Allometries in Plants as Drivers of Forage Nutritive Value: A Review
Bibliographic record
Abstract
The nutritive value of forage for herbivores has been for a long time determined by the concentration in protein and, hence in nitrogen (N), the concentration in different minerals (P, K, Ca, Mg, and oligo-elements), and the in vivo dry matter (DM) digestibility. Forage DM digestibility, the proportion of ingested DM being metabolized by ruminant animals has been related to different components of plant tissue composition such as Neutral Detergent Fiber (NDF) and Acid Detergent Fiber (ADF); the NDF concentration represents an estimate of cell wall content while the ADF concentration is an estimate of the more lignified cell wall content. Forage nutritive value is generally analyzed by relating the attributes of nutritive value to plant phenology, in order to predict the decline of these attributes with plant age. A more functional approach, initially developed for the analysis of N concentration dynamic analysis (Lemaire et al. 2008 and Lemaire et al. 2019), and extended for digestibility for this review, is based on the assumption that above-ground plant mass (W) is composed of two compartments: (i) the metabolic compartment (Wm), associated with plant growth process scaling with leaf area, having a high N concentration (%N), and a high Digestibility (%D); (ii) the structural compartment (Ws) associated with architectural plant development, scaling with plant height and thickness and having low %N and %D. With the postulate that Wm is allometrically related to W (Wm = c × Wα with α < 1), the ontogenetic decline of both %N and %D as the plant gets bigger and forage mass increases can be explained, and the purely empirical statistical approach of forage quality based on plant phenology can be replaced by a more mechanistic and comprehensive analysis linking forage production and forage quality dynamics within the same functional approach for a better understanding of genotype-environment-management interactions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".