[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2912":3,"related-2912":153},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":37,"classification":50,"search_phrases":102,"slug":105,"view_count":85,"doi":106,"paper":107,"created_at":152},2912,"Spatial optimization of water-saving irrigation in Chinese rice paddies: Balancing yield, greenhouse gases, and cost using NSGA-II","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104976","CONTEXT Optimizing the spatial allocation of water-saving irrigation (WSI) promotion is critical for balancing rice production, greenhouse gas (GHG) mitigation, and economic costs, yet remains challenging at a national scale due to computational intractability. OBJECTIVE This study aims to develop a spatially explicit optimization framework to identify WSI promotion pathways across China's rice paddies that simultaneously maximize yield gain, maximize GHG reduction, and minimize implementation cost. METHODS We coupled K-means clustering with a multi-objective evolutionary algorithm (NSGA-II). Based on machine-learning predicted yield and GHG emissions for 157,417 flooded irrigation grids, we first clustered these grids into 500 environmentally and agronomically homogeneous groups. We then formulated a continuous optimization problem with cluster-level conversion rates as decision variables, simultaneously maximizing national yield gain, maximizing GHG reduction, and minimizing implementation cost. RESULTS AND CONCLUSIONS The Pareto front comprised 100 non-dominated solutions spanning promotion rates from 61.8% to 80.3%. The optimal solution, selected by normalized scoring, achieved 6.47 Mt. yield gain and 63.5 Mt. CO 2 e GHG reduction at a cost of 10.14 billion CNY, corresponding to a national promotion rate of 80.3%. Cluster-scale conversion rates exhibited significant spatial heterogeneity and positive correlation with cluster size ( r = 0.23), revealing economies of scale in WSI promotion. Compared with a random promotion strategy at 90% adoption, our optimized solution delivered 74% higher yield gain with 9.7 percentage points lower promotion effort while achieving comparable GHG reduction. SIGNIFICANCE Our framework provides a spatially explicit decision-support tool for precision agricultural policy, demonstrating that smart spatial allocation can substantially enhance the efficiency of limited resources in scaling climate-smart agricultural practices.","背景 优化节水灌溉（WSI）推广的空间配置对于平衡水稻生产、温室气体（GHG）减排和经济成本至关重要，但由于计算上的不可处理性，在全国尺度上仍具挑战性。目的 本研究旨在开发一个空间显式优化框架，以识别中国稻田的WSI推广路径，同时最大化产量增益、最大化GHG减排并最小化实施成本。方法 我们将K-means聚类与多目标进化算法（NSGA-II）相结合。基于机器学习预测的157,417个淹水灌溉网格的产量和GHG排放，我们首先将这些网格聚类为500个在环境和农艺上同质的组。然后，我们构建了一个以组级转换率为决策变量的连续优化问题，同时最大化全国产量增益、最大化GHG减排并最小化实施成本。结果与结论 Pareto前沿包含100个非支配解，推广率从61.8%到80.3%不等。通过归一化评分选出的最优解实现了6.47 Mt的产量增益和63.5 Mt CO₂e的GHG减排，成本为101.4亿元人民币，对应全国推广率为80.3%。组尺度转换率表现出显著的空间异质性，并与组规模呈正相关（r = 0.23），揭示了WSI推广中的规模经济。与90%采纳率的随机推广策略相比，我们的优化方案在推广力度低9.7个百分点的情况下实现了高出74%的产量增益，同时实现了相当的GHG减排。意义 我们的框架为精准农业政策提供了一个空间显式的决策支持工具，表明智能空间配置可以显著提高有限资源在推广气候智慧型农业实践中的效率。",null,"Agricultural Systems","2026-09-18T00:00:00Z","论文",10,true,89,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},24,23,19,14,9,1,"基于NSGA-II的全国稻田节水灌溉空间优化框架，数据规模大、结论具体，对气候智慧型农业政策有决策参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","水稻","空间优化","节水灌溉","农业减排",{"code":33,"slug":34,"name":28,"path":35,"parent_code":36},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",[38,44],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":51,"locked":52,"version":53,"method":54,"evidence":55},"classified",false,"2026-10-22.2","rules",{"candidates":56,"agricultural_evidence":14},[57,63,69,75,83,90,97],{"code":33,"score":58,"supported":14,"title":59,"abstract":60,"tags":62},18,[34],[28,61,34],"稻田",[28],{"code":45,"score":64,"supported":14,"title":65,"abstract":66,"tags":68},15,[46],[67,46],"灌溉",[30],{"code":39,"score":20,"supported":14,"title":70,"abstract":72,"tags":74},[71],"greenhouse",[73,71],"温室",[],{"code":76,"score":77,"supported":14,"title":78,"abstract":79,"tags":82},"J1",6,[],[80,81],"农业政策","agricultural