[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4212":3,"related-4212":151},{"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":24,"tags":26,"primary_topic":33,"secondary_topics":38,"classification":39,"search_phrases":121,"slug":124,"view_count":125,"doi":126,"paper":127,"created_at":150},4212,"Transforming the rice–wheat system in India for climate resilience, resource-use efficiency, and food security","https:\u002F\u002Fdoi.org\u002F10.59797\u002Fija.v71.i2.5710","The rice–wheat cropping system (RWCS) of the Indo-Gangetic Plains is fundamental to India’s food and nutritional security, accounting for a substantial share of national rice and wheat production while sustaining the livelihoods of millions of smallholder farmers. However, decades of intensive cultivation have led to groundwater depletion, soil degradation, declining factor productivity, residue burning, greenhouse gas emissions, and increasing climate vulnerability. This review synthesizes recent advances in technologies, management practices, and institutional innovations for the sustainable transformation of the RWCS. It examines the major biophysical, socio-economic, and policy constraints limiting system performance. The greatest gains in productivity, profitability, resource-use efficiency, and environmental performance arise from integrated production systems that combine improved genetics, conservation agriculture, precision resource management, mechanization, digital technologies, and supportive institutions and policies. Future priorities should focus on scaling proven innovations through efficient seed and machinery delivery systems, climate-smart extension, digital decision support, climate finance, and coordinated water–energy–food governance. Long-term system research integrating agronomic, environmental, economic, and social dimensions will be essential for developing location-specific solutions and informing evidence-based policy. An integrated system approach offers the strongest pathway for enhancing climate resilience, improving resource-use efficiency, reducing environmental footprints, and securing India’s future food and nutritional security.","印度河-恒河平原的水稻-小麦种植制度（RWCS）是印度粮食和营养安全的基础，在全国水稻和小麦产量中占有相当大的份额，同时维系着数百万小农户的生计。然而，数十年的集约化种植导致了地下水枯竭、土壤退化、要素生产率下降、秸秆焚烧、温室气体排放以及气候脆弱性加剧。本文综述了RWCS可持续转型在技术、管理实践和制度创新方面的最新进展，考察了制约系统表现的主要生物物理、社会经济和政策限制因素。生产力、盈利能力、资源利用效率和环境绩效的最大提升，源于将改良遗传材料、保护性农业、精准资源管理、机械化、数字技术以及支持性制度和政策相结合的综合生产系统。未来优先事项应侧重于通过高效的种子和机械配送系统、气候智慧型推广服务、数字决策支持、气候融资以及协调的水-能源-粮食治理来推广已验证的创新成果。整合农学、环境、经济和社会维度的长期系统研究，对于制定因地制宜的解决方案和提供循证政策依据至关重要。综合系统方法为增强气候韧性、提高资源利用效率、减少环境足迹以及保障印度未来的粮食和营养安全提供了最强有力的路径。",null,"Indian Journal of Agronomy","2026-10-04T00:00:00Z","论文",10,false,83,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},22,20,18,14,9,1,"核心期刊综述，系统梳理印度稻麦轮作体系的气候韧性转型路径，对农业信息化与智慧农业决策支持有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31,32],"数字农业","粮食安全","保护性农业","气候韧性","水稻小麦轮作","资源利用效率",{"code":34,"slug":35,"name":36,"path":37,"parent_code":9},"A","field-crops","大田作物","\u002Ftopic\u002Ffield-crops",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"partial","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47,"reason":120},[45,53,60,67,73,81,88,95,102,109,115],{"code":46,"score":20,"supported":47,"title":48,"abstract":50,"tags":52},"A1",true,[49],"rice",[51,49],"水稻",[],{"code":54,"score":20,"supported":47,"title":55,"abstract":57,"tags":59},"A2",[56],"wheat",[58,56],"小麦",[],{"code":61,"score":62,"supported":47,"title":63,"abstract":65,"tags":66},"H7",12,[64],"climate resilience",[64],[30],{"code":68,"score":21,"supported":47,"title":69,"abstract":71,"tags":72},"A8",[70],"food security",[],[28],{"code":74,"score":75,"supported":47,"title":76,"abstract":77,"tags":80},"B7",6,[],[78,79],"温室","greenhouse",[],{"code":82,"score":75,"supported":47,"title":83,"abstract":84,"tags":87},"G2",[],[85,86],"小农户","smallholder",[],{"code":89,"score":75,"supported":47,"title":90,"abstract":91,"tags":94},"H2",[],[92,93],"土壤","soil",[],{"code":96,"score":97,"supported":47,"title":98,"abstract":99,"tags":101},"H8",4,[],[100],"conservation agriculture",[29],{"code":103,"score":104,"supported":47,"title":105,"abstract":106,"tags":108},"F5",3,[],[107],"机械化",[],{"code":110,"score":104,"supported":47,"title":111,"abstract":112,"tags":114},"I2",[],[113],"decision support",[],{"code":116,"score":22,"supported":14,"title":117,"abstract":118,"tags":119},"I1",[],[],[27],"一级领域明确，二级方向需要细分",[122,123],"印度 稻麦轮作 气候韧性","恒河平原 保护性农业 粮食安全","印度稻麦轮作气候韧性-4212",0,"10.59797\u002Fija.v71.i2.5710",{"doi":126,"openalex_id":128,"authors":129,"venue":10,"cited_by_count":125,"oa_url":141,"card":142,"direction":148,"ingested_from":149},"W7219812077",[130,133,135,137,139],{"name":131,"orcid":132},"Sudhanshu Singh","https:\u002F\u002Forcid.org\u002F0009-0001-4721-6971",{"name":134,"orcid":9},"VIRENDAR KUMAR",{"name":136,"orcid":9},"Ashish Kumar Srivastava",{"name":138,"orcid":9},"Eazhilkrishna Narayanasamy",{"name":140,"orcid":9},"Sunil Kumar","https:\u002F\u002Fpub.isa-india.in\u002Findex.php\u002Fija\u002Farticle\u002Fdownload\u002F7229\u002F5428\u002F12491",{"tldr":143,"method":144,"finding":145,"direction":146,"opportunity":147},"综述印度稻麦系统转型，提出整合技术、管理与政策以提升气候韧性和资源效率。","综述生物物理、社会经济与政策约束，整合遗传、保护性农业、数字技术等。","综合生产系统可最大提升生产力、利润、资源效率与环境表现。","农业绿色发展与碳","可研究数字决策支持与气候融资在稻麦系统规模化转型中的协同机制。","智慧农业 \u002F 农业物联网","openalex","2026-10-05T23:30:09.058853Z",{"total":75,"page":22,"page_size":75,"items":152},[153,256,353,425,505,558],{"id":154,"title":155,"url":156,"summary":157,"summary_zh":158,"content":9,"source_name":159,"source_url":156,"published_at":160,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":161,"score_detail":162,"sources":167,"tags":169,"primary_topic":174,"secondary_topics":175,"classification":176,"search_phrases":209,"slug":212,"view_count":125,"doi":213,"paper":214,"created_at":255},5550,"Integrated effects of fermented organic manure on soil fertility, productivity, and economic performance of the rice–wheat cropping system in Uttar Pradesh","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1968370","The rice–wheat cropping system (RWCS) is a major cereal-based production system in Uttar Pradesh, India, where sustaining crop productivity and soil fertility under intensive cultivation is a major challenge. This study evaluated the field-level performance of fermented organic manure (FOM) integrated with inorganic fertilizers in terms of crop productivity, selected soil fertility indicators, and economic returns during 2025–2026. A total of 224 frontline demonstrations (FLDs) were conducted through 25 Krishi Vigyan Kendras (KVKs) across Uttar Pradesh. The analysis included demonstrations with sufficiently complete and consistent field-level records, comprising 40 rice and 110 wheat demonstrations under farmers’ field