Euphytica · 2021 · 17 citations · 37 references
AgricultureCrop SciencesPlant SciencesGermplasm EvaluationPlant GeneticsQuantitative GeneticsBreeding ValueGenetic EvaluationPlant Breeding GeneticsQuality Trait EvaluationYield Potential AssessmentBreeding Line EvaluationGenetic Variance AnalysisGenotypic Yield PotentialHeritability Of Yield TraitsLocation EffectsVariance Components AnalysisYield-related Trait AnalysisYield-related Trait Assessment
Abstract The objective of this study was to determine genetic potentials in eight sets of cowpea lines for grain yield (GY), hundred seed weight (HSDWT) and days to 50% flowering (DT50FL). A total of 614 F 6 genotypes constituting the sets, grouped by maturity, were evaluated across two locations in Northern Nigeria, in an alpha lattice design, two replications each. Data were recorded on GY, HSDWT and DT50FL.Variance components, genotypic coefficient of variation (GCV), and genetic advance (G A ) were used to decode the magnitude of genetic variance within and among sets. Genetic usefulness ( Up ) which depends on mean and variance to score the genetic merits in historically bi-parental populations was applied to groups of breeding lines with mixed parentage. Principal component analysis (PCA) was used to depict contribution of traits to observed variations. GY and DT50FL explained the variance within and between sets respectively. Genotypes were significantly different, although genotype-by-location and set-by-location interaction effects were also prominent. Genetic variance (δ 2 G ) and GCV were high for GY in Prelim2 (δ 2 G = 45,897; GCV = 19.58%), HSDWT in Prelim11 (δ 2 G = 7.137; GCV = 17.07%) and DT50F in Prelim5 (δ 2 G = 4.54; GCV = 4.4%). Heritability varied among sets for GY (H = 0.21 to 0.57), HSDWT (H = 0.76 to 0.93) and DT50FL (H = 0.20 to 0.81). G A and percentage G A (G APM ) were high for GY in Prelim2 (G APM = 24.59%; G A = 269.05Kg/ha), HSDWT in Prelim11 (G APM = 28.54%; G A = 4.47 g), and DT50F in Prelim10 (G APM = 6.49%; G A = 3.01 days). These sets also registered high values of genetic usefulness, suggesting potential application in non-full sib populations. These approaches can be used during preliminary performance tests to reinforce decisions in extracting promising lines and choose among defined groups of lines.
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R: A Language and Environment for Statistical Computing
R Core Team · 2000 · 353K citations · Full text
Natural Sciences, Computer Science, Statistical Computing +10
Fitting Linear Mixed-Effects Models Using <b>lme4</b>
Douglas M. Bates, Martin Mächler, Benjamin M. Bolker et al. · Journal of Statistical Software · 2015 · 85.6K citations · Full text