[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2637":3,"related-2637":115},{"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,"primary_topic":9,"secondary_topics":31,"classification":32,"search_phrases":86,"slug":89,"view_count":21,"doi":90,"paper":91,"created_at":114},2637,"Determinants of CSA Adoption in Coastal Bangladesh: An Application of Structural Path Analysis","https:\u002F\u002Fdoi.org\u002F10.59978\u002Far04030018","Climate-smart agriculture (CSA) is widely promoted to reconcile productivity, resilience, and emissions goals among smallholder farmers, yet adoption of capital-intensive CSA technologies remains low and uneven in climate-vulnerable coastal agriculture. This study examines socio-demographic, institutional, and behavioral determinants of CSA adoption among farming households in selected coastal districts of Bangladesh. This cross-sectional study interviewed 386 farmers from 3 selected coastal districts of Bangladesh following a multi-stage random sampling. Results were interpreted using descriptive statistics, Spearman’s correlation, adoption-difficulty (quadrant) analysis, and structural path analysis. Adoption levels varied widely across 30 CSA practices. Low-cost, knowledge-based practices such as raised bed homestead gardening, airtight seed storage, and heat and salinity-tolerant crops were widely adopted, whereas capital-intensive technologies such as poly-net houses, solar irrigation, and the sorjan method remained rare. Extension communication showed the strongest association with extension strategies for promoting CSA (β = 0.551), whereas strategies for promoting CSA, knowledge of CSA, and climate change perception were important predictors of CSA adoption. Importantly, the ICT usage had a negative relationship with climate change perception, indicating a digital paradox whereby the higher connectivity does not lead to a better understanding of the issues related to climate change. The research shows that there is a necessity for combined extension approaches based on face-to-face extension efforts, participatory learning platforms, and credible digital technologies combined with strong farmer organizations for increasing CSA adoption in coastal Bangladesh.","气候智慧型农业(CSA)被广泛推广，以期在小农户中协调生产力、韧性与排放目标，但在气候脆弱的沿海农业中，资本密集型CSA技术的采用率仍然较低且不均衡。本研究考察了孟加拉国部分沿海地区农户采用CSA的社会人口、制度与行为决定因素。本横断面研究采用多阶段随机抽样方法，访谈了孟加拉国3个选定沿海地区的386名农民。研究结果通过描述性统计、斯皮尔曼相关分析、采用难度(象限)分析和结构路径分析进行解读。30项CSA实践的采用水平差异很大。低成本、知识型实践，如高垄庭院种植、密封种子储藏以及耐热耐盐作物，被广泛采用；而资本密集型技术，如Poly-net温室、太阳能灌溉和sorjan法，则仍然很少见。推广沟通与促进CSA的推广策略关联最强(β = 0.551)，而促进CSA的策略、CSA知识和气候变化认知是CSA采用的重要预测因素。重要的是，信息通信技术(ICT)使用与气候变化认知呈负相关，表明存在一种数字悖论，即更高的连通性并未带来对气候变化相关问题更好的理解。研究表明，有必要采取综合推广方式，以面对面推广工作、参与式学习平台和可信数字技术为基础，并结合强大的农民组织，以提高孟加拉国沿海地区对CSA的采用。",null,"Agricultural & Rural Studies","2026-09-16T00:00:00Z","论文",10,false,73,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":13,"relevant":21,"comment":22},12,21,17,13,1,"基于386户问卷的结构路径分析，揭示ICT使用与气候变化认知的“数字悖论”，对气候智慧型农业推广与数字乡村建设有实证参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"农业信息化","小农户","农技推广","数字鸿沟","气候智慧型农业",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":41,"reason":85},[38,46,53,61,68,75,80],{"code":39,"score":40,"supported":41,"title":42,"abstract":43,"tags":45},"G2",7,true,[],[27,44],"smallholder",[27],{"code":47,"score":40,"supported":41,"title":48,"abstract":49,"tags":52},"H7",[],[50,51],"气候变化","climate change",[30],{"code":54,"score":55,"supported":41,"title":56,"abstract":57,"tags":60},"H3",6,[],[58,59],"灌溉","irrigation",[],{"code":62,"score":63,"supported":41,"title":64,"abstract":65,"tags":67},"I1",4,[],[66],"smart agriculture",[26],{"code":69,"score":70,"supported":41,"title":71,"abstract":72,"tags":74},"B7",3,[],[73],"温室",[],{"code":76,"score":21,"supported":14,"title":77,"abstract":78,"tags":79},"G1",[],[],[28],{"code":81,"score":21,"supported":14,"title":82,"abstract":83,"tags":84},"I6",[],[],[29],"多个产业证据相当，需要确认主归属",[87,88],"气候智慧型农业 农业信息化 农技推广 数字鸿沟","气候智慧型农业 农业信息化","气候智慧型农业农业信息化农技推广数字鸿沟-2637","10.59978\u002Far04030018",{"doi":90,"openalex_id":92,"authors":93,"venue":10,"cited_by_count":105,"oa_url":6,"card":106,"direction":112,"ingested_from":113},"W7213241723",[94,97,100,102],{"name":95,"orcid":96},"Md. Sumon