[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-587":3,"related-587":55},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":26,"secondary_topics":32,"classification":33,"search_phrases":51,"slug":52,"view_count":53,"doi":8,"paper":8,"created_at":54},587,"Pathways Toward Sustainable Dairy Production: Scale Economies and Human Capital Barriers in the Heterogeneous Adoption of Digital Technologies in China","https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8238","山东石油化工学院、南京农业大学、南京财经大学、东营市畜牧发展服务中心研究团队基于规模化奶牛场调研数据,采用工具变量 Probit 模型实证检验养殖规模与信息密集型、知识嵌入型两类数字技术采用及牧场经济可持续性的关系。结果显示养殖规模与两类数字技术采用均正相关;扩张规模可降低物质成本与学习成本,但提高与场特异性人力资本相关的沉没成本;两类数字技术的采用均与牧场经济可持续性显著正相关。",null,"Logical Operator Operator\n\nSearch Text\n\nSearch Type\n\n_add\\_circle\\_outline_\n\n_remove\\_circle\\_outline_\n\n[![Image 1: sustainability-logo](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fjournals\u002Fsustainability-logo.png?3798e4e58c765aed)](https:\u002F\u002Fwww.mdpi.com\u002Fjournal\u002Fsustainability)\n\n## Article Menu\n\nFont Type:\n\n_Arial_ _Georgia_ _Verdana_\n\nFont Size:\n\nAa Aa Aa\n\nLine Spacing:\n\n__ __ __\n\nColumn Width:\n\n__ __ __\n\nBackground:\n\nOpen Access Article\n\nby \nYuwen Qi\n\n 1[](mailto:2023049@sdipct.edu.cn), \nJiqin Han\n\n 2,*[](mailto:jhan@njau.edu.cn)[![Image 2: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1786617101)](https:\u002F\u002Forcid.org\u002F0000-0002-2512-7855), \nXue Chang\n\n 3,*[](mailto:changxue@nufe.edu.cn) and \nChunhua Yang\n\n 4\n\n1\n\nSchool of Economics and Management, Shandong Institute of Petroleum and Chemical Technology, Dongying 257061, China\n\n2\n\nCollege of Economics and Management, Nanjing Agricultural University, Nanjing 210095, China\n\n3\n\nSchool of Economics, Nanjing University of Finance and Economics, Nanjing 210023, China\n\n4\n\nAnimal Husbandry Development Service Center, Dongying 257061, China\n\n*\n\nAuthors to whom correspondence should be addressed.\n\nSubmission received: 17 June 2026 \u002F Revised: 8 August 2026 \u002F Accepted: 9 August 2026 \u002F Published: 11 August 2026\n\n## Abstract\n\nLivestock digital technologies are pivotal to advancing sustainable agricultural development. However, their adoption rates in China’s livestock sector remain relatively low. Existing studies have largely overlooked the heterogeneous mechanisms through which herd size relates to the adoption of different types of agricultural digital technologies. Drawing on survey data from scaled dairy farms, this research employs an Instrumental Variable Probit (IV Probit) model to empirically examine the relationship between herd size and the adoption of information-intensive and knowledge-embedded digital technologies, as well as their effects on farm economic sustainability. The results indicate that herd size is positively associated with the adoption of both types of digital technologies. Further analysis reveals that herd expansion is associated with lower material and learning costs and higher sunk costs of farm-specific human capital. Moreover, the results suggest that the adoption of either type of digital technology is positively and significantly correlated with farm economic sustainability. These findings suggest that it is imperative to actively promote moderate-scale farm operations. Additionally, improving agricultural subsidy policies may promote the uptake of digital technologies to advance sustainable dairy farming.