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A review of artificial intelligence applications for predicting crop performance and enhancing food security under drought-induced water stress 人工智能在干旱胁迫下作物生产性能预测和粮食安全中的应用综述
IF 6.5 1区 农林科学 Q1 AGRONOMY Pub Date : 2026-03-31 Epub Date: 2026-02-10 DOI: 10.1016/j.agwat.2026.110212
Ali Fares, Anoop Valiya Veettil, Atikur Rahman, Ripendra Awal
Artificial Intelligence (AI) has emerged as a vital tool for predicting crop performance, including crop growth, diseases, and yield, and assisting in informed decision-making, such as predicting precise water management practices for sustainable water use, thereby maximizing growers’ profits. Climate change and the increasing frequency of severe droughts have significantly affected crop yields, posing a challenge to global food security. Therefore, predicting crop performance, particularly expected yield, is crucial during water stress. AI algorithms, including Random Forest (RF), Gradient Boosting Machines (GBM), Artificial Neural Networks (ANNs), and Convolutional Neural Networks (CNNs), enable the integration of weather, soil, crop, and irrigation management data to predict crop performance under drought with higher accuracy. This review focuses on the applications of AI-based models in quantifying the impact of drought on crop performance, considering nonlinear soil–plant–atmosphere interactions and the integration of spatial data through remote sensing. However, data limitations, modeling dynamic drought responses, and addressing spatial heterogeneity in drought indices pose significant challenges for AI-based predictions. Case studies demonstrate AI's potential to predict crop yields under drought conditions, with applications ranging from traditional machine learning (ML) models to advanced deep learning (DL) frameworks. However, there is no clear consensus on the best AI model for drought-induced water stress management, although ANNs and RFs are more commonly used than others in precision-based irrigation and crop yield prediction. DL-based models, such as CNN and Long Short-Term Memory (LSTM), are less frequently applied, despite their strengths in handling complex and unstructured data, as well as modeling long-term drought impacts. Refining AI algorithms to support precision irrigation scheduling, integrating AI with conventional water management practices, and enabling data-driven decision support systems will be vital for mitigating the adverse effects of drought on crop performance.
人工智能(AI)已经成为预测作物性能(包括作物生长、病害和产量)的重要工具,并协助做出明智的决策,例如预测精确的水资源管理实践,以实现可持续用水,从而使种植者的利润最大化。气候变化和日益频繁的严重干旱严重影响了作物产量,对全球粮食安全构成挑战。因此,在缺水条件下预测作物性能,特别是预期产量是至关重要的。包括随机森林(RF)、梯度增强机(GBM)、人工神经网络(ANNs)和卷积神经网络(cnn)在内的人工智能算法,能够整合天气、土壤、作物和灌溉管理数据,以更高的精度预测干旱条件下的作物表现。本文综述了基于人工智能模型在考虑土壤-植物-大气非线性相互作用和遥感空间数据整合的干旱对作物生产影响量化方面的应用。然而,数据限制、干旱动态响应建模以及干旱指数的空间异质性对基于人工智能的预测构成了重大挑战。案例研究表明,人工智能在干旱条件下预测作物产量的潜力,应用范围从传统的机器学习(ML)模型到高级深度学习(DL)框架。然而,尽管人工神经网络和RFs在基于精确的灌溉和作物产量预测中比其他模型更常用,但对于干旱诱导的水胁迫管理的最佳人工智能模型尚无明确的共识。基于dl的模型,如CNN和长短期记忆(LSTM),尽管它们在处理复杂和非结构化数据以及模拟长期干旱影响方面具有优势,但应用频率较低。改进人工智能算法以支持精确灌溉调度,将人工智能与传统的水管理实践相结合,以及启用数据驱动的决策支持系统,对于减轻干旱对作物性能的不利影响至关重要。
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引用次数: 0
Spatial distributed management strategies for maize high-yield and high-efficiency under different production-demand scenarios in Northwest China 西北不同生产需求情景下玉米高产高效空间分布管理策略
IF 6.5 1区 农林科学 Q1 AGRONOMY Pub Date : 2026-03-31 Epub Date: 2026-01-17 DOI: 10.1016/j.agwat.2026.110166
Honghang Zhang , Chuanbin Liang , Wenxin Zhang , Manoj Shukla , Yu Fang , Shichao Chen , Taisheng Du
