Agricultural Water Management最新文献

{"title":"A machine learning-driven semi-mechanistic model for estimating actual evapotranspiration: Integrating photosynthetic indicators with vapor pressure deficit","authors":"Yao Li ,&nbsp;Xiongbiao Peng ,&nbsp;Zhunqiao Liu ,&nbsp;Xiaoliang Lu ,&nbsp;Xiaobo Gu ,&nbsp;Lianyu Yu ,&nbsp;Jiatun Xu ,&nbsp;Huanjie Cai","doi":"10.1016/j.agwat.2025.109563","DOIUrl":"10.1016/j.agwat.2025.109563","url":null,"abstract":"<div><div>Accurate estimation of actual crop evapotranspiration (ET_{c act}) is essential for optimizing water resource management and irrigation strategies, particularly in arid and semi-arid agricultural regions. Traditional models rely on extensive meteorological data, limiting their applicability in data-scarce areas. This study used on-site ground observation data with a 30-minute temporal resolution from a winter wheat field at the Yangling Station on the Guanzhong Plain, China, to evaluate the performance of machine learning-driven semi-mechanistic models driven by three machine learning methods (Ridge regression, Random Forest, and Support Vector Machine) in estimating ET_{c act}. These machine learning-driven semi-mechanistic models integrate photosynthetic indicators (Gross Primary Production, GPP; solar-induced chlorophyll fluorescence, SIF; near-infrared reflectance of vegetation, NIRv) with the square root of vapor pressure deficit (VPD^0.5) to enhance ET_{c act} estimation accuracy. The results showed that among the photosynthetic indicators, GPP and SIF exhibited a strong correlation with ET_{c act}. When combined with VPD^0.5, their correlation with ET_{c act} further increased by 0.10 and 0.05, respectively, while their response time to ET_{c act} variations was reduced by 2 hours and 1 hour. Notably, NIRv exhibited the weakest correlation with ET_{c act}, with a Pearson correlation coefficient of only 0.31, significantly lower than SIF (0.78) and GPP (0.69), indicating its limited effectiveness as an independent predictor. Furthermore, machine learning-driven semi-mechanistic models driven by machine learning achieved higher accuracy in ET_{c act} estimation than single-factor machine learning models and the Penman-Monteith equation incorporating the single crop coefficient method. Among them, the RF model based on SIF × VPD^0.5 achieved the best performance, with an R² of 0.86 and an RMSE of 0.69 mm/day. This study demonstrates that machine learning-driven semi-mechanistic models can significantly improve ET_{c act} estimation accuracy while reducing dependence on meteorological data. The proposed approach provides a new theoretical framework for improving water resource management and irrigation efficiency in arid and semi-arid agricultural regions, while also offering a scientific basis for future ET_{c act} estimation methods integrating remote sensing data.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"315 ","pages":"Article 109563"},"PeriodicalIF":5.9,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced estimation of reference evapotranspiration using hybrid deep learning models and remote sensing variables","authors":"Tze Ying Fong,&nbsp;Yuk Feng Huang,&nbsp;Ren Jie Chin,&nbsp;Chai Hoon Koo","doi":"10.1016/j.agwat.2025.109534","DOIUrl":"10.1016/j.agwat.2025.109534","url":null,"abstract":"<div><div>Effective water resources management and irrigation scheduling for agricultural sector highly depend on the precise estimation of reference evapotranspiration, ET_o. This study aims to develop ET_o estimation models using deep learning algorithms with remote sensing variables as the input variables at Pulau Langkawi and Kuantan stations, located in Peninsular Malaysia. Support vector regressor (SVR) was found to satisfactorily estimate the daytime land surface temperature (LST) using a set of significant variables including meteorological and remote sensing variables. It was then used along with downward shortwave radiation and surface reflectance bands to estimate ET_o. Both long short-term memory (LSTM) and gated recurrent unit (GRU) showed their equivalent capability in estimating ET_o and achieved the highest R² of 0.695 and 0.796, respectively. The proposed hybrid deep learning models, combined model of convolutional neural network (CNN) with LSTM and GRU, respectively, achieved higher accuracy compared to individual models. They managed to improve the accuracy of the prediction in most of the cases, with the highest R² = 0.805 and the lowest prediction errors, MAE = 0.265 mm/day, RMSE = 0.343 mm/day and NRMSE = 0.096. It was shown that the incorporation of surface reflectance bands and auxiliary variables (day length, Julian day and solar zenith angle) enhanced the performance of the models. This study provides valuable insights into deep learning algorithms and further confirms the potential of remote sensing variables as an alternative data source for ET_o estimation.