[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3844":3,"related-3844":119},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":36,"classification":37,"search_phrases":89,"slug":92,"view_count":21,"doi":93,"paper":94,"created_at":118},3844,"Assessing Rice Food Availability Resilience Under Paddy Field Land Use Change in Sukabumi Regency","https:\u002F\u002Fdoi.org\u002F10.59188\u002Feduvest.v6i9.53513","Rice food availability is increasingly challenged by paddy field conversion driven by socioeconomic development and land-use transformation. However, reductions in paddy field extent do not necessarily indicate decreased food availability, as agricultural systems may respond through intensification and productivity improvement. This study evaluated rice food availability resilience under paddy field land-use change in Sukabumi Regency, West Java, Indonesia, during 2000–2020 by integrating Landsat-based spatial analysis with agricultural production indicators. Paddy field dynamics were analyzed using multi-temporal Landsat imagery through Object-Based Image Analysis (OBIA) and Random Forest classification. Agricultural responses were assessed using harvested area, cropping intensity, productivity, rice production, and the Production Efficiency Index (PEI).The results showed that paddy field area declined from 71,641.44 ha in 2000 to 65,982.15 ha in 2020, representing a 7.9% reduction, with the largest decline occurring in rainfed paddy fields. Despite this reduction, rice production increased from 622,189 tons to 931,328 tons, supported by increases in harvested area, cropping intensity, and productivity, particularly within irrigated paddy field systems. The PEI increased from 8.68 to 14.12 ton ha⁻¹, indicating improved production efficiency relative to available land resources. These findings indicated that rice food availability resilience in Sukabumi Regency was supported by agricultural adaptation strategies, particularly through intensification and productivity enhancement rather than land expansion.Nevertheless, productivity improvements may not indefinitely compensate for continued paddy field conversion due to ecological and biophysical limitations. Integrating remote sensing-based land-use change analysis with agricultural performance indicators provides a useful framework for evaluating food availability resilience and supporting sustainable paddy field management.","水稻粮食供给日益受到社会经济发展和土地利用转型所驱动的稻田转换的挑战。然而，稻田面积的减少并不必然意味着粮食供给下降，因为农业系统可能通过集约化和生产力提升作出响应。本研究通过将基于Landsat的空间分析与农业生产指标相结合，评估了印度尼西亚西爪哇省苏加武眉县2000—2020年稻田土地利用变化下的水稻粮食供给韧性。稻田动态采用多时相Landsat影像，通过面向对象影像分析（OBIA）和随机森林分类进行分析。农业响应则通过收获面积、种植强度、生产力、水稻产量和生产效率指数（PEI）进行评估。结果表明，稻田面积从2000年的71,641.44公顷下降至2020年的65,982.15公顷，减少了7.9%，其中雨养稻田的降幅最大。尽管稻田面积减少，水稻产量却从622,189吨增加至931,328吨，这得益于收获面积、种植强度和生产力的提高，尤其是在灌溉稻田系统中。