AGRICULTURAL SUSTAINABILITY UNDER CLIMATE VARIABILITY: COUPLING CROP PHYSIOLOGY WITH PREDICTIVE STATISTICAL MODELS
Abstract
Agricultural systems are increasingly challenged by climate variability, which disrupts crop productivity and threatens long-term sustainability. Existing approaches often separate physiological understanding from predictive modeling, limiting their ability to capture the complexity of crop responses to environmental stress. This study aims to develop an integrative framework that couples crop physiological processes with predictive statistical models to improve the accuracy and interpretability of agricultural sustainability assessments. A mixed-methods design was employed, combining field-based physiological measurements with advanced statistical and machine learning modeling. Data were collected across multiple agricultural sites, including climatic variables, soil conditions, and key physiological indicators such as photosynthetic rate, stomatal conductance, and water-use efficiency. Predictive models were developed and evaluated using regression analysis and machine learning techniques with cross-validation procedures. Results indicate that models incorporating physiological variables significantly outperform those based solely on climatic data in predicting crop yield. Physiological indicators function as critical mediators between environmental stress and productivity, enhancing both predictive accuracy and explanatory depth. Nonlinear modeling approaches further improve performance by capturing complex interactions among variables. Findings demonstrate that integrating crop physiology with predictive modeling provides a robust framework for understanding and managing agricultural systems under climate variability. This approach supports more adaptive and sustainable agricultural strategies.
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References
Arshad, A., Zhang, Y., Zhang, P., Wang, X., Chen, Y., Ahmed, M., & Zhang, L. (2025). APSIM-cotton model calibration for phenology-driven sowing and yield optimization in drip irrigated arid climate. Smart Agricultural Technology, 12, 101325. https://doi.org/https://doi.org/10.1016/j.atech.2025.101325
Bai, Y., Zhang, F., Ma, X., Ma, H., & Liu, Q. (2025). Enhancing crop yield predictions under drought: Integrating Accumulated Drought Degree Days into the WOFOST model. Ecological Modelling, 508, 111224. https://doi.org/https://doi.org/10.1016/j.ecolmodel.2025.111224
Bao, L., Yu, L., Yu, E., Li, R., Cai, Z., Yu, J., & Li, X. (2025). Improving the simulation of maize growth using WRF-Crop model based on data assimilation and local maize characteristics. Agricultural and Forest Meteorology, 365, 110478. https://doi.org/https://doi.org/10.1016/j.agrformet.2025.110478
Becker, R., Schüth, C., Merz, R., Khaliq, T., Usman, M., Beek, T. aus der, Kumar, R., & Schulz, S. (2023). Increased heat stress reduces future yields of three major crops in Pakistan’s Punjab region despite intensification of irrigation. Agricultural Water Management, 281, 108243. https://doi.org/https://doi.org/10.1016/j.agwat.2023.108243
Cao, H., Ruan, S., Wu, S., Li, W., Zhu, Y., Guo, Y., Chen, Z., Wu, W., & Yang, P. (2024). A study on parameter calibration of a general crop growth model considering non-foliar green organs. Computers and Electronics in Agriculture, 226, 109362. https://doi.org/https://doi.org/10.1016/j.compag.2024.109362
Cheng, C., Kuang, Z., Wu, J., Shao, Z., Fei, L., Xu, Y., Lin, Y., & Yu, P. (2025). Exploring the spatiotemporal dynamics and driving mechanism of cropland ecosystem health: A perspective based on the contributions of climate change and human activities. Ecological Indicators, 181, 114426. https://doi.org/https://doi.org/10.1016/j.ecolind.2025.114426
Cheng, M., Song, N., Penuelas, J., McCabe, M. F., Jiao, X., Lv, Y., Sun, C., & Jin, X. (2025). A framework of crop water productivity estimation from UAV observations: A case study of summer maize. Agricultural Water Management, 317, 109621. https://doi.org/https://doi.org/10.1016/j.agwat.2025.109621
