Objective: Stakeholders—farmers from four different European areas (Campania (IT), Kujawsko-Pomorskie (PL), Limburg (NL), Andalusia (ES))—are asked to share, from the OPERA project, their opinions on five criteria that all aim at improving the use of irrigation advisory services (IASs). Each criterion has different characteristics that affect the way farmers rank it. The present study has two objectives. The first is to individuate the priorities of the preferences expressed by the stakeholders. The second objective is to carry out a ranking of the weights of the criteria by case study, ranking the groups and their associated properties among farmers’ profiles. Methods: The answers to 120 questionnaires dispensed to the future users of IASs in the four agricultural sites were analyzed in detail, and then the given priorities were evaluated through the analytic hierarchy process (AHP). The AHP methodology was used to determine the relative weights of the five assessment criteria, and finally, to select the one with major value. Results and conclusions: The results show that A5 (assuring economic sustainability) was the most important criterion. The contributions provided by this study are twofold: Firstly, it presents an application of a methodology that involves the conversion of a linguistic judgement of farmers in a correspondence weight. Secondly, it tackles decision making regarding improving the use of IASs, evaluating the preferences expressed by the stakeholders. Irrigation advisory services can play a key role in assisting users to adopt new techniques and technologies for more efficient water use and increased production.

The analytic hierarchy process (AHP) was applied to six agronomic scenarios for durum wheat (Triticum durum Desf.) in the Capitanata plain (Apulia, southern Italy), combining three sowing dates (15 October, 1 November, and 15 November) with two water regimes (rainfed; supplemental irrigation at flowering). Five performance indicators were derived from AquaCrop-GIS simulations coupled with cradle-to-gate life-cycle assessment: grain yield, CO₂-equivalent emissions (CO₂_eq), carbon footprint (CFP), total water consumption (TotW), and water footprint (WFP). Six theoretical decision profiles were defined through a symmetric weight scheme (w = 0.60 for the dominant criterion, w = 0.10 for each of the remaining four; balanced profile with equal weights). The rankings revealed a systematic inversion between absolute and ratio indicators: under absolute-metric profiles, the lowest-yielding scenario paradoxically ranked first because reduced productivity mechanically lowered per-hectare resource consumption, whereas under ratio-metric and balanced profiles, early-November rainfed sowing consistently led the rankings. Switching point analyses quantified the weight thresholds at which leadership transitions occurred, providing a continuous sensitivity assessment of the dominant weight, and the AHP procedure was also applied to the 72 simulation replicates spanning the soil × climatic-cell variability of the 2013–2023 dataset to obtain empirical rank distributions for each scenario under each profile. The results highlight that the choice between absolute and ratio environmental indicators is a substantive methodological decision that directly affects the ranking of agronomic alternatives in multi-criteria evaluation.

The Analytic Hierarchy Process (AHP) was applied to six agronomic scenarios for durum wheat (Triticum durum Desf.) in the Capitanata plain (Apulia, southern Italy), combining three sowing dates (15 October, 1 November, 15 November) with two water regimes (rainfed; supplemental irrigation at flowering, 13–41 mm season⁻¹). Five performance indicators — grain yield (4983–5722 kg ha⁻¹), CO₂-equivalent emissions (CO₂_eq, 1190–1214 kg ha⁻¹), carbon footprint (CFP, 0.19–0.24 kg CO₂ eq kg⁻¹), total water consumption (TotW, 5555–6387 m³ ha⁻¹) and water footprint (WFP, 1.08–1.18 m³ kg⁻¹) — were derived from AquaCrop-GIS simulations coupled with cradle-to-gate life cycle assessment. A symmetric weight scheme (w = 0.60 for the dominant criterion, w = 0.10 for each of the remaining four) defined six decision profiles, with all pairwise comparison matrices perfectly consistent (CI = 0, CR = 0). The rankings revealed a systematic inversion between absolute indicators (CO₂_eq, TotW) and ratio indicators (CFP, WFP): under absolute-metric profiles, the lowest-yielding scenario (4983 kg ha⁻¹) paradoxically ranked first because reduced productivity mechanically lowered per-hectare resource consumption. Under ratio-metric and balanced profiles, early-November sowing consistently led the rankings, combining the lowest carbon footprint (0.19 kg CO₂ eq kg⁻¹) and the lowest water footprint (1.08 m³ kg⁻¹) among the six scenarios. Switching point analyses quantified the weight thresholds at which leadership changed: w₁ = 0.20 (S1 → S2) and w₁ = 0.60 (S2 → S5) along the Yield axis, w₃ = 0.60 (S2 → S5) along the CFP axis, with rainfed early-November sowing retaining leadership across the full range of WFP weights. The AHP procedure was also applied to the 72 simulation replicates spanning all combinations of soil profile, climatic cell and cropping year in the 2013 – 2023 dataset, providing the empirical rank distribution for each scenario under each profile and extending the mean-based analysis to the full pedo-climatic variability of the region.

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.