Why Do Farmers Disadopt Successful Innovations? Socio-Ecological Niches and Rice Intensification
Bibliographic record
Abstract
The adoption of innovations in rice cultivation is presumed to operate in a rational manner, wherein new technologies or practices that successfully increase productivity or resource efficiency are adopted by target farmers based on cost-benefit calculations. In contrast, this paper examines a case of a public initiative to promote the system of rice intensification (SRI), wherein farmers widely disadopted the technique despite reporting increasing yields and reduced water consumption. To explain this paradox, we use the concept of the socio-ecological niche to examine a range of social and institutional factors that shape farmers’ decision-making. These included (1) access to land and labour; (2) water management capacity; (3) the quality of networks for knowledge sharing. The research suggests that small variations in these categories among otherwise similar smallholder households can markedly shape farmers’ risk perceptions and tangible outcomes with SRI. The implication is that agricultural innovations should be judged within their wider social context rather than on narrow evaluations of agronomic efficiency. Importantly, this must involve greater feedback mechanisms from smallholders with a variety of socio-economic profiles to help shape the character of agricultural innovations and extension strategies.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".