Dead Cover and Agronomic Characteristics of Cowpea
Bibliographic record
Abstract
Dead cover, or mulch, consisting of plant residues, plays an important role for the success of diverse agricultural crops, working as an insulating layer protecting the soil from daytime temperature variations and maintaining the soil moist and rich in organic matter. Cowpea is a source of proteins, carbohydrates, vitamins and minerals. Its importance in the North, Northeast and Midwest regions of the country is associated with economic and social aspects, since it is an important food for low-income populations, supplying their nutritional needs. This study was carried out under greenhouse conditions in Manaus, state of Amazonas, with the purpose of assessing the effect of different dead covers on the agronomic characteristics of cowpea cultivars. It consisted of a completely randomized design in a 4 × 4 factorial arrangement. The treatments comprised four cowpea cultivars (BRS Caldeirão, BRS Tumucumaque, BRS Guariba and BRS Tracuateua) and three species of cover plants (Brachiaria decumbens, Brachiaria ruziziensis and Mucuna pruriens) and one control treatment, without soil cover, in a total of 16 treatments, with four replications and two plants per experimental unit. Analysis of variance was applied to the data, and the means were compared by the Scott-Knott’s test at 5% probability level. The following characteristics were examined: number of pods per plant, pod length, number of seeds per pod, weight of shoot dry matter, and grain yield. Mulching provided better results for all characteristics assessed in the four cultivars when compared to the control. BRS Caldeirão is the recommended cultivar for the state of Amazonas and the other regions with similar edaphoclimatic characteristics (high air temperature, rainfall, air humidity, and low-fertility tropical soils) because it exhibited the greatest number of pods per plant, number of seeds per pod, shoot dry matter, and the highest average grain yield (Freire Filho et al., 2011; Souza et al., 2016).
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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".