The main revenue source for beef cattle farmers is the price they are awarded for carcasses based on carcass value (i.e., carcass weight, conformation and fat score) which is influenced by genetic and environmental factors (e.g., herd management). In order to improve profitability, accurate means of evaluating and improving both sets of factors influencing carcass trait performance are necessary. This would entail optimal management of genetic resources and herd practices. Furthermore, access to a large international germplasm pool would facilitate faster genetic gain. The objective of this thesis was to generate tools for the enhancement of carcass trait genetic and herd management evaluations both at a national and international level. The data used in the thesis originated from the Irish and UK national cattle databases and consisted of 336,944 Irish and 147,876 UK cattle of multiple beef and dairy breeds from 9,572 Irish and 3,385 UK commercial herds. Livestock mature at different rates depending on a number of factors including the genetic background; therefore, the optimum age at which to slaughter the progeny of different sires may differ. Chapter 2 examined sire level genetic profiles for three carcass traits (carcass weight, conformation and fat score) in cattle using data from the Republic of Ireland. Variance components for each trait across age at slaughter were estimated using sire random regression models. Heritability estimates of carcass traits across ages at slaughter varied depending on gender (heifers, steers, young bulls) and the trait in question, and ranged from 0.08 (± 0.02) to 0.34 (± 0.02) for carcass weight, from 0.24 (± 0.02) to 0.42 (± 0.02) for conformation score and from 0.16 (± 0.03) to 0.40 (± 0.02) for fat score. Genetic correlations between traits across ages at slaughter were all significantly less than unity, indicating that different genetic mechanisms control these traits across life. The results from chapter 2 show that genetic variability in the progeny growth trajectory of sires exists and that this variability in the growth profiles of sires for carcass traits may be exploited in breeding programmes. As carcass traits are a function of both the genetics of the animal and the environment in which the animal is reared, chapter 3 aimed to quantify the contribution of the herd environment to the same three beef carcass traits, with particular emphasis on generating finishing herd-specific profiles for carcass traits across different ages at slaughter. The data analysed in chapter 3 was from animals slaughtered in UK abattoirs. Genetic and finishing-herd-year of slaughter parameters were generated using random regression analysis. Across slaughter age and gender, the proportion of phenotypic variance accounted for by finishing-herd-year of slaughter variance was between 30.83%-71.48% for carcass weight, 21.38%-26.29% for conformation score and between 10.88%-44.04% for fat score. These parameters indicate that the finishing herd environment is an at least equally important contributor to carcass trait variability as the genetic background of animals, and amenable to improvement with appropriate management practices. The final study of the thesis was to investigate the feasibility of across-country carcass trait genetic evaluations. Examination of the level of genetic connectedness between Ireland and the UK found 225 distinct bulls common to both countries. These common bulls were related to 80,707 Irish and 23,162 UK animals with carcass records in each population. Genetic correlations for carcass traits between Ireland and the UK were almost unity, ranging from 0.92 (± 0.31) for fat score to 0.96 (± 0.17) for carcass weight, indicating that the carcass traits recorded in both countries are genetically essentially equivalent. These strong genetic correlations between carcass traits in both countries enabled the direct pooling of carcass data for the purpose of across-country genetic evaluations (breeding value estimation). An increased rate of genetic gain for carcass traits per generation was predicted from across-country selection compared to within country selection ranging from 2% (conformation score in Ireland) to 33.77% (conformation score in the UK). This improved gain was primarily due to greater intensity of selection and somewhat more accurate estimated breeding values when carcass records and pedigree information from both countries were combined. The results presented in this thesis demonstrate that routinely collected abattoir data in Ireland and the UK can be exploited to produce additional selection and on-farm management tools. The results also show that access to across-country carcass trait genetic evaluations would allow UK and Irish beef farmers to make more informed decisions on the selection of seed stock needed to increase genetic gain and profits. Outcomes of this thesis pave the way to improvements in national carcass traits genetic evaluations in Ireland and the UK based on appropriate age at slaughter and also demonstrate the feasibility of across-country carcass trait genetic evaluations between Ireland and the UK. The scope for further areas of research includes the identification of specific management practices for optimal herd performance for carcass traits. Additionally, across-country carcass trait genetic evaluations based on random regression models across different ages at slaughter would also be of benefit to beef producers in Ireland and the UK. Finally, the viability of across-country genetic evaluations for additional carcass traits, such as carcass cut weights should be explored.

