Camelina sativa (L. Crantz) Fresh Forage Productive Performance and Quality at Different Vegetative Stages: Effects of Dietary Supplementation in Ionica Goats on Milk Quality

The research meant to study the productive performances of Camelina sativa and the effects of feeding Camelina fresh forage harvested during five phenological stages (I: main stem elongation; II: maximum stem elongation: III: inflorescence appearance; IV: flowering; V: fruit set visible) on the yield, chemical composition and fatty acid profile of milk from autochthonous Ionica goats. Goats were randomly assigned to two groups (n = 15) that received a traditional forage mixture (Control) or Camelina forage harvested at different stages (CAM). The field experiment was conducted in two years; no significant differences between years were recorded for any of the Camelina production traits. The total biomass increased (p < 0.05) from phase I (1.4 t/ha) to phase V (5.2 t/ha). The distribution of stem, leaves and pod also changed during growth, showing a significant increase of stem from 40.8 to 45.6% and of pod from 0 to 19.4%, whereas leaves decreased from 59.2 to 35.1%. The milk yield and chemical composition were unaffected by the diet, while supplementation with Camelina forage increased milk CLA content (on average 1.14 vs. 0.78%). A markedly higher concentration of PUFAs was found in milk from goats fed Camelina harvested during the last three phenological stages. The index of thrombogenicity of milk from the CAM fed goats was significantly lower compared to the control group. In conclusion, Camelina sativa is a multi-purpose crop that may be successfully cultivated in Southern Italy regions and used as fresh forage for goat feeding. Milk obtained from Camelina fed goats showed satisfactory chemical and fatty acid composition, with potential benefits for human health.

Genomic Selection for Milk Production Traits in Xinjiang Brown Cattle

One-step genomic selection is a method for improving the reliability of the breeding value estimation. This study aimed to compare the reliability of pedigree-based best linear unbiased prediction (PBLUP) and single-step genomic best linear unbiased prediction (ssGBLUP), single-trait and multitrait models, and the restricted maximum likelihood (REML) and Bayesian methods. Data were collected from the production performance records of 2207 Xinjiang Brown cattle in Xinjiang from 1983 to 2018. A cross test was designed to calculate the genetic parameters and reliability of the breeding value of 305 daily milk yield (305 dMY), milk fat yield (MFY), milk protein yield (MPY), and somatic cell score (SCS) of Xinjiang Brown cattle. The heritability of 305 dMY, MFY, MPY, and SCS estimated using the REML and Bayesian multitrait models was approximately 0.39 (0.02), 0.40 (0.03), 0.49 (0.02), and 0.07 (0.02), respectively. The heritability and estimated breeding value (EBV) and the reliability of milk production traits of these cattle calculated based on PBLUP and ssGBLUP using the multitrait model REML and Bayesian methods were higher than those of the single-trait model REML method; the ssGBLUP method was significantly better than the PBLUP method. The reliability of the estimated breeding value can be improved from 0.9% to 3.6%, and the reliability of the genomic estimated breeding value (GEBV) for the genotyped population can reach 83%. Therefore, the genetic evaluation of the multitrait model is better than that of the single-trait model. Thus, genomic selection can be applied to small population varieties such as Xinjiang Brown cattle, in improving the reliability of the genomic estimated breeding value.

The Addition of Epistatic Genetic Effects Increases Genomic Prediction Accuracy for Reproduction and Production Traits in Duroc Pigs Using Genomic Models

Background: Genomic selection has been implemented in livestock genetic evaluations for years. However, currently most genomic selection models only consider the additive effects associated with SNP markers and nonadditive genetic effects have been for the most part ignored. Methods: Production traits for 26,735 to 27,647 Duroc pigs and reproductive traits for 5,338 sows were used, including off-test body weight (WT), off-test back fat (BF), off-test loin muscle depth (MS), number born alive (NBA), number born dead (NBD), and number weaned (NW). All animals were genotyped with the PorcineSNP60K Bead Chip. Variance components were estimated using a linear mixed model that includes inbreeding coefficient, additive, dominance, additive-by-additive, additive-by-dominance, dominance-by-dominance effect, and common litter environmental effect. Genomic prediction performance, including all nonadditive genetic effects, was compared with a reduced model that included only additive genetic effect. Results: Significant estimates of additive-by-additive effect variance were observed for NBA, BF, and WT (31%, 9%, and 10%, respectively). Production traits showed significant large estimates of additive-by-dominance variance (9%-23%). MS also showed large estimate of dominance-by-dominance variance (10%). Dominance effect variance estimates were low for all traits (0%-2%). Compared to the reduced model, prediction accuracies using the full model, including nonadditive effects, increased significantly by 12%, 12%, and 1% for NBA, WT, and MS, respectively. A strong dominance association signal with BF was identified near AK5.Conclusions: Sizable estimates of epistatic effects were found for the reproduction and production traits, while the dominance effect was relatively small for all traits yet significant for all production traits. Including nonadditive effects, especially epistatic effects in the genomic prediction model, significantly improved prediction accuracy for NBA, WT, and MS.