Vol. 50996): 185-192. Estimation of genetic parameters for test-day milk production at different stages of lactation of Finnish Ayrshire heifers Anne Kettunen and Esa A. Mäntysaari Agricultural Research Centre ofFinland, Institute ofAnimal Production, FIN-31600 Jokioinen, Finland Genetic parameters for test-day milk production at different stages of lactation of Finnish Ayrshire heifers were estimated with the REML method using the AI algorithm and animal model. The data consisted of 38 679 first lactation test-day milk yields of 4205 cows from 231 herds in three geo- graphical regions (North Savo, Central Ostrobothnia and Lapland). To identify different test days, records were numbered according to the days in milk after calving, and were further categorized into three part-lactations according to the test-day classification. Expressions in the three part-lactations were considered as separate traits, and tests were treated as repeated observations within the trait. Heritability estimates for test-day milk yield varied between 0.11 and 0.17, being lowest at the begin- ning of lactation. Genetic correlations between test-day milk yields at different trimesters ranged from 0.64 to 0.91, being highest between consecutive trimesters. Standard errors of the estimates of genetic parameters varied between 0.02 and 0.08. Genetic interrelationships differed from 1.0, sup- porting the assumption that genetic variation exists in the shape of the lactation curve. The necessity of considering deviations from the general lactation curve in the test-day model, e.g. fitting random regression coefficients, is discussed. Key words: animal model, test-day model, lactation curve ntroduction In Finland, as in many other countries, the estim- ation of breeding values for production traits of dairy cattle is based on 305-day lactation records of milk, protein and fat production, and on the protein and fat percentages in the corres- ponding milk. For milk-recorded herds, indi- vidual milk yields are measured every month at regular (approximately 30-day) intervals, and the concentrations of protein and fat in milk every other month. The lactation records are formed by combining the first ten monthly test-day yields, weighted by the length of the testing periods, into a measure of production. In estimation of breeding values and in an- imal selection scheme it is advantageous to use the original test day measurements rather than the entire lactation production. There is then no need to combine individual test-day results into an entire lactation yield nor to extend incomplete © Agricultural and Food Science in Finland Manuscript received June 1996 185 AGRICULTURAL AND FOOD SCIENCE IN FINLAND https://www.c-info.fi/en/info/?token=4kwAeaCRsiFdfeLN.W71H-Rj5A13ebMLNBcWHGQ.7W1gzZlTpPuCiWKVQyU_nXaipSV4UlL3me_AAW6vLAQ-QtIrk83fgUmmBjuzXBjge7LsVkVDQ8awh8Ty3LPuGrhsKMuknqegO_IBpa7U5fYodOjHKWWFxyG26DmJCyJnHODwH78erXQ9RtYU2xCBzmaEAZskHdHO1mxdU2Tq7LqvdjD2D9U0fIsoKb6Alcu0yWkzwrZyKIMimZJ-HPWk_gxIm8JfzWcMiPGV0AeJqgRgNtzlyt349QgpcRQOML483RNP_g4TsQ6ahk7qcbIxDTIE3ig47IEEz7fEyHcAjw Kettunen, A. & Mäntysaari, E.: Geneticparameters for test-day milk lactation records. In the test-day model, the spe- cific environmental conditions of the sample day can be accounted for by modelling the contem- porary group to include all the animals sampled on a certain test day within the herd. This should result in smaller residual variance (Ptak and Schaeffer 1993). In the test-day model the amount of information is greater; instead of one measure ofproduction per lactation, all informa- tion obtained during lactation can be used and the accuracy of evaluations may improve (Pan- der et al. 1992, Ptak and Schaeffer 1993). Fur- thermore, it is possible to weight test-day meas- urements according to their (co)variance struc- ture (Ali and Schaeffer 1987). When the animal evaluation is based on 305- day production, a common underlying lactation curve for all animals is assumed. In the test-day model, factors for modelling the curve of lacta- tion can be included (Ptak and