conditions and 6 rice and 11 wheat demonstrations at KVK instructional farms. Seven nutrient management treatments were evaluated at KVK farms, compared with three treatments under farmers’ field conditions. Crop performance, post-harvest soil fertility indicators, and economic returns were assessed. The treatment combining 100% recommended inorganic fertilizers with 125% FOM (T₃) recorded the highest numerical means for most growth and yield attributes and grain yield in both crops. Compared with 100% recommended inorganic fertilizers alone, T₃ increased grain yield by 6.4–7.3% in rice and 10.8–12.7% in wheat. FOM-integrated treatments also showed higher numerical values of soil organic carbon (SOC) and available nitrogen, phosphorus, and potassium after the rice–wheat cropping cycle. Under T₃, SOC increased from 0.271% to 0.304% at KVK instructional farms and from 0.447% to 0.515% under farmers’ field conditions. Although T₃ generally recorded the highest numerical gross returns, the additional costs of FOM reduced net returns and benefit–cost (B) ratios in several cases. Under farmers’ field conditions, inorganic fertilizers alone recorded higher numerical net returns and B ratios for both crops. The findings indicate that integrating FOM with recommended inorganic fertilizers was associated with improved crop productivity and selected soil fertility indicators under the demonstrated rice–wheat systems. However, the economic advantage depended on the costs of FOM production, transportation, and application. As the demonstrations were participatory and lacked formal plot-level randomization and inferential statistical testing, the observed differences represent descriptive numerical responses rather than statistically established treatment effects. Further controlled trials and cost optimization are needed to establish the agronomic and economic viability of FOM-integrated nutrient management.","稻麦种植制度（RWCS）是印度北方邦以谷类作物为主的重要生产体系，在集约化种植下维持作物生产力与土壤肥力是该体系面临的主要挑战。本研究评估了发酵有机肥（FOM）与无机肥料配合施用在2025—2026年度对作物生产力、选定土壤肥力指标及经济效益的田间表现。研究通过北方邦25个农业科技推广中心（KVKs）开展了224项前线示范（FLDs）。分析纳入了田间记录足够完整且一致的示范，包括农民田块条件下的40项水稻和110项小麦示范，以及KVK教学农场的6项水稻和11项小麦示范。在KVK农场评估了7个养分管理处理，并与农民田块条件下的3个处理进行比较。评估了作物表现、收获后土壤肥力指标及经济效益。100%推荐无机肥料配合125% FOM的处理（T₃）在两种作物的多数生长和产量性状及籽粒产量上记录了最高的数值均值。与单施100%推荐无机肥料相比，T₃使水稻籽粒产量提高6.4%—7.3%，小麦提高10.8%—12.7%。整合FOM的处理在稻麦轮作周期后也表现出较高的土壤有机碳（SOC）及有效氮、磷、钾数值。在T₃处理下，KVK教学农场的SOC从0.271%提高到0.304%，农民田块条件下从0.447%提高到0.515%。尽管T₃总体上记录了最高的数值总收益，但FOM的额外成本在多种情况下降低了净收益和效益成本比（B）。在农民田块条件下，单施无机肥料在两种作物上均记录了较高的数值净收益和B比。研究结果表明，在示范的稻麦系统中，FOM与推荐无机肥料配合施用与作物生产力及选定土壤肥力指标的改善相关。然而，经济优势取决于FOM的生产、运输和施用成本。由于示范为参与式，缺乏正式的田块水平随机化和推断性统计检验，观察到的差异代表描述性数值响应，而非统计学上确立的处理","Frontiers in Sustainable Food Systems","2026-10-21T00:00:00Z",71,{"impact":163,"substance":19,"depth":164,"authority":165,"freshness":21,"relevant":22,"comment":166},16,15,13,"基于224个前线示范的实地数据，结论有参考价值，但缺乏随机化与统计检验，且为印度区域研究，属细分领域进展。",[168],{"name":159,"url":156},[170,171,172,173,31],"有机肥","土壤肥力","养分管理","印度农业",{"code":34,"slug":35,"name":36,"path":37,"parent_code":9},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":177},{"candidates":178,"agricultural_evidence":47,"reason":120},[179,183,187,191,198,204],{"code":89,"score":164,"supported":47,"title":180,"abstract":181,"tags":182},[93],[92,93],[171],{"code":46,"score":20,"supported":47,"title":184,"abstract":185,"tags":186},[49],[51,49],[],{"code":54,"score":20,"supported":47,"title":188,"abstract":189,"tags":190},[56],[58,56],[],{"code":192,"score":193,"supported":47,"title":194,"abstract":195,"tags":197},"F3",8,[],[196,172],"肥料",[172,170],{"code":199,"score":104,"supported":47,"title":200,"abstract":201,"tags":203},"A7",[],[202],"轮作",[],{"code":205,"score":22,"supported":14,"title":206,"abstract":207,"tags":208},"J3",[],[],[173],[210,211],"发酵有机肥 稻麦轮作","Uttar Pradesh 稻麦系统","发酵有机肥稻麦轮作-5550","10.3389\u002Ffsufs.2026.1968370",{"doi":213,"openalex_id":215,"authors":216,"venue":159,"cited_by_count":125,"oa_url":249,"card":250,"direction":146,"ingested_from":149},"W7223144837",[217,219,222,223,226,229,231,232,234,237,239,242,244,246],{"name":218,"orcid":9},"Raghwendra Singh",{"name":220,"orcid":221},"Ajay Kumar Singh","https:\u002F\u002Forcid.org\u002F0000-0002-8743-6477",{"name":220,"orcid":221},{"name":224,"orcid":225},"Preeti P. Yadav","https:\u002F\u002Forcid.org\u002F0000-0001-7843-9783",{"name":227,"orcid":228},"Seema Yadav","https:\u002F\u002Forcid.org\u002F0000-0002-9773-6641",{"name":230,"orcid":9},"Abhishek Singh",{"name":230,"orcid":9},{"name":233,"orcid":9},"Saurabh Raj Pandey",{"name":235,"orcid":236},"Vinod Kumar Singh","https:\u002F\u002Forcid.org\u002F0009-0004-2583-6565",{"name":238,"orcid":9},"Narendra Raghubanshi",{"name":240,"orcid":241},"Yash Pal Singh","https:\u002F\u002Forcid.org\u002F0000-0001-8345-9084",{"name":243,"orcid":9},"Mahesh Kumar",{"name":245,"orcid":9},"Shivangi Singh",{"name":247,"orcid":248},"Reena Kumari","https:\u002F\u002Forcid.org\u002F0000-0002-1101-5473","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1968370\u002Fpdf",{"tldr":251,"method":252,"finding":253,"direction":146,"opportunity":254},"评估发酵有机肥配施化肥对稻麦轮作产量、土壤肥力与经济收益的综合影响。","印度北方邦224个农户与KVK示范基地田间示范，比较7种养分管理处理。","全量化肥配125%发酵有机肥增产稻6.4-7.3%、麦10.8-12.7%并提升有机碳，但净收益常因","可研究有机肥成本分摊与碳交易补偿机制，量化其经济可行性与土壤固碳协同效益。","2026-10-22T11:20:16.098808Z",{"id":257,"title":258,"url":259,"summary":260,"summary_zh":261,"content":9,"source_name":262,"source_url":259,"published_at":263,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":264,"score_detail":265,"sources":267,"tags":269,"primary_topic":274,"secondary_topics":275,"classification":276,"search_phrases":321,"slug":324,"view_count":325,"doi":326,"paper":327,"created_at":352},3886,"Impact of high day and night temperatures on major food crops: A comprehensive review","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.14740","Understanding plant responses to both day and night heat is essential for developing climate smart agriculture systems to sustain global food production. High daytime (> 35 °C) and nighttime (> 25 °C) temperatures are increasingly threatening global food production. Major crops such as rice, wheat, maize, soybean and barley are especially vulnerable during flowering and grain filling. However, the distinct and combined effects of day versus night heat on crop performance remain underexplored in a comparative manner. This review synthesises findings from recent physiological, biochemical and molecular studies on heat stress responses in staple food crops. It compares the impacts of high day temperature and high night temperature on photosynthesis, respiration, reproductive development, oxidative stress, antioxidant defenses, grain yield and quality. Data from field experiments, controlled environment studies and meta-analyses are integrated. Daytime