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

Moshood A Bakare; Siraj Ismail Kayondo; Cynthia I Aghogho; Marnin D Wolfe; Elizabeth Y Parkes; Peter Kulakow; Chiedozie Egesi; Jean-Luc Jannink; Ismail Yusuf Rabbi

doi:10.3389/fpls.2022.978248

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

Front Plant Sci. 2022 Sep 21:13:978248. doi: 10.3389/fpls.2022.978248. eCollection 2022.

Authors

Moshood A Bakare^{1

2}, Siraj Ismail Kayondo², Cynthia I Aghogho^{2

3}, Marnin D Wolfe^{1

4}, Elizabeth Y Parkes², Peter Kulakow², Chiedozie Egesi^{1

2

5}, Jean-Luc Jannink^{1

6}, Ismail Yusuf Rabbi²

Affiliations

¹ Plant Breeding and Genetics Section, School of Integrative Plant Science, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY, United States.
² International Institute of Tropical Agriculture, Ibadan, Nigeria.
³ West Africa Centre for Crop Improvement, University of Ghana, Legon, Ghana.
⁴ Department of Crop, Soil and Environmental Sciences, College of Agriculture, Auburn University, Auburn, AL, United States.
⁵ National Root Crops Research Institute (NRCRI), Umudike, Umuahia, Nigeria.
⁶ USDA-ARS, Robert W. Holley Center for Agriculture and Health, Ithaca, NY, United States.

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 (AIC). 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.

Keywords: environmental covariables; factor analytic model; factor loadings; genotype-by-environment interaction; genotypic scores; hybrid relationship matrix; partial least squares regression; variance structure.