Impact of agriculture finance in modern technologies adoption for enhanced productivity and rural household economic wellbeing in Ghana: A case study of rice farmers in Shai-Osudoku District.
Bibliographic record
Abstract
Rural and agricultural finance innovations have significant potential to improve the livelihoods and food security of the poor. Although microfinance has been widely studied, an extensive knowledge gap still exists on the nuts and bolts of expanding access to rural and agricultural finance. This study uses focus group discussion, key informant interview, and quantitative household survey to explore how smallholders access credits and loans influence adoption of modern production technologies and what are perceived limitations to access these financial instruments in the Shia-Osuduku District in the Greater Accra Region of Ghana. The specific objectives of the study are; (1) to assess the challenges rice farmers face in accessing finance, (2) to determine if access to finance impacts the adoption of modern rice production technologies and (3) to determine whether loan investments in improved technologies increase productivity and income levels of farmers. The study noted that issues of mistrust for smallholder farmers by financial institutions act as barriers to facilitating their access to loans and credits. Banks and financial institutions relay their mistrust through actions such as requesting outrageous collateral, guarantors, a high sum of savings capital, and a high interest rate for agriculture loans, delays, and bureaucratic processes in accessing loans. The study suggested that enabling policy environment and frameworks with a supportive rural infrastructure such as warehouse receipt systems can significantly increase farmers' access to credit instruments for investment in modern technologies to increase agricultural productivity, which is essential to address food insecurities and rural poverty issues in 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 | 0.001 | 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 | 0.002 | 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) | 0.003 | 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".