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Houlahan K, Schenkel FS, Miglior F, Jamrozik J, Stephansen RB, González-Recio O, Charfeddine N, Segelke D, Butty AM, Stratz P, VandeHaar MJ, Tempelman RJ, Weigel K, White H, Peñagaricano F, Koltes JE, Santos JEP, Baldwin RL, Baes CF. Estimation of genetic parameters for feed efficiency traits using random regression models in dairy cattle. J Dairy Sci 2024; 107:1523-1534. [PMID: 37690722 DOI: 10.3168/jds.2022-23124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 08/05/2023] [Indexed: 09/12/2023]
Abstract
Feed efficiency has become an increasingly important research topic in recent years. As feed costs rise and the environmental impacts of agriculture become more apparent, improving the efficiency with which dairy cows convert feed to milk is increasingly important. However, feed intake is expensive to measure accurately on large populations, making the inclusion of this trait in breeding programs difficult. Understanding how the genetic parameters of feed efficiency and traits related to feed efficiency vary throughout the lactation period is valuable to gain understanding into the genetic nature of feed efficiency. This study used 121,226 dry matter intake (DMI) records, 120,500 energy-corrected milk (ECM) records, and 98,975 metabolic body weight (MBW) records, collected on 7,440 first-lactation Holstein cows from 6 countries (Canada, Denmark, Germany, Spain, Switzerland, and the United States), from January 2003 to February 2022. Genetic parameters were estimated using a multiple-trait random regression model with a fourth-order Legendre polynomial for all traits. Weekly phenotypes for DMI were re-parameterized using linear regressions of DMI on ECM and MBW, creating a measure of feed efficiency that was genetically corrected for ECM and MBW, referred to as genomic residual feed intake (gRFI). Heritability (SE) estimates varied from 0.15 (0.03) to 0.29 (0.02) for DMI, 0.24 (0.01) to 0.29 (0.03) for ECM, 0.55 (0.03) to 0.83 (0.05) for MBW, and 0.12 (0.03) to 0.22 (0.06) for gRFI. In general, heritability estimates were lower in the first stage of lactation compared with the later stages of lactation. Additive genetic correlations between weeks of lactation varied, with stronger correlations between weeks of lactation that were close together. The results of this study contribute to a better understanding of the change in genetic parameters across the first lactation, providing insight into potential selection strategies to include feed efficiency in breeding programs.
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Affiliation(s)
- K Houlahan
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada, N1G 2W1
| | - F S Schenkel
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada, N1G 2W1
| | - F Miglior
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada, N1G 2W1; Lactanet, Guelph, ON, Canada, N1K 1E5
| | - J Jamrozik
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada, N1G 2W1; Lactanet, Guelph, ON, Canada, N1K 1E5
| | - R B Stephansen
- Center for Quantitative Genetics and Genomics, Aarhus University, Blichers Alle 20, 8830 Tjele, Denmark
| | - O González-Recio
- Departamento de Producción Animal, ETSI Agrónomos, Universidad Politécnica, Ciudad Universitaria s/n, 28040 Madrid, Spain
| | | | - D Segelke
- Vereinigte Informationssysteme Tierhaltung w.V. 27283 Verden/Aller
| | | | - P Stratz
- Qualitas AG, 6300 Zug, Switzerland
| | - M J VandeHaar
- Department of Animal Science, Michigan State University, East Lansing, MI 48824
| | - R J Tempelman
- Department of Animal Science, Michigan State University, East Lansing, MI 48824
| | - K Weigel
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53706
| | - H White
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53706
| | - F Peñagaricano
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53706
| | - J E Koltes
- Department of Animal Science, Iowa State University, Ames, IA 50011
| | - J E P Santos
- Department of Animal Sciences, University of Florida, Gainesville, FL 32611
| | - R L Baldwin
- Animal Genomics and Improvement Laboratory, USDA, Beltsville, MD 20705
| | - C F Baes
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada, N1G 2W1; Institute of Genetics, Vetsuisse Faculty, University of Bern, 3012 Bern, Switzerland.
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de Oliveira Padilha DA, Evangelista AF, Valloto AA, Zadra LEF, de Almeida R, de Almeida Teixeira R, Dias LT. Genetic association between fat-to-protein ratio and traits of economic interest in early lactation Holstein cows in Brazil. Trop Anim Health Prod 2024; 56:90. [PMID: 38413494 DOI: 10.1007/s11250-024-03937-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 02/20/2024] [Indexed: 02/29/2024]
Abstract
The aims of this study were to estimate the genetic parameters for fat-to-protein ratio (F:P) within the first 90 days of lactation and to examine their genetic associations with daily milk yield (MY), somatic cell score (SCS), and calving interval between the first and second calving (IFSC) and between the second and third calving (ISTC) during the first three lactations of Holstein cows. We utilized 200,626 production-related data officially recorded from 77,436 cows milked two or three times a day from 2012 to 2022, sourced from the Holstein Cattle Breeders Association of Paraná State, Brazil. The (co)variance components were estimated using animal models, adopting the restricted maximum likelihood (REML) method with single-trait analysis (for heritability and repeatability) and two-trait analysis (for genetic and phenotypic correlations), per lactation. Regardless of lactation number, heritability estimates were relatively low, ranging from 0.08 ± 0.005 to 0.10 ± 0.003 for F:P; 0.08 ± 0.01 to 0.18 ± 0.005 for MY; 0.04 ± 0.01 to 0.07 ± 0.004 for SCS; and 0.03 ± 0.01 for both IFSC and ISTC. Repeatability estimates within the same lactation were low for F:P (ranging from 0.17 ± 0.002 to 0.19 ± 0.03), high for MY (between 0.50 ± 0.003 and 0.53 ± 0.002), and moderate to high for SCS (between 0.39 ± 0.003 and 0.44 ± 0.004). Genetic correlations between F:P and MY ranged from -0.26 ± 0.03 to -0.15 ± 0.02; F:P and SCS, from -0.06 ± 0.03 to -0.03 ± 0.08; F:P and IFSC, 0.31 ± 0.01; F:P and ISTC, 0.20 ± 0.01; MY and IFSC, 0.24 ± 0.05; and MY and ISTC, 0.13 ± 0.08. The fat-to-protein ratio during early lactation showed low genetic variability, regardless of lactation number. Furthermore, it was genetically correlated with MY, IFSC, and ISTC, although there is an antagonistic and unfavorable correlation between traits that can limit genetic progress.
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Affiliation(s)
| | - Amauri Felipe Evangelista
- Postgraduate Program in Animal Science, Department of Animal Science, UFPR, Curitiba, PR, 80035-050, Brazil
| | - Altair Antônio Valloto
- Holstein Cattle Breeders Association of Paraná State (APCBRH), Curitiba, PR, 81200-404, Brazil
| | - Lenira El Faro Zadra
- Advanced Beef Cattle Research Center, Institute of Animal Science, Sertãozinho, SP, 13380-011, Brazil
| | - Rodrigo de Almeida
- Postgraduate Program in Animal Science, Department of Animal Science, UFPR, Curitiba, PR, 80035-050, Brazil
| | | | - Laila Talarico Dias
- Postgraduate Program in Animal Science, Department of Animal Science, UFPR, Curitiba, PR, 80035-050, Brazil
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Frizzarin M, Miglior F, Berry DP, Gormley IC, Baes CF. Usefulness of mid-infrared spectroscopy as a tool to estimate body condition score change from milk samples in intensively fed dairy cows. J Dairy Sci 2023; 106:9115-9124. [PMID: 37641249 DOI: 10.3168/jds.2023-23290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 07/02/2023] [Indexed: 08/31/2023]
Abstract
Directly measuring individual cow energy balance is not trivial. Other traits such as body condition score (BCS) and BCS change (ΔBCS) can, however, be used as an indicator of cow energy status. Body condition score is a metric used worldwide to estimate cow body reserves, but the estimation of ΔBCS was, until now, conditional on the availability of multiple BCS assessments. The aim of the present study was to estimate ΔBCS from milk mid-infrared (MIR) spectra and days in milk (DIM) in intensively fed dairy cows using statistical prediction methods. Daily BCS was interpolated from cubic splines fitted through the BCS records and daily ΔBCS was calculated from these splines. The ΔBCS records were merged with milk MIR spectra recorded on the same week. The dataset comprised 37,077 ΔBCS phenotypes across 9,403 lactations from 6,988 cows in 151 herds based in Quebec, Canada. Partial least squares regression (PLSR) and a neural network (NN) were then used to estimate ΔBCS from (1) MIR spectra only, (2) DIM only, or (3) MIR spectra and DIM together. The ΔBCS data in both the first 120 and 305 DIM of lactation were used to develop the estimates. Daily ΔBCS had a standard deviation of 4.40 × 10-3 BCS units in the 120-d dataset and of 3.63 × 10-3 BCS units in the 305-d dataset. A 4-fold cross-validation was used to calibrate and test the prediction equations. External validation was also conducted using more recent years of data. Irrespective of whether based on the first 120 or 305 DIM, or when MIR spectra only, DIM only or MIR spectra and DIM were jointly used as prediction variables, NN produced the lowest root mean square error (RMSE) of cross-validation (1.81 × 10-3 BCS units and 1.51 × 10-3 BCS units, respectively, using the 120-d and 305-d dataset). Relative to predictions for the entire 305 DIM, the RMSE of cross-validation was 15.4% and 1.5% lower in the first 120 DIM when using PLSR and NN, respectively. Predictions from DIM only were more accurate than those using just MIR spectra data but, irrespective of the dataset and of the prediction model used, combining DIM information with MIR spectral data as prediction variables reduced the RMSE compared with the inclusion of DIM alone, albeit the benefit was small (the RMSE from cross-validation reduced by up to 5.5% when DIM and spectral data were jointly used as model features instead of DIM only). However, when predicting extreme ΔBCS records, the MIR spectral data were more informative than DIM. Model performance when predicting ΔBCS records in future years was similar to that from cross-validation demonstrating the ability of MIR spectra of milk and DIM combined to estimate ΔBCS, particularly in early lactation. This can be used to routinely generate estimates of ΔBCS to aid in day-to-day individual cow management.