\n\n## 1. Introduction\n\nDigital technologies are increasingly recognized as pivotal instruments in reducing costs, enhancing operational efficiency, and promoting sustainability in agricultural production [[1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8238#B1-sustainability-18-08238)]. Within the livestock sector, the development and adoption of these technologies are essential for ensuring stable supplies of meat, eggs, and dairy products, increasing the income of farming households, and ultimately contributing to rural revitalization. Recognizing this imperative, the Chinese government has released a series of policies to accelerate the advancement of digital technologies in livestock production. For instance, in 2024, the Ministry of Agriculture and Rural Affairs released the Guiding Opinions on Vigorously Developing Smart Agriculture, which explicitly promotes intelligent large-scale breeding. More recently, the “No. 1 central document” for 2025 underscored the need to expand the application of data-driven and artificial intelligence technologies across the agricultural sector. Despite these policy imperatives, the adoption of digital technologies is still constrained within China’s livestock farming, and their application differs sharply by technology type. According to the China Digital Rural Development Report (2022) [[2](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8238#B2-sustainability-18-08238)], the informatization rate of agricultural production was a mere 25.4% in 2021, substantially below the target of over 40% set by national guidelines for 2035. At the farm level, evidence from a 2020 survey of large-scale dairy farms in Shandong Province reveals a stark disparity: while 25.8% of farms had adopted digital estrus detection systems, only 14.2% utilized digital manure management systems [[3](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8238#B3-sustainability-18-08238)]. Under these conditions, the sluggish diffusion of digital production and management systems has constrained productivity growth in the livestock sector [[4](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8238#B4-sustainability-18-08238)]. Accelerating the adoption of digital technologies has become a critical challenge in China’s pursuit of agricultural modernization.\n\nA growing body of literature has examined technology adoption in livestock production from various perspectives. However, systematic analysis of digital technology adoption remains relatively scarce. The first strand of this literature focuses on traditional livestock technologies. For example, studies have found that memory limitations negatively affect the application of disease control technologies [[5](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8238#B5-sustainability-18-08238)]. Farm size and technical characteristics also significantly influence the adoption of manure detection technologies [[6](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8238#B6-sustainability-18-08238)], while the economic, social, and environmental factors critically influence the adoption of feeding technologies [[7](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8238#B7-sustainability-18-08238)]. A second, more recent strand of research has begun to investigate digital technologies in the livestock sector. Several studies have highlighted the importance of herd size in the adoption of such technologies [[8](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8238#B8-sustainability-18-08238)], with larger farms more likely to adopt at least one form of precision agriculture technology [[9](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8238#B9-sustainability-18-08238)]. Moreover, farmers’ human capital, digital knowledge, gender, perceptions, experience of extreme weather, cooperative membership, and family characteristics have been found to significantly influence farmers’ willingness to adopt digital agricultural technologies [[10](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8238#B10-sustainability-18-08238),[11](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8238#B11-sustainability-18-08238),[12](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8238#B12-sustainability-18-08238),[13](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8238#B13-sustainability-18-08238),[14](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8238#B14-sustainability-18-08238)].