生产效率指数从8.68吨\u002F公顷上升至14.12吨\u002F公顷，表明相对于可用土地资源的生产效率有所提高。这些发现表明，苏加武眉县的水稻粮食供给韧性得益于农业适应策略，特别是通过集约化和生产力提升而非土地扩张来实现。然而，由于生态和生物物理限制，生产力的提高可能无法无限期地弥补稻田的持续转换。将基于遥感的土地利用变化分析与农业绩效指标相结合，为评估粮食供给韧性和支持可持续稻田管理提供了一个有用的框架。",null,"Eduvest - Journal Of Universal Studies","2026-09-29T00:00:00Z","论文",10,false,60,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":20,"relevant":21,"comment":22},8,18,16,9,1,"印尼区域案例，方法（Landsat+OBIA+随机森林）与结论有参考价值，但影响范围限于地方，适合作为遥感与耕地变化主题的补充素材。",[24],{"name":10,"url":6},[26,27,28,29,30],"水稻","粮食安全","耕地保护","遥感监测","土地利用",{"code":32,"slug":33,"name":26,"path":34,"parent_code":35},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",[],{"status":38,"locked":14,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45},[43,51,59,67,74,79,84],{"code":32,"score":44,"supported":45,"title":46,"abstract":48,"tags":50},26,true,[33,47],"paddy",[26,49,33,47],"稻田",[26],{"code":52,"score":53,"supported":45,"title":54,"abstract":55,"tags":58},"I3",7,[],[56,57],"遥感","remote sensing",[29],{"code":60,"score":61,"supported":45,"title":62,"abstract":63,"tags":66},"I4",6,[],[64,65],"空间分析","spatial analysis",[],{"code":68,"score":69,"supported":45,"title":70,"abstract":71,"tags":73},"H3",3,[],[72],"灌溉",[],{"code":75,"score":21,"supported":14,"title":76,"abstract":77,"tags":78},"A8",[],[],[27],{"code":80,"score":21,"supported":14,"title":81,"abstract":82,"tags":83},"H1",[],[],[28],{"code":85,"score":21,"supported":14,"title":86,"abstract":87,"tags":88},"H6",[],[],[30],[90,91],"Sukabumi 稻田 遥感","水稻 耕地转换 粮食供给","Sukabumi稻田遥感-3844","10.59188\u002Feduvest.v6i9.53513",{"doi":93,"openalex_id":95,"authors":96,"venue":10,"cited_by_count":110,"oa_url":6,"card":111,"direction":115,"ingested_from":117},"W7214788951",[97,99,102,104,107],{"name":98,"orcid":9},"Tatang Kurniawan",{"name":100,"orcid":101},"Widiatmaka Widiatmaka","https:\u002F\u002Forcid.org\u002F0000-0001-8613-9561",{"name":103,"orcid":9},"M. Yanuar J. Purwanto",{"name":105,"orcid":106},"Yudi Setiawan","https:\u002F\u002Forcid.org\u002F0000-0002-1482-1905",{"name":108,"orcid":109},"Iman Firmansyah","https:\u002F\u002Forcid.org\u002F0000-0001-5180-8964",0,{"tldr":112,"method":113,"finding":114,"direction":115,"opportunity":116},"评估印尼苏卡布米稻田2000-2020年土地利用变化下稻米供应韧性。","Landsat多时相影像、OBIA与随机森林分类，结合农业生产指标。","稻田减少7.9%但稻米产量增49.7%，靠集约化与生产力提升维持韧性。","农业遥感与作物表型","可研究集约化补偿的生态阈值及多区域遥感-生产指标耦合的韧性评估框架。","openalex","2026-09-30T23:30:29.307801Z",{"total":61,"page":21,"page_size":61,"items":120},[121,190,258,312,352,400],{"id":122,"title":123,"url":124,"summary":125,"summary_zh":126,"content":9,"source_name":127,"source_url":124,"published_at":128,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":129,"score_detail":130,"sources":134,"tags":136,"primary_topic":139,"secondary_topics":140,"classification":141,"search_phrases":163,"slug":166,"view_count":110,"doi":167,"paper":168,"created_at":189},5319,"Synergistic