Submitted by RAFAEL ESPIGOLAN (espigolan@yahoo.com.br) on 2017-03-17T22:04:14Z No. of bitstreams: 1 Tese_Rafael_Espigolan.pdf: 1532864 bytes, checksum: c79ad7471b25137c47529f25762a83a2 (MD5) / Approved for entry into archive by Juliano Benedito Ferreira (julianoferreira@reitoria.unesp.br) on 2017-03-22T12:50:50Z (GMT) No. of bitstreams: 1 espigolan_r_dr_jabo.pdf: 1532864 bytes, checksum: c79ad7471b25137c47529f25762a83a2 (MD5) / Made available in DSpace on 2017-03-22T12:50:50Z (GMT). No. of bitstreams: 1 espigolan_r_dr_jabo.pdf: 1532864 bytes, checksum: c79ad7471b25137c47529f25762a83a2 (MD5) Previous issue date: 2017-02-23 / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / O melhoramento genético animal visa melhorar a produtividade econômica das futuras gerações de espécies domésticas por meio da seleção. A maioria das características de interesse econômico na pecuária é de expressão quantitativa e complexa, isto é, são influenciadas por vários genes e afetadas por fatores ambientais. As análises estatísticas de informações de fenótipo e pedigree permite estimar os valores genéticos dos candidatos à seleção com base no modelo infinitesimal. Uma grande quantidade de dados genômicos está atualmente disponível para a identificação e seleção de indivíduos geneticamente superiores com o potencial de aumentar a acurácia de predição dos valores genéticos e, portanto, a eficiência dos programas de melhoramento genético animal. Vários estudos têm sido conduzidos com o objetivo de identificar metodologias apropriadas para raças e características específicas, o que resultará em estimativas de valores genéticos genômicos (GEBVs) mais acurados. Portanto, o objetivo deste estudo foi verificar a possibilidade de aplicação de modelos semiparamétricos para a seleção genômica e comparar a habilidade de predição com os modelos paramétricos para dados reais (características de carcaça, qualidade da carne, crescimento e reprodutiva) e simulados. As informações fenotípicas e de pedigree utilizadas foram fornecidas por onze fazendas pertencentes a quatro programas de melhoramento genético animal. Para as características de carcaça e qualidade da carne, o banco de dados continha 3.643 registros para área de olho de lombo (REA), 3.619 registros para espessura de gordura (BFT), 3.670 registros para maciez da carne (TEN) e 3.378 observações para peso de carcaça quente (HCW). Um total de 825.364 registros para peso ao sobreano (YW) e 166.398 para idade ao primeiro parto (AFC) foi utilizado para as características de crescimento e reprodutiva. Genótipos de 2.710, 2.656, 2.749, 2.495, 4.455 e 1.760 animais para REA, BFT, TEN, HCW, YW e AFC foram disponibilizados, respectivamente. Após o controle de qualidade, restaram dados de, aproximadamente, 450.000 polimorfismos de base única (SNP). Os modelos de análise utilizados foram BLUP genômico (GBLUP), single-step GBLUP (ssGBLUP), Bayesian LASSO (BL) e as abordagens semiparamétricas Reproducing Kernel Hilbert Spaces (RKHS) e Kernel Averaging (KA). Para cada característica foi realizada uma validação cruzada composta por cinco “folds” e replicada aleatoriamente trinta vezes. Os modelos estatísticos foram comparados em termos do erro do quadrado médio (MSE) e acurácia de predição (ACC). Os valores de ACC variaram de 0,39 a 0,40 (REA), 0,38 a 0,41 (BFT), 0,23 a 0,28 (TEN), 0,33 a 0,35 (HCW), 0,36 a 0,51 (YW) e 0,49 a 0,56 (AFC). Para todas as características, os modelos GBLUP e BL apresentaram acurácias de predição similares. Para REA, BFT e HCW, todos os modelos apresentaram ACC similares, entretanto a regressão RKHS obteve o melhor ajuste comparado ao KA. Para características com maior quantidade de registros fenotípicos comparada ao número de animais genotipados (YW e AFC) o modelo ssGBLUP é indicado. Considerando o desempenho geral, para todas as características estudadas, a regressão RKHS é, particularmente, uma