Optimal harvest timing vs. harvesting for animal forage supply: Impacts on production and quality of lucerne on the Loess Plateau, China
Bibliographic record
Abstract
Abstract The current promotion of larger areas of lucerne ( Medicago sativa ) production on the Loess Plateau in China prompted this study, which investigated lucerne harvesting practices by farmers and the scope for improved harvest yield and quality by optimizing harvest date, interval and height above ground. On‐farm surveys were conducted to document the dominant harvesting practices used by farmers and their perceptions of barriers to adoption of alternative harvesting practices. In districts with less emphasis on livestock, less labour and inadequate facilities to store conserved lucerne, smaller areas of lucerne are grown and it is often harvested daily to meet demand from penned livestock. The consequence is that much of the lucerne is harvested either before or after flowering, resulting in suboptimal yield of biomass and crude protein. Field experiments conducted at low and high rainfall locations on the Loess Plateau over three seasons showed that delaying the start to harvest until after mid‐June (the date of first flowering), while not affecting total biomass harvested for the season, does reduce leaf biomass harvested and hence crude protein concentration and yield. Lower crude protein is a consequence of a decline in both leaf percentage in harvested biomass and stem nitrogen concentration. Commencing harvests well before flowering with short (3 week) harvest intervals also penalized total and leaf biomass harvested. Raising cutting height from ground level (current farmer practice) to 50 mm (likely with the advent of mechanized harvesting) did not penalize harvested total or leaf biomass.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".