[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3764":3,"related-3764":74},{"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":9,"secondary_topics":32,"classification":33,"search_phrases":51,"slug":54,"view_count":55,"doi":56,"paper":57,"created_at":73},3764,"Exploring the gender wage gap in Indian agriculture—an interaction-based panel regression approach","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1872202","This study investigates the gender wage disparities in Indian agriculture utilizing a pseudo-panel dataset and an interaction-based panel regression framework. The study seeks to examine the association of gender on agricultural wages over the years, especially in response to the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA), which was enacted in 2005. The study examines whether MGNREGA, which mandates equal wages for male and female workers in public works, is associated with differential gender wage dynamics in the agriculture by shifting wage expectations and enhancing women’s bargaining power. The analysis uses a random effects model with a clustered panel-level standard errors to overcome the problem of serial correlation and heteroscedasticity in the datasets. The findings indicate persistent gender-based wage disparities across various agricultural activities, where male labourers are consistently paid higher wages than their female counterparts. At the same time, the implementation of MGNREGA revealed a significantly positive change observed on rural agricultural wages, with consistent post-policy wage gains reported across most of the farm activities. However, the interaction-based panel regression estimates do not consistently show statistically significant differential gender wage trends during the post-MGNREGA period, suggesting that while overall wages increased, gender disparities remained largely unchanged. Based on these findings, the study discusses policy implications associated with the task-specific nature of gender wage disparities in Indian agriculture. While not directly estimated, the persistence of gender wage gaps highlights the importance of strengthening wage transparency and monitoring mechanisms in informal labour markets, as well as promoting skill development initiatives to enhance women’s participation in higher-paying agricultural activities.","本研究利用伪面板数据集和基于交互效应的面板回归框架，考察了印度农业中的性别工资差异。研究旨在探讨性别与农业工资在多年间的关联，尤其是对2005年颁布的《圣雄甘地国家农村就业保障法》（MGNREGA）的响应。研究检验了MGNREGA——该法案规定公共工程中男女工人同工同酬——是否通过改变工资预期和增强女性议价能力，与农业中差异化的性别工资动态相关联。分析采用随机效应模型，并辅以聚类面板层面标准误，以克服数据集中的序列相关和异方差问题。研究发现，各类农业活动中持续存在基于性别的工资差异，男性劳动者始终获得比女性同行更高的工资。与此同时，MGNREGA的实施对农村农业工资产生了显著的正向变化，大多数农业活动在政策实施后均报告了持续的工资增长。然而，基于交互效应的面板回归估计并未一致显示MGNREGA实施后存在统计上显著的差异化性别工资趋势，这表明尽管总体工资有所上升，性别差异在很大程度上仍未改变。基于这些发现，本研究讨论了与印度农业中性别工资差异的任务特定性相关的政策含义。尽管未直接进行估计，性别工资差距的持续存在凸显了在非正规劳动力市场中加强工资透明度和监督机制的重要性，以及推动技能发展举措以促进女性参与更高报酬农业活动的重要性。",null,"Frontiers in Sustainable Food Systems","2026-09-28T00:00:00Z","论文",10,false,77,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,21,18,13,9,1,"基于伪面板与交互项回归的实证研究，揭示印度农业性别工资差距在MGNREGA实施后依然持续，方法规范、结论有政策参考价值，但属国别研究，对国内三农信息化直接关联有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"非农就业","印度农业","性别工资差距","MGNREGA","农村劳动力",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"unclassified","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":49,"reason":50},[39,44],{"code":40,"score":22,"supported":14,"title":41,"abstract":42,"tags":43},"G6",[],[],[27],{"code":45,"score":22,"supported":14,"title":46,"abstract":47,"tags":48},"J3",[],[],[28],true,"缺少足够的主题证据",[52,53],"印度农业 