Effect of Different Mulching Materials on the Growth and Yield of Green Bean (Phaseolus vulgaris L.) in Nfonta the Western Highlands of Cameroon
Bibliographic record
Abstract
Mulching is a common technique used across the world by farmers to especially conserve soil moisture in vegetable production but farmers in Nfonta and the entire western highlands of Cameroon have not practiced the uses of mulching. In this experiment, a randomized complete block designe with 5 treatments and 3 replications was set up to study the effect of elephant grass, saw dust and white plastic as mulching materials on the growth and yield of green bean (Phaseolusvulgaris L.) in Nfonta. Data was collected on plant height; number of leaves per plants, leaf area index and yield of mature pods per plant. Data was analyzed using one way ANOVA from stat graphics centurion xv and means were separation using the Fischer least significant difference (LSD) test at 95% confidence interval. Results showed white plastic, and elephant grass mulches to have significantly (P<0.05) affected the growth and yield of green bean. White plastic mulched plants exhibited the highest growth parameters and subsequently produced the highest yield of 12.00 mature pods per plant with average pod length of 11.97cm and average mature pod weight of 4.22g compared to the other mulch treatments. There were no significant (P>0.05) differences in yield of green bean grown with no mulch (control), saw dust mulch and corn stalk mulch. Corn stalk mulched bean plants produced the lowest yield of 7.83 mature pods per plant with average pod length of 9.17cm and an average mature pod weight of 2.83 which was not much different from that produced by the control. These results call for more investigations to the potentials of white plastic as best mulch material for achieving optimum green beans yield in Nfonta and the entire western highlands of Cameroon.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".