On Technological Change and Yield Resiliency in Canadian Crop Yields
Bibliographic record
Abstract
Feeding nine billion people by 2050, yield resiliency, climate change, and remaining economically competitive have received significant attention in the literature. Technological change in agriculture will largely dictate our ability to meet these challenges. Although there is significant literature on technological change in U.S. crop yields, very little has been done with Canadian yields. Moreover, the adoption and effect of various technologies and their interaction with climate tend to be crop-region specific. To this end, we model technological change in county-level yields for barley, canola, corn, oats, soybean and wheat in Canada. We use mixtures to allow and test for heterogeneous rates of technological change within the yield data generating process. While we tend to find increasing but heterogeneous rates of technological change, increasing and asymmetric yield volatility, and increasing absolute but decreasing relative yield resiliency, our results do differ across crops and exhibit spatial bifurcations within a crop. Using a standard attribution model, we find changing climate has differing effects across crops. We also consider the public funding implications of technological change for Canadian Business Risk Management programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".