Two approaches to account for genotype-by-environment interactions for production traits and age at first calving in South African Holstein cattle

BACKGROUND

If not accounted for, genotype x environment (G×E) interactions can decrease the accuracy of genetic evaluations and the efficiency of breeding schemes. These interactions are reflected by genetic correlations between countries lower than 1. In countries that are characterized by a heterogeneity of production systems, they are also likely to exist within country, especially when production systems are diverse, as is the case in South Africa. We illustrate several alternative approaches to assess the existence of G×E interactions for production traits and age at first calving in Holsteins in South Africa. Data from 257,836 first lactation cows were used. First, phenotypes that were collected in different regions were considered as separate traits and various multivariate animal models were fitted to calculate the estimates of heritability for each region and the genetic correlations between them. Second, a random regression approach using long-term averages of climatic variables at the herd level in a reaction norm model, was used as an alternative way to account for G×E interactions. Genetic parameter estimates and goodness-of-fit measures were compared.

RESULTS

Genetic correlations between regions as low as 0.80 or even lower were found for production traits, which reflect strong G×E interactions within South Africa that can be linked to the production systems (pasture vs total mixed ration). A random regression model including average rainfall during several decades in the herd surroundings gave the best goodness-of-fit for production traits. This can be related to a preference for total mixed ration on farms with limited rainfall. For age at first calving, the best model was based on a random regression on maximum relative humidity and maximum temperature in summer.

CONCLUSIONS

Our results indicate that G×E interactions can be accounted for when genetic evaluations of production traits are performed in South Africa, by either considering production records in different regions as different correlated traits or using a reaction norm model based on herd management characteristics. From a statistical point of view, climatic variables such as average rainfall over a long period can be included in a random regression model as proxies of herd production systems and climate.

The incorporation of fertility indices in genetic improvement programmes

In recent years there has been considerable genetic progress in milk production. Yet, increases in yield have been accompanied by an apparent lengthening of calving intervals, days open, days to first heat and a decline in conception rates, which appears to be both at the genetic and phenotypic level. Fertility has a high relative economic value compared to production traits such as protein, making it attractive to include in a breeding programme. To do this there needs to be genetic variance in fertility. Measures of fertility calculated from service dates have a small genetic compared to phenotypic variance, hence heritability estimates are small, typically less than 5%, although coefficients of genetic variance are comparable to those of production traits. Heritabilities of commencement of luteal activity determined using progesterone profiles are generally higher, and have been reported as being from 0.16 to 0.28, which could be because of a more precise quantification of genetic variance, as management influences such as delaying insemination and heat detection rates are excluded. However, it might not be the use of progesterone profiles alone, as days to first heat observed by farm staff has a heritability of 0.15. The most efficient way to breed for improved fertility is to construct a selection index using the genetic and phenotypic parameter estimates of all traits of interest in addition to their respective economic values. Index traits for fertility could include measures such as calving interval, days open, days to first service, or days to first heat but there may also be alternative measures. Examples include traits related to energy balance, such as live weight and condition score (change), both of which have higher heritabilities than fertility measures and have genetic correlations of sufficient magnitude to make genetic progress by using them feasible. To redress the balance between fertility and production, some countries already publish genetic evaluations of fertility including: Denmark, Finland, France, Germany, Israel, The Netherlands, Norway and Sweden.

Genetic parameters for an alternative criterion to improve productive longevity of Nellore cows

Number of calvings at 53 mo (NC53) was proposed as an alternative selection criterion to improve productive longevity of Nellore cows. This study was carried out to estimate variance components for NC53 by assuming different models, so that the potential for using this selection criterion to improve fertility of Nellore cows could be assessed. Genetic correlations between NC53, number of calvings at 89 mo (NC89), and 2 selection indexes used in this breed were also estimated. The NC53 trait is moderately heritable (posterior mean heritability ≈ 0.17) and selecting for this criterion could improve productive longevity of Nellore cows. Greater response to selection is expected by fitting a threshold animal model for this trait, rather than a linear animal model. Greater accuracy of prediction for this criterion could be achieved by fitting a threshold-linear model, considering this trait and a selection index composed by traits evaluated at weaning and long-yearling.

Short Communication: Changes in the Association Between Milk Yield and Age at First Calving in Holstein Cows with Herd Environment Level for Milk Yield

The aim of this study was to evaluate the effect of herd environment class on the genetic and phenotypic relationships of mature equivalent milk yield (MY) on age at first calving (AFC). Data analyzed were 248,230 first parity records of Holstein cows, daughters of 588 sires in 3,042 herds in the United States. Heritability for AFC was 0.33 +/- 0.01 and 0.20 +/- 0.01 in high and low environment herds, respectively, and 0.47 +/- 0.01 in the complete data set. The correlation between AFC sires' predicted breeding values of low and high classes was 0.69. Genetic correlations between MY and AFC were -0.52 +/- 0.02 and -0.31 +/- 0.03 in high and low environment herds, respectively, and -0.44 +/- 0.02 in the complete data set representing intermediate environments. If selection is based on the whole data set, expected correlated responses for AFC estimated as a result of 1,000 kg of genetic gain in MY, for high and low herd environment herds were -26.1 and -15.3 d, respectively, and -32.6 for the complete data set; hence the highest reduction in AFC occurs in intermediate environment herds. Different estimates of the heritability of AFC, the correlation between AFC breeding values of low and high classes as well as changes in the genetic correlation between MY and AFC across environments indicate genotype x environment interaction. Caution in interpretation is warranted because genetic relationships are dynamic, especially in populations undergoing selection. Current relationships may differ from those during the time period of the present study (1987-1994). Notwithstanding this possibility, methods and findings from the present study provide insight about the complexity of genetic association and genotype x environment interactions between AFC and MY.

