A divide-and-conquer approach for genomic prediction in rubber tree using machine learning

International audienceRubber tree ( Hevea brasiliensis ) is the main feedstock for commercial rubber; however, its long vegetative cycle has hindered the development of more productive varieties via breeding programs. With the availability of H. brasiliensis genomic data, several linkage maps with associated quantitative trait loci have been constructed and suggested as a tool for marker-assisted selection. Nonetheless, novel genomic strategies are still needed, and genomic selection (GS) may facilitate rubber tree breeding programs aimed at reducing the required cycles for performance assessment. Even though such a methodology has already been shown to be a promising tool for rubber tree breeding, increased model predictive capabilities and practical application are still needed. Here, we developed a novel machine learning-based approach for predicting rubber tree stem circumference based on molecular markers. Through a divide-and-conquer strategy, we propose a neural network prediction system with two stages: (1) subpopulation prediction and (2) phenotype estimation. This approach yielded higher accuracies than traditional statistical models in a single-environment scenario. By delivering large accuracy improvements, our methodology represents a powerful tool for use in Hevea GS strategies. Therefore, the incorporation of machine learning techniques into rubber tree GS represents an opportunity to build more robust models and optimize Hevea breeding programs

A divide-and-conquer approach for genomic prediction in rubber tree using machine learning

International audienceRubber tree ( Hevea brasiliensis ) is the main feedstock for commercial rubber; however, its long vegetative cycle has hindered the development of more productive varieties via breeding programs. With the availability of H. brasiliensis genomic data, several linkage maps with associated quantitative trait loci have been constructed and suggested as a tool for marker-assisted selection. Nonetheless, novel genomic strategies are still needed, and genomic selection (GS) may facilitate rubber tree breeding programs aimed at reducing the required cycles for performance assessment. Even though such a methodology has already been shown to be a promising tool for rubber tree breeding, increased model predictive capabilities and practical application are still needed. Here, we developed a novel machine learning-based approach for predicting rubber tree stem circumference based on molecular markers. Through a divide-and-conquer strategy, we propose a neural network prediction system with two stages: (1) subpopulation prediction and (2) phenotype estimation. This approach yielded higher accuracies than traditional statistical models in a single-environment scenario. By delivering large accuracy improvements, our methodology represents a powerful tool for use in Hevea GS strategies. Therefore, the incorporation of machine learning techniques into rubber tree GS represents an opportunity to build more robust models and optimize Hevea breeding programs

Ruminal Degradability Of Agro-industrial Fruit Residues

The aim of this study was to evaluate the chemical composition and ruminal degradability of the dry matter (DM), crude protein (CP) and neutral detergent fiber (NDF) of fruit residues. Three fistulated sheep were held collectively in a pen, and fed daily with the studied residues in a diet consisting of canarana grass (Echinochloa pyramidalis) and a concentrate of corn and soybeans. The animals were allowed an adjustment period of seven days. The residues were dried in the sun, crushed in a forage machine, sorted using a 4.0-mm sieve, and incubated for 3, 6, 12, 24, 48, 72, and 96 h using nonwoven bags (weight 60g/m2, 14 ×12 cm2). Chemical analyses of the residues were performed using a randomized block experimental design with split plots. The cherimoya and tamarind residues showed the highest concentrations of CP (12.66% and 11.79%) the ether extract of cherimoya residue was the highest at 22.30%stands out the sour soup residue. The cashew and guava residues showed the highest levels of lignin (22.13 and 18.34%). The effective degradability of DM for the pineapple and tamarind residues to a passage rate of 5%/h were 53.04% and 42.61%, respectively. The guava, cherimoya, and cashew residues showed lower values at 19.16%, 26.86%, and 29.21%, respectively. The cherimoya, guava and pineapple residues showed the highest values of potential degradability for CP at 87%, 81%, 86.02% and 90.94%, respectively, with an average effective degradability of 50.0% at the rate of 5%/h. The pineapple (35.38%) and tamarind residues (34.49%) showed higher values of the effective degradability of NDF at a passage rate of 5%/h. Among the studied residues, the pineapple residue showed the greatest potential for use in animal feed based on chemical composition and rates of degradability.37127929

Density perturbations in an Universe dominated by the Chaplygin gas

We study the fate of density perturbations in an Universe dominate by the Chaplygin gas, which exhibit negative pressure. We show that it is possible to obtain the value for the density contrast observed in large scale structure of the Universe by fixing a free parameter in the equation of state of this gas. The negative character of pressure must be significant only very recently.Comment: Latex file, 5 page

Sistema de produção de leite em Terra Alta, Pará.

bitstream/item/60880/1/CPATU-Doc103.pd

Dissimilaridade genética de clones de Brachiaria ruziziensis baseada em características morfológicas e de produção de forragem.

Em programas de melhoramento tem-se sempre o interesse de conhecer melhor a variabilidade disponível para os principais caracteres morfoagronômicos. Nesse trabalho objetivou-se realizar a caracterização morfológica e da produção de forragem de clones de B. ruziziensis do programa de melhoramento genético da Embrapa Gado de Leite visando quantificar a variabilidade genética entre eles e ainda determinar a importância relativa desses caracteres para fins avaliativos. O experimento foi conduzido no Campo Experimental de Coronel Pacheco (MG). Foram avaliados 81 clones de B. ruziziensis, mais quatro testemunhas (?Comum? (B. ruziziensis), ?Basilisk? (B. decumbens), ?Marandu? (B. brizantha) e a população do primeiro ciclo de seleção (C0)). O delineamento foi de blocos casualisados, com três repetições e parcela de dois metros quadrados. Procedeu-se ao teste dos efeitos e estimação das herdabilidades (h2 c) com base na análise de variância dos caracteres em separado e posteriormente estimou-se a dissimilaridade entre os clones pela distância de Mahalanobis. O agrupamento dos clones foi feito pelo método de Tocher e a importância dos caracteres pelo método de Singh. Foram detectadas diferenças significativas (P<0,05) entre os clones avaliados para a maioria dos caracteres, demonstrando a existência de variabilidade genética. Verificou-se que sete caracteres de produção apresentaram uma contribuição relativa de 65,60% de toda a divergência genética, enquanto os reprodutivos e vegetativos contribuíram com apenas 34,40%, com destaque para as características VIG, PV e PS que apresentaram as maiores contribuições relativas. Os clones foram agrupados em nove grupos, sendo que 83,53% desses ficaram alocados no primeiro grupo formado. Em síntese, evidencia-se que há divergência entre os clones testados pelo programa de melhoramento genético de B. ruziziensis da Embrapa Gado de Leite e que é possível gerar populações com potencial para o desenvolvimento de cultivares superiores