Agricultural Water Management最新文献_第10页

Simulation of soil moisture and drought prediction in middle reaches of the Yellow River based on machine learning 基于机器学习的黄河中游土壤水分与干旱预测模拟

IF 6.5 1区农林科学 Q1 AGRONOMY

Agricultural Water Management

Pub Date : 2025-12-10 DOI: 10.1016/j.agwat.2025.110068

Siying Yan , Baisha Weng , Zhaoyu Dong , Denghua Yan , Qiang Fu

Under global climate change, agricultural and ecological droughts in the Middle Reaches of the Yellow River (MRYR) severely threaten agricultural production and ecological security. However, the lack of large-scale, high-spatiotemporal-resolution layered soil moisture data constrains the precise identification of droughts. This study innovatively integrates a Multi-Layer Perceptron (MLP) model with RegCM4 climate scenario data to generate a layered daily-scale soil moisture dataset for the 0–289 cm profile in the MRYR from 2001 to 2100 (Abbreviated as MLP_D). The MLP_D dataset features a spatial resolution of 0.01°× 0.01° and demonstrates superior performance to traditional data products across multiple metrics. Analysis of the MLP_D dataset reveals: During 2001–2022, surface soil moisture (0–7 cm) exhibited a slight, non-significant increasing trend at a rate of 0.0002 m³ /m³ /year, while soil moisture in layers below 7 cm declined, in 100–289 cm, the soil moisture decreasing significantly at 0.0016 m³ /m³ /year. Moreover, MLP_D data accurately captured typical drought events, demonstrating high consistency between simulated and actual observations. Future drought frequency and duration in the MRYR increase with more intense scenarios, under the RCP8.5 scenario, areas experiencing a significant increase in drought duration account for 71 % of the total region. By bridging a critical data gap in high-resolution, long-term, layered soil moisture data for the MRYR, this study provides pivotal insights into climate change impacts on soil moisture and drought regimes. It thereby serves as a scientific basis for enhancing precision agriculture and water management, with profound implications for mitigating drought risks and safeguarding regional agro-ecological security.

在全球气候变化背景下，黄河中游地区的农业和生态干旱严重威胁着农业生产和生态安全。然而，缺乏大尺度、高时空分辨率的分层土壤水分数据限制了干旱的精确识别。本文创新性地将多层感知器（Multi-Layer Perceptron， MLP）模型与RegCM4气候情景数据相结合，生成了2001 - 2100年MRYR 0-289 cm剖面（简称MLP_D）分层日尺度土壤湿度数据集。MLP_D数据集的空间分辨率为0.01°× 0.01°，在多个指标上表现出优于传统数据产品的性能。MLP_D数据集的分析显示:在2001 - 2022,表面土壤水分(鹿 厘米)表现出轻微的,与0.0002的速度增长趋势 m³ / m³ /年,而土壤水分在下层7 厘米下降,在100 - 289年 厘米,土壤水分显著减少0.0016 m³ / m³ /年。此外，MLP_D数据准确地捕获了典型的干旱事件，显示了模拟和实际观测之间的高度一致性。在RCP8.5情景下，干旱持续时间显著增加的区域占总区域的71% %。通过弥合MRYR高分辨率、长期、分层土壤湿度数据的关键数据缺口，本研究为气候变化对土壤湿度和干旱状况的影响提供了关键见解。为加强精准农业和水资源管理提供了科学依据，对缓解干旱风险、保障区域农业生态安全具有深远意义。

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引用次数: 0

Characterization of single-cropping rice net irrigation water requirements in China's major rice-producing regions using time-frequency domain methods 利用时频域方法表征中国主要水稻产区单季水稻净灌溉需水量

IF 6.5 1区农林科学 Q1 AGRONOMY

Agricultural Water Management

Pub Date : 2025-12-10 DOI: 10.1016/j.agwat.2025.110071

Miao Hou , Xing Yang , Wenye Zhang , Yugeng Guo , Fei Qi , Linpeng Zhai , Chaozhen He

