Challenges and opportunities for farmer-recorded data in health and welfare selection (original) (raw)
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The value of health data from dairy farmers in the United States
In the United States, most dairy farmers who use on-farm dairy management systems voluntarily record health incidences to facilitate effective cattle management. However, there is no national effort to organize or regulate the recording of health data such as enforcing standard or consistent definitions of health conditions. But when the data have been aggregated into experimental databases, several researchers have been able to compute lactation incidence rates, heritabilities and reliabilities at levels that are relatively comparable with other studies. PCDART from DRMS is one of three primary on-farm software systems that service dairy farmers and provide typical methods for data recording such as flexible health definitions, unlimited number of events and assistance with consistent within farm recording. There are 3250 herds (845K cows) that are managed by producers using PCDART and also enrolled on DHIA. Herds are representative of U.S. herd sizes and breeds. Of these herds, 44% deliver health incidences for off-farm backup at DRMS. Another 45% of herds also record health incidences at a lower rate, but these herds do not routinely deliver data files for off-farm backup at DRMS. Data recording histories for 'backup' herds were assessed for calving years 2009 through 2011 for entry of 34 recognized mature cow conditions of varying value to the dairy industry. Lactation incidence rates were similar to those found in earlier studies under more controlled environments. Additionally, the rates of entry of health events for large 'non-backup' herds were comparable to those of large 'backup' herds. 'Backup' herds recorded a mean of 123 events per 100 cows per year and 65% of herds recorded a minimum of 10 usable events per 100 cows per year. Larger herds (number of cows>500) recorded useful data at almost twice the rate of smaller herds. The most prevalent conditions were mastitis, lameness, metritis, cystic ovaries, other reproductive problems, retained placenta, Johne's and ketosis. There is sufficient potential in both volume and quality of U.S. health data to contribute to computation of meaningful genetic measures for selection using conditions of concern to producers.
Genomic selection for producer-recorded health event data in US dairy cattle
Journal of Dairy Science, 2014
Emphasizing increased profit through increased dairy cow production has revealed a negative relationship of production with fitness and health traits. Decreased cow health can affect herd profitability through increased rates of involuntary culling and decreased or lost milk sales. The development of genomic selection methodologies, with accompanying substantial gains in reliability for low-heritability traits, may dramatically improve the feasibility of genetic improvement of dairy cow health. Producer-recorded health information may provide a wealth of information for improvement of dairy cow health, thus improving profitability. The principal objective of this study was to use health data collected from on-farm computer systems in the United States to estimate variance components and heritability for health traits commonly experienced by dairy cows. A single-step analysis was conducted to estimate genomic variance components and heritabilities for health events, including cystic ovaries, displaced abomasum, ketosis, lameness, mastitis, metritis, and retained placenta. A blended H matrix was constructed for a threshold model with fixed effects of parity and year-season and random effects of herd-year and sire. The single-step genomic analysis produced heritability estimates that ranged from 0.02 (standard deviation = 0.005) for lameness to 0.36 (standard deviation = 0.08) for retained placenta. Significant genetic correlations were found between lameness and cystic ovaries, displaced abomasum and ketosis, displaced abomasum and metritis, and retained placenta and metritis. Sire reliabilities increased, on average, approximately 30% with the incorporation of genomic data. From the results of these analyses, it was concluded that genetic selection for health traits using producer-recorded data are feasible in the United States, and that the inclusion of genomic data substantially improves reliabilities for these traits.
Journal of Dairy Science, 2012
The principal objective of this study was to analyze the plausibility of health data recorded through on-farm recording systems throughout the United States. Substantial progress has been made in the genetic improvement of production traits while health and fitness traits of dairy cattle have declined. Health traits are generally expensive and difficult to measure, but health event data collected from on-farm computer management systems may provide an effective and low-cost source of health information. To validate editing methods, incidence ...
