A Regional Assessment of Four Green Manure/Cover Crop Species Suited to Tropical Southeast Asia
Bibliographic record
Abstract
While maintaining adequate levels of soil fertility can be a challenge on any farm, maintaining those levels on the resource-limited smallholder farms of the tropics requires options that are also affordable, practical, and appropriate in such challenging conditions. This research endeavor was designed to compare the adaptability and potential of four legume species promoted as Green Manure/Cover Crops (GMCC’s) in Southeast Asia. Cowpea (Vigna unguiculata), Jackbean (Canavalia ensiformis), Lablab (Lablab purpureus), and Ricebean (Vigna umbellata) were planted in field trials in five diverse countries across Southeast Asia in 2016, including Cambodia, Myanmar, Thailand, Bangladesh, and the Philippines. Data was collected to assess the production of above-ground biomass, percentage of ground cover, and timing of growth cycles at each site. Although results varied from country to country based on soil-type, climatic conditions, and growing degree days, Jackbean consistently outperformed other GMCC species in terms of biomass production, yielding up to 12 t ha-1 on a dry-weight basis in Bangladesh and the Philippines. Of the four crops compared, cowpea consistently delivered the shortest growth cycle, reaching the pod formation stage in the fewest number of days across all five sites. These results provide informative answers regarding the growth habits and life cycles of these four crops across five diverse sites, and serve to enhance the capability of smallholders in Southeast Asia to select appropriate species needed for soil improvement purposes in a wide-ranging set of cropping systems.
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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 | 0.000 | 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".