alternativa interessante para a aplicação na seleção genômica, especialmente para características de baixa herdabilidade. No estudo de simulação, genótipos, pedigree e fenótipos para quatro características (A, B, C e D) foram simulados utilizando valores de herdabilidade baseados nos obtidos com os dados reais (0,09, 0,12, 0,36 e 0,39 para cada característica, respectivamente). O genoma simulado consistiu de 735.293 marcadores e 1.000 QTLs distribuídos aleatoriamente por 29 pares de autossomos, com comprimento variando de 40 a 146 centimorgans (cM), totalizando 2.333 cM. Assumiu-se que os QTLs explicavam 100% da variação genética. Considerando as frequências do alelo menor maiores ou iguais a 0,01, um total de 430.000 marcadores foram selecionados aleatoriamente. Os fenótipos foram obtidos pela soma dos resíduos (aleatoriamente amostrados de uma distribuição normal com média igual a zero) aos valores genéticos verdadeiros, e todo o processo de simulação foi replicado 10 vezes. A ACC foi calculada por meio da correlação entre o valor genético genômico estimado e o valor genético verdadeiro, simulados da 12a a 15a geração. A média do desequilíbrio de ligação, medido entre os pares de marcadores adjacentes para todas as características simuladas foi de 0,21 para as gerações recentes (12a, 13a e 14a), e 0,22 para a 15a geração. A ACC para as características simuladas A, B, C e D variou de 0,43 a 0,44, 0,47 a 0,48, 0,80 a 0,82 e 0,72 a 0,73, respectivamente. Diferentes metodologias de seleção genômica implementadas neste estudo mostraram valores similares de acurácia de predição, e o método mais adequado é dependente da característica explorada. Em geral, as regressões RKHS obtiveram melhor desempenho em termos de ACC com menor valor de MSE em comparação com os outros modelos. / Animal breeding aims to improve economic productivity of future generations of domestic species through selection. Most of the traits of economic interest in livestock have a complex and quantitative expression i.e. are influenced by a large number of genes and affected by environmental factors. Statistical analysis of phenotypes and pedigree information allows estimating the breeding values of the selection candidates based on infinitesimal model. A large amount of genomic data is now available for the identification and selection of genetically superior individuals with the potential to increase the accuracy of prediction of genetic values and thus, the efficiency of animal breeding programs. Numerous studies have been conducted in order to identify appropriate methodologies to specific breeds and traits, which will result in more accurate genomic estimated breeding values (GEBVs). Therefore, the objective of this study was to verify the possibility of applying semi-parametric models for genomic selection and to compare their ability of prediction with those of parametric models for real (carcass, meat quality, growth and reproductive traits) and simulated data. The phenotypic and pedigree information used were provided by farms belonging to four animal breeding programs which represent eleven farms. For carcass and meat quality traits, the data set contained 3,643 records for rib eye area (REA), 3,619 records for backfat thickness (BFT), 3,670 records for meat tenderness (TEN) and 3,378 observations for hot carcass weight (HCW). A total of 825,364 records for yearling weight (YW) and 166,398 for age at first calving (AFC) were used as growth and reproductive traits of Nelore cattle. Genotypes of 2,710, 2,656, 2,749, 2,495, 4,455 and 1,760 animals were available for REA, BFT, TEN, HCW, YW and AFC, respectively. After quality control, approximately 450,000 single nucleotide polymorphisms (SNP) remained. Methods of analysis were genomic BLUP (GBLUP), single-step GBLUP (ssGBLUP), Bayesian LASSO (BL) and the semi-parametric approaches Reproducing Kernel