Single-cropping rice dominates rice production in China, yet its regional differences and long-term variability in net irrigation water requirements (RIWR) remain poorly characterized at the national scale. This study estimates RIWR from 1951 to 2023 across five major rice-producing regions using a water balance method with the Penman-Monteith formula as the core meteorological component. Wavelet and time-frequency analyses are used to investigate the periodic variability of RIWR and its associated meteorological and circulation factors. Future changes are projected under SSP2–4.5 and SSP5–8.5 scenarios to quantify potential shifts in irrigation demand. Results show substantial temporal and spatial variations in RIWR. National annual averages ranged from 524.6 to 791.5 mm and declined by 0.93 mm/yr on average, being above the long-term mean between 1951 and 1980, below it from 1981 to 2000, and alternating from 2001 to 2023. Regionally, the variation range of RIWR follows Northeast > Central China > East China > South China > Southwest, with province-level values spanning 315.0–1250.1 mm/yr. RIWR exhibits an alternating pattern across regions, with high periods in Northeast China corresponding to low periods in South China, and vice versa. Cycles with periods of 15–25 years dominate RIWR variability, with interregional phase differences (i.e., differences in the phase of these same-frequency cycles) of up to 180°, contributing to spatial heterogeneity. Using the proposed time-frequency approach, effective precipitation, sunshine duration, maximum air temperature, and relative humidity were identified as the primary meteorological drivers, confirmed by sensitivity analysis, with relative influences varying across regions. Future projections suggest the historical decline in RIWR may slow or reverse in some regions. Taken together, these findings underscore the importance of accounting for spatial, temporal, and frequency-domain variations when planning adaptive irrigation strategies.

单作水稻在中国水稻生产中占主导地位，但在全国范围内，其区域差异和净灌溉需水量（RIWR）的长期变化特征仍然很差。本研究采用以Penman-Monteith公式为核心气象成分的水分平衡方法估算了1951 - 2023年五个主要水稻产区的RIWR。利用小波分析和时频分析研究了黄河水势及其相关气象和环流因子的周期变化。在SSP2-4.5和SSP5-8.5情景下预测了未来的变化，以量化灌溉需求的潜在变化。结果表明，RIWR存在明显的时空差异。全国年平均524.6 ~ 791.5 mm，平均下降0.93 mm/年，1951 ~ 1980年高于长期平均值，1981 ~ 2000年低于长期平均值，2001 ~ 2023年交替变化。从区域上看，RIWR的变化幅度依次为东北>； 华中>； 华东>； 华南>； 西南，省际范围为315.0 ~ 1250.1 mm/yr。RIWR在区域间表现为交替变化，东北高周期对应华南低周期，反之亦然。周期为15-25年的周期在RIWR变率中占主导地位，区域间的相位差异（即这些同频率周期的相位差异）可达180°，导致空间异质性。利用提出的时频方法，确定了有效降水、日照时数、最高气温和相对湿度是主要的气象驱动因素，并通过敏感性分析证实了这一点，各区域的相对影响存在差异。未来的预测表明，在一些地区，RIWR的历史下降可能会放缓或逆转。综上所述，这些发现强调了在规划适应性灌溉策略时考虑空间、时间和频域变化的重要性。

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引用次数: 0

Change in microbial population in farm ponds and irrigation distribution systems throughout two crop production seasons in Georgia Coastal Plains 在两个作物生产季节中，乔治亚海岸平原农场池塘和灌溉分配系统中微生物种群的变化

IF 6.5 1区农林科学 Q1 AGRONOMY

Agricultural Water Management

Pub Date : 2025-12-10 DOI: 10.1016/j.agwat.2025.110073

Rawane Raad , Blanca Ruiz-Llacsahuanga , Henk C. den Bakker , Charles Bency Appolon , Mia Gale , Halle Greenbaum , Ruben Vinueza , Manpreet Singh , Faith Critzer

Studies on the distribution of microbial populations within irrigation lines are limited. We evaluated the ecology, diversity, and composition of the microbial communities present in different irrigation distribution systems in the Georgia Coastal Plains during the 2023 (May-November) and 2024 (March-October) growing seasons. DNA samples (n = 499) were collected using HiCap swabs from irrigation systems (drip systems or center pivots) in six fresh produce commercial farms and sequenced for taxonomy determination. Oxford Nanopore Technologies’ 16S microbiome sequencing kit was used, followed by bioinformatics analysis using Sepia. Water samples (n = 57) from irrigation sources or from the drip line were tested for physicochemical properties, total coliforms, and generic E. coli. Overall, there was a wide diversity of microbial populations with Exiguobacterium, Thiobacillus, Pseudomonas, Aeromonas, and Bacillus abundant in most farms throughout the two sampling years. No significant differences between alpha diversity indices were observed across the farms (p > 0.05), and an increase in microbial diversity at the end of the drip line was observed compared to the beginning. Regardless of the sampling year, all diversity indices were significantly different by month (p < 0.001). Bray-Curtis’ beta diversity metric resulted in no significant clusters across the farm and some seasonal variation. Water quality and microbial composition across water samples revealed significant fluctuations. This work provides the first comprehensive evaluation of microbial communities within active produce farm irrigation systems, capturing their natural dynamics across seasons, water sources, and management practices.

