[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2911":3,"related-2911":54},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"search_phrases":30,"slug":33,"view_count":34,"doi":35,"paper":36,"created_at":53},2911,"Hidden costs of rice intensification: Life cycle and economic assessment in Vietnam's Mekong Delta","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104987","Hidden costs of rice intensification: Life cycle and economic assessment in Vietnam's Mekong Delta。Agricultural Systems",null,"Agricultural Systems","2026-09-18T00:00:00Z","论文",10,false,80,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,21,14,9,1,"核心期刊发表的水稻集约化生命周期与经济评估研究，数据与结论有实质增量，但属越南区域案例，对国内三农信息化参考价值有限，可入精选但非重点。",[23],{"name":9,"url":6},[25,26,27,28,29],"水稻","农业可持续","生命周期评价","湄公河三角洲","水稻集约化",[31,32],"湄公河三角洲 水稻 生命周期评价","越南 水稻集约化 经济评估","湄公河三角洲水稻生命周期评价-2911",0,"10.1016\u002Fj.agsy.2026.104987",{"doi":35,"openalex_id":37,"authors":38,"venue":9,"cited_by_count":34,"oa_url":6,"card":8,"direction":8,"ingested_from":52},"W7213535887",[39,41,44,46,49],{"name":40,"orcid":8},"Van Tinh Thai",{"name":42,"orcid":43},"Julia Checco","https:\u002F\u002Forcid.org\u002F0000-0002-1269-1561",{"name":45,"orcid":8},"Jaquie Mitchell",{"name":47,"orcid":48},"Md. Ali Akber","https:\u002F\u002Forcid.org\u002F0000-0002-5507-1055",{"name":50,"orcid":51},"Ammar Abdul Aziz","https:\u002F\u002Forcid.org\u002F0000-0003-3470-2062","openalex","2026-09-19T23:30:05.428434Z",{"total":55,"page":20,"page_size":55,"items":56},6,[57,101,137,205,235,256],{"id":58,"title":59,"url":60,"summary":61,"summary_zh":62,"content":8,"source_name":63,"source_url":60,"published_at":64,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":65,"score_detail":66,"sources":71,"tags":73,"search_phrases":78,"slug":81,"view_count":34,"doi":82,"paper":83,"created_at":100},2951,"Comparative Analysis of Geographical Factors Affecting Paddy (Oryza sativa L.) Yields in Türkiye Using Random Forest and ANOVA: The Case of Kırıkkale, Balıkesir, Diyarbakır and Şanlıurfa","https:\u002F\u002Fdoi.org\u002F10.24925\u002Fturjaf.v14i9.2678-2694.8977","Rice (Oryza sativa L.) is a staple food for nearly half of the global population and a strategic crop for Turkey, where inter-provincial yield disparities remain pronounced. This study aims to classify provincial rice yield levels in Turkey for the 2004–2024 period using TurkStat data and to quantify the relative contribution of 14 environmental, edaphic and agronomic parameters driving these differences. Preliminary analyses identified Kırıkkale (21-year mean 908.3 kg\u002Fda) as the high-yield province, Balıkesir (747.7 kg\u002Fda) as the medium-yield province, and Diyarbakır (448.1 kg\u002Fda) and Şanlıurfa (440.5 kg\u002Fda) as the low- and lowest-yield provinces, respectively. A 14-parameter dataset compiled from field measurements and published province-level studies was analysed using Principal Component Analysis (PCA), Random Forest (RF) classification and one-way Analysis of Variance (ANOVA).With a 70\u002F30 train\u002Ftest split, the RF model achieved 93.47% accuracy, 0.9764 ROC-AUC and a mean variance of 0.0145. Gini-based