heat impairs photosynthesis and carbon assimilation, while nighttime heat increases respiration rates, depletes carbohydrate reserves and disrupts nutrient partitioning. Both conditions reduce pollen viability and spikelet fertility, causing yield losses of 5–40 % depending on the crop and growth stage. Heat stress triggers reactive oxygen species accumulation, leading to cellular damage and membrane lipid peroxidation. Antioxidant systems (superoxide dismutase, catalase, ascorbate peroxidase) provide only partial protection under prolonged heat. Crop-specific vulnerabilities include grain chalkiness in rice, reduced kernel weight in maize and impaired nodulation in legumes. Breeding tools such as clustered regularly interspaced short palindromic repeats\u002F CRISPR-associated protein 9 (CRISPR\u002FCas9) and agronomic practices like adjusted sowing dates and optimised irrigation help mitigate these effects. This review uniquely compares daytime and nighttime heat effects across multiple staple crops, integrating physiological, molecular and agronomic perspectives with quantitative yield data.","理解植物对白天和夜间高温的响应，对于发展气候智慧型农业系统以维持全球粮食生产至关重要。白天高温（>35 °C）和夜间高温（>25 °C）正日益威胁全球粮食生产。水稻、小麦、玉米、大豆和大麦等主要作物在开花和灌浆期尤其脆弱。然而，白天高温与夜间高温对作物表现的独立效应和联合效应，仍缺乏比较性的深入研究。本文综述了近年来关于主粮作物热胁迫响应的生理学、生物化学和分子学研究结果，比较了白天高温和夜间高温对光合作用、呼吸作用、生殖发育、氧化胁迫、抗氧化防御、籽粒产量和品质的影响，并整合了田间试验、可控环境研究和荟萃分析的数据。白天高温损害光合作用和碳同化，而夜间高温则提高呼吸速率、消耗碳水化合物储备并扰乱养分分配。两种条件均会降低花粉活力和小穗结实率，导致产量损失5%–40%，具体取决于作物种类和生育阶段。热胁迫触发活性氧积累，导致细胞损伤和膜脂过氧化。抗氧化系统（超氧化物歧化酶、过氧化氢酶、抗坏血酸过氧化物酶）在持续高温下仅能提供部分保护。作物特异性脆弱性包括水稻的垩白粒、玉米的粒重降低以及豆科作物的结瘤受损。成簇规律间隔短回文重复序列\u002FCRISPR相关蛋白9（CRISPR\u002FCas9）等育种工具以及调整播期、优化灌溉等农艺措施有助于缓解这些影响。本综述独特地比较了白天和夜间高温对多种主粮作物的影响，将生理学、分子和农艺学视角与定量产量数据相结合。","Plant Science Today","2026-09-30T00:00:00Z",77,{"impact":19,"substance":18,"depth":19,"authority":165,"freshness":193,"relevant":22,"comment":266},"系统比较昼夜高温对主要粮食作物的影响，数据翔实，对气候智慧型农业与耐热育种有参考价值。",[268],{"name":262,"url":259},[270,28,271,272,273],"智慧农业","耐热性","气候变化","作物育种",{"code":34,"slug":35,"name":36,"path":37,"parent_code":9},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":277},{"candidates":278,"agricultural_evidence":47,"reason":120},[279,283,287,294,301,308,313,317],{"code":46,"score":75,"supported":47,"title":280,"abstract":281,"tags":282},[],[51,49],[],{"code":54,"score":75,"supported":47,"title":284,"abstract":285,"tags":286},[],[58,56],[],{"code":288,"score":75,"supported":47,"title":289,"abstract":290,"tags":293},"A3",[],[291,292],"玉米","maize",[],{"code":295,"score":75,"supported":47,"title":296,"abstract":297,"tags":300},"A4",[],[298,299],"大豆","soybean",[],{"code":302,"score":75,"supported":47,"title":303,"abstract":304,"tags":307},"H3",[],[305,306],"灌溉","irrigation",[],{"code":116,"score":97,"supported":47,"title":309,"abstract":310,"tags":312},[],[311],"smart agriculture",[270],{"code":68,"score":22,"supported":14,"title":314,"abstract":315,"tags":316},[],[],[28],{"code":61,"score":22,"supported":14,"title":318,"abstract":319,"tags":320},[],[],[272],[322,323],"水稻 小麦 玉米 高温胁迫","CRISPR 耐热育种","水稻小麦玉米高温胁迫-3886",5,"10.14719\u002Fpst.14740",{"doi":326,"openalex_id":328,"authors":329,"venue":262,"cited_by_count":125,"oa_url":259,"card":347,"direction":148,"ingested_from":149},"W7214983452",[330,333,335,338,341,344],{"name":331,"orcid":332},"S Devadharshini","https:\u002F\u002Forcid.org\u002F0009-0002-1913-2970",{"name":334,"orcid":9},"N K Sathyamoorthy",{"name":336,"orcid":337},"S. Kokilavani","https:\u002F\u002Forcid.org\u002F0000-0003-3548-6146",{"name":339,"orcid":340},"V. Manivannan","https:\u002F\u002Forcid.org\u002F0000-0002-1623-0877",{"name":342,"orcid":343},"K. Chandrakumar","https:\u002F\u002Forcid.org\u002F0000-0002-6905-722X",{"name":345,"orcid":346},"K. Vanitha","https:\u002F\u002Forcid.org\u002F0000-0002-3447-3813",{"tldr":348,"method":349,"finding":350,"direction":146,"opportunity":351},"综述比较了白天与夜间高温对水稻、小麦、玉米、大豆和大麦等主粮作物的生理、分子及产量影响。","整合田间试验、控制环境研究和荟萃分析，比较昼夜高温对光合、呼吸、生殖和产量的影响","白天高温抑制光合，夜间高温增加呼吸消耗，两者均降低花粉活力和结实率，导致5-40%减产。","可探究昼夜高温交互效应及作物特异性耐热机制，开发基于CRISPR和智能农艺的缓解策略。","2026-10-01T23:30:09.973225Z",{"id":354,"title":355,"url":356,"summary":357,"summary_zh":9,"content":358,"source_name":359,"source_url":9,"published_at":360,"category":361,"cover_url":9,"hotness":13,"is_selected":47,"score":362,"score_detail":363,"sources":366,"tags":368,"primary_topic":372,"secondary_topics":373,"classification":374,"search_phrases":419,"slug":422,"view_count":423,"doi":9,"paper":9,"created_at":424},2811,"《全国种植业发展\"十五五\"规划》全文印发:2030年粮食综合生产能力达1.45万亿斤左右","https:\u002F\u002Fnync.pingliang.gov.cn\u002Fzfxxgk\u002Ffdzdgknr\u002Flzyj\u002Fsjzc\u002Fart\u002F2026\u002Fart_e2a08515b7f94c689d80f5bc4d8b5a2e.html","农业农村部9月7日印发《全国种植业发展\"十五五\"规划》,明确到2030年,我国粮食综合生产能力达1.45万亿斤左右,大豆、油料、棉花、糖料产能巩固提升。规划在发挥农业新质生产力引领作用方面作出部署:选育推广突破性品种、推广水稻智能育秧与精准灌溉、推广气力式精量播种机与大载荷植保无人机等先进机型、加强智能化场景推广应用,并部署构建苗情、墒情、病虫情、灾情数据库增强防灾减灾能力。","## **农业农村部关于印发《全国种植业发展“十五五”规划》的通知**\n\n各省、自治区、直辖市农业农村（农牧）厅（局、委），新疆生产建设兵团农业农村局：\n\n为贯彻落实《中华人民共和国国民经济和社会发展第十五个五年规划纲要》、《加快农业农村现代化“十五五”规划》有关部署，科学引领“十五五”种植业高质量发展，增强粮食等重要农产品供给保障能力，我部编制了《全国种植业发展“十五五”规划》。现印发给你们，请结合实际，认真组织实施。\n\n农业农村部\n\n2026年9月7日\n\n[![Image 1: 图片29.png](https:\u002F\u002Fnync.pingliang.gov.cn\u002Fcms_files\u002Ffilemanager\u002F1111806365\u002Fpicture\u002F20268\u002FS6847b378f40147e9ad8a38d755a71496-800.png)](https:\u002F\u002Fnync.pingliang.gov.cn\u002Fcms_files\u002Ffilemanager\u002F1111806365\u002Fpicture\u002F20268\u002F6847b378f40147e9ad8a38d755a71496.png)[](https:\u002F\u002Fnync.pingliang.gov.cn\u002Fzfxxgk\u002Ffdzdgknr\u002Flzyj\u002Fsjzc\u002Fart\u002F2026\u002Fart_e2a08515b7f94c689d80f5bc4d8b5a2e.html)\n\n[![Image 2: 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图片40.png](https:\u002F\u002Fnync.pingliang.gov.cn\u002Fcms_files\u002Ffilemanager\u002F1111806365\u002Fpicture\u002F20268\u002FS17bce7b119d249c9a14b90efa47638bb-800.png)](https:\u002F\u002Fnync.pingliang.gov.cn\u002Fcms_files\u002Ffilemanager\u002F1111806365\u002Fpicture\u002F20268\u002F17bce7b119d249c9a14b90efa47638bb.png)[](https:\u002F\u002Fnync.pingliang.gov.cn\u002Fzfxxgk\u002Ffdzdgknr\u002Flzyj\u002Fsjzc\u002Fart\u002F2026\u002Fart_e2a08515b7f94c689d80f5bc4d8b5a2e.html)\n\n[![Image 13: 图片41.png](https:\u002F\u002Fnync.pingliang.gov.cn\u002Fcms_files\u002Ffilemanager\u002F1111806365\u002Fpicture\u002F20268\u002FS1d704feb9cf4489ba11ac9c670f2e4c4-800.png)](https:\u002F\u002Fnync.pingliang.gov.cn\u002Fcms_files\u002Ffilemanager\u002F1111806365\u002Fpicture\u002F20268\u002F1d704feb9cf4489ba11ac9c670f2e4c4.png)[](https:\u002F\u002Fnync.pingliang.gov.cn\u002Fzfxxgk\u002Ffdzdgknr\u002Flzyj\u002Fsjzc\u002Fart\u002F2026\u002Fart_e2a08515b7f94c689d80f5bc4d8b5a2e.html)\n\n[![Image 14: 