Farmer networks and agrobiodiversity interventions: the unintended outcomes of intended change
Bibliographic record
Abstract
Agroecosystem strategies to enhance agrobiodiversity can curb many of the negative impacts associated with current food production systems.With rising interest in agrobiodiversity and agroforestry as farming interventions that confer ecological and socioeconomic benefits, understanding the intended pathways of interventions is important for successful agroecological transformations.Yet, the patterns of agrobiodiversity introduction and adoption remain elusive.Drawing upon social network research from the regions of Ghana where cocoa (Theobroma cacao) is grown, we synthesize the relationships between agroforestry interventions, information networks, and the adoption of diversified agroecosystems.We illustrate middle-level patterns from independent studies in three regions of Ghana and nearly 500 farmer interviews.Strong structural indicators at the network level are linked to agrobiodiversity; farmers in larger, less dense information networks with ties to external organizations tend to have higher reported and measured agrobiodiversity.Remarkably, these trends were found in environmentally and socio-culturally different contexts in Ghana.However, these trends do not, in all cases, scale to the community level.For example, we did not observe any clear relationship between the density of community networks and the measures of agrobiodiversity at the community scale.This may be on account of the type of agrobiodiversity measure applied (above-ground biomass) to assess community-level outcomes.Selection of environmental attributes with meaningful spillover effects, such as pest management, would more likely uncover nontrivial network effects at the collective level.Our findings support that both innovation and cooperation are indispensable for successful agrobiodiversity interventions, and that networks can operate to overcome negative outcomes of agrobiodiversity.Based on these studies, we conclude that agrobiodiversity adoption via interventions and established farmer-to-farmer networks may trigger the formation of other, observation-based networks that draw in socially distant actors.Our research strategy of ex-post qualitative comparisons allowed for in-depth insight into the complexities of information networks and agrobiodiversity adoption but also generated new hypotheses on the role of social networks in diversified farming systems.
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.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".