[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3151":3,"related-3151":60},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":59},3151,"From economic rationality to ecological rationality: how does non-pastoral labor allocation affect grazing substitution behavior?","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1891104","Introduction Grazing substitution behavior (GSB), such as cultivating pasture, purchasing forage, and barn-feeding, are increasingly recognized as key strategies to mitigate overgrazing in grasslands and enhance the livelihoods of herding households. In contrast to the free-range grazing method, which relies on natural grasslands, GSB methods typically require a greater labor investment. However, as urbanization progresses swiftly in pastoral regions, the trend of herding households’ labor force shifting to non-pastoral industries is becoming increasingly prominent, which is likely to significantly impact the adoption of GSB by herding households. Consequently, examining the impacts of non-pastoral labor allocation on GSB is essential. Methods This research, utilizing survey data collected from 863 herding households situated in the agro-pastoral ecotone of Inner Mongolia, empirically investigates the effects, underlying mechanisms, and variations in how non-pastoral labor allocation influences the GSB of herding households. Results The findings indicate: (1) A notable “inverted U -shaped” correlation exists between the non-pastoral labor allocation and the GSB of herding households, indicating that a moderate level of non-pastoral labor allocation enhances the adoption of GSB. (2) The utilization of socialized services in animal husbandry moderates the relationship between non-pastoral labor allocation and GSB; as the use of socialized livestock services increases, the inverted U -curve between the two becomes steeper, and the inflection point shifts to the left. (3) Heterogeneity analysis reveals that, compared to local non-pastoral labor allocation, out-migrating non-pastoral employment has a lesser promoting effect on herding households’ GSB, indicating a lower likelihood of GSB adoption among these households. Discussion In light of these insights, it is recommended to encourage a measured engagement in non-pastoral employment, energetically develop the market for socialized services, reinforce support across various dimensions of these services, and implement tailored support policies for non-pastoral labor allocation aimed specifically at enhancing the adoption of GSB.","引言 放牧替代行为（Grazing Substitution Behavior，GSB），如种植人工草地、购买饲草和舍饲圈养，日益被认为是缓解草原过度放牧、提升牧户生计的关键策略。与依赖天然草原的自由放牧方式相比，放牧替代行为通常需要更多的劳动力投入。然而，随着牧区城镇化进程的快速推进，牧户劳动力向非牧产业转移的趋势日益显著，这很可能对牧户采纳放牧替代行为产生重要影响。因此，考察非牧劳动力配置对放牧替代行为的影响至关重要。方法 本研究利用在内蒙古农牧交错带收集的863户牧户调查数据，实证检验了非牧劳动力配置对牧户放牧替代行为的影响效应、内在机制及其差异。结果 研究结果表明：（1）非牧劳动力配置与牧户放牧替代行为之间存在显著的“倒U型”关系，即适度的非牧劳动力配置能够促进放牧替代行为的采纳。（2）畜牧业社会化服务的利用对非牧劳动力配置与放牧替代行为之间的关系具有调节作用；随着畜牧业社会化服务使用程度的提高，二者之间的倒U型曲线变得更加陡峭，且拐点向左移动。（3）异质性分析表明，与本地非牧劳动力配置相比，外出非牧就业对牧户放牧替代行为的促进效应较小，表明此类牧户采纳放牧替代行为的可能性较低。讨论 基于上述发现，建议鼓励适度参与非牧就业，大力发展社会化服务市场，加强社会化服务各维度的支持，并针对非牧劳动力配置实施差异化支持政策，以促进放牧替代行为的采纳。",null,"Frontiers in Sustainable Food Systems","2026-09-21T00:00:00Z","论文",10,false,79,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,8,1,"基于内蒙古863户牧户调查，揭示非牧劳动力配置与放牧替代行为的倒U型关系，数据扎实、结论有政策参考价值，值得进入每日精选。",[24],{"name":10,"url":6},[26,27,28,29,30],"社会化服务","草原生态","农牧交错带","牧户生计","非牧就业",[32,33],"内蒙古 农牧交错带 牧户","非牧就业 放牧替代","内蒙古农牧交错带牧户-3151",0,"10.3389\u002Ffsufs.2026.1891104",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":6,"card":52,"direction":56,"ingested_from":58},"W7213940558",[40,43,45,47,49],{"name":41,"orcid":42},"Wenjie Ouyang","https:\u002F\u002Forcid.org\u002F0009-0004-3500-0155",{"name":44,"orcid":9},"Fang Ju",{"name":46,"orcid":9},"Qingsong Zhao",{"name":48,"orcid":9},"Na Zhuo",{"name":50,"orcid":51},"Zhiyi Gai","https:\u002F\u002Forcid.org\u002F0009-0002-1348-3171",{"tldr":53,"method":54,"finding":55,"direction":56,"opportunity":57},"基于内蒙古863户牧户调查，研究非牧劳动力配置如何影响放牧替代行为。","内蒙古农牧交错带863户牧户问卷，实证分析中介与异质性。","非牧劳动力配置与放牧替代行为呈倒U型关系，社会化服务使曲线更陡、拐点左移。","数字乡村与农业信息化","可探究社会化服务数字化平台如何调节劳动力外流与牧户绿色生产行为的关系。","openalex","2026-09-22T23:30:08.758170Z",{"total":61,"page":21,"page_size":61,"items":62},5,[63,128,183,208,232],{"id":64,"title":65,"url":66,"summary":67,"summary_zh":68,"content":9,"source_name":69,"source_url":66,"published_at":70,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":71,"score_detail":72,"sources":75,"tags":77,"search_phrases":82,"slug":85,"view_count":35,"doi":86,"paper":87,"created_at":127},3016,"Afforestation enhances ecosystem stability in the farming-pastoral ecotone of northern China, but its effects are constrained by water availability","https:\u002F\u002Fdoi.org\u002F10.1093\u002Fjpe\u002Frtag232","Abstract The farming-pastoral ecotone of northern China (FPENC), a typical semi-agricultural and semi-pastoral ecologically