图片42.png](https:\u002F\u002Fnync.pingliang.gov.cn\u002Fcms_files\u002Ffilemanager\u002F1111806365\u002Fpicture\u002F20268\u002FS4c400a690e5348a585246dbe7e0c7726-800.png)](https:\u002F\u002Fnync.pingliang.gov.cn\u002Fcms_files\u002Ffilemanager\u002F1111806365\u002Fpicture\u002F20268\u002F4c400a690e5348a585246dbe7e0c7726.png)[](https:\u002F\u002Fnync.pingliang.gov.cn\u002Fzfxxgk\u002Ffdzdgknr\u002Flzyj\u002Fsjzc\u002Fart\u002F2026\u002Fart_e2a08515b7f94c689d80f5bc4d8b5a2e.html)\n\n扫一扫在手机打开当前页","农业农村部 \u002F 平凉市农业农村局","2026-09-07T00:00:00Z","政策",89,{"impact":364,"substance":17,"depth":19,"authority":164,"freshness":75,"relevant":22,"comment":365},28,"农业农村部印发的全国种植业五年规划，明确2030年粮食综合生产能力1.45万亿斤目标，属国家级政策重磅文件，权威性与信息增量高，值得进入每日精选。",[367],{"name":359,"url":356},[270,369,28,370,371],"十五五规划","种植业","产能提升",{"code":34,"slug":35,"name":36,"path":37,"parent_code":9},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":375},{"candidates":376,"agricultural_evidence":47,"reason":120},[377,381,385,391,397,401,406,410,414],{"code":46,"score":104,"supported":47,"title":378,"abstract":379,"tags":380},[],[51],[],{"code":295,"score":104,"supported":47,"title":382,"abstract":383,"tags":384},[],[298],[],{"code":386,"score":104,"supported":47,"title":387,"abstract":388,"tags":390},"A6",[],[389],"棉花",[],{"code":392,"score":104,"supported":47,"title":393,"abstract":394,"tags":396},"F4",[],[395],"植保",[],{"code":302,"score":104,"supported":47,"title":398,"abstract":399,"tags":400},[],[305],[],{"code":61,"score":104,"supported":47,"title":402,"abstract":403,"tags":405},[],[404],"防灾减灾",[],{"code":68,"score":22,"supported":14,"title":407,"abstract":408,"tags":409},[],[],[28],{"code":116,"score":22,"supported":14,"title":411,"abstract":412,"tags":413},[],[],[270],{"code":415,"score":22,"supported":14,"title":416,"abstract":417,"tags":418},"J1",[],[],[369],[420,421],"十五五规划 产能提升 智慧农业 粮食安全","十五五规划 产能提升","十五五规划产能提升智慧农业粮食安全-2811",2,"2026-09-18T00:03:24.338983Z",{"id":426,"title":427,"url":428,"summary":429,"summary_zh":430,"content":9,"source_name":159,"source_url":428,"published_at":431,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":432,"score_detail":433,"sources":435,"tags":437,"primary_topic":442,"secondary_topics":443,"classification":444,"search_phrases":477,"slug":480,"view_count":104,"doi":481,"paper":482,"created_at":504},2762,"Food-supply resilience under policy, market, and biosecurity shocks: a diagnostic of import vulnerability, supplier substitution, and short-run callable capacity in RCEP agri-food networks","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1924874","Introduction Policy, market, and biosecurity shocks test whether regional trade networks can maintain physical food availability when established sourcing channels are disrupted. This narrower dimension of food security is termed food-supply resilience. This study treats the Regional Comprehensive Economic Partnership (RCEP) as a policy-relevant regional supply space and examines whether its agri-food networks contain short-run callable capacity for import-vulnerable economies. The analysis advances the claim that connectivity is not capacity: trade links support resilience only when they connect vulnerable importers to suppliers with callable surplus, stable ties, and usable access. Methods This study constructs 126 commodity-year networks for six agri-food categories over 2001–2021 using CEPII-BACI trade data, with FAOSTAT quantities used as an independent cross-source check. Import vulnerability is measured through supplier dependence, concentration, and tie stability. Supplier substitution is assessed through regional supplier persistence, lower-bound callable surplus, gross-export upper-bound coverage, and shock-specific access conditions. Post-2021 observations are used as descriptive monitoring checks rather than as agreement-effect estimates. Results Regional Comprehensive Economic Partnership agri-food networks became more connected, but resilience profiles diverged by commodity. Rice was the only distributed-supplier profile. Milk-powder dairy and palm oil combined large regional surplus with exporter cores concentrated around New Zealand and Australia, and Indonesia and Malaysia, respectively. Soybean and pork remained weak regional-substitution cases, while selected tropical and subtropical fruit was retained only as a compositional regional-trade profile. China showed a centrality paradox: it became more central where regional alternatives already existed but remained peripheral in soybean and pork, where extra-regional dependence mattered most. Lower-bound and gross-export upper-bound checks confirmed pervasive regional substitution gaps in soybean and pork across eligible importer profiles, not only for China. The Philippines-rice case showed the contrasting profile of an import-vulnerable economy backed by substantial regional surplus. Discussion The shock episodes support the diagnostic. Rice export restrictions, Indonesia’s palm-oil export ban, and the African swine fever pork shock show that regional networks support food-supply resilience only when vulnerable importers are connected to suppliers with callable surplus, stable ties, and usable access. Regional agreements can reduce frictions and improve coordination, but they do not automatically create deployable supply. Food-security governance should therefore monitor commodity-importer vulnerability, callable surplus, gross-export upper bounds, tie persistence, and access readiness rather than treating network connectivity as resilience.","引言 政策、市场和生物安全冲击检验了区域贸易网络在既有采购渠道中断时能否维持实体粮食可获得性。这一较窄维度的粮食安全被称为粮食供应韧性。本研究将《区域全面经济伙伴关系协定》（RCEP）视为具有政策相关性的区域供应空间，并考察其农食网络是否对进口脆弱型经济体包含短期可调用能力。分析提出，连通性不等于能力：贸易联系只有在将脆弱进口方连接到拥有可调用盈余、稳定联系和可用准入的供应方时，才能支撑韧性。方法 本研究利用CEPII-BACI贸易数据构建了2001—2021年间六大农食类别的126个商品—年份网络，并使用FAOSTAT数量数据作为独立的跨来源校验。进口脆弱性通过供应方依赖、集中度和联系稳定性来衡量。供应方替代通过区域供应方持续性、下限可调用盈余、总出口上限覆盖率和冲击特定准入条件来评估。2021年后的观察结果被用作描述性监测检查，而非协定效应估计。结果 RCEP农食网络变得更为连通，但韧性特征因商品而异。大米是唯一呈现分布式供应方特征的商品。奶粉乳制品和棕榈油分别将大规模区域盈余与集中在新西兰和澳大利亚、印度尼西亚和马来西亚的出口核心相结合。大豆和猪肉仍是区域替代较弱的案例，而部分热带和亚热带水果仅作为构成性区域贸易特征得以保留。中国表现出中心性悖论：在区域替代品已经存在的地方，中国变得更为中心，但在大豆和猪肉这些域外依赖最为重要的领域，中国仍处于边缘。下限和总出口上限检查证实，在符合条件的进口方特征中，大豆和猪肉普遍存在区域替代缺口，不仅限于中国。菲律宾—大米案例则展示了另一种特征：一个进口脆弱型经济体得到大量区域盈余的支撑。讨论 冲击事件支持了这一诊断。