Global Planting Suitability of Wheat Under the 1.5 °C and 2 °C Warming Goals
Bibliographic record
Abstract
Global Planting Suitability of Wheat Under the 1.5 °C and 2 °C Warming Goals Authors: Xi Guo; Puying Zhang; Yaojie Yue This is the outcome data of our research which is under submission. Though the impact of climate change on potential crop distributions has been extensively explored, there are few studies on potential wheat distributions at specific global warming levels (GWLs), e.g., 1.5 °C and 2 °C. Here, a grided (0.5 degree × 0.5 degree) dataset of global potential wheat distribution under the 1.5 °C and 2 °C GWLs is proposed. This dataset is produced using the MaxEnt model with support of multi-model data(GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, and NorESM1-M). The predictive accuracy of the proposed dataset was carefully validated between the predicted global wheat distribution and multiple known datasets. For more details of the approach used to predict the global wheat distribution please refer to: Yue, Y., Zhang, P., Shang, Y., 2019. The potential global distribution and dynamics of wheat under multiple climate change scenarios. Sci Total Environ 688, 1308-1318. https://coi.org/10.1016/j.scitotenv.2019.06.153. The results indicate the regional differences in the potential suitability of wheat cultivation under different GWLs. Eastern Europe, Pakistan, Northern India, Russia, and Canada witnessed a significant increase in wheat planting suitability. In contrast, Central Eastern Africa, Southeastern Australia, Southeastern China, Southern Brazil, France, Spain, and Italy demonstrated a significant decrease in wheat suitability. Compared with 1.5 °C GWLs, wheat planting suitability decreases more evidently in 2 °C GWLs in Central and Eastern Africa, Central and Southern India, Southeastern China, Australia, Mexico, Southern Brazil, and Argentina. Simultaneously, regions such as Russia, Pakistan, Canada, and the Great Lakes area of the United States observed further increases in wheat planting suitability. To ensure favorable conditions for the cultivation of wheat, it is crucial to limit the global average temperature increase to less than 2 °C. Our findings demonstrate the influence of different GWLs on potential global wheat distribution, highlighting the regional differences in the potential suitability of wheat cultivation under different GWLs. We argue that the potential global wheat distribution datasets under different GWLs are a valuable complement to currently available products. This potential global wheat distribution is one of the few products to take into account 1.5 °C and 2 °C GWLs based on multi-modal data. We believe that it can provide more valuable information for policymakers to make decisions for the warming world. The data of the Global Planting Suitability of Wheat Under the 1.5 °C and 2 °C Warming Goals is stored in a zip package, that is Global Planting Suitability of Wheat.zip. This package consists of 1 folder, i.e., SR1.5&2.0. This subfolder contains GeoTIFF files for the Global Planting Suitability of Wheat Under the 1.5 °C and 2 °C Warming Goals. Correspondingly Wheat_SR15.tif and Wheat_SR20.tif. The grid value of each file ranges from 0 to 1, indicating the possibility of wheat planting in each grid, and the higher the value, the higher the possibility that wheat exists. Reference: Yue, Y., Zhang, P., Shang, Y., 2019. The potential global distribution and dynamics of wheat under multiple climate change scenarios. Sci Total Environ 688, 1308-1318. https://coi.org/10.1016/j.scitotenv.2019.06.153.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".