fragile zone, has long faced controversy over afforestation due to its water-limited environment, raising concerns about how afforestation affects this fragile ecosystem. In this study, based on Google Earth Engine and a Random Forest model, we derive afforestation distribution (1985-2020), and further investigated ecosystem stability under afforestation from the perspectives of short-term responses and long-term dynamic changes. The results showed: (1) By 2020, 67,700 km2 had been afforested in FPENC (6.20% of its area). Afforestation survival strongly depended on precipitation, with relatively low retention rates (40%-70%) in the zones with \u003C 400 mm. (2) At short-term timescales, afforestation mitigated ecosystem state deviations caused by extreme drought events and improved surrounding ecosystems within a 150 m buffer. (3) Over long-term timescales, NDVI across the FPENC exhibited a significant increasing trend over the past four decades, accompanied by an overall enhancement in ecosystem recovery capacity. In zones with > 400 mm annual precipitation and with forest cover exceeding 40%, ecosystem recovery capacity increased significantly, showing there might be a potential linkage among afforestation, precipitation, and ecosystem recovery processes. This study revealed despite water scarcity and low tree survival, afforestation under suitable precipitation can effectively enhance regional ecosystem stability.","中国北方农牧交错带（FPENC）是典型的半农半牧生态脆弱区，由于环境水分受限，长期以来在造林问题上存在争议，引发了人们对造林如何影响这一脆弱生态系统的担忧。本研究基于Google Earth Engine和随机森林模型，反演了1985—2020年造林分布，并进一步从短期响应和长期动态变化两个角度研究了造林条件下的生态系统稳定性。结果表明：（1）截至2020年，FPENC已造林67,700 km²，占其面积的6.20%。造林存活强烈依赖降水，在降水量小于400 mm的区域，保存率相对较低（40%—70%）。（2）在短期时间尺度上，造林缓解了极端干旱事件引起的生态系统状态偏离，并改善了150 m缓冲区内周边生态系统。（3）在长期时间尺度上，过去四十年FPENC的NDVI呈显著增加趋势，同时生态系统恢复能力总体增强。在年降水量大于400 mm且森林覆盖率超过40%的区域，生态系统恢复能力显著提高，表明造林、降水与生态系统恢复过程之间可能存在潜在联系。本研究揭示，尽管存在水分短缺和树木存活率低的问题，但在适宜降水条件下，造林能够有效增强区域生态系统稳定性。","Journal of Plant Ecology","2026-09-17T00:00:00Z",80,{"impact":17,"substance":18,"depth":17,"authority":73,"freshness":20,"relevant":21,"comment":74},14,"基于遥感与随机森林量化北方农牧交错带造林成效及水分约束，数据规模大、结论有新意，对生态修复与农业信息化有参考价值。",[76],{"name":69,"url":66},[78,79,80,28,81],"生态修复","遥感监测","造林","水资源约束",[83,84],"北方农牧交错带 造林 水资源","Google Earth Engine 造林 遥感","北方农牧交错带造林水资源-3016","10.1093\u002Fjpe\u002Frtag232",{"doi":86,"openalex_id":88,"authors":89,"venue":69,"cited_by_count":35,"oa_url":120,"card":121,"direction":125,"ingested_from":58},"W7213445880",[90,93,96,98,100,102,104,106,108,110,113,115,118],{"name":91,"orcid":92},"Yuchao Luo","https:\u002F\u002Forcid.org\u002F0000-0003-0063-3505",{"name":94,"orcid":95},"Haile Zhao","https:\u002F\u002Forcid.org\u002F0000-0002-5870-062X",{"name":97,"orcid":9},"Qianhe Wang",{"name":99,"orcid":9},"Yi Zhou",{"name":101,"orcid":9},"Xin Chen",{"name":103,"orcid":9},"Yuling Jin",{"name":105,"orcid":9},"Xingjie Yin",{"name":107,"orcid":9},"Guoliang Zhang",{"name":109,"orcid":9},"Haorui Sun",{"name":111,"orcid":112},"Jun Bai","https:\u002F\u002Forcid.org\u002F0000-0002-1408-4271",{"name":114,"orcid":9},"Huiyao Shi",{"name":116,"orcid":117},"Zhihua Pan","https:\u002F\u002Forcid.org\u002F0000-0002-8187-1574",{"name":119,"orcid":9},"Pingli An","https:\u002F\u002Facademic.oup.com\u002Fjpe\u002Fadvance-article-pdf\u002Fdoi\u002F10.1093\u002Fjpe\u002Frtag232\u002F71179175\u002Frtag232.pdf",{"tldr":122,"method":123,"finding":124,"direction":125,"opportunity":126},"基于GEE和随机森林反演1985-2020年北方农牧交错带造林分布，评估其对生态系统稳定性的影响。","Google Earth Engine遥感数据与随机森林模型，分析造林分布及ND","造林可缓解干旱影响并提升恢复力，但存活率与效果受降水制约，400mm为关键阈值。","农业遥感与作物表型","可探究不同降水梯度下造林-水分-恢复力耦合机制，优化生态修复的水资源约束阈值。","2026-09-20T23:30:22.592517Z",{"id":129,"title":130,"url":131,"summary":132,"summary_zh":133,"content":9,"source_name":134,"source_url":131,"published_at":135,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":136,"score_detail":137,"sources":142,"tags":144,"search_phrases":148,"slug":151,"view_count":35,"doi":152,"paper":153,"created_at":182},2536,"Mapping Native Grass Cover with Random Forest Models: Sentinel-2 Versus Sentinel-2 Combined with Sentinel-1 SAR-Derived GLCM Texture Metrics","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183150","Temperate native grasslands in southeastern Australia have been extensively cleared for agriculture, and the remaining patches are under growing pressure from further land use change, climate variability, and invasive species. Mapping and monitoring their distribution and the cover of native and exotic grasses are critical for their conservation and management. Field-based methods are not always scalable or time-effective, and this study aimed to develop a scalable method to map and monitor the fractional cover-class maps of native C3 and native C4 grass cover as a component of remnant native grasslands on the western outskirts of Melbourne, Victoria, Australia. Field-based reference data for training and validation of random forest machine learning