Economic Implications of Environmentally Smart System of Rice Intensification in Nigeria
Bibliographic record
Abstract
Rice is a staple food in almost all parts of the world, especially Africa and Asia, as a rich source of carbohydrate. However, rice cultivation contributes greatly to climate change through the emission of greenhouse gases involved in soil and plant management, fertiliser application as well as water management practices. While the environmental benefits of environmentally smart rice farming practices in Nigeria have been established, the same has not been for the economic implications of environmentally smart rice farming. Prior to this study, there was inadequate information on the profit margins in different rice production systems in Nigeria. The research aimed at understanding and evaluating the method that gives a higher profit margin while having lesser environmental impact. The study’s primary sources of information require two distinct research methodologies and as such, two major types of data collection methods; qualitative and quantitative as suitable for participatory action research viz: questionnaire, focus group discussion, participant observation and key informant interview. The research findings demonstrate that the environmentally-smart system of rice intensification is more profitable than the conventional method.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".