Best linear unbiased prediction

Gene

Biplot

Gene–environment interaction

Genotype

Animal science

Horticulture

Randomized block design

Artificial intelligence

Heritability

Ecology

Leaf area index

Ammi

Mathematics

Computer science

Wet season

Genetics

Biochemistry

Agronomy

Selection (genetic algorithm)

Biology

Scientia Horticulturae

An experiment was conducted with 25 cassava genotypes in Randomized Block Design with three replications in rainy seasons 2017−18 and 2018−19; water stress 2018−19 and 2019−20 to select the stable and superior genotypes that effectively tolerate water stress. AMMI ANOVA indicated significant G × E interaction for all the traits, Leaf Area Index (LAI), Yield per Plant (YPP), Harvest Index (HI), Dry Matter (DM) and Starch Yield per Plant (SYP). A high environment effect of 68.38 % of the total sum of squares was observed for DM followed by SYP, LAI, YPP and HI. A significant residual variation in G × E for YPP, HI and DM demonstrated that the presence of considerable noise in the model for these traits. Higher PCV and GCV values, moderate level of broad-sense heritability and high Genetic Advance as per cent of mean was observed for LAI, YPP and SYP indicating the possibility of high genetic gains for these traits through selection. CI-158 for LAI, CR43-11 for YPP, M4 for HI, Sree Pavitra and 9S127 for DM were identified as stable based on mean vs. WAAS biplot. Among all the environments, 8S501 and CR43-7 were ‘all-time winners’ for YPP. The rainy season and water stress were the two mega environments whose influence on traits was different. The accuracy of selection was very high for the traits HI followed by YPP and SYP. 8S501 followed by CR43-7 for YPP; 8S501 followed by CR43-7 for HI; 8S501 followed by Sree Athulya for DM; CI-130 followed by 8S501 for SYP had the highest predicted mean values. Based on MTSI, 8S501 followed by Sree Athulya, CR 43-7 and 9S127 were selected considering their mean performance and stability.

Genetic parameters, stability and selection of cassava genotypes between rainy and water stress conditions using AMMI, WAAS, BLUP and MTSI