A Wheat Grazing Model for Simulating Grain and Beef Production: Part I—Model Development
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.
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".