Solar radiation management impacts on agriculture in China: A case study in the Geoengineering Model Intercomparison Project (GeoMIP)
Bibliographic record
Abstract
Abstract Geoengineering via solar radiation management could affect agricultural productivity due to changes in temperature, precipitation, and solar radiation. To study rice and maize production changes in China, we used results from 10 climate models participating in the Geoengineering Model Intercomparison Project (GeoMIP) G2 scenario to force the Decision Support System for Agrotechnology Transfer (DSSAT) crop model. G2 prescribes an insolation reduction to balance a 1% a−1 increase in CO2 concentration (1pctCO2) for 50 years. We first evaluated the DSSAT model using 30 years (1978–2007) of daily observed weather records and agriculture practices for 25 major agriculture provinces in China and compared the results to observations of yield. We then created three sets of climate forcing for 42 locations in China for DSSAT from each climate model experiment: (1) 1pctCO2, (2) G2, and (3) G2 with constant CO2 concentration (409 ppm) and compared the resulting agricultural responses. In the DSSAT simulations: (1) Without changing management practices, the combined effect of simulated climate changes due to geoengineering and CO2 fertilization during the last 15 years of solar reduction would change rice production in China by −3.0 ± 4.0 megaton (Mt) (2.4 ± 4.0%) as compared with 1pctCO2 and increase Chinese maize production by 18.1 ± 6.0 Mt (13.9 ± 5.9%). (2) The termination of geoengineering shows negligible impacts on rice production but a 19.6 Mt (11.9%) reduction of maize production as compared to the last 15 years of geoengineering. (3) The CO2 fertilization effect compensates for the deleterious impacts of changes in temperature, precipitation, and solar radiation due to geoengineering on rice production, increasing rice production by 8.6 Mt. The elevated CO2 concentration enhances maize production in G2, contributing 7.7 Mt (42.4%) to the total increase. Using the DSSAT crop model, virtually all of the climate models agree on the sign of the responses, even though the spread across models is large. This suggests that solar radiation management would have little impact on rice production in China but could increase maize production.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".