Scientia Agrícola
URI permanente para esta coleção${dspace.url}/handle/123456789/12094
Navegar
2 resultados
Resultados da Pesquisa
Item Genomic prediction of leaf rust resistance to Arabica coffee using machine learning algorithms(Escola Superior de Agricultura "Luiz de Queiroz", 2021) Sousa, Ithalo Coelho de; Nascimento, Moysés; Silva, Gabi Nunes; Nascimento, Ana Carolina Campana; Cruz, Cosme Damião; Silva, Fabyano Fonseca e; Almeida, Dênia Pires de; Pestana, Kátia Nogueira; Azevedo, Camila Ferreira; Zambolim, Laércio; Caixeta, Eveline TeixeiraGenomic selection (GS) emphasizes the simultaneous prediction of the genetic effects of thousands of scattered markers over the genome. Several statistical methodologies have been used in GS for the prediction of genetic merit. In general, such methodologies require certain assumptions about the data, such as the normality of the distribution of phenotypic values. To circumvent the non-normality of phenotypic values, the literature suggests the use of Bayesian Generalized Linear Regression (GBLASSO). Another alternative is the models based on machine learning, represented by methodologies such as Artificial Neural Networks (ANN), Decision Trees (DT) and related possible refinements such as Bagging, Random Forest and Boosting. This study aimed to use DT and its refinements for predicting resistance to orange rust in Arabica coffee. Additionally, DT and its refinements were used to identify the importance of markers related to the characteristic of interest. The results were compared with those from GBLASSO and ANN. Data on coffee rust resistance of 245 Arabica coffee plants genotyped for 137 markers were used. The DT refinements presented equal or inferior values of Apparent Error Rate compared to those obtained by DT, GBLASSO, and ANN. Moreover, DT refinements were able to identify important markers for the characteristic of interest. Out of 14 of the most important markers analyzed in each methodology, 9.3 markers on average were in regions of quantitative trait loci (QTLs) related to resistance to disease listed in the literature.Item Bioprospecting endophytic bacteria for biological control of coffee leaf rust(Escola Superior de Agricultura "Luiz de Queiroz", 2006-01) Shiomi, Humberto Franco; Silva, Harllen Sandro Alves; Melo, Itamar Soares de; Nunes, Flávia Vieira; Bettiol, WagnerSuppression of plant diseases due to the action of endophytic microorganisms has been demonstrated in several pathosystems. Experiments under controlled conditions involving endophytic bacteria isolated from leaves and branches of Coffea arabica L and Coffea robusta L were conducted with the objective of evaluating the inhibition of germination of Hemileia vastatrix Berk. & Br., race II, urediniospores and the control of coffee leaf rust development in tests with leaf discs, detached leaves, and on potted seedling of cv. Mundo Novo. The endophytic bacterial isolates tested proved to be effective in inhibiting urediniospore germination and/or rust development, with values above 50%, although the results obtained in urediniospore germination tests were inferior to the treatment with fungicide propiconazole. Endophytic isolates TG4-Ia, TF2-IIc, TF9-Ia, TG11-IIa, and TF7-IIa, demonstrated better coffee leaf rust control in leaf discs, detached leaves, and coffee plant tests. The endophytic isolates TG4-Ia and TF9-Ia were identified as Bacillus lentimorbus Dutky and Bacillus cereus Frank. & Frank., respectively. Some endophytic bacterial isolates were effective in controlling the coffee leaf rust, although some increased the severity of the disease. Even though a relatively small number of endophytic bacteria were tested, promising results were obtained regarding the efficiency of coffee leaf rust biocontrol. These selected agents appears to be an alternative for future replacement of chemical fungicide.