Farmer perception and utilization of leaf functional traits in managing agroecosystems
Bibliographic record
Abstract
Abstract Using knowledge of leaf functional traits, such as those forming the leaf economics spectrum (LES), to understand plant responses to environmental change is well‐established and now being more widely applied to agroecosystems. Yet, little is known about how farm managers invoke leaf functional traits to inform management decisions. The objectives of this research were to (1) evaluate whether farmers use knowledge of intraspecific trait variation (ITV) in LES traits (or trait proxies) of target crops as response indicators of management conditions; (2) determine whether LES trait values are ranked consistently among multiple farmers along a “Farmer Leaf Economics Spectrum” (FES); (3) evaluate how a FES corresponds to the LES; and (4) identify the farmer and farm attributes that best predict the agreement between the FES and the LES. We collaborated with coffee ( Coffea arabica ) farmers in the Turrialba Valley, Costa Rica. We used a visual elicitation tool of fresh leaves along an intraspecific spectrum of leaf size, leaf thickness and leaf colour (as a proxy for leaf nutrients); respondents were asked to rank leaves in response to shade and nutrient scenarios as well as yield potential. On‐farm biophysical data, management practices and socio‐economic attributes were also collected. The majority of farmers demonstrated a developed system of utilizing coffee leaf and whole‐plant ITV as indicators of management practices. Farmers managing smaller farms tended to more commonly acknowledge ITV in LES chemical–morphological traits, as compared to those managing large farms. The agreement between a respondent‐identified ranking of leaf thickness ITV as a function of light environment and an empirically defined thickness‐to‐light ranking was partially explained by farmers’ physical engagement with plants. Synthesis and applications . In scientific literature, analyses of crop intraspecific trait variation have provided important insights into the mechanistic bases of multiple key agroecological processes. We demonstrate that farmers use crop leaf trait variation as an indicator to both evaluate management prescriptions and to initiate management actions including shade‐tree species selection and abundance, crop‐ and shade‐tree pruning regimes and fertilization treatments. These findings signify that functional traits represent a key nexus between scientific and local knowledge.
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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".