Rice yield response to climate and price policy in high-latitude regions of China
Bibliographic record
Abstract
Abstract Climate change has renewed interest in the production capacity of agriculture. Few researchers paid attention to price policy and heteroscedasticity in yield model. We incorporate rice price policy into the yield model at the expected price using a Tobit procedure and take Kalman filter theory to explore useful information, and then estimate the rice yield response to climate and rice price using a spatial autoregressive combined model in high-latitude regions of China from 1992 to 2018. Meanwhile, we apply two different Breusch-Pagan tests to examine heteroscedasticity. Our results suggest that spatial correlation of the error term is a more critical source of heteroscedasticity and cannot be completely solved by only allowing spatially autocorrelated errors due to possible technology diffusion effects. The results also show that rice price support policy is useful for constructing rice expected prices, and the price elasticities of rice and corn on rice yield are 0.194 and -0.097, respectively. Among climate variables, the total growing degree days in the growing season has positive effects, and monthly accumulated growing degree days also matter, especially in June. Precipitation in July and August has a significant effect with an inverse U shape. Projections of future climate change suggest that rice yield will mainly increase, ranging from 0.095% to 1.769%, but the rate of increase in yield will slow down in the higher-rate global warming. This study shows how price policy could be incorporated into yield response model and highlights the importance of climate factors and crop price policy for rice yield.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".