Genotype by environment interaction for milk production traits between organic and conventional dairy cattle production in The Netherlands

Estimates of genetic parameters for organic dairy farming have not been published previously, and neither is information available on the magnitude of genotype by environment interaction (G×E) between organic and conventional farming. However, organic farming is growing worldwide and basic information about genetic parameters is needed for future breeding strategies for organic dairy farming. The goal of this study was to estimate heritabilities of milk production traits under organic farming conditions and to estimate the magnitude of G×E between organic and conventional dairy farming. For this purpose, production records of first-parity Holstein heifers were used. Heritabilities of milk, fat and protein yield, and somatic cell score (SCS) were higher under organic farming conditions. For percentages of fat and protein, heritabilities of organic and conventional production were very similar. Genetic correlations between preorganic and organic, and organic and conventional milk production were 0.79 and 0.80, respectively. For fat yield, these correlations were 0.86 and 0.88, and for protein yield, these were 0.78 and 0.71, respectively. Our findings indicate that moderate G×E was present for yield traits. For percentage of fat and protein and SCS, genetic correlations between organic and conventional and preorganic production were close to unity, indicating that there was no G×E for these traits.     

Environmental sensitivity in New Zealand dairy cattle

This study quantifies the extent of within-breed sire reranking for milk production traits in a range of environments encountered within New Zealand. Character states of herds were formed within the environmental ranges of herd fat plus protein (MS) yield, summer heat load index (HLI), herd size, and altitude. Single-trait and bivariate sire models across breeds were then applied for estimation of genetic parameters and genetic correlations between extreme character states. A low degree of sire reranking occurred, as measured by genetic correlations around 0.9, between herd environments that differed widely in MS yield (227 vs. 376 kg of MS per cow), and HLI (61.4 vs. 69.6). The HLI of 61.4 and 69.6 are approximately equivalent to average summer maximum temperatures of 19 and 25°C at 80% humidity. Correlations of sire estimated breeding values in extreme character states were low, but only one was below an expected correlation accounting for the reliability of prediction. The results show the environment in New Zealand is not sufficiently diverse to warrant separate breeding schemes for different environments.     

A genome scan for quantitative trait locus by environment interactions for production traits

Genotype by environment interactions between milk production traits and production level have often been observed. To increase the power of quantitative trait loci (QTL) detection, QTL by environment interaction was included in QTL analyses for the milk, protein, and fat yields. The aim of the study was to detect QTL with interaction effects with the production environment. The QTL effects were modeled through random regression models for within-herd production level. All autosomes except Bos taurus autosome 6 were included in the analysis. A more detailed study of chromosome 6 is planned. For milk yield, 5 QTL were observed, 2 of which had interaction effects with production level (suggestive linkage). For protein yield, 5 QTL were observed, 3 of which had interaction effects (suggestive linkage). For fat yield, 3 QTL were observed, none of which had interaction effects with the environment (suggestive linkage). Thus, some QTL with interaction effects seemingly exist for milk yield and protein yield. For such QTL, estimated correlations between slope and intercept of the effect (close to 1 or −1) indicated that only 2 alleles were segregating. The study indicates that QTL by environment interactions exist, and that random regression models that describe the environment as herd production level can detect this interaction.     

Fertility traits of Holstein,Brown Swiss,Simmental, and Alpine Grey cows are differently affected by herd productivity and milk yield of individual cows

