This paper advances an analytical framework that links soil fertility parameters to agricultural output efficiency, enabling evidence-based nutrient management under variable biophysical and socioeconomic conditions. The framework integrates harmonized data layers field measurements (pH, soil organic carbon, total nitrogen, available phosphorus and potassium, micronutrients, cation exchange capacity), soil physical properties (texture, bulk density, structure, infiltration), moisture dynamics, and proximal/remote-sensing covariates into a unified, quality-assured repository. Feature engineering derives stoichiometric ratios (C:N:P), base saturation, acidity indices, and water availability metrics, while geostatistical kriging and digital soil mapping close spatial gaps at farm-to-landscape scales. Agricultural output efficiency is quantified via frontier methods data envelopment analysis and stochastic frontier analysis yielding technical efficiency, marginal abatement cost of nutrient stress, and partial factor productivity for N, P, and K. Causal identification leverages directed acyclic graphs, panel fixed effects, instrumental variables (rainfall anomalies, legacy liming), and difference-in-differences around soil health interventions to disentangle fertility effects from confounders such as climate, cultivar, and management. A hierarchical Bayesian layer pools information across sites and seasons, estimating nonlinear response functions and credible intervals for yield elasticities with respect to pH, SOC, and available P. Spatial-econometric components (SAR/SEM) capture neighborhood effects including nutrient runoff and shared management, while machine-learning ensembles with SHAP values provide model-agnostic interpretability and variable importance. Decision analytics convert elasticities and frontier gaps into prescriptive levers lime rates, balanced NPK blends, micronutrient triggers, organic amendments, and residue management optimized under cost, water, and emission constraints. Practical deployment follows a staged roadmap: data audit and calibration; baseline frontier estimation; causal learning with intervention pilots; and operational dashboards that fuse NDVI/EVI, in-situ sensors, and market signals for adaptive recommendations. Key performance indicators include gains in technical efficiency, fertilizer use efficiency, yield stability, profit per hectare, and reductions in nutrient surpluses and nitrous oxide intensity. By unifying measurement, causality, and decision optimization, the framework makes soil fertility actionable for both smallholders and commercial farms, supports climate-smart intensification, and guides sustainable input allocation across heterogeneous agroecosystems. Governance is embedded through data standards, uncertainty audits, and reproducible pipelines, enabling traceable recommendations for extension services, cooperatives, and agribusinesses while aligning with SDG 2, SDG 12, and fertilizer policies.