Dairy Records Management Systems

Raleigh, NC, United States

Dairy Records Management Systems

Raleigh, NC, United States
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Hinde K.,Harvard University | Hinde K.,Smithsonian Conservation Biology Institute | Hinde K.,University of California at Davis | Carpenter A.J.,Kansas State University | And 2 more authors.
PLoS ONE | Year: 2014

Mammalian females pay high energetic costs for reproduction, the greatest of which is imposed by lactation. The synthesis of milk requires, in part, the mobilization of bodily reserves to nourish developing young. Numerous hypotheses have been advanced to predict how mothers will differentially invest in sons and daughters, however few studies have addressed sex-biased milk synthesis. Here we leverage the dairy cow model to investigate such phenomena. Using 2.39 million lactation records from 1.49 million dairy cows, we demonstrate that the sex of the fetus influences the capacity of the mammary gland to synthesize milk during lactation. Cows favor daughters, producing significantly more milk for daughters than for sons across lactation. Using a sub-sample of this dataset (N = 113,750 subjects) we further demonstrate that the effects of fetal sex interact dynamically across parities, whereby the sex of the fetus being gestated can enhance or diminish the production of milk during an established lactation. Moreover the sex of the fetus gestated on the first parity has persistent consequences for milk synthesis on the subsequent parity. Specifically, gestation of a daughter on the first parity increases milk production by ∼445 kg over the first two lactations. Our results identify a dramatic and sustained programming of mammary function by offspring in utero. Nutritional and endocrine conditions in utero are known to have pronounced and long-term effects on progeny, but the ways in which the progeny has sustained physiological effects on the dam have received little attention to date. © 2014 Hinde et al.


Parker Gaddis K.L.,University of Florida | Cole J.B.,U.S. Department of Agriculture | Clay J.S.,Dairy Records Management Systems | Maltecca C.,North Carolina State University
Journal of Dairy Science | Year: 2016

Genetic improvement of dairy cattle health through the use of producer-recorded data has been determined to be feasible. Low estimated heritabilities indicate that genetic progress will be slow. Variation observed in lowly heritable traits can largely be attributed to nongenetic factors, such as the environment. More rapid improvement of dairy cattle health may be attainable if herd health programs incorporate environmental and managerial aspects. More than 1,100 herd characteristics are regularly recorded on farm test-days. We combined these data with producer-recorded health event data, and parametric and nonparametric models were used to benchmark herd and cow health status. Health events were grouped into 3 categories for analyses: mastitis, reproductive, and metabolic. Both herd incidence and individual incidence were used as dependent variables. Models implemented included stepwise logistic regression, support vector machines, and random forests. At both the herd and individual levels, random forest models attained the highest accuracy for predicting health status in all health event categories when evaluated with 10-fold cross-validation. Accuracy (SD) ranged from 0.61 (0.04) to 0.63 (0.04) when using random forest models at the herd level. Accuracy of prediction (SD) at the individual cow level ranged from 0.87 (0.06) to 0.93 (0.001) with random forest models. Highly significant variables and key words from logistic regression and random forest models were also investigated. All models identified several of the same key factors for each health event category, including movement out of the herd, size of the herd, and weather-related variables. We concluded that benchmarking health status using routinely collected herd data is feasible. Nonparametric models were better suited to handle this complex data with numerous variables. These data mining techniques were able to perform prediction of health status and could add evidence to personal experience in herd management. © 2016 American Dairy Science Association.


PubMed | University of Florida, Dairy Records Management Systems, North Carolina State University and U.S. Department of Agriculture
Type: Journal Article | Journal: Journal of dairy science | Year: 2016

Genetic improvement of dairy cattle health through the use of producer-recorded data has been determined to be feasible. Low estimated heritabilities indicate that genetic progress will be slow. Variation observed in lowly heritable traits can largely be attributed to nongenetic factors, such as the environment. More rapid improvement of dairy cattle health may be attainable if herd health programs incorporate environmental and managerial aspects. More than 1,100 herd characteristics are regularly recorded on farm test-days. We combined these data with producer-recorded health event data, and parametric and nonparametric models were used to benchmark herd and cow health status. Health events were grouped into 3 categories for analyses: mastitis, reproductive, and metabolic. Both herd incidence and individual incidence were used as dependent variables. Models implemented included stepwise logistic regression, support vector machines, and random forests. At both the herd and individual levels, random forest models attained the highest accuracy for predicting health status in all health event categories when evaluated with 10-fold cross-validation. Accuracy (SD) ranged from 0.61 (0.04) to 0.63 (0.04) when using random forest models at the herd level. Accuracy of prediction (SD) at the individual cow level ranged from 0.87 (0.06) to 0.93 (0.001) with random forest models. Highly significant variables and key words from logistic regression and random forest models were also investigated. All models identified several of the same key factors for each health event category, including movement out of the herd, size of the herd, and weather-related variables. We concluded that benchmarking health status using routinely collected herd data is feasible. Nonparametric models were better suited to handle this complex data with numerous variables. These data mining techniques were able to perform prediction of health status and could add evidence to personal experience in herd management.