Schaeffer 1993), and different lactation curves can be delineated for different subgroups if necessary. The use of continuous evaluation and the test-day model rather than one-shot evaluation would make it possible to obtain updated evaluations of a particular herd immediately after each new test day. Solutions for herd-test day effects and indi- vidual animal deviations from the predicted pro- duction can be used in dairy management and to detect problems in herd feeding or health pro- grammes. In most studies the variance components of test-day measurement data have been estimated for either individual test days or for yields cu- mulated periodically (see review by Swalve 1995b). Heritability estimates for the mid-part of lactation are of the same order of magnitude as those estimated for 305-day production, and considerably lower for the beginning and end of lactation (Meyer et al. 1989, Pander et al. 1992, Swalve 1995a). Genetic correlations between consecutive test days are close to unity, decreas- ing when the distance between test days increas- es (Meyer et al. 1989, Pander etal. 1992, Kahten- brink and Swalve 1993, Rekaya et al. 1995). Genetic relationships between test-day yields and the entire lactationproduction depend on the stage of lactation, being lowest during the first two test days and highest during the mid-part of lactation (Pander et al. 1992, Kahtenbrink and Swalve 1993). One parameter of the lactation curve that is of economic importance to the dairy producer is persistency (Graven and Baptist 1976). Gener- ally persistency describes how sharply the lacta- tion curve peaks, and how strong the decrease in daily production is after the peak. Cows main- taining consistently high production throughout lactation are consideredpersistent. First lactating cows are usually more persistent than cows in later parities (Danell 1982).At the same level of production, persistent cows are under much less physiological stress, and thus metabolic dis- orders or problems with health and fertility are less probable (Leukkunen 1985, Swalve 1995c). It is much easier to meet the feeding require- ments of a persistent cow, and therefore it is possible to keep the proportion of roughage in the daily diet high (Danell 1982, Leukkunen 1985,Swalve 1995c). This has economic import- ance, especially in geographical areas where self-sufficiency in feedstuff production is based on hay and silage, and grain or concentrates being mainly bought elsewhere. Persistency cor- relates favourably with 305-day milk yield and the test-day milk production, except in early lactation (Danell 1982, Swalve 1995c). The shape of the lactation curve can be estimated with various mathematical models, of which those widely used are Wood’s gamma function (Wood 1967) and the multiple regres- sion model (Ali and Schaeffer 1987). As there is evidence of genetic variation in persistency (Danell 1982), individual differences in the shape of the lactation curve may have to be con- sidered. This can be done with a multitrait model in which each test-day yield is considered as a different trait. Another alternative is to use a single-trait model in which test-day yields are considered as repeated observations and indi- vidual deviations from the general lactation curve are accounted for by estimating lactation curve parameters within cows (Schaeffer and Dekkers 1994).The breeding value of an animal is then a 186 AGRICULTURAL AND FOOD SCIENCE IN FINLAND Vol. 5 (1996): 185-192. combination of the genetic value determining the height of production and the genetic value of the shape of the lactation curve. Here, heritabilities were estimated for test- day milk yields at different stages of the lacta- tion ofFinnish Ayrshire heifers with a test-day model, and the genetic correlations between test- day milk yields at different stages of lactation were determined. The need to include lactation persistency in the breeding value estimation is discussed in the light of the results. Material and methods Test day data were extracted from the national milk-recording