The principal objective of this study was to analyze the plausibility of health data recorded through on-farm recording systems throughout the United States. Substantial progress has been made in the genetic improvement of production traits while health and fitness traits of dairy cattle have declined. Health traits are generally expensive and difficult to measure, but health event data collected from on-farm computer management systems may provide an effective and low-cost source of health information. To validate editing methods, incidence rates of on-farm recorded health event data were compared with incidence rates reported in the literature. Putative relationships among common health events were examined using logistic regression for each of 3 timeframes: 0 to 60, 61 to 90, and 91 to 150 d in milk. Health events occurring on average before the health event of interest were included in each model as predictors when significant. Calculated incidence rates ranged from 1.37% for respiratory problems to 12.32% for mastitis. Most health events reported had incidence rates lower than the average incidence rate found in the literature. This may partially represent underreporting by dairy farmers who record disease events only when a treatment or other intervention is required. Path diagrams developed using odds ratios calculated from logistic regression models for each of 13 common health events allowed putative relationships to be examined. The greatest odds ratios were estimated to be the influence of ketosis on displaced abomasum (15.5) and the influence of retained placenta on metritis (8.37), and were consistent with earlier reports. The results of this analysis provide evidence for the plausibility of on-farm recorded health information.
Frontiers in Genetics, 2020
Genomic breeding programs have been paramount in improving the rates of genetic progress of productive efficiency traits in livestock. Such improvement has been accompanied by the intensification of production systems, use of a wider range of precision technologies in routine management practices, and high-throughput phenotyping. Simultaneously, a greater public awareness of animal welfare has influenced livestock producers to place more emphasis on welfare relative to production traits. Therefore, management practices and breeding technologies in livestock have been developed in recent years to enhance animal welfare. In particular, genomic selection can be used to improve livestock social behavior, resilience to disease and other stress factors, and ease habituation to production system changes. The main requirements for including novel behavioral and welfare traits in genomic breeding schemes are: (1) to identify traits that represent the biological mechanisms of the industry breeding goals; (2) the availability of individual phenotypic records measured on a large number of animals (ideally with genomic information); (3) the derived traits are heritable, biologically meaningful, repeatable, and (ideally) not highly correlated with other traits already included in the selection indexes; and (4) genomic information is available for a large number of individuals (or genetically close individuals) with phenotypic records. In this review, we (1) describe a potential route for development of novel welfare indicator traits (using ideal phenotypes) for both genetic and genomic selection schemes; (2) summarize key indicator variables of livestock behavior and welfare, including a detailed assessment of thermal stress in livestock; (3) describe the primary statistical and bioinformatic methods available for large-scale data analyses of animal welfare; and (4) identify major advancements, challenges, and opportunities to generate high-throughput and large-scale datasets to enable genetic and genomic selection for improved welfare in livestock. A wide variety of novel welfare indicator traits can be derived from information captured by modern technology such as sensors, automatic feeding systems, milking robots, activity monitors, video cameras, and
Genomic evaluation of health traits in dairy cattle
There is growing interest from dairy producers in traits related to health and fitness of cattle, which often have low heritabilities but high economic values. Traits with low heritability can be improved by genetic selection, but large numbers of daughter records are required to produce predicted transmitting abilities with high reliability. Producer-recorded health event data collected from on-farm computer systems were used to estimate variance components and compute traditional predicted transmitting abilities (PTA) for several health traits (digestive problems, displaced abomasum, ketosis, lameness, mastitis, metritis, reproductive problems, and retained placenta) using single-trait threshold sire models. Heritabilities ranged from 0.01 for lameness to 0.30 for displaced abomasum using only first lactation data. Results were similar when only first lactation or first through fifth parity data were used. Multiple trait models also were used to estimate genetic correlations among those traits, which ranged from-0.29 (ketosis, lameness) to +0.81 (displaced abomasum, ketosis). Only three traits (displaced abomasum, mastitis, metritis) had 300 or more bulls with traditional reliabilities of at least 0.50. A multiple-trait sire threshold model was used to compute genomic PTA for 2,649 genotyped bulls. The increase in reliability from including the genomic data ranged from 0.38 (displaced abomasum) to 0.48 (lameness). These results suggest that enough data may exist in on-farm computer systems to enable the routine calculation of genetic and genomic evaluations for the most common health disorders in US Holstein cattle.
