De correctie van melkproduktiegegevens voor leeftijd, seizoen en lactatiestadium

Het doel van dit onderzoek was het berekenen van correctiefactoren om verschillen in leeftijd, seizoen van afkalven en lactatiestadium te elimineren. Daartoe is eerst het definitieve model vastgesteld voor de verdere analyse. In dit model dient naast de vaste hoofdeffecten bedrijfsjaren, leeftijd, seizoen en lactatiestadium rekening te worden gehouden met de interactie bedrijfsniveau/leeftijd. De correctiefactoren voor lactatieprodukties en voor dagproduktie zijn daarna berekend. Tenslotte is de nauwkeurigheid van de ontwikkelde correctiefactoren getoetst aan de hand van melkproduktiegegevens van koeien die 1 jaar later afkalfde

Studies on test-day and lactation milk, fat and protein yield of dairy cows

Data of milk recording provides the basis to control herd management and genetic improvement of cows. Different management guides can be presented to dairy farmers. Breeding values are predicted for 305-day yields in order to select bulls and cows. However, breeding values should be predicted as early as practicable, so as to increase genetic progress and minimize prediction bias by selection.To calculate mangement guides and predict breeding values, adjustment of individual milk, fat and protein records for age and season of calving and prediction of 305-day yields should be made. For early selection, a number of information sources other than 305-day yield in first lactation, may be interesting. The research in this thesis was directed towards 1) improvement of procedures to correct milk, fat and protein records 2) prediction of 305-day yield and 3) development of a procedure to predict breeding values for 305-day milk, fat and protein as early as possible.In the first chapter, influence of age at calving on 305-day milk, fat and protein yield was studied in relation to herd and population level of production. Age differences per herd level of production were estimated by a repeatability model using 49669 305-day lactation records of pure-bred Dutch Friesians, calving from June 1979 until June 1982. Herds were grouped in 3 levels according to average 305-day milk yield. Second and third degree polynomial functions and a linear function including log of age at calving were fit to estimated age differences within and over parities for medium herd level. Best fit was a second degree polynomial for parity 1 (R 2=0.996) and for parity 2 (R 2=0.995), seperately. Age differences in third and higher parities were fit best by the linear function including log of age at calving (R 2=0.967). Using these functions, age adjustment factors were calculated. At medium herd level, age factors increased from 0.717 for heifers, aged 22-23 months at calving to 1.000 for cows of about 7.5 years old. Age factors between herd levels differed by only 0.017. It was concluded that one set of age factors could be used for all herd levels. However, age factors differed by 0.021-0.068 from factors estimated on records of cows calving in 1970-1971 and producing about 1,000 kg less milk on heifer basis. These differences were less if level of production was updated. It was concluded that a more accurate age adjustment was obtained by using mathematical functions and updating mean production of heifers.Age differences at various stages of lactation for test-day milk, fat and protein yield were estimated by generalized least square methods on records of 14275 Dutch Friesians (chapter 2). Mathematical functions of days in milk and age at calving (in months) were fit to age differences. Adjustment for age at calving and stage in lactation by factors calculated from these functions was compared with adjustment by factors derived from constant estimates. F-values for age at calving decreased by 0.5, 0.8 and 0.4 for milk, fat and protein, when factors derived from functions were used. Adjustment factors for month of calving were derived from constant estimates. It was concluded that use of mathematic 1 functions in adjustment of test- day records for age at calving and lactation stage resulted in more precise correction.In chapter 3, regression and factor analysis models for predicting 305-day milk yield, using means calculated from within herd lactation curves, were compared. In multiple regression and factor analysis all known test-day yields in current lactation were used as information sources, whereas last known test-day yield was used in single regression. Prediction of 305-day milk yield was split in 1) calculation of averages within herd-age- season subclasses and 2) estimation of predicting factors. Averages were calculated from lactation curves pertaining to a group of cows of the same age, freshening in the same month and performing the lactation in the same herd. These lactation curves were obtained by regression of the parameters in the lactation curve on mean test-day yield at 50 days post partum and mean 305-day yield per herd and by back-adjustment for age and season of calving.Using these averages the correlation between predicted and realized 305-day milk yield for parity 1, 2 and 3 and later increased from 0.86 to 0.99 when day post partum for the last known test progressed from 50 to 210. 305-day milk yield was predicted more accurately for heifers than for older cows. In all cases average predicted minus realized 305-day yield was positive which was influenced by year influences. Correlations between predicted and realized 305-day milk yield and standard deviations of prediction errors agreed with theoretical values. No differences were found between methods. Prediction factors were insensitive to differences in age and season of calving, whereas average prediction error within low level herds differed by only 96 kg from average prediction error in high level herds when last known test was at 50 days post partum. Using the estimation procedure of means, accuracy of prediction was improved. The single regression model was recommended for prediction purposes in practice.In chapter 6 it was concluded that 305-day fat and protein yield was predicted as accurate as 305-day milk yield, when utilizing the prediction method as presented in chapter 3.Efficiency of selection defined as the ratio of correlated and direct response in 305-day yield on different cumulative or extended milk, fat and protein yield in first lactation, was studied (chapter 4). Genetic parameters were estimated from records of 20260 Black and White heifers, sired by 204 young and 97 proven bulls. Genetic correlations between cumulative milk, fat and protein yield in mid-lactation (120-180 days post partum) or extended 180-day yields and the 305-day equivalents were unity. Genetic correlations between second trimester and 305-day yield were 0.985 for milk, 0.998 for fat and 0.996 for protein. Heritabilities for second trimester milk and protein yield and for extended 180-day fat yield were close to values for 305-day equivalents. Heritabilities for 305-day yield were 0.309 for milk, 0.372 for fat and 0.331 for protein.Heritability for second trimester fat yield was 0.318. It was concluded that selection on second trimester milk and protein yield and extended 180-day fat yield were the best alternatives to select for 305-day yield, in which case annual genetic response in 305-day yield would be increased by 5, 6 and 4% respectively. Selection on second trimester fat yield resulted in 2% additional annual response in 305-day yield. Considering efficiency of selection on part lactation yields, an increase in efficiency was obtained when cumulative yields in the first months of lactation were eliminated in calculating part lactation yields.Inclusion of incomplete first lactations to estimate genetic parameters for second month, second trimester and 305-day yield did increased heritabilities for second month and second trimester yield (chapter 5). Genetic variances were increased for second month milk, fat and protein yield, and for second trimester and 305-day milk yield, indicating that milk yield was most directly related to culling decisions. Genetic correlations between second month or second trimester yield and 305-day yields were almost not affected when incomplete first lactations were included. As a result, selection on second trimester yields would still be the best alternative to selection for 305-day yield.When incomplete lactations were extended genetic correlation between 60-day milk yield, extended to 305 days, and 305-day milk yield was increased, but remained constant for fat and protein. Heritabilities for extended 60-day yields increased when incomplete lactations were included. It was concluded that it was justified to extend incomplete lactations with the procedures presented in chapter 3.Alternatives for predicting breeding values for 305-day milk, fat and protein yield, together with minimization of prediction bias by selection of heifers, are discussed in the last chapter. Selection on breeding values for second trimester or extended yields would result in higher annual gain for 305-day yield than selection based on 305-day yields, whereas potential bias by selection of heifers was minimized. When predicting breeding values for extended yields, some bias by extending part lactations might be encountered. Genetic correlations between persistency and second trimester yield were not different from genetic correlations between persistency and 305-day yield for fat and protein. It was concluded that selection for second trimester yield is an attractive alternative for selection for 305-day yield