variable importance ranked soil moisture, organic matter, soil pH, rainfall and temperature as the most influential drivers of yield, and ANOVA confirmed statistically significant differences across yield classes for these variables (all p \u003C 0.001). Findings indicate that low yields in south-eastern Anatolia are largely driven by inadequate soil moisture management, low organic matter content, elevated soil pH and summer heat stress, whereas Kırıkkale’s high yields are associated with more balanced soil–water relations. Results provide evidence-based guidance for region-specific rice production policies and data-driven decision support in Türkiye.","水稻（Oryza sativa L.）是全球近半数人口的主粮，也是土耳其的战略性作物，但该国各省之间的产量差异依然显著。本研究旨在利用土耳其统计局（TurkStat）数据，对2004—2024年期间土耳其各省水稻产量水平进行分类，并量化14项环境、土壤和农艺参数对上述差异的相对贡献。初步分析确定，Kırıkkale省（21年均值908.3 kg\u002Fda）为高产区，Balıkesir省（747.7 kg\u002Fda）为中产区，Diyarbakır省（448.1 kg\u002Fda）和Şanlıurfa省（440.5 kg\u002Fda）分别为低产区和最低产区。基于田间实测数据和已发表的省级研究，构建了包含14项参数的数据集，并采用主成分分析（PCA）、随机森林（RF）分类和单因素方差分析（ANOVA）进行分析。在70\u002F30的训练\u002F测试集划分下，RF模型达到了93.47%的准确率、0.9764的ROC-AUC值以及0.0145的平均方差。基于基尼系数的变量重要性排序显示，土壤水分、有机质、土壤pH、降雨量和温度是影响产量最重要的驱动因素，ANOVA证实这些变量在不同产量类别间均存在统计学显著差异（均p \u003C 0.001）。研究结果表明，安纳托利亚东南部地区的低产主要归因于土壤水分管理不足、有机质含量低、土壤pH偏高以及夏季高温胁迫，而Kırıkkale省的高产则与更为均衡的土壤—水分关系有关。研究结果为土耳其制定区域特异性水稻生产政策和数据驱动的决策支持提供了循证依据。","Turkish Journal of Agriculture - Food Science and Technology","2026-09-17T00:00:00Z",74,{"impact":67,"substance":68,"depth":16,"authority":69,"freshness":19,"relevant":20,"comment":70},12,22,13,"基于21年省级数据与随机森林、ANOVA量化水稻产量驱动因子，方法规范、结论可靠，但属土耳其区域研究，对国内三农实践参考价值有限。",[72],{"name":63,"url":60},[25,74,75,76,77],"产量预测","农业大数据","精准农业","土壤墒情",[79,80],"土耳其 水稻 产量 随机森林","Kırıkkale Balıkesir 水稻 产量","土耳其水稻产量随机森林-2951","10.24925\u002Fturjaf.v14i9.2678-2694.8977",{"doi":82,"openalex_id":84,"authors":85,"venue":63,"cited_by_count":34,"oa_url":92,"card":93,"direction":99,"ingested_from":52},"W7213465585",[86,89],{"name":87,"orcid":88},"Mehmet ÖZCANLI","https:\u002F\u002Forcid.org\u002F0000-0003-2228-8298",{"name":90,"orcid":91},"Kerim Karadağ","https:\u002F\u002Forcid.org\u002F0000-0001-5167-4054","https:\u002F\u002Fwww.agrifoodscience.com\u002Findex.php\u002FTURJAF\u002Farticle\u002Fdownload\u002F8977\u002F4317",{"tldr":94,"method":95,"finding":96,"direction":97,"opportunity":98},"用随机森林和方差分析比较土耳其四省水稻产量差异，识别关键地理驱动因子。","基于2004–2024年TurkStat数据，用PCA、随机森林分类和单因素AN","土壤水分、有机质、pH、降雨和温度是产量主因；东南部低产源于土壤水分不足、有机质低、pH高和夏季热胁","农业人工智能与决策模型","可引入时序遥感与土壤传感器数据，构建跨区域可迁移的产量预测与精准水肥管理模型。","农业遥感与作物表型","2026-09-19T23:30:34.632573Z",{"id":102,"title":103,"url":104,"summary":105,"summary_zh":106,"content":8,"source_name":107,"source_url":104,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":108,"score_detail":109,"sources":112,"tags":114,"search_phrases":119,"slug":122,"view_count":34,"doi":123,"paper":124,"created_at":136},2920,"Breeding rice for optimal maturity across diverse sowing windows under future climate change scenarios in Chongqing area","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1899019","Introduction It is of great significance to optimize rice cultivars for different sowing dates under future climate change