[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1108":3,"related-1108":60},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":55,"slug":58,"view_count":21,"doi":8,"paper":8,"created_at":59},1108,"赋能芒果产业提质升级 国家香蕉芒果产业技术体系专家赴滇开展岗站对接交流活动","https:\u002F\u002Fyaas.org.cn\u002F","近日,国家香蕉芒果产业技术体系芒果种质资源鉴定与新种质创制岗位专家高爱平研究员一行赴云南开展岗站对接交流活动,推动云南芒果产业提质升级。同期,8月25日至27日,专家团深入热经所开展学术交流和现场技术指导。",null,"云南省农业科学院(云南日报转载)","2026-08-28T07:00:00Z","报道",10,false,53,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},15,12,8,11,7,1,"省级科研机构推动芒果产业技术对接，有一定行业价值，但内容偏通稿，深度一般。",[24],{"name":9,"url":6},[26,27,28,29],"岗站对接","云南省农科院","芒果","香蕉芒果产业体系",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[],{"status":38,"locked":13,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":44},[43,48],{"code":31,"score":17,"supported":44,"title":45,"abstract":46,"tags":47},true,[28],[28],[28],{"code":49,"score":50,"supported":44,"title":51,"abstract":52,"tags":54},"F1",3,[],[53],"种质资源",[],[56,57],"香蕉芒果产业体系 云南省农科院 岗站对接 芒果","香蕉芒果产业体系 云南省农科院","香蕉芒果产业体系云南省农科院岗站对接芒果-1108","2026-08-30T00:11:10.295038Z",{"total":61,"page":21,"page_size":61,"items":62},6,[63,163,205,246,325,364],{"id":64,"title":65,"url":66,"summary":67,"summary_zh":68,"content":8,"source_name":69,"source_url":66,"published_at":70,"category":71,"cover_url":8,"hotness":12,"is_selected":13,"score":72,"score_detail":73,"sources":78,"tags":80,"primary_topic":85,"secondary_topics":86,"classification":98,"search_phrases":130,"slug":133,"view_count":50,"doi":134,"paper":135,"created_at":162},3167,"Temporal trend analysis and multi-temporal satellite feature integration for mango orchard acreage estimation using machine learning algorithms approach","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-65828-3","Accurate estimation of horticultural crop acreage is essential for agricultural planning, market forecasting and evidence-based policy formulation. The present study investigated long-term trends in mango cultivation and developed a multi-temporal remote sensing framework for mango orchard acreage estimation in Navsari District using integrated optical, SAR and machine learning approaches. Time-series (temporal) data spanning 23 years (2001–02 to 2023–24) were analyzed using polynomial regression models to evaluate trends in mango area and production. Linear regression best represented area expansion trends (Adj. R² = 0.969), whereas cubic regression better captured production variability (Adj. R² = 0.634), indicating climatic and seasonal influences on productivity. For orchard classification and acreage estimation, multi-temporal Sentinel-2 imagery acquired from November 2023 to March 2024 was processed within a phenology-guided framework. Monthly composites were generated and integrated with Sentinel-1 SAR backscatter data, vegetation indices (NDVI, GNDVI, NDRE, SAVI, EVI and NDMI) and texture metrics derived from Gray Level Co-occurrence Matrix (GLCM) analysis. A comprehensive 72-band feature stack was developed for classification. Four machine learning algorithms, namely Random