Adaptive model of precision irrigation for cereal crops based on yield prediction modeling and water resource optimization

Анотація

The relevance of the study is driven by increasing climate instability, growing water scarcity, and the need to improve the efficiency of water use in cereal crop production. Traditional irrigation approaches fail to adequately adapt to the variability of agroclimatic and soil conditions, resulting in yield losses and inefficient water consumption. In this context, the need for implementing adaptive models based on predictive assessment of agricultural performance becomes particularly актуальною. The purpose of the study is to improve the efficiency of water resource use and ensure stable yields of cereal crops through the development of an adaptive precision irrigation model based on yield prediction modeling. Methods. The study applies methods of generalization and systematization of scientific approaches, structural-functional analysis, modeling, and comparative analysis to substantiate the conceptual framework of irrigation management. Results. The influence of agroclimatic, soil, and technological factors on yield formation under irrigation conditions has been investigated. Modern approaches to irrigation management within precision agriculture have been systematized, and methodological approaches to the use of yield prediction modeling in water management have been generalized. Key scientific and practical problems of implementing adaptive models have been identified, including data instability, limited model transferability, and infrastructure constraints. It has been proven that the integration of predictive yield parameters into irrigation management ensures more rational water allocation and improves agricultural efficiency.Conclusions. As a result, an adaptive precision irrigation model has been substantiated, combining yield prediction with water resource optimization and functioning as a cyclic decision-making system. The findings confirm the feasibility of transitioning to prediction-oriented irrigation management as a basis for enhancing the resilience of agricultural production. Future research should focus on the development of hybrid models, improvement of data processing algorithms, and expansion of the integration of adaptive systems into digital agricultural management environments.