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Scientific Evaluation of Five Coffee Cooperative Organizations in Oromia, Ethiopia: Digital Traceability, Value Conversion, Institutional Dependencies and Five-Year Transformation Strategies","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23504407","Comparative Scientific Evaluation of Five Coffee Cooperative Organizations in Oromia, Ethiopia: Digital Traceability, Value Conversion, Institutional Dependencies and Five-Year Transformation Strategies Authors: Kedir Nefo Biftu, Kebede Hail, and Tadila Tadessa QajelaPublication year: 2026Publication type: Comparative scientific research article \u002F Preprint Description This scientific article presents a comparative evaluation of five coffee cooperative organizations in Oromia, Ethiopia: Chercher Oda Bultum Farmers Cooperative Union, Homa Multipurpose Farmers Cooperative, Kata Muduga Multipurpose Farmers Cooperative Union, Torban Anfillo Multipurpose Farmers Cooperative Union, and Sadetan Chora Farmers Cooperative Union. The research investigates variability in biological production, institutional organization, processing efficiency, digital traceability, product quality, market participation and financial value realization. The study uses institutional questionnaires, cooperative administrative records and previously developed case assessments, supported by recent international, African and Ethiopian scientific literature. Its integrated analytical framework comprises 18 end-to-end value-chain components, nine value-creation and conversion gates, three institutional levels and the eight-stage Big Win methodology. The comparative results demonstrate significant differences in organizational scale, coffee-growing ecology, farmer registration, geospatial mapping, production measurement, processing infrastructure, quality assurance, certification, export-market participation, financial settlement and digital readiness. The findings also identify common weaknesses in upstream traceability, data consistency, coffee-lot genealogy, processing mass balance, producer-level quality differentiation and verifiable links between commercial transactions and farmer payments. The scientific discussion examines how biological productivity, environmental sustainability, quality conservation, processing efficiency, value addition, buyer acceptance, and realized commercial value depend on reliable institutional coordination and information exchange. The analysis draws on recent research concerning digitalization and sustainability (Hidalgo et al., 2023), Ethiopian coffee traceability and buyer trust (Mbakop et al., 2023), digital traceability capabilities (Tadesse et al., 2024), geographical-origin compliance (Urugo et al., 2025), Ethiopian coffee-quality differentiation (Worku, Astatkie, and Boeckx, 2023), and sustainability performance in southwestern Oromia (Botola, Werke, and Birbirsa, 2026). International institutional frameworks from the International Coffee Organization, UNIDO, FAO, GS1, and ISO inform the proposed approaches to value-chain resilience, circular resource management, digital interoperability, and product traceability. The article proposes an integrated Smart Digital Coffee Value Chain architecture linking market intelligence, genetic resources, nursery development, producer and plot identity, extension services, production, harvesting, aggregation, processing, quality assurance, storage, certification, market contracts, buyer acceptance, financial settlement and feedback. A five-year scientific transformation proposal identifies differentiated improvement priorities for each organization, together with harmonized Business Requirements (BR), Functional Requirements (FR), institutional data dependencies, Key Performance Indicators (KPIs), Key Risk Indicators (KRIs) and corrective and preventive actions. The principal scientific contribution is a comparative framework demonstrating how cooperative organizations with different ecological, institutional, and commercial characteristics can implement a common, interoperable digital value-chain system while following locally appropriate transformation pathways. The study contributes to scientific understanding of cooperative value-chain performance, digital agriculture, institutional coordination, biological and economic efficiency, environmental sustainability and commercial value realization. The findings are based principally on documentary and organizational evidence. Reported performance indicators are distinguished from calculated measures and proposed interventions. Differences in reporting periods, measurement units and data completeness limit direct statistical comparability, while the proposed five-year improvements require implementation and independent evaluation. Keywords Smart Digital Coffee Value Chain; Comparative Scientific Evaluation; Oromia; Ethiopia; Coffee Cooperative Unions; Chercher Oda Bultum; Homa Cooperative; Kata Muduga; Torban Anfillo; Sadetan Chora; Digital Traceability; Value Creation; Value Conservation; Value Addition; Biological Efficiency; Economic Efficiency; Institutional Dependency; Big Win Methodology; Sustainable Coffee Production.","埃塞俄比亚奥罗米亚地区五家咖啡合作社组织的比较科学评估：数字可追溯性、价值转化、制度依赖与五年转型战略\n\n作者：Kedir Nefo Biftu、Kebede Hail、Tadila Tadessa Qajela\n\n出版年份：2026年\n\n出版类型：比较科学研究论文\u002F预印本\n\n描述\n\n本科学论文对埃塞俄比亚奥罗米亚地区五家咖啡合作社组织进行了比较评估：Chercher Oda Bultum农民合作社联盟、Homa多用途农民合作社、Kata Muduga多用途农民合作社联盟、Torban Anfillo多用途农民合作社联盟和Sadetan Chora农民合作社联盟。该研究考察了生物生产、制度组织、加工效率、数字可追溯性、产品质量、市场参与和财务价值实现方面的变异性。研究采用机构问卷、合作社行政记录和先前开发的案例评估，并以近期国际、非洲和埃塞俄比亚科学文献为支撑。其综合分析框架由18个端到端价值链组成部分、九个价值创造与转化关口、三个制度层面和八阶段“大赢”方法组成。比较结果揭示了在组织规模、咖啡种植生态、农民登记、地理空间测绘、生产计量、加工基础设施、质量保证、认证、出口市场参与、财务结算和数字准备度方面存在显著差异。研究结果还识别了上游可追溯性、数据一致性、咖啡批次谱系、加工物料平衡、生产者层面质量分级以及商业交易与农民付款之间可验证联系方面的共同弱点。科学讨论审视了生物生产力、环境可持续性、质量保持、加工效率、价值增值、买方接受度和已实现商业价值如何依赖于可靠的制度协调和信息交换。分析借鉴了近期关于数字化与可持续性（Hidalgo等，2023）、埃塞俄比亚咖啡可追溯性与买方信任（Mbakop等，2023）、数字可追溯能力（Tadesse等，2024）、地理原产地合规（Urugo等，2025）、埃塞俄比亚咖啡质量分级（Worku","Zenodo (CERN European Organization for Nuclear Research)","2026-10-20T00:00:00Z","论文",false,59,{"impact":173,"substance":361,"depth":584,"authority":585,"freshness":585,"relevant":455,"comment":586},15,9,"该研究以埃塞俄比亚咖啡合作社为对象，提出数字价值链框架，方法有一定新意，但属区域性案例研究，对国内三农信息化仅有间接参考价值。",[588],{"name":578,"url":575},[590,591,592,593,594],"数字农业","价值链","合作社","数字溯源","咖啡产业",{"code":294,"slug":295,"name":296,"path":297,"parent_code":271},[],{"status":598,"locked":581,"version":4,"method":599,"evidence":600},"classified","rules",{"candidates":601,"agricultural_evidence":603},[602,609,614],{"code":294,"score":584,"supported":603,"title":604,"abstract":606,"tags":608},true,[605],"coffee",[607,605],"咖啡",[594],{"code":496,"score":400,"supported":603,"title":610,"abstract":611,"tags":613},[],[612],"农民合作社",[592],{"code":16,"score":400,"supported":603,"title":615,"abstract":616,"tags":618},[],[617],"digital agriculture",[590],[620,621],"埃塞俄比亚 咖啡合作社 数字溯源","咖啡产业 数字农业 数字溯源 价值链","埃塞俄比亚咖啡合作社数字溯源-5479","10.5281\u002Fzenodo.23504407",{"doi":623,"openalex_id":625,"authors":626,"venue":578,"cited_by_count":424,"oa_url":575,"card":633,"direction":639,"ingested_from":640},"W7223041235",[627,629,631],{"name":628,"orcid":11},"Kedir Biftu",{"name":630,"orcid":11},"Kebede Hailu",{"name":632,"orcid":11},"Tadila Tadessa Qajela",{"tldr":634,"method":635,"finding":636,"direction":637,"opportunity":638},"比较埃塞俄比亚奥罗米亚五个咖啡合作社的数字化追溯与价值转化差异。","机构问卷、合作社行政记录与案例评估，构建18环节价值链分析框架。","各社在组织规模、数字追溯与支付可验证性差异显著，上游追溯普遍薄弱。","数字乡村与农业信息化","可研究小农户合作社低成本数字追溯与批次谱系方案，打通交易与农户付款的可验证链路。","智慧农业 \u002F 农业物联网","openalex","2026-10-21T23:30:09.167175Z",{"id":643,"title":644,"url":645,"summary":646,"summary_zh":647,"content":11,"source_name":578,"source_url":645,"published_at":579,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":648,"score_detail":649,"sources":651,"tags":653,"primary_topic":658,"secondary_topics":659,"classification":660,"search_phrases":683,"slug":686,"view_count":424,"doi":687,"paper":688,"created_at":699},5477,"Smart Digital Coffee Value Chain Transformation: A Scientific Implementation Manual for Kata Muduga Multipurpose Farmers Cooperatives Union, Oromia, Ethiopia Authors: Kedir Nefo Biftu, Kebede Hail, and Tadila Tadessa Qajela","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23488198","Smart Digital Coffee Value Chain Transformation: A Scientific Implementation Manual for Kata Muduga Multipurpose Farmers Cooperatives Union, Oromia, EthiopiaAuthors: Kedir Nefo Biftu, Kebede Hail, and Tadila Tadessa Qajela Publication year: 2026 Publication type: Scientific technical manual and five-year strategic implementation guide DescriptionThis 30-page scientific implementation manual provides a practical, evidence-based framework for transforming the coffee value chain of Kata Muduga Multipurpose Farmers Cooperatives Union in Jimma Zone, Oromia, Ethiopia. The manual translates institutional assessment findings, scientific knowledge, digital-system requirements, and value-chain management principles into operational procedures suitable for cooperative organizations and other agricultural value-chain institutions. The manual draws on the Union's institutional questionnaire completed on 7 October 2026 and relevant scientific publications on international, African, and Ethiopian coffee production, processing, traceability, sustainability, and cooperative development. Its integrated methodological structure consists of 18 end-to-end coffee value-chain components, nine value-creation and conversion points, three institutional levels, and eight stages of the Big Win delivery methodology. The institutional baseline includes 28,164 members, 61,270 hectares of coffee-growing land, 49,287 productive hectares, an annual production of 315,337 quintals, 62 washing and collection facilities, 5,127 GPS-mapped farms, and reported direct-export experience. The manual evaluates these existing institutional capacities and identifies priority gaps in farmer-level digital identification, planting-material genealogy, production information, product traceability, processing utilization, quality testing, environmental compliance, market contracts, payment verification, and coordinated performance management. It establishes differentiated responsibilities for three institutional levels: Core value-chain actors: Farmers, primary cooperatives, cooperative unions, processors, exporters, and buyers responsible for biological production, collection, processing, commercial transactions, and payment. Supporting