Response of Upland Rice Cultivars to Nitrogen Fertilizer in the Savannas of Nigeria
Bibliographic record
Abstract
In the dry savannas of West Africa, cultivation of upland rice (Oryza spp.) under rain‐fed conditions is expanding due to the introduction of the New Rice for Africa (NERICA; WARDA, Bouake, Cote d'Ivoire) but appropriate N recommendations for these new cultivars are lacking. The present study evaluated the response of four NERICA rice cultivars, their parents (WAB 56‐104 (O. sativa, tropical japonica type), CG 14 [O. glaberrima]) to four rates of N (0, 30, 60, and 90 kg ha−1) at Sabon‐Gari (Sudan savanna, SS) and Tilla (northern Guinea savanna, NGS) in 2007 and 2008. There was no interaction between N rates and rice cultivars for grain yield, suggesting that the rice cultivars responded similarly to N application. Grain yield of NERICA rice cultivars responded significantly to N application in the Nigerian dry savannas following a linear response, even at 90 kg N ha−1, thus optimum rate might be above this N level. NERICA 1, NERICA 3, and NERICA 4 had comparable yields and produced 1.4 to 1.7 times more grains than the other cultivars in Sabon‐Gari; while in Tilla, NERICA 4 and WAB56‐104 produced the highest yield. NERICA 1, NERICA 3, and NERICA 4 may be more suitable for cultivation in Sabon‐Gari (SS) while NERICA 4 and WAB56‐104 can be recommended for Tilla (NGS).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".