1. Introduction
Agriculture accounts for approximately 70% of global water usage [1,2]. Although the use of water in agriculture yields considerable gains in productivity, thereby contributing significantly to food production and security, the agricultural sector is increasingly required to justify its use of water and is under growing scrutiny [3,4,5]. To address water governance and ensure fair access to water across sectors and scales, efficient water management, and environmental protection, it is critical to safeguard the sustainability of irrigation agriculture [6,7].
In the last few decades, considerable efforts have been made to improve water-use efficiency (WUE) across different sectors. According to a recent publication by the Food and Agriculture Organization of the United Nations (FAO) [8], there was an increase of 23% in WUE (expressed in USD m−3) between 2015 and 2022, indicating a positive outcome under Sustainable Development Goal (SDG) target 6.4.1—Change in WUE over time. Gains in agricultural water productivity have been achieved thanks to agronomic technological advancements, like higher efficiency irrigation methods, remote-sensing integration, or smart irrigation systems [9,10,11].
As stated by the FAO (2025) [1] “Future gains must come from smarter—not just increased—food production, by closing yield gaps, diversifying the selection of suitable and resilient crops, and applying locally adapted, resource-efficient practices suited to land, soil and water conditions. There is no single pathway—no one-size-fits-all solution.”
Currently, the issue of sustainable irrigation involves making optimal decisions amid uncertainty. Climate change, resource degradation and availability, the water–energy–food–environment (WEFE) nexus, and institutional and political issues often constrain the advancement of agricultural systems [12,13,14].
The second edition of the Special Issue (SI) “Agricultural practices to improve irrigation sustainability”, published in Water in 2025, builds upon the first edition published in 2024 [3] and expands the scope in irrigation research from classic studies at the field or agricultural plot scale to research on the most effective tools to support decision-making under uncertainty. To achieve these advances, decision-making must be supported by reliable data and tools that are simple enough for stakeholders to interpret and can effectively inform their decisions.
The contributions for this SI highlight this shift in research, comprising articles that address best practices at the plot level, with a focus on increasing efficiency; refocus agricultural decision-making on data and model analysis; and expose and reflect on trade-offs and governance related to the WEFE nexus.
2. An Overview of the Contributions to the Special Issue
2.1. Field-Scale Practices
Contributions from Baydar et al., Poma-Chamana et al., and Garofalo et al. (contributions 1–3) focused on efficiency, performance, and adaptation at the farm scale. Baydar et al. (contribution 1) studied the hydraulic performance and theoretical efficiency in drip irrigation systems in Nectarine Orchards (Prunus persica var. nucipersica) in the Mediterranean Region. The drip systems’ performance was evaluated using statistical uniformity and efficiency parameters, highlighting the need to train farmers to address low performance and increase profitability.
Poma-Chamana et al. (contribution 2) examined how water conservation and economic productivity can be achieved through the integration of deficit irrigation (DI) and structural erosion control measures in potato (Solanum tuberosum L.) production. Their results suggested that drip irrigation enhanced efficiency while maintaining yield and profitability in terraced crops.
Garofalo et al. (contribution 3) studied the potential of sorghum (Sorghum bicolor L. Moench) for bioethanol production under DI strategies. They found higher biomass and ethanol water productivity under moderate deficit irrigation, underscoring the importance of adjusting irrigation practices to specific environmental conditions to improve WUE and yield.
2.2. Data, Models, and Decision Support
The contributions from Shan et al., Garofalo et al., Tomaz et al., and Scobie et al. (contributions 4–7) demonstrated a shift from research at the field-scale to data-driven agricultural decision-making. Shan et al. (contribution 4) reported a study using the DSSAT–CROPGRO–Tomato model to optimize water-saving irrigation and nitrogen-use efficiency in greenhouse tomato cultivation in North China. Their results showed that the maximum tomato yield and water–nitrogen-use efficiency were achieved with irrigation volumes of 320–340 mm and nitrogen application rates of 360–400 kg·ha−1.
Garofalo et al. (contribution 5) studied data-driven irrigation management in watermelon (Citrullus lanatus, (Thunb.) Matsum & Nakai, 1916). They estimated actual evapotranspiration (ETa) using machine learning (ML) algorithms (Random Forest, Elastic Net, and Support Vector Machine) based on measured data and remote sensing.
Tomaz et al. (contribution 6) researched the prediction of irrigation water quality under drought conditions using multivariate statistical tools and ML models. Random Forest regression and Gradient Boosting Machine algorithms were employed to predict water quality parameters from chemical and climatic variables, providing a promising framework for early warning and informed decision-making in the context of increasing drought vulnerability across Mediterranean agro-environments.
Scobie et al. (contribution 7) examined the basis for specifying minimum data requirements for irrigation decision-making among small-scale Vietnamese coffee farmers. They found that Low-Data models (LDMs) are best suited to more informed irrigation scheduling decisions, which have the potential to improve the likelihood of success for farmers.