Rapid warming and increasingly stringent water allocations in arid Northwest China have exacerbated the inherent trade-offs among maize yield, water productivity (WPc) and economic returns. This study employed a statistical modeling-optimization pipeline (PLS-GA) and a machine learning-optimization pipeline (RF-GA) to build a framework for better maize production in the Hexi Corridor of Gansu Province, an arid region in northwest China. The framework uses yield and actual evapotranspiration (ETc act) prediction as well as multi-objective optimization calculations. The optimal irrigation, nitrogen application, and planting density were proposed for five production-demand scenarios and the consequent impacts on yield, WPc, and economic returns under future climate change were systematically assessed. Results showed that random forest (RF) outperformed partial least squares (PLS) regression in capturing non-linear relationships (R2= 0.80 vs. 0.51) for yield simulation, whereas PLS provided superior explanatory power for individual factors. Findings also showed that all scenarios in the historical period could have benefited from an increase in planting density by at least 13.1 % and precision planting, leading to improvements in yield, WPc, and economic returns of at least 20.2 %, 31.4 %, and 15.1 %, respectively, alongside reductions in nitrogen application and irrigation of at least 13.7 % and 6.3 %, respectively. During mid-century (2041–2050), planting density and irrigation were projected to decline 0.4–1.1 % and 0.1–3.5 %, respectively, while nitrogen application to increase by 4.7–9.9 %. These adaptive measures lead to enhanced yield (5.8–6.2 %) and economic returns (13.8–14.7 %), albeit with a decline in WPc (13.1–14.5 %). This study presents an integrated maize management strategy that simultaneously optimizes grain yield, WPc, and economic returns in the Hexi Corridor, while also contributing a scalable methodological framework for advancing climate-resilient agricultural practices in arid, irrigated agroecosystems of Northwest China and comparable regions.
中国西北干旱地区的快速变暖和日益严格的水资源分配加剧了玉米产量、水分生产力和经济效益之间的内在权衡。本研究采用统计建模-优化管道(PLS-GA)和机器学习-优化管道(RF-GA)构建了中国西北干旱区甘肃省河西走廊玉米优化生产的框架。该框架采用产量和实际蒸散量预测以及多目标优化计算。提出了5种生产需求情景下的最佳灌溉、施氮量和种植密度,并系统评估了未来气候变化对产量、WPc和经济回报的影响。结果表明,随机森林(RF)在捕获非线性关系(R2= 0.80 vs. 0.51)的产量模拟方面优于偏最小二乘(PLS)回归,而PLS对单个因素提供了更好的解释能力。研究结果还表明,在历史时期的所有情景中,种植密度至少增加13.1% %和精确种植都可以使产量、WPc和经济回报分别提高至少20.2% %、31.4% %和15.1% %,同时氮肥施用和灌溉分别减少至少13.7% %和6.3% %。本世纪中叶(2041 ~ 2050年),预计种植密度和灌溉分别下降0.4 ~ 1.1 %和0.1 ~ 3.5 %,施氮量增加4.7 ~ 9.9 %。这些适应性措施提高了产量(5.8-6.2 %)和经济回报(13.8-14.7 %),尽管WPc下降了(13.1-14.5 %)。本研究提出了一种玉米综合管理策略,可同时优化河西走廊的粮食产量、WPc和经济回报,同时也为促进西北干旱灌溉农业生态系统的气候适应性农业实践提供了可扩展的方法框架。
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引用次数: 0
Spatio-temporal HYDRUS-1D soil water balance simulations as support for precision irrigation in North-Eastern Germany 德国东北部HYDRUS-1D土壤水分平衡时空模拟:为精准灌溉提供支持
IF 6.5 1区 农林科学 Q1 AGRONOMY Pub Date : 2026-03-31 Epub Date: 2026-02-05 DOI: 10.1016/j.agwat.2026.110157
Jan Lukas Wenzel , Christopher Conrad , Talha Mahmood , Matthias Kunz , Martin Volk , Julia Pöhlitz
Accurate spatio-temporal information on the soil water balance is critical for an efficient and sustainable irrigation. Large effort requirements limit the applicability of complex simulations for precision irrigation. The spatially distributed application of one-dimensional models can reconcile the need for precise soil water balance simulations with the complexity of root-zone water flow processes. This study uses HYDRUS-1D to simulate the daily depth-specific (0 cm to 60 cm, in 10 cm increments) soil water balance from 1st April to 30th September 2021 (2022). Simulations at 70 m spatial resolution covered a 1600 ha farm in Mecklenburg-Western Pomerania, Germany. Results were validated against in-situ soil water content (SWC) and two remotely-sensed SWC data sets (“Soil Moisture Active Passive”, SMAP; Sentinel-1, S1-SWC). Further analysis explored crop-specific irrigation efficiencies and potential farm-scale water savings. Spatially distributed HYDRUS-1D simulations showed good accuracy compared to in-situ SWC (RMSEmean = 0.020 m3 m−3; MAEmean = 0.017 m3 m−3; R2mean = 0.676; bias = −0.008 m3 m−3). The agreement with remotely-sensed SWC was moderate to weak (RMSEmean = 0.059 (0.150) m3 m−3, MAEmean = 0.049 (0.123) m3 m−3, R2mean = 0.208 (0.141), mean bias = 0.021 (0.108) m3 m−3 for SMAP (S1-SWC)). Irrigation efficiencies were 65.0 % (potato), 47.3 % (wheat), 40.5 % (rye), and 58.2 % (sugar beet). Potential water savings amounted to 87,006.9 m³ (11.2 % of total irrigation water; 2021) and 71,396.6 m³ (10.4 %; 2022). The proposed approach reduces the trade-offs between accurately representing the soil water balance in the root-zone and keeping the practical effort reasonable.
