Climatic Variability and Adaptive Zoning of Maize Cultivation in High-Latitude Cold Regions
Bibliographic record
Abstract
Climate change induces widespread effects on crop production, influencing multiple developmental stages and associated agronomic outcomes. Using long-term meteorological data from Jilin Province, Northeast China, this study examined temporal and spatial variations in climatic conditions through trend analysis, Mann–Kendall tests, and inverse distance weighting interpolation. A fuzzy comprehensive evaluation model was applied to classify maize cultivation suitability into four levels across major production areas, with Level I representing the most suitable regions, Level II highly suitable regions, Level III moderately suitable regions, and Level IV low suitable regions. Changes in suitable areas were analyzed before and after abrupt climatic shifts. From 1976 to 2020, Jilin Province experienced a significant rise in annual mean temperature, a marked decline in sunshine duration, and a slight increase in precipitation. The area of Level I suitability remained stable, while Level II expanded to approximately 1.3 times its original area. Conversely, Level III and IV areas decreased by 4.59% and 28.77%, respectively, compared with the pre-transition period. Spatially, the most suitable maize cultivation areas shifted from central to northern and eastern Jilin due to climatic warming. Although rising temperatures enhanced thermal conditions for maize production, reduced sunshine and variable precipitation constrained further expansion. These findings provide a scientific basis for optimizing maize variety selection and cropping structure in high-latitude regions, supporting yield improvement and sustainable development of the maize industry under a changing climate.
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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 | 0.001 |
| 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".