Genetic relationships among body condition score, body weight, milk yield, and fertility in dairy cows

Genetic (co)variances between body condition score (BCS), body weight (BW), milk production, and fertility-related traits were estimated. The data analyzed included 8591 multiparous Holstein-Friesian cows with records for BCS, BW, milk production, and/or fertility from 78 seasonal calving grass-based farms throughout southern Ireland. Of the cows included in the analysis, 4402 had repeated records across the 2 yr of the study. Genetic correlations between level of BCS at different stages of lactation and total lactation milk production were negative (-0.51 to -0.14). Genetic correlations between BW at different stages of lactation and total lactation milk production were all close to zero but became positive (0.01 to 0.39) after adjusting BW for differences in BCS. Body condition score at different stages of lactation correlated favorably with improved fertility; genetic correlations between BCS and pregnant 63 d after the start of breeding season ranged from 0.29 to 0.42. Both BW at different stages of lactation and milk production tended to exhibit negative genetic correlations with pregnant to first service and pregnant 63 d after the start of the breeding season and positive genetic correlations with number of services and the interval from first service to conception. Selection indexes investigated illustrate the possibility of continued selection for increased milk production without any deleterious effects on fertility or average BCS, albeit, genetic merit for milk production would increase at a slower rate.

Test-day genetic analysis of condition score and heart girth in Holstein Friesian cows

This study aimed to estimate heritability for condition score and heart girth using a test-day model, to investigate the genetic relationships between condition score, heart girth, and milk yield traits and to analyze the genetic relationships of condition score and heart girth measured at different stages of lactation. Cows from 25 dairy herds were scored for body condition and measured for heart girth at 3-mo intervals for 2 yr. Approximately 5000 test-day observations on condition score, heart girth, and milk fat and protein yield from 1344 Italian Friesian cows were analyzed using two approaches: 1) repeated observations for a trait were considered repeated measurements of the same trait; 2) observations for a trait collected in different stages of lactation (dry period, 1 to 75, 76 to 130, 131 to 210, and 211 to 300 DIM) were treated as different traits. (Co)variance components and related parameters were estimated using REML multiple-trait procedures and animal models with unequal design for different traits. Heritability estimates for fat and protein test-day yield and for test-day condition score and heart girth were 0.22, 0.18, 0.29, and 0.33, respectively. Condition score was negatively correlated with yield traits and positively correlated with heart girth, whereas genetic relationships between heart girth and milk yield traits were negligible. Heritability estimates were 0.27 for condition score recorded in the first half of lactation (1 to 75 and 76 to 130 DIM), 0.36 for condition score in the second half of lactation (131 to 210 and 211 to 300 DIM) and 0.32 for condition score recorded on dry cows. Genetic correlations between condition scores measured in different lactation stages were generally high (0.85 or more), with the exception of the relationships between the first and the last stage of lactation (0.74) and between the first half of lactation and the dry period (0.7). Heritability estimates for heart girth in different lactation stages ranged from 0.31 to 0.40, and genetic correlations between high girth measured in different lactation stages were higher than 0.80.

Genetic parameters and trends of milk, fat, days open, and body weight after calving in North Carolina experimental herds

The main objective of this study was to estimate genetic trends for 3.7% FCM, fat yield, days open, and predicted body weight after calving in six experimental dairy herds owned by the State Farm Division of the North Carolina Department of Agriculture. Body weights were predicted from heart girths measured at or before the first test day after calving. Data analyzed were 23,052 records from 8575 cows, daughters of 681 bulls. Heritabilities and breeding values were estimated using the multiple-trait, derivative-free REML programs and a single-trait repeatability model. Breeding values of cows were averaged by and regressed on birth year to estimate genetic trends. Genetic correlations between traits were estimated by correlating breeding values. Estimates of heritability were 0.25 for 3.7% FCM, 0.28 for fat yield, 0.03 for days open, and 0.17 for predicted body weight. Unfavorable genetic relationships were found between yields and days open and between yields and body weight. Genetically, cows that were heavier after calving produced less milk and fat but conceived earlier than smaller cows. Genetic changes in yields and days open were greater for cows born after 1970, but the greatest genetic changes were after 1980 (FCM, 94.7 kg/yr; fat yield, 3.46 kg/yr; days open, 1.1 d/yr). Breeding values for body weight increased for cows born from 1950 to 1970, decreased until 1980, and increased for later parities. The results of our study suggest that AI organizations may need to include fertility traits in progeny testing and relax the emphasis on increased body weight.

Selection for economic efficiency of dairy cattle using information on live weight and feed intake: a review

The objective of this study was to review some of the latest evidence of genetic variation in feed intake and feed utilization and to determine how this variation might be used. The most important sources of genetic variation in gross efficiency are likely to be the quantities of feed eaten and used for yield or maintenance and the extent to which body tissue is mobilized. Accounting for just one of these components when selection is for improved feed efficiency might result in undesirable genetic changes. For example, in an ad libitum feeding system, the heritability of body condition score is reported to be 0.43 for heifers; genetic correlations of body condition score with milk production and live weight were -0.46 and 0.66, respectively. Also, the genetic correlation between milk yield and live weight depends on lactation stage. For example, over the first 26 wk of lactation, this correlation was reported to be -0.09, but, after genetic adjustment for body condition score, the correlation was 0.29. When economic values are being derived, energy norms or genetic correlations can be used, and double counting of the feed costs needs to be avoided. An index that contained linear type traits, however, gave high accuracy of selection. Hence, although there appears to be great potential to improve economic efficiency by selecting for feed intake and live weight or by possible indicator traits, there is still uncertainty about some of the genetic parameters, especially among traits related to health, reproduction, and energy balance.