Resumo O objetivo deste trabalho foi avaliar o potencial de rendimento de grãos de cultivares de soja (Glycine max) modernas submetidas a níveis alto e baixo de manejo de insumos, em diferentes datas de semeadura, na região Sudoeste do Paraná, Brasil. O experimento foi realizado nas safras de 2017/2018 e 2018/2019, no município de Itapejara D’Oeste. Cinco cultivares de soja (BMX Zeus IPRO, BMX Ativa RR, BMX Lança IPRO, NS 5445 IPRO e NA 5909 RG) foram avaliadas em quatro ambientes formados pela combinação de nível de manejo de insumos (alto e baixo) e datas de semeadura (primeira e segunda). O delineamento experimental foi de blocos ao acaso, com três repetições. As características avaliadas foram: potencial de rendimento de grãos (kg ha-1), no estádio fenológico R5; e rendimento de grãos (kg ha-1) e seus componentes, no estádio R8. A cultivar, a época de semeadura e o manejo dos insumos são determinantes para a maximização do potencial produtivo dos grãos. Na primeira data de semeadura, em outubro, a cultivar BMX Zeus IPRO apresenta melhor resposta ao manejo de alto nível, com maior potencial produtivo no estádio R5 (19.682 kg ha-1) e maior produtividade de grãos (8.248 kg ha-1), enquanto NA 5909 RG apresenta os melhores resultados com baixo manejo de insumos.

Abstract The objective of this work was to evaluate the grain yield potential of modern soybean (Glycine max) cultivars subjected to high- and low-input management levels on different sowing dates, in the southwestern region of the state of Paraná, Brazil. The experiment was carried out in the 2017/2018 and 2018/2019 crop seasons in the municipality of Itapejara D’Oeste. Five soybean cultivars (BMX Zeus IPRO, BMX Ativa RR, BMX Lança IPRO, NS 5445 IPRO, and NA 5909 RG) were evaluated in four environments formed by the combination of input management levels (high and low) and sowing dates (first and second). The experimental design was a randomized complete block with three replicates. The evaluated traits were: grain yield potential (kg ha-1), in the R5 phenological stage; and grain yield (kg ha-1) and its components, in the R8 stage. Cultivar, sowing date, and input management are determinant for maximizing grain yield potential. In the first sowing date, in October, the BMX Zeus IPRO cultivar shows a better response to the high level management, with a higher yield potential in the R5 stage (19,682 kg ha-1) and a higher grain yield (8,248 kg ha-1), whereas NA 5909 RG shows the best results with the low input management.

Abstract System fertilization is characterized by partial or total fertilizer application at the predecessor crop; and it can be a viable practice to soybean crop. This study aimed to determine the fertilizer management and fertilizer levels for black oat-soybean cropping system, in high fertility soils and no-tillage system. The field trial was conducted in a bifactorial scheme, consisting of six environments, by combination of locations (Bom Sucesso do Sul - Paraná, Itapejara d'Oeste - Paraná) and fertilization management (all fertilization in black oat; splitting with 50% in black oat and 50% in soybean, all fertilization in soybean), and four fertilizer levels (0, 100, 200 and 300%) defined according to soil analysis and production expected. The evaluated traits were dry mass production, N, P and K nutrient accumulation of straw, dry mass remaining of black oat crop; and plant height, number of pods per plant, thousand grain weight, grain yield for soybean crop. Higher black oat dry mass production was observed at higher fertilization level. The fertilizer anticipation in black oat crop had better performance. Phosphorus and potassium accumulation increased linearly with fertilizer level increase. For N, the highest accumulated value occurred at the 200%, decreasing at the 300% of fertilizer level. The soybean crop had no influence in grain yield considering fertilization management, anticipation or splitting, and fertilizer levels. Thus, the system fertilization can be a viable practice, and favor black oat dry mass production and soybean development.

RESUMO No Brasil, o feijoeiro (Phaseolus vulgaris L.) é cultivado em diferentes safras e ambientes, com diferentes respostas do genótipo devido à interação genótipo x ambiente. Objetivou-se identificar genótipos de feijão com melhor estabilidade produtiva em cada ambiente de cultivo. Os experimentos foram conduzidos em delineamento de blocos casualizados, com três repetições, envolvendo três anos agrícolas, nove genótipos de feijão comum e quatro ambientes (primeira e segunda safras com baixo e alto nível tecnológico de insumos, como fertilizantes, controle de pragas e doenças). Análises biplot foram realizadas utilizando-se o software GGE Biplot. O uso de alto nível tecnológico de insumos resulta em aumento de 14,4 % na produtividade, é mais representativo nos ambientes de cultivo e mais adequado para a seleção de genótipos. IPR Campos Gerais, BRS Esplendor e BRS Campeiro mostraram alta estabilidade e obtiveram o melhor desempenho produtivo em ambos os níveis tecnológicos.

ABSTRACT In Brazil, common bean (Phaseolus vulgaris L.) is cultivated in different growing seasons and environments, with different genotypes responses due to the genotype x environment interaction. This study aimed to identify common bean genotypes with a better production stability in each growing environment. The trials were conducted in a randomized block design, with three replications, involving three growing years, nine common bean genotypes and four environments (first and second growing seasons with high and low levels of technological input, such as fertilizers and pest and disease control). Biplot analyses were performed using the GGE Biplot software. The use of high levels of technological input results in an average increase of 14.4 % for yield and is more representative in the crop environments, as well as more adequate for genotype selection. IPR Campos Gerais, BRS Esplendor and BRS Campeiro showed a high stability and presented the best productive performance under both technological levels.

Resumo: O objetivo deste trabalho foi identificar locais-chave de teste em duas macrorregiões sojícolas no Brasil, para avaliação e seleção de linhagens de soja (Glycine max). Foram testadas 22 cultivares em 23 locais, nessas duas macrorregiões (MR1 e MR2), durante quatro anos, de 2012 a 2015. Os ensaios foram conduzidos em delineamento de blocos ao acaso, com três repetições. Todas as análises foram realizadas com uso do programa GGEbiplot. As análises efeito principal de genótipo mais interação genótipo × local + efeito principal de genótipo mais interação genótipo × ambiente e efeito genotípico vs. efeito do genótipo x ambiente foram utilizadas para identificar locais de teste, isto é, locais com alta representatividade e consistência de resultados. Chapada, no Rio Grande do Sul, e Maracaju, no Mato Grosso do Sul, foram os principais locais em MR1 e MR2, respectivamente. Estes locais foram os mais representativos e consistentes ao longo dos anos, e o efeito genotípico explicou uma alta proporção da variância fenotípica.

Abstract: The objective of this work was to identify core locations in two soybean macroregions in Brazil for the evaluation and selection of soybean (Glycine max) lineages. Twenty-two cultivars were tested in 23 locations in these two macroregions (MR1 and MR2), during four years, from 2012 to 2015. Trials were conducted in a randomized complete block design with three replicates. All analyses were performed using the GGEbiplot software. The genotype main effects plus genotype × location interaction + genotype main effects plus genotype × environment interaction and genotypic effect vs. genotype x environment effect analyses were used to identify core locations, i.e., locations with high representativeness and consistency of results. Chapada, in the state of Rio Grande do Sul, and Maracaju, in the state of Mato Grosso do Sul, were the core locations in MR1 and MR2, respectively. These locations were the most representative and consistent over the years, and the genotypic effect explained a high proportion of phenotypic variance.