African agri-entrepreneurship in the face of the COVID-19 pandemic
Bibliographic record
Abstract
Background: The African continent is known for high entrepreneurial activity, especially in the agricultural sector. Despite this, the continent's economic development is below expectations, due to numerous factors constraining the growth and sustainability of agricultural SMEs. These constraints have been exacerbated by the COVID-19 pandemic. The purpose of this study was to understand the pathways through which the pandemic affected agri-SMEs, with specific focus on assessing the differentiated effects arising from the size of the agri-SME and the gender of the owner-manager. Methods: Data was collected from over 100 agri-SMEs, ranging in size from sole proprietorships with one employee to agri-SMEs employing up to 100 people, in six African countries. Mixed methods were used to analyse the data with changes in business operations arising from changing market access, regimented health and safety guidelines and constrained labour supply assessed using visualisations and descriptive statistics. Logistic regression modelling was employed to determine the set of variables contributing to agri-SME business downturn during the COVID-19 pandemic. Results: All surveyed agri-SMEs were negatively affected by COVID-19-associated restrictions with the size of the firm and gender of the owner-managers resulting in differentiated impacts. The smallest agri-SMEs, mainly owner-managed by women, were more likely to experience disruptions in marketing their goods and maintaining their labour supply. Larger agri-SMEs made changes to their business operations to comply with government guidelines during the pandemic and made investments to manage their labour supply, thus sustaining their business operations. In addition, logistic regression modelling results show that financing prior to the pandemic, engaging in primary agricultural production, and being further from urban centres significantly influenced the likelihood of a firm incurring business losses. Conclusions: These findings necessitate engendered multi-faceted agri-SME support packages that are tailored for smaller-sized agri-SMEs. Any such support package should include support for agri-SMEs to develop sustainable marketing strategies and help them secure flexible financing that considers payment deferrals and debt moratorium during bona fide market shocks such as the COVID-19 pandemic.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| 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".