Influence of Management Practices on Selected Cowpea Growth Attributes and Soil Organic Carbon
Bibliographic record
Abstract
Cowpea is a multi-purpose nitrogen fixing crop that can be grown as a vegetable, grain legume and a fodder. The objectives of this study were to investigate the growth response of cowpea to different cropping systems at different locations and determine nitrogen fertilization on cowpea growth and soil organic carbon content. Three cropping systems were used, namely, maize-cowpea rotation, cowpea monocropping and maize-cowpea intercropping at three locations (Potchefstroom, Taung, and Rustenburg) in South Africa during 2011/12 and 2012/13 planting seasons. Nitrogen fertilizer was applied at two rates where no application was the control at all locations and application according to soil analysis recommendation for maize requirement was applied at each location. The variables measured for cowpea growth were days to 100% flowering and physiological maturity, number of leaves and nodules per cowpea plant. Soil organic carbon was determined for each treatment. The results showed that, maize-cowpea rotation and monocropping reached days to 100% flowering and maturity significantly earlier compared to intercropping. Cowpea planted at Potchefstroom and Rustenburg reached days to 100% flowering and physiological maturity significantly earlier than cowpea planted at Taung. Cowpea planted at Taung had significantly higher number of nodules per plant than cowpea planted at Potchefstroom and Rustenburg. There was also a positive correlation between soil organic carbon and cowpea growth. It is concluded that the positive effect of cowpea in agronomic systems is enhanced by the correct cropping system, although it is affected by location.
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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 | 0.001 |
| 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".