Bibliographic record
Abstract
Precision agriculture is rapidly emerging as a cornerstone of transformation in modern farming as farmers adjust to changing needs in their management and dissemination of technology. While technological innovations associated with precision agriculture--such as remote sensing, GIS mapping, and drone usage--are widely recognized, there remains a substantial gap in understanding the economic implications and behavioral responses that accompany its adoption. This research addresses that gap by exploring three key questions related to the economics of precision agriculture, each examined through a distinct empirical lens. Chapter two investigates the current state of PA adoption in the United States and GIS mapping use in Canada, drawing on data from the Census of Agriculture. The analysis explores the relationship between farm size and adoption behavior, revealing that larger farms are significantly more likely to implement precision technologies. Capital availability and investment capacity also appear to be key drivers of adoption. Chapter three evaluates the impact of a pro-precision agriculture policy on farmers' risk behavior, proxied by crop insurance coverage rates. Using county-level data from the Summary of Business and Risk Management Agency (RMA) for corn producers and applying a two-way fixed effects difference-in-differences framework, the study finds that farmers in Virginia--following the implementation of the tax credit policy--reduced their insurance coverage rates relative to North Carolina. This suggests that PA adoption may influence farmers' perceived need for insurance and their attitudes toward risk. Chapter four shifts the focus to Australia, analyzing how farmers planned adoption of drones relates to their technical efficiency. Using survey-level data and a two-stage approach with truncated regression and subsample bootstrapping, the study finds a positive association between drone adoption plans and technical efficiency. This chapter contributes to the growing literature on how emerging technologies may support productivity improvements in agriculture. Collectively, these three chapters offer new empirical evidence on the adoption, policy impacts, and efficiency outcomes associated with precision agriculture, providing insights that are vital for researchers, policymakers, and practitioners alike.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".