institutions: Research centers, agricultural extension services, financial institutions, quality laboratories, logistics providers, and digital technology organizations responsible for specialized technical and operational services. Policy and enabling institutions: The Oromia Cooperative Promotion Commission, agricultural and trade bureaus, regulatory organizations, and regional and national governments responsible for governance, standards, policy coordination, and enabling infrastructure. The manual specifies practical Business Requirements (BR), Functional Requirements (FR), data ownership, information exposing and consuming relationships, interoperable digital identification, transaction records, quality assurance, institutional accountability, and measurable performance indicators. Recent international and African experiences are incorporated to identify transferable best practices in cooperative modernization, agricultural digitalization, specialty-coffee differentiation, sustainable production, traceability, and circular value creation. The proposed five-year implementation roadmap applies the Big Win methodology through Direction, Mini-Lab, Open Day, Roadmap, KPI\u002FKRI Development, Implementation, Verification, and Annual Performance Reporting. Implementation priorities include reliable farmer and farm registration, verified production data, digital collection receipts, coffee-lot genealogy, quality-preserving processing, transparent market contracts, buyer acceptance, producer-payment reconciliation, and feedback-driven corrective actions. The principal contribution of this manual is a structured and adaptable implementation model for converting agricultural production potential into traceable products, conserved quality, added commercial value, accepted market transactions, and realized producer benefits. The manual is designed for practical application by farmers, cooperatives, cooperative unions, researchers, government organizations, development partners, technology developers, exporters, and investors in Ethiopia, Africa, and other coffee-producing regions. It serves as an operational companion to the scientific case study and provides guidance for institutional capacity development, digital-system implementation, monitoring, evaluation, and replication of verified best practices. The five-year roadmap and performance targets represent proposed interventions requiring stakeholder approval, practical implementation, and independent validation. KeywordsSmart Digital Coffee Value Chain; Kata Muduga; Scientific Implementation Manual; Coffee Cooperatives; Jimma; Oromia; Ethiopia; Digital Agriculture; Value Creation; Value Conservation; Value Addition; Biological Efficiency; Economic Efficiency; Business Requirements; Functional Requirements; Institutional Dependency; Big Win Methodology; CAPA; Coffee Sustainability.","智能数字咖啡价值链转型：埃塞俄比亚奥罗米亚州Kata Muduga多用途农民合作社联盟科学实施手册\n\n作者：Kedir Nefo Biftu、Kebede Hail 和 Tadila Tadessa Qajela\n出版年份：2026年\n出版类型：科学技术手册与五年战略实施指南\n\n描述\n\n这本30页的科学实施手册为埃塞俄比亚奥罗米亚州吉马地区的Kata Muduga多用途农民合作社联盟的咖啡价值链转型提供了一个实用的、基于证据的框架。该手册将机构评估结果、科学知识、数字系统要求和价值链管理原则转化为适用于合作社组织及其他农业价值链机构的操作程序。该手册依据该联盟于2026年10月7日完成的机构问卷，以及有关国际、非洲和埃塞俄比亚咖啡生产、加工、可追溯性、可持续性和合作社发展的相关科学出版物。其综合方法结构由18个端到端咖啡价值链组成部分、九个价值创造与转化点、三个机构层级以及“大赢”（Big Win）交付方法的八个阶段组成。机构基线包括28，164名成员、61，270公顷咖啡种植土地、49，287公顷生产性土地、年产量315，337公担、62个水洗和收集设施、5，127个经GPS测绘的农场，以及已报告的直接出口经验。该手册评估了这些现有机构能力，并识别出农民层面数字身份识别、种植材料谱系、生产信息、产品可追溯性、加工利用率、质量检测、环境合规、市场合同、付款验证和协调绩效管理等方面的优先差距。该手册为三个机构层级确立了差异化责任：核心价值链参与者：负责生物生产、收集、加工、商业交易和付款的农民、初级合作社、合作社联盟、加工商、出口商和买家。支持性机构：负责专业技术和运营服务的研究中心、农业推广服务机构、金融机构、质量实验室、物流服务提供商和数字技术组织。政策与赋能机构：奥罗",79,{"impact":361,"substance":321,"depth":361,"authority":416,"freshness":585,"relevant":455,"comment":650},"该手册面向埃塞俄比亚咖啡合作社，提供数字化价值链实施框架，数据详实、方法结构清晰，对农业信息化有参考价值，但属区域性案例，影响范围有限。",[652],{"name":578,"url":645},[590,654,655,656,657],"智慧农业","咖啡产业链","农业合作社","产品溯源",{"code":294,"slug":295,"name":296,"path":297,"parent_code":271},[],{"status":598,"locked":581,"version":4,"method":599,"evidence":661},{"candidates":662,"agricultural_evidence":603},[663,667,674,678],{"code":294,"score":345,"supported":603,"title":664,"abstract":665,"tags":666},[605],[607,605],[],{"code":488,"score":668,"supported":603,"title":669,"abstract":670,"tags":673},6,[],[671,672],"农业推广","agricultural extension",[],{"code":16,"score":408,"supported":603,"title":675,"abstract":676,"tags":677},[],[617],[590,654],{"code":496,"score":679,"supported":603,"title":680,"abstract":681,"tags":682},3,[],[612],[],[684,685],"Kata Muduga 咖啡 合作社","埃塞俄比亚 咖啡 数字价值链","KataMuduga咖啡合作社-5477","10.5281\u002Fzenodo.23488198",{"doi":687,"openalex_id":689,"authors":690,"venue":578,"cited_by_count":424,"oa_url":645,"card":694,"direction":639,"ingested_from":640},"W7223028195",[691,692,693],{"name":628,"orcid":11},{"name":630,"orcid":11},{"name":632,"orcid":11},{"tldr":695,"method":696,"finding":697,"direction":637,"opportunity":698},"为埃塞俄比亚咖啡合作社联盟制定数字化价值链转型的科学实施手册。","基于机构问卷与文献，构建18个价值链环节、三级机构与八阶段交付方法。","识别出农户数字身份、溯源、质检与支付核验等优先数字化缺口。","可研究小农户合作社场景下低成本溯源与数字身份落地的效果评估。","2026-10-21T23:30:08.879737Z",{"id":701,"title":702,"url":703,"summary":704,"summary_zh":705,"content":11,"source_name":578,"source_url":703,"published_at":706,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":45,"score_detail":707,"sources":709,"tags":711,"primary_topic":713,"secondary_topics":714,"classification":715,"search_phrases":732,"slug":735,"view_count":424,"doi":736,"paper":737,"created_at":748},5377,"Title: Smart Digital Guji Cooperative Coffee Value Chain Transformation: Scientific Evaluation, Value Conversion, Digital Traceability and Five-Year Strategic Implementation Framework in Oromia, Ethiopia","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23481514","Title: Smart Digital Guji Cooperative Coffee Value Chain Transformation: Scientific Evaluation, Value Conversion, Digital Traceability, and Five-Year Strategic Implementation Framework in Oromia, Ethiopia Author: Kedir Nefo Biftu, Kebede Hailu, Tadila Tadessa Qajela DescriptionThis scientific paper presents an evidence-based evaluation and five-year strategic transformation framework for Homa Multipurpose Farmers Cooperative in Abaya Woreda, West Guji Zone, Oromia, Ethiopia. The study investigates how smallholder coffee production, cooperative aggregation, processing, quality assurance, international marketing, and financial transactions can be integrated into a smart digital coffee value chain that improves traceability, value conservation, economic efficiency, and producer benefits. The research integrates a structured cooperative questionnaire, a farmer-level administrative registry containing 2,000 records, and a comprehensive Business and Functional Requirements Specification (BFRS). The analytical framework comprises 18 end-to-end coffee value-chain components, nine points of value creation (conservation, addition, and conversion), three institutional levels (core value-chain actors, supporting institutions, and the policy environment), and the eight-stage Big Win delivery methodology. The assessment documents 2,000 cooperative members, including 449 women and 204 youth, managing approximately 1,585.2 hectares of coffee production. The cooperative reports annual red-cherry production of 4,968.696 tonnes, while its farmer registry records 4,832.929 tonnes. Reported processing volume is 2,388.254 tonnes of red cherry, equivalent to approximately 477.651 tonnes of green coffee under an indicative 5:1 conversion assumption. Direct green-coffee exports total 264.240 tonnes, with a reported export value of US$2,069,127.329 and an implied export unit value of approximately US$7.83\u002Fkg. The results identify important organizational strengths, including nursery operations, mapped smallholder farms, two washing stations, specialty coffee production, export-market participation, and reported certification activities. However, the analysis reveals weaknesses in planting-material genealogy, reliable source identification, digital transaction continuity, coffee-lot traceability, quality-linked producer payments, data validation, and institutional interoperability. Six farmer-coordinate records require further verification, while discrepancies between questionnaire and registry production figures indicate the need for improved administrative data governance. The scientific discussion compares these findings with recent international, African, and Ethiopian publications on coffee sustainability, digital traceability, geographical differentiation, processing quality, cooperative governance, and market competitiveness. Particular emphasis is placed on the relationship between biological productivity, product quality preservation, processing recovery, buyer acceptance, and realized economic value. The paper proposes an integrated Smart Digital Coffee Value Chain architecture linking market intelligence, research and genetics, nursery systems, farmer and plot identification, agricultural services, production, harvesting, collection, processing, storage, certification, buyer contracts, export acceptance, financial settlement, and performance feedback. It further translates the evaluation into prioritized Business Requirements (BR), Functional Requirements (FR), institutional data-supply and consumption responsibilities, interoperable information flows, corrective and preventive actions (CAPA), and measurable Key Performance and Key Risk Indicators (KPI\u002FKRI). A five-year implementation proposal establishes a phased pathway from baseline reconciliation and digital identity verification to transaction-level traceability, quality conservation, commercial integration, producer-payment transparency, and evidence-based scaling. The principal scientific contribution is a practical and replicable framework for transforming fragmented smallholder coffee activities into an integrated, traceable, and economically accountable value chain. The study demonstrates how biological production, quality conservation, value addition, market conversion, and financial realization can be assessed through connected institutional responsibilities and verifiable digital records. The findings provide useful evidence and implementation guidance for coffee farmers, primary cooperatives, cooperative unions, the Oromia Cooperative Promotion Commission, regional and national government institutions, researchers, exporters, investors, and digital-system developers in Ethiopia and other coffee-producing regions. The reported organizational findings describe the available 2026 baseline, while proposed interventions, performance targets, and anticipated economic benefits require field implementation and independent verification. Publication classificationArticle type: Scientific case study, institutional performance assessment, and prospective five-year transformation proposal. Keywords: Smart Digital Coffee Value Chain; Guji Coffee; Homa Multipurpose Farmers Cooperative; Oromia; Ethiopia; Coffee Traceability; Biological Efficiency; Economic Efficiency; Value Conservation; Value Addition; Cooperative Development; Business Requirements; Functional Requirements; Big Win Methodology; Digital Agriculture; Coffee Sustainability.","标题：智能数字古吉合作社咖啡价值链转型：埃塞俄比亚奥罗米亚州科学评估、价值转化、数字追溯与五年战略实施框架\n\n作者：Kedir Nefo Biftu, Kebede Hailu, Tadila Tadessa Qajela\n\n描述：本科学论文针对埃塞俄比亚奥罗米亚州西古吉区阿巴亚沃雷达的霍马多用途农民合作社，提出了一项基于证据的评估与五年战略转型框架。