Crop niche modeling projects major shifts in common bean growing areas
Bibliographic record
Abstract
Crops experience different climate stresses during development. The magnitude of damage will depend on the phenological stage of the crop and the stress duration. Climate change could intensify some or all of these stresses, thus negatively impacting agriculture. An assessment of staple crop productivity,quality and climatically suitable areas under climate change conditions is necessary to undertake any global initiatives to tackle food security issues. The common bean ('Phaseolus vulgaris' L.) is a staple crop and the main source of proteins and nutrients in Africa and Latin America. The purpose of this study is to develop a process-oriented niche model to assess the impacts of climate change on the current and future potential distribution of common bean and to use this model to investigate the changes in heat, cold, dry and wet stresses under climate change. We used A2 and A1B emission scenarios and two different global climate models, CSIRO-Mk3.0 and MIROC-H, for the years 2050 and 2100. Our results indicate future climate conditions are more favorable for common bean cultivation in the Northern Hemisphere, but are less favorable in the Southern Hemisphere. Heat and dry stresses are the main factors limiting and reducing common bean distribution under current and future projected conditions. Africa and Latin America are projected to decrease with respect to suitability for common bean cultivation. The model projections indicate that a shift in the common bean productive areas is highly likely with a loss of suitability of the current common bean cultivation areas and an increase in cold regions such as Canada, the Nordic countries and Russia. The results indicate the likelihood of changes in climatic suitability and the distribution of common bean at a global scale under a future climate, which will affect regions where this legume is a staple crop and an important source of household income. Regions in the Northern Hemisphere could take advantage of the increase in suitability by increasing the production and exportation of this grain.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".