Response of Sorghum Production in Kenya to Prices and Public Investments
Bibliographic record
Abstract
Expansion of sorghum production in the arid and semi-arid areas in Kenya has been singled out as a potential for addressing food security challenges due to climate shocks affecting maize production and reduced availability of arable land in the medium and high potential areas. Towards achieving this the government has used guaranteed minimum output prices, input subsidies and public investments to promote agricultural developments as some of the instruments of policy to provide incentives to farmers. Literature is deficient of studies on production behaviour of sorghum in the country with respect to market prices and public investments. This study provides an empirical evidence on the response of sorghum production to output and input prices as well as to public investments. The study used data spanning the period 1978 to 2014 to fit an autoregressive distributed lag (ARDL) specification of the output response equation using the EViews statistical software. The findings show that sorghum production in Kenya does not respond to increases in its output price and is not adversely affected by input prices. Increased development spending in agriculture lead to increased sorghum production and also increase use of fertilisers and certified seeds. The findings suggest that policy interventions based on output prices and input prices alone would not yield the desired increased expansion in sorghum production. The government should increase budgetary allocations to agricultural development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".