Role of Variety and Fertilizer Practices on Cowpeas (Vigna unguiculata) Yield and Field Incidence of the Parasitic Weed Alectra vogelii (Benth) in Central Malawi
Bibliographic record
Abstract
Grain legumes are an important component of the food systems in Malawi. The parasitic legume witchweed species Alectra vogelii (Benth) is among the problem pests with serious infestations in groundnuts (Arachis hypogea), soybeans (Glycine max), cowpeas (Vigna unguiculata) and other legumes. A study was conducted in 2013/14 and ‘14/15 seasons to evaluate the effects of three cowpea varieties (IT82E-16, Sudan 1 and Alectra-resistant Mkanakaufi) and fertilizer practices (no fertilizer applied, 5 t ha-1 cattle manure and 100 kg ha-1 of inorganic 23:21:0+4S on cowpea grain yield, yield components and Alectra emergence at three sites in central Malawi. Cowpea grain yields ranged 400-2400 kg ha-1. There were significant (P < 0.05) variety effects on yield in 4 of 6 site-years, with variety IT82-16 consistently giving the highest yields (range 1200-2400 kg ha-1). There were significant variety effects on A. vogelii emergence with Mkanakaufiti having no Alectra throughout. Application of cattle manure strongly suppressed A. vogelii in 6 site-years all at 60 days after planting, while inorganic fertilizer suppressed Alectra in 3 of the 6 site-years. Cattle significantly but marginally (about 250 kg ha-1) increased yield in 2 of the 6 site-years. The results show potential to suppress A. vogelii with cattle manure application. However, further studies are required to understand the causes of the limited yield response under manure or fertilizer application to make the practices attractive to farmers. More variety improvement studies to produce a range of varieties with better local adaptability and response to fertility amendments are recommended.
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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) | 0.000 | 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".