6 resultados para Beef production

em Repositório Alice (Acesso Livre à Informação Científica da Embrapa / Repository Open Access to Scientific Information from Embrapa)


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Changes in beef demand has been driven by the growth of human population, income and urbanization.

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The current Amazon landscape consists of heterogeneous mosaics formed by interactions between the original forest and productive activities. Recognizing and quantifying the characteristics of these landscapes is essential for understanding agricultural production chains, assessing the impact of policies, and in planning future actions. Our main objective was to construct the regionalization of agricultural production for Rondônia State (Brazilian Amazon) at the municipal level. We adopted a decision tree approach, using land use maps derived from remote sensing data (PRODES and TerraClass) combined with socioeconomic data. The decision trees allowed us to allocate municipalities to one of five agricultural production systems: (i) coexistence of livestock production and intensive agriculture; (ii) semi-intensive beef and milk production; (iii) semi-intensive beef production; (iv) intensive beef and milk production, and; (v) intensive beef production. These production systems are, respectively, linked to mechanized agriculture (i), traditional cattle farming with low management, with (ii) or without (iii) a significant presence of dairy farming, and to more intensive livestock farming with (iv) or without (v) a significant presence of dairy farming. The municipalities and associated production systems were then characterized using a wide variety of quantitative metrics grouped into four dimensions: (i) agricultural production; (ii) economics; (iii) territorial configuration, and; (iv) social characteristics. We found that production systems linked to mechanized agriculture predominate in the south of the state, while intensive farming is mainly found in the center of the state. Semi-intensive livestock farming is mainly located close to the southwest frontier and in the north of the state, where human occupation of the territory is not fully consolidated. This distributional pattern reflects the origins of the agricultural production system of Rondônia. Moreover, the characterization of the production systems provides insights into the pattern of occupation of the Amazon and the socioeconomic consequences of continuing agricultural expansion.

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Feed efficiency and carcass characteristics are late-measured traits. The detection of molecular markers associated with them can help breeding programs to select animals early in life, and to predict breeding values with high accuracy. The objective of this study was to identify polymorphisms in the functional and positional candidate gene NEUROD1 (neurogenic differentiation 1), and investigate their associations with production traits in reference families of Nelore cattle. A total of 585 steers were used, from 34 sires chosen to represent the variability of this breed. By sequencing 14 animals with extreme residual feed intake (RFI) values, seven single nucleotide polymorphisms (SNPs) in NEUROD1 were identified. The investigation of marker effects on the target traits RFI, backfat thickness (BFT), ribeye area (REA), average body weight (ABW), and metabolic body weight (MBW) was performed with a mixed model using the restricted maximum likelihood method. SNP1062, which changes cytosine for guanine, had no significant association with RFI or REA. However, we found an additive effect on ABW (P ≤ 0.05) and MBW (P ≤ 0.05), with an estimated allele substitution effect of -1.59 and -0.93 kg0.75, respectively. A dominant effect of this SNP for BFT was also found (P ≤ 0.010). Our results are the first that identify NEUROD1 as a candidate that affects BFT, ABW, and MBW. Once confirmed, the inclusion of this SNP in dense panels may improve the accuracy of genomic selection for these traits in Nelore beef cattle as this SNP is not currently represented on SNP chips.

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Seventy-one mature Brangus cows, 38 nonlactating (NL) and 33 in late stage of lactation (L) were fed for 192 days (Phase I) a low energy diet (L). During Phase II (65 days) 19 NL and 17 L cows were fed a high energy diet (H). The other nonlactating (19) and lactating (16) cows remained on the low energy diet. Energy restriction during Phase I did not affect (P> 0.05) cyclic ovarian activity although losses in body weight and condition were substantial. Rapid changes in body weight, condition, and percent empty body lipe (EBLP) during Phase II did not substantially influencefertility, although a five-fold difference in EBLP was observed (NL0H vs. L-L). Treatment groups did not differ (P> 0.05) in conception rate, days from the beginning of the breeding season to breeding and to conception, conception at first service, and number of services per conception. Values observed for these parameters for NL-H, L-H, NL-L, and L-L groups were respectively: 68,4%, `3,.2, 23.3, 36.8% and 1.68; 82,4% 12.7, 19.5, 58.8% and 1.29; 68.4%, 10.2, 17.4, 47.4%, and 1.41; 68.8%, 12.4, 19.5, 43.7%, and 1.50.

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Background: Copy number variations (CNVs) have been shown to account for substantial portions of observed genomic variation and have been associated with qualitative and quantitative traits and the onset of disease in a number of species. Information from high-resolution studies to detect, characterize and estimate population-specific variant frequencies will facilitate the incorporation of CNVs in genomic studies to identify genes affecting traits of importance. Results: Genome-wide CNVs were detected in high-density single nucleotide polymorphism (SNP) genotyping data from 1,717 Nelore (Bos indicus) cattle, and in NGS data from eight key ancestral bulls. A total of 68,007 and 12,786 distinct CNVs were observed, respectively. Cross-comparisons of results obtained for the eight resequenced animals revealed that 92 % of the CNVs were observed in both datasets, while 62 % of all detected CNVs were observed to overlap with previously validated cattle copy number variant regions (CNVRs). Observed CNVs were used for obtaining breed-specific CNV frequencies and identification of CNVRs, which were subsequently used for gene annotation. A total of 688 of the detected CNVRs were observed to overlap with 286 non-redundant QTLs associated with important production traits in cattle. All of 34 CNVs previously reported to be associated with milk production traits in Holsteins were also observed in Nelore cattle. Comparisons of estimated frequencies of these CNVs in the two breeds revealed 14, 13, 6 and 14 regions in high (>20 %), low (<20 %) and divergent (NEL > HOL, NEL < HOL) frequencies, respectively. Conclusions: Obtained results significantly enriched the bovine CNV map and enabled the identification of variants that are potentially associated with traits under selection in Nelore cattle, particularly in genome regions harboring QTLs affecting production traits.