A Framework for Soil Health Ontology and Assessment Modeling

Thesis Abstract

Soil health assessment remains a complex, multidimensional construct, hindered by inconsistent definitions, disparate measurement protocols, and fragmented data that obstructs comparability and scalable decision-making for land management. This study addresses the gap by developing a formal ontology of soil health—SoilHealthOntology (SHO)—and integrating it with a robust assessment modeling framework capable of harmonizing diverse indicators across pedogenic processes, soil functions, and management regimes. The aim is to produce a theoretically grounded, mechanistically informed platform that supports consistent measurement, interoperability, and predictive inference for soil health status under varying agro-ecological contexts. Specific objectives are (i) to delineate a comprehensive conceptualization of soil health rooted in ecosystem services and soil governance; (ii) to construct SHO, including classes, properties, and logical constraints that capture the relationships among physical, chemical, biological, and functional indicators; (iii) to develop an assessment modeling framework that links SHO to standardized data inputs, scoring rules, and decision-support outputs; (iv) to evaluate the framework using empirical data from 60 soil profiles across three agro-ecoregions under contrasting management practices; and (v) to validate the framework through hypothesis testing and expert elicitation to ensure ecological validity and practical relevance. The methodology adopts a mixed-methods design within an ontological and modeling paradigm. The population comprises soils from temperate, tropical, and Mediterranean agricultural landscapes, with purposive sampling to select 60 profiles representing soil orders, texture classes, and management histories. Data collection employs standardized soil property measurements (pH, organic carbon, total nitrogen, cation exchange capacity, bulk density, available P, microbial biomass carbon, soil respiration) following international soil survey protocols, complemented by management data (tillage intensity, residue coverage, fertilizer regimes). In addition, 20 expert interviews are conducted to elicit conceptual boundaries and validation of SHO relations, analyzed via thematic analysis. Instrument validity is ensured through pilot testing, content validity indices with a panel of five soil scientists, and inter-rater reliability checks for field measurements. Data analysis integrates quantitative modeling and qualitative insights structural equation modeling (SEM) and partial least squares SEM (PLS-SEM) test the causal pathways among SHO-defined constructs; regression analyses quantify the contribution of individual indicators to the overall soil health score; and a rule-based reasoning engine, implemented in OWL/RDF with SPARQL queries, operationalizes the ontology into the assessment model. The analytical framework is grounded in systems theory and the ecosystem services framework, with specific reference to the soil health concept proposed by the International Union of Soil Sciences and the Sustainable Intensification paradigm. Expected findings include (i) a coherent SHO with explicit axioms linking soil physical, chemical, and biological properties to soil functional outcomes such as nutrient cycling, soil structure stability, and resilience to disturbance; (ii) a transparent, interoperable soil health scoring mechanism enabling cross-site comparisons and temporal monitoring; (iii) empirically validated causal pathways demonstrating the contribution of organic matter, biodiversity indices, and aggregate stability to overall soil health under different management regimes; and (iv) a decision-support toolkit that translates SHO scores into actionable management recommendations at field and landscape scales. The study anticipates that SHO will improve interpretability and comparability of soil health assessments, reduce methodological heterogeneity, and enable integration with precision agriculture and land-use planning systems. Contributions to knowledge include (a) the formalization of soil health as an ontology-based construct with explicit relations among indicators, processes, and services; (b) a generalizable assessment modeling framework that can be adapted to diverse soil types and management contexts; (c) methodological advances by integrating ontological reasoning with SEM-based validation and traditional soil science measurements; and (d) practical guidelines for policymakers and practitioners on using standardized SHO outputs to drive soil-health-centric decision-making. The study concludes that an ontology-driven assessment framework enhances consistency, traceability, and scalability of soil health evaluations, and recommends the adoption of SHO in national soil information systems, incorporation into certification schemes for sustainable land management, and ongoing refinement through longitudinal data collection and cross-regional validation.