Agronomic Evaluation of Some Drought Tolerant NERICA Rice Varieties to Arbuscular Mycorrhizal Fungi (AMF) Inoculation in the Rainforest Transitory Zone of Nigeria
Bibliographic record
Abstract
Erratic rainfall distribution pattern poses serious challenge to food production in the rainforest transitory zone of Nigeria. Low input technology through the adoption of drought tolerant varieties (NERICA) and the application of Arbuscular Mycorrhiza Fungi (AMF) inoculum could be an alternative option for double cropping of upland rice. Field trials were conducted in the Teaching and Research Farm of the Federal University of Agriculture, Abeokuta in the early and late cropping season of 2012 in the transitory rainforest agro-ecology of Nigeria. The objective was to investigate the performance of some selected upland NERICA rice to the application of AMF inoculum. A 6 x 2 factorial experiment was conducted; six upland rice varieties selected with and without the application of AMF, laid out in split plot arrangement, with AMF in the main block, while variety was in the sub-plot. Each treatment was replicated three times. In both seasons, significant (P < 0.05) effect of AMF inoculation was observed on vegetative, reproductive growth and development parameters in the order + AMF > - AMF. In the early season there was no significant (P > 0.05) varietal variability on grain yield/ha. Conversely, in late season NERICA 4 recorded significantly (P < 0.05) higher grain yield (1050kg/ha) as suggested in significantly (P < 0.05) higher panicle/m2 (59.50). Performance of NERICA 4 was significantly increased by application of AMF inoculation in the late cropping season.
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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".