Yield and net return from alfalfa cultivars under irrigation in Southern Alberta
Bibliographic record
Abstract
Field studies with two types of alfalfa (Medicago sativa L.) cultivars were conducted at Lethbridge in 2012 and 2013 and at Picture Butte in 2012 to determine the effects of irrigation on the dry matter (DM) yield and on net returns. The irrigated cultivars (Longview and Blue J) and dryland cultivars (Rangelander and Rambler) were arranged on plots in a randomized complete block design with four irrigation treatments and replicated five times. For the optimal irrigation treatment (W1), soil water content was maintained between 60 and 90% of available water in the designated root zone. Other irrigation treatments received 75% (W2), 50% (W3), and 25% (W4) of the irrigation water applied to the optimal treatment. The mean DM yields of irrigated alfalfa cultivars were greater than one of the dryland cultivars in both locations. The mean total DM yields for W2 and W3 at Lethbridge for Blue J, Longview and Rambler were greater than those of W1, although the differences were not always significant. The net returns, calculated by using the same price for all alfalfa harvests were similar across the cultivars and irrigation treatments excepting Rangelander, where the returns were lower. The results obtained from this study indicated a trend towards comparable yields and net returns between the optimal and the 75% irrigation treatment with 40% depletion of available water at the root zone, for the irrigated alfalfa cultivars and a dryland type Rambler.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".