Agricultural Water Management最新文献

{"title":"Water and energy savings using variable rate sprinkler irrigation on a large maize farm in northern Italy","authors":"Alice Mayer ,&nbsp;Bianca Ortuani ,&nbsp;Alberto Crema ,&nbsp;Mirco Boschetti ,&nbsp;Arianna Facchi","doi":"10.1016/j.agwat.2026.110184","DOIUrl":"10.1016/j.agwat.2026.110184","url":null,"abstract":"<div><div>Maize is a key crop both globally and in Italy. In the Po Valley, it is cultivated on 500,000 ha, primarily for use in livestock production. Here, maize cultivation is highly dependent on irrigation, traditionally performed using border irrigation. However, due to increasing water scarcity, more efficient irrigation strategies will be required in the future. This study develops and tests an innovative integrated framework combining soil characterisation, in-field monitoring devices, agro-hydrological modelling and remote sensing to save water and energy. In 2021, a variable rate (VR) irrigation strategy was implemented in a 15-ha center pivot in a large livestock farm in northern Italy using: i) soil mapping based on an electromagnetic induction (EMI) sensor to delineate homogeneous zones, ii) a modelling workflow coupling soil moisture probes and weather forecasts to determine irrigation timing and amounts, and iii) a speed-controlled pivot for spatially variable application. This approach reduced water and energy use by 20 %, while maintaining yield and reducing grain moisture at harvest, although operational constraints imposed by the tenant limited the achievable savings. The framework was then scaled up to the entire farm for the 2016–2021 period using a semi-distributed agro-hydrological model supported by remote sensing data. Simulations indicated a mean reduction of 19 % in irrigation and energy use, consistent with field results. Overall, the developed modelling framework proved to be effective in optimizing irrigation and can be transferred to other crop-growing areas relying on sprinkler systems.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"325 ","pages":"Article 110184"},"PeriodicalIF":6.5,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025213","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":"Long-term biochar addition improves post-rice wheat production by ameliorating soil mechanical impedance and moisture condition as well as promoting root growth","authors":"Zhi Wang ,&nbsp;Wei Ma ,&nbsp;Yunfei Lu ,&nbsp;Xinyu Liu ,&nbsp;Jiawen Han ,&nbsp;Xinxin Ye","doi":"10.1016/j.agwat.2026.110200","DOIUrl":"10.1016/j.agwat.2026.110200","url":null,"abstract":"<div><div>Biochar has been widely applied as an efficiency soil additive to modify the quality of cultivated field. However, the effects of long-term biochar addition on spatial and temporal dynamics of soil compaction, and the changes in soil moisture condition and plant root growth remain unclear. Hence, an eight-year (2015/16–2023/24) consecutive field experiment on wheat was conducted in the subtropical humid region of east China, using three treatments: no N fertilizer (PK), chemical fertilizer (NPK), NPK plus biochar (5 t ha⁻¹ yr⁻¹, NPKB). Relative to NPK, across nine growing seasons of wheat, NPKB decreased the soil bulk density by 0.019 and 0.013 units (g cm⁻³ yr⁻¹), and decreased the soil penetration resistance by 0.028 and 0.015 units (MPa yr⁻¹) in 0–10 cm and 10–20 cm depths, respectively. Biochar addition improved soil water content from seeding to flowering, increased wheat root distribution during the whole growth period, and enhanced soil N supply capacity by promoting N adsorption, which gave rise to greater biomass and N accumulation and more biomass allocation in grain. As a result, NPKB increased wheat yield by 14.8 %, N recovery efficiency by 55.1 %, and crop water productivity by 14.9 %, relative to NPK, on average across four growing seasons of wheat. Therefore, long-term biochar addition has potential to substantially increase grain yield of post-rice wheat, water productivity, and N recovery efficiency. Hence, for the sustainable intensification cropping in the long-run, successive biochar addition could be a finable management for wheat production on the rainfed Yangtze River Region of China.