Funding of Agricultural Research and Development in Ghana: The Case of Council for Scientific and Industrial Research (CSIR)
Bibliographic record
Abstract
Agricultural Research and Development (R&D) investments contribute greatly to economic growth, agricultural development and poverty reduction in developing countries. This paper examines the financial investment and expenditure trends in agricultural R&D in Ghana with emphasis on the Council for Scientific and Industrial Research (CSIR) and the implication for the policies driving agricultural research in Ghana. Data from Agricultural Science & Technology Indicator (ASTI) and in-depth studies on agricultural R&D in Ghana were used. Purposive sampling was used to gather data in thirteen agricultural research institutes and five public universities in Ghana. Through questionnaire administration, data were collected and analyzed using descriptive statistics. The study revealed that, total public agricultural R&D expenditure had increased by 59 per cent from 42.5 million (2005 PPP) dollars in 2000 to 67.7 million (2005 PPP) dollars in 2011 and with an average expenditure of 54.1 million (2005 PPP) dollars per year. The total expenditure by CSIR constitutes about 50 per cent of the total agricultural research expenditure in Ghana. The study however, showed a drastic decline in capital investments from 6.7 per cent in 2000 to 0.1 per cent in 2011 of the total government funding with operational cost following similar declining pattern. Still, when considering the totality of funding including salaries and wages, government support is the main source of funding for agricultural R&D in Ghana (85 per cent) with donors (7.3 per cent), sale of goods and services (6.7 per cent) and others serving as complementary sources. Though there have been considerable government investments in agricultural R&D in CSIR over the period, impact on operational and research activities has been minimal as the chunk of it went into payment of salaries and wages. The fundamental challenge is funding the very important operational and research activities which lead to technology development and innovation. Increasing commercialization of research technologies and government investment in agricultural R&D in Ghana, are recommended to address this investment challenge.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".