A Wheat Grazing Model for Simulating Grain and Beef Production: Part I—Model Development
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
It is a common practice to grow winter wheat ( Triticum aestivum L.) as a dual‐purpose crop in the U.S. Southern Great Plains to decrease production risk and to increase profit margin through cattle ( Bos taurus ) production. Crop management of the dual‐purpose wheat is complex because of the tradeoffs between beef production and wheat grain yield. A wheat grazing model helps in making optimal decision. The objective of this study was to develop and incorporate a grazing and metabolizable energy‐based cattle growth module into the Decision Support Systems for Agrotechnology Transfer (DSSAT) to simulate beef and wheat grain production. The wheat grazing model was comprised of wheat growth, wheat–cattle interaction, and cattle growth components. Wheat growth was simulated by the cropping system model (CSM) of DSSAT. For the wheat–cattle interface, removals of canopy biomass and leaf area by grazing were estimated daily. Predicted grain yield was also reduced by 50 kg ha −1 per day for each day of grazing past the first hollow stem stage. Cattle growth rate was based on a metabolizable energy intake. Maximum voluntary daily intake was estimated based on stocker body weight and forage quality, and is further adjusted for actual forage availability, temperature, and adaptation status during the first 14 d of grazing to estimate the actual daily intake. Changes in wheat growth processes brought about by grazing, including a grazing effect on the delay of plant phenological development, are not simulated in the model. Field experiments to characterize any such effects are needed to help fine‐tune the model.
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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 | 0.001 | 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) | 0.000 | 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