Effect of Forage Species as Monoculture or in Binary Mixtures on Forage Characteristics, Animal Preference, and Grazing Behaviour
Bibliographic record
Abstract
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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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".