Exploring the value of routinely collected herd data for estimating dairy cattle welfare
2014
Routine on-farm assessment of dairy cattle welfare is time consuming and, therefore, expensive. A promising strategy to assess dairy cattle welfare more efficiently is to estimate the level of animal welfare based on herd data available in national databases. Our aim was to explore the value of routine herd data (RHD) for estimating dairy cattle welfare at the herd level. From November 2009 through March 2010, 7 trained observers collected data for 41 welfare indicators in a selected sample of 183 loose-housed and 13 tethered Dutch dairy herds (herd size: 10 to 211 cows) using the Welfare Quality protocol for cattle. For the same herds, RHD relating to identification and registration, management, milk production and composition, and fertility were extracted from several national databases. The RHD were used as potential predictors for each welfare indicator in logistic regression at the herd level. Nineteen welfare indicators were excluded from the predictions, because they showed a prevalence below 5% (15 indicators), or were already listed as RHD (4 indicators). Predictions were less accurate for 7 welfare indicators, moderately accurate for 14 indicators, and highly accurate for 1 indicator. By forcing to detect almost all herds with a welfare problem (sensitivity of at least 97.5%), specificity ranged from 0 to 81%. By forcing almost no herds to be incorrectly classified as having a welfare problem (specificity of at least 97.5%), sensitivity ranged from 0 to 67%. Overall, the best-performing prediction models were those for the indicators access to at least 2 drinkers (resource based), percentage of very lean cows, cows lying outside the supposed lying area, and cows with vulvar discharge (animal based). The most frequently included predictors in final models were percentages of on-farm mortality in different lactation stages. It was concluded that, for most welfare indicators, RHD have value for estimating dairy cattle welfare. The RHD can serve as a prescreening tool for detecting herds with a welfare problem, but this should be followed by a verification of the level of welfare in an on-farm assessment to identify false-positive herds. Consequently, the number of farm visits needed for routine welfare assessments can be reduced. The RHD also hold value for continuous monitoring of dairy cattle welfare. Prediction models developed in this study, however, should first be validated in additional field studies.
Health traits in current dairy cattle breeding: A review
Czech Journal of Animal Science, 2021
The review focuses on breeding practices aimed at improving resistance to diseases and health disorders that are associated with better efficiency, welfare and longevity of cows. It is commonly known that diseases like mastitis, foot and claw disorders, metabolic and reproductive issues seriously violate dairy cows’ well-being. The cause of prevailing health and fertility deterioration has been the intensive selection merely based on higher milk production starting after the Second World War. Therefore since the last decades of the 20th-century genetic selection programs have been increasingly focused on increasing resistance to diseases and improving fertility traits using several omics techniques, including genomics. The first steps for maintaining the goal of genetic breeding for disease resistance were the introduction of disease data collection at national levels followed by an elaboration of gene evaluation systems. It was proved that diseases exhibit additive genetic variabil...
Genetics Selection Evolution, 2015
Background: Genetic selection has been successful in achieving increased production in dairy cattle; however, corresponding declines in fitness traits have been documented. Selection for fitness traits is more difficult, since they have low heritabilities and are influenced by various non-genetic factors. The objective of this paper was to investigate the predictive ability of two-stage and single-step genomic selection methods applied to health data collected from on-farm computer systems in the U.S. Methods: Implementation of single-trait and two-trait sire models was investigated using BayesA and single-step methods for mastitis and somatic cell score. Variance components were estimated. The complete dataset was divided into training and validation sets to perform model comparison. Estimated sire breeding values were used to estimate the number of daughters expected to develop mastitis. Predictive ability of each model was assessed by the sum of χ 2 values that compared predicted and observed numbers of daughters with mastitis and the proportion of wrong predictions. Results: According to the model applied, estimated heritabilities of liability to mastitis ranged from 0.05 (SD = 0.02) to 0.11 (SD = 0.03) and estimated heritabilities of somatic cell score ranged from 0.08 (SD = 0.01) to 0.18 (SD = 0.03). Posterior mean of genetic correlation between mastitis and somatic cell score was equal to 0.63 (SD = 0.17). The single-step method had the best predictive ability. Conversely, the smallest number of wrong predictions was obtained with the univariate BayesA model. The best model fit was found for single-step and pedigree-based models. Bivariate single-step analysis had a better predictive ability than bivariate BayesA; however, the latter led to the smallest number of wrong predictions. Conclusions: Genomic data improved our ability to predict animal breeding values. Performance of genomic selection methods depends on a multitude of factors. Heritability of traits and reliability of genotyped individuals has a large impact on the performance of genomic evaluation methods. Given the current characteristics of producer-recorded health data, single-step methods have several advantages compared to two-step methods.