\n\nHowever, these studies have largely treated digital technologies as a homogeneous category, and little empirical attention has been paid to the divergent associations between herd size and the uptake of heterogeneous digital technologies within the dairy sector. This research gap warrants attention, given the high capital requirements and substantial learning costs associated with digital technology adoption [[15](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8238#B15-sustainability-18-08238)]. To address this, we draw on a theoretical framework that classifies digital technologies into two distinct types according to their value-delivery mode and required operator skill level: information-intensive and knowledge-embedded digital technologies [[16](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8238#B16-sustainability-18-08238)].\n\nInformation-intensive digital technologies are defined as those that employ controllers, onboard sensors, and actuators to capture production data, convert it into digital information, and provide timely information to support decision-making. However, their effective use requires operators to invest time and resources in acquiring additional specialized skills [[17](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8238#B17-sustainability-18-08238)]. For example, digital estrus detection systems rely on wearable sensors to record daily step count data, generating monitoring information that supports decision-making in the dairy sector. The core value of such technologies lies in mitigating information asymmetry in traditional agricultural production. However, this value cannot be realized if operators lack the ability to interpret the provided information. In contrast, knowledge-embedded technologies are defined as those that integrate advanced intelligent digital systems and deliver value that is inherently embedded in the technology itself, without requiring additional operator skills [[18](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8238#B18-sustainability-18-08238)]. For instance, digital manure management systems enable automated continuous manure removal in dairy farms. The core value of such technologies lies in addressing inefficiency of manual agricultural management.\n\nThis conceptual distinction raises several interrelated questions with both theoretical and policy-related significance: Do these two categories exhibit heterogeneous adoption patterns? Is herd size associated with the heterogeneous adoption of digital technologies? And which policy instruments are most effective in encouraging digital technology application in dairy production? This study aims to provide rigorous answers to these questions by analyzing survey data from scaled dairy farms in Shandong Province. Our empirical investigation focuses on the relationship between herd size and the uptake of both technology types, as well as the mechanisms underpinning this relationship. Our findings reveal three main results. First, herd size is positively associated with the adoption of heterogeneous agricultural digital technologies. Second, larger herd size exhibits lower material and learning costs for both technology types, but with higher sunk costs of farm-specific human capital for information-intensive technologies. Third, compared with non-adoption, the joint adoption of digital technologies on larger farms is associated with higher dairy productivity and farm profitability.\n\nThis research makes three marginal contributions to the existing literature. First, by explicitly distinguishing between information-intensive and knowledge-embedded digital technologies [[19](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8238#B19-sustainability-18-08238)], we offer nuanced empirical evidence on the scale-technology adoption nexus within China’s dairy sector, a context that remains empirically underexplored. Although the general relationship between farm size and technology adoption has been extensively studied [[20](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8238#B20-sustainability-18-08238),[21](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8238#B21-sustainability-18-08238)]. Our disaggregated approach reveals whether this relationship holds