Use of Multitemporal SAR and Optical Data with Random Forest Approach for Rice Mapping in Bangladesh","https:\u002F\u002Fdoi.org\u002F10.31223\u002Fx5f22w","Accurate in-season crop mapping is vital for national food security, yet traditional ground-based estimation methods in Bangladesh often lack precision. This study develops a robust machine-learning framework to map Boro rice in the Manikganj district by integrating Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical imagery. To overcome the challenge of seasonal cloud cover, Sentinel-1 VH polarization was utilized to identify unique backscatter reductions during the transplantation phase (late February to early March) caused by specular reflection from flooded fields. By combining these SAR signatures with high-resolution spectral indices (NDVI, EVI, and BSI) from Sentinel-2 during peak growth stages, the research captures the distinct phenological signatures of paddy rice. A pixel-based Random Forest algorithm was applied within the Google Earth Engine (GEE) platform, utilizing about 200 field-verified reference points per sub-district (1,385 in total) for training and validation. The results demonstrated high classification performance, with overall accuracies ranging from 88.1% to 94.9% across the seven sub-districts. Furthermore, a comparison with official government statistics revealed a minimal total area difference of only 2.34%, underscoring the reliability of this integrated satellite approach for operational crop monitoring.","准确的当季作物制图对国家粮食安全至关重要，然而孟加拉国传统的基于地面估计的方法往往缺乏精度。本研究开发了一个稳健的机器学习框架，通过整合Sentinel-1合成孔径雷达（SAR）和Sentinel-2光学影像，对马尼格甘杰县的Boro水稻进行制图。为克服季节性云覆盖的挑战，研究利用Sentinel-1 VH极化识别插秧期（2月下旬至3月上旬）由淹水田块镜面反射所引起的独特后向散射降低特征。通过将这些SAR特征与Sentinel-2在生长旺盛期的高分辨率光谱指数（NDVI、EVI和BSI）相结合，研究捕捉到了水稻独特的物候特征。在Google Earth Engine（GEE）平台上应用了基于像元的随机森林算法，利用每个乡约200个实地验证参考点（共计1，385个）进行训练和验证。结果表明分类性能较高，七个乡的总体精度在88.1%至94.9%之间。此外，与政府官方统计数据相比，总面积差异仅为2.34%，凸显了这种综合卫星方法在业务化作物监测中的可靠性。","OpenAlex","2026-10-18T00:00:00Z",79,{"impact":18,"substance":131,"depth":18,"authority":132,"freshness":20,"relevant":21,"comment":133},22,12,"方法扎实、精度可靠的水稻遥感制图研究，对农业遥感业务化监测有参考价值，但属区域性案例，影响层级有限。",[135],{"name":127,"url":124},[26,27,137,138,56],"机器学习","作物制图",{"code":32,"slug":33,"name":26,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":142},{"candidates":143,"agricultural_evidence":45},[144,148,153,159],{"code":32,"score":18,"supported":45,"title":145,"abstract":146,"tags":147},[33],[26,33,47],[26],{"code":75,"score":53,"supported":45,"title":149,"abstract":150,"tags":152},[],[27,151],"food security",[27],{"code":154,"score":155,"supported":45,"title":156,"abstract":157,"tags":158},"I2",4,[],[137],[137],{"code":52,"score":21,"supported":14,"title":160,"abstract":161,"tags":162},[],[],[56],[164,165],"Sentinel-1 Sentinel-2 