database in three geographical regions (North Savo, Central Ostrobothnia and Lapland). For this study, the data were restrict- ed to the first lactation records of Ayrshire cows calving between April 1991 and March 1995. As in the national milk recording system, cows pro- ducing less than 6.0kg of milk on a test day were considered dry. To enable the computations to be made with a multitrait model, the data were further edited with respect to the number of an- imals in the contemporary comparison group, only the herds with more than threeheifers calv- ing in each herd-year class being included in the analyses. A total of 38 679 test day milk pro- duction records for 4205 cows from 231 herds were used in the estimation of genetic para- meters. Test days (TD) were identified by number- ing records according to the days in milk (DIM) after calving. Equal 30-day intervals were used to classify test days 1-9; the test made during days 271 to 305 after calving was classified as test day 10. If a cow’s two successive test-day samples fell in the same test-day class, only the last one was considered. For the estimation of genetic parameters, the test-day classes were further grouped into three part-lactations accord- ing to the stage of lactation. The first trimester consisted of the first two test days (1-60 DIM), the second trimester ranged from the third to sixth test day (61-180 DIM) and the last trimes- ter from the seventh to tenth test day (181-305 DIM). No restriction was made according to the number oftest day records during lactation. The average test-day milk production peaked during 31-90 days after calving and then declined quite linearly to the end oflactation (Fig. 1). The vari- ances of the test-day milk yields were fairly Fig.l. Lactation curve for milk yield from average phenotypic test-day milk production (n = 38 679). 187 AGRICULTURAL AND FOOD SCIENCE IN FINLAND i Kettunen, A. & Mäntysaari, E.: Geneticparameters for test-day milk Table 1. Number of observations (N), means and standard deviations (SD) of test-day milk yield classified according to days in milk (DIM). Test day (DIM) N Mean SD 1. (< 31 d) 3744 20.8 4.3 2. (31-60 d) 3994 23.0 4.4 3. (61-90 d) 4017 22.9 4.4 4. (91-120 d) 3995 22.3 4.5 5. (121-150d) 3977 21.5 4.5 6. (151-180 d) 3970 20.7 4.4 7. (181-210 d) 3931 19.8 4.4 8. (211-240 d) 3892 18.8 4.3 9. (241-270 d) 3746 17.6 4.3 10. (271-305 d) 3413 15.8 4.4 homogeneous throughout lactation (sd. 4.3-4.5). Characteristics of the data are presented in Table 1 and Table 2. Genetic parameters were estimated using a multitraitanimal model in which expressions in each trimester were considered as repeated ob- servations of a different trait. The statistical model used for describing the test-day milk yield for each trait was: y,... -HD + AGE + DCC. + YS. + b *DIM ..J ijklmno i j k I ijklmno +HY +pe + a + e...m r n n ijklmno where: yUkimno = test- day yield (milk kg), HD = fixed effect of herd, i= 1-231, I AGE. = fixed effect of calving age, j= 1-8, DCCk - fixed effect of days carried calf, for traits 2 and 3, k = 1-5, YS = fixed effect of calving year-season, 1= 1-23, b = regression coefficient for test-day milk pro- duction on days in milk (DIM), D1M..., = days in milk at test,ijklmno J HYm - random effect of herd-calving year, pen - random effect of permanent environment, an = additive genetic effect of animal, e.. = random residual error pertaining t0y.... ijklmno 1 osijklmno The effect of calving age was divided into eight classes according to the age of the heifer on the day of calving (Table 3). As the effect of pregnancy on milk yield cannot be found in the Table 2. Number of observations (N), means and standard deviations (SD) of test-day milk yield in trimesters grouped according to test day classification (TD). Trimester N Mean SD I.