policy",[],{"code":84,"score":85,"supported":14,"title":86,"abstract":87,"tags":89},"I1",4,[],[88],"精准农业",[27],{"code":91,"score":92,"supported":14,"title":93,"abstract":94,"tags":96},"I2",3,[],[95],"机器学习",[],{"code":98,"score":22,"supported":52,"title":99,"abstract":100,"tags":101},"H4",[],[],[31],[103,104],"中国稻田 节水灌溉 空间优化","NSGA-II 水稻 温室气体 减排","中国稻田节水灌溉空间优化-2912","10.1016\u002Fj.agsy.2026.104976",{"doi":106,"openalex_id":108,"authors":109,"venue":10,"cited_by_count":144,"oa_url":6,"card":145,"direction":149,"ingested_from":151},"W7213551595",[110,112,115,118,120,122,124,126,129,131,134,137,140,142],{"name":111,"orcid":9},"Qiang Xu",{"name":113,"orcid":114},"Fan Yao","https:\u002F\u002Forcid.org\u002F0000-0002-4393-7296",{"name":116,"orcid":117},"Dan Wei","https:\u002F\u002Forcid.org\u002F0000-0003-4401-567X",{"name":119,"orcid":9},"Hui Gao",{"name":121,"orcid":9},"Min Jiang",{"name":123,"orcid":9},"Wenya Chen",{"name":125,"orcid":9},"Yourui Cao",{"name":127,"orcid":128},"Peng Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-3036-1507",{"name":130,"orcid":9},"A. I. Abdo",{"name":132,"orcid":133},"Liujun Xiao","https:\u002F\u002Forcid.org\u002F0000-0002-1900-1586",{"name":135,"orcid":136},"Hao Liang","https:\u002F\u002Forcid.org\u002F0000-0002-9955-6492",{"name":138,"orcid":139},"Xiaoqing Cui","https:\u002F\u002Forcid.org\u002F0000-0002-1970-5145",{"name":141,"orcid":9},"Xia Liang",{"name":143,"orcid":9},"Huiqing Bai",0,{"tldr":146,"method":147,"finding":148,"direction":149,"opportunity":150},"构建空间优化框架，为中国稻田节水灌溉推广寻找兼顾产量、温室气体与成本的路径。","耦合K-means聚类与NSGA-II多目标进化算法，基于15.7万网格的机器学","最优方案增产6.47 Mt、减排63.5 Mt CO2e，成本101.4亿元，推广率80.3%，存在","农业绿色发展与碳","可将该空间优化框架扩展到其他气候智慧型农业技术，并耦合农户采纳行为与政策激励。","openalex","2026-09-19T23:30:05.508592Z",{"total":77,"page":22,"page_size":77,"items":154},[155,222,310,387,474,547],{"id":156,"title":157,"url":158,"summary":159,"summary_zh":9,"content":9,"source_name":160,"source_url":9,"published_at":161,"category":12,"cover_url":9,"hotness":13,"is_selected":52,"score":162,"score_detail":163,"sources":166,"tags":168,"primary_topic":172,"secondary_topics":173,"classification":180,"search_phrases":208,"slug":211,"view_count":92,"doi":9,"paper":212,"created_at":221},4159,"Dynamic regulation of grid-scale high-efficiency precision irrigation for rice under climate variability（基于气候变化的网格尺度水稻精准灌溉动态调控）","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs44264-026-00172-7","Dong Wenhao、Xu YaoWen、Zhangzhong Lili、Xu Xianghui、Wei Shiyu、Sun Ziqiang、Chen Yingshan、Zhang Pingan、Li Mo（npj Sustainable Agriculture 2026, 4(1): 61）。针对气候变化与区域水量供需失衡，文章提出「模拟-优化」协同的网格尺度水稻精准灌溉调度框架：使用WOFOST模型结合Sobol敏感性分析与粒子群优化，与多目标优化模型耦合生成可动态调整的灌溉策略。