性别工资差距","MGNREGA 农村就业","印度农业性别工资差距-3764",5,"10.3389\u002Ffsufs.2026.1872202",{"doi":56,"openalex_id":58,"authors":59,"venue":10,"cited_by_count":65,"oa_url":6,"card":66,"direction":70,"ingested_from":72},"W7214649896",[60,63],{"name":61,"orcid":62},"Padma Lakshmi Govindarajan","https:\u002F\u002Forcid.org\u002F0000-0003-1701-1681",{"name":64,"orcid":9},"K. Thomas Felix",0,{"tldr":67,"method":68,"finding":69,"direction":70,"opportunity":71},"研究印度农业性别工资差距，并评估MGNREGA政策对工资动态的影响。","伪面板数据与交互项面板回归，随机效应模型加聚类标准误。","农业性别工资差距持续存在，MGNREGA虽提高整体工资但未显著缩小性别差距。","数字乡村与农业信息化","可探索数字平台与工资透明机制如何结合技能培训缩小农业性别工资差距。","openalex","2026-09-29T23:30:07.348373Z",{"total":75,"page":22,"page_size":75,"items":76},6,[77,194,292,353,430,508],{"id":78,"title":79,"url":80,"summary":81,"summary_zh":82,"content":9,"source_name":10,"source_url":80,"published_at":83,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":84,"score_detail":85,"sources":88,"tags":90,"primary_topic":95,"secondary_topics":100,"classification":101,"search_phrases":146,"slug":149,"view_count":65,"doi":150,"paper":151,"created_at":193},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","2026-10-21T00:00:00Z",71,{"impact":17,"substance":19,"depth":86,"authority":20,"freshness":21,"relevant":22,"comment":87},15,"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。",[89],{"name":10,"url":80},[91,92,93,28,94],"有机肥","土壤肥力","养分管理","水稻小麦轮作",{"code":96,"slug":97,"name":98,"path":99,"parent_code":9},"A","field-crops","大田作物","\u002Ftopic\u002Ffield-crops",[],{"status":102,"locked":14,"version":35,"method":36,"evidence":103},"partial",{"candidates":104,"agricultural_evidence":49,"reason":145},[105,112,120,127,134,141],{"code":106,"score":86,"supported":49,"title":107,"abstract":109,"tags":111},"H2",[108],"soil",[110,108],"土壤",[92],{"code":113,"score":114,"supported":49,"title":115,"abstract":117,"tags":119},"A1",14,[116],"rice",[118,116],"水稻",[],{"code":121,"score":114,"supported":49,"title":122,"abstract":124,"tags":126},"A2",[123],"wheat",[125,123],"小麦",[],{"code":128,"score":129,"supported":49,"title":130,"abstract":131,"tags":133},"F3",8,[],[132,93],"肥料",[93,91],{"code":135,"score":136,"supported":49,"title":137,"abstract":138,"tags":140},"A7",3,[],[139],"轮作",[],{"code":45,"score":22,"supported":14,"title":142,"abstract":143,"tags":144},[],[],[28],"一级领域明确，二级方向需要细分",[147,148],"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":150,"openalex_id":152,"authors":153,"venue":10,"cited_by_count":65,"oa_url":186,"card":187,"direction":191,"ingested_from":72},"W7223144837",[154,156,159,160,163,166,168,169,171,174,176,179,181,183],{"name":155,"orcid":9},"Raghwendra Singh",{"name":157,"orcid":158},"Ajay Kumar Singh","https:\u002F\u002Forcid.org\u002F0000-0002-8743-6477",{"name":157,"orcid":158},{"name":161,"orcid":162},"Preeti P. Yadav","https:\u002F\u002Forcid.org\u002F0000-0001-7843-9783",{"name":164,"orcid":165},"Seema Yadav","https:\u002F\u002Forcid.org\u002F0000-0002-9773-6641",{"name":167,"orcid":9},"Abhishek Singh",{"name":167,"orcid":9},{"name":170,"orcid":9},"Saurabh Raj Pandey",{"name":172,"orcid":173},"Vinod Kumar Singh","https:\u002F\u002Forcid.org\u002F0009-0004-2583-6565",{"name":175,"orcid":9},"Narendra Raghubanshi",{"name":177,"orcid":178},"Yash Pal Singh","https:\u002F\u002Forcid.org\u002F0000-0001-8345-9084",{"name":180,"orcid":9},"Mahesh Kumar",{"name":182,"orcid":9},"Shivangi