Parker Gaddis K.L.,North Carolina State University | Tiezzi F.,North Carolina State University | Cole J.B.,U.S. Department of Agriculture | Clay J.S.,Dairy Records Management Systems | Maltecca C.,North Carolina State University
Genetics Selection Evolution | Year: 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 (S D=0.02) to 0.11 (S D=0.03) and estimated heritabilities of somatic cell score ranged from 0.08 (S D=0.01) to 0.18 (S D=0.03). Posterior mean of genetic correlation between mastitis and somatic cell score was equal to 0.63 (S D=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. © 2015 Parker Gaddis et al.; licensee BioMed Central.


Tiezzil F.,North Carolina State University | Parker-Gaddis K.L.,North Carolina State University | Parker-Gaddis K.L.,U.S. Department of Agriculture | Cole J.B.,U.S. Department of Agriculture | And 2 more authors.
PLoS ONE | Year: 2015

Clinical mastitis (CM) is one of the health disorders with large impacts on dairy farming profitability and animal welfare. The objective of this study was to perform a genome-wide association study (GWAS) for CM in first-lactation Holstein. Producer-recorded mastitis event information for 103,585 first-lactation cows were used, together with genotype information on 1,361 bulls from the Illumina BovineSNP50 BeadChip. Single-step genomic-BLUP methodology was used to incorporate genomic data into a threshold-liability model. Association analysis confirmed that CM follows a highly polygenic mode of inheritance. However, 10-adjacent- SNP windows showed that regions on chromosomes 2, 14 and 20 have impacts on genetic variation for CM. Some of the genes located on chromosome 14 (LY6K, LY6D, LYNX1, LYPD2, SLURP1, PSCA) are part of the lymphocyte-antigen-6 complex (LY6) known for its neutrophil regulation function linked to the major histocompatibility complex. Other genes on chromosome 2 were also involved in regulating immune response (IFIH1, LY75, and DPP4), or are themselves regulated in the presence of specific pathogens (ITGB6, NR4A2). Other genes annotated on chromosome 20 are involved in mammary gland metabolism (GHR, OXCT1), antibody production and phagocytosis of bacterial cells (C6, C7, C9, C1QTNF3), tumor suppression (DAB2), involution of mammary epithelium (OSMR) and cytokine regulation (PRLR). DAVID enrichment analysis revealed 5 KEGG pathways. The JAK-STAT signaling pathway (cell proliferation and apoptosis) and the 'Cytokine-cytokine receptor interaction' (cytokine and interleukines response to infectious agents) are co-regulated and linked to the 'ABC transporters' pathway also found here. Gene network analysis performed using GeneMania revealed a co-expression network where 665 interactions existed among 145 of the genes reported above. Clinical mastitis is a complex trait and the different genes regulating immune response are known to be pathogen-specific. Despite the lack of information in this study, candidate QTL for CM were identified in the US Holstein population.


Parker Gaddis K.L.,North Carolina State University | Cole J.B.,U.S. Department of Agriculture | Clay J.S.,Dairy Records Management Systems | Maltecca C.,North Carolina State University
Journal of Dairy Science | Year: 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 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. © 2012 American Dairy Science Association.


Avendano-Reyes L.,Mississippi State University | Avendano-Reyes L.,Autonomous University of Baja California | Fuquay J.W.,Mississippi State University | Moore R.B.,Mississippi State University | And 4 more authors.
Tropical Animal Health and Production | Year: 2010