在黑龙江省三江平原与松嫩平原（332,500 km²）应用：模型精度提高，区域产量提升1.5%、灌溉用水减少8.0%；动态调度策略相比传统灌溉节水10.1%。","npj Sustainable Agriculture 2026, 4(1): 61","2026-09-28T00:00:00Z",83,{"impact":164,"substance":18,"depth":58,"authority":20,"freshness":77,"relevant":22,"comment":165},22,"核心期刊论文，提出模拟-优化协同的网格尺度水稻精准灌溉框架，在三江平原等区域验证节水增产效果，方法新颖、数据规模大，对智慧农业与气候适应有参考价值。",[167],{"name":160,"url":158},[27,28,169,170,171],"气候适应","精准灌溉","三江平原",{"code":33,"slug":34,"name":28,"path":35,"parent_code":36},[174,175],{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"code":176,"slug":177,"name":178,"path":179,"parent_code":49},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"status":51,"locked":52,"version":53,"method":54,"evidence":181},{"candidates":182,"agricultural_evidence":14},[183,188,192,198,204],{"code":33,"score":184,"supported":14,"title":185,"abstract":186,"tags":187},20,[28,34],[28],[28],{"code":45,"score":184,"supported":14,"title":189,"abstract":190,"tags":191},[67,46],[67],[170],{"code":176,"score":193,"supported":14,"title":194,"abstract":196,"tags":197},12,[195],"气候变化",[195],[169],{"code":199,"score":92,"supported":14,"title":200,"abstract":201,"tags":203},"H8",[],[202],"sustainable agriculture",[],{"code":84,"score":22,"supported":52,"title":205,"abstract":206,"tags":207},[],[],[27],[209,210],"三江平原 水稻 精准灌溉","WOFOST 水稻 灌溉调度","三江平原水稻精准灌溉-4159",{"doi":9,"openalex_id":9,"authors":213,"venue":9,"cited_by_count":144,"oa_url":9,"card":214,"direction":218,"ingested_from":220},[],{"tldr":215,"method":216,"finding":217,"direction":218,"opportunity":219},"提出模拟-优化协同的网格尺度水稻精准灌溉调度框架，实现动态调控。","WOFOST作物模型、Sobol敏感性分析、粒子群优化与多目标优化耦合。","区域产量提升1.5%、灌溉用水减少8.0%，动态调度较传统灌溉节水10.1%。","智慧农业 \u002F 农业物联网","可探索将网格尺度灌溉调度与实时物联网墒情监测、遥感数据同化结合，实现气候变率下的在线滚动优化。","agent","2026-10-04T00:09:10.154416Z",{"id":223,"title":224,"url":225,"summary":226,"summary_zh":227,"content":9,"source_name":228,"source_url":225,"published_at":229,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":230,"score_detail":231,"sources":233,"tags":235,"primary_topic":239,"secondary_topics":240,"classification":242,"search_phrases":281,"slug":284,"view_count":22,"doi":285,"paper":286,"created_at":309},4371,"Intelligent greenhouse irrigation control via multilayer perceptron neural network climate prediction integrated with fuzzy logic control","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compeleceng.2026.111587","Conventional irrigation methods rely on indoor sensor feedback, which fails to anticipate rapid microclimate fluctuations and often leads to over-irrigation or water stress. Additionally, traditional controllers used in the watering process lack the ability to adjust water flow rate dynamically, resulting in inefficient water usage and energy waste. Intelligent Greenhouse Irrigation Management (IGIM) remains a critical challenge for achieving sustainable, high-yield greenhouse agriculture. Precise irrigation management is especially important for crops like tomatoes, where both timing and quantity directly influence productivity and energy consumption. To address these challenges, this paper proposes a hybrid intelligent system that integrates a Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) with an Adaptive Fuzzy Logic Controller (AFLC). The MLP-ANN forecasts key indoor variables such as temperature, humidity, and soil moisture, achieving R² values of 0.999. The Root Mean Square Errors of 0.266 °C, 0.254%, and 0.808%, respectively. These predictions directly feed into the AFLC, which employs an adaptive rule base to dynamically adjust irrigation flow rates in real time (0–18 LPH), significantly enhancing water-use efficiency and reducing waste to save energy consumption in the watering process. The key novelty of this work is the direct integration of MLP-predicted microclimate variables into the antecedent of adaptive fuzzy rules, enabling the controller to anticipate and respond to climate variations before they affect crop conditions. Unlike conventional reactive or static rule-based systems, the proposed MLP-AFLC framework creates a closed-loop coupling between forecasting and actuation, allowing predictive, rather than threshold-based, irrigation decisions. By integrating predictive intelligence with adaptive control, the proposed MLP-AFLC framework provides a robust, autonomous, and sustainable solution for greenhouse management, advancing precision agriculture in highly dynamic environments.","传统灌溉方法依赖室内传感器反馈，无法预判微气候的快速波动，常导致过度灌溉或水分胁迫。