Modeling the Impact of Climate Change on Soil Health Using Predictive Analytics

Climate change has had ongoing impacts, each of which brings with them challenges to the sustainability of soil health by way of its impact on agricultural productivity. Reliable methods for predicting and managing changes in soil properties in response to increasing temperature fluctuations, shifting precipitation patterns, and extreme weather events are needed, as these changes are occurring in the face of soil properties. To answer the problem of soil degradation under climate stress, this research develops and evaluates predictive models capable of predicting soil health indicators. For modeling the key soil parameters (pH, organic matter, and moisture content), the proposed system uses machine learning techniques, namely Linear Regression, Support Vector Machine (SVM), Neural Networks, etc. In terms of these methods, the Random Forest model proved to be the most accurate and robust one since it consistently performs well in predicting soil health under various climate scenarios. The model's capacity to have sophisticated and nonlinear relationships within the data and its high precision and recall rate makes it a perfect instrument for soil health management. This study's results provide evidence of the strength of the Random Forest model to produce actionable, sustainable soil management insights that can appropriately ameliorate the problems associated with climate change in agricultural systems. Just as important, such predictive tools have to be implemented to promote resilience in agricultural practices and to achieve long-term soil health.