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"315 ","pages":"Article 109534"},"PeriodicalIF":5.9,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accelerating soil water recovery in alfalfa-converted cropland: Critical roles of fallow season mulch, crop selection, and precipitation","authors":"Xu-Long Zhang ,&nbsp;Ge Li ,&nbsp;Yang-Yang Zhao ,&nbsp;Bai-Rui Han ,&nbsp;Wen-Feng Cong ,&nbsp;Fusuo Zhang ,&nbsp;Feng-Min Li","doi":"10.1016/j.agwat.2025.109554","DOIUrl":"10.1016/j.agwat.2025.109554","url":null,"abstract":"<div><div>Alfalfa (<em>Medicago sativa</em> L.) crops rotation has been proposed as a sustainable strategy for dryland farming. However, limited understanding of the spatio-temporal dynamics of deep soil water recovery following alfalfa conversion constrains broader adoption of this practice. This study investigates soil water recovery in alfalfa-converted cropland (AC) compared to conventional cropland (CC) under a plastic-mulched maize-potato rotation over 12 years (2010–2021). We further examined variations in soil water recharge across crop types (maize vs. potato) and seasons (growing vs. fallow). At alfalfa conversion, the water deficit (DS) in the 0–500 cm profile was −0.37 (the relative change in soil water content in AC compared to CC). Following conversion, DS increased exponentially with conversion duration. Soil water in the upper 60 cm recovered within 2 years, while deeper layers (0–500 cm) recovered after 12 years. Most importantly, rapid recovery in the upper 60 cm enabled AC to achieve equivalent evapotranspiration and crop water productivity compared to CC. Soil water recharge in the 0–500 cm profile was similar during growing and fallow seasons, demonstrating the importance of precipitation storage under plastic mulch during fallow periods despite much lower precipitation. Potato cropping-years resulted in significantly greater soil water recharge than maize years, suggesting that increasing potato frequency in crop rotations could further accelerate soil water recovery. Soil water recharge showed a strong linear relationship with precipitation. A minimum annual precipitation threshold of 322 mm was identified for positive recharge, with fallow season precipitation contributing disproportionately to deep-layer replenishment. Based on these findings, we recommend implementing alfalfa rotation in regions with &gt; 322 mm annual precipitation, prioritizing potato in rotations, and optimizing water-saving management during fallow periods to maximize water capture. These findings advance strategies for reconciling agricultural productivity with hydrological sustainability in water-limited ecosystems.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"315 ","pages":"Article 109554"},"PeriodicalIF":5.9,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144088732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Probabilistic approach to monitoring vegetation water stress using solar-induced chlorophyll fluorescence data","authors":"Muhammad Abrar Faiz ,&nbsp;Qiumei Wang ,&nbsp;Shehakk Muneer ,&nbsp;Yongqiang Zhang ,&nbsp;Faisal Baig ,&nbsp;Farah Naz","doi":"10.1016/j.agwat.2025.109559","DOIUrl":"10.1016/j.agwat.2025.109559","url":null,"abstract":"<div><div>Solar-induced chlorophyll fluorescence (SIF) provides valuable insights into plant stress by detecting reductions in photosynthesis that frequently occur during drought. Unlike climate-based drought indices, SIF directly measures the photosynthetic activity and vitality of vegetation, providing a unique and real-time perspective for examining the effects of water stress. The vegetation water stress index (SIF-Di) is calculated using a probabilistic method, and a meteorological composite drought index (CDI) is employed to monitor vegetation health and drought conditions. The probabilistic approach categorizes monthly SIF anomalies based on percentiles, with lower percentiles indicating more severe vegetation water stress. A dynamic time warping approach is employed to investigate how SIF responds to climatic drought. The SIF-Di captures vegetation water stress activity well across global river basins. The results revealed that the Amazon basin has a CDI that leads the SIF-Di by 5.94 ± 6.24 lag times, suggesting that vegetation water stress develops gradually due to the dense rainforest canopy, as deep-rooted vegetation allows plants to tap into subsurface water, which increases resiliency and delays stress during prolonged dry periods. The SIF-Di and CDI offer a new approach to drought intensity, particularly in basins where climate drought affects vegetation with a relatively small lag. For example, the Mackenzie and Danube basins, with lags of 0.68 ± 1.63 and 0.84 ± 1.89 months, respectively, are vulnerable to drought and act as models for estimating drought response mechanisms. This study could enhance the predictability of drought onset and severity by anticipating the time difference between vegetation water stress and climatic drought.