Hilbert Spaces (RKHS) regression and Kernel Averaging (KA). A five-fold cross-validation with thirty random replicates was carried out and models were compared in terms of their prediction mean squared error (MSE) and accuracy of prediction (ACC). The ACC ranged from 0.39 to 0.40 (REA), 0.38 to 0.41 (BFT), 0.23 to 0.28 (TEN), 0.33 to 0.35 (HCW), 0.36 to 0.51 (YW) and 0.49 to 0.56 (AFC). For all traits, the GBLUP and BL models showed very similar prediction accuracies. For REA, BFT and HCW, models provided similar prediction accuracies, however RKHS regression had the best fit across traits considering multiple-step models and compared to KA. For traits which have a higher number of animals with phenotypes compared to the number of those with genotypes (YW and AFC), the ssGBLUP is indicated. Judged by overall performance, across all traits, the RKHS regression is particularly appealing for application in genomic selection, especially for low heritability traits. Simulated genotypes, pedigree, and phenotypes for four traits A, B, C and D were obtained using heritabilities based on real data (0.09, 0.12, 0.36 and 0.39 for each trait, respectively). The simulated genome consisted of 735,293 markers and 1,000 QTLs randomly distributed over 29 pairs of autosomes, with length varying from 40 to 146 centimorgans (cM), totaling 2,333 cM. It was assumed that QTLs explained 100% of genetic variance. Considering Minor Allele Frequencies greater or equal to 0.01, a total of 430,000 markers were randomly selected. The phenotypes were generated by adding residuals, randomly drawn from a normal distribution with mean equal to zero, to the true breeding values and all simulation process was replicated 10 times. ACC was quantified using correlations between the predicted genomic breeding value and true breeding values simulated for the generations of 12 to 15. The average linkage disequilibrium, measured between pairs of adjacent markers for all simulated traits was 0.21 for recent generations (12, 13 and 14), and 0.22 for generation 15. The ACC for simulated traits A, B, C and D ranged from 0.43 to 0.44, 0.47 to 0.48, 0.80 to 0.82 and 0.72 to 0.73, respectively. Different genomic selection methodologies implemented in this study showed similar accuracies of prediction, and the optimal method was sometimes trait dependent. In general, RKHS regressions were preferable in terms of ACC and provided smallest MSE estimates compared to other models. / FAPESP: 2014/00779-0 / FAPESP: 2015/13084-3

Beef carcasses from F2 Nellore × Angus (n = 181) and half-blood Bos indicus × Bos taurus (n = 57) were used to evaluate the responsiveness of sire and family groups nested within sires to post-mortem electrical stimulation (ES). In the F2 population, biological response to ES was identified for myofibrillar fragmentation index, and 6 h post-mortem pH. The genetic contributions of sire and families nested within sires were found for the average Warner-Bratzler shear force (WBS), location of shear core extraction, post-mortem carcass temperatures, and carcass pH. ES sides had lower WBS values, higher carcass temperatures, and lower carcass pH. In the half-blood population, biological response to ES was found for WBS core location. Sire and families nested within sires significantly affected WBS core location and carcass temperature. The ES sides had lower WBS values, higher carcass temperatures, and lower carcass pH in the half-blood population. From a carcass temperature and pH standpoint, carcass weight and fat thickness were used as covariates in the analysis of variance. This covariate analysis still showed a genetic component to carcass temperature and pH. There are genetic factors that impact how carcasses respond to electrical stimulation, which is the first work to demonstrate this relationship between genetics and a post-mortem tenderization treatment.

