Balancing co-benefits and trade-offs between climate change mitigation and adaptation innovations under mixed crop-livestock systems in semi-arid Zimbabwe
Bibliographic record
Abstract
Abstract Achieving Zimbabwe’s national and international commitments to food systems transformation and climate resilience building is of high priority. Integrated simulation-based research approaches developed under the Agricultural Model Intercomparison and Improvement Project (AgMIP) are important sources of evidence to guide policy decisions towards sustainable intensification. Through the identification of economically viable, socially inclusive and environmentally sustainable development pathways, the analysis in this study evaluates co-benefits and trade-offs between climate change adaptation and mitigation interventions for vulnerable smallholder crop-livestock holdings in the semi-arid regions of Zimbabwe. We explore how climate effects disrupt the livelihoods and food security for diverse farm types, the extremely vulnerable and those better resource endowed but facing high risks. In an iterative process with experts and stakeholders, we co-developed context specific development pathways. They include market-oriented adaptation and mitigation interventions and social protection mechanisms that would support the transition towards more sustainable intensified, diversified and better integrated crop-livestock systems. We assess the trade-offs associated with adoption of climate-smart interventions aimed at improving incomes and food security but that may have consequences on GHG emissions for the different pathways and farm types. The approach and results inform the discussion on drivers that can bring about sustainable intensification, and the extent to which socio-economic benefits could enhance the uptake of emission reducing technologies thereof. Through this strategy we evaluate interventions that can result in win–win outcomes, that is, adaptation-mitigation co-benefits, and what this would imply for policies that aim at transforming agri-food systems.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".