Milk yield has a strong effect on fertility, but it may vary across different herds and individual cows. Therefore, the aim of this study was to assess the effects of breed and its interaction with level of milk production at the herd level (Herd-L) and at a cow-within-herd level (Cow-L) on fertility traits in dairy cattle. Data were gathered from Holstein (n = 17,688), Brown Swiss (n = 32,697), Simmental (n = 27,791), and Alpine Grey (n = 13,689) cows in northeastern Italy. The analysis was based on records from the first 3 lactations in the years 2011 to 2014. A mixed model was fitted to establish milk production levels of the various herds (Herd-L) and individual cows (Cow-L) using milk as a response variable. The interval fertility traits were interval from calving to first service, interval from first service to conception, and number of days open. The success traits were nonreturn rate at 56 d after first service, pregnancy rate at first service, and the number of inseminations. The interval from calving to first service, interval from first service to conception, and number of days open were analyzed using a Cox's proportional hazards model. The nonreturn rate at 56 d after first service, pregnancy rate at first service, and the number of inseminations were analyzed using logistic regression. There was a strong interaction between breed and productivity class at both Herd-L and Cow-L on all traits. The effects of herd and cow productivity differed from each other and differed among breeds. The dual-purpose Simmental and Alpine Grey breeds had better fertility than the specialized Holstein and Brown Swiss dairy cows; this difference is only partly attributable to different milk yields. Greater herd productivity can result in higher fertility in cows, whereas higher milk yield of individual cows within a herd results in lower fertility. These effects at both Herd-L and Cow-L are curvilinear and are stronger in dual-purpose breeds, which was more evident from low to intermediate milk yield levels than from central to high productivity classes. Disentangling the effects of milk productivity on fertility at Herd-L and Cow-L and taking the nonlinearity of response into account could lead to better modeling of populations within breed. It could also help with management—for example, in precision dairy farming of dairy and dual-purpose cattle. Moreover, assessing the fertility of various breeds and their different responses to herd and individual productivity levels could be useful in devising more profitable crossbreeding programs in different dairy systems.     

Quantitative trait loci affecting fertility and calving traits in Swedish dairy cattle

Impaired fertility is the main reason for involuntary culling of dairy cows in Sweden. The objective of this study was to map quantitative trait loci (QTL) influencing fertility and calving traits in the Swedish dairy cattle population. The traits analyzed were number of inseminations, 56-d nonreturn rate, interval from calving to first insemination, fertility treatments, heat intensity score, stillbirth, and calving performance. A genome scan covering 20 bovine chromosomes was performed using 145 microsatellite markers. The mapping population consisted of 10 sires and their 417 sons in a granddaughter design. Nine of the sires were of the Swedish Red Breed, and one was a Swedish Holstein. Least squares regression was used to map loci affecting the analyzed traits, and permutation tests were used to set significance thresholds. Cofactors were used in the analyses of individual chromosomes to adjust for QTL found on other chromosomes. The use of cofactors increased both the number of QTL found and the significance level. In the initial analysis, we found 13 suggestive QTL that were mapped to chromosomes 6, 7, 9, 11, 13, 15, 20, and 29. When cofactors were included, 30 QTL were detected on chromosomes 1, 3, 4, 18, 19, 22, and 25, in addition to the 8 previously mentioned chromosomes. Some of the results from the cofactor analysis may be false positives and require further validation. In conclusion, we were able to map several QTL affecting fertility and calving traits in Swedish dairy cattle.     

Fertility responses of Mexican Holstein cows to US sire selection

Genetic relationships between 2 fertility traits and milk production were investigated using mature-equivalent lactation records of 55,162 daughters of 1,339 Holstein sires in Mexico and 499,401 daughters of 663 Holstein sires in the northeastern United States. Data sets contained yields in first and second lactation, age at first calving (AFC), and calving interval (CI). There were 474 US sires in common between countries. A herd-year standard deviation criterion defined nonoverlapping low- (≤ 1,300 kg) and high- (≥ 1,600 kg) opportunity Mexican herd environments and a low-opportunity (≤ 1,024 kg) US environment. Genetic variances for the average Mexican herd (all data) for AFC and CI were 65 and 85% as large as those obtained from half-sisters in the average US herd. Genetic correlations for first-lactation milk in the average US herd and AFC and CI in the average Mexican environment were unfavorable (0.18 and 0.10). Regression coefficients of AFC in Mexican environments on US genetic gain in milk ranged from 2 to 7 d/1,000 kg. However, the favorable predicted response in AFC from genetic gain in milk in Mexican environments, like those in average US herds, ranged from − 4 to − 7 d/1,000 kg (r_g = − 0.20). This unequal AFC response may indicate genotype by environment interaction in fitness performance or differential breeding management of high and low yielding Mexican cows. The potential effects of age at first service of breeding females need to be disentangled to accurately assess genetic improvement needs for Mexican Holstein herds.     