models were collected across multiple sites in 2021. Sentinel-2 optical spectral bands and vegetation indices were used as the primary input data, and Sentinel-1 Synthetic Aperture Radar (SAR)-derived Grey Level Co-occurrence Matrix (GLCM) texture metrics were assessed for their capacity to improve the model. Results show that random forest models trained on Sentinel-2 data without GLCM texture information derived from Sentinel-1 SAR data provided a moderate overall accuracy (C3: 59.1%, C4: 78.1%). Class-specific metrics showed that reliability was highest for better represented lower-cover classes, particularly the 6–25% native C3 class and the 0–5% native C4 class, while higher-cover classes were less reliable because of the limited number of training and validation samples. Grass cover fractions were modelled well for sparse to moderate grass cover, but dense grass cover was not modelled accurately, probably due to limited high-cover samples in the training dataset. Model performance was not improved by the inclusion of Sentinel-1 SAR-derived GLCM texture metrics, indicating that C-band VH-polarised SAR is not sensitive to the fine-scale structural heterogeneity that characterises native grassland ecosystems. Sparse native C3 and C4 grasses could be mapped most reliably in the lower-cover classes as a component of grasslands with optical remote sensing, and the method developed here can now be applied to enable evidence-based management of grasslands, biodiversity conservation and the monitoring of grassland composition in the WGR and elsewhere. Higher-resolution structural datasets and more sophisticated machine learning approaches may be required to accurately predict native C3 and C4 grass cover fractions in denser grasslands.","澳大利亚东南部的温带原生草原已被大面积开垦用于农业，残余斑块正面临土地利用进一步变化、气候变率和入侵物种日益增大的压力。对其分布以及原生和外来草类覆盖度进行制图和监测，对于草原的保护和管理至关重要。基于实地调查的方法并非总是可扩展或省时的，本研究旨在开发一种可扩展的方法，以制图和监测作为残余原生草原组成部分的原生C3和原生C4草类覆盖度的分数覆盖等级图，研究区位于澳大利亚维多利亚州墨尔本西郊。用于训练和验证随机森林机器学习模型的实地参考数据于2021年在多个样点采集。研究以Sentinel-2光学光谱波段和植被指数作为主要输入数据，并评估了Sentinel-1合成孔径雷达（SAR）衍生的灰度共生矩阵（GLCM）纹理指标对提升模型性能的能力。结果表明，仅使用Sentinel-2数据（不含Sentinel-1 SAR衍生的GLCM纹理信息）训练的随机森林模型提供了中等的总体精度（C3：59.1%，C4：78.1%）。分类别指标显示，对于代表性较好的低覆盖度类别，可靠性最高，尤其是6–25%原生C3类别和0–5%原生C4类别，而较高覆盖度类别的可靠性较低，原因是训练和验证样本数量有限。草类覆盖度分数在稀疏至中等草类覆盖条件下建模效果良好，但茂密草类覆盖未能准确建模，可能是由于训练数据集中高覆盖度样本有限。纳入Sentinel-1 SAR衍生的GLCM纹理指标并未改善模型性能，表明C波段VH极化SAR对原生草原生态系统所特有的精细尺度结构异质性不敏感。作为草原的组成部分，稀疏的原生C3和C4草类在低覆盖度类别中利用光学遥感可最可靠地制图，本研究开发的方法现可应用于西维多利亚草原（WGR）及其他地区，以实现基于证据的草原管理、生物多样性保护和草原组成监测。准确预测原生C3和C4草类覆盖度分数可能需要更高分辨率的结构数据集和更先进的机器学习方法。","Remote Sensing","2026-09-13T00:00:00Z",71,{"impact":138,"substance":139,"depth":140,"authority":73,"freshness":20,"relevant":21,"comment":141},12,20,17,"方法新颖、结论明确（SAR纹理未提升精度），对草地遥感监测有参考价值，但属细分领域研究，影响范围有限。",[143],{"name":134,"url":131},[145,146,79,27,147],"智慧农业","机器学习","植被覆盖",[149,150],"智慧农业 机器学习 植被覆盖 草原生态","智慧农业 机器学习","智慧农业机器学习植被覆盖草原生态-2536","10.3390\u002Frs18183150",{"doi":152,"openalex_id":154,"authors":155,"venue":134,"cited_by_count":35,"oa_url":131,"card":177,"direction":125,"ingested_from":58},"W7212561645",[156,159,162,165,167,169,172,174],{"name":157,"orcid":158},"Sabah Sabaghy","https:\u002F\u002Forcid.org\u002F0000-0002-9453-8922",{"name":160,"orcid":161},"M. Abuzar","https:\u002F\u002Forcid.org\u002F0000-0002-6101-1307",{"name":163,"orcid":164},"Steve J. Sinclair","https:\u002F\u002Forcid.org\u002F0000-0002-4282-1021",{"name":166,"orcid":9},"Tony Dugdale",{"name":168,"orcid":9},"Vanessa Hutchins",{"name":170,"orcid":171},"Yogendra K. Karna","https:\u002F\u002Forcid.org\u002F0000-0002-2120-4710",{"name":173,"orcid":9},"Jonathan Wilson",{"name":175,"orcid":176},"Kathryn Sheffield","https:\u002F\u002Forcid.org\u002F0000-0003-2624-9739",{"tldr":178,"method":179,"finding":180,"direction":125,"opportunity":181},"用随机森林结合Sentinel-2与Sentinel-1纹理特征，绘制澳洲原生草地C3\u002FC4草覆盖度","2021年野外样点训练随机森林，Sentinel-2光谱与植被指数为主，Sent","仅用Sentinel-2精度中等（C3 59.1%、C4 78.1%），加入SAR纹理未提升，高覆盖","高覆盖度草地样本不足且C波段SAR不敏感，可探索高分辨率结构数据与深度模型提升密草覆盖反演。","2026-09-15T23:30:21.287053Z",{"id":184,"title":185,"url":186,"summary":187,"summary_zh":9,"content":188,"source_name":189,"source_url":9,"published_at":190,"category":191,"cover_url":9,"hotness":13,"is_selected":14,"score":192,"score_detail":193,"sources":197,"tags":199,"search_phrases":203,"slug":206,"view_count":35,"doi":9,"paper":9,"created_at":207},1929,"中国农科院资划所举办\"天然草原智能放牧与草畜精准管控技术\"项目培训班","https:\u002F\u002Fiarrp.caas.cn\u002Fysdt\u002Fzhxw\u002F8a849f901c884b10b461fd8945988996.htm","中国农业科学院农业资源与农业区划研究所联合全国畜牧总站主办的\"天然草原智能放牧与草畜精准管控技术\"项目培训班（一期）8月20—23日在内蒙古呼伦贝尔市举行。来自中国农业科学院、中国科学院地理科学与资源研究所、中国农业大学、北京理工大学及中科星图股份有限公司的多位专家学者围绕天然草原植被参数遥感监测、机器视觉在畜牧养殖中的应用、机器人放牧应用展望、刈牧草地地力精准诊断系统等前沿议题作深入系统的报告；并前往谢尔塔拉天然草原现场观摩智能放牧演示。","为加快推进“十四五”国家重点研发计划“天然草原智能放牧与草畜精准管控技术”项目相关技术、配套设备及成果的落地应用，全面提升农牧民和基层技术人员的现代牧场管理水平，由中国农业科学院农业资源与农业区划研究所联合全国畜牧总站主办的“天然草原智能放牧与草畜精准管控技术”项目培训班（一期）于2026年8月20-23日在内蒙古呼伦贝尔市举行。该培训班依托内蒙古呼伦贝尔国家野外科学观测研究站（呼伦贝尔站）举办，吸引了来自甘肃、内蒙古、黑龙江等地的多位行业专家、基层业务骨干及科研人员共同参与。