Effect of Forage Species as Monoculture or in Binary Mixtures on Forage Characteristics, Animal Preference, and Grazing Behaviour
Notice bibliographique
Résumé
In Canada, new forage varieties need not undergo grazing trials before registration and sale. As such, little is known about forage performance under grazing, or how animal preference and temperament affect grazing behaviour. To determine these effects, eight cool-season forage species/varieties including meadow bromegrass (Bromus riparius Rehm.), orchardgrass (Dactylis glomerata L.), sainfoin (Onobrychis viciifoila Scop. ssp. Viciifolia), cicer milkvetch (Astralagus cicer L.), birdsfoot trefoil (Lotus corniculatus L.), and three alfalfa varieties (Medicago sativa L.) were established in monoculture and grass-legume binary mixtures (20 treatments) at the Livestock and Forage Centre of Excellence (Saskatchewan, Canada) in 2018. Forages were seeded in randomized adjacent 0.3 ha (21 × 125 m) strips within each of three, 5 ha paddock replicates. Cicer milkvetch and birdsfoot trefoil failed to establish. In 2019, sixty-nine Bos taurus crossbred steers (396 ± 34 kg BW), and in 2020 one hundred and forty-nine (362 ± 28 kg BW) of the same, were homogenously allocated to the three paddocks for grazing observations. Individual steer temperament was characterized via novel object, corridor, human approach, flight speed, and pen score tests. The animals showing the most bold and shy temperaments were labelled for identification while grazing (2019 n = 18; 2020 n = 48). There was one grazing period in 2019 lasting 19 d, and two grazing periods in 2020: 19 d and 9 d. Grazing observations took place over the first 3 to 10 d of grazing. Observers determined forage preference based upon the number of animals grazing each forage type every 30 min for 2 h in the morning and 2 h in the evening. Animal preference differed (P<0.05) among forage treatments in the first grazing periods of 2019 and 2020 as follows: alfalfa monocultures ≥ sainfoin monoculture ≥ binary mixtures and meadow bromegrass (MBG) ≥ orchardgrass (OG). Forage preference was positively correlated with dry matter yield, protein, energy, fiber, and Ca content (P<0.05). Forage yield differed (P<0.05) among treatments in all grazing events (1384 ± 455 kg DM ha-1), where alfalfa monocultures ≥ sainfoin monoculture ≥ MBG = binary mixtures ≥ OG. Grass component yield was higher (P<0.05) in MBG-legume mixtures than OG-legume mixtures in the first grazing period of 2020. Legume component yield did not differ (P>0.05) between monocultures or binary mixtures. Botanical composition did not change throughout the study (P>0.05). Leaf area index (1.93 ± 0.92) was greater in alfalfa monocultures than all other treatments (P<0.01). Estimated ME (2.23 ± 0.08 Mcal kg-1) was highest in sainfoin; crude protein (11.6 ± 2.57 g kg-1 DM) in the alfalfas, and ADF (34.2 ± 3.1 g kg-1 DM) and NDF (47.7 ± 8.1 g kg-1 DM) highest in MBG. Etiolated growth (27.2 ± 7.7 g DM cm-2) differed (P<0.05) among binary mixtures but not monocultures. An economic analysis indicated that legume monocultures were the only treatments to generate positive net returns after two years, with alfalfas being the most profitable. Steer temperament tended to affect (P=0.06) animal distribution, with bold steers travelling further from the center of the paddock than shy steers or average herd animals. These results indicate that differences in grazing behaviour may be more related to individual animal temperament than forage preference or performance. This study found limited benefit of Killarney OG in western Canada but demonstrated promise for the increased use of newly registered alfalfa varieties under intensive grazing. In summation, the forages tested in this study demonstrated that alfalfa monocultures had greater forage yields and quality than sainfoin monocultures, simple grass-legume binary mixtures, or grass monocultures. Therefore, alfalfa monocultures led to the greatest profitability and greater animal preference than for other forage treatments due to the positive correlation between increasing forage quality and grazing preference. However, forage preference and quality were not related to the grazing distribution of the animals, which was therefore attributed to differences in animal temperament.
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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 | 0,001 |
| 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 ».