研究探讨如何将小农咖啡生产、合作社聚合、加工、质量保证、国际营销和金融交易整合为智能数字咖啡价值链，以提升可追溯性、价值保存、经济效率和生产者收益。研究整合了结构化合作社问卷、包含2000条记录的农户级行政登记册以及全面的业务与功能需求规范（BFRS）。分析框架包括18个端到端咖啡价值链组成部分、九个价值创造点（保存、增加和转化）、三个制度层面（核心价值链参与者、支持机构和政策环境）以及八阶段“大赢”交付方法。评估记录了2000名合作社成员，其中包括449名妇女和204名青年，管理约1585.2公顷咖啡生产。合作社报告的年红果产量为4968.696吨，而其农户登记册记录为4832.929吨。报告的加工量为2388.254吨红果，在指示性5：1转化假设下约相当于477.651吨生咖啡。直接生咖啡出口总量为264.240吨，报告的出口价值为2，069，127.329美元，隐含出口单价约为7.83美元\u002F千克。结果识别出重要的组织优势，包括苗圃运营、已测绘的小农农场、两个水洗站、精品咖啡生产、出口市场参与以及报告的认证活动。然而，分析揭示了在种植材料谱系、可靠来源识别、数字交易连续性、咖啡批次可追溯性、与质量挂钩的生产者付款、数据验证和制度互操作性方面的弱点。六条农户坐标记录需要进一步核实，而问卷之间的差异","2026-10-19T00:00:00Z",{"impact":416,"substance":321,"depth":361,"authority":173,"freshness":585,"relevant":455,"comment":708},"基于2000条农户登记与18个价值链环节的实证评估，数据扎实、框架可复制，但属区域案例，公共影响有限。",[710],{"name":578,"url":703},[654,592,593,712,655],"埃塞俄比亚",{"code":294,"slug":295,"name":296,"path":297,"parent_code":271},[],{"status":598,"locked":581,"version":4,"method":599,"evidence":716},{"candidates":717,"agricultural_evidence":603},[718,722,728],{"code":294,"score":345,"supported":603,"title":719,"abstract":720,"tags":721},[605],[607,605],[],{"code":496,"score":723,"supported":603,"title":724,"abstract":725,"tags":727},7,[],[612,726],"smallholder",[592],{"code":16,"score":400,"supported":603,"title":729,"abstract":730,"tags":731},[],[617],[654],[733,734],"Guji 咖啡 数字溯源","埃塞俄比亚 咖啡合作社 价值链","Guji咖啡数字溯源-5377","10.5281\u002Fzenodo.23481514",{"doi":736,"openalex_id":738,"authors":739,"venue":578,"cited_by_count":424,"oa_url":703,"card":743,"direction":639,"ingested_from":640},"W7222944563",[740,741,742],{"name":628,"orcid":11},{"name":630,"orcid":11},{"name":632,"orcid":11},{"tldr":744,"method":745,"finding":746,"direction":637,"opportunity":747},"为埃塞俄比亚Guji咖啡合作社设计智慧数字价值链转型与五年战略框架。","合作社问卷、2000条农户登记数据与BFRS需求规范，18环节价值链分析。","合作社具备出口能力，但缺乏批次追溯、数字交易连续性与质量挂钩支付。","可研究小农户咖啡区块链追溯与质量溢价支付机制，填补数据互操作空白。","2026-10-20T23:30:08.394987Z",{"id":750,"title":751,"url":752,"summary":753,"summary_zh":754,"content":11,"source_name":755,"source_url":752,"published_at":756,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":757,"score_detail":758,"sources":762,"tags":764,"primary_topic":770,"secondary_topics":771,"classification":772,"search_phrases":781,"slug":784,"view_count":424,"doi":785,"paper":786,"created_at":808},5142,"Comparative biochemical and transcriptomic profiling of açaí and cacao fruits from Eastern Amazon agroforestry systems","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1970644","Agroforestry is an agricultural practice that combines production with sustainability. In the Eastern Amazon region, this system is used to harvest native fruits such as açaí and cacao. However, different management strategies may be associated with contrasting biochemical and molecular characteristics of these fruits. To evaluate these differences, a comparative observational analysis of açaí pulps and cacao seeds with adhering arils was performed using samples from two neighboring agroforestry systems in Tomé-Açu (Pará, Brazil), with one farm representing an organic management regime (AFS19) and the other representing a conventional management regime (AFS27). The analysis focused on trace element composition, metabolic diversity, and gene expression. The elemental composition of the fruits was broadly similar, although açaí from AFS19 had 35% and 38% higher concentrations of phosphorus and copper, respectively, while cacao from AFS27 had 17%–30% higher concentrations of calcium, manganese, magnesium, and sulfur. The metabolic profiles of both açaí and cacao from AFS19 were more diverse and included several putative compounds, such as apigenin and esculin, tentatively annotated only in samples from AFS19. Transcriptomic data showed that açaí pulps from AFS19 had higher expression of genes associated with antioxidative processes and stress resistance, whereas AFS27 showed higher expression of genes related to ripening, proteolysis, and flavonoid biosynthesis. In cacao seeds, AFS19 showed higher expression of transcripts associated with seed protection and carbohydrate metabolism on reserve mobilization, while AFS27 showed higher expression of genes related to pathogen resistance, seed coating, and maturation. Overall, the combined analysis revealed distinct biochemical and transcriptomic profiles between the two agroforestry systems, providing comparative data on fruit characteristics associated with contrasting management regimes in tropical agroforestry systems.","农林复合经营是一种将生产与可持续性相结合的农业实践。在东亚马逊地区，该系统被用于采收巴西莓（açaí）和可可等本土果实。然而，不同的管理策略可能与该类果实在生化和分子特征上的差异相关。为评估这些差异，本研究对来自巴西帕拉州托梅-阿苏（Tomé-Açu）两个相邻农林复合系统的巴西莓果肉和带假种皮的可可种子进行了比较性观察分析，其中一个农场代表有机管理方式（AFS19），另一个代表常规管理方式（AFS27）。分析聚焦于微量元素组成、代谢多样性和基因表达。果实的元素组成总体相似，但AFS19的巴西莓中磷和铜的浓度分别高出35%和38%，而AFS27的可可中钙、锰、镁和硫的浓度高出17%–30%。AFS19的巴西莓和可可的代谢谱均更为多样，并包含若干推定化合物，如芹菜素（apigenin）和秦皮苷（esculin），这些化合物仅在AFS19样品中被初步注释。转录组数据显示，AFS19的巴西莓果肉中与抗氧化过程和抗逆性相关的基因表达更高，而AFS27中与成熟、蛋白质水解和类黄酮生物合成相关的基因表达更高。在可可种子中，AFS19中与种子保护和碳水化合物代谢（储备动员）相关的转录本表达更高，而AFS27中与病原体抗性、种子包被和成熟相关的基因表达更高。总体而言，联合分析揭示了两类农林复合系统之间不同的生化和转录组谱，为热带农林复合系统中与不同管理方式相关的果实特征提供了比较数据。","Frontiers in Sustainable Food Systems","2026-10-16T00:00:00Z",64,{"impact":173,"substance":361,"depth":759,"authority":760,"freshness":585,"relevant":455,"comment":761},16,13,"该研究对比了亚马逊东部有机与常规管理下巴西莓和可可的生化与转录组差异，方法扎实、数据具体，但属区域性基础研究，公共影响有限，适合作为论文类资讯收录，不进入每日精选。",[763],{"name":755,"url":752},[765,766,767,768,769],"农林复合系统","亚马逊水果","代谢组学","转录组学","有机管理",{"code":294,"slug":295,"name":296,"path":297,"parent_code":271},[],{"status":598,"locked":581,"version":4,"method":599,"evidence":773},{"candidates":774,"agricultural_evidence":603},[775],{"code":294,"score":345,"supported":603,"title":776,"abstract":778,"tags":780},[777],"cacao",[779,777],"可可",[],[782,783],"Tomé-Açu 农林复合系统 巴西莓","可可 农林复合系统 转录组","Tomé-Açu农林复合系统巴西莓-5142","10.3389\u002Ffsufs.2026.1970644",{"doi":785,"openalex_id":787,"authors":788,"venue":755,"cited_by_count":424,"oa_url":801,"card":802,"direction":806,"ingested_from":640},"W7222624325",[789,791,793,795,797,799],{"name":790,"orcid":11},"R. B. Dextro",{"name":792,"orcid":11},"A. Virgilio",{"name":794,"orcid":11},"S. M. Alencar",{"name":796,"orcid":11},"A. F. Cruz",{"name":798,"orcid":11},"N. Ono",{"name":800,"orcid":11},"S. M. Tsai","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1970644\u002Fpdf",{"tldr":803,"method":804,"finding":805,"direction":806,"opportunity":807},"比较亚马逊东部两种农林复合管理下巴西莓与可可果实的生化及转录组差异。","对有机与常规管理样品的果肉\u002F种子做元素、代谢组和转录组比较分析。","有机管理果实代谢物更多样、抗氧化基因表达更高，常规管理则成熟与抗病基因更活跃。","农业绿色发展与碳","可延伸研究管理方式如何通过代谢与基因表达影响热带农林果实的品质与功能性成分。","2026-10-17T23:30:06.787420Z",{"id":810,"title":811,"url":812,"summary":813,"summary_zh":11,"content":11,"source_name":814,"source_url":11,"published_at":11,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":815,"score_detail":816,"sources":818,"tags":820,"primary_topic":826,"secondary_topics":827,"classification":828,"search_phrases":851,"slug":854,"view_count":424,"doi":855,"paper":856,"created_at":864},4786,"From classical practices to precision agriculture","https:\u002F\u002Fwww.frontiersin.org\u002Farticles\u002F10.3389\u002Ffpls.2026.1848455\u002Ffull","Yildiz等学者在Frontiers in Plant Science发表茶叶精准农业综述,首次整合茶树长期驯化史与近年精准农业、数字技术进展,跨越历史、生物、农业生态、技术四维度。综述揭示茶树产量和品质受基因型—环境—管理交互作用强烈影响,气候波动不断加剧这种关系的不确定性。文章提出区域特化的精准技术集成、低成本可扩展技术研发、气候适应性栽培策略三大未来重点。","Frontiers in Plant Science 2026",73,{"impact":361,"substance":508,"depth":361,"authority":345,"freshness":679,"relevant":455,"comment":817},"核心期刊发表的茶树精准农业综述，整合驯化史与技术进展并提出三大方向，专业增量足但时效性偏弱，可入精选备选。",[819],{"name":814,"url":812},[821,822,823,824,825],"精准农业","茶树","数字农业技术","农业综述","气候适应性栽培",{"code":294,"slug":295,"name":296,"path":297,"parent_code":271},[],{"status":598,"locked":581,"version":4,"method":599,"evidence":829},{"candidates":830,"agricultural_evidence":603},[831,836,841,846],{"code":16,"score":416,"supported":603,"title":832,"abstract":834,"tags":835},[833],"precision agriculture",[821],[821],{"code":294,"score":668,"supported":603,"title":837,"abstract":838,"tags":840},[],[839,822],"茶叶",[],{"code":176,"score":679,"supported":603,"title":842,"abstract":843,"tags":845},[],[844],"农业生态",[],{"code":184,"score":679,"supported":603,"title":847,"abstract":848,"tags":850},[],[849],"气候适应",[],[852,853],"茶树 精准农业","Yildiz 茶树 数字技术","茶树精准农业-4786","10.3389\u002Ffpls.2026.1848455\u002Ffull",{"doi":855,"openalex_id":11,"authors":857,"venue":11,"cited_by_count":424,"oa_url":11,"card":858,"direction":639,"ingested_from":863},[],{"tldr":859,"method":860,"finding":861,"direction":639,"opportunity":862},"综述茶叶精准农业，整合驯化史与数字技术，提出三大未来重点。","文献综述，跨历史、生物、农业生态与技术四维度整合分析。","茶树产量品质受基因型—环境—管理交互影响，气候波动加剧不确定性。","可探索低成本可扩展的茶园传感与区域特化精准技术集成方案。","agent","2026-10-13T00:08:38.597555Z",{"id":866,"title":867,"url":868,"summary":869,"summary_zh":870,"content":11,"source_name":871,"source_url":868,"published_at":872,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":873,"score_detail":874,"sources":876,"tags":878,"primary_topic":883,"secondary_topics":884,"classification":886,"search_phrases":909,"slug":912,"view_count":424,"doi":913,"paper":914,"created_at":939},4685,"Farm management and yield gaps in Robusta (Coffea canephora) coffee production across the three largest Asian origins","https:\u002F\u002Fdoi.org\u002F10.25186\u002F.v21i.2515","Robusta coffee is a key livelihood crop in Asia, and its productivity differs largely across main regions. However, the factors underlying these differences remain limited. This study examines yield variation across Vietnam, Indonesia, and India, the three largest Robusta-producing countries in Asia. Using 13,525 farm-level surveys, the study applied descriptive statistics, correlation analysis, and regression-based threshold methods to assess the relationships between yield and key management practices. The results show that median yields reach 2,643 kg GBE ha⁻¹ in Vietnam, compared with 1,359 kg ha⁻¹ in India-Karnataka, 509 kg ha⁻¹ in India-Kerala, and 383 kg ha⁻¹ in Indonesia. Correlation analysis shows that inorganic fertilizer use is positively associated with yield across the three countries, with coefficients ranging from 0.326 to 0.408. Pruning practices are also associated with higher yields in several regions, whereas shade levels are negatively related to yield in Vietnam. These findings indicate that yield gaps in Asian Robusta systems arise from different constraints across countries. Input scarcity appears to constrain productivity in Indonesia, while inefficiencies in nutrient management and structural characteristics of production systems play a larger role in Vietnam and India. The results highlight the need for context-specific strategies to improve productivity and sustainability in Robusta coffee production. Key words: Yield differences; fertilizer efficiency; shade management; plantation rejuvenation; Robusta coffee.","罗布斯塔咖啡（Robusta coffee）是亚洲重要的生计作物，但其生产力在主产区之间差异显著。