Forest (RF), XGBoost (XGB), Support Vector Machine (SVM) and Multinomial Logistic Regression (MNLR) were evaluated for orchard discrimination. Among the tested models, RF achieved the highest classification performance with an Overall Accuracy of 99.80% and a Kappa coefficient of 0.997, followed by SVM (99.30%), MNLR (98.21%) and XGB (9.20%). The RF model estimated mango orchard area at 36,099.94 ha, showing the closest agreement with official horticultural statistics (34,363 ha) with only 5.05% estimation error. In contrast, SVM and MNLR overestimated orchard extent by 16.03% and 43.37%, respectively. The proposed framework provides a reliable and scalable methodology for operational horticultural monitoring, crop inventory generation and precision agricultural planning in tropical orchard ecosystems.","准确估算园艺作物种植面积对于农业规划、市场预测和循证政策制定至关重要。本研究探讨了芒果种植的长期趋势，并开发了一个多时相遥感框架，结合光学、合成孔径雷达（SAR）和机器学习方法，用于纳夫萨里县芒果园种植面积估算。利用多项式回归模型分析了跨越23年（2001—02年至2023—24年）的时间序列数据，以评估芒果面积和产量的变化趋势。线性回归最能表征面积扩张趋势（调整R² = 0.969），而三次回归更能捕捉产量变异性（调整R² = 0.634），表明气候和季节性因素对生产力具有影响。在果园分类和面积估算方面，基于物候指导框架处理了2023年11月至2024年3月获取的多时相Sentinel-2影像。生成了月度合成影像，并将其与Sentinel-1 SAR后向散射数据、植被指数（NDVI、GNDVI、NDRE、SAVI、EVI和NDMI）以及基于灰度共生矩阵（GLCM）分析提取的纹理指标进行整合。构建了一个包含72个波段的综合特征集用于分类。评估了四种机器学习算法，即随机森林（RF）、XGBoost（XGB）、支持向量机（SVM）和多项逻辑回归（MNLR），用于果园判别。在测试的模型中，RF取得了最高的分类性能，总体精度为99.80%，Kappa系数为0.997，其次是SVM（99.30%）、MNLR（98.21%）和XGB（9.20%）。RF模型估算的芒果园面积为36,099.94公顷，与官方园艺统计数据（34,363公顷）最为接近，估算误差仅为5.05%。相比之下，SVM和MNLR分别高估了果园面积16.03%和43.37%。所提出的框架为热带果园生态系统中的业务化园艺监测、作物清单生成和精准农业规划提供了一种可靠且可扩展的方法。","Scientific Reports","2026-09-22T00:00:00Z","论文",78,{"impact":74,"substance":75,"depth":76,"authority":74,"freshness":12,"relevant":21,"comment":77},14,22,18,"方法扎实、数据规模大且精度高，但属区域性作物遥感估产研究，产业影响有限，可作为技术方法类精选。",[79],{"name":69,"url":66},[81,82,28,83,84],"智慧农业","机器学习","遥感监测","作物估产",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[87,93],{"code":88,"slug":89,"name":90,"path":91,"parent_code":92},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"code":94,"slug":95,"name":96,"path":97,"parent_code":92},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":38,"locked":13,"version":39,"method":40,"evidence":99},{"candidates":100,"agricultural_evidence":44},[101,106,111,117,124],{"code":31,"score":16,"supported":44,"title":102,"abstract":104,"tags":105},[103],"orchard",[28,103],[28],{"code":88,"score":16,"supported":44,"title":107,"abstract":109,"tags":110},[108],"machine learning",[82,108],[82],{"code":94,"score":18,"supported":44,"title":112,"abstract":113,"tags":116},[],[114,115],"遥感","remote sensing",[84,83],{"code":118,"score":119,"supported":44,"title":120,"abstract":121,"tags":123},"I1",4,[],[122],"精准农业",[81],{"code":125,"score":50,"supported":44,"title":126,"abstract":127,"tags":129},"J1",[],[128],"农业规划",[],[131,132],"Navsari 芒果 遥感估产","Sentinel-2 芒果 果园面积","Navsari芒果遥感估产-3167","10.1038\u002Fs41598-026-65828-3",{"doi":134,"openalex_id":136,"authors":137,"venue":69,"cited_by_count":154,"oa_url":66,"card":155,"direction":159,"ingested_from":161},"W7213920056",[138,140,143,145,147,150,152],{"name":139,"orcid":8},"V. Raju",{"name":141,"orcid":142},"Yogesh