Singh",{"name":184,"orcid":185},"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":188,"method":189,"finding":190,"direction":191,"opportunity":192},"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","农业绿色发展与碳","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":195,"title":196,"url":197,"summary":198,"summary_zh":199,"content":9,"source_name":200,"source_url":197,"published_at":201,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":202,"score_detail":203,"sources":206,"tags":208,"primary_topic":213,"secondary_topics":219,"classification":220,"search_phrases":261,"slug":264,"view_count":65,"doi":265,"paper":266,"created_at":291},5249,"Strawberry (Fragaria × ananassa) cultivation in India: A critical review of status, constraints and future directions","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.15408","Strawberry (Fragaria × ananassa Duch.) is an economically important berry crop worldwide, due to its distinctive flavour, nutritional value and health benefits. In recent years, it has been cultivated not only in temperate regions, but also in subtropical and tropical regions. It has become one of the high-value horticultural crops in India, due to the growing consumer demand and good returns to farmers. This review critically examines the trends in yield, constraints and technological factors influencing strawberry cultivation across major strawberry producing states such as Maharashtra, Himachal Pradesh, Uttarakhand, Jammu and Kashmir and other emerging regions in India. Evidence from existing studies indicates that the adoption of improved cultivation practices such as balanced fertilisation, integrated pest management, mulching, protected cultivation and soilless systems has substantially improved fruit yield and quality compared to conventional open-field methods. Despite these advancements, productivity remains limited by climatic sensitivity, pest and disease factors, soil and nutrient management constraints and postharvest losses due to the highly perishable nature of the fruit. Moreover, disparities in technology adoption and the lack of cold-chain infrastructure continue to constrain sectoral growth. Emerging technologies such as precision agriculture and decision-support systems offer considerable potential to improve resource-use efficiency and sustainable production. The review discusses the status of strawberry production in India and its integration with production trends, challenges and future research in the field. It also points out gaps in the current knowledge and limited production systems and also outlines the future technological interventions needed to enhance the productivity, profitability and sustainability of strawberry production under Indian conditions.","草莓（Fragaria × ananassa Duch.）因其独特风味、营养价值和健康益处，成为全球范围内具有重要经济价值的浆果作物。近年来，草莓不仅在中温带地区种植，也在亚热带和热带地区得到栽培。由于消费者需求不断增长且种植者收益良好，草莓已成为印度高值园艺作物之一。本文综述批判性地考察了印度主要草莓产区（如马哈拉施特拉邦、喜马偕尔邦、北阿坎德邦、查谟和克什米尔以及其他新兴地区）的产量趋势、限制因素及影响草莓栽培的技术因素。现有研究证据表明，与传统的露地栽培方法相比，采用平衡施肥、病虫害综合防治、覆盖栽培、设施栽培和无土栽培等改良栽培措施已显著提高了果实产量和品质。尽管取得了这些进展，生产力仍受限于气候敏感性、病虫害因素、土壤和养分管理约束以及果实高度易腐性造成的采后损失。此外，技术采用程度的差异和冷链基础设施的缺乏继续制约着产业增长。精准农业和决策支持系统等新兴技术为提高资源利用效率和可持续生产提供了巨大潜力。