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Increasing selection gain for the target environment

1. What is the selection and the target environment
The selection environment is usually managed by the breeder with regard to field design, soil homogeneity and fertilizer application.
The selection environment can include managed trials genotypes are grown under a clearly defined stress.
The target environment is the set of fields and future seasons in which the varieties produced by a breeding program will be grown.
Usually, performance is evaluated on breeding stations under the assumption that screening in those stations can predict future performance in farmers’ fields. This assumption is only valid if indirect selection on breeding stations is as efficient (or even better, more efficient) than direct selection in the target environment.

 
2. When is indirect selection efficient
Assuming the same selection intensity under direct and indirect selection, the relative efficiency of indirect selection is a function of the square root of the heritability of grain yield under indirect (2) and direct (1) selection as well as the genetic correlation between both selection environments:
 RE=\frac{r_{g}h_{1}}{h_{2}}
Indirect selection was expected to be more efficient than direct selection, when the ratio between the two was more than 1 (Falconer and Mackay, 1996). See also selection efficiency.
Note: If the genetic correlation is estimated across several years, the phenotypic correlation cannot be applied to estimate genotypic correlation among environments as g:y affects the subregion factor and thus is part of the phenotypic variance (see also ways to estimate the genetic correlation).

 

3. What is genotype-by-environment interaction (GxE)
The yield (or any other trait) of one genotype is the sum out of the mean performance of all entries included, the genotype effect (G), the environmental effect (E) and the genotype environment interaction (G:E)
 Y= \mu + G + E + G:E

Genetic variability (G)
Variability that can be attributed to genes that encode specific traits, and can be transmitted from one generation to the next, is described as genetic or heritable variation.  The degree of expression of a heritable trait is impacted by its environment.
 G= v_{A}+ v_{D} + v_{I}
vA= additive variance, vD= dominant variance, vI= epistatic variance, variance from interaction between genes

As only the additive variance can be estimated, it is common to partition the genetic variance into additive variance versus all other kinds of variance.
Narrow sense heritability:
 h^{2}= \frac{v_{A}}{v_{P}}

Environmental variation (E)
The fields in which genotypes are grown are often heterogeneous with respect to plant growth factors such as nutrients, moisture, light and temperature. Additionally disease and pest agents may not uniformly infect plants in the field. Inferior genotypes can outperform superior genotypes under uneven environmental conditions. In general the selecting environment should be as uniform as possible and close to the one in which the crop will be commercially grown.

Genotype-by-environment interaction
Genotypes may react differently to environmental variation resulting in genotypic rank changes. This genotype × environment interaction complicates selection for broad adaptation. If genotype × environment interaction is large within a TPE and associated with consistent subgroupings of environments within the TPE, greater gains from selection may result by subdividing the breeding target than by selecting for broad adaptation. See also genotype-by-environment interaction

 
4. When is the subdivision of a target region appropriate
The subdivision of a target region into uniform subregions will only increase selection efficiency if (i) genotype-by-subregion interactions are repeatable (Atlin et al., 2001), (ii) genotypic correlation among subregions is low (Presterl et al., 2003) (iii) increase in genotypic variance can counterbalance loss in precision of genotypic means associated with division of testing resources (Windhausen et al., 2012).

 

5. References

Atlin, G.N., M. Cooper, and Å. Bjørnstad. 2001. A comparison of formal and participatory breeding approaches using selection theory. Euphytica 122(3): 463–475.

Falconer, D.S., and T.F.C. Mackay. 1996. Introduction to Quantitative Genetics.

Presterl, T., G. Seitz, M. Landbeck, E.M. Thiemt, W. Schmidt, H.H. Geiger, and M. Zea. 2003. Improving nitrogen-use efficiency in European maize: Estimation of quantitative genetic parameters. Crop Sci. 43(4): 1259–1265.

Windhausen, V.S., S. Wagener, C. Magorokosho, D. Makumbi, B. Vivek, H.-P. Piepho, A.E. Melchinger, and G.N. Atlin. 2012. Strategies to Subdivide a Target Population of Environments: Results from the CIMMYT-Led Maize Hybrid Testing Programs in Africa. Crop Sci. 52(5): 2143–2152.

 

December 3rd, 2013
Topic: Crop Science, Plant breeding Tags: None

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