Agricultural Water Management最新文献

{"title":"Farmers agronomic management responses to extreme drought and rice yields in Bihar, India","authors":"Maxwell Mkondiwa ,&nbsp;Avinash Kishore ,&nbsp;Prakashan Chellattan Veetil ,&nbsp;Sonam Sherpa ,&nbsp;Satyam Saxena ,&nbsp;Bhavani Pinjarla ,&nbsp;Anton Urfels ,&nbsp;Shishpal Poonia ,&nbsp;Anurag Ajay ,&nbsp;Peter Craufurd ,&nbsp;Ram Malik ,&nbsp;Andrew McDonald","doi":"10.1016/j.agwat.2025.109830","DOIUrl":"10.1016/j.agwat.2025.109830","url":null,"abstract":"<div><div>In 2022, the Indian state of Bihar experienced its sixth driest year in over a century. To document the consequences and farmer responses to the meteorological drought, real-time survey data was collected across 11 districts of Bihar. We then developed a causal machine learning model to quantify drought impacts on rice production and to characterize how access to affordable irrigation from electric pumps mitigated productivity losses. This model addresses the empirical challenge of conducting a counterfactual causal analysis when a factor like drought affects nearly all sampled farmers. In the 2022 event, drought led to rice acreage reduction, transplanting delays, damage to seedling nurseries, and higher use rates of supplemental irrigation. For fields that were planted, average yield losses from water stress were estimated as 0.94 t/ha (∼23 % yield loss) with these losses reduced by 0.3 t/ha in fields with access to electric tubewells. Agronomic management practices such as earlier transplanting were also identified as complementary strategies that increased the adaptation value of investments in irrigation. To reduce the impact of drought in Bihar, additional investments in electric irrigation infrastructure are needed along with focused extension efforts and decision support systems that empower farmers to make economically and sustainably rational use of available water resources to maintain yield and profitability.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"320 ","pages":"Article 109830"},"PeriodicalIF":6.5,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145156630","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 manganese and copper concentrations on emitter clogging in brackish water drip irrigation systems","authors":"Peng Hou ,&nbsp;Changjian Ma ,&nbsp;Yayu Wang ,&nbsp;Thair Muhammad ,&nbsp;Kai Zhang ,&nbsp;Shance Hou ,&nbsp;Jingzhi Li ,&nbsp;Yang Xiao ,&nbsp;Yunkai Li","doi":"10.1016/j.agwat.2025.109844","DOIUrl":"10.1016/j.agwat.2025.109844","url":null,"abstract":"<div><div>The use of brackish water in drip irrigation has proven to be an effective strategy for alleviating water scarcity in arid regions. However, the problem of emitter clogging remains a major barrier to the widespread adoption of this technology. The application of manganese (Mn) and copper (Cu) offers a promising dual benefit: mitigating emitter clogging while simultaneously supplying essential trace element fertilizers. Nevertheless, the optimal concentrations of Mn and Cu for this purpose remain unclear. This study investigated the effects of seven different manganese ion (Mn^2 +) concentrations (0–3.0 mg/L) and seven different copper ion (Cu²⁺) concentrations (0–0.3 mg/L) on emitter clogging in brackish water drip irrigation systems. The results demonstrate that emitter clogging in brackish water drip irrigation systems can be effectively mitigated by regulating Mn²⁺ and Cu²⁺ concentrations. As concentrations of Mn²⁺ and Cu²⁺ increased, the dry weight of emitter clogging substances initially increased and then decreased. Compared to water sources without ion addition, Mn²⁺ concentrations of 0–1.5 mg/L exacerbated emitter clogging, while concentrations of 1.5–3.0 mg/L mitigated it. This was due to 0–1.5 mg/L promoting the formation of clogging substances calcite and Na-feldspar, whereas 1.5–3.0 mg/L reduced the formation of substances quartz, muscovite, and chlorite. Conversely, Cu²⁺ concentrations of 0–0.3 mg/L reduced the fouling accumulation process, with the optimal emitter clogging control observed at 0.3 mg/L. This was attributed to Cu²⁺ reducing the formation of substances quartz and calcite. Based on the findings, it is recommended that Mn²⁺ concentrations be maintained above 2.0 mg/L and Cu²⁺ concentrations no less than 0.15 mg/L in drip irrigation systems. These results provide practical guidance for emitter clogging control and contribute to the sustainable utilization of brackish water resources in agriculture.