Yield Gap Survey in Sudanno-Guinea Savanna Agro-Ecological Zones of Ghana
Bibliographic record
Abstract
Yield gap survey (YGS) was carried out in nine selected communities in the Sudanno-Guinea Savannah (SGS) rice growing ecologies of Ghana in the 2012 and 2013 cropping seasons. One lead farmer was selected from each community to host researcher-managed fields. The specific objectives of the study were to determine the causes of yield gaps in rain fed and irrigated ecologies of the SGS of Ghana and to suggest ways by which the gaps can be bridged to enhance the production of the crop in Ghana In both 2012 and 2013 seasons, farmers in the Sudan savannah zone recorded higher gaps in rice yields (average 55%) than their compatriots in the Guinea Savannah (average 49%). The analysis of data collected from YGS indicated that factors such as difficult weather and soil conditions, insufficient access to mechanization services/improper land preparation, lack of fertilizers, the use of unimproved rice seed, untimely field operations and inadequate water and pest (in particular weed) management, were some of the key factors that adversely influence rice yields in the study area. Unrestricted access to mechanization services delivery, intensified use of improved rice seed, appropriate melioration with organic and inorganic fertilizers, together with robust soil and water management interventions are essential requirements for bridging experiment-based yield gaps for enhanced rice yields in the Sudanno-Guinea Savannah zones of Ghana.
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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 | 0.001 | 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".