[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5537":3,"related-5537":75},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":59,"slug":62,"view_count":63,"doi":8,"paper":64,"created_at":74},5537,"农业产业园促进小农户融入数字化供应链：协同机制与制度逻辑","https:\u002F\u002Fkyy.nwafu.edu.cn\u002Fkjjz\u002Fxdkjjz\u002F557ffd662c8e4af98e46fbf90f35d91f.htm","西北农林科技大学阮俊虎教授团队联合德国哥廷根大学于晓华教授，以大荔县国家现代农业产业园等为典型案例，从交易成本理论出发围绕技术赋能、组织整合、规则治理与价值分配四个机制开展案例内分析与跨案例比较，揭示农业产业园作为中国特色制度平台促进小农户融入数字化供应链的深层逻辑。",null,"中国农村经济 2026-10-21","2026-10-21T00:00:00Z","论文",10,true,86,{"impact":16,"substance":16,"depth":17,"authority":18,"freshness":19,"relevant":20,"comment":21},22,18,15,9,1,"核心期刊论文，以国家现代农业产业园为案例揭示小农户融入数字化供应链的机制，理论增量与政策参考价值兼具，值得进入每日精选。",[23],{"name":9,"url":6},[25,26,27,28,29],"数字农业","数字乡村","小农户","数字化供应链","农业产业园",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"G2","farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[],{"status":38,"locked":39,"version":40,"method":41,"evidence":42},"classified",false,"2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":13},[44,49,54],{"code":31,"score":45,"supported":13,"title":46,"abstract":47,"tags":48},12,[27],[27],[27],{"code":50,"score":20,"supported":39,"title":51,"abstract":52,"tags":53},"I1",[],[],[25],{"code":55,"score":20,"supported":39,"title":56,"abstract":57,"tags":58},"I6",[],[],[26],[60,61],"大荔县 国家现代农业产业园","阮俊虎 小农户 数字化供应链","大荔县国家现代农业产业园-5537",3,{"doi":8,"openalex_id":8,"authors":65,"venue":8,"cited_by_count":66,"oa_url":8,"card":67,"direction":71,"ingested_from":73},[],0,{"tldr":68,"method":69,"finding":70,"direction":71,"opportunity":72},"以农业产业园为案例，揭示其促进小农户融入数字化供应链的机制与制度逻辑。","交易成本理论，大荔县等典型案例的案例内分析与跨案例比较。","技术赋能、组织整合、规则治理与价值分配四机制共同推动小农户融入数字化供应链。","数字乡村与农业信息化","可量化检验四机制对农户融入绩效的影响，并比较不同产业园模式的适用边界。","agent","2026-10-22T00:07:10.192700Z",{"total":76,"page":20,"page_size":76,"items":77},6,[78,123,171,235,308,349],{"id":79,"title":80,"url":81,"summary":82,"summary_zh":8,"content":83,"source_name":84,"source_url":8,"published_at":85,"category":11,"cover_url":8,"hotness":12,"is_selected":39,"score":86,"score_detail":87,"sources":91,"tags":93,"primary_topic":99,"secondary_topics":100,"classification":101,"search_phrases":117,"slug":120,"view_count":121,"doi":8,"paper":8,"created_at":122},539,"ICT-Based Versus Human-Based Climate Information: Implications for Agronomic Decisions Among Smallholder Farmers in the Eastern Cape, South Africa","https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114","基于南非东开普省 217 户小农户的横断面调查与多阶段抽样,采用 logistic 回归识别 ICT 气候信息采纳决定因素,并用倾向得分匹配(PSM)评估其对农事决策的影响。结果显示 65% 农户通过 ICT 平台获取气候信息,35% 依赖人力渠道;ICT 采纳驱动因素包括教育、数字素养、移动网络可靠性、实时更新感知价值与智能手机持有;PSM 估计显示基于 ICT 的气候信息在影响种植决策、投入品使用与农事时机方面比人力渠道效果低 10%—15%,提示数字接入并不自动转化为有效使用。","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 \nJabulile Zamokuhle Manyike\n\n *[](mailto:jmanyike@ufh.ac.za)[![Image 2: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1786617101)](https:\u002F\u002Forcid.org\u002F0000-0001-6529-9574) and \nYanga-Inkosi Nocezo\n\n[](mailto:yanganocezo12@gmail.com)[![Image 3: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1786617101)](https:\u002F\u002Forcid.org\u002F0000-0002-7607-495X)\n\nDepartment of Agricultural Economics, Extension, and Agri-Business, Faculty of Science and Agriculture, University of Fort Hare, 1 King William’s Town Road, Alice 5700, South Africa\n\n*\n\nAuthor to whom correspondence should be addressed.\n\nSubmission received: 3 February 2026 \u002F Revised: 7 March 2026 \u002F Accepted: 8 March 2026 \u002F Published: 9 August 2026\n\n## Abstract\n\nSmallholder farmers in South Africa face increasing climate variability, yet their agronomic decision-making depends on timely and reliable climate information. Although digital ICT platforms are expanding, limited evidence exists on how their effectiveness compares to human-based advisory systems. The study addresses this gap by examining the determinants of ICT-based climate information adoption using logistic regression and assessing its influence on agronomic decisions through propensity score matching (PSM). A cross-sectional survey and multistage sampling were used to collect data from 217 smallholder crop farmers in the Eastern Cape. The results indicate that 65% of farmers accessed climate information through ICT platforms, while 35% relied on human-based sources. The adoption of ICT-based information is driven by education, digital literacy, mobile network reliability, the perceived value of real-time updates, and smartphone ownership, whereas habitual dependence on traditional channels hinders digital uptake. PSM estimates show that ICT-based climate information is 10–15% less effective than human-based sources in shaping planting decisions, input use, and the timing of farm operations, likely due to digital literacy and infrastructure constraints. The study demonstrates that access to digital tools does not automatically translate into effective use and recommends a hybrid information model integrating digital platforms with trusted human intermediaries to strengthen climate resilience and agronomic decision-making.