Short Communication: Climate change and biofuel wheat: A case study of southern Saskatchewan
Bibliographic record
Abstract
Wang, H., He, Y., Qian, B., McConkey, B., Cutforth, H., McCaig, T., McLeod, G., Zentner, R., DePauw, R., Lemke, R., Brandt, K., Liu, T., Qin, X., White, J., Hunt, T. and Hoogenboom, G. 2012. Short Communication: Climate change and biofuel wheat: A case study of southern Saskatchewan. Can. J. Plant Sci. 92: 421–425. This study assessed potential impacts of climate change on wheat production as a biofuel crop in southern Saskatchewan, Canada. The Decision Support System for Agrotechnology Transfer-Cropping System Model (DSSAT-CSM) was used to simulate biomass and grain yield under three climate change scenarios (CGCM3 with the forcing scenarios of IPCC SRES A1B, A2 and B1) in the 2050s. Synthetic 300-yr weather data were generated by the AAFC stochastic weather generator for the baseline period (1961–1990) and each scenario. Compared with the baseline, precipitation is projected to increase in every month under all three scenarios except in July and August and in June for A2, when it is projected to decrease. Annual mean air temperature is projected to increase by 3.2, 3.6 and 2.7°C for A1B, A2 and B1, respectively. The model predicted increases in biomass by 28, 12 and 16% without the direct effect of CO2 and 74, 55 and 41% with combined effects (climate and CO2) for A1B, A2 and B1, respectively. Similar increases were found for grain yield. However, the occurrence of heat shock (>32°C) will increase during grain filling under the projected climate conditions and could cause severe yield reduction, which was not simulated by DSSAT-CSM. This implies that the future yield under climate scenarios might have been overestimated by DSSAT-CSM; therefore, model modification is required. Several measures, such as early seeding, must be taken to avoid heat damages and take the advantage of projected increases in temperature and precipitation in the early season.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".