Agricultural Water Management最新文献

{"title":"Assessing the long-term water footprint of olive grove under changing climate (Apulia, Italy)","authors":"M. Leone, O. Attar, Y. Brouziyne, E.M. El Khalki, L. Bouchaou, A.M. De Girolamo","doi":"10.1016/j.agwat.2025.109875","DOIUrl":"https://doi.org/10.1016/j.agwat.2025.109875","url":null,"abstract":"Changes in the water balance and an increase in agricultural water requirements are generally expected for the future due to climate change (CC). In this context, sustainable water resources management will play a crucial role in balancing human and ecosystem demands. Going beyond a case study (Locone basin, Apulia, Italy), this paper aims to analyze the water consumption in olive cultivation under CC through the water footprint (WF) approach. Two climate model projections were adopted, MPI-ESM1–2-LR and CMCC-CM-COSMO-CLM, and different scenarios were developed for analyzing the potential effects of the increase in temperature and atmospheric CO<ce:inf loc=\"post\">2</ce:inf> concentration on the WF components. The Soil and Water Assessment Tool (SWAT+) was used to estimate the WF<ce:inf loc=\"post\">green</ce:inf> and WF<ce:inf loc=\"post\">blue</ce:inf> (WF<ce:inf loc=\"post\">g,b</ce:inf>), and crop yield under different environmental conditions. For the baseline (2000–2009), the WF<ce:inf loc=\"post\">green</ce:inf> was 831 m<ce:sup loc=\"post\">3</ce:sup> t<ce:sup loc=\"post\">−1</ce:sup> and WF<ce:inf loc=\"post\">blue</ce:inf> was 116 m<ce:sup loc=\"post\">3</ce:sup> t<ce:sup loc=\"post\">−1</ce:sup>. For the future (2040–2049), the climate models project an increase in temperature (up to 1.12°C) and a decrease in rainfall (up to −17 %) compared to the baseline. The results showed that the impact of CC constitutes an important risk for the productivity of olive (up to −17 %). The positive effect of CO<ce:inf loc=\"post\">2</ce:inf> fertilization (up to 500 ppm) on the crop yield is insufficient to maintain the baseline productivity. To preserve the latter, an increase in irrigation (up to 135 %) is needed with a consequent rise in WF<ce:inf loc=\"post\">g,b</ce:inf> (up to 18 %). These results provide useful insights for agricultural water management under CC.","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"1 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145229159","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":"Wide-precision planting improves winter wheat yield, nitrogen use efficiency and water productivity in China: A meta-analysis","authors":"Liwei Fei, Lihua Xiao, Ying Zhang, Yuanjie Dong, Runqiang Liu, Chuan Zhong, Mingrong He, Xinglong Dai","doi":"10.1016/j.agwat.2025.109869","DOIUrl":"https://doi.org/10.1016/j.agwat.2025.109869","url":null,"abstract":"Wide-precision planting has been introduced to wheat production systems in China to improve grain yield. However, the effects of wide-precision planting on wheat yield, nitrogen (N) use efficiency (NUE), and water productivity (WP) have not yet been comprehensively evaluated. A meta-analysis was conducted using 699 observations from 79 studies to quantify the contributions of wide-precision planting to wheat yield, NUE, and WP. Compared with conventional-cultivation planting, wide-precision planting increased wheat yield by 9.9 %, NUE by 9.3 %, and WP by 4.8 %. Soil conditions had the greatest importance on wheat yield, NUE, and WP under wide-precision planting, particularly suitable soil organic matter and available potassium. Fertilizer factors also influenced the wheat's comprehensive productivity under wide-precision planting. The random forest regression model revealed that more attention should be paid to the application of potassium and phosphorus fertilizers in wheat production. Additionally, wide-precision planting under high mean annual precipitation (&gt; 600 mm) and medium mean annual temperature (10–13℃) more easily coordinated improvements in yield, NUE and WP. In conclusion, wide-precision planting can significantly increase wheat comprehensive productivity. Considering the factors of climate, soil fertility and different fertilizers, the study holds novel insights for informing the widespread adoption of wide-precision planting practices in wheat production.","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"49 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145229160","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":"Phosphate source apportionment across the agriculture-urban gradient in Asia's longest river: Combining machine learning and multi-isotope techniques","authors":"Xing Chen ,&nbsp;Tianqi Ma ,&nbsp;Fazhi Xie ,&nbsp;Zhi Tang","doi":"10.1016/j.agwat.2025.109874","DOIUrl":"10.1016/j.agwat.2025.109874","url":null,"abstract":"<div><div>Excessive phosphorus (P) can have serious impacts on water quality and ecosystems, and accurately identifying P sources is crucial for preventing and controlling eutrophication in watersheds. Traditional