Which farmer(s) should we target? How do extension approaches influence social learning and spread of agricultural innovations?

我们应该瞄准哪些农民？

• 批准号: ES/J009008/1
• 负责人: Erwin Bulte
• 金额: $ 56.8万
• 依托单位: Wageningen University & Research
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2012
• 资助国家: 英国
• 起止时间: 2012 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=ES%2FJ009008%2F1
• 关键词: farmer; should; target; How; do

Theme: 'Agriculture'. In this research we investigate if and how extension services can influence levels of spread of a new agricultural technology through the selection of different lead farmers, i.e. farmers who implement and maintain demonstration plots of a new technology. These farmers learn about the most effective way to implement the new technology and spread this information to family and friends. Different methods to select lead farmers may have profound effects on subsequent learning on and spread of a new technology. This we propose to investigate in a rural African setting, and analyse if one selection method works better than others (in some locations), in terms of subsequent spread and adoption levels. We randomly select sixty different villages in our region of study, around lake Kivu in the Democratic Republic of Congo. Here, our local partner implements a regular extension program. In each selected village they promote the use of nitrogen-fixing legumes. However, for the purpose of this approach, the method by which lead and satellite farmers are selected will differs from one village to the other. We thereby built upon approaches to select lead farmers as are commonly used by extension services and compare two different approaches, each in 30 villages. The methods included selection, or election, based on a participatory group meeting, versus a selection in consultation with village heads. To ensure that our research is well connected to actual practice, a final decision on treatments is taken at the start of the project in close consultation with various local stakeholders.In order to better understand if, and why some selection methods work better than others, for example, in reaching specific farmer groups such as female farmers, or work better in selected areas only, we collect additional data on village and household characteristics before and after the implementation of the extension program. A baseline survey documents village and household characteristics. In addition, we map social networks by documenting which farmers are linked, and the nature of such links, and we measure social characteristics of the villages experimentally that could explain, or could be explained from, characteristics of the households and social networks surveyed. Such measures include trust or the willingness to invest in a public good. At two points in time after the initial extension activity -one and two years after the initial training- we document levels of awareness, experimentation and adoption levels of the new technologies in an end-line survey. For each village we determine the total number of adopters and number of fields on which the technology is practiced. We use statistical analyses to determine if one of the treatments, i.e. the methods for selecting lead farmers, has led to a significantly higher level of awareness on, or adoption of the technologies. Subsequent statistical analyses, combining data from the baseline and end-line surveys, is deployed to better understand how information on technologies diffuses, and why diffusion processes may have been different across the treatments. We thereby analyse if variation in the treatment response can be related to specific village characteristics and we determine if the structure of some social networks, and or decisions by others in social networks, has played an important role in facilitating adoption or information spread. These analyses provide important information on the processes of technology diffusion and serve to identify best approaches of agricultural technology promotion and diffusion. This in turns helps agricultural development programs to increase their reach amongst rural African smallholders.

主题：“农业”。在这项研究中，我们通过选择不同的主导农户，即实施和维护新技术示范区的农民，来调查推广服务是否以及如何影响新农业技术的传播水平。这些农民了解实施新技术的最有效方式，并将这一信息传播给家人和朋友。选择领头人的不同方法可能会对一项新技术的后续学习和推广产生深远影响。我们建议在非洲农村环境中进行调查，并分析在随后的传播和采用率方面，一种选择方法是否比其他方法(在某些地方)效果更好。我们在我们的研究区域中随机选择了60个不同的村庄，这些村庄位于刚果民主共和国基伍湖周围。在这里，我们的当地合作伙伴实施了一个常规的扩展计划。在每个选定的村庄，他们都推广使用固氮豆类植物。然而，就这一方法而言，每个村庄选择龙头农户和卫星农户的方法将有所不同。因此，我们以推广服务机构常用的选择带头农民的方法为基础，比较了两种不同的方法，每种方法都在30个村庄进行了比较。这些方法包括基于参与性小组会议的选举，而不是与村长协商后的选举。为了确保我们的研究与实际实践很好地联系在一起，我们在项目开始时与当地各利益相关者密切协商，就处理方法做出最终决定。为了更好地了解某些选择方法是否以及为什么比其他方法效果更好，例如，在接触到特定的农民群体(如女农民)方面，或者只在选定的地区效果更好，我们收集了推广计划实施前后村庄和家庭特征的额外数据。基线调查记录了村庄和家庭的特征。此外，我们通过记录哪些农民被联系在一起以及这种联系的性质来绘制社会网络图，并通过实验测量村庄的社会特征，这些特征可以解释或可以解释所调查的家庭和社会网络的特征。这些措施包括信任或愿意投资于公共利益。在最初推广活动后的两个时间点--最初培训后一年和两年--我们在结束调查中记录新技术的认识、试验和采用水平。对于每个村庄，我们确定采用该技术的总人数和实践该技术的田地数量。我们使用统计分析来确定其中一种处理方法，即选择带头农民的方法，是否大大提高了人们对这些技术的认识或采用程度。随后的统计分析结合了基线调查和终点调查的数据，以更好地了解关于技术传播的信息是如何传播的，以及为什么传播过程在不同的处理中可能是不同的。因此，我们分析治疗反应的变化是否与特定的村庄特征有关，并确定一些社会网络的结构，或社会网络中其他人的决定，是否在促进采用或信息传播方面发挥了重要作用。这些分析提供了有关技术推广过程的重要信息，有助于确定农业技术推广和推广的最佳途径。这反过来又有助于农业发展项目扩大对非洲农村小农的影响。

项目成果

Social relationships, local institutions, and the diffusion of improved variety seed and field management techniques in rural communities: six case studies in South Kivu, DRC

社会关系、地方机构以及改良品种种子和田间管理技术在农村社区的传播：刚果民主共和国南基伍省的六个案例研究

• 发表时间: 2016
• 作者: Kendzior, J.

Agricultural Extension and Input Subsidies to Reduce Food Insecurity. Evidence from a Field Experiment in the Congo

农业推广和投入补贴，以减少粮食不安全。

• 作者: Leuveld, K.

Social Networks and Technology Diffusion in DRC

刚果民主共和国的社交网络和技术传播

• 作者: Hofman, P.

Farm Households in Eastern Congo Baseline Survey Report

刚果东部农户基线调查报告

• 发表时间: 2016
• 作者: Bulte, E.

Impact assessment of agricultural extension and input subsidies - baseline data for Eastern DRC

农业推广和投入补贴的影响评估 - 刚果民主共和国东部的基线数据

• 发表时间: 2016
• 作者: Bulte, E.