[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1457":3,"related-1457":70},{"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":22,"tags":24,"primary_topic":29,"secondary_topics":35,"classification":36,"search_phrases":64,"slug":67,"view_count":68,"doi":8,"paper":8,"created_at":69},1457,"农民日报 9 月 3 日头版头条：洪水刚退\"药方\"到田——吉林梅河口姜连武千亩稻田秋粮田管纪实","https:\u002F\u002Fszb.farmer.com.cn\u002Fnmrb\u002Fhtml\u002F2026\u002F20260903\u002F20260903_1\u002Fnmrb_20260903_13395_1_2095256867819327504.html","9 月 3 日农民日报头版头条报道：吉林梅河口市曙光镇西太平村种粮户姜连武的千亩稻田在汛情最急时部分地块被水没过苗顶。洪水一退，中国水稻研究所、吉林省农业科学院专家踩着泥巴查苗情，给农户送上\"药方\"。灌浆初期可喷施芸苔素内酯、磷酸二氢钾，缓解生育期推迟影响；在降雨更猛的辽源市东丰县横道河镇（24 小时降雨 220 毫米），农技人员指导农户抓紧排水清田、同步做好防病防虫。技术下沉到地头后，灾害损失往下降、丰收底气往上涨。",null,"农民日报·头版要闻","2026-09-02T22:00:00Z","报道",10,false,77,{"impact":16,"substance":17,"depth":18,"authority":18,"freshness":19,"relevant":20,"comment":21},20,18,15,9,1,"央媒头版报道灾后农技下沉，体现政策与技术结合，对秋粮稳产有指导意义。",[23],{"name":9,"url":6},[25,26,27,28],"农技服务","防灾减灾","秋粮生产","田间管理",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[],{"status":37,"locked":13,"version":38,"method":39,"evidence":40},"classified","2026-10-22.2","rules",{"candidates":41,"agricultural_evidence":44},[42,49,54,59],{"code":30,"score":43,"supported":44,"title":45,"abstract":47,"tags":48},14,true,[46],"稻田",[32,46],[],{"code":50,"score":20,"supported":13,"title":51,"abstract":52,"tags":53},"G1",[],[],[25],{"code":55,"score":20,"supported":13,"title":56,"abstract":57,"tags":58},"H7",[],[],[26],{"code":60,"score":20,"supported":13,"title":61,"abstract":62,"tags":63},"J4",[],[],[27],[65,66],"农技服务 田间管理 秋粮生产 防灾减灾","农技服务 田间管理","农技服务田间管理秋粮生产防灾减灾-1457",2,"2026-09-03T00:06:40.100940Z",{"total":71,"page":20,"page_size":71,"items":72},6,[73,115,162,213,276,310],{"id":74,"title":75,"url":76,"summary":77,"summary_zh":8,"content":78,"source_name":79,"source_url":8,"published_at":80,"category":81,"cover_url":8,"hotness":12,"is_selected":44,"score":14,"score_detail":82,"sources":87,"tags":89,"primary_topic":91,"secondary_topics":92,"classification":93,"search_phrases":109,"slug":112,"view_count":17,"doi":113,"paper":8,"created_at":114},185,"生成式人工智能赋能水稻系统农场级农技咨询的综述","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1808028\u002Ffull","原文题名：Bridging Agronomic Science and Context Specific Farm-Level Advisory through Generative AI for Rice Systems in India。论文综述生成式AI、知识图谱、作物模拟器、卫星与无人机影像结合的农技咨询路径，强调专业语料、数据主权、基础设施和“人在回路”是可靠落地的前提。研究对象主要为印度水稻系统，属于综述和框架讨论，未提供统一田间效果验证。","## Abstract\n\nAgriculture is increasingly characterized by a data paradox, while the sector generates massive volumes of genomic, climatic, remote sensing, and field data. Translating this information into actionable, farm-level insights remains a critical bottleneck. Traditional advisory mechanisms cannot operate at the spatial scales or provide the context-specificity needed for climate adaptation and food security. The work presents GenAI as a transformative interface that makes advanced agricultural science, including crop simulation models and remote sensing diagnostics, accessible to rice farmers in India through natural language, provided that challenges related to data sovereignty, infrastructure gaps, and the need for human oversight in advisory systems are effectively addressed. We trace the shift from static, rule-based expert systems to dynamic foundation models capable of complex reasoning and multimodal analysis. The paper critically reviewed the rise of crop-specific LLM agro-advisory model architectures, such as SeedLLM-Rice and IPM-AgriGPT, which outperform general-purpose models by utilizing specialized scientific corpora to reduce hallucinations. Main attention is on the hybrid integration framework of LLMs with Knowledge Graphs (KGs) for factual grounding, and connecting with process-based simulators (e.g., DSSAT, APSIM) to make biophysical modelling more accessible through natural language. Furthermore, the manuscript examines the fusion of satellite, drone, and smartphone imagery with vision-language models, enabling real-time, context-aware diagnostics for smallholder farmers. The review concludes that while GenAI has the potential to support more equitable forms of precision agriculture, it can only do so under specific conditions. Its success depends on resolving infrastructure gaps, ensuring data sovereignty, and maintaining a “human-in-the-loop” architecture to guarantee scientific rigour and social inclusion.