A Predictive Framework for Soil Health Reconstruction under Climate Variability

Thesis Abstract

Soil degradation and climate variability threaten agricultural productivity and ecosystem services, necessitating a predictive approach to reconstruct soil health through integrated management that anticipates socio-environmental changes. The study aims to develop and validate a predictive framework for soil health reconstruction that integrates soil physics, chemistry, biology, and climate projections to guide adaptive restoration strategies. Specific objectives are (1) to quantify relationships among soil organic carbon, aggregation, nutrient cycling, microbial functional genes, and climate variables across 21 representative farming sites; (2) to develop a mechanistic-empirical model linking climatic stressors (temperature, precipitation, drought indices) to soil health indicators under different management regimes; (3) to identify threshold conditions for irreversible soil degradation and derive actionable restoration targets; (4) to test the framework’s predictive accuracy under hindcast and scenario analyses using regional climate projections; and (5) to formulate decision-support tools for farmers and policymakers that optimize soil health recovery under climate variability. A mixed-methods, multi-site, longitudinal design is employed. The population comprises agricultural soils across temperate, semi-arid, and tropical agroecologies, with a stratified random sample of 630 soil profiles from 70 farms observed over a five-year period. Data collection integrates quantitative soil physicochemical properties (pH, bulk density, texture, SOC, total N, available P, CN ratio, aggregate stability), biological indicators (soil enzyme activities such as dehydrogenase and phosphatase; microbial biomass C and N; functional gene abundances via quantitative PCR), and climate data (monthly rainfall, temperature, drought indices, soil moisture). Management practices (tillage, cover cropping, residue management, organic amendments) are recorded to encode interactions between climate stress and agronomic inputs. In-depth interviews with extension agents and farmers supplement quantitative data to capture management adaptation strategies. The analytical framework combines mechanistic process modeling with machine learning and econometric techniques. Structural equation modeling assesses causal pathways among climate drivers, soil processes, and health indicators. A process-based soil carbon and nutrient dynamics model is calibrated with field data and extended to simulate climate-driven trajectories of SOC, aggregate stability, and nutrient cycling under alternative management scenarios. Gradient-boosted decision trees and random forest algorithms are used to detect nonlinear relationships and identify key predictors of soil health restoration across agroecosystems. Model validation employs k-fold cross-validation, hindcasting against observed data, and regional climate projection ensembles (RCP4.5 and RCP8.5) to evaluate robustness under climate variability. Sensitivity analyses quantify the influence of uncertainties in climate input and management practices. Expected findings indicate that accelerated restoration of soil health is attainable with integrated practices that enhance organic matter inputs, promote earthworm activity, and improve aggregation, slowing declines in SOC and nutrient leakage under droughts. The framework is anticipated to reveal threshold values for SOC%, aggregate stability, and microbial functional gene diversity, beyond which recovery accelerates or decelerates, and to delineate scenario-based management portfolios that optimize long-term soil health and resilience. The study contributes to knowledge by bridging process-based soil physics and biogeochemistry with data-driven predictive analytics within a climate-smart agriculture paradigm, offering a scalable framework applicable across diverse biomes. The developed decision-support tool will translate model outputs into farm-level recommendations, including precise input rates, timing of amendments, and contingency actions during extreme events, while informing policy on soil health targets and incentive structures. The main conclusion posits that predictive reconstruction of soil health under climate variability is achievable through an integrated framework that couples climate-informed management with dynamic soil process models and data-driven predictors. Recommendations emphasize the adoption of adaptive, precise management strategies—prioritizing organic matter enrichment, reduced-till or no-till systems, diversified crop rotations, and targeted soil amendments—that collectively enhance soil structure, nutrient cycling, and microbial resilience under projected climate scenarios. Future research should extend the framework to include socioeconomic constraints, regional governance mechanisms, and long-term monitoring to continuously refine predictive capabilities and guide large-scale soil restoration programs.