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"325 ","pages":"Article 110200"},"PeriodicalIF":6.5,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146074897","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":"Toward sustainable Brassica napus production: Optimizing fertilization regimes for yield, water, and nutrient efficiency","authors":"Qiang Zheng ,&nbsp;Peng Song ,&nbsp;Xin Wang ,&nbsp;Siqi Han ,&nbsp;Kai Zhang ,&nbsp;Peng Hou ,&nbsp;Peiling Yang","doi":"10.1016/j.agwat.2026.110145","DOIUrl":"10.1016/j.agwat.2026.110145","url":null,"abstract":"<div><div>Enhancing both crop productivity and resource-use efficiency is essential for sustainable intensification, particularly in high-value vegetable systems. Conventional surface irrigation combined with one-time application of compound fertilizer often leads to poor synchronization between nutrient supply and crop demand, resulting in inefficient resource use and elevated environmental risks. This study investigated the effects of split N P K application via precision drip fertigation on soil conditions, crop performance, and water productivity in <em>Brassica napus</em> cultivated for edible shoots. Four fertilization treatments were compared: (T1) conventional surface irrigation with a single top-dressing of compound fertilizer, and three drip-fertigation regimes using organic water-soluble fertilizer-(T2) fertilizer applied in two equal splits, (T3) fertilizer applied in three equal splits, and (T4) fertilizer applied in four equal splits-synchronized with successive shoot-harvest stages. Results demonstrated that drip fertigation significantly improved subsoil moisture and reduced soil electrical conductivity. Notably, treatments T3 and T4 enhanced nitrate nitrogen availability, stem diameter, plant height, and biomass accumulation. Compared with T1, T3 and T4 increased shoot yield by 17.1 % and 9.31 %, irrigation water productivity (WP_I) by 17.1 % and 9.17 %, and partial factor productivity of N fertilizer (PFP_N) by 1.62 % and 7.63 %, respectively. Structural equation modeling identified stem diameter, dry weight, and inflorescence number as key yield drivers, while PFP_N was affected by both morphological and physiological traits. A combined AHP-EWM evaluation framework identified T3 as the optimal fertilization regime. The fertigation strategy and evaluation framework developed here offer a practical and scalable pathway for enhancing water-nutrient efficiency in multi-cut vegetable systems, supporting sustainable intensification beyond the study region.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"325 ","pages":"Article 110145"},"PeriodicalIF":6.5,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145969401","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":"Linking anatomy and physiology of almond to irrigation strategies: Towards standardized thresholds and decision-support tools for water-limited environments","authors":"S. Gutiérrez-Gordillo ,&nbsp;L. Conti ,&nbsp;G. Egea ,&nbsp;S. Vélez ,&nbsp;R. Martínez-Peña ,&nbsp;D. Andima ,&nbsp;V. Blanco ,&nbsp;M.A. Sarıdaş ,&nbsp;B. Kapur ,&nbsp;T.A. Paço ,&nbsp;E. Kullaj ,&nbsp;Ş.E. Aslan ,&nbsp;O. Sperling ,&nbsp;K. Vukićević ,&nbsp;P. Losciale","doi":"10.1016/j.agwat.2026.110162","DOIUrl":"10.1016/j.agwat.2026.110162","url":null,"abstract":"<div><div>Almond (<em>Prunus dulcis</em> (Mill.) D.A. Webb) is one of the most important nut crops cultivated in arid and semi-arid regions, where water availability is a key factor determining yield and nut quality. Its domestication in dry environments has favoured traits such as deep rooting and early phenology, which confer a moderate tolerance to drought. However, under prolonged or severe water stress, these adaptations become insufficient, leading to declines in yield. Understanding the balance between tolerance and vulnerability is therefore essential for developing irrigation strategies that ensure yield stability, nut quality and long-term orchard resilience under climatic conditions and modern cultivation systems increasingly dependent on irrigation. This review provides an integrative overview of almond’s anatomical and ecophysiological responses to water availability, emphasizing key physiological indicators, such as water potential, stomatal conductance, and leaf temperature, as tools to guide irrigation management. The reliability of these variables depends on environmental conditions, phenological stages, and cultivar-specific traits, which complicates the definition of universal thresholds. By integrating anatomical and physiological evidence with recent advances in monitoring technologies, this review aims to support the development of standardized, adaptive irrigation protocols that enhance water use efficiency of almond trees while preserving yield and nut quality. Understanding cultivar adaptation and physiological thresholds is critical to ensure resilient almond production under increasing climate and water challenges.