Survival analysis was used to study the effects of composite and descriptive linear type traits on functional herd life of Quebec Holsteins. Functional herd life was defined as the length of life from first calving to death, culling, or censoring, and was adjusted for 305-d milk production. The dataset contained information from 331,105 cows from Quebec province calving for the first time between 1981 and 1995; 58% of the records had type information. Weibull models were fitted to analyze the data. The hazard function was described as the product of a baseline hazard function and the time-independent effects of age at first calving and type, and the time-dependent effects of year of calving, stage of lactation x lactation number, annual change in herd size, 305-d milk production, and herd-year (random). Analyses were done one at a time for each type trait. The strongest relationships between survival and composite type traits were found for final score, mammary system, and feet and legs. Among the linear type traits, the highest impact on functional herd life was found for traits related to the udder.

RESUMO -Neste estudo, avaliaram-se os efeitos de alguns fatores ambientais sobre a produção, a composição química e a contagem de células somáticas do leite em rebanhos vinculados às cooperativas no Rio Grande do Sul. Foram utilizados dados de um programa de controle leiteiro durante cinco anos, totalizando 165.311 observações, para analisar os seguintes efeitos ambientais: ano e mês do controle leiteiro, idade ao parto, tempo em controle leiteiro e estádio da lactação. A produção média de leite foi de 19,36 L/vaca/dia. Foi encontrada maior produção de leite em vacas de 5 a 6 anos de idade ao parto, nos primeiros 60 dias de lactação, nos meses de inverno e em rebanhos com mais tempo em controle leiteiro. Maior número de células somáticas foi relacionado à redução na produção de leite. Houve menor teor de gordura e de proteína no leite nos primeiros 60 dias de lactação e aumento desses componentes com o aumento no número de células somáticas. A concentração de lactose no leite diminuiu significativamente à medida que aumentaram as células somáticas e a idade ao parto. A contagem de células somáticas aumentou com a idade da vaca e à medida que avançou a lactação. Nos meses de inverno, verificaram-se valores mais elevados de proteína, gordura e lactose, possivelmente como conseqüência da alimentação com gramíneas temperadas.Os resultados revelam a importância das variações ambientais no estudo da composição do leite.Palavras-chave: células somáticas, composição do leite, fatores ambientais, Sul do Brasil Effects of environmental factors on milk yield and composition of dairy herds assisted by cooperatives in Rio Grande do Sul, BrazilABSTRACT -The effects of some environmental factors on milk yield and milk chemical composition and also on somatic cells count in dairy herds assisted by cooperatives (DHI) in Rio Grande do Sul, Brazil were investigated in this trial.Data were obtained from a 5-year period of dairy control program totalizing 165,311 observations and were used to investigate the effects of year and month of dairy control, age at calving, time of farm on dairy control and stage of lactation on milk yield and composition. Milk yield including all observations averaged 19.36 L/cow/day. Greater milk yield was observed in cows varying from 5 to 6 years of age at calving, at the first 60 days of lactation, in the winter months and in herds with longer time in dairy control. Increased somatic cells count was associated to reduction in milk yield. Lower contents of milk fat and milk protein and greater somatic cells number were observed at the first 60 days of lactation. Concentration of milk lactose decreased with both the increment in somatic cells count and age at calving. Somatic cells count increased as the age of cows advanced and as lactation progressed. Milk contents of protein, fat and lactose all increased during the winter months possibly because of feeding temperate grasses. Results from this study showed that is important taking into account the effects of environmental factors on milk composition.

