Abstract
Spring barley (Hordeum vulgare L.) is the most important cereal in Iceland and its national breeding program aims to select barley genotypes adapted to its environment. A critical step to understand the adaptation of Nordic barley material to a cool maritime climate is to assess the genotype by environment interaction (GxE). In this study, we evaluated the yield and thousand-kernel weight (TKW) of 32 spring barley genotypes in seven Icelandic environments. We applied three methods to analyze GxE: the additive main effects and multiplicative interaction model, a factorial model, and a linear mixed model. For yield, GxE was mainly caused by a better response of six-row genotypes compared to two-row genotypes in high fertility soils. For TKW, GxE showed a pattern along a gradient of daily mean temperatures. This pattern translated into a divergent TKW response between the 2-row and 6-row genotypes, with substantial crossovers along the temperature gradient. This GxE pattern was disentangled using all three methods, illustrating the value of cross-analysis. As yield is the main trait of interest for barley cultivation in Iceland, and few crossovers of genotype performance have been observed between environments, the definition of one mega-environment was recommended for Icelandic cultivation and breeding. We identified promising genetic material for both traits and highlighted the superiority of six-row genotypes for yield.
Highlights
Barley (Hordeum vulgare L.) is the fourth most cultivated cereal crop in the world after maize, wheat, and rice [1]
To study GxE for yield and thousandkernel weight (TKW), a set of statistical models were applied to the dataset, all derived from a base model as follows: Yger = μ + αg + βe +ge + ωer + Eger where Yger is the measured phenotype of genotype g in the environment e and block r, μ is the intercept, αg is the main effect of genotype g, βe is the main effect of the environment e,ge is the GxE effect of genotype g in environment e, ωer is the effect of block r in environment e, and Eger is the error associated with genotype g in environment e and block r with Eger ∼ N 0, σE2 independent and identically distributed (IID), σE2 being the error variance
6r genotypes had both a higher mean yield and IPC1 score compared to 2r genotypes. These results indicated that 6r genotypes yielded better than 2r genotypes in the high fertility environments N_HF and W_HF that had both the highest mean yield and IPC1 scores
Summary
Barley (Hordeum vulgare L.) is the fourth most cultivated cereal crop in the world after maize, wheat, and rice [1]. Factorial regression models have been used to describe the crop yield of wheat genotype reaction to environmental covariables [23] and more recently linear mixed models have shown high predictive accuracy in cross-validation for GxE analysis [24]. Trials carried out in Scandinavia during the period 1987–1989, including Iceland, showed significant GxE across the Nordic region [6,30], and supported the use of AMMI for the analysis of GxE This method has been used to identify stable and high-yielding genotypes in barley trials [31,32]. Soil type has3boef e15n shown to cause variability in the yield and kernel weight of spring barley in Iceland, where many barley genotypes were found to be unstable and seemed to perform better in boanrelesyoiilntyIcpeelatnhdan, wthheeorethmera[n3y6]b.arley genotypes were found to be unstable and seemed to perTfhoermevbaeluttaetrioinn oonf eGsxoEil htyaps entohtabneethnecoatrhrieerd[3o6u]t. The soil texture that was considered least fertile, received the highest amounts of nitrogen (N) while the soil types considered most fertile received the lowest amount of N
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