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"320 ","pages":"Article 109844"},"PeriodicalIF":6.5,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145156626","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":"Assessing the impact of soil water deficit and supplemental irrigation scenarios on Ohio’s maize and soybean yields using machine learning models","authors":"Rajveer Dhillon ,&nbsp;Susanta Das ,&nbsp;Vinayak S. Shedekar ,&nbsp;Vivek Sharma","doi":"10.1016/j.agwat.2025.109834","DOIUrl":"10.1016/j.agwat.2025.109834","url":null,"abstract":"<div><div>To understand the relationship between climate variables and irrigation requirements with crop yield, this study evaluates the role of monthly precipitation, temperature, soil water deficit (SWD), and supplemental irrigation on the spatio-temporal variability of county-level maize and soybean yields across Ohio. We combined a soil water balance approach with machine learning to identify key climatic and soil hydrological variables influencing the effects of supplemental irrigation on county-level maize and soybean yields. To model the effect of SWD and weather parameters on yield variability, the Random Forest model performed best with an RMSE of 0.60 Mt/ha and an R² of 0.77 for maize, and with an RMSE of 0.21 Mt/ha and an R² of 0.64 for soybean yields. Maize yields were most influenced by July soil water deficit, September maximum temperature, and August precipitation, whereas soybean yields were primarily affected by precipitation in May and August. Supplemental irrigation of 50.8 mm/month during the summer improved maize yields more than soybean yields, with an average maize yield increase of 598 kg/ha (∼0.6 Mt/ha) relative to rainfed conditions. However, a statistically significant reduction in inter-annual yield variability was found with supplemental irrigation under all conditions for both crops. Irrigation beyond 50.8 mm/month did not yield further significant gains. Yield improvement varied over the years, with higher improvement seen during dry years, particularly for maize and it was more pronounced in years with cooler September months. Southwest Ohio showed a higher average yield (1991–2022) increase for maize, with an average increase of up to 22.6 %, which corresponds to an increase of 1224 kg/ha (∼1.2 Mt/ha). For soybeans, an average yield increase of up to 9.2 % was found, which corresponds to an increase of 207.5 kg/ha (∼0.21 Mt/ha), and counties with higher average yield increases were found in different regions of Ohio.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"320 ","pages":"Article 109834"},"PeriodicalIF":6.5,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145120501","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":"Deep learning for intelligent irrigation decision-making: A review","authors":"Jingxin Yu ,&nbsp;Qinglin Qu ,&nbsp;Shuyi Peng ,&nbsp;Xiaoming Wei ,&nbsp;Yinkun Li ,&nbsp;Congcong Sun","doi":"10.1016/j.agwat.2025.109836","DOIUrl":"10.1016/j.agwat.2025.109836","url":null,"abstract":"<div><div>Global agriculture faces the dual challenges of water scarcity and climate change, making efficient and precise irrigation management increasingly important. This review analyzes the role of deep learning (DL) technologies in intelligent irrigation decision-making: (1) DL technologies have shifted irrigation management from experience-based decisions to data-driven precision prediction. (2) Deep learning architectures demonstrate distinct advantages in different aspects of irrigation management, including spatial identification, soil water content prediction, long-term forecasting, and optimization of water use. (3) Hybrid DL models often demonstrate superior performance in practical applications. (4) Edge-cloud collaborative architectures are particularly effective, reducing communication volume and decreasing response times from minutes to seconds. Despite progress, intelligent irrigation using DL faces challenges related to data quality, model generalization ability, and computational resource limitations, as well as application barriers such as cost, acceptance, and regional adaptability. Future work should prioritize climate-adaptive models, extreme-weather response, and ultra-precise management in water-scarce regions, while evaluating federated, few-shot learning and large language models as enabling methods.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"320 ","pages":"Article 109836"},"PeriodicalIF":6.5,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145120499","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":"Spatiotemporal modeling for enhancing winter wheat yield and water productivity in dryland farming with supplemental irrigation under variable rainfall conditions","authors":"Simin Mashouqi ,&nbsp;Seyed Hamid Ahmadi ,&nbsp;Bahareh Kamali","doi":"10.1016/j.agwat.2025.109825","DOIUrl":"10.1016/j.agwat.2025.109825","url":null,"abstract":"<div><div>Water scarcity induced by climate change poses a significant challenge to sustainable crop production in dryland regions. This study employed the water-driven AquaCrop model