From the ground up: Patterns and perceptions of herbaceous diversity in organic coffee agroecosystems
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.
Bibliographic record
Abstract
Abstract Smallholder farms that transition to organic and biodiverse production are increasingly recognized as strongholds of agrobiodiversity, with emerging work identifying important outcomes such as enhancing crop portfolios, mitigating extreme climate events and contributing to farmer well‐being. Yet the emergent herbaceous communities in these organic systems remain understudied, with the functional diversity and management of this stratum relatively unknown. This study identifies the taxonomic and functional diversity of the herbaceous community in organic coffee agroforestry systems, and describes the extent of this diversity with farm, and farmer, attributes. We measured leaf‐level functional traits (e.g. specific leaf area) of the herbaceous community to derive functional diversity indices and collected localized environmental conditions on 15 organic coffee farms in Central Valley, Costa Rica. We also conducted semi‐structured interviews with nine farmers to construct mental models on herbaceous community management using a cognitive mapping approach. In total, 38 species from 20 taxonomic families were present in these organic coffee systems. The herbaceous communities were functionally diverse; however, functional evenness increased with canopy openness, suggesting that farms adopting agroforestry tend to have a more functionally diverse herbaceous stratum. Farmer perception of plant traits in the herbaceous community was differentiated into competitive (weeds) or neutral/positive effects. These perceptions aligned with well‐established functional trait trade‐offs. The mental models representing farmer decision‐making processes were highly variable, with a nearly 30% increase in cognitive map density from the simplest map to the most complex; this complexity in mental models was a key explanatory variable in the level of functional diversity of the herbaceous community. Organic management practices that support agroforestry practices also, in turn, promote a functionally diverse herbaceous stratum. We show that functional trait syndromes in these herbaceous communities in agroforestry systems are linked with farmer perceptions of traits, and that highly interconnected farm decision‐making is related to greater functional diversity in the herbaceous community. Understanding pathways of farmer decision‐making on managing this herbaceous community can appropriately situate on‐farm practice and policy for the transition to organic production, and inform emerging agri‐environmental programs.
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 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it