大米出口限制、印度尼西亚棕榈油出口禁令和非洲猪瘟猪肉冲击表明，区域网络支撑粮食供应韧性","2026-09-16T00:00:00Z",80,{"impact":19,"substance":17,"depth":19,"authority":165,"freshness":21,"relevant":22,"comment":434},"基于CEPII-BACI与FAOSTAT构建126个商品-年份网络，提出“连通性不等于可调用产能”的诊断框架，对大豆、猪肉等进口脆弱品类有实质结论，但属学术论文，产业落地影响有限。",[436],{"name":159,"url":428},[28,298,438,439,440,441],"RCEP","供应链韧性","农产品贸易","猪肉",{"code":34,"slug":35,"name":36,"path":37,"parent_code":9},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":445},{"candidates":446,"agricultural_evidence":47,"reason":120},[447,452,456,460,466,472],{"code":295,"score":448,"supported":47,"title":449,"abstract":450,"tags":451},7,[],[298,299],[298],{"code":68,"score":448,"supported":47,"title":453,"abstract":454,"tags":455},[],[28,70],[28],{"code":46,"score":104,"supported":47,"title":457,"abstract":458,"tags":459},[],[49],[],{"code":461,"score":104,"supported":47,"title":462,"abstract":463,"tags":465},"C1",[],[464],"swine",[],{"code":467,"score":104,"supported":47,"title":468,"abstract":469,"tags":471},"C5",[],[470],"非洲猪瘟",[],{"code":473,"score":22,"supported":14,"title":474,"abstract":475,"tags":476},"E5",[],[],[440],[478,479],"供应链韧性 农产品贸易 粮食安全 大豆","供应链韧性 农产品贸易","供应链韧性农产品贸易粮食安全大豆-2762","10.3389\u002Ffsufs.2026.1924874",{"doi":481,"openalex_id":483,"authors":484,"venue":159,"cited_by_count":125,"oa_url":497,"card":498,"direction":502,"ingested_from":149},"W7213434130",[485,487,490,492,494],{"name":486,"orcid":9},"Guo Ru",{"name":488,"orcid":489},"Bolu Wei","https:\u002F\u002Forcid.org\u002F0009-0003-4854-2036",{"name":491,"orcid":9},"Mingrong Zeng",{"name":493,"orcid":9},"Pan Liu",{"name":495,"orcid":496},"Bixiang Shi","https:\u002F\u002Forcid.org\u002F0000-0003-2034-1710","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1924874\u002Fpdf",{"tldr":499,"method":500,"finding":501,"direction":502,"opportunity":503},"基于RCEP农食贸易网络，诊断进口脆弱性、供应商替代与短期可调用产能。","用CEPII-BACI 2001–2021年126个商品-年份网络，FAOSTA","RCEP连通性增强但韧性分化，大豆和猪肉普遍存在区域替代缺口。","农业人工智能与决策模型","可构建可调用剩余与准入条件的动态预警模型，识别替代缺口商品。","2026-09-17T23:30:08.362367Z",{"id":506,"title":507,"url":508,"summary":509,"summary_zh":9,"content":510,"source_name":511,"source_url":9,"published_at":512,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":9,"score_detail":9,"sources":513,"tags":515,"primary_topic":520,"secondary_topics":521,"classification":522,"search_phrases":553,"slug":556,"view_count":97,"doi":9,"paper":9,"created_at":557},711,"基于无人机多光谱的玉米与大豆盐胁迫响应指数及盐分管理干预比较评估","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769","北京市农林科学院联合中国农业科学院烟草研究所在山东滨海盐渍区开展 14 块玉米与 13 块大豆试验,基于 UAV 多光谱与偏最小二乘路径建模(PLS-PM)融合基线土壤盐分与叶面积指数、株高、SPAD、覆盖度,构建盐胁迫响应指数 SSRI;SSRI 与产量呈显著负相关(r=−0.670 至 −0.764),EN 模型在玉米拔节期 R²=0.774、PLSR 在开花期 R²=0.819,生成的空间 SSRI 地图可捕捉田间异质性,为盐渍农业 UAV 作物胁迫监测与精准管理干预评估提供概念验证框架。","## 1. Introduction\n\nSoil salinization is one of the major abiotic stressors constraining sustainable agricultural development worldwide [[1](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B1-agriculture-16-01769)]. Excessive soil salinity not only impairs water and mineral nutrient uptake and transport but also directly suppresses photosynthetic activity, thereby inducing coordinated changes in a range of morphological and physiological traits, including plant height, leaf area, chlorophyll content, and dry matter accumulation. These changes ultimately manifest as distinct phenotypic characteristics, such as inhibited plant growth and altered canopy spectral properties [[2](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B2-agriculture-16-01769),[3](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B3-agriculture-16-01769)]. Traditionally, salt stress has been diagnosed through manual field observations based on phenotypic traits, whereby crop growth status is visually assessed using geometric characteristics, morphology, and color. However, this approach is labor-intensive, time-consuming, and often yields results with limited spatial representativeness. More importantly, the pronounced spatial heterogeneity of soil salinity leads to highly discontinuous and patchy crop growth patterns that cannot be adequately captured by conventional field observations [[4](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B4-agriculture-16-01769)]. In China, where saline cropland is widely distributed, considerable efforts have been devoted to developing chemical, biological, and agronomic amelioration strategies. The effectiveness of these approaches has typically been evaluated using field-based observations of crop growth, yield, and soil properties. Nevertheless, their practical performance under conditions of pronounced spatial variability remains insufficiently understood [[5](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B5-agriculture-16-01769)]. Therefore, accurate and efficient monitoring of salt stress through the response of crops is essential for providing a scientific basis for optimizing soil amelioration and crop management strategies, improving the utilization efficiency of saline cropland, and identifying effective amelioration practices.\n\nCurrent remote sensing approaches for salinity monitoring have been developed across multiple platforms. Satellite sensors provide large-scale and long-term observations but are often limited by spatial resolution when characterizing field-scale salinity heterogeneity. UAV platforms provide centimeter-level spatial information and flexible data acquisition, making them suitable for crop-scale stress monitoring; however, most UAV-based studies still rely on individual spectral indices or limited indicators. Although hyperspectral and ground-based measurements can provide detailed physiological information, their operational efficiency and scalability remain limited. Therefore, integrating multi-dimensional crop responses with UAV observations remains necessary for more comprehensive salt stress characterization. At the crop scale, UAV multispectral observations can capture stress-related changes in canopy structure and pigment status through variations in spectral reflectance, providing a basis for quantitative monitoring of crop salt stress [[6](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B6-agriculture-16-01769)]. Stress-induced response in crop internal structure and chlorophyll content can lead to regular variations in reflectance at specific spectral bands, which lays the theoretical foundation for using UAV-based multispectral data to monitor salt stress and provides a technical means for objectively evaluating the effectiveness of soil amelioration. To date, various abiotic and biotic stresses, such as drought, nitrogen deficiency, and disease, have been extensively explored in major crops, including maize, soybean, and wheat [[7](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B7-agriculture-16-01769),[8](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B8-agriculture-16-01769),[9](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B9-agriculture-16-01769),[10](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B10-agriculture-16-01769),[11](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B11-agriculture-16-01769),[12](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B12-agriculture-16-01769),[13](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B13-agriculture-16-01769)]. For salt stress monitoring, Zhao et al. used biomass as an indicator and constructed an alfalfa salt stress index with UAV multispectral data, realizing