Assessing Soil Health and Agroecosystem Resilience in Gujarat Dairy Farms

Thesis Abstract

Assessing soil health and agroecosystem resilience in Gujarat dairy farms addresses the declining soil quality and vulnerability of dairy production systems to climate variability, land-use intensification, and nutrient imbalances. The study aims to quantify soil health indicators and evaluate agroecosystem resilience across representative dairy farm enterprises in Gujarat, with specific objectives to (i) characterize soil physical, chemical, and biological properties under traditional and intensified dairy farming, (ii) assess the influence of farm management practices on soil health and resilience indicators, (iii) develop a parsimonious agroecosystem resilience index integrating biophysical and socio-economic dimensions, (iv) identify thresholds for soil health indicators associated with sustained productivity, and (v) propose targeted management options to enhance sustainability. The theoretical framework integrates the Soil Health paradigm and the Resilience Theory as applied to agricultural systems, drawing on the Theory of Planned Behavior to contextualize farmer decision-making and the Ecological Thresholds concept to interpret resilience dynamics. A mixed-methods approach combines quantitative soil analyses with qualitative stakeholder engagement to triangulate evidence. The research adopts an explanatory sequential design. The population comprises dairy farms across three agro-climatic zones of Gujarat (Northern, Central, and Southern regions). A stratified random sample of 120 dairy farms will be selected, with 40 farms per zone, and within each farm, composite soil samples (0–15 cm and 15–30 cm) will be collected from five representative paddocks, totaling 1,200 samples. Instrumentation includes laboratory analyses of soil physical properties (bulk density, total porosity, infiltration rate), chemical properties (pH, electrical conductivity, organic carbon, total nitrogen, available phosphorus and potassium, micronutrients), and biological indicators (microbial biomass carbon, dehydrogenase activity, soil enzyme activities). Farmer surveys and interview guides will capture management practices (fodder crop rotation, manure management, tillage, compost use, irrigation), input costs, and perceived resilience. Data collection will occur over two cropping seasons to account for seasonal variability. Validity and reliability will be ensured through pilot testing of surveys, calibration of soil laboratories (standard reference materials), and inter-laboratory cross-checks for a subset of samples. Quantitative data will be analyzed using multivariate statistics, including principal component analysis to reduce dimensionality of soil health indicators, cluster analysis to categorize farms by soil health and management regimes, and multiple regression and structural equation modeling to test causal pathways linking management practices to soil health and resilience outcomes. ANOVA will compare soil health metrics across zones and farming intensity levels. The resilience index will be constructed by integrating biophysical indicators with socio-economic stability measures (income variability, feed self-sufficiency, and risk exposure) using a weighted composite scoring approach, with weights derived from expert elicitation and confirmatory factor analysis. Qualitative data from farmer interviews will be analyzed thematically to elucidate perceived drivers, barriers, and adaptation strategies, with triangulation to quantify alignment with quantitative findings. Expected findings include (i) identification of soil health constraints most limiting dairy productivity, such as declining Soil Organic Matter and aggregation in intensively managed paddocks, (ii) evidence that integrated manure management and diverse fodder rotations improve microbial activity and nutrient cycling, (iii) a resilient farm typology distinguishing high-, medium-, and low-resilience systems in Gujarat, and (iv) threshold values for key soil health metrics beyond which dairy productivity and resilience decline markedly. The study contributes to knowledge by operationalizing a context-specific soil health-resilience framework for semi-arid, mixed livestock-crop systems and by providing a validated resilience index adaptable to policy and extension services. It offers data-driven recommendations for management interventions—such as optimized compost-manure integration, residue retention, precision irrigation, and paddock zoning—that enhance soil health, ecosystem services, and farm-level resilience under climate stress. The main conclusion anticipates that sustaining soil health is essential for durable dairy system resilience, with targeted management leveraging organic amendments, crop-livestock integration, and farmer knowledge to close nutrient cycles and stabilize productivity. Recommendations include policy incentives for soil amendment adoption, capacity-building programs for soil health monitoring, and the scaling of the resilience framework to other dairy-dominated regions facing similar agroecological constraints.