2.3. Trade-Offs and Governance
Institutional conflicts and governance may limit the sustainability of agricultural water use, as reported by Gupta & Rowan (contribution 8). These authors employed a WEFE nexus analytical framework to examine the socio-political, economic, and environmental factors driving unsustainable irrigation practices in a river basin of Peninsular India. They integrated spatially explicit analysis using digitized irrigation census data, theoretical energy modelling, and crop water requirements simulations to assess groundwater use and energy consumption for irrigation, and to examine their links with governance and economic growth. The findings highlight the need for holistic, catchment-wide planning that is aligned with the SDGs.
3. Conclusions
This SI addresses the challenge of improving irrigation sustainability under uncertainty related to climate and energy, water availability (quantity and quality), environmental degradation, institutional conflicts, and governance complexity.
Three contributions (Baydar et al., Poma-Chamana et al., and Garofalo et al.) reported that water-use efficiency and productivity can be enhanced through improvements in irrigation system performance, deficit irrigation strategies, and the adaptation of management options to crops and environmental conditions. The growing role of data-driven approaches in irrigation management was highlighted by four contributions (Shan et al., Garofalo et al., Tomaz et al., and Scobie et al.). One of the contributions (Gupta and Rowan) emphasized the need for integrated, basin-scale planning aligned with the SDG, which is critical for the effectiveness of technical solutions and irrigation sustainability.
Sustainable irrigation relies on integrating complementary approaches. The articles published in this SI demonstrate the need for improved decision-making in irrigated agriculture, whether informed by field data, modelling, or forecasting using new digital tools.
Author Contributions
Conceptualization, P.P. and A.T.; writing—original draft preparation, P.P. and A.T.; writing—review and editing, P.P. and A.T.; funding acquisition, P.P. and A.T. All authors have read and agreed to the published version of the manuscript.
Funding
The work is funded by national funds through FCT—Fundação para a Ciência e Tecnologia, I.P., in the framework of the UID/06107/2025—Centro de Investigação em Ciência e Tecnologia para o Sistema Terra e Energia (CREATE) and the UID/04035/2025—GeoBioTec.
Conflicts of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Abbreviations
The following abbreviations are used in this manuscript:
| DI | Deficit Irrigation |
| ETa | Actual Evapotranspiration |
| FAO | Food and Agriculture Organization of the United Nations |
| LDM | Low-Data Model |
| ML | Machine Learning |
| SDG | Sustainable Development Goal |
| WEFE | Water–Energy–Food–Environment |
| WUE | Water-Use Efficiency |
List of Contributions
- Baydar, A.; Bozkurt Çolak, Y.; Küçükyumuk, C.; Dalkılıç, B. Evaluation of Hydraulic and Irrigation Performances of Drip Systems in Nectarine Orchards (Prunus iersica var. nucipersica) in The Mediterranean Region. Water 2025, 17, 758. https://doi.org/10.3390/w17050758.
- Poma-Chamana, R.; Flores-Marquez, R.; Cordova-Tadeo, J.; Quello, A.; Arapa-Quispe, J.; Solórzano-Acosta, R. Transformation of Terraces with Irrigation Systems: Profitability and Water Savings in Potato Crop (Solanum tuberosum L.). Water 2025, 17, 668. https://doi.org/10.3390/w17050668.
- Garofalo, S.P.; Modugno, A.F.; de Carolis, G.; Campi, P. Energy of Sorghum Biomass Under Deficit Irrigation Strategies in the Mediterranean Area. Water 2025, 17, 578. https://doi.org/10.3390/w17040578.
- Shan, Z.; Chen, J.; Zhang, X.; Si, Z.; Yi, R.; Fan, H. Optimizing Irrigation and Nitrogen Application for Greenhouse Tomato Using the DSSAT–CROPGRO–Tomato Model. Water 2025, 17, 426. https://doi.org/10.3390/w17030426.
- Garofalo, S.P.; Ardito, F.; Sanitate, N.; De Carolis, G.; Ruggieri, S.; Giannico, V.; Rana, G.; Ferrara, R.M. Robustness of Actual Evapotranspiration Predicted by Random Forest Model Integrating Remote Sensing and Meteorological Information: Case of Watermelon (Citrullus lanatus, (Thunb.) Matsum. & Nakai, 1916). Water 2025, 17, 323. https://doi.org/10.3390/w17030323.
- Tomaz, A.; Catarino, A.; Tomaz, P.; Fabião, M.; Palma, P. Patterns, Risks, and Forecasting of Irrigation Water Quality Under Drought Conditions in Mediterranean Regions. Water 2025, 17, 1783. https://doi.org/10.3390/w17121783.
- Scobie, M.; Freebairn, D.; Mushtaq, S.; Donahue, D. How Much Is Enough? Data Requirements for Practical Irrigation Decision-Making in Vietnamese Coffee Production. Water 2025, 17, 646. https://doi.org/10.3390/w17050646.
- Gupta, B.; Rowan, J.S. Understanding Unsustainable Irrigation Practices in a Regionally Contested Large River Basin in Peninsular India Through the Lens of the Water–Energy–Food–Environment (WEFE) Nexus. Water 2025, 17, 1644. https://doi.org/10.3390/w17111644.
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