准确的土壤水分平衡时空信息是实现高效、可持续灌溉的关键。大的工作量限制了复杂模拟对精确灌溉的适用性。一维模型在空间分布上的应用,可以满足精确土壤水分平衡模拟的需要和根区水流过程的复杂性。本研究利用HYDRUS-1D模拟了2021(2022)年4月1日至9月30日土壤水分平衡的日深度(0 cm至60 cm,增量为10 cm)。70 米空间分辨率的模拟覆盖了德国梅克伦堡-西波美拉尼亚一个1600 公顷的农场。利用原位土壤含水量(SWC)和两个遥感土壤含水量数据集(“土壤水分主动被动”,SMAP; Sentinel-1, S1-SWC)对结果进行验证。进一步的分析探讨了特定作物的灌溉效率和潜在的农场规模节水。与原位SWC相比,空间分布的HYDRUS-1D模拟具有较好的精度(RMSEmean = 0.020 m3 m−3;MAEmean = 0.017 m3 m−3;R2mean = 0.676; bias = - 0.008 m3 m−3)。SMAP (S1-SWC)与遥感SWC的一致性为中弱(RMSEmean = 0.059 (0.150) m3 m−3,MAEmean = 0.049 (0.123)m3 m−3,R2mean = 0.208(0.141),平均偏差= 0.021 (0.108)m3 m−3)。灌溉效率分别为65.0% %(马铃薯)、47.3% %(小麦)、40.5% %(黑麦)和58.2% %(甜菜)。节水潜力分别为87,006.9 m³ (占2021年灌溉水量的11.2 %)和71,396.6 m³ (10.4 %;2022年)。该方法减少了准确表征根区土壤水分平衡与保持实际工作合理性之间的权衡。
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引用次数: 0
From past to future: Spatiotemporal insights into drought stress on Belgian pome fruit production for 1991–2090 从过去到未来:1991-2090年干旱胁迫对比利时梨果生产的时空影响
IF 6.5 1区 农林科学 Q1 AGRONOMY Pub Date : 2026-03-31 Epub Date: 2026-01-16 DOI: 10.1016/j.agwat.2026.110151
Brecht Bamps , Anne Gobin , Ben Somers , Jos Van Orshoven
Recurring drought episodes cause significant economic losses and uncertainty in the Belgian pome fruit sector, requiring effective risk management strategies. Considering the expected increase in frequency, intensity and duration of drought episodes, our study focuses on quantifying the associated location-specific hazard for apple and pear orchards in Belgium. A spatially distributed implementation of the growth model AquaCrop was used to calculate a daily soil water balance at a 12.5 × 12.5 km spatial resolution based on nine bias corrected regional climate model projections from the EURO-CORDEX ensemble for emission scenarios RCP4.5 and RCP8.5. The spatiotemporal evolution of the precipitation deficit (mm), transpiration deficit (mm) and net irrigation requirement (mm) were used to assess current and future meteorological and agricultural drought hazards. We find a spatially averaged increase of the transpiration deficit of 61.4% (39.0%) and the net irrigation requirement of 32.3 % (18.5%) comparing a period in the far future (2061–2090) to a baseline period (1991–2020) under scenario RCP8.5 (RCP4.5). In addition, corresponding interquartile ranges of 204.2% (77.6%) and 30.8% (10.3%) show a large inter-climate model variability in the simulation results. The projected increase of the precipitation deficit during the summer months causes agricultural drought in a spatially heterogeneous manner, relating to the soil total available water content and depth to the shallow groundwater table. Despite the overall increase in drought hazard, the suitability of the current production regions is not projected to change relative to other agricultural regions in Belgium regarding agricultural drought.
