Simulating Dryland Water Availability and Spring Wheat Production in the Northern Great Plains
Bibliographic record
Abstract
Agricultural system models are useful tools to synthesize field experimental data and to extrapolate results to longer periods of weather and to other cropping systems. The objectives of this study were: (i) to quantify the effects of crop management practices and tillage on soil water and spring wheat (Triticum aestivum L.) production in a continuous spring wheat system using the RZWQM2 model (coupled with CERES‐Wheat) under a dryland condition, and (ii) to extend the RZWQM2 model results to longer term weather conditions and propose alternate cropping systems and management practices. Measured soil water content, yield, and total aboveground biomass under different tillage and plant management practices were used to calibrate and evaluate the RZWQM2 model. The model showed no impacts of tillage but late planting greatly reduced grain yield and biomass, in agreement with observed differences among treatments. The hydrologic analysis under long‐term climate variability showed a large water deficit (32.3 cm) for spring wheat. Fallowing the cropland every other year conserved 4.2 cm of water for the following wheat year, of which only 1.7 cm water was taken up by wheat, resulting in a yield increase of 249 kg ha−1 (13.7%); however, the annualized mean yield decreased 782 kg ha−1 (43.1%) due to 1 yr of fallow. Other long‐term simulations showed that optimal planting dates ranged from 1 March to 10 April and the seeding rates with optimum economic return were 3.71 and 3.95 × 106 seeds ha−1 for conventional and ecological management treatments, respectively.
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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 | 0.000 |
| 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 | 0.001 | 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".