Chicken Manure: An Alternative in Increasing Production of Sweet Potato (Ipomoea batatas)
Bibliographic record
Abstract
Chicken manure is accessible to family farmers, is produced in large volumes, and has a low cost. Therefore, it can be an alternative to increase the productivity of sweet potatoes (Ipomoea batatas), which is a culture of socioeconomic importance. The objective of the study was to evaluate the productivity of sweet potatoes under different doses of chicken manure, with and without liming, comparing to the use of chemical fertilizer NPK, in very clayey soil, from the perspective of economic efficiency. The experiment was conducted in the field from September 2019 to March 2020 in Manaus, Amazonas, Brazil. The experimental design was in randomized blocks with four replications, in a split-plot scheme 2 × 5 + 1 (presence or absence of liming; five doses of chicken manure; and NPK, respectively. The evaluated parameters were: total and commercial productivity; number of total and commercial tuberous roots; harvest index; individual fresh mass, length, and diameter of tuberous roots. The effect of chicken manure was not influenced by the liming and there are no differences in agronomic values related to NPK. The dose of chicken manure that results in maximum production of the queen sweet potato variety is approximately 13 t ha-1, producing 25.2 t ha-1, while the resulting dose in maximum profit is approximately 12 t ha-1 with a production of the 25.1 t ha-1. The selling production directly to the final consumer increased profit by approximately 57.3%. Purchasing the input directly from the manufacturer reduced costs by approximately 74.5%. The use of chicken manure has a greater economic advantage compared to NPK in very clayey soil.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".