The benefits of flushing with Lemuru fish oil as a source of docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) on the performance of reproductive parameters in Garut ewes.

This study aimed to evaluate and analyze the effects of a flushing diet containing Docosahexaenoic acid (DHA) and Eicosapentaenoic acid (EPA) from Lemuru (Sardinella sp) fish oil on the reproductive performance parameters of Garut ewes. Forty (n = 40) primiparous Garut ewes aged 12-14 months with an average body weight of 28.92 ± 4.94 kg were assigned into four experimental treatment groups. The experimental diets contained roughage: concentrate (30:70%) designated as control concentrate (CNT), flushing concentrate with 6% palm oil (PO), flushing concentrate with 3% palm oil mixed with 3% lemuru oil as DHA and EPA sources (PFO), and flushing concentrate with the addition of 6% lemuru oil (FO). Treatment animals were fed two weeks before and after conception and parturition (8 weeks of total flushing treatment). The addition of fish oil at either 3% (PFO) or 6% (FO) resulted in significantly higher reproductive performance of ewes by increasing the litter size, as reflected by the birth of multiple kids (P < 0.05) compared to CNT and PO. Adding fish oil (PFO and FO) also maintains gestation, resulting in increased lamb yield, especially in the FO treatment, which yields the highest lamb yield (0% single lamb birth). The lamb male ratio was also higher with fish oil supplementation (PFO and PO) (P < 0.05). This research revealed a positive effect of 6% Lemuru oil on decreasing embryo loss and increasing the proportion of twin births. These findings thus support the hypothesis that ration flushing with double the required DHA and EPA from 6% Lemuru fish oil (FO) resulted in significantly higher reproductive performance in Garut sheep.

Meta-analysis of the relationship between dietary starch intake and enteric methane emissions in cattle from in vivo experiments.

Objective: Different sources and levels of starch in the total mixed ration might result in different rumen fermentation profiles, rumen microbial composition, and enteric methane production. The current study aimed to evaluate the effect of dietary starch intake on enteric methane production through a meta-analytical approach by integrating related published studies. Materials and Methods: Papers that provided study results on enteric methane production from cattle fed different sources and levels of starch were selected. A total of 52 publications were filtered based on some specified criteria, comprised of 73 studies focused on enteric methane production in cattle fed a basal diet supplemented with starch. The collected data were subjected to statistical meta-analysis through a mixed methodology model. The p-value and root mean square error (RMSE) were applied as the statistical models. Results: Results showed that increasing the level of dietary starch intake and its digestibility in the rumen as well as increasing propionate significantly decreased the acetate to propionate (A/P) ratio as well as enteric methane production (p < 0.01). Conclusions: It is concluded that the addition of starch to diets mitigates enteric methane production from cattle, primarily through two ways, i.e., manipulation of the protozoa population and alteration to a lower A/P ratio.

Body weight prediction of Belgian Blue crossbred using random forest.

Objective: The aim of this study was to predict the body weight (BW) of a Belgian Blue X Friesian Holstein (BB X FH) crossbred in Indonesia based on morphometrics using random forest. Materials and Methods: A total of 26 BB X FH crossbreds were observed for BW, chest weight (CW), body length (BL), hip height (HH), wither height (WH), and chest girth (CG) from 0, 30, 60, 90, 120, 150, 180, 210, 240, 270, and 300 days of age. Stepwise regression and random forest were performed using R 3.6.1. Results: The random forest results show that CG is an important variable in estimating BW, with an important variable value of 24.49%. Likewise, the results obtained by stepwise regression show that CG can be an indicator of selection for the BB X FH crossbred. The R squared value obtained from the regression is 0.83, while the R squared value obtained from the random forest (0.86) is greater than the regression. Conclusion: In conclusion, random forest produces a better model than stepwise regression. However, a good simple equation to use to estimate BW is CG.