Forage harvest management impacts “Kernza” intermediate wheatgrass productivity across North America
Bibliographic record
Abstract
Abstract Intermediate wheatgrass [IWG, Thinopyrum intermedium (Host) Barkworth & D.R. Dewey, trade name Kernza] is a widely adapted, cool‐season forage grass, actively bred for perennial grain production. Most of IWG's net primary productivity is directed to nonreproductive structures, so dual‐use strategies to harvest both grain and forage represent a potentially viable pathway to increase its productivity and profitability. We conducted a 3‐year trial at nine diverse environments across North America to evaluate grain and forage yields and forage nutritive value of an early IWG breeding line under contrasting forage harvest managements. These included control (no forage harvest), summer forage harvest immediately after grain harvest, and summer forage harvest with spring or fall forage harvests. Across all sites, IWG grain yields averaged 745, 296, and 221 kg ha −1 for the first, second, and third years, respectively. Grain yields were influenced more by stand age than site. Summer forage mass after grain harvest averaged 6.0, 4.5, and 5.7 Mg ha −1 respectively for the first 3 years. Forage mass was less influenced by stand age, and more by site and forage harvest frequency. Fall forage harvest increased grain yields while spring forage harvests decreased grain yields and both treatments increased total relative feed nutritive values. Collectively, our results demonstrate that harvesting forage can improve both grain yield and forage nutritive values. Farmers growing IWG as a perennial grain can benefit from dual‐use management by harvesting both grain and forage.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".