Howlader","https:\u002F\u002Forcid.org\u002F0009-0009-8951-3307",{"name":98,"orcid":99},"Md. Mamun-ur-Rashid","https:\u002F\u002Forcid.org\u002F0000-0001-7632-1829",{"name":101,"orcid":9},"Md. Kamrul Hasan",{"name":103,"orcid":104},"Md. Ektear Uddin","https:\u002F\u002Forcid.org\u002F0000-0002-8003-8134",0,{"tldr":107,"method":108,"finding":109,"direction":110,"opportunity":111},"研究孟加拉沿海农户气候智慧型农业(CSA)采纳的社会、制度与行为决定因素。","386户多阶段随机抽样，用描述统计、Spearman相关、象限分析和结构路径分析","推广沟通影响最强；ICT使用与气候变化认知负相关，存在数字悖论。","数字乡村与农业信息化","可探究ICT使用为何未提升气候认知，检验数字鸿沟、信息质量与信任的中介机制。","智慧农业 \u002F 农业物联网","openalex","2026-09-16T23:30:09.724808Z",{"total":55,"page":21,"page_size":55,"items":116},[117,167,231,301,354,436],{"id":118,"title":119,"url":120,"summary":121,"summary_zh":9,"content":122,"source_name":123,"source_url":9,"published_at":124,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":125,"score_detail":126,"sources":131,"tags":133,"primary_topic":139,"secondary_topics":144,"classification":145,"search_phrases":161,"slug":164,"view_count":165,"doi":9,"paper":9,"created_at":166},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":17,"substance":127,"depth":128,"authority":17,"freshness":129,"relevant":21,"comment":130},20,18,2,"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。",[132],{"name":123,"url":120},[134,135,26,27,136,137,29,138],"数字农业","数字乡村","气候信息","ICT采纳","发展中国家",{"code":39,"slug":140,"name":141,"path":142,"parent_code":143},"farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[],{"status":146,"locked":14,"version":34,"method":35,"evidence":147},"classified",{"candidates":148,"agricultural_evidence":41},[149,153,157],{"code":39,"score":17,"supported":41,"title":150,"abstract":151,"tags":152},[44],[27],[27],{"code":62,"score":129,"supported":14,"title":154,"abstract":155,"tags":156},[],[],[26,134],{"code":81,"score":129,"supported":14,"title":158,"abstract":159,"tags":160},[],[],[135,29],[162,163],"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":168,"title":169,"url":170,"summary":171,"summary_zh":172,"content":9,"source_name":173,"source_url":170,"published_at":174,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":175,"score_detail":176,"sources":179,"tags":181,"primary_topic":185,"secondary_topics":186,"classification":187,"search_phrases":205,"slug":208,"view_count":105,"doi":209,"paper":210,"created_at":230},5105,"Behavioral Readiness for Climate‐Smart Agriculture Adoption Among Smallholder Farmers in Southwest Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fcli2.70071","ABSTRACT Understanding the behavioral mechanisms underlying farmers' readiness to adopt climate‐smart agriculture (CSA) is important for informing climate adaptation efforts. This study examines CSA adoption readiness among 384 smallholder farmers in Kaffa zone, Southwest Ethiopia, using covariance‐based structural equation modeling. The study extends the Theory of Planned Behavior by incorporating technology perceptions, previous CSA experience, risk aversion, and institutional support. The result shows attitude, subjective norms, perceived behavioral control (PBC), perceived ease of use, previous CSA adoption experience, and institutional support were positively associated with CSA adoption readiness, whereas perceived usefulness and risk aversion were not significant. Perceived ease of use showed the strongest association with adoption intention ( β = 0.377, p = 0.001). Institutional support was positively associated with PBC ( β = 0.243, p \u003C 0.001) and future intention ( β = 0.112, p = 0.030), explaining 5.9% of variance in PBC and 41.4% of the variance in adoption readiness. PBC showed a significant indirect association between institutional support and future intention ( β = 0.058, p = 0.013; 95% bootstrap CI: 0.015–0.102), alongside a remaining direct association. The significant indirect association through perceived behavioral control suggests that farmers’ perceptions of their ability to implement CSA may represent an important pathway linking institutional support with adoption intentions. The findings highlight the relevance of integrating institutional support with efforts to strengthen farmers' perceived capability to implement CSA and to enhance behavioural readiness for future adoption.","摘要 理解农户采用气候智慧型农业（CSA）意愿背后的行为机制，对于为气候适应工作提供依据具有重要意义。