quantitative diagnosis of alfalfa salt stress [[14](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B14-agriculture-16-01769)]. Shi et al. established an evaluation system for saline soil improvement effectiveness using UAV-derived leaf area index at the jointing stage of winter wheat [[15](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B15-agriculture-16-01769)]. Although UAV-based stress monitoring has evolved from single spectral indicators to multi-trait crop responses, existing composite indices often determine trait contributions based on statistical variance, empirical assumptions, or yield-related relationships, rather than the response relationships between crops and environmental stressors. This limitation is particularly relevant because crop growth indicators exhibit distinct sensitivities and response dynamics under salt stress. Previous studies have shown that salt stress initially affects leaf area index, followed by fractional vegetation cover and photosynthetically active radiation, whereas physiological indicators such as leaf water content and chlorophyll-related traits generally respond at later stages [[16](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B16-agriculture-16-01769)]. These asynchronous responses highlight the difficulty of accurately characterizing salt stress based on individual traits, which may fail to capture the integrated effects of salinity on crop morphological structure and physiological function [[17](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B17-agriculture-16-01769)]. Therefore, an index that integrates multiple crop indicators according to their responses to soil salinity is needed to better characterize crop salt stress and facilitate comparative assessment of management practices.\n\nThis study develops a salt stress response index (SSRI) by integrating multiple crop growth indicators according to their responses to soil salinity. We hypothesized that integrating multiple growth indicators could provide a more comprehensive characterization of crop salt stress than individual traits and that UAV-based SSRI retrieval performance would vary across crop growth stages due to differences in canopy development and stress expression. The specific objectives of this study are to (i) quantify the responses of key crop growth traits to soil salinity; (ii) identify salt stress-sensitive spectral features and develop models for SSRI retrieval; and (iii) compare crop salt stress responses under different management practices using the proposed SSRI.\n\n## 2. Materials and Methods\n\n### 2.1. Study Area\n\nThe study area is located at the experimental base of the Shandong Yellow River Delta Agricultural High-tech Industry Demonstration Zone, China, spanning 118°50′–119°03′ E and 37°36′–38°17′ N ([Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#fig_body_display_agriculture-16-01769-f001)). This region is representative of a coastal saline soil transition zone, where chloride-dominated saline soils prevail and are characterized by high salinity and pronounced spatial heterogeneity. The area experiences a warm-temperate, semi-humid continental monsoon climate, with a mean annual temperature of 13.3 °C, an accumulated temperature above 10 °C of approximately 4300 °C, and an average annual precipitation of 537 mm. The local cropping system follows a two-year, three-crop rotation, primarily consisting of maize, soybean, and winter wheat. Baseline soil physicochemical properties were characterized to describe the initial soil conditions. The 0–20 cm soil layer had a pH of 8.93, electrical conductivity of 1716 μS cm−1, bulk density of 1.55 g cm−3, organic carbon content of 7.81 g kg−1, available phosphorus of 7.3 mg kg−1, and available potassium of 204.4 mg kg−1. The soil texture consisted of 16.97% sand, 61.30% silt, and 21.73% clay. As a key research base for saline land improvement and sustainable agricultural production, this region provides a critical setting for remote sensing-based monitoring of salt stress. As shown in [Table 1](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#table_body_display_agriculture-16-01769-t001), 14 experimental plots were established for maize and another 14 for soybean, each measuring 15 m × 50 m. Each management practice was assigned to one experimental plot.\n\n**Figure 1.** Geographic location of the study area and distribution of sampling sites. (**a**) Location of the Shandong Yellow River Delta Agricultural High-tech Industry Demonstration Zone. (**b**) Location of the experimental sites. (**c**) Distribution of the experimental treatments and sampling sites.\n\n**Table 1.** Experimental treatments for maize and soybean.\n\nThe experimental management practices consisted of soil amendment practices, cultivar selection, and agronomic management practices. For maize, the evaluated practices included shared practices (Treatments 1–10) and maize-specific practices (Treatments M1–M4). Treatment 1 represented conventional management and served as the reference practice, whereas Treatment 2 received 75% of the conventional fertilizer rate. Treatments 3–10 involved soil amendment practices, including a waste-derived soil conditioner, phosphorus-rich mineral amendment, and other additive measures. The maize-specific practices included cultivar selection (M1 and M2, Zhongdan 1158 and Zhongdan 1191), ridge-furrow planting (M3), and rhizosphere application of a composite microbial agent (M4). Except for M1 and M2, all maize practices used Zhengdan 958 as the cultivar. For soybean, Treatments 1–10 followed the same shared management practices as those applied to maize, whereas Treatments S1–S4 represented cultivar selections involving four soybean varieties. These management practices represented different approaches for regulating crop growth conditions under saline environments. Notably, soybean Treatment 9 was excluded from subsequent analyses because this plot underwent separate replanting during the growing season due to field management issues. The replanting resulted in inconsistent crop establishment and growth timing compared with the other plots, making it unsuitable for direct comparison. The evaluated practices originated from different research teams and were compared exploratorily under the field conditions of this study.