Crop simulation models as decision tools to enhance agricultural system productivity and sustainability – a critical review

Feeding the growing global population necessitates increased agricultural production, yet agriculture remains a vulnerable sector facing significant challenges from limited land resources, environment, and climatic constraints. To enhance agricultural productivity and farm profitability, it is crucial to quantify the interactions among soil, plant growth, environmental factors, and management practices. This aids policymakers and farmers in making informed decisions to mitigate risks associated with agricultural productivity. The advent of BIG-DATA in agriculture and various crop simulation modeling approaches at multiple spatiotemporal scales offers valuable insights into seasonal and in-field soil-crop variability, enabling accurate crop yield, and quality estimation. Agricultural models serve as essential tools for management and planning, facilitating the adaptation of new technologies to site-specific factors such as climate, soils, and cropping patterns. Despite their importance, there is a lack of comprehensive understanding regarding the principles, utility, and challenges of these crop models. This review critically evaluates the utility, simulation processes, datasets, predictability, advantages, and disadvantages of diverse crop simulation modeling approaches. It examines the current state-of-the-art agricultural systems models and their implications for crop management, growth forecasting, nutrient management, and yield prediction, highlighting complex interactions between agro-meteorology, soil, and crops across different scales. Furthermore, it addresses future challenges for agricultural system models in the context of climate change and environmental shifts, underscoring the necessity for additional research. The emergence of BIG-DATA and high-performance computing in agricultural simulation modeling presents new challenges that demand innovative solutions to improve forecasting and mitigate production risks caused by environmental constraints and associated stresses.