Combinations of cover crop mixtures and bio-waste composts enhance biomass production and nutrients accumulation: a greenhouse study
Bibliographic record
Abstract
Abstract Improved farming practices are needed to produce more food in a sustainable way. This study assessed 12 combinations of cover crop mixtures and amendment treatments and their effects on shoot and root dry (matter (DM) weights, nitrogen (N), phosphorus (P) and potassium (K) uptakes in plants, Mehlich-3 extractable P (PM3) and K (KM3). Shoot and root DM weights were increased by 30–63% with combinations of clover-based cover crop mixtures and 65 Mg ha−1of municipal solid food waste (MSFW) compared with synthetic fertilizer. The combination of clover-based cover crop mixtures with MSFW increased N uptake by 38 and 30%, P uptake by 57 and 40% and K uptake by 77 and 77% compared with fertilized and unfertilized treatments, respectively. The combination of vetch-based cover crop mixtures with MSFW had no effect on N uptake, but increased P uptake on average by 43%, and K uptake on average by 11% compared with fertilized and unfertilized treatments. The highest soil PM3and KM3values were obtained with additions of MSFW, while the lowest were obtained with synthetic fertilizer indicating that the amount of P and K added with MSFW were greater than cover crop needs. Combining cover crop mixtures and MSFW at levels recommended for N fertilization allows meeting cover crops’ nutrient needs and increases biomass inputs to agricultural soils, but long-term monitoring of soil P is required to limit potential P build-up.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".