[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3510":3,"related-3510":61},{"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":18,"tags":20,"search_phrases":26,"slug":29,"view_count":15,"doi":30,"paper":31,"created_at":60},3510,"Bridging the in situ data gap: a satellite- and model-based framework for operational monitoring of marine harmful algal blooms and temperature-related threats in emerging economies","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffmars.2026.1934010","Emerging and developing economies often face constrained financial, technical, and institutional capacities to establish and maintain the ocean-based in situ observational networks that underpin marine decision support applications. This limitation heightens their exposure and sensitivity to the cumulative impacts of marine environmental hazards, including harmful algal blooms and marine heatwaves. In this perspective, we present an example of an operational, co-developed ocean monitoring framework that is explicitly structured around user requirements, local scientific expertise, established software engineering practices, and openly available satellite and numerical model data and algorithms. The framework is instantiated through the South African National Oceans and Coastal Information Management System (OCIMS) Fisheries and Aquaculture Decision Support Tool, which is employed as a representative implementation case study and potential reference architecture for application in other regional contexts. We demonstrate that satellite-derived and model-based products can markedly accelerate the establishment of operational marine environmental monitoring capabilities in settings where dense, long-term in situ observing systems are absent or sparse, thereby enhancing societal preparedness and adaptive capacity in relation to intensifying, climate-driven marine risks. Furthermore, we discuss how such frameworks can underpin resilient, equitable, and evidence-based blue economy development in the Global South.","新兴和发展中经济体在建立和维持支撑海洋决策支持应用的海洋原位观测网络方面，往往面临财政、技术和制度能力的制约。这一局限加剧了它们对海洋环境危害累积影响的暴露度和敏感性，这些危害包括有害藻华和海洋热浪。在本视角文章中，我们展示了一个可业务化运行、共同开发的海洋监测框架实例，该框架明确围绕用户需求、地方科学专业知识、成熟的软件工程实践以及公开可用的卫星和数值模型数据与算法来构建。该框架通过南非国家海洋与海岸信息管理系统（OCIMS）渔业与水产养殖决策支持工具得以实例化，并作为代表性实施案例和潜在参考架构，供其他区域情境应用。我们证明，在缺乏密集长期原位观测系统或此类系统稀疏的环境中，卫星衍生和基于模型的产品能够显著加速业务化海洋环境监测能力的建立，从而增强社会对日益加剧的气候驱动海洋风险的防范和适应能力。此外，我们讨论了此类框架如何支撑全球南方具有韧性、公平且基于证据的蓝色经济发展。",null,"Frontiers in Marine Science","2026-09-22T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"主题为海洋有害藻华与热浪监测框架，属海洋环境与蓝色经济范畴，与三农、农业信息化、智慧农业无直接关联，相关性门槛未通过。",[19],{"name":10,"url":6},[21,22,23,24,25],"决策支持系统","有害藻华","蓝色经济","海洋遥感","海洋热浪",[27,28],"OCIMS 渔业 水产 决策支持","南非 海洋监测 框架","OCIMS渔业水产决策支持-3510","10.3389\u002Ffmars.2026.1934010",{"doi":30,"openalex_id":32,"authors":33,"venue":10,"cited_by_count":15,"oa_url":52,"card":53,"direction":57,"ingested_from":59},"W7213986050",[34,36,39,41,44,46,48,50],{"name":35,"orcid":9},"Marié E. Smith",{"name":37,"orcid":38},"Stewart