\n\nGraphical Abstract\n\n## 1 Introduction\n\n### 1.1 Background\n\nAgriculture is operating under the combined pressures of ensuring food security, adapting to climate variability, and reducing environmental impacts, which will shape food systems in the coming decades ([Sapkota et al., 2025](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1808028\u002Ffull#ref147)). By 2050, the world’s agricultural systems will need to grow food for an estimated 10 billion people apart from adapting to unpredictable changes in climate patterns and achieving sustainable intensification that preserves natural resources and the environment ([Wise, 2013](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1808028\u002Ffull#ref175)). This convergence of demographic pressure, environmental uncertainty, and sustainability imperatives demands transformative innovation in how agricultural knowledge is generated, disseminated, and applied across diverse farming contexts. Traditional agricultural advisory systems face fundamental limitations despite their historical contributions ([Manimozhi and Krishnamoorthy, 2025](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1808028\u002Ffull#ref97)). Extension services operate at a limited scale with delayed responses to emerging threats due to low advisor-to-farmer ratios ([Bonilla et al., 2024](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1808028\u002Ffull#ref23)). Generic recommendations fail to address diverse farm conditions, particularly affecting underserved smallholder farmers in developing regions ([Tumwebaze et al., 2025](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1808028\u002Ffull#ref163); [Prajapati et al., 2025](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1808028\u002Ffull#ref137)). The digital revolution has transformed agriculture’s data landscape through IoT sensors, satellite imagery, and genomic sequencing ([Birner et al., 2021](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1808028\u002Ffull#ref20); [Marudamuthu et al., 2023](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1808028\u002Ffull#ref100)). However, translating these massive, varied datasets into practical, actionable insights for farmers remains extraordinarily difficult.\n\nGenerative AI (GenAI) fundamentally differs from traditional machine learning approaches in agriculture ([Sai et al., 2025](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1808028\u002Ffull#ref145)). While conventional systems focus on prediction and classification, including forecasting yields, detecting diseases, and optimizing irrigation ([Paudel et al., 2021](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1808028\u002Ffull#ref129); [Bharadiya et al., 2023](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1808028\u002Ffull#ref15)), GenAI employs large language models (LLMs), diffusion models, and generative adversarial networks to generate content, facilitate reasoning, and enable natural conversations ([Shaikh et al., 2025](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1808028\u002Ffull#ref150); [Yang et al., 2025a](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1808028\u002Ffull#ref181)). Unlike standard deep learning models that merely estimate disease likelihood from images, GenAI systems engage farmers conversationally, explain reasoning, integrate multi-source knowledge, provide personalized advice, and generate synthetic training data for data-limited conditions ([Sai et al., 2025](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1808028\u002Ffull#ref145); [Chirra et al., 2025](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1808028\u002Ffull#ref35)).