本研究采用基于协方差的结构方程模型，考察了埃塞俄比亚西南部卡法地区384户小农户的CSA采用意愿。研究在计划行为理论的基础上，纳入了技术感知、既往CSA经验、风险规避和制度支持。结果表明，态度、主观规范、感知行为控制（PBC）、感知易用性、既往CSA采用经验和制度支持与CSA采用意愿呈正相关，而感知有用性和风险规避则不显著。感知易用性与采用意愿的关联最强（β = 0.377，p = 0.001）。制度支持与PBC（β = 0.243，p \u003C 0.001）和未来意愿（β = 0.112，p = 0.030）呈正相关，分别解释了PBC方差的5.9%和采用意愿方差的41.4%。PBC在制度支持与未来意愿之间表现出显著的间接关联（β = 0.058，p = 0.013；95% bootstrap CI：0.015–0.102），同时仍存在直接关联。通过感知行为控制产生的显著间接关联表明，农户对自身实施CSA能力的感知可能是连接制度支持与采用意愿的重要路径。研究结果凸显了将制度支持与增强农户实施CSA的感知能力、提升未来采用行为意愿的努力相结合的重要性。","Climate Resilience and Sustainability","2026-10-14T00:00:00Z",67,{"impact":177,"substance":18,"depth":19,"authority":20,"freshness":177,"relevant":21,"comment":178},8,"基于扩展计划行为理论对埃塞俄比亚小农户气候智慧型农业采纳意愿的实证研究，方法规范、数据扎实，但属区域性学术成果，对国内农业信息化实践的直接参考价值有限。",[180],{"name":173,"url":170},[27,30,182,183,184],"埃塞俄比亚","采纳意愿","行为机制",{"code":39,"slug":140,"name":141,"path":142,"parent_code":143},[],{"status":146,"locked":14,"version":34,"method":35,"evidence":188},{"candidates":189,"agricultural_evidence":41},[190,195,200],{"code":39,"score":191,"supported":41,"title":192,"abstract":193,"tags":194},15,[44],[27,44],[27],{"code":62,"score":196,"supported":41,"title":197,"abstract":198,"tags":199},11,[66],[66],[],{"code":47,"score":63,"supported":41,"title":201,"abstract":202,"tags":204},[],[203],"气候适应",[30],[206,207],"埃塞俄比亚 小农户 气候智慧型农业","气候智慧型农业 埃塞俄比亚 行为机制 采纳意愿","埃塞俄比亚小农户气候智慧型农业-5105","10.1002\u002Fcli2.70071",{"doi":209,"openalex_id":211,"authors":212,"venue":173,"cited_by_count":105,"oa_url":223,"card":224,"direction":229,"ingested_from":113},"W7222349215",[213,216,218,221],{"name":214,"orcid":215},"Tesfaye Gafaro","https:\u002F\u002Forcid.org\u002F0009-0006-7838-5264",{"name":217,"orcid":9},"Kassa Tarekegn Erekalo",{"name":219,"orcid":220},"Fresenbet Zeleke","https:\u002F\u002Forcid.org\u002F0000-0002-9792-9240",{"name":222,"orcid":9},"Sime Shiferaw","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1002\u002Fcli2.70071",{"tldr":225,"method":226,"finding":227,"direction":110,"opportunity":228},"基于扩展计划行为理论，分析埃塞俄比亚小农户气候智慧型农业采纳意愿的行为机制。","对384户小农户问卷调查，用协方差结构方程模型分析。","感知易用性影响最强，制度支持通过感知行为控制间接提升采纳意愿。","可探索数字服务与制度支持如何协同提升农户感知能力，促进气候智慧型农业采纳。","农业绿色发展与碳","2026-10-16T23:31:01.112802Z",{"id":232,"title":233,"url":234,"summary":235,"summary_zh":236,"content":9,"source_name":237,"source_url":234,"published_at":238,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":239,"score_detail":240,"sources":243,"tags":245,"primary_topic":249,"secondary_topics":250,"classification":251,"search_phrases":268,"slug":271,"view_count":21,"doi":272,"paper":273,"created_at":300},4807,"Climate Smart Agriculture Adaptation Resource Inequalities Among Smallholder Farmers in the Barind Tract of Bangladesh: The Role of Governance Inclusion and Institutional Access","https:\u002F\u002Fdoi.org\u002F10.47191\u002Fijsshr\u002Fv9-i10-18","Climate-smart agriculture (CSA) is increasingly discussed as an approach for responding to climate risks while supporting agricultural productivity, resilience and sustainable resource use. However, access to the institutional and governance resources that enable farmers to adopt CSA may be uneven. This manuscript develops an empirical framework for examining adaptation-resource inequalities among smallholder farmers in the drought-prone Barind Tract of Bangladesh, with particular attention to governance inclusion and institutional access. The study is aligned with an approved doctoral research design focused on CSA adoption and its contribution to livelihood resilience in Rajshahi, Naogaon and Chapainawabganj. A mixed-methods design will combine a structured household survey, GPS-based spatial information, key-informant interviews, focused ethnographic fieldwork, policy and institutional document review, and relevant secondary data. The quantitative component