此外，灌溉过程中使用的传统控制器缺乏动态调节流速的能力，造成水资源利用效率低下和能源浪费。智能温室灌溉管理（IGIM）仍是实现可持续、高产温室农业的关键挑战。对于番茄等作物而言，精准灌溉管理尤为重要，因为灌溉时机和水量直接影响产量和能耗。为解决这些挑战，本文提出了一种将多层感知器人工神经网络（MLP-ANN）与自适应模糊逻辑控制器（AFLC）相集成的混合智能系统。MLP-ANN预测温度、湿度和土壤湿度等关键室内变量，R²值达到0.999，均方根误差分别为0.266 °C、0.254%和0.808%。这些预测结果直接输入AFLC，后者采用自适应规则库实时动态调节灌溉流速（0–18 LPH），显著提高水资源利用效率并减少浪费，从而在灌溉过程中节约能耗。本工作的关键创新在于将MLP预测的微气候变量直接集成到自适应模糊规则的前件中，使控制器能够在气候变化影响作物状况之前进行预判和响应。与传统反应式或静态规则系统不同，所提出的MLP-AFLC框架在预测与执行之间建立了闭环耦合，实现了基于预测而非基于阈值的灌溉决策。通过将预测智能与自适应控制相结合，所提出的MLP-AFLC框架为温室管理提供了一种稳健、自主且可持续的解决方案，推动了高度动态环境下的精准农业发展。","Computers & Electrical Engineering","2026-10-06T00:00:00Z",81,{"impact":58,"substance":164,"depth":58,"authority":20,"freshness":21,"relevant":22,"comment":232},"提出MLP-ANN与自适应模糊控制融合的温室预测性灌溉框架，R²达0.999，方法新颖且数据可靠，对精准农业节水节能有实质参考价值。",[234],{"name":228,"url":225},[27,236,237,88,238],"农业人工智能","智能灌溉","温室种植",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[241],{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":52,"version":53,"method":54,"evidence":243},{"candidates":244,"agricultural_evidence":14},[245,250,254,260,267,273,277],{"code":45,"score":58,"supported":14,"title":246,"abstract":247,"tags":249},[46],[67,248,46],"水资源",[237],{"code":39,"score":64,"supported":14,"title":251,"abstract":252,"tags":253},[71],[73,71],[238],{"code":84,"score":255,"supported":14,"title":256,"abstract":257,"tags":259},8,[],[88,258],"precision agriculture",[27,88],{"code":261,"score":77,"supported":14,"title":262,"abstract":263,"tags":266},"H2",[],[264,265],"土壤","soil",[],{"code":268,"score":92,"supported":14,"title":269,"abstract":270,"tags":272},"B1",[],[271],"番茄",[],{"code":176,"score":92,"supported":14,"title":274,"abstract":275,"tags":276},[],[195],[],{"code":91,"score":22,"supported":52,"title":278,"abstract":279,"tags":280},[],[],[236],[282,283],"MLP-AFLC 温室灌溉","神经网络 模糊控制 灌溉","MLP-AFLC温室灌溉-4371","10.1016\u002Fj.compeleceng.2026.111587",{"doi":285,"openalex_id":287,"authors":288,"venue":228,"cited_by_count":144,"oa_url":225,"card":303,"direction":308,"ingested_from":151},"W7220746994",[289,292,295,297,300],{"name":290,"orcid":291},"Jamel Riahi","https:\u002F\u002Forcid.org\u002F0009-0006-1434-4367",{"name":293,"orcid":294},"Hamza Nasri","https:\u002F\u002Forcid.org\u002F0009-0008-7343-4199",{"name":296,"orcid":9},"Abdelkader Mami",{"name":298,"orcid":299},"Mario Carpentieri","https:\u002F\u002Forcid.org\u002F0000-0001-5165-5873",{"name":301,"orcid":302},"Silvano Vergura","https:\u002F\u002Forcid.org\u002F0000-0002-0826-9237",{"tldr":304,"method":305,"finding":306,"direction":218,"opportunity":307},"提出MLP-ANN预测温室微气候并驱动自适应模糊控制器，实现预测性智能灌溉。","MLP神经网络预测温湿度与土壤水分，结合自适应模糊逻辑控制流量。","预测R²达0.999，模糊控制器动态调节0-18 LPH流量，显著节水节能。","可探索多作物多环境迁移学习与边缘部署，降低预测模型算力需求。","农业人工智能与决策模型","2026-10-07T23:30:43.848308Z",{"id":311,"title":312,"url":313,"summary":314,"summary_zh":315,"content":9,"source_name":316,"source_url":313,"published_at":317,"category":12,"cover_url":9,"hotness":13,"is_selected":52,"score":318,"score_detail":319,"sources":322,"tags":324,"primary_topic":328,"secondary_topics":329,"classification":336,"search_phrases":366,"slug":369,"view_count":370,"doi":371,"paper":372,"created_at":386},3825,"Hybrid Rule-Based and Machine Learning Decision-Support