Crop biotechnology, structure of primary production and socioeconomic changes in rural communities
Bibliographic record
Abstract
Lower production costs, higher productivity, and reduced chemical requirements are generally claimed as attributes of biotechnology in crop production. These claims are not substantiated through the analysis of the changes in production structure. The changes in the structure of production are manifested in farm structural changes in the long-run. It is often argued that farm structural changes affect rural socioeconomic conditions. Hence, the relevant policy questions are what changes in the structure of crop production are caused by biotechnology, and how those changes can potentially affect farm-based rural economies. In order to generate empirical answers to these questions, this study investigates the impacts of crop biotechnology on the structure of production and their implications for socioeconomic changes in rural communities. Specifically, this study estimates the changes in the structure of production of corn and soybeans attributable to biotechnology, and the impacts of changes in overall structure of production on rural socioeconomic changes in Southern Ontario. First, an index for crop biotechnology is developed on the basis of the rates of adoption and translog variable cost models are estimated employing the index along with a time trend. As evident in the results, the index explains about 2% of the total factor productivity growth. The results also indicate that the technology has contributed to an increase in the total production costs, increases in the costs of seed and custom work, and decreases in the costs of fertilizer, chemicals and the use of machinery. At the second stage, a model for population and employment changes in rural communities is developed and estimated by incorporating measures of relative changes in the structure of sectoral production in the region. The results indicate that the long-run impact of regional employment growth in the primary sector on local population change is negative, while that in the tertiary sector is positive. The results also suggest that the impacts of regional income in the primary sector and agglomeration of production on local employment growth are positive.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".