\n\nMaize was sown on 22 May 2024 and harvested on 2 October 2024 at a density of 82,500 plants ha−1; soybean was sown on 30 May 2024 and harvested on 15 October 2024 at a density of 210,000 plants ha−1. All plots received the same irrigation management, whereas Treatment 2 differed from the other practices in fertilizer application rate. The conventional fertilizer regime was 140 kg N ha−1, 60 kg P 2 O 5 ha−1, and 60 kg K 2 O ha−1 for maize, and 60 kg N ha−1, 75 kg P 2 O 5 ha−1, and 60 kg K 2 O ha−1 for soybean. All fertilizers were applied as basal fertilizer by broadcasting followed by rotary tillage before sowing, and Treatment 2 received 75% of the conventional fertilizer rate. After sowing, one irrigation event was conducted using drip irrigation belts with an irrigation amount of 60 mm. No further irrigation was applied during the growing season, and the irrigation water salinity was 484 mg L−1.\n\n### 2.2. Data Acquisition and Processing\n\n#### 2.2.1. Soil Sampling Data\n\nThree sampling sites were established within each plot to capture within-plot spatial variability. Soil samples were collected in May 2024, prior to crop sowing and following the seasonal peak of soil salt accumulation, and were used to characterize baseline soil salinity. Three sampling sites were evenly distributed within each plot ([Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#fig_body_display_agriculture-16-01769-f001)). At each sampling site, three surface soil samples (0–20 cm) were collected and thoroughly mixed to form one composite sample. Therefore, each plot contained three independent soil sampling locations, and the resulting composite samples were used for soil salinity (SC) determination. Accordingly, 42 SC observations for maize and 39 SC observations for soybean were obtained and used in subsequent analyses. The samples were air-dried, passed through a 2 mm sieve, and prepared as soil–water extracts at a soil-to-water ratio of 1:5. After shaking for 5 min and allowing the extracts to stand for 8 h, electrical conductivity (EC) was measured using a FE38 conductivity meter (Mettler Toledo, Zurich, Switzerland). The measured EC values (EC 1:5, mS\u002Fcm) were converted to soil salt content (SC, g\u002Fkg) using the following conversion equation established for the same saline soil region and soil salinity type [[18](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B18-agriculture-16-01769)]: SC = 2.9863·EC 1:5 − 0.1019. The equation was established based on saline soils with SC ranging from 0.78 to 39.54 g\u002Fkg.\n\nThe soil salt content across the experimental plots ranged from 0.79 to 2.72 g\u002Fkg. According to the Chinese soil salinity classification standard [[19](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B19-agriculture-16-01769)], the soils were classified as non-saline (\u003C1 g\u002Fkg), slightly saline (1–2 g\u002Fkg), and moderately saline (2–4 g\u002Fkg). No artificial salt gradient was imposed in this study. Instead, the baseline salinity of the experimental site encompassed the typical range of soil salinity found in regional maize and soybean production areas [[20](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B20-agriculture-16-01769)]. For each plot, the reported SC value was calculated as the mean of three sampling sites within the plot.\n\n#### 2.2.2. Crop Sampling Data\n\nCrop growth measurements were collected at each sampling site on 18 July and 23 August 2024. These dates corresponded to the maize jointing stage and soybean branching stage and to the flowering stage of both crops, respectively. A total of 42 sets of growth measurements were collected for maize and 39 for soybean at each sampling campaign.\n\nGiven the relatively small size of each experimental plot, all measurements were obtained using non-destructive in situ methods to avoid disturbing crop growth and maintain the integrity of the field experiment. Four key growth indicators were measured directly in the field: leaf area index (LAI), plant height (PH), relative chlorophyll content (SPAD), and fractional vegetation cover (FVC). These indicators were selected because of their sensitivity to salt stress and their suitability for retrieval from UAV-based remote sensing data. LAI characterizes canopy density and is closely associated with photosynthetic productivity [[21](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B21-agriculture-16-01769)]. FVC reflects horizontal canopy coverage and effectively captures reductions in canopy cover caused by salt stress [[22](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B22-agriculture-16-01769)]. SPAD provides an indirect measure of leaf chlorophyll content and serves as an indicator of salt-induced changes in photosynthetic physiology [[23](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B23-agriculture-16-01769),[24](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B24-agriculture-16-01769)]. PH, a fundamental morphological trait, is highly responsive to salt stress and complements the assessment of crop growth status [[25](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B25-agriculture-16-01769),[26](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B26-agriculture-16-01769)]. Collectively, these four indicators represent complementary aspects of crop structure, function, and physiology, while all can be retrieved from remote sensing data, making them well suited for field-scale monitoring.\n\nSpecifically, LAI was measured using an LAI-2200C Plant Canopy Analyzer (LI-COR Biosciences, Lincoln, NE, USA). Three measurements were taken at each sampling site and averaged to obtain the site-level LAI value. SPAD was measured using a SPAD-502Plus Chlorophyll Meter (Konica Minolta, Tokyo, Japan). Readings were collected from the midpoint of the fully expanded functional leaf of 10 randomly selected plants at each sampling site. PH was determined by measuring the vertical distance from the soil surface to the apex of 10 randomly selected plants at each site using a ruler.\n\nFVC was estimated using the digital photography method described by [[27](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B27-agriculture-16-01769)]. Three RGB images were captured along the diagonal of each sampling site using a Huawei P20 smartphone camera (1632 × 1224 pixels) with the camera positioned approximately 2 m above the canopy under nadir viewing geometry. Images were processed in MATLAB R2016b (MathWorks Inc., Natick, MA, USA) by converting RGB images into the CIE L*a*b* color space. The a* channel was used to separate vegetation from background, and vegetation pixels were extracted using a fixed threshold (a* = −8.0). FVC was calculated as the proportion of vegetation pixels within each image, and the mean value of the three images was used as the site-level FVC.\n\nAt crop maturity, grain yield was determined at both sampling-site and plot scales through manual harvesting. For site-level yield measurement, 10 representative plants were harvested at each sampling site, whereas the entire experimental plot was harvested for plot-level yield determination. After threshing and cleaning, the grains were air-dried to constant weight, and yield was expressed as kg\u002Fha based on dry grain weight.