对灌溉线内微生物种群分布的研究是有限的。在2023年（5 - 11月）和2024年（3 - 10月）生长季节，对乔治亚海岸平原不同灌溉分配系统中存在的微生物群落的生态、多样性和组成进行了评估。采用HiCap拭子从6个新鲜农产品商业农场的灌溉系统（滴灌系统或中心枢纽）中收集DNA样本（n = 499），并进行测序以确定其分类。使用Oxford Nanopore Technologies的16S微生物组测序试剂盒，随后使用Sepia进行生物信息学分析。对来自灌溉水源或滴灌管道的水样（n = 57）进行了理化性质、总大肠菌群和一般大肠杆菌的测试。总体而言，在两个采样年期间，大多数农场的微生物种群具有广泛的多样性，其中出口杆菌、硫杆菌、假单胞菌、气单胞菌和芽孢杆菌丰富。不同养殖场之间的α多样性指数差异不显著（p >； 0.05），滴灌管道末端的微生物多样性比开始时有所增加。无论采样年份如何，各月份的多样性指数均存在显著差异（p <； 0.001）。布雷-柯蒂斯的beta多样性指标显示，整个农场没有明显的集群，并且存在一些季节性变化。各水样的水质和微生物组成显示出显著的波动。这项工作首次全面评估了活跃农产品灌溉系统中的微生物群落，捕捉了它们在季节、水源和管理实践中的自然动态。

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引用次数: 0

Unveiling the double-edged sword effect of saline water irrigation through meta-analysis: Reduced soil organic carbon storage versus mitigated CO2 and N2O emissions 通过荟萃分析揭示盐水灌溉的双刃剑效应：减少土壤有机碳储量与减少CO2和N2O排放

IF 6.5 1区农林科学 Q1 AGRONOMY

Agricultural Water Management

Pub Date : 2025-12-09 DOI: 10.1016/j.agwat.2025.110062

Qi Wei , Shengyu Chen , Qi Wei , Junzeng Xu , Peng Chen , Xue Zhou , Zihao Liu , Ruiqi Wu , Zhiming Qi , Ziwei Li

Saline water irrigation serves as a critical strategy for sustainable agriculture amid freshwater scarcity. While its growing application, the effects of saline water irrigation on soil organic carbon (SOC) storage and CO₂ and N₂O emissions remain inconsistent and poorly quantified. This study aims to address this knowledge gap through a meta-analysis of 50 studies encompassing 374 field observations, with the specific objective of evaluating the impacts of saline water irrigation on SOC content, as well as on emissions of CO₂ and N₂O, considering factors such as climate, soil properties, crop types and agronomic practices. Results showed that relative to freshwater irrigation, saline water irrigation reduced SOC content by 10.2 % and CO₂ and N₂O emissions by 19.1 % and 13.8 %. Saline water with electrical conductivity greater than 8 dS·m⁻¹ intensified this effect, reducing CO₂ and N₂O emissions by 32.3 % and 31.4 %. This finding suggests that saline water irrigation markedly reduce CO₂ and N₂O emissions in arid or semi-arid regions. Saline water irrigation in soils with bulk density below 1.35 g·cm⁻³ and total nitrogen content above 1 g·kg⁻¹ promoted the reduction of CO₂ and N₂O emissions and delayed SOC losses. Cash crops outperformed grain crops in SOC retention and CO₂ and N₂O emissions reduction under saline water irrigation. Additionally, furrow irrigation and a nitrogen application rate below 150 kg·ha⁻¹ demonstrated superior potential CO₂ and N₂O emissions mitigation. Future agronomic management should be optimized based on regional climate and soil characteristics to promote SOC sequestration and climate-smart agriculture. This current research demonstrated that saline water irrigation is a viable strategy for regions with limited freshwater availability, providing a practical approach to both transforming agricultural systems and reducing emissions of CO₂ and N₂O.

