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Correspondence between genetic structure and farmers' taxonomy – a case study from dry-season sorghum landraces in northern Cameroon

Published online by Cambridge University Press:  29 November 2012

Clelia Soler*
Affiliation:
CIRAD, UMR CEFE, Campus du CNRS, 1919 route de Mende, 34293Montpellier cedex 5, France
Abdoul-Aziz Saidou
Affiliation:
CIRAD, UMR CEFE, Campus du CNRS, 1919 route de Mende, 34293Montpellier cedex 5, France Institut Supérieur du Sahel, Université de Maroua, BP 46, Maroua, Cameroon
Tuong Vi Cao Hamadou
Affiliation:
CIRAD, UMR AGAP, Campus Agropolis, 34398Montpellier, France
Marco Pautasso
Affiliation:
UMR 5175 CNRS CEFE, Campus CNRS, 1919 route de Mende, 34293Montpellier, France FRB, Centre de Synthèse et d'Analyse sur la Biodiversité (CESAB), 13857Aix-en-Provence, France
Jean Wencelius
Affiliation:
CNRS, UMR LESC, Laboratoire d'Ethnologie et de Sociologie Comparative, 21 allée de l'Université, 92023Nanterre, France
Hélène H. I. Joly
Affiliation:
CIRAD, UMR CEFE, Campus du CNRS, 1919 route de Mende, 34293Montpellier cedex 5, France
*
*Corresponding author. E-mail: clelia.soler@cefe.cnrs.fr

Abstract

The study of the genetic structure of cultivated plant populations maintained by farmers is of great importance for evolutionary and conservation biology. Such studies help understand the bases of crop evolution and conservation in relation to farmers' practices. In this study, we assessed the genetic structure underlying landrace diversity of dry-season sorghum. This crop constitutes a historical model of innovation developed by farmers to extend sorghum cultivation to the dry season. Two types of dry-season sorghum are cultivated. We aimed to assess the link between farmers' taxonomy and molecular genetic structure. We collected both types of dry-season sorghum in two villages of northern Cameroon which represented 20 landraces. These landraces were genotyped using eight polymorphic microsatellite markers. This study compared two clustering methods: a Bayesian method (STRUCTURE) which is based on explicit genetic assumptions and the discriminant analysis of principal component method. The latter, more recently proposed, is based on the combination of principal component analysis and discriminant analysis. We noticed a general congruence between these two methods. We also used both methods to infer the genetic structure of our sample. Our results showed strong genetic structuring of the landraces, with K= 14 genetic clusters. We then analysed the fit between farmers' taxonomy and genetic structure. The data suggested that each type and each landrace corresponds to a given genetic entity. This pattern was robust across both villages, despite differences in cultural practices.

Type
Research Article
Copyright
Copyright © NIAB 2012

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