Genotype x environment interaction for grazing versus confinement. I. Production traits

The objective of this study was to investigate the possible existence of a genotype x environment interaction (GxE) for production traits of US Holsteins in grazing versus confinement herds. Grazing herds were defined as those that utilized grazing for at least 6 mo and were enrolled in dairy herd improvement (DHI). Control herds were confinement DHI herds of comparable size in similar regions. The performance of daughters in grazing herds and control herds was examined using linear regression of mature equivalent milk, fat, and protein yield on the November 2000 USDA-DHI predicted transmitting abilities (PTA) of their sires for those traits. Heritabilities and genetic correlations were estimated using restricted maximum likelihood in a bivariate animal model that considered the same trait in different environments as different traits. Product-moment and rank correlations were calculated between sires' estimated breeding values, estimated separately in both environments. For grazing herds, the coefficient of regression of milk, fat and protein on PTA were 0.78, 0.76, and 0.78, respectively. Corresponding coefficients in the control herds were 0.99, 0.96, and 0.98. Estimates of heritability for the traits ranged from 0.2 to 0.25, and differences between grazing and control environments were small. Estimates of the genetic correlations for the traits in both environments were 0.89, 0.88, and 0.91 for milk, fat, and protein, respectively. Within-quartile analyses revealed a lower correlation for milk and protein between the upper and lower grazing quartiles, while the same quartiles for the control herds did not differ from unity. Rank correlation coefficients between sire estimated breeding values from the 2 environments were 0.59, 0.63, and 0.66 for milk, fat, and protein, respectively. The mean rank change for the top 100 sires between the two environments was 27. The regression coefficients indicate that expected daughter differences may be overstated by current sire PTA in grazing herds. Genetic correlations less than unity suggests that there is, at least, some reranking among sires in both environments, while the rank correlations indicate the possibility of sire reranking when evaluations were performed within management system. However, differences are not so large as to justify separate genetic evaluations for each system.     

Genotype x environment interaction for age at first calving in Brazilian and Colombian Holsteins

The objective was to determine whether there is a genotype x environment interaction for age at first calving (AFC) in Holstein cattle in Brazil and Colombia. Data included 51,239 and 25,569 first-lactation records from Brazil and Colombia, respectively. Of 4230 sires in the data, 530 were North American sires used in both countries. Analyses were done using the REML bi-trait animal model, and AFC was considered as a distinct characteristic in each country. Fixed effects of contemporary group (herd-calving year), sire genetic group, and cow genetic group, and random effects of animal and residual variation were included in the model. Average AFC in Brazil and Colombia were 29.5 +/- 4.0 and 32.1 +/- 3.5 mo, respectively. Additive and residual genetic components and heritability coefficient for AFC in Brazil were 2.21 mo2, 9.41 mo2, and 0.19, respectively, whereas for Colombia, they were 1.02 mo2, 6.84 mo2, and 0.13, respectively. The genetic correlation of AFC between Brazil and Colombia was 0.78, indicating differences in ranking of sires consistent with a genotype x environment interaction. Therefore, in countries with differing environments, progeny of Holstein sires may calve at relatively younger or older ages compared with contemporary herdmates in one environment versus another.     

