Trends in Agriculturally-Relevant Rainfall Characteristics for Small-scale Agriculture in Northern Ghana
Bibliographic record
Abstract
This study set out to investigate the trends of agriculturally-relevant rainfall characteristics among small-scale farmers in the rainfall-sensitive dry savanna agro ecological zone of northern Ghana. Interviews are used to identify characteristics of rainfall which are deemed by the farmers as important in their food production. Time series daily rainfall data from 1960-2007 is then used to identify trends in these variables which include the amount and temporal distribution of rainfall, occurrence of extreme daily rainfall events, the onset of rains, risk of dry spells and coefficient of variability of rains. The risk of dry spells for varying number of days following the planting period is computed using first-order Markov chain modeling. We find that there is a significant increase in mean rainfall per rain day and the coefficient of variation or summer rainfall amounts. No significant change in the onset of rains, the annual rainfall amount and maximum rainfall days are established. However, a significant decrease in the number of rain days and the probability of dry spells of up to seven and eleven days in the first four weeks of the planting season is revealed. There is need for development of an agricultural policy framework designed to understand the growing risks associated with agricultural production among small-scale farmers, and to improve management practices to accommodate and adapt to the new challenges of varying rainfall.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".