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"315 ","pages":"Article 109559"},"PeriodicalIF":5.9,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of straw mulching and plastic mulching on maize yield and crop water productivity in China: A meta-analysis","authors":"Xuegui Zhang ,&nbsp;Yao Li ,&nbsp;Yanan Sun ,&nbsp;Lianyu Yu ,&nbsp;Jiatun Xu ,&nbsp;Xiaobo Gu ,&nbsp;Huanjie Cai","doi":"10.1016/j.agwat.2025.109549","DOIUrl":"10.1016/j.agwat.2025.109549","url":null,"abstract":"<div><div>With the increasing severity of global climate change and water scarcity, improving crop water productivity (<span><math><msub><mrow><mi>WP</mi></mrow><mrow><mi>c</mi></mrow></msub></math></span>) has become essential for sustainable agriculture. As a major staple crop, maize (Zea mays L) plays a crucial role in China’s food security. Straw and plastic mulching are widely used to alleviate water stress and improve maize yield and <span><math><msub><mrow><mi>WP</mi></mrow><mrow><mi>c</mi></mrow></msub></math></span>; however, comprehensive comparisons under varying environmental and management conditions are limited. This study employed meta-analysis to evaluate the effects of straw and plastic mulching on maize yield and <span><math><msub><mrow><mi>WP</mi></mrow><mrow><mi>c</mi></mrow></msub></math></span> across different climatic zones, soil types, and agronomic practices in China. The results revealed that plastic film mulching significantly enhanced yield and <span><math><msub><mrow><mi>WP</mi></mrow><mrow><mi>c</mi></mrow></msub></math></span> when growing season precipitation was ≥ 600 mm, temperature ≤ 19°C, and annual sunshine ranged from 2300 to 2600 h. In contrast, straw mulching performed better when precipitation was ≤ 400 mm, temperature ranged from 19 to 23°C, and sunshine duration exceeded 2600 h. Plastic mulching was more effective across various soil types, especially under conditions of spring sowing, high planting density (&gt;6.75^×10⁴ plants·ha⁻¹), low nitrogen input (&lt;120 kg·ha⁻¹), and irrigation. Straw mulching effectiveness was primarily influenced by temperature and planting density, while plastic mulching was governed by soil organic matter and nitrogen input. Economic analysis showed that plastic mulching yielded higher profits under rain-fed conditions, whereas straw mulching was more profitable under irrigation due to lower costs. These findings offer practical guidance for region-specific mulching strategies, contributing to efficient water use, enhanced crop productivity, and sustainable farming.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"315 ","pages":"Article 109549"},"PeriodicalIF":5.9,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study on the coupled coordination of water resources consumption and economic development in Heilongjiang province under different scenarios based on SD model","authors":"Meiyun Tao ,&nbsp;Youzhu Zhao ,&nbsp;Qiuxiang Jiang,&nbsp;Zilong Wang,&nbsp;Baohan Li","doi":"10.1016/j.agwat.2025.109560","DOIUrl":"10.1016/j.agwat.2025.109560","url":null,"abstract":"<div><div>Promoting the coordinated development of water resources consumption and economic growth is crucial for advancing modern society. The study constructed a novel methodological framework that utilizes the water footprint (WF) as the primary indicator. First employed a system dynamics (SD) model to construct a coupled water resources-economy composite system for Heilongjiang Province. Next, the Logarithmic Mean Divisia Index (LMDI) model and shock detection method were applied to identify and quantify the driving factors of WF. By adjusting variables, four development scenarios were designed. Finally, a coupling coordination degree model (CCDM) was employed to evaluate and select the optimal scenario with the highest system coupling coordination. This methodological framework effectively overcomes the limitations of subjective selection in control variables and scenario design. The main findings are as follows: (1) the intensity effect had the strongest inhibition of the total water footprint (WF_T) from 2000 to 2021, with a contribution value of −1545.05 × 10⁸ m³ ; structural effects had a minor impact on the WF of each sector; scale effects showed a promoting effect to the WF in all industries. (2) Simulated future trends under different development scenarios reveal that under the status quo type of development (Scenario Ⅰ), water consumption across sectors remains relatively high during 2021–2050, the WF_T increase by 229.79 × 10⁸ m³ , and with relatively slower economic growth. In contrast, under the integrated development (Scenario IV), the increase in water consumption across sectors is relatively small, the WF_T increase by 145.41 × 10⁸ m³ , alongside faster economic growth. (3) Scenario Ⅰ exhibits a low degree of coupling between the water consumption and the economic development subsystem. Scenario IV demonstrates strong coupling and a high degree of coordination between the two subsystems. Therefore, Scenario IV is identified as the optimal development scenario for the composite system. The study provides a scientific foundation for conserving regional water resources while promoting sustainable economic growth.