will construct a Climate-Smart Agriculture Adoption Index (CSAAI) and examine socioeconomic, livelihood-capital, institutional, environmental and behavioural determinants using a Fractional Logit model. The institutional component will examine stakeholder roles, financing, extension, policy implementation and barriers through stakeholder mapping, a Power–Interest Matrix, a Barrier Severity Index and thematic analysis. Governance inclusion and institutional access will therefore be treated as mechanisms and explanatory conditions within the broader CSA adoption framework rather than as unsupported outcome measures. The manuscript does not report empirical results because the household survey and fieldwork are scheduled for the subsequent fieldwork phase of the doctoral project. This design provides a transparent basis for assessing whether differences in institutional access and governance inclusion are associated with unequal opportunities to adopt CSA and, subsequently, with livelihood resilience.","气候智慧型农业(CSA)日益被视为一种应对气候风险、同时支撑农业生产力、韧性与可持续资源利用的路径。然而，农民获取使其能够采用CSA的制度与治理资源的机会可能并不均衡。本文构建了一个实证框架，用于考察孟加拉国易受干旱影响的巴林德地区小农户之间的适应资源不平等，尤其关注治理包容性与制度可及性。本研究与一项已获批准的博士研究设计相一致，该设计聚焦于拉杰沙希、瑙冈和查帕伊纳瓦布甘杰地区CSA的采用及其对生计韧性的贡献。研究采用混合方法设计，将结构化农户调查、基于GPS的空间信息、关键信息人访谈、聚焦民族志田野工作、政策与制度文件审查以及相关二手数据相结合。定量部分将构建气候智慧型农业采用指数(CSAAI)，并使用分数对数模型考察社会经济、生计资本、制度、环境与行为等方面的决定因素。制度部分将通过利益相关者映射、权力—利益矩阵、障碍严重程度指数和主题分析，考察利益相关者角色、融资、推广、政策实施及障碍。因此，治理包容性与制度可及性将被视为更广泛的CSA采用框架中的机制与解释性条件，而非缺乏支撑的结果测量指标。本文不报告实证结果，因为农户调查与田野工作安排在博士项目后续的田野阶段进行。该设计为评估制度可及性与治理包容性方面的差异是否与采用CSA的不平等机会相关、并进而与生计韧性相关，提供了透明的依据。","International Journal of Social Science and Human Research","2026-10-12T00:00:00Z",51,{"impact":177,"substance":13,"depth":17,"authority":17,"freshness":241,"relevant":21,"comment":242},9,"研究设计类论文，尚无实证结果，方法框架有一定参考价值但信息增量有限，暂不建议进入每日精选。",[244],{"name":237,"url":234},[27,30,246,247,248],"农业治理","孟加拉","机构可及性",{"code":39,"slug":140,"name":141,"path":142,"parent_code":143},[],{"status":146,"locked":14,"version":34,"method":35,"evidence":252},{"candidates":253,"agricultural_evidence":41},[254,258,262],{"code":39,"score":191,"supported":41,"title":255,"abstract":256,"tags":257},[44],[27,44],[27],{"code":62,"score":196,"supported":41,"title":259,"abstract":260,"tags":261},[66],[66],[],{"code":47,"score":40,"supported":41,"title":263,"abstract":264,"tags":267},[],[265,266],"干旱","drought",[30],[269,270],"Barind Tract 小农户 气候智慧型农业","孟加拉 农业治理 机构可及性","BarindTract小农户气候智慧型农业-4807","10.47191\u002Fijsshr\u002Fv9-i10-18",{"doi":272,"openalex_id":274,"authors":275,"venue":237,"cited_by_count":105,"oa_url":294,"card":295,"direction":112,"ingested_from":113},"W7221555223",[276,278,280,283,286,288,290,292],{"name":277,"orcid":9},"Md Shahabuddin",{"name":279,"orcid":9},"Most Jesmin Ara",{"name":281,"orcid":282},"Banani Biswas","https:\u002F\u002Forcid.org\u002F0000-0001-5773-1574",{"name":284,"orcid":285},"Rui Miguel Moutinho Sá","https:\u002F\u002Forcid.org\u002F0000-0001-6407-4541",{"name":287,"orcid":9},"Faizah Sadik Tapti",{"name":289,"orcid":9},"Kazi Afnan Maha",{"name":291,"orcid":9},"Sumia Akter Mim",{"name":293,"orcid":9},"Tanisha Tabassum Khan","https:\u002F\u002Fijsshr.in\u002Fv9i10\u002FDoc\u002F18.pdf",{"tldr":296,"method":297,"finding":298,"direction":110,"opportunity":299},"构建混合方法框架，研究孟加拉Barind Tract小农气候智慧农业采纳中的制度与治理资源不平等。","混合方法设计，含家庭调查、GPS空间数据、访谈、政策审查及分数Logit模型。","论文为研究设计，尚未报告实证结果，将治理包容与制度获取作为解释机制。","可延伸至数字治理工具如何提升小农制度获取公平性及气候适应韧性。","2026-10-13T23:30:10.181875Z",{"id":302,"title":303,"url":304,"summary":305,"summary_zh":306,"content":9,"source_name":307,"source_url":304,"published_at":308,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":309,"score_detail":310,"sources":312,"tags":314,"primary_topic":317,"secondary_topics":318,"classification":319,"search_phrases":334,"slug":337,"view_count":129,"doi":338,"paper":339,"created_at":353},4175,"Integrating Agricultural Technology Adoption and Financial Inclusion as a Strategy to Improve Micro-Scale Farm Productivity in Rural Areas of South Sumatra","https:\u002F\u002Fdoi.org\u002F10.38124\u002Fijisrt\u002F26sep241","Micro-scale farming faces various