本文讨论了印度草莓生产现状及其与生产趋势、挑战和未来研究的融合，指出了当前知识空白和生产体系的局限性，并概述了在印度条件下提高草莓生产力、盈利能力和可持续性所需的未来技术干预措施。","Plant Science Today","2026-10-17T00:00:00Z",72,{"impact":17,"substance":19,"depth":204,"authority":20,"freshness":129,"relevant":22,"comment":205},17,"系统综述印度草莓产业现状与制约，对国内草莓种植与设施农业有参考价值，但非本土资讯，时效与影响层级有限。",[207],{"name":200,"url":197},[209,210,28,211,212],"设施栽培","病虫害防治","草莓种植","无土栽培",{"code":214,"slug":215,"name":216,"path":217,"parent_code":218},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[],{"status":221,"locked":14,"version":35,"method":36,"evidence":222},"classified",{"candidates":223,"agricultural_evidence":49},[224,230,235,239,246,251,257],{"code":214,"score":114,"supported":49,"title":225,"abstract":227,"tags":229},[226],"strawberry",[228,226],"草莓",[],{"code":128,"score":75,"supported":49,"title":231,"abstract":232,"tags":234},[],[233,93],"施肥",[],{"code":106,"score":75,"supported":49,"title":236,"abstract":237,"tags":238},[],[110,108],[],{"code":240,"score":75,"supported":49,"title":241,"abstract":242,"tags":245},"I1",[],[243,244],"精准农业","precision agriculture",[],{"code":247,"score":55,"supported":49,"title":248,"abstract":249,"tags":250},"B7",[],[212],[212,209],{"code":252,"score":136,"supported":49,"title":253,"abstract":254,"tags":256},"E3",[],[255],"冷链",[],{"code":45,"score":22,"supported":14,"title":258,"abstract":259,"tags":260},[],[],[28],[262,263],"印度 草莓 栽培","Fragaria ananassa 印度","印度草莓栽培-5249","10.14719\u002Fpst.15408",{"doi":265,"openalex_id":267,"authors":268,"venue":200,"cited_by_count":65,"oa_url":197,"card":284,"direction":290,"ingested_from":72},"W7222796615",[269,272,275,278,281],{"name":270,"orcid":271},"A Dharshni","https:\u002F\u002Forcid.org\u002F0009-0002-8809-3557",{"name":273,"orcid":274},"C. P. Ravindran","https:\u002F\u002Forcid.org\u002F0000-0002-7514-4848",{"name":276,"orcid":277},"S. Muthulakshmi","https:\u002F\u002Forcid.org\u002F0009-0007-2071-9919",{"name":279,"orcid":280},"S. Shenbagavalli","https:\u002F\u002Forcid.org\u002F0000-0001-9344-277X",{"name":282,"orcid":283},"T Anitha","https:\u002F\u002Forcid.org\u002F0000-0003-0723-5948",{"tldr":285,"method":286,"finding":287,"direction":288,"opportunity":289},"综述印度草莓种植的产量趋势、制约因素与技术现状，并提出未来方向。","基于印度主产州文献证据，评述栽培技术、病虫害与采后损失等制约。","改良栽培与无土系统可提产提质，但气候敏感、冷链缺失与技术采纳不均仍限产。","智慧农业 \u002F 农业物联网","可研究面向小农户的低成本精准栽培与决策支持系统，并补齐采后冷链信息化短板。","农业人工智能与决策模型","2026-10-18T23:31:06.966374Z",{"id":293,"title":294,"url":295,"summary":296,"summary_zh":297,"content":9,"source_name":10,"source_url":295,"published_at":298,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":299,"score_detail":300,"sources":303,"tags":305,"primary_topic":310,"secondary_topics":316,"classification":317,"search_phrases":335,"slug":338,"view_count":65,"doi":339,"paper":340,"created_at":352},4874,"Estimating the level of rural household resilience to food insecurity: evidence from Rushinga Rural District in northern Zimbabwe","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1875364","Introduction Rural households in semi-arid Zimbabwe face recurrent food insecurity because their livelihoods depend heavily on climate-sensitive agriculture, limited assets, and weak rural service systems. However, household-level evidence on the dimensions that construct resilience to food insecurity in Southern Africa remains limited. The study estimates multidimensional rural household resilience to food insecurity in Rushinga District, Zimbabwe; identifies the relative contributions of eight livelihood-related resilience dimensions; and examines resilience patterns by livelihood orientation and household headship, using cross-sectional survey data from 300 households collected across three wards. Methods The study applied a two-stage procedure informed by the multidimensional structure of the FAO-RIMA-II framework. In the first stage, observable household indicators