Bernard","https:\u002F\u002Forcid.org\u002F0000-0001-6537-3682",{"name":40,"orcid":9},"Giles Fearon",{"name":42,"orcid":43},"Marjolaine Krug","https:\u002F\u002Forcid.org\u002F0000-0002-5719-3240",{"name":45,"orcid":9},"Raymond Molapo",{"name":47,"orcid":9},"Jennifer Veitch",{"name":49,"orcid":9},"Lufuno Vhengani",{"name":51,"orcid":9},"L. A. Williams","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fmarine-science\u002Farticles\u002F10.3389\u002Ffmars.2026.1934010\u002Fpdf",{"tldr":54,"method":55,"finding":56,"direction":57,"opportunity":58},"提出基于卫星与模型数据的海洋监测框架，弥补新兴经济体现场观测不足，监测有害藻华与热浪。","以南非OCIMS渔业决策工具为案例，结合用户需求、开源卫星与数值模型数据。","卫星与模型产品可快速建立业务化海洋环境监测能力，提升社会适应力。","农业遥感与作物表型","可迁移该框架至农业领域，利用遥感与模型弥补地面观测不足，监测作物病害与热胁迫。","openalex","2026-09-25T23:30:35.360231Z",{"total":62,"page":63,"page_size":62,"items":64},6,1,[65,100,145,177,206,239],{"id":66,"title":67,"url":68,"summary":69,"summary_zh":9,"content":9,"source_name":70,"source_url":68,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":71,"score_detail":72,"sources":79,"tags":81,"search_phrases":86,"slug":89,"view_count":15,"doi":90,"paper":91,"created_at":99},3274,"Farmer-Friendly Decision Support System Using Explainable Orangutan Artificial Intelligence for Soil Fertility","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs42979-026-05329-2","Farmer-Friendly Decision Support System Using Explainable Orangutan Artificial Intelligence for Soil Fertility。SN Computer Science","SN Computer Science",68,{"impact":73,"substance":74,"depth":75,"authority":76,"freshness":77,"relevant":63,"comment":78},12,18,16,13,9,"论文提出可解释猩猩优化算法驱动的土壤肥力决策支持系统，方法新颖且面向农户，但尚属学术探索阶段，产业影响有限。",[80],{"name":70,"url":68},[82,83,84,21,85],"智慧农业","农业人工智能","可解释AI","土壤肥力",[87,88],"农业人工智能 决策支持系统 土壤肥力 智慧农业","农业人工智能 决策支持系统","农业人工智能决策支持系统土壤肥力智慧农业-3274","10.1007\u002Fs42979-026-05329-2",{"doi":90,"openalex_id":92,"authors":93,"venue":70,"cited_by_count":15,"oa_url":9,"card":9,"direction":98,"ingested_from":59},"W7214018467",[94,96],{"name":95,"orcid":9},"K. Komala Devi",{"name":97,"orcid":9},"Josephine Prem Kumar","智慧农业 \u002F 农业物联网","2026-09-23T23:30:12.314548Z",{"id":101,"title":102,"url":103,"summary":104,"summary_zh":105,"content":9,"source_name":106,"source_url":103,"published_at":107,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":108,"score_detail":109,"sources":113,"tags":115,"search_phrases":120,"slug":123,"view_count":15,"doi":124,"paper":125,"created_at":144},3200,"Timing Matters: Optimising the Early Blight Spraying Strategy in Potatoes in the Netherlands","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11540-026-10146-4","Abstract Potato early blight is a fungal disease in potato, caused by the fungus Alternaria solani . We analysed 25 experiments (2009–2023) in different locations with different cultivars to assess effects of the disease on yield, disease progression curves and effects of fungicides. Results showed large unexplained variation in the yield losses versus AUDPC, which varied between 0 and −20% and 0 to 20 ton fresh\u002Fha. Disease control with fungicides