Mega‐Environmental Design: Using Genotype × Environment Interaction to Optimize Resources for Cultivar Testing

Abstract

The efficient use of testing resources is one of the key factors for successful plant breeding programs. Controlling microand macroenvironmental variability is an effective way of improving the testing efficiency and the selection of superior genotypes. Common experimental designs in genotypic testing usually use replicated or augmented experiments at each location, but they are balanced across locations. Some studies suggest that the increase in population size even at the expense of balanced experiments might be beneficial if genotype ́ environment interaction (GEI) is modeled. The objective of this study was to compare strategies for micro and macro-environmental variability control that include GEI information to optimize resource allocation in multi-environment trials (METs). Six experimental designs combined with four spatial correction models were compared for efficiency under three experimental sizes using simulations under a real yield variability map. Additionally, six resource allocation strategies were evaluated in terms of accuracy and the expected response to selection. The a-lattice (ALPHA) experimental design was the best one at controlling micro-environmental variability. The moderate mega-environmental design (MED) strategy had the largest response to selection. This strategy uses historical megaenvironments (MEs) to unbalance genotypic testing within MEs while modeling GEI. The MED was the best resource allocation strategy and could potentially increase selection response up to 43% in breeding programs when genotypes are evaluated in METs. P. González-Barrios and L. Gutiérrez, Dep. of Agronomy, Univ. of Wisconsin–Madison, 1575 Linden Dr., Madison, WI 53706; P. González-Barrios and L. Gutiérrez, Statistics Dep., Facultad de Agronomía, Univ. de la República, Garzón 780, Montevideo, Uruguay; L. Diaz-García, Dep. of Horticulture, Univ. of Wisconsin– Madison, 1575 Linden Dr., Madison, WI 53706; L. Diaz-García, Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias, Aguascalientes, Mexico. Received 19 Nov. 2018. Accepted 30 May 2019. *Corresponding author ([email protected]). Assigned to Associate Editor Marcio Resende Jr. Abbreviations: ALPHA, a-lattice incomplete block design; AR1, spatially correlated error model with first-order autoregressive process; BEST, proportion of times the true 15% superior genotypes are recovered; BLUE, best linear unbiased estimate; COR, Pearson correlation coefficient between predicted and observed genotypic values; CRD, completely randomized design; EXP, spatially correlated error model with two-dimensional exponential process; FA1, factor analytic of order 1; GEI, genotype ́ environment interaction; GGE, genotype + genotype ́ environment interaction; MAF, marker allele frequency; ME, mega-environment; MED, mega-environmental design; MET, multi-environment trial; NSC, no spatial correction model; PREP, partially replicated design; RCBD, randomized complete block design; SED, standard error of the difference between genotypic means; S2D, two-dimensional spline model; UOPN, uniformity oat performance nurseries; WOBP, Wisconsin Oat Breeding Program. Published in Crop Sci. 59:1899–1915 (2019). doi: 10.2135/cropsci2018.11.0692 © 2019 The Author(s). This is an open access article distributed under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Published August 15, 2019