Effect of Organic (Jatropha Cake) and NPK Fertilizers on Improving Biochemical Components and Antioxidant Properties of Five Cowpea (Vigna unguiculata L. Walp.) Genotypes
Bibliographic record
Abstract
Organic and inorganic fertilizers improve the nutritional quality of crop. An experiment in pots of four treatments was conducted in a randomized complete block design in order to evaluate the effect of the application of organic and NPK fertilizers on the biochemical components and antioxidant activities of five cowpea genotypes. Results showed that organic fertilizer and the mixture of organic and NPK fertilizer have significantly (p < 0.05) increased the growth parameters and leaves quality traits of cowpea. The response of cowpea under the different treatment is also function of genotype type. The PCA and the dendrogram performed basis on the different fertilizers effect on growth parameters and leaves quality trait divided the treatments into three main classes. Class I comprising NPK + Jatropha treatment strongly contributed to lycopene, β-carotene and total chlorophyll contents, height and number of branches. Class II comprising NPK treatment solely and Jatropha treatment solely are highly linked to the variables FRAP, DPPH and protein content. Class III comprising control treatment strongly contributed to salicylic, soluble sugar, phenolics and flavonoids contents, and trypsin inhibition activity. The Jatropha cake could be used solely or in mixture with NPK to improve growth and the quality traits of cowpea leaves.
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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) | 0.001 | 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".