在淡水短缺的情况下，盐水灌溉是可持续农业的一项重要战略。在盐碱水灌溉应用日益广泛的同时，其对土壤有机碳（SOC）储量和CO2、N2O排放的影响仍不一致且缺乏量化。本研究旨在通过对50项研究的荟萃分析来解决这一知识缺口，其中包括374项实地观测，具体目标是评估盐水灌溉对有机碳含量的影响，以及对CO2和N2O排放的影响，考虑到气候、土壤性质、作物类型和农艺实践等因素。结果表明，与淡水灌溉相比，盐水灌溉使土壤有机碳含量降低了10. %，CO2和N2O排放量分别降低了19.1 %和13.8 %。电导率大于8 dS·m−1的盐水强化了这一效应，减少了32.3% %和31.4% %的CO2和N2O排放。这一发现表明，在干旱或半干旱地区，盐水灌溉显著减少了CO2和N2O的排放。在容重低于1.35 g·cm−3、总氮含量高于1 g·kg−1的土壤中，盐水灌溉促进了CO2和N2O排放的减少，延缓了有机碳的损失。在盐水灌溉条件下，经济作物在有机碳保持和CO2、N2O减排方面优于粮食作物。此外，沟灌和氮肥施用量低于150 kg·ha - 1显示出更好的CO2和N2O减排潜力。未来的农业管理应根据区域气候和土壤特征进行优化，以促进有机碳的固存和气候智能型农业。目前的研究表明，对于淡水资源有限的地区，盐水灌溉是一种可行的策略，为农业系统转型和减少CO2和N2O的排放提供了一种实用的方法。

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引用次数: 0

Drought and irrigation requirements: Adaptation strategies and economic impacts on Italian arable farms under different first-pillar CAP scenarios 干旱和灌溉需求：不同第一支柱CAP情景下意大利可耕地的适应策略和经济影响

IF 6.5 1区农林科学 Q1 AGRONOMY

Agricultural Water Management

Pub Date : 2025-12-09 DOI: 10.1016/j.agwat.2025.110049

Rebecca Buttinelli , Edmondo Di Giuseppe , Sara Quaresima , Arianna Di Paola , Raffaele Cortignani

Under drought conditions, the growing demand for irrigation water, coupled with its declining availability, poses serious risks to agricultural productivity. Meanwhile, European farmers operate within a policy framework that requires the agricultural sector not only to adapt to climate change impacts, such as drought, but also to contribute to their mitigation through the sustainable management of natural resources. In this context, this study aims to assess how drought pressure interacts with the EU Common Agricultural Policy (CAP) in shaping the adaptation strategies of Italian arable farms, with a particular focus on land use, economic outcomes, and irrigation water use. By integrating high-resolution climatic data with the AGRITALIM agro-supply economic model and a Farm Accountancy Data Network sample of 3089 Italian farms, this study estimates crop irrigation requirements and simulates drought scenarios affecting increasingly large areas of the country. Results show that northern regions are especially exposed to drought, with farms responding in the short term by relying more on groundwater and reducing irrigated area. Adaptation strategies are strongly influenced by CAP frameworks: under the CAP 2014–2022 first-pillar reform, farms tend to expand rainfed cereal areas, while the 2023–2027 reform promotes the expansion of legumes and oilseeds and reduces the area under cereals such as wheat. The current CAP reform partially offsets income losses for small farms through internal convergence and redistributive payments, while increasing pressure on larger ones. Overall, these findings highlight the need for targeted policies capable of balancing climate adaptation, productivity, and equity in the distribution of support.

在干旱条件下，对灌溉用水的需求不断增加，加上可获得的灌溉用水不断减少，对农业生产力构成严重威胁。与此同时，欧洲农民在一个政策框架内开展活动，该框架要求农业部门不仅要适应气候变化的影响，如干旱，而且要通过对自然资源的可持续管理，为减轻这些影响作出贡献。在此背景下，本研究旨在评估干旱压力如何与欧盟共同农业政策（CAP）相互作用，形成意大利耕地的适应策略，特别关注土地利用、经济成果和灌溉用水。通过将高分辨率气候数据与AGRITALIM农业供应经济模型和意大利3089个农场的农场会计数据网络样本相结合，本研究估算了作物灌溉需求，并模拟了影响该国越来越大地区的干旱情景。结果表明，北方地区特别容易受到干旱的影响，农场在短期内通过更多地依赖地下水和减少灌溉面积来应对。适应战略受到共同农业政策框架的强烈影响：根据共同农业政策2014-2022年第一支柱改革，农场倾向于扩大旱作谷物种植面积，而2023-2027年改革促进了豆类和油籽的扩大，并减少了小麦等谷物的种植面积。目前的CAP改革通过内部趋同和再分配支付部分抵消了小农场的收入损失，同时增加了对大农场的压力。总的来说，这些发现突出表明，需要有针对性的政策，能够平衡气候适应、生产力和支持分配的公平性。