source-tracing models are limited in their ability to identify the driving factors influencing P dynamics, leading to the accuracy of source apportionment being influenced by potential driving factors. Therefore, this study introduces machine learning (ML) methods, combined with various receptor models and isotope techniques, to quantitatively analyze the sources and driving factors of phosphate in the Yangtze River Basin (YRB). The ecological condition in the YRB is relatively favorable, with an average concentration of soluble reactive phosphorus (SRP) at 0.076 mg/L and phosphate saturation levels ranging from 18 % to 95 %. The results of δ¹⁸O_(PO4) indicate that agricultural discharges, livestock discharges, phosphate rock, and mixed sources predominantly composed of sewage discharges are the main P sources in the YRB. In the upstream, phosphate rock (54.7 %) and livestock sources (33.9 %) are the primary phosphate sources, whereas in the midstream, agricultural sources (66.1 %) dominate phosphate sources. In the downstream, agricultural sources (48.3 %) and mixed sources (33.3 %) are the main contributors to phosphate. Consequently, the application of ML provides an effective approach for the analysis of P pollution sources and the identification of driving factors, offering important scientific evidence for the management of eutrophication in large river basins across the agriculture-urban gradient.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"320 ","pages":"Article 109874"},"PeriodicalIF":6.5,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145218418","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":"Bias correction of satellite based crop water stress index using machine learning methods","authors":"Elahe Zoratipour ,&nbsp;Shadman Veysi ,&nbsp;Amir Soltani Mohammadi ,&nbsp;Saeed Boroomand Nasab ,&nbsp;Abd Ali Naseri","doi":"10.1016/j.agwat.2025.109862","DOIUrl":"10.1016/j.agwat.2025.109862","url":null,"abstract":"<div><div>Accurate estimation of the Crop Water Stress Index (CWSI) is essential for supporting irrigation scheduling in water-limited regions. Traditionally, CWSI has been computed from field-based canopy temperature and meteorological measurements; however, these approaches are often limited by high costs and sparse spatial coverage. Satellite remote sensing offers a practical alternative by providing large-scale and repeated observations of crop water stress. Nevertheless, uncertainties in satellite-derived inputs, particularly land surface temperature (LST), can introduce significant biases in CWSI computations. This study addresses the challenge of mitigating biases in the CWSI computations, utilizing satellite data within sugarcane fields in southwest Iran. For this goal, twenty-four Landsat 8/9 satellite images were acquired (June–September 2023). Concurrent with the satellite overpass times, data from 18 points across the sugarcane fields, were gathered to compute CWSI based on Idso method as a benchmark for bias correction. Field measurement includes, canopy temperature (T_c) and meteorological variables (i.e. T_min, T_max, RH_min, and RH_max) from sensor-equipped points. LST was calculated using the single-channel algorithm (SC) in google earth engine (GEE) platform. Three distinct machine learning (ML) methods, namely Random Forest (RF), Support Vector Machine (SVM), and extreme gradient boosting (XGBoost), were employed to mitigate bias in LST as main driver of CWSI based on satellite data. The results demonstrated that the RF model performed exceptionally well for LST bias correction. Overall, CWSI enhance accuracy, about with R² = 35 %, nRMSE= 47 % and rMBE= 50 %. This research highlights the effectiveness of ML methods in improving CWSI estimates based on satellite imagery and taking a big step towards calculating CWSI for optimizing irrigation management in arid regions, supporting sustainable water use and food security with minimal ground monitoring.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"320 ","pages":"Article 109862"},"PeriodicalIF":6.5,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145218033","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 soil water sensor placement and irrigation thresholds for winter wheat: A data-driven approach to efficient water management","authors":"Xudong Feng ,&nbsp;Zongzheng Yan ,&nbsp;Na Liu ,&nbsp;Qingshan Liu ,&nbsp;Liwei Shao ,&nbsp;Xiuwei Liu","doi":"10.1016/j.agwat.2025.109861","DOIUrl":"10.1016/j.agwat.2025.109861","url":null,"abstract":"<div><div>Soil moisture monitoring plays an important role in precision irrigation in modern agriculture. While wireless soil moisture sensors (SMSs) have revolutionized data collection for irrigation decision-making, critical knowledge gaps exist regarding optimal sensor placement strategies and dynamic threshold determination, particularly for deep-rooted crops like winter wheat (<em>Triticum aestivum L.