RESUMOA precipitação é uma variável de grande importância para a agricultura, sobretudo na região Nordeste do Brasil que sofre com intensos períodos de déficit hídrico. O objetivo deste trabalho foi caracterizar a precipitação dos municípios piauienses de Bom Jesus, Parnaíba, Picos e Teresina e observar a influência dessa variável climática sobre a evapotranspiração de referência. Os dados históricos mensais de precipitação dos anos 1994 a 2018 utilizados foram obtidos das estações meteorológicas do Instituto Nacional de Meteorologia (INMET) presentes nos municípios estudados. Realizou-se a análise exploratória, sendo obtidas, para cada mês do ano, as estatísticas: média aritmética ( ), desviopadrão (s), coeficiente de variação (CV), valor mínimo (Min) e valor máximo (Max), a fim de se verificar a dispersão dos dados, bem como a representação gráfica da distribuição das médias mensais da precipitação e a relação entre a precipitação e evapotranspiração de referência (ETo) para os municípios estudados. Os resultados evidenciam que há pouca variação dos períodos secos e chuvosos entre os municípios observados, porém há variação do volume de precipitação e da ETo. Observou-se que há relação inversa entre precipitação e evapotranspiração de referência para essa região. Palavras-chave: caatinga; cerrado, Nordeste Brasileiro; bacia do Rio Parnaíba ABSTRACTPrecipitation is a variable of great importance for agriculture, especially in the Northeast region of Brazil, which suffers from intense periods of water deficit. The objective of this work was to characterize the precipitation of Piauí cities and to observe the influence of this climatic variable on reference evapotranspiration. The monthly historical precipitation data from 1994 to 2018 were obtained from the meteorological stations of the National Meteorological Institute (INMET) of the cities of Bom Jesus, Parnaíba, Picos and Teresina. The statistical analysis was performed for each month of the year: arithmetic mean ( ), standard deviation (s), coefficient of variation (CV), minimum value (Min) and maximum value (Max), in order to verify the dispersion of the data, as well as the graphical Meio ambiente, paisagem e qualidade ambiental Submissãorepresentation of the distribution of monthly precipitation averages and the relation between the precipitation and reference evapotranspiration (ETo) for the municipalities studied. The results show that there is little variation in the dry and rainy periods between the municipalities observed, but there is variation in the volume of precipitation and ETo. It was observed that there is an inverse relationship between precipitation and reference evapotranspiration for this region.
Genotype × environment (G×E) interaction is an important source of variation in soybean yield, which can significantly influence selection in breeding programs. This study aimed to select superior soybean genotypes for performance and yield stability, from data from multi-environment trials (METs), through GGE biplot analysis that combines the main effects of the genotype (G) plus the genotype-by-environment (G×E) interaction. As well as, through path analysis, determine the direct and indirect influences of yield components on soybean grain yield, as a genotype selection strategy. Eight soybean genotypes from the breeding program of Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA) were evaluated in field trials using a randomized block experimental design, in an 8 x 8 factorial scheme with four replications in eight different environments of the Cerrado of Northeastern Brazil during two crop seasons. Phenotypic performance data were measured for the number of days to flowering (NDF), height of first pod insertion (HPI), final plant height (FPH), number of days to maturity (NDM), mass of 100 grains (M100) and grain yield (GY). The results revealed that the variance due to genotype, environment, and G×E interaction was highly significant (P < 0.001) for all traits. The ST820RR, BRS 333RR, BRS SambaíbaRR, M9144RR and M9056RR genotypes exhibited the greatest GY stability in the environments studied. However, only the BRS 333RR genotype, followed by the M9144RR, was able to combine good productive performance with high yield stability. The study also revealed that the HPI and the NDM are traits that should be prioritized in the selection of soybean genotypes due to the direct and indirect effects on the GY.
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