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引用次数: 0

From grain to ground: How hydrologic uncertainty drives shifts in crop patterns across the Yellow River Basin 从粮食到土地：水文不确定性如何推动黄河流域作物模式的变化

IF 6.5 1区农林科学 Q1 AGRONOMY

Agricultural Water Management

Pub Date : 2025-12-08 DOI: 10.1016/j.agwat.2025.110058

Zhongwen Xu , Shiqi Tan

China’s arable land exploitation has intensified water scarcity, threatening food security. To address the interdependent land-water-food nexus, this study develops a robust bi-objective optimization model for agricultural resource allocation under hydrologic uncertainty. Applied to the Yellow River Basin, the model balances water use efficiency with land productivity. Results indicate that (1) optimal planting patterns vary significantly across regions to balance efficiency and equity; (2) under 10–30 % water reduction scenarios, water-intensive paddy areas decrease by up to 15 %, while wheat and maize expand by 5–12 %, particularly in arid regions; (3) higher risk-awareness reduces overall efficiency but enhances inter-provincial fairness; and (4) irrigation technology innovation serves as a transformative pathway to sustain productivity under climate risk. By integrating hydrologic uncertainty into a comprehensive land-water-food framework, this research offers robust, policy-relevant solutions for safeguarding food security, promoting sustainable land use, and improving water management practices in water-limited regions, thereby supporting global sustainability transitions.

中国的耕地开发加剧了水资源短缺，威胁到粮食安全。为了解决土地-水-粮食之间的相互依赖关系，本研究建立了水文不确定性条件下农业资源配置的稳健双目标优化模型。应用于黄河流域，该模型平衡了水资源利用效率和土地生产力。结果表明：(1)为了平衡效率和公平，不同区域的最优种植模式存在显著差异；(2)在减水量10 ~ 30% %的情景下，水稻田面积最多减少15 %，而小麦和玉米面积增加5 ~ 12% %，特别是在干旱区；(3)较高的风险意识降低了整体效率，但提高了省际公平；(4)灌溉技术创新是气候风险下维持生产力的变革性途径。通过将水文不确定性纳入陆地-水-粮食综合框架，本研究为保障粮食安全、促进土地可持续利用和改善水资源有限地区的水管理实践提供了强有力的、与政策相关的解决方案，从而支持全球可持续性转型。

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引用次数: 0

Grain yield and resource efficiency responses to water-nitrogen coupled input reduction: A global meta-analytical perspective 粮食产量和资源效率对水氮耦合投入减少的响应：全球元分析视角

IF 6.5 1区农林科学 Q1 AGRONOMY

Agricultural Water Management

Pub Date : 2025-12-07 DOI: 10.1016/j.agwat.2025.110059

Qianwen Fan, Liangjun Fei, Youliang Peng, Yalin Gao, Fangyuan Shen

Global water scarcity and excessive use of fertilizers have become important challenges for sustainable agricultural development. The effects of water-nitrogen coupling management under water-reduced and fertilizer-reduced conditions on the yield, water use efficiency (WUE), and nitrogen partial factor productivity (NPFP) of field crops, such as maize, wheat, and potatoes, were evaluated in this study via a meta-analysis. The findings demonstrated that while maintaining high yields, modest water and nitrogen reductions (about 10 %) substantially increased crop WUE and nitrogen use efficiency. On the other hand, extreme decreases in nitrogen and water led to a significant drop in production, especially for potatoes, which suffered the largest yield loss. Additionally, the effects of water-nitrogen coupling were strongly influenced by soil types, climate, and organic matter content. Compared to semi-humid environments, crops grown in arid and semi-arid environments were more sensitive to decreases in fertilizer and water. Water-nitrogen coupling management could optimize resource allocation, reduce water and fertilizer inputs, and improve crop productivity and resource utilization efficiency. The ideal levels of fertilization and irrigation for maize, wheat, and potatoes under water-nitrogen coupling conditions were 0.87I (I: full irrigation volume) and 0.81 N (N: maximum nitrogen application), 0.92I and 0.91 N, and 0.98I and 0.85 N, respectively. The maximum yields for maize, wheat, and potatoes at these levels were 1.3 × 10⁴ kg·ha⁻¹, 8.9 × 10³ kg·ha⁻¹, and 9.2 × 10⁴ kg·ha⁻¹, respectively. The yields achieved through integrated water-nitrogen management were significantly higher than those obtained from separate irrigation or fertilization treatments. Furthermore, when compared to isolated water or nitrogen management practices, the coupling of water and nitrogen not only enhanced yield but also reduced the overall input of both water and fertilizer. These findings provided robust scientific evidence to support the optimization of water-nitrogen coupling strategies. This approach held considerable practical significance, especially in the context of escalating water scarcity and growing environmental concerns related to agricultural practices.