Cheng, Z., Gu, X., Zhou, Z., Zhang, Y., Yin, H., Li, W., Chang, T., & Du, Y. (2024). Enhancing in-season yield forecast accuracy for film-mulched wheat: A hybrid approach coupling crop model and UAV remote-sensing data by ensemble learning technique. European Journal of Agronomy, 156, 127174. https://doi.org/https://doi.org/10.1016/j.eja.2024.127174
Dahri, S. H., Shaikh, I. A., Talpur, M. A., Mangrio, M. A., Dahri, Z. H., Hoogenboom, G., & Knox, J. W. (2024). Modelling the impacts of climate change on the sustainability of rainfed and irrigated maize in Pakistan. Agricultural Water Management, 296, 108794. https://doi.org/https://doi.org/10.1016/j.agwat.2024.108794
Dash, S. K., Sembhi, H., Langsdale, M., Wooster, M., Dodd, E., Ghent, D., & Sinha, R. (2025). Assessing the field-scale crop water condition over an intensive agricultural plain using UAV-based thermal and multispectral imagery. Journal of Hydrology, 655, 132966. https://doi.org/https://doi.org/10.1016/j.jhydrol.2025.132966
de Melo, M. L. A., de Jong van Lier, Q., da Silva, E. H. F. M., Pereira, R. A. de A., van Dam, J. C., Heinen, M., & Marin, F. R. (2025). Field-scale modeling of root water uptake and crop growth in a tropical scenario. Field Crops Research, 322, 109749. https://doi.org/https://doi.org/10.1016/j.fcr.2025.109749
Deng, C., Xiong, Q., Harrison, M. T., Xu, F., Xiang, Q., Li, L., & Liu, K. (2025). Declining frequency yet increasing intensity of crop waterlogging events under future climates. Journal of Hydrology, 663, 134178. https://doi.org/https://doi.org/10.1016/j.jhydrol.2025.134178
Dias, H. B., Cuadra, S. V., Boote, K. J., Lamparelli, R. A. C., Figueiredo, G. K. D. A., Suyker, A. E., Magalhães, P. S. G., & Hoogenboom, G. (2023). Coupling the CSM-CROPGRO-Soybean crop model with the ECOSMOS Ecosystem Model – An evaluation with data from an AmeriFlux site. Agricultural and Forest Meteorology, 342, 109697. https://doi.org/https://doi.org/10.1016/j.agrformet.2023.109697
Efrat, N., Dubinin, V. (Moshe), Baram, S., & Paz-Kagan, T. (2025). Modeling seasonal water status and predicting yield in almond orchards using UAV multi-sensor and meteorological data. Agricultural Water Management, 320, 109868. https://doi.org/https://doi.org/10.1016/j.agwat.2025.109868
González-Jiménez, J., Andersson, B., Wiik, L., & Zhan, J. (2023). Modelling potato yield losses caused by Phytophthora infestans: Aspects of disease growth rate, infection time and temperature under climate change. Field Crops Research, 299, 108977. https://doi.org/https://doi.org/10.1016/j.fcr.2023.108977
Guo, X., Bayat, B., Bates, J. S., Herbst, M., Schmidt, M., Vereecken, H., & Montzka, C. (2025). Enhancing carbon flux estimation in a crop growth model by integrating UAS-derived leaf area index. Agricultural and Forest Meteorology, 374, 110776. https://doi.org/https://doi.org/10.1016/j.agrformet.2025.110776
Han, X., Ma, Z., Zhang, B., Peng, Z., Zhang, K., & Li, X. (2025). Regional dynamics in the irrigation requirement of maize and wheat crops in relation to changes in the climate and cultivated area in the Shandong’s Yellow River Basin. Agricultural Water Management, 322, 109979. https://doi.org/https://doi.org/10.1016/j.agwat.2025.109979
Han, Y., Lu, H., & Qiao, D. (2024). Response of net water productivity to climate and edaphic moisture in wheat-maize rotation system. Soil and Tillage Research, 237, 105965. https://doi.org/https://doi.org/10.1016/j.still.2023.105965
Hu, T., Zhang, X., Khanal, S., & Zhao, K. (2025). Beyond radiation use efficiency: A mechanistic biochemical photosynthesis model for crop growth simulation and agroecosystem modeling. Computers and Electronics in Agriculture, 233, 110199. https://doi.org/https://doi.org/10.1016/j.compag.2025.110199
Kamdi, P. J., Swain, D. K., & Wani, S. P. (2023). Developing climate change agro-adaptation strategies through field experiments and simulation analyses for sustainable sorghum production in semi-arid tropics of India. Agricultural Water Management, 286, 108399. https://doi.org/https://doi.org/10.1016/j.agwat.2023.108399
