@article{MAKHILLAJ202116620731,
    title = {Kernel Yield Stability Analysis in Groundnut (<i>Arachis hypogea</i> L.)},
    journal = {Agricultural Journal},
    volume = {16},
    number = {6},
    pages = {57-64},
    year = {2021},
    issn = {1816-9155},
    doi = {aj.2021.57.64},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9155&doi=aj.2021.57.64},
    author = {Zekeria,Wassu,Shimelis,Arno and},
    keywords = {AMMI Model,Favorable environments,Genotype x environment interaction (GEI),Mega environments,GGE biplot model},
    abstract = {Multiple-environment trials identify genotypes
that thrive in different environments since the occurrence
of genotype x environment interaction (GEI) produces
stable performance of genotypes. This research was
conducted to determine the effect of GEI on the stability
of groundnut genotypes for kernel yield. The field
experiment was conducted for 16 groundnut genotypes
evaluated for kernel yield in a Randomized Complete
Block Design (RCBD) across six locations in Ethiopia.
The additive main effect and multiplicative interaction
(AMMI) Model analysis of variance (ANOVA) revealed
that the largest proportion of the observed kernel yield
variation was due to GEI (41.5%) and G (38.5%) rather
than environment (19%). The mean yield, stability
parameters from linear regression, AMMI and genotype
main effect and genotype x environment (GGE) biplot
models selected Bahagudo as the best genotype in across
environments and Tole-1, Werer-962 and Manipeter
genotypes with second to fourth highest kernel yield
identified as best in favorable, representative and
unfavorable environments, respectively. The GGE biplot
has shown that the six environments fell into two sectors
with different winning genotypes. Babile and Guba were
identified as representative and discriminating
environments, respectively. Therefore, it is necessary to
grow groundnut genotypes in the environments where
they performed best and testing genotypes in most
discriminating environments to reduce the cost related to
testing genotypes over locations.}
    }