Soil water dynamics over 12 seasons on irrigated dry bean–potato–wheat–sugar beet rotations
Bibliographic record
Abstract
Dry bean (Phaseolus vulgaris L.), potato (Solanum tuberosum L.), wheat (Triticum aestivum L.), and sugar beet (Beta vulgaris L.) are mainstays of irrigated crop production in southern Alberta. Concerns about soil quality and sustainability instigated a 12 yr (2000–2011) rotation study to compare conventional (CONV) with conservation (CONS) management practices (reduced tillage, narrow-row dry bean, compost addition, and cover cropping). Plant-available water (PAW) was measured using a neutron probe (10–16 count days·season−1, n = 148) on all phases of 4 yr (dry bean–potato–wheat–sugar beet) rotations under CONS and CONV management. A visual monitoring approach was used for irrigation scheduling. For dry bean and sugar beet, management allowable depletion (MAD) was exceeded on only 11%–15% of neutron probe count days over 12 yr. However, MAD was exceeded on 30% of count days for wheat and 43% for potato. Significant crop × management interactions showed that PAW was higher with CONS management most frequently on potato, followed by dry bean, wheat, and sugar beet. This order reflected the prevalence of CONS practices directly impacting each crop. Regression analyses showed that potato, wheat, and sugar beet yield increased significantly as mean growing season water table depth (WTD) increased. This was explained by yield suppression due to excessive soil wetness in seasons with high rainfall and shallow WTD. This study provided comparative soil water dynamics for four major irrigated crops in southern Alberta, over a 12 yr period, which included record high and low growing season precipitation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".