uniformly across different types of digital technologies. Second, we propose a novel analytical framework that links herd size to technology adoption through three mediating channels: material costs, learning costs, and sunk costs of farm-specific human capital. While prior work has examined the role of material costs [[22](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8238#B22-sustainability-18-08238),[23](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8238#B23-sustainability-18-08238)], the mediating roles of learning costs and the sunk costs of human capital (recognized as critical barriers to adoption [[24](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8238#B24-sustainability-18-08238),[25](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8238#B25-sustainability-18-08238)]) have been largely overlooked. Our research directly addresses this gap. Third, we extend the analysis to examine the economic implications, exploring linkages between the interaction of herd size and digital technology adoption, dairy productivity, and farm profitability. While numerous studies have linked farm size, technology, and productivity indicators such as technical efficiency [[26](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8238#B26-sustainability-18-08238)] and total factor productivity [[27](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8238#B27-sustainability-18-08238)], the specific links between herd size, heterogeneous technology adoption, and yields and profits remain unclear. Farm profit is a central goal of agricultural production, yet data on farm profit are difficult to collect. Drawing on a comprehensive survey, this study contributes to the literature by shedding light on these previously underexplored effects.\n\n## 2. Technical Background and Theoretical Analysis\n\n### 2.1. Background of Digital Livestock Technologies\n\nDigital technologies have significantly enhanced both resource allocation efficiency and productivity in livestock production systems [[28](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8238#B28-sustainability-18-08238)]. Digital technologies applied in dairy production include digital estrus detection systems and digital manure management systems. We select digital estrus detection systems as representative of information-intensive digital technologies, given that reproductive performance constitutes a core determinant of lactation output. Timely and accurate estrus detection is essential for successful insemination, conception, and calving, which is a prerequisite for milk production. Delayed detection reduces conception rates and prolongs the non-pregnant period, thereby increasing feeding and labor costs and disrupting milk supply. Digital manure management systems are chosen as representative of knowledge-embedded digital technologies, as manure management is essential to dairy operations. Manure decomposition generates ammonia (NH 3) and hydrogen sulfide (H 2 S). Elevated NH 3 levels reduce cow immunity and productivity, while H 2 S impairs reproductive performance.\n\nAs digital technologies, digital estrus detection systems and digital manure management systems share several common characteristics. On the one hand, both technologies exhibit a degree of indivisibility. Digital estrus detection systems require the joint usage of signal receivers and computing systems, while digital manure management systems are usually developed as fixed facilities and typically cannot be separated into independent components. On the other hand, both are marked by substantial capital requirements and uncertain returns, with effective use depending on learning processes and information sharing.