A Computationally Robust Decision-Support System forMountain Agriculture: Validated Fuzzy-AHP-GIS Modelingwith Dynamic Threshold Optimization

Authors

  • Bertrand Kenzong Département des Sciences du Sol, Faculté d'Agronomie et des Sciences Agricoles, Université de Dschang, B.P 222 Dschang, Cameroun
  • Primus Azinwi Tamfuh Département des Sciences du Sol, Faculté d'Agronomie et des Sciences Agricoles, Université de Dschang, B.P 222 Dschang, Cameroun
  • Roger Kogge Enang Département des Sciences du Sol, Faculté d'Agronomie et des Sciences Agricoles, Université de Dschang, B.P 222 Dschang, Cameroun
  • Georges Kogge Kome Department of Land Surveying, National Advanced School of Public Works, Yaounde P.O. Box 510, Cameroon

Keywords:

Land suitability, Fuzzy-AHP, Model validation, Multi-class ROC, Precision agriculture, Mountain ecosystem

Abstract

The development of computationally robust decision-support tools is critical for achieving sustainable agricultural intensification in vulnerable mountain ecosystems. This study presents an integrated Fuzzy-AHP-GIS framework to assess land suitability for soybean cultivation in the western highlands of Cameroon. Unlike conventional approaches, the proposed model incorporates dynamic threshold optimization and a novel multi-faceted validation protocol to enhance reliability for practical agricultural decision-making. Nine biophysical and accessibility criteria were weighted using the Analytic Hierarchy Process (AHP), standardized through fuzzy membership functions, and integrated within a Geographic Information System (GIS) to produce a continuous suitability map classified into four ordinal categories. Validation against 93 ground-truth points employed traditional metrics, multi-class Receiver Operating Characteristic (ROC) analysis, and the Fuzzy Kappa statistic. Results show that 10.12% of the area is highly suitable, 23.84% moderately suitable, 62.31% marginally suitable, and 1.24% unsuitable, with topography identified as the dominant limiting factor. The validation framework demonstrated excellent discriminative capacity (multi-class AUC = 0.841) and strong ordinal agreement (Fuzzy Kappa = 0.782), significantly outperforming standard Cohen’s Kappa (0.713). The introduction of a dynamic threshold optimization algorithm reduced severe misclassifications by 57%. The study advances the field of agricultural informatics by demonstrating that sophisticated validation frameworks and adaptive thresholds are essential for ensuring the reliability and practical applicability of land suitability models in precision agriculture. These findings offer a scalable, computation-ready decision-support system tailored for mountainous regions, with clear implications for sustainable intensification and climate resilience planning.

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Published

2026-04-30

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Vol. 8 No. 1 (2026): IJESKA

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Copyright (c) 2026 Bertrand Kenzong, Primus Azinwi Tamfuh, Roger Kogge Enang, Georges Kogge Kome

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