[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5543":3,"related-5543":61},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":8,"secondary_topics":30,"classification":31,"search_phrases":39,"slug":42,"view_count":43,"doi":44,"paper":45,"created_at":60},5543,"Factors linked to glyphosate use patterns in French arable cropping systems","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.105012","Factors linked to glyphosate use patterns in French arable cropping systems。Agricultural Systems",null,"Agricultural Systems","2026-10-21T00:00:00Z","论文",10,false,69,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},12,18,16,14,9,1,"核心期刊论文，聚焦法国耕作制度中草甘膦使用模式的影响因素，对农药减量与耕作制度研究有参考价值，但属国外区域性研究，公共影响有限。",[24],{"name":9,"url":6},[26,27,28,29],"法国农业","草甘膦","耕作制度","农药使用",[],{"status":32,"locked":13,"version":33,"method":34,"evidence":35},"unclassified","2026-10-22.2","rules",{"candidates":36,"agricultural_evidence":37,"reason":38},[],true,"缺少足够的主题证据",[40,41],"法国 草甘膦 耕作制度","农药使用 法国农业 耕作制度 草甘膦","法国草甘膦耕作制度-5543",0,"10.1016\u002Fj.agsy.2026.105012",{"doi":44,"openalex_id":46,"authors":47,"venue":9,"cited_by_count":43,"oa_url":8,"card":8,"direction":8,"ingested_from":59},"W7223136278",[48,50,53,56],{"name":49,"orcid":8},"Michael James McCallum",{"name":51,"orcid":52},"Romain Nandillon","https:\u002F\u002Forcid.org\u002F0009-0007-5182-0462",{"name":54,"orcid":55},"Nicolas M. Munier-Jolain","https:\u002F\u002Forcid.org\u002F0000-0002-0871-3692",{"name":57,"orcid":58},"Xavier Reboud","https:\u002F\u002Forcid.org\u002F0000-0001-6430-153X","openalex","2026-10-22T11:20:12.773278Z",{"total":62,"page":21,"page_size":62,"items":63},3,[64,127,201],{"id":65,"title":66,"url":67,"summary":68,"summary_zh":69,"content":8,"source_name":70,"source_url":67,"published_at":71,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":72,"score_detail":73,"sources":77,"tags":79,"primary_topic":83,"secondary_topics":89,"classification":90,"search_phrases":104,"slug":107,"view_count":108,"doi":109,"paper":110,"created_at":126},4041,"Trade-off between investment and private withdrawals: assessing the return on investment for farms in France","https:\u002F\u002Fdoi.org\u002F10.1108\u002Fafr-07-2025-0098","Purpose The wealth generated on a farm can be allocated either to the immediate remuneration of the self-employed workers or to the financing of investments, which can be considered as a deferred remuneration to be realized in the future. We contribute to the study of the profitability of capital invested in agriculture by estimating an internal rate of return (IRR) for a sample of French commercial farms and proposing five strategies reflecting how the generated wealth is allocated to either investment or private withdrawals. Design\u002Fmethodology\u002Fapproach The adopted approach relies on the net present value theory and the estimation of an IRR. Based on the analysis of annual cash flows and balance sheet assets, we study the profitability of having invested in agriculture and document how the trade-off between investment and private withdrawals was implemented by a balanced sample of 1,254 French commercial farms over the period 2002–2018. Findings Estimated IRRs are positive in most cases, with an average of 1.8%, which is of the same order of magnitude as the assumed weighted average cost of capital. We further propose five strategies named “balanced”, “spender”, “well-paid”, “accumulator” and “prudent”, based on the respective shares of the total operating cash flow dedicated to either investments or private withdrawals. Originality\u002Fvalue Our analysis provides original insights into measuring farm performance and farmers' investment behaviour in the long term, which should help agricultural advisers to better target their decision support towards farmers' investment and financing decisions.","目的 农场创造的财富既可以用于直接报酬自雇劳动者，也可以用于投资融资，后者可视为未来实现的递延报酬。我们通过估算法国商业化农场样本的内部收益率（IRR），并提出五种反映所创造财富在投资或私人提款之间分配方式的策略，为农业投资资本盈利能力研究作出贡献。设计\u002F方法\u002F路径 所采用的方法依托净现值理论和内部收益率估算。