in agricultural systems and support the sustainability of global food production.

 

See the article:

Liu W, Bai Y, Du T, Li M, Yang H, Chen S, Liang C, Kang S. 2025. Advances in regional-scale crop growth and associated process modeling. Science China Earth Sciences, 68(3): 669-684, https://doi.org/10.1007/s11430-024-1477-2

---

---