Comparative effects of food processing liquid slurry and inorganic fertilizers on tanner grass (<i>Brachiaria arrecta</i>) pasture: grass yield, crude protein and P levels and residual soil N and P
Bibliographic record
Abstract
Abstract This small‐plot field study evaluated food processing liquid slurry (FPLS) as a potential fertilizer for tanner grass (Brachiaria arrecta) production on an acidic loam soil. The treatments, arranged in a randomized complete block design with three replicates, consisted of an unfertilized control, inorganic fertilizer applied at 50 and 200 kg nitrogen (N) ha−1with and without phosphorus (P) at 50 kg P ha−1, andFPLSapplied at 50 and 200 kg N ha−1. Compared to the unfertilized control, theFPLSapplied at 200 kg N ha−1significantly increased grass dry‐matter yield (DMY), herbage crude protein (CP) and P content, and N and P uptake in the second of two trials and P uptake in both trials. However,DMYand contents, ofCPand P were generally lower for theFPLStreatments compared to the inorganic fertilizers. Apparent N recovery was higher for the inorganic fertilizer treatments thanFPLStreatments in trial 1, while apparent P recovery was similar among all treatments in both trials. TheFPLStreatments did not significantly increase soilNO3‐N and P concentrations, but increasedNH4‐N in the 0–15 cm layer. The results suggest that application ofFPLSto tanner grass pastures is an alternative to its disposal in landfill.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".