A. Garde","https:\u002F\u002Forcid.org\u002F0000-0002-0297-316X",{"name":144,"orcid":8},"Dr. V. S. Thorat",{"name":146,"orcid":8},"V. T. Shinde",{"name":148,"orcid":149},"Nitin Varshney","https:\u002F\u002Forcid.org\u002F0000-0001-9144-5475",{"name":151,"orcid":8},"Alok Shrivastava",{"name":153,"orcid":8},"A. P. Chaudhary",0,{"tldr":156,"method":157,"finding":158,"direction":159,"opportunity":160},"融合多时相Sentinel-1\u002F2与机器学习，估算印度芒果园面积并分析23年种植趋势。","23年时序回归分析；Sentinel-2月合成+SAR+植被指数+GLCM纹理共","RF精度最高（总体精度99.80%，Kappa 0.997），面积估算误差仅5.05%，优于SVM和","农业遥感与作物表型","可迁移该多时相SAR-光学特征框架至其他热带果园，并探索深度学习与物候自适应特征优化。","openalex","2026-09-22T23:30:22.619289Z",{"id":164,"title":165,"url":166,"summary":167,"summary_zh":8,"content":8,"source_name":168,"source_url":8,"published_at":169,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":170,"score_detail":171,"sources":175,"tags":177,"primary_topic":185,"secondary_topics":186,"classification":187,"search_phrases":199,"slug":202,"view_count":203,"doi":8,"paper":8,"created_at":204},965,"云南农科院发布3项农业科研成果 为澜湄区域农业产业提供「云南技术方案」","https:\u002F\u002Fwww.lmcchina.org\u002Flmhzdt\u002F202608\u002Ft20260825_12010002.html","近日云南省农业科学院质量标准与检测技术研究所受邀参加云南省商务厅联合南亚东南亚七国驻昆明总领事馆主办的「共享大市场•出口中国」系列活动，发布3项核心农业科研成果：一、葡萄产业绿色发展及标准化体系构建——搭建起科学完善的葡萄产业标准体系，提升果品质量安全水平；二、中国猕猴桃品质评价技术体系——为澜湄区域各类特色果蔬品质评定提供标准化参考依据；三、花卉智能检测技术及颜色图像分析系统——打破传统花卉育种与检测对人工肉眼主观判定的依赖，实现花卉颜色的定量、定性精准分析。三项科研成果精准对接产业需求与跨境贸易痛点，为澜湄区域农业产业协同发展筑牢技术根基。","澜沧江-湄公河合作 2026-08-25","2026-08-25T08:20:00Z",65,{"impact":76,"substance":172,"depth":17,"authority":12,"freshness":173,"relevant":21,"comment":174},16,9,"省级农科院发布三项农业科研成果，涉及葡萄、猕猴桃、花卉检测技术，对澜湄区域农业合作有实质推动，但影响范围限于区域，深度一般。",[176],{"name":168,"url":166},[178,179,180,27,181,182,183,184],"猕猴桃","标准化","花卉","葡萄","澜湄","智能检测","品质评价",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":188},{"candidates":189,"agricultural_evidence":44},[190,194],{"code":31,"score":18,"supported":44,"title":191,"abstract":192,"tags":193},[],[181,178],[178,181],{"code":195,"score":119,"supported":44,"title":196,"abstract":197,"tags":198},"B4",[],[180],[180],[200,201],"云南省农科院 品质评价 智能检测 标准化","云南省农科院 品质评价","云南省农科院品质评价智能检测标准化-965",2,"2026-08-26T00:09:31.281710Z",{"id":206,"title":207,"url":208,"summary":209,"summary_zh":8,"content":210,"source_name":211,"source_url":8,"published_at":212,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":213,"score_detail":214,"sources":216,"tags":218,"primary_topic":222,"secondary_topics":223,"classification":224,"search_phrases":241,"slug":244,"view_count":61,"doi":8,"paper":8,"created_at":245},597,"广西田东县推动芒果产业全链条智慧化转型:智慧农运系统覆盖率 71%","https:\u002F\u002Fwww.agri.cn\u002Fzx\u002Fxxlb\u002Fgx\u002F202608\u002Ft20260812_8863374.htm","广西田东县林逢镇东养芒果基地建成智慧果园,集成物联网、AI 识别、区块链溯源等技术,无人机单台日均运力 12-15 吨,运输效率提升 3 倍、成本降低 40%。截至目前,全县 63 个千亩以上基地接入智慧农运系统,配备作业无人机超 280 架,覆盖率达 71%。首批 60 名学员通过粤桂协作促就业培训获 CAAC 无人机执照。","盛夏八月，右江河谷暑气蒸腾。田东县林逢镇东养芒果核心智慧示范基地里，种植大户陆秀缎没往山上走，却往屋里钻。她站在果园中控大屏前，指尖轻滑，200亩果园的土壤湿度、虫情预警、长势热力图等信息呈现在屏幕上。\n\n“以前施肥浇水全凭感觉，现在土壤‘喊渴’、虫子‘报警’，手机上都看得清清楚楚。”