for rice sustainable production in Chongqing. Methods In this study, using the APSIM-Rice model and Coupled Model Intercomparison Project Phase 6 (CMIP6) Shared Socioeconomic Pathways (SSP) scenarios, we investigated the changes of yield, water consumption and water use efficiency (WUE) across six sowing dates and three cultivars (early, normal and late-maturing cultivars) under baseline period (1981 – 2010) and future climate 2scenarios (2031-2100, SSP2-4.5 and SSP5-8.5). In this study, the climate model BCC-CSM2-MR was selected due to its reliable simulation of China’s climate and compatibility with the APSIM-Rice model. Results Results showed that rice yield with normal cultivar in the baseline period peaked on March 30, with the value of 6,310 kg ha− −1 , and reached its minimum on March 1, with the value of 4,580 kg ha− −1 . Water consumption during the rice growing period increased with the delayed sowing dates (212 mm on March 1 to 276 mm on April 20). The response trend of water use efficiency (WUE) to different sowing dates was identical to that of yield, with the maximum WUE of 23.30 kg ha− −1 mm −1 achieved when sown on March 30. Under future SSP2-4.5 and SSP5-8.5 scenarios, early sowing (March 1–20) consistently enhanced yield and WUE for normal cultivars (maximum increments were 25.40% and 29.00% for yield and WUE), while late sowing (March 30–April 20) caused severe losses (up to 26.90% and 34.60% for yield and WUE for April 10 under SSP5-8.5 in the 2060s), with water consumption rising across all sowing dates. Early-maturing cultivars reduced yield (10.60%–44.60%) and WUE (7.70%–43.90%) across all sowing dates under future scenarios, whereas late-maturing cultivars synergized with early sowing to boost yield (up to 25.62%) and WUE (up to 32.85%) but exacerbated losses for late sowing, with water consumption increasing significantly (up to 56.30% under SSP5-8.5 in the 2060s). Discussion These findings provide critical scientific support for optimizing sowing date and cultivar combinations, thereby enhancing the climate resilience and sustainability of rice production in Chongqing and similar subtropical monsoon regions. However, in the future, more climate models, extreme climate impacts, and agronomic factors should be considered","引言 优化不同播期下的水稻品种对重庆未来气候变化背景下的水稻可持续生产具有重要意义。方法 本研究利用APSIM-Rice模型和耦合模式比较计划第六阶段（CMIP6）共享社会经济路径（SSP）情景，研究了基准期（1981—2010年）和未来气候情景（2031—2100年，SSP2-4.5和SSP5-8.5）下6个播期和3个品种（早熟、中熟和晚熟品种）的产量、耗水量和水分利用效率（WUE）变化。本研究选择气候模式BCC-CSM2-MR，因其对中国气候的模拟可靠且与APSIM-Rice模型兼容。结果 结果表明，基准期中熟品种水稻产量在3月30日达到峰值，为6 310 kg ha⁻¹，在3月1日降至最低，为4 580 kg ha⁻¹。水稻生育期耗水量随播期推迟而增加（3月1日的212 mm增至4月20日的276 mm）。水分利用效率（WUE）对不同播期的响应趋势与产量一致，3月30日播种时WUE最高，为23.30 kg ha⁻¹ mm⁻¹。在未来SSP2-4.5和SSP5-8.5情景下，早播（3月1—20日）持续提高中熟品种的产量和WUE（产量和WUE的最大增幅分别为25.40%和29.00%），而晚播（3月30日—4月20日）造成严重损失（在2060年代SSP5-8.5情景下，4月10日播种的产量和WUE损失分别高达26.90%和34.60%），且所有播期的耗水量均增加。在未来情景下，早熟品种在所有播期均降低产量（10.60%—44.60%）和WUE（7.70%—43.90%），而晚熟品种与早播协同提高产量（最高25.62%）和WUE（最高32.85%），但加剧了晚播的损失，耗水量显著增加（在2060年代SSP5-8.5情景下最高达56.30%）。讨论 这些发现为优化播期和品种组合提供了关键科学支撑，从而增强重庆及类似亚热带季风区水稻生产的气候韧性和可持续性。