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5](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Fimg\u002F20240301dzv.png)0\n\n[](javascript:; \"新浪微博\")[](javascript:; \"微信\")\n\n科技日报记者 马爱平\n\n记者获悉，2026中国农学会年会暨第十三届中国现代农业发展论坛将于11月26日—28日在中山大学广州校区举办。\n\n本次会议主题为“人工智能+”农业农村现代化。中国农学会、农业农村部人力资源开发中心为主办单位，中山大学为承办单位，中国作物学会、中国土壤学会、华南农业大学、广东省农业科学院、仲恺农业工程学院为协办单位。\n\n据悉，本次会议主会场包括主旨报告与院士报告、成果发布、新场景路演、前沿对话、创新成果展示等活动；分会场包括若干场专题活动。院士专家将围绕世界科技前沿发展态势、人工智能赋能农业农村各领域发展、各专业领域最新研究进展等作前沿报告。会议将发布《2026全球农业科技热点前沿》《2026中国涉农企业创新报告》《2026农业领域重大科学问题产业技术问题》《2024—2026基础农学学科发展报告》《2026中国农业重大新技术新产品新场景》《2026年度农业“火花技术”》等成果，举行学会英文科技新刊首发式。\n\n据介绍，本次会议将选择具有典型意义和示范引领作用，由企业主导的2026农业新场景进行现场路演，突出企业科技创新主体地位。聚焦人工智能农业深度应用，特邀企业、金融、教育、科研等领域的代表对话交流，分享人工智能赋能农业农村现代化的实际案例和前沿思考，促进人工智能与农业发展相结合。设置提升农业综合生产能力和质量效益、强化农业科技装备支撑、推进农业绿色低碳转型与可持续发展、践行大食物观构建多元化食物供给体系、和美乡村建设与全球农业合作以及夯实乡村人才基础、提升科学传播效能等6大板块31场专题活动。设置农业科技创新成果展示专区，采用展板与小型实物相结合的方式集中展示有代表性的农业科技成果，包括近年来入选的农业新技术新产品新场景、农业主导品种主推技术、重大引领性技术、“火花技术”等部分重大科技成果以及公开征集的相关成果，助推农业科技成果高效转化应用。\n\n责任编辑：陈可轩\n\n相关稿件：\n\n![Image 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3.暂不升级，继续浏览\n\n[继续浏览](javascript:;)","科技日报·2026年10月3日","2026-10-03T00:00:00Z","报道",false,79,{"impact":584,"substance":361,"depth":345,"authority":345,"freshness":585,"relevant":455,"comment":586},24,9,"全国性农业科技年会预告，主题聚焦人工智能赋能农业农村，含成果发布与路演安排，信息量足、时效新，值得进入每日精选。",[588],{"name":578,"url":575},[590,591,592,593,594],"智慧农业","农业人工智能","成果转化","农业科技","学术会议",{"code":556,"slug":557,"name":558,"path":559,"parent_code":534},[],{"status":598,"locked":581,"version":4,"method":599,"evidence":600},"classified","rules",{"candidates":601,"agricultural_evidence":604},[602,609,614,618,622],{"code":549,"score":603,"supported":604,"title":605,"abstract":606,"tags":608},3,true,[],[607],"农业农村现代化",[],{"code":556,"score":603,"supported":604,"title":610,"abstract":611,"tags":613},[],[612],"全球农业",[],{"code":488,"score":455,"supported":581,"title":615,"abstract":616,"tags":617},[],[],[592],{"code":16,"score":455,"supported":581,"title":619,"abstract":620,"tags":621},[],[],[590],{"code":24,"score":455,"supported":581,"title":623,"abstract":624,"tags":625},[],[],[591],[627,628],"中国农学会年会 中山大学","人工智能 农业农村现代化 论坛","中国农学会年会中山大学-4133",6,"2026-10-04T00:09:07.062175Z",{"id":633,"title":634,"url":635,"summary":636,"summary_zh":637,"content":11,"source_name":638,"source_url":635,"published_at":639,"category":640,"cover_url":11,"hotness":385,"is_selected":581,"score":582,"score_detail":641,"sources":644,"tags":646,"primary_topic":652,"secondary_topics":653,"classification":654,"search_phrases":673,"slug":676,"view_count":603,"doi":677,"paper":678,"created_at":698},2763,"How do productive service inputs enhance a country’s position in the global agricultural value chain?","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1887232","Introduction The ongoing restructuring of global agricultural value chains has reshaped the pattern of international agricultural competition. Optimizing countries’ positional structure within production networks and improving domestic value added capture capacity have emerged as central issues for advancing highquality agricultural development across economies. As modern productive services gradually penetrate agricultural production systems, they have evolved into critical endogenous drivers for optimizing agricultural