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Affiliation(s)
- M Frizzarin
- School of Mathematics and Statistics, University College Dublin, Dublin, D04 V1W8, Ireland; Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy, P61 P302, Co. Cork, Ireland
| | - F Miglior
- Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, ON, N1G 2W1, Canada; Lactanet Canada, Guelph, ON, N1K 1E5, Canada
| | - D P Berry
- Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy, P61 P302, Co. Cork, Ireland
| | - I C Gormley
- School of Mathematics and Statistics, University College Dublin, Dublin, D04 V1W8, Ireland
| | - C F Baes
- Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, ON, N1G 2W1, Canada; Vetsuisse Faculty, Institute of Genetics, University of Bern, Bern, 3002, Switzerland.
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Nishiura A, Sasaki O, Tanigawa T, Kubota A, Takeda H, Saito Y. Prediction of energy balance from milk traits of Holsteins in Japan. Anim Sci J 2022; 93:e13757. [PMID: 35781727 DOI: 10.1111/asj.13757] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 05/20/2022] [Accepted: 06/09/2022] [Indexed: 11/30/2022]
Abstract
We predicted the energy balance (EB) of Holstein cows in Japan from milk traits obtained by herd testing. Records covered 156 lactations of 102 cows. The number of artificial inseminations was highest, and the first conception rate was lowest in the low-EB group. Four prediction models were developed-for the whole lactation and for the early, middle, and late stages of lactation-with 20 variables, covering days in milk (DIM), milk yield, and milk composition traits. The actual and predicted EB means agreed well within DIM classes; the means of the residuals were smaller in the lactation stage models than in the all-lactation model, but the standard deviations (SDs) of the residuals were similar among models. After data reduction, the SDs of the residuals for 100 iterations were <1 throughout lactation in both types of models when n = 100. After model reduction, including the daily change of milk yield as a variable minimized the SDs of the residuals. Our equations for herd-level EB prediction have potential for use in genetic evaluation.
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Affiliation(s)
- Akiko Nishiura
- Institute for Livestock and Grassland Science, National Agriculture and Food Research Organization, Tsukuba, Japan
| | - Osamu Sasaki
- Institute for Livestock and Grassland Science, National Agriculture and Food Research Organization, Tsukuba, Japan
| | - Tamako Tanigawa
- Agricultural Research Department, Hokkaido Research Organization, Dairy Research Center, Nakashibetsu, Japan
| | - Asuka Kubota
- Agricultural Research Department, Hokkaido Research Organization, Dairy Research Center, Nakashibetsu, Japan
| | - Hisato Takeda
- Institute for Livestock and Grassland Science, National Agriculture and Food Research Organization, Tsukuba, Japan
| | - Yuriko Saito
- Institute for Livestock and Grassland Science, National Agriculture and Food Research Organization, Tsukuba, Japan
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Revisiting the Relationships between Fat-to-Protein Ratio in Milk and Energy Balance in Dairy Cows of Different Parities, and at Different Stages of Lactation. Animals (Basel) 2021; 11:ani11113256. [PMID: 34827986 PMCID: PMC8614280 DOI: 10.3390/ani11113256] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 11/07/2021] [Accepted: 11/12/2021] [Indexed: 11/24/2022] Open
Abstract
Simple Summary Data from 840 Holstein-Friesian cows (1321 lactations) were used to evaluate trends in fat-to-protein ratios in milk (FPR), and the use of FPR as an indicator of energy balance (EB). The fat-to-protein ratio was negatively related to EB, and this relationship became more negative with increased parity. Regression slopes describing linear relationships between FPR and EB differed over time, although trends were inconsistent. Similarly, ‘High’ FPR scores in milk (≥1.5) were consistently associated with a greater negative energy balance, milk yields, body weight loss, and plasma non-esterified fatty acid concentrations; however, their relationships with dry matter intake did not follow a clear trend. Although FPR can provide an indication of EB at a herd level, this analysis suggests that FPR cannot accurately predict the EB of individual cows. Abstract A statistical re-assessment of aggregated individual cow data was conducted to examine trends in fat-to-protein ratio in milk (FPR), and relationships between FPR and energy balance (EB, MJ of ME/day) in Holstein-Friesian dairy cows of different parities, and at different stages of lactation. The data were collected from 27 long-term production trials conducted between 1996 and 2016 at the Agri-Food and Biosciences Institute (AFBI) in Hillsborough, Northern Ireland. In total, 1321 lactations (1 to 20 weeks in milk; WIM), derived from 840 individual cows fed mainly grass silage-based diets, were included in the analysis. The energy balance was calculated daily and then averaged weekly for statistical analyses. Data were further split in 4 wk. intervals, namely, 1–4, 5–8, 9–12, 13–16, and 17–20 WIM, and both partial correlations and linear regressions (mixed models) established between the mean FPR and EB during these periods. Three FPR score categories (‘Low’ FPR, <1.0; ‘Normal’ FPR, 1.0–1.5; ‘High’ FPR, >1.5) were adopted and the performance and EB indicators within each category were compared. As expected, multiparous cows experienced a greater negative EB compared to primiparous cows, due to their higher milk production relative to DMI. Relatively minor differences in milk fat and protein content resulted in large differences in FPR curves. Second lactation cows displayed the lowest weekly FPR, and this trend was aligned with smaller BW losses and lower concentrations of non-esterified fatty acids (NEFA) until at least 8 WIM. Partial correlations between FPR and EB were negative, and ‘greatest’ in early lactation (1–4 WIM; r = −0.38 on average), and gradually decreased as lactation progressed across all parities (17–20 WIM; r = −0.14 on average). With increasing parity, daily EB values tended to become more negative per unit of FPR. In primiparous cows, regression slopes between FPR and EB differed between 1–4 and 5–8 WIM (−54.6 vs. −47.5 MJ of ME/day), while differences in second lactation cows tended towards significance (−57.2 vs. −64.4 MJ of ME/day). Irrespective of the lactation number, after 9–12 WIM, there was a consistent trend for the slope of the linear relationships between FPR and EB to decrease as lactation progressed, with this likely reflecting the decreasing milk nutrient demands of the growing calf. The incidence of ‘High’ FPR scores was greatest during 1–4 WIM, and decreased as lactation progressed. ‘High’ FPR scores were associated with increased energy-corrected milk (ECM) yields across all parities and stages of lactation, and with smaller BW gains and increasing concentrations (log transformed) of blood metabolites (non-esterified fatty acid, NEFA; beta-hydroxybutyrate, BHB) until 8 WIM. Results from the present study highlight the strong relationships between FPR in milk, physiological changes, and EB profiles during early lactation. However, while FPR can provide an indication of EB at a herd level, the large cow-to-cow variation indicates that FPR cannot be used as a robust indicator of EB at an individual cow level.