were grouped a priori into eight theoretically informed resilience dimensions, such as assets, income and food access, social protection, access to basic services, access to natural resources, adaptive capacity, support services, and stability. PCA was used within each dimension to reduce the observed indicators into dimension-level component scores. In the second stage, the retained dimension scores were entered into a factor analysis to examine their joint structure and construct the multidimensional household resilience index. Results The second-order factor analysis retained three factors explaining 59.28% of the variance, with comparatively high absolute loadings for income and food access (0.891), assets (0.793), adaptive capacity (-0.777), stability (0.783), basic services (0.623) and support services (0.813). Access to natural resources did not load strongly on the retained factors, indicating a comparatively weaker contribution within the estimated multidimensional structure. Non-farming households recorded a higher mean resilience score relative to farming households, while female-headed households recorded a lower mean resilience score compared to male-headed households. Conclusion The findings suggest that strengthening rural resilience to food insecurity in Zimbabwe may require a policy approach that supports productive assets, non-farm livelihood diversification, access to basic services, and gender-responsive support systems, alongside agricultural interventions.","引言 津巴布韦半干旱地区的农村家庭面临反复出现的粮食不安全问题，因为其生计高度依赖对气候敏感的农业、资产有限且农村服务体系薄弱。然而，关于南部非洲粮食不安全复原力构成维度的家庭层面证据仍然有限。本研究利用在三个选区收集的300户家庭的横截面调查数据，评估津巴布韦Rushinga区农村家庭多维粮食不安全复原力；识别八个与生计相关的复原力维度的相对贡献；并按生计取向和户主性别考察复原力模式。方法 本研究采用基于FAO-RIMA-II框架多维结构的两阶段程序。第一阶段，将可观测的家庭指标先验地归入八个具有理论依据的复原力维度，如资产、收入与食物获取、社会保护、基本服务获取、自然资源获取、适应能力、支持服务和稳定性。在每个维度内使用PCA将观测指标降维为维度层面的成分得分。第二阶段，将保留的维度得分纳入因子分析，以考察其联合结构并构建多维家庭复原力指数。结果 二阶因子分析保留了三个因子，解释了59.28%的方差，其中收入与食物获取（0.891）、资产（0.793）、适应能力（-0.777）、稳定性（0.783）、基本服务（0.623）和支持服务（0.813）的绝对载荷相对较高。自然资源获取在保留因子上的载荷不强，表明其在所估计的多维结构中贡献相对较弱。非农业家庭的平均复原力得分高于农业家庭，而女性户主家庭的平均复原力得分低于男性户主家庭。结论 研究结果表明，加强津巴布韦农村粮食不安全复原力可能需要一种政策路径，在农业干预措施之外，支持生产性资产、非农生计多样化、基本服务获取和性别响应型支持体系。","2026-10-13T00:00:00Z",66,{"impact":129,"substance":301,"depth":17,"authority":20,"freshness":21,"relevant":22,"comment":302},20,"基于FAO-RIMA-II框架的300户实证研究，方法规范、结论具体，但属津巴布韦地方性案例，对国内三农信息化参考价值有限，可作国际比较素材。",[304],{"name":10,"url":295},[306,27,307,308,309],"粮食安全","农户生计","农村抗逆力","性别差异",{"code":311,"slug":312,"name":313,"path":314,"parent_code":315},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[],{"status":221,"locked":14,"version":35,"method":36,"evidence":318},{"candidates":319,"agricultural_evidence":49},[320,325,330],{"code":311,"score":136,"supported":49,"title":321,"abstract":322,"tags":324},[],[323],"干旱",[],{"code":40,"score":326,"supported":14,"title":327,"abstract":328,"tags":329},2,[],[],[307,27],{"code":331,"score":22,"supported":14,"title":332,"abstract":333,"tags":334},"A8",[],[],[306],[336,337],"津巴布韦 Rushinga 农户抗逆力","FAO-RIMA-II 粮食安全","津巴布韦Rushinga农户抗逆力-4874","10.3389\u002Ffsufs.2026.1875364",{"doi":339,"openalex_id":341,"authors":342,"venue":10,"cited_by_count":65,"oa_url":295,"card":347,"direction":70,"ingested_from":72},"W7221657887",[343,345],{"name":344,"orcid":9},"Onismo Muzah",{"name":346,"orcid":9},"Tafadzwa