to simulate wheat yield in the southwestern Iran divided into dryland and irrigated regions. Using long-term meteorological data, wheat grain yield (GY) and crop water productivity (WPc) were simulated under three water level scenarios including dryland, supplemental irrigation (SI), and fully irrigation. In this study we introduced a newly defined modified rainfall shape index (MRSI), which takes into account both rainfall amount and temporal distribution, especially early- or late-season events- that strongly influence dryland wheat growth. Analysis showed a very significant correlation of MRSI with GY (r = 0.41), particularly in the moderate-rainfall regions (e.g., 300–500 mm). Simulations revealed that applying 50 mm supplemental irrigation before flowering notably increased irrigation water productivity (SIWP) to 0.6–2.4 kg m^-³ . Despite the model's insights, reliance on monthly rainfall data led to over- or under-estimations of GY due to the AquaCrop interpolation method. Furthermore, a regression model showed transpiration-based and evapotranspiration water productivity of 1.86 and 1.64 kg m^-³ , suggesting to apply agronomic practices in dryland regions to reduce soil evaporation and select high-transpiration-efficiency wheat genotypes. In the irrigated regions, deficit irrigation maintained yields while improving WPc. Inverse modeling of light extinction coefficient (<em>k</em>) ranged from 0.43 to 0.68, emphasizing the importance of canopy structure in optimizing water use efficiency. Overall, the study highlights the important role of rainfall pattern and irrigation management in sustaining wheat grain yield in semi-arid regions.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"320 ","pages":"Article 109825"},"PeriodicalIF":6.5,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145120498","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":"Optimizing regional irrigation management in arid saline areas using a process-based hydro-salt-crop model and fallow strategy","authors":"Qihua Yu ,&nbsp;Shaozhong Kang ,&nbsp;Hui Wu ,&nbsp;Jian Song ,&nbsp;Hui Wang ,&nbsp;David Parsons","doi":"10.1016/j.agwat.2025.109832","DOIUrl":"10.1016/j.agwat.2025.109832","url":null,"abstract":"<div><div>Agriculture in arid regions faces water scarcity and spatially heterogeneous soil salinization, compelling consideration of brackish groundwater irrigation and strategic fallowing under conditions of extreme water scarcity. A key challenge for agricultural managers is optimizing limited surface and groundwater allocation in complex, heterogeneous saline environments with varying water availability. This study introduces a gridded regional irrigation water optimization model under total irrigation water control, integrating salt effects, fallow strategy and multi-source water management. The model: (1) incorporates salt impacts on crop growth, saline groundwater use, and spatial salt heterogeneity; (2) generates surface/saline groundwater allocation and marginal land use guidance; (3) balances water scarcity and salinization trade-offs. An empirical study was conducted using cotton field data in the First Division of the Tarim Irrigation District in Xinjiang, China. The results suggest that the optimized water allocation scheme could increase cotton lint yield in the Tarim Irrigation District by up to 9959 tons (+3.3 %) compared to traditional uniform allocation. Soil salt content dominated allocation decisions. When surface water availability is limited, water distribution should prioritize high-yield fields (non-severe salinization), and supplemental brackish groundwater irrigation can mitigate yield losses. Rational fallowing can enhance total yield in the irrigation district while reducing input costs, with severely saline areas being prime candidates for fallowing policies. This research provides a scientific basis for optimizing water-salt management in cotton production, groundwater extraction, and irrigation water allocation in saline arid regions, while future work could integrate ion-specific chemistry and its crop response functions for wider applications.