Land restoration in food security programmes: synergies with climate change mitigation
Bibliographic record
Abstract
Food-insecure households in many countries depend on international aid to alleviate acute shocks and chronic shortages. Some food security programmes (including Ethiopia’s Productive Safety Net Program–PSNP – which provides a case study for this article) have integrated aid in exchange for labour on public works to reduce long-term dependence by investing in the productive capacity and resilience of communities. Using this approach, Ethiopia has embarked upon an ambitious national programme of land restoration and sustainable land management. Although the intent was to reduce poverty, here we show that an unintended co-benefit is the climate-change mitigation from reduced greenhouse gas (GHG) emissions and increased landscape carbon stocks. The article first shows that the total reduction in net GHG emissions from PSNP’s land management at the national scale is estimated at 3.4 million Mg CO2e y−1 – approximately 1.5% of the emissions reductions in Ethiopia’s Nationally Determined Contribution for the Paris Agreement. The article then explores some of the opportunities and constraints to scaling up of this impact.Key policy insights Food security programmes (FSPs) can contribute to climate change mitigation by creating a vehicle for investment in land and ecosystem restoration.Maximizing mitigation, while enhancing but not compromising food security, requires that climate projections, and mitigation and adaptation responses should be mainstreamed into planning and implementation of FSPs at all levels.Cross-cutting oversight is required to integrate land restoration, climate policy, food security and disaster risk management into a coherent policy framework.Institutional barriers to optimal implementation should be addressed, such as incentive mechanisms that reward effort rather than results, and lack of centralized monitoring and evaluation of impacts on the physical environment.Project implementation can often be improved by adopting best management practices, such as using productive living livestock barriers where possible, and increasing the integration of agroforestry and non-timber forest products into landscape regeneration.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".