Improving Carbon Dioxide Concentration, Growth and Yield of Tomato Using Fresh Manure and Agronet Cover
Bibliographic record
Abstract
Arable land area is declining in many tropical and sub-tropical regions and increasing tomato (Solanum lycopersicum L.) production is necessary due to its high demand. Food security amid scarcity of arable land could be achieved through intensification as a way of maximizing productivity per unit area of available arable land. Trials were conducted at the Horticulture Research and Teaching Field, Egerton University, Njoro, Kenya, to evaluate effects of agronet cover and fresh manure on carbon dioxide (CO2) concentration in the air around the crop canopy and tomato plant development. In addition to CO2 concentration levels, stem diameter, plant height, number of internodes and branches, number of fruit and fresh fruit weight were determined. Use of agronet cover and fresh manure resulted in higher CO2 concentration and enhanced tomato growth and yield. The highest CO2 concentration in the air around the crop canopy was in plots treated with fresh goat dung and those covered with agronet; the lowest CO2 concentration was in plots with no manure and those without agronet at all data collection dates. Application of fresh cow dung and covering plots with agronet stimulated tomato stem elongation; application of fresh goat dung and covering with agronet enhanced stem diameter, number of internodes and branches. Higher tomato yields were obtained with use of fresh manure and agronet cover. There were differences in response of tomato plants to fresh manure source with fresh goat dung showing greater potential for use in CO2 enrichment and enhancing tomato crop performance. Use of fresh manure and agronet covers could enrich CO2 levels in open field tomato production leading to improved growth and yield.
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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".