A cultivar/soil selection protocol for simulating weather impacts on regional crop yield
Bibliographic record
Abstract
Crop yield simulation can facilitate understanding and support policy response to climate warming. DSSAT modelling is dependable when the crop varieties characterised can manifest realistic yield-responses to weather and hence climate, in a location of interest. We strived to minimise field trial and cultivar calibration resources. Our primary objective was to identify in-built DSSAT cultivars, which when used with representations of local soils can simulate the observed maize and soybean yields of 1987–2016, across 12 counties in Southwestern Ontario. These cultivars/soils were then utilised to disaggregate historic weather contribution to trending yields, but they can otherwise serve climate projection impact studies, as envisioned. Technology had contributed to progressive time-based increases in measured yields. Hence, a mixed nRMSE and r2 factor (MSF ≤ 1) enabled quick and decisive cultivar/soil selections, conditioned on closer matching simulated yield levels and variations with measured. Pio-3563 (maize) and Pio-9202 (soybean) were among the cultivars selected. Northern and southern subregional composites of simulated county yields, from final selections, compared with measured at nRMSE ≤ 0.20 and r2 ≥ 0.53, the best values being nRMSE ≈ 0.08 with r2 ≈ 0.80 (soybean) in the south. Modelling indicated 60% and 52% climate warming contributions to increased maize and soybean yields, respectively, across Southwestern Ontario. These percentages mostly agreed with those of other investigators using statistical methods for weather impact evaluations, which were utilised for final validation of our selections. An additional finding was that northern weather contributed 35%, while southern weather contributed 70% to increased subregional soybean yields.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".