To estimate the relationship between heat stress during the last 60 days prepartum, body condition score and certain reproductive traits in the subsequent lactation of Holstein cows, 564 multiparous cows and 290 primiparous cows from four dairy herds were used in a hot, humid region. Maximum prepartum degree days were estimated to quantify the degree of heat stress. Multiple regressions analyses and logistic regression analysis were performed to determine the effect of prepartum heat stress and body condition change on reproductive parameters, which were obtained from DHIA forms at the end of the lactation. Multiparous and primiparous cows which gained body condition score from calving to 60 d postpartum exhibited 28 and 27 fewer days open (P < 0.05), respectively, than cows not gaining. There was no effect (P > 0.05) of heat stress measurement on days open or services per conception in either multiparous or primiparous cows. During hotter months of calving, multiparous cows showed higher services per conception and primiparous cows showed higher days open and services per conception (P < 0.05). Maximum prepartum degree-days were positively associated (P < 0.05) with calving difficulty score. Multiparous cows with high body condition score at calving were 1.47 times more likely to present a very difficult calving than cows that calved in October (P < 0.05). Collectively, these results suggest that reproductive performance was not affected by cumulative prepartum heat stress although it was associated with very difficult calving score. © Springer Science+Business Media B.V. 2009.


Parker Gaddis K.L.,North Carolina State University | Cole J.B.,U.S. Department of Agriculture | Clay J.S.,Dairy Records Management Systems | Maltecca C.,North Carolina State University
Journal of Dairy Science | Year: 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. © 2014 American Dairy Science Association.


Dhakal K.,Bayer | Tiezzi F.,North Carolina State University | Clay J.S.,Dairy Records Management Systems | Maltecca C.,North Carolina State University
Livestock Science | Year: 2016

Complex relationships exist between udder susceptibility to mastitis and milk production traits. Identifying causal association between these traits could help to disentangle these complex relationships. The main objective of the study was to use producer-recorded health data to examine the causal relationship between mastitis events, milk yield and lactation persistency. A total of 48,058 first lactation cows, daughters of 2213 Holstein bulls and raised across 207 herds were analyzed using structural equation models. Traits included in the dataset were mastitis events and average test day milk yields recorded in three different periods: period 1 (5-60 DIM), period 2 (61-120 DIM) and period 3 (121-180 DIM). In addition, lactation persistency was also included. A subset including 28,867 daughters of 1809 Holstein sires having both first and second lactation across 201 herds was further investigated. In these datasets, mastitis events were defined on a lactation basis as binary trait; either a cow was assigned a score of 1 (had a mastitis event in that lactation) or a score of 0 (healthy) for that particular lactation, regardless of the time of occurrence. Total milk yield from first and second lactation were also included in the analyses. We estimated negative structural coefficient (-0.032) between clinical mastitis and test day milk production in early lactation period suggesting that mastitis results in a direct decline in milk production in early lactation. We nonetheless elicited little impact of mastitis on test day milk production of mid and late lactation periods, and on milk yield lactation persistency. Likewise the positive estimate of the structural coefficient (0.123) from mastitis event in first lactation to second lactation suggests an increased risk of mastitis in second lactation if a case of mastitis occurs in the primiparous cow. Heritability estimates obtained from the structural equation models were low for mastitis (ranged 0.04 to 0.07), and negative genetic correlations were found between mastitis events and milk yield. The study illustrates how mastitis events and production are causally linked. Through the use of structural equation models we elicited the causal effect among mastitis and production traits that evolve over the course of cow life. © 2016 Elsevier B.V.


PubMed | Dairy Records Management Systems and North Carolina State University
Type: Journal Article | Journal: Journal of dairy science | Year: 2015

Hoof lesions contributing to lameness are crucial economic factors that hinder the profitability of dairy enterprises. Producer-recorded hoof lesions data of US Holsteins were categorized into infectious (abscess, digital and interdigital dermatitis, heel erosion, and foot rot) and noninfectious (korn, corkscrew, sole and toe ulcer, sole hemorrhage, white line separation, fissures, thin soles, and upper leg lesions) categories of hoof lesions. Pedigree- and genomic-based univariate analyses were conducted to estimate the variance components and heritability of infectious and noninfectious hoof lesions. A threshold sire model was used with fixed effects of year-seasons and random effects of herd and sire. For genomic-based analysis, a single-step procedure was conducted, incorporating H matrix to estimate genomic variance components and heritability for hoof lesions. The pedigree-based analysis produced heritability estimates of 0.11 (0.05) for infectious hoof lesions and 0.08 (0.05) for noninfectious hoof lesions. The single-step genomic analysis produced heritability estimates of 0.14 (0.06) for infectious hoof lesions and 0.12 (0.08) for noninfectious hoof lesions. Approximated genetic correlations between hoof lesion traits and hoof type traits along with productive life and net merit were all low and ranged between -0.25 and 0.14. Sire reliabilities increased, on average, by 0.24 and 0.18 for infectious and noninfectious hoof lesions, respectively, with incorporation of genomic data.

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