\n\nAt the same time, however, the two technologies differ in functional terms. Digital estrus detection systems are decision-support tools designed to identify reproductive status based on monitoring outputs from sensing technologies. They replace human observation experience with data-driven analysis to support production decisions. In conventional farm management, estrus identification has depended on manual observation, with farmers drawing on their experience to judge outward behavioral and physiological signs, including mounting behavior, rumination, body temperature, and feed intake. Under large-scale production conditions, however, the detection rate achieved through such observation typically falls below 50% [[29](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8238#B29-sustainability-18-08238)]. This constraint depresses reproductive efficiency, which in turn reduces total milk production. In operational terms, digital estrus detection technologies depend on wearable sensors affixed to animals, most commonly on the leg or neck, to capture daily step count data via a signal receiver. Those data are then sent to computer software for statistical analysis, generating the monitoring information system for an analysis report. By displacing manual, experience-based assessment with automated, data-driven monitoring, such systems improve estrus detection accuracy markedly, thereby contributing to higher milk yield.\n\nIn contrast, digital manure management systems are directly implemented in the production process by replacing conventional equipment, such as loaders, and improving production efficiency. Large-scale and intensive farming have increasingly led to a decoupling of crop cultivation from livestock raising, resulting in the generation of substantial volumes of dairy manure. Under the conventional system of farming, manure removal involves manual labor or use of loaders, which slows the process and often leads to severe environmental pollution [[30](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8238#B30-sustainability-18-08238)]. Powered by electricity, digital manure management systems automate continuous manure removal, effectively overcoming the inefficiencies of traditional methods and ensuring sanitary farm conditions [[31](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8238#B31-sustainability-18-08238)].\n\n### 2.2. Theoretical Analysis and Research Hypotheses\n\nDigital technologies share several common characteristics, including indivisibility, high capital intensity, and uncertain returns [[32](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8238#B32-sustainability-18-08238)]. First, according to economies of scale, an expansion in production size reduces material costs. Given the indivisibility of both types of digital technologies, their unit acquisition costs decline as herd size increases, thereby promoting adoption. Second, knowledge acquisition also exhibits size economies, implying that larger herd size reduces average learning costs [[33](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8238#B33-sustainability-18-08238)]. Since the returns to both types of digital technologies are uncertain, effective learning and information exchange are required prior to adoption. As herd size expands, the per-unit learning cost decreases, which in turn facilitates the adoption of both types of digital technologies.\n\nIn addition, the two types of digital technologies exhibit distinct characteristics. Information-intensive digital technologies provide knowledge that substitutes for experiential know-how and supports production decision-making, whereas knowledge-embedded digital technologies derive value from the technologies themselves and substitute for other capital inputs to improve productivity [[34](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8238#B34-sustainability-18-08238)]. Drawing on asset specificity theory, production experience constitutes a form of farm-specific human capital that is accumulated through on-farm learning and tailored to its production environment. Unlike general human capital, which is readily transferable across farms, this specific human capital offers limited value outside the current operation. When a farmer considers adopting an information-intensive technology that disrupts herd management and decision-making routines, the accumulated