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The aim of this study was to assess yield components and grain yield of soybean cultivars in response to sowing densities. For this, two soybean cultivars and five sowing densities were tested, in a two-factor scheme. The following yield components were measured by the end of the cycle: plant height; insertion height of the first pod; number of nodes per plant; number of pods with one, two, three and four grains; number of pods per plant; number of grains per plant; weight of a thousand grains; humidity and grain yield. Sowing densities did not cause significant variations of grain yield (bags ha-1) for any cultivar, however, higher populational densities promoted a reduction in the number of pods with two and three grains, as well as a reduction in the total number of pods and grains per plant for both cultivars. Cultivar NS 5700 IPRO was the most productive, with a higher number of pods with two and three grains and number of pods and grains per plant. [ABSTRACT FROM AUTHOR]

Genotype x environment interaction (GEI) causes constant interference in soybean [Glycine max (L.) Merr.] grain yield. This complexity tends to increase when comparing highland and lowland cultivation systems, and there has been little referenced work for these in the situation of subtropical conditions. Hence, the aims of this study were to verify the effects of genotypes, environments and GEI for soybean grain yield in highland and lowland areas with subtropical climate and to compare the adaptability and stability methodologies. The trials were carried out in two locations of the state of Rio Grande do Sul, Brazil, with 20 soybean cultivars on three sowing dates for each location. The experiments were conducted in a randomised block design with three replications. Given the observed magnitude of the GEI, its simple and complex parts were quantified for further analysis of adaptability and stability, using the Additive Main Effect and Multiplicative Interaction (AMMI), Genotype plus Genotype–Environment interaction (GGE) and Best Linear Unbiased Prediction (BLUP) modelling approaches, followed by the verification of similarity between the methodologies via Spearman's correlation coefficient. The complex part of the GEI represented 82.11% of the total variation associated with the GEI, whereas the simple part accounted for 17.89% of that variation. The second sowing date, in both locations, was the one that presented the best rankings according to the models used, and also, cases of specific genotype adaptability were identified in each environment. The highest yield averages were obtained in the highlands. The GGE and BLUP techniques presented genotypic ranking concordance. [ABSTRACT FROM AUTHOR]