Future crop yields and water productivity changes for Nebraska rainfed and irrigated crops
Bibliographic record
Abstract
We assessed future rainfed and irrigated crop yield and water productivity changes in Nebraska across multiple climate and emission scenarios using an empirical modeling approach. We found rainfed crops showed slightly increased crop water productivity while irrigated crops showed no change or decreased water productivity. Contrary to U.S.-wide studies reporting declines in crop yields, we projected Nebraska crop yields to increase overall with greatest increases in current rainfed fields due to combined effects from maximum and minimum temperatures. However, the increased rainfed yields are not sufficient to fully close the gap between rainfed and irrigated yields.Abbreviations: USDA: U.S. Department of Agriculture; RegCM4.3: ICTP Regional Climate Model version 4.3; NCEP: National Centers for Environmental prediction; DOE: U.S. Department of Energy; CGCM: Canadian Climate Centre general circulation model; GFDL: Geophysical Fluid Dynamics Laboratory general circulation model; CRCM: Canadian Climate Centre regional climate model; CCSM: National Center for Atmospheric Research general circulation model; HRM3: Hadley Centre’s Regional Model 3; HADCM3: Hadley Centre’s general circulation model; WRFG: the NCAR Weather Research and Forecasting model; CCCma: Canadian Centre for Climate Modelling and Analysis; CanESM2: Canadian Centre Earth System Model 2; ICHEC-EC: A European community Earth-System Model; IPCC: Intergovernmental Panel on Climate Change; RMSE: Root Mean Square Error
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".