文章基本信息

标题：Seed exchange networks, ethnicity, and sorghum diversity
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作者：Vanesse Labeyrie ; Mathieu Thomas ; Zachary K. Muthamia 等
期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2016
卷号：113
期号：1
页码：98-103
DOI：10.1073/pnas.1513238112
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：Recent studies investigating the relationship between crop genetic diversity and human cultural diversity patterns showed that seed exchanges are embedded in farmers’ social organization. However, our understanding of the social processes involved remains limited. We investigated how farmers’ membership in three major social groups interacts in shaping sorghum seed exchange networks in a cultural contact zone on Mount Kenya. Farmers are members of residence groups at the local scale and of dialect groups clustered within larger ethnolinguistic units at a wider scale. The Chuka and Tharaka, who are allied in the same ethnolinguistic unit, coexist with the Mbeere dialect group in the study area. We assessed farmers’ homophily, propensity to exchange seeds with members of the same group, using exponential random graph models. We showed that homophily is significant within both residence and ethnolinguistic groups. At these two levels, homophily is driven by the kinship system, particularly by the combination of patrilocal residence and ethnolinguistic endogamy, because most seeds are exchanged among relatives. Indeed, residential homophily in seed exchanges results from local interactions between women and their in-law family, whereas at a higher level, ethnolinguistic homophily is driven by marriage endogamy. Seed exchanges and marriage ties are interrelated, and both are limited between the Mbeere and the other groups, although frequent between the Chuka and Tharaka. The impact of these social homophily processes on crop diversity is discussed.
关键词：crop diversity dynamics ; local seed systems ; social organization ; social networks analysis ; exponential random graph models