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Civiero M, Cabezas-Garcia EH, Ribeiro-Filho HMN, Gordon AW, Ferris CP. Relationships between energy balance during early lactation and cow performance, blood metabolites, and fertility: A meta-analysis of individual cow data. J Dairy Sci 2021; 104:7233-7251. [PMID: 33685685 DOI: 10.3168/jds.2020-19607] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 01/15/2021] [Indexed: 11/19/2022]
Abstract
This study was designed to contribute to the understanding of the relationships between energy balance (EB) in early lactation [4 to 21 d in milk (DIM)] and fertility traits [interval to start of luteal activity (SLA), interval to first observed heat (FOH), and conception to first artificial insemination (AI)], and their associated relationships with cow performance and blood metabolites between 4 to 150 DIM. Individual cow data (488 primiparous and 1,020 multiparous lactations) from 27 experiments was analyzed. Data on cow performance, EB (on a metabolizable energy basis), and fertility traits were available for all cows, whereas milk progesterone data (to determine SLA) and periodic blood metabolite data were available for 1,042 and 1,055 lactations, respectively. Data from primiparous and multiparous cows were analyzed separately, with the data sets for the 2 parity groups divided into quartiles (Q1-Q4) according to the average EB during 4 to 21 DIM (EB range for Q1 to Q4: primiparous, -120 to -49, -49 to -24, -24 to -3, and -3 to 92 MJ/d, respectively: multiparous, -191 to -79, -79 to -48, -48 to -22, and -22 to 93 MJ/d, respectively). Differences between EB quartiles for production and fertility traits were compared. In early lactation (4 to 21 DIM), moving from Q1 to Q4 mean DMI and metabolizable energy intake increased whereas mean ECM decreased. During the same period, moving from Q1 to Q4 milk fat content, milk fat-to-protein ratio, and plasma nonesterified fatty acid and β-hydroxybutyrate concentrations decreased, whereas milk protein content and plasma glucose concentrations increased in both primiparous and multiparous cows. When examined over the entire experimental period (4 to 150 DIM), many of the trends in intakes and milk production remained, although the magnitude of the difference between quartiles was much reduced, whereas milk fat content did not differ between quartiles in primiparous cows. The percentage of cows with FOH before 42 DIM increased from Q1 to Q4 (from 46 to 72% in primiparous cows, and from 41 to 58% in multiparous cows). Interval from calving to SLA and to FOH decreased with increasing EB during 4 to 21 DIM, with these occurring 9.8 and 10.2 d earlier, respectively, in Q4 compared with Q1 (primiparous cows), and 7.4 and 5.9 d earlier, respectively, in Q4 compared with Q1 (multiparous cows). For each 10 MJ/d decrease in mean EB during 4 to 21 DIM, FOH was delayed by 1.2 and 0.8 d in primiparous and multiparous cows, respectively. However, neither days to first AI nor the percentage of cows that conceived to first AI were affected by daily EB during 4 to 21 DIM in either primiparous or multiparous cows, and this is likely to reflect a return to a less metabolically stressed status at the time of AI. These results demonstrate that interval from calving to SLA and to FOH were reduced with increasing EB in early lactation, whereas early lactation EB had no effect on conception to the first service.
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Affiliation(s)
- M Civiero
- Agri-Food and Biosciences Institute, Hillsborough, Co. Down, BT26 6DR, United Kingdom; Departamento de Produção Animal e Alimentos, Universidade do Estado de Santa Catarina, Av. Luiz de Camões, 2090, Lages, SC, Brazil, 88520-000.
| | - E H Cabezas-Garcia
- Agri-Food and Biosciences Institute, Hillsborough, Co. Down, BT26 6DR, United Kingdom.
| | - H M N Ribeiro-Filho
- Departamento de Produção Animal e Alimentos, Universidade do Estado de Santa Catarina, Av. Luiz de Camões, 2090, Lages, SC, Brazil, 88520-000
| | - A W Gordon
- Agri-Food and Biosciences Institute, Belfast, Co. Antrim, BT9 5PX, United Kingdom
| | - C P Ferris
- Agri-Food and Biosciences Institute, Hillsborough, Co. Down, BT26 6DR, United Kingdom
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Fourier transform infrared spectroscopy of milk samples as a tool to estimate energy balance, energy- and dry matter intake in lactating dairy cows. J DAIRY RES 2020; 87:436-443. [PMID: 33256860 DOI: 10.1017/s0022029920001004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The objective of the study was to evaluate the potential of Fourier transform infrared spectroscopy (FTIR) analysis of milk samples to predict body energy status and related traits (energy balance (EB), dry matter intake (DMI) and efficient energy intake (EEI)) in lactating dairy cows. The data included 2371 milk samples from 63 Norwegian Red dairy cows collected during the first 105 days in milk (DIM). To predict the body energy status traits, calibration models were developed using Partial Least Squares Regression (PLSR). Calibration models were established using split-sample (leave-one cow-out) cross-validation approach and validated using an external test set. The PLSR method was implemented using just the FTIR spectra or using the FTIR together with milk yield (MY) or concentrate intake (CONCTR) as predictors of traits. Analyses were conducted for the entire first 105 DIM and separately for the two lactation periods: 5 ≤ DIM ≤ 55 and 55 < DIM ≤ 105. To test the models, an external validation using an independent test set was performed. Predictions depending on the parity (1st, 2nd and 3rd-to 6th parities) in early lactation were also investigated. Accuracy of prediction (r) for both cross-validation and external test set was defined as the correlation between the predicted and observed values for body energy status traits. Analyzing FTIR in combination with MY by PLSR, resulted in relatively high r-values to estimate EB (r = 0.63), DMI (r = 0.83), EEI (r = 0.84) using an external validation. Only moderate correlations between FTIR spectra and traits like EB, EEI and dry matter intake (DMI) have so far been published. Our hypothesis was that improvements in the FTIR predictions of EB, EEI and DMI can be obtained by (1) stratification into different stages of lactations and different parities, or (2) by adding additional information on milking and feeding traits. Stratification of the lactation stages improved predictions compared with the analyses including all data 5 ≤ DIM ≤105. The accuracy was improved if additional data (MY or CONCTR) were included in the prediction model. Furthermore, stratification into parity groups, improved the predictions of body energy status. Our results show that FTIR spectral data combined with MY or CONCTR can be used to obtain improved estimation of body energy status compared to only using the FTIR spectra in Norwegian Red dairy cattle. The best prediction results were achieved using FTIR spectra together with MY for early lactation. The results obtained in the study suggest that the modeling approach used in this paper can be considered as a viable method for predicting an individual cow's energy status.
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Bresolin T, Dórea JRR. Infrared Spectrometry as a High-Throughput Phenotyping Technology to Predict Complex Traits in Livestock Systems. Front Genet 2020; 11:923. [PMID: 32973876 PMCID: PMC7468402 DOI: 10.3389/fgene.2020.00923] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Accepted: 07/24/2020] [Indexed: 12/17/2022] Open
Abstract
High-throughput phenotyping technologies are growing in importance in livestock systems due to their ability to generate real-time, non-invasive, and accurate animal-level information. Collecting such individual-level information can generate novel traits and potentially improve animal selection and management decisions in livestock operations. One of the most relevant tools used in the dairy and beef industry to predict complex traits is infrared spectrometry, which is based on the analysis of the interaction between electromagnetic radiation and matter. The infrared electromagnetic radiation spans an enormous range of wavelengths and frequencies known as the electromagnetic spectrum. The spectrum is divided into different regions, with near- and mid-infrared regions being the main spectral regions used in livestock applications. The advantage of using infrared spectrometry includes speed, non-destructive measurement, and great potential for on-line analysis. This paper aims to review the use of mid- and near-infrared spectrometry techniques as tools to predict complex dairy and beef phenotypes, such as milk composition, feed efficiency, methane emission, fertility, energy balance, health status, and meat quality traits. Although several research studies have used these technologies to predict a wide range of phenotypes, most of them are based on Partial Least Squares (PLS) and did not considered other machine learning (ML) techniques to improve prediction quality. Therefore, we will discuss the role of analytical methods employed on spectral data to improve the predictive ability for complex traits in livestock operations. Furthermore, we will discuss different approaches to reduce data dimensionality and the impact of validation strategies on predictive quality.