\n\n## 1. Introduction\n\nAgriculture remains central to rural livelihoods in South Africa, particularly in provinces such as the Eastern Cape, where smallholder farmers form the majority of agricultural producers [[1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B1-sustainability-18-08114),[2](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B2-sustainability-18-08114),[3](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B3-sustainability-18-08114)]. Despite its importance, this sector is highly vulnerable to climate-related risks, as smallholder farmers often operate with limited resources, weak extension support, and poor access to climate adaptation tools [[4](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B4-sustainability-18-08114),[5](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B5-sustainability-18-08114),[6](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B6-sustainability-18-08114),[7](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B7-sustainability-18-08114)]. Increasingly frequent droughts, delayed rainfall, rising temperatures, and shifting pest and disease pressures threaten food security and economic stability in the region [[8](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B8-sustainability-18-08114),[9](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B9-sustainability-18-08114),[10](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B10-sustainability-18-08114)]. Timely, reliable climate information is therefore critical for guiding agronomic decisions such as planting dates, crop choices, water management, and input application [[11](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B11-sustainability-18-08114),[12](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B12-sustainability-18-08114),[13](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B13-sustainability-18-08114),[14](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B14-sustainability-18-08114),[15](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B15-sustainability-18-08114)]. Access to such information enhances farmers’ preparedness for climate extremes and strengthens their adaptive capacity [[15](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B15-sustainability-18-08114),[16](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B16-sustainability-18-08114),[17](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B17-sustainability-18-08114),[18](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B18-sustainability-18-08114)]. Climate information services typically include rainfall onset and cessation, seasonal duration, temperature trends, soil moisture, wind patterns, and early warnings for droughts or floods [[18](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B18-sustainability-18-08114),[19](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B19-sustainability-18-08114),[20](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B20-sustainability-18-08114),[21](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B21-sustainability-18-08114),[22](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B22-sustainability-18-08114)]. Traditionally, this information has been disseminated through human-based channels—extension officers, farmer groups, NGOs, and community networks [[14](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B14-sustainability-18-08114),[22](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B22-sustainability-18-08114),[23](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B23-sustainability-18-08114),[24](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B24-sustainability-18-08114)]. These sources are valued for their contextual relevance and interpersonal trust [[21](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B21-sustainability-18-08114)]. However, in South Africa, high extension-to-farmer ratios, inconsistent service delivery, communication gaps, and sociocultural barriers, including gendered access to advisory support, limit the effectiveness of these channels [[25](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B25-sustainability-18-08114),[26](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B26-sustainability-18-08114),[27](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B27-sustainability-18-08114),[28](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B28-sustainability-18-08114),[29](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B29-sustainability-18-08114)].