PSLBI-24 Optimizing crop byproduct inclusion in beef cattle diets: Utilization of wheat straw to improve operational economics and oilseed screenings as protein supplements
Notice bibliographique
Résumé
Abstract Inclusion of crop residues and byproducts in beef cattle diets has become a significant practice in beef cattle production as producers are faced with conventional feed shortages, increased prices, and disrupted supply chains. Wheat straw is a widely available crop byproduct in the western Canadian prairies whose inclusion in beef cattle diets has been prevented by its low nutritional content, necessitating supplementation strategies to enhance its nutritional value. The present study explored the effect of incorporating wheat straw in backgrounding cattle diets with canola or flax screening supplementation to improve the nutritional profile of the diet and determine the sustainability of such inclusion through cattle performance measurements. The experiment was a completely randomized design. Steers [n = 300; initial body weight (BW): 297 ± 18 kg] were randomly assigned to 5 treatment diets, each diet having 4 pen replicates (15 steers/pen); control (CTL), low straw canola (LSC), low straw flax (LSF), high straw canola (HSC) and high straw flax (HSF). The CTL diet was a conventional Western Canadian backgrounding diet (60% barley silage:40% dry rolled barley grain-based concentrate). Low straw diets had a 25% wheat straw inclusion and high straw diets had 50% wheat straw inclusion on a dry matter (DM) basis. Respective screenings were included at 12.50% inclusion of diet DM. The steers were fed for a total of 84 d divided into four periods of 21 d. Statistical analyses were performed using the MIXED procedure of SAS 9.4 with diet treatment included as a fixed effect and pen within diet as a random effect. Treatment means were compared using the LSMEANS statement adjusted for the Tukey-Kramer method. Final BW, total BW gain, average daily gain (ADG) and gain:feed were greater (P < 0.001) for the CTL steers (Table 1). Dry matter intake (DMI) was greatest for CTL steers and decreased with increasing straw inclusion in the diet (P < 0.001). The type of screenings (canola or flax) did not affect (P > 0.05) steers final BW, total BW gain, ADG and gain:feed. Increasing the inclusion of wheat straw in backgrounding diets decreased growth performance parameters. The high level of neutral digestible fiber in wheat straw limited DMI and reduced growth performance compared with CTL steers. The addition of screenings to the diet provided protein content to offset the low nutritional value of the wheat straw; however, the different types of screenings did not result in a cattle performance advantage over the other. Although these findings demonstrate the challenges of including low nutritional crop byproducts, further research is needed to analyze the effects different diets have on operational economics, rumen fermentation parameters and greenhouse gas emissions to further outline any advantages or disadvantages.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
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 | 0,001 | 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 ».