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Affiliation(s)
- Tiago Bresolin
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI, United States
| | - João R R Dórea
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI, United States
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Ho PN, Marett LC, Wales WJ, Axford M, Oakes EM, Pryce JE. Predicting milk fatty acids and energy balance of dairy cows in Australia using milk mid-infrared spectroscopy. ANIMAL PRODUCTION SCIENCE 2020. [DOI: 10.1071/an18532] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Mid-infrared spectroscopy (MIRS) is traditionally used for analysing milk fat, protein and lactose concentrations in dairy production, but there is growing interest in using it to predict difficult, or expensive-to-measure, phenotypes on a large scale. The resulting prediction equations can be applied to MIRS data from commercial herd-testing, to facilitate management and feeding decisions, or for genomic selection purposes. We investigated the ability of MIRS of milk samples to predict milk fatty acids (FAs) and energy balance (EB) of dairy cows in Australia. Data from 240 Holstein lactating cows that were part of two 32-day experiments, were used. Milk FAs were measured twice during the experimental period. Prediction models were developed using partial least-square regression with a 10-fold cross-validation. Measures of prediction accuracy included the coefficient of determination (R2cv) and root mean-square error. Milk FAs with a chain length of ≤16 were accurately predicted (0.89 ≤ R2cv ≤ 0.95), while prediction accuracy for FAs with a chain length of ≥17 was slightly lower (0.72 ≤ R2cv ≤ 0.82). The accuracy of the model prediction was moderate for EB, with the value of R2cv of 0.48. In conclusion, the ability of MIRS to predict milk FAs was high, while EB was moderately predicted. A larger dataset is needed to improve the accuracy and the robustness of the prediction models.
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Smith SL, Denholm SJ, Coffey MP, Wall E. Energy profiling of dairy cows from routine milk mid-infrared analysis. J Dairy Sci 2019; 102:11169-11179. [PMID: 31587910 DOI: 10.3168/jds.2018-16112] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Accepted: 07/24/2019] [Indexed: 01/04/2023]
Abstract
The balance of body energy within and across lactations can have health and fertility consequences for the dairy cow. This study aimed to create a large calibration data set of dairy cow body energy traits across the cow's productive life, with concurrent milk mid-infrared (MIR) spectral data, to generate a prediction tool for use in commercial dairy herds. Detailed phenotypic data from 1,101 Holstein Friesian cows from the Langhill research herd (SRUC, Scotland) were used to generate energy balance (EB) and effective energy intake (EI), both in megajoules per day. Pretreatment of spectral data involved standardization to account for drift over time and machine. Body energy estimates were aligned with their spectral data to generate a prediction of these traits based on milk MIR spectroscopy. After data edits, partial least squares analysis generated prediction equations with a coefficient of determination from split sample 10-fold cross validation of 0.77 and 0.75 for EB and EI, respectively. These prediction equations were applied to national milk MIR spectra on over 11 million animal test dates (January 2013 to December 2016) from 4,453 farms. The predictions generated from these were subject to phenotypic analyses with a fixed regression model highlighting differences between the main dairy breeds in terms of energy traits. Genetic analyses generated heritability estimates for EB and EI ranging from 0.12 to 0.17 and 0.13 to 0.15, respectively. This study shows that MIR-based predictions from routinely collected national data can be used to generate predictions of dairy cow energy turnover profiles for both animal management and genetic improvement of such difficult and expensive-to-record traits.
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Affiliation(s)
- S L Smith
- Scotland's Rural College (SRUC), Edinburgh EH9 3JG, UK
| | - S J Denholm
- Scotland's Rural College (SRUC), Edinburgh EH9 3JG, UK.
| | - M P Coffey
- Scotland's Rural College (SRUC), Edinburgh EH9 3JG, UK
| | - E Wall
- Scotland's Rural College (SRUC), Edinburgh EH9 3JG, UK
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11
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Li B, Fang L, Null DJ, Hutchison JL, Connor EE, VanRaden PM, VandeHaar MJ, Tempelman RJ, Weigel KA, Cole JB. High-density genome-wide association study for residual feed intake in Holstein dairy cattle. J Dairy Sci 2019; 102:11067-11080. [PMID: 31563317 DOI: 10.3168/jds.2019-16645] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Accepted: 07/19/2019] [Indexed: 01/27/2023]
Abstract
Improving feed efficiency (FE) of dairy cattle may boost farm profitability and reduce the environmental footprint of the dairy industry. Residual feed intake (RFI), a candidate FE trait in dairy cattle, can be defined to be genetically uncorrelated with major energy sink traits (e.g., milk production, body weight) by including genomic predicted transmitting ability of such traits in genetic analyses for RFI. We examined the genetic basis of RFI through genome-wide association (GWA) analyses and post-GWA enrichment analyses and identified candidate genes and biological pathways associated with RFI in dairy cattle. Data were collected from 4,823 lactations of 3,947 Holstein cows in 9 research herds in the United States. Of these cows, 3,555 were genotyped and were imputed to a high-density list of 312,614 SNP. We used a single-step GWA method to combine information from genotyped and nongenotyped animals with phenotypes as well as their ancestors' information. The estimated genomic breeding values from a single-step genomic BLUP were back-solved to obtain the individual SNP effects for RFI. The proportion of genetic variance explained by each 5-SNP sliding window was also calculated for RFI. Our GWA analyses suggested that RFI is a highly polygenic trait regulated by many genes with small effects. The closest genes to the top SNP and sliding windows were associated with dry matter intake (DMI), RFI, energy homeostasis and energy balance regulation, digestion and metabolism of carbohydrates and proteins, immune regulation, leptin signaling, mitochondrial ATP activities, rumen development, skeletal muscle development, and spermatogenesis. The region of 40.7 to 41.5 Mb on BTA25 (UMD3.1 reference genome) was the top associated region for RFI. The closest genes to this region, CARD11 and EIF3B, were previously shown to be related to RFI of dairy cattle and FE of broilers, respectively. Another candidate region, 57.7 to 58.2 Mb on BTA18, which is associated with DMI and leptin signaling, was also associated with RFI in this study. Post-GWA enrichment analyses used a sum-based marker-set test based on 4 public annotation databases: Gene Ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, Reactome pathways, and medical subject heading (MeSH) terms. Results of these analyses were consistent with those from the top GWA signals. Across the 4 databases, GWA signals for RFI were highly enriched in the biosynthesis and metabolism of amino acids and proteins, digestion and metabolism of carbohydrates, skeletal development, mitochondrial electron transport, immunity, rumen bacteria activities, and sperm motility. Our findings offer novel insight into the genetic basis of RFI and identify candidate regions and biological pathways associated with RFI in dairy cattle.
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Affiliation(s)
- B Li
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705-2350
| | - L Fang
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705-2350; Department of Animal and Avian Sciences, University of Maryland, College Park 20742; Medical Research Council Human Genetics Unit at the Medical Research Council Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, United Kingdom
| | - D J Null
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705-2350
| | - J L Hutchison
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705-2350
| | - E E Connor
- Department of Animal and Food Sciences, University of Delaware, Newark 19716
| | - P M VanRaden
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705-2350
| | - M J VandeHaar
- Department of Animal Science, Michigan State University, East Lansing 48824
| | - R J Tempelman
- Department of Animal Science, Michigan State University, East Lansing 48824
| | - K A Weigel
- Department of Dairy Science, University of Wisconsin, Madison 53706
| | - J B Cole
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705-2350.
