Avena sativa is the sixth most produced cereal in the world. It is widely used for human consumption. Due to the bromatological quality of its forage, it is used for direct grazing, hay and silage. Due to the large number of interesting characteristics of forage white oats, the selection of unique characteristics becomes difficult and expensive for breeders. In this sense, the use of analysis with multiple characteristics can facilitate the process. Therefore, the objective of this work was to estimate genetic parameters of morphological characteristics, productivity, and quality of forages, as well as to define multiple characteristics that assist in the selection of promising white oat genotypes with forage profile through factor analysis. Field trials were carried out during the agricultural year of 2013 in the municipality of Capão do Leão, RS. The experimental design was in randomized blocks, with treatments arranged in four replications. The treatments corresponded to the genotypes CHIARASUL (G1), FAEM006 (G2), BARBARASUL (G3), BRISASUL, (G4) CGF03006 (G5), CGF07023-1 (G6), CGF07-74 027-1 (G7), CGF07033 (G8), CGF07033-1 (G9), CGF07041 (G10), CGF0705-7 (G11), CGF07060-2 (G12) and CGF07060-3 (G13). The ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 20 (2): gmr18451 T.C. da Rosa et al. 2 characteristics analyzed were: plant height, leaf area, weight of fresh plants, weight of dry plants, number of tillers and levels of nitrogen, crude protein, phosphorus, potassium, calcium, magnesium, copper, zinc, manganese and iron. The data were submitted to the normality test and to various components of variance. Statistical analyses were performed using Selegen®, SAS® and Genes® software. The white oat genotypes expressed high genetic variability and possibility of selection for leaf area, fresh forage mass, dry forage mass, number of tillers and calcium content. Simultaneously the magnesium content with multiple traits + zinc content, dry matter + fresh mass, nitrogen content + calcium content, crude protein + potassium content and number of tillers, showing potential to select genotypes of interest for genetic improvement.
oil were determined in intact soybeans by the technique of reflectance in the near infrared. There was genetic dissimilarity among the 54 soybean genotypes. The protein character was the most decisive factor for genetic variability, based on Singh's relative contribution. Using patterns such as standardized Euclidean mean distance, Tocher's optimized method and the UPGMA clustering method, it was possible to show patterns in the genotype clusters. Based on these results, we can define crosses between accessions with genetic potential for the formation of genotypes with superior centesimal characteristics.
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