Control of spatial variability in safflower (Carthamus tinctorius L.) germplasm evaluation under dryland conditions

Effective assessment of genotypes superiority is possible by identification of spatial variability and selection of appropriate methods of statistical analysis. In order to select suitable genotypes for advanced trials in dryland conditions, 100 safflower pure lines were evaluated using simple lattice by accounting for spatial variability. Results showed that, the lattice design with correlated errors along rows, the lattice design and the randomized complete block, design with first order auto-correlated errors along rows were the best models and most effective in accounting for spatial variability in grain yield, plant height and days to flowering, respectively. There was not any evidence for fixed trend in this experiment. Broad sense heritability estimates revealed a significant genetic potential for selection for all of the measured characters. Based on the selected models, the best linear unbiased estimates for all genotypes were calculated and the superior genotypes were selected regarding related standard errors. Since the criterion used was based on maximum information of the data, the interpretations drawn from the best model for each trait would give the most realistic assessment of the performance of genotypes. Hence, for breeding objectives, it is recommended that at first a best model be identified to describe the spatial variation in the data, followed by evaluation of the genotypes based on that model.