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12
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Li B, Fikse W, Løvendahl P, Lassen J, Lidauer M, Mäntysaari P, Berglund B. Genetic heterogeneity of feed intake, energy-corrected milk, and body weight across lactation in primiparous Holstein, Nordic Red, and Jersey cows. J Dairy Sci 2018; 101:10011-10021. [DOI: 10.3168/jds.2018-14611] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2018] [Accepted: 06/25/2018] [Indexed: 01/19/2023]
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13
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Dennis N, Stachowicz K, Visser B, Hely F, Berg D, Friggens N, Amer P, Meier S, Burke C. Combining genetic and physiological data to identify predictors of lifetime reproductive success and the effect of selection on these predictors on underlying fertility traits. J Dairy Sci 2018; 101:3176-3192. [DOI: 10.3168/jds.2017-13355] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Accepted: 12/04/2017] [Indexed: 11/19/2022]
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14
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Meta-analysis of the relationships between reproduction, milk yield and body condition score in dairy cows. Livest Sci 2018. [DOI: 10.1016/j.livsci.2018.01.017] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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15
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Seeker LA, Ilska JJ, Psifidi A, Wilbourn RV, Underwood SL, Fairlie J, Holland R, Froy H, Bagnall A, Whitelaw B, Coffey M, Nussey DH, Banos G. Longitudinal changes in telomere length and associated genetic parameters in dairy cattle analysed using random regression models. PLoS One 2018; 13:e0192864. [PMID: 29438415 PMCID: PMC5811042 DOI: 10.1371/journal.pone.0192864] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2017] [Accepted: 01/31/2018] [Indexed: 11/18/2022] Open
Abstract
Telomeres cap the ends of linear chromosomes and shorten with age in many organisms. In humans short telomeres have been linked to morbidity and mortality. With the accumulation of longitudinal datasets the focus shifts from investigating telomere length (TL) to exploring TL change within individuals over time. Some studies indicate that the speed of telomere attrition is predictive of future disease. The objectives of the present study were to 1) characterize the change in bovine relative leukocyte TL (RLTL) across the lifetime in Holstein Friesian dairy cattle, 2) estimate genetic parameters of RLTL over time and 3) investigate the association of differences in individual RLTL profiles with productive lifespan. RLTL measurements were analysed using Legendre polynomials in a random regression model to describe TL profiles and genetic variance over age. The analyses were based on 1,328 repeated RLTL measurements of 308 female Holstein Friesian dairy cattle. A quadratic Legendre polynomial was fitted to the fixed effect of age in months and to the random effect of the animal identity. Changes in RLTL, heritability and within-trait genetic correlation along the age trajectory were calculated and illustrated. At a population level, the relationship between RLTL and age was described by a positive quadratic function. Individuals varied significantly regarding the direction and amount of RLTL change over life. The heritability of RLTL ranged from 0.36 to 0.47 (SE = 0.05-0.08) and remained statistically unchanged over time. The genetic correlation of RLTL at birth with measurements later in life decreased with the time interval between samplings from near unity to 0.69, indicating that TL later in life might be regulated by different genes than TL early in life. Even though animals differed in their RLTL profiles significantly, those differences were not correlated with productive lifespan (p = 0.954).
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Affiliation(s)
- Luise A. Seeker
- Animal & Veterinary Sciences, SRUC, Easter Bush, Midlothian, United Kingdom
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Midlothian, United Kingdom
- * E-mail:
| | - Joanna J. Ilska
- Animal & Veterinary Sciences, SRUC, Easter Bush, Midlothian, United Kingdom
| | - Androniki Psifidi
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Midlothian, United Kingdom
- Royal Veterinary College, University of London, Hatfield, United Kingdom
| | - Rachael V. Wilbourn
- Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, Midlothian, United Kingdom
| | - Sarah L. Underwood
- Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, Midlothian, United Kingdom
| | - Jennifer Fairlie
- Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, Midlothian, United Kingdom
| | - Rebecca Holland
- Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, Midlothian, United Kingdom
| | - Hannah Froy
- Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, Midlothian, United Kingdom
| | | | - Bruce Whitelaw
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Midlothian, United Kingdom
| | - Mike Coffey
- Animal & Veterinary Sciences, SRUC, Easter Bush, Midlothian, United Kingdom
| | - Daniel H. Nussey
- Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, Midlothian, United Kingdom
| | - Georgios Banos
- Animal & Veterinary Sciences, SRUC, Easter Bush, Midlothian, United Kingdom
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Midlothian, United Kingdom
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16
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Li B, Berglund B, Fikse W, Lassen J, Lidauer M, Mäntysaari P, Løvendahl P. Neglect of lactation stage leads to naive assessment of residual feed intake in dairy cattle. J Dairy Sci 2017; 100:9076-9084. [DOI: 10.3168/jds.2017-12775] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2017] [Accepted: 07/24/2017] [Indexed: 01/24/2023]
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17
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Newsome R, Green M, Bell N, Bollard N, Mason C, Whay H, Huxley J. A prospective cohort study of digital cushion and corium thickness. Part 2: Does thinning of the digital cushion and corium lead to lameness and claw horn disruption lesions? J Dairy Sci 2017; 100:4759-4771. [DOI: 10.3168/jds.2016-12013] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2016] [Accepted: 02/19/2017] [Indexed: 12/27/2022]
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18
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McParland S, Berry DP. The potential of Fourier transform infrared spectroscopy of milk samples to predict energy intake and efficiency in dairy cows. J Dairy Sci 2017; 99:4056-4070. [PMID: 26947296 DOI: 10.3168/jds.2015-10051] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2015] [Accepted: 01/14/2016] [Indexed: 12/18/2022]
Abstract
Knowledge of animal-level and herd-level energy intake, energy balance, and feed efficiency affect day-to-day herd management strategies; information on these traits at an individual animal level is also useful in animal breeding programs. A paucity of data (especially at the individual cow level), of feed intake in particular, hinders the inclusion of such attributes in herd management decision-support tools and breeding programs. Dairy producers have access to an individual cow milk sample at least once daily during lactation, and consequently any low-cost phenotyping strategy should consider exploiting measureable properties in this biological sample, reflecting the physiological status and performance of the cow. Infrared spectroscopy is the study of the interaction of an electromagnetic wave with matter and it is used globally to predict milk quality parameters on routinely acquired individual cow milk samples and bulk tank samples. Thus, exploiting infrared spectroscopy in next-generation phenotyping will ensure potentially rapid application globally with a negligible additional implementation cost as the infrastructure already exists. Fourier-transform infrared spectroscopy (FTIRS) analysis is already used to predict milk fat and protein concentrations, the ratio of which has been proposed as an indicator of energy balance. Milk FTIRS is also able to predict the concentration of various fatty acids in milk, the composition of which is known to change when body tissue is mobilized; that is, when the cow is in negative energy balance. Energy balance is mathematically very similar to residual energy intake (REI), a suggested measure of feed efficiency. Therefore, the prediction of energy intake, energy balance, and feed efficiency (i.e., REI) from milk FTIRS seems logical. In fact, the accuracy of predicting (i.e., correlation between predicted and actual values; root mean square error in parentheses) energy intake, energy balance, and REI from milk FTIRS in dairy cows was 0.88 (20.0MJ), 0.78 (18.6MJ), and 0.63 (22.0MJ), respectively, based on cross-validation. These studies, however, are limited to results from one research group based on data from 2 contrasting production systems in the United Kingdom and Ireland and would need to be replicated, especially in a range of production systems because the prediction equations are not accurate when the variability used in validation is not represented in the calibration data set. Heritable genetic variation exists for all predicted traits. Phenotypic differences in energy intake also exists among animals stratified based on genetic merit for energy intake predicted from milk FTIRS, substantiating the usefulness of such FTIR-predicted phenotypes not only for day-to-day herd management, but also as part of a breeding strategy to improve cow performance.
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Affiliation(s)
- S McParland
- Animal and Grassland Research and Innovation Center, Teagasc, Moorepark, Fermoy, Co. Cork, Ireland.
| | - D P Berry
- Animal and Grassland Research and Innovation Center, Teagasc, Moorepark, Fermoy, Co. Cork, Ireland
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19
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Bilal G, Cue R, Hayes J. Genetic and phenotypic associations of type traits and body condition score with dry matter intake, milk yield, and number of breedings in first lactation Canadian Holstein cows. CANADIAN JOURNAL OF ANIMAL SCIENCE 2016. [DOI: 10.1139/cjas-2015-0127] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The objective of the present study was to estimate genetic parameters of milk yield (MY), intake traits, type traits, body condition score (BCS), and number of breedings (NOB) in first lactation Canadian Holsteins with a focus on the possibility of using type traits as an indicator of feed intake. Data were obtained from the Canadian Dairy Network and Valacta. A mixed linear model was fitted under REML for the statistical analysis. The multivariate (five traits) model included the fixed effects of age at calving, stage of lactation, and herd-round-classifier for type traits; age at calving, stage of lactation, and herd–year–season of calving (HYS) for BCS; age at calving and HYS for MY, feed intake, and NOB. Animal and residual effects were fitted as random effects for all traits. Estimates of heritabilities for MY, dry matter intake (DMI), angularity, body depth, stature, dairy strength, final score, BCS, and NOB were 0.41, 0.13, 0.24, 0.30, 0.50, 0.30, 0.22, 0.20, and 0.02, respectively. Genetic correlations between type traits and DMI ranged from 0.16 to 0.60. Results indicate that type traits appear to have the potential to predict DMI as a combination/index of two or more traits.