全球水资源短缺和化肥过度使用已成为农业可持续发展面临的重要挑战。通过荟萃分析，研究了减水减肥条件下的水氮耦合管理对玉米、小麦和马铃薯等大田作物产量、水分利用效率（WUE）和氮偏因子生产率（NPFP）的影响。研究结果表明，在保持高产的同时，适度减少水分和氮（约10 %）可显著提高作物水分利用效率和氮利用效率。另一方面，氮和水的极度减少导致产量大幅下降，特别是马铃薯，其产量损失最大。此外，水氮耦合效应受土壤类型、气候和有机质含量的强烈影响。与半湿润环境相比，干旱和半干旱环境中生长的作物对肥料和水分的减少更为敏感。水氮耦合管理可以优化资源配置，减少水肥投入，提高作物生产力和资源利用效率。水氮耦合条件下，玉米、小麦和马铃薯的理想施肥和灌溉水平分别为0.87I（全灌水量）和0.81 N（最大施氮量），0.92I和0.91 N， 0.98I和0.85 N。在此水平下，玉米、小麦和马铃薯的最大产量分别为1.3 × 104 kg·ha - 1、8.9 × 103 kg·ha - 1和9.2 × 104 kg·ha - 1。水氮综合管理的产量显著高于单独灌溉或施肥处理的产量。此外，与孤立的水氮管理相比，水氮耦合不仅提高了产量，而且减少了水和肥料的总投入。这些发现为优化水氮耦合策略提供了有力的科学依据。这种做法具有相当大的实际意义，特别是在水资源日益短缺和与农业做法有关的环境问题日益严重的情况下。

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引用次数: 0

ENT-YOLO: An improved lightweight YOLO for cotton organ detection in mulched drip irrigation systems in southern Xinjiang ENT-YOLO：用于南疆覆盖滴灌系统棉花器官检测的改进型轻量化YOLO

IF 6.5 1区农林科学 Q1 AGRONOMY

Agricultural Water Management

Pub Date : 2025-12-07 DOI: 10.1016/j.agwat.2025.110054

Jingcui Shao , Qingqing Zhao , Zhi Gong , Xinhua Guo , Shiyi Geng , Zhaoyang Li , Dongwei Li

The quantity and spatial distribution of cotton organs reflect the plant’s growth responses to soil water, fertilizer, and salinity conditions. Precise organ detection can provide a scientific basis for the regulation of water, fertilizer and salt in mulched drip irrigation. However, cotton organ detection in field environments remains challenging owing to the presence of complex occlusions and the highly variable morphology of target organs, with these difficulties being particularly pronounced in arid regions such as southern Xinjiang. To address these challenges, this study proposes ENT-YOLO, a compact yet high precision detection model built upon the YOLOv11n. First, the CSP-EDLAN module is introduced to replace the original C3k2 structure, effectively reducing model parameters. Second, CIoU loss is integrated with NWD loss to enhance bounding box regression accuracy. Finally, a TADDH detection head is incorporated to enhance feature representation and localization robustness for organs exhibiting variable morphology and blurred texture. Experimental results on the self-constructed cotton organ detection dataset (COD-DS) show that ENT-YOLO achieves 76.36 % precision, 73.44 % recall, and 79.77 % [email protected], with only 8.4 GFLOPs and 2.10 M parameters. The AP for the bud-flower class and the boll class increases by 3.13 % and 1.17 %, respectively, compared with the baseline model. The overall model size is merely 4.2 MB, representing a 19.2 % reduction relative to YOLOv11n. In comparison with mainstream detectors, ENT-YOLO demonstrates an improved balance between accuracy and compactness. Moreover, the spatial distribution map of the target organs is constructed using ENT-YOLO detection outputs, it lays a methodological foundation for the subsequent quantitative analysis of the spatial distribution and quantity of key organs such as buds, flowers and bolls. The results provide useful references for cotton growth stage determination and irrigation management.