increased yields by on average 2–3 ton\u002Fha fresh weight. We observed large interannual variation in onset of the disease (first symptoms) which implies that in a year with late onset, earliest sprayings are probably too early to be effective and can be skipped without any yield loss. Results showed fungicides delay onset of the disease, but not the growth rate of the disease severity. Once well established, the disease grows equally strong with and without fungicide use. This finding implies that once well established, one could just as well stop using fungicides, as they are at that stage no longer effective, which means latest spraying(s) can be skipped. A Pdays based model was tested to predict onset of the disease. The model had some skill in predicting the onset of the disease, although large uncertainty remains. The model was implemented in a decision support system where it advises farmers from which day onwards to start spraying. Our analysis suggests scope for reducing fungicide by up to ~50% without yield penalty, by skipping the first one or two sprayings in years where early blight comes at normal or late date and skipping the last one or two sprayings in years where early blight comes at early or normal date.","摘要 马铃薯早疫病是一种由茄链格孢菌(Alternaria solani)引起的真菌性病害。我们分析了2009至2023年间在不同地点、不同品种上开展的25项试验，以评估该病害对产量、病害进展曲线的影响以及杀菌剂的效果。结果表明，产量损失与AUDPC之间的关系存在较大的无法解释的变异，产量损失变化范围为0至−20%，即0至20吨鲜重\u002F公顷。使用杀菌剂进行病害防治使产量平均增加2至3吨\u002F公顷鲜重。我们观察到病害始发期(首次出现症状)存在较大的年际变异，这意味着在始发期较晚的年份，最早的喷药可能因过早而无效，可以跳过而不造成任何产量损失。结果表明，杀菌剂延迟了病害的始发，但不影响病害严重度的增长率。一旦病害充分建立，无论是否使用杀菌剂，其增长强度相同。这一发现意味着，一旦病害充分建立，就可以停止使用杀菌剂，因为在该阶段杀菌剂已不再有效，这意味着最后的几次喷药可以跳过。我们测试了一个基于Pdays的模型来预测病害始发期。该模型在预测病害始发期方面具有一定的能力，但仍存在较大的不确定性。该模型已被整合到一个决策支持系统中，用于建议农民从哪一天开始喷药。我们的分析表明，通过在不来或晚来早疫病的年份跳过前一到两次喷药，以及在早来或正常来早疫病的年份跳过后一到两次喷药，有将杀菌剂使用量减少约50%而不造成产量损失的空间。","Potato Research","2026-09-19T00:00:00Z",79,{"impact":74,"substance":110,"depth":74,"authority":76,"freshness":111,"relevant":63,"comment":112},22,8,"基于25个试验的新结论显示可减少约50%杀菌剂用量而不减产，对精准施药与智慧植保具参考价值。",[114],{"name":106,"url":103},[116,21,117,118,119],"马铃薯","农药减量","智慧植保","早疫病",[121,122],"马铃薯 早疫病 杀菌剂 减量","荷兰 马铃薯 早疫病 预测模型","马铃薯早疫病杀菌剂减量-3200","10.1007\u002Fs11540-026-10146-4",{"doi":124,"openalex_id":126,"authors":127,"venue":106,"cited_by_count":15,"oa_url":137,"card":138,"direction":142,"ingested_from":59},"W7213645010",[128,131,134],{"name":129,"orcid":130},"P.A.J. van Oort","https:\u002F\u002Forcid.org\u002F0000-0001-7617-5382",{"name":132,"orcid":133},"Bert Evenhuis","https:\u002F\u002Forcid.org\u002F0000-0002-6895-1190",{"name":135,"orcid":136},"Geert J. T. Kessel","https:\u002F\u002Forcid.org\u002F0000-0003-4559-4896","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs11540-026-10146-4.pdf",{"tldr":139,"method":140,"finding":141,"direction":142,"opportunity":143},"基于25年试验分析马铃薯早疫病发病时间与产量损失，提出可减少约50%杀菌剂用量的精准喷施策略。","分析2009–2023年25个田间试验，构建Pdays模型预测发病起点并集成决策","杀菌剂仅延迟发病不降低病害增长率，晚发年可跳过早期喷施、早发年可跳过末期喷施而不减产。","农业人工智能与决策模型","可结合气象与遥感数据提升Pdays模型预测精度，并开发实时变量喷施决策系统。","2026-09-22T23:30:50.511266Z",{"id":146,"title":147,"url":148,"summary":149,"summary_zh":150,"content":9,"source_name":151,"source_url":148,"published_at":152,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":71,"score_detail":153,"sources":156,"tags":158,"search_phrases":161,"slug":164,"view_count":15,"doi":165,"paper":166,"created_at":176},3197,"YieldVision: Model-Driven Decision Support System for Crop Yield Prediction","https:\u002F\u002Fdoi.org\u002F10.56201\u002Frjpst.vol.9.no1.2026.pg176.194","Agriculture, a cornerstone of global food security, faces unprecedented challenges in achieving sustainable productivity. Crop yield prediction, a crucial aspect of agricultural planning, suffers from inefficiencies rooted in technological and methodological gaps. While traditional approaches rely on historical data and empirical estimations, they often fail to address the complexity of environmental interactions because they lack real-time data integration and limited access to accurate data, leading to inaccuracies and resource mismanagement. Addressing these issues requires the development of advanced Machine Learning (ML) models-based system that can predict crop yield with high accuracy in dynamic environments. This paper presents YieldVision, a decision support system driven by advanced ML models for Crop Yield Prediction based on environmental, soil, and crop-specific factors. In this work, we proposed three crop prediction models: Random Forest (RF), Long Short Time Memory (LSTM) and Hybrid (RF LSTM). RF regressor effectively captured the complex, nonlinear relationships among soil nutrients, fertilizer application, and temperature. The LSTM network learned the way short-term weather fluctuations impact crop development over time, and the hybrid model combined LSTM for environmental-related factors, and RF for soil-related factors. Experimental results on the Crop Yield Prediction Dataset containing soil and weather parameters showed that RF has the highest accuracy 𝑅2 = 99.10% and outperforms both LSTM and hybrid which have 87.50% and 97.30% accuracy respectively, confirming its suitability for the dataset. RF has the lowest RMSE and MAE compared to LSTM and hybrid models. The models are integrated into a decision support system that is deployed to operationalize the models in real-world settings with user interfaces accessible via mobile or web-delivered real-time alerts and yield forecasts to farmers and agronomists.","农业作为全球粮食安全的基石，在实现可持续生产力方面面临着前所未有的挑战。作物产量预测作为农业规划的关键环节，因技术和方法上的不足而效率低下。传统方法依赖历史数据和经验估算，但由于缺乏实时数据集成且难以获取准确数据，往往无法应对环境相互作用的复杂性，导致预测不准确和资源管理不善。解决这些问题需要开发基于先进机器学习（ML）模型的系统，能够在动态环境中高精度地预测作物产量。本文提出了YieldVision，一个由先进机器学习模型驱动的决策支持系统，用于基于环境、土壤和作物特定因素的作物产量预测。在本研究中，我们提出了三种作物预测模型：随机森林（RF）、长短期记忆网络（LSTM）和混合模型（RF-LSTM）。随机森林回归器有效捕捉了土壤养分、施肥量和温度之间复杂的非线性关系。LSTM网络学习了短期天气波动如何随时间影响作物发育，而混合模型将LSTM用于环境相关因素，RF用于土壤相关因素。在包含土壤和天气参数的作物产量预测数据集上的实验结果表明，随机森林具有最高的准确率𝑅2 = 99.10%，优于LSTM和混合模型，后两者的准确率分别为87.50%和97.30%，证实了随机森林对该数据集的适用性。与LSTM和混合模型相比，随机森林具有最低的RMSE和MAE。