然而，造成这些差异的因素仍缺乏充分研究。本研究考察了亚洲三大罗布斯塔咖啡生产国——越南、印度尼西亚和印度之间的产量差异。研究利用13,525份农场层面调查数据，采用描述性统计、相关分析和基于回归的阈值方法，评估产量与关键管理措施之间的关系。结果表明，越南的产量中位数达到2,643 kg GBE ha⁻¹，而印度卡纳塔克邦为1,359 kg ha⁻¹，印度喀拉拉邦为509 kg ha⁻¹，印度尼西亚为383 kg ha⁻¹。相关分析显示，无机肥料施用与三国产量均呈正相关，系数范围为0.326至0.408。修剪措施在若干地区也与较高产量相关，而越南的遮阴水平与产量呈负相关。这些发现表明，亚洲罗布斯塔咖啡系统的产量差距源于各国不同的限制因素。投入品短缺似乎制约了印度尼西亚的生产力，而养分管理效率低下和生产系统的结构性特征在越南和印度发挥了更大作用。研究结果凸显了制定因地制宜策略以提高罗布斯塔咖啡生产力和可持续性的必要性。关键词：产量差异；肥料效率；遮阴管理；种植园更新；罗布斯塔咖啡。","Coffee Science","2026-10-09T00:00:00Z",77,{"impact":759,"substance":321,"depth":361,"authority":760,"freshness":173,"relevant":455,"comment":875},"基于13,525份农户调查揭示亚洲三大罗布斯塔产区的产量差与管理约束，数据规模大、结论有区域针对性，对咖啡产业信息化与精准施肥有参考价值。",[877],{"name":871,"url":868},[879,594,880,881,882],"小农户","施肥管理","产量差","遮荫栽培",{"code":294,"slug":295,"name":296,"path":297,"parent_code":271},[885],{"code":96,"slug":97,"name":98,"path":99,"parent_code":72},{"status":598,"locked":581,"version":4,"method":599,"evidence":887},{"candidates":888,"agricultural_evidence":603},[889,893,900,905],{"code":294,"score":584,"supported":603,"title":890,"abstract":891,"tags":892},[605],[607,605],[594],{"code":96,"score":585,"supported":603,"title":894,"abstract":895,"tags":899},[],[896,897,898],"肥料","养分管理","fertilizer",[],{"code":32,"score":679,"supported":603,"title":901,"abstract":902,"tags":904},[],[903],"估产",[],{"code":496,"score":455,"supported":581,"title":906,"abstract":907,"tags":908},[],[],[879],[910,911],"越南 印尼 印度 罗布斯塔 咖啡","罗布斯塔 咖啡 产量差 施肥","越南印尼印度罗布斯塔咖啡-4685","10.25186\u002F.v21i.2515",{"doi":913,"openalex_id":915,"authors":916,"venue":871,"cited_by_count":424,"oa_url":932,"card":933,"direction":938,"ingested_from":640},"W7221151398",[917,920,923,926,929],{"name":918,"orcid":919},"Hue Tran","https:\u002F\u002Forcid.org\u002F0000-0003-0606-0525",{"name":921,"orcid":922},"Thuy Phan Thi","https:\u002F\u002Forcid.org\u002F0000-0001-5451-7185",{"name":924,"orcid":925},"Long Nhat Cao","https:\u002F\u002Forcid.org\u002F0000-0002-5345-7472",{"name":927,"orcid":928},"Jacob Schenthal","https:\u002F\u002Forcid.org\u002F0000-0002-1265-033X",{"name":930,"orcid":931},"Ketan Patel","https:\u002F\u002Forcid.org\u002F0009-0007-1813-2449","https:\u002F\u002Fcoffeescience.ufla.br\u002Findex.php\u002FCoffeescience\u002Farticle\u002Fdownload\u002F2515\u002F2608",{"tldr":934,"method":935,"finding":936,"direction":637,"opportunity":937},"基于13525份农户调查，分析越南、印尼、印度罗布斯塔咖啡产量差异及管理因素。","13,525份农户调查，描述统计、相关分析与回归阈值法。","无机肥与产量正相关；印尼受投入不足限制，越南和印度则受养分管理低效与结构因素制约。","可构建分区域咖啡产量差诊断与精准施肥决策模型，结合遥感与物联网实现管理处方推荐。","农业遥感与作物表型","2026-10-11T23:30:31.779147Z",{"id":941,"title":942,"url":943,"summary":944,"summary_zh":945,"content":11,"source_name":946,"source_url":943,"published_at":947,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":948,"score_detail":949,"sources":953,"tags":955,"primary_topic":960,"secondary_topics":961,"classification":963,"search_phrases":983,"slug":986,"view_count":455,"doi":987,"paper":988,"created_at":1012},4598,"Seasonal compatibility of RGB and multispectral vegetation indices in Amazon Cacao","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12518-026-00815-x","Abstract Precision agriculture in cacao cultivation is constrained by multispectral sensor costs, limiting accessibility for smallholder farmers. This study evaluated the seasonal compatibility between RGB-derived visible reflectance and multispectral vegetation indices (NDVI, GNDVI, NDRE) to establish a low-cost monitoring approach for Eastern Amazon cacao plantations. We collected spectral data from 1,431 random sampling points across a 0.9-ha commercial cacao plantation during four seasonal periods (February, May, August, and November 2025) using UAV-mounted RGB and multispectral sensors. Pearson correlations and linear regression models assessed relationships between mean visible reflectance [(R + G+B)\u002F3] and multispectral indices. Geostatistical analysis characterized spatial dependence structure across seasons. GNDVI demonstrated superior compatibility across all seasons (mean R² = 59.3%; range: 52.5–62.9%), outperforming NDRE (44.2%) and NDVI (40.8%) by 15–19% points. Temporal stability was 2.9× higher for GNDVI (SD = 4.7 pp) than NDRE (14.1 pp) and NDVI (12.7 pp). Peak compatibility occurred in May (R² = 62.9%) during wet-to-dry transition. The conversion equation (GNDVI = 0.509–0.000012 × Visible Reflectance; RMSE = 0.077) enables reliable estimation, retaining 59% of multispectral information. Strong spatial dependence (Co\u002F(Co + C) \u003C 10%) was confirmed across all periods, with August providing optimal structure (range = 11.4 m) for management zone delineation. GNDVI is the most reliable RGB proxy for cacao monitoring, democratizing precision agriculture access through affordable technology while maintaining robust spatial and temporal performance year-round.","摘要 可可种植中的精准农业受到多光谱传感器成本的制约，限制了小农户的可及性。本研究评估了RGB衍生的可见光反射率与多光谱植被指数（NDVI、GNDVI、NDRE）之间的季节性兼容性，以建立一种面向东亚马逊可可种植园的低成本监测方法。我们使用无人机搭载的RGB和多光谱传感器，在四个季节时段（2025年2月、5月、8月和11月）从一片0.9公顷商业可可种植园的1，431个随机采样点采集了光谱数据。采用Pearson相关分析和线性回归模型评估平均可见光反射率[（R + G + B）\u002F3]与多光谱指数之间的关系。地统计学分析刻画了各季节的空间依赖结构。GNDVI在所有季节均表现出最优兼容性（平均R² = 59.3%；范围：52.5–62.9%），较NDRE（44.2%）和NDVI（40.8%）高出15–19个百分点。GNDVI的时间稳定性（SD = 4.7个百分点）是NDRE（14.1个百分点）和NDVI（12.7个百分点）的2.9倍。最高兼容性出现在5月（R² = 62.9%），即湿季向干季过渡期。转换方程（GNDVI = 0.509–0.000012 × 可见光反射率；RMSE = 0.077）可实现可靠估算，保留了59%的多光谱信息。所有时期均确认存在强空间依赖性（Co\u002F(Co + C) \u003C 10%），其中8月提供了最优结构（变程 = 11.4 m），适合用于管理分区划分。GNDVI是可可监测中最可靠的RGB替代指标，通过可负担的技术使精准农业更广泛可及，同时全年保持稳健的空间和时间性能。","Applied Geomatics","2026-10-08T00:00:00Z",68,{"impact":173,"substance":950,"depth":951,"authority":760,"freshness":585,"relevant":455,"comment":952},21,17,"以低成本RGB替代多光谱监测亚马逊可可，数据扎实、结论明确，对精准农业降本有参考价值，但属细分作物研究，公共影响有限。",[954],{"name":946,"url":943},[654,956,957,958,959],"遥感","植被指数","可可种植","无人机监测",{"code":294,"slug":295,"name":296,"path":297,"parent_code":271},[962],{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"status":598,"locked":581,"version":4,"method":599,"evidence":964},{"candidates":965,"agricultural_evidence":603},[966,970,975,979],{"code":294,"score":345,"supported":603,"title":967,"abstract":968,"tags":969},[777],[779,777],[],{"code":32,"score":760,"supported":603,"title":971,"abstract":973,"tags":974},[972],"multispectral",[972],[957,956],{"code":16,"score":723,"supported":603,"title":976,"abstract":977,"tags":978},[],[821,833],[654],{"code":496,"score":668,"supported":603,"title":980,"abstract":981,"tags":982},[],[879,726],[],[984,985],"亚马逊 可可 无人机 遥感","GNDVI RGB 植被指数 可可","亚马逊可可无人机遥感-4598","10.1007\u002Fs12518-026-00815-x",{"doi":987,"openalex_id":989,"authors":990,"venue":946,"cited_by_count":424,"oa_url":1006,"card":1007,"direction":938,"ingested_from":640},"W7221039911",[991,994,997,1000,1003],{"name":992,"orcid":993},"Charlyngton da Silva e Silva","https:\u002F\u002Forcid.org\u002F0009-0003-9332-4302",{"name":995,"orcid":996},"Luiz Antônio Soares Cardoso","https:\u002F\u002Forcid.org\u002F0000-0001-6868-7847",{"name":998,"orcid":999},"Ronilson de Souza Santos","https:\u002F\u002Forcid.org\u002F0000-0002-2683-456X",{"name":1001,"orcid":1002},"Carlos Rodrigo Tanajura Caldeira","https:\u002F\u002Forcid.org\u002F0000-0001-7102-7735",{"name":1004,"orcid":1005},"Norberto Cornejo Noronha","https:\u002F\u002Forcid.org\u002F0000-0001-7216-8687","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs12518-026-00815-x.pdf",{"tldr":1008,"method":1009,"finding":1010,"direction":938,"opportunity":1011},"评估RGB与多光谱植被指数在亚马逊可可园的季节性兼容性，以低成本监测替代多光谱传感器。","无人机RGB与多光谱传感器采集1431个样点，用相关、回归和地统计分析四季数据。","GNDVI兼容性最佳（R²=59.3%），时空稳定性最高，5月表现最优，可保留59%多光谱信息。","可探索RGB代理指数在其他热带作物和不同冠层结构下的普适性，并优化低成本管理分区方法。","2026-10-10T23:30:31.142887Z",{"id":1014,"title":1015,"url":1016,"summary":1017,"summary_zh":1018,"content":11,"source_name":1019,"source_url":1016,"published_at":947,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":815,"score_detail":1020,"sources":1022,"tags":1024,"primary_topic":1029,"secondary_topics":1030,"classification":1033,"search_phrases":1074,"slug":1077,"view_count":424,"doi":1078,"paper":1079,"created_at":1101},4570,"Agroecological practices, perceived benefits, and determinants of adoption in the Yayu Coffee Forest Biosphere Reserve, Southwest Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44274-026-01113-4","Agroecology offers a promising pathway for reconciling forest conservation with rural livelihoods in biodiversity-rich tropical landscapes, yet empirical evidence on the breadth, intensity, and determinants of adoption remains limited, particularly in UNESCO biosphere reserves. This study investigates agroecological practices, perceived contributions, and determinants of adoption in the Yayu Coffee Forest Biosphere Reserve (YCFBR), Southwest Ethiopia. A cross-sectional concurrent mixed-methods design was employed, combining 293 randomly selected households, complemented by key informant interviews, focus group discussions, and field observations. Binary logistic regression identified determinants of adoption (extensive margin), while ordinal logistic regression examined adoption intensity using the Agroecological Adoption Index (AAI) (intensive margin). Number of Practices Adopted (NPA), Weighted Adoption Intensity (WAI), and Duration of Adoption (DUR) provided complementary insights into adoption