这位与芒果树打了多年交道的老把式，如今成了“数据控”。\n\n从凭经验种果到靠数据管园，陆秀缎的“屏幕种田”，是田东芒果产业数字化转型的缩影。当物联网、AI识别、区块链溯源等新技术嵌入种植、采收、加工、销售全链条，一颗芒果从枝头到市场，每一步都留下了“数据脚印”。\n\n1智管入园，老把式遇上新算法\n\n上午10时，艳阳高照。陆秀缎坐在自家院子的竹椅上，笑着查看手机里的果园实况，“往年要去果地观察有没有虫咬、长没长病，全靠这双眼睛。现在不用了，手机一目了然”。\n\n笑容背后，是一张在果园里织就的“科技网”：微型气象站、土壤墒情监测点、虫情AI识别设备全域布点；高清摄像头实时回传画面，水肥一体化系统精准补给；空天地一体化监测叠加AI分析，覆盖芒果全生长周期。\n\n技术落地，果农感受最直观。班彩帽边演示边说：“只需选定果树片区，设置水肥配比，山下泵房便会自动输送营养液直达树根。”\n\n数字化管护实惠算得明白：人工减少约一成，农资降了三成，亩均省三成水肥，优质果率攀升，今年产量又比去年高出20%。传统粗放种植每亩纯收入约3800元，智慧果园亩均收益稳定在5000元以上。\n\n种得好，还要运得出。祥周镇百银村芒果种植基地山高坡陡，往年采摘后全靠人工一趟趟往下扛。如今启用无人机点对点直运，单台无人机日均运力可达12—15吨，运输效率提升3倍，成本降低四成，商品果损耗率下降18%。\n\n运力上去了，飞手队伍也在壮大。田东县首批参加粤桂协作促就业培训的60名学员，日前通过考核，拿到了民航局CAAC无人机执照和植保专项技能证书。\n\n截至目前，田东县已有63个千亩以上基地接入智慧农运系统，配备作业无人机超280架，覆盖率达71%。\n\n2码上溯源，好果子卖出好身价\n\n好芒果下山，第一件事是“验明正身”。\n\n在平马镇小龙村芒果标准园打包车间，工人们正给一箱箱芒果贴上二维码标签。“以前最怕假货砸招牌。”田东县小龙村臻芒合作社理事长黄忠伟拿起一箱芒果说：“现在一扫码，承诺达标合格证、产品产地、质量认证、检测报告、主体信息、农事记录全跳出来，从枝头到舌尖，透明得跟玻璃似的。”\n\n这张“数字身份证”背后，是田东县大力推行的产地承诺达标合格证制度。全县35家芒果绿色食品认证经营主体、17.7万亩认证面积，年产绿色认证芒果20万吨，筑牢“百色芒果”地理标志品质防线。\n\n但好果子不该只卖原料价。鲜友食品智能化车间里，芒果排着队“体检”——AI分拣线机器一扫，果型、着色、果径即刻分档，速度是人工的5倍。分级后的果子随即送入智能恒温冷链仓储，保鲜期延长12天，赶早市变成了错峰卖，好果子再也不怕卖不上价。\n\n车间另一头，自动化生产线正把芒果“吃干榨尽”：果肉变果干、果浆、芒果冰棒，一颗芒果衍生出十多种形态。田东县已引进培育精深加工企业5家，建成20多条自动化生产线，年加工鲜果能力超过8万吨。\n\n3云上出圈，新农人闯出新市场\n\n田间车间忙完了，直播间里又热闹起来。\n\n晚8时，平马镇百林村，新农人黄尚善支起三脚架，补光灯把满桌芒果照得透亮。“家人们，这是咱们百色田东的桂七芒，标准的S型身材还带个小翘嘴，核薄肉厚……”他对着镜头侃侃而谈。而谁能想到，这个如今张口就来产品卖点的年轻人，2021年第一次直播时，对着镜头半晌憋不出一句整话。\n\n“近两个月，直播芒果带货营业额近80万元。”黄尚善说，“手机成了新农具，直播成了新农活。我自己就是果农，对着镜头讲得出门道，观众看得明白，买得放心。”\n\n一个人走得快，一群人走得远。为壮大新农人队伍，6月25日，田东县组织50名芒果种植户、电商从业者、合作社负责人及乡村创业者，参加了2026年粤桂协作百色芒果“新农人”传播人才培育活动。系统教学后，学员们又在芒果标准园开展实地直播带货练习，现学现卖。\n\n“2025年，芒果线上销售额突破7.6亿元。”田东县电子商务协会会长蒙天庚说，“今年我们提前对接各大电商平台、直播团队、外地采购商，持续优化销售渠道，完善分级筛选、冷链保鲜、物流配送体系，同时规范果品分级标准和包装标准，销售额预计会有新的突破”。\n\n从枝头到舌尖，从果园到云端，数字技术正让右江河谷的芒果产业焕发出新的生机。","中国农业信息网 2026-08-12 田东数字农业","2026-08-12T00:00:00Z",74,{"impact":76,"substance":75,"depth":76,"authority":12,"freshness":61,"relevant":21,"comment":215},"报道田东县芒果产业全链条智慧化转型，含具体数据与案例，具行业参考价值，但属地方性进展，时效性稍弱。",[217],{"name":211,"url":208},[219,220,28,221],"数字农业","无人机","区块链溯源",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":225},{"candidates":226,"agricultural_evidence":44},[227,231,237],{"code":31,"score":17,"supported":44,"title":228,"abstract":229,"tags":230},[28],[28],[28],{"code":232,"score":50,"supported":44,"title":233,"abstract":234,"tags":236},"I5",[],[235],"物联网",[],{"code":118,"score":21,"supported":13,"title":238,"abstract":239,"tags":240},[],[],[219],[242,243],"区块链溯源 数字农业 无人机 芒果","区块链溯源 数字农业","区块链溯源数字农业无人机芒果-597","2026-08-16T23:55:38.819914Z",{"id":247,"title":248,"url":249,"summary":250,"summary_zh":251,"content":8,"source_name":252,"source_url":249,"published_at":253,"category":71,"cover_url":8,"hotness":12,"is_selected":13,"score":254,"score_detail":255,"sources":259,"tags":261,"primary_topic":267,"secondary_topics":268,"classification":275,"search_phrases":296,"slug":299,"view_count":154,"doi":300,"paper":301,"created_at":324},5547,"Apple quality and soil biological diversity in long-term organic and IPM apple orchards","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1943487","Introduction