Mabhaudhi",{"tldr":348,"method":349,"finding":350,"direction":70,"opportunity":351},"基于津巴布韦300户调查，用FAO-RIMA-II框架估算农村家庭粮食不安全恢复力指数。","两阶段法：各维度内PCA降维，再对维度得分做二阶因子分析构建综合指数。","收入与食物获取、资产、支持服务贡献最大；非农家庭恢复力更高，女性户主家庭更低。","可将恢复力指数与农户数字服务可及性、移动支付等信息化指标结合，检验数字赋能对恢复力的调节作用。","2026-10-14T23:30:07.234699Z",{"id":354,"title":355,"url":356,"summary":357,"summary_zh":358,"content":9,"source_name":10,"source_url":356,"published_at":359,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":360,"score_detail":361,"sources":365,"tags":367,"primary_topic":372,"secondary_topics":378,"classification":379,"search_phrases":398,"slug":401,"view_count":65,"doi":402,"paper":403,"created_at":429},4482,"Development and validation of a multidimensional behavioural change index and its application to farmer producer organisations in India: implications for sustainable smallholder food systems","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1915367","Realising the Sustainable Development Goals in ending poverty (SDG 1), achieving zero hunger through sustainable agriculture (SDG 2), and reducing inequalities (SDG 10) hinges on the adaptive capacity of smallholder farmers, and Farmer Producer Organisations (FPOs) are widely promoted to strengthen it, yet their evaluation has concentrated on short-term economic gains, neglecting the behavioural transformation that sustains improvement and underpins resilient food systems, and limited validated multidimensional instruments are available to measure such behavioural differences across farmers. This study developed a Behavioural Change Index (BCI) and applied it to examine the association between FPO membership and farmer behvioural status in Odisha, India. An initial pool of 142 items was refined to 89 items across twelve theoretically grounded, expert-weighted dimensions through relevancy analysis by an 80-member expert panel, reliability testing, exploratory factor analysis, confirmatory factor analysis, composite reliability, average variance extracted, measurement invariance, discriminant validity and common-method-bias assessment, after which the index was applied to 300 FPO members and 100 non-members, with selection bias addressed through propensity-score matching, inverse-probability weighting, kernel and full matching, doubly-robust estimation and E-value sensitivity analysis. The BCI demonstrated excellent reliability (Cronbach’s α = 0.95), a clean twelve-factor structure explaining 68.3% of variance, and satisfactory discriminant validity; members scored significantly higher across all twelve dimensions(Cohen’s d = 1.29–2.21) and the raw advantage of 22.12 points remained robust after matching (19.34 points; doubly-robust ATT = 19.71; E -value = 6.69), indicating minimal sensitivity to unmeasured confounding. The cross-sectional study thus delivers both a psychometrically supported instrument and robust observational evidence of differences in behavioural status between FPO members and non-members after adjustment for observed covariates. By rendering behavioural status measurable, the BCI enables agencies to monitor progress beyond income and target support effectively, advancing the transition toward sustainable, equitable and resilient smallholder food systems.","实现消除贫困（可持续发展目标1）、通过可持续农业实现零饥饿（可持续发展目标2）以及减少不平等（可持续发展目标10），关键在于小农户的适应能力，而农民生产者组织（Farmer Producer Organisations, FPOs）被广泛推广以增强这一能力，然而对其评估一直集中于短期经济收益，忽视了支撑持续改善和韧性食物系统的行为转变，且可用于测量农户间此类行为差异的经过验证的多维工具十分有限。本研究开发了行为变化指数（Behavioural Change Index, BCI），并将其应用于印度奥里萨邦，以考察FPO成员身份与农户行为状况之间的关联。初始的142个题项经由80人专家组的关联性分析、信度检验、探索性因子分析、验证性因子分析、组合信度、平均方差抽取量、测量不变性、区分效度及共同方法偏差评估，在十二个有理论依据且经专家赋权的维度上精炼为89个题项，随后将该指数应用于300名FPO成员和100名非成员，并通过倾向得分匹配、逆概率加权、核匹配和全匹配、双重稳健估计及E值敏感性分析来处理选择偏差。