经常性的干旱给比利时的梨类水果部门造成重大的经济损失和不确定性,需要有效的风险管理战略。考虑到预计干旱事件的频率、强度和持续时间会增加,我们的研究重点是量化比利时苹果和梨果园相关的特定地点危害。利用生长模式AquaCrop的空间分布式实现,基于EURO-CORDEX系统对RCP4.5和RCP8.5排放情景的9个校正偏差的区域气候模式预估,计算了12.5 × 12.5 km空间分辨率下的日土壤水分平衡。利用降水亏缺量(mm)、蒸腾亏缺量(mm)和净灌溉需要量(mm)的时空演变特征来评估当前和未来的气象和农业干旱灾害。研究发现,在RCP8.5 (RCP4.5)情景下,远未来时期(2061-2090)与基准期(1991-2020)相比,蒸腾亏缺的空间平均增幅为61.4%(39.0%),净灌溉需水量为32.3% %(18.5%)。另外,对应的四分位数区间为204.2%(77.6%)和30.8%(10.3%),在模拟结果中表现出较大的气候间模式变率。预估夏季降水亏缺的增加导致农业干旱的空间异质性,与土壤总有效含水量和浅层地下水位深度有关。尽管干旱灾害总体上有所增加,但预计当前生产区在农业干旱方面相对于比利时其他农业区的适宜性不会发生变化。
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引用次数: 0
Effects of deficit irrigation at different phenological periods on yield, quality, and water productivity of Xanthoceras sorbifolium Bunge in the Horqin Sandy Land of China 不同物候期亏缺灌溉对科尔沁沙地文冠果产量、品质和水分生产力的影响
IF 6.5 1区 农林科学 Q1 AGRONOMY Pub Date : 2026-03-31 Epub Date: 2026-01-16 DOI: 10.1016/j.agwat.2026.110130
Shuqin Song , Benye Xi , Yang Liu , Jing Zhong , Xiaoxia Wang , Liming Jia
In arid and semi-arid regions, improving water efficiency is imperative for the sustainable advancement of both forestry and agriculture. The water demand period for Xanthoceras sorbifolium Bunge, an economically and ecologically important tree grown in the Horqin Sandy Land of China, is unknown. This knowledge gap has hindered the development of optimized deficit irrigation (DI) schemes aimed at conserving water while maintaining yield and quality. To identify this key period and better understand the water-use dynamics of this species, we conducted a two-year field experiment (2021–2022). Eight irrigation treatments were applied across three key phenological stages: flowering (F), fruit setting to expansion (S), and fruit-expansion to maturity (M). The irrigation treatments included full-stage (FSM), two-stage (FS, FM, and SM), single-stage (F, S, and M), and no irrigation (NI). The application of DI decreased fruit yields by 8.36–58.01 % (p < 0.05), while two-stage irrigation significantly reduced water consumption and evapotranspiration compared with full-stage irrigation (p < 0.01). FS significantly improved water productivity (WP), irrigation water productivity (WPI), and fruit quality. All two-stage irrigation treatments demonstrated yield response factors (ky) < 1. The FS treatment reduced irrigation volume by 43.7 %, while the yield decreased by only 8.36 %, suggesting that the irrigation savings did not significantly compromise yield. In summary, the FS treatment is recommended as the most optimal irrigation schedule, followed by SM and FM, for the production of X. sorbifolium in drylands. This approach conserves water while minimally impacting productivity, thus representing a sustainable water management strategy.
在干旱和半干旱地区,提高用水效率对于林业和农业的可持续发展至关重要。中国科尔沁沙地上生长着一种具有重要经济和生态意义的乔木——文冠(Xanthoceras sorbifolium Bunge),其需水周期尚不清楚。这种知识差距阻碍了旨在节水同时保持产量和质量的优化亏缺灌溉(DI)方案的发展。为了确定这一关键时期,更好地了解该物种的水分利用动态,我们进行了为期两年的野外试验(2021-2022)。8个灌溉处理跨越3个物候阶段:开花(F)、坐果至膨大(S)和果实膨大至成熟(M)。灌溉处理包括全阶段(FSM)、两阶段(FS、FM和SM)、单阶段(F、S和M)和不灌溉(NI)。直接灌水使果实产量降低了8.36 ~ 58.01 % (p <; 0.05),两期灌溉较全期灌溉显著降低了耗水量和蒸散量(p <; 0.01)。FS显著提高了水分生产力(WP)、灌溉水分生产力(WPI)和果实品质。所有两期灌溉处理均表现出产量响应因子(ky) < 1。FS处理减少了43.7%的灌水量,而产量仅下降了8.36%,表明节水对产量没有显著影响。综上所述,旱地枸杞生产的最佳灌溉方式为FS处理,其次为SM和FM处理。这种方法节约用水,同时对生产力的影响最小,因此是一种可持续的水管理战略。
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引用次数: 0
Impact of the soil freeze-thaw process on runoff generation and water balance in an alpine region of the northeast Qinghai-Tibet plateau 青藏高原东北高寒地区土壤冻融过程对产流和水分平衡的影响
IF 6.5 1区 农林科学 Q1 AGRONOMY Pub Date : 2026-03-31 Epub Date: 2026-01-27 DOI: 10.1016/j.agwat.2026.110191
Linshan Yang , Jingru Wang , Tiaoxue Lu , Wanghan He , Xingyi Zou , Honghua Xia , Raffaele Albano , Bogdan Ozga-Zielinski , Jan Adamowski , Qi Feng