While several studies have compared fruit quality in organic and conventional apple production, integrated assessments of fruit quality and soil biological diversity remain limited. Majority of previous studies have been based on relatively short-term experimental comparisons, although responses of soil biota and soil properties often require many years of consistent management to become fully expressed. Methods This study compared yield, fruit quality, and soil biological diversity in long-term organic (ORG) apple orchard and long-term integrated pest management (IPM) orchard under Nordic conditions. The study was conducted in 2022–2024 at the Polli Horticultural Research Centre (Estonia) using the cultivars “Katre”, “Krista,” and “Alesja”. Apple, yield, fruit quality attributes (soluble solids and titratable acids content and their ratio, ascorbic acid content and total polyphenols, total anthocyanins, fruit firmness, fruit colour, fruit mineral elements content), soil chemical parameters, earthworm and springtail communities were analysed. Results and Discussion Higher total and fresh-market yields were observed in the IPM orchard in two of the three cultivars, whereas the proportion of fresh-market fruit did not differ consistently between the two orchards. When averaged across cultivars, apples from ORG orchard exhibited more intense peel coloration. Differences in taste-related traits and antioxidant composition between cultivation systems were cultivar- and year-dependent rather than consistently associated with cultivation system. Total polyphenol content was consistently higher under ORG management only in “Katre,” while “Alesja” exhibited the highest total polyphenol and anthocyanin contents irrespective of cultivation system, highlighting the dominant role of genotype. Soil in the ORG orchard had higher pH and greater phosphorus and calcium contents, supporting a higher proportion of anecic earthworms, particularly Aporrectodea longa . Total earthworm abundance was higher in the IPM orchard, springtail abundance did not differ significantly between systems. These results suggest that the conditions in the long-term ORG were more favourable for ecologically important earthworm groups without consistently affecting internal fruit quality compared to IPM orchard.","引言 尽管已有若干研究比较了有机与常规苹果生产中的果实品质，但结合果实品质与土壤生物多样性的综合评价仍较为有限。以往研究大多基于相对短期的试验比较，而土壤生物区系和土壤性质的响应往往需要多年持续一致的管理才能充分显现。方法 本研究在北欧条件下比较了长期有机（ORG）苹果园与长期综合病虫害管理（IPM）苹果园的产量、果实品质及土壤生物多样性。研究于2022—2024年在爱沙尼亚Polli园艺研究中心开展，供试品种为“Katre”“Krista”和“Alesja”。分析了苹果产量、果实品质指标（可溶性固形物与可滴定酸含量及其比值、抗坏血酸含量、总多酚、总花青素、果实硬度、果实色泽、果实矿质元素含量）、土壤化学参数、蚯蚓和跳虫群落。结果与讨论 在3个品种中的2个品种上，IPM果园的总产量和鲜食市场产量较高，而鲜食市场果实比例在两种果园间并无一致差异。按品种平均，ORG果园苹果果皮色泽更深。栽培系统间口感相关性状和抗氧化成分的差异因品种和年份而异，而非与栽培系统稳定相关。总多酚含量仅在“Katre”中于ORG管理下持续较高，而“Alesja”无论栽培系统如何均表现出最高的总多酚和花青素含量，凸显了基因型的主导作用。ORG果园土壤具有较高的pH以及较高的磷和钙含量，支持了较高比例的内栖型蚯蚓，尤其是长穴居蚯蚓（Aporrectodea longa）。IPM果园蚯蚓总丰度较高，跳虫丰度在系统间无显著差异。