\n\nIndia’s rice production faces critical sustainability challenges that threaten the nation’s food security ([Gathorne-Hardy et al., 2016](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1808028\u002Ffull#ref51)). [Kumar et al. (2021)](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1808028\u002Ffull#ref87) documented that long-term intensive rice cultivation in northern Indian plains has led to severe deterioration of natural resources, declining factor productivity, multiple nutrient deficiencies, depleting groundwater, and labor scarcity. The rice-wheat cropping system in the Indo-Gangetic Plains shows particularly alarming trends, with groundwater tables declining approximately one meter per year while crop productivity stagnates ([Khedwal et al., 2023](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1808028\u002Ffull#ref83)). Rice demand in India is expected to rise sharply by mid-century, while resource degradation and environmental stresses threaten sustainable production. GenAI presents transformative solutions to address these multifaceted challenges. The AgriLLM initiative, developed by Consultative Group on International Agricultural Research (CGIAR) in partnership with AI71, provides region-aware, role-specific responses supporting farmers, advisors, and decision-makers with multilingual capabilities ([CGIAR, 2025](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1808028\u002Ffull#ref29)). GenAI is not here to replace what farmers already know, but to be a powerful helper that makes expert farming advice accessible to everyone. It breaks down technical barriers, allowing small farmers who may not read well or use technologies to ask questions in their own language and receive informed answers ([Kumar et al., 2025](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1808028\u002Ffull#ref86); [Sai et al., 2025](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1808028\u002Ffull#ref145); [Chirra et al., 2025](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1808028\u002Ffull#ref35)). It does not replace local agricultural advisors, but it helps them reach more farmers more quickly with up-to-date information and tools.\n\n### 1.2 Scope and objectives\n\nThis review addresses the central research question: What is the current landscape of generative AI applications for rice systems in India, and how do these applications bridge agronomic science with farm-level advisory delivery? This scoping review establishes clear boundaries, providing focused yet comprehensive coverage of generative AI applications in agriculture and food systems. While the agricultural challenges faced are universal, this analysis examines global trends with a particular focus on rice production systems. Rice serves as the staple food for more than half of humanity, predominantly in Asia, Africa, and Latin America, where smallholder farming remains the dominant production model ([Asma et al., 2023](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1808028\u002Ffull#ref7); [Mohapatra et al., 2025](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1808028\u002Ffull#ref113)). The recent development of SeedLLM, a specialised large language model for rice cultivation, exemplifies how crop-specific AI systems are emerging to address the unique agronomic complexities of individual commodities ([Lu et al., 2024](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1808028\u002Ffull#ref96); [Yang et al., 2025a](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1808028\u002Ffull#ref181)). This focus allows us to explore both the technical innovations and practical implementation challenges that characterize the frontier of agricultural AI development.