Cassava is an important staple food in sub-Saharan Africa. Breeding experiments were conducted at the International Institute of Tropical Agriculture in cassava to select elite parents. Taking into account the heterogeneity in the field while evaluating these trials can increase the accuracy in estimation of breeding values. The authors used an exploratory approach using the parametric spatial kernels Power, Spherical, and Gaussian to determine the best kernel for a given scenario.
This work is part of the “Next Generation Cassava Breeding Project” which is supported by the UK Department for International Development, in partnership with the Bill & Melinda Gates Foundation.
Ani A. Elias, Ismail Rabbi, Peter Kulakow Jean-Luc Jannink. Improving Genomic Prediction in Cassava Field Experiments Using Spatial Analysis. G3: Genes, Genomes, Genetics; 1 January 2018 vol. 8 no. 1 53-62; https://doi.org/10.1534/g3.117.300323
Improving Genomic Prediction in Cassava Field Experiments Using Spatial Analysis