Irrigation Criteria and Sweetpotato Cultivar Performance in the Treasure Valley of Eastern Oregon
Bibliographic record
Abstract
In the United States, sweetpotato [ Ipomoea batatas L. (Lam)] is predominately grown in the southeastern states and in California, but production farther north is limited. To determine if sweetpotato could be successfully produced in semiarid Pacific North West, four sweetpotato cultivars (Covington, Beauregard, Diane, and Evangeline) were subjected to four soil water tension (SWT) irrigation criteria treatments (40, 60, 80, 100 kPa in 2011 and 25, 40, 60, and 80 kPa in 2012) using drip irrigation at Ontario, OR. The four SWT criteria were maintained by an automated irrigation system. Sweetpotato cultivars were evaluated for the percentage of early groundcover, number of vines per hill, vine length, and yield. The total applied water decreased with the increase in the targeted SWT. The highest amount of water was applied at the 25 kPa criterion (1184 mm) and the least amount at the 100 kPa SWT criterion (146 mm). Cultivars varied in the average number of vines per hill, with ‘Covington’ having the fewest at 6 vines per hill compared with ‘Beauregard’ and ‘Evangeline’ that averaged 10 vines and ‘Diane’ averaging 11. The average vine length increased with the decrease in SWT criteria during both years. The total, marketable, and U.S. No. 1 sweetpotato yield was influenced by cultivars and varied among irrigation criteria and years. In general, the sweetpotato yield decreased with the increase in SWT, with the highest yield attained at the lowest SWT tested, 40 kPa in 2011 and 25 kPa in 2012. For ‘Beauregard’ grown with irrigation onset criteria of 40 and 25 kPa, the marketable yields were 49 and 87 Mg·ha −1 and U.S. No. 1 yields were 35 and 27 Mg·ha −1 in 2011 and 2012, respectively. The results suggested that sweetpotato could be grown in eastern Oregon and would be capable of producing yields comparable to those obtained in California. However, yearly weather variations could delay transplanting and early harvest could be necessary to avoid frost damage.
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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 | 0.001 | 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) | 0.000 | 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".