Modeling the effect of social networks on adoption of multifunctional agriculture.
Modeling the effect of social networks on adoption of multifunctional agriculture.
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DOI：
10.1016/j.envsoft.2014.09.015
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发表时间：
2016-01
期刊：
影响因子：
--
通讯作者：
Brummel RF
中科院分区：
文献类型：
--
作者：
Manson SM;Jordan NR;Nelson KC;Brummel RF
轮牧（RG）作为动物系统中多功能农业（MFA）的基石引起了广泛关注，有可能为农业景观和农村社区的不同利益攸关方提供一系列有价值的商品和服务，并带来更广泛的社会效益。尽管有这些好处，但全球对MFA的采用并不均衡，一些地方积极参与，而另一些地方增长有限。最近的MFA概念模型强调自下而上的过程和社会与环境系统之间的联系的潜力，以促进多功能。社交网络对这些解释至关重要，但这些网络如何以及为什么起作用尚不清楚。我们调查了美国三个州（纽约州、威斯康星州、宾夕法尼亚州）的53个农场，并开发了一个程式化的社会网络模型和奶牛养殖系统的系统性变化。我们发现，社交网络对RG的采用很重要，但其影响取决于社会和空间因素。网络对农民决策的影响取决于它们是否包含弱联系关系（连接不同的人员和组织）或强联系关系（由成员彼此熟悉的群体共享）。RG的采用还取决于社会景观的特征，包括奶牛家庭的数量、邻近农民共享牢固联系的可能性，以及空间在网络形成中的作用。该模型复制了美国东部实际采用RG实践的特征，并说明了在乳制品领域实现更多功能的途径。这些模型在以网络为中心的农业发展战略中可能具有启发式价值。
Rotational grazing (RG) has attracted much attention as a cornerstone of multifunctional agriculture (MFA) in animal systems, potentially capable of producing a range of goods and services of value to diverse stakeholders in agricultural landscapes and rural communities, as well as broader societal benefits. Despite these benefits, global adoption of MFA has been uneven, with some places seeing active participation, while others have seen limited growth. Recent conceptual models of MFA emphasize the potential for bottom-up processes and linkages among social and environmental systems to promote multifunctionality. Social networks are critical to these explanations but how and why these networks matter is unclear. We investigated fifty-three farms in three states in the United States (New York, Wisconsin, Pennsylvania) and developed a stylized model of social networks and systemic change in the dairy farming system. We found that social networks are important to RG adoption but their impact is contingent on social and spatial factors. Effects of networks on farmer decision making differ according to whether they comprise weak-tie relationships, which bridge across disparate people and organizations, or strong-tie relationships, which are shared by groups in which members are well known to one another. RG adoption is also dependent on features of the social landscape including the number of dairy households, the probability of neighboring farmers sharing strong ties, and the role of space in how networks are formed. The model replicates features of real-world adoption of RG practices in the Eastern US and illustrates pathways toward greater multifunctionality in the dairy landscape. Such models are likely to be of heuristic value in network-focused strategies for agricultural development.