\n\n8月21日，培训班举行了开班仪式与专题学术报告会。开班式上，呼伦贝尔站站长辛晓平研究员致欢迎辞，会议主持人资划所邵长亮研究员代表项目组对各位远道而来的领导、特邀专家以及基层学员表示了热烈的欢迎，并就本次培训班举办的战略背景、核心目标及培训纪律要求作了全面阐述。全国畜牧总站副处长齐晓、内蒙古自治区农牧业技术推广中心饲料饲草技术处处长王永杰以及呼伦贝尔市畜牧技术推广中心主任祁航分别作了开班讲话，对项目的战略意义及未来应用前景提出了殷切期望与明确要求。随后，来自中国农业科学院、中国科学院地理科学与资源研究所、中国农业大学、北京理工大学及中科星图股份有限公司的多位知名专家学者围绕天然草原植被参数遥感监测、大型野生食草动物与家畜调查方法、高时频植被冠层生产力监测仪研发、草原牧场“一张图”原型系统、机器视觉在畜牧养殖中的应用、机器人放牧应用展望、草地改良修复与合理利用技术、刈牧草地地力精准诊断系统以及天然草原放牧智能管控云平台等前沿议题作了深入系统的报告。\n\n![Image 1](https:\u002F\u002Fiarrp.caas.cn\u002Fimages\u002F2026-09\u002Fd7bc90fabd7e4429aec79fc0017c021d.jpeg)![Image 2](https:\u002F\u002Fiarrp.caas.cn\u002Fimages\u002F2026-09\u002Fff1876356c3444cdb547b314492e7add.jpeg)\n\n![Image 3](https:\u002F\u002Fiarrp.caas.cn\u002Fimages\u002F2026-09\u002F0d1fdd3cbfec4ab6a5d97dd1238f7c5a.jpeg)![Image 4](https:\u002F\u002Fiarrp.caas.cn\u002Fimages\u002F2026-09\u002F5434a9570d1d49e48a309498459ffc52.jpeg)\n\n![Image 5](https:\u002F\u002Fiarrp.caas.cn\u002Fimages\u002F2026-09\u002F54ee61c5e4cf4145af2d8bf048ac28f5.jpeg)![Image 6](https:\u002F\u002Fiarrp.caas.cn\u002Fimages\u002F2026-09\u002F6a3159c84ca14403a17d9d6d2c700dea.jpeg)\n\n在理论授课之外，培训班高度注重实践与示范。8月22日，与会人员前往谢尔塔拉天然草原现场观摩智能放牧演示，并实地考察了呼伦贝尔楚拉乳业示范基地与陈巴尔虎旗草原修复平台，通过现场演示与成果展示，直观感受到了智能放牧与草畜精准管控技术在现代草原生态保护及高效利用中的巨大潜力和显著成效。\n\n![Image 7](https:\u002F\u002Fiarrp.caas.cn\u002Fimages\u002F2026-09\u002F2bd853d3d5704fcc97b92519e8e43ed4.jpeg)![Image 8](https:\u002F\u002Fiarrp.caas.cn\u002Fimages\u002F2026-09\u002F9b8ebba8168549919e6b72ebc45d9bd5.jpeg)\n\n![Image 9](https:\u002F\u002Fiarrp.caas.cn\u002Fimages\u002F2026-09\u002Fe2a0272fa0bc4c2ba5d37c35265178d7.jpeg)![Image 10](https:\u002F\u002Fiarrp.caas.cn\u002Fimages\u002F2026-09\u002F2658923e49a84b3b967251e3df682c6a.jpeg)\n\n![Image 11](https:\u002F\u002Fiarrp.caas.cn\u002Fimages\u002F2026-09\u002F4787d987d6824fe6adb28f9fc52b6c7d.jpeg)![Image 12](https:\u002F\u002Fiarrp.caas.cn\u002Fimages\u002F2026-09\u002Ffc6516914f30418cabce1b7ce04ad730.jpeg)\n\n本次培训班紧密围绕国家生态文明建设与草原畜牧业高质量发展需求，内容涵盖理论前沿、核心技术攻关以及基层应用示范。与会代表纷纷表示，通过此次系统培训，不仅拓宽了视野、掌握了前沿智能技术，更为今后在基层的推广应用奠定了坚实基础，将为助力我国草原生态保护与草原畜牧业转型升级积极贡献力量。","中国农业科学院农业资源与农业区划研究所 2026-08-23","2026-08-23T00:00:00Z","报道",69,{"impact":139,"substance":17,"depth":194,"authority":73,"freshness":195,"relevant":21,"comment":196},15,2,"国家级科研机构主办的智能放牧技术培训，内容详实，但时效性较低，仍具行业参考价值。",[198],{"name":189,"url":186},[145,200,201,27,202],"技术培训","智能放牧","草畜平衡",[204,205],"技术培训 智慧农业 智能放牧 草原生态","技术培训 智慧农业","技术培训智慧农业智能放牧草原生态-1929","2026-09-09T00:03:54.176268Z",{"id":209,"title":210,"url":211,"summary":212,"summary_zh":9,"content":213,"source_name":214,"source_url":9,"published_at":215,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":216,"score_detail":217,"sources":219,"tags":221,"search_phrases":225,"slug":228,"view_count":229,"doi":230,"paper":9,"created_at":231},109,"Does Digital Literacy Promote Farmers' Socialized Agricultural Service Adoption? Evidence from China's Thousand Villages Survey","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1907316\u002Ffull","Li & Wu基于中国千村调查数据,实证检验数字素养对农户社会化农业服务采纳行为的影响。研究发现数字素养显著提升农户对社会化农业服务的采纳概率,且在不同代际、不同经营规模、不同地区之间存在异质性效应。","## Abstract\n\nThe rapid development of digital technologies is reshaping rural production, daily life, rural governance, and patterns of social interaction. Digital tools such as smartphones make farmers obtain market information and engage in rural governance. Digital literacy (DL) works as a crucial capability empowering farmers to engage in production and managerial decision-making, thereby affecting socialized agricultural service adoption. However, few studies have directly examined the influence of DL upon farmers’ adoption of socialized agricultural services. Our research investigated the effect of DL upon farmers’ socialized agricultural service adoption, using microdata from the 2023 China Thousand Villages Survey. It utilized a logit model to analyze the data and employed instrumental variables and other methods to conduct robustness checks. The study found that DL dramatically raised the probability that farmers adopted socialized agricultural services. As indicated by mechanism analysis, DL propels farmers’ socialized agricultural service adoption mainly through three channels: broadening information access, alleviating financing constraints, and facilitating the transformation of agricultural production and management practices. Moderation analysis showed that the higher the dependence of DL upon agricultural operating income and the degree of marketization in agricultural production and management, the stronger the driving force. However, if the level of digital village governance is higher, its marginal effect will be lower. In heterogeneity analysis, the effect of DL was more evident among male-headed households, households headed by individuals aged over 50 years, and households headed by individuals with junior high school education or below. These findings expand the analytical framework for the determinants of socialized agricultural service adoption, while providing evidence and policy perceptions of introducing modern agriculture based on division of labor into small farms through digital empowerment.