(TDI-TD2) 7 738 21.9 4.5 2. (TD3-TD6) 15 959 21.9 4.5 3. (TD7-TD10) 14 982 18.1 4.6 first trimester, its effect was considered only in later trimesters. The DCC effect was divided into five classes according to the duration of preg- nancy on the sample day (Table 3). The calving year-seasons were classified into bimonthly periods within each calving year across herds, forming 23 calving year-season classes. From the lactation curve, estimated by phenotypic test-day means, production was observed to be approxim- ately linearly dependent on the time from calv- ing to sample day within each trimester (Fig. 1). Therefore DIM was included as a covariate in the model. Calving years, each from April to March and the years from 1991 to 1995, were classified within herds to form 867 herd-calv- ing year classes. The variances Var(hy)=l®HY0 , Var(pe)=l®PE0, Var(a)=A®G0 and Var(e)=l®R 0 were assumed where I is an identity matrix and A is the additive relationship matrix of animal effects. The matrices HY 0, PE 0, G 0 and R 0 are matri- ces of order 3, describing the (co)variance struc- ture of the traits. For computational reasons, pedigree information from the dam side was re- Table 3. Classification of calving age (AGE) and days car- ried calf (DCC) effects. Class AGE (days) DCC (days) 1 missing < 119 2 <690 120-149 3 691-720 150-179 4 721-750 180-209 5 751-780 > 210 6 781-840 7 841-900 8 >9Ol 188 AGRICULTURAL AND FOOD SCIENCE IN FINLAND Vol. 5 (1996): 185-192. Table 4, Structure of pedigree information. Number of Sires Dams progeny N N 1 153 (22%) 2613 (82%) 2-5 319 (47%) 589 (18%) 6-10 124 (18%) 11-20 58 (9%) >2O 27 (4%) total 681 (100%) 3202 (100%) stricted to include only female relatives whose first production year was 1982 or after. Cows with records were daughters of 681 sires and 3202 dams. On average sires had 6.2, and dams 1.3, daughters in the data. The total number of dam-daughter pairs with records in the dataset was 570. The structure of the pedigree informa- tion is presented in Table 4. Variance components were estimated with the restricted maximum likelihood (REML) method using the AI algorithm (average information). The average information matrix is taken as an average from Fisher’s information matrix and the Newton-Raphson second derivative matrix (Johnson and Thompson 1995). Standard errors of the estimates of heritabilities and genetic cor- relations were estimated using the elements of the inverse of the average informationmatrix and a Taylor series expansion. Results and discussion Variance components and estimates of herit- abilities and repeatabilities for test-day milk yields at different stages of lactation are presented in Table 5. Heritability estimates were 0.11, 0.17 and 0.17 for the first, second and third trimes- ters, respectively. Standard errors of the estim- ates of heritabilities were 0.02 for all three tri- mesters. Repeatability estimates within trimes- ters ranged from 0.52 to 0.71 (Table 5). Genetic correlations between trimesters were 0.88, 0.64 Table 5. Variance components for herd-year (HY), perman- ent environment (pe), animal effect and residual, and estim- ates of heritabilities and repeatabilities for test-day milk yield in lactation trimesters. Variance Ist trimester 2nd trimester 3rd trimester component HY 0.68 0.90 0.82 pe 5.27 6.84 6.32 animal 1.42 2.07 2.22 residual (Ul 4.42 h 2 0.11+0.02 0.17 ±0.02 0.17 ±0.02 r 0.52 ±O.Ol 0.71 ±O.OO 0.66 ±O.Ol and 0.91 between the first and second, the first and third and the second and third trimesters, respectively. Standard errors of the estimates of genetic correlations were low, ranging from 0.03 to 0.08. Additive genetic variance was lowest during the first trimester, increasing towards the end of lactation. Residual variance, in contrast, was considerably higher at the beginning oflactation than in the following two trimesters, indicating that the model did not describe early lactation as well as it did the rest of lactation (Table 5). Heritabilities are typically estimated for in- dividual test days (Meyer et al. 1989, Pander et al. 1992,Kahtenbrink and Swalve 1993, Reents et al. 1994, Rekaya et al. 1995, Swalve 1995a) or for cumulated or averaged yields in standard- ized intervals (Kahtenbrink and Swalve 1993, Swalve 1995a, b). Somewhat higher heritabilities for test-day