factor allocation and promoting value chain upgrading. Methods Based on panel data of 55 economies from 2001 to 2020, this study employs fixed-effects models, mediation models, and moderation models to systematically explore how productive service inputs affect a country’s positions in the global agricultural value chain and clarify the underlying influencing mechanisms. Results The empirical results show that productive service input significantly improves a country’s agricultural GVC status. Mechanism tests confirm that productive services empower GVC upgrading through two core pathways: improving agricultural labor productivity and facilitating technological innovation. Meanwhile, the promotional effect is positively strengthened by increasing technical complexity in agricultural exports and human capital accumulation. Further heterogeneity analysis verifies evident differences in the empowerment effect across economic development levels, time periods, and GVC participation patterns. Discussion This study concludes that accelerating high quality productive service inputs and matching them with advanced human capital and export technology endowments provides a vital feasible path for countries to enhance their competitive advantages and achieve highvalue upgrading in global agricultural value chains.","引言 全球农业价值链的持续重构重塑了国际农业竞争格局。优化各国在生产网络中的位置结构、提升国内附加值捕获能力，已成为推动各经济体农业高质量发展的核心议题。随着现代生产性服务逐步渗透农业生产体系，其已演变为优化农业要素配置、促进价值链升级的关键内生驱动力。方法 本研究基于2001年至2020年55个经济体的面板数据，采用固定效应模型、中介模型和调节模型，系统探讨生产性服务投入如何影响一国在全球农业价值链中的位置，并阐明其潜在影响机制。结果 实证结果表明，生产性服务投入显著提升了一国的农业全球价值链地位。机制检验证实，生产性服务通过两条核心路径赋能价值链升级：提升农业劳动生产率与促进技术创新。同时，农业出口技术复杂度和人力资本积累的提高正向强化了这一促进效应。进一步的异质性分析验证了赋能效应在经济发展水平、时间段和全球价值链参与模式上存在明显差异。讨论 本研究认为，加快高质量生产性服务投入，并使其与先进人力资本和出口技术禀赋相匹配，为各国增强竞争优势、实现全球农业价值链高价值升级提供了重要的可行路径。","Frontiers in Sustainable Food Systems","2026-09-16T00:00:00Z","论文",{"impact":361,"substance":321,"depth":361,"authority":642,"freshness":173,"relevant":455,"comment":643},13,"基于55个经济体20年面板数据的实证研究，揭示生产性服务投入通过提升劳动生产率与技术创新推动农业全球价值链升级，结论对农业高质量发展有参考价值。",[645],{"name":638,"url":635},[647,648,649,650,651],"农业科技创新","农产品出口","农业价值链","农业生产性服务","农业人力资本",{"code":556,"slug":557,"name":558,"path":559,"parent_code":534},[],{"status":598,"locked":581,"version":4,"method":599,"evidence":655},{"candidates":656,"agricultural_evidence":604},[657,661,665,669],{"code":556,"score":603,"supported":604,"title":658,"abstract":659,"tags":660},[],[612],[],{"code":472,"score":455,"supported":581,"title":662,"abstract":663,"tags":664},[],[],[648],{"code":503,"score":455,"supported":581,"title":666,"abstract":667,"tags":668},[],[],[649],{"code":549,"score":455,"supported":581,"title":670,"abstract":671,"tags":672},[],[],[647],[674,675],"农业生产性服务 农业人力资本 农业科技创新 农业价值链","农业生产性服务 农业人力资本","农业生产性服务农业人力资本农业科技创新农业价值链-2763","10.3389\u002Ffsufs.2026.1887232",{"doi":677,"openalex_id":679,"authors":680,"venue":638,"cited_by_count":424,"oa_url":690,"card":691,"direction":695,"ingested_from":697},"W7213434328",[681,683,685,687],{"name":682,"orcid":11},"Longling Li",{"name":684,"orcid":11},"Xin Bai",{"name":686,"orcid":11},"Yuhan Ren",{"name":688,"orcid":689},"Qiang