Culling reasons in organic and conventional dairy herds and genotype by environment interaction for longevity

Dairy cow longevity combines all functional traits and is thought to be especially important in organic production, which is an established, increasing part of Swedish dairy production, representing approximately 6% of the market. The aim of this study was to compare dynamics in culling reasons between organic and conventional production and to analyze genotype by environment interactions for longevity. The data contained information from all organic herds with information available from official recording (n = 402) and from approximately half of the conventional herds (n = 5,335). Records from Swedish Holsteins (n = 155,379) and Swedish Red cows (n = 160,794) that had their first calf between January 1998 and September 2003 were included. The opportunity period for longevity was at least 6 yr. Six longevity traits were defined: length of productive life; survival through first, second, and third lactations; fertility-determined survival; and udder health-determined survival. Twenty codes were used to describe the cause of culling, and these were divided into 8 groups: udder health, low fertility, low production, leg problems, metabolic diseases, other diseases, other specified causes, and unspecified cause. The main reason for culling cows in organic herds was poor udder health, whereas for cows in conventional herds it was low fertility. Furthermore, the shift in main culling reason from fertility, which was most common in first lactation regardless of production system, to udder health occurred at a lower age in organic production. Heritabilities and genetic correlations for the longevity traits expressed in organic and conventional herds were estimated from a bivariate animal model. The genetic correlations were close to unity (>0.88), except for fertility-determined survival in the Swedish Red breed (0.80). Heritabilities were low to moderate, and no clear pattern was identified for production system or breed. In general, the results indicate that farmers’ culling criteria differ between organic and conventional production. Different preferences may influence the need for alternative selection indexes for organic production, with different weightings of traits, or a separate breeding program. However, no genotype by environment interaction of importance was found between the production systems.     

Short communication: Effect of environment on the expression of breed and heterosis effects for production traits

Character states of New Zealand herds were formed within the environmental ranges of herd average total lactation yield of fat plus protein (MS), which is a proxy for feeding level, summer heat load index (HLI), herd size, and altitude. A univariate multibreed sire model was applied to first-lactation (2 yr old) records of milk, fat, and protein within each environmental character state to estimate breed and heterosis effects. A scaling effect was observed for MS yield between overseas Holstein-Friesian (OHF) and New Zealand Jersey (NZJ) animals when comparing breed performance in extreme MS character states. For example, differences for milk, fat, and protein yield between these breeds were 561, 1.3, and 9.3 kg, respectively, in the character state averaging 227 kg of MS/cow, much smaller than the differences of 1,151, 3.1, and 23.0 in the character state averaging 376 kg of MS/cow. Heterosis levels for milk, fat, and protein yields were highest for OHF × NZJ, followed by New Zealand Friesian (NZF) × NZJ and OHF × NZF with average heterosis for all traits of 7.3, 5.7, and 2.7%, respectively. Heterosis levels for OHF × NZF were suppressed in very low MS yield environments and in many cases were not significantly different from zero. Heterosis was suppressed in crosses with OHF in the high HLI environment. Crossbred animals (OHF × NZJ, NZF × NZJ, and OHF × NZF) generally achieved higher fat yields than any of the straight-bred animals.     

Genotype x environment interaction for grazing vs. confinement. II. Health and reproduction traits

Continual selection for increased milk yield for more than 40 yr, combined with the antagonistic relationship between increasing yield, somatic cell count, and fertility, have resulted in sires that may not be optimal for producing daughters for grazing systems where seasonal calving is very important. The objective of this study was to investigate the possible existence of a genotype x environment interaction (G x E) in grazing vs. confinement herds within the United States for lactation average somatic cell score (LSCS), days open (DO), days to first service (DFS), and number of services per conception (SPC). Grazing herds were defined as those that utilized grazing for at least 6 mo each year and were enrolled in Dairy Herd Improvement (DHI). Control herds were confinement DHI herds of similar size in the same states. For LSCS, the performance of daughters in grazing and control herds was examined using linear regression of LSCS on the November 2000 USDA-DHIA sire predicted transmitting abilities (PTA) for SCS. Genetic parameters for all traits were estimated using REML in a bivariate animal model that treated the same trait in different environments as different traits. Rank correlations were calculated between sires' estimated breeding values for LSCS, calculated separately for sires in both environments. The coefficient of regression of daughter LSCS on sire PTA was less in grazing herds than in control herds. The coefficient of regression for control herds was closer to expectation. Estimates of heritability were approximately 0.12 for LSCS, and less than 0.05 for the reproduction traits. Heritabilities for DO, DFS, and SPC were slightly higher for control herds. Estimates of genetic correlation for each reproductive trait between the 2 environments were high and not significantly different from unity. Generally, these traits appear to be under similar genetic control, but a lower coefficient of regression of LSCS on sire PTA for SCS in grazing herds suggests differences in daughter performance in grazing herds may be overstated based on current PTA for SCS.     