production experience may become a sunk cost. Accordingly, more extensive production experiences raise the sunk costs of adopting information-intensive digital technologies. By contrast, because the value of knowledge-embedded digital technologies is embodied in the technologies themselves, their adoption does not increase the sunk cost of farm-specific human capital. Moreover, as farms expand in scale, the degree of internal specialization tends to increase, which may further raise sunk costs [[35](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8238#B35-sustainability-18-08238)]. Within the context of dairy production, farm expansion is likely to inhibit the adoption of information-intensive digital technologies by raising the sunk costs associated with production experience as a form of farm-specific human capital, while having no such effect on the adoption of knowledge-embedded digital technologies.\n\nBased on the above analysis, the following hypotheses are proposed:\n\n**Hypothesis****1.**\n\nAn increase in herd size promotes the adoption of both information-intensive and knowledge-embedded dig","Sustainability 2026, 18(16): 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累计推广83.31万亩","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7697083643218477622\u002F","黑龙江省黑土保护利用研究院研究员高洪生团队依托国家重点研发计划、黑龙江省农科院创新工程等项目历经近10年攻关,成功研发出适配寒区农业生产节律的奶牛液体粪肥还田成套技术体系;2022—2024年累计推广应用面积83.31万亩、消纳还田奶牛液体粪肥467.41万立方米;2026年该技术正式入选黑龙江省农业主推技术名单。","[](https:\u002F\u002Fwww.toutiao.com\u002F)\n\n*   关注 \n*   推荐 \n*   北京   \n*   视频 \n*   财经 \n*   科技 \n*   热点 \n*   国际 \n*   更多 \n\n    *   军事 \n    *   体育 \n    *   娱乐 \n    *   历史 \n    *   美食 \n    *   [直播](https:\u002F\u002Flive.ixigua.com\u002F) \n    *   旅游 \n    *   [懂车帝](https:\u002F\u002Fwww.dongchedi.com\u002F?zt=tt_pc_home_channel) \n\n搜索\n\n[消息](https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7697083643218477622\u002F)\n\n[发布](https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7697083643218477622\u002F)\n\n[登录](https:\u002F\u002Fsso.toutiao.com\u002Flogin\u002F?service=https%3A%2F%2Fwww.toutiao.com%2Farticle%2F7697083643218477622%2F%3Fwid%3D1792368503998)\n\n2\n\n评论\n\n收藏\n\n分享\n\n*   转发到头条 \n*   复制链接 \n*   微信  微信扫码分享  \n*   新浪微博 \n*   QQ空间 \n\n# 寒区奶牛液体粪肥还田技术变废为宝\n\n2026-10-16 10:15·[中工网](https:\u002F\u002Fwww.toutiao.com\u002Fc\u002Fuser\u002Ftoken\u002FMS4wLjABAAAAMnblevmY0Qt-QgRg_pKWHVMrbv_lg0VMmEMpEEOk6Vw\u002F?source=tuwen_detail)\n\n科技日报记者 朱虹\n\n前不久，寒区奶牛液体粪肥还田技术推介观摩现场会暨畜禽液体粪污资源化利用专题培训会在哈尔滨举行。本次会议由黑龙江省黑土保护利用研究院牵头，联合黑龙江省奶业协会、省奶牛产业技术协同创新推广体系主办，聚焦寒区畜禽粪污资源化利用、绿色种养循环农业发展，开展技术研讨与现场观摩交流。\n\n黑龙江作为全国核心优质奶源基地，规模化奶牛养殖产业体量庞大。但受高寒气候制约，区域土壤封冻期长、粪污还田窗口期短，叠加粪污贮存风险高、转运难度大、施用技术粗放、专用装备短缺等痛点，长期存在奶牛液体粪污“存不住、运不出、还田难”困境。这造成养殖端有机养分资源与种植端耕地肥力需求时空错配，既污染养殖区域生态环境，又浪费珍贵的有机农业资源，制约种养循环农业高质量发展。\n\n针对寒区养殖粪污资源化利用的技术短板，黑龙江省黑土保护利用研究院研究员高洪生团队，依托国家重点研发计划、黑龙江省农业科学院创新工程等项目，历经近10年持续攻关、多点田间试验与示范验证，成功研发出适配寒区农业生产节律的奶牛液体粪肥还田成套技术体系，破解高寒地区粪污资源化利用的核心难题。\n\n这套技术构建起一条“冬天存得住、春天运得出、田里施得准”的流水线：先把粪水集中起来，固液分离后把液体粪肥密封贮存发酵，解决封冻期不能还田、容易污染的问题；等土地解冻，用像“脐带”一样的泵和软管把粪肥直接从贮存池送到田间，替代罐车拉运，降低成本；还田前检测粪肥养分、测算地块承受力，再按季节用喷洒、深埋注入或滴灌等方式精准施入，抓住春、夏、秋的还田窗口。这样就把粪污从“污染负担”变成了“肥料资源”。同时，项目配套的托管式（脐带）输送装备国产化率超95%，大幅降低大型牧场粪肥田间输送成本、提升作业效率，为技术规模化落地筑牢装备根基。\n\n多年示范应用数据显示，该技术提质增效成果显著。2022—2024年，黑龙江省累计推广应用面积达83.31万亩，消纳还田奶牛液体粪肥467.41万立方米。示范片区实现化肥减量10%—20%，玉米粮食作物增产7.58%以上，同步达成粪污无害化处置、化肥减量增效、黑土地力培肥三重效益，构建起生态、农业、产业共赢的发展模式。2026年，寒区奶牛液体粪肥还田技术正式入选黑龙江省农业主推技术名单，标志着该项科研成果全面完成试验示范迭代，迈入标准化、规范化、规模化推广新阶段。\n\n来源：科技日报\n\n举报\n\n评论 0\n\n请先 登录 后发表评论～\n\n今日头条推荐引擎有 9 条更新\n\n[稻花香里说丰年｜向科技要丰收，看大国粮仓如何“藏粮于技”？](https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7696519606655124009\u002F)\n*   [![Image 1: 稻花香里说丰年｜向科技要丰收，看大国粮仓如何“藏粮于技”？-图1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002Fbdf7992fbac1d9184325c16ee97c9901~tplv-tt-cs0:1280:853.jpg?_iz=31826&bid=10&from=article.headline&gid=7696519606655124009&lk3s=06827d14&x-expires=1794960507&x-signature=UVjsVt3yiBPoAmtiKXAKGfjAM48%3D)](https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7696519606655124009\u002F) \n*   [![Image 2: 稻花香里说丰年｜向科技要丰收，看大国粮仓如何“藏粮于技”？