milk production are presented in the literature than those found in three trimes- ters in this study. The definitions used in the records vary, however, and the statistical models and methods of estimation differ from those used here (see review by Swalve 1995b). Espe- cially test-day milk production at mid-lactation is reported to be much more heritable than was found here (Pander et al. 1992, Kahtenbrink and Swalve 1993, Swalve 1995b). Comparison with estimates of heritabilities for 305-day milk pro- duction is invalid, because the trimesters are as- sumed to be different traits. However, with the 189 AGRICULTURAL AND FOOD SCIENCE IN FINLAND Kettunen, A. & Mäntysaari, E.: Geneticparameters for test-day milk Table 6. Variance components forherd-year (HY), perman- ent environment (pe), animal effect and residual, and estim- ates of heritabilities and repeatabilities for test-day milk yield in lactation trimesters when fixed herd effect was ex- cluded from model. Variance Ist trimester 2nd trimester 3rd trimester component HY 3.93 5.52 4.66 pe 4.52 5.87 5.65 animal 2.35 3.32 3.07 residual 6AO 4.42 h 2 0.18 ±0.03 0.26 ±0.03 0.23 ±0.03 r 0.53 ±O.Ol 0.72 ±O.OO 0.66 ±O.Ol aid of heritabilities and repeatabilities it was possible to construct selection indices for cumu- lative yields over each period. The squared ac- curacy of such indices (comparable to h 2) would then be 0.14, 0.22 and 0.23 for the first, second and third trimesters, respectively. In the literature, higher heritabilities are es- timated for the test-day records with herd-test day models than for the traditional herd-year- season (HYS) classification (Swalve 1994, Rekaya et al. 1995). In our study the herd-test day classification could not be used because of the small size of the contemporary comparison group; the random herd-calving year (HY) was used as a contemporary comparison group in- stead. The fixed herd effect was included in the model to account for between-herd variation. Such an HY definition pools together the test months of a period of one year or more, and hence does not have the accuracy of even HYS- calving groups in other studies. Exclusion of the fixed effect of herd in the model affected heritability estimates (Table 6). When the herd effect was removed from the model, herd-year variation increased signific- antly. There was also some increase in the additive genetic variance, with a decrease in the compo- nent ofpermanent environmental variance. It was assumed that ignoring the fixed herd effect would make it impossible to fully differentiate between the part of the variation due to between-herd dif- ferences and the part due to the within-herd variation between calving years. Part of the be- tween-herd variation seemed to enter the addi- tive genetic component, and the effect of per- manent environment became less detectable. No change in residual variance component was noted, thus leading to overestimation of herit- abilities when the fixed herd effect was excluded from the model (Table 6). On the other hand, the structure of the data could have caused problems in distinguishing the effects of the factors in the model. Because the young sires in the Attest- ing program (artificial insemination) are used for only limited periods, and possibly in only a few herds, the low heritability estimates may result from the fact that sires and herds and/or sires and calving year-season effects are confounded. Conclusions As a result of lower additive genetic variance and higher residual variance, the heritability es- timate for test-day milk production in the first trimester of lactation was lower than that in the two following trimesters.The first test-day yield at the beginning of lactation, which is often sam- pled only a few days after calving, is presum- ably a less reliable measure of test-day milk pro- duction than later yields. Many environmental factors, such as feeding before calving, may sig- nificantly affect the cows’ performance on the first test day. The variation caused by these nuis- ance factors might be very hard to account