Urbanization and adoption of sustainable agricultural practices in the rural‐urban interface of Bangalore, India
Bibliographic record
Abstract
Abstract Urban expansion often takes place on the most productive agricultural lands, affecting how the remaining agricultural land is used. Evidence on the adoption of sustainable agricultural practices in urbanizing areas is scarce and mostly based on cross‐sectional data. Cross‐sectional studies, however, cannot reflect the dynamics of urbanization and adoption. We use household panel data from 2017 and 2020 to analyze the adoption of sustainable agricultural practices among peri‐urban farmers in Bangalore, India, a rapidly urbanizing region. We focus on practices for water and erosion management, integrated pest management and soil fertility management, and an integrated package of sustainable practices. Using random effects probit models with the Mundlak approach, we consider various factors besides urbanization, including exposure to weather variability, awareness of climate change, connection with institutional actors, and household and farm characteristics. Results show that urbanization, measured as changes in the percentage of built‐up area, reduces the probability that farmers adopt sustainable agricultural practices. Like prior studies, we find that wealth indicators, market access, knowledge of climate change, and rainfall variability facilitate adoption. However, contact with institutional actors largely reduces farmers’ probability of adoption. Policies should promote the integration of sustainable farming technologies at the institutional level and in information and training programs to achieve sustainable intensification of peri‐urban agriculture.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".