BCI表现出极好的信度（Cronbach's α = 0.95）、清晰的十二因子结构（解释68.3%的方差）以及令人满意的区分效度；成员在所有十二个维度上的得分均显著更高（Cohen's d = 1.29–2.21），22.12分的原始优势在匹配后依然稳健（19.34分；双重稳健ATT = 19.71；E值 = 6.69），表明对未测量混杂因素的敏感性极低。因此，这项横断面研究既提供了一种心理测量学支持的测量工具，也提供了在调整可观测协变量后FPO成员与非成员之间行为状况差异的稳健观察证据。通过使行为状况可测量，BCI使各机构能够监测收入之外的进展并有效定向支持，从而推进小农户食物系统向可持续、公平和有韧性的方向转型。","2026-10-08T00:00:00Z",74,{"impact":362,"substance":363,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":364},12,22,"该研究开发了行为改变指数并验证其对印度小农组织成员行为差异的测量，方法严谨、数据扎实，对可持续小农食物系统评估有参考价值，但地域限于印度、影响层级偏学术细分领域。",[366],{"name":10,"url":356},[368,369,28,370,371],"小农户","可持续农业","农民合作社","行为改变指数",{"code":373,"slug":374,"name":375,"path":376,"parent_code":377},"G2","farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[],{"status":221,"locked":14,"version":35,"method":36,"evidence":380},{"candidates":381,"agricultural_evidence":49},[382,387,394],{"code":373,"score":17,"supported":49,"title":383,"abstract":385,"tags":386},[384],"smallholder",[368,384],[370,368],{"code":388,"score":389,"supported":49,"title":390,"abstract":391,"tags":393},"H8",7,[],[369,392],"sustainable agriculture",[369],{"code":45,"score":22,"supported":14,"title":395,"abstract":396,"tags":397},[],[],[28],[399,400],"行为改变指数 农民合作社 可持续农业 印度农业","行为改变指数 农民合作社","行为改变指数农民合作社可持续农业印度农业-4482","10.3389\u002Ffsufs.2026.1915367",{"doi":402,"openalex_id":404,"authors":405,"venue":10,"cited_by_count":65,"oa_url":356,"card":424,"direction":70,"ingested_from":72},"W7221022600",[406,408,410,413,416,418,420,422],{"name":407,"orcid":9},"Badigi Pavan Naik",{"name":409,"orcid":9},"Mahamaya Prasad Nayak",{"name":411,"orcid":412},"Bishnupriya Mishra","https:\u002F\u002Forcid.org\u002F0009-0007-6590-8967",{"name":414,"orcid":415},"Abhiram Dash","https:\u002F\u002Forcid.org\u002F0000-0002-6683-6594",{"name":417,"orcid":9},"Khitish Kumar Sarangi",{"name":419,"orcid":9},"Madhumita Jena",{"name":421,"orcid":9},"Indira Priyadarsini Pattnaik",{"name":423,"orcid":9},"Srabani Ghosal",{"tldr":425,"method":426,"finding":427,"direction":70,"opportunity":428},"构建并验证多维行为改变指数，评估印度农民生产者组织成员的行为差异。","专家赋权、因子分析、倾向得分匹配等，调查300名成员与100名非成员。","指数信效度良好，成员在12个维度均显著更高，匹配后优势仍稳健。","可将该行为指数嵌入数字平台，用于小农户行为监测与精准干预评估。","2026-10-09T23:30:06.254620Z",{"id":431,"title":432,"url":433,"summary":434,"summary_zh":435,"content":9,"source_name":436,"source_url":433,"published_at":437,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":438,"score_detail":439,"sources":441,"tags":443,"primary_topic":448,"secondary_topics":454,"classification":455,"search_phrases":479,"slug":482,"view_count":136,"doi":483,"paper":484,"created_at":507},4432,"Horticulture acreage estimation: remote sensing vis-a-vis sample surveys","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-74697-9","Timely and spatially explicit estimates of horticultural crop acreage are required to support agricultural statistics, resource allocation, and production planning. This study assessed the agreement between satellite-derived acreage estimates and house-to-house survey data for selected villages in Erode, Chittoor, and Nashik districts of India. Multispectral imagery from Resourcesat-2 LISS-IV and Sentinel-2 was subjected to radiometric and geometric correction, band stacking, mosaicking, and normalized difference vegetation index-based vegetation masking. Maximum Likelihood Classification (MLC), Spectral Angle Mapper (SAM), and Support Vector Machine (SVM) algorithms were evaluated for mapping banana, tapioca, mango, and