Assessing the Potential for Private Sector Engagement in Integrated Landscape Approaches: Insights from Value-Chain Analyses in Southern Zambia
Bibliographic record
Abstract
Agricultural and forested landscapes in Africa are changing rapidly in response to socio-economic and environmental pressures. Integrated landscape approaches provide an opportunity for a more holistic and coordinated resource management strategy through the engagement of multiple stakeholders. Despite their influence as landscape actors, participation of private businesses in such initiatives has thus far been limited. This study focuses on the Kalomo District in southern Zambia, which provides an example of a rural landscape characterized by high levels of poverty, low agricultural productivity, and widespread deforestation and forest degradation. The study applied a value-chain analysis approach to better understand how the production of four locally important commodities (maize, tobacco, cattle, and charcoal) impacts land use, local livelihoods, and environmental objectives in this landscape, focusing on the role and influence of private sector actors. Data were collected through focus group discussions and key informant semi-structured interviews. Qualitative content analysis was employed to analyze the data and contextualize the findings. Results indicate three key potential entry points for increased private sector engagement: (1) improving water security for smallholders; (2) empowering small and medium-sized enterprises (SMEs) as private sector actors; and (3) collective planning for sustainable landscape activities with deliberate measures to involve private sector actors. We discuss options for optimizing benefits from the identified entry points.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".