Climate change-driven shifts in the global distribution of tomato and potato crops and their associated bacterial pathogens
Bibliographic record
Abstract
Introduction Climate change is increasingly affecting the global distribution and productivity of critical food crops, including Solanum lycopersicum (tomato) and Solanum tuberosum (potato). In particular, bacterial pathogens such as Clavibacter michiganensis and Ralstonia solanacearum are expected to shift their geographic ranges, posing new risks to these crops. This study hypothesizes that under future climate scenarios, the geographic overlap between these crops and their pathogens will increase in certain regions, leading to heightened agricultural risks, especially in areas currently considered safe from these pathogens. Methods To test our hypotheses, the objective was to evaluate the potential impact of climate change on the geographic distribution of two key food crops (tomato and potato) and their bacterial pathogens for the current time and by 2050. This study used four species distribution models (SDMs) to predict current and future habitat suitability for both tomato and potato crops, as well as their associated pathogens, under two shared socioeconomic pathways (SSP4.5 and SSP8.5) and four global circulation models (GCMs). Results The models projected significant poleward shifts in suitable habitats for tomatoes and potatoes, with notable expansions in higher-latitude regions such as Canada, northern Europe, and Russia, and contractions in current major production zones such as the United States (US), Brazil, parts of Africa, and China. For Clavibacter michiganensis , the overlap with tomatoes was substantial, whereas the overlap between potatoes and Ralstonia solanacearum was comparatively smaller. Discussion Our hypothesis was partially supported by the results. While the overall overlap between crop and pathogen habitats remains limited, the risk areas for both pathogens are expected to expand under future climate conditions in regions such as eastern Australia, Japan, Spain, and France. These findings underscore the importance of region-specific agricultural planning and pathogen management strategies to mitigate the risks posed by climate change. Future efforts should focus on vulnerable areas to prevent significant economic losses and ensure food security.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".