Intercropping Pineapple With Rice or Cowpea: An Alternative for Family Farming in the State of Tocantins, Brazil
Bibliographic record
Abstract
Pineapple is commonly planted in monoculture systems. It is a long-cycle crop that takes time to monetize, which hinders its cropping by small farmers. The objective of this work was to evaluate the production and quality of pineapple with short-cycle crops, at the beginning of the growing period, as an alternative for family farming in the state of Tocantins. The experimental design was randomized complete block with three treatments and four replications. The pineapple was intercropped with rice and cowpea. The treatments consisted of T1: pineapple + rice; T2: pineapple + cowpea and T3: pineapple in monoculture. The evaluated variables of the pineapple fruit were pH, soluble solids, titratable acidity, yield, fruit mass, fruit length with crown, fruit length without crown and fruit circumference. For the rice and cowpea, the yield and the Area Equivalence Index (AEI) were determined. The cropping system did not influence the pineapple fruits quality. The pineapple yielded less. Cowpea yielded more when intercropped with pineapple. The AEI of the pineapple + rice intercropping was 2.07, being feasible for increasing the use of the area by 100%. The AEI of the pineapple + cowpea intercropping was 2.48, being feasible as it increased the use of the area by 148%. The results obtained showed that it is possible to intercrop pineapple with rice or cowpea at the beginning of pineapple cultivation, and it can be a viable alternative for family farming.
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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 | 0.001 |
| 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) | 0.002 | 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".