Genome-wide mapping of 10 calving and fertility traits in Holstein dairy cattle with special regard to chromosome 18

Over the last decades, a dramatic decrease in reproductive performance has been observed in Holstein cattle and fertility problems have become the most common reason for a cow to leave the herd. The premature removal of animals with high breeding values results in both economic and breeding losses. For efficient future Holstein breeding, the identification of loci associated with low fertility is of major interest and thus constitutes the aim of this study. To reach this aim, a genome-wide combined linkage disequilibrium and linkage analysis (cLDLA) was conducted using data on the following 10 calving and fertility traits in the form of estimated breeding values: days from first service to conception of heifers and cows, nonreturn rate on d 56 of heifers and cows, days from calving to first insemination, days open, paternal and maternal calving ease, paternal and maternal stillbirth. The animal data set contained 2,527 daughter-proven Holstein bulls from Germany that were genotyped with Illumina's BovineSNP50 BeadChip (Illumina Inc., San Diego, CA). For the cLDLA, 41,635 sliding windows of 40 adjacent single nucleotide polymorphisms (SNP) were used. At each window midpoint, a variance component analysis was executed using ASReml. The underlying mixed linear model included random quantitative trait locus (QTL) and polygenic effects. We identified 50 genome-wide significant QTL. The most significant peak was detected for direct calving ease at 59,179,424 bp on chromosome 18 (BTA18). Next, a mixed-linear model association (MLMA) analysis was conducted. A comparison of the cLDLA and MLMA results with special regard to BTA18 showed that the genome-wide most significant SNP from the MLMA was associated with the same trait and located on the same chromosome at 57,589,121 bp (i.e., about 1.5 Mb apart from the cLDLA peak). The results of 5 different cLDLA and 2 MLMA models, which included the fixed effects of either SNP or haplotypes, suggested that the cLDLA method outperformed the MLMA in accuracy and precision. The haplotype-based cLDLA method allowed for a more precise mapping and the definition of ancestral and derived QTL alleles, both of which are essential for the detection of underlying quantitative trait nucleotides.     

Genetic analysis of new progesterone-based fertility traits in dairy cows measured on-farm

