Yield to the data: some perspective on crop productivity and pesticides
Bibliographic record
Abstract
The scientific consensus is that pesticide use maximizes crop yields in the face of pest and disease pressures. Often, the debate then becomes a "so what" question (e.g., a percent or two increase in yield is inconsequential, so why use pesticides at all?). We set out to help give technical and lay audiences an objective and quantitative sense of what it means for pesticides to protect crop yields from two perspectives: (i) the number of additional hectares required to produce the same amount of food without the use of pesticides; and (ii) increased calorie production and people fed. Using available seeding and yield data for Canada and United States from 2015 to 2019 for common field crops, a user-friendly interface was developed that allows for the coarse calculation of land preserved and caloric increases for specific scenarios (e.g., jurisdiction, crop, percent yield increase). We found that land preserved would range from 145 883 to 11 590 255 ha and the number of adults fed would range from 1 333 814 to 100 016 319 depending on the crop and the country. Our hope is that this simple tool will provide a fuller sense of what changes in crop yields mean, and their implications for environmental protection and food security. © 2022 Society of Chemical Industry.
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".