Qualidade textural de tomates cultivados em substratos orgânicos submetidos à aplicação de substâncias húmicas

The objective of this work was to evaluate the effects of humic substances and of different organic substrates on the textural quality of tomato fruit of the Vênus hybrid, in a protected environment. Four types of substrates were used: coconut fiber; coconut fiber and carbonized coffee husk 1/3 (v/v); coconut fiber and carbonized coffee husk 2/3 (v/v); and carbonized coffee husk. The humic substances (humic acid, 10% + fulvic acid, 10,2%) dosages of 0, 20, 40 and 80 L ha-1 were applied onto the substrates fortnightly from the eighth day after transplanting. The experimental design was in randomized block in a 4x4 factorial arrangement. Evaluations were done for fruit firmness, percentage of pectin solubilization, and enzymatic activity (pectinmethylesterase and polygalacturonase). Different enzymatic activities of pectinmethylesterase and polygalacturonase were observed according to the doses of humic substances added to the different substrates. The humic substances dosage effects of the humic substances in fruit firmness, pectin solubility and enzymatic activity were dependent upon the substrate used. Fruit grown in coconut fiber had an increase in firmness and a reduction in the percentage of pectin solubilization with the application of increasing doses of humic substances.O objetivo deste trabalho foi avaliar os efeitos de substâncias húmicas e de diferentes substratos orgânicos na qualidade textural dos frutos do tomateiro híbrido Vênus, em ambiente protegido. Utilizaram-se, quatro tipos de substratos: fibra de coco; fibra de coco e casca de café carbonizada 1/3 (v/v); fibra de coco e casca de café carbonizada 2/3 (v/v); e casca de café carbonizada. As doses de substâncias húmicas (ácido húmico, 10% + ácido fúlvico 10,2%) utilizadas foram 0, 20, 40 e 80 L ha-1, aplicadas ao substrato quinzenalmente, a partir do oitavo dia após o transplantio. O delineamento utilizado foi o de blocos ao acaso, em arranjo fatorial 4x4. Avaliaram-se firmeza dos frutos, percentagem de solubilização péctica e atividade enzimática (pectinametilesterase e poligalacturonase). Observou-se variação na atividade das enzimas pectinametilesterase e poligalacturonase, em consequência das doses de substâncias húmicas adicionadas, nos diferentes substratos. O efeito das doses de substâncias húmicas sobre a firmeza, solubilidade de pectinas e atividade enzimática, em frutos de tomate, depende do substrato utilizado. Frutos obtidos de plantas cultivadas em fibra de coco apresentaram aumento de firmeza e redução da percentagem de solubilização péctica com a aplicação de doses crescentes de substâncias húmicas

Tournaments between markers as a strategy to enhance genomic predictions.

Analysis of a large number of markers is crucial in both genome-wide association studies (GWAS) and genome-wide selection (GWS). However there are two methodological issues that restrict statistical analysis: high dimensionality (p≫n) and multicollinearity. Although there are methodologies that can be used to fit models for data with high dimensionality (eg, the Bayesian Lasso), a big problem that can occurs in this cases is that the predictive ability of the model should perform well for the individuals used to fit the model, but should not perform well for other individuals, restricting the applicability of the model. This problem can be circumvent by applying some selection methodology to reduce the number of markers (but keeping the markers associated with the phenotypic trait) before adjusting a model to predict GBVs. We revisit a tournament-based strategy between marker samples, where each sample has good statistical properties for estimation: n>p and low collinearity. Such tournaments are elaborated using multiple linear regression to eliminate markers. This method is adapted from previous works found in the literature. We used simulated data as well as real data derived from a study with SNPs in beef cattle. Tournament strategies not only circumvent the p≫n issue, but also minimize spurious associations. For real data, when we selected a few more than 20 markers, we obtained correlations greater than 0.70 between predicted Genomic Breeding Values (GBVs) and phenotypes in validation groups of a cross-validation scheme; and when we selected a larger number of markers (more than 100), the correlations exceeded 0.90, showing the efficiency in identifying relevant SNPs (or segregations) for both GWAS and GWS. In the simulation study, we obtained similar results