Framework for Smart Paddy Farming","https:\u002F\u002Fdoi.org\u002F10.4038\u002Fatrsj.v6i1.70","Paddy farming decisions are affected by changing weather conditions, water availability, soil conditions, and crop health, creating a need for decision-support approaches that combine agronomic knowledge with data-driven prediction. This research presents a hybrid rule-based and machine-learning decision-support solution for paddy management that combines interpretable agronomic rules and lightweight predictive models. The temporal alignment, interpolation, normalization, feature engineering, and crop-stage aggregation of weather and NDVI data were integrated and preprocessed. It is a rule-based engine with 63 expert rules that was fused with Decision Tree, Logistic Regression, and Random Forest models using a weighted decision-fusion mechanism. The hybrid model performed with 90% accuracy, 0.91 precision, 0.88 recall, and 0.89 F1-score, while the rule-based model had 72% accuracy, and the best standalone Random Forest model had 86% accuracy. The hybrid model also yielded the minimum RMSE of 7.6. The results of the simulation scenarios showed increases in yield of 3–17% and savings in irrigation-water use of 12.4%. There were 10 farmers and 2 agricultural officers who participated in the preliminary usability test and were scored from 4.0 to 4.8 out of 5. The key is an adaptive machine-learning prediction integrated with clear agronomic logic, in a lightweight advisory system. The yields and water-saving benefits, however, are simulated, and experimental testing on a wider range of locations and seasons will be needed to determine the full agronomic benefits.","水稻种植决策受到天气条件变化、水资源可用性、土壤条件和作物健康状况的影响，因此需要将农艺知识与数据驱动预测相结合的决策支持方法。本研究提出了一种基于规则与机器学习混合的水稻管理决策支持方案，将可解释的农艺规则与轻量级预测模型相结合。研究对天气和NDVI数据进行了时间对齐、插值、归一化、特征工程和作物阶段聚合的集成与预处理。构建了一个包含63条专家规则的基于规则的引擎，并通过加权决策融合机制将其与决策树、逻辑回归和随机森林模型相融合。混合模型的准确率为90%，精确率为0.91，召回率为0.88，F1分数为0.89，而基于规则的模型准确率为72%，最佳独立随机森林模型准确率为86%。混合模型还取得了最低的RMSE值7.6。模拟情景结果显示，产量提高了3%–17%，灌溉用水节省了12.4%。共有10名农民和2名农业官员参与了初步可用性测试，评分为4.0至4.8分（满分5分）。其核心在于将自适应机器学习预测与清晰的农艺逻辑相结合，构建一个轻量级咨询系统。然而，产量提升和节水效益均为模拟结果，需要在更广泛的地点和季节进行实验测试，以确定其完整的农艺效益。","Journal of Agro-Technology and Rural Sciences","2026-09-29T00:00:00Z",68,{"impact":193,"substance":184,"depth":320,"authority":13,"freshness":21,"relevant":22,"comment":321},17,"提出规则与机器学习融合的水稻决策支持框架，指标与仿真结果具体，但验证样本小、增产节水为模拟值，属细分领域方法学进展。",[323],{"name":316,"url":313},[27,236,325,326,327],"遥感监测","水稻种植","灌溉节水",{"code":33,"slug":34,"name":28,"path":35,"parent_code":36},[330,335],{"code":91,"slug":331,"name":332,"path":333,"parent_code":334},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":52,"version":53,"method":54,"evidence":337},{"candidates":338,"agricultural_evidence":14},[339,344,349,353,357,361],{"code":33,"score":64,"supported":14,"title":340,"abstract":342,"tags":343},[341],"paddy",[28,341],[326],{"code":91,"score":193,"supported":14,"title":345,"abstract":347,"tags":348},[346],"machine learning",[95],[236],{"code":45,"score":21,"supported":14,"title":350,"abstract":351,"tags":352},[],[67,248,46],[],{"code":261,"score":77,"supported":14,"title":354,"abstract":355,"tags":356},[],[264,265],[],{"code":84,"score":22,"supported":52,"title":358,"abstract":359,"tags":360},[],[],[27],{"code":362,"score":22,"supported":52,"title":363,"abstract":364,"tags":365},"I3",[],[],[325],[367,368],"水稻 决策支持 机器学习","NDVI 水稻 灌溉","水稻决策支持机器学习-3825",2,"10.4038\u002Fatrsj.v6i1.70",{"doi":371,"openalex_id":373,"authors":374,"venue":316,"cited_by_count":144,"oa_url":380,"card":381,"direction":218,"ingested_from":151},"W7214735276",[375,378],{"name":376,"orcid":377},"Sawjanya