Agricultural Water Management最新文献

{"title":"Modeling the impact of agricultural managed aquifer recharge (Ag-MAR) on soil water and nitrogen dynamics of the growing season","authors":"Wenyi Cui ,&nbsp;Tiantian Zhou ,&nbsp;Elad Levintal ,&nbsp;Cristina Prieto García ,&nbsp;Isaya Kisekka ,&nbsp;Helen E. Dahlke","doi":"10.1016/j.agwat.2025.109623","DOIUrl":"10.1016/j.agwat.2025.109623","url":null,"abstract":"<div><div>Agricultural managed aquifer recharge (Ag-MAR) is a practice aimed at sustaining groundwater reserves by utilizing farmland to recharge excess surface water into aquifers. Because fields are fertilized for crop production there are questions about how Ag-MAR may affect the leaching of legacy nitrate from fertilizer applications to groundwater or nitrogen transformations such as mineralization or denitrification. In this study, DSSAT was used to model the impact of different Ag-MAR practices (e.g. continuous or intermittent flooding for 8–20 days) prior to the growing season to estimate their effect on the growing season water and nitrogen budgets. The study uses soil physico-chemical, hydrologic, and processing tomato yield data from an Ag-MAR experiment in the Central Valley, California. Results show that the timing (e.g. Jan–Apr) and intermittency of flooding (e.g. continuous vs. wet-dry cycles) directly impact the amount of nitrate leached, and the residual amount of nitrate available at the beginning (May) of the growing season. Scheduling Ag-MAR closer to the growing season benefits the root zone water content and crop yield and can reduce growing season irrigation demand by 10 %, although these effects are less pronounced in sandy soils. Together these results show that continuous flooding for longer (e.g. 8–20 days) periods of time, well in advance of the growing season (&gt; 2 months), provides the greatest recharge potential with no adverse effects on the growing season nitrogen budget. Early and continuous Ag-MAR promotes the mineralization of new nitrate which offsets nitrate leaching and allows for water and nitrogen fertilizer savings during the growing season.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"317 ","pages":"Article 109623"},"PeriodicalIF":5.9,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365970","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":"Effect of alternating canal and marginal groundwater irrigation on banana yield, water use efficiency, and soil salinity under furrow plantation","authors":"Nazar Gul ,&nbsp;Hafiz Abdul Salam ,&nbsp;Muhammad Ashraf ,&nbsp;Majid Taie Semiromi","doi":"10.1016/j.agwat.2025.109603","DOIUrl":"10.1016/j.agwat.2025.109603","url":null,"abstract":"<div><div>Banana is one of the most important cash crops in Sindh, Pakistan; however, its production is increasingly compromised due to declining freshwater availability. Currently, banana is cultivated using conventional flat basin irrigation, which results in low water use efficiency (WUE). Groundwater in the region is predominantly marginal to saline in quality but could potentially supplement limited freshwater resources. Therefore, it is essential to evaluate the conjunctive use of marginal-quality groundwater and canal water to address freshwater shortages without compromising crop yields. In this study, the Dwarf Cavendish banana variety (locally known as Basrai) was cultivated over a four-year period (October 2015–June 2019) on furrows (0.90 m furrow width, 1.20 m bed width, and 0.23 m bed height). Alternate irrigation using canal and marginal groundwater was applied at three levels of available soil moisture depletion (ASMD): 50 %, 40 %, and 30 %. Traditional farmer irrigation practices were also monitored for comparison. At ASMD levels of 50 %, 40 %, and 30 %, annual water applications were 1228 mm, 1403 mm, and 1592 mm, respectively. In contrast, farmers applied 2866 mm of water annually. Irrigation at 50 % ASMD resulted in 12–23 % higher yield and 23–41 % higher WUE compared to 30 % and 40 % ASMD treatments. Compared to the 50 % ASMD treatment, farmer practices produced 24 % lower yield and 67 % lower WUE. Importantly, soil salinity remained within acceptable threshold limits across all treatments. These results suggest that banana can be successfully cultivated on furrows using alternate irrigation with canal and marginal groundwater at 50 % ASMD. This strategy offers substantial water savings while improving yield and WUE, making it a viable and sustainable solution for banana production in water-scarce regions.