Abstract
Maize breeding programs conduct multi-environment trials every year to assess the performance of new cultivars in pre-releasing tests. The data are combined across sites and seasons to perform a joint analysis in order to obtain information that will help breeders to select the best cultivars for different environments. Beyond this, it is essential to understand the different factors that can hamper the selection and genetic progress (i.e., genetic variability, selection intensity and genotype-by-environment interactions). In this study, the genetic progress (GP) was estimated and the adaptability and stability of 81 maize genotypes were evaluated in a series of trials for the value of cultivation and use (VCU) between the 2010/11 and 2014/15 growing seasons. The genotypes were composed of open-pollinated varieties, topcross hybrids, intervarietal hybrids, and single, double and three-way cross hybrids and were assessed in 117 environments in the central region of Brazil, from which 22 presented environmental stresses. For grain yield, an annual GP of 331.5 kg ha-1 was observed, thus showing efficiency in the selection of superior cultivars. Additionally, it was observed that some low-cost seed cultivars showed yield potential, adaptability and stability estimates that were compatible with commercial hybrids, thus making them quite attractive for cultivation in environments with or without abiotic stresses.
Highlights
In several countries, genotypes are evaluated in independent trial experiments (Smith, Ganesalingam, Kuchel, & Cullis, 2015)
In this study, we propose the use of mixed multi-environment models to study the performance of maize cultivars under value for cultivation and use (VCU) tests in order to estimate the genetic progress (GP) and the genotypic gain (GG) in unbalanced trials
Given the multi-environment EBLUP matrix obtained in the joint analysis, the singular value decomposition (SVD) was applied (Gauch, Piepho, & Annicchiarico, 2008) to the confounded GGE matrix
Summary
Genotypes are evaluated in independent trial experiments (Smith, Ganesalingam, Kuchel, & Cullis, 2015). A key point to maximize the profitability and food security is to provide reliable information to farms on the potential of cultivars in order to enable them to choose the best cultivars for the most diverse environmental conditions In this sense, the data reliability and models chosen for analysis become essential in multiple environment trials (MET). One way to evaluate the GP is to measure the performance of previous and current cultivars in the same test or those estimated by genotypic means in different trials These biased means are influenced by the different environmental and experimental conditions, which can mask the true value of the actual GP. The mixed-model approach for the analysis of MET experiments has become widely used (Stefanova & Buirchell, 2010). In this study, we propose the use of mixed multi-environment models to study the performance of maize cultivars under VCU tests (unbalanced) in order to estimate the genetic progress (GP) and the genotypic gain (GG) in unbalanced trials
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