Sathyaseelan","https:\u002F\u002Forcid.org\u002F0009-0003-1194-527X",{"name":379,"orcid":9},"K. Sriram","https:\u002F\u002Fatrsj.sljol.info\u002Farticles\u002F70\u002Ffiles\u002F6abb528a0792a.pdf",{"tldr":382,"method":383,"finding":384,"direction":308,"opportunity":385},"提出规则与机器学习混合的水稻决策支持框架，提升决策精度并节水增产。","融合63条专家规则与决策树、逻辑回归、随机森林，处理天气和NDVI数据。","混合模型准确率90%，模拟增产3-17%，节水12.4%，可用性评分4.0-4.8。","当前增产节水为模拟结果，未来需在多地点多季节开展田间试验验证农艺效益。","2026-09-30T23:30:09.772909Z",{"id":388,"title":389,"url":390,"summary":391,"summary_zh":392,"content":9,"source_name":393,"source_url":390,"published_at":394,"category":12,"cover_url":9,"hotness":13,"is_selected":52,"score":395,"score_detail":396,"sources":399,"tags":401,"primary_topic":406,"secondary_topics":407,"classification":413,"search_phrases":449,"slug":452,"view_count":144,"doi":453,"paper":454,"created_at":473},3168,"Spatial and Temporal Analysis of Rice Yield Using the DSSAT Model in the Kommamuru Canal Command Area, Guntur (Dist), Andhra Pradesh, India","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjeai\u002F2026\u002Fv48i104524","Rice is the predominant crop cultivated in the Kommamuru Canal Command Area of Guntur district, Andhra Pradesh. Rice productivity is strongly influenced by irrigation water availability and climatic conditions. The present study aimed to analyse the spatial and temporal variability of rice productivity using the DSSAT-CERES Rice model integrated with Sentinel-2 remote sensing data during the kharif seasons of 2022, 2023 and 2024. Rice-cultivated areas were mapped using multi-temporal Sentinel-2 imagery, while weather, soil, crop management and cultivar data were used as inputs for DSSAT simulations. The model was calibrated and validated using field observations and yield data collected from representative locations within the command area. Simulated rice yields showed considerable variation among mandals and across years, ranging from 1,560.58 to 4,111.06 kg ha⁻¹. The highest productivity was observed in 2024 due to favourable rainfall distribution, adequate canal water supply and improved crop growth conditions, whereas relatively lower productivity was recorded in 2023 owing to moisture stress and irregular irrigation availability. Spatial yield maps generated through the integration of DSSAT outputs and GIS-based rice maps identified high- and low-productivity zones across the command area. The study demonstrated the usefulness of combining crop simulation modelling and remote sensing techniques for rice yield assessment, irrigation planning, yield forecasting and sustainable water resource management in canal command areas. The study confirmed that the DSSAT-CERES Rice model effectively simulated rice productivity in the Kommamuru Canal Command Area during 2022-2024. Overall, the study provides a reliable framework for sustainable rice production and efficient water management in canal command areas and can be applied for future agricultural planning and decision-making. The validation results showed low percentage deviation values ranging from 0.94% to 4.39%, indicating good agreement between simulated and actual farmer yields. Low percentage deviation values between simulated and actual yields indicated good model accuracy and reliability for yield prediction. The integration of DSSAT, GIS and remote sensing techniques was useful for sustainable rice production planning and efficient irrigation management.","水稻是安得拉邦贡土尔县科马穆鲁灌区种植的主要作物。水稻生产力受灌溉可用水量和气候条件的强烈影响。本研究旨在利用DSSAT-CERES水稻模型结合Sentinel-2遥感数据，分析2022年、2023年和2024年kharif季水稻生产力的时空变异性。