The soil freeze-thaw process profoundly influences runoff generation through complex and interconnected mechanisms, yet its quantitative impact remains poorly understood, particularly across different vegetation types and elevations. Addressing this issue is critical for improving hydrological predictions in cold regions. In this study, we established an integrated atmosphere-vegetation-soil observation system across varying elevations and vegetation types in the Qilian Mountains (QLM) and employed the Simultaneous Heat and Water (SHAW) model to quantitatively assess soil hydrothermal dynamics and runoff generation during different freeze-thaw stages from 2015 to 2023. The results demonstrate that the SHAW model could accurately simulate soil hydrothermal processes across all vegetation types (NSE > 0.80 for soil temperature; NSE > 0.69 for soil moisture) in an alpine region. Soil water content and water balance components varied significantly across both freeze-thaw stages and vegetation types. There was almost no surface runoff formed in desert steppe and mountainous steppe, and deep seepage was also low. In contrast, shrub meadow exhibited substantial deep seepage (89.29 mm) during the completely thawed stage and could be a major source of recharging to streamflow. The major water fluxes for the four vegetation types occurred during thawing and completely thawed stages, dominated by evapotranspiration. Evapotranspiration accounted for 93 %, 94 %, 81 %, and 62 % of annual precipitation in desert steppe, mountainous steppe, coniferous forest, and shrub meadow, respectively. While the component of evapotranspiration differed, it was dominated by soil evaporation in desert steppe (79 % of total ET) and mountainous steppe (92 %), and by vegetation transpiration in coniferous forest (59 %) and shrub meadow (78 %). These findings offer critical insights into water partitioning within the soil-vegetation-atmosphere continuum, enabling more accurate predictions of streamflow and water availability in alpine regions.
土壤冻融过程通过复杂和相互关联的机制深刻地影响径流生成,但其定量影响仍然知之甚少,特别是在不同植被类型和海拔之间。解决这一问题对于改善寒冷地区的水文预测至关重要。本研究在祁连山地区建立了不同海拔和不同植被类型的大气-植被-土壤综合观测系统,采用同步热水(SHAW)模型对2015 - 2023年不同冻融阶段土壤热液动力学和产流进行了定量评价。结果表明,SHAW模型能准确模拟高寒地区所有植被类型的土壤热液过程(土壤温度NSE >; 0.80,土壤湿度NSE >; 0.69)。土壤含水量和水分平衡组分在不同冻融阶段和不同植被类型间存在显著差异。荒漠草原和山地草原几乎没有地表径流形成,深层渗流也较低。灌丛草甸在完全融化阶段表现出较大的深度渗流(89.29 mm),可能是径流补给的主要来源。4种植被类型的水通量主要发生在融化和完全融化阶段,以蒸散发为主。荒漠草原、山地草原、针叶林和灌丛草甸的蒸散发分别占年降水量的93 %、94 %、81 %和62 %。土壤蒸腾在荒漠草原(占总蒸散发的79 %)和山地草原(占92 %)以及针叶林(59 %)和灌丛草甸(78 %)以土壤蒸腾为主。这些发现为土壤-植被-大气连续体中的水分分配提供了重要的见解,使我们能够更准确地预测高寒地区的河流流量和水分可用性。
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引用次数: 0
Integrating water evaluation and adaptation planning with enhanced sustainable development goals impact assessment for sustainable irrigation optimization in water stressed agricultural basin 将水资源评价与适应规划与强化可持续发展目标相结合,实现水资源紧张农业流域可持续灌溉优化
IF 6.5 1区 农林科学 Q1 AGRONOMY Pub Date : 2026-03-31 Epub Date: 2026-01-22 DOI: 10.1016/j.agwat.2026.110132
Naveed Ahmed , Jiang Ming , Youssef M. Youssef , Shahid Ali , Nassir Alarifi , Waqas Ul Hussan , Faten Nahas , Mahmoud E. Abd-Elmaboud
This study advances prior approaches by integrating Water Evaluation and Adaptation Planning (WEAP) modelling with a novel Enhanced Sustainable Development Goals (SDG) Impact Assessment Framework (ESIAF) to holistically evaluate irrigation scenarios in water-stressed agricultural regions such as the Indus Basin. Applied to the Chaj Doab region within Pakistan's Indus Basin, this research fills a critical gap in canal-specific, SDG-linked planning for water-stressed agricultural regions. In this integrated approach, the WEAP model simulated physical system responses, quantifying that the optimal scenario (Scenario 3) improves water-use efficiency by 32.4 % (to 67.55 kg/m³) and increases crop yields by 74 %. The ESIAF sustainability assessment reveals this scenario achieves a Final Sustainability Score (FSS) of 0.66 with a Sustainability Balance Index (SBI) of 0.86, demonstrating robust alignment with SDG 6 (Clean Water and Sanitation), SDG 2 (Zero Hunger), and SDG 8 (Decent Work and Economic Growth). Conversely, climate change projections (Scenario 5) indicate system reliability could plummet to 34–35 %, underscoring the urgency of adaptive measures. The WEAP-ESIAF coupling provides a replicable decision-support tool that offers direct insights for implementing Pakistan's National Water Policy (2018) and guiding sustainable irrigation investments in semi-arid, glacier-dependent basins.