然而，未来应考虑更多气候模式、极端气候影响和农艺因素。","Frontiers in Sustainable Food Systems",78,{"impact":110,"substance":68,"depth":16,"authority":69,"freshness":19,"relevant":20,"comment":111},16,"基于APSIM-Rice与CMIP6情景模拟重庆水稻播期与品种组合，数据扎实、结论对区域气候适应性育种有参考价值，但属细分领域研究，影响力有限。",[113],{"name":107,"url":104},[115,25,116,117,118],"智慧农业","气候变化","品种选育","播期优化",[120,121],"APSIM-Rice 水稻 播期","重庆 水稻 品种 气候","APSIM-Rice水稻播期-2920","10.3389\u002Ffsufs.2026.1899019",{"doi":123,"openalex_id":125,"authors":126,"venue":107,"cited_by_count":34,"oa_url":104,"card":131,"direction":97,"ingested_from":52},"W7213555399",[127,129],{"name":128,"orcid":8},"Jianzhao Tang",{"name":130,"orcid":8},"Jianping Zhang",{"tldr":132,"method":133,"finding":134,"direction":97,"opportunity":135},"用APSIM-Rice与CMIP6情景模拟重庆不同播期和品种水稻产量、耗水与水分利用效率。","APSIM-Rice模型结合CMIP6 SSP2-4.5\u002FSSP5-8.5情景及","未来早播配晚熟品种可增产提效，晚播则大幅减产，各播期耗水均上升。","可引入多模型集合、极端气候与氮肥管理等农艺因素，优化播期-品种组合的适应策略。","2026-09-19T23:30:08.567885Z",{"id":138,"title":139,"url":140,"summary":141,"summary_zh":142,"content":8,"source_name":9,"source_url":140,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":143,"score":144,"score_detail":145,"sources":150,"tags":152,"search_phrases":156,"slug":159,"view_count":34,"doi":160,"paper":161,"created_at":204},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。",true,89,{"impact":146,"substance":147,"depth":148,"authority":18,"freshness":19,"relevant":20,"comment":149},24,23,19,"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。",[151],{"name":9,"url":140},[115,25,153,154,155],"空间优化","节水灌溉","农业减排",[157,158],"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":160,"openalex_id":162,"authors":163,"venue":9,"cited_by_count":34,"oa_url":140,"card":198,"direction":202,"ingested_from":52},"W7213551595",[164,166,169,172,174,176,178,180,183,185,188,191,194,196],{"name":165,"orcid":8},"Qiang Xu",{"name":167,"orcid":168},"Fan Yao","https:\u002F\u002Forcid.org\u002F0000-0002-4393-7296",{"name":170,"orcid":171},"Dan Wei","https:\u002F\u002Forcid.org\u002F0000-0003-4401-567X",{"name":173,"orcid":8},"Hui Gao",{"name":175,"orcid":8},"Min Jiang",{"name":177,"orcid":8},"Wenya Chen",{"name":179,"orcid":8},"Yourui Cao",{"name":181,"orcid":182},"Peng Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-3036-1507",{"name":184,"orcid":8},"A. I. Abdo",{"name":186,"orcid":187},"Liujun Xiao","https:\u002F\u002Forcid.org\u002F0000-0002-1900-1586",{"name":189,"orcid":190},"Hao Liang","https:\u002F\u002Forcid.org\u002F0000-0002-9955-6492",{"name":192,"orcid":193},"Xiaoqing Cui","https:\u002F\u002Forcid.org\u002F0000-0002-1970-5145",{"name":195,"orcid":8},"Xia Liang",{"name":197,"orcid":8},"Huiqing Bai",{"tldr":199,"method":200,"finding":201,"direction":202,"opportunity":203},"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。","2026-09-19T23:30:05.508592Z",{"id":206,"title":207,"url":208,"summary":209,"summary_zh":8,"content":8,"source_name":210,"source_url":8,"published_at":211,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":212,"score_detail":213,"sources":215,"tags":217,"search_phrases":222,"slug":225,"view_count":34,"doi":8,"paper":226,"created_at":234},2900,"Crop-GPA 2.0:安徽农业大学团队发布跨物种基因-表型预测深度学习工具,登The Crop Journal","https:\u002F\u002Fwww.global-agriculture.com\u002Fag-tech-research-news\u002Fchinese-researchers-launch-ai-tool-that-predicts-crop-gene-trait-links-across-species\u002F","安徽农业大学岳振宇教授团队(第一作者高玉佳)开发的Crop-GPA 2.0跨物种基因-表型预测深度学习工具于2026年9月10日发表在The Crop Journal。