constraints in improving productivity, particularly in relation to farmers' ability to adopt technology and access financing that meets their business needs. Agricultural technology can improve the efficiency of input use, access to information, farm management, and decision-making. However, technology adoption among smallholder farmers is still influenced by digital literacy, infrastructure, economic conditions, and institutional factors. On the other hand, access to credit can improve farm productivity and technical efficiency by enabling farmers to obtain production inputs and make productive investments. This study aims to: (1) analyze the effect of agricultural technology adoption on micro-scale farm productivity; and (2) analyze the effect of financial inclusion access on micro-scale farm productivity. The study employs a quantitative explanatory approach using multiple linear regression analysis. A total of 300 farm observations are used as simulated data to demonstrate the proposed analytical framework; therefore, the numerical results do not represent findings from an actual field survey. The independent variables consist of an agricultural technology adoption index and a financial inclusion access index, while farm productivity serves as the dependent variable. Farmers' age, education level, and land area are included as control variables. The results indicate that agricultural technology adoption and financial inclusion access have positive and statistically significant effects on farm productivity. The model explains a substantial proportion of the variation in productivity and provides an initial basis for further empirical investigation into the role of technology and financing in micro-scale farming. These findings are illustrative in nature and should be validated using primary data collected from farmers.","微观农业在提高生产力方面面临各种制约，尤其是农民采用技术以及获取满足其经营需求的融资能力方面的制约。农业技术可以提高投入品使用效率、信息获取、农场管理和决策效率。然而，小农户的技术采用仍受到数字素养、基础设施、经济条件和制度因素的影响。另一方面，获得信贷可以通过使农民能够获取生产投入并进行生产性投资来提高农场生产力和技术效率。本研究旨在：(1) 分析农业技术采用对微观农场生产力的影响；(2) 分析普惠金融获取对微观农场生产力的影响。本研究采用定量解释性方法，运用多元线性回归分析。共使用300个农场观测值作为模拟数据来展示所提出的分析框架；因此，数值结果不代表实际实地调查的发现。自变量包括农业技术采用指数和普惠金融获取指数，农场生产力为因变量。农民年龄、教育水平和土地面积作为控制变量纳入分析。结果表明，农业技术采用和普惠金融获取对农场生产力具有正向且统计显著的影响。该模型解释了生产力变异的相当大比例，为进一步实证研究技术和融资在微观农业中的作用提供了初步基础。这些发现具有示例性质，应使用从农民处收集的一手数据加以验证。","International Journal of Innovative Science and Research Technology (IJISRT)","2026-10-03T00:00:00Z",41,{"impact":177,"substance":13,"depth":177,"authority":55,"freshness":241,"relevant":21,"comment":311},"主题相关但数据为模拟生成、期刊权威性一般，仅具方法参考价值，不建议进入每日精选。",[313],{"name":307,"url":304},[26,27,315,316],"普惠金融","农业技术采纳",{"code":39,"slug":140,"name":141,"path":142,"parent_code":143},[],{"status":146,"locked":14,"version":34,"method":35,"evidence":320},{"candidates":321,"agricultural_evidence":41},[322,326,330],{"code":39,"score":40,"supported":41,"title":323,"abstract":324,"tags":325},[],[27,44],[27],{"code":76,"score":21,"supported":14,"title":327,"abstract":328,"tags":329},[],[],[316],{"code":62,"score":21,"supported":14,"title":331,"abstract":332,"tags":333},[],[],[26],[335,336],"南苏门答腊 小农户 生产力","农业技术采纳 普惠金融","南苏门答腊小农户生产力-4175","10.38124\u002Fijisrt\u002F26sep241",{"doi":338,"openalex_id":340,"authors":341,"venue":307,"cited_by_count":105,"oa_url":304,"card":348,"direction":110,"ingested_from":113},"W7218524136",[342,345],{"name":343,"orcid":344},"Lisa Hermawati","https:\u002F\u002Forcid.org\u002F0009-0001-5824-7052",{"name":346,"orcid":347},"Ema Pusvita","https:\u002F\u002Forcid.org\u002F0009-0002-2813-5567",{"tldr":349,"method":350,"finding":351,"direction":110,"opportunity":352},"用模拟数据回归分析农业技术采纳与金融普惠对微规模农场生产力的影响。","多元线性回归，300条模拟数据，构建技术采纳与金融普惠指数。","技术采纳和金融普惠均显著正向提升微规模农场生产力。","需用真实农户调查数据验证，并探究数字素养与信贷可得性的交互机制。","2026-10-04T23:30:30.503599Z",{"id":355,"title":356,"url":357,"summary":358,"summary_zh":359,"content":9,"source_name":360,"source_url":357,"published_at":361,"category":12,"cover_url":9,"hotness":362,"is_selected":14,"score":363,"score_detail":364,"sources":368,"tags":374,"primary_topic":378,"secondary_topics":384,"classification":385,"search_phrases":420,"slug":423,"view_count":21,"doi":424,"paper":425,"created_at":435},4008,"Agroecology