\n\n## 1 Introduction\n\nIn many developing countries, agricultural production is experiencing deep-going transformation. With rapid urbanization, continuous rural labor outflow, an aging agricultural workforce, and rising household labor opportunity costs, traditional small farming faces increasingly severe constraints on labor supply, production efficiency, technology adoption, and risk management (; ; ; ). Socialized agricultural services, a critical institutional arrangement linking smallholders to modern agriculture, can help farmers overcome the constraints arising from lack of household labor, machinery, and professional expertise by offering services such as mechanized field operations, agricultural input procurement, pest and disease control, technical guidance, harvesting, drying, storage, transportation, and full process management. Thus, they improve production efficiency and reduce production risks (; ). However, because of factors such as limited access to information, ability-to-pay constraints, difficulties in assessing service quality, and traditional production habits, farmers’ potential demand for socialized agricultural services often cannot be fully translated into actual adoption behavior (; ).\n\nChina forges a representative context, aimed at examining this issue. As a major agricultural country with a large number of smallholders (), China’s agricultural modernization partially depends upon land consolidation and farm expansion. Instead, it increasingly emphasizes integrating smallholders into the trajectory of modern agricultural development through socialized agricultural services, enabling them to indirectly share the benefits of mechanization, specialization, and scaled-up operations without having to transfer their land or transform themselves into large-scale farming entities (; ). Therefore, socialized agricultural service development is regarded as an important pathway to organically integrate smallholders into modern agriculture. Although the market for socialized agricultural services has rapidly expanded recently, the extent of adoption significantly differ among farmers; the key factors underlying these differences are yet to be fully identified. Clarifying these factors is of great significance to facilitate better integration of smallholders into modern agriculture based on division of labor and effectively transform their service needs into actual adoption behaviors.\n\nThe rapid development of digital technologies is reshaping rural production, daily life, rural governance, and patterns of social interaction (; ; ). Digital tools such as smartphones, the mobile Internet, digital finance, e-commerce platforms, short-video applications, and online government service platforms have gradually become embedded in rural society, offering farmers novel approaches to obtaining market information, obtain financial services, compare service providers, connect with agricultural business organizations, and participate in rural governance. However, the dividends of digital technology do not automatically translate into improvements in farmers’ production and operational decisions; the key lies in whether farmers are able to effectively access, understand, access, and utilize digital information (). Digital literacy (DL), a crucial capability that enables farmers to engage in production and managerial decision-making in the digital era, may shape the process through which digital resources are translated into actual farming practices, thereby affecting socialized agricultural service adoption. Against this backdrop, our research examined whether digital literacy empirically promotes farmers’ socialized agricultural service adoption.\n\nExisting studies have mainly examined the determinants of farmers’ socialized agricultural service adoption from the following viewpoints: household resource endowments, farm size, labor allocation, risk perception, social networks, dependence on agricultural income, and village-level service supply conditions. argue that the shrinking size of household labor has become a key factor in driving smallholders’ utilization of socialized agricultural services. Surveying 1,174 smallholders in Jiangsu Province, China. A sample survey of farmers in Ecuador show that income functions as a pivotal factor in impacting agricultural socialized service adoption (). According to investigation data from 787 farm households in Jiangxi, Fujian, and Zhejiang, China, risk perception dramatically inhibits farmers’ socialized agricultural service adoption (). According to survey information from China’s 638 farm households, reveal that farm size exerts a nonlinear impact on socialized agricultural service adoption; an expansion in farm size significantly promotes large-scale farmers’ actual purchase behavior but suppresses that of smallholders. In addition, the study finds that household income exerts a remarkably positive effect upon adopting socialized agricultural services; if the household burden is heavier and the degree of land fragmentation is greater, farmers will trend less to accept such services. Moreover, the presence of local providers of socialized agricultural services can significantly increase farmers’ adoption behaviors.