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Genetic selection has been a very successful tool for the long-term improvement of livestock populations, and the rapid adoption of genomic selection over the last decade has doubled the rate of gain in some populations. Breeding programs seek to identify genetically superior parents of the next generation, typically as a function of an index that combines information about many economically important traits into a single number. In the United States, the data that drive this system are collected through the national dairy herd improvement program that began more than a century ago. The resulting information about animal performance, pedigree, and genotype is used to compute genomic evaluations for comparing and ranking animals for selection. However, the full expression of genetic potential requires that animals are placed in environments that can support such performance. The Agricultural Research Service of the US Department of Agriculture and the Council on Dairy Cattle Breeding collaborate to deliver state-of-the-art genomic evaluations to the dairy industry. Today, most breeding stock are selected and marketed using the net merit dollars (NM$) selection index, which evolved from 2 traits in 1926 (milk and fat yield) to a combination of 36 individual traits following the last NM$ update in 2018. Updates to NM$ require the estimation of many different values, and it can be difficult to achieve consensus from stakeholders on what should be added to, or removed from, the index at each review, and how those traits should be weighted. Over time, the majority of the emphasis in the index has shifted from yield traits to fertility, health, and fitness traits. Phenotypes for some of these new traits are difficult or expensive to measure, or require changes to on-farm habits that have not been widely adopted. This is driving interest in sensor-based systems that provide continuous measurements of the farm environment, individual animal performance, and detailed milk composition. There is also a need to capture more detailed data about the environment in which animals perform, including information about feeding, housing, milking systems, and infectious and parasitic load. However, many challenges accompany these new technologies, including a lack of standardization or validation, need for high-speed internet connections, increased computational requirements, and interpretations that are often not backed by direct observations of biological phenomena. This work will describe how US selection objectives are developed, as well as discuss opportunities and challenges associated with new technologies for measuring and recording animal performance.

Diversos cultivares de Cynodon dactylon têm sido cultivados no Rio Grande do Sul para alimentação do rebanho leiteiro, na forma de pastejo ou feno. A rápida determinação do valor nutritivo dessas forrageiras pode ser útil para seu manejo e para o planejamento da dieta dos animais. Este trabalho teve como objetivo desenvolver curvas de calibração para análise do valor nutritivo de quatro cultivares de Cynodon (Tifton 68, Tifton 85, Florakirk, Coastcross), utilizando o método de reflectância no infravermelho proximal (NIRS). Foram utilizadas 129 amostras de forragem verde, coletadas e analisadas entre 1998 e 2001. Os coeficientes de determinação para proteína bruta, fibra insolúvel em detergente neutro, fibra insolúvel em detergente ácido, matéria seca, cálcio, fósforo, potássio e magnésio foram, respectivamente: 0,98; 0,97; 0,99; 1; 0,92; 0,97; 0,99 e 0,72%. Os erros-padrão de calibração foram de 0,38; 0,60; 0,35; 0,14; 0,02; 0,01; 0,05 e 0,01%, respectivamente. As equações obtidas foram consideradas de excelente resolução para todos os parâmetros estimados, o que indica a acurácia do método para a espécie avaliada.

The aim of this simulation study was to investigate whether it is possible to detect the effect of genomic preselection on Mendelian sampling (MS) means or variances obtained by the MS validation test. Genomic preselection of bull calves is 1 additional potential source of bias in international evaluations unless adequately accounted for in national evaluations. Selection creates no bias in traditional breeding value evaluation if the data of all animals are included. However, this is not the case with genomic preselection, as it excludes culled bulls. Genomic breeding values become biased if calculated using a multistep procedure instead of, for example, a single-step method. Currently, about 60% of the countries participating in international bull evaluations have already adopted genomic selection in their breeding schemes. The data sent for multiple across-country evaluation can, therefore, be very heterogeneous, and a proper validation method is needed to ensure a fair comparison of the bulls included in international genetic evaluations. To study the effect of genomic preselection, we generated a total of 50 replicates under control and genomic preselection schemes using the structures of the real data and pedigree from a medium-size cow population. A genetic trend of 15% of the genetic standard deviation was created for both schemes. In carrying out the analyses, we used 2 different heritabilities: 0.25 and 0.10. From the start of genomic preselection, all bulls were genomically preselected. Their MS deviations were inflated with a value corresponding to selection of the best 10% of genomically tested bull calves. For cows, the MS deviations were unaltered. The results revealed a clear underestimation of bulls' breeding values (BV) after genomic preselection started, as well as a notable deviation from zero both in true and estimated MS means. The software developed recently for the MS validation test already produces yearly MS means, and they can be used to devise an appropriate test. Mean squared true MS of genomically preselected bulls was clearly inflated. After correcting for the simulated preselection bias, the true genetic variance was smaller than the parametric value used to simulate BV, and also below the variance based on the estimated BV. Based on this study, the lower the trait's heritability, the stronger the bias in estimated BV and MS means and variances. Daughters of genomically preselected bulls had higher true and estimated BV compared with the control scheme and only slightly elevated MS means, but no effect on genetic variances was observed.