基于对年度现金流和资产负债表资产的分析，我们研究了投资农业的盈利能力，并记录了2002—2018年期间由1，254个法国商业化农场组成的平衡样本如何在投资与私人提款之间进行权衡。研究发现 估算的内部收益率在大多数情况下为正，平均为1.8%，与假设的加权平均资本成本处于同一数量级。我们进一步根据总经营现金流分别用于投资或私人提款的比例，提出五种策略，分别命名为“平衡型”“消费型”“高薪型”“积累型”和“审慎型”。原创性\u002F价值 我们的分析为衡量农场绩效和农民长期投资行为提供了新的见解，这应有助于农业顾问更好地将决策支持定位于农民的投资和融资决策。","Agricultural Finance Review","2026-10-01T00:00:00Z",70,{"impact":74,"substance":75,"depth":17,"authority":19,"freshness":74,"relevant":21,"comment":76},8,22,"基于1254家法国农场2002-2018年数据测算农业投资内部收益率并提出五种财富分配策略，方法扎实、结论有参考价值，但属国外研究且与国内三农信息化关联较弱，不宜进入每日精选。",[78],{"name":70,"url":67},[80,81,26,82],"农业金融","农业投资","农场盈利",{"code":84,"slug":85,"name":86,"path":87,"parent_code":88},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":91,"locked":13,"version":33,"method":34,"evidence":92},"classified",{"candidates":93,"agricultural_evidence":37},[94,99],{"code":84,"score":62,"supported":37,"title":95,"abstract":96,"tags":98},[],[97],"decision support",[],{"code":100,"score":21,"supported":13,"title":101,"abstract":102,"tags":103},"G3",[],[],[81],[105,106],"法国农场 投资回报率","农业投资 农业金融 农场盈利 法国农业","法国农场投资回报率-4041",2,"10.1108\u002Fafr-07-2025-0098",{"doi":109,"openalex_id":111,"authors":112,"venue":70,"cited_by_count":43,"oa_url":8,"card":119,"direction":125,"ingested_from":59},"W7215098266",[113,116],{"name":114,"orcid":115},"Philippe Jeanneaux","https:\u002F\u002Forcid.org\u002F0000-0001-8715-9185",{"name":117,"orcid":118},"Laurent Piet","https:\u002F\u002Forcid.org\u002F0000-0002-5656-5479",{"tldr":120,"method":121,"finding":122,"direction":123,"opportunity":124},"估算法国1254个农场2002-2018年的内部收益率，并划分五种投资与私人提取策略。","基于净现值理论和内部收益率法，分析年度现金流与资产负债表资产。","多数农场IRR为正，平均1.8%，与加权平均资本成本相当。","数字乡村与农业信息化","可结合农场微观财务数据与数字化工具，构建投资决策支持系统，优化长期资本配置。","农业人工智能与决策模型","2026-10-02T23:30:49.549999Z",{"id":128,"title":129,"url":130,"summary":131,"summary_zh":132,"content":8,"source_name":133,"source_url":130,"published_at":71,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":134,"score_detail":135,"sources":137,"tags":139,"primary_topic":144,"secondary_topics":150,"classification":151,"search_phrases":170,"slug":173,"view_count":43,"doi":174,"paper":175,"created_at":200},3964,"Use of smartphone images in the evaluation of weed control with glyphosate: A computational approach using Random Forest","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112497","The evaluation of herbicide control efficacy is essential in integrated weed management, and this information is fed back into the database for future crop protection actions. The evaluation of weed control using herbicides is carried out based on on-site visual evaluations, which can be time-consuming and imprecise. In this study, models capable of evaluating the efficacy of Urochloa brizantha control by glyphosate were proposed using computational image analysis with Random Forest. The images were obtained with a smartphone in an area with U. brizantha infestation desiccated by glyphosate. To stratify the control response, glyphosate doses of 0, 144, 288, 576, 864, 1152, and 1440 g a.e. ha −1 were applied. For model estimation, 27 vegetation indices were used as input variables, with the objective of estimating control based on visual evaluation data and on control percentage data determined by the plant mass killed by the action of the herbicide. The model developed with data from visual control scores presented excellent performance, both in training (r 2 of 0.92) and in validation (r 2 of 0.92). The model trained with the percentage of dead plant mass also presented satisfactory performance, with an r 2 of 0.88 for the training sample and 0.90 for the validation sample. The proposed evaluation method presents advantages over the usual methods, due to the ease of obtaining smartphone images and the possibility of inserting the indices and models developed in mobile applications.","除草剂防效评价是杂草综合治理中的关键环节，其信息可反馈至数据库以指导未来的作物保护决策。目前，除草剂杂草防效评价主要依赖田间目测，该方法耗时且不够精确。本研究利用随机森林（Random Forest）结合计算图像分析，构建了能够评估草甘膦对俯仰臂形草（Urochloa brizantha）防效的模型。图像通过智能手机在草甘膦灭杀的俯仰臂形草侵染区域获取。为划分防效梯度，设置了0、144、288、576、864、1152和1440 g a.e. ha⁻¹的草甘膦剂量处理。