Improving Carrot Yield and Quality through the Use of Bio-slurry Manure
Bibliographic record
Abstract
Continuous cultivation of farms has led to decline in soil fertility due to constant removal of nutrients leading to reduction of carrot (Daucus carota L.) yields. A field study was carried out at Egerton University, Horticulture Research and Teaching field in two seasons (October 2010 to January 2011 and February to May 2011) with the aim of investigating the effects of decomposed cattle bio-slurry manure on carrot growth and performance. The experimental design was a Randomized Complete Block Design (RCBD) with 3 replications. Treatments comprised four levels (0, 2.6, 5.2 and 7.8 t/ha) of decomposed bio-slurry manure. Growth, yield and quality parameters were recorded and used to discern the treatment effects. Application of bio-slurry manure generally improved growth, yield and quality of carrots. Application of 7.8 t/ha of bio-slurry increased yields by 8.8% in season 1 and 23.5% in season 2 compared to the control. Leaf numbers, plant height, dry weights of shoot and roots and root volume were also generally higher for the 7.8 t/ha treatment compared to other treatments. Total Soluble Solids of roots from plant treated with 7.8 t/ha were higher by 12.7% in season 1 and 13.2% in season 2 compared to the control. The study recommends 7.8 t/ha of bio-slurry manure for enhanced yield and quality of carrot.
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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 | 0.001 | 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".