Mathematical applications in plant agrophysiology and its impact on modern agriculture

Subject Areas : Journal of plant ecophysiology

Masoumeh Samareh Fekri ¹ , Kavos Soleimani Dameneh ²

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Received: 2025-11-29 Accepted : 2025-12-01 Published : 2025-12-02

Keywords: Agricultural productivity, Crop growth simulation, Environmental factors, Mathematical modeling,

Abstract :

Objective: This study explores the role of mathematical applications in plant agrophysiology and their implications for enhancing modern agricultural practices. It aims to demonstrate how mathematical modeling and quantitative analysis can improve understanding of plant growth dynamics and optimize agricultural productivity.

Methods: The research employs a comprehensive review of existing literature on mathematical models used in plant agrophysiology. It analyzes various methodologies, including statistical modeling, simulation techniques, and data-driven approaches, to assess their effectiveness in predicting plant responses to environmental factors and management practices.

Results: The findings indicate that mathematical applications significantly enhance the understanding of complex plant physiological processes. They facilitate the development of predictive models that can inform decision-making in crop management, leading to improved yields and resource efficiency. The integration of these models into agricultural practices demonstrates a positive correlation with sustainability and productivity.

Conclusions: This study contributes to the field by highlighting the critical intersection of mathematics and plant sciences, emphasizing the need for interdisciplinary approaches in modern agriculture. It underscores the value of mathematical tools in addressing challenges posed by climate change and food security, offering insights for future research and practical applications in agrophysiology.

References:



Altieri, M. A. (1999). The ecological role of biodiversity in agroecosystems. Agriculture, Ecosystems & Environment, 74(1-3), 19-31.
Gao, X., Zhang, Y., & Xu, J. (2016). Estimating leaf area index from spectral reflectance using a hybrid model. Remote Sensing of Environment, 186, 135-142.
Jones, J. W., Hoogenboom, G., Porter, C. H., & Boote, K. J. (2017). The DSSAT cropping system model. In Crop Modeling and Decision Support (pp. 1-27). Springer.
Keating, B. A., Carberry, P. S., & Hammer, G. L. (2003). An overview of APSIM: A model designed for farming systems simulation. European Journal of Agronomy, 18(3-4), 267-288.
Lobell, D. B., Schlenker, W., & Costa-Roberts, J. (2011). Climate trends and global crop production since 1980. Science, 333(6042), 616-620.
Munns, R., & Tester, M. (2008). Mechanisms of salinity tolerance. Annual Review of Plant Biology, 59, 651-681.
Peters, J. R., & Ritchie, J. T. (2014). Optimizing crop rotations for sustainable agriculture. Agricultural Systems, 130, 1-10.
Thornley, J. H. M., & Johnson, I. R. (1990). Plant and crop modeling: A mathematical approach to the study of plant growth and development. New York: Wiley.
Thenkabail, P. S., Lyon, J. G., & Huete, A. (2018). Advances in remote sensing and GIS for crop management. In Remote Sensing for Agriculture, Ecosystems, and Hydrology XX (Vol. 10796, p. 1079609). SPIE.
Zhang, X., Wang, H., & Chen, J. (2019). Modeling crop phenology using growing degree days: A review. Agricultural and Forest Meteorology, 265, 78-90.