Effects of Improved Nursery Management, Seedling Transplanting Age and Split Nitrogen Fertilizer Application on Growth and Yield of Lowland Rice in Eastern Uganda
Bibliographic record
Abstract
Rice yields in Uganda are still low due to poor rice production methods on smallholder farms in particular poor nursery and nitrogen fertilizer management practices. The study was set up to investigate the effect of nitrogen (N) fertilizer, nursery management and age of seedlings at transplanting on the yield of four rice cultivars (WITA 9, GSR 007, K 85 and K 5) in eastern Uganda. The nursery experiment was established with five treatments: 1) control (no chemical + transplanting 30-day old seedling), 2) di-ammonium phosphate (DAP) + fungicide + transplanting 14-day old seedlings, 3) DAP + transplanting 14-day old seedlings, 4) DAP + transplanting 30-day seedlings and 5) fungicide + transplanting 30-day old seedlings. Effect of split application of N on yield, was studied with urea (46% N) as the fertilizer source using a split-plot design with control (no fertilizer added) and 23 and 46 kg N ha-1 applied either basally or in two splits. Applying fertilizer in the nursery and transplanting 14-day old seedlings increased yields by 23-30% relative to the control, while using 30-day old seedlings did not result in any yield gain irrespective of the treatment. Splitting N applications increased yields by 0.1-0.3 t ha-1 and increased agronomic efficiency marginally. Applying 23 kg of N in two splits gave the highest return over fertilizer cost (US$ 855/ha). This demonstrates that lowland rice production in Uganda can be increased by a combination of nutrient management in the nursery, transplanting young seedlings and splitting applications of nitrogen fertilizer and represents a simple and economical option for farmers to increase rice yields. This is especially important considering that fertilizer use among smallholder farmers is restricted by high prices and limited availability. Improving N- and nursery management has great prospects for increasing rice yields on all cultivars in smallholder farms at minimal costs.
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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 | 0.001 |
| 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) | 0.000 | 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".