The impact of sustainable management systems on the sustainable development of water resources in agriculture and the reduction of agricultural carbon footprint

Environmental and economic Sustainability increasingly demands effective water resource management and reduction of the carbon footprint in agriculture. This meta-analysis synthesizes the results of 18 studies published in 2015-2024 to assess the performance of sustainable management systems in optimizing agricultural water resources and reducing carbon emissions. The review specifically focuses on integrating the Internet of Things and Artificial Intelligence into agricultural water management, employing advanced computational methods, including text clustering algorithms, support vector machines (SVM), and deep learning models for analysis. Results show that IoT-driven water management systems achieve, on average, a water economy of 27.3% (95% CI: 24.1-30.5%) against conventional approaches and reduce agricultural emissions by approximately 31.2% (95% CI: 28.4-34.0%). AI-driven optimization algorithms demonstrate remarkable effectiveness; machine learning algorithms provide an average increase in water use efficiency by 35.6% in precision irrigation applications, while deep learning models achieve 42.3% accuracy in crop water requirement predictions. The computational analysis revealed that integrated IoT-AI systems show the highest potential for agricultural Sustainability, with neural network models achieving 89.5% accuracy in resource optimization predictions. The results, therefore, show that large-scale diffusion of these technologies can make major contributions to agricultural sustainability goals while sustaining or improving crop yields.