棉花器官的数量和空间分布反映了植物对土壤水、肥、盐条件的生长响应。精确的器官检测可为膜下滴灌的水、肥、盐调控提供科学依据。然而，由于存在复杂的闭塞和目标器官形态的高度变化，在田间环境中进行棉花器官检测仍然具有挑战性，这些困难在新疆南部等干旱地区尤其明显。为了应对这些挑战，本研究提出了一种基于YOLOv11n的紧凑高精度检测模型ENT-YOLO。首先，引入CSP-EDLAN模块取代原有的C3k2结构，有效降低了模型参数。其次，将CIoU损失与NWD损失相结合，提高边界盒回归精度。最后，结合TADDH检测头，增强形态学变化和纹理模糊器官的特征表示和定位鲁棒性。在自构建的棉花器官检测数据集（COD-DS）上的实验结果表明，在仅有8.4个GFLOPs和2.10个 M参数的情况下，nt - yolo的准确率为76.36 %，召回率为73.44 %，召回率为79.77 % mAP@0.5。与基线模型相比，花蕾类和铃类的AP分别提高了3.13 %和1.17 %。整体模型大小仅为4.2 MB，相对于YOLOv11n减少了19.2% %。与主流探测器相比，ENT-YOLO在准确性和紧凑性之间取得了更好的平衡。利用ENT-YOLO检测输出构建目标器官的空间分布图，为后续对芽、花、铃等关键器官的空间分布和数量进行定量分析奠定了方法学基础。研究结果可为棉花生育期的确定和灌溉管理提供参考。

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引用次数: 0

A deep learning-based composite agricultural drought index for monitoring and impact assessment in Central Asia 基于深度学习的中亚农业干旱综合指数监测与影响评价

IF 6.5 1区农林科学 Q1 AGRONOMY

Agricultural Water Management

Pub Date : 2025-12-06 DOI: 10.1016/j.agwat.2025.110043

Xiuwei Xing , Shujie Wei , Xi Chen , Jing Qian , Shuhong Peng , Jiayu Sun , Bo Sun , Chaoliang Chen

Drought is a major natural hazard that seriously threatens agricultural production and food security. However, most existing drought indices rely on a single variable or scale, limiting their ability to capture the complexity of agricultural drought. In this study, we propose a Composite Agricultural Drought Index (CAEDI), developed using an unsupervised Convolutional Autoencoder (CAE) to integrate multiple drought-related indicators, including precipitation, land surface temperature, vegetation condition, and soil moisture. Guided by soil moisture anomalies as a weak physical prior, the model extracts drought-relevant features through nonlinear multivariate fusion. We evaluated CAEDI using established drought indices, in-situ observations, and crop yield data across Central Asia. CAEDI exhibits strong correlations with SPEI-2 and SPEI-3 (R > 0.80, p < 0.01) and outperforms individual indices in identifying agricultural drought. It also demonstrates high spatial and temporal consistency with conventional drought indices, strong associations with yield variability, and effectively distinguishes between wet and dry zones, showing good agreement with meteorological observations. Furthermore, we developed an Agricultural Drought Impact Index (ADI) by integrating CAEDI with Extreme Gradient Boosting (XGBoost) and SHapley Additive Explanations (SHAP). ADI effectively captures yield losses and highlights drought-sensitive phenological stages, particularly during heading and grain filling. Overall, CAEDI provides an objective, scalable, and physically grounded approach for agricultural drought monitoring and impact assessment, especially in data-scarce regions. Its integration of multi-source indicators through deep learning shows promises for enhancing early warning systems and supporting climate-resilient agricultural management.

干旱是严重威胁农业生产和粮食安全的重大自然灾害。然而，大多数现有的干旱指数依赖于单一变量或尺度，限制了它们捕捉农业干旱复杂性的能力。在这项研究中，我们提出了一个综合农业干旱指数（CAEDI），该指数使用无监督卷积自编码器（CAE）来整合多个与干旱相关的指标，包括降水、地表温度、植被状况和土壤湿度。该模型以土壤水分异常作为弱物理先验为指导，通过非线性多元融合提取干旱相关特征。我们利用中亚地区已建立的干旱指数、原位观测和作物产量数据对CAEDI进行了评估。CAEDI与SPEI-2和SPEI-3表现出较强的相关性（R > 0.80, p <； 0.01），在识别农业干旱方面优于单项指标。该方法与常规干旱指数具有较高的时空一致性，与产量变异有较强的相关性，并能有效区分干湿区，与气象观测结果吻合良好。此外，我们将CAEDI与极端梯度提升（XGBoost）和SHapley加性解释（SHAP）相结合，建立了农业干旱影响指数（ADI）。ADI有效地捕获了产量损失，并突出了干旱敏感物候阶段，特别是在抽穗和灌浆期间。总体而言，CAEDI为农业干旱监测和影响评估提供了一种客观、可扩展和有实际依据的方法，特别是在数据匮乏的地区。它通过深度学习整合多源指标，显示了加强预警系统和支持气候适应型农业管理的前景。