这些模型被集成到一个决策支持系统中，该系统已部署用于在实际环境中运行这些模型，其用户界面可通过移动端或网页访问，向农民和农艺师提供实时警报和产量预测。","RESEARCH JOURNAL OF PURE SCIENCE AND TECHNOLOGY","2026-09-21T00:00:00Z",{"impact":73,"substance":154,"depth":75,"authority":73,"freshness":111,"relevant":63,"comment":155},20,"提出RF、LSTM及混合模型并集成决策支持系统，方法对比与精度数据扎实，对智慧农业产量预测有参考价值，但属单篇论文且来源期刊影响力有限。",[157],{"name":151,"url":148},[82,83,159,160,21],"产量预测","机器学习",[162,163],"YieldVision 作物产量预测","RF LSTM 混合模型 产量预测","YieldVision作物产量预测-3197","10.56201\u002Frjpst.vol.9.no1.2026.pg176.194",{"doi":165,"openalex_id":167,"authors":168,"venue":151,"cited_by_count":15,"oa_url":9,"card":171,"direction":142,"ingested_from":59},"W7213883348",[169],{"name":170,"orcid":9},"D.J.S. Sako",{"tldr":172,"method":173,"finding":174,"direction":142,"opportunity":175},"提出YieldVision决策支持系统，用RF、LSTM及混合模型预测作物产量并部署应用。","基于土壤、环境与作物数据，构建RF、LSTM和RF-LSTM混合模型并集成到We","随机森林表现最佳，R²达99.10%，优于LSTM（87.50%）和混合模型（97.30%）。","可探索多源实时数据融合与模型可解释性，提升跨区域泛化能力并降低对高精度历史数据的依赖。","2026-09-22T23:30:43.591303Z",{"id":178,"title":179,"url":180,"summary":181,"summary_zh":9,"content":9,"source_name":182,"source_url":9,"published_at":183,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":71,"score_detail":184,"sources":186,"tags":188,"search_phrases":193,"slug":196,"view_count":15,"doi":9,"paper":197,"created_at":205},3049,"土壤压实与灌溉管理：对精准农业中土壤水力变化的启示","https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F18\u002F1853","意大利帕多瓦大学A.C.与L.B.评估土壤压实通过改变土壤水力特性对精准农业灌溉管理的综合影响。研究维护土壤结构作为维持土壤水力功能、提升灌溉效率与农业系统长期可持续性最有效途径，使用HYPROP水力特性分析仪测定田间持水量（FC）、永久萎蔫点（PWP）、饱和水力传导度（Ksat）等关键参数，结合无人机遥感（UAV）与决策支持系统（DSS）实现精准灌溉调度。研究获SOILWAT（BIRD 2026）项目资助，为精准农业管理决策提供可量化水力参数基础。","MDPI Agronomy 16(18):1853","2026-09-20T00:00:00Z",{"impact":73,"substance":74,"depth":75,"authority":76,"freshness":77,"relevant":63,"comment":185},"学术论文，方法结合HYPROP与无人机遥感，对精准灌溉有参考价值，但属细分领域研究，公共影响有限。",[187],{"name":182,"url":180},[21,189,190,191,192],"精准农业","无人机遥感","智慧灌溉","土壤压实",[194,195],"帕多瓦大学 土壤压实 灌溉","HYPROP 水力特性 精准灌溉","帕多瓦大学土壤压实灌溉-3049",{"doi":9,"openalex_id":9,"authors":198,"venue":9,"cited_by_count":15,"oa_url":9,"card":199,"direction":98,"ingested_from":204},[],{"tldr":200,"method":201,"finding":202,"direction":98,"opportunity":203},"评估土壤压实改变水力特性对精准灌溉管理的影响，并提出维护土壤结构的对策。","用HYPROP测FC、PWP、Ksat，结合无人机遥感与决策支持系统调度灌溉。","维护土壤结构是保持水力功能、提升灌溉效率与长期可持续性的最有效途径。","可探索压实-水力参数-遥感反演耦合模型，实现压实风险与灌溉调度的实时协同优化。","agent","2026-09-21T00:04:39.395594Z",{"id":207,"title":208,"url":209,"summary":210,"summary_zh":211,"content":9,"source_name":212,"source_url":209,"published_at":213,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":214,"score_detail":215,"sources":219,"tags":221,"search_phrases":224,"slug":226,"view_count":15,"doi":227,"paper":228,"created_at":238},2682,"Machine Learning-Based Decision Support System for Greenhouse Crop Management Under Mite Infestation Conditions","https:\u002F\u002Fdoi.org\u002F10.36099\u002Fjess.v1i3.001","This paper presents an integrated machine learning-based Decision Support System (DSS) for greenhouse crop management under mite infestation conditions, specifically designed for Sri Lankan agricultural contexts. The research addresses