这些结果表明，与IPM果园相比，长期ORG的条件更有利于生态上重要的蚯蚓类群，但并未对果实内部品质产生一致影响。","Frontiers in Sustainable Food Systems","2026-10-21T00:00:00Z",66,{"impact":18,"substance":256,"depth":172,"authority":257,"freshness":173,"relevant":21,"comment":258},20,13,"北欧长期定位试验对比有机与IPM苹果园的果实品质与土壤生物多样性，结论有新意但属区域尺度研究，公共影响有限。",[260],{"name":252,"url":249},[262,263,264,265,266],"长期定位试验","有机果园","土壤生物多样性","苹果品质","IPM",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[269],{"code":270,"slug":271,"name":272,"path":273,"parent_code":274},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":38,"locked":13,"version":39,"method":40,"evidence":276},{"candidates":277,"agricultural_evidence":44},[278,285,290],{"code":31,"score":279,"supported":44,"title":280,"abstract":282,"tags":284},17,[281],"apple",[283,103,281],"苹果",[],{"code":270,"score":74,"supported":44,"title":286,"abstract":287,"tags":289},[271],[288,271],"土壤",[],{"code":291,"score":50,"supported":44,"title":292,"abstract":293,"tags":295},"H6",[],[294],"生物多样性",[],[297,298],"爱沙尼亚 Polli 苹果 有机 IPM","苹果园 蚯蚓 跳虫 土壤","爱沙尼亚Polli苹果有机IPM-5547","10.3389\u002Ffsufs.2026.1943487",{"doi":300,"openalex_id":302,"authors":303,"venue":252,"cited_by_count":154,"oa_url":249,"card":318,"direction":322,"ingested_from":161},"W7223104118",[304,307,310,312,315],{"name":305,"orcid":306},"Ulvi Moor","https:\u002F\u002Forcid.org\u002F0000-0002-8808-8494",{"name":308,"orcid":309},"Annely Kuu","https:\u002F\u002Forcid.org\u002F0000-0002-0335-6243",{"name":311,"orcid":8},"K. Kahu",{"name":313,"orcid":314},"P. Põldma","https:\u002F\u002Forcid.org\u002F0000-0002-7906-823X",{"name":316,"orcid":317},"Reelika Rätsep","https:\u002F\u002Forcid.org\u002F0000-0002-8673-9978",{"tldr":319,"method":320,"finding":321,"direction":322,"opportunity":323},"比较长期有机与IPM苹果园的果实品质和土壤生物多样性。","在爱沙尼亚长期试验园测定三个品种的产量、品质及蚯蚓和跳虫群落。","有机园果皮色泽更深、土壤pH和磷钙更高，但果实内在品质差异多随品种和年份变化。","农业绿色发展与碳","可结合长期定位试验，量化土壤动物群落与果实品质的关联机制，并拓展多区域验证。","2026-10-22T11:20:15.557635Z",{"id":326,"title":327,"url":328,"summary":329,"summary_zh":8,"content":330,"source_name":331,"source_url":8,"published_at":332,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":170,"score_detail":333,"sources":335,"tags":337,"primary_topic":341,"secondary_topics":342,"classification":343,"search_phrases":359,"slug":362,"view_count":154,"doi":8,"paper":8,"created_at":363},5516,"上海农科院葡萄团队赴银川参加第47届世界葡萄与葡萄酒大会","https:\u002F\u002Fwww.saas.sh.cn\u002Fhzjl\u002Fgjhz\u002Fcontent_57128","10月12日至16日第47届世界葡萄与葡萄酒大会在银川举办，智慧农业研究中心葡萄团队负责人奚晓军研究员作专题报告，阐明疏穗与环剥处理对果实糖、花青素及脱落酸积累的源库调控机制。团队还观摩酿酒葡萄AI综合作业机器人成果展示，与研发单位围绕葡萄智慧装备与果园智能化管理深入交流。","2026年10月12-16日，第47届世界葡萄与葡萄酒大会在银川举办，智慧农业研究中心葡萄团队负责人奚晓军研究员一行赴会交流。团队围绕“提升葡萄种植生产韧性与可持续发展能力”主题，向全球同行展示最新科研成果。\n\n会上，奚晓军作“Changes in sugar, anthocyanin and ABA accumulation in response to cluster thinning and girdling in ‘Jumeigui’grape”专题报告。报告围绕葡萄疏穗与环剥处理开展研究，阐明源库比对果实糖、花青素及脱落酸积累的调控机制，为葡萄优质栽培技术提供理论支撑，报告内容引发国内外园艺专家广泛研讨。参会期间，团队前往银川国际会展中心观摩酿酒葡萄 AI 综合作业机器人成果展示，详细了解该装备在葡萄园智能作业方面的技术特点与应用前景，并与研发单位围绕葡萄智慧装备研发、果园智能化管理等方向开展深入交流。