Exploring the Feasibility of Enhancing Indigenous Arbuscular Mycorrhizal Fungi Communities for Sustainable Soybean Production in northern Ontario
Bibliographic record
Abstract
Arbuscular mycorrhizal (AM) fungi have been widely recognized as a promising approach to attain sustainability in the agricultural industry. These fungi have been known to offer numerous benefits to crops and have been commercially employed as inoculants due to their capacity to enhance yields and protect crops from biotic and abiotic stressors. However, it is widely believed that certain conventional agricultural practices, including high fertilization, monocultures, and non-mycorrhizal crops, can cause a decline in the abundance and diversity of AM fungal communities. This, in turn, can lead to a decrease in the transfer of benefits from AM fungi to crops, thereby compromising crop productivity and sustainability. This thesis aimed to investigate the feasibility of enhancing the preexisting indigenous AM