\n\nA growing number of studies have begun to focus on the role of the digital economy and digital technologies in farmers’ decision-making in agricultural production and management, as well as in agricultural sustainable transformation. examine how the digital economy affects Chinese farmers’ adoption of eco-agricultural technologies along dimensions such as digital sales, digital finance, and digital production. Other studies further explore the impacts of digital technologies on farmers’ credit access, labor mobility, and participation in supply-chain integration (; ; ).\n\nCompared with research on the adoption of specific digital technologies—which mainly emphasizes whether farmers use particular tools such as the Internet, smartphones, digital finance platforms, or e-commerce platforms—digital literacy (DL) places greater emphasis on farmers’ ability to understand, use, and transform digital information and digital tools. Therefore, digital literacy not only affects whether farmers come into contact with digital technologies, but also determines whether they can convert digital resources into actual capabilities for agricultural production and operational decision-making. Accordingly, in recent years, an increasing body of literature has directly investigated the importance of digital literacy in fields such as agricultural production, rural entrepreneurship, inclusive finance, and farmers’ market participation. construed survey information from 643 farm households in Shaanxi and Shandong, China, suggesting that enhancements in DL dramatically facilitated farmers’ adoption of green production technology, including water-saving irrigation, pest and disease control, use of contamination-free pesticides, and the return of straw. Analyzing 742 survey questionnaires from Shandong province, find that digital literacy dramatically increases farmers’ adoption probability for integrated pest management, conservation tillage, soil testing, and formula fertilization technologies. draw a similar conclusion from 923 valid household samples of the China Land Economic Survey. show that digital literacy helps rural entrepreneurs expand their businesses and connect with markets, thereby promoting rural entrepreneurship. According to micro-survey information from China’s five national e-commerce demonstration counties, Internet use significantly increases farmers’ probability of partaking in e-commerce entrepreneurship ([Song et al., 2024](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1907316\u002Ffull#ref9002)). Using survey data from 360 farm households in Nigeria, [Nwangwu et al. (2024)](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1907316\u002Ffull#ref9001) show that digital technology dramatically reinforces farmers’ market participation decisions and intensity of participation.\n\nHowever, existing studies have mainly examined the effects of the digital economy, digital technologies, or digital literacy on ecological agricultural technology adoption, rural entrepreneurship, and market participation, while relatively little attention has been paid to integrating digital literacy and the adoption of agricultural socialized services into a unified analytical framework. Agricultural socialized services provide an important pathway for smallholders to participate in the modern agricultural division of labor and connect with the modern agricultural system. Therefore, examining the effect of digital literacy on farmers’ adoption of agricultural socialized services can help extend the micro-level empirical evidence on digitally enabled agricultural modernization. Few studies have incorporated digital literacy and socialized agricultural services into a unified analysis framework, aimed at directly examining the influence of digital literacy upon farmers’ adoption of socialized agricultural services. Just a small amount of research provides preliminary evidence on this issue (; ; ). Nevertheless, such research relies on relatively simple measures of digital literacy and have not sufficiently uncovered the potential mechanisms through which DL affects farmers’ acceptation of socialized agricultural services. Our study explores the influence of DL on farmers’ socialized agricultural service adoption and interprets its potential mechanisms. The purpose is to deal with this problem. We used large-scale household information from China’s Thousand Villages Survey (CTVS), implemented by Shanghai University of Finance and Economics.