\n\nAt the same time, the expansion of digital infrastructure and mobile technologies has enabled Information and Communication Technologies (ICTs) such as mobile weather alerts, agricultural apps, WhatsApp groups, and voice-based advisory platforms to play an increasingly prominent role in climate information delivery [[23](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B23-sustainability-18-08114),[30](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B30-sustainability-18-08114),[31](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B31-sustainability-18-08114),[32](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B32-sustainability-18-08114),[33](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B33-sustainability-18-08114),[34](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B34-sustainability-18-08114),[35](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B35-sustainability-18-08114),[36](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B36-sustainability-18-08114)]. ICT-based tools have the potential to provide rapid, localized, and scalable climate information [[37](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B37-sustainability-18-08114)]. However, their adoption remains uneven due to persistent digital barriers, including poor network coverage, low digital literacy, high data costs, and language mismatches [[33](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B33-sustainability-18-08114),[38](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B38-sustainability-18-08114),[39](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B39-sustainability-18-08114),[40](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B40-sustainability-18-08114)]. Moreover, farmers often continue to trust interpersonal sources more than digital platforms, creating a dual system where ICT-based and human-based information coexist but are not equally utilized or valued [[30](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B30-sustainability-18-08114),[39](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B39-sustainability-18-08114)]. These dynamics raise important questions regarding why some farmers adopt ICT-based climate information while others rely on traditional channels, and whether ICT-based information is as effective as human-based sources in influencing agronomic decisions. Addressing these questions is essential for designing climate information systems that are both accessible and impactful.\n\nThis study, therefore, seeks to bridge this gap by (i) identifying the factors that influence smallholder farmers’ adoption of ICT-based climate information over human-based sources, and (ii) evaluating the impact of ICT-based climate information on agronomic decision-making compared to human-based sources. Focusing on the Eastern Cape Province, where both ICT infrastructure and extension systems are active but unevenly distributed, the study provides timely insights into how information flows influence climate adaptation at the farm level. By shedding light on these dynamics, the study contributes to policy discussions on how to design integrated climate information services that are inclusive, accessible, and actionable. It also offers practical recommendations for strengthening both digital and interpersonal communication channels, ensuring that no farmer is left behind in the shift toward climate-resilient agriculture.\n\n## 2. Materials and Methods\n\n### 2.1. Description of the Study Area\n\nThe study was conducted in Elundini Local Municipality, located within the Joe Gqabi District of the Eastern Cape. The Municipality is made up of three towns, Mount Fletcher in the north, Maclear in the centre, and Ugie in the South. [Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#fig_body_display_sustainability-18-08114-f001) illustrates their locations along with the distribution of sampled households across the study area. The municipality is located near Mthatha [[41](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B41-sustainability-18-08114)], the third largest city within the province [[42](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B42-sustainability-18-08114)]. Elundini’s economy is driven by agriculture, social services, and retail trade, with the agricultural sector comprising commercial, emerging, and subsistence farming [[43](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B43-sustainability-18-08114)]. Farmers commonly grow maize, potatoes, and cabbages, and keep both large and small ruminants [[41](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B41-sustainability-18-08114)]. The area receives an annual average rainfall of approximately 1200 mm, mostly received in the summer planting season [[44](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B44-sustainability-18-08114)], but experiences significant climate variability, including erratic rainfall and periodic droughts that heighten production risks [[43](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B43-sustainability-18-08114)]. Furthermore, Elundini has many natural resources and a good climate that makes it possible for households to engage in agriculture [[45](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B45-sustainability-18-08114)]. However, its agricultural performance remains below its potential, constrained by limited market access, infrastructure, and technology uptake—conditions that contrast sharply with highly mechanized and well-serviced agricultural zones [[46](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B46-sustainability-18-08114)]. In the area, government support is available through extension services and farmer assistance programmes such as the provision of seedlings, livestock facilities, and fencing materials [[41](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B41-sustainability-18-08114),[46](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B46-sustainability-18-08114)]. Nonetheless, the extent of their reach is limited due to high farmer-to-officer ratios and communication constraints [[25](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B25-sustainability-18-08114),[26](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B26-sustainability-18-08114),[27](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B27-sustainability-18-08114),[28](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B28-sustainability-18-08114),[29](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B29-sustainability-18-08114)].\n\n**Figure 1.** Map of study area. Source: Department of GIS, University of Fort Hare. Note: Numbers 1–17 show the municipal wards; red dots mark the locations of the sampled households.\n\nICT infrastructure is provided through public libraries equipped with computers and internet access [[46](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B46-sustainability-18-08114)], though network reliability and digital literacy remain barriers for many community members. Given the combination of high agricultural potential, exposure to climate-related risks, uneven technological advancement, and mixed advisory and ICT service environments, Elundini provides an ideal setting for analyzing differences in the adoption and effectiveness of ICT-based versus human-based climate information sources.\n\n### 2.2. Research Design\n\nThis study adopted a quantitative cross-sectional research design to investigate the adoption and impact of ICT-based climate information among smallholder crop farmers in the Eastern Cape Province of South Africa. A cross-sectional approach was deemed appropriate for capturing data at a single point in time, enabling an assessment of current patterns of ICT use, climate information access, and related agronomic decisions.\n\n### 2.3. Sampling Procedure and Sample Size\n\nA multistage sampling technique was employed to ensure representativeness and practicality in data collection. In the first stage, Elundini municipality was purposively selected. Within the municipality, eight wards were randomly selected based on their agricultural relevance and reported farming activity levels. Within each ward, one or two villages were randomly selected, leading to a total of twelve participating villages. Finally, within each village, a random sampling technique was used to select 217 smallholder farmers. [Table 1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#table_body_display_sustainability-18-08114-t001) presents the distribution of sampled farmers across the three towns. From the total sample, 167 farmers indicated that they had access to some form of climate information, either through ICT-based platforms or human-based sources, while 50 had no access, and were excluded from the impact assessment component of the analysis. Additionally, among the 167 farmers included, 69 were from Maclear, and 65 from Mount Fletcher, with Ugie represented by 33 farmers.\n\n**Table 1.** Sampling size per location.