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Affiliation(s)
- G. Bilal
- Department of Animal Science, McGill University, Macdonald Campus, Ste-Anne-de-Bellevue, QC H9X 3V9, Canada
- Laboratories of Animal Breeding and Genetics, Department of Livestock Production and Management, PMAS-Arid Agriculture University, Rawalpindi 46300, Pakistan
| | - R.I. Cue
- Department of Animal Science, McGill University, Macdonald Campus, Ste-Anne-de-Bellevue, QC H9X 3V9, Canada
| | - J.F. Hayes
- Department of Animal Science, McGill University, Macdonald Campus, Ste-Anne-de-Bellevue, QC H9X 3V9, Canada
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20
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Cardona SJC, Cadavid HC, Corrales JD, Munilla S, Cantet RJC, Rogberg-Muñoz A. Longitudinal data analysis of polymorphisms in the κ-casein and β-lactoglobulin genes shows differential effects along the trajectory of the lactation curve in tropical dairy goats. J Dairy Sci 2016; 99:7299-7307. [PMID: 27423955 DOI: 10.3168/jds.2016-10954] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2016] [Accepted: 06/07/2016] [Indexed: 01/19/2023]
Abstract
The κ-casein (CSN-3) and β-lactoglobulin (BLG) genes are extensively polymorphic in ruminants. Several association studies have estimated the effects of polymorphisms in these genes on milk yield, milk composition, and cheese-manufacturing properties. Usually, these results are based on production integrated over the lactation curve or on cross-sectional studies at specific days in milk (DIM). However, as differential expression of milk protein genes occurs over lactation, the effect of the polymorphisms may change over time. In this study, we fitted a mixed-effects regression model to test-day records of milk yield and milk quality traits (fat, protein, and total solids yields) from Colombian tropical dairy goats. We used the well-characterized A/B polymorphisms in the CSN-3 and BLG genes. We argued that this approach provided more efficient estimators than cross-sectional designs, given the same number and pattern of observations, and allowed exclusion of between-subject variation from model error. The BLG genotype AA showed a greater performance than the BB genotype for all traits along the whole lactation curve, whereas the heterozygote showed an intermediate performance. We observed no such constant pattern for the CSN-3 gene between the AA homozygote and the heterozygote (the BB genotype was absent from the sample). The differences among the genotypic effects of the BLG and the CSN-3 polymorphisms were statistically significant during peak and mid lactation (around 40-160 DIM) for the BLG gene and only for mid lactation (80-145 DIM) for the CSN-3 gene. We also estimated the additive and dominant effects of the BLG locus. The locus showed a statistically significant additive behavior along the whole lactation trajectory for all quality traits, whereas for milk yield the effect was not significant at later stages. In turn, we detected a statistically significant dominance effect only for fat yield in the early and peak stages of lactation (at about 1-45 DIM). The longitudinal analysis of test-day records allowed us to estimate the differential effects of polymorphisms along the lactation curve, pointing toward stages that could be affected by the gene.
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Affiliation(s)
- Samir Julián Calvo Cardona
- Grupo de Investigación en Genética, Mejoramiento y Modelación Animal (GaMMA), Facultad Ciencias Agrarias, Universidad de Antioquia, Calle 67, no 53-108, AA 1226, Medellín, Colombia 005043
| | - Henry Cardona Cadavid
- Grupo de Investigación en Genética, Mejoramiento y Modelación Animal (GaMMA), Facultad Ciencias Agrarias, Universidad de Antioquia, Calle 67, no 53-108, AA 1226, Medellín, Colombia 005043
| | - Juan David Corrales
- Facultad Ciencias Agropecuarias, Universidad de La Salle, Bogotá, Colombia 110231; Departamento de Producción, Facultad de Agronomía, Universidad de Buenos Aires, San Martín 4453 (1417), Ciudad Autónoma de Buenos Aires, Argentina
| | - Sebastián Munilla
- Departamento de Producción, Facultad de Agronomía, Universidad de Buenos Aires, San Martín 4453 (1417), Ciudad Autónoma de Buenos Aires, Argentina
| | - Rodolfo J C Cantet
- Departamento de Producción, Facultad de Agronomía, Universidad de Buenos Aires, San Martín 4453 (1417), Ciudad Autónoma de Buenos Aires, Argentina; Unidad Ejecutora de Investigaciones en Producción Animal (INPA), Universidad de Buenos Aires - Consejo Nacional de Investigaciones Científicas y Técnicas, Cdad. Atma. Buenos Aires (1417), Argentina
| | - Andrés Rogberg-Muñoz
- Departamento de Producción, Facultad de Agronomía, Universidad de Buenos Aires, San Martín 4453 (1417), Ciudad Autónoma de Buenos Aires, Argentina; IGEVET-Instituto de Genética Veterinaria "Ing. Fernando Noel Dulout" (UNLP - CONICET La Plata), Facultad de Ciencias Veterinarias, Universidad Nacional de La Plata, Calle 60 y 118 S/N, La Plata, Argentina 1900.
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21
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Reproductive performance and survival of Holstein and Holstein × Simmental crossbred cows. Trop Anim Health Prod 2016; 48:1409-13. [PMID: 27344664 DOI: 10.1007/s11250-016-1103-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2016] [Accepted: 06/15/2016] [Indexed: 10/21/2022]
Abstract
Crossbreed dairy breeds, such as Holstein × dairy type of Simmental, have been generally used to improve fertility, udder health, and longevity of dairy herds. The aim was to compare the reproductive performance and survival of Holstein and Holstein × Simmental crossbred cows. Data from two farms were used as follows: one located in Bom Retiro, in the state of Santa Catarina, Brazil. and another in Carambeí, Paraná state. Information concerning birth, inseminations, and parity date were obtained from the management software of the farms, generating information regarding the calving interval, days between calving to first service, conception rate, and age at first calving. At one of the farms, calving was monitoring to quantify dystocia. Live weight as well as body condition score (BCS) of cows and information of culling were obtained to determine the survival rate. Data were analyzed by variance analysis and by logistic regression. Crossbred Holstein × Simmental cows had better reproductive performance than the Holstein cows, characterized by lower calving interval (381 vs. 445 days), higher conception rate (37.3 vs. 33.6 %), and shorter calving to first service interval (65 vs. 89 days). These results were related to a higher BCS in crossbred cows (3.63 vs. 2.94 points). Crossbred Holstein × Simmental cows had higher survival rate than Holstein cows on the second parity (83 vs. 92 %). No differences between genetic groups were observed (P > 0.05) for body weight and dystocia. In conclusion, Holstein × Simmental crossbred cows have better reproductive performance and higher survival rate than Holstein cows.
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22
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Bastin C, Théron L, Lainé A, Gengler N. On the role of mid-infrared predicted phenotypes in fertility and health dairy breeding programs. J Dairy Sci 2016; 99:4080-4094. [DOI: 10.3168/jds.2015-10087] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2015] [Accepted: 11/02/2015] [Indexed: 12/21/2022]
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23
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McParland S, Kennedy E, Lewis E, Moore S, McCarthy B, O’Donovan M, Berry D. Genetic parameters of dairy cow energy intake and body energy status predicted using mid-infrared spectrometry of milk. J Dairy Sci 2015; 98:1310-20. [DOI: 10.3168/jds.2014-8892] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2014] [Accepted: 11/04/2014] [Indexed: 02/03/2023]
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24
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McParland S, Lewis E, Kennedy E, Moore S, McCarthy B, O’Donovan M, Butler S, Pryce J, Berry D. Mid-infrared spectrometry of milk as a predictor of energy intake and efficiency in lactating dairy cows. J Dairy Sci 2014; 97:5863-71. [DOI: 10.3168/jds.2014-8214] [Citation(s) in RCA: 63] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2014] [Accepted: 06/05/2014] [Indexed: 11/19/2022]
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25
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Hazel A, Heins B, Seykora A, Hansen L. Production, fertility, survival, and body measurements of Montbéliarde-sired crossbreds compared with pure Holsteins during their first 5 lactations. J Dairy Sci 2014; 97:2512-25. [DOI: 10.3168/jds.2013-7063] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2013] [Accepted: 12/20/2013] [Indexed: 11/19/2022]
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26
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von Leesen R, Tetens J, Stamer E, Junge W, Thaller G, Krattenmacher N. Effect of genetic merit for energy balance on luteal activity and subsequent reproductive performance in primiparous Holstein-Friesian cows. J Dairy Sci 2013; 97:1128-38. [PMID: 24359817 DOI: 10.3168/jds.2013-7185] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2013] [Accepted: 11/05/2013] [Indexed: 11/19/2022]
Abstract
Postpartum energy status is critically important to fertility. However, studies dealing with the relationship between both traits are rare and most refer only to the phenotypic level. In this study, random regression models were used to generate cow-specific lactation curves for daily breeding values (BV) of energy balance (EB) to assess the effect of genetic merit for energy status on different traits derived from progesterone profiles and on subsequent reproductive performance of high-producing dairy cows. Individual feed intake, milk yield, and live weight were recorded for lactation d 11 to 180, and EB was estimated on a daily basis. The results provided the basis for the estimation of BV for 824 primiparous Holstein-Friesian cows. For a subset of these cows (n = 334), progesterone profiles for the resumption of ovarian activity were available. Four different traits describing the genetic merit for EB were defined to evaluate their relationship with fertility. Two EB traits referred to the period in which the average daily EB across all cows was negative (d 11 to 55 postpartum), and 2 parameters were designed considering only daily BV for d 11 to 180 in lactation that were negative. We found that cows with a high genetic merit for EB had a significantly earlier resumption of ovarian activity postpartum. Thus, an EB (indicator) trait should be included in future breeding programs to reduce the currently prolonged anovulatory intervals after parturition.