水稻制图","孟加拉国 Boro 水稻 遥感","Sentinel-1Sentinel-2水稻制图-5319","10.31223\u002Fx5f22w",{"doi":167,"openalex_id":169,"authors":170,"venue":9,"cited_by_count":110,"oa_url":183,"card":184,"direction":115,"ingested_from":117},"W7222810209",[171,173,176,179,181],{"name":172,"orcid":9},"Mst. Farida Perveen",{"name":174,"orcid":175},"Sravan Shrestha","https:\u002F\u002Forcid.org\u002F0000-0001-6979-1726",{"name":177,"orcid":178},"Abdullah Al Mehadi","https:\u002F\u002Forcid.org\u002F0000-0002-9002-633X",{"name":180,"orcid":9},"Saima Kazmina",{"name":182,"orcid":9},"Amio Al Saem","https:\u002F\u002Feartharxiv.org\u002Frepository\u002Fobject\u002F15552\u002Fdownload\u002F26951\u002F",{"tldr":185,"method":186,"finding":187,"direction":115,"opportunity":188},"融合Sentinel-1 SAR与Sentinel-2光学影像，用随机森林在GEE上绘制孟加拉国Bo","Sentinel-1 VH后向散射与Sentinel-2 NDVI\u002FEVI\u002FBS","七个分县总体精度88.1%–94.9%，与官方统计面积仅差2.34%。","可将该SAR-光学协同框架迁移至其他多云雨稻区，并探索多时相特征优选与跨季泛化。","2026-10-19T23:30:22.772950Z",{"id":191,"title":192,"url":193,"summary":194,"summary_zh":195,"content":9,"source_name":196,"source_url":193,"published_at":197,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":198,"score_detail":199,"sources":202,"tags":204,"primary_topic":208,"secondary_topics":209,"classification":215,"search_phrases":238,"slug":241,"view_count":242,"doi":243,"paper":244,"created_at":257},4431,"A multimodal deep learning for rice crop health monitoring using vegetation index feature selection","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-74895-5","Crop health monitoring is essential for sustaining agricultural productivity and ensuring stable production, particularly in major crop-producing regions that face challenges due to climate variability, resource constraints, and spatial heterogeneity. Traditional methods struggle to effectively integrate spatial heterogeneity and environmental variability from multi-source remote sensing (RS) data. To address this problem, the proposed work explores the role of satellite imagery in monitoring rice crop health with the help of Vegetation Indices (VIs) under the impact of changing weather conditions. Multi-temporal Sentinel-1 and Sentinel-2 satellite data were acquired from a rice cultivation field in Gorakhpur, Uttar Pradesh, India, during three Kharif growing seasons (2023-2025). In this work, we propose a supervised MI-based feature selection framework employing Otsu’s adaptive thresholding for selection of VIs that strongly determine the health of rice crop. To ensure data integrity, cloud-contaminated observations were systematically excluded through a two-stage cleaning process. Further, we propose a multimodal deep learning (DL) framework that fuses a Swin Transformer (Swin-T) backbone for spatial feature extraction from vegetation index imagery with a Multi-Layer Perceptron (MLP) for structured numerical modeling of meteorological variables. The proposed framework achieved 94.05% accuracy on an independent held-out test set and a mean accuracy of \\(92.12 \\pm 5.28\\) % under 10-fold cross-validation, consistently outperforming competing hybrid deep learning baselines. These results highlight that integrating transformer-based spatial representations with environmental information provides an efficient approach for rice crop health classification. The proposed framework can be extended to multi-region crop monitoring by incorporating additional vegetation index modalities and diverse climatic conditions.","作物健康监测对于维持农业生产力、保障稳定生产至关重要，尤其是在面临气候变异性、资源约束和空间异质性挑战的主要作物产区。传统方法难以有效整合多源遥感（RS）数据中的空间异质性和环境变异性。为解决这一问题，本研究探索了卫星影像在天气条件变化影响下借助植被指数（VIs）监测水稻作物健康的作用。研究在印度北方邦戈勒克布尔的稻田中，于三个水稻生长季（2023—2025年）获取了多时相Sentinel-1和Sentinel-2卫星数据。在本研究中，我们提出了一种基于互信息（MI）的监督式特征选择框架，采用Otsu自适应阈值法筛选对水稻作物健康具有强判别能力的植被指数。为确保数据完整性，通过两阶段清洗流程系统性地剔除了受云污染的观测数据。此外，我们提出了一种多模态深度学习（DL）框架，该框架融合了Swin Transformer（Swin-T）主干网络用于从植被指数影像中提取空间特征，以及多层感知机（MLP）用于气象变量的结构化数值建模。所提框架在独立留出测试集上达到了94.05%的准确率，在10折交叉验证下平均准确率为\\(92.12 \\pm 5.28\\)%，持续优于竞争的混合深度学习基线模型。这些结果表明，将基于Transformer的空间表征与环境信息相结合，为水稻作物健康分类提供了一种高效方法。所提框架可通过纳入更多植被指数模态和多样化气候条件，扩展至多区域作物监测。","Scientific Reports","2026-10-07T00:00:00Z",81,{"impact":18,"substance":131,"depth":18,"authority":200,"freshness":20,"relevant":21,"comment":201},14,"方法新颖、数据跨三个生长季且精度可靠，属农业遥感与AI交叉的细分领域进展，值得进入每日精选。",[203],{"name":196,"url":193},[205,206,26,29,207],"智慧农业","农业人工智能","作物长势",{"code":32,"slug":33,"name":26,"path":34,"parent_code":35},[210],{"code":154,"slug":211,"name":212,"path":213,"parent_code":214},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":38,"locked":14,"version":39,"method":40,"evidence":216},{"candidates":217,"agricultural_evidence":45},[218,222,229,233],{"code":32,"score":18,"supported":45,"title":219,"abstract":220,"tags":221},[33],[26,49,33],[26],{"code":154,"score":223,"supported":45,"title":224,"abstract":226,"tags":228},15,[225],"deep learning",[227,225],"深度学习",[206],{"code":52,"score":53,"supported":45,"title":230,"abstract":231,"tags":232},[],[56,57],[29],{"code":234,"score":21,"supported":14,"title":235,"abstract":236,"tags":237},"I1",[],[],[205],[239,240],"Sentinel-2 水稻 遥感监测","Swin Transformer 作物健康","Sentinel-2水稻遥感监测-4431",2,"10.1038\u002Fs41598-026-74895-5",{"doi":243,"openalex_id":245,"authors":246,"venue":196,"cited_by_count":110,"oa_url":193,"card":252,"direction":115,"ingested_from":117},"W7220838898",[247,250],{"name":248,"orcid":249},"Shashank Gupta","https:\u002F\u002Forcid.org\u002F0000-0003-1291-7951",{"name":251,"orcid":9},"Shefali Arora 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年福建省农业主导品种。","10月15日，我院水稻所在尤溪县西城镇麻洋村举行新品种“禾两优631”作再生稻高产栽培示范验收会。中国科学院韩斌院士、钱前院士以及南京农业大学、中国农业科学院作物科学研究所、上海市农业科学院、福建省农业农村厅、福建省农学会和三明市农业农村局的专家莅临现场测产。