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Record W4408011439 · doi:10.1016/j.envdev.2025.101180

A qualitative framework to identify variables influencing ecological sustainability in tropical small-scale agriculture

2025· article· en· W4408011439 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEnvironmental Development · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainable Agricultural Systems Analysis
Canadian institutionsMcGill University
FundersInstituto para la Formación y Aprovechamiento de Recursos HumanosNatural Sciences and Engineering Research Council of CanadaSecretaría Nacional de Ciencia, Tecnología e Innovación
KeywordsSustainabilityScale (ratio)AgricultureEnvironmental resource managementEcologyGeographyAgroforestryEnvironmental scienceBiologyCartography

Abstract

fetched live from OpenAlex

Small-scale agriculture continues to be the sector with the largest number of food-producing farms worldwide. According to literature, this sector is highly diverse, not highly mechanized, and has low environmental impact. As a result, smallholders play a crucial role in ensuring food security and sustainability. Despite their small scale, these systems must be evaluated and compared based on a wide variety of factors influenced by their specific contexts. Environmental conditions, personal preferences, economic constraints, government regulations, and social norms all contribute to these contexts. A comparison of the ecological sustainability of agricultural systems has shown potential, but is often hindered by substantial limitations. Many of these approaches fail to engage stakeholders comprehensively and elucidate the intricate structures, components, and feedback mechanisms of agricultural ecosystems. Incomplete portrayals of these systems' complex interdependencies lead to inaccurate sustainability assessments. A novel method for analyzing and comparing the ecological sustainability of small farming systems in the tropics is presented using semi-structured interviews, content analysis, and causal loop diagrams. Using interviews, we identified key drivers and challenges in the development of these systems. Through causal loop diagrams, we visualized each system and identified its feedback loops. Several important conclusions have been drawn from the study of these systems in Mariato, Panama: 1.Ecological sustainability is driven by production, regenerative practices, and soil quality 2.Subsistence and respect for nature motivated the farmers 3.Degradation of soil and extreme dry seasons were major challenges 4.All three system types that were compared tended towards equilibrium • A bottom-up approach was used to develop conceptual models for small-scale farming systems in the tropics. • Causal loop diagrams illustrated the interactions between variables affecting the ecological sustainability of agricultural systems. • The methodology identified significant barriers and drivers impacting sustainability. • The methodology was tested for small farms located in Mariato, Panama. • The methodology facilitated the creation of conceptual models representing the shared vision of the stakeholders.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.277
Teacher spread0.270 · 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