Future crop yields and water productivity changes for Nebraska rainfed and irrigated crops
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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) | 0.001 | 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 it