Validation of Predictive Equations of Pre‐Harvest Forage Nutritive Value for Alfalfa–Grass Mixtures
Bibliographic record
Abstract
Core Ideas Predictive equations can help producers determine when to harvest their forage fields. Equations developed in New York State could be used to predict aNDFom, ADFom, RFV, and RFQ in Quebec. Equations developed in New York State cannot be used to predict NDFdom in Quebec. Predictive equations of pre‐harvest nutritive attributes of alfalfa ( Medicago sativa L.)–grass mixtures using simple plant or climate data were developed in New York State for the spring growth, but they must be validated before being used outside their development area. Our objective was to validate these predictive equations for their use in Quebec, Canada. Samples ( n = 679) of alfalfa–grass mixtures were collected during spring growth at three sites for 2 consecutive years and analyzed for several nutritive attributes. Alfalfa maximum height, the most mature stage of development of alfalfa, growing degree days, grass proportion, and grass maximum height were also measured and used as input in several existing predictive equations. Predicted values were then compared with laboratory‐determined values using several validation statistics. The most promising predictive equations of neutral detergent fiber (NDF) and acid detergent fiber concentrations, relative feed value, and relative forage quality had coefficients of determination ( r 2 ) of the linear regression between observed and predicted values between 0.74 and 0.81, and an index of agreement ( d ) between 0.87 and 0.93. Several equations were, however, significantly biased as indicated by slopes and intercepts of the regressions. The NDF digestibility was not predicted satisfactorily with the New York State equations. Among all equations evaluated, an equation for NDF concentration has the most potential for use to predict the spring growth pre‐harvest nutritive value of alfalfa–grass mixtures in Quebec.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".