as a pathway for scaling climate-smart agriculture and strengthening food system resilience in Benin","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23087822","Climate-Smart Agriculture (CSA) is widely promoted to address food insecurity, climate variability, and environmental degradation in sub-Saharan Africa. However, evidence on scalable and context-specific pathways for CSA adoption in smallholder systems remains limited, particularly regarding agroecological transitions. This study assesses agroecology as a pathway for scaling CSA and strengthening food system resilience in three communes of Benin (Dassa-Zoumè, Bohicon and Parakou), examining contributions to the three CSA pillars of adaptation, mitigation and productivity. A mixed-methods design combined a structured household survey (n = 210), semi-structured interviews with key informants (n = 32), and secondary agricultural and climate data (2010-2024). A composite resilience index was constructed using principal component analysis (PCA) to compare agroecological and conventional systems. Results show that agroecological farms achieve a 23% higher soil fertility index, a 31% higher resilience capacity, and a 17% reduction in yield variability under climate stress, relative to conventional systems. Crop diversification is 28% higher among agroecological producers, while dependence on synthetic inputs declines by up to 40%, improving system stability and adaptive capacity. Despite these benefits, scaling remains constrained by limited access to climate information services (reported by 35% of respondents), weak extension coverage (30%), and low access to climate finance (20%). The study concludes that agroecology provides a robust, evidence-based pathway for scaling CSA in Benin, and that strengthening digital climate services, extension systems and inclusive climate finance is critical to accelerate resilient and sustainable agricultural transformation in West Africa.","气候智慧型农业（Climate-Smart Agriculture, CSA）被广泛推广以应对撒哈拉以南非洲的粮食不安全、气候变异和环境退化问题。然而，关于小农系统中可规模化且因地制宜的CSA采纳路径的证据仍然有限，尤其是在农业生态转型方面。本研究评估了农业生态学作为贝宁三个市镇（Dassa-Zoumè、Bohicon和Parakou）推广CSA和增强粮食系统韧性的路径，考察其对CSA三大支柱——适应、减缓和生产力——的贡献。研究采用混合方法设计，结合了结构化农户调查（n = 210）、关键信息人半结构化访谈（n = 32）以及二手农业和气候数据（2010—2024年）。通过主成分分析（PCA）构建了综合韧性指数，以比较农业生态体系与常规体系。结果表明，相较于常规体系，农业生态农场实现了更高的土壤肥力指数（提高23%）、更强的韧性能力（提高31%），并在气候胁迫下将产量变异性降低了17%。农业生态生产者的作物多样化程度高出28%，而对合成投入品的依赖下降了多达40%，从而提升了系统稳定性和适应能力。尽管有这些益处，规模化推广仍受到气候信息服务获取有限（35%的受访者报告）、推广覆盖薄弱（30%）以及气候融资获取不足（20%）的制约。研究结论认为，农业生态学为在贝宁推广CSA提供了一条稳健的、基于证据的路径，而加强数字气候服务、推广体系和包容性气候融资对于加速西非韧性和可持续农业转型至关重要。","Agriculture, Food, and Natural Resources Journal","2026-10-01T00:00:00Z",40,77,{"impact":365,"substance":366,"depth":128,"authority":20,"freshness":177,"relevant":21,"comment":367},16,22,"基于贝宁三省210户调查的混合方法研究，量化了生态农业对气候智慧型农业三大支柱的贡献，数据扎实、结论明确，对西非及类似小农体系有参考价值，但属区域性学术成果，公共影响层级有限。",[369,370,372],{"name":360,"url":357},{"name":360,"url":371},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23087821",{"name":360,"url":373},"https:\u002F\u002Fjournals.unizik.edu.ng\u002Findex\u002Farticle\u002Fview\u002F9044",[27,30,375,376,377],"生态农业","粮食系统韧性","气候信息服务",{"code":379,"slug":380,"name":381,"path":382,"parent_code":383},"H6","biodiversity","生态保护与生物多样性","\u002Ftopic\u002Fenvironment\u002Fbiodiversity","H",[],{"status":146,"locked":14,"version":34,"method":35,"evidence":386},{"candidates":387,"agricultural_evidence":41},[388,395,399,406,410,416],{"code":379,"score":389,"supported":41,"title":390,"abstract":392,"tags":394},14,[391],"agroecology",[393,391],"农业生态",[],{"code":62,"score":196,"supported":41,"title":396,"abstract":397,"tags":398},[66],[66],[],{"code":400,"score":55,"supported":41,"title":401,"abstract":402,"tags":405},"H2",[],[403,404],"土壤","soil",[],{"code":39,"score":63,"supported":41,"title":407,"abstract":408,"tags":409},[],[44],[27],{"code":411,"score":63,"supported":41,"title":412,"abstract":413,"tags":415},"H8",[],[414],"可持续农业",[375],{"code":47,"score":21,"supported":14,"title":417,"abstract":418,"tags":419},[],[],[30],[421,422],"贝宁 