Profitability and Water Productivity of Small Scale Irrigation Schemes in Northern Ghana
Bibliographic record
Abstract
The Savelugu-Nanton District of Northern Ghana is a beneficiary of irrigation projects mostly on small scale basis schemes. Poor data situation due to inadequate appraisal of these schemes results in difficulty to track their progress and impacts, which threatens their sustainability. This study was conducted to assess the profitability and productivity of the Libga and Bunglung small scale irrigation schemes in the District between 2013 and 2015. Sixty households were selected using random sampling techniques. Production data, costs, yield and soil data were gathered using structured questionnaires and field measurements. Data on traditional rainfed systems were gathered from secondary information. The results indicated that yields of rice were greater in Bunglung than in Libga scheme but both schemes had greater yields than rainfed systems, resulting in greater profits under irrigation. However, yields of pepper were greater in Libga than in Bunglung. Crop water productivity (CWP) in terms of harvested weight of rice was 0.50 and 0.58 kilogram per cubic meter in Libga and Bunglung respectively while CWPs in terms of gross value of harvested rice were 0.38 and 0.41 Ghana cedis per cubic meter respectively. For pepper, the CWPs were 0.74 and 0.64 kilogram per cubic meter in terms of crop weight in Libga and Bunglung respectively while CWPs in terms of gross value were 1.23 and 1.07 Ghana cedis per cubic meter respectively. Irrigation improved farmers’ incomes, however, pepper production was more profitable than rice production at both schemes. More investments by farmers are important to achieving maximum yields.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".