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Partial substitution of fish oil for linseed oil enhances beneficial fatty acids from rumen biohydrogenation but reduces ruminal fermentation and digestibility in growing goats.

Lam Phuoc ThanhNoppharat PhakachoedWisitiporn SuksombatJuan J LoorTran Thi Thuy Hang
Published in: Translational animal science (2021)
This study was performed to investigate effects of partial replacement of fish oil (FO) for linseed oil (LO) on digestibility, ruminal fermentation and biohydrogenation in growing goats. Experiment 1 was carried out in four growing male goats aged 6 months in a 4 × 4 Latin square design. Goats were fed a basal diet supplemented with 25 g/kg dry matter either LO alone or in combination with tuna FO. Treatments were developed by replacing FO for LO at ratios of 0, 5, 10 and 15 g/kg DM corresponding to FO-0, FO-5, FO-10 and FO-15, respectively. Experiment 2 was carried out in an in vitro incubation system including 12 fermenters with the same four treatments. Each fermenter consisted of 40 mL goat ruminal fluid, 160 mL warm buffer, 2 g mixed substrates, and 50 mg FO-0, FO-5, FO-10 or FO-15. Fish oil inclusion reduced (P < 0.05) digestibility and nitrogen retention in Experiment 1. Increasing doses of FO in the diet induced a strong drop (P < 0.001) in ruminal total volatile fatty acid (VFA) concentration and protozoa population at 3 h post incubation, but did not affect individual VFA proportions. Substitution of FO for LO decreased mean concentrations of C18:0 (P = 0.057), c-9,c-12 C18:2 and C18:3n-3 (P < 0.001), but increased (P < 0.001) C20:5n-3 and C22:6n-3. Feeding FO-10 enhanced formation of ruminal c-9,t-11 conjugated linoleic acid (CLA) concentration compared with FO-0. Overall, combined data suggest that to improve ruminal concentrations of C20:5n-3, C22:6n-3, and c-9,t-11 CLA for deposition in tissues or milk with minimal risk of affecting digestibility and ruminal fermentation, a dietary supplementation of 15 g/kg LO and 10 g/kg FO would be suitable.
Keyphrases
  • fatty acid
  • gene expression
  • metabolic syndrome
  • type diabetes
  • insulin resistance
  • weight loss
  • electronic health record
  • artificial intelligence
  • data analysis