本研究通过将水资源评估和适应规划(WEAP)模型与新的增强型可持续发展目标(SDG)影响评估框架(ESIAF)相结合,对印度河流域等水资源紧张农业区的灌溉情景进行了全面评估,从而推进了先前的方法。该研究应用于巴基斯坦印度河流域的Chaj Doab地区,填补了水资源紧张农业区与运河相关的可持续发展目标规划的关键空白。在这种综合方法中,WEAP模型模拟了物理系统响应,量化了最佳方案(方案3)将水利用效率提高了32.4% %(至67.55 kg/m³),并将作物产量提高了74 %。ESIAF可持续性评估显示,该情景的最终可持续性得分(FSS)为0.66,可持续性平衡指数(SBI)为0.86,与可持续发展目标6(清洁水和卫生设施)、可持续发展目标2(零饥饿)和可持续发展目标8(体面工作和经济增长)保持一致。相反,气候变化预测(情景5)表明,系统可靠性可能暴跌至34-35 %,强调了适应性措施的紧迫性。weapon - esiaf耦合提供了一种可复制的决策支持工具,为实施巴基斯坦国家水资源政策(2018年)和指导半干旱、依赖冰川的流域的可持续灌溉投资提供了直接见解。
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引用次数: 0
Response of soil water–salt distribution and maize growth to aerated subsurface drip irrigation in saline soils 盐碱地土壤水盐分布及玉米生长对加气地下滴灌的响应
IF 6.5 1区 农林科学 Q1 AGRONOMY Pub Date : 2026-03-31 Epub Date: 2026-01-22 DOI: 10.1016/j.agwat.2026.110173
Wenxiu Li , Kai Chang , Xiangping Wang , Changcheng He , Qiancheng Gao , Rongjiang Yao , Wenping Xie , Dongxiang Ma , Hai Zhu , Wei Zhu
Aerated subsurface drip irrigation (ASDI) is a promising strategy for improving crop productivity in saline soils, yet its effects on root–zone water–salt dynamics and crop adaptation remain unclear. Here, based on a two–year field experiment, we assessed the effects of ASDI on soil water–salt distribution, root development, crop yield, and irrigation water productivity under conventional (W1) and 20 % water–saving (W0.8) irrigation regimes, each combined with two dissolved oxygen concentrations (12 and 20 mg /L), using non–aerated irrigation as the control (CK). ASDI improved the soil water–salt environment by enhancing water storage, infiltration, and salt suppression, with salinity responses showing clear irrigation–dependent and temporal patterns. Under W1, aeration consistently reduced salt accumulation over two seasons, maintaining soil salt storage below CK. The initially smaller low–salinity zone under W1 compared with W0.8 likely reflected feedbacks between root growth and water–salt redistribution. In contrast, under W0.8, the low–salinity zone progressively contracted by 2024, accompanied by soil salt storage exceeding CK, indicating a potential risk of salt accumulation under sustained water–saving irrigation. Increasing the aeration mitigated this trend and enhanced soil enzyme activity. An improved root–zone environment promoted root development, increasing the maximum root density by 16 % and other root traits. These responses sustained grain yield under water–saving conditions, while increasing the harvest index by 2.5 % and irrigation water productivity by 25.0 %. Overall, aeration alleviated salt accumulation and supported crop performance, while its longer–term effectiveness under continuous water–saving requires further validation.
