Why Farmers Are Hesitant to Adopt What Appears Good on the Basis of Science: Understanding Farmers’ Perceptions of Biophysical Research
Bibliographic record
Abstract
This study conducted a series of extension events that were followed by farmer interviews with 394 farmers who had participated in an initial household survey in 2018, involving four farmer categories: 1] those actively participating in the Trees for Food Security (T4FS) project from phase 1 (2014); 2] farmers neighbouring those actively participating in the T4FS project from phase 1; 3] farmers actively participating in the T4FS project from phase 2 (2017) and; 4] farmers living distant and unaware of the T4FS project. The study drew upon knowledge generated from biophysical experiments on tree water use, shade tree planting and management in smallholder coffee-bean agroforestry systems to assess farmers’ perceptions and willingness to adopt practices emanating from the study following exposure to the research outputs. The main form of extension used was through display and viewing of posters and a translated power point presentation of the research outputs on impact of tree canopy pruning on tree and coffee plant water use and productivity of coffee and common beans. We present the key messages obtained by the participants from the extension activities conducted, their preferred crop and management combinations, perceptions towards the research outputs and willingness to adopt the practices recommended by the study. We contend that smallholder farmers are hesitant to adopt innovations due to an underlying culture of financial expectancy leading to ‘pseudo adoption’, underutilisation of existing social networks during research and extension, period of exposure to a technology, and limitations in measuring and predicting adoption. We align the four farmer categories to the Process of Agricultural Utilisation Framework (PAUF) criteria, leading to a better understanding of the impact of research and development projects and agroforestry tree planting and management adoption pathways among smallholder farmers. This would enable introduction of socially and biophysically appropriate agroforestry interventions into local realities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".