Abstract
Abstract The objective of this work was to identify environmental factors with significant effects on the genotype by environment interaction (GEI) of sugarcane, and to generate thematic maps yield adaptability of genotypes for the state of Goiás, Brazil, through the integrated use of factorial regression models and the geographic information system (GIS). The study was based on the yield of recoverable sugar (YRS) from cultivar field trials carried out in nine locations. Fourteen environmental factors were used, out of which 11 were divided into 10 crop growth phases, totaling 113 environmental covariates (ECs). The selection of ECs was done by successive simple linear regressions, and the respective genotypic sensitivity coefficients were used to generate adaptability maps. Approximately 57% of the GEI effects were related to the covariates longitude, average temperature at crop germination phase, and maximum temperature at the beginning of the phase of greatest growth. For YRS, the RB034128 and RB034021 clones show specific yield adaptations, and the RB034045 cultivar can share the growing area with the RB867515 check cultivar.
Highlights
The efficiency of sugarcane farming systems could be increased by matching genotypes adapted to specific environmental conditions with different growing locations
Such approach assumes that genotypes are differentially affected by environmental factors, which is a biological phenomenon known as genotype by environment interaction (GEI)
Eleven environmental factors were used for ten crop stages, plus geographic coordinates and altitude, for analyzing the role that environmental covariates (ECs) played in GEI in sugarcane
Summary
The efficiency of sugarcane farming systems could be increased by matching genotypes adapted to specific environmental conditions with different growing locations. Such approach assumes that genotypes are differentially affected by environmental factors, which is a biological phenomenon known as genotype by environment interaction (GEI). Most of the GEI studies on sugarcane focused on identifying mega-environments (Todd et al, 2018), similarity of test sites for selection (Guilly et al, 2017), and genotypic stability (Silveira et al, 2013) These empirical approaches do not take into account the environmental factors, which allows of a limited biological interpretation of GEI. The importance of analytical approaches integrating environmental factors have been well recognized (Ramburan et al, 2011, 2012)
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