scholarly journals Use of Herd Solutions from a Random Regression Test-Day Model for Diagnostic Dairy Herd Management

2007 ◽  
Vol 90 (5) ◽  
pp. 2563-2568 ◽  
Author(s):  
M. Koivula ◽  
J.I. Nousiainen ◽  
J. Nousiainen ◽  
E.A. Mäntysaari
Keyword(s):  
Dairy Herd ◽  
Random Regression ◽  
Herd Management ◽  
Regression Test

scholarly journals Effect of Business and Dairy Herd Management Practices on the Variable Cost of Producing Milk

1987 ◽  
Vol 70 (8) ◽  
pp. 1701-1709 ◽  
Author(s):  
C.B. Williams ◽  
P.A. Oltenacu ◽  
C.A. Bratton ◽  
R.A. Milligan
Keyword(s):  
Management Practices ◽  
Dairy Herd ◽  
Variable Cost ◽  
Herd Management

scholarly journals The Evaluation of Real Time Milk Analyse Result Reliability in the Czech Republic

2016 ◽  
Vol 64 (4) ◽  
pp. 1155-1166 ◽  
Author(s):  
Oto Hanuš ◽  
Luděk Stádník ◽  
Marcela Klimešová ◽  
Martin Tomáška ◽  
Lucie Hasoňová ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Real Time ◽  
Near Infrared ◽  
Explanatory Power ◽  
Dairy Herd ◽  
Advanced Technology ◽  
Cell Counting ◽  
Measurement Unit ◽  
Real Time System ◽  
Herd Management

The good result reliability of regular analyzes of milk composition could improve the health monitoring of dairy cows and herd management. The aim of this study was the analysis of measurement of abilities and properties of RT (Real Time) system (AfiLab = AfiMilk (NIR measurement unit (near infrared spectroscopy) and electrical conductivity (C) of milk by conductometry) + AfiFarm (calibration and interpretation software)) for the analysis of individual milk samples (IMSs). There were 2 × 30 IMSs in the experiment. The reference values (RVs) of milk components and properties (fat (F), proteins (P), lactose (L), C and the somatic cell count (SCC)) were determined by conventional (direct and indirect: conductometry (C); infrared spectroscopy 1) with the filter technology and 2) with the Fourier transformations (F, P, L); fluoro-opto-electronic cell counting (SCC) in the film on the rotation disc (1) and by flow cytometry (2)) methods. AfiLab method (alternative) showed less close relationships as compared to the RVs as relationships between reference methods. This was expected. However, these relationships (r) were mostly significant: F from .597 to .738 (P ≤ 0.01 and ≤ 0.001); P from .284 to .787 (P > 0.05 and P ≤ 0.001); C .773 (P ≤ 0.001). Correlations (r) were not significant (P > 0.05): L from −.013 to .194; SCC from −.148 to −.133. Variability of the RVs explained the following percentages of variability in AfiLab results: F to 54.4 %; P to 61.9 %; L only 3.8 %; C to 59.7 %. Explanatory power (reliability) of AfiLab results to the animal is increasing with the regularity of their measurements (principle of real time application). Correlation values r (x minus 1.64 × sd for confidence interval (one-sided) at a level of 95 %) can be used for an alternative method in assessing the calibration quality. These limits are F 0.564, P 0.784 and C 0.715 and can be essential with the further implementation of this advanced technology of dairy herd management.

scholarly journals Definition of subgroups for fixed regression in the test-day animal model for milk production ofHolsteincattle in theCzech Republic

2011 ◽  
Vol 50 (No. 1) ◽  
pp. 7-13 ◽  
Author(s):  
L. Zavadilová ◽  
E. Němcová ◽  
J. Přibyl ◽  
J. Wolf
Keyword(s):  
Animal Model ◽  
Milk Production ◽  
Legendre Polynomials ◽  
Environmental Effect ◽  
Random Regression ◽  
Breeding Values ◽  
Regression Test ◽  
Days Open ◽  
Calving Season ◽  
Definition Of