\n\n### 2.4. Data Collection\n\nData was collected in May 2022 using a structured questionnaire, administered through face-to-face interviews to ensure comprehension and minimize non-response. The questionnaire consisted of questions on farmer’s socio-demographic characteristics (age, education, income, household size, and gender), sources of climate information (e.g., extension agents, radio, mobile apps, WhatsApp groups, local farmer associations), ICT access and use (mobile phone ownership, digital literacy, access to internet or network), and agronomic decision-making (e.g., planting time, crop choices, irrigation practices, input application). The instrument was pre-tested with a small group of farmers outside the sample area to ensure clarity and relevance, and necessary adjustments were made before final data collection. During the data collection phase, authors ensured that verbal consent was obtained from farmers, they were treated with respect and allowed to refuse to be part of the research or withdraw from it at any given time, and that their information was kept confidential and used for academic purposes only. All of these requirements were guided by the ethical approval letter [MAN011SNOC01], which was obtained from the department of agricultural economics and extension, faculty of Science and Agriculture at the University of Fort Hare.\n\n### 2.5. Data Analysis\n\nData was first analyzed using descriptive statistics to summarize the characteristics of the sample, including age distribution, education levels, climate information sources, and ICT use. To identify the factors influencing the adoption of ICT-based climate information, a binary logistic regression model was employed. Stata\u002FSE 15.1 (Stata Corp LLC) was used for the analysis.\n\n#### 2.5.1. Logistic Regression Model\n\nThe dependent variable was a binary indicator of whether a farmer accessed climate information via ICT (1) or through human-based sources (0). The probability (\n\n$P_{i}$\n) that farmers receive climate information via ICT platforms is represented as\n\n$$\nY_{i } = \\beta_{0} + \\sum_{i = 1}^{n} \\beta_{i} X_{i ,}\n$$\n\n(1)\n\nThe equation represents a binary choice, which involves the estimation of the probability of receiving climate information via ICT platforms (Y) as a function of independent variables (X).\n\n$\\beta_{0 }$\nis constant and\n\n$Y_{i }$\nis equal to one 1 when farmers receive ICT-based climate information and 0 if through human-based sources. The logit model uses a logistic cumulative distributive function to estimate, P given by\n\n$$\nP = \\left(\\right. Y = \\frac{1}{X} \\left.\\right) = \\frac{e^{y}}{1 + e^{y}}\n$$\n\n(2)\n\n$$\nP = \\left(\\right. Y = \\frac{0}{X} \\left.\\right) = 1 - \\frac{e^{y}}{1 + e^{y}}\n$$\n\n(3)\n\n$$\nY = \\mathsf{\\beta}_{1} X_{1} + \\mathsf{\\beta}_{2} X_{2} + \\ldots + \\mathsf{\\beta}_{k} X = \\sum_{i = 1}^{k} \\mathsf{\\beta}_{i} X_{i ,}\n$$\n\n(4)\n\nwhere k represents the number of explanatory predictors that are to be included in the analysis. The model is as follows:\n\n$$\nY = \\mathit{Ln} \\left(\\right. \\frac{P}{1 - P} \\left.\\right) = \\beta_{0} + \\beta_{k} + \\epsilon ,\n$$\n\n(5)\n\nwhere Y = farmer accessed climate information via ICT; Ln\n\n$\\left(\\right. \\frac{P}{1 - P} \\left.\\right)$\nthe ratio of probability of accessing climate information via ICT (p) to receiving it through human-based sources (1 − P); β = slope of coefficient;\n\n$X_{k}$\n= vector of the independent variables presented in [Table 2](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#table_body_display_sustainability-18-08114-t002); ε = error term.\n\n**Table 2.** Independent variables and expected signs in ICT adoption model.\n\n#### 2.5.2. Measuring Agronomic Decision\n\nThe Agronomic Decision Index (ADI) was employed to assess the extent of agronomic decisions made by smallholder farmers. This index serves as a tool to quantify the degree to which each household adopts key agronomic practices. While the existing literature commonly measures agronomic decision-making by simply noting whether a farmer made a decision or not [[17](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B17-sustainability-18-08114)], this approach does not capture the depth of adoption. In contrast, ADI provides a more comprehensive measure. Each agronomic decision was coded as 1 if undertaken by the farmer and 0 otherwise. The index for each farmer was then calculated as shown in Equation (6):\n\n$$\nA D I = \\frac{D_{1} + D_{2} + D_{3} + D_{4} + D_{5} + D_{6} + D_{7}}{7}\n$$\n\n(6)\n\nwhere D1 to D7 include decisions on land preparation, planting date