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

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 - Cornell University; Jannink, Jean-Luc; RABBI, ISMAIL - International Institute Of Tropical Agriculture (IITA)

Submitted to: Frontiers in Plant Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/8/2022
Publication Date: 9/21/2022
Citation: Bakare, M.A., Kayondo, I.S., Aghogho, C.I., Wolfe, M.D., Parkes, E.Y., Kulakow, P., Egesi, C., Jannink, J., Rabbi, I.Y. 2022. Parsimonious genotype by environment interaction covariance models for cassava (Manihot esculenta). Frontiers in Plant Science. 13:978248. https://doi.org/10.3389/fpls.2022.978248.
DOI: https://doi.org/10.3389/fpls.2022.978248

Interpretive Summary: As all crops, cassava is affected by genotype-by-environment interaction (GEI): varieties respond differently to different growing conditions. This phenomenon is considered a critical challenge for plant breeders in developing cassava varieties. This study used the data from variety trials across 11 locations in Nigeria over four cropping seasons from 2016 to 2020. We evaluated a total of 96 varieties across 48 trials. We used statistical models that can fit GEI and were able to account for almost 80% of the genetic variability among the varieties. We found that minimum temperature, precipitation and relative humidity are weather conditions influencing the genotypic response in Southern Nigeria, and maximum temperature, wind speed, and temperature range are important in the northern region of Nigeria. We conclude that the statistical models that we described are effective for analyzing GEI variability.

Technical Abstract: The assessment of cassava clones across multiple environments is often carried out at the uniform yield trial, a late evaluation stage, before variety release. This is to assess the differential response of the varieties across the testing environments, a phenomenon referred to as genotype-by-environment interaction (GEI). This phenomenon is considered a critical challenge confronted by plant breeders in developing crop varieties. This study used the data from variety trials established as randomized complete block design (RCBD) in three replicates across 11 locations in different agro-ecological zones in Nigeria over four cropping seasons (2016–2017, 2017–2018, 2018–2019, and 2019–2020). We evaluated a total of 96 varieties, including five checks, across 48 trials. We exploited the intricate pattern of GEI by fitting variance–covariance structure models on fresh root yield. The goodness-of-fit statistics revealed that the factor analytic model of order 3 (FA3) is the most parsimonious model based on Akaike Information Criterion. The three-factor loadings from the FA3 model explained, on average across the 27 environments, 53.5% [FA (1)], 14.0% [FA (2)], and 11.5% [FA (3)] of the genetic effect, and altogether accounted for 79.0% of total genetic variability. The association of factor loadings with weather covariates using partial least squares regression (PLSR) revealed that minimum temperature, precipitation and relative humidity are weather conditions influencing the genotypic response across the testing environments in the southern region and maximum temperature, wind speed, and temperature range for those in the northern region of Nigeria. We conclude that the FA3 model identified the common latent factors to dissect and account for complex interaction in multi-environment field trials, and the PLSR is an effective approach for describing GEI variability in the context of multi-environment trials where external environmental covariables are included in modeling.