Agricultural production systems are complex socio-technical and bio-physical constructs shaped by soil properties, plant physiological processes, technological interventions, water availability, and decision-making under uncertainty. The present research develops a comprehensive, theoretically grounded, and methodologically integrated framework for understanding and optimizing tillage systems, land-reclamation technologies, and water use practices within modern agriculture. Drawing strictly on established agronomic, physiological, engineering, and systems-analysis literature, the article synthesizes classical foundations of crop rotation and soil management with contemporary approaches to multicriteria optimization, intelligent decision support, and uncertainty handling in land-reclamation technologies.

The study situates tillage and soil preparation as central mediating processes between natural soil dynamics and anthropogenic agricultural objectives. Special attention is given to the physical transformation of soil structure, resistance, aggregation stability, and organic carbon dynamics as influenced by traditional and innovative tillage practices. These transformations are examined not as isolated mechanical effects but as system-level phenomena that directly affect water infiltration, root development, nutrient uptake, and ultimately plant productivity. By integrating plant physiology perspectives, the article demonstrates how soil mechanical and hydrological conditions interact with plant metabolic processes, stress responses, and growth regulation.

Methodologically, the article advances a descriptive but rigorous framework for parameter optimization in agricultural and land-reclamation technologies, grounded in systems analysis and fuzzy approximation theory. Multicriteria decision-making approaches are examined as essential tools for navigating the inherent trade-offs among productivity, energy efficiency, soil conservation, and water sustainability. The role of uncertainty, both epistemic and environmental, is analyzed in depth, emphasizing the need for intelligent decision support systems capable of synthesizing heterogeneous criteria without reducing complex realities to simplistic numerical optima.

The results of this integrative analysis reveal that optimal agricultural outcomes cannot be achieved through singular technological interventions or isolated efficiency metrics. Instead, resilience-oriented optimization emerges as a guiding principle, where adaptive tillage strategies, informed water use policies, and flexible decision-support algorithms collectively enhance system stability and long-term productivity. The discussion critically evaluates limitations of existing approaches, including over-mechanization, data scarcity, and institutional constraints, while outlining future research pathways focused on intelligent, context-sensitive agricultural systems.

By unifying agronomy, plant physiology, agricultural engineering, and systems optimization theory, this article contributes a holistic conceptual foundation for sustainable land management in water-constrained and uncertainty-prone agricultural environments.