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Affiliation(s)
- R von Leesen
- Institute of Animal Breeding and Husbandry, Christian-Albrechts-University, D-24118 Kiel, Germany
| | - J Tetens
- Institute of Animal Breeding and Husbandry, Christian-Albrechts-University, D-24118 Kiel, Germany
| | - E Stamer
- TiDaTier und Daten GmbH, D-24259 Westensee/Brux, Germany
| | - W Junge
- Institute of Animal Breeding and Husbandry, Christian-Albrechts-University, D-24118 Kiel, Germany
| | - G Thaller
- Institute of Animal Breeding and Husbandry, Christian-Albrechts-University, D-24118 Kiel, Germany
| | - N Krattenmacher
- Institute of Animal Breeding and Husbandry, Christian-Albrechts-University, D-24118 Kiel, Germany.
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Tiezzi F, Maltecca C, Cecchinato A, Penasa M, Bittante G. Thin and fat cows, and the nonlinear genetic relationship between body condition score and fertility. J Dairy Sci 2013; 96:6730-41. [DOI: 10.3168/jds.2013-6863] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2013] [Accepted: 06/18/2013] [Indexed: 11/19/2022]
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Loker S, Bastin C, Miglior F, Sewalem A, Schaeffer L, Jamrozik J, Osborne V, Ali A. Development of a genetic evaluation for body condition score for Canadian Holsteins. J Dairy Sci 2013; 96:3994-4004. [DOI: 10.3168/jds.2012-6148] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2012] [Accepted: 02/18/2013] [Indexed: 11/19/2022]
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Relationships between conception rate in Holstein heifers and cows and milk yield at various stages of lactation. Animal 2013; 7:1423-8. [PMID: 23597286 DOI: 10.1017/s1751731113000633] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
We investigated the relationships between conception rates (CRs) at first service in Japanese Holstein heifers (i.e. animals that had not yet had their first calf) and cows and their test-day (TD) milk yields. Data included records of artificial insemination (AI) for heifers and cows that had calved for the first time between 2000 and 2008 and their TD milk yields at 6 through 305 days in milk (DIM) from first through third lactations. CR was defined as a binary trait for which first AI was a failure or success. A threshold-linear animal model was applied to estimate genetic correlations between CRs of heifers or cows and TD milk yield at various lactation stages. Two-trait genetic analyses were performed for every combination of CR and TD milk yield by using the Bayesian method with Gibbs sampling. The posterior means of the heritabilities of CR were 0.031 for heifers, 0.034 for first-lactation cows and 0.028 for second-lactation cows. Heritabilities for TD milk yield increased from 0.324 to 0.433 with increasing DIM but decreased slightly after 210 DIM during first lactation. These heritabilities from the second and third lactations were higher during late stages of lactation than during early stages. Posterior means of the genetic correlations between heifer CR and all TD yields were positive (range, 0.082 to 0.287), but those between CR of cows and milk yields during first or second lactation were negative (range, -0.121 to -0.250). Therefore, during every stage of lactation, selection in the direction of increasing milk yield may reduce CR in cows. The genetic relationships between CR and lactation curve shape were quite weak, because the genetic correlations between CR and TD milk yield were constant during the lactation period.
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Spurlock DM, Dekkers JCM, Fernando R, Koltes DA, Wolc A. Genetic parameters for energy balance, feed efficiency, and related traits in Holstein cattle. J Dairy Sci 2013; 95:5393-5402. [PMID: 22916946 DOI: 10.3168/jds.2012-5407] [Citation(s) in RCA: 71] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2012] [Accepted: 05/27/2012] [Indexed: 01/21/2023]
Abstract
Objectives of the current study were to estimate genetic parameters in Holstein cows for energy balance (EB) and related traits including dry matter intake (DMI), body weight (BW), body condition score (BCS), energy-corrected milk (ECM) production, and gross feed efficiency (GFE), defined as the ratio of total ECM yield to total DMI over the first 150 d of lactation. Data were recorded for the first half of lactation on 227 and 175 cows in their first or later lactation, respectively. Random regression models were fitted to longitudinal data. Also, each trait was averaged over monthly intervals and analyzed by single and multivariate animal models. Heritability estimates ranged from 0.27 to 0.63, 0.12 to 0.62, 0.12 to 0.49, 0.63 to 0.72, and 0.49 to 0.53 for DMI, ECM yield, EB, BW, and BCS, respectively, averaged over monthly intervals. Daily heritability estimates ranged from 0.18 to 0.30, 0.10 to 0.26, 0.07 to 0.22, 0.43 to 0.67, and 0.25 to 0.38 for DMI, ECM yield, EB, BW, and BCS, respectively. Estimated heritability for GFE was 0.32. The genetic correlation of EB at 10d in milk (DIM) with EB at 150 DIM was -0.19, suggesting the genetic regulation of this trait differs by stage of lactation. Positive genetic correlations were found among DMI, ECM yield, and BW averaged over monthly intervals, whereas correlations of these traits with BCS depended upon stage of lactation. Total ECM yield for the lactation was positively correlated with DMI, but a negative genetic correlation between total ECM yield and EB was found. However, the genetic correlation between total ECM yield and EB in the first month of lactation was -0.02, indicating that total production is not genetically correlated with EB during the first month of lactation, when negative EB is most closely associated with diminished fitness. The genetic correlation between GFE and EB ranged from -0.73 to -0.99, indicating that selection for more efficient cows would favor a lower energy status. However, the genetic correlation between EB in the first month of lactation and GFE calculated from 75 to 150 DIM was not significant, indicating that the unfavorable correlation between GFE and EB in early lactation may be minimized with alternative definitions of efficiency. Thus, EB, GFE and related traits will likely respond to genetic selection in Holstein cows. However, the impact of selection for improved feed efficiency on EB must be carefully considered to avoid potential negative consequences of further reductions in EB at the onset of lactation.