The investigation was based on roughly 3.9, 2.7 and 1.7 million test-day records from first, second and third lactation, respectively, sampled from 596 200 Czech Holstein cows between the years 1991 and 2002. Breeding values were estimated from multi-lactation random-regression test-day models which contained the fixed effect of herd-test day, fixed regression on days in milk and random regressions on the animal level and the permanent environmental effect. Third degree Legendre polynomials (with four coefficients) were used for both the fixed and random regressions. The models differed in fixed regression. In Analysis I, 96 subclasses were defined according to age at calving, season and year of calving within lactation. In Analysis II, days open were additionally included as a grouping factor resulting in 480 subclasses. Rank correlations over 0.98 between both analyses were observed for breeding values for sires. Grouping according to Analysis I was recommended.  

scholarly journals Genetic Analysis of Somatic Cell Score in Norwegian Cattle Using Random Regression Test-Day Models

2003 ◽  
Vol 86 (12) ◽  
pp. 4103-4114 ◽  
Author(s):  
J. Ødegard ◽  
J. Jensen ◽  
G. Klemetsdal ◽  
P. Madsen ◽  
B. Heringstad
Keyword(s):  
Genetic Analysis ◽  
Somatic Cell ◽  
Somatic Cell Score ◽  
Random Regression ◽  
Regression Test

scholarly journals Development of a Benchmarking Tool for Dairy Herd Management Using Routinely Collected Herd Records

Animals ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1689
Author(s):  
Daniel Warner ◽  
Elsa Vasseur ◽  
Marianne Villettaz Robichaud ◽  
Steve Adam ◽  
Doris Pellerin ◽  
...  
Keyword(s):  
Assessment Tool ◽  
Composite Index ◽  
Dairy Herd ◽  
Dairy Herds ◽  
High Status ◽  
Continuous Assessment ◽  
Livestock Farming ◽  
Eastern Canada ◽  
Herd Management ◽  
The Individual

Continuous assessment of the herd status is important in order to monitor and adjust to changes in the welfare and health status but can be time consuming and expensive. In this study, herd status indicators from routinely collected dairy herd improvement (DHI) records were used to develop a remote herd assessment tool with the aim to help producers and advisors benchmark the herd status and identify herd management issues affecting welfare and health. Thirteen DHI indicators were selected from an initial set of 72 potential indicators collected on 4324 dairy herds in Eastern Canada. Data were normalized to percentile ranks and aggregated to a composite herd status index (HSI) with equal weights among indicators. Robustness analyses indicated little fluctuation for herds with a small HSI (low status) or large HSI (high status), suggesting that herds in need of support could be prioritized and effectively monitored over time, limiting the need for time-consuming farm visits. This tool allows evaluating herds relative to their peers through the composite index and highlighting specific areas with opportunities for improvements through the individual indicators. This procedure could be applied to similar multidimensional livestock farming issues, such as environmental and socio-economic studies.

scholarly journals Fit of different functions to the individual deviations in random regression test day models for milk yield in dairy cattle

2007 ◽  
Vol 6 (sup1) ◽  
pp. 153-155
Author(s):  
N. P. P. Macciotta ◽  
F. Miglior ◽  
A. Cappio-Borlino ◽  
L. R. Schaeffer
Keyword(s):  
Dairy Cattle ◽  
Milk Yield ◽  
Random Regression ◽  
Regression Test ◽  
The Individual

scholarly journals Inferring Genetic Parameters of Lactation in Tropical Milking Criollo Cattle with Random Regression Test-Day Models

2008 ◽  
Vol 91 (11) ◽  
pp. 4393-4400 ◽  
Author(s):  
E. Santellano-Estrada ◽  
C.M. Becerril-Pérez ◽  
J. de Alba ◽  
Y.M. Chang ◽  
D. Gianola ◽  
...  
Keyword(s):  
Genetic Parameters ◽  
Random Regression ◽  
Regression Test

Application of Radio Frequency Identification (RFID) Technology in Dairy Herd Management

2014 ◽  
Vol 4 (1) ◽  
pp. 10 ◽  
Author(s):  
Akhilesh Singh ◽  
Sudipta Ghosh ◽  
Biswajit Roy ◽  
Deepak Tiwari ◽  
RPS Baghel
Keyword(s):  
Radio Frequency ◽  
Radio Frequency Identification ◽  
Dairy Herd ◽  
Rfid Technology ◽  
Herd Management ◽  
Frequency Identification
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