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"317 ","pages":"Article 109603"},"PeriodicalIF":5.9,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144366421","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":"A review of saline-alkali soil improvements in China: Efforts and their impacts on soil properties","authors":"Sihong Lei ,&nbsp;Xiaoxu Jia ,&nbsp;Chunlei Zhao ,&nbsp;Mingan Shao","doi":"10.1016/j.agwat.2025.109617","DOIUrl":"10.1016/j.agwat.2025.109617","url":null,"abstract":"<div><div>It is a great challenge to manage and sustain agricultural production in saline-alkali soils due to their high salt content, poor structure, and low fertility, posing global threats to soil health, land sustainability, and food security. China, accounting for 10.1 % of the world’s saline-alkali land, faces increased pressure as arable land decreases, population increases, and economic grows. Since the 1960s, region-specific reclamation techniques, such as flood irrigation, drip irrigation with mulching, and drainage systems have been employed in China, and have brought about obvious reductions in salinity by over 60 % and pH improvements, with values decreasing from above 9.5 to below 8.5 for saline-alkali soils. More recent efforts have enabled China to develop extensive theoretical knowledge and management practices, culminating in a “Chinese solution” for saline-alkali land management. This review provides an overview of the occurrence, evolution, and challenges of saline-alkali soils in China. It also employs meta-analysis to synthesize management techniques, evaluate their effectiveness, and summarize the mechanisms that mitigate saline-alkali soil problems. This review highlights successful strategies and outcomes from typical saline-alkali regions in China, providing insights for future management practices and research of their global applicability.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"317 ","pages":"Article 109617"},"PeriodicalIF":5.9,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144338645","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":"Ammonium and potassium effects on carrot response to water deficit","authors":"Ayman M.S. Elshamly ,&nbsp;Saudi A. Rekaby ,&nbsp;A.S. Abaza ,&nbsp;Kassem A.S. Mohammed ,&nbsp;Mohamed S. Elshikh ,&nbsp;Humaira Rizwana ,&nbsp;Rashid Iqbal ,&nbsp;Maximilian Lackner","doi":"10.1016/j.agwat.2025.109614","DOIUrl":"10.1016/j.agwat.2025.109614","url":null,"abstract":"<div><div>For a comprehensive understanding of carrot drought tolerance, studying nutrient homeostasis alongside physiochemical modifications, especially under different drought intensities and nutrient supplements is essential. In this work, the effect of soil ammonium nitrate and foliar potassium humate applications in different combinations and rates on nutrient concentrations, agronomic traits, and irrigation water use efficiency was investigated. Three deficit water levels in the field in southern of Egypt were used, namely well-watered irrigation level, continuous mild deficit level, and moderate deficit level. The current work was organized in a split-plot with three replicates. Adopting the mild deficit level and applying 250 kg ha⁻¹ ammonium nitrate combined with 200 g 100 L⁻¹ potassium humate yielded beneficial impacts that relieved carrot plants stress and lead to enhanced irrigation water use efficiency. Adopting this approach in the short mild deficit term increased leaf concentrations of K (+29 %), Mg (+45 %), Na (+24 %), and Zn (+12 %) and decreased leaf concentrations of N (-10 %), P (-13)%, Fe (-18 %), Mn (-21 %), and Cu (-25 %) compared to well-watered conditions, while in the long mild deficit term, the leaf concentrations of P (+25 %), K (+44 %), Ca (+11 %), Mg (+21 %), Na (+8 %), and Cu (+7 %) increased and leaf concentrations of N (-8 %), Fe (-12 %), and Mn (-30 %) decreased compared to well-watered irrigation. The adaptive responses in nutrient homeostasis improve carrot resilience to deficit stress and allow plants to maintain their developing processes and water use. Interestingly, when the potassium humate rate was increased up to 400 g 100 L⁻¹ and applied in a combination with 250 kg N ha⁻¹ under the well-watered level, negative impacts were observed on water use of carrots which decreased by 52 % and 33 % compared to mild and moderate deficit, respectively.