Irrigation Requirements for Seed Production of Two Eriogonum Species in a Semiarid Environment
Bibliographic record
Abstract
Seeds of native plants are needed for rangeland restoration in the Intermountain West. Many of these plants are rarely cultivated and relatively little is known about the cultural practices required for their seed production. Irrigation trials were conducted over multiple years for two perennial Eriogonum species, Eriogonum umbellatum Torr. and Eriogonum heracleoides Nutt. The two species grown at the Oregon State University Malheur Experiment Station, Ontario, Ore., received 0, 100, or 200 mm·year −1 of drip irrigation. 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. For E. umbellatum , over 11 years, highest seed yields averaged 260 kg·ha −1 and ranged from 207 to 508 kg·ha −1 . For E. heracleoides , over 6 years, highest yields averaged 353 kg·ha −1 and ranged from 168 to 588 kg·ha −1 . Adding spring precipitation to applied water improved the accuracy of estimated water requirements for maximum seed production of E. umbellatum. For E. heracleoides , adding precipitation to applied water did not improve the accuracy of estimated water requirements for maximum seed production. Averaged over 11 years, seed yield of E. umbellatum was maximized by 209 mm·year −1 of spring precipitation plus irrigation. Averaged over 6 years, seed yield of E. heracleoides was maximized by 126 mm·year −1 of applied water. Both species required relatively small amounts of irrigation to help assure seed yield, and the irrigation needed for E. umbellatum could be adjusted by taking spring precipitation into account.
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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 | 0.001 |
| 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 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".