加气地下滴灌(ASDI)是提高盐碱地作物生产力的一种有前景的策略,但其对根区水盐动态和作物适应性的影响尚不清楚。在此,基于为期两年的田间试验,我们评估了ASDI对土壤水盐分布、根系发育、作物产量和灌溉水生产力的影响,在常规(W1)和20% %节水(W0.8)灌溉方案下,每个方案都结合两种溶解氧浓度(12和20 mg /L),以无通气灌溉为对照(CK)。ASDI通过加强水分储存、入渗和抑盐等措施改善了土壤水盐环境,其盐度响应具有明显的灌溉依赖和时间模式。在W1条件下,通气连续两季减少了土壤盐分积累,使土壤盐分储量保持在CK以下。与W0.8相比,W1初始低盐度区较小,可能反映了根系生长与水盐再分配之间的反馈。W0.8条件下,到2024年,低盐区逐渐收缩,土壤盐储量超过CK,表明持续节水灌溉存在积盐风险。增加曝气量可以缓解这一趋势,提高土壤酶活性。根区环境的改善促进了根系发育,最大根密度提高了16% %,其他根系性状也有所提高。这些响应维持了节水条件下的粮食产量,同时使收获指数提高了2.5 %,灌溉水生产力提高了25.0 %。总体而言,曝气缓解了盐积累,有利于作物生产,但在持续节水条件下,其长期有效性有待进一步验证。
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引用次数: 0
Precision nanobubble irrigation tailors plant physiology to drive sustainable lettuce growth with water savings 精确的纳米气泡灌溉可以调节植物的生理机能,以节水驱动生菜的可持续生长
IF 6.5 1区 农林科学 Q1 AGRONOMY Pub Date : 2026-03-31 Epub Date: 2026-01-21 DOI: 10.1016/j.agwat.2026.110149
Jesús Morón-López , Aislinn Varela , Renato Montenegro-Ayo , Andrea Maya , Mariana Hernandez-Molina , Kenneth Flores , Emily E. Matula , John Graf , Onur Apul , Sergi Garcia-Segura
Under accelerating climate-driven water scarcity, improving irrigation efficiency for high-value leafy crops has become an urgent challenge for global food security. Nanobubbles (NBs), gas-filled cavities < 500 nm in diameter, have emerged as a promising irrigation technology capable of enhancing root-zone processes and early plant development with minimal additional water inputs. However, their effects in soil-based systems remain poorly resolved, particularly for leafy vegetables grown in water-stressed regions. Here, we systematically evaluate the influence of four gas types (O2, CO2, N2, and air), delivered as NBs at three dilution levels (100 %, 50 %, and 10 %; corresponding to >108 particles mL−1 at 100 %), on early-stage growth and water productivity (WP) in lettuce (Lactuca sativa, var. 'Little Gem') grown in a peat moss, coconut coir, and vermiculite mixture. Our results reveal that moderately diluted O2 NBs (10 %–50 %) accelerate germination, boost biomass accumulation, and improve water savings by up to ∼23 %. In contrast, high concentrations (100 %) of O2 NBs reduced overall performance and induced elongated but narrow leaf morphology, consistent with stress-related growth allocation. The use of CO2 NBs, particularly at higher concentrations, stimulates root expansion and leaf area development, while moderate N2 NBs concentration enhanced ammonium uptake and root elongation. Air NBs produce modest and variable effects, serving as a baseline but never outperforming pure gas NBs. Together, these results demonstrate that gas-specific NBs treatments can be strategically tuned to regulate distinct physiological pathways during early plant development, supporting the potential of NBs-based irrigation as a tool for water-efficient, climate-resilient leafy crop production.