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Affiliation(s)
- D M Spurlock
- Department of Animal Science, Iowa State University, Ames 50011.
| | - J C M Dekkers
- Department of Animal Science, Iowa State University, Ames 50011
| | - R Fernando
- Department of Animal Science, Iowa State University, Ames 50011
| | - D A Koltes
- Department of Animal Science, Iowa State University, Ames 50011
| | - A Wolc
- Department of Animal Science, Iowa State University, Ames 50011; Department of Genetics and Animal Breeding, Poznan University of Life Sciences, Poland
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32
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Advances in predicting nutrient partitioning in the dairy cow: recognizing the central role of genotype and its expression through time. Animal 2013; 7 Suppl 1:89-101. [DOI: 10.1017/s1751731111001820] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
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Loker S, Miglior F, Koeck A, Neuenschwander TO, Bastin C, Jamrozik J, Schaeffer L, Kelton D. Relationship between body condition score and health traits in first-lactation Canadian Holsteins. J Dairy Sci 2012; 95:6770-80. [DOI: 10.3168/jds.2012-5612] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2012] [Accepted: 07/25/2012] [Indexed: 11/19/2022]
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35
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Tiezzi F, Maltecca C, Cecchinato A, Penasa M, Bittante G. Genetic parameters for fertility of dairy heifers and cows at different parities and relationships with production traits in first lactation. J Dairy Sci 2012; 95:7355-62. [PMID: 23063160 DOI: 10.3168/jds.2012-5775] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2012] [Accepted: 08/30/2012] [Indexed: 11/19/2022]
Abstract
The objectives of this study were to estimate genetic parameters for fertility of Brown Swiss cattle, considering reproductive measures in different parities as different traits, and to estimate relationships between production traits of first lactation and fertility of heifers and first-parity and second-parity cows. Reproductive indicators were interval from parturition to first service, interval from first service to conception, interval from parturition to conception, number of inseminations to conception, conception rate at first service, and nonreturn rate at 56 d after first service. Production traits were peak milk yield (pMY), lactation milk yield, and lactation length (LL). Data included 37,546 records on heifers, and 24,098 and 15,653 records on first- and second-parity cows, respectively. Cows were reared in 2,035 herds, calved from 1999 to 2007, and were progeny of 527 AI bulls. Gibbs sampling was implemented to obtain (co)variance components using both univariate and bivariate threshold and censored linear sire models. Estimates of heritability for reproductive traits in heifers (0.016 to 0.026) were lower than those in first-parity (0.017 to 0.142) and second-parity (0.026 to 0.115) cows. Genetic correlations for fertility in first- and second-parity cows were very high (>0.920), whereas those between heifers and lactating cows were moderate (0.348 to 0.709). The latter result indicates that fertility in heifers is a different trait than fertility in lactating cows, and hence it cannot be used as robust indicator of cow fertility. Heifer fertility was not related to production traits in first lactation (genetic correlations between -0.215 and 0.251). Peak milk yield exerted a moderate and unfavorable effect on the interval from parturition to first service (genetic correlations of 0.414 and 0.353 after first and second calving, respectively), and a low and unfavorable effect on other fertility traits (genetic correlations between -0.281 and 0.295). Infertility after first calving caused a strong elongation of the lactation, and LL was negatively correlated with fertility of cows after second calving, so that LL can itself be regarded as a measure of fertility. Lactation milk yield depends on both pMY and LL, and, as such, is a cause and consequence of (in)fertility.
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Affiliation(s)
- F Tiezzi
- Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, Legnaro (PD), Italy
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McParland S, Banos G, McCarthy B, Lewis E, Coffey MP, O'Neill B, O'Donovan M, Wall E, Berry DP. Validation of mid-infrared spectrometry in milk for predicting body energy status in Holstein-Friesian cows. J Dairy Sci 2012; 95:7225-35. [PMID: 23040020 DOI: 10.3168/jds.2012-5406] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2012] [Accepted: 08/01/2012] [Indexed: 11/19/2022]
Abstract
Cow energy balance is known to be associated with cow health and fertility; therefore, routine access to data on energy balance can be useful in both management and breeding decisions to improve cow performance. The objective of this study was to determine if individual cow milk mid-infrared spectra (MIR) could be useful to predict cow energy balance across contrasting production systems. Direct energy balance was calculated as the differential between energy intake and energy output in milk and maintenance (maintenance was predicted using body weight). Body energy content was calculated from (change in) body weight and body condition score. Following editing, 2,992 morning, 2,742 midday, and 2,989 evening milk MIR records from 564 lactations on 337 Scottish cows, managed in a confinement system on 1 of 2 diets, were available. An additional 844 morning and 820 evening milk spectral records from 338 lactations on 244 Irish cows offered a predominantly grazed grass diet were also available. Equations were developed to predict body energy status using the milk spectral data and milk yield as predictor variables. Several different approaches were used to test the robustness of the equations calibrated in one data set and validated in another. The analyses clearly showed that the variation in the validation data set must be represented in the calibration data set. The accuracy (i.e., square root of the coefficient of multiple determinations) of predicting, from MIR, direct energy balance, body energy content, and energy intake was 0.47 to 0.69, 0.51 to 0.56, and 0.76 to 0.80, respectively. This highlights the ability of milk MIR to predict body energy balance, energy content, and energy intake with reasonable accuracy. Very high accuracy, however, was not expected, given the likely random errors in the calculation of these energy status traits using field data.
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Affiliation(s)
- S McParland
- Animal and Grassland Research and Innovation Centre, Teagasc, Moorepark, Fermoy, Co. Cork, Ireland.
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37
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Thorup V, Edwards D, Friggens N. On-farm estimation of energy balance in dairy cows using only frequent body weight measurements and body condition score. J Dairy Sci 2012; 95:1784-93. [DOI: 10.3168/jds.2011-4631] [Citation(s) in RCA: 75] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2011] [Accepted: 11/29/2011] [Indexed: 11/19/2022]
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38
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Banos G, Coffey M. Technical note: Prediction of liveweight from linear conformation traits in dairy cattle. J Dairy Sci 2012; 95:2170-5. [DOI: 10.3168/jds.2011-4838] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2011] [Accepted: 12/12/2011] [Indexed: 11/19/2022]
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39
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Cole JB, Newman S, Foertter F, Aguilar I, Coffey M. BREEDING AND GENETICS SYMPOSIUM: Really big data: Processing and analysis of very large data sets1. J Anim Sci 2012; 90:723-33. [DOI: 10.2527/jas.2011-4584] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- J. B. Cole
- Animal Improvement Programs Laboratory, ARS, USDA, Beltsville, MD 20705-2350
| | | | | | - I. Aguilar
- Instituto Nacional de Investigación Agropecuaria (INIA) Las Brujas, Las Piedras, Canelones, Uruguay, 90200
| | - M. Coffey
- Scottish Agricultural College, Easter Bush Campus, Midlothian, United Kingdom EH25 9RG
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40
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McParland S, Banos G, Wall E, Coffey MP, Soyeurt H, Veerkamp RF, Berry DP. The use of mid-infrared spectrometry to predict body energy status of Holstein cows. J Dairy Sci 2011; 94:3651-61. [PMID: 21700055 DOI: 10.3168/jds.2010-3965] [Citation(s) in RCA: 76] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2010] [Accepted: 03/22/2011] [Indexed: 11/19/2022]
Abstract
Energy balance, especially in early lactation, is known to be associated with subsequent health and fertility in dairy cows. However, its inclusion in routine management decisions or breeding programs is hindered by the lack of quick, easy, and inexpensive measures of energy balance. The objective of this study was to evaluate the potential of mid-infrared (MIR) analysis of milk, routinely available from all milk samples taken as part of large-scale milk recording and milk payment operations, to predict body energy status and related traits in lactating dairy cows. The body energy status traits investigated included energy balance and body energy content. The related traits of body condition score and energy intake were also considered. Measurements on these traits along with milk MIR spectral data were available on 17 different test days from 268 cows (418 lactations) and were used to develop the prediction equations using partial least squares regression. Predictions were externally validated on different independent subsets of the data and the results averaged. The average accuracy of predicting body energy status from MIR spectral data was as high as 75% when energy balance was measured across lactation. These predictions of body energy status were considerably more accurate than predictions obtained from the sometimes proposed fat-to-protein ratio in milk. It is not known whether the prediction generated from MIR data are a better reflection of the true (unknown) energy status than the actual energy status measures used in this study. However, results indicate that the approach described may be a viable method of predicting individual cow energy status for a large scale of application.
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Affiliation(s)
- S McParland
- Animal and Bioscience Research Department, Animal and Grassland Research and Innovation Centre, Teagasc, Moorepark, Co. Cork, Ireland.
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41
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Loker S, Bastin C, Miglior F, Sewalem A, Schaeffer L, Jamrozik J, Osborne V. Short communication: Estimates of genetic parameters of body condition score in the first 3 lactations using a random regression animal model. J Dairy Sci 2011; 94:3693-9. [DOI: 10.3168/jds.2010-4122] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2010] [Accepted: 03/09/2011] [Indexed: 11/19/2022]
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42
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Buch L, Sørensen M, Lassen J, Berg P, Jakobsen J, Johansson K, Sørensen A. Udder health and female fertility traits are favourably correlated and support each other in multi-trait evaluations. J Anim Breed Genet 2010; 128:174-82. [DOI: 10.1111/j.1439-0388.2010.00904.x] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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