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"325 ","pages":"Article 110162"},"PeriodicalIF":6.5,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146006383","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":"Bridging watershed conservation preferences: A multi-basin comparative study of willingness to pay for watershed services among farming communities in India and Sri Lanka","authors":"Menuka Udugama ,&nbsp;Kaushika Seelanatha ,&nbsp;Lahiru Udayanga ,&nbsp;Mohanasundari Thangavel ,&nbsp;Mohamed M.M. Najim ,&nbsp;Savinda Arambawatta Lekamge ,&nbsp;Bader Alhafi Alotaibi","doi":"10.1016/j.agwat.2026.110182","DOIUrl":"10.1016/j.agwat.2026.110182","url":null,"abstract":"<div><div>Watersheds play a major role in ensuring human well-being, while conserving ecosystems. Due to extreme changes in climate and human interference, watersheds remain significantly threatened. Current study aimed to compare the preferences and Willingness to Pay (WTP) of two farmer communities in South Asia; India and Sri Lanka for conserving watershed services, employing a Contingent Valuation Model (CVM). Bargi catchment area in the Jabalpur District of Madhya Pradesh state, India and the Upper Mahaweli watershed’s Knuckles mountain range in Sri Lanka were selected as the study areas. A pre-tested interviewer administered structured questionnaire was administered for primary data collection from a total of 600 farmers residing in the study areas. Binary Logistic Regression (BLR) was used for statistical analysis. The findings revealed that in case of the Indian farmers, secondary education (r = 0.482, P &lt; 0.038) gross monthly income below Rs 20,000 (coefficient [r]=0.581, P = 0.039), being a female household head (r = 0.041, P = 0.046) and awareness of ecosystem services (r = 0.561, P = 0.032) and awareness on sustainable farming practices (r = 0.332, P = 0.043) emerged as significant driving factors influencing the community’s preferences and payment for conservation among the Indian farmers. For the Sri Lankan counterpart, significant factors influencing WTP were being a farmer age over 50 years (r = 5.930, P &lt; 0.066), the bid (r = 0.004, P &lt; 0.001), land area (r = 5.621, P = 0.039) and the awareness of ecosystem services (r = 17.717, P = 0.003). Sri Lankan farmers were willing to contribute by USD 4.47 year/household, while Indian farmers preferred to pay USD 6.23 year/household for conservation of watershed services. The findings underscore the importance of context-specific, tailored watershed management policies that align with local priorities to encourage community-based watershed conservation efforts.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"325 ","pages":"Article 110182"},"PeriodicalIF":6.5,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134096","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":"Integrating UAV multispectral and thermal images with machine learning models to estimate cotton leaf area index under varying water and nitrogen regimes","authors":"Shengzhao Pei ,&nbsp;Fuquan Lv ,&nbsp;Xiaoshu Pan ,&nbsp;Yunfang Mu ,&nbsp;Yulong Dai ,&nbsp;Zhenqi Liao ,&nbsp;Xiaoqiang Liu ,&nbsp;Fucang Zhang ,&nbsp;Junliang Fan ,&nbsp;Feihu Yin","doi":"10.1016/j.agwat.2026.110193","DOIUrl":"10.1016/j.agwat.2026.110193","url":null,"abstract":"<div><div>Leaf area index (LAI) is a critical indicator bridging crop dynamic growth and agricultural management implementation. Different irrigation amounts and nitrogen (N) application rates influence crop growth and LAI. Accurate and dynamic LAI monitoring is essential for improving modern agricultural quality and efficiency. While unmanned aerial vehicles (UAVs) equipped with multispectral (MS) or thermal infrared (TIR) sensors can estimate LAI by extracting various features from remote sensing images, reliance on a single data source often limits estimation accuracy. To achieve more precise LAI estimation for cotton, this study acquired UAV-based remote sensing images—incorporating both MS and TIR data—across different growth stages under different irrigation amounts (60 % ET_c, 80 % ET_c and 100 % ET_c, ET_c denotes crop evapotranspiration) and N application regimes (0, 200, 300 and 400 kg N ha⁻¹) in Xinjiang, China. The multi-feature and multi-dimensional information, including canopy coverage (CC), vegetation indices (VIs), canopy thermal information (CTs) and texture-related information (texture features, TFs; general texture indices, GTIs; three-texture indices, TTIs), was extracted from multi-source images. Five machine learning (ML) models, namely Support Vector Regression (SVR), Light Gradient Boosting Machine (LGBM), Random