\n\nThe geographic scope of this review is centred on Indian agricultural contexts but draws on international evidence where Indian-specific literature is sparse, including from related institutional bodies such as the International Rice Research Institute (IRRI), CGIAR, and the Food and Agriculture Organization (FAO) [CABI (2024)](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1808028\u002Ffull#ref26). Of the 183 sources included in the scoping synthesis, 25.7% directly address Indian agricultural contexts, while the remainder contribute methodological, architectural, or international policy perspectives that inform a synthesis with implications for Indian smallholder rice systems. This India-centred framing with transferable international evidence is consistent with the realities of an emerging research field in which crop-specific generative AI work for any single national context remains, by necessity, a small fraction of the global literature.\n\nTemporally, this work concentrates on the transformer era in AI, beginning with the architectural breakthroughs that enable modern LLMs and extending through the present moment. Since around 2017, model capabilities have proliferated, foundation models have emerged, and methods for domain adaptation have advanced significantly. By focusing on this transformative period, the analysis demonstrates how advances in general-purpose AI were adapted and refined for agricultural applications. Earlier rule-based expert systems and traditional machine-learning methods are mentioned only to provide historical context and to highlight the major shift introduced by generative artificial intelligence.\n\nThis manuscript outlines five objectives illustrating agricultural AI development and future directions. It traces evolution from early rule-based systems through machine learning, deep learning, to modern generative AI, emphasizing how innovations have transformed agricultural knowledge while highlighting risks of losing valuable earlier approaches. The review examines crop-specific LLMs like SeedLLM for rice versus broad foundation models, discussing domain adaptation, data requirements, and specialization trade-offs. It presents integration frameworks combining AI tools, knowledge graphs, sensing systems, and crop simulation models, and acknowledging development complexities. The global policy environment is evaluated, with particular emphasis on India’s agricultural AI, offering lessons on regulation, investment, digital access, and smallholder farmer benefits. Finally, it identifies transformative opportunities like personalized advisory systems and accelerated crop improvement, while addressing challenges including data gaps, infrastructure limitations, technical issues, and adoption barriers. Together, these objectives map the current landscape of generative AI in agricultural advisory, expose integration gaps across sensing, knowledge, model, interface, and governance layers, and identify the institutional and policy questions that will shape responsible deployment in Indian rice systems.\n\n## 2 Materials and methods\n\nIn this scoping study, a comprehensive search approach has been employed to encompass both formal research studies and practical examples of generative AI in agriculture. Because the field is advancing at an exponential rate, important innovations often appear first on preprint servers, blogs, or open-source platforms before they are published in peer-reviewed journals. This indicates that our approach extends beyond conventional academic databases to capture the full range of relevant evidence in this fast-moving field. We conducted structured searches across multiple complementary databases to ensure comprehensive coverage of the literature. Web of Science and Scopus provided access to established peer-reviewed literature, capturing research that has undergone formal quality assessment. Google Scholar has extended its reach to include conference proceedings, technical reports, and publications from regional journals that may not be indexed in primary services. To maintain reproducibility, Google Scholar results were limited to the top 180 relevance-ranked records, consistent with established scoping review practice ([Haddaway et al., 2015](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1808028\u002Ffull#ref57)). Recognizing that cutting-edge AI research often appears first as preprints, we systematically searched arXiv’s computer science and quantitative biology sections, as well as bioRxiv, for agricultural applications with computational components. This multi-database approach enabled coverage across formally vetted academic literature, emerging preprint research, and grey literature ([Figure 1](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1808028\u002Ffull#fig1)).