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"320 ","pages":"Article 109832"},"PeriodicalIF":6.5,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145156631","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":"Species asynchrony and species richness stabilize grassland productivity under different rainfall addition regimes","authors":"Zhenyu Yao ,&nbsp;Tianming Gao ,&nbsp;Yue Xin ,&nbsp;Jianying Guo ,&nbsp;Ru Tian ,&nbsp;Ting Yuan ,&nbsp;Jing Liu ,&nbsp;Ende Xing ,&nbsp;Jiatao Zhang","doi":"10.1016/j.agwat.2025.109840","DOIUrl":"10.1016/j.agwat.2025.109840","url":null,"abstract":"<div><div>Changes in global precipitation patterns are increasingly impacting the structure, function, and stability of terrestrial ecosystems, especially in water-limited grasslands. These changes lead to reduced ecosystem stability, as manifested by enhanced productivity fluctuations, shifts in biodiversity, and ecological uncertainty. Understanding how different rainfall addition treatments influence grassland ecosystem stability is, therefore, essential for predicting ecosystem responses under increasingly variable precipitation regimes. In this three-year study, we examined the effects of control (natural rainfall), phenology-based rainfall addition (applied during the green-up, tillering, and flowering stages, 40 mm/month from May to July), and full-season rainfall addition (40 mm /month from April to September) on aboveground net primary productivity (ANPP), species richness, species asynchrony, and the temporal stability of community ANPP in a semi-arid grassland in Inner Mongolia. Both rainfall addition treatments markedly increased ANPP (by 100.8 % and 145.8 % for phenology-based and full-season, respectively) and enhanced its temporal stability. Full-season rainfall addition increased productivity via dominant species growth, with no significant effect on species diversity. In contrast, phenology-based rainfall addition simultaneously boosted productivity and significantly increased species richness. Structural equation modeling revealed that community ANPP stability was mainly driven by species asynchrony and richness, with minimal contribution from dominant species stability. These findings highlight the pivotal roles of biodiversity and asynchronous species responses in buffering productivity fluctuations. From a management perspective, phenology-based rainfall addition offers a more sustainable approach to enhance ecosystem function by improving productivity while conserving biodiversity. These insights provide practical guidance for sustainable grassland management in the face of shifting precipitation patterns.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"320 ","pages":"Article 109840"},"PeriodicalIF":6.5,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145120500","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":"Establishing and assessing a new two-dimensional module in CERES-maize for maize production under mulched drip irrigation","authors":"Dan Wang ,&nbsp;Yan Mo ,&nbsp;Juan Xiao ,&nbsp;Guangyong Li","doi":"10.1016/j.agwat.2025.109824","DOIUrl":"10.1016/j.agwat.2025.109824","url":null,"abstract":"<div><div>In view of the problem that the traditional CERES-maize model does not consider plastic film covering and the water balance module describes a one-dimensional movement of soil water, this study modified the input data of air temperature, which was compensated by the soil accumulative temperature under plastic film mulching, based on the invariance of growing degree days principle in the model, and established a two-dimensional (2D) CERES-Maize model. The 2D model was calibrated and verified with maize phenological period, aboveground biomass (AB), grain yield and yield components, and maize actual evapotranspiration (ET_{c act}) during the whole growing season in 2015 and 2016 under mulched drip irrigation. Results indicated that the modified model effectively improved the simulating accuracy of maize phenological period and main growth indexes, which made the Absolute Relative Error (ARE) decrease by 11.2 %, 1.8 % and 2.1 % points for maize emergence, anthesis and maturity date respectively, and made the normalized Root Mean Square Error (nRMSE) reducing 1.5 % and 5.9 % points for grain number per ear and AB respectively, due to the compensation of soil temperature under film mulching for the air temperature during maize Sowing∼V6 (the sixth leaf) period. The 2D CERES-Maize model was established with a consideration of plastic film mulching effect on soil water evaporation and the two-dimensional water movement characteristics of drip irrigation. The simulating accuracy of 2D model was improved with a decrease of 1.5–2.5 % and 2.5–4.8 % points in nRMSE for grain yield and AB, respectively, compared with those of 1D model. The 2D model could simulate the differences of maize ET_{c act} during the whole growing season under different irrigation quotas very well (nRMSE&lt;10 %), and the simulating accuracy of 2D model was significantly improved with a decrease of 2.2–5.9 % points of nRMSE for maize ET_{c act}, compared with that of 1D model. In conclusion, the 2D CERES-Maize model preliminarily established in this study basically realized the simulation of maize production under mulched drip irrigation, but how to consider the warming effect of plastic film mulching in the model remains to be further improved.