Phosphorus Fertilizer Rating and Rhizobia Inoculation for Improved Productivity of Cowpea in Northern Uganda
Bibliographic record
Abstract
Cowpea (Vigna unguiculata) is an important legume crop in the tropics and subtropical regions of the world. It is mainly grown for its leaves and grains, and to a lesser extent as a fodder crop. Cowpea is considered as the most important food grain legume in the dry savannas of tropical Africa. This study compared the yield of local (Agondire) and improved (SECOW 2W) cowpea varieties grown on an Oxisol. Inorganic P at levels of 0, 10, 20, and 40 kg·ha-1 was tested on each variety with or without rhizobia inoculation. The experiments were set up in a randomized complete block design and replicated thrice during the short and long rains of the 2015/2016 seasons on fifteen fields in Arua district, northern Uganda. Agondire responded significantly (P -1) were applied. A significant increase of 26.4% and 28.4% in grain yield of Agondire and SECOW 2W, respectively was obtained after inoculation with rhizobia. We concluded that inoculation and P fertilizer application increased the yield of both varieties, but with inoculation, SECOW 2W performs much better at lower P fertilizer rates than Agondire. Therefore, we recommend growing of SECOW 2W under inoculation with 20 kg P ha-1 and an application of 40 kg P ha-1 for Agondire local cowpea variety in northern Uganda.
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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) | 0.001 | 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".