Partitioning of herd, year and season variation in milk production

Chauhan, V. P. S.

doi:10.1016/0301-6226(87)90013-3

Cited by 9 publications

(5 citation statements)

References 11 publications

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“…Fixed effects of herd, season, and year have a definite influence on milk production of dairy cows. Approximately 40% of the variance in milk and fat yields was accounted for by these three factors and their interactions in a study by Chauhan (1987). He concluded that direct comparisons within and between herds should be limited to dams calving within a period of four mo or less to reduce variation caused by season and year.…”

Section: Chapter II Literature Reviewmentioning

confidence: 99%

Relationship of milk production, milk expected progeny difference, and calf weaning weight in angus and simmental cow-calf pairs

Marston

Simms

Schalles

et al. 1992

Journal of Animal Science

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Milk EPD, used to predict the milk production potential of a parent's daughters, have been reported by all major cattle breed associations. Our objectives were to determine the relationship of milk EPD of a dam to actual milk production (both fluid and components) and offspring weaning weight. Angus (AN; n = 114) and Simmental (SM; n = 82) cows were machine-milked at approximately 60, 104, and 196 d postpartum after overnight calf removal. In addition, one herd of AN was also milked at approximately 35 and 145 d postpartum. A lactation curve was fitted to these measurements to estimate total milk production during lactation. Simple correlations between 205-d total milk yields (TMY) and adjusted 205-d calf weaning weight (WW) were .30 (P < .001) and .47 (P < .001) for AN and SM, respectively. Furthermore, milk EPD was positively correlated to adjusted WW (r = .38 P < .001; r = .39, P < .001) and TMY (r = .32, P < .001; r = .44, P < .001) for AN and SM cows, respectively. A 1-kg change in TMY changed WW by .014 +/- .006 kg (P < .001) in AN and by .032 +/- .009 kg (P < .001) in SM. A 1-kg change in milk EPD resulted in a 4.85 +/- 1.14 kg change in WW (P < .001) in AN and a 3.74 +/- 1.73 kg (P < .05) change in SM. Corresponding changes in TMY were 42.1 +/- 16.6 kg (P < .01) and 69.3 +/- 16.0 kg (P < .001) for AN and SM, respectively.(ABSTRACT TRUNCATED AT 250 WORDS)

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Section: Chapter II Literature Reviewmentioning

confidence: 99%

Relationship of milk production, milk expected progeny difference, and calf weaning weight in angus and simmental cow-calf pairs

Marston

Simms

Schalles

et al. 1992

Journal of Animal Science

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“…Chauhan (1987b) investigated alternative year, season within year and month within season groupings for first lactation Holstein-Friesians and concluded that a suitable model would be herd-period (period of 12 months) as a fixed effect and herd-period-season (season of 3 or 4 months) and herd-period-month as random effects. Chauhan (1987a) also conducted analyses for milk yield and protein yield and generally obtained the same partition of variance. The accuracy of sire evaluation was, however.…”

Section: Discussionmentioning

confidence: 99%

Components of environmental variance of yields of cows in different lactations

1989

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With a view to incorporating identifiable environmental factors such as month of calving as random effects in models for dairy sire and cow breeding value estimation, an analysis was undertaken of environmental components of variance for fat yield in lactations 1 to 5 of pedigree British Friesian-Holstein cattle. Records of fat yield and of log fat yield on daughters of widely used sires were first corrected for age and month of calving and for sire in each lactation on a national basis. Variance components for herd-year, herd-year x lactation, herd-year x month of calving, herd-year x month of calving x lactation and error were then estimated for pairs of lactations.The results were most consistent for log fat yield, where heterogeneity of variance was less. Correlations were, approximately, 0-45 and 0-42 for records in the same herd-year and lactation for lactation 1 or a later lactation, respectively, and approximately 0-40 for records in the same herd-year and different lactations. Within herd-year and lactation, correlations of records in the same month were approximately 0-10 for lactation 1 and 0-08 for later lactations, and for different lactations the correlations were 0-06 if lactation 1 was included and 0-08 otherwise. Thus, the effects appear to be similar for later lactations, which can be pooled, but effects for month and month x lactation 1 v. later should be fitted.

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“…Although showing promising results, a number of studies explain that this is only suitable if sires are randomly spread across CGs (Chauhan, 1987;Meyer, 1987;Visscher and Goddard, 1993). This is generally not the case with the Pakistani data set used in this study and hence it was removed from the final analysis.…”

Section: Estimation Of Genetic Parametersmentioning

confidence: 99%

“…These are generally fitted as fixed effects within the evaluation model and account for~40% of the variation seen in milk and fat yield (Chauhan, 1987;Van Bebber et al, 1997). It is well established that ignoring fixed effects can lead to biased predictions of breeding values (Henderson, 1975a).…”

Section: Introductionmentioning

confidence: 99%

Selecting an appropriate genetic evaluation model for selection in a developing dairy sector

McGill

Mulder

Thomson

et al. 2014

Animal

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This study aimed to identify genetic evaluation models (GEM) to accurately select cattle for milk production when only limited data are available. It is based on a data set from the Pakistani Sahiwal progeny testing programme which includes records from five government herds, each consisting of 100 to 350 animals, with lactation records dating back to 1968. Different types of GEM were compared, namely: (1) multivariate v. repeatability model when using the first three lactations, (2) an animal v. a sire model, (3) different fixed effects models to account for effects such as herd, year and season; and (4) fitting a model with genetic parameters fixed v. estimating the genetic parameters as part of the model fitting process. Two methods were used for the comparison of models. The first method used simulated data based on the Pakistani progeny testing system and compared estimated breeding values with true breeding values. The second method used cross-validation to determine the best model in subsets of actual Australian herd-recorded data. Subsets were chosen to reflect the Pakistani data in terms of herd size and number of herds. Based on the simulation and the cross-validation method, the multivariate animal model using fixed genetic parameters was generally the superior GEM, but problems arise in determining suitable values for fixing the parameters. Using mean square error of prediction, the best fixed effects structure could not be conclusively determined. The simulation method indicated the simplest fixed effects structure to be superior whereas in contrast, the cross-validation method on actual data concluded that the most complex one was the best. In conclusion it is difficult to propose a universally best GEM that can be used in any data set of this size. However, some general recommendations are that it is more appropriate to estimate the genetic parameters when evaluating for selection purposes, the animal model was superior to the sire model and that in the Pakistani situation the repeatability model is more suitable than a multivariate.

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Partitioning of herd, year and season variation in milk production

Cited by 9 publications

References 11 publications

Relationship of milk production, milk expected progeny difference, and calf weaning weight in angus and simmental cow-calf pairs

Relationship of milk production, milk expected progeny difference, and calf weaning weight in angus and simmental cow-calf pairs

Components of environmental variance of yields of cows in different lactations

Selecting an appropriate genetic evaluation model for selection in a developing dairy sector

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