Itzel Inti Maria Donati, Andrea Martelli, Anna Dalla Marta, Pasquale Garofalo, Antonella Petrillo, Filiberto Altobelli

Using the Analytic Hierarchy Process to prioritise criteria to enhance AquaCrop from the user perspective

Introduction

Using the analytic hierarchy process to prioritise criteria to enhance aquacrop from the user perspective. Prioritize AquaCrop model enhancements for precision agriculture using AHP & user feedback. Improve data management & IoT integration for sustainable water productivity.

Abstract

The successful advancement of precision agriculture depends on the availability of agrometeorological and agroclimatic data with high spatiotemporal resolution. However, such data must also be high quality, consistent, and nationally valid, allowing for long-term model calibration and short-term decision support systems. This study explores potential improvements to AquaCrop, a widely used crop water productivity model developed by the Food and Agriculture Organization, by incorporating feedback from interviewees to enhance its applicability and to promote more sustainable water resource use. Interviewees (farmers, researchers, and information technology experts) were surveyed using structured questionnaires, and responses were analysed through the Analytic Hierarchy Process (AHP) using the Super Decisions software. A three-level hierarchical model was developed to assess and prioritise desirable model features. The results demonstrate that the AHP effectively captures user needs and identifies concrete areas for model improvement. Notably, the criterion related to data management (C3) emerged as a key priority, particularly the capability for automatic communication with external Internet of Things platforms. This study emphasises the importance of involving both users (farmers and researchers) and information technology experts in evaluating the technical feasibility of proposed upgrades. The significant preference weights expressed by farmers (0.15) and researchers (0.18) further underscore the importance of aligning model development with real-world operational needs.

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