Evaluation of cotton planting suitability in Xinjiang based on climate change and soil fertility factors simulated by coupled machine learning model
Bibliographic record
Abstract
Cotton is the world’s most widely cultivated fiber crop and holds great significance in Xinjiang. However, unsuitable planting environments can hinder farmer income and result in a substantial waste of agricultural resources.This study explores suitability of cotton planting areas in Xinjiang to reduce agricultural inputs and pollution. The goal is to promote sustainable agricultural development by considering both climate change and soil fertility, factors often overlooked in previous research. We analyzed climate change trends in Xinjiang and used machine learning-transfer component analysis to build a transferable coupling model for total nitrogen (TN) and soil organic carbon (SOC) indicators, resulting in a cotton suitability zoning that accounts for climate and soil fertility factors. Xinjiang has seen an overall increase in cumulative temperature and rainfall, with southern Xinjiang showing the most significant rise (4.02% in temperature and 16.26% in rainfall). The random forest model (RF) outperformed multivariate linear regression (MLR) and support vector machines (SVM) in predicting soil fertility indicators (TN: R 2 = 0 . 80 , SOC: R 2 = 0 . 77 ). The RF-TCA coupling model enhanced adaptability, with better performance in TN prediction compared to SOC. The Xinjiang cotton suitability zoning, based on meteorological and soil data, indicates a northward shift in suitable cotton planting areas in northern Xinjiang, while southern Xinjiang continues to maintain a substantial number of suitable planting zones. Notably, the disparity in suitability between the two regions has been narrowing over time. The research offers valuable insights for optimizing cotton planting locations, enhancing resource efficiency, and promoting sustainable development in Xinjiang. • Gray relation and full subset analysis of remote sensing indices improved model prediction accuracy. • An RF-TCA coupled machine learning model was developed to enhance transferability. • A comprehensive system was established to identify suitable cotton planting areas in Xinjiang, using meteorology and soil fertility factors.
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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 | 0.003 | 0.001 |
| 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".