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"320 ","pages":"Article 109824"},"PeriodicalIF":6.5,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145109675","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":"Cross-scale soil moisture content monitoring of winter wheat by integrating UAV and sentinel-1/2 data","authors":"Xingjiao Yu ,&nbsp;Qi Yin ,&nbsp;Long Qian ,&nbsp;Chaoyue Zhang ,&nbsp;Lingjia Shao ,&nbsp;Danjie Ran ,&nbsp;Wen’e Wang ,&nbsp;Baozhong Zhang ,&nbsp;Xiaotao Hu","doi":"10.1016/j.agwat.2025.109831","DOIUrl":"10.1016/j.agwat.2025.109831","url":null,"abstract":"<div><div>Accurate estimation of soil moisture content (SMC) is critical for agricultural irrigation, water resource management, and monitoring the ecological environment. The development of multi-sensor UAV platforms offers a novel approach to cross-scale SMC monitoring. This study presents an innovative framework that integrates ground, UAV, and satellite data to estimate SMC and generate county-scale spatial distribution maps of SMC in winter wheat fields. Firstly, UAV images were utilized at the subplot scale to extract winter wheat planting areas through supervised classification, and SMC was estimated by employing partial least squares regression (PLSR). Subsequently, the UAV SMC mapping results were upscaled and integrated with Sentinel-1 synthetic aperture radar (SAR) features and Sentinel-2 multispectral features to develop XGBoost-based satellite-scale SMC estimation model. The study demonstrated that at the plot scale, combining vegetation indices and texture features achieved the highest accuracy (0–20 cm: R² = 0.775, RMSE = 0.018 m³/m³; 20–40 cm: R² = 0.723, RMSE = 0.021 m³/m³). At the satellite scale, the XGBoost model also performed well (0–20 cm: R² = 0.901, RMSE = 0.0071 m³/m³; 20–40 cm: R² = 0.884, RMSE = 0.011 m³/m³). Furthermore, compared to traditional ground-satellite models, the integrated ground-UAV-satellite approach improved accuracy, with R² increasing by 9.53–10.52 %, RMSE decreasing by 11.11–1.25 %, and MAE reducing by 18.19–25.00 %. This cross-scale remote sensing framework enhances SMC monitoring efficiency and accuracy, offering a robust solution for large-scale applications.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"320 ","pages":"Article 109831"},"PeriodicalIF":6.5,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145109676","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":"Assessing environmental impacts of agricultural water table management: A global meta-analysis","authors":"Ruiqi Wu ,&nbsp;Ziwei Li ,&nbsp;Zhiming Qi ,&nbsp;Junzeng Xu ,&nbsp;Qi Wei ,&nbsp;Junliang Jin","doi":"10.1016/j.agwat.2025.109833","DOIUrl":"10.1016/j.agwat.2025.109833","url":null,"abstract":"<div><div>Global climate change intensifies the need for adaptive water management in agriculture, particularly as extreme rainfall and drought events jeopardize yield stability and water quality. This study provides a comprehensive global meta-analysis comparing controlled drainage (CD) and controlled drainage with subirrigation (CDSI) against free drainage (FD), based on 84 publications encompassing 52 sites (1788 paired site-years).</div><div>Overall, both CD and CDSI significantly reduced subsurface drainage discharge and nutrient losses (NO₃⁻, TN, DRP, TP) compared to FD, while improving crop yields. CD had no significant effects on surface runoff or surface NO₃⁻ loss, and CDSI increased both. Neither practice significantly influenced subsurface NH₄⁺, PP, or DP losses, and CDSI showed only limited evidence for CH₄ reduction, with no consistent effects on other greenhouse gases. Significant differences between CD and CDSI were observed only for subsurface NO₃⁻, TN, and TP losses, with CDSI achieving greater reductions in NO₃⁻ (CD: 59.4 %, CDSI: 72.5 %) and TN (CD: 16.3 %, CDSI: 55.9 %), whereas CD was more effective in reducing TP (CD: 56.8 %, CDSI: 28.6 %).</div><div>Moderator effects revealed notable patterns. Crop types influenced subsurface drainage discharge under both CD and CDSI, with small grain crops producing the largest reductions (CD: 68.1 %, CDSI: 83.6 %). Under CDSI, drainage responses were further shaped by N input rate, with higher inputs associated with greater reductions (77.3 %). For CD, subsurface NO₃⁻ loss was strongly affected by precipitation and soil texture, with wetter years (79.2 %) and medium-textured soils (63.6 %) offering the greatest reduction potential. This study presents the first comparative assessment of the environmental impacts of CD and CDSI, highlighting their context-dependent nature and providing insights to inform more informed drainage management decisions.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"320 ","pages":"Article 109833"},"PeriodicalIF":6.5,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145120436","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}