\n\n本次参会是葡萄团队持续推进科研国际化、对标国际前沿开展研究的重要实践。","上海市农业科学院 2026-10-19","2026-10-19T00:00:00Z",{"impact":172,"substance":279,"depth":257,"authority":257,"freshness":61,"relevant":21,"comment":334},"国际学术会议交流并涉及葡萄AI作业机器人观摩，有实质科研内容与行业参考价值，但属团队参会动态，影响面有限。",[336],{"name":331,"url":328},[81,338,181,339,340],"智能农机","栽培技术","国际交流",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":344},{"candidates":345,"agricultural_evidence":44},[346,350,354],{"code":31,"score":17,"supported":44,"title":347,"abstract":348,"tags":349},[181],[181],[181],{"code":118,"score":119,"supported":44,"title":351,"abstract":352,"tags":353},[],[81],[81],{"code":355,"score":21,"supported":13,"title":356,"abstract":357,"tags":358},"F5",[],[],[338],[360,361],"上海农科院 葡萄 银川","世界葡萄与葡萄酒大会 奚晓军","上海农科院葡萄银川-5516","2026-10-22T00:07:04.061911Z",{"id":365,"title":366,"url":367,"summary":368,"summary_zh":369,"content":8,"source_name":252,"source_url":367,"published_at":370,"category":71,"cover_url":8,"hotness":12,"is_selected":13,"score":371,"score_detail":372,"sources":375,"tags":377,"primary_topic":383,"secondary_topics":384,"classification":385,"search_phrases":404,"slug":407,"view_count":154,"doi":408,"paper":409,"created_at":434},5468,"Farm-level technical efficiency in temperate horticulture: evidence from apple, pear, and cherry growers","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1928386","Improving resource-use efficiency is fundamental to enhancing agricultural productivity and promoting sustainable horticultural development, particularly in regions where opportunities for expanding cultivated land and increasing input use are limited. This study evaluates the technical efficiency of apple, pear, and cherry growers in the Kashmir Valley and examines the socioeconomic, institutional, and regional factors associated with farm-level technical efficiency. Primary data were collected from 418 horticultural households during 2023 using a multi-stage sampling approach. Technical efficiency was first estimated separately for apple, pear, and cherry growers using an input-oriented Data Envelopment Analysis (DEA) framework under both Constant Returns to Scale (CRS) and Variable Returns to Scale (VRS) assumptions. Scale efficiency was subsequently estimated, while a pooled DEA model was employed to generate farm-level technical efficiency scores for the second-stage analysis. The determinants of technical efficiency were examined using Tobit regression, supported by a fractional logit model as a robustness check, and regional disparities were assessed using Kruskal–Wallis and Mann–Whitney non-parametric tests. The results indicate that apple growers achieved the highest overall technical efficiency, followed by pear and cherry growers. The VRS estimates reveal substantially higher levels of pure technical efficiency, whereas the scale efficiency analysis suggests that a considerable proportion of technical inefficiency arises from farms operating below their optimal production scale. The second-stage analysis identifies family size, education, irrigation facilities, access to credit, and cold storage infrastructure as significant positive factors associated with farm-level technical efficiency, while significant regional disparities are observed across the