In:
PLOS ONE, Public Library of Science (PLoS), Vol. 17, No. 7 ( 2022-7-18), p. e0268189-
Abstract:
Variety advancement decisions for root quality and yield-related traits in cassava are complex due to the variable patterns of genotype-by-environment interactions (GEI). Therefore, studies focused on the dissection of the existing patterns of GEI using linear-bilinear models such as Finlay-Wilkinson (FW), additive main effect and multiplicative interaction (AMMI), and genotype and genotype-by-environment (GGE) interaction models are critical in defining the target population of environments (TPEs) for future testing, selection, and advancement. This study assessed 36 elite cassava clones in 11 locations over three cropping seasons in the cassava breeding program of IITA based in Nigeria to quantify the GEI effects for root quality and yield-related traits. Genetic correlation coefficients and heritability estimates among environments found mostly intermediate to high values indicating high correlations with the major TPE. There was a differential clonal ranking among the environments indicating the existence of GEI as also revealed by the likelihood ratio test (LRT), which further confirmed the statistical model with the heterogeneity of error variances across the environments fit better. For all fitted models, we found the main effects of environment, genotype, and interaction significant for all observed traits except for dry matter content whose GEI sensitivity was marginally significant as found using the FW model. We identified TMS14F1297P0019 and TMEB419 as two topmost stable clones with a sensitivity values of 0.63 and 0.66 respectively using the FW model. However, GGE and AMMI stability value in conjunction with genotype selection index revealed that IITA-TMS-IBA000070 and TMS14F1036P0007 were the top-ranking clones combining both stability and yield performance measures. The AMMI-2 model clustered the testing environments into 6 mega-environments based on winning genotypes for fresh root yield. Alternatively, we identified 3 clusters of testing environments based on genotypic BLUPs derived from the random GEI component.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0268189
DOI:
10.1371/journal.pone.0268189.g001
DOI:
10.1371/journal.pone.0268189.g002
DOI:
10.1371/journal.pone.0268189.g003
DOI:
10.1371/journal.pone.0268189.g004
DOI:
10.1371/journal.pone.0268189.g005
DOI:
10.1371/journal.pone.0268189.g006
DOI:
10.1371/journal.pone.0268189.g007
DOI:
10.1371/journal.pone.0268189.g008
DOI:
10.1371/journal.pone.0268189.g009
DOI:
10.1371/journal.pone.0268189.t001
DOI:
10.1371/journal.pone.0268189.t002
DOI:
10.1371/journal.pone.0268189.s001
DOI:
10.1371/journal.pone.0268189.s002
DOI:
10.1371/journal.pone.0268189.s003
DOI:
10.1371/journal.pone.0268189.s004
DOI:
10.1371/journal.pone.0268189.s005
DOI:
10.1371/journal.pone.0268189.s006
DOI:
10.1371/journal.pone.0268189.s007
DOI:
10.1371/journal.pone.0268189.s008
DOI:
10.1371/journal.pone.0268189.s009
DOI:
10.1371/journal.pone.0268189.s010
DOI:
10.1371/journal.pone.0268189.s011
DOI:
10.1371/journal.pone.0268189.s012
DOI:
10.1371/journal.pone.0268189.s013
DOI:
10.1371/journal.pone.0268189.s014
DOI:
10.1371/journal.pone.0268189.s015
DOI:
10.1371/journal.pone.0268189.s016
DOI:
10.1371/journal.pone.0268189.s017
DOI:
10.1371/journal.pone.0268189.s018
DOI:
10.1371/journal.pone.0268189.s019
DOI:
10.1371/journal.pone.0268189.s020
DOI:
10.1371/journal.pone.0268189.s021
DOI:
10.1371/journal.pone.0268189.s022
DOI:
10.1371/journal.pone.0268189.s023
DOI:
10.1371/journal.pone.0268189.s024
DOI:
10.1371/journal.pone.0268189.s025
DOI:
10.1371/journal.pone.0268189.s026
DOI:
10.1371/journal.pone.0268189.s027
DOI:
10.1371/journal.pone.0268189.s028
DOI:
10.1371/journal.pone.0268189.r001
DOI:
10.1371/journal.pone.0268189.r002
DOI:
10.1371/journal.pone.0268189.r003
DOI:
10.1371/journal.pone.0268189.r004
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2022
detail.hit.zdb_id:
2267670-3