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Record W4241339778 · doi:10.4102/sajems.v19i1.1183

Contract farming risks: A quantitative assessment

2016· article· en· W4241339778 on OpenAlexaff
Arkins M Kabungo, Glenn P. Jenkins

Bibliographic record

VenueSouth African Journal of Economic and Management Sciences · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsQueen's University
Fundersnot available
KeywordsExchange rateAgricultureBusinessLoanNet present valueCommodityYield (engineering)SustainabilityStakeholderRisk managementValue (mathematics)EconomicsFinanceProduction (economics)Microeconomics

Abstract

fetched live from OpenAlex

The objective of this study is to identify the key risks facing each of the stakeholders in the export-focused paprika value chain in Zambia. Although a deterministic cost-benefit analysis indicated that this outgrower scheme would have a very satisfactory net present value (NPV), a Monte Carlo analysis using an integrated financial–economic–stakeholder model identifies a number of risk variables that could make this system unsustainable. The major risks include the variability of the real exchange rate in Zambia; the international price of paprika; and the farm yield rates. This analysis points out that irrigation systems are very important for both stabilising and increasing yields. The analysis also shows the limitations of loan financing for such outgrower arrangements when at the sector level it is difficult or even impossible to mitigate the risks from real exchange rate movements and changes in international commodity prices. This micro-level analysis shows how critical real exchange rate management policies are in achieving sustainability of such export-oriented value chains.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.821
Threshold uncertainty score0.317

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.055
GPT teacher head0.303
Teacher spread0.248 · 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 designTheoretical or conceptual
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
Published2016
Admission routes1
Has abstractyes

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