Li, S., Li, Y., Zhang, G., Wang, L., Song, M., Fan, Y., & Siddique, K. H. M. (2025). Compound dry/wet and hot extremes decreased wheat/maize yield revealed by SHAP-RF and R-Vine Copula. Field Crops Research, 334, 110161. https://doi.org/https://doi.org/10.1016/j.fcr.2025.110161
Lu, J., Li, J., Fu, H., Zou, W., Kang, J., Yu, H., & Lin, X. (2025). Estimation of rice yield using multi-source remote sensing data combined with crop growth model and deep learning algorithm. Agricultural and Forest Meteorology, 370, 110600. https://doi.org/https://doi.org/10.1016/j.agrformet.2025.110600
Mamassi, A., Nhamo, N., Gummadi, S., Ammar, K., Dhanhani, M. A. H. Al, Hashmi, H. A. Al, Bouras, H., Accatino, F., & Zaaboul, R. (2025). Climate change and irrigation management shape crop resilience in UAE arid agriculture: APSIM model-driven assessment. Agricultural Water Management, 319, 109819. https://doi.org/https://doi.org/10.1016/j.agwat.2025.109819
Moghbel, F., Fazel, F., Aguilar, J., Mosaedi, A., & Lollato, R. P. (2024). Long-term investigation of the irrigation intervals and supplementary irrigation strategies effects on winter wheat in the U.S. Central High Plains based on a combination of crop modeling and field studies. Agricultural Water Management, 304, 109077. https://doi.org/https://doi.org/10.1016/j.agwat.2024.109077
Nakhavali, M. (André), Lessa Derci Augustynczik, A., Repo, A., Vangi, E., & Havlík, P. (2025). ForestScope: Comprehensive tool for analysing soil, climate, and stand data in forest ecosystems. Ecological Modelling, 508, 111251. https://doi.org/https://doi.org/10.1016/j.ecolmodel.2025.111251
Pasquel, D., Cammarano, D., Roux, S., Castrignanò, A., Tisseyre, B., Rinaldi, M., Troccoli, A., & Taylor, J. A. (2023). Downscaling the APSIM crop model for simulation at the within-field scale. Agricultural Systems, 212, 103773. https://doi.org/https://doi.org/10.1016/j.agsy.2023.103773
Pei, J., Tan, S., Zou, Y., Liao, C., He, Y., Wang, J., Huang, H., Wang, T., Tian, H., Fang, H., Wang, L., & Huang, J. (2025). The role of phenology in crop yield prediction: Comparison of ground-based phenology and remotely sensed phenology. Agricultural and Forest Meteorology, 361, 110340. https://doi.org/https://doi.org/10.1016/j.agrformet.2024.110340
Penny, J., Ordens, C. M., Barnett, S., Djordjevi?, S., & Chen, A. S. (2023). Vineyards, vegetables or business-as-usual? Stakeholder-informed land use change modelling to predict the future of a groundwater-dependent prime-wine region under climate change. Agricultural Water Management, 287, 108417. https://doi.org/https://doi.org/10.1016/j.agwat.2023.108417
Qiu, S., Zhang, Y., Dai, J., & Dong, H. (2025). Physiological mechanisms and agronomic strategies underlying flood tolerance variability in dryland crops: A global meta-analysis. Field Crops Research, 334, 110146. https://doi.org/https://doi.org/10.1016/j.fcr.2025.110146
Ring, N. L., & Brunsell, N. A. (2025). A Bayesian information theoretic approach to assessing stomatal conductance dynamics in a perennial agricultural crop. Ecological Indicators, 178, 114017. https://doi.org/https://doi.org/10.1016/j.ecolind.2025.114017
Sanad, H., Moussadek, R., Zouahri, A., Lhaj, M. O., Mouhir, L., & Dakak, H. (2025). Machine learning-integrated hydrogeochemical and spatial modeling of groundwater quality indices for seawater intrusion and irrigation sustainability in coastal agroecosystems of Skhirat Region, Morocco. Journal of Hydrology: Regional Studies, 62, 102848. https://doi.org/https://doi.org/10.1016/j.ejrh.2025.102848
Shrestha, O., Khan, A., Torrion, J. A., Sigler, W. A., McVay, K., Powell, S. L., & Stoy, P. C. (2025). Actual crop coefficients for cereal crops in Montana USA from eddy covariance observations. Agricultural Water Management, 316, 109561. https://doi.org/https://doi.org/10.1016/j.agwat.2025.109561