\n\nFigure 1\n\nThe keyword strategy struck a balance between specificity and comprehensiveness to identify relevant literature. Core search terms included “large language model agriculture,” “generative AI crop,” “agricultural knowledge graph,” “multimodal agricultural AI,” and “crop simulation LLM.” The related terms-, such as “foundation model farming,” “transformer agriculture,” “conversational AI advisory,” “diffusion model crop breeding,” and crop-specific queries like “rice language model” and “wheat phenotype generation,” were supplemented. Named-system queries were also included to capture specific crop-adapted models reported in the field, such as “AgriLLM,” “AgriGPT,” “SeedLLM,” “AgriLLaVA,” “AgroLLM.” Boolean operators enabled the combination of domain terms (such as agriculture, farming, crop, and agronomy) with AI technology terms (LLM, generative AI, GPT, transformer, and diffusion model) to capture interdisciplinary work. The study primarily focused on literature from 2020 to 2026, a period marked by rapid growth in generative AI capabilities and agricultural applications. This timeframe encompasses the transition from early transformer models to sophisticated foundation models, as well as their domain adaptation for agricultural applications. Selected literature from 1980 to 2019 provides essential background on the evolution from rule-based expert systems to conventional machine learning approaches and establishes the historical context necessary to understand the paradigm shift that generative AI represents. Grey literature formed a crucial component of the evidence base, reflecting the reality that much innovation in applied AI occurs outside traditional academic publishing. Systematically reviewed reports from bodies such as the Ministry of Agriculture and Farmers Welfare, the Department of Science and Technology, Government of India, and the Indian Space Research Institute, which capture policy perspectives and national strategic initiatives, are also part of the detailed scoping.\n\nSources were eligible for inclusion if they addressed generative AI, large language models, foundation models, diffusion models, or knowledge graph–integrated systems with relevance to agriculture, with priority given to rice, related cereals, and Indian agricultural contexts. Peer-reviewed journal articles, conference proceedings, substantive preprints, book chapters, and institutional grey literature were all included. Studies addressing agricultural AI without generative or foundation-model components, opinion pieces lacking technical substance, non-English publications, and duplicate records were excluded.\n\nRecords identified across all sources were exported and de-duplicated, after which a two-stage screening process was applied: title and abstract review against the eligibility criteria, followed by full-text assessment of records passing the first stage. Discrepancies were resolved through discussion among the authors. A total of 632 records were identified, of which 500 remained after duplicate removal. Title and abstract screening excluded 278 records, and a further 32 were excluded at full-text review, yielding 190 sources included in the scoping synthesis. The complete screening process is summarized in [Figure 1](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1808028\u002Ffull#fig1).