PSVI-11 Effects of nutrient management and cropping strategies in a dual-crop forage production system of silage corn and perennial grass on nutritional quality and predicted milk production of dairy cattle
Bibliographic record
Abstract
Abstract The objectives of this study were to determine the effects of incrementally applied enhanced nutrient management, cropping practices, and advanced production technologies on nutrient composition and in vitro degradability of whole plant corn and perennial grass (tall fescue) and the predicted milk production of dairy cattle. Farm management strategies included: conventional system with manure slurry broadcast, late harvest corn, and grass cut 5 times per year (F1); improved nutrient management with manure sludge applied to corn and liquid applied to grass (F2); improved nutrient management and cropping practices with separated manure, an early harvest corn inter-seeded with a relay crop (Italian ryegrass), and grass cut 3 times per year (F3); and advanced technologies that included a nitrification inhibitor (diacyandiamide, DCD; F4). The field trial was conducted as a randomized complete block design over 2 years with 4 blocks each divided into corn and grass, 4 subplots for each crop, and 2 replicates within each subplot. Enhanced nutrient, cropping, and advanced management increased (P < 0.05) the crude protein (CP) concentration in corn compared to the conventional system (Table 1). The DCD reduced (P < 0.05) the CP concentration and the highly degradable fiber of the relay crop compared to management without DCD. Decreasing the number of cuts of grass reduced (P < 0.05) the CP concentration in the spring harvest, increased (P < 0.05) the fiber concentration in spring and summer harvests, and reduced (P < 0.05) fiber degradability in all harvests. Milk production predicted from the nutritional quality and representative proportions of forages using the Cornell Net Carbohydrate and Protein System was increased with enhanced management. The lower forage quality of grass cut 3 times compared to 5 times annually was offset by the improved quality of corn and relay crop under enhanced field management of the dairy farm.
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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) | 0.000 | 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".