Impact of climate change scenarios on Canadian agroclimatic indices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Qian, B., De Jong, R., Gameda, S., Huffman, T., Neilsen, D., Desjardins, R., Wang, H. and McConkey, B. 2013. Impact of climate change scenarios on Canadian agroclimatic indices. Can. J. Soil Sci. 93: 243-259. The Canadian agricultural sector is facing the impacts of climate change. Future scenarios of agroclimatic change provide information for assessing climate change impacts and developing adaptation strategies. The goal of this study was to derive and compare agroclimatic indices based on current and projected future climate scenarios and to discuss the potential implications of climate change impacts on agricultural production and adaptation strategies in Canada. Downscaled daily climate scenarios, including maximum and minimum temperatures and precipitation for a future time period, 2040-2069, were generated using the stochastic weather generator AAFC-WG for Canadian agricultural regions on a 0.5°×0.5° grid. Multiple climate scenarios were developed, based on the results of climate change simulations conducted using two global climate models - CGCM3 and HadGEM1 - forced by IPCC SRES greenhouse gas (GHG) emission scenarios A2, A1B and B1, as well as two regional climate models forced by the A2 emission scenario. The agroclimatic indices that estimate growing season start, end and length, as well as heat accumulations and moisture conditions during the growing season for three types of field crops, cool season, warm season and over-wintering crops, were used to represent agroclimatic conditions. Compared with the baseline period 1961-1990, growing seasons were projected to start earlier, on average 13 d earlier for cool season and over-wintering crops and 11 d earlier for warm season crops. The end of the growing season was projected on average to be 10 and 13 d later for over-wintering and warm season crops, respectively, but 11 d earlier for cool season crops because of the projected high summer temperatures. Two indices quantifying the heat accumulation during the growing season, effective growing degree days (EGDD) and crop heat units (CHU) indicated a notable increase in heat accumulation: on average, EGDD increased by 15, 55 and 34% for cool season, warm season and over-wintering crops, respectively. The magnitudes of the projected changes were highly dependent on the climate models, as well as on the GHG emission scenarios. Some contradictory projections were observed for moisture conditions based on precipitation deficit accumulated over the growing season. This confirmed that the uncertainties in climate projections were large, especially those related to precipitation, and such uncertainties should be taken into account in decision making when adaptation strategies are developed. Nevertheless, the projected changes in indices related to temperature were fairly consistent.
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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) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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 it