The availability of accurate genetic parameters for important economic traits in milking buffaloes is critical for implementation of a genetic evaluation program. In the present study, heritabilities and genetic correlations for fat (FY305), protein (PY305), and milk (MY305) yields, milk fat (%F) and protein (%P) percentages, and SCS were estimated using Bayesian methodology. A total of 4,907 lactations from 1,985 cows were used. The (co)variance components were estimated using multiple-trait analysis by Bayesian inference method, applying an animal model, through Gibbs sampling. The model included the fixed effects of contemporary groups (herd-year and calving season), number of milking (2 levels), and age of cow at calving as (co)variable (quadratic and linear effect). The additive genetic, permanent environmental, and residual effects were included as random effects in the model. The posterior means of heritability distributions for MY305, FY305, PY305, %F, P%, and SCS were 0.22, 0.21, 0.23, 0.33, 0.39, and 0.26, respectively. The genetic correlation estimates ranged from -0.13 (between %P and SCS) to 0.94 (between MY305 and PY305). The permanent environmental correlation estimates ranged from -0.38 (between MY305 and %P) to 0.97 (between MY305 and PY305). Residual and phenotypic correlation estimates ranged from -0.26 (between PY305 and SCS) to 0.97 (between MY305 and PY305) and from -0.26 (between MY305 and SCS) to 0.97 (between MY305 and PY305), respectively. Milk yield, milk components, and milk somatic cells counts have enough genetic variation for selection purposes. The genetic correlation estimates suggest that milk components and milk somatic cell counts would be only slightly affected if increasing milk yield were the selection goal. Selecting to increase FY305 or PY305 will also increase MY305, %P, and %F.

The aim of this paper was to analyze the drying kinetics, test the Akaike information criterion (AIC) and Schwarz’s Bayesian information criterion (BIC) in the selection of models, determine the effective diffusivity and activation energy of the crushed mass of ‘jambu’ leaves for different conditions of temperature and layer thicknesses. The experiment was carried out at the Food Laboratory of the Brazilian Agricultural Research Corporation (Embrapa) in Macapá-AP. Drying was carried out in air circulation oven with speed of 1.0 m s^-1 at various temperatures (60, 70 and 80 ºC) and layer thicknesses (0.005 and 0.010 m). The experimental data were fitted to 11 mathematical models. Coefficient of determination (R²), mean relative error (P), mean estimated error (SE), Chi-square test (χ²), AIC and BIC were the selection criteria for the models. For the effective diffusivity, the Fick’s diffusion model was used considering the flat plate geometry. It was found that Midilli and Logarithmic models showed the best fit to the experimental data of drying kinetics. Effective diffusion coefficient increases with increment in the thickness of the material and with the temperature elevation. Activation energy of the material was of 16.61 kJ mol^-1 for the thickness of 0.005 m, and 16.97 kJ mol^-1 for the thickness of 0.010 m. AIC and BIC can be additionally included to select models of drying.

When the environment on which the animals are raised is very diverse, selecting the best sires for different environments may require the use of models that account for genotype by environment interaction (G × E). The main objective of this study was to evaluate the existence of G × E for yearling weight (YW) in Nellore cattle using reaction norm models with only pedigree and pedigree combined with genomic relationships. Additionally, genomic regions associated with each environment gradient were identified. A total of 67,996 YW records were used in reaction norm models to calculate EBV and genomic EBV. The method of choice for genomic evaluations was single-step genomic BLUP (ssGBLUP). Traditional and genomic models were tested on the ability to predict future animal performance. Genetic parameters for YW were obtained with the average information restricted maximum likelihood method, with and without adding genomic information for 5,091 animals. Additive genetic variances explained by windows of 200 adjacent SNP were used to identify genomic regions associated with the environmental gradient. Estimated variance components for the intercept and the slope in traditional and genomic models were similar. In both models, the observed changes in heritabilities and genetic correlations for YW across environments indicate the occurrence of genotype by environment interactions. Both traditional and genomic models were capable of identifying the genotype by environment interaction; however, the inclusion of genomic information in reaction norm models improved the ability to predict animals’ future performance by 7.9% on average. The proportion of genetic variance explained by the top SNP window was 0.77% for the regression intercept (BTA5) and 0.82% for the slope (BTA14). Single-step GBLUP seems to be a suitable model to predict genetic values for YW in different production environments.

We estimated genetic parameters for various phases of body and testicular growth until 550 days of age in Nelore cattle, using Bayesian inference, including correlation values and error estimates. Weight and scrotal records of 54,182 Nelore animals originating from 18 farms participating in the Brazilian Nelore Breeding Program (PMGRN) were included. The following traits were measured: weight at standard ages of 120 (W120), 210 (W210), 365 (W365), 450 (W450), and 550 (W550) days; weight gain between 120/210 (WG1), 210/365 (WG2), 365/450 (WG3), 450/550 (WG4), 120/365 (WG5), 120/450 (WG6), 120/550 (WG7), 210/450 (WG8), 210/550 (WG9), and 365/550 (WG10) days of age; scrotal circumference at 365 (SC365), 450 (SC450) and 550 (SC550) days of age, and testicular growth between 365/450 (TG1), 450/550 (TG2) and 365/550 (TG3) days of age. The model included contemporary group (current farm, year and two-month period of birth, sex, and management group) and age of dam at calving, divided into classes as fixed effects. The model also included random effects for direct additive, maternal additive and maternal permanent environmental, and residual effects. The direct heritability estimates ranged from 0.23 to 0.39, 0.13 to 0.39 and 0.32 to 0.56 for weights at standard ages, weight gains and testicular measures, respectively. The genetic correlations between weights (0.69 to 0.94) and scrotal circumferences (0.91 to 0.97) measured at standard ages were higher than those between weight gain and testicular growth (0.18 to 0.97 and 0.36 to 0.77, respectively). The weights at standard ages responded more effectively to selection, and also gave strong correlations with the other traits.

Buffalo milk has excellent physical and chemical qualities as a consequence of the high percentage of constituents. This milk property is desirable for the dairy industry because it facilitates manufacture of mozzarella cheese. We estimated genetic parameters for milk yield, milk fat and protein and their effects on mozzarella cheese production using Bayesian inference. Using information from 4907 lactation records of buffaloes, genetic and non-genetic parameters were estimated for accumulated 305-day milk yield (MY), milk fat (%F) and protein (%P) percentages and mozzarella production per lactation (MP). The (co)variance components were obtained by Bayesian inference using a multiple trait model, which included as fixed effects contemporary group, milking number and buffalo age at calving as covariables (linear and quadratic), along with the additive genetic, permanent environmental and residual random effects. Mean a posteriori heritability distributions for MY, %F, %P, and MP were 0.25, 0.30, 0.38, and 0.23, respectively. The genetic correlation estimates between MY with %P and %F were negative and moderate. Positive genetic correlation estimates varying from 0.19 (%P/MP) to 0.95 (MY/MP) were obtained among the traits. Milk yield, milk components, and mozzarella production in Murrah buffaloes have enough genetic variation for selection purposes. We conclude that selection to increase milk yield would be effective in improving mozzarella production.