Two similarly managed trials were conducted to investigate serial ultrasound measures of body composition (longissimus muscle area (ULMA), 12th - rib fat thickness (UFAT), and percentage of intramuscular fat (UIMF)) early in the lives of feeder calves as they compared to carcass traits. Group 1 cattle were Charolais-sired by Brahman-British crossbred dams whereas Group 2 cattle were purebred Beefmaster. Both groups were fed at the same commercial feedlot (Graham Land and Cattle Co.) in Gonzales, Texas. In both data sets classifications were developed for ribeye area of Lower (less than 70.95 cm2, Middle (between 70.95 cm2 and 90.3 cm2) and Upper (over 90.3 cm2) based on a range that fit within the ribeye specifications of such branded beef programs as Certified Angus Beef and Nolan Ryan?s Tender Aged Beef. Differences among ribeye area and quality grade (Choice vs. Select) categories were evaluated for ultrasound and carcass traits. As reported previously, correlations between ultrasound measures and carcass traits became larger at times closer to harvest. In both sets of cattle, there were no differences in fat thickness or intramuscular fat at the ultrasound scan sessions or in these carcass traits due to ribeye area category. The same trend for quality grade classification was not seen across both groups of cattle however. In Group 1, there were no differences in early measures of body composition between carcass quality grade classes except for ultrasound fat thickness at weaning. However, in Group 2 cattle there were differences in ultrasound fat at times 1 and 2, IMF at time 1, and ribeye area at time 2 between cattle that graded choice verses those that graded select. Correlations between ultrasound measures of REA (r of .26 to .50) and ultrasound REA and carcass REA (r of .16 to .81) appeared to be lower in Group 1 vs. Group 2 (r of .55, and .64 to 81 respectively). Results from this project imply that changes in ribeye area will not automatically result in changes of marbling and vice versa. Furthermore, these results also show that ultrasound is useful to help predict beef carcass traits, but that early measures of body composition used alone do not explain a large portion of the variation in the carcass measures and specific methods should be developed by different biological cattle types.

The cost of feed remains an important factor affecting the profitability of beef production, and the difficulty of recording feed intake is a major limitation in an industry-wide selection program. Novel genomics approaches offer opportunities to select for efficient cattle. Therefore, the main objective of this work was to identify genetic markers responsible for genetic variation in feed efficiency traits as well as to understand the molecular basis of feed efficiency traits. The candidate gene approach revealed new single nucleotide polymorphisms (SNPs) in the Cholecystokinin B receptor (CCKBR) and pancreatic anionic trypsinogen (TRYP8) genes that showed strong evidence of association with feed efficiency traits. An in silico approach was proposed as a cost-effective method for SNP discovery. SNPs within genes Pyruvate carboxylase, ATPaseH+, UBQEI, UCP2, and PTI showed evidence of association with carcass traits without negatively affecting feed efficiency traits. The polymorphisms within genes CCKBR and TRYP8 were associated with pancreas mass and pancreatic exocrine secretion. A fine-mapping study on 1,879 SNPs revealed 807 SNPs with significant associations corresponding to 1,012 genes. These 807 SNPs represented a genomic heritability of 0.32 and 89% of the genetic variance of residual feed intake (RFI). Genomic breeding values estimated from the SNP set (807) were highly correlated (0.96) to the breeding values estimated from a mixed animal model. The 10 most influential SNPs were located in chromosomes 16, 17, 9, 11, 12, 20, 15, and 19. Enrichment analysis for the identified genes (1,012) suggested 110 biological processes and 141 pathways contributed to variation in RFI. The 339 newly identified SNPs corresponding to 180 genes identified by fine-mapping were tested for association with feed efficiency, growth, and carcass traits. Strong evidence of associations for RFI was located on chromosomes 8, 15, 16, 18, 19, 21, and 28. Combing validated SNPs from fine-mapping and the candidate gene approach may help develop a DNA test panel for commercial use and increase our understanding of the biological basis of feed efficiency in beef cattle. / The Ministry of Higher Education of Egypt

No description available.

A series of experiments were conducted to investigate the nutritional value of a new oat variety developed by the Crop Development Centre at the University of Saskatchewan. Trials 1 and 2 evaluated performance of steers fed a low lignin hull, high oil groat (LLH-HOG) oat as a replacement for barley or corn. In trial 1, 400 steers were fed one of two diets with barley or the LLH-HOG oat at 37.8% of the diet DM. Dry matter intake was lower (P=0.02) and gain to feed improved (P0.01) for steers fed the oat-based diet. In trial 2, 240 steers were finished diets with barley, corn or the LLH-HOG oat at 88.2% of the finishing diet (DM). During finishing, steers on the oat diet had lower (P0.01) ADG, body and carcass (P<0.01) weights than barley or corn-fed cattle reflecting lower (P0.01) DMI. In trial 3, 20 steers were fed one of seven diets consisting of barley silage and 0, 28, 56, or 84% LLH-HOG oat or barley grain (DM basis) to compare nutrient digestibility. Apparent DM, OM, ADF and NDF digestibility coefficients were lower (P<0.05) for LLH-HOG oat-based diets compared to barley-based diets. Apparent CP and EE digestibility coefficients were higher (P<0.05) for the LLH-HOG oat diets. Trial 4 was conducted to assess ruminal fermentation differences between LLH-HOG oat- or barley-based finishing diets using four rumen cannulated steers. No diet effects (P>0.05) were observed for total ruminal VFA concentration or molar proportions of individual VFA however mean ruminal pH was lower (P=0.01) for steers fed the LLH-HOG oat-finishing diet. Further, the extent of pH decline in oat-fed cattle was greater (P<0.01) than for barley-fed cattle. The results indicate that the energy value of the LLH-HOG oat is equivalent or superior to that of barley for growing cattle. However, further research is required to identify factors limiting feed intake of cattle fed this new oat type in finishing diets.