The objectives of this study were (1) to analyze the agreement of a standard laboratory ELISA for progesterone (P4) with an automated on-farm ELISA kit operated under commercial conditions in 1,297 milk samples from 50 dairy cows; (2) to study the influence of the method of detection of luteal activity on genetic parameters of fertility traits based on P4 measured with an automated on-farm ELISA once weekly from wk 3 to 9 postpartum in the milk of 1,304 cows; and (3) to study the influence of sampling frequency (once or twice weekly from wk 3 to 9) on the same traits from 296 cows. Luteal activity can be detected when there is an active corpus luteum in the ovary producing P4 and indicating the onset of reproductive cyclicity after calving. The on-farm ELISA overestimated P4 contents by a mean square error of prediction of 2.76 ng/mL and had an intermediate Spearman correlation with the laboratory kit (0.54). For the second objective, the postpartum interval to the commencement of luteal activity (C-LA), proportion of luteal activity between d 15 and 63 postpartum (P-LA), calculated as the number of samples above the threshold for high P4 values divided by the number of all samples, and delay of first ovulation (DOV1), defined as C-LA occurring later than d 45 postpartum, were derived from the P4 profiles. Both C-LA and DOV1 were determined by (a) thorough qualitative visual inspection of the profile, (b) the profile's mean as threshold for the first increase in P4 postpartum, indicating commencement of luteal activity, and (c) 3 ng/mL as threshold for the first increase in P4, a value that has been used by many other studies. Similarly, P-LA was determined by using methods (b) and (c). Estimates of heritability were 0.04 to 0.13 for C-LA, 0.12 to 0.23 for P-LA, and 0.03 to 0.07 for DOV1. Genetic correlation of P-LA with C-LA and with the profile's mean P4 was ?1.00. The profile's mean had a higher estimate of heritability (0.11–0.12) than C-LA or DOV1. It can be calculated as the arithmetic mean of all P4 values of a profile, whereas C-LA, P-LA, and DOV1 need a definition of a threshold for high P4 values. We therefore suggest the profile's mean as a promising candidate for further research. For the third objective, once-weekly sampling was mimicked by neglecting every second sample, and C-LA and DOV1 shifted toward a later onset of cyclicity. Thus, a common standard for sampling regimen and detection algorithm is essential to avoid incompatibility between studies.     

Genetics of body condition score in New Zealand dairy cows

Body condition score (BCS) data were collected on 169,661 first-parity cows from herds participating in progeny testing schemes and linear type assessment. Genetic and residual variances for BCS estimated across time using a quadratic random regression model were found to be largest at the start of lactation. Heritability estimates ranged from 0.32 to 0.23 from d 1 to 200 of lactation, with a mean of 0.26. Genetic correlations between BCS and other traits were estimated using 2 approaches: 1) a multivariate analysis that included BCS and live weight, both adjusted for stage of lactation; 270-d cumulative yields of milk, fat, and protein; average somatic cell score; and 2 measures of fertility; and 2) a bivariate random regression analysis in which BCS was considered to be a longitudinal trait across time, with the same measurements as in approach 1 for all other traits. Genetic correlations of BCS with the 2 fertility traits were 0.43 and 0.50 using the multivariate analysis; the corresponding random regression estimates between BCS as a longitudinal trait across time and 2 measures of fertility were 0.35 to 0.44 and 0.40 to 0.49, and tended to increase with stage of lactation. Genetic correlations estimated using the random regression model fluctuated around the multivariate estimates for live weight and somatic cell score, which were 0.50 and −0.12, respectively. Genetic correlations estimated using the multivariate analysis of BCS with fat and protein yields were close to zero. With the random regression model, genetic correlations between BCS and fat and protein yields were positive at d 1 of lactation (0.16 and 0.08, respectively) and were negative by d 200 of lactation (−0.25 and −0.20, respectively). In pastoral production systems, such as those typical in New Zealand, there appears to be an advantage in the total lactation yields of fat and protein for cows of higher BCS in early lactation, which is likely to be because these cows have body reserves that are available to be mobilized in later lactation, when feed resources are sometimes limited.     

Genetic and phenotypic relationships among endocrine and traditional fertility traits and production traits in Holstein-Friesian dairy cows

