A Framework for Predicting Soil Fertility Changes Using Machine Learning Models

Thesis Abstract

Soil fertility deterioration poses a significant threat to sustainable agricultural productivity globally, particularly in regions experiencing intensified land use and climate variability. Despite the critical importance of maintaining soil health, current predictive models for soil fertility changes are often limited by their reliance on traditional statistical techniques that fail to capture complex, nonlinear relationships among soil properties, environmental factors, and land management practices. This study aims to develop a comprehensive framework leveraging advanced machine learning models to improve the prediction accuracy of soil fertility fluctuations over time and across diverse agro-ecological zones. The specific objectives are to identify key soil and environmental predictors of fertility changes, compare the performance of various machine learning algorithms, and propose an integrated predictive model adaptable to different farming contexts. The research adopts a quantitative, cross-sectional research design, utilizing observational data collected from 300 farms across three distinct soil zones within the agricultural region. The population comprises soil samples and associated agronomic data collected from farming households, with stratified random sampling employed to ensure representative coverage of different soil types, cultivation practices, and climatic conditions. Data collection instruments include standardized soil testing kits for laboratory analysis of physical, chemical, and biological soil properties, supplemented by structured questionnaires capturing land use history, crop rotation patterns, fertilization regimes, and climatic data from local weather stations. The validity and reliability of the laboratory analyses adhere to ISO standards, and questionnaire tools are pretested to ensure clarity and consistency. Data analysis employs descriptive statistics to summarize soil property distributions and exploratory data analysis techniques to identify patterns. Several machine learning algorithms, including Random Forest, Support Vector Machines, Gradient Boosting Machines, and Artificial Neural Networks, are trained and validated using an 80/20 train-test split. Model performance is evaluated through metrics such as coefficient of determination (R²), root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC), where applicable. Feature importance analyses identify the most influential predictors, while cross-validation techniques ensure robustness against overfitting. The analytical framework draws on systems theory and the predictive analytics paradigm, integrating principal component analysis for feature reduction and hyperparameter tuning strategies to optimize model performance. Expected findings suggest that machine learning models, particularly Random Forest and Gradient Boosting, outperformed traditional regression approaches in predicting soil fertility changes with higher accuracy and interpretability. Key predictors are anticipated to include soil organic matter, pH, nutrient content (N, P, K), land management practices, and climate variables such as rainfall and temperature. Results are expected to demonstrate localized variability in predictor importance, emphasizing the need for region-specific models. The development of an operational predictive framework with user-friendly visualization tools aims to facilitate decision-making among farmers, extension agents, and policymakers. This study contributes to knowledge by advancing the application of machine learning techniques within soil fertility management, offering a scientifically validated, scalable framework adaptable for various agricultural systems. It underscores the potential of integrating data-driven models into existing soil monitoring protocols, providing precise, timely predictions to mitigate fertility decline and promote sustainable land use. The main conclusion underscores the superior predictive capacity of machine learning models over traditional approaches, advocating for their broader adoption in soil health monitoring systems. Recommendations include integrating the framework into national soil information systems, fostering capacity-building for local stakeholders, and promoting further research into hybrid modeling approaches that incorporate remote sensing data for enhanced spatial prediction accuracy. The study ultimately aims to fill critical gaps in predictive soil science and support sustainable agricultural productivity through innovative technological solutions.