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.

Experimental designs for evaluation of genetic variability and selection of ancient grapevine varieties: a simulation study

“…We should also remember that an experimental design supposed entirely suitable might later prove to be inefficient in controlling for spatial variation. That is why recourse to spatial models for data analysis is another important option for the selection success (Cullis and Gleeson, 1991;Federer, 1998;Qiao et al, 2000;Dutkowski et al, 2006;Gonçalves et al, 2007). It is important to emphasize, however, that even using spatial analysis, a good experimental design is always essential.…”

“…Thus, according to what is observed in large grapevine field trials (around 70 in Portugal), two values of error variance were considered, s e 2 ¼ 1 and 3, corresponding respectively to level 1 (the most frequent) and level 2 of environmental variation. In respect to spatial variability, in these trials the percentage of spatially independent variation is usually around 60%, but can oscillate between 50 and 70% of the total error variance (values obtained from current yield data analysis of 70 grapevine initial selection trials and also supported by Gonçalves et al, 2007). In this study, we assumed that 60% of the total error variance is attributable to the spatially independent variance and 40% to the spatially dependent variance.…”

“…They were established values for the parameters y row and y col of 20 and 15 m, respectively (values frequently obtained from current yield data analysis of 70 grapevine initial selection trials and also supported by Gonçalves et al, 2007). These errors were generated in Proc SIM2D of SAS version 9.1 (SAS Institute, 2003), 100 simulations for each field layout.…”

The preservation of genetic resources of the vine requires cohabitation between institutional clonal selection, mass selection and private clonal selection

“…Data analysis was based on average yield values observed over several years and performed using PROC MIXED of SAS version 9.2 [10]. Mixed models were fitted to yield and quality traits data [11,12]. The parameters involved in the model were estimated by residual maximum likelihood (REML).…”

Genetic Variability Evaluation and Selection in Ancient Grapevine Varieties

“…Although quantitative traits may also show a wide range of variation in some populations, this is not a sufficient criterion for their sub-division into distinct varieties. From this set of circumstances arises the fact that the yield can vary within the ancient variety up to tenfold, while some important characteristics of quality (soluble solids, acidity and anthocyanins) can vary up to twofold (Martins et al, 2006;Martins, 2007;Martins, 2009). All this variability is useful for important theoretical analysis regarding evolution and other topics as well as for the practical purposes of mass and clonal selection.…”