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引用次数: 0

Wheat-WSI: Development and estimation of a seedling-stage waterlogging stress index using multimodal image features 小麦- wsi：利用多模态图像特征建立和估算苗期涝渍胁迫指数

IF 6.5 1区农林科学 Q1 AGRONOMY

Agricultural Water Management

Pub Date : 2025-12-06 DOI: 10.1016/j.agwat.2025.110048

Jianliang Wang , Jianjun Sun , Jiacheng Wang , Dongwei Han , Yuanyuan Zhao , Chunyan Li , Tanjo Yui , Chengming Sun , Wenshan Guo , Tao Liu

Waterlogging stress during the seedling stage significantly restricts wheat growth and biomass accumulation, posing a major constraint on yield formation. While multimodal sensing technologies are increasingly applied to crop stress monitoring, challenges remain in achieving timely and accurate estimation under short-term stress conditions. This study addresses the need for a rapid, non-destructive, and quantifiable method to monitor wheat waterlogging stress. A composite index, Wheat-WSI (Waterlogging Stress Index), was developed using principal component analysis (PCA) based on six key physiological and agronomic parameters. Two estimation models were constructed: Wheat-WSI (HSM), a hyperspectral single-modality model using four spectral indices; and Wheat-WSI (MFM), a multimodal fusion model incorporating eight features derived from hyperspectral, thermal infrared, and chlorophyll fluorescence images. Both models employed the random forest (RF) algorithm, with feature selection and performance evaluation conducted using SHAP values, Pearson correlation, feature importance scores, and incremental modeling curves. Wheat-WSI (MFM) showed consistent estimation performance across treatments, with optimal results under 9-day stress (R² = 0.92, CCC = 0.96) and reduced accuracy under 3-day stress (R² = 0.75, CCC = 0.82). Wheat-WSI (HSM), despite relying solely on spectral indices, achieved reasonable accuracy (R² = 0.78, NRMSE = 0.16). Field validation demonstrated the robustness and application potential of Wheat-WSI (MFM) under prolonged stress, though its early detection capability under short-term conditions requires further enhancement. The proposed Wheat-WSI enables effective quantification of seedling-stage waterlogging stress and provides a robust modeling framework for monitoring crop stress responses.

苗期涝渍胁迫严重制约小麦的生长和生物量积累，是制约小麦产量形成的主要因素。虽然多模态传感技术越来越多地应用于作物胁迫监测，但在短期胁迫条件下实现及时和准确的估计仍然存在挑战。本研究旨在寻求一种快速、无损、可量化的小麦涝渍胁迫监测方法。采用主成分分析（PCA）方法，以小麦涝渍胁迫6个关键生理农艺参数为基础，建立了小麦涝渍胁迫指数（wsi）。构建了两种估算模型：小麦- wsi （HSM）是一种采用4个光谱指标的高光谱单模态模型；Wheat-WSI （MFM）是一种多模态融合模型，融合了来自高光谱、热红外和叶绿素荧光图像的8个特征。两种模型均采用随机森林（random forest， RF）算法，通过SHAP值、Pearson相关性、特征重要性评分和增量建模曲线进行特征选择和性能评价。小麦- wsi （MFM）在不同处理下表现出一致的估计性能，其中9 d胁迫下的结果最优（R2 = 0.92, CCC = 0.96）， 3 d胁迫下的结果较差（R2 = 0.75, CCC = 0.82）。小麦- wsi （HSM）仅依赖于光谱指数，但仍能获得合理的准确度（R2 = 0.78, NRMSE = 0.16）。田间验证结果表明，小麦- wsi （MFM）在长期胁迫条件下的稳健性和应用潜力，但其短期条件下的早期检测能力有待进一步提高。提出的小麦- wsi能够有效地量化苗期涝渍胁迫，并为监测作物胁迫反应提供了一个强大的建模框架。

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引用次数: 0