</em>). This four-year experimental study (2018–2022) systematically investigated sensor placement optimization through multi-depth (10–200 cm) soil moisture monitoring under six water supply regimes. Subsequent validation trials (2022–2024) evaluated the proposed threshold values in balancing yield and water productivity. The results showed that shallow soil moisture (≤20 cm depth) exhibited significant variability and was not suitable to be used for irrigation decision. The threshold values for highest yield and highest water productivity were not the same, with the former being higher than the latter. The values in using relative soil water contents (soil water contents/field capacity, RSW) and fraction of available soil water (FASW) were 55 % and 32 %, respectively, at 30 cm for highest WP; and they were 60 % and 45 %, respectively, for highest yield. The threshold values were also depth-dependent. Down to 60 cm, the values were changing to 65 % and 50 % for highest WP, and 70 % and 60 % for highest yield, respectively. Soil moisture in deep soil layers (&gt;90 cm) could indicate the crop water status after anthesis. Implementation of depth-specific thresholds reduced irrigation inputs by 18–23 % in wet seasons (p &lt; 0.05) while maintaining yield stability and enhanced water productivity. Therefore, it was recommended that the depths of the sensor placement should be considered in deciding the threshold values for irrigation management for a deep-rooted crop. The threshold values for maximizing yield and water productivity should be separately decided.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"320 ","pages":"Article 109861"},"PeriodicalIF":6.5,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145218031","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":"Evaluation and comparison of OpenET models for estimating soil water depletion of irrigated alfalfa in Arizona","authors":"Said Attalah ,&nbsp;Elsayed Ahmed Elsadek ,&nbsp;Peter Waller ,&nbsp;Douglas J. Hunsaker ,&nbsp;Kelly R. Thorp ,&nbsp;Eduardo Bautista ,&nbsp;Clinton Williams ,&nbsp;Gerard Wall ,&nbsp;Ethan Orr ,&nbsp;Diaa Eldin M. Elshikha","doi":"10.1016/j.agwat.2025.109850","DOIUrl":"10.1016/j.agwat.2025.109850","url":null,"abstract":"<div><div>Growers in arid regions often estimate soil water depletion (D_r) of the root zone in making irrigation scheduling decisions. While growers might use soil water sensors, actual D_r in fields may be only vaguely assessed. In this study, daily crop evapotranspiration (ET_sat) data from the six OpenET satellite-based models (ALEXI/DisALEXI, eeMETRIC, geeSEBAL, PT-JPL, SIMS, SSEBop) and the OpenET ensemble were incorporated into a soil water balance (SWB) to estimate D_r (D_{r sat}) for alfalfa (<em>Medicago sativa</em> L.) within a 46-ha, center-pivot field in Buckeye, Arizona. A daily SWB for each model input the average ET_sat data acquired at four locations for both 30-m and 90-m pixel sizes, assuming an alfalfa root-zone of 1.8-m. Net irrigation (I_n) inputs were based on gross irrigation amounts, modified for evaporation losses using weather data from the AZMET station in Buckeye. D_{r sat} values were compared to the average observed root-zone D_r (D_{r obs}), as determined at the four locations by neutron moisture meter measurements on 21 dates between 05/23/2023 and 12/11/2023. Cumulative values were 841 mm for I_n plus effective precipitation, 776 mm (ALEXI/DisALEXI) to 1130 mm (SSEBop) for ET_sat, and 816 mm of ET obtained by a seasonal SWB (ET_swb) based on the average change in D_{r obs} (ΔD_{r obs}) from first to last measurements. Average D_{r obs} fluctuated between 59 and 133 mm, and ΔD_{r obs} was −25.0 ± 42.0 mm. Model variations in ET_sat resulted in different estimates of D_{r sat} and agreements, where mean bias error (MBE) ranged from ≈ -35 % (PT-JPL) to 195 % (SSEBop). Despite notable uncertainties in the SWB parameters and observed data, the SWB based on both the ensemble and ALEXI/DisALEXI ET_sat capably tracked D_{r obs} for most of the observations, excluding those made from late-July to mid-August. However, the best overall agreement was with the ensemble data, where differences between cumulative ET_sat and ET_swb and ΔD_{r sat} and ΔD_{r obs} were small, and MBE for D_{r sat} was less than 9 % and 15 % at 30-m and 90-m pixels, respectively. Findings suggest that using the single value OpenET ensemble daily ET could be a reliable source for estimating the D_r in arid climate alfalfa fields.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"320 ","pages":"Article 109850"},"PeriodicalIF":6.5,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145218417","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 feature engineering technique for enhancing the generalization of machine learning models in estimating crop evapotranspiration","authors":"Gaku Yokoyama ,&nbsp;Sohta Harigai ,&nbsp;Shigehiro Kubota ,&nbsp;Koichi Nomura ,&nbsp;Gregory R. Goldsmith ,&nbsp;Daisuke Yasutake ,&nbsp;Tomoyoshi Hirota ,&nbsp;Masaharu Kitano","doi":"10.1016/j.agwat.2025.109854","DOIUrl":"10.1016/j.agwat.2025.109854","url":null,"abstract":"<div><div>Accurate and precise estimation of evapotranspiration (<em>ET</em>) is crucial for understanding the terrestrial carbon, water, and energy cycles. While process-based models of <em>ET</em>, such as the Penman–Monteith model offer robust generalization capabilities, they are limited by the need for detailed parameters (<em>e.g.