Cost Analysis of Corn Cultivation in the Setup of the Crop-Livestock-Forest Integration System to Recover Degraded Pastures
Bibliographic record
Abstract
The objective of this study is to estimate the production costs and profitability of corn cultivation in the setup phase of the crop-livestock-forest integration system for pasture recovery in the municipality of Pindaré-Mirim/MA, Brazil. The study was developed at the Technological Reference Unit (TRU) for the Integration of Crop-Livestock-Forest (ICLF) of Embrapa Cocais, located in the municipality of Pindaré-Mirim/MA, Brazil. Data collection occurred during the agricultural year 2015/2016. The management of the ICLF system was carried out following the molds of the “Santa Fé” technique. The cost of production was used to calculate the Total Operational Cost (TOC) and were extrapolated per hectare. For the economic analysis of corn production, three different prices were considered: (a) the price received by the producer; (b) the historical average of the last 30 months to the date of actual sale of the product; and (c) the minimum guarantee price of the federal government. The TOC was found to be US$ 1,672.72 per hectare. The economic efficiency indicators showed promising profit values, demonstrating that in this study with corn production in the 1st year, it would be possible to pay for the implementation of the ICLF system as an alternative for the recovery of degraded pasture.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".