The objective of this study was to estimate the heritability of a number of traditional and endocrine fertility traits in addition to d-56 predicted milk yield (MY56), and the genetic and phenotypic correlations between these traits. Various fixed effects such as season, year, herd, lactation number, diet, percentage Holstein (PCH) of the cow, and occurrence of uterine infection (UI), dystocia (DYS), and retained placenta (RP) were also investigated. Data collected for 1212 lactations of 1080 postpartum (PP) Holstein-Friesian dairy cows in eight commercial farms between 1996 and 1999 included thrice weekly milk progesterone samples, calving and insemination dates, various reproductive health records, monthly/bimonthly production records, three-generation pedigrees, and PCH information. Genetic models were fitted to the data to obtain heritabilitites and correlations using ASREML. Estimates of heritability for interval to commencement of luteal activity PP (lnCLA), length of the first luteal phase PP (lnLutI) and occurrence of persistent CL type I (PCLI) were 0.16, 0.17, and 0.13, respectively. Heritabilities for pregnancy to first service (PFS), interval to first service (IFS), and MY56 were 0.14, 0.13, and 0.50, respectively. Genetic regressions of lnCLA and lnLutI on PTA of the sire for milk, fat, and protein yields, and PIN95 were investigated. Regressions of lnCLA were positive and significant on fat yield, while regressions of lnLutI on both protein yield and PIN95 were negative and significant. Genetic correlations of endocrine fertility traits (lnCLA, lnLutI, and PCLI) with MY56 were high (0.36, P < 0.05; -0.51, P < 0.05; and -0.31, P < 0.1, respectively). Percentage Holstein of the cows had no significant effect on any of the fertility parameters monitored. This work emphasizes the strong genetic correlation of fertility with production traits and, therefore, highlights the urgent requirement for selective breeding for fertility in the United Kingdom. The high heritability of endocrine fertility traits stress their potential value for inclusion in a selection index to improve fertility.     

Phenotypic and genetic effects of season on milk production traits in dairy cattle in the Netherlands

Milk production systems in several countries show considerable differences between seasons. For example, in the Netherlands, cows are kept inside and fed silage in winter, whereas they are on pasture in summer. The differences between seasons affect milk yield and composition and might influence the genetic background of milk production traits. The objective of this study was to estimate phenotypic and genetic effects of season on milk production traits. For this purpose, 19,286 test-day milk production records of 1,800 first-parity Dutch Holstein-Frisian cows were available, and these cows were genotyped using a 50K SNP panel. Phenotypic effects of season were significant for all milk production traits. Effects of season were large for milk fat yield, fat content, and protein content. Genetic correlations between milk production traits in different seasons showed that genotype by season interaction effects were relatively small for most milk production traits. The genetic background of protein content and lactose content seems to be sensitive to seasonal effects. Furthermore, the genetic correlations between spring and autumn differed significantly from unity for almost all milk production traits. A genome-wide association study for genotype by season interaction identified chromosomal regions on BTA3, BTA14, BTA20, and BTA25 that showed genotype by season interaction effects, including a region containing DGAT1, which showed interaction effects for fat content and protein content.     

Genotype x environment interaction for yield and somatic cell score with automatic and conventional milking systems

The objective of this study was to quantify genotype by environment interaction (G x E) between automatic milking systems (AMS) and conventional milking systems (CMS) for test-day milk, fat, and protein yield and for test-day somatic cell score (SCS) in The Netherlands. The G x E was studied in 2 ways: 1) between AMS farms and CMS farms in the same period and 2) within farms comparing the period before introduction of AMS with the period after introduction of AMS. For both sub-objectives, a separate data set was generated. Test-day records were used to be more flexible with respect to the introduction date of AMS. Multivariate, fixed regression, test-day sire models were used to estimate variance components. Genetic correlations between AMS farms and CMS farms in the same period were 0.93, >0.99, 0.98, and 0.79 for test-day milk yield, fat yield, protein yield, and SCS, respectively. Genetic correlations within farms between the period before and after introduction of AMS were lower for production traits and higher for SCS: 0.89, 0.91, 0.87, and >0.99, respectively, for test-day milk yield, fat yield, protein yield, and SCS. Heterogeneity of variance was observed between AMS and CMS in both data sets. Especially the residual variance increased with automatic milking. As a consequence, the heritability tended to be lower for automatic milking. It was concluded that effects of G x E are small between AMS and CMS. Therefore, AMS farms can select sires accurately based on national rankings.