</em>, stomatal conductance,) that are challenging to measure continuously. On the other hand, machine learning models can estimate <em>ET</em> by capturing relationships between <em>ET</em> and environmental variables without experimentally measuring model parameters. However, machine learning models face the challenge of limited generalizability. This issue is particularly significant given the uncertainty introduced by changing climatic conditions, which can restrict the model's predictive performance when it is applied to different environmental contexts. Therefore, we propose a hybrid modeling approach that combines feature engineering using process-based models with machine learning to improve generalizability while maintaining practicality. Our model first converts environmental variables into leaf-scale <em>ET</em> using mechanistic process-based models and then uses these features along with the leaf area index to estimate the canopy-scale <em>ET</em> using an artificial neural network (ANN). We evaluated the generalization of the hybrid model against a pure ANN model using FLUXNET2015 data. Results show that the hybrid model significantly outperformed the pure ANN model, especially when tested on data beyond the range of the training dataset. Furthermore, the estimation accuracy of the hybrid model was stable even when the values of the model parameters in the process-based models used for feature engineering were varied by ±50 %. This indicates that incorporating a mechanistic understanding of plant environmental responses enhances the generalizability and robustness of <em>ET</em> predictions. These findings underscore the potential of hybrid models to combine the strengths of process-based and machine learning approaches.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"320 ","pages":"Article 109854"},"PeriodicalIF":6.5,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145218048","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":"Interactive effects of tillage and straw mulching on surface runoff, nutrient loss, and maize yield on sloping farmland with purple soil in China","authors":"Hong Wang ,&nbsp;Yueying Wu ,&nbsp;Li Yao ,&nbsp;Lin Wang ,&nbsp;Haitao Liu ,&nbsp;Limei Zhai ,&nbsp;Chaowen Lin ,&nbsp;Kai Wei ,&nbsp;Honglin Chen ,&nbsp;Huan Liu ,&nbsp;Dinghui Liu","doi":"10.1016/j.agwat.2025.109860","DOIUrl":"10.1016/j.agwat.2025.109860","url":null,"abstract":"<div><div>Investigating the impacts of different management practices on soil erosion and nutrient loss in purple soil sloping farmlands can contribute to the optimization of management strategies. The best tillage system for sustainable agriculture within this region remains unknown. Therefore, a seven-year field experiment was carried out at 15 randomly selected experimental sites, aiming to determine the effects of no tillage (TR1), ridge tillage (TR2), strip tillage (TR3), ridge tillage with straw mulching (TR4), and strip tillage with straw mulching (TR5) methods on runoff depth, nutrient loss, soil chemical properties, and maize yield. The results indicated that, compared with those under no-till, runoff depth and nutrient losses (nitrogen: N and phosphorus: P) under all conservation tillage practices were reduced over 60–90 %. The runoff depth was the lowest in TR4, while the nutrient losses were the lowest in TR4 in dry years and normal years, and in TR5 in wet years. The dissolved form made up the majority of N (71–90 %) and P (58–79 %) losses in runoff. Straw mulching was positively correlated with soil chemical properties, and the annual soil organic carbon (SOC) increased significantly under TR4 (up to 31 %) and TR5 (up to 17 %) compared with other treatments. Compared with those of the TR1 method, maize yield and nutrient uptake responses were positive for the TR4 method in dry years and normal years, and for the TR5 method in wet years. Overall, these results will offer more precise management suggestions for the optimization of the interaction between straw return and soil tillage, reducing nutrient loss by water erosion, as well as advancing the conservation and sustainable utilization on the sloping land of purple soil in Southwest China.