breadth, intensity, and persistence. Qualitative data were analyzed thematically to contextualize quantitative findings. Results showed that 72% of households adopted at least one agroecological practice, including coffee agroforestry, homegarden agroforestry, soil and water conservation, climate-smart agriculture, intercropping, crop rotation, mixed tree planting, afforestation and reforestation, organic soil fertility management, and biological pest and disease management. Adoption intensity varied: 34.6% low, 41.2% moderate, and 24.2% high. Practices were perceived to improve soil fertility, biodiversity conservation, climate resilience, forest management, and livelihood diversification while reducing pressure on natural forests and strengthening ecological connectivity and adaptive capacity. Sex, education, farm size, extension services, training, credit access, and cooperative membership significantly determined adoption and intensity, whereas age, household size, farming experience, and distance to forest were non-significant. Agroecology provides a multifunctional, context-specific pathway for advancing sustainable forest management, biodiversity conservation, and livelihood resilience. Strengthening agroecology-oriented extension, gender-responsive training, access to finance, indigenous knowledge integration, and policy mainstreaming are essential for long-term environmental sustainability and community resilience in the YCFBR and comparable forest-dependent landscapes.","农业生态学为在生物多样性丰富的热带景观中协调森林保护与农村生计提供了一条有前景的路径，但关于其采用广度、强度及决定因素的实证证据仍然有限，尤其是在联合国教科文组织生物圈保护区中。本研究调查了埃塞俄比亚西南部亚尤咖啡森林生物圈保护区（YCFBR）的农业生态实践、感知贡献及采用决定因素。研究采用横断面同步混合方法设计，结合293户随机选取的家庭，并辅以关键知情人访谈、焦点小组讨论和实地观察。二元逻辑回归识别了采用的决定因素（外延边际），而有序逻辑回归则使用农业生态采用指数（AAI）考察了采用强度（内涵边际）。采用实践数量（NPA）、加权采用强度（WAI）和采用持续时间（DUR）为采用广度、强度和持续性提供了补充性见解。定性数据采用主题分析法，以对定量发现进行情境化解释。结果表明，72%的家庭至少采用了一项农业生态实践，包括咖啡农林复合、家庭园圃农林复合、水土保持、气候智能型农业、间作、轮作、混合植树、植树造林和再造林、有机土壤肥力管理以及生物病虫害管理。采用强度存在差异：低度34.6%、中度41.2%、高度24.2%。受访者认为这些实践改善了土壤肥力、生物多样性保护、气候韧性、森林管理和生计多样化，同时减轻了对天然森林的压力，并增强了生态连通性和适应能力。性别、教育程度、农场规模、推广服务、培训、信贷获取和合作社成员资格显著决定了采用及采用强度，而年龄、家庭规模、耕作经验和距森林距离则不显著。农业生态学为推进可持续森林管理、生物多样性保护和生计韧性提供了一条多功能、因地制宜的路径。加强面向农业生态学的推广、性别响应型培训、融资渠道、本土知识整合和政策主流化，对于YCFBR及类似依赖森林的地区实现长期环境可持续性和社区韧性至关重要。","Discover Environment",{"impact":416,"substance":321,"depth":951,"authority":760,"freshness":585,"relevant":455,"comment":1021},"基于293户调查的实证研究，数据扎实、结论具体，对热带林区农业生态化与咖啡产业可持续发展有参考价值，但属区域性案例研究，公共影响层级有限。",[1023],{"name":1019,"url":1016},[1025,594,1026,1027,1028],"可持续生计","生态农业","农林复合经营","生物多样性保护",{"code":294,"slug":295,"name":296,"path":297,"parent_code":271},[1031,1032],{"code":256,"slug":257,"name":258,"path":259,"parent_code":200},{"code":176,"slug":177,"name":178,"path":179,"parent_code":128},{"status":598,"locked":581,"version":4,"method":599,"evidence":1034},{"candidates":1035,"agricultural_evidence":603},[1036,1040,1048,1055,1060,1065,1070],{"code":294,"score":584,"supported":603,"title":1037,"abstract":1038,"tags":1039},[605],[607,605],[594],{"code":256,"score":585,"supported":603,"title":1041,"abstract":1042,"tags":1047},[],[1043,1044,1045,1046],"轮作","间作","crop rotation","intercropping",[],{"code":176,"score":585,"supported":603,"title":1049,"abstract":1050,"tags":1054},[],[1051,844,1052,177,1053],"生物多样性","水土保持","agroecology",[],{"code":144,"score":668,"supported":603,"title":1056,"abstract":1057,"tags":1059},[],[1058,145],"土壤",[],{"code":184,"score":679,"supported":603,"title":1061,"abstract":1062,"tags":1064},[],[1063],"climate resilience",[],{"code":16,"score":679,"supported":603,"title":1066,"abstract":1067,"tags":1069},[],[1068],"smart agriculture",[],{"code":192,"score":455,"supported":581,"title":1071,"abstract":1072,"tags":1073},[],[],[1026],[1075,1076],"埃塞俄比亚 亚尤咖啡林 生物圈保护区","农林复合经营 采纳影响因素","埃塞俄比亚亚尤咖啡林生物圈保护区-4570","10.1007\u002Fs44274-026-01113-4",{"doi":1078,"openalex_id":1080,"authors":1081,"venue":1019,"cited_by_count":424,"oa_url":1095,"card":1096,"direction":639,"ingested_from":640},"W7221057925",[1082,1085,1088,1091,1093],{"name":1083,"orcid":1084},"Alemayehu Abera","https:\u002F\u002Forcid.org\u002F0000-0002-6225-9304",{"name":1086,"orcid":1087},"Samuel Taye","https:\u002F\u002Forcid.org\u002F0000-0001-9662-3939",{"name":1089,"orcid":1090},"Adanech Asfaw","https:\u002F\u002Forcid.org\u002F0009-0007-2586-0559",{"name":1092,"orcid":11},"Chaluma Kicha",{"name":1094,"orcid":11},"Endashew Mogessie","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44274-026-01113-4.pdf",{"tldr":1097,"method":1098,"finding":1099,"direction":806,"opportunity":1100},"研究埃塞俄比亚亚尤咖啡森林生物圈保护区农户采用农业生态实践的程度、感知效益及影响因素。","混合方法：293户问卷+访谈+焦点小组，二元与有序logistic回归分析采用广","72%农户至少采用一项实践，采用强度分低中高；性别、教育、推广、信贷等显著影响采用。","可延伸研究农业生态实践采用对森林保护与生计韧性的长期因果效应及数字化推广干预。","2026-10-10T23:30:11.538537Z",{"id":1103,"title":1104,"url":1105,"summary":1106,"summary_zh":1107,"content":11,"source_name":1108,"source_url":1105,"published_at":947,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1109,"score_detail":1110,"sources":1113,"tags":1115,"primary_topic":1120,"secondary_topics":1121,"classification":1122,"search_phrases":1143,"slug":1146,"view_count":424,"doi":1147,"paper":1148,"created_at":1168},4507,"Nearly ninety percent of Indonesian coffee area is classified as deforestation-free under the European Union Deforestation Regulation (EUDR)","https:\u002F\u002Fdoi.org\u002F10.1088\u002F1748-9326\u002Faeb1e2","Abstract Indonesia is the third-largest coffee producer globally and exports a substantial share of its production to European Union (EU) countries. Recent implementation of the EU Deforestation Regulation (EUDR), which requires deforestation-free supply chains, highlights the need for spatially-explicit coffee mapping to support the assessment of coffee-associated deforestation. In this study, we utilized multitemporal and multi-sensor remote sensing to produce annual maps of coffee distribution from 2016 to 2024. We analyzed the spatial patterns of annual coffee distribution during this period, and evaluated the potential implications of EUDR for coffee production in Indonesia. Our maps of coffee agroecosystems demonstrate an overall classification accuracy exceeding 80%, and are broadly consistent with independent coffee datasets (i.e., national statistics and coffee probability maps from the Forest Data Partnership). We detected a net increase of ~15% in coffee area over the eight-year period, although the patterns of change vary across provinces. Based on these maps, we estimate that 87.9% of the coffee area in 2024 meets the EUDR deforestation-free criterion, and only a relatively small proportion of agroecosystems are classified as non-deforestation-free. These findings highlight opportunities for spatially targeted EUDR implementation, where deforestation-free coffee agroecosystems can be supported towards meeting broader EUDR requirements, while areas associated with forest conversion can be supported towards other pathways, such as sustainable agricultural practices, restoration, and forest conservation.","摘要：印度尼西亚是全球第三大咖啡生产国，并将相当大比例的产量出口至欧盟（EU）国家。近期实施的《欧盟零毁林条例》（EUDR）要求供应链不涉及毁林，这凸显了开展空间显式咖啡制图以支持咖啡相关毁林评估的必要性。本研究利用多时相和多传感器遥感技术，制作了2016年至2024年咖啡分布的年度地图。我们分析了这一时期咖啡年度分布的空间格局，并评估了EUDR对印度尼西亚咖啡生产的潜在影响。我们的咖啡农业生态系统地图总体分类精度超过80%，并与独立咖啡数据集（即国家统计数据以及森林数据伙伴关系提供的咖啡概率图）大体一致。我们检测到八年间咖啡面积净增加约15%，但各省变化格局有所不同。基于这些地图，我们估计2024年87.9%的咖啡面积符合EUDR无毁林标准，仅有相对较小比例的农业生态系统被归类为非无毁林。这些发现凸显了在空间上有针对性地实施EUDR的机会，即可以支持无毁林咖啡农业生态系统满足EUDR更广泛的要求，同时可以支持与森林转化相关的区域转向其他路径，如可持续农业实践、恢复和森林保护。","Environmental Research Letters",86,{"impact":321,"substance":1111,"depth":361,"authority":345,"freshness":585,"relevant":455,"comment":1112},23,"基于多时相多源遥感生成印尼咖啡分布图，量化EUDR合规比例，方法扎实、结论对咖啡出口与可持续供应链有直接参考价值。",[1114],{"name":1108,"url":1105},[1116,594,1117,1118,1119],"遥感监测","供应链溯源","EUDR","毁林零容忍",{"code":294,"slug":295,"name":296,"path":297,"parent_code":271},[],{"status":598,"locked":581,"version":4,"method":599,"evidence":1123},{"candidates":1124,"agricultural_evidence":603},[1125,1129,1134,1138],{"code":294,"score":584,"supported":603,"title":1126,"abstract":1127,"tags":1128},[605],[607,605],[594],{"code":32,"score":723,"supported":603,"title":1130,"abstract":1131,"tags":1133},[],[956,1132],"remote sensing",[1116],{"code":176,"score":679,"supported":603,"title":1135,"abstract":1136,"tags":1137},[],[844],[],{"code":192,"score":679,"supported":603,"title":1139,"abstract":1140,"tags":1142},[],[1141],"可持续农业",[],[1144,1145],"印尼 咖啡 遥感制图","EUDR 咖啡 毁林","印尼咖啡遥感制图-4507","10.1088\u002F1748-9326\u002Faeb1e2",{"doi":1147,"openalex_id":1149,"authors":1150,"venue":1108,"cited_by_count":424,"oa_url":1105,"card":1163,"direction":938,"ingested_from":640},"W7221019615",[1151,1154,1156,1158,1160],{"name":1152,"orcid":1153},"Tin Widyani Satriawan","https:\u002F\u002Forcid.org\u002F0000-0003-4580-9973",{"name":1155,"orcid":11},"Xiangzhong (Remi) Luo",{"name":1157,"orcid":11},"Shafira Nur Ramdhania",{"name":1159,"orcid":11},"Agustín Escobar-López",{"name":1161,"orcid":1162},"Anggun Tridawati","https:\u002F\u002Forcid.org\u002F0000-0002-0070-883X",{"tldr":1164,"method":1165,"finding":1166,"direction":938,"opportunity":1167},"利用多时相多源遥感绘制印尼2016-2024年咖啡分布图，评估EUDR合规性。","多时相多传感器遥感分类，结合国家统计与森林数据伙伴关系概率图验证。","印尼咖啡面积净增约15%，2024年87.9%咖啡区符合EUDR无毁林标准。","可延伸研究：针对非合规小区域，结合高分辨率遥感与供应链追溯，设计精准干预与可持续转型路径。","2026-10-09T23:30:26.065388Z",{"id":1170,"title":1171,"url":1172,"summary":1173,"summary_zh":11,"content":1174,"source_name":1175,"source_url":11,"published_at":1176,"category":1177,"cover_url":11,"hotness":385,"is_selected":581,"score":329,"score_detail":1178,"sources":1180,"tags":1182,"primary_topic":1187,"secondary_topics":1188,"classification":1189,"search_phrases":1208,"slug":1211,"view_count":69,"doi":11,"paper":11,"created_at":1212},4278,"云南保山小粒咖啡数中求精 云智农链平台综合成本降83%亩均增收1500元","https:\u002F\u002Fnews.qq.com\u002Frain\u002Fa\u002F20261004A03PSF00","中国移动云南公司保山分公司联合保山学院浙江理工大学西北工业大学等高校科研团队打造云智农链平台，集农事管理水肥智能控制病虫害预警产品溯源于一体，辛家山咖啡种植基地应用后综合成本下降超83%、鲜果亩均增产超21%、瑕疵率降至3.34%、亩均增收近1500元。2025年保山精品咖啡种植面积18.06万亩、产量2.68万吨、全产业链产值223.28亿元。","辛家山咖啡种植基地负责人杨江春介绍，基地2020年建立之初，便瞄准精品化赛道，与云南省农业科学院热带亚热带经济作物研究所等科研团队合作筛选品种，并在咖啡地里间种香蕉、木豆等，用于遮阴、固氮等。\n\n咖啡树铺满山坡后，人工管护逐渐力不从心：不同坡向、地块的水肥需求不一，何时灌溉、施肥，多靠经验判断；夜间值守时，疲劳还易带来误判。“最棘手的是病虫害。过去，一些病害往往要等到巡园时才能发现，容易错过最佳防治期，影响咖啡品质。”杨江春说。\n\n2024年，庄园接入“云智农链”平台，管护开始有了“数字帮手”。该平台除后台远程调度外，土壤墒情低于预设阈值时，系统还可自动启动灌溉施肥；AI巡检无人机在咖啡林间往返，通过图像识别发现锈病等早期病害，并生成植保建议。“两年多来，庄园综合成本下降超83%，鲜果亩均增产超21%，瑕疵率降至3.34%，亩均增收近1500元。”