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"317 ","pages":"Article 109614"},"PeriodicalIF":5.9,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365971","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":"Modeling water and salt migration in groundwater and vadose zones to assess agricultural sustainability in Karamay Irrigation District","authors":"Jiawei Ren ,&nbsp;Tongkai Guo ,&nbsp;Changyan Tian ,&nbsp;Wenxuan Mai ,&nbsp;Xiaomin Mao","doi":"10.1016/j.agwat.2025.109611","DOIUrl":"10.1016/j.agwat.2025.109611","url":null,"abstract":"<div><div>The agricultural region of Karamay in northern Xinjiang, China, faces serious challenges to agricultural sustainability due to primary salinization, arid climatic conditions and a lack of effective drainage systems. To evaluate the influence of groundwater depth and salinity on soil salinization and analyze the sustainability of agricultural development, this study employed a three-dimensional (3D) water and solute transport model (FEFLOW) to simulate water-salt dynamics in both groundwater and vadose zones across cropland, forest land, and desert. The model was calibrated and validated using 2006–2021 data, showing good agreement with observed groundwater levels (R²=0.70, RMSE=0.27 m), groundwater salinity (R²=0.72, RMSE=2.08 dS/m), and soil salinity (R²=0.71, RMSE=0.56 dS/m). Results demonstrate that during the early stage of irrigation district development (2006–2010), flood irrigation effectively leached salts and mitigated salinization, but also caused a rapid rise in groundwater levels. The subsequent adoption of water-saving irrigation slowed the groundwater rise, but due to its limited leaching capacity and strong evaporation, salt accumulated in the surface soil, intensifying salinization phenomenon. The study reveals that groundwater depth and salinity significantly influence soil salinity distribution, with agricultural development playing a critical role in regional water-salt dynamics. Scenario simulations for 2022–2030 indicate continued groundwater rise to an average of 1.7 m and a 20 % increase in surface soil salinity under current irrigation practices. These findings highlight the unsustainability of current water management and emphasize the urgent need for measures, such as effective drainage systems, or cultivation of salt-tolerant crops to ensure sustainable agricultural development.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"317 ","pages":"Article 109611"},"PeriodicalIF":5.9,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365972","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":"The effects of dynamic soil moisture-dependent parameters on runoff and crop yield estimation for the APEX model","authors":"Quinn Pallardy,&nbsp;Claire Baffaut","doi":"10.1016/j.agwat.2025.109612","DOIUrl":"10.1016/j.agwat.2025.109612","url":null,"abstract":"<div><div>The APEX, or Agricultural Policy/Environmental Extender Model, can be used to evaluate the effects of management practices on important agricultural outcomes, including water and soil quality, erosion, and crop yields. APEX simulates many of the bio-physical processes that take place on a farm or small watershed. The parameters APEX uses to detail how these processes are simulated are typically determined through calibration and validation procedures based on a comparison of model output to observational data. However, traditional methods of parameterization assume a static set of optimal parameters for the full simulation. To investigate the validity of the assumption of stationarity in optimal parameter values, this study assessed whether the introduction of parameters allowed to vary based on soil moisture conditions could offer improved runoff and crop yield estimation for the APEX model. The null hypothesis examined was that optimal parameters under dry conditions were similar to those under wet conditions. Results indicated that dynamic parameters resulted in improvements in model performance for an objective function that combined mean absolute error of modeled output water yields and crop yields under calibration scenarios. Validation performance also improved, though at less magnitude. The calibration process for several dynamic parameters indicated changes in optimal values based on soil moisture levels. These results have potential implications for both the accuracy and confidence in APEX outcomes under current scenarios and for APEX outcomes under changing environmental conditions.