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Crop Growth Models: Development, Applications, Recent Advances, and Future Perspectives
Guoan Li1,2, Ying Wang3, Yin Zhao3
1Key Laboratory of Crop Water Physiology and Drought-Tolerance Germplasm Improvement, Ministry of Agriculture and Rural Affairs, College of Agronomy, Shandong Agricultural University, Tai'an 271018, China.
Crop growth models are crucial for agricultural production and food security, especially with climate change. This review details their development, types, applications, and future directions, including AI integration for sustainable agriculture.
Area of Science:
- Agricultural Science
- Climate Science
- Computational Modeling
Background:
- Global climate change significantly threatens agricultural production and food security.
- Crop growth models are essential tools for simulating, predicting, and guiding agricultural practices.
- Understanding the evolution and application of these models is vital for sustainable agriculture.
Purpose of the Study:
- To review the research progress of crop growth models.
- To summarize their development history, categorization, and applications.
- To identify future research directions for enhanced model utility.
Main Methods:
- Historical review of crop growth model development stages (process modeling, system simulation, application, algorithm expansion).
- Categorization of models based on driving factors (solar radiation, soil moisture, meteorological, integrated factors).
- Analysis of model applications (adaptability, resource management, climate change evaluation) and advancements (remote sensing integration, AI combination).
Main Results:
- Crop growth models have evolved significantly, enhancing mechanistic understanding and spatial scale (point to global).
- Integration with remote sensing and intelligent algorithms has improved yield estimation accuracy and optimized management practices.
- Key development countries include the Netherlands, USA, Australia, and China.
Conclusions:
- Crop growth models are increasingly sophisticated, incorporating complex processes and advanced technologies.
- Future research should focus on mechanistic modeling, 3D soil transport, remote sensing integration, and AI.
- These advancements will provide crucial technical support for sustainable agriculture and food security.
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