Reaction norm model to describe environmental sensitivity across first lactation in dairy cattle under tropical conditions

Heat stress on breeding value prediction for milk yield and composition of a Brazilian Holstein cattle population

Due to the high milk production of Holstein cows, many countries have chosen to import semen to improve local dairy herds. This strategy would be more effective if this semen was used in the same environment conditions in which the bulls were selected. If the effect of genotype by environment (G × E) interaction is not considered, the estimated breeding values (EBVs) may vary, potentially reducing the selection response. We evaluate the impact of heat stress on selection for milk yield and composition of Holstein cows using random regression models. To verify the interference of heat stress in milk yield (MY) and composition traits (fat, protein, total saturated, and total unsaturated fatty acids content in milk), temperature-humidity index (THI) on test-day milk records was used. The threshold value to divide the environments using test-day information from Brazilian Holstein cows was 72 units of THI, i.e., < 72 represented no heat stress and > 72 represented heat stress. Legendre polynomials of second-order (Leg 2) model and two lactation points (33 and 122 DIM) were used to estimate heritabilities and EBVs for five important dairy traits. The heritabilities of milk components and fatty acids were low (0.09-0.29), regardless of lactation period and degree of heat stress, with the exception of protein content (0.30-0.35). Fat content was the only milk component that was reduced according to the degree of heat stress and lactation period. The EBVs tended to decrease in heat stress conditions, thus animals with high genetic potential demonstrated evidence of G × E interaction. However, acclimatization of dairy cows to heat stress in the farm production systems may have been responsible for the low differences among genetic parameters and EBVs with and without heat stress found in this study.

Genetic principal components for reproductive and productive traits in Holstein cows reared under tropical conditions

The objective of this study was to compare the standard multi-trait model and five reduced-rank models fitted to the first principal components and genetic parameter estimates in order to determine the most appropriate method to model the covariance structure of reproductive and productive traits in Brazilian Holstein cows. Individual records of the following traits from 5217 cows were analyzed: 305-day milk yield (MY305), peak yield, milk yield per day of calving interval, days from calving to first estrus, days from calving to last service (CLS), calving interval (CI), and gestation length. Schwarz's Bayesian information criterion was used to compare the different models. The results indicated that four principal components were necessary to model the genetic (co)variance structure, reducing the number of parameters to be estimated. Analysis of genetic and phenotypic correlations showed that milk production-related traits were strongly correlated with each other (ranging from 0.74 to 0.99), while the correlation of these traits with the reproductive traits was weak (ranging from - 0.14 to 0.27). Heritability estimates for the traits ranged from 0.03 to 0.18. The reproductive traits CLS and CI and the production trait MY305 should be included as selection criteria in dairy cattle breeding programs because they are correlated with the first two principal components, retaining 91% of the genetic variability of the data.

Assessment of Genotype by Environment Interaction Via Reaction Norms for Milk Yield in Holstein Cattle of Southern Brazil

The experiments reported in this research paper were aimed at assessing the genetic responses of a Holstein cow population, as a response to the variations in environmental temperature, through the analysis of the effects resulting from the genotype by environment interaction (GEI), based on reaction norms. Therefore, milk production data was collected from the database of the Paraná Holstein Breeders Association in Brazil for 67,360 primiparous cows born between 1990 and 2015, with the purpose of evaluating the temperature effect, considered as an environmental variable, distinguished under six gradients (17 ºC to 19.5 ºC) over the region. A random regression model was adopted, utilizing the fourth order under the Legendre polynomials, applying the mixed models of analysis by the REML method, and using the WOMBAT software. Additionally, the breeding value of the 15 most representative bulls was assessed, in response to the changes in the temperature gradient. The total milk production on average was estimated at 8,412.83 ± 2,012.08 kg. The heritabilities estimates were found in the low to moderate range, from 0.18 to 0.23, displaying a decline with a rise in the temperature, highlighting the influence it exerted on the heritabilities. Variations in the genetic expression of some bulls were noted to show differences of up to 289 kg of milk in response to the increase in the temperature from 17 ºC to 19.5 ºC. However, all the genetic correlations between the gradients for milk yield were above 0.80, in the range of 0.873 to 0.998, revealing no remarkable interaction between the genotype and environment. This result indicates that the application of the temperature variable in the models of genetic analysis in southern Brazil is not required.

Genotype-environment interaction for productive traits of Holstein cows in Brazil described by reaction norms

Brazil is the world's fourth largest milk producer; this activity extends throughout the national territory, with productions of approximate 34 billion liters of milk per year. The objective of this work was to evaluate the genotype-environment interaction (GEI) in purebred Holstein cows for milk (M), protein (P), and fat (F) yields. The statistical models used were the standard animal model (AM), which disregards the GEI, and hierarchical reaction norm models with homoscedastic (HRNMHO) and heteroscedastic (HRNMHE) residual variance, and one (HRNMHO1S and HRNMHE1S) and two (HRNMHO2S and HRNMHE2S) steps. HRNMHO1S presented a better fit of the data for all traits, with higher heritability for the best environments. Most bulls presented robust phenotypes; however, GEI was found with a reclassification of the bulls in the environmental gradient. Although few, and less used, bulls with plastic phenotypes were found for all traits, and the use of them can optimize genetic gains in specific environments.