-图2](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F41252e896104b44737e8937b61d290cd~tplv-tt-cs0:600:461.jpg?_iz=31826&bid=10&from=article.headline&gid=7696519606655124009&lk3s=06827d14&x-expires=1794960507&x-signature=8F6QAbPohV55KRdkKGB%2BigcxbcY%3D)](https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7696519606655124009\u002F) \n*   [![Image 3: 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头像](https:\u002F\u002Fsf1-cdn-tos.toutiaostatic.com\u002Fimg\u002Fuser-avatar\u002F18143f27479eac65f70b88df5d2035da~300x300.image)](https:\u002F\u002Fwww.toutiao.com\u002Fc\u002Fuser\u002Ftoken\u002FMS4wLjABAAAAMnblevmY0Qt-QgRg_pKWHVMrbv_lg0VMmEMpEEOk6Vw\u002F?source=tuwen_detail&entrance_gid=7697083643218477622)[中工网](https:\u002F\u002Fwww.toutiao.com\u002Fc\u002Fuser\u002Ftoken\u002FMS4wLjABAAAAMnblevmY0Qt-QgRg_pKWHVMrbv_lg0VMmEMpEEOk6Vw\u002F?source=tuwen_detail&entrance_gid=7697083643218477622)\n中工网官方账号\n\n关注\n\nTA的热门作品\n\n*   [](https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7697982998863528486\u002F)[从卖产品到卖方案！广交会上“产品+服务”新模式受青睐](https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7697982998863528486\u002F)\n158阅读 11小时前 \n*   [](https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7697988220289598002\u002F)[中国制造从“我会做”走到“我懂你日子怎么过” 外贸向技术、方案、服务出口升级](https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7697988220289598002\u002F)\n157阅读 11小时前 \n*   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[9 为什么要把社保缴费的尺子统一起来](https:\u002F\u002Fwww.toutiao.com\u002Ftrending\u002F7697117829618958377\u002F?rank=9)\n11.   [10 马克龙谴责法国多地暴力事件](https:\u002F\u002Fwww.toutiao.com\u002Ftrending\u002F7698154320520302118\u002F?rank=10)\n\n扫码下载今日头条APP\n\n看最新、最热资讯内容\n\n## 精彩视频\n\n![Image 19](https:\u002F\u002Flf-dw.toutiaostatic.com\u002Fobj\u002Ftoutiao-duanwai\u002Ftoutiao\u002Ftoutiao_web_pc\u002Fsvgs\u002Frotate_new.3094a784.svg)换一换\n\n[01:03](https:\u002F\u002Fwww.toutiao.com\u002Fvideo\u002F7457786404332896820\u002F)\n\n[安徽人真的太幸福了！ 这个一袋铜钱就能逛的小镇](https:\u002F\u002Fwww.toutiao.com\u002Fvideo\u002F7457786404332896820\u002F \"安徽人真的太幸福了！ 这个一袋铜钱就能逛的小镇\")\n13万次播放\n\n[02:01](https:\u002F\u002Fwww.toutiao.com\u002Fvideo\u002F7456732325234082345\u002F)\n\n[柳州市被行政拘留人矛盾纠纷人民调解委员会揭牌成立](https:\u002F\u002Fwww.toutiao.com\u002Fvideo\u002F7456732325234082345\u002F \"柳州市被行政拘留人矛盾纠纷人民调解委员会揭牌成立\")\n13万次播放\n\n[18:18](https:\u002F\u002Fwww.toutiao.com\u002Fvideo\u002F7457813323883479602\u002F)\n\n[科技与艺术的桥梁：专访跨界创客，解码DigiShow的创新之路](https:\u002F\u002Fwww.toutiao.com\u002Fvideo\u002F7457813323883479602\u002F \"科技与艺术的桥梁：专访跨界创客，解码DigiShow的创新之路\")\n14万次播放\n\n[21:25](https:\u002F\u002Fwww.toutiao.com\u002Fvideo\u002F7457868032962986535\u002F)\n\n[【姆罗解析】深度解析《灵魂骇客2》剧情缘何惊艳](https:\u002F\u002Fwww.toutiao.com\u002Fvideo\u002F7457868032962986535\u002F \"【姆罗解析】深度解析《灵魂骇客2》剧情缘何惊艳\")\n12万次播放\n\n[02:20](https:\u002F\u002Fwww.toutiao.com\u002Fvideo\u002F7457813195646829093\u002F)\n\n[应急科普 | 高原地震如何针对性救援？ #西藏地震救援](https:\u002F\u002Fwww.toutiao.com\u002Fvideo\u002F7457813195646829093\u002F \"应急科普 | 高原地震如何针对性救援？ #西藏地震救援\")\n13万次播放\n\n*   点击回到头条首页 首页\n*   反馈\n*   下载 扫码下载今日头条  \n*   顶部\n\n登录后内容更精彩","今日头条中工网(2026-10-16)","2026-10-16T00:00:00Z",{"impact":18,"substance":125,"depth":68,"authority":149,"freshness":56,"relevant":21,"comment":340},"省级主推技术，含83.31万亩推广数据与成套技术细节，具行业参考价值，可入精选。",[342],{"name":337,"url":334},[344,345,346,347,348],"黑土地保护","主推技术","奶牛养殖","粪污资源化","种养循环",{"code":27,"slug":28,"name":29,"path":30,"parent_code":31},[],{"status":34,"locked":14,"version":35,"method":36,"evidence":352},{"candidates":353,"agricultural_evidence":41},[354,358,362,367],{"code":27,"score":149,"supported":41,"title":355,"abstract":356,"tags":357},[49],[49],[346],{"code":219,"score":21,"supported":14,"title":359,"abstract":360,"tags":361},[],[],[345],{"code":363,"score":21,"supported":14,"title":364,"abstract":365,"tags":366},"H1",[],[],[344],{"code":89,"score":21,"supported":14,"title":368,"abstract":369,"tags":370},[],[],[348],[372,373],"黑龙江省黑土保护利用研究院 粪肥还田","粪污资源化 黑土地保护 主推技术 奶牛养殖","黑龙江省黑土保护利用研究院粪肥还田-5276","2026-10-19T00:07:41.105960Z"]