\n\nOur research has four key findings: For starters, DL can dramatically raise the likelihood of farmers’ acceptation of socialized agricultural services. Second, digital literacy promotes farmers’ socialized agricultural service adoption mainly through three channels (broadening information acquisition, alleviating financing constraints, and driving the transition of agricultural production and management). Third, the extent of dependence on agricultural operating income and the marketization level exert apparent positive moderating effects on digital literacy’s role in driving socialized agricultural service adoption, whereas the digital governance level in villages exerts a remarkably negative moderating effect. Fourth, the driving force of DL is stronger among households with male heads, heads over the age of 50, and heads with junior high school education or below.\n\nThe following three aspects of our study reflect its potential marginal contributions: First, it combines DL with the analysis framework of farmers’ socialized agricultural service adoption, thereby extending associated studies according to digital literacy. Second, this research systematically examines the mechanisms via which DL affects the acceptation of socialized agricultural services from three perspectives: broadening information acquisition channels, easing financing constraints, and facilitating the transition of agricultural production and management modes, thereby deepening our understanding of the internal theoretical logic underlying the relationship between the two. Third, by combining a moderating effect analysis and heterogeneity analysis, our research identifies situational conditions and group differences in the impact of DL upon farmers’ socialized agricultural service adoption, compensates for existing studies’ insufficiency in examining contextual conditions and heterogeneity characteristics, and provides empirical proof for formulating differentiated digital capability enhancement policies and boosting the advancement of high-quality socialized agricultural services.\n\nThe rest of this article is structured as below: Section 2 describes the theoretical analysis and assumptions, along with the theoretical mechanisms via which DL impacts farmers’ adoption of socialized agricultural services. Section 3 elaborates on the information sources, variables, and econometric models. Section 4 exhibits the results of the benchmark regression, mechanism analysis, robustness tests, moderating effect analysis, and heterogeneity analysis. Section 5 discusses and interprets the empirical results. Section 6 concludes the paper and provides policy suggestions.\n\n## 2 Theoretical analysis and assumption development\n\n### 2.1 Digital literacy’s impact upon socialized agricultural service adoption\n\nFor farmers, deciding whether to adopt socialized agricultural services is essentially the result of a cost–benefit trade-off under multiple constraints, including labor, capital, technology, risk, and transaction costs (). However, some farmers have long relied on family labor and traditional production experience, and often have insufficient knowledge of specialized external services, limited trust in such services, and concerns about uncertain service outcomes and uncontrollable transaction processes. These factors inhibit their willingness to adopt socialized agricultural services.\n\nDigital literacy helps alleviate cognitive, trust, and decision-making constraints, thereby encouraging farmers to adopt socialized agricultural services. First, digital literacy reduces cognitive constraints. Farmers showing higher digital literacy levels usually have better learning capacity and stronger capacity to accept novel technologies (), making it easier to understand the effect of socialized agricultural services in saving labor input, ameliorating operational efficiency, reducing production costs, and enhancing business performance. This, in turn, improves their awareness and understanding of such services. Second, digital literacy alleviates trust constraints. Farmers with superior DL can better understand service contracts, operational standards, and service procedures, evaluate feedback, and are more likely to accept platform-based transactions and digital supervision methods, thereby reducing their distrust of external specialized services (). Finally, digital literacy eases decision-making constraints. Socialized agricultural service adoption involves multiple aspects including service selection, fee payment, quality assessment, and expected returns. Farmers showing superior DL can better compare different production options, accurately weigh cost–benefit relationships between self-production and service outsourcing, and reduce perceived uncertainty in the adoption process (). Therefore, digital literacy makes farmers content to accept socialized agricultural services, while raising the likelihood of adoption by improving their cognitive level, degree of trust, and decision-making ability. Our analysis puts forward the following assumption:\n\n> _A1_: DL significantly increases farmers’ socialized agricultural service adoption.