Understanding hydrometeorological conditions and their relationship with crop production in the upper east region, Ghana
Bibliographic record
Abstract
This study analyzes long-term patterns in precipitation, (P), evapotranspiration (ET), and meteorological and agricultural drought indices in the Upper East Region (UER) of Ghana from 1981 to 2017. The study further investigates the relationship between these hydrometeorological variables and yields of groundnut, maize, millet, rice, and sorghum from 1993 to 2017. Results indicate statistically non-significant trends in P and ET over the study period, corresponding with fairly consistent crop yields. However, both linear and non-linear relationships between crop yield and hydrometeorological conditions were observed, with extreme soil moisture (SM) and P levels negatively impacting yields, likely due to waterlogging exceeding optimal thresholds for crops, or drought stress. Regression analyses show moderate R-squared values (0.1 to 0.5), suggesting that while hydrometeorological variables are key drivers, other factors, such as farming practices and socio-economic conditions, also influence yield variability. These findings underscore the need for water management strategies to optimize soil moisture and mitigate the impact of both droughts and extreme waterlogging on crop production. The study recommends adopting modern agricultural technologies, such as precision irrigation and drought-resistant crop varieties, to enhance crop yields and ensure sustainable farming in the UER. • Analyzes 37-year hydroclimatic data for crop yields in Ghana’s UER. • Integrates geospatial data to map agricultural productivity trends. • Excess soil moisture and rain negatively impact staple crop yields. • Demonstrates geospatial analysis for climate–agriculture links in data-scarce regions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".