Potential of Species of Green Coverage in Entisol
Bibliographic record
Abstract
The intensive use of the soil, associated with the application of chemical fertilizers, contribute to degradation processes that affect soil quality and crop production. The use of techniques that contribute to soil conservation and reduction of external inputsis important for the success of the agriculture, and hedging is an alternative. The objective of this study was to evaluate the phytomass production nutrient accumulation and contents, as well as rate of decomposition of different species used as cover plants. The experimental design was randomized blocks, with nine treatments distributed in three blocks. The treatments were: Canavalia ensiformes (L.) DC., Crotalaria spectabilis Roth, Crotalaria ochroleuca G. Don., Crotalaria juncea L., Dolichos lablab L., Stizolobium niveum (Roxb.) Kuntze, Stizolobium aterrimum Piper & Tracy, Neonotonia wightii (Wight & Arn.) J.A. Lackey and Pennisetum glaucum (L.) R.Br. The most recommended species as cover plants are C. ochroleuca and C. spectabilis for they produce more dry matter on the tops, 5.89 and 4.04 kg ha-1, in addition to greater accumulation of nutrients. C. ochroleuca and C. spectabilis had higher accumulation of N, P, K, Mg and S, and soil coverage with those species can be a good source of green manure. The recommended species for the highest coverage rate are: N. wightii; S. niveum and S. aterrimum. The recommended species for the lowest rate of decomposition are: S. aterrimum; S. niveum; P. glaucum and C. ochroleuca (0.245; 0.260; 0.264 and 0.276% month-1).
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".