A whole genome scan was conducted to identify and fine map QTL regions for ultrasound and carcass merit traits in beef cattle. A total of 465 steers and bulls, genotyped for 4592 SNPs, were analysed for 16 ultrasound and carcass merit traits using interval mapping, single marker regression and Bayesian shrinkage approaches. Thirty QTL and 22 SNPs associated with traits were identified by interval mapping and single marker regression respectively. In Bayesian shrinkage estimation, 218 QTL were identified, wherein 11 of the 30 QTL identified by interval mapping were confirmed. The proportions of QTL variance on the trait variations estimated by Bayesian shrinkage analysis were relatively small. They ranged from 0.1 to 4.8% compared to 6.1 to 11.7% in interval mapping because the QTL in Bayesian approach were adjusted to remove effects of other QTL in the genome. These results are useful for detection of underlying causative QTN variants. / Animal Science

Made available in DSpace on 2014-06-11T19:33:33Z (GMT). No. of bitstreams: 0 Previous issue date: 2007-11-30Bitstream added on 2014-06-13T20:05:38Z : No. of bitstreams: 1 monteiro_jmc_dr_jabo.pdf: 1222391 bytes, checksum: acc3bc476ca5877b6c0800b21ad83071 (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / Objetivou-se comparar genótipos de suínos relativos ao desempenho, parâmetros e características de carcaça e da carne em animais abatidos aos 161 dias de idade. Utilizaram-se os seguintes genótipos: G1 - ½ Topigs© (Toppi) x ½ Naïma®; G2 - ½ DB Danbred© (Frederik) x ½ Naïma®; G3 - ½ PIC© (AGPIC 412) x ½ Naïma®; G4 - ½ SG 2030© (Duroc) x ½ Naïma®; e G5 - ½ Pen Ar Lan© (P76) x ½ Naïma®. Estudaram-se o ganho de peso total (GPT), consumo de ração total (CRT), conversão alimentar (CA) e eficiência alimentar (EA). As meias carcaças esquerdas foram, inicialmente, avaliadas quanto ao peso da carcaça quente (PCQ) e fria (PCF), comprimento (CC), área do olho de lombo (AOL), comprimento de olho do lombo (COL) e profundidade do toucinho (PT10ª). Foram feitas ainda, com a pistola de tipificação eletrônica Hennessy, medidas de espessura do músculo (EM1 e EM2) e profundidade do toucinho (PT1 e PT2). Foram feitas medidas de espessura de toucinho na altura da primeira costela (ET1), última costela (ET2), última lombar (ET3) e máxima lombar (ETM), com o auxílio de paquímetro digital. A carcaça foi desdobrada em seus cortes primários: pernil, carré, barriga, barriga ventral, fraldinha, paleta, sobre paleta, ponta do peito, filezinho, antebraço, perna e papada. Após a pesagem desses cortes, foi realizada a desossa dos mesmos, para se determinar os pesos da carne, gordura interna, ossos, pele e gordura subcutânea de cada um. A porcentagem de carne magra (PCM) foi determinada dividindo-se o total de carne magra desossada pelo peso da carcaça fria. Foram estudados os seguintes parâmetros físico-químicos da carne: pH post-mortem, perda de líquido por gotejamento, cor da carne,... / The aim of this work it was to compare different swine genotypes with the performance, characteristics of carcass and meat quality in swine slaughtered at 161 days of age. The following genotypes had been used: G1 - ½ Topigs© (Toppi) x ½ Naïma©; G2 - ½ DB Danbred© (Frederik) x ½ Naïma©; G3 - ½ PIC© (AGPIC 412) x ½ Naïma©; G4 - ½ SG 2030© (Duroc) x ½ Naïma©; e G5 - ½ Pen Ar Lan© (P76) x ½ Naïma©. They had been studied the profit of all up weight (BW), consumption of total ration (CTR), feed conversion (FC) and alimentary efficiency (AE). The half left carcasses had been, initially, evaluated to the hot carcass weight (HCW) and cold weight (CCW), carcass length (CL), loin eye area (LEA), loin eye length (LEL) and backfat thickness in tenth rib (BT10th). They had been made still, with the pistol electronic Hennessy, measures of muscle depth (MD1 and MD2) and fat depth (FD1 and FD2). Backfat thickness was measured at four locations: in the first rib (BT1), last rib (BT2), last lumbar (BT3) and maximum lumbar (BTM), with digital paquimeter. The carcass was unfolded in these cuts: ham, loin, belly, ventral belly, cranial belly, shoulder, neck, jowl, tenderloin, front shank, hind shank and cheek. After the weight of these cuts, the composition of each one was determined by physical dissection into lean, fat, bone, and skin. The lean meat percentage (LMP) was determined, dividing the total of boned lean meat by the weight of the cold carcass. The following parameters had been studied in the meat: pH post-mortem, ...