Crop Water Use and a Gravity Model Exploration of Virtual Water Trade in Ghana’s Cereal Agriculture
Bibliographic record
Abstract
Agricultural water productivity is crucial for sustainability amidst the escalating demand for food. Cereals are pivotal in providing nutritious food at affordable prices. This study was based on Ghanaian data spanning from 1992 to 2021 to evaluate water usage in the cultivation of major cereals. It also examined the virtual water losses or gains in cereal trade alongside influencing factors. The analysis utilized secondary data encompassing the virtual water content, production quantity, export and import quantities, distance, GDP per capita, population, and land per capita of Ghana and its 75 trade partners. In the last 5 years, crop water use (CWU) reached an average of 7.08 billion m3/yr for maize, 3.48 billion m3/yr for rice, 1.08 billion m3/yr for sorghum, and 0.63 billion m3/yr for millet production. Ghana’s major partners for exported virtual water (EVW) were Niger, Burkina Faso, South Africa, and Togo. Major partners for imported virtual water (IVW) were Argentina, South Africa, Ukraine, Togo, Russia, Burkina Faso, Canada, Senegal, Nigeria, Portugal, UK, Niger, and the USA. The Panel Least Squares Method of regression was used to apply the Gravity Model principle in assessing influencing factors. The findings indicate that Ghana is a net importer of virtual water in the cereal trade, with significant influences from geographical distance, GDP per capita, population, land per capita, and cereal water use.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".