Identification of Traits Contributing to High and Stable Yields in Different Soybean Varieties Across Three Chinese Latitudes

Abstract

Soybean yield is a complex quantitative trait, which is greatly affected by environmental conditions. The main objective of this study is not only to identify specific traits contributing to yield in different latitudes, which can be further used in breeding, but also to identify the outperforming varieties, as this can help to select new lines with these traits. One hundred and seventy-three soybean genotypes were tested in three different ecological environments, including Harbin, Changchun, and Shenyang in China during 2015-2016 cropping seasons. The evaluation on the different agronomic and physiological traits indicated that the soybean varieties with higher plant height, more nodes of main stem, branches, pods, grains, and 100-grain weight, or longer growth periods may have higher yield. Pods, grains and 100-grain weight can be used as direct selection criteria for yield increase, and likewise the other traits such as plant height, nodes of main stem, branches, growth periods indirectly affected yield by affecting the three traits above. The effect of genotype × environment (G × E) interaction on different agronomic traits was significant. The representativeness and discriminability for grains yield per plant was the most significant in Harbin, which could be used to screen varieties with high yield and wider adaptability. Genotype "Suinong 1" was considered stable with higher value of grain yield per plant than other genotypes used in this study. As the yield of certain soybean cultivars may be significantly reduced if they are grown in a region as little as 2°N beyond its normal cultivation latitudes, therefore, the identification and analysis on the stable and widely adaptive soybean genotypes would be very important, and it would provide the significant reference accordance of soybean variety selection for the soybean breeders.

Construction of a high-density genetic map and QTL analysis for yield, yield components and agronomic traits in chickpea (Cicer arietinum L.)

“…Detailed analysis of phenotyping data revealed a substantial genotypic variation among the RILs for all studied traits across three years. In the present study, the extent of genotypic variances was more than their corresponding environmental variances for PLHT, PPP, 100SW and YPP, indicating a greater contribution of the genotypic component to the total variation in these traits [19,[38][39][40]. All traits, except 100SW, displayed transgressive segregation in both directions, suggesting that both parental lines contributed favorable alleles for these traits.…”

Stability of yield and seed composition in early maturing soybean genotypes assessed by AMMI analysis

“…The predominant influence of environment on yield variation was expected given the meteorological conditions during 2011 (moderate drought) and 2012 (extreme drought), and was in accordance with results of numerous studies on soybean yield variation (YAN and RAJCAN, 2002;MILADINOVIĆ et al, 2006). Similar to findings presented by SUDARIĆ et al (2006a) and LI et al (2020), although significant, G ×E interaction was generally of less importance than effects of genotypes and environments.…”

“…Plant introductions are in general poorly adapted, so for the successful integration of genes into elite soybean gene pool, it is necessary to determine the stability of the introduced sources (PALOMEQUE et al, 2009). In order to provide a deeper understanding of G× E interaction for important agronomic traits in soybean, several models of stability analysis were conducted: GGE biplot (YAN and RAJCAN, 2002;ZHE et al, 2010), additive main effects and multiplicative interaction -AMMI analysis (SUDARIĆ et al, 2006a;LI et al, 2020) and linear regression (KARASU et al, 2009;BALEŠEVIĆ-TUBIĆ et al, 2011), with AMMI and GGE models being the most commonly used to determine genotypes' response patterns across different environments. AMMI method gives the possibility of graphical representation of the interaction on the biplot, where the values of the main effects (genotypes, environments) are presented on the abscissa, and the values of the first interaction axis (IPC1) on the ordinate (CROSSA et al, 1990).…”

Soybean grain yield in highland and lowland cultivation systems: A genotype by environment interaction approach

“…characteristics, geographic location and cultivation systems also directly affect soybean grain yield (Branquinho, Duarte, Souza, Neto, & Pacheco, 2014;Li et al, 2020;Zanon et al, 2015). As a consequence, phenotypic variations are expressed between genotypes cultivated in different environments, which creates the significance of the genotype x environment interaction (GEI - Cruz, Carneiro, & Regazzi, 2014;Van Eeuwijk, Bustos-Korts, & Malosetti, 2016) for grain yield, which is usually the most important response measured in plant breeding programmes.…”