Comparison of Response to Nitrogen between Upland NERICAs and ITA (Oryza sativa) Rice Varieties
Bibliographic record
Abstract
Average yields of upland rice are the lowest in Uganda, and most of the productivity gains attributed to improved varieties are related to increased area of production from clearing virgin lands for rice production. In a bid to optimize productivity, we compared the effect of four nitrogen fertilizer treatments: 0, 40, 80 and 120 kgN/ha and two variety types (ITAs (Oryza sativa) and NERICAs (New rice for Africa)) on grain yield and yield parameters in four locations. Combined analysis of variance revealed that nitrogen fertilizer increased mean grain yields from 2116-5200 kg/ha in the NERICAs and 2331-5100 kg/ha in the ITAs. In all the study areas, NERICA 4 and NARIC 2 outperformed NARIC 1 and NERICA 1, and yield trends were consistent over the years suggesting that the two varieties respond better to N fertilizer application. However, the productivity gains are probably related to genetic potential of the varieties rather than the N fertilizer effect, as reflected by the consistent relative performance between 0 N and other N rates. The heavier grains of NARIC 2 and NERICA 4 suggest greater dry matter accumulation before heading, as these varieties have a longer period of vegetative growth. The significant interaction of location x fertilizer and location x variety reveals the need for evaluating the nitrogen-supplying power of soils in the various cropping systems in the country.
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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) | 0.000 | 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".