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Record W2787226870 · doi:10.5539/sar.v7n2p19

Response of Sorghum Production in Kenya to Prices and Public Investments

2018· article· en· W2787226870 on OpenAlexvenueno aff
Perez Ayieko Onono-Okelo

Bibliographic record

VenueSustainable Agriculture Research · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Innovations and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsSorghumProduction (economics)EconomicsSubsidyAgricultural economicsFood securityIncentiveAgricultural productivityAgricultureBusinessMacroeconomicsMarket economyAgronomy

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.873
Threshold uncertainty score0.377

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.005
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.067
GPT teacher head0.345
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2018
Admission routes1
Has abstractyes

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