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"320 ","pages":"Article 109860"},"PeriodicalIF":6.5,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145218032","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":"On the suitability of non-dormant alfalfa to tolerate off-season groundwater recharge during winter and spring periods","authors":"Helen E. Dahlke ,&nbsp;Yonatan Ganot ,&nbsp;Nicholas Clark ,&nbsp;Khaled Bali ,&nbsp;Daniel H. Putnam","doi":"10.1016/j.agwat.2025.109859","DOIUrl":"10.1016/j.agwat.2025.109859","url":null,"abstract":"<div><div>Winter groundwater recharge, which involves flooding farmland with excess surface water during the dormant season to replenish underlying aquifers, is a promising water conservation strategy, and alfalfa represents a particularly promising crop for this practice. In this study, we investigated yield and forage quality effects of intentional winter or spring flooding of non-dormant alfalfa (<em>Medicago sativa L.</em>) for groundwater recharge. A replicated randomized complete block design study was implemented at the University of California Kearney Research and Extension Center (KARE) in Parlier, CA testing three treatments (3 days of flooding followed by 4 days without flooding, 4 days of flooding followed by 10 days without flooding, and control) for a duration of 6 weeks in winter 2019 and spring 2020. A total of 1.6 and 3.6 m³ m⁻² (2019) and 1.3 and 2.2 m³ m⁻² (2020) were recharged in the 4 on 10 off and 3 on 4 off flood treatments at a recharge efficiency of &gt; 88 % (81 % – 217 % of annual irrigation demand), respectively. The 2019 (Feb – Mar) experiment did not show significant differences in alfalfa yield in the first and second cutting after flooding but resulted in slight but non-significant decline in forage quality (e.g. crude protein; fair hay quality). The 2020 flooding experiment, conducted much later in the year (April – May), resulted in no significant differences in alfalfa yield but poor feed quality (utility grade), partly due to delays in harvesting. Together these results indicate that while caution is appropriate to prevent oxygen deprivation and impacts on alfalfa yield and quality, winter recharge in alfalfa fields in highly permeable soils appears to be a viable practice to conserve large quantities of surface water when available in excess.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"320 ","pages":"Article 109859"},"PeriodicalIF":6.5,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145218049","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":"Nitrogen and phosphorus enrichment synergistically alter carbon-water exchange in desert steppe ecosystems","authors":"Xuying Hai ,&nbsp;Jianping Li ,&nbsp;Qing Qu ,&nbsp;Junwen Yang ,&nbsp;Zhouping Shangguan ,&nbsp;Lei Deng","doi":"10.1016/j.agwat.2025.109858","DOIUrl":"10.1016/j.agwat.2025.109858","url":null,"abstract":"<div><div>Semi-arid grasslands are simultaneously limited by nitrogen (N) and phosphorus (P), yet the effects of their additions on carbon–water coupling remain poorly understood. This knowledge gap hampers predictions of ecosystem responses to nutrient deposition under global climate change. We hypothesized that N and P inputs would alter ecosystem carbon sequestration and water-use efficiency (WUE), and that these effects would be strongly modulated by precipitation. To test this, we conducted a randomized block design experiment in 2020–2022 with N (10 g N m⁻² yr⁻¹) and P (8 g P m⁻² yr⁻¹) additions in a <em>Stipa breviflora</em>-dominated desert steppe in Yanchi County, China. Our results showed that precipitation emerged as a dominant regulator of nutrient effects. Under higher early-growing-season precipitation, NP addition enhanced intrinsic WUE (iWUE) while reducing impacts on evapotranspiration (ET) and ecosystem WUE (W_G). Conversely, under drier conditions, NP addition weakened iWUE gains but amplified ET and W_G responses. At the carbon flux level, net ecosystem CO₂ exchange (NEE) was more strongly controlled by gross ecosystem production (GEP) than by ecosystem respiration (ER), indicating photosynthetic activity as the primary driver of net carbon sequestration. Single N addition increased the GEP, ER but reduced NEE. In contrast, the N effect under the condition of NP co-addition suppressed GEP, ER, and NEE. Single P addition increased GEP, ER, and NEE, whereas the P effect under the condition of NP co-addition promoted ER but inhibited both GEP and NEE. This study elucidates threshold effects in nutrient-driven carbon-water coupling, where stoichiometric interactions between N and P regulate carbon cycling and plant hydraulic strategies. The observed precipitation-dependent synergism/antagonism highlights the nonlinear responses of arid ecosystems to future nutrient deposition scenarios. The present study improves our understanding of the coupled mechanisms of future global C, N, P, and water dynamics, and provides insights for adaptive grassland and water resource management under climate change.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"320 ","pages":"Article 109858"},"PeriodicalIF":6.5,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145218165","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}