Jin","https:\u002F\u002Forcid.org\u002F0009-0005-1581-1759","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1887232\u002Fpdf",{"tldr":692,"method":693,"finding":694,"direction":695,"opportunity":696},"基于55个经济体面板数据，检验生产性服务投入如何提升一国全球农业价值链地位。","2001-2020年55经济体面板数据，固定效应、中介与调节模型。","生产性服务投入通过提升农业劳动生产率和促进技术创新显著提升农业GVC地位。","数字乡村与农业信息化","可探究数字农业服务投入对GVC地位的异质性影响，及与发展中国家数字基础设施的匹配机制。","openalex","2026-09-17T23:30:08.429911Z",{"id":700,"title":701,"url":702,"summary":703,"summary_zh":11,"content":704,"source_name":705,"source_url":11,"published_at":706,"category":640,"cover_url":11,"hotness":385,"is_selected":581,"score":707,"score_detail":708,"sources":711,"tags":713,"primary_topic":718,"secondary_topics":719,"classification":721,"search_phrases":736,"slug":739,"view_count":69,"doi":11,"paper":11,"created_at":740},1149,"基于FAO全球农业数据的机器学习作物产量预测:线性回归 随机森林 XGBoost比较研究","https:\u002F\u002Fwww.ijfmr.com\u002Fresearch-paper.php?id=86667","International Journal for Multidisciplinary Research (IJFMR) 2026年第8卷第4期(2026-08-28发表):基于FAO全球农业数据集(覆盖212个国家和地区、10种主要作物、1961-2016年56,717条记录),比较线性回归、随机森林、XGBoost三种机器学习模型。80:20 train-test split,Random Forest取得最佳性能:R²=0.9586、MAE=6724.19 hg\u002Fha、RMSE=13829.60 hg\u002Fha;XGBoost: R²=0.8115;线性回归:R²=0.0608。特征重要性分析识别作物类型(Item Code)为最具影响力预测因子,其次是地理区域和时间因素。[注:本论文使用2016年截止数据,模型泛化能力可能存在边界]","**Call for Paper Volume 8, Issue 4 (July-August 2026)**[Submit your research](https:\u002F\u002Fwww.ijfmr.com\u002Fsubmit-research-paper.php)**before last 3 days of August** to publish your research paper in the issue of July-August.\n\n| Author(s) | Abdulhafedh Mohammed Alsarfi, Archana Harsing Sable, Aymen Mosleh Al-Hejri |\n| --- |\n| Country | India |\n| Abstract | Accurate crop yield prediction is essential for food security planning, agricultural policy-making, and sustainable farming management. This study develops and compares three machine learning models Linear Regression, Random Forest, and XGBoost for predicting crop yield using the FAO (Food and Agriculture Organization) global agricultural dataset. The dataset spans 212 countries and regions, covers 10 major crop types, and encompasses the period 1961–2016, comprising 56,717 records. Following a systematic preprocessing pipeline including missing value verification, duplicate removal, and label encoding, all models were trained and evaluated under identical experimental conditions using an 80:20 train-test split (45,373 training \u002F 11,344 testing records). Performance was assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). Random Forest achieved the best predictive performance with R² = 0.9586, MAE = 6,724.19 hg\u002Fha, and RMSE = 13,829.60 hg\u002Fha. XGBoost demonstrated competitive performance with R² = 0.8115, while Linear Regression showed limited effectiveness with R² = 0.0608, confirming its inability to model the nonlinear dynamics of global agricultural data. Feature importance analysis identified Item Code (crop type) as the most influential predictor, followed by geographic region and temporal factors. These findings confirm that ensemble learning methods, particularly Random Forest, are well-suited for modelling complex agricultural yield patterns at global scale. |\n| Keywords | Crop yield prediction1; Machine learning2; Random Forest3; XGBoost4; Linear Regression5; FAO dataset6; Precision agriculture7; Feature importance8; Food security9. |\n| Field | Computer |\n| Published In | [Volume 8, Issue 4, July-August 