利用多时相Sentinel-2影像绘制水稻种植区，同时将气象、土壤、作物管理和品种数据作为DSSAT模拟的输入。利用从灌区内代表性地点收集的田间观测数据和产量数据对模型进行校准和验证。模拟水稻产量在不同mandal和年份间表现出相当大的变异，范围为1,560.58至4,111.06 kg ha⁻¹。2024年由于有利的降雨分布、充足的渠水供应和改善的作物生长条件，生产力最高；而2023年由于水分胁迫和不规律的灌溉可用性，生产力相对较低。通过整合DSSAT输出和基于GIS的水稻分布图生成的空间产量图，识别出灌区内的高产区和低产区。研究表明，将作物模拟模型与遥感技术相结合，对于灌区水稻产量评估、灌溉规划、产量预测和可持续水资源管理具有实用价值。研究证实，DSSAT-CERES水稻模型有效模拟了2022-2024年科马穆鲁灌区的水稻生产力。总体而言，本研究为灌区可持续水稻生产和高效水资源管理提供了可靠的框架，可应用于未来的农业规划和决策。验证结果显示，百分比偏差值较低，范围为0.94%至4.39%，表明模拟产量与实际农民产量之间具有良好的一致性。模拟产量与实际产量之间较低的百分比偏差值表明模型在产量预测方面具有良好的准确性和可靠性。DSSAT、GIS和遥感技术的整合有助于可持续水稻生产规划和高效灌溉管理。","Journal of Experimental Agriculture International","2026-09-22T00:00:00Z",72,{"impact":193,"substance":184,"depth":320,"authority":397,"freshness":13,"relevant":22,"comment":398},13,"印度区域尺度的DSSAT与Sentinel-2融合估产研究，方法成熟、验证可靠，对遥感估产与灌溉管理有参考价值，但属区域性案例，公共影响有限。",[400],{"name":393,"url":390},[28,402,403,404,405],"产量预测","遥感","作物模型","灌溉管理",{"code":33,"slug":34,"name":28,"path":35,"parent_code":36},[408,409],{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"code":362,"slug":410,"name":411,"path":412,"parent_code":334},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":51,"locked":52,"version":53,"method":54,"evidence":414},{"candidates":415,"agricultural_evidence":14},[416,420,425,430,434,440,445],{"code":33,"score":64,"supported":14,"title":417,"abstract":418,"tags":419},[34],[28,34],[28],{"code":45,"score":13,"supported":14,"title":421,"abstract":422,"tags":424},[],[67,248,46,423],"water resource",[405],{"code":362,"score":255,"supported":14,"title":426,"abstract":427,"tags":429},[],[403,428],"remote sensing",[402,403],{"code":261,"score":77,"supported":14,"title":431,"abstract":432,"tags":433},[],[264,265],[],{"code":435,"score":92,"supported":14,"title":436,"abstract":437,"tags":439},"I4",[],[438],"GIS",[],{"code":76,"score":92,"supported":14,"title":441,"abstract":442,"tags":444},[],[443],"农业规划",[],{"code":91,"score":22,"supported":52,"title":446,"abstract":447,"tags":448},[],[],[404],[450,451],"DSSAT CERES Rice 水稻","Sentinel-2 水稻 遥感估产","DSSATCERESRice水稻-3168","10.9734\u002Fjeai\u002F2026\u002Fv48i104524",{"doi":453,"openalex_id":455,"authors":456,"venue":393,"cited_by_count":144,"oa_url":390,"card":467,"direction":471,"ingested_from":151},"W7213966772",[457,459,461,463,465],{"name":458,"orcid":9},"Rana Prathap",{"name":460,"orcid":9},"G. Ravi Babu",{"name":462,"orcid":9},"V. Muthayya Chowdary",{"name":464,"orcid":9},"K.Krupavathi",{"name":466,"orcid":9},"K. Chandrasekhar",{"tldr":468,"method":469,"finding":470,"direction":471,"opportunity":472},"用DSSAT-CERES水稻模型结合Sentinel-2遥感，分析印度Guntur灌区2022-20","DSSAT-CERES水稻模型、Sentinel-2多时相影像、GIS空间制图与","模拟产量1560-4111 kg\u002Fha，2024年最高、2023年因水分胁迫最低，验证偏差仅0.94","农业遥感与作物表型","可引入机器学习同化遥感与作物模型，提升灌区尺度产量预报精度并支撑灌溉决策。","2026-09-22T23:30:22.711640Z",{"id":475,"title":476,"url":477,"summary":478,"summary_zh":9,"content":9,"source_name":10,"source_url":477,"published_at":394,"category":12,"cover_url":9,"hotness":13,"is_selected":52,"score":479,"score_detail":480,"sources":482,"tags":484,"primary_topic":489,"secondary_topics":490,"classification":492,"search_phrases":508,"slug":511,"view_count":22,"doi":512,"paper":513,"created_at":546},3136,"Integrated water–carbon management reshapes trade-offs among greenhouse gas emissions, productivity, and economic returns in rice systems","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104991","Integrated water–carbon management reshapes trade-offs among greenhouse gas emissions, productivity, and economic returns in rice systems。