Analysis of Genebank Evaluation Data by using Geostatistical Methods

Genebanks often characterize accessions based on evaluation trials. This paper evaluates geostatistical methods as a tool to increase the utility of evaluation data. These methods were selected to overcome limitations resulting from a relative lack of replication and the scarcity of standards or check varieties. The data employed in the present study comprise nine characteristics of spring and winter barley, evaluated mostly as ratings. Ratings with quasi-metric scales were transformed by using the folded exponential transformation. To estimate the genetic component of the total effect, we compared two methods: Method 1 whereby a variogram is fitted by non-linear regression, and subsequently the implied spatial correlation is embedded into a mixed model analysis, which estimates the genetic effect by Best Linear Unbiased Prediction (BLUP); and Method 2 where each data value is re-estimated by kriging to correct for spatial effects and then the corrected data are submitted to a mixed model analysis. For practical application we propose Method 1 (though occasionally we met convergence problems): Fit the short range of the empirical variogram, visually choose the suitable covariance model. Use this and the initial values from non-linear regression fit with the mixed model, fixing the spatial parts at their starting values from non-linear regression, and estimate genetic effects by BLUP by using the fitted mixed model. To improve performance, we recommend that more standard or check varieties be used and, wherever possible, replace rating scales with metric scales or free-percentage scales (without categories).