Advances in crop growth modeling: A review of perennial crop and beneficial soil microorganism approaches
World food systems are subject to many challenges related to land degradation, rapid population growth, climate change, and limited resources. Crop growth models are being recognized as efficient tools for agricultural research to investigate trends in crop yield production and address these challen...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
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| Series: | Agricultural Water Management |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0378377425002628 |
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| Summary: | World food systems are subject to many challenges related to land degradation, rapid population growth, climate change, and limited resources. Crop growth models are being recognized as efficient tools for agricultural research to investigate trends in crop yield production and address these challenges under various pedoclimatic, genotypic, and management conditions. Crop growth models have come a long way in terms of development and use in recent decades but are still bound to be improved, especially for various perennial crops and the incorporation of beneficial soil microorganisms. Based on research papers published since 1965 across all continents, this review gives a brief history of crop models, explores 44 selected process-based crop growth models, their origin, usefulness, and applicability, and discusses some of their characteristics and their application in water management in arid and semi-arid areas. For the first time, this review highlights the modeling approaches in simulating the effects of beneficial soil microorganisms on crop growth, including plant growth-promoting rhizobacteria and mycorrhizal fungi, and discusses the advances in modeling perennial crops by exploring 35 studies found for fruit trees, perennial legumes, and vegetables, as well as 45 studies on perennial forage and bioenergy grasses. In addition, the review discusses crop modeling applications in the context of precision agriculture when combined with machine learning and remote sensing. The review concludes by emphasizing key limitations and challenges facing the use of crop growth models. Accordingly, this review can be a valuable resource for researchers, providing insights into existing crop models with a view to what needs to be improved. |
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| ISSN: | 1873-2283 |