decis","Sustainability","2026-08-08T22:00:00Z",64,{"impact":45,"substance":88,"depth":17,"authority":45,"freshness":89,"relevant":20,"comment":90},20,2,"论文聚焦南非小农气候信息获取，对农业信息化有参考价值，但地域性强，时效性低。",[92],{"name":84,"url":81},[25,26,94,27,95,96,97,98],"农业信息化","气候信息","ICT采纳","数字鸿沟","发展中国家",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":39,"version":40,"method":41,"evidence":102},{"candidates":103,"agricultural_evidence":13},[104,109,113],{"code":31,"score":45,"supported":13,"title":105,"abstract":107,"tags":108},[106],"smallholder",[27],[27],{"code":50,"score":89,"supported":39,"title":110,"abstract":111,"tags":112},[],[],[94,25],{"code":55,"score":89,"supported":39,"title":114,"abstract":115,"tags":116},[],[],[26,97],[118,119],"农业信息化 发展中国家 数字乡村 数字农业","农业信息化 发展中国家","农业信息化发展中国家数字乡村数字农业-539",5,"2026-08-15T00:02:55.675693Z",{"id":124,"title":125,"url":126,"summary":127,"summary_zh":8,"content":8,"source_name":128,"source_url":8,"published_at":8,"category":11,"cover_url":8,"hotness":12,"is_selected":39,"score":129,"score_detail":130,"sources":134,"tags":136,"primary_topic":140,"secondary_topics":141,"classification":142,"search_phrases":159,"slug":162,"view_count":20,"doi":8,"paper":163,"created_at":170},4972,"数字赋能:助推小农户融入现代农业发展轨道——基于全国农户微观数据的考察","http:\u002F\u002Fwww.qikanzj.com\u002Fhek\u002Fjnxb\u002Fmulu\u002F633014.html","钟文晶、李丹、罗必良(华南农业大学经济管理学院)基于有限理性假设和信息不对称理论,探讨互联网信息技术助推小农户数字赋能的内在机理,并利用中国劳动力动态调查(CLDS)2018年的微观数据进行实证检验。结果表明,互联网使用有效促进了小农户农业生产的机械化、土地经营的规模化以及对农业的生产性投资;异质性分析表明,数字赋能在大规模农户、中老年农户、中部和东北地区的农户组中的作用尤为显著。文章强调应加快推进农村地区的数字基础设施建设,提升农户的数字素养。","《农业经济问题》",79,{"impact":16,"substance":131,"depth":17,"authority":132,"freshness":89,"relevant":20,"comment":133},23,14,"基于全国微观数据的实证研究，结论扎实且具政策参考价值，但时效性偏弱，适合主题聚合而非当日精选。",[135],{"name":128,"url":126},[26,137,27,138,139],"数字素养","互联网使用","农村数字基建",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":39,"version":40,"method":41,"evidence":143},{"candidates":144,"agricultural_evidence":13},[145,149,155],{"code":31,"score":45,"supported":13,"title":146,"abstract":147,"tags":148},[27],[27],[27],{"code":150,"score":63,"supported":13,"title":151,"abstract":152,"tags":154},"F5",[],[153],"机械化",[],{"code":55,"score":89,"supported":39,"title":156,"abstract":157,"tags":158},[],[],[26,137],[160,161],"华南农业大学 小农户 数字赋能","CLDS 互联网使用 农业生产","华南农业大学小农户数字赋能-4972",{"doi":8,"openalex_id":8,"authors":164,"venue":8,"cited_by_count":66,"oa_url":8,"card":165,"direction":71,"ingested_from":73},[],{"tldr":166,"method":167,"finding":168,"direction":71,"opportunity":169},"基于CLDS数据实证检验互联网使用如何助推小农户融入现代农业发展。","有限理性与信息不对称理论，CLDS 2018农户微观数据实证。","互联网使用促进小农户机械化、规模化和生产性投资，中老年及中部东北农户效应更显著。","可探究数字素养培训与数字基础设施如何协同提升小农户数字赋能效果。","2026-10-15T00:06:09.096458Z",{"id":172,"title":173,"url":174,"summary":175,"summary_zh":176,"content":8,"source_name":177,"source_url":174,"published_at":178,"category":11,"cover_url":8,"hotness":12,"is_selected":39,"score":179,"score_detail":180,"sources":183,"tags":185,"primary_topic":189,"secondary_topics":190,"classification":191,"search_phrases":217,"slug":220,"view_count":66,"doi":221,"paper":222,"created_at":234},4601,"Digital Agriculture and Sector Transformation in South Africa","https:\u002F\u002Fdoi.org\u002F10.36096\u002Fijbes.v8i4.1274","Digitalisation is increasingly promoted as a catalyst for agricultural productivity, climate resilience, and market integration. However, there is limited systematic analysis of how digital agricultural transformation unfolds within South Africa’s institutional and socio-economic context. This study examines the trajectory, drivers, and governance implications of digitalisation in South African agriculture. Anchored in an integrated Socio-Technical Systems and Digital Agriculture Ecosystem framework, the study applies a structured qualitative document analysis of 82 academic and policy documents published between 2013 and 2025. The findings reveal highly uneven digital adoption patterns that reflect a structural dualism between capital-intensive commercial agriculture and resource-constrained smallholder systems. While South Africa has developed a relatively advanced digital policy architecture, implementation fragmentation, infrastructure constraints, and limited capacity for extension impede inclusive digital outcomes. The study contributes by integrating socio-technical and ecosystem perspectives to analyse digital transitions in emerging economies and by providing a transparent analytical protocol for document-based research. Policy implications highlight the need for coordinated governance, investment in rural connectivity, and targeted support mechanisms to ensure equitable digital transformation.","数字化日益被视为提升农业生产力、增强气候韧性和促进市场整合的催化剂。