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"317 ","pages":"Article 109612"},"PeriodicalIF":5.9,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144338643","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":"A framework of crop water productivity estimation from UAV observations: A case study of summer maize","authors":"Minghan Cheng ,&nbsp;Ni Song ,&nbsp;Josep Penuelas ,&nbsp;Matthew F. McCabe ,&nbsp;Xiyun Jiao ,&nbsp;Yuping Lv ,&nbsp;Chengming Sun ,&nbsp;Xiuliang Jin","doi":"10.1016/j.agwat.2025.109621","DOIUrl":"10.1016/j.agwat.2025.109621","url":null,"abstract":"<div><div>This investigation establishes Crop Water Productivity (CWP) - quantified as yield per unit water consumption (kg/m³) - as a pivotal metric for agricultural water resource optimization. However, current methodologies face limitations in estimation accuracy and operational efficiency due to the multidisciplinary complexity integrating agronomic and hydrological expertise. To address this challenge, our research develops an innovative UAV-based monitoring framework through systematic integration of long-term multispectral/thermal infrared observations with multi-model fusion: (1) Surface Energy Balance Algorithm for Land (SEBAL) and FAO-56 Penman-Monteith models for evapotranspiration (ET) estimation; (2) Random Forest algorithm incorporating four phenotypical growth indicators for yield estimation, ultimately enabling CWP quantification. Key scientific findings demonstrate: (1) SEBAL outperformed FAO-56 in daily ET estimation (R² = 0.76 vs. 0.71, RMSE = 1.15 vs. 1.31 mm/d). (2) The machine learning yield model exhibited robust predictive capability (R² = 0.77, RMSE = 0.98 t/ha), successfully capturing yield variability across treatments. (3) Error propagation analysis validated framework reliability (CWP RMSE = 0.67 kg/m³), effectively differentiating CWP performance among management practices. This breakthrough validates the operational efficacy of UAV remote sensing for precision agricultural water assessment, providing decision-support for field-scale irrigation scheduling optimization, drought-resilient cultivar selection through CWP benchmarking and sustainable intensification strategies. The methodology establishes novel methodological benchmarks for crop-water relationship studies through its innovative fusion of multi-source remote sensing data and multiple model combination.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"317 ","pages":"Article 109621"},"PeriodicalIF":5.9,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144338644","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":"Soil moisture retrieval and spatiotemporal variation analysis based on deep learning","authors":"Zihan Zhang ,&nbsp;Jinjie Wang ,&nbsp;Jianli Ding ,&nbsp;Jinming Zhang ,&nbsp;Liya Shi ,&nbsp;Wen Ma","doi":"10.1016/j.agwat.2025.109622","DOIUrl":"10.1016/j.agwat.2025.109622","url":null,"abstract":"<div><div>Soil moisture is a key factor in soil-atmosphere energy and material exchanges, playing a crucial role in the hydrological cycle and agricultural management. Traditional monitoring methods are limited in large-scale and real-time applications, and the complex mechanisms of soil processes complicate modeling. However, deep learning provides a promising approach to capturing the complex nonlinear relationships between feature parameters and soil moisture content. Here, Sentinel-1 and Landsat data, along with <em>in-situ</em> measurements (0–10 cm) from the Wei-Ku Oasis, were used to extract 36 feature parameters. The Boruta algorithm and correlation analysis were applied to select key variables. Nine deep learning models, including three basic architectures (Convolutional Neural Networks (CNN), Long Short-Term Memory Networks (LSTM), Transformer) and six hybrid structures (CNN-LSTM, LSTM-CNN, CNN-with-LSTM, CNN-Transformer, GAN-LSTM, Transformer-LSTM), were systematically compared to evaluate the impact of neural network structure on model performance. The optimal model was then used to perform spatiotemporal mapping of soil moisture. Results indicated that both single-structure and hybrid models were effective for soil moisture retrieval, with CNN-based models (either standalone or hybrid) performing better. Among them, the CNN-LSTM hybrid model achieved the best performance with an R²of 0.72 on the test set. The soil moisture map produced by the optimal model reveals a spatial distribution pattern in the Weiku Oasis, characterized by higher moisture levels in the center and lower levels at the periphery. Temporally, from 2017–2024, soil moisture at the 0–10 cm depth exhibited an overall increasing trend. We demonstrate that the design of efficient neural network architectures is essential for soil moisture inversion, and provides valuable insights for deep learning applications in hydrological parameter estimation and other challenges in complex environmental contexts.