Soil and Water Quality Rapidly Responds to the Perennial Grain Kernza Wheatgrass
Bibliographic record
Abstract
Perennial grain cropping systems could address a number of contemporary agroecological problems, including soil degradation, NO3 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 NO3 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 NO3 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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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 | 0.000 | 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".