Growth Performance and Carcass Quality of Grass-Fed Beef Raised on Tropical Forages/Legumes
Bibliographic record
Abstract
Most beef producers in Hawaii ship cattle to the continental United States and Canada for feedlot-finishing, slaughtering and processing. Reasons for this include high transportation costs associated with shipping grain and a lack of production and slaughtering capacity in the state.There is a niche market of consumers that prefer grass-fed over grain-fed beef. However, studies have proven variation in growth performance, carcass quality and nutritional value of beef when comparing these production systems. Typically, grass-fed cattle reach slaughter weight slower, have leaner carcasses, and the meat may be healthier from a consumer standpoint. However, little research has been done to evaluate how nutrient content of different species/varieties of tropical grasses and legumes can affect these parameters in cattle grazing on Hawaii pastures. Two studies were conducted. The objectives of study one were to determine nutrient profiles of guinea grass (GG) and the tropical legume leucaena (L), and to evaluate growth performance and carcass characteristics of steers grazed on these pastures at Ranch A on Hawaii Island. The objective of study two was to compare differences in nutrient profiles of GG and kikuyu grass (KK) due to: grass type (GG vs KK), season (summer vs winter) and ranch location (B vs C vs D vs E) on Hawaii Island. Nutrient composition of all samples was determined by near-infrared spectroscopy. Variables determined included: percent crude protein (CP), acid detergent fiber (ADF), ash-free neutral detergent fiber (aNDFom), ash, total digestible nutrients (TDN), relative feed value (RFV), energy for maintenance (NEM, Mcal/kg), and ash-free neutral detergent fiber digestibility (NDFDom). Growth performance and carcass characteristics (hot carcass weight, backfat thickness, rib-eye area, marbling score, USDA quality grade, and Warner-Bratzler shear force) of steers grazed on GG and GL pastures were determined. Chemical composition of rib- eye samples (moisture, fat, protein, ash, and pH) was determined as well. Higher average daily gain (ADG) and desirable carcass characteristics were found for steers that grazed on GL pastures compared to GG pastures, which can be attributed to L having an overall higher nutrient value (CP 18.8 vs 27.3, ADF 39.2 vs 27.0, aNDFom 51.5 vs 31.5, Ash 16.3 vs 12.3, TDN 52.4 vs 59.6 % of GG vs L, respectively, P<0.05). Cattle grazed on GL pastures had higher average daily gains (0.46 vs 0.62 kg), shortened stay on pasture (707 vs 532 days), and carcasses with higher marbling score (8.96 vs 10.3), thicker backfat (0.38 vs 0.57 cm), and bigger rib-eye size (81.0 vs 87.9 cm2) than GG grazed steers (P<0.05). Rib-eye of GG had higher intramuscular fat content than that of GL (6.77 vs 4.60%, respectively, P<0.05) and GL rib-eyes were found to be less tender than that of GG (4.09 vs 4.99 kg, respectively, P<0.05). In study two, KK grass had a higher nutrient value when comparing GG to KK (CP 12.03 vs 16.6, ADF 40.1 vs 35.4, Ash 12.7 vs 9.4, TDN 54.7 vs 58.8, RFV 90.1 vs 101.2%, NEM 0.22 vs 0.24 Mcal/kg, NDFDom 120h 67.1 vs 72.9%, respectively, P<0.05). Significant differences were also found due to location and season. All summer samples were found to have a higher nutritional value compared to winter samples (P<0.05) and samples collected from Ranch B had the highest nutritional value as well (P<0.05). When comparing GG to L, L had a higher RFV (101.0 vs 200.5%, respectively, P<0.001) and more TDN (52.4 vs 59.6%, respectively, P<0.001), which resulted in GL steers having higher ADG (0.46 vs 0.62 kg), shortened stay on pasture (707 vs 532 days) and better carcass characteristics (P<0.05). In conclusion, GL pastures produced animals with higher ADG and more desirable carcass characteristics. Results support producers should practice more grass- fed beef production in Hawaii. It is suggested that producers allow cattle to graze mixed legume/forage pastures due to high ADG and desirable carcass characteristics found for GL cattle. Due to differences found for season and ranch location when comparing GG and KK, producers also need to consider variations in pasture nutrient profiles due to location and season in order to make decisions about when environmental conditions are appropriate to take advantage of grass-fed beef production.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".