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"317 ","pages":"Article 109622"},"PeriodicalIF":5.9,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144335971","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 driving factors of spring agricultural drought in Northeast China from the perspective of atmosphere and snow cover","authors":"Wei Pei ,&nbsp;Yi Su ,&nbsp;Qiang Fu ,&nbsp;Yongtai Ren ,&nbsp;Tianxiao Li","doi":"10.1016/j.agwat.2025.109620","DOIUrl":"10.1016/j.agwat.2025.109620","url":null,"abstract":"<div><div>Drought is closely related to the early stages of the water cycle. Due to the influence of snow cover and soil freeze<img>thaw processes, the water cycle process in cold regions presents specific and complex characteristics in the early spring. Therefore, the driving factors of spring drought in cold regions are different from those in warmer regions. This work takes the cold region of Northeast China as the study area. The early spring period was divided into four periods: the prefreezing period, rapid freezing period, stable freezing period, and melting period. Precipitation, evapotranspiration, and snow depth were selected as candidate driving factors in each period. The rough set conditional information entropy method was used to calculate the weights of the driving factors, and the main driving factors were preferably selected. The results indicated that as soil depth increased, the range of drought expanded. Spring drought was mainly concentrated in the western, central, and central western regions of the study area, whereas the northern, eastern, and southeastern regions were relatively humid. The precipitation and evapotranspiration during the prefreezing and melting periods had a considerable impact on spring drought, and for shallow spring drought, the impact of precipitation was greater, whereas the impact of evapotranspiration during deep drought was greater. The impermeability and evaporation suppression function of frozen soil resulted in early water retention in the soil, which in turn affected the soil moisture of the following year. During the rapid freezing period, snow depth was the main factor driving spring drought. The snow during the freezing period helped to suppress soil moisture evaporation, and the infiltration of snowmelt effectively increased the soil moisture content. Studying the driving factors of spring drought can help identify the formation mechanism of agricultural drought in cold regions and provide a reference for disaster prevention.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"317 ","pages":"Article 109620"},"PeriodicalIF":5.9,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330315","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":"Precise irrigation of dryland cotton under canal irrigation system constraints based on the CERES-CROPGRO-Cotton model","authors":"Lei Wang ,&nbsp;Liang He ,&nbsp;Weihong Sun ,&nbsp;Chen Gao ,&nbsp;Zhenxiang Han ,&nbsp;Meiwei Lin","doi":"10.1016/j.agwat.2025.109624","DOIUrl":"10.1016/j.agwat.2025.109624","url":null,"abstract":"<div><div>Xinjiang agriculture faces significant challenges due to water resource scarcity and uneven distribution, making accurate predictions of irrigation's impact on cotton yield crucial for decision-making. Existing studies primarily examine the relationship between irrigation and soil but often overlook the combined effects of irrigation networks, time constraints, and the interactions between crop growth patterns, weather, soil, and irrigation strategies. This study integrates the Decision Support System for Agrotechnology Transfer (DSSAT) model with machine learning models, accounting for weather, soil, crop conditions, and irrigation time constraints, to propose an intelligent irrigation decision-making framework. Meteorological data from 1980 to 2024, 13 sets of soil data, and field experiments conducted in 2023 and 2024 were used to calibrate the DSSAT model (with a calibration rate of 0.856). Additionally, water channel quota time constraints were established to determine the optimal irrigation timing. The framework analyzes the interactions between weather, soil, irrigation strategies, and other factors, enhancing cotton yield prediction. The results indicate that the intelligent decision-making algorithm outperforms traditional methods under data limitations, reducing the irrigation water consumption-yield ratio (<span><math><msub><mrow><mi>U</mi></mrow><mrow><mi>i</mi></mrow></msub></math></span>) by 3.99 %, while increasing yield by 8.5 % to 9724 kg/ha, thus achieving both water-saving and yield-enhancing objectives. This research offers a refined solution for intelligent irrigation decision-making in cotton cultivation in arid regions and paves the way for the application of intelligent agricultural decision systems.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"317 ","pages":"Article 109624"},"PeriodicalIF":5.9,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144335970","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}