\n\nDuring synthesis, included sources were organized according to a consistent set of characteristics to enable thematic comparison across the evidence base. These characteristics included publication type, the primary AI architecture or approach, the target crop and agricultural task, geographic focus, the stage of evaluation reported (distinguishing benchmark performance, experimental evaluation, and real-world deployment), and the principal limitations noted. Consistent with PRISMA-ScR guidance ([Tricco et al., 2018](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1808028\u002Ffull#ref162)), formal critical appraisal of individual sources was not undertaken, as the review aims to map the breadth of evidence and identify thematic patterns rather than to evaluate study-level methodological quality. Given the heterogeneity of the included sources, a narrative synthesis approach was adopted, supplemented by structured comparative tables and an integrative conceptual framework.\n\n## 3 An integrative framework for generative AI in rice advisory\n\nGenerative AI in agriculture is usually described as a series of separate advances: a crop-specific language model, a knowledge graph, a sensing pipeline, a chatbot, a policy paper. Each is useful alone, but the literature says little about how they connect into a working advisory system. A farmer benefits only when data from the field is structured, reasoned over, and returned as usable guidance. To organise the synthesis that follows, we propose a framework tha","Frontiers in Artificial Intelligence","2026-07-29T00:00:00Z","论文",{"impact":17,"substance":83,"depth":84,"authority":43,"freshness":85,"relevant":20,"comment":86},22,19,4,"综述系统梳理了生成式AI在农业咨询中的应用，对智慧农业有参考价值，但时效性稍差。",[88],{"name":79,"url":76},[90,25,32],"农业大模型",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":94},{"candidates":95,"agricultural_evidence":44},[96,100,104],{"code":30,"score":18,"supported":44,"title":97,"abstract":98,"tags":99},[32],[32,31],[32],{"code":50,"score":20,"supported":13,"title":101,"abstract":102,"tags":103},[],[],[25],{"code":105,"score":20,"supported":13,"title":106,"abstract":107,"tags":108},"I2",[],[],[90],[110,111],"农业大模型 农技服务 水稻","农业大模型 农技服务","农业大模型农技服务水稻-185","10.3389\u002Ffrai.2026.1808028\u002Ffull","2026-08-02T23:54:41.232179Z",{"id":116,"title":117,"url":118,"summary":119,"summary_zh":8,"content":120,"source_name":121,"source_url":8,"published_at":122,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":123,"score_detail":124,"sources":128,"tags":130,"primary_topic":136,"secondary_topics":137,"classification":138,"search_phrases":156,"slug":159,"view_count":160,"doi":8,"paper":8,"created_at":161},5450,"福建农科院水稻所「禾两优631」再生稻示范 两季亩产 1364.52 公斤","https:\u002F\u002Fwww.faas.cn\u002Fcms\u002Fhtml\u002Ffjsnykxy\u002F2026-10-19\u002F849994658.html","10 月 15 日，福建省农业科学院水稻所在尤溪县西城镇麻洋村举行「禾两优631」作再生稻高产栽培示范验收会，中国科学院韩斌、钱前院士等到场测产。经测产该品种再生季亩产 484.72 公斤，加上 8 月头季亩产 879.8 公斤，两季合计亩产 1364.52 公斤，是麻洋村基地连续第 26 年开展再生稻高产示范的新进展。「禾两优631」为两系杂交中稻，2025 年通过福建省中稻审定（闽审稻 20251009），抗白叶枯病、中抗稻瘟病、再生力强，2026 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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个省市审定或引种备案。湖北省农科院首席科学家游艾青表示，引入粳稻可打造差异化产品。","10月9日，湖北省农业科学院粮食作物研究所和南京农业大学水稻研究所联合邀请华中农业大学、湖北省种子管理局、湖北省农业科学院植保土肥研究所、黄冈市农业科学院、黄冈市农村专业技术协会等单位的专家，来到团风县回龙山镇梅家墩村三组，对优质食味粳稻新品种“宁香粳9号”百亩示范方进行现场测产。\n\n阳光下，“宁香粳9号”长势清秀、秆青籽黄、穗大粒饱、稻穗齐整。专家组观摩了百亩示范方后，随机抽取1个田块进行测产，确定采用收割机进行收割脱粒，当场装袋称重计产。\n\n团风县邦农水稻种植农民专业合作社负责人梅新华介绍，示范方面积112亩，前茬作物为油菜。今年5月30日按照用种量7公斤\u002F亩，实施无人机飞播。示范种植期间，水稻长势均衡、结实率高，无明显病虫危害。\n\n专家组通过取样测定杂质率和水分含量，最终，“宁香粳9号”百亩示范方稻谷折合标准产量为亩产805.25公斤。\n\n“宁香粳9号”是南京农业大学万建民院士团队选育的优质、高产、早熟的优质食味粳稻新品种。该品种不仅表现出产量高、米质优、抗病强等优点，还显著改善了软米的外观，米饭香、软、弹、滑，深受市场广泛欢迎，曾获第三届、第四届全国优质稻品种食味品质鉴定（粳稻）金奖。2023年被农业农村部认定为超级稻品种，成为农业农村部2023年、2024年水稻主导品种。已通过江苏、安徽、上海、湖北、浙江、云南、贵州、河南等8个省市的审定或引种备案。\n\n2024年，“宁香粳9号”落户湖北。分别在黄冈、仙桃、公安、潜江、枝江、当阳、孝昌、宜城、随州设立9个种植示范区。当年，黄冈市在黄湖农场示范方组织专家进行实地测产，平均亩产达811.6公斤。\n\n“‘宁香粳9号’产量高、米质优、口感好，每亩平均增收约400元。”黄冈市农科院院长涂军明介绍说，今年示范方生产的稻谷，已被当地大米加工企业订购。\n\n湖北是我国重要水稻产区，以籼稻为主。全省粳稻种植面积300万亩，约占中稻种植面积的十分之一。发展优质粳稻有基础、有优势、有潜力、有需求。\n\n“‘宁香粳9号’是‘十四五’国家农作物育种联合攻关成果，在我省进行引种，是助力水稻育种创新、品种结构调整、产业提质增效、满足消费升级的重要举措。”湖北省农科院首席科学家、研究员游艾青表示，目前“江汉大米”大多以长粒籼米为主。引入粳稻品种，能够打造差异化产品，丰富品牌产品线，提升区域公用品牌的竞争力。\n\n好品种要有好的栽培方法。下一步，省粮作所、南农大学水稻研究所将联合示范方，探索机插、精量条播等种植方式，为湖北粮农种植“宁香粳9号”等粳稻品种提供更多、更好的“技术支撑”。（湖北日全媒记者汪彤 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