Implicações da interação de genótipos com ambientes na recomendação de cultivares de feijoeiro comum: validação de regras e importância de fatores ambientais
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2019-11-14
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Universidade Federal de Goiás
Resumo
The genotypes by environments interaction (GxA) can be defined as the differential
phenotypic response of genotypes in different environments. This phenomenon is the main
complicating factor in recommending broadly adapted cultivars in common bean and others
crops. The value of cultivation and use (VCU) tests are required for registration of new
cultivars. These tests are intended to generate agronomic information about the performance
of candidate lines for new cultivars in the various cultivation environments. The rules for
conducting VCU tests were very restrictive as they require many tests to register the new
cultivar. This step in the development process of new cultivars is the most costly for common
bean breeding programs for logistical and operational reasons. Because of this, the standard
rule has been relaxed since 2010 and was considers the regionalization of Brazil in
edaphoclimatic regions. Thus, ten environments are currently accepted for regions I (South)
and II (Central), and six environments for Region III (Northeast). Of which three
environments are required per sowing season for the season in which the cultivar is to be
indicated. The sowing seasons are for region I “waters” (águas) and “drought” (seca); and for
region II "waters" and "winter" (inverno). The tests must be conducted for two years. Thus,
this work aims to: validate the number of environments (VCU assays) currently accepted for
registration of new cultivars, through computer simulations with real data, and; to evaluate
environmental factors to determine their relevance to the phenotypic variation of candidate
lines. Grain yield data were used for the study. Data were obtained from 406 VCU trials during
17 years of the common bean breeding program of Embrapa Rice and Beans. During this
period 101 candidate lines and 19 commercial cultivars were evaluated as control. The trials
were distributed among the three edaphoclimatic regions that contribute most for of the
common common bean grain production. For the simulation study an algorithm was built to
randomly sample the environments in various combinations. The combinations represent
several scenarios, which vary in the number of environments. 288646 simulations were
performed and the five best classified genotypes were compared, by coincidence, with the five
classified in the complete joint analysis. This analysis uses all available environments in each
VCU cycle (two years). Nonlinear modeling was used to adjust estimates to the asymptotic
curve to obtain the adjusted averages of coincidence. The curve equation was derived to
obtain the instantaneous rate of change. For the criterion of determining the minimum
number of environments, the mean value theorem was used to estimate the average rate of
change (∆dM) between scenarios, where the x value for the average rate represents the
minimum number of environments. For the study of environmental factors two approaches
were used: the modeling by mixed models to estimate the variance components and; the
classical approach to analysis of variance with decomposition of GxA interaction. In addition to
these analyzes, the GxA interaction was decomposed into the simple and complex parts. The
results of the simulation study indicated high average coincidence between genotypes even in
scenarios with few environments. The elevation of the coincidence was progressive until the
scenario with eight environments in regions I and II, which represents the point of ∆dM.
However, the number of currently accepted environments (ten) for these regions was more
appropriate. For region III, the ∆dM occurred 6.25 indicating that the minimum number of
environments for this region is capable of detecting the genotypes most adapted to this
region. For sowing seasons, three environments resulted in estimates of over 60% of average
coincidence, except for the winter season (53.4%). Thus, it is concluded that the number of
environments currently accepted for registration of new cultivars is capable of indicating the
superior genotypes. The mixed model evaluation of the environmental factors analysis by
region indicated that the GxLxExA interaction is the component of variance that contributes
most to the total variance, followed by the effect of locations for regions I and II. In region III
the effect of sites was the most important of the components. The analysis of variance of the
factors and their partial decompositions indicated that in region I that the isolated effects of
times and places together with the GxL interaction were more relevant. In region II, GxE
interaction was the most significant componet involving genotypes. The isolated
environmental components varied in importance between cycles in the region II. The local
effect and GxL interaction are the most expressive components in region III. The
decomposition of the interaction was predominantly complex in all studied cycles for all
regions. It is concluded, therefore, that in region II the environmental factors sowing seasons,
years and location were the ones that participated with most of the total variation. GxE was
the most significant among the interactions of environmental factors involving genotypes in
region II. In region III the main sources of variation for the isolated effects were location and
years, in that order. The variance components indicated that the interaction of genotypes with
the environmental components were more important for regions I and II, and for region III
the location effect was more relevant. The location effect is the isolated variance component
that most contributes to the total variation in all regions. The type of complex interaction was
predominant among the combined assays in all regions.
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Citação
BARROS, M. S. Implicações da interação de genótipos com ambientes na recomendação de cultivares de feijoeiro comum: validação de regras e importância de fatores ambientais. 2019. 94 f. Tese (Doutorado em Genética e Melhoramento de Plantas) - Universidade Federal de Goiás, Goiânia, 2019.