Economic Analysis of Taro-Sunn Hemp Intercropping With Broccoli, Green Maize and Snow Pea as Successive Crops
Bibliographic record
Abstract
Although several studies about intercropping green manures with vegetable crops have already been carried out, there are few studies in which the economic analysis was performed. The aim of the present study was to evaluate the economic profitability of the taro and Crotalaria juncea consortium, as well as broccoli, green maize and snow pea grown in succession. The treatments correspond to 10 different cutting dates of the sunn hemp during its intercropping with taro (55, 70, 85, 100, 115, 130, 145, 160, 190 and 220 DAS-days after sowing), plus the monoculture of the taro. Under the legumious cover crop straw were grown in succession: broccoli, green maize and snow pea. The use of Crotalaria juncea in a consortium does not affect the productivity and economic profitability of the taro when cutting up to 130 DAS. The residual effect of Crotalaria juncea grown in consortium with taro increases the economic yield of broccoli when the cutting is performed from 145 DAS. However, it does not affect green maize and snow pea. When the taro is cultivated intercropped with Crotalaria juncea and in succession broccoli, green maize and snow pea are grown, higher yields and economic incomes are obtained by cutting the leguminous green manure up to 130 DAS.
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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".