Irrigation Requirements for Seed Production of Three Leguminous Wildflowers of the U.S. Intermountain West
Bibliographic record
Abstract
Increasing the supply of native wildflower seed is essential for restoring burned and degraded wildlands in the Intermountain West. Limitations to wildland seed collection necessitate development of effective cultural practices to improve reliability of seed production in agricultural fields. Irrigation trials were conducted over multiple years for three perennial species in the Fabaceae family [ Dalea ornata (Douglas ex Hook.) Eaton & J. Wright, Dalea searlsiae (A. Gray) Barneby, and Astragalus filipes Torr. ex A. Gray]. Each of the three species was grown at the Oregon State University Malheur Experiment Station, Ontario, OR and received 0, 100, or 200 mm·year −1 of drip irrigation in four equal biweekly increments during bud formation and flowering. Seed yield responses to irrigation were evaluated by linear and quadratic regression against 1) applied water, 2) applied water plus spring precipitation, 3) applied water plus winter and spring precipitation, and 4) applied water plus fall, winter, and spring precipitation. In general, seed yields responded quadratically to irrigation. Adding fall, winter, and spring precipitation to applied water improved the accuracy of estimated water requirements for maximum seed production of D. ornata and D. searlsiae . For D. ornata , the highest yields averaged 396 kg·ha −1 and ranged from 146 to 545 kg·ha −1 . Averaged over 6 years, seed yield of D. ornata was highest with applied water plus fall, winter, and spring precipitation totaling 393 mm. For D. searlsiae , the highest yields averaged 260 kg·ha −1 and ranged from 51 to 424 kg·ha −1 . Averaged over 6 years, seed yield of D. searlsiae was highest with applied water plus fall, winter, and spring precipitation totaling 412 mm. Seed yields of A. filipes ranged from 7 to 110 kg·ha −1 depending on year and averaged 40 kg·ha 1 over 6 years. Seed yields of A. filipes did not respond to irrigation in any of the 5 years of testing.
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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 | 0.001 |
| 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".