Forest (RF), Elman Neural Network (Elman), and Transformer, were adopted to estimate the LAI by utilizing the fused MS and TIR data, and the model with the optimal predictive performance was then employed to generate spatial distribution maps of LAI across the study area. The results indicated that texture indices enhanced LAI estimation, with TTIs demonstrating particularly strong potential. Multispectral data outperformed thermal data in standalone LAI estimation, while the integration of MS and TIR features greatly enhanced accuracy. Specifically, the Transformer model with CC + VIs + CTs + GTIs + TTIs as input variables obtained the best estimation accuracy (R² = 0.87, RMSE = 0.42, MAE = 0.36 for calibration; R² = 0.85, RMSE = 0.45, MAE = 0.37 for validation). The resulting LAI spatial distribution maps effectively characterized cotton growth dynamics under different irrigation and N treatments. These findings provided a reliable technical basis and practical guidance for the precision management of water and N in cotton fields.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"325 ","pages":"Article 110193"},"PeriodicalIF":6.5,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134108","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 precision irrigation and nitrogen management for corn using SWAP model under changing humid climates","authors":"Suman Budhathoki ,&nbsp;Ryan Stewart ,&nbsp;William Hunter Frame ,&nbsp;Julie Shortridge","doi":"10.1016/j.agwat.2025.110087","DOIUrl":"10.1016/j.agwat.2025.110087","url":null,"abstract":"<div><div>Climate change is expected to alter crop productivity and nitrogen dynamics, yet limited research has quantified how different irrigation strategies can mitigate these impacts, particularly in humid regions where erratic rainfall complicates water and nutrient management. This study employs the agro-hydrological model SWAP to examine the performance of rainfed, calendar, and precision irrigation with both single (1 N) and split (2 N) nitrogen applications. SWAP model calibration and evaluation were conducted using observed volumetric water content across multiple soil depths as well as nitrate concentration data. Corn yield, nitrogen uptake, nitrate leaching, and irrigation water productivity were compared for each integrated irrigation and nitrogen strategy under different climate scenarios. To distinguish the effects of irrigation and nitrogen application strategy, precision irrigation was simulated using both a single nitrogen application (Precision-1N) and a split application (Precision-2N). Results indicated that nitrogen application timing (1 N vs. 2 N) had less of an impact on yields, leaching, and water productivity compared to irrigation strategy. Precision-2N consistently outperformed the Calendar-1N system across all scenarios, with higher yields and nitrogen uptake, and significantly better water productivity. The greatest long-term benefits of the Precision-2N treatment compared to Calendar-1N were observed under Scenario 4, which featured increased rainfall variability without an increase in total precipitation. In contrast, the smallest disparities between the irrigation treatments were observed in climate scenarios where precipitation increased. An analysis of interannual variability demonstrated that the Precision-2N benefits were most pronounced during years with frequent extreme temperature events. These findings reinforce the effectiveness of Precision-2N to achieve a favorable balance between higher yields and reduced NO₃ leaching.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"325 ","pages":"Article 110087"},"PeriodicalIF":6.5,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146014836","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":"Irrigation management for sustainable intensification of faba bean production: Synergizing yield, soil health, and water use efficiency","authors":"Qiang Zheng ,&nbsp;Peng Song ,&nbsp;Xin Wang ,&nbsp;Bin Sun ,&nbsp;Peng Hou","doi":"10.1016/j.agwat.2026.110208","DOIUrl":"10.1016/j.agwat.2026.110208","url":null,"abstract":"<div><div>Efficient water–fertilizer management is essential for enhancing the productivity and sustainability of faba bean (<em>Vicia faba L.