谢华安院士，我院党委委员、副院长魏辉研究员到会指导，院科研处处长赵健研究员主持验收会。\n\n经测产，“禾两优631”再生季亩产达484.72公斤，加上今年8月头季亩产879.8公斤，两季合计亩产1364.52公斤。水稻所又一新品种实现再生稻栽培高产，也是该基地连续第26年开展再生稻高产栽培示范取得的新进展。验收专家们表示，“禾两优631”头季与再生季表现均衡，再生季484.72公斤的产量表明该品种具有较强再生力和推广潜力，也验证了配套栽培技术在大面积示范中的稳定性。两季合计1364.52公斤，对提升稻田综合效益、稳定粮食生产、促进农民增收具有重要意义。\n\n尤溪县是全国高产再生稻发祥地。自2000年以来，我院水稻所的科研人员风雨无阻、年复一年，在同一片土地上持续开展再生稻品种试验、高产栽培和示范推广。截至目前，麻洋村百亩示范基地已示范种植15个强再生力杂交稻品种。麻洋村百亩示范基地已成为福建再生稻品种与技术的重要展示窗口。谢华安院士团队经数十年持续攻关，不仅选育出一批高产、抗逆、优质、广适的强再生力水稻新品种；还系统构建了水稻再生力评价体系，创建了以“双调双控”为核心的再生稻高效生产关键技术体系和省工节本的机械化生产技术体系，相关科研成果获得福建省科技进步一等奖2项。这既是26年厚积薄发的集中体现，也是对持之以恒开展基础研究和应用推广的最好肯定。\n\n“禾两优631”是我院水稻所主持选育的两系杂交中稻品种，2025年通过福建省中稻审定（闽审稻20251009），进入国家长江中下游中籼迟熟组区域试验（第2年）。 “禾两优631”田间表现产量高（LAX1\u002FPTB1\u002FNOG1）、抗白叶枯病（Xa23）、中抗稻瘟病（Pi1\u002FPi2\u002FPikh）、再生力强（psr1）、适应性强（OsLG3）、抗倒伏，是一个综合性状优良的杂交稻品种。“禾两优631”被评为2024年全国种子双交会水稻地展评价优良品种、2025年福建省优质稻田间展示综合表现优异十大品种、2026年福建省农业主导品种。“禾两优631”的育成和应用，将为福建省乃至长江中下游地区的粮食生产作出更大贡献。\n\n站在新的历史起点上，水稻所将继续保持“咬定青山不放松”的战略定力，以更高标准、更实举措推进再生稻科研与产业高质量发展，在融合分子育种等现代技术手段强化育种攻关、推进农机农艺融合、切实提升生产效率和深化示范推广与产业带动等方面，加快技术成果在全省乃至南方稻区的推广应用，为南方稻区再生稻产业发展提供可借鉴的样板，为保障国家粮食安全、促进农业增效和农民增收作出新的更大贡献。（文\u002F水稻所 朱永生，图\u002F水稻所 蔡秋华）","福建省农业科学院 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日上午，水稻良种和高效肥展示观摩会在淮南市潘集区贺疃镇「绿色吨半粮」示范基地举行，来自中国农业大学、南京农业大学、安徽省农业科学院、安徽农业大学的专家教授以及种粮大户、基层农技人员参加。示范田内多个水稻新品种分区种植，同步设置高效肥与常规施肥对照试验田块，通过机收和人员测产直观展示良种搭配高效肥料的增产效果，为做好新一年店集贡米品种选育与宣传推广提供技术依据，推动水稻单产提升行动。","为加快优良水稻品种、高效新型肥料示范推广，推动良种良法配套落地，助力粮食稳产增产，10月14日上午，水稻良种和高效肥展示观摩会在贺疃镇“绿色吨半粮”示范基地举行。来自中国农业大学、南京农业大学、安徽省农业科学院、安徽农业大学的专家教授以及种粮大户、基层农技人员齐聚基地，现场观摩对比，开展实收测产。\n\n在示范田内，多个水稻新品种分区种植，同步设置高效肥与常规施肥对照试验田块。观摩会现场，通过查看不同品种的长势、抗倒伏、抗病性，对比施用高效肥和常规肥料水稻的株型、穗粒、结实情况，现场讲解品种特性、科学施肥、田间管护等关键技术要点，为种粮大户答疑解惑，做好新一年的店集贡米的品种选育、宣传推广。现场通过机收和人员测产，直观展示了优良品种搭配高效肥料的增产效果，用真实数据验证良种与高效肥相结合的增产提质、节肥增效优势，对推进水稻单产提升行动具有积极作用。\n\n潘集区农技推广中心主任杨中保表示：“近年来，我们立足本地农业生产实际，聚焦粮食安全底线、农业增效目标、农民增收核心，扎实推进农作物良种筛选推广、配套栽培良法落地、新型肥料示范应用等一系列重点工作，通过建示范、抓培训、强指导、树样板，良种良法配套、科学施肥增效的农业生产新模式全面落地，让农作物抗灾能力、单产水平、农产品品质稳步提升，农业生产综合效益持续提高，农业生产取得了阶段性成效。”\n\n通过现场观摩不同品种的水稻生长特性，选用适配本地气候土壤的水稻良种，配套应用高效肥料，能够提升肥料利用率，对于减少化肥投入，改善稻米品质，推动水稻绿色高产，壮大潘集水稻产业的有效路径。此次观摩测产活动，搭建起科研院所、农技部门与种植主体交流对接的平台，打通农业科技成果转化 “最后一公里”。\n\n潘集区农技推广中心主任杨中保表示：“下一步，我们农技部门要锚定农业高质量发展总目标，紧扣稳粮、提质、增效、绿色核心任务，持续深耕良种良法推广、科学施肥示范工作，推动农业生产提质增效、提档升级，为稳定粮食生产、壮大特色农业、助力乡村产业振兴提供有力支撑。”","淮南市潘集区人民政府 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保护视力色：\n\n2026年10月12日，我院的安徽省财政农业科技成果转化项目“粮油农文旅融合集成技术转化应用”，在黄山市休宁县商山镇荪田村实施的水稻新品种“银月丝苗”百亩示范片进行现场测产验收。该项目由农业机械装备与工程所牵头、联合水稻研究所共同实施。\n\n专家组听取了实施情况汇报，实地考察了项目示范片并机收测产。示范片面积331亩，种植了水稻新品种“银月丝苗”，配套应用了水稻机直播、机插同步侧深施肥、稻田杂草绿色防控、优质稻病虫害绿色防控等绿色高产优质栽培技术。示范片处于黄熟期，专家组随机选取代表性田块，实收折算干谷亩产达605.2公斤。示范片单产比去年黄山市统计的水稻平均亩产460.0公斤高出145.2公斤，增产幅度达31.6%，远超项目增产50公斤的预期目标。\n\n示范片田间生长整齐平衡，熟相好，未发现明显病虫草害，充分展现了该品种高产、优质、抗倒伏能力较强的综合优势，项目区生态环境优良，稻米产量高、品质优，同时展现了稻丰田园的大地景观，为游客初秋乡村旅游和美食体验提供了良好去处。","安徽省农业科学院 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