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Record W2293398842 · doi:10.1016/j.agee.2015.05.013

Ecosystem-based adaptation for smallholder farmers: Definitions, opportunities and constraints

2015· article· en· W2293398842 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAgriculture Ecosystems & Environment · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Innovations and Practices
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNatural Environment Research CouncilCentre de Coopération Internationale en Recherche Agronomique pour le DéveloppementBundesministerium für Umwelt, Naturschutz, Bau und ReaktorsicherheitEconomic and Social Research CouncilDepartment for International Development
KeywordsAdaptation (eye)EcosystemEnvironmental resource managementEcosystem servicesEcologyAgroforestryBusinessEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

• 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.758
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.152
GPT teacher head0.227
Teacher spread0.075 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it