Effect of Poultry Manure and Different Combinations of Inorganic Fertilizers on Growth and Yield of Four Tomato Varieties in Ghana
Bibliographic record
Abstract
The agronomic response of four tomato (Solanum lycopersicum L.) varieties to fertilizer application was examined at the CSIR-Crops Research Institute, Kwadaso-Kumasi in the Forest agro-ecological zone of Ghana during the 2013 growing season. The four tomato varieties Shasta, Heinz, CRI POO and CRI 034 were evaluated on five different fertilizer types using a split plot arrangements in randomized complete block design with three replications. The Tomato varieties were the main plots, with the fertilizer treatments as the subplots. The CSIR-CRI breeding lines (CRI P00 and CRI P034) were able to yield higher than the exotic varieties. Using Winner fertilizer (6 g/plant at two weeks after transplanting (WAT) ) and Sulfan (3 g/plant at 4 WAT) CRI P00 produced the highest yield (26.4 t/ha) followed by chicken manure (250 g/plant at 2 and 4 WAT) (23.1 t/ha). CRI P00 with Winner + Sulfan fertilizer application also produced significantly (p≤0.05) higher fruit yield (26.4 t/ha). Fertilizer application however did not have any significant effect on the days to flowering over the control.Fertilizer application however, increased the number of branching for the tomato plants with Unik15 + Urea having significantly more branches compared to the control. Results from this study showed that tomato yields in the Forest zones in Ghana can be increased using improved varieties and recommended fertilizer rates.
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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".