Evaluation of the Impacts of Regional Climate Factors and Crop Management on Corn Yields in Different Climate Regimes of China Using the DayCent Model
Bibliographic record
Abstract
Corn is one of most important agricultural products in China. Understanding impacts of regional climate change, as well as agricultural management practices, on corn yields is critical for maintaining stable corn production. Using the DayCent model and observed climatic data in Sichuan province (a humid and hot environment) and Hebei province (a cold and dry environment) in China, corn yields in 1948-2010 were simulated. The spatial variations of simulated corn yields and the relationship between regional climate variability and warming with corn yields in these two environments were analyzed. The results demonstrated that: (1) corn yields in Zhangjiakou of Hebei and most regions of Sichuan decreased significantly after 2000 compared to other regions; (2) relative humidity and precipitation exhibit a significant negative correlation with observed crop yields in the growing season in Hebei province; (3) air temperature from 23.33 °C to 29 °C constitutes the ideal range influencing the increase of corn yields in Sichuan; (4) the planting of the large amount of silage maize in Sichuan compensated the negative impact of the rising air temperature on corn yields; (5) sensitivity tests for different fertilization levels and OMAD suggest that an increasing fertilization level significantly affects corn yields in Hebei province, a cold and dry environment, while a decreasing fertilization level has a significant negative effect in Sichuan province, a hot and humid environment. The overarching goal of these analyses is to provide the theoretical basic for maintaining stable corn production under regional climate warming and different agricultural management practices.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".