Phosphorus Response Efficiency in Cowpea Genotypes
Bibliographic record
Abstract
Phosphorus is important for cowpea production and is inherently low in many tropical soils. Selection of cowpea genotypes that produce good yield under low soil P or those with high P response efficiency can be a low input approach in solving this problem. Therefore, the effect of root architecture and P application on the yield of cowpea and its P uptake were investigated. A screen house experiment was conducted at the International Institute of Tropical Agriculture (IITA) Ibadan, Nigeria. Three hundred cowpea genotypes obtained from the germplasm collection Unit of IITA were screened for number of root whorls and total number of roots using a completely randomized design with 12 replicates. Ten cowpea genotypes were further selected from the initial screening to determine the effect of three levels of P (0, 20 and 40 mg P kg-1 soil) application on their growth and nutrient uptake. The experiment was replicated three times. Phosphorus application significantly (p<0.05) enhanced shoot and root dry weight, total biomass, number of nodules, nodules dry weight, N and P uptake of the cowpea genotypes investigated. Six cowpea genotypes exhibited increases in nodules dry weight with P application. There was no significant relationship between root architecture and P uptake (r = -0.13). The N and P uptake were enhanced by the application of P in the cowpea genotypes studied. In low P soil, IT97K-414-5 was efficient in P uptake and IT92KD-267-2 was efficient in N uptake. The two genotypes may produce cowpea lines suitable in low P soils when used as breeder lines.
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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 | 0.001 | 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".