Yield and income risk‐efficiency analysis of alternative systems for rice production in the Guinea Savannah of Northern Ghana
Bibliographic record
Abstract
Abstract Risk efficiency of rice grain yield and returns to farm operators' household resources generated from an improved short‐duration cover crop fallow system were compared with (traditional) natural bush fallow, and continuous rice‐cropping systems. The improved fallow system involved maintaining Calopogonium mucunoides, seeded into a natural bush fallow for 2 years before planting to rice. With no chemical fertilizer application, which reflects farmers' practice in the area, average grain yield for continuous rice (1,185 kg/ha) and the cropping sequence incorporating a natural bush fallow (1,175 kg/ha) did not differ, but were higher for the improved fallow system (1,304 kg/ha). This suggests that nutrient contribution from the leguminous cover crop made up for critical crop N requirements in the improved fallow. Stochastic dominance of grain yield distributions from the improved fallow system, relative to the other two cropping systems, was more dramatic with no N fertilizer application compared to treatments with 30 kg/ha N. Average returns were highest for the improved fallow system, followed by the natural bush fallow‐cropping system, and then continuous rice, under the no N fertilizer treatment regime. With 30 kg/ha N fertilizer, income risk efficiency was less clear (compared to treatments with no N fertilizer), especially between continuous rice and the improved fallow treatment, because of faster N mineralization effects on continuous rice. In contrast, the improved cover crop fallow system completely dominated the natural bush fallow treatment under both fertilizer regimes. Rice production systems that incorporated the leguminous cover crop fallow were superior to the natural bush fallow system, based on both grain yield and average farm income risk‐efficiency considerations.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".