Agro-physiology of grasses and legumes destined for biorefining of protein – effects of defoliation and fertilization regimes
Bibliographic record
Abstract
Perennial herbaceous plants after defoliation follow one or a combination of two ‘reserve dependent’ regrowth strategies, to either photosynthesise with remaining leaves or halt root growth and remobilize stored assimilate when defoliation is severe (Wang et al., 2021). Their productivity thus integrates photosynthesis, soil nitrogen (N) uptake and internal remobilization, but remain poorly understood for different species subjected to fertilizer and defoliation regimes and targeting biorefining of feed protein. Field experiment was conducted from 2019 to 2021 on sandy soil in Denmark with either fertilized grasses (perennial ryegrass, tall fescue), unfertilized legumes (alfalfa, red clover) or their fertilized mixture (grass-legume), each defoliated in high (2), medium (4) or low frequency (6 weeks) at either 7-9 or 12-14 cm height. Differences between treatment means (excluding the establishment year 2019) were evaluated by mixed-effects model fitted to annual biomass and N yield. Grass species, defoliation frequency and height were highly significant factors for both biomass and N yield, whereas N fertilization as influential factor was likely offset by the unfertilized legumes, prompting for data analysis method integrating multiple two-way interactions as more suitable. The largest biomass was obtained by tall fescue (11.8-14.2 Mg ha-1) and grass-legume mixture (12.5-13.3 Mg ha-1), both defoliated at medium to low frequency, regardless of N fertilization and defoliation height. For N yield, the systems with red clover (395-440 kg N ha-1) and grass-clover mixture (360-400 kg N ha-1) defoliated at high to medium frequency were significantly more productive than the others. Within the season, the results clearly showed the contribution of the spring and main-season defoliation event to the annual biomass (Zhang et al., 2021). We also estimated canopy radiation use efficiency and measured extractable protein to elucidate to which extend it reflects the N yield and thus support the integration of environmental and economic sustainability of perennial systems targeting biorefining of feed protein. This study also provides novel insights in perennial productivity modulated in dynamic terms by management.
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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) | 0.001 | 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".