模型估算以27个植被指数作为输入变量，目标为基于目测评价数据和由除草剂作用导致的植株死亡质量确定的防效百分比数据进行防效估算。基于目测防效评分数据开发的模型表现优异，训练集r²为0.92，验证集r²为0.92。基于植株死亡质量百分比训练的模型同样表现良好，训练样本r²为0.88，验证样本r²为0.90。所提出的评价方法相较常规方法具有优势，原因在于智能手机图像获取便捷，且所开发的指数和模型可嵌入移动应用程序中。","Computers and Electronics in Agriculture",79,{"impact":18,"substance":75,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":136},"核心期刊论文，用手机图像与随机森林实现除草效果快速评估，方法新颖且可落地移动应用，值得进入每日精选。",[138],{"name":133,"url":130},[140,141,142,27,143],"智慧农业","随机森林","杂草防控","手机图像识别",{"code":145,"slug":146,"name":147,"path":148,"parent_code":149},"F4","crop-protection","农药与绿色防控","\u002Ftopic\u002Finputs-equipment\u002Fcrop-protection","F",[],{"status":91,"locked":13,"version":33,"method":34,"evidence":152},{"candidates":153,"agricultural_evidence":37},[154,161,166],{"code":145,"score":17,"supported":37,"title":155,"abstract":157,"tags":160},[156],"weed control",[158,159,156],"除草","crop protection",[142],{"code":162,"score":21,"supported":13,"title":163,"abstract":164,"tags":165},"I1",[],[],[140],{"code":84,"score":21,"supported":13,"title":167,"abstract":168,"tags":169},[],[],[141],[171,172],"手机图像 杂草防控 草甘膦","Random Forest 杂草 评估","手机图像杂草防控草甘膦-3964","10.1016\u002Fj.compag.2026.112497",{"doi":174,"openalex_id":176,"authors":177,"venue":133,"cited_by_count":43,"oa_url":130,"card":195,"direction":125,"ingested_from":59},"W7215049668",[178,180,183,186,188,190,192],{"name":179,"orcid":8},"Israel Gomes Cruz",{"name":181,"orcid":182},"Alcinei Mí­stico Azevedo","https:\u002F\u002Forcid.org\u002F0000-0001-5196-0851",{"name":184,"orcid":185},"Richardson Fernandes de Souza","https:\u002F\u002Forcid.org\u002F0000-0001-9065-9527",{"name":187,"orcid":8},"Gabriel Martins Afonso",{"name":189,"orcid":8},"Luan Silva Donato",{"name":191,"orcid":8},"Victor Augustus Vasconcelos Oliveira",{"name":193,"orcid":194},"Leonardo David Tuffi Santos","https:\u002F\u002Forcid.org\u002F0000-0002-9362-778X",{"tldr":196,"method":197,"finding":198,"direction":125,"opportunity":199},"用智能手机图像和随机森林模型评估草甘膦对杂草的防效。","智能手机拍摄杂草图像，提取27个植被指数，随机森林建模。","基于视觉评分和死株率的模型验证r²分别达0.92和0.90，效果优异。","可开发移动端杂草防效实时评估应用，并拓展至其他除草剂与作物场景。","2026-10-02T23:30:01.575485Z",{"id":202,"title":203,"url":204,"summary":205,"summary_zh":8,"content":8,"source_name":206,"source_url":204,"published_at":207,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":208,"score_detail":209,"sources":211,"tags":213,"primary_topic":216,"secondary_topics":220,"classification":221,"search_phrases":234,"slug":237,"view_count":108,"doi":238,"paper":239,"created_at":251},3520,"Factors influencing digital technology investments on French farms","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10429-3","Factors influencing digital technology investments on French farms。Precision Agriculture","Precision Agriculture","2026-09-25T00:00:00Z",68,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":74,"relevant":21,"comment":210},"核心期刊论文，方法数据扎实，但研究对象为法国农场，对国内三农实践的直接参考价值有限，时效性尚可。",[212],{"name":206,"url":204},[214,140,215,81,26],"数字农业","精准农业",{"code":162,"slug":217,"name":218,"path":219,"parent_code":88},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture",[],{"status":91,"locked":13,"version":33,"method":34,"evidence":222},{"candidates":223,"agricultural_evidence":37},[224,230],{"code":162,"score":225,"supported":37,"title":226,"abstract":227,"tags":229},6,[],[228],"precision agriculture",[214,140,215],{"code":100,"score":21,"supported":13,"title":231,"abstract":232,"tags":233},[],[],[81],[235,236],"法国农场 数字技术 投资","Precision Agriculture 法国","法国农场数字技术投资-3520","10.1007\u002Fs11119-026-10429-3",{"doi":238,"openalex_id":240,"authors":241,"venue":206,"cited_by_count":43,"oa_url":8,"card":8,"direction":123,"ingested_from":59},"W7214319682",[242,245,248],{"name":243,"orcid":244},"Maha Ben Jaballah","https:\u002F\u002Forcid.org\u002F0000-0002-4489-278X",{"name":246,"orcid":247},"Aude Ridier","https:\u002F\u002Forcid.org\u002F0000-0002-7893-735X",{"name":249,"orcid":250},"Karine Daniel","https:\u002F\u002Forcid.org\u002F0000-0001-9550-5666","2026-09-26T23:30:02.695117Z"]