该工具通过分层基因组表征、跨物种预训练、性状感知学习三种联动技术,在水稻、玉米、小麦测试中跨性状(产量、抗病性、抗逆性、籽粒品质)均优于现有方法,且能在新物种或训练数据有限时保持较好性能,在线平台为育种者提供免费SNP排名服务,极大降低标记辅助选择门槛。","Global Agriculture \u002F The Crop Journal","2026-09-14T00:00:00Z",83,{"impact":68,"substance":147,"depth":16,"authority":18,"freshness":55,"relevant":20,"comment":214},"跨物种基因-表型预测工具登核心期刊，方法新颖、结论可靠，对智能育种有实质推动，值得进入每日精选。",[216],{"name":210,"url":208},[115,218,25,219,220,221],"农业人工智能","智能育种","玉米","基因-表型预测",[223,224],"安徽农业大学 Crop-GPA 2.0","Crop-GPA 2.0 基因-表型预测","安徽农业大学Crop-GPA2.0-2900",{"doi":8,"openalex_id":8,"authors":227,"venue":8,"cited_by_count":34,"oa_url":8,"card":228,"direction":97,"ingested_from":233},[],{"tldr":229,"method":230,"finding":231,"direction":97,"opportunity":232},"安徽农业大学团队发布跨物种基因-表型预测深度学习工具Crop-GPA 2.0，并在水稻、玉米、小麦中","分层基因组表征、跨物种预训练、性状感知学习，基于多物种SNP与表型数据。","跨性状预测均优于现有方法，新物种或小样本下仍表现良好，并提供免费在线SNP排名服务。","可探索将跨物种预训练模型与田间表型组、环境数据融合，提升复杂性状预测与育种决策的可解释性。","agent","2026-09-19T00:06:08.595080Z",{"id":236,"title":237,"url":238,"summary":239,"summary_zh":8,"content":8,"source_name":240,"source_url":8,"published_at":64,"category":241,"cover_url":8,"hotness":12,"is_selected":143,"score":144,"score_detail":242,"sources":245,"tags":247,"search_phrases":251,"slug":254,"view_count":34,"doi":8,"paper":8,"created_at":255},2897,"湖北活力中国调研行:稻功芯育种芯片+高蛋白玉米(13.2%)+全球首个稻米造血一类创新药","https:\u002F\u002Fwww.cinic.org.cn\u002Fxy\u002Fgdcj\u002F1653313.html","9月14日,2026活力中国调研行湖北主题采访活动情况介绍会召开。湖北首次发现水稻玉米增产关键基因并解析水稻耐高温机制,入选《科学》2025年度十大突破;国内首个水稻主效功能基因分子模块育种液相芯片稻功芯打破国外知识产权壁垒;水稻新品种E两优2300小面积超高产攻关田平均亩产超900公斤;全球首个稻米造血一类创新药获批上市,实现年产100万支注射液规模化量产;高蛋白玉米蛋白含量达13.2%,每提升1个百分点可减少进口大豆近800万吨。","中国产业经济信息网\u002F科技日报","报道",{"impact":243,"substance":146,"depth":16,"authority":69,"freshness":55,"relevant":20,"comment":244},28,"湖北多项种业与生物制造突破集中亮相，含全球首个稻米造血一类新药等硬核数据，产业级价值突出，值得进入每日精选。",[246],{"name":240,"url":238},[25,248,249,220,250],"种业振兴","生物育种","农业科技",[252,253],"湖北 稻功芯 育种芯片","高蛋白玉米 13.2%","湖北稻功芯育种芯片-2897","2026-09-19T00:06:07.538826Z",{"id":257,"title":258,"url":259,"summary":260,"summary_zh":8,"content":8,"source_name":261,"source_url":8,"published_at":10,"category":241,"cover_url":8,"hotness":12,"is_selected":143,"score":144,"score_detail":262,"sources":265,"tags":267,"search_phrases":271,"slug":274,"view_count":34,"doi":8,"paper":8,"created_at":275},2883,"中国农科院作科所万建民院士团队解析水稻抽穗信号桥梁基因,登The Plant Cell","https:\u002F\u002Fnew.qq.com\u002Frain\u002Fa\u002F20260918A068DI00","近日,万建民院士领衔的中国农科院作科所作物功能基因组研究创新团队解析了关键基因介导水稻光周期信号传递、精准调控水稻抽穗期的分子机制。发现了串联上下游抽穗信号的桥梁基因,该基因编码蛋白可在细胞核中与成花素蛋白互作,通过负反馈调控精准把控水稻抽穗时间;启动子自然变异可分为不同功能单倍型,早抽穗单倍型经人工选择已在高纬度短生长季稻区广泛应用,完善了水稻抽穗调控分子网络。","中国农业科学院作物科学研究所\u002F腾讯新闻",{"impact":146,"substance":147,"depth":16,"authority":263,"freshness":19,"relevant":20,"comment":264},15,"国家级团队在核心期刊发表的水稻抽穗调控分子机制突破，兼具学术价值与育种应用前景，值得进入每日精选。",[266],{"name":261,"url":259},[25,248,268,269,270],"分子育种","抽穗期","光周期",[272,273],"万建民 水稻 抽穗期 基因","中国农科院作科所 水稻 光周期","万建民水稻抽穗期基因-2883","2026-09-19T00:06:06.090532Z"]