critical challenges faced by greenhouse farmers regarding pest management and crop productivity optimization through a comprehensive system combining NetLogo simulation for synthetic data generation, ensemble machine learning models, and a web-based interface. Field research with Sri Lankan greenhouse farmers revealed that mite infestations cause up to 40% crop losses, driving panic-induced pesticide overuse and knowledge gaps in pest management timing. The system integrates environmental monitoring, pest prediction, and crop yield forecasting to provide actionable recommendations for farmers. The mite infestation prediction model achieved 86% accuracy, while the system successfully addresses data scarcity challenges through agent-based modeling. The DSS demonstrates potential for transforming reactive farming practices into predictive, data-driven approaches while accommodating the technological constraints of developing agricultural contexts.","本文提出了一种基于机器学习的集成决策支持系统（DSS），用于螨虫侵染条件下的温室作物管理，专为斯里兰卡农业情境设计。该研究针对温室农户在害虫管理和作物生产力优化方面面临的关键挑战，通过一个综合系统加以解决，该系统结合了用于合成数据生成的NetLogo仿真、集成机器学习模型以及基于网络的界面。针对斯里兰卡温室农户的实地研究表明，螨虫侵染可导致高达40%的作物损失，进而引发恐慌性农药过度使用以及害虫管理时机方面的知识缺口。该系统整合了环境监测、害虫预测和作物产量预测，为农户提供可操作的推荐建议。螨虫侵染预测模型达到了86%的准确率，同时该系统通过基于智能体的建模成功应对了数据稀缺的挑战。该决策支持系统展现出将被动应对式耕作实践转变为预测性、数据驱动方法的潜力，同时兼顾了发展中农业情境的技术约束。","Journal of Environmental and Sustainability Science","2026-09-16T00:00:00Z",74,{"impact":216,"substance":154,"depth":217,"authority":73,"freshness":13,"relevant":63,"comment":218},15,17,"将机器学习与智能体仿真结合用于温室螨害预测与决策支持，方法新颖、数据翔实，对设施农业植保信息化有参考价值。",[220],{"name":212,"url":209},[82,83,222,21,223],"设施农业","病虫害预警",[225,88],"农业人工智能 决策支持系统 病虫害预警 智慧农业","农业人工智能决策支持系统病虫害预警智慧农业-2682","10.36099\u002Fjess.v1i3.001",{"doi":227,"openalex_id":229,"authors":230,"venue":212,"cited_by_count":15,"oa_url":209,"card":233,"direction":142,"ingested_from":59},"W7213235680",[231],{"name":232,"orcid":9},"S. Nasiketha",{"tldr":234,"method":235,"finding":236,"direction":142,"opportunity":237},"为斯里兰卡温室农户开发基于机器学习的决策支持系统，预测螨害并优化作物管理。","NetLogo仿真生成合成数据，集成机器学习模型与网页界面。","螨害预测准确率达86%，可缓解数据稀缺并减少农药滥用。","可探索小样本下合成数据与迁移学习结合，提升发展中国家温室病虫害预测泛化能力。","2026-09-16T23:30:51.573509Z",{"id":240,"title":241,"url":242,"summary":243,"summary_zh":244,"content":9,"source_name":245,"source_url":242,"published_at":246,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":247,"sources":249,"tags":251,"search_phrases":254,"slug":257,"view_count":63,"doi":258,"paper":259,"created_at":281},2631,"Examining the shades of blue in the blue economy: a perspective on disaggregating impacts on food security in Puerto Rico","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1854048","The Blue Economy has been promoted globally as a framework for sustainable ocean development, promising economic growth, conservation, and improved well-being through sectors such as tourism, fisheries, and aquaculture. It has been particularly championed as a win-win approach in low and middle-income countries and territories across the Global South. Yet its success is typically