\n\n### 2.2 Mechanism analysis of digital literacy’s impact upon socialized agricultural service adoption\n\n#### 2.2.1 Broadening information acquisition channels\n\nFarmers’ decisions to accept socialized agricultural services depend heavily on the extent to which they have access to relevant service information (). In accordance with transaction cost and information search theories, incomplete and asymmetric information increases farmers’ search, comparison, and monitoring costs in the service adoption process. When timely information about service providers, service prices, operational quality, service reputation, and policy subsidies is not available, farmers feel uncertain about service effectiveness and transaction security, which reduces their willingness to adopt such services (). In traditional rural societies, farmers mainly obtain information about agricultural services through acquaintances, neighborhood communications, or notifications from village cadres. Such information sources are relatively limited, which easily leads to mismatches between service supply and demand and insufficient adoption.\n\nDL can effectively broaden farmers’ information channels. Farmers showing superior DL levels can use digital tools and platforms excellently, such as smartphones, agricultural information platforms, WeChat groups, short-video platforms, agricultural service mini-programs, and government service apps, and obtain timely data relevant to socialized agricultural services (). Through multichannel information comparison, farmers can accurately assess service providers’ qualifications, reasonableness of service prices, reliability of service quality, and availability of subsidies, thereby reducing uncertainty in service selection. Digital literacy also allows farmers to evaluate information, helps them identify false advertising and low-quality information, improves the accuracy of their judgments, and reduces information-related risks in adoption decisions. The foregoing analysis leads to the following assumption:\n\n> _A2_: Digital literacy strongly motivates farmers to adopt socialized agricultural services by broadening their information acquisition channels.\n\n#### 2.2.2 Alleviating financing constraints\n\nSocialized agricultural service adoption usually requires farmers to pay service fees. While farmers facing tight financial conditions may recognize that agricultural socialized services can enhance production efficiency, decrease labor intensity, and enhance business performance, they may be unable to make timely purchases because of short-term liquidity problems. Therefore, financing constraints and insufficient payment capacity are important factors influencing farmers’ decisions to adopt socialized agricultural services ().\n\nIn rural financial markets, farmers generally face problems such as insufficient collateral, lack of credit information, long distances to financial service outl","Frontiers in Sustainable Food Systems Vol. 10 Article 1907316","2026-07-06T01:00:00Z",74,{"impact":17,"substance":18,"depth":17,"authority":73,"freshness":195,"relevant":21,"comment":218},"基于千村调查数据，实证分析数字素养对农户采纳社会化服务的影响，方法严谨，结论有政策启示，但发表于2026年7月，时效性较低。",[220],{"name":214,"url":211},[222,26,223,224],"数字素养","农户采纳","千村调查",[226,227],"社会化服务 农户采纳 千村调查 数字素养","社会化服务 农户采纳","社会化服务农户采纳千村调查数字素养-109",9,"10.3389\u002Ffsufs.2026.1907316\u002Ffull","2026-07-31T00:00:55.984490Z",{"id":233,"title":234,"url":235,"summary":236,"summary_zh":9,"content":9,"source_name":237,"source_url":9,"published_at":238,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":239,"score_detail":240,"sources":243,"tags":245,"search_phrases":248,"slug":251,"view_count":13,"doi":252,"paper":9,"created_at":253},30,"数字素养与农民对社会化服务的采纳：基于CRRS数据验证的U型关系","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1722049\u002Ffull","基于2020年中国社会科学院乡村振兴调查（CRRS）数据，采用因子分析和回归方法考察农民数字素养与社会化服务规模经营的关系。研究发现数字素养对服务规模经营整体呈先抑制后促进的U型关系，转折点约为0.47，机制上通过自然、社会、金融和人力资本四重中介路径发挥作用；分维度看数字信息与社会素养呈U型，数字商业素养呈线性正向；异质性上偏远农村和东部地区U型显著、中部不显著、西部线性正向，城乡郊区因机会成本高而无显著效应。","Frontiers in Sustainable Food Systems · 2026-07-24","2026-07-24T00:00:00Z",76,{"impact":17,"substance":18,"depth":17,"authority":73,"freshness":241,"relevant":21,"comment":242},4,"基于CRRS数据的实证研究，揭示数字素养与社会化服务采纳的U型关系，对数字乡村建设有参考价值。",[244],{"name":237,"url":235},[246,222,26,247],"农业信息化","U型关系",[249,250],"农业信息化 社会化服务 数字素养 U型关系","农业信息化 社会化服务","农业信息化社会化服务数字素养U型关系-30","10.3389\u002Ffsufs.2026.1722049\u002Ffull","2026-07-28T03:05:26.994652Z"]