Mixed spatial models for data analysis of yield on large grapevine selection field trials

Abstract

In large field trials, it may be desirable to adjust for spatial correlation due to variation in soil fertility and in other environmental factors. Spatial correlation within a field trial can mask differences in the genotypic values of clones, consequently reducing the possibility of identifying superior genotypes. This paper describes a strategy to improve the precision of statistical data analysis of grapevine selection trials through the use of mixed spatial models. The efficiency of mixed spatial models was compared with that of a classical randomized complete block model (with independent and identically distributed errors). The comparisons were based on yield data from three large experimental populations of clones of the Arinto, Aragonez (Tempranillo) and Viosinho grapevine varieties. The fit of the spatial mixed models applied to yield data was significantly better than that of the classical approach, resulting in a positive impact on selection decisions and increasing the accuracy of genetic gain prediction.