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

Authors: BAKARE, MOSHOOD - Cornell University; KAYONDO, ISMAIL - International Institute Of Tropical Agriculture (IITA); AGHOGHO, CYNTHIA - International Institute Of Tropical Agriculture (IITA); WOLFE, MARNIN - Cornell University; PARKES, ELIZABETH - International Institute Of Tropical Agriculture (IITA); KULAKOW, PETER - International Institute Of Tropical Agriculture (IITA); EGESI, CHIEDOZIE - International Institute Of Tropical Agriculture (IITA); RABBI, ISMAIL - International Institute Of Tropical Agriculture (IITA); Jannink, Jean-Luc

Submitted to: PLOS ONE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/23/2022
Publication Date: 7/18/2022
Citation: Bakare, M.A., Kayondo, I.S., Aghogho, C.I., Wolfe, M.D., Parkes, E.Y., Kulakow, P., Egesi, C., Rabbi, I.Y., Jannink, J. 2022. Exploring genotype by environment interaction on cassava yield and yield related traits using classical statistical methods. PLOS ONE. 17(7): Article e0268189. https://doi.org/10.1371/journal.pone.0268189.
DOI: https://doi.org/10.1371/journal.pone.0268189

Interpretive Summary: Variety advancement decisions for root quality and yield in cassava are difficult due to genotype-by-environment interactions (GEI). 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. Genetic correlation coefficients and heritability estimates among environments found intermediate to high values while also finding differential clonal ranking among the environments indicating the existence of GEI. 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 only marginally significant. We identified two clones, TMS14F1297P0019 and TMEB419, as stable clones. However, combining yield and stability in an index revealed that IITA-TMS-IBA000070 and TMS14F1036P0007 were the top-ranking clones. We clustered the testing environments into 6 mega-environments based on winning genotypes for fresh root yield.

Technical 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.