Agricultural Systems",82,{"impact":58,"substance":164,"depth":58,"authority":20,"freshness":13,"relevant":22,"comment":481},"核心期刊新研究，提出水碳协同管理以平衡稻田减排、产量与收益，方法新颖、结论有实质增量，对农业绿色低碳与智慧灌溉有参考价值。",[483],{"name":10,"url":477},[28,485,486,487,488],"农业温室气体","水碳协同","稻田减排","农业经济评估",{"code":33,"slug":34,"name":28,"path":35,"parent_code":36},[491],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":51,"locked":52,"version":53,"method":54,"evidence":493},{"candidates":494,"agricultural_evidence":14},[495,499,504],{"code":33,"score":193,"supported":14,"title":496,"abstract":497,"tags":498},[34],[34],[28],{"code":39,"score":500,"supported":14,"title":501,"abstract":502,"tags":503},11,[71],[71],[],{"code":98,"score":22,"supported":52,"title":505,"abstract":506,"tags":507},[],[],[485],[509,510],"水稻 水碳管理 温室气体","稻田 减排 经济收益","水稻水碳管理温室气体-3136","10.1016\u002Fj.agsy.2026.104991",{"doi":512,"openalex_id":514,"authors":515,"venue":10,"cited_by_count":144,"oa_url":477,"card":9,"direction":9,"ingested_from":151},"W7213974400",[516,518,520,522,524,526,529,532,534,536,538,540,543,544],{"name":517,"orcid":9},"Zewei Jiang",{"name":519,"orcid":9},"Shihong Yang",{"name":521,"orcid":9},"Qingqing Pang",{"name":523,"orcid":9},"Pete Smith",{"name":525,"orcid":9},"Mohamed Abdalla",{"name":527,"orcid":528},"Matthias Kuhnert","https:\u002F\u002Forcid.org\u002F0000-0003-3284-2133",{"name":530,"orcid":531},"Saeed Karbin","https:\u002F\u002Forcid.org\u002F0000-0002-8327-8037",{"name":533,"orcid":9},"Yi Xu",{"name":535,"orcid":9},"Jie Zhang",{"name":537,"orcid":9},"Haonan Qiu",{"name":539,"orcid":9},"Xishan Song",{"name":541,"orcid":542},"Qinbo Yang","https:\u002F\u002Forcid.org\u002F0000-0002-5327-8837",{"name":533,"orcid":9},{"name":545,"orcid":9},"Yakov Kuzyakov","2026-09-22T23:30:05.426576Z",{"id":548,"title":549,"url":550,"summary":551,"summary_zh":9,"content":552,"source_name":553,"source_url":9,"published_at":554,"category":555,"cover_url":9,"hotness":13,"is_selected":52,"score":556,"score_detail":557,"sources":559,"tags":561,"primary_topic":567,"secondary_topics":568,"classification":570,"search_phrases":597,"slug":600,"view_count":144,"doi":9,"paper":9,"created_at":601},5040,"\"深东盐稻1号\"耐盐碱水稻新品系在山东东营全生育期3‰~4‰咸水灌溉下亩产476.09公斤","https:\u002F\u002Fwww.caas.cn\u002F?id=3804","10月8-9日,耐盐碱水稻新品系展示示范与测产观摩会在山东东营举行。经专家组现场实收测产,在全生育期3‰~4‰盐度咸水灌溉条件下,\"深东盐稻1号\"实收产量476.09公斤\u002F亩,较耐盐对照品种增产23%。该新品系突破传统盐碱地水稻产量瓶颈,为滨海盐碱地综合利用与粮食安全拓展新增耕地空间。","[人民日报头版头条]力度更大 “四个面向”为基础研究划重点（总书记的关切·落地的回响）\n\n7月10日，海南商业航天发射场，长征十号乙运载火箭首飞成功，我国运载火箭首次实现可控回收。中国航天一次次创造历史，离不开材料制备、流体动力、空间物理等方面的基础研究突破。","中国农业科学院 2026年10月14日","2026-10-14T00:00:00Z","报道",79,{"impact":164,"substance":184,"depth":20,"authority":64,"freshness":255,"relevant":22,"comment":558},"国家级科研机构发布的耐盐碱水稻新品系在3‰~4‰咸水灌溉下实现亩产476.09公斤，属盐碱地综合利用与种业创新的实质性突破，数据具体、信源权威，值得进入每日精选。",[560],{"name":553,"url":550},[562,563,564,565,566],"种业振兴","盐碱地治理","耐盐碱水稻","山东东营","咸水灌溉",{"code":33,"slug":34,"name":28,"path":35,"parent_code":36},[569],{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":52,"version":53,"method":54,"evidence":571},{"candidates":572,"agricultural_evidence":14},[573,577,581,586,592],{"code":33,"score":193,"supported":14,"title":574,"abstract":575,"tags":576},[28],[28],[564],{"code":45,"score":500,"supported":14,"title":578,"abstract":579,"tags":580},[67],[67],[],{"code":261,"score":85,"supported":14,"title":582,"abstract":583,"tags":585},[],[584],"盐碱地",[563],{"code":587,"score":92,"supported":14,"title":588,"abstract":589,"tags":591},"A8",[],[590],"粮食安全",[],{"code":593,"score":22,"supported":52,"title":594,"abstract":595,"tags":596},"F1",[],[],[562],[598,599],"深东盐稻1号 东营 耐盐碱水稻","咸水灌溉 水稻 亩产476.09公斤","深东盐稻1号东营耐盐碱水稻-5040","2026-10-16T00:13:11.145577Z"]