VILLAGE SURVEYS FOR TECHNOLOGY UPTAKE MONITORING: CASE OF TILLAGE DYNAMICS IN THE TRANS-GANGETIC PLAINS
Bibliographic record
Abstract
SUMMARY Agricultural research and development (R&D) would benefit from reliable yet cheap technology uptake indicators to guide decision making. The paper explores the use of village surveys to monitor technology use and illustrates this through two empirical case studies into tillage dynamics in the Trans-Gangetic Plains in northwest India. The first case study is a revisit of 50 communities surveyed earlier in Haryana State. The second case study is a new and wider representative sample of 120 villages across Haryana and Punjab States. The case studies illustrate that after an initial rapid spread of tractor-drawn zero tillage drills for wheat seeding in these intensive systems, the zero + reduced tillage area seems to have stabilized there at between a fifth and a quarter of the wheat area. Conventional tillage for wheat continues to decline, with an increased use of rotavators making up the difference – but its intensive shallow tillage goes against the conservation agriculture tenets. The paper illustrates the potential of village surveys to provide timely and cost-effective feedback to agricultural R&D.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".