measured through narrow quantitative indicators such as production levels, revenues, and growth rates, while overlooking the social, cultural, and equity dimensions that shape how benefits and burdens are distributed across sectors and communities. As these initiatives expand, understanding their impacts on local food systems, community well-being, and equity implications becomes increasingly urgent, particularly in (neo)colonial and non-sovereign places where governance structures constrain local control, such as Puerto Rico. Yet the indicators used to evaluate progress for Blue Economy policies often fail to capture implications for wellbeing, food security, and equity. Recent scholarship on equitable Blue Economies calls for social indicators that assess differentiated impacts across groups. Building on Cisneros-Montemayor et al. (2025), this study draws from author experience and exploratory data from Puerto Rico to examine the desegregated impacts, and equity implications of tourism and aquaculture, while gesturing toward possible directions for indicator development grounded in the author’s lived Puerto Rico experience. It argues that without equity-oriented evaluation frameworks and justice-centered governance design, Blue Economy agendas risk reproducing historical inequities rather than improving livelihoods in Puerto Rico and across the Global South.","蓝色经济已在全球范围内被推广为可持续海洋发展的框架，承诺通过旅游、渔业和水产养殖等部门实现经济增长、生态保护与福祉改善。它尤其被推崇为全球南方中低收入国家和地区的一种双赢路径。然而，其成功通常以产量、收入和增长率等狭隘的量化指标来衡量，而忽视了塑造部门与社区之间利益和负担分配方式的社会、文化和公平维度。随着这些倡议不断扩展，理解其对地方粮食系统、社区福祉和公平影响变得日益紧迫，尤其是在治理结构制约地方控制权的（新）殖民和非主权地区，如波多黎各。然而，用于评估蓝色经济政策进展的指标往往无法捕捉其对福祉、粮食安全和公平的影响。近期关于公平蓝色经济的学术研究呼吁采用能够评估群体间差异化影响的社会指标。本研究以Cisneros-Montemayor等（2025）为基础，结合作者经验和来自波多黎各的探索性数据，考察旅游和水产养殖的分解影响及公平意涵，同时基于作者在波多黎各的生活经验，指向指标开发的可能方向。研究认为，若缺乏以公平为导向的评估框架和以正义为中心的治理设计，蓝色经济议程有可能在波多黎各乃至整个全球南方复制历史性的不平等，而非改善生计。","Frontiers in Sustainable Food Systems","2026-09-15T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":248},"研究聚焦波多黎各蓝色经济对粮食安全与公平的影响，属海洋经济与食物系统议题，与三农、农业信息化、智慧农业等平台主题无直接关联，不建议进入每日精选。",[250],{"name":245,"url":242},[252,23,253],"粮食安全","渔业治理",[255,256],"渔业治理 粮食安全 蓝色经济","渔业治理 粮食安全","渔业治理粮食安全蓝色经济-2631","10.3389\u002Ffsufs.2026.1854048",{"doi":258,"openalex_id":260,"authors":261,"venue":245,"cited_by_count":15,"oa_url":274,"card":275,"direction":279,"ingested_from":59},"W7213256548",[262,265,267,270,272],{"name":263,"orcid":264},"Liliana Sierra Castillo","https:\u002F\u002Forcid.org\u002F0000-0002-6931-6869",{"name":266,"orcid":9},"Nicolás X. Gómez Andújar",{"name":268,"orcid":269},"Luis Alexis Rodríguez-Cruz","https:\u002F\u002Forcid.org\u002F0000-0002-2229-8448",{"name":271,"orcid":9},"Megan Elaine Considine",{"name":273,"orcid":9},"Nathania Martínez-González","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1854048\u002Fpdf",{"tldr":276,"method":277,"finding":278,"direction":279,"opportunity":280},"以波多黎各为例，探讨蓝色经济对粮食安全与公平的差异化影响。","基于作者经验与波多黎各探索性数据，分析旅游与水产养殖。","缺乏公平导向评估框架，蓝色经济可能加剧而非改善历史不平等。","其他","可开发面向小岛屿非主权地区的蓝色经济公平与粮食安全社会指标体系。","2026-09-16T23:30:08.046982Z"]