Evaluation of sugarcane genotypes and production environments in Paraná by GGE biplot and AMMI analysis

Pedro Henrique Costa De Mattos,Mario Alvaro Aloiso Veríssimo,Ricardo Augusto De Oliveira,Edelclaiton Daros,João Carlos Bespalhok Filho

doi:10.1590/s1984-70332013000100010

Pedro Henrique Costa De Mattos, Mario Alvaro Aloiso Veríssimo + Show 3 more

Open Access

https://doi.org/10.1590/s1984-70332013000100010

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Abstract

The purpose of this study was to evaluate sugarcane genotypes for the trait tons of sugar per hectare (TSH), stratifying five production environments in the state of Paraná. The performance of 20 genotypes and 2 standard cultivars was analyzed in three consecutive growing seasons by the statistical methods AMMI and GGE Biplot. The GGE Biplot grouped the locations into two mega-environments and indicated the best-performing genotypes for each one, facilitating the selection of superior genotypes. Another advantage of GGEBiplot is the definition of an ideal genotype (G) and environment (E), serving as reference for the evaluation of genotypes and choice of environments with greater GE interaction. Both models indicated RB006970, RB855156 and RB855453 as the genotypes with highest TSH and São Pedro do Ivai as the environment with the greatest GE interaction. Both approaches explained a high percentage of the sum of squares, with a slight advantage of AMMI over GGE Biplot analysis.

Highlights

The development of sugarcane and other crops is affected by effects of the environment (E), genotype (G) and their interaction (GE), of which the latter causes significant variations in cultivar performance between different locations (Mohammadi et al 2007).The evaluation of genotypes, aside from the stratification of production environments, is fundamental for the study of relations between genotypes and environments (GE), especially to identify similar response patterns of genotypes in the environments of the experimental network (Cruz et al 2001).One of the most recent evaluation methods is the AMMI (Additive Main Effects and Multiplicative Interaction) analysis
Both models indicated RB006970, RB855156 and RB855453 as the genotypes with highest tons of sugar per hectare (TSH) and São Pedro do Ivai as the environment with the greatest GE interaction. Both approaches explained a high percentage of the sum of squares, with a slight advantage of AMMI over GGE Biplot analysis
The number of stalks per plot was counted, to determine the number of stalks per meter (NSM). These values were used to define the traits of tons of stalks per hectare (TSH) and tons of sugar per hectare (TSH), by the following expressions: TSH = NCM x MIC x 7.142, where the fixed value 7.142 indicates the area estimated for planting, according to the spacing and TSH = (TSH x pol % cane (PC))/100

Summary

Introduction

One of the most recent evaluation methods is the AMMI (Additive Main Effects and Multiplicative Interaction) analysis. In this model, statistical techniques such as analysis of variance and principal component analysis, respectively, are combined to adjust the main effects and GE interaction effects (Duarte and Vencovsky 1999). Yan et al (2000) proposed the GGE Biplot method and pointed out that the yield data are the combined effect of genotype (G), environment (E) and the interaction of both (GE), only G and GE are relevant and should be considered simultaneously in the evaluation of genotypes. The biplot technique is used to approach and evidence the G and GE effect in a multi-environmental trial, which coined the term “GGE Biplot”

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