Ecosystem-based adaptation for smallholder farmers: Definitions, opportunities and constraints
Bibliographic record
Abstract
• Smallholders’ farmers are vulnerable to the impacts of climate extreme events. • Ecosystem-based Adaptation (EbA) practices can help reduce or avoid these impacts. • Adoption of these practices by smallholders is conditioned by key barriers and trade-offs. • Existing experiences in promoting agroecology and agroforestry provide key lessons to promote adoption. Despite the growing interest in Ecosystem-based Adaptation, there has been little discussion of how this approach could be used to help smallholder farmers adapt to climate change, while ensuring the continued provision of ecosystem services on which farming depends. Here we provide a framework for identifying which agricultural practices could be considered ‘Ecosystem-based Adaptation’ practices, and highlight the opportunities and constraints for using these practices to help smallholder farmers adapt to climate change. We argue that these practices are (a) based on the conservation, restoration or management of biodiversity, ecosystem processes or services, and (b) improve the ability of crops and livestock to maintain crop yields under climate change and/or by buffering biophysical impacts of extreme weather events or increased temperatures. To be appropriate for smallholder farmers, these practices must also help increase their food security, increase or diversify their sources of income generation, take advantage of local or traditional knowledge, be based on local inputs, and have low implementation and labor costs. To illustrate the application of this definition, we provide some examples from smallholders’ coffee management practices in Mesoamerica. We also highlight three key obstacles that currently constrain the use of Ecosystem-based Adaptation practices (i) the need for greater understanding of their effectiveness and the factors that drive their adoption, (ii) the development supportive and integrated agriculture and climate change policies that specifically promote them as part of a broader agricultural adaptation program; and (iii) the establishment and maintaining strong and innovative extension programs for smallholder farmers. Our framework is an important starting point for identifying which Ecosystem-based Adaptation practices are appropriate for smallholder farmers and merit attention in international and national adaptation efforts.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| 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".