Evaluation of Alfalfa and Grass Species in Binary and Complex Mixtures on Performance under Soil Salinity Conditions
Bibliographic record
Abstract
The effect of synergies between saline-tolerant grass and legume species grown in saline soil, on yield, quality, composition, persistence, and weed suppression was studied in 2019–2021 in a moderately saline soil located in the dark brown soil zone near Saskatoon, Saskatchewan, Canada. ‘Halo’ alfalfa (ALF), a salt-tolerant cultivar of alfalfa, was seeded in binary mixtures with ‘Revenue’ slender wheatgrass (ALF-SWG), ‘Garrison’ creeping meadow foxtail (ALF-CMF), and ‘Radisson’ smooth bromegrass (ALF-SBG). A complex mixture (quaternary) with all three grasses was also included. Four replicated treatments (n = 4) were randomly allocated to 6.2 × 1.2 m plots in spring 2019. The binary mixtures had similar or slightly better stand establishment compared to the quaternary mixture over the two years (83.3 vs. 76.9%), the quaternary stand showed greater (p = 0.01) establishment in Yr 2 than in Yr 1 (85 vs. 68.8%). There was high weed infestation in all the stands, but ALF-SWG had less weed infestation (23.8 vs. 44.1%) than ALF-CMF, especially in Yr 2 (p = 0.05). There were no significant differences among mixtures in yield, nutritional composition, and nutrient uptake. The ALF-CMF and ALF-SWG binary mixtures had 9 to 23% greater mean total DMY than the other mixtures in the moderately saline soil. The cost of establishing forage mixtures on unproductive saline land can be up to 89% recovered after only two years. The results suggested that binary mixtures of ‘Halo’ alfalfa with ‘Revenue’ slender wheatgrass or ‘Garrison’ creeping meadow foxtail could be reasonable alternatives for adequate forage production and quality, high N-use efficiency, and ultimately livestock gain per hectare, as well as for controlling soil salinity and improving soil fertility in this saline area in the dark brown soil zone.
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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".