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"315 ","pages":"Article 109560"},"PeriodicalIF":5.9,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using generalized additive models to investigate drivers and controls on stream water nitrate concentrations in four agricultural catchments","authors":"Jason Galloway ,&nbsp;Golnaz Ezzati ,&nbsp;Daniel Hawtree ,&nbsp;Karl Richards ,&nbsp;Bridget Lynch ,&nbsp;Edward Burgess ,&nbsp;Ogjnen Zurovec ,&nbsp;Macdara O’Neill ,&nbsp;Per-Erik Mellander","doi":"10.1016/j.agwat.2025.109552","DOIUrl":"10.1016/j.agwat.2025.109552","url":null,"abstract":"<div><div>Nitrogen (N) is essential for agricultural production and additional inputs of N-containing mineral or organic fertilizers as part of modern agricultural practice followed by the subsequent loss of surplus N has led to a deterioration in water quality globally. In order to design effective mitigation measures and accurately assess progress towards meeting water quality improvement goals, an understanding of the processes that govern N loss is prerequisite. However, the complexity which governs N cycling in agricultural catchments and the timescales over which they occur make understanding the relative importance of the drivers and controls of N loss challenging. Here, we used an eight-year dataset to investigate stream water nitrate concentrations across four catchments with contrasting characteristics where agriculture accounted for greater than 95 % of the land use. We subdivided each catchment into subcatchments and investigated trends in stream water nitrate concentrations using explanatory variables representing farming intensity, land management, climatic conditions, and soil drainage. We adopted a systematic approach using generalized additive mixed models (GAMM) to capture complex relationships between explanatory variables and nitrate concentrations within each catchment and across all catchments. We found no clear relationship between source N loadings and stream water nitrate concentrations, with the most likely explanation for this being that N cycling in the study sites were transport- and not source-limited. Our results highlighted the key role played by climate and the hydrological characteristics of catchments driving N loss from agricultural catchments. We also found that site specific characteristics mediated the relative importance of the drivers and controls of N losses which suggests that effective mitigation measures should be determined by hydraulic properties of (sub)catchments.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"315 ","pages":"Article 109552"},"PeriodicalIF":5.9,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Temporal variations of soil saturated hydraulic conductivity under different land use types and its impact on water balance components","authors":"Tianqi Guo ,&nbsp;Yalin Ji ,&nbsp;Xiaoying Yan ,&nbsp;Ahmed Yehia Mady ,&nbsp;Rui Liu ,&nbsp;Mingbin Huang","doi":"10.1016/j.agwat.2025.109557","DOIUrl":"10.1016/j.agwat.2025.109557","url":null,"abstract":"<div><div>Soil saturated hydraulic conductivity (K_s) is one of the important soil hydraulic properties impacting the dynamic changes in soil water content and water balance components. Determining the impact of the temporal variation of K_s on these components is helpful for water resource management on the Chinese Loess Plateau. The main objective of this work was to estimate the efficiency of dual-porosity model in Hydrus-1D for simulating soil water content and water balance components with and without the temporal variation of K_s. Moreover, the effect of temporal variation of K_s on water balance components was estimated under different land use types. In this study, the double ring infiltrometers were used to measure K_s values for corn field and forestland sites from April to October 2022. The dynamic changes of soil water content and water balance components for both land use types were simulated using the calibrated and validated dual-porosity equations in the Hydrus-1D model under the two scenarios: (1) constant K_s, an average over the whole measuring period, and (2) temporally variable K_s. The results showed the temporal variation of K_s was significant for the corn field site due to tillage and not significant for the forestland site due to the lack of disturbance. The accuracy of the dual-porosity equations in the Hydrus-1D model increased by 14 % for the corn field site but by only 5 % for the forestland site when considering temporally varying K_s vs. constant K_s. In addition, the temporal variation of K_s resulted in evaporation increasing by 1.27 %, deep percolation decreasing by 14.92 %, and no obvious changes in transpiration and soil water storage of 300 cm for the corn field site, while almost consistent water balance components for the forestland site for both scenarios. These results indicated the temporal variation of K_s should be considered to improve simulations of soil water content and water balance components, particularly in farmland, which are useful for managing soil and water resources.