然而，对于数字农业转型如何在南非的制度与社会经济语境中展开，尚缺乏系统性分析。本研究考察南非农业数字化的演进轨迹、驱动因素及治理意涵。研究以社会技术系统与数字农业生态系统的整合框架为依托，对2013年至2025年间发表的82份学术与政策文件进行了结构化定性文献分析。研究发现，数字技术采纳模式高度不均衡，反映出资本密集型商业农业与资源受限的小农体系之间的结构性二元分化。尽管南非已建立起相对先进的数字政策架构，但实施碎片化、基础设施制约以及推广能力有限等因素阻碍了包容性数字成果的实现。本研究的贡献在于整合社会技术与生态系统视角以分析新兴经济体的数字转型，并为基于文献的研究提供了透明的分析方案。政策启示强调，需通过协调治理、农村互联互通投资以及有针对性的支持机制，确保公平的数字转型。","International Journal of Business Ecosystem and Strategy (2687-2293)","2026-10-09T00:00:00Z",80,{"impact":17,"substance":16,"depth":17,"authority":181,"freshness":19,"relevant":20,"comment":182},13,"基于82份文献的系统性分析，揭示南非数字农业的结构性二元分化与治理碎片化，对新兴经济体数字乡村研究有参考价值。",[184],{"name":177,"url":174},[25,186,27,187,188],"智慧农业","南非农业","农村数字治理",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":39,"version":40,"method":41,"evidence":192},{"candidates":193,"agricultural_evidence":13},[194,200,205,211],{"code":50,"score":195,"supported":13,"title":196,"abstract":198,"tags":199},16,[197],"digital agriculture",[25,197],[25,186],{"code":31,"score":201,"supported":13,"title":202,"abstract":203,"tags":204},4,[],[106],[27],{"code":206,"score":63,"supported":13,"title":207,"abstract":208,"tags":210},"H6",[],[209],"农业生态",[],{"code":212,"score":63,"supported":13,"title":213,"abstract":214,"tags":216},"H7",[],[215],"climate resilience",[],[218,219],"南非 数字农业 转型","南非 小农户 数字鸿沟","南非数字农业转型-4601","10.36096\u002Fijbes.v8i4.1274",{"doi":221,"openalex_id":223,"authors":224,"venue":177,"cited_by_count":66,"oa_url":174,"card":228,"direction":71,"ingested_from":233},"W7221113328",[225],{"name":226,"orcid":227},"Mokgadi Julia Ngoepe-Ntsoane","https:\u002F\u002Forcid.org\u002F0000-0001-7995-4824",{"tldr":229,"method":230,"finding":231,"direction":71,"opportunity":232},"系统分析南非农业数字化轨迹、驱动因素与治理影响，揭示数字鸿沟与政策碎片化。","社会技术系统与数字农业生态框架，对82份2013-2025年文献政策进行结构化质","数字采用高度不均，商业农业与小农二元分化；政策先进但实施碎片化、基建与推广能力不足阻碍包容性转型。","可延伸至新兴经济体小农数字包容性治理机制与农村连接性投资评估的实证研究。","openalex","2026-10-10T23:30:34.991942Z",{"id":236,"title":237,"url":238,"summary":239,"summary_zh":240,"content":8,"source_name":241,"source_url":238,"published_at":242,"category":11,"cover_url":8,"hotness":12,"is_selected":39,"score":179,"score_detail":243,"sources":245,"tags":247,"primary_topic":250,"secondary_topics":251,"classification":258,"search_phrases":288,"slug":291,"view_count":76,"doi":292,"paper":293,"created_at":307},3512,"AI-Driven Agricultural Advisory and Diagnostic Systems for Smallholder Farming: Technical Architectures, Evidence and Deployment Priorities for North-East India","https:\u002F\u002Fdoi.org\u002F10.9734\u002Farja\u002F2026\u002Fv19i4919","Artificial intelligence (AI) is being introduced into agricultural advisory services through machine learning, computer vision, conversational large language models, retrieval-augmented generation and multimodal interfaces. For smallholder farming, the central question is not whether these technologies can produce technically plausible outputs, but whether they can provide locally correct, actionable and safe recommendations under heterogeneous agronomic, linguistic and connectivity conditions. This critical narrative review integrates evidence on digital extension, AI-enabled agricultural advice, image-based diagnosis and responsible digital agriculture, with particular reference to North-East India. Literature published from 1 January 2010 to 17 July 2026 was considered, with emphasis on peer-reviewed field evaluations, technical validation studies, reviews and regionally relevant research. Evidence from digital extension provides the strongest causal baseline: mobile and personalised advisory services frequently improve information recall, agronomic knowledge and adoption of recommended practices, yet effects on yield, profit and welfare are inconsistent. Recent generative-AI studies show that large language models can produce useful agricultural responses, but site-specific rates, timing and local practice remain recurrent failure points. Retrieval grounding and expert feedback improve local relevance, although multi-season farm-level effectiveness evidence remains scarce. Image-based plant-disease systems achieve high accuracy in curated datasets, but performance can deteriorate sharply under field domain shift, class novelty and variable image quality. North-East Indian studies of mobile advisory systems in Meghalaya, Nagaland and Tripura demonstrate a valuable institutional foundation based on interactive voice response, local expert networks and user-centred service design; they do not, however, establish the effectiveness of autonomous AI. The most defensible deployment model is therefore an offline-tolerant, multilingual, multimodal and human-supervised architecture that grounds recommendations in curated regional knowledge, represents uncertainty, preserves provenance and escalates high-risk or out-of-distribution cases. Future research should prioritise prospective district- and season-spanning evaluations that connect model quality to farmer decisions, agronomic outcomes, equity, safety and cost-effectiveness.","人工智能（AI）正通过机器学习、计算机视觉、对话式大语言模型、检索增强生成和多模态界面被引入农业咨询服务。