在气候驱动的水资源短缺加剧的情况下,提高高价值叶作物的灌溉效率已成为全球粮食安全面临的紧迫挑战。纳米气泡(NBs)是一种直径为 500 nm的充满气体的空腔,已经成为一种很有前途的灌溉技术,能够以最少的额外水输入促进根区过程和早期植物发育。然而,它们对土壤系统的影响仍然没有得到很好的解决,特别是对在缺水地区种植的叶菜。在这里,我们系统地评估了四种气体类型(O2, CO2, N2和空气),以三种稀释水平(100 %,50 %和10 %;对应于100 %的>;108颗粒mL−1)作为NBs输送,对生菜(Lactuca sativa, var.)早期生长和水分生产力(WP)的影响。“小宝石”)生长在泥炭苔藓、椰子椰子和蛭石的混合物中。我们的研究结果表明,适度稀释的O2 NBs(10 % -50 %)加速发芽,促进生物量积累,并提高节水高达23 %。相比之下,高浓度(100 %)的O2 NBs降低了植株的整体性能,诱导叶片形态变长但变窄,与胁迫相关的生长分配一致。CO2 NBs的使用,特别是在较高浓度下,促进了根的扩张和叶面积的发育,而适度的N2 NBs浓度促进了铵的吸收和根的伸长。空气NBs产生适度和可变的影响,作为基准,但永远不会超过纯气体NBs。总之,这些结果表明,气体特异性NBs处理可以在植物早期发育过程中进行战略性调整,以调节不同的生理途径,支持NBs灌溉作为节水、气候适应型叶作物生产工具的潜力。
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引用次数: 0
Reduced tillage and cover crop effects on soil moisture and infiltration 减少耕作和覆盖作物对土壤水分和入渗的影响
IF 6.5 1区 农林科学 Q1 AGRONOMY Pub Date : 2026-03-31 Epub Date: 2026-01-27 DOI: 10.1016/j.agwat.2025.110108
Carson Roberts , Drew Gholson , Martin Locke , Zachary Simpson , Nicolas Quintana-Ashwell , G. Dave Spencer , Steven Pires , Brian Pieralisi , Whitney Crow , L. Jason Krutz
Cropping systems that conserve soil moisture are needed to improve yield or reduce irrigation water demand. This study assessed the ability of different tillage systems, subsoiling, and cover crops to conserve soil moisture, reduce soil water depletion, and augment infiltration in cotton (Gossypium hirsutum L.) production on a Dubbs silt loam (Typic Hapludalfs) and a Bosket very fine sandy loam (Mollic Hapludalfs). This multi-year field study used a randomized complete block design to manage irrigation based on sensor data and matric potential thresholds, with agronomic and sensor-based methods used for data collection.Conventionally tilled soils had ≥ 59 % lower soil matric potential (less moisture; P > F < 0.0001) than conservation practices before irrigation. Cover crops increased soil moisture (-20 kPa) compared to winter fallow (-34 kPa). All conservation practices improved season-long soil moisture by ≥ -19 kPa over conventional tillage (P > F < 0.0001). Each Mg ha⁻¹ increase in preplant biomass raised soil matric potential by ≥ 7.3 kPa. Irrigation at −80 kPa to replenish soil moisture did not alter treatment differences. The conventional method (control) required irrigation every year with up to 7.8 cm more supplemental irrigation than the studied conservation practices. Cover crop treatments did not require irrigation at least 2 out of the 3 seasons in the experiment. Cost savings from reduced irrigation of up to $18 ha−1 do not fully compensate for crop yield penalties. Infiltration rates on bed tops increased by 23 % with cover crops (P > F = 0.0627). Cover crops and subsoiling enhanced infiltrated rainfall by 13 % (P > F = 0.003) and 16 % (P > F = 0.009), respectively, compared to winter fallow. Reduced tillage and cover crops improve season-long soil moisture and infiltration, offering a viable strategy for conserving irrigation water.
为了提高产量或减少灌溉用水需求,需要保持土壤水分的种植系统。本研究评估了在Dubbs粉壤土(Typic Hapludalfs)和Bosket极细砂壤土(Mollic Hapludalfs)上生产棉花时,不同耕作制度、深埋和覆盖作物保持土壤水分、减少土壤水分枯竭和增加入渗的能力。这项为期多年的实地研究采用随机完全块设计,根据传感器数据和基质潜在阈值管理灌溉,并使用农艺和基于传感器的方法收集数据。常规耕作土壤的土壤基质电位(水分更少;P >; F < 0.0001)比灌溉前的保护性耕作土壤低≥ 59 %。与冬季休耕(-34 kPa)相比,覆盖作物增加了土壤水分(-20 kPa)。与传统耕作相比,所有保护措施都使整个季节的土壤湿度提高了≥ -19 kPa (P >; F < 0.0001)。每增加Mg ha(⁻¹ ),种植前生物量就会使土壤基质电位增加≥ 7.3 kPa。−80 kPa灌水对土壤水分补充影响不大。常规方法(对照)需要每年灌溉7.8 cm,比研究的保护措施多补充灌溉。覆盖作物处理在试验3个季节中至少2个季节不需要灌溉。减少灌溉可节省高达18美元 ha - 1的成本并不能完全补偿作物产量损失。覆盖作物使床顶入渗率提高了23% % (P >; F = 0.0627)。与冬季休耕相比,覆盖作物和深埋土壤使入渗降雨量分别增加了13 % (P >; F = 0.003)和16 % (P >; F = 0.009)。减少耕作和覆盖作物改善了整个季节的土壤水分和入渗,为节约灌溉用水提供了可行的策略。
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引用次数: 0
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Agricultural Water Management
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