Exploring genotype by environment interaction on cassava yield and yield related traits using classical statistical methods

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.

Detection of Superior Rice Genotypes and Yield Stability under Different Nitrogen Levels Using AMMI Model and Stability Statistics

“…Accordingly, the genotypes G25, MTU1010, BG 304, and Yabani LuLu were the best genotypes having the lowest IPCAs values and located in close proximity to the origin. Using AMMI biplots, several investigations were able to identify their corresponding genotypes such as in barley [ 44 ], rice [ 40 ], Sorghum [ 45 ], cassava [ 46 ], and maize [ 47 ]. MTU1010 and Yabani LuLu are among the two groups most productive and stable.…”

Parsimonious genotype by environment interaction covariance models for cassava (Manihot esculenta)

“…Studying the patterns of MET data for decision making cannot be adequately investigated using conventional statistical methods due to some limitations as pointed out by Bakare et al (2022) . Therefore, factor analytic structures fitted in the linear mixed model framework as used in this study are flexible and robust for modeling complex genetic variance structure and more parsimonious for MET analyses than unstructured models ( Smith et al, 2001a ).…”

“…The phenotypic panel for evaluating GEI is often called a multi-environment trial (MET). Traditionally, the resulting empirical data from METs are often analyzed using classical statistical methods ( Bakare et al, 2022 ). These methods include ANOVA, fixed linear bilinear model such as additive main effect and multiplicative interaction (AMMI) model ( Gauch and Zobel, 1997 ; Gauch, 2016 ) and site regression (SREG) or genotype main effect and genotype-by-environment (GGE) model ( Yan et al, 2000 ), and linear regression type model like Finlay and Wilkinson (1963) .…”

“…These methods include ANOVA, fixed linear bilinear model such as additive main effect and multiplicative interaction (AMMI) model ( Gauch and Zobel, 1997 ; Gauch, 2016 ) and site regression (SREG) or genotype main effect and genotype-by-environment (GGE) model ( Yan et al, 2000 ), and linear regression type model like Finlay and Wilkinson (1963) . These classical analyses are inefficient in handling unbalanced datasets that often arise in METs ( Bakare et al, 2022 ), resulting in unreliable estimates of genetic effects.…”

Increasing cassava root yield: Additive-dominant genetic models for selection of parents and clones

“…In several phases of the breeding program, vegetative propagation allows the maintenance of high heterozygosity and phenotypic plasticity expression for several traits (Oliveira et al, 2015a). In addition, it allows hybrids to be evaluated and selected in different locations and crop seasons (Barandica et al, 2016), thus allowing the separation of genetic and environmental effects, through the effects of the genotype by environment interactions (Ceballos et al, 2016a;Bakare et al, 2022). Due to vegetative propagation and the high heterozygosity of the parents (Ceballos et al, 2016a), genetic variability within families represents approximately 90% of total genetic variability (Ceballos et al, 2016b), supporting the idea that elite clones can be obtained within any family.…”