对于小农户而言，核心问题不在于这些技术能否产生技术上看似合理的输出，而在于它们能否在异质的农艺、语言和网络连接条件下提供本地正确、可操作且安全的建议。本批判性叙事综述整合了数字推广、AI赋能的农业建议、基于图像的诊断和负责任数字农业方面的证据，并特别关注印度东北部。本文考察了2010年1月1日至2026年7月17日期间发表的文献，重点关注同行评议的田间评估、技术验证研究、综述及区域相关研究。来自数字推广的证据提供了最强的因果基线：移动化和个性化咨询服务经常改善信息记忆、农艺知识和对推荐措施的采纳，但对产量、利润和福利的影响并不一致。近期生成式AI研究表明，大语言模型能够产生有用的农业回答，但针对具体地点的用量、时机和本地实践仍是反复出现的失败点。检索 grounding 和专家反馈可提高本地相关性，但多季农场层面的有效性证据仍然稀缺。基于图像的植物病害系统在精选数据集上达到高准确率，但在田间域偏移、类别新颖性和图像质量多变的情况下，性能可能急剧下降。印度东北部在梅加拉亚邦、那加兰邦和特里普拉邦开展的移动咨询系统研究展示了基于交互式语音应答、本地专家网络和以用户为中心的服务设计的宝贵制度基础；然而，这些研究并未确立自主AI的有效性。因此，最可辩护的部署模式是一种容忍离线、多语言、多模态且有人工监督的架构，该架构将建议建立在精选的区域知识之上，表征不确定性，保留来源信息，并对高风险或分布外案例进行升级处理。未来研究应优先开展前瞻性的跨区县和跨季节评估，将模型质量与农户决策、农艺结果、公平性、安全性和成本效益联系起来。","Asian Research Journal of Agriculture","2026-09-24T00:00:00Z",{"impact":17,"substance":16,"depth":17,"authority":181,"freshness":19,"relevant":20,"comment":244},"系统综述AI农业咨询与诊断系统在印度东北小农场景的技术架构与落地证据，指出人机协同、离线多语言与检索增强是可行路径，对智慧农业落地有参考价值。",[246],{"name":241,"url":238},[26,186,248,249,27],"农业人工智能","农业技术推广",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[252],{"code":253,"slug":254,"name":255,"path":256,"parent_code":257},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":38,"locked":39,"version":40,"method":41,"evidence":259},{"candidates":260,"agricultural_evidence":13},[261,265,274,279,284],{"code":31,"score":18,"supported":13,"title":262,"abstract":263,"tags":264},[106],[27,106],[27],{"code":253,"score":12,"supported":13,"title":266,"abstract":267,"tags":273},[],[268,269,270,271,272],"机器学习","计算机视觉","machine learning","artificial intelligence","computer vision",[248],{"code":50,"score":275,"supported":13,"title":276,"abstract":277,"tags":278},7,[],[25,197],[186],{"code":280,"score":20,"supported":39,"title":281,"abstract":282,"tags":283},"G1",[],[],[249],{"code":55,"score":20,"supported":39,"title":285,"abstract":286,"tags":287},[],[],[26],[289,290],"印度东北部 农业AI 小农户","农业智能诊断 多语言 离线","印度东北部农业AI小农户-3512","10.9734\u002Farja\u002F2026\u002Fv19i4919",{"doi":292,"openalex_id":294,"authors":295,"venue":241,"cited_by_count":66,"oa_url":238,"card":301,"direction":71,"ingested_from":233},"W7214205238",[296,298],{"name":297,"orcid":8},"Pravangkar Boruah",{"name":299,"orcid":300},"Rubul Kumar 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2026）","https:\u002F\u002Fwww.sciopen.com\u002Flocal\u002Farticle_pdf\u002F10.1016\u002Fj.jia.2026.04.021.pdf","《Journal of Integrative Agriculture》2026年专刊导论，IAMO孙占力博士、华中农大熊航教授、北大易红梅教授等担任客座编辑。本专刊收录Liu et al. (2026)研究——使用面板数据和多种估计方法调查数字技术使用如何影响中国易地搬迁农户收入稳定性，发现数字技术显著改善收入水平和稳定性；Hu et al. (2026)使用来自5省1,833个农场的倾向得分匹配数据，发现网络信息使用实际上增加了小农户的化肥支出，反思了农业数字化的复杂性；Bai et al. (2026)研究数字素养通过改善农民对气候灾害风险的感知促进气候适应性生产行为；Amolegbe et al. (2026)研究尼日利亚数字知识与电商估值。","《Journal of Integrative Agriculture》2026专刊","2026-09-17T00:00:00Z",{"impact":17,"substance":16,"depth":357,"authority":132,"freshness":76,"relevant":20,"comment":358},19,"国际期刊专刊导论，汇集多国小农户数字化实证研究，方法扎实、结论具反思性，对农业信息化研究与实践有参考价值。",[360],{"name":354,"url":352},[25,362,137,27,97],"农村电商",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":39,"version":40,"method":41,"evidence":366},{"candidates":367,"agricultural_evidence":13},[368,372,378,383,387,392],{"code":31,"score":45,"supported":13,"title":369,"abstract":370,"tags":371},[106],[27],[27],{"code":373,"score":63,"supported":13,"title":374,"abstract":375,"tags":377},"G6",[],[376],"农户收入",[],{"code":212,"score":63,"supported":13,"title":379,"abstract":380,"tags":382},[],[381],"气候适应",[],{"code":55,"score":89,"supported":39,"title":384,"abstract":385,"tags":386},[],[],[137,97],{"code":388,"score":20,"supported":39,"title":389,"abstract":390,"tags":391},"E5",[],[],[362],{"code":50,"score":20,"supported":39,"title":393,"abstract":394,"tags":395},[],[],[25],[397,398],"Journal of Integrative Agriculture 数字农业 专刊","易地搬迁农户 数字技术 收入稳定性","JournalofIntegrativeAgriculture数字农业专刊-3327","10.1016\u002Fj.jia.2026.04.021.pdf",{"doi":400,"openalex_id":8,"authors":402,"venue":8,"cited_by_count":66,"oa_url":8,"card":403,"direction":71,"ingested_from":73},[],{"tldr":404,"method":405,"finding":406,"direction":71,"opportunity":407},"该专刊导论综述小农户农业数字化的采纳与影响，涵盖中国及尼日利亚的多项实证研究。","基于面板数据、倾向得分匹配等方法的专刊导论，整合多国小农户实证研究。","数字技术可提升收入稳定性，但也可能增加化肥支出，数字化影响具有复杂性。","可深入探究数字技术对不同农户群体的异质性影响及化肥增投的机制与调控路径。","2026-09-24T00:04:03.016677Z"]