Kashmir Valley. The robustness analysis confirms the consistency of the estimated relationships. These findings highlight that improving horticultural performance requires not only efficient farm-level resource management but also stronger institutional support, investments in irrigation and post-harvest infrastructure, improved access to rural credit, and region-specific development strategies. The study contributes to the literature by integrating crop-specific and pooled efficiency analyses with socioeconomic and regional assessments, providing policy-relevant evidence for promoting resource-efficient and sustainable horticultural production.","提高资源利用效率是提升农业生产力、促进园艺产业可持续发展的基础，在耕地扩张和投入增加机会有限的地区尤为如此。本研究评估了克什米尔谷地苹果、梨和樱桃种植者的技术效率，并考察了与农场层面技术效率相关的社会经济、制度和区域因素。研究采用多阶段抽样方法，于2023年从418个园艺农户收集了一手数据。首先，在规模报酬不变（CRS）和规模报酬可变（VRS）两种假设下，运用投入导向的数据包络分析（DEA）框架分别估算苹果、梨和樱桃种植者的技术效率。随后估算规模效率，并采用混合DEA模型生成农场层面技术效率得分用于第二阶段分析。技术效率的决定因素通过Tobit回归进行检验，并以分数logit模型作为稳健性检验加以支持，区域差异则采用Kruskal–Wallis和Mann–Whitney非参数检验进行评估。结果表明，苹果种植者总体技术效率最高，梨和樱桃种植者次之。VRS估计显示纯技术效率水平显著更高，而规模效率分析表明，相当比例的技术无效率源于农场经营规模低于最优生产规模。第二阶段分析识别出家庭规模、教育、灌溉设施、信贷获取和冷库基础设施是与农场层面技术效率显著正相关的因素，同时在克什米尔谷地观察到显著的区域差异。稳健性分析证实了所估计关系的一致性。这些发现表明，提升园艺产业绩效不仅需要有效的农场层面资源管理，还需要更强的制度支持、对灌溉和采后基础设施的投资、改善农村信贷获取以及因地制宜的发展策略。本研究通过将作物特定效率分析与混合效率分析同社会经济和区域评估相结合，为促进资源利用效率提升提供了具有政策相关性的证据。","2026-10-20T00:00:00Z",73,{"impact":17,"substance":373,"depth":76,"authority":257,"freshness":173,"relevant":21,"comment":374},21,"基于418户果农一手数据，用DEA与Tobit回归分析技术效率，方法规范、结论有政策参考价值，但属区域性研究，影响范围有限。",[376],{"name":252,"url":367},[378,379,380,381,382],"可持续农业","园艺产业","苹果种植","技术效率","数据包络分析",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":386},{"candidates":387,"agricultural_evidence":44},[388,392,399],{"code":31,"score":74,"supported":44,"title":389,"abstract":390,"tags":391},[281],[283,281],[],{"code":393,"score":61,"supported":44,"title":394,"abstract":395,"tags":398},"H3",[],[396,397],"灌溉","irrigation",[],{"code":400,"score":21,"supported":13,"title":401,"abstract":402,"tags":403},"H8",[],[],[378],[405,406],"数据包络分析 可持续农业 园艺产业 技术效率","数据包络分析 可持续农业","数据包络分析可持续农业园艺产业技术效率-5468","10.3389\u002Ffsufs.2026.1928386",{"doi":408,"openalex_id":410,"authors":411,"venue":252,"cited_by_count":154,"oa_url":367,"card":428,"direction":432,"ingested_from":161},"W7223025732",[412,414,417,420,423,426],{"name":413,"orcid":8},"Seerat Sajjad",{"name":415,"orcid":416},"Khursheed Hussain Dar","https:\u002F\u002Forcid.org\u002F0000-0002-3925-0758",{"name":418,"orcid":419},"Anam Ul Haq Ganie","https:\u002F\u002Forcid.org\u002F0000-0002-4404-5011",{"name":421,"orcid":422},"Ghadda Mohamed Awad Yousif","https:\u002F\u002Forcid.org\u002F0000-0003-4183-2957",{"name":424,"orcid":425},"Mohd Salim","https:\u002F\u002Forcid.org\u002F0000-0003-0162-8433",{"name":427,"orcid":8},"Mahdy S. Othman",{"tldr":429,"method":430,"finding":431,"direction":432,"opportunity":433},"评估克什米尔谷地苹果、梨、樱桃种植户的技术效率及其影响因素。","418户调查数据，DEA测效率，Tobit回归与分数logit分析影响因素。","苹果效率最高，规模效率不足是主因；灌溉、信贷、冷库等显著提升效率。","数字乡村与农业信息化","可结合遥感与物联网数据构建果园投入产出实时效率监测，弥补自报数据局限。","2026-10-21T23:30:07.145395Z"]