A framework for optimizing nutrient management in sustainable crop production

Thesis Abstract

The increasing global demand for sustainable agricultural production necessitates the development of effective nutrient management frameworks that maximize crop yields while minimizing environmental impacts. Despite advancements in agro-technologies, current nutrient management practices often remain inefficient, leading to nutrient wastage, soil degradation, and water pollution, which compromise long-term crop productivity. This study aims to develop a comprehensive, context-specific framework for optimizing nutrient management in sustainable crop production, integrating principles of precision agriculture, soil fertility management, and environmental sustainability. The specific objectives include identifying critical factors influencing nutrient efficiency, evaluating the effectiveness of existing nutrient application strategies, and constructing an integrative decision-support model that guides resource-efficient fertilization practices. The research adopted a mixed-methods approach, combining quantitative and qualitative techniques to ensure a holistic understanding of nutrient management challenges and solutions. The quantitative component employed a quasi-experimental design involving a representative sample of 150 farmers selected through stratified random sampling across three cropping districts. Data collection instruments comprised structured questionnaires assessing fertilizer application practices, soil testing results, crop yield records, and environmental parameters. The qualitative component involved focus group discussions and key informant interviews with agronomists, extension officers, and farmers, providing contextual insights into local practices and perceptions. Ensuring instrument validity and reliability involved pre-testing questionnaires with a pilot group of 20 farmers, and applying Cronbach’s alpha with a cutoff of 0.8 to confirm internal consistency. Data analyses utilized multiple regression analysis to identify key predictors of nutrient use efficiency, factor analysis to reduce variable dimensionality, and thematic analysis for qualitative data evaluation. The development of the proposed framework incorporated system dynamics modeling and decision tree algorithms, with model validation conducted through cross-validation techniques and expert consultations. Expected findings suggest significant variability in nutrient management efficiency linked to factors such as knowledge levels, access to soil testing services, crop type, and socioeconomic status. The analysis is anticipated to reveal critical gaps in current practices, including over-application of inorganic fertilizers and underutilization of organic amendments. The proposed decision-support framework is expected to demonstrate improved alignment of fertilizer application with crop needs, enhanced resource use efficiency, and reduced environmental pollution, validated through simulation scenarios and field trials. The study's contribution to knowledge lies in bridging the gap between empirical data and theoretical models by integrating principles from the Theory of Planned Behavior and the Ecological Modernization Theory into a practical decision-support framework tailored to diverse agro-ecological settings. This framework advances existing models by offering a participatory, adaptive, and scalable tool that aligns with sustainable intensification goals. In conclusion, this research underscores the importance of context-specific, scientifically grounded nutrient management strategies for sustainable crop production. The framework developed provides policymakers, extension services, and farmers with a tool to optimize fertilizer use, enhance productivity, and safeguard environmental health. Policy recommendations include integrating the framework into national agricultural development programs and promoting capacity-building initiatives for its effective adoption. Future research should focus on refining the model through longitudinal assessments and integrating emerging technologies such as remote sensing and machine learning to further improve predictive capabilities and decision accuracy. Ultimately, this study provides a scientifically sound, practically feasible pathway toward achieving more sustainable and efficient agricultural nutrient management systems that are adaptable across diverse cropping systems and regions.