\n\n#### 2.2.3. UAV Multispectral Data\n\nUAV multispectral imagery was acquired on the same dates as the field measurements using a DJI Phantom 4 Multispectral (P4M) UAV (SZ DJI Technology Co., Shenzhen, China). The basic parameters of the P4M camera are summarized in [Table 2](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#table_body_display_agriculture-16-01769-t002). Flights were conducted between 10:00 and 14:00 under stable illumination conditions with a flight speed of 3 m s−1. Before each flight, radiometric calibration was performed using the DJI P4M reflectance calibration panel. The UAV was flown at an altitude of 40 m, resulting in a ground sampling distance (GSD) of approximately 2.1 cm, with 75% forward and side overlaps. The RTK positioning module of the UAV was enabled during data acquisition for image georeferencing. Images were acquired with the multispectral camera oriented nadir to the ground. Orthomosaic generation, radiometric processing, and inter-band alignment were performed using DJI Terra to generate reflectance mosaics for the blue, green, red, red-edge, and near-infrared bands, with a spatial resolution of approximately 2.1 cm. Regions of interest (ROIs) corresponding to each sampling site were delineated as 3 m × 3 m square areas around the sampling locations in ENVI 5.3. The mean reflectance values of each spectral band within each ROI were extracted for subsequent analysis.\n\n**Table 2.** Basic parameters of the P4M camera.\n\n### 2.3. Method\n\nThis study developed a framework for estimating crop salt stress and exploring differences among salinity-management practices by integrating UAV remote sensing, crop growth measurements, and soil data, as illustrated in [Figure 2](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#fig_body_display_agriculture-16-01769-f002). First, partial least squares path modeling (PLS-PM) was used to derive relative weights for LAI, PH, SPAD, and FVC based on their relationships with baseline soil salinity, and these weighted indicators were integrated to con","Agriculture 2026, 16(16), 1769 (MDPI) 2026-08-18","2026-08-18T00:00:00Z",[514],{"name":511,"url":508},[27,516,517,518,519],"农业无人机","精准农业","多光谱","盐胁迫",{"code":34,"slug":35,"name":36,"path":37,"parent_code":9},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":523},{"candidates":524,"agricultural_evidence":47,"reason":120},[525,530,534,540,545,549],{"code":288,"score":526,"supported":47,"title":527,"abstract":528,"tags":529},11,[291],[291],[],{"code":295,"score":526,"supported":47,"title":531,"abstract":532,"tags":533},[298],[298],[],{"code":535,"score":193,"supported":47,"title":536,"abstract":538,"tags":539},"I3",[537],"无人机多光谱",[],[],{"code":386,"score":104,"supported":47,"title":541,"abstract":542,"tags":544},[],[543],"烟草",[],{"code":89,"score":104,"supported":47,"title":546,"abstract":547,"tags":548},[],[92],[],{"code":116,"score":423,"supported":14,"title":550,"abstract":551,"tags":552},[],[],[27,517],[554,555],"农业无人机 数字农业 精准农业 多光谱","农业无人机 数字农业","农业无人机数字农业精准农业多光谱-711","2026-08-18T23:59:19.258819Z",{"id":559,"title":560,"url":561,"summary":562,"summary_zh":9,"content":563,"source_name":564,"source_url":9,"published_at":565,"category":361,"cover_url":9,"hotness":13,"is_selected":14,"score":566,"score_detail":567,"sources":569,"tags":571,"primary_topic":577,"secondary_topics":578,"classification":579,"search_phrases":603,"slug":606,"view_count":22,"doi":9,"paper":9,"created_at":607},298,"云南省9部门联合印发《加快补上粮油产地烘干能力短板行动实施方案》——构建「中心带动+节点覆盖+移动补充+共享共用」三级烘干网络","https:\u002F\u002Fnync.yn.gov.cn\u002Fhtml\u002F2026\u002Fzuixinwenjian_0805\u002F1427908.html","云南省农业农村厅等9部门2026年8月4日联合印发《云南省加快补上粮油产地烘干能力短板行动实施方案》,承接农业农村部等9部委《加快补上粮油产地烘干能力短板行动方案》(农机发〔2026〕2号)部署。聚焦玉米、水稻等主要粮油作物,构建\"烘干中心+烘干点+移动式烘干设施\"三级烘干网络,力争到\"十五五\"末,全省粮油产地烘干能力显著提升、粮油品质和附加值明显提高。","（一）优化粮油产地烘干能力布局。结合各地地形地貌、种植结构、产业基础和交通条件，科学编制粮油产地烘干能力建设规划，构建“烘干中心+烘干点+移动式烘干设施”三级烘干网络。常年玉米播种面积超过30万亩的宣威、镇雄等35个县（市、区），水稻播种面积超过10万亩的腾冲、广南等20个县（市、区），重点依托已建成的粮食烘干中心（点），发挥其辐射能力强、功能完善的服务体系作用，承担大规模、标准化烘干及配套服务；在其他县（市、区）和粮油生产相对集中乡镇，合理布局中型化、专业化烘干中心（点），服务辖区内粮油生产。在偏远山区、交通不便区域，大力推广移动式烘干设施，开展流动服务、应急作业，服务小农户粮油生产。按照“宜大则大、宜小则小、合理覆盖”原则，进一步明确不同区域的粮食烘干中心（点）服务半径与建设规模，避免重复建设与资源浪费，形成布局合理、供需匹配、运行高效的烘干设施格局。\n\n（二）培育粮油产地烘干提质意识。强化宣传引导与技术培训，多渠道、多形式普及粮油烘干提质减损知识，提升广大农户、新型农业经营主体和基层干部对产地烘干重要性的认识。充分利用广播电视、新媒体平台、乡村大喇叭、现场观摩等方式，宣传烘干技术在减少霉变、提升品质、增加收益等方面的显著成效，破除“靠天晾晒”的传统观念，引导主动应用烘干设施。将烘干技术培训纳入新型职业农民培育、农机手培训等项目，分批次开展实操培训，推广先进适用烘干工艺与操作规范，提升科学烘干水平。培育一批烘干提质示范主体，总结推广典型经验，发挥示范引领作用，营造重视烘干、应用烘干、提质增效的良好氛围。\n\n（三）提升粮食烘干中心（点）效能。推进现有粮食烘干中心（点）改造升级，完善烘干、清选、仓储、运输、检测等配套设施，补齐设备短板，提升标准化、智能化、一体化服务能力。支持粮食烘干中心（点）拓展服务范围，从单一烘干向“烘干+清选+仓储+加工+销售+技术指导”综合服务延伸，为农户提供全链条粮食产后服务。建立健全运营管理机制，鼓励采用“合作社+农户”“企业+基地+农户”等模式，与小农户建立紧密利益联结机制，通过订单作业、托管服务、分红返利等方式，带动农户共享发展收益。推进粮食烘干中心（点）信息化建设，实现供需对接、智能调度、远程监控、质量追溯，提高运营效率与服务便捷度，打造高效便民的粮食产后服务综合体。\n\n（四）大力推广环保烘干设施设备。坚持绿色发展导向，大力推广节能环保、清洁高效的烘干设施设备，加快淘汰高污染、高能耗老旧装备。因地制宜推广固定式、移动式、共享式三类烘干设施。在产粮大县规模化建设固定式烘干中心，配备循环式、连续式高效烘干机，满足大规模烘干需求；在丘陵山区、偏远地区推广移动式烘干设备，灵活开展流动烘干作业；鼓励各地通过设施改进、技术优化等方式，将现有的果蔬、坚果等烘干设备改造为粮油烘干设备，实现一改两用，提高设备利用率，提升烘干产业全链条效益。“十五五”期间，重点推广生物质、天然气、电能等清洁能源烘干设备，支持对现有燃煤烘干设施进行环保改造，加装除尘、脱硫或生物质燃烧机等环保装置，确保达标排放。\n\n（五）提升平急两用综合服务效益。统筹平时服务与应急保障，推动粮油烘干设施平急两用、综合利用，提升设施综合效益与应急保障能力。依托区域农机社会化服务中心、现代农事综合服务中心、农机应急救灾队伍等，平时烘干设施重点保障粮油正常烘干作业，提供优质高效产后服务；在洪涝、阴雨、霉变等灾害发生后，快速开展抢烘作业，最大限度减少灾害损失，保障粮食安全。建立平急转换机制，完善应急预案，加强应急演练，储备充足烘干物资，确保应急时快速响应、高效运转。鼓励烘干主体拓展功能，在非烘干季节开展种子加工、农产品初加工、农资仓储、农机停放等业务，提高设施利用率与经营效益，实现“一设施、多用途，平急结合、综合增效”。\n\n（六）一体推进“玉米机收+烘干+仓储”。聚焦玉米主产区，大力推进玉米机械化收获、产地烘干、科学仓储一体化发展，打通玉米生产“最后一公里”，实现全程机械化、标准化、高效化。推广先进适用玉米联合收获机，提升机收率，减少收获环节损失；同步配套建设玉米烘干设施，确保收获后及时烘干，避免霉变发芽。完善玉米仓储设施，建设标准化粮仓，配备通风、测温、防潮等设备，实现烘干后科学储存，保障粮食品质。加强机收、烘干、仓储各环节衔接，建立联动机制，推广“机收即运、运到即烘、烘好即储”一条龙服务模式，提高作业效率，降低生产成本。培育一体化服务主体，支持农机合作社、粮食企业等提供“机收+烘干+仓储”全程托管服务，带动小农户融入现代农业发展链条。","云南省农业农村厅 2026-08-04","2026-08-04T00:00:00Z",78,{"impact":18,"substance":17,"depth":19,"authority":62,"freshness":75,"relevant":22,"comment":568},"省级政策，构建三级烘干网络，内容具体，有实质条款，时效稍差。",[570],{"name":564,"url":561},[572,28,573,574,575,576],"十五五","云南","粮油产地烘干","农机化","9部门联合",{"code":34,"slug":35,"name":36,"path":37,"parent_code":9},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":580},{"candidates":581,"agricultural_evidence":47,"reason":120},[582,586,590,595,599],{"code":46,"score":104,"supported":47,"title":583,"abstract":584,"tags":585},[],[51],[],{"code":288,"score":104,"supported":47,"title":587,"abstract":588,"tags":589},[],[291],[],{"code":103,"score":104,"supported":47,"title":591,"abstract":592,"tags":594},[],[593],"农机",[],{"code":68,"score":22,"supported":14,"title":596,"abstract":597,"tags":598},[],[],[28],{"code":415,"score":22,"supported":14,"title":600,"abstract":601,"tags":602},[],[],[572],[604,605],"粮油产地烘干 9部门联合 粮食安全 农机化","粮油产地烘干 9部门联合","粮油产地烘干9部门联合粮食安全农机化-298","2026-08-08T23:53:54.079645Z"]