NITROGEN ACQUISITION BY WHEAT, CANOLA, AND SOYBEAN INOCULATED WITH THE N2-FIXING BACTERIUM GLUCONACETOBACTER DIAZOTROPHICUS
Bibliographic record
Abstract
Demands for food and fiber continue to increase, as does the global population. As a consequence, the use of nitrogen (N) fertilizers also is increasing globally. However, as the use of N fertilizer increases, so do concerns about the impacts of N loss to the environment. Efforts to reduce reliance on synthetic N fertilizers involve multiple strategies, including the development of biofertilizers. Capitalizing on microorganisms that fix N is one potential avenue for reducing the need for fertilizer N. There exist in nature many species of soil microorganisms (collectively referred to as diazotrophs) that can fix atmospheric N. One group of diazotrophs, including the species Gluconacetobacter diazotrophicus, are endophytes that invade plant roots and either colonize the spaces between the cells (intercellular) or the cell matrix itself (intracellular). Azotic North America recently began marketing a G. diazotrophicus-based inoculant (Envita™) reported to have beneficial effects including N fixation and yield improvement in crops as diverse as rice, wheat, corn, and soybean. This research aimed to quantify N-fixation in crops important to Saskatchewan producers (e.g., wheat, canola, and soybean) that were inoculated with Envita™. Experiments relied on the use of 15N tracing to verify N-fixation and a new droplet digital polymerase chain reaction (ddPCR) method to verify the presence of G. diazotrophicus in plant tissues of the Envita™-inoculated crops. Different methods of introducing the inoculant to the plants also were evaluated. On average, soybean showed no significant difference between treatments, though a few plants did show evidence of N-fixation. Moreover, while some treatment differences in 15N content were found for wheat and canola when the inoculant was introduced using the root bath application (RBA) method, the inoculation status from the ddPCR was inconsistent and yielded conflicting results. The data suggest that Envita™ has the potential to provide some N through N fixation to wheat, canola, and soybean but that more research is needed to optimize inoculation approaches and to fully understand the conditions necessary for N fixation to take place.
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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) | 0.001 | 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".