2026](https:\u002F\u002Fwww.ijfmr.com\u002Fpublication-archive.php?volume=8&issue=4#papers-index) |\n| Published On | 2026-08-28 |\n| DOI | [https:\u002F\u002Fdoi.org\u002F10.36948\u002Fijfmr.2026.v08i04.86667](https:\u002F\u002Fdoi.org\u002F10.36948\u002Fijfmr.2026.v08i04.86667) |\n\n#### Share this\n\n![Image 1](https:\u002F\u002Fwww.ijfmr.com\u002Fimages\u002Floading.gif)","IJFMR 2026, 8(4)","2026-08-28T00:00:00Z",65,{"impact":416,"substance":508,"depth":361,"authority":173,"freshness":709,"relevant":455,"comment":710},7,"基于FAO全球数据比较三种机器学习模型预测作物产量，数据规模大，方法系统，结论可靠，对农业信息化研究有参考价值，但来源期刊权威性一般。",[712],{"name":705,"url":702},[714,715,716,717],"机器学习","随机森林","作物产量预测","FAO数据",{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},[720],{"code":556,"slug":557,"name":558,"path":559,"parent_code":534},{"status":598,"locked":581,"version":4,"method":599,"evidence":722},{"candidates":723,"agricultural_evidence":604},[724,728,732],{"code":24,"score":642,"supported":604,"title":725,"abstract":726,"tags":727},[714],[714],[714,715],{"code":556,"score":524,"supported":604,"title":729,"abstract":730,"tags":731},[612],[612],[],{"code":32,"score":455,"supported":581,"title":733,"abstract":734,"tags":735},[],[],[716],[737,738],"作物产量预测 机器学习 随机森林 FAO数据","作物产量预测 机器学习","作物产量预测机器学习随机森林FAO数据-1149","2026-08-31T00:08:57.364320Z",{"id":125,"title":742,"url":743,"summary":744,"summary_zh":11,"content":11,"source_name":745,"source_url":11,"published_at":746,"category":640,"cover_url":11,"hotness":385,"is_selected":581,"score":747,"score_detail":748,"sources":750,"tags":752,"primary_topic":756,"secondary_topics":757,"classification":758,"search_phrases":770,"slug":773,"view_count":524,"doi":774,"paper":11,"created_at":775},"撒哈拉以南非洲农业创新扩散仍偏重横向推广：基于系统综述的研究议程","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1891557\u002Fabstract","按PRISMA范围综述方法，从2,070项研究中筛选2016—2026年间的54项实证研究。结果显示86%研究集中在2016—2021年发布；地理分布以肯尼亚（28%）、埃塞俄比亚（12%）和多国SSA研究（16%）为主；方法偏量化，随机对照试验占47%、比较调查26%、面板数据16%；65%研究聚焦农资与增产技术，仅9%关注价值链或市场导向创新；70%研究将扩展框架为'横向扩散'。促进因素以信息\u002F推广\u002F社会网络（60%）和金融可及性（30%）为主，主要障碍为资金约束（23%）、可及性（21%）和知识缺口（16%）；仅不足15%研究显式考虑系统维度，提示需从扩散中心论转向系统导向整合方法。","Frontiers in Sustainable Food Systems · 2026-07-22 接收","2026-07-22T00:00:00Z",73,{"impact":361,"substance":321,"depth":361,"authority":642,"freshness":69,"relevant":455,"comment":749},"系统综述方法规范，数据详实，对非洲农业推广模式有深刻洞察，但时效性差且地域远离中国，影响有限。",[751],{"name":745,"url":743},[753,754,755],"农业技术推广","系统综述","撒哈拉以南非洲",{"code":556,"slug":557,"name":558,"path":559,"parent_code":534},[],{"status":598,"locked":581,"version":4,"method":599,"evidence":759},{"candidates":760,"agricultural_evidence":604},[761,766],{"code":556,"score":585,"supported":604,"title":762,"abstract":764,"tags":765},[763],"非洲农业",[],[755],{"code":488,"score":455,"supported":581,"title":767,"abstract":768,"tags":769},[],[],[753],[771,772],"撒哈拉以南非洲 农业技术推广 系统综述","撒哈拉以南非洲 农业技术推广","撒哈拉以南非洲农业技术推广系统综述-36","10.3389\u002Ffsufs.2026.1891557\u002Fabstract","2026-07-28T03:05:27.127667Z",{"technologies":777,"keywords":780},[778,779],{"name":591,"count":455},{"name":714,"count":455},[781,782,783,784,785,786,787,788,789,790],{"name":716,"count":455},{"name":649,"count":455},{"name":753,"count":455},{"name":647,"count":455},{"name":648,"count":455},{"name":592,"count":455},{"name":755,"count":455},{"name":590,"count":455},{"name":754,"count":455},{"name":715,"count":455}]