Growth and Yield Performance of Cowpea (Vigna Unguiculata (L.) Walp) as influenced by Row-Spacing and Period of Weed interference in South-West Nigeria
Bibliographic record
Abstract
Weed problem appears to be the most deleterious factor causing between 25 and 60% reduction in potential yield of cowpea. Field trials were therefore conducted to study the effect of inter-row spacing and period of weed interference on growth and yield of cowpea (Vigna unguiculata (L) Walp) at the Teaching and Research Farm of the Federal University of Agriculture, Abeokuta (07° 15'; 03° 25' E) in South Western Nigeria during the early and late wet seasons of 2009. The experiment consisted of eight main plots of weed interference which included initial weed removal for 3, 6, 9, and 12 weeks after sowing (WAS) and subsequently weed –infested until harvest as well as initial weed infestation for corresponding periods and thereafter kept weed free until harvest. There were also sub-plot treatments of three inter-row spacing of 60, 75, and 90 cm. All treatments in different combinations were laid out in a split-plot design with three replications. In both trials, the use of inter-row spacing of 60 cm resulted in significant reduction in weed growth as evident in lower weed dry matter production and subsequent higher cowpea pod and grain yields than those of 75 and 90 cm inter-row spacing. Initial weed infestation of up to 3 WAS did not have any adverse effect on crop growth and cowpea grain yields provided the weeds were subsequently removed. On the other hand, cowpea grain yield loss was not significantly averted by keeping the crop weed free for only 3 WAS without subsequent weed removal. In this study, initial weed-infestation for 6 WAS and beyond significantly depressed various crop growth parameter and cowpea grain yield compared with the crop kept weed free throughout its life cycle. In order to obtain optimum yields similar to that of the weed free cowpea field, it was required to keep the crop weed free for 6 WAS and beyond. However, frequent weeding beyond 9 weeks after sowing did not improve cowpea yield significantly and as a matter of fact it may even result in reduction of cowpea grain yield due to mechanical damage of hoe weeding. The practical implication of this finding is that early weeding starting from 3 WAS is very crucial for cowpea production while the critical period of weed removal for optimum yield in cowpea is between 3 and 9 WAS in the forest-savannah transitional zone of south Western Nigeria.
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 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) | 0.000 | 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 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".