生态农业 气候智慧型农业","西非 小农户 粮食系统韧性","贝宁生态农业气候智慧型农业-4008","10.5281\u002Fzenodo.23087822",{"doi":424,"openalex_id":426,"authors":427,"venue":360,"cited_by_count":105,"oa_url":357,"card":430,"direction":112,"ingested_from":113},"W7215033617",[428],{"name":429,"orcid":9},"Maanzou Boukari",{"tldr":431,"method":432,"finding":433,"direction":229,"opportunity":434},"评估贝宁农业生态学作为气候智慧型农业推广路径，提升食物系统韧性。","混合方法：210户调查、32位关键信息人访谈、2010-2024年数据及PCA韧","农业生态农场土壤肥力高23%、韧性高31%、产量波动低17%，但推广受气候信息、推广服务和资金限制。","可研究数字气候信息服务与包容性气候融资如何协同破解农业生态转型的推广瓶颈。","2026-10-02T23:30:11.597021Z",{"id":437,"title":438,"url":439,"summary":440,"summary_zh":441,"content":9,"source_name":442,"source_url":439,"published_at":443,"category":12,"cover_url":9,"hotness":444,"is_selected":14,"score":445,"score_detail":446,"sources":448,"tags":452,"primary_topic":456,"secondary_topics":460,"classification":461,"search_phrases":488,"slug":491,"view_count":129,"doi":492,"paper":493,"created_at":510},3880,"DETERMINANTS DE L'ADOPTION DES PRATIQUES ENDOGENES CLIMATO-INTELLIGENTES PAR LES MENAGES AGRICOLES A DJIDJA, AU SUD-BENIN","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23066853","Résumé Le secteur agricole béninois, bien que moteur de l'économie, est fragilisé par une forte variabilité climatique perçue par 97,6 % des producteurs de la commune de Djidja. Dans ce contexte, l’adoption des Pratiques Endogènes Climato-Intelligentes (PECI) apparaît comme une solution de résilience incontournable. Cette recherche vise à identifier les facteurs socio-économiques et institutionnels qui conditionnent l'adoption de ces pratiques. Dans cette étude, une approche mixte qui combine des variables qualitatives et quantitatives a été adoptée pour évaluer les effets des PECI sur la capacité d'adaptation et la sécurité alimentaire des ménages agricoles. Les collectes ont été faites dans quatre arrondissements de la commune de Djidja. Il s’agit de Setto, Mougnon, Djidja-centre et Monsourou. Prenant en compte les facteurs tels que la géographie et le nombre élevé de ménage agricole dans ces villages. L'étude s'appuie sur un échantillon de 123 ménages, obtenu grâce à la formule de Schwartz. La population est majoritairement sans instruction formelle (58,5 %) et disposant de revenus modestes (52,8 % gagnent moins de 500 000 FCFA\u002Fan). Un modèle de régression linéaire multiple a été utilisé pour tester l'influence de six variables clés sur le score d'adoption des PECI. Les résultats du modèle (R²=0,112 ; F=2,446 ; p=0,029) indiquent que l’adoption ne dépend pas des caractéristiques sociodémographiques classiques : le sexe (p=0,794), le niveau d’éducation (p=0,679), l’âge (p=0,110) et la superficie exploitée (p=0,419) n'ont aucun effet significatif. En revanche, l’expérience agricole (B=0,690 ; p=0,007) et l’accès à l’encadrement et à l’information agricole et météorologique (B=1,104 ; p=0,014) sont les déterminants majeurs et positifs. Cela démontre que l’appropriation des PECI à Djidja repose sur l’apprentissage empirique et la qualité du conseil agricole de proximité plutôt que sur le capital foncier ou l'instruction scolaire. L'étude conclut sur la nécessité de renforcer les dispositifs de vulgarisation et de favoriser les groupements d’agriculteurs (13 %) pour massifier la transition vers une agriculture climato-intelligente. Mots-clés : Changement climatique, PECI, Déterminants de l’adoption des PECI, Djidja, Bénin Abstract Benin’s agricultural sector, although a driving force of the economy, is weakened by significant climate variability, as reported by 97.6% of farmers in the municipality of Djidja. In this context, the adoption of Climate-Smart Local Practices (CSLP) appears to be an essential solution for building resilience. This research aims to identify the socioeconomic and institutional factors that influence the adoption of these practices. In this study, a mixed-methods approach combining qualitative and quantitative variables was adopted to assess the effects of Endogenous Climate-Smart Practices (ECSPs) on the adaptive capacity and food security of farming households. Data were collected