(Complete abstract click electronic access below)

Doctor of Philosophy / Department of Animal Sciences and Industry / Megan Rolf / In the future, water may not be as readily available due to an increase in competition from a growing human population, wildlife, and other agricultural sectors. To better understand water demands in the beef industry, water intake has to be accurately measured. It also critical to understand if water intake is a heritable trait and to determine its relationship to other production traits. This dissertation examines the number of days to accurately measure water intake in beef cattle, how to predict water intake in beef cattle using individual intakes, and estimates genetic parameters for water intake, dry matter intake (DMI), average daily gain (ADG), water efficiency measures, feed efficiency measures, and carcass traits. Study 1 investigates the test duration required to accurately measure water intake. Water intakes were collected over 70 d and shortened test periods (7 day intervals) were correlated with the full 70 day test to determine the minimum number of days required to accurately measure water intake. Water intake can be collected over a 35 to 42-day test period, with a minimal decrease in accuracy. Study 2 developed a water intake prediction equation that included different weather variables and average daily temperature (TAVG), average relative humidity (HVAG), solar radiation (SRAD), and wind speed (WSPD). Water intakes and feed intakes on individual animals were collected over a 70-day period along with (TAVG), (HVAG), (SRAD), (WSPD) for each day. Five different prediction equations were developed: summer, winter, slick bunk feed management, ad libitum feed management, and overall. All models included variables of DMI, metabolic mid test weight, TAVG, HAVG, SRAD, and WSPD, with R-squared values ranging from 0.34 to 0.41. Study 3 investigated the relationships between water intake and DMI, ADG, and water and feed efficiency traits. Variance components and genetic correlations were estimated using single-step genomic best linear unbiased prediction (GBLUP), incorporating genotypes on approximately 150,000 single nucleotide polymorphisms. Water intake was moderately heritable (0.39) and had moderate genetic correlations with DMI and residual feed intake, high genetic correlations with residual water intake, water to gain ratio, and feed to gain ratio, and had a low genetic correlation with ADG. Study 4 investigated the relationship between water intake and carcass traits. Single-Step GBLUP was used to estimate variance components and genetic correlations between water intake and carcass traits. Similar to study 3, water intake was moderately heritable (0.42). Water intake was moderately correlated with hot carcass weight (0.38), back fat (0.36), yield grade (0.29), and final body weight (0.29), but had a low genetic correlation with longissimus muscle area (0.08) and marbling (0.17). More research must be done to determine the relationships between water intake and other economically important traits in beef cattle and to better understand how environment and genetic background affect water intake. Improvements in water efficiency could decrease the amount of water cattle consume and assist producers in managing on-farm water resources during times of water scarcity.