for, and at least was not distinguished by the model used here. This was indicated by the larger re- sidual variance for the first trimester thanfor the other two trimesters. Genetic correlations between the trimesters of the lactation were estimated to range from 0.64 to 0.91, being highest between consecutive trimesters. The fact that the interrelationships between different parts of lactation were lower than one indicates that the shape of the lactation curve is not genetically the same for all the indi- 190 AGRICULTURAL AND FOOD SCIENCE IN FINLAND Vol. 5 (1996): 185-192. viduals. Furthermore, estimating breeding values and ranking animals on the basis of test-day records for the first two test days may not al- ways lead to selection of the same animals that would have been selected if the decisions had been based on the later test day records. On the basis of these results, it wouldbe reas- onable to take individual deviations from the general lactation curve into account either by estimating regression coefficients describing the lactation curve within cows, as in an approach chosen in Canada (Jamrozik et al. 1995), or by considering individual test-day observations as different traits. Fitting random regression coef- ficients to describe the individual lactation curves would enable lactation persistency to be included as a new trait in animal evaluation. The animal’s breeding value for test-day milk pro- duction would be determined by two compon- ents: one determining the genetic mean produc- tion throughout lactation and another describing the genetic value of the shape of the lactation curve. Problems in defining the contemporary comparison groups in test-day models, the pos- sibility of reducing the number of regression parameters in the lactation curve submodel and establishing the genetic background of the shape of the lactation curve in the Finnish dairy cow population are subjects that need to be studied in the future. Acknowledgements. We are grateful to theAgricultural Data Processing Centre in supplying the research data. The study is a part ofa largerproject on utilization of test day records funded by Finnish Animal Breeding Association, Agricul- tural Data Processing Centre, Valio Co., Association ofRural Advisory Centres and Ministry ofAgriculture and Forestry. References Ali,T. E. & Schaeffer, L. R. 1987. Accounting for covari- ances among test day milk yields in dairy cows. Cana- dian Journal of Animal Science 67: 637-644. Danell, B. 1982. Studies on lactation yield and individual test-day yields of Swedish dairy cows. 111. Persistency of milk yield and its correlation with lactation yield. Acta Agriculturae Scandinavica 32: 93-101. Gravert, H. O. & Baptist, R. 1 976. Breeding for persistency of milk yield. Livestock Production Science 3: 27-31. Jamrozik, J., Schaeffer, L. R. & Dekkers, J. C. M. 1 995. Genetic evaluation of dairy cattle using test day yields and random regression model. Journal of Dairy Science 78, Supplement 1: 249. Johnson, D. L. & Thompson, R. 1995. Restricted max- imum likelihood estimation of variance components for univariate animal models using sparse matrix techniques and average information. Journal of Dairy Science 78: 449-456. Kahtenbrink, S. & Swalve H. H. 1993. Untersuchungen zur Erblichkeit von Leistungsvariablen der ersten Lakta- tion von Milchktihen. Zuchtungskunde 65: 254-266. Leukkunen, A. 1985. Genetic parameters for the per- sistency of milk yield in the Finnish Ayrshire cattle. Zeitschrift fur Tierzuchtung und Zuchtungsbiologie 102: 117-124. Meyer, K., Graser, H.