杨江春说。","腾讯新闻 中新网 2026-10-04","2026-10-04T00:00:00Z","报道",{"impact":759,"substance":508,"depth":584,"authority":173,"freshness":173,"relevant":455,"comment":1179},"智慧农业平台在咖啡种植中的落地案例，成本降幅与增收数据具体，具备行业参考价值。",[1181],{"name":1175,"url":1172},[1183,654,1184,1185,1186],"数字乡村","水肥一体化","咖啡种植","AI巡检",{"code":294,"slug":295,"name":296,"path":297,"parent_code":271},[],{"status":598,"locked":581,"version":4,"method":599,"evidence":1190},{"candidates":1191,"agricultural_evidence":603},[1192,1196,1200,1204],{"code":294,"score":416,"supported":603,"title":1193,"abstract":1194,"tags":1195},[607],[607],[1185],{"code":96,"score":455,"supported":581,"title":1197,"abstract":1198,"tags":1199},[],[],[1184],{"code":16,"score":455,"supported":581,"title":1201,"abstract":1202,"tags":1203},[],[],[654],{"code":56,"score":455,"supported":581,"title":1205,"abstract":1206,"tags":1207},[],[],[1183],[1209,1210],"云智农链 保山小粒咖啡","保山 咖啡 智慧种植","云智农链保山小粒咖啡-4278","2026-10-06T00:05:03.287378Z",{"id":1214,"title":1215,"url":1216,"summary":1217,"summary_zh":1218,"content":11,"source_name":1219,"source_url":1216,"published_at":1220,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1221,"score_detail":1222,"sources":1224,"tags":1226,"primary_topic":1229,"secondary_topics":1230,"classification":1232,"search_phrases":1259,"slug":1262,"view_count":455,"doi":1263,"paper":1264,"created_at":1294},4081,"Aerial Phenotyping of Coffee Flowering Using Deep Learning for Early Yield Prediction","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fplants15193019","Early yield estimation is important for improving decision-making and management practices in coffee production systems. However, approaches that use aerial imagery to characterize flowering and relate it to subsequent yield remain limited. This study evaluated the potential of RGB imagery acquired by remotely piloted aircraft (RPA) combined with Deep Learning techniques to classify coffee plants according to three levels of a flowering proxy and to investigate its relationship with yield. A dataset comprising 1593 segmented RGB images was classified into Low, Medium, and High flowering levels using a custom convolutional neural network and three transfer learning architectures: VGG16, ResNet50V2, and MobileNetV2. ResNet50V2 was selected for final evaluation based on its validation performance and achieved 92.12% accuracy, 92.40% macro-precision, 91.81% macro-recall, and 92.06% macro-F1 on the test dataset. The flowering proxy showed a positive association with field-observed yield, with Spearman’s ρ = 0.701 (p \u003C 0.001) and R2 = 0.528 for the combined dataset of Arara and Mundo Novo. In consolidated out-of-sample predictions, the model achieved R2 = 0.489, MAE = 1.03 kg plant−1, and RMSE = 1.18 kg plant−1. Performance differed between cultivars, with consolidated R2 values of 0.567 for Arara and 0.329 for Mundo Novo. Spatial analysis also showed correspondence between the distribution of flowering levels derived from RGB imagery and the subsequent spatial distribution of yield. These findings indicate that RPA-based RGB imagery and Deep Learning can provide spatialized information related to coffee flowering and its productive performance, with potential applications in precision agriculture. Further evaluation across production cycles, cultivars, and environmental conditions is needed to assess the robustness and generalization of the approach.","早期产量估测对于改善咖啡生产系统中的决策与管理实践具有重要意义。然而，利用航空影像表征开花并将其与后续产量相关联的方法仍然有限。本研究评估了由遥控驾驶航空器（RPA）获取的RGB影像结合深度学习技术，根据开花代理指标的三个水平对咖啡植株进行分类，并探讨其与产量之间关系的潜力。利用自定义卷积神经网络和三种迁移学习架构——VGG16、ResNet50V2和MobileNetV2，将包含1593张分割RGB图像的数据集分类为低、中、高开花水平。基于验证性能，选择ResNet50V2进行最终评估，其在测试数据集上达到了92.12%的准确率、92.40%的宏精确率、91.81%的宏召回率和92.06%的宏F1值。开花代理指标与田间观测产量呈正相关，Arara和Mundo Novo合并数据集的Spearman's ρ = 0.701（p \u003C 0.001），R² = 0.528。在合并的样本外预测中，模型达到了R² = 0.489、MAE = 1.03 kg plant⁻¹和RMSE = 1.18 kg plant⁻¹。不同品种间的表现存在差异，Arara和Mundo Novo的合并R²值分别为0.567和0.329。空间分析还表明，由RGB影像得出的开花水平分布与后续产量的空间分布之间存在对应关系。这些发现表明，基于RPA的RGB影像和深度学习能够提供与咖啡开花及其生产性能相关的空间化信息，在精准农业中具有应用潜力。需要在不同生产周期、品种和环境条件下进行进一步评估，以检验该方法的稳健性和泛化能力。","Plants","2026-10-02T00:00:00Z",78,{"impact":759,"substance":321,"depth":361,"authority":760,"freshness":585,"relevant":455,"comment":1223},"该研究将无人机RGB影像与深度学习结合用于咖啡花期分类和早期产量预测，方法新颖、数据扎实、结论可靠，对智慧农业和精准农业有参考价值，但属于细分作物研究，影响范围有限。",[1225],{"name":1219,"url":1216},[654,1227,1228,956,1185],"农业人工智能","产量预测",{"code":294,"slug":295,"name":296,"path":297,"parent_code":271},[1231],{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"status":598,"locked":581,"version":4,"method":599,"evidence":1233},{"candidates":1234,"agricultural_evidence":603},[1235,1239,1245,1249,1255],{"code":294,"score":584,"supported":603,"title":1236,"abstract":1237,"tags":1238},[605],[607,605],[1185],{"code":24,"score":584,"supported":603,"title":1240,"abstract":1242,"tags":1244},[1241],"deep learning",[1243,1241],"深度学习",[1227],{"code":16,"score":723,"supported":603,"title":1246,"abstract":1247,"tags":1248},[],[821,833],[654],{"code":40,"score":668,"supported":603,"title":1250,"abstract":1251,"tags":1254},[],[1252,1253],"空间分析","spatial analysis",[],{"code":32,"score":69,"supported":581,"title":1256,"abstract":1257,"tags":1258},[],[],[1228,956],[1260,1261],"无人机 RGB 咖啡 花期 产量预测","ResNet50V2 咖啡 深度学习","无人机RGB咖啡花期产量预测-4081","10.3390\u002Fplants15193019",{"doi":1263,"openalex_id":1265,"authors":1266,"venue":1219,"cited_by_count":424,"oa_url":1216,"card":1289,"direction":938,"ingested_from":640},"W7216244707",[1267,1269,1272,1275,1277,1280,1283,1286],{"name":1268,"orcid":11},"Elias Horácio Zavala",{"name":1270,"orcid":1271},"Gabriel Araújo e Silva Ferraz","https:\u002F\u002Forcid.org\u002F0000-0001-6403-2210",{"name":1273,"orcid":1274},"Felipe Andrés Obando Vega","https:\u002F\u002Forcid.org\u002F0000-0001-7241-7065",{"name":1276,"orcid":11},"Mirian de Lourdes Oliveira",{"name":1278,"orcid":1279},"Milene Alves de Figueiredo Carvalho","https:\u002F\u002Forcid.org\u002F0000-0001-6138-8182",{"name":1281,"orcid":1282},"Samuel Henrique Braga da Cunha","https:\u002F\u002Forcid.org\u002F0000-0002-8783-0378",{"name":1284,"orcid":1285},"Andrea Pagliai","https:\u002F\u002Forcid.org\u002F0000-0002-0455-7426",{"name":1287,"orcid":1288},"Daniele Sarri","https:\u002F\u002Forcid.org\u002F0000-0001-6990-7573",{"tldr":1290,"method":1291,"finding":1292,"direction":938,"opportunity":1293},"用无人机RGB影像和深度学习对咖啡开花分级，实现早期产量预测。","1593张分割RGB图，自定义CNN与VGG16\u002FResNet50V2\u002FMobi","ResNet50V2开花分级准确率92.12%，开花等级与产量正相关（ρ=0.701）。","可跨生产周期、品种和环境验证模型泛化性，并融合多源遥感提升早期产量预测稳健性。","2026-10-03T23:30:27.930711Z",{"id":1296,"title":1297,"url":1298,"summary":1299,"summary_zh":1300,"content":11,"source_name":871,"source_url":1298,"published_at":1301,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1302,"score_detail":1303,"sources":1305,"tags":1307,"primary_topic":1309,"secondary_topics":1310,"classification":1312,"search_phrases":1331,"slug":1334,"view_count":455,"doi":1335,"paper":1336,"created_at":1356},3893,"Modeling the productive potential of coffee trees in the Cerrado of Minas Gerais through hierarchical landscape analysis and remote sensing data","https:\u002F\u002Fdoi.org\u002F10.25186\u002F.v21i.2516","Brazil stands out as the world’s largest producer and exporter of Arabica coffee, one of the world’s most important commodities, which plays a crucial role in the country’s economy. In this context, this study develops a model to classify the productive potential of coffee trees based on hierarchical landscape analysis and coffee plant metrics, applied to different farms in the Córrego Feio watershed, Patrocínio, Minas Gerais. We employed a model based on landscape metrics (slope, water flow, vegetation, and slope shape), inspired by Ross’s (1994) proposal for Environmental Fragility. Each variable was reclassified according to hierarchical levels from the literature and empirical observations of the area. The data were integrated to generate two products: Factor T, which represents the potential of the area based on static soil and relief variables, and Calculated Potential, which combines Factor T with the NDVI vegetation index for a dynamic assessment. The application of the model demonstrated a strong capacity to interpret the qualitative hierarchy of productive potential, supporting the delineation of differentiated management zones. The results indicated that productive potential is more strongly associated with soil and topographic factors than with the vegetation index. It is concluded that the proposed method is a valuable tool for precision agriculture, allowing the delineation of management zones for the application of inputs in a more efficient and sustainable manner, contributing to resource conservation and environmental protection. Key words: Arabica coffee; environmental fragility; hierarchical analysis; vegetation index; watershed.","巴西是全球最大的阿拉比卡咖啡（Arabica coffee）生产国和出口国，阿拉比卡咖啡作为全球最重要的商品之一，在该国经济中发挥着至关重要的作用。在此背景下，本研究开发了一个基于层次景观分析和咖啡植株指标的咖啡树生产潜力分类模型，并将其应用于米纳斯吉拉斯州帕特罗西尼奥市Córrego Feio流域的不同农场。我们采用了一个基于景观指标（坡度、水流、植被和坡形）的模型，其灵感来源于Ross（1994）提出的环境脆弱性方案。每个变量根据文献和该区域经验观察所得的层次等级进行重新分类。数据经过整合生成了两个产品：因子T，代表基于静态土壤和地形变量的区域潜力；以及计算潜力，将因子T与NDVI植被指数相结合以进行动态评估。模型的应用展现出强大的能力，能够解释生产潜力的定性层次，支持差异化经营区的划分。结果表明，生产潜力与土壤和地形因子的关联性强于与植被指数的关联性。研究认为，所提出的方法是一种有价值的精准农业工具，能够以更高效和可持续的方式划定管理区以施用投入品，有助于资源保护和环境保护。关键词：阿拉比卡咖啡；环境脆弱性；层次分析；植被指数；流域。","2026-09-30T00:00:00Z",70,{"impact":416,"substance":508,"depth":951,"authority":760,"freshness":173,"relevant":455,"comment":1304},"基于景观分析与NDVI的咖啡生产潜力分级模型，方法新颖、结论可靠，对精准农业管理分区有实用价值，但属区域性研究，影响范围有限。",[1306],{"name":871,"url":1298},[654,821,956,1308,1185],"管理分区",{"code":294,"slug":295,"name":296,"path":297,"parent_code":271},[1311],{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"status":598,"locked":581,"version":4,"method":599,"evidence":1313},{"candidates":1314,"agricultural_evidence":603},[1315,1319,1323,1327],{"code":294,"score":584,"supported":603,"title":1316,"abstract":1317,"tags":1318},[605],[607,605],[1185],{"code":32,"score":585,"supported":603,"title":1320,"abstract":1321,"tags":1322},[1132],[],[956],{"code":16,"score":173,"supported":603,"title":1324,"abstract":1325,"tags":1326},[],[821,833],[654,821],{"code":144,"score":668,"supported":603,"title":1328,"abstract":1329,"tags":1330},[],[1058,145],[],[1332,1333],"Minas Gerais 咖啡 遥感","Córrego Feio 流域 咖啡 生产潜力","MinasGerais咖啡遥感-3893","10.25186\u002F.v21i.2516",{"doi":1335,"openalex_id":1337,"authors":1338,"venue":871,"cited_by_count":424,"oa_url":1298,"card":1351,"direction":938,"ingested_from":640},"W7214888407",[1339,1342,1345,1348],{"name":1340,"orcid":1341},"Jorge Eduardo Ferreira Cunha","https:\u002F\u002Forcid.org\u002F0000-0002-3679-6376",{"name":1343,"orcid":1344},"Mateus Duarte Segismundo","https:\u002F\u002Forcid.org\u002F0009-0001-1289-406X",{"name":1346,"orcid":1347},"George Deroco Martins","https:\u002F\u002Forcid.org\u002F0000-0001-9738-7325",{"name":1349,"orcid":1350},"João Vítor Meza Bravo","https:\u002F\u002Forcid.org\u002F0000-0002-5457-3192",{"tldr":1352,"method":1353,"finding":1354,"direction":938,"opportunity":1355},"基于景观指标与NDVI构建模型，对巴西塞拉多咖啡树生产潜力进行分级分类。","层次化景观分析（坡度、水流、植被、坡形）结合NDVI，生成因子T与计算潜力。","生产潜力与土壤和地形因素关联强于植被指数，可划分差异化精准管理区。","可引入多时相遥感与机器学习，动态预测咖啡生产潜力并优化管理区划分。","2026-10-01T23:30:28.485534Z",{"id":1358,"title":1359,"url":1360,"summary":1361,"summary_zh":11,"content":11,"source_name":1362,"source_url":1360,"published_at":1301,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1363,"score_detail":1364,"sources":1366,"tags":1368,"primary_topic":1372,"secondary_topics":1373,"classification":1374,"search_phrases":1389,"slug":1392,"view_count":69,"doi":1393,"paper":1394,"created_at":1400},3884,"Sustainable Coffee Production: Biotechnology, Circular Bioeconomy, and Climate-Smart Agriculture","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-11196777\u002Fv1","Sustainable Coffee Production: Biotechnology, Circular Bioeconomy, and Climate-Smart Agriculture。