Nitrogen fixation in annual Trifolium species in alkaline soils as assessed by the 15N natural abundance method
Bibliographic record
Abstract
Annual clover species such as Trifolium purpureum Loisel., T. resupinatum L., and T. alexandrinum L. are adapted to alkaline soil conditions and provide certain agronomic advantages over annual medics (Medicago spp.). Annual clovers have not been widely grown in alkaline soils in Australia, and quantifying their dinitrogen (N2) fixation in alkaline soils is important in understanding their potential role in mixed farming systems of southern Australia. Using the 15N natural abundance technique, it was estimated that annual clovers fixed 101–137 kg N/ha at Roseworthy and 59–62 kg N/ha at Mallala, on Calcarosols with soil pH of 8.0 and 8.5, respectively. Species differed in the percentages of fixed N2 estimated in shoot dry matter, which was highest in T. alexandrinum (77–85%), moderate in T. resupinatum (76%), and lowest in T. purpureum (65–74%). Naturally occurring soil rhizobia (Rhizobium leguminosarum bv. trifolii) provided adequate nodulation, as inoculation with different strains of rhizobia had little influence on nodulation or N2 fixation. These results indicate that clovers can provide a significant contribution of fixed N2 to mixed farming systems. Examination of nodules indicated variable nodule occupancy by the inoculant rhizobia and that 69% of shoot N was fixed when clovers were nodulated by the soil populations of rhizobia. A simple model is defined to identify the potential interactions between inoculated legumes and soil rhizobia, and the options for enhancing symbiotic effectiveness are discussed.
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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 | 0.000 |
| 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".