Site-Specific Management Zone (SSMZ) delineation is a key decision problem in precision agriculture that seeks to partition a field into the fewest spatially contiguous regions while ensuring a prescribed level of within-zone homogeneity. Existing approaches face a fundamental trade-off: exact optimization models guarantee optimality but are limited by exponential memory requirements and poor scalability, while heuristic and metaheuristic methods are faster but depend on parameter tuning and lack quality guarantees. To address these limitations, this study proposes a hybrid analytics-driven optimization framework comprising three components. First, a memory-efficient Mixed Integer Nonlinear Programming (MINLP) formulation based on lazy constraints dynamically generates connectivity constraints during the branch-and-cut process, avoiding exponential enumeration. Second, two parameter-free disconnection heuristics, H 1 and H 2 , exploit the spanning-tree structure of feasible solutions to produce near-optimal partitions in sub-second time. Third, hybrid strategies integrate heuristic solutions as warm starts for both a flow-based and the lazy-constraint MINLP formulation. Computational experiments on synthetic and real-world agricultural datasets show that H 2 achieves mean relative errors ranging from 2.8% to 12.6% at α = 0 . 9 across all benchmark classes, substantially outperforming H 1 . The hybrid strategy H 2 +F matches or improves upon state-of-the-art solutions in 92.5% of real-world instances, reducing solution times by up to fifteen times compared to standalone optimization models. The lazy-constraint formulation requires significantly less memory than the flow-based model, making it suitable for resource-constrained environments. • Develop a decision analytics framework for spatial zone partitioning under homogeneity requirements. • Design memory-efficient optimization models using dynamic constraint generation. • Propose a fast and parameter-free heuristic for near-optimal zone delineation. • Integrate heuristics with optimization models to significantly reduce solution time. • Provide actionable guidance for real-time decision-making in precision agriculture systems.
Urbán-Rivero et al. (Mon,) studied this question.