The Evolution of Data-Driven Management Zone Delineation: A Systematic Review

Abstract

By partitioning agricultural fields into units with similar yield-limiting factors, Management Zone (MZ) delineation provides the spatial basis for variable-rate application of inputs such as nitrogen, seed, and irrigation. To evaluate the operational implementation of MZ methodologies, this paper analyzes 137 peer-reviewed papers published between 2000 and 2025, extracting data on agronomic contexts, sensing inputs, computational workflows, and validation strategies. Our analysis reveals a clear methodological shift: while early studies relied heavily on data such as soil properties, recent literature is dominated by multisource data fusion that combines static soil proxies (e.g., apparent electrical conductivity) with dynamic remote sensing vegetation indices. Methodologically, the literature relies heavily on similarity-based clustering, specifically fuzzy c-means and k-means, often applied to raw spatial grids or Principal Component Analysis (PCA) transformations. Although machine learning and optimization-based approaches have increased in recent years, rigorous agronomic and economic validation remains limited, while internal cluster validity indices (e.g., FPI, NCE) and inferential statistical tests (e.g., ANOVA) are widely used to assess delineated zones, only 13 of the reviewed papers explicitly evaluated the economic or environmental net returns of the delineated zones. To transition MZ delineation from a classification problem to an operational decision-support tool, the current literature suggests a need to shift validation efforts away from internal clustering metrics toward multi-year yield stability assessments and direct economic cost–benefit analyses.