Growth Performance and Carcass Quality of Grass-Fed Beef Raised on Tropical Forages/Legumes
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
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Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».