-U. & Hammond, K. 1989. Estim- ates of genetic parameters for first lactation test day production of Australian Black and White cows. Livestock Production Science 21: 177-199. Pander, B. L., Hill, W. G. & Thompson R. 1992. Gen- etic parameters of test day records of British Holstein- Friesian heifers. Animal Production 55: 11-21. Ptak, E. & Schaeffer, L. R. 1993. Use of test day yields for genetic evaluation of dairy sires and cows. Livestock Production Science 34: 23-34. Reents, R., Dekkers, J. C. M. & Schaeffer, L. R. 1994. Genetic parameters of test day somatic cell counts and production traits. Proceedings of the sth5 thWorld Congress on Genetics Applied to Livestock Production. Guelph, Ontario, Canada, Vol. 17: 120-123. Rekaya, R., Béjar, F., Carabano M. J. & Alenda, R. 1995. Genetic parameters for test day measurements in Spanish Holstein-Friesian. Proceedings of the Open Ses- sion of the INTERBULL Annual Meeting. Prague, Czech Republic, Bulletin no. 11.8 p. Schaeffer, L. R. & Dekkers, J. C. M. 1994. Random re- gressions in animal models for test-day production in dairy cattle. Proceedings of the sth5th World Congress on Gen- etics Applied to Livestock Production. Guelph, Ontario, Canada, Vol. 18: 443-446. Swalve, H. H. 1994, Estimation of breeding values using testday models. Proceedings of the Open Session of the INTERBULL Annual Meeting. Ottawa, Canada, Bulletin no. 10. 13 p. -1995a.The effect of test day models on the estimation of genetic parameters and breeding values for dairy yield traits. Journal of Dairy Science 78: 929-938. -1995b. Test day models in the analysis of dairy pro- duction data - a review. Archiv fur Tierzucht 38: 11-32. -1995c. Genetic relationship between dairy lactation persistency and yield. Journal of Animal Breeding and Genetics 112: 303-311. Wood, P. D. P. 1967. Algebraic model of the lactation curve in cattle. Nature 216: 164-165. 191 AGRICULTURAL AND FOOD SCIENCE IN FINLAND Kettunen , A. & Mäntysaari, E.: Geneticparameters for test-day milk SELOSTUS Ayrshire-ensikoiden koelypsykohtaisen maidontuotannon perinnölliset tunnusluvut laktaation eri vaiheissa Anne Kettunen ja Esa A. Mäntysaari Maatalouden tutkimuskeskus Tutkimuksessa tarkasteltiin ayrshire-ensikoiden mai- dontuotannon perinnöllisiä tunnuslukuja laktaation eri vaiheissa koelypsykohtaisten mittausten perusteel- la. Aineisto koottiin Maatalouden Laskentakeskuksen karjantarkkailurekisteristä kolmen eri maaseutukes- kuksen alueelta (Pohjois-Savo, Keski-Pohjanmaa ja Lappi). Koelypsytulokset numeroitiin kymmeneen luokkaan sen mukaan, kuinka monta päivää oli ku- lunut poikimisesta mittauspäivään mennessä (DIM). Numeroidut koelypsyt luokiteltiin edelleen kolmek- si ominaisuudeksi lypsykauden vaiheen mukaan si- ten, että kaksi ensimmäistä koelypsyä muodostivat ensimmäisen ominaisuuden (1-60 DIM), neljä seu- raavaa toisen ominaisuuden (61-180 DIM) ja neljä viimeistä kolmannen ominaisuuden (181-305 DIM). Varianssikomponentit arvioitiin usean ominaisuuden toistuvuus-eläinmallilla käyttämällä REML-menetel- mää (restricted maximum likelihood) ja Al-algorit- mia (average information). Periytymisasteiden arviot koelypsykohtaisille maitotuotoksille laktaation eri vaiheissa (0,11-0,17) olivat alhaisempia kuin kirjal- lisuudessa esitetyt. Erityisesti laktaation keskivai- heen maitotuotos on yleisesti arvioitu voimakkaammin periytyväksi ominaisuudeksi kuin mihin tämän tutki- muksen tulos viittaa. Ominaisuuksien väliset geneet- tiset korrelaatiot vaihtelivat välillä 0,64-0,91. Periy- tymisasteiden ja geneettisten korrelaatioiden keski- virheet olivat pieniä (0,02-0,08). Alhaiset periytymis- asteiden arviot voivat olla seurausta käytetystä mal- lista tai aineiston rakenteesta aiheutuvasta mahdolli- sesta mallin tekijöiden päällekkäisyydestä. Tulosten perusteella voidaan päätellä, että maidontuotanto lyp- sykauden eri vaiheessa ei määräydy täydellisesti sa- mojen geenien perusteella. On todennäköistä, että eläinten paremmuusjärjestys jonkin verran poikkeaa koko lypsykauden koelypsytulosten perusteella teh- dyn arvostelun paremmuusjärjestyksestä, jos eläimet arvostellaan lypsykauden alun koelypsytulosten pe- rusteella. Tutkimuksen tulokset tukevat oletusta, että lypsykäyrän muodossa on additiivista geneettistä vaihtelua. Eläimen poikkeama yleisestä lypsykäyrästä on syytä mallintaa esimerkiksi satunnaisten regres- siokertoimien avulla, kun arvioidaan eläimen jalos- tusarvoa koelypsymallilla. Tällöin eläimen jalostus- arvoksi tulee sen tuotostason ja lypsykäyrän muodon yhdistelmä. 192 AGRICULTURAL AND FOOD SCIENCE IN FINLAND