Soil and Water Quality Rapidly Responds to the Perennial Grain Kernza Wheatgrass
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
Perennial grain cropping systems could address a number of contemporary agroecological problems, including soil degradation, NO 3 leaching, and soil C loss. Since it is likely that these systems will be rotated with other agronomic crops, a better understanding of how rapidly perennial grain systems improve local ecosystem services is needed. We quantified soil moisture, lysimeter NO 3 leaching, soil labile C accrual, and grain yields in the first 2 yr of a perennial grain crop under development [kernza wheatgrass, Thinopyrum intermedium (Host) Barkworth & D.R. Dewey] relative to annual winter wheat ( Triticum aestivum L.) under three management systems. Overall, differences between annual and perennial plants were much greater than differences observed due to management. In the second year, perennial kernza reduced soil moisture at lower depths and reduced total NO 3 leaching (by 86% or more) relative to annual wheat, indicating that perennial roots actively used more available soil water and captured more applied fertilizer than annual roots. Carbon mineralization rates beneath kernza during the second year were increased 13% compared with annual wheat. First‐year kernza grain yields were 4.5% of annual wheat, but second year yields increased to 33% of wheat with a harvest index of 0.10. Although current yields are modest, the realized ecosystem services associated with this developing crop are promising and are a compelling reason to continue breeding efforts for higher yields and for use as a multipurpose crop (e.g., grain, forage, and biofuel).
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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 | 0.001 | 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) | 0.002 | 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