</em>), particularly in systems where conventional practices rely on excessive inputs and contribute to resource waste and non-point source pollution. This study compared three irrigation amounts (190, 163, and 136 mm) and two irrigation frequencies (12 and 24 events) under drip fertigation, alongside conventional surface irrigation (CK), to identify an optimized water-management strategy that improves yield, water use efficiency, and partial factor productivity of fertilizer (PFP). Drip fertigation markedly increased yield and PFP by optimizing root-zone water supply, enhancing nutrient availability, and promoting dry-matter accumulation. The DH (high-frequency and high-amount drip irrigation) treatment achieved the greatest improvements, with 80.8 %–85.4 % higher yield and 143.6 %–161.9 % greater PFP than CK. Yield was primarily driven by stem dry weight, stem diameter, and 100-grain weight, while PFP was influenced by stem traits and branch number. Among all treatments, the low irrigation amount combined with high irrigation frequency (136 mm, 24 irrigations) provided the most effective water strategy, delivering the highest integrated performance in productivity and resource efficiency. This optimized water strategy is recommended for regions experiencing seasonal water scarcity, offering a practical pathway to enhance yield while reducing resource inputs and environmental impacts. Future research should assess the long-term stability and broader applicability of this water strategy across different soil conditions, climates, and legume production systems.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"325 ","pages":"Article 110208"},"PeriodicalIF":6.5,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146710","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":"Mapping spatiotemporal agricultural droughts from 2019 to 2024 in Northeast Thailand using multi-temporal and multiple sensor data together with random forest algorithm","authors":"Nudthawud Homtong ,&nbsp;Savittri Ratanopad Suwanlee ,&nbsp;Surasak Keawsomsee ,&nbsp;Kemin Kasa ,&nbsp;Jaturong Som-ard ,&nbsp;Sarawut Ninsawat ,&nbsp;Narissara Nuthammachot ,&nbsp;Dario Spiller ,&nbsp;Filippo Sarvia","doi":"10.1016/j.agwat.2026.110216","DOIUrl":"10.1016/j.agwat.2026.110216","url":null,"abstract":"<div><div>Agricultural drought exerts a direct impacts food production and livelihoods, highlighting the critical importance of effective natural resource management in regions frequently affected by these events. Earth observation (EO) satellites offer rich geometric, spectral, and temporal resolution data that are increasingly used to map and monitor drought conditions. However, accurately mapping agricultural droughts in tropical countries like Thailand remains challenging due to the need for extensive training datasets, variable precipitation patterns, persistent cloud cover, and small field sizes. To address these challenges, this study performed a series of analyses: i) investigated the relationship between the Soil Moisture Index (SMI) and the Standardized Precipitation Evapotranspiration Index (SPEI) across wet and dry seasons; ii) developed Random Forest Regression (RFR) models integrating multi-temporal Sentinel-2 (S2) imagery with several vegetation indices and SMI derived reference data to map drought occurrence from 2019 to 2024; and iii) assessed agricultural drought trends for major crops, including rice, sugarcane, cassava, and rubber trees within the Northeast Thailand. The SMI showed the strongest correlation with SPEI-3, with R values ranging from 0.7 to 0.8. The RFR models were highly efficient for all years, with R values exceeding 0.65. Spatiotemporal analysis indicated that the most severe drought events occurred consistently between March and May annually. Regions exhibiting the steepest drought trends were often located in irrigated areas, reflecting changes in water availability and cropping practices over time, despite generally low drought severity. Overall, SMI proved to be a robust reference dataset, while the RFR models showed high reliability for monitoring agricultural droughts in cloud regions. This work offers a valuable approach for generating training data in areas with limited ground observations. The results provide support for agricultural drought mitigation and crop water management.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"325 ","pages":"Article 110216"},"PeriodicalIF":6.5,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146774","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":"Sweet corn yield, evapotranspiration, production functions, basal crop coefficients, water productivity and soil-water extraction","authors":"Suat Irmak","doi":"10.1016/j.agwat.2026.110312","DOIUrl":"https://doi.org/10.1016/j.agwat.2026.110312","url":null,"abstract":"","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"14 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147501664","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}