in four arrondissements of the commune of Djidja: Setto, Mougnon, Djidja-Centre, and Monsourou. These areas were selected based on geographical considerations and the high number of farming households in the selected villages. The study was conducted with a sample of 123 farming households, determined using the Schwartz formula. The majority of whom have no formal education (58.5%) and modest incomes (52.8% earn less than 500,000 CFA francs per year). A multiple linear regression model was used to test the influence of six key variables on the PECI adoption score. The model results (R² = 0.112; F = 2.446; p = 0.029) indicate that adoption does not depend on traditional sociodemographic characteristics: gender (p = 0.794), education level (p = 0.679), age (p = 0.110), and farm size (p = 0.419) have no significant effect. In contrast, agricultural experience (B=0.690; p=0.007) and access to technical guidance and agricultural information (B=1.104; p=0.014) are the major positive determinants. This demonstrates that the adoption of CSLPs in Djidja is based on empirical learning and the quality of local agricultural extension services rather than on land ownership or educational attainment. The study concludes that it is necessary to strengthen extension services and support farmers’ groups (13 %) to scale up the transition to climate-smart agriculture. Keywords: Climate change, CSLP, Determinants of CSLP adoption, Djidja, Benin","摘要 贝宁的农业部门虽然是经济的驱动力，却因97.6%的迪吉贾（Djidja）镇生产者所感受到的强烈气候变异性而变得脆弱。在此背景下，采用气候智能型本土实践（PECI）似乎成为不可或缺的韧性解决方案。本研究旨在识别制约这些实践采用的社会经济与制度因素。本研究采用定性与定量变量相结合的混合方法，评估PECI对农户适应能力和粮食安全的影响。数据采集在迪吉贾镇的四个区进行，即塞托（Setto）、穆尼翁（Mougnon）、迪吉贾中心（Djidja-centre）和蒙苏鲁（Monsourou），选取时考虑了地理因素以及这些村庄农户数量较多的情况。研究基于123户样本，通过Schwartz公式获得。该人群大多未接受正规教育（58.5%），收入微薄（52.8%年收入低于500 000非洲法郎）。采用多元线性回归模型检验六个关键变量对PECI采用得分的影响。模型结果（R²=0.112；F=2.446；p=0.029）表明，采用并不取决于传统的社会人口学特征：性别（p=0.794）、教育水平（p=0.679）、年龄（p=0.110）和经营面积（p=0.419）均无显著影响。相反，农业经验（B=0.690；p=0.007）以及获得农业与气象指导及信息的机会（B=1.104；p=0.014）是主要且正向的决定因素。这表明，迪吉贾对PECI的掌握依赖于经验学习和就近农业咨询的质量，而非土地资本或学校教育。研究结论指出，有必要加强推广机制并促进农民组织（13%），以大规模推进向气候智能型农业的转型。关键词：气候变化，PECI，PECI采用的决定因素，迪吉贾，贝宁 摘要 贝宁的农业部门虽然是经济的驱动力，却因迪吉贾镇97.6%的农民所报告的显著气候变异性而变得脆弱。在此背景下，采用气候智能型本土","African Scientific Journal","2026-09-30T00:00:00Z",25,65,{"impact":177,"substance":127,"depth":365,"authority":17,"freshness":241,"relevant":21,"comment":447},"基于123户调查的实证研究，发现农业经验与推广信息服务而非教育程度决定气候智慧型本土实践的采纳，对发展中国家农业信息化推广有参考价值，但属区域性个案，影响力有限。",[449,450],{"name":442,"url":439},{"name":442,"url":451},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23066852",[453,27,30,454,455],"农户采纳","农业推广","贝宁农业",{"code":47,"slug":457,"name":458,"path":459,"parent_code":383},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",[],{"status":146,"locked":14,"version":34,"method":35,"evidence":462},{"candidates":463,"agricultural_evidence":41},[464,468,475,480,484],{"code":47,"score":40,"supported":41,"title":465,"abstract":466,"tags":467},[],[50,51],[30],{"code":469,"score":55,"supported":41,"title":470,"abstract":471,"tags":474},"A8",[],[472,473],"粮食安全","food security",[],{"code":76,"score":63,"supported":41,"title":476,"abstract":477,"tags":479},[],[478],"agricultural extension",[454],{"code":62,"score":70,"supported":41,"title":481,"abstract":482,"tags":483},[],[66],[],{"code":39,"score":129,"supported":14,"title":485,"abstract":486,"tags":487},[],[],[453,27],[489,490],"Djidja 气候智慧型农业 农户采纳","贝宁 农业推广 气候适应","Djidja气候智慧型农业农户采纳-3880","10.5281\u002Fzenodo.23066853",{"doi":492,"openalex_id":494,"authors":495,"venue":442,"cited_by_count":105,"oa_url":439,"card":505,"direction":112,"ingested_from":113},"W7214914541",[496,498,500,502],{"name":497,"orcid":9},"Imelda DANNOU",{"name":499,"orcid":9},"Gervais ATCHADE",{"name":501,"orcid":9},"Océane GANSA",{"name":503,"orcid":504},"Ibouraïma Yabi","https:\u002F\u002Forcid.org\u002F0000-0002-8465-9405",{"tldr":506,"method":507,"finding":508,"direction":110,"opportunity":509},"分析贝宁Djidja农户采用本土气候智慧实践的社会经济与制度决定因素。","123户混合问卷+多元线性回归，检验六变量对采用得分影响。","农业经验与推广信息获取显著促进采用，性别、教育、年龄和土地面积无显著影响。","可研究数字气象与农技信息服务如何替代传统推广，提升小农户气候智慧实践采用率。","2026-10-01T23:30:09.405648Z"]