Sow, S., Senghor, Y., Sadio, K., Vezy, R., Roupsard, O., Affholder, F., N’dienor, M., Clermont-Dauphin, C., Gaglo, E. K., Ba, S., Tounkara, A., Balde, A. B., Agbohessou, Y., Seghieri, J., Sall, S. N., Couedel, A., Leroux, L., Jourdan, C., Diaite, D. S., & Falconnier, G. N. (2024). Calibrating the STICS soil-crop model to explore the impact of agroforestry parklands on millet growth. Field Crops Research, 306, 109206. https://doi.org/https://doi.org/10.1016/j.fcr.2023.109206
Su, Z., Yu, Z., Gu, Z., Zhao, D., & Peng, J. (2025). Unravelling the hidden drivers of crop sensitivity to precipitation in the arid and semi-arid regions of Northwest China. Agricultural Water Management, 320, 109866. https://doi.org/https://doi.org/10.1016/j.agwat.2025.109866
Sun, X., Zhang, B., Zhang, Z., Jing, C., Gu, L., Zhen, W., & Gu, X. (2025). Coupling decision of water and nitrogen application in winter wheat via UAV hyperspectral imaging. Field Crops Research, 334, 110159. https://doi.org/https://doi.org/10.1016/j.fcr.2025.110159
Vanalli, C., Radici, A., Casagrandi, R., Gatto, M., & Bevacqua, D. (2024). Phenological and epidemiological impacts of climate change on peach production. Agricultural Systems, 218, 103997. https://doi.org/https://doi.org/10.1016/j.agsy.2024.103997
Wang, J., Huang, H., Ariyasena, H. H. S., Zhao, J., Zhang, X., Gao, X., Zhao, X., & Zhao, Y. (2025). A UAV-based method for root zone soil moisture modeling of different farmland scale with grain and economic crops. Agricultural Water Management, 321, 109932. https://doi.org/https://doi.org/10.1016/j.agwat.2025.109932
Wang, L., He, L., Sun, W., Gao, C., Han, Z., & Lin, M. (2025). Precise irrigation of dryland cotton under canal irrigation system constraints based on the CERES-CROPGRO-Cotton model. Agricultural Water Management, 317, 109624. https://doi.org/https://doi.org/10.1016/j.agwat.2025.109624
Wang, X., Miao, Y., Dong, L., Mi, G., Batchelor, W. D., & Kusnierek, K. (2025). Precision nitrogen management for maize based on crop modeling, remote sensing and machine learning. Computers and Electronics in Agriculture, 239, 111124. https://doi.org/https://doi.org/10.1016/j.compag.2025.111124
Xie, J., Zhang, D., Jin, N., Cheng, T., Zhao, G., Han, D., Niu, Z., & Li, W. (2025). Coupling crop growth models and machine learning for scalable winter wheat yield estimation across major wheat regions in China. Agricultural and Forest Meteorology, 372, 110687. https://doi.org/https://doi.org/10.1016/j.agrformet.2025.110687
Yuan, X., Zhou, S., Wei, F., Ma, L., Sun, Y., Peng, D., Liang, R., Luo, Y., Chen, B., Yao, W., & Wang, Z. (2025). Global synthesis of nitrogen management in sugarcane systems: Decoding climate-soil-management drivers of Brazil-China contrasts. Field Crops Research, 333, 110075. https://doi.org/https://doi.org/10.1016/j.fcr.2025.110075
Zhang, C., Gao, J., Liu, L., & Wu, S. (2024). Compound drought and hot stresses projected to be key constraints on maize production in Northeast China under future climate. Computers and Electronics in Agriculture, 218, 108688. https://doi.org/https://doi.org/10.1016/j.compag.2024.108688
Zhang, W., Zheng, X., Li, S., Han, S., Liu, C., Yao, Z., Wang, R., Wang, K., Chen, X., Yu, G., Chen, Z., Wu, J., Wang, H., Yan, J., & Li, Y. (2025). Modelling forest-atmosphere exchanges of carbon and water using an improved hydro-biogeochemical model in subtropical and temperate monsoon climates. Ecological Modelling, 507, 111174. https://doi.org/https://doi.org/10.1016/j.ecolmodel.2025.111174
Zhang, X., Li, Y., Deng, Z., Zhao, Z., Gu, X., Yu, L., Xu, J., & Cai, H. (2025). Revealing nonlinear thresholds and interactions in maize yield and water productivity responses to straw incorporation via an integrated meta-analysis and explainable machine learning framework. Agricultural Water Management, 322, 110024. https://doi.org/https://doi.org/10.1016/j.agwat.2025.110024
Zheng, J., Ren, W., Wang, J., Tao, B., & Luo, Y. (2025). Projecting potential planting dates of global soybean and wheat under future climate change. Environmental and Sustainability Indicators, 28, 100909. https://doi.org/https://doi.org/10.1016/j.indic.2025.100909
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