Research Square","Research Square",61,{"impact":361,"substance":345,"depth":416,"authority":585,"freshness":173,"relevant":455,"comment":1365},"主题契合农业科技与可持续生产，但摘要信息量有限，属细分领域研究进展，未达重大突破层级。",[1367],{"name":1362,"url":1360},[1369,594,1370,1371],"气候智慧农业","农业生物技术","循环生物经济",{"code":294,"slug":295,"name":296,"path":297,"parent_code":271},[],{"status":598,"locked":581,"version":4,"method":599,"evidence":1375},{"candidates":1376,"agricultural_evidence":603},[1377,1381,1385],{"code":294,"score":416,"supported":603,"title":1378,"abstract":1379,"tags":1380},[605],[605],[594],{"code":16,"score":524,"supported":603,"title":1382,"abstract":1383,"tags":1384},[1068],[1068],[],{"code":184,"score":455,"supported":581,"title":1386,"abstract":1387,"tags":1388},[],[],[1369],[1390,1391],"可持续咖啡生产 生物技术","气候智慧农业 咖啡","可持续咖啡生产生物技术-3884","10.21203\u002Frs.3.rs-11196777\u002Fv1",{"doi":1393,"openalex_id":1395,"authors":1396,"venue":1362,"cited_by_count":424,"oa_url":1360,"card":11,"direction":639,"ingested_from":640},"W7214969071",[1397],{"name":1398,"orcid":1399},"Mitiku Muanenda","https:\u002F\u002Forcid.org\u002F0000-0003-3022-9175","2026-10-01T23:30:09.668346Z",{"id":1402,"title":1403,"url":1404,"summary":1405,"summary_zh":1406,"content":11,"source_name":755,"source_url":1404,"published_at":1301,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1407,"score_detail":1408,"sources":1410,"tags":1412,"primary_topic":1417,"secondary_topics":1418,"classification":1420,"search_phrases":1444,"slug":1447,"view_count":723,"doi":1448,"paper":1449,"created_at":1473},3869,"Full-process mechanization of tea production: technological advances and prospects from mechanization-friendly planting to mechanical harvesting","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1963634","To address labor shortages and rising production costs in the tea industry, as well as the limited adaptability of machinery to hilly and mountainous tea plantations, this study systematically screened and synthesized the literature published between 1990 and 2026 following the PRISMA framework, ultimately including 216 studies. The results indicate that tea plantation mechanization is transitioning from stand-alone machinery and manual assistance toward lightweight, precision-based, intelligent, and fully coordinated operations. However, several challenges remain, including inadequate adaptability to complex terrain, insufficient integration of agricultural machinery with agronomic practices, limited reliability and standardization of key components, inadequate accuracy and efficiency in shoot and leaf recognition, localization, and low-damage harvesting, and an insufficient understanding of the relationship between mechanized operations and tea quality. Future research should promote the development of standardized, machinery-compatible tea plantations; develop modular operation platforms, high-mobility chassis, active terrain-following systems, and flexible end effectors; advance the application of multimodal sensing, autonomous navigation, digital twins, and multi-machine coordination; and establish long-term evaluation systems for operational performance and tea quality across regions and seasons. These efforts will help build an integrated, full-process mechanization system that closely coordinates tea plant characteristics, agronomic practices, mechanical equipment, and information-based control, thereby supporting the efficient, high-quality, green, and sustainable development of the tea industry.","为解决茶产业劳动力短缺、生产成本上升，以及机械对丘陵山区茶园适应性不足等问题，本研究遵循PRISMA框架，系统筛选并综合了1990年至2026年间发表的文献，最终纳入216项研究。结果表明，茶园机械化正从单机作业和人工辅助向轻量化、精准化、智能化和全程协同作业方向转型。然而，仍存在若干挑战，包括对复杂地形的适应性不足、农机与农艺融合不够、关键零部件可靠性和标准化程度有限、芽叶识别、定位和低损伤采摘的精度与效率不足，以及对机械化作业与茶叶品质之间关系的认识不充分。未来研究应推动标准化、宜机化茶园建设；开发模块化作业平台、高通过性底盘、主动仿形系统和柔性末端执行器；推进多模态感知、自主导航、数字孪生和多机协同的应用；并建立跨区域、跨季节的作业性能与茶叶品质长期评价体系。这些努力将有助于构建茶树特性、农艺措施、机械装备与信息化控制紧密协调的一体化全程机械化体系，从而支撑茶产业高效、优质、绿色和可持续发展。",80,{"impact":361,"substance":321,"depth":361,"authority":345,"freshness":173,"relevant":455,"comment":1409},"基于216篇文献的系统综述，梳理茶园全程机械化技术路径与瓶颈，对丘陵山区智能农机研发有参考价值。",[1411],{"name":755,"url":1404},[654,1413,1414,1415,1416],"农业机器人","智能农机","丘陵山区","茶叶机械化",{"code":112,"slug":113,"name":114,"path":115,"parent_code":72},[1419],{"code":294,"slug":295,"name":296,"path":297,"parent_code":271},{"status":598,"locked":581,"version":4,"method":599,"evidence":1421},{"candidates":1422,"agricultural_evidence":603},[1423,1430,1435,1440],{"code":112,"score":385,"supported":603,"title":1424,"abstract":1425,"tags":1429},[],[1426,1427,1428],"农机","机械化","agricultural 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科技助茶促振兴","https:\u002F\u002Fcys.aaas.org.cn\u002F4304505\u002F71246029.html","9月21日至23日黟县第二届干臣杯手工红茶制作大赛在洪星乡成功举办。安徽农科院茶叶所加工团队成员为赛事提供技术指导，设置理论考试、现场技能考核、干茶品质等模块，来自杭州、绍兴、六安及黄山各区县30余名茶人同台竞技。","为传承传统手工红茶制作技艺，规范制茶工艺标准，培育专业制茶人才，助推黟县红茶产业标准化、精品化发展，2026年9月21日至23日，黟县第二届“干臣杯”手工红茶制作大赛在洪星乡成功举办。本次赛事由黟县人力资源和社会保障局、黟县洪星乡人民政府主办，院茶叶所加工团队成员王辉、刘亚芹为赛事提供技术指导。\n\n大赛立足制茶人才综合能力培养，设置理论考试、现场技能考核、干茶品质等模块，来自杭州、绍兴、六安及黄山各区县30余名茶人同台竞技。赛事全程，专家团队驻场把关，主导技术标准、竞赛流程规范、茶叶审评等核心工作，严守赛事公平、公正、专业准则，保障赛事公信力。大赛以赛促学、以赛促训、以赛促改，既是制茶技能竞技，更是精准落地的技能人才培育。\n\n下一步，院茶叶所将持续深化属地服务职能，坚持以茶农需求为导向、以产业发展为牵引，常态化下沉基层开展技术帮扶、技能培训、工艺完善、品种优化等产学研服务。以科技赋能、人才赋能、技术赋能持续为安徽省茶产业高质量发展注入新动能。","安徽省农业科学院茶叶研究所","2026-09-23T12:00:00Z",46,{"impact":173,"substance":416,"depth":585,"authority":385,"freshness":723,"relevant":455,"comment":1484},"县级制茶技能赛事，有院所技术指导与产学研服务内容，但属地方动态、信息增量有限，不宜进入每日精选。",[1486],{"name":1480,"url":1477},[1488,1489,1490,1491,1492],"茶产业","科技助农","红茶","制茶技艺","技能大赛",{"code":294,"slug":295,"name":296,"path":297,"parent_code":271},[],{"status":598,"locked":581,"version":4,"method":599,"evidence":1496},{"candidates":1497,"agricultural_evidence":603},[1498,1502],{"code":294,"score":400,"supported":603,"title":1499,"abstract":1500,"tags":1501},[],[839],[1488],{"code":488,"score":455,"supported":581,"title":1503,"abstract":1504,"tags":1505},[],[],[1489],[1507,1508],"黟县 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Villar","https:\u002F\u002Fwww.researchsquare.com\u002Farticle\u002Frs-10767247\u002Flatest.pdf","2026-09-27T23:30:34.698079Z",{"id":1612,"title":1613,"url":1614,"summary":1615,"summary_zh":11,"content":1616,"source_name":1617,"source_url":11,"published_at":1618,"category":1177,"cover_url":11,"hotness":385,"is_selected":581,"score":757,"score_detail":1619,"sources":1621,"tags":1623,"primary_topic":1629,"secondary_topics":1630,"classification":1632,"search_phrases":1657,"slug":1660,"view_count":455,"doi":11,"paper":11,"created_at":1661},3427,"湖北省科技服务茶叶产业链'515'行动暨山地茶园机械化生产及碾茶加工技术现场观摩培训活动在保康举办","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7687205549078888979\u002F","9月15日湖北省科技服务茶叶产业链'515'行动（协同推广）暨山地茶园机械化生产及碾茶加工技术现场观摩培训活动在襄阳市保康县举办。活动由湖北省农业科学院果树茶叶研究所主办，襄阳市农业科学院、保康县农业农村局承办，110余人参会。无人机修剪、茶园采摘、施肥、植保等机械化作业轮番演示；山地轨道运输系统沿坡而上，智慧化茶园管理应用实时呈现；碾茶加工车间标准化生产流程一目了然。","湖北日报讯（通讯员桂安辉）无人机掠过茶行，轨道车爬坡运叶，智能化碾茶生产线匀速运转……9月15日，湖北省科技服务茶叶产业链“515”行动（协同推广）暨山地茶园机械化生产及碾茶加工技术现场观摩培训活动在襄阳市保康县举办。活动由湖北省农业科学院果树茶叶研究所主办，襄阳市农业科学院、保康县农业农村局承办，襄阳市竹林翠峰茶业有限责任公司、湖北荆襄红茶业有限公司协办。\n\n省果茶办公室副主任赵昆松，省农科院果茶所党委书记陈沫，省农科院果茶所副所长、省科技服务茶叶产业链“515”行动技术牵头人高士伟，襄阳市农业农村局副局长程建胜，襄阳市农业科学院院长罗敬东、纪委书记罗小刚，保康县委副书记王晓静等领导出席活动。\n\n会议特别邀请中国农业科学院茶叶研究所尹军峰研究员、农业农村部南京农业机械化研究所宋志禹研究员作专题报告，省农科院果茶所研究员郑鹏程、金孝芳，华中农业大学副教授余志三名科技服务茶叶产业链“515”行动课题负责人作技术培训。来自全省各茶叶主产区市州农业技术推广部门、农科院、协作企业及媒体记者等110余人参加本次观摩培训活动。\n\n![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F963832cca88a4b15950d56c467cda61a~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1790900173&x-signature=pzZBqp%2FPTphYnz%2B7GXA1E53oRIM%3D)\n\n**从茶园到车间，看“机器换人”如何落地**\n\n上午，与会人员先后走进保康县黄堡镇黄龙沟村茶园、荆襄红罗仕沟茶园和保茶集团。在黄龙沟村茶园，无人机修剪、茶园采摘、施肥、植保等机械化作业轮番演示，山地茶园高效管理的最新成果“看得见、摸得着”；在罗仕沟茶园，山地轨道运输系统沿坡而上，智慧化茶园管理应用实时呈现，碾茶加工车间标准化生产流程一目了然；在保茶集团，智能化名优茶和碾茶加工车间里，设备运行、成品展示吸引代表驻足询问。\n\n一路“看现场、听讲解、观流程”，与会人员直观感受到山地茶园机械化生产与碾茶加工提质增效带来的新气象，对破解山地茶园劳动力短缺、降低生产成本、提高夏秋茶原料利用率有了更真切的认识。\n\n![Image 2](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F22552e8436c11e65007d5e9f374cad79~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1790900173&x-signature=4EF81UWTPlLxUpeH1KZ3ZjqLp18%3D)\n\n**从趋势到实操，听专家如何“开方”**\n\n下午的培训交流会上，保康县政府、襄阳市农业农村局及省农科院果茶所领导分别致辞。尹军峰研究员围绕碾（抹）茶产业发展现状与未来前景，宋志禹研究员围绕山地茶园机械装备与应用，分别作深入讲解，为湖北省茶产业未来发展提供了有益参考。\n\n随后，金孝芳、余志、郑鹏程分别就“鄂茶201”等省主导新品种示范推广、宜机化茶园改造技术、茶叶智匀摊青机研发与应用开展技术培训。台上讲趋势、教方法，台下听门道、找答案，培训既有产业趋势的“望远镜”，也有落地操作的“工具包”。\n\n**从短板到路径，让“515”行动走深走实**\n\n赵昆松在总结讲话中指出，本次观摩培训活动是落实全省茶产业链建设部署、推动科技赋能茶产业高质量发展的重要举措。他要求，科技服务茶叶产业链“515”行动团队和各级农业技术推广部门要紧紧围绕产业所需、农民所要，聚焦山地茶园机械化短板和茶叶加工提质关键环节，加大新技术、新装备、新模式的示范推广力度，切实发挥“515”行动在科技支撑茶产业链建设中的引领作用；要认真学习借鉴各位专家的宝贵经验和先进技术，不断提升全省茶园建设生态化与茶叶加工标准化水平，全力做好茶文化、茶产业、茶科技三篇文章，奋力推进全省茶产业高质量发展。\n\n![Image 3](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F9b4c908be606656ffef8a5e1edddc579~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1790900173&x-signature=k1IptgyIHZz0YyV4oMFsfcblQFM%3D)\n\n**从“金叶子”到强链条，襄阳茶产业拔节生长**\n\n此次活动的成功举办，对襄阳茶产业高质量发展起到了积极的支撑和推动作用。近年来，襄阳市将茶产业作为推进乡村振兴和农业农村现代化的特色产业来抓，全市茶园面积稳步扩大，茶叶加工能力持续提升，茶产业链条不断延伸。截至目前，襄阳高香茶产业综合产值已突破80亿元，培育省级以上龙头企业18家，茶产品出口40多个国家和地区，带动近50万人增收致富；机械化采摘已覆盖全市80%茶区，建成30多条国内领先的优质茶生产线，茶产业已成为山区群众增收致富的“金叶子”。\n\n保康县作为“襄阳高香茶”核心产区，全县茶园面积达20.1万亩，投产茶园15万亩，综合产值突破20亿元。通过创新应用“1234”绿色防控技术，全县化肥农药使用量减少80%以上；持续引导企业加大宜机化茶园改造和智能化加工装备引进力度，已投建6条现代化碾茶生产线及5000亩碾茶基地，茶园生产机械化、标准化水平不断提高。