Diversified crop sequencing for suppression of creeping perennial weeds in organic production systems
Bibliographic record
Abstract
In the Northern Great Plains, organic production is often limited by creeping perennial weeds. Producers have typically relied on alfalfa to control these weeds but this is not always practical. Therefore, we investigated Canada thistle, field bindweed, and perennial sowthistle suppression from three diversified crop sequences: (i) ALF, three years of alfalfa followed by a fourth year of hard red spring wheat (HRSW); (ii) LENCL, lentil the first year, HRSW interseeded with yellow sweetclover the second year, yellow sweetclover green manure the third year, and HRSW the fourth year; and (iii) CCPLY, nine species cool-season cover crop (CC) polyculture the first year, HRSW the second year, nine-species cool and warm season CC the third year, and HRSW the fourth year. From 2019 to 2021, at peak weed emergence, Canada thistle density declined in ALF, but remained unchanged in LENCL and CCPLY. During peak weed vegetative growth, Canada thistle density in ALF was greatest during 2020 (7 shoots per m−2), then declined during 2021 and 2022 (1 and 0.6 shoots m−2, respectively). Within LENCL, Canada thistle density did not change from 2019 to 2020 (15 and 14 shoots m−2, respectively), but then declined to 0.3 shoots m-2 in 2021 before rebounding again to 5 shoots m−2 in 2022. Within CCPLY, Canada thistle density did not change over time (mean = 9 shoots m−2). Canada thistle biomass at peak weed vegetative growth followed similar patterns. Likewise, ALF was shown to suppress perennial sowthistle and field bindweed more effectively than LENCL or CCPLY. Hence, alfalfa remains a good choice for creeping perennial weed management. However, none of these weed species increased substantially in LENCL and CCPLY, and the final year HRSW yield did not differ among cropping sequences. Therefore, these two sequences may provide crop diversification options along with creeping perennial weed management.
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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 | 0.000 |
| 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) | 0.000 | 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".