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"315 ","pages":"Article 109557"},"PeriodicalIF":5.9,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Water saving benefits of autumn-sown sugar beet as a climate adaptation strategy for Iran","authors":"Reza Deihimfard ,&nbsp;Esmaeil Mohammadi Ahmad-Mahmoudi ,&nbsp;Sajjad Rahimi-Moghaddam ,&nbsp;Arash Shakeri ,&nbsp;Ehsan Eyshi Rezaei","doi":"10.1016/j.agwat.2025.109540","DOIUrl":"10.1016/j.agwat.2025.109540","url":null,"abstract":"<div><div>Climate change poses significant challenges for sugar beet cultivation in Iran, where this industrial crop accounts for over 50 % of national sugar production but relies heavily on scarce water resources. This study evaluated the potential of autumn sowing as an adaptation strategy compared to conventional spring sowing under baseline (1980–2010) and future climate projections (2040–2070, RCP4.5 and RCP8.5) across 21 diverse agricultural locations in Iran. The SUCROS crop growth model was modified to simulate sugar beet response to frost damage and applied to assess yields under full and supplementary irrigation regimes. Results showed that spring-sown sugar beet failed under all supplementary irrigation scenarios, requiring full irrigation for feasibility. Autumn-sown sugar beet yields averaged 23.25 t ha⁻¹ under supplementary irrigation compared to 89.85 t ha⁻¹ under full irrigation at baseline. Climate change projections indicated autumn-sown yields would increase by 21.87 % (RCP4.5) and 27.80 % (RCP8.5) compared to baseline, with significant spatial variability across locations. Frost events during autumn-sown growing seasons declined substantially under future scenarios (63 % fewer in RCP4.5 and 76 % fewer in RCP8.5), with frost intensity decreasing by 4.21 % on average. Southern regions exhibited no frost events, while northeastern locations experienced the most severe frost damage. Supplementary irrigation at mid-growth stage produced the highest autumn-sown yields across most locations. These findings demonstrate that autumn sowing offers a practical adaptation strategy for sugar beet cultivation in Iran under climate change, with reduced frost risk and improved water use efficiency, particularly in warmer regions and under supplementary irrigation regimes.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"315 ","pages":"Article 109540"},"PeriodicalIF":5.9,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143947248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the sustainability of wheat irrigation systems: Using life cycle assessment to monitor the Water-Energy-Food-Environment Nexus","authors":"Fereshteh Batoukhteh ,&nbsp;Abdullah Darzi-Naftchali ,&nbsp;Ali Motevali ,&nbsp;Fatemeh Karandish ,&nbsp;Markus Berger","doi":"10.1016/j.agwat.2025.109521","DOIUrl":"10.1016/j.agwat.2025.109521","url":null,"abstract":"<div><div>This pioneering study employs the Water-Energy-Food-Environment Nexus (WEFEN) framework to evaluate the sustainability of various wheat irrigation systems—furrow (FI), sprinkler (SI), and drip irrigation (DI)—by integrating renewable energy (solar PV and small-scale wind turbines) and life cycle assessment (LCA). Field data were collected from representative farms across five climates: hyper-arid, arid, semi-arid, dry-sub-humid, and humid. A structured questionnaire was designed to obtain information on cropping areas, input consumption, field management practices, crop yield, costs, and income. Renewable energy scenarios, including 100 % renewables (RE100 %) and a 50 % grid mix (RE50 %), were modeled using Homer Pro software, and environmental impacts (EI) were assessed via LCA. Transitioning from FI to DI significantly enhanced both water and energy productivity, up to 107 % and 60 %, respectively, in hyper-arid and arid climates. The incorporation of renewable energy further improved energy productivity by 33 % and reduced EI by up to 61 %, with the most pronounced benefits observed in arid regions. Key EI hotspots identified across all climates and irrigation systems included electricity, chemical fertilizers, and diesel fuel. The WEFEN index ranked DI highest (0.75), followed by SI (0.53) and FI (0.45). These results offer critical insights for enhancing resource efficiency, especially in regions facing water and energy scarcity, and provide actionable strategies for reducing environmental burdens. The findings of this research may significantly advance several global Sustainable Development Goals, particularly those focused on Zero Hunger, Affordable and Clean Energy, and Climate Action.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"315 ","pages":"Article 109521"},"PeriodicalIF":5.9,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143948526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}