Open AccessRelative contribution of expected sum of squares values for soybean genotypes × growing environments interaction
Author(s) -
Ivan Ricardo Carvalho,
J. A. G. da Silva,
Luiz Leonardo Ferreira,
Vinícius Jardel Szareski,
Gustavo Henrique Demari,
Paulo Henrique Karling Facchinello,
Natã Balssan Moura,
Rémi Schneider,
Tiago Corazza da Rosa,
Deivid Araújo Magano,
Velci Queiróz de Souza
Publication year - 2020
Publication title -
australian journal of crop science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.304
H-Index - 44
eISSN - 1835-2693
pISSN - 1835-2707
DOI - 10.21475/ajcs.20.14.03.p1515
Subject(s) - interaction , explained sum of squares , gene–environment interaction , mathematics , biology , genotype , statistics , factorial , grain yield , agronomy , genetics , mathematical analysis , gene
The objective of this work was to assess the effects and tendencies weighted by genotypes x environments interaction for soybean, as well as to employ a biometric approach through the relative contribution of the sum of squares expected values (RCSS) and to define which levels of the variation sources determine the differential effects of the interaction. The experimental design was randomized blocks arranged in a factorial scheme (four growing environments x 20 soybean genotypes). The relative contribution of expected sums of squares values to soybean genotypes x growing environments interaction defined that the environment Tenente Portela - RS significantly influence plant height, number of pods per plant, number of reproductive nodes in the main stem, number of reproductive nodes in the ramifications, number of grains per plant and grain yield. The variation factor soybean genotypes define that number of pods per plant, number of reproductive nodes in the ramifications, number of grains per plant and grain yield are potentiated by genotype TMG 7161 RR. The biometric approach is efficient to understand the treatment levels and the slicing of simple effects of a factorial experiment, being possible to apply this methodology extensively in soybean.