onion. Classification performance varied among crops. MLC achieved overall accuracies of 84.83% for banana and 76.81% for mango, with kappa coefficients of 0.767 and 0.917, respectively. SAM produced an overall accuracy of 78.70% and a kappa coefficient of 0.797 for tapioca, whereas SVM performed best for onion, attaining an overall accuracy of 76.41% and a kappa coefficient of 0.713. Satellite-derived acreage estimates were strongly associated with survey estimates for banana (R 2 = 0.94), tapioca (R 2 = 0.93), and mango (R 2 = 0.94). Compared with the survey estimates, remote sensing underestimated the total areas of banana, tapioca, mango, and onion by 9.04%, 5.35%, 5.88%, and 26.08%, respectively. The results indicate that classification performance depends on crop structure, field size, spatial fragmentation, and classifier selection. Satellite imagery provided more reliable estimates for relatively large and contiguous orchards than for small and fragmented crop fields. Integrating satellite-based mapping with targeted field verification can therefore provide a scalable and spatially explicit framework for operational horticultural acreage estimation.","及时且具有空间明确性的园艺作物种植面积估算，是支撑农业统计、资源分配和生产规划的必要条件。本研究评估了卫星遥感估算面积与印度埃罗德、奇图尔和纳西克地区部分村庄逐户调查数据之间的一致性。对来自Resourcesat-2 LISS-IV和Sentinel-2的多光谱影像进行了辐射校正和几何校正、波段叠加、镶嵌以及基于归一化植被指数（NDVI）的植被掩膜处理。评估了最大似然分类（MLC）、光谱角制图（SAM）和支持向量机（SVM）算法在香蕉、木薯、芒果和洋葱制图中的应用效果。不同作物的分类性能存在差异。MLC对香蕉和芒果的总体精度分别为84.83%和76.81%，Kappa系数分别为0.767和0.917。SAM对木薯的总体精度为78.70%，Kappa系数为0.797；而SVM对洋葱的表现最佳，总体精度为76.41%，Kappa系数为0.713。卫星估算面积与调查估算值在香蕉（R²=0.94）、木薯（R²=0.93）和芒果（R²=0.94）上呈强相关。与调查估算值相比，遥感分别低估了香蕉、木薯、芒果和洋葱的总面积，低估幅度为9.04%、5.35%、5.88%和26.08%。结果表明，分类性能取决于作物结构、田块大小、空间破碎化程度和分类器选择。对于相对较大且连片的果园，卫星影像提供的估算值比小而破碎的农田更为可靠。因此，将基于卫星的制图与有针对性的实地验证相结合，可为业务化园艺种植面积估算提供一个可扩展且具有空间明确性的框架。","Scientific Reports","2026-10-07T00:00:00Z",79,{"impact":17,"substance":363,"depth":19,"authority":114,"freshness":21,"relevant":22,"comment":440},"该研究以多光谱遥感与入户调查对比估算印度园艺作物面积，方法扎实、数据具体，对农业遥感统计具有参考价值，但属区域性案例，影响力有限。",[442],{"name":436,"url":433},[444,28,445,446,447],"遥感监测","园艺作物","作物面积估算","农业统计",{"code":449,"slug":450,"name":451,"path":452,"parent_code":453},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":221,"locked":14,"version":35,"method":36,"evidence":456},{"candidates":457,"agricultural_evidence":49},[458,465,471,475],{"code":449,"score":19,"supported":49,"title":459,"abstract":461,"tags":464},[460],"remote sensing",[462,460,463],"遥感","multispectral",[444],{"code":466,"score":136,"supported":49,"title":467,"abstract":468,"tags":470},"B2",[],[469],"芒果",[],{"code":218,"score":22,"supported":14,"title":472,"abstract":473,"tags":474},[],[],[445],{"code":45,"score":22,"supported":14,"title":476,"abstract":477,"tags":478},[],[],[28],[480,481],"Resourcesat-2 Sentinel-2 园艺作物 面积估算","Erode Chittoor Nashik 遥感 抽样调查","Resourcesat-2Sentinel-2园艺作物面积估算-4432","10.1038\u002Fs41598-026-74697-9",{"doi":483,"openalex_id":485,"authors":486,"venue":436,"cited_by_count":65,"oa_url":433,"card":501,"direction":505,"ingested_from":72},"W7220842723",[487,490,492,495,498],{"name":488,"orcid":489},"Tauqueer Ahmad","https:\u002F\u002Forcid.org\u002F0000-0002-2573-9667",{"name":491,"orcid":9},"Prachi Misra Sahoo",{"name":493,"orcid":494},"A.K. Biswas","https:\u002F\u002Forcid.org\u002F0000-0002-1708-7363",{"name":496,"orcid":497},"Gopal Krishna","https:\u002F\u002Forcid.org\u002F0000-0002-1070-9916",{"name":499,"orcid":500},"Karunesh K. 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