\n\n![Image 4](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F12d004a4567f1e8c66825a75b4204f73~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1790900173&x-signature=tBbnmEm7gq5IjJIumPrYUeAnKiU%3D)\n\n本次观摩推广的山地茶园机械化生产及碾茶生产加工技术，有效缓解了山区茶园劳动力短缺、生产成本高的瓶颈，推动夏秋茶原料高效利用，为襄阳市标准化茶园建设提供了有力的技术保障，为全省茶叶产业链高水平建设和茶业强省战略实施贡献了科技力量。","湖北日报 2026-09-19","2026-09-19T00:00:00Z",{"impact":361,"substance":951,"depth":345,"authority":385,"freshness":408,"relevant":455,"comment":1620},"省级茶叶产业链科技服务行动落地报道，含机械化生产与碾茶加工技术细节及产业数据，对山地茶园宜机化改造有参考价值。",[1622],{"name":1617,"url":1614},[1624,1625,1626,1627,1628],"515行动","山地茶园","茶叶产业链","机械化生产","碾茶加工",{"code":294,"slug":295,"name":296,"path":297,"parent_code":271},[1631],{"code":112,"slug":113,"name":114,"path":115,"parent_code":72},{"status":598,"locked":581,"version":4,"method":599,"evidence":1633},{"candidates":1634,"agricultural_evidence":603},[1635,1639,1643,1647,1652],{"code":294,"score":524,"supported":603,"title":1636,"abstract":1637,"tags":1638},[839],[839],[],{"code":112,"score":524,"supported":603,"title":1640,"abstract":1641,"tags":1642},[1427],[1427],[],{"code":286,"score":679,"supported":603,"title":1644,"abstract":1645,"tags":1646},[],[288],[],{"code":96,"score":679,"supported":603,"title":1648,"abstract":1649,"tags":1651},[],[1650],"施肥",[],{"code":104,"score":679,"supported":603,"title":1653,"abstract":1654,"tags":1656},[],[1655],"植保",[],[1658,1659],"湖北 保康 碾茶 机械化","山地茶园 机械化 碾茶加工","湖北保康碾茶机械化-3427","2026-09-25T00:09:31.863361Z",{"id":1663,"title":1664,"url":1665,"summary":1666,"summary_zh":11,"content":1667,"source_name":1668,"source_url":11,"published_at":1669,"category":1177,"cover_url":11,"hotness":385,"is_selected":581,"score":253,"score_detail":1670,"sources":1673,"tags":1675,"primary_topic":1681,"secondary_topics":1682,"classification":1683,"search_phrases":1699,"slug":1702,"view_count":455,"doi":11,"paper":11,"created_at":1703},3232,"2026年中国热带作物产业大会在昆明举办——云南农科院发布咖啡重大科技成果 云南咖啡种植面积占全国98%以上","https:\u002F\u002Fwww.jingjiribao.cn\u002Fstatic\u002Fdetail.jsp?id=684363","9-17 经济日报报道：2026年中国热带作物产业大会9-17在云南昆明举行。本次大会聚焦咖啡这一热带作物，以\"咖位中国·链动全球\"为主题。云南省农业科学院发布咖啡重大科技成果，包括咖啡园\"乔—灌—草\"立体复合种植技术、咖啡化肥农药减施增效技术、咖啡鲜果无水加工技术、咖啡定向发酵关键技术等，覆盖种植、植保与加工环节。云南省市场监管局宣布成立云南省咖啡技术标准化委员会并揭牌。\"十四五\"期间，云南咖啡豆精品率从8%跃升至41.7%，精深加工率从20%提升到85%。2025年云南咖啡种植面积达151.88万亩、产量达17.07万吨，均占全国98%以上，咖啡全产业链产值达995亿元。","2026年中国热带作物产业大会9月17日在云南昆明举行。本次大会聚焦咖啡这一热带作物，以“咖位中国·链动全球”为主题，200余名专家学者、企业家、业界代表等围绕咖啡提质、品牌建设与国际合作展开研讨，推动我国咖啡向标准化、精品化、品牌化方向发展。\n\n![Image 1](https:\u002F\u002Fstatic.jingjiribao.cn\u002F2026\u002F9\u002F20\u002F20260920103055_7096.JPEG)\n\n开幕式上，云南省市场监管局宣布成立云南省咖啡技术标准化委员会并揭牌。云南省农业科学院发布咖啡重大科技成果，包括咖啡园“乔—灌—草”立体复合种植技术、咖啡化肥农药减施增效技术、咖啡鲜果无水加工技术、咖啡定向发酵关键技术等，覆盖种植、植保与加工环节。云南日报报业集团发布《中式咖啡文化传播影响力报告》。\n\n据介绍，云南热区面积约8.1万平方公里，是全国热区资源富集省份之一。近年来，云南立足多样立体的气候资源禀赋，大力发展热作产业，咖啡成为最具辨识度、最有竞争力的热作产业之一。“十四五”期间，云南咖啡豆精品率从8%跃升至41.7%，精深加工率从20%提升到85%。2025年，云南咖啡种植面积达151.88万亩、产量达17.07万吨，均占全国98%以上，咖啡全产业链产值达995亿元。\n\n本次大会设“变局与新机——全球咖啡格局重塑中的中国路径与中非、GMS（大湄公河次区域）机遇”“标准筑基·科技赋能——中国咖啡的品质根基与创新驱动”“从产地到品牌——云南咖啡的精品化跃迁”“世界咖啡·中国表达——中国咖啡文化国际传播”4个平行分论坛。\n\n本次大会由农业农村部南亚热带作物中心、云南省农业农村厅、昆明市人民政府共同主办，大会期间，云南还举办咖啡加工工职业技能大赛、花咖之夜、2026云南咖啡展、滇池国际咖啡文化嘉年华·“秋韵啡扬”、滇池国际咖啡文化嘉年华·翠湖咖啡主题市集等活动，让参会嘉宾、游客与本地消费者体验云南丰富多彩的咖啡文化。","经济日报","2026-09-17T00:00:00Z",{"impact":1671,"substance":1111,"depth":951,"authority":345,"freshness":668,"relevant":455,"comment":1672},27,"全国性热带作物产业大会落地昆明，云南农科院发布覆盖种植到加工的重大科技成果，并给出精品率、精深加工率与全产业链产值等关键数据，产业级信息增量充足，值得进入每日精选。",[1674],{"name":1668,"url":1665},[1676,1677,594,1678,1679,1680],"品牌建设","标准化","精深加工","热带作物","农业科技成果",{"code":294,"slug":295,"name":296,"path":297,"parent_code":271},[],{"status":598,"locked":581,"version":4,"method":599,"evidence":1684},{"candidates":1685,"agricultural_evidence":603},[1686,1690,1695],{"code":294,"score":416,"supported":603,"title":1687,"abstract":1688,"tags":1689},[607],[607],[594],{"code":104,"score":668,"supported":603,"title":1691,"abstract":1692,"tags":1694},[],[1693,1655],"农药",[],{"code":472,"score":455,"supported":581,"title":1696,"abstract":1697,"tags":1698},[],[],[1676],[1700,1701],"云南农科院 咖啡 科技成果","云南咖啡 种植面积 全产业链产值","云南农科院咖啡科技成果-3232","2026-09-23T00:04:30.734823Z",{"id":1705,"title":1706,"url":1707,"summary":1708,"summary_zh":1709,"content":11,"source_name":1710,"source_url":1707,"published_at":1711,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1712,"score_detail":1713,"sources":1715,"tags":1717,"primary_topic":1719,"secondary_topics":1720,"classification":1722,"search_phrases":1747,"slug":1750,"view_count":408,"doi":1751,"paper":1752,"created_at":1785},3165,"Explainable growth stage classification of cacao (Theobroma cacao L.) leaves and key feature visualization using vision transformer and transfer learning","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1885906","Accurate and objective plant phenotyping is crucial for optimizing agricultural practices, understanding plant development, and enabling rapid responses to environmental changes. Traditional methods, often relying on visual observation, can be subjective, time-consuming, and may overlook subtle but important differences. This study demonstrates the power of combining digital imaging with deep learning to classify plant material with high accuracy, even when visual differences are minimal. We focused on differentiating between stage D (light green) and stage E (dark green) leaves of cacao ( Theobroma cacao L. ), which are visually very similar in size and overall structure. Using cleared and stained leaves of the SCA 6 genotype to highlight the venation network, we trained a Vision Transformer (ViT) model, a deep learning architecture, on image patches. At the patch level, the model achieved an overall accuracy of 97.03% on an independent test set, with a recall of 96.0% for stage D and 98.3% for stage E. At the whole-leaf level, majority voting correctly classified 14 of 15 independent test leaves (93.3%). Attention maps indicated that image regions containing the midrib and primary lateral veins contributed strongly to classification. These attention maps identify discriminative image regions, but they do not by themselves determine the biological mechanism underlying the signal. The major-vein signal may reflect developmental differences in vein-associated structure, stage-associated differences in Safranin O uptake or optical density, tissue thickness, or a combination of these factors. Because vascular anatomy, lignification, hydraulic conductance, phloem loading, and source–sink status were not directly measured, these mechanisms are treated as hypotheses requiring future anatomical, histochemical, and physiological validation. Thus, this study provides a proof-of-concept for interpretable image-based classification of stage D and stage E leaves within greenhouse-grown SCA 6 cacao. Extension to other cacao genotypes, field-grown plants, independent seasons, staining batches, stress detection, species identification, genotype discrimination, or precision-agriculture deployment will require external validation.","准确、客观的植物表型分析对于优化农业实践、理解植物发育以及快速响应环境变化至关重要。传统方法通常依赖视觉观察，可能具有主观性、耗时，并且可能忽略细微但重要的差异。本研究展示了将数字成像与深度学习相结合，即使在视觉差异极小的情况下，也能以高精度对植物材料进行分类。我们聚焦于区分可可（Theobroma cacao L.）的D期（浅绿色）和E期（深绿色）叶片，这些叶片在大小和整体结构上视觉上非常相似。利用SCA 6基因型的透明染色叶片以突出脉序网络，我们在图像块上训练了Vision Transformer（ViT）模型，一种深度学习架构。在图像块水平上，该模型在独立测试集上达到了97.03%的总体准确率，D期的召回率为96.0%，E期为98.3%。在整叶水平上，多数投票正确分类了15片独立测试叶片中的14片（93.3%）。注意力图表明，包含中脉和初级侧脉的图像区域对分类贡献显著。这些注意力图识别了具有判别力的图像区域，但它们本身并不能确定信号背后的生物学机制。主脉信号可能反映了脉相关结构的发育差异、番红O摄取或光密度的阶段相关差异、组织厚度，或这些因素的组合。由于未直接测量维管解剖结构、木质化、水力导度、韧皮部装载和源–库状态，这些机制被视为假设，需要未来的解剖学、组织化学和生理学验证。因此，本研究为温室种植的SCA 6可可中D期和E期叶片的可解释图像分类提供了概念验证。扩展到其他可可基因型、田间种植植株、独立季节、染色批次、胁迫检测、物种鉴定、基因型区分或精准农业部署将需要外部验证。","Frontiers in Plant Science","2026-09-21T00:00:00Z",71,{"impact":416,"substance":508,"depth":951,"authority":760,"freshness":585,"relevant":455,"comment":1714},"方法新颖、数据可靠的可解释作物表型概念验证研究，但属实验室小样本，产业影响有限。",[1716],{"name":1710,"url":1707},[654,1227,1243,779,1718],"作物表型",{"code":294,"slug":295,"name":296,"path":297,"parent_code":271},[1721],{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"status":598,"locked":581,"version":4,"method":599,"evidence":1723},{"candidates":1724,"agricultural_evidence":603},[1725,1729,1733,1739,1743],{"code":294,"score":584,"supported":603,"title":1726,"abstract":1727,"tags":1728},[777],[779,777],[779],{"code":24,"score":173,"supported":603,"title":1730,"abstract":1731,"tags":1732},[],[1243,1241],[1227,1243],{"code":324,"score":668,"supported":603,"title":1734,"abstract":1735,"tags":1738},[],[1736,1737],"温室","greenhouse",[],{"code":16,"score":400,"supported":603,"title":1740,"abstract":1741,"tags":1742},[],[821],[654],{"code":32,"score":455,"supported":581,"title":1744,"abstract":1745,"tags":1746},[],[],[1718],[1748,1749],"可可 叶片 生长阶段 分类","Vision Transformer 作物表型","可可叶片生长阶段分类-3165","10.3389\u002Ffpls.2026.1885906",{"doi":1751,"openalex_id":1753,"authors":1754,"venue":1710,"cited_by_count":424,"oa_url":1707,"card":1780,"direction":938,"ingested_from":640},"W7213901640",[1755,1757,1760,1762,1764,1766,1769,1772,1775,1777],{"name":1756,"orcid":11},"Ezekiel Ahn",{"name":1758,"orcid":1759},"Eun-Sung Park","https:\u002F\u002Forcid.org\u002F0000-0001-6826-2865",{"name":1761,"orcid":11},"Moon S. Kim",{"name":1763,"orcid":11},"Hangi Kim",{"name":1765,"orcid":11},"Lalit M. Kandpal",{"name":1767,"orcid":1768},"Sunchung Park","https:\u002F\u002Forcid.org\u002F0000-0002-7398-9476",{"name":1770,"orcid":1771},"Seunghyun Lim","https:\u002F\u002Forcid.org\u002F0000-0003-3023-4863",{"name":1773,"orcid":1774},"Lyndel W. Meinhardt","https:\u002F\u002Forcid.org\u002F0000-0001-8299-2629",{"name":1776,"orcid":11},"Byoung-Kwan Cho",{"name":1778,"orcid":1779},"Insuck Baek","https:\u002F\u002Forcid.org\u002F0000-0003-1044-349X",{"tldr":1781,"method":1782,"finding":1783,"direction":938,"opportunity":1784},"用ViT和迁移学习对可可叶D、E期进行可解释分类，准确率达97%。","透明染色叶片图像块训练ViT，注意力图可视化关键区域。","模型准确区分D\u002FE期，中脉和主侧脉区域贡献最大。","可扩展到多基因型、田间、胁迫检测，并验证脉信号生物学机制。","2026-09-22T23:30:19.972949Z",{"technologies":1787,"keywords":1795},[1788,1789,1790,1791,1792,1793,1794],{"name":956,"count":408},{"name":1227,"count":400},{"name":821,"count":679},{"name":1116,"count":69},{"name":1718,"count":455},{"name":1413,"count":455},{"name":1243,"count":455},[1796,1797,1798,1799,1800,1801,1802,1803,1804,1806,1808,1809],{"name":654,"count":759},{"name":594,"count":524},{"name":1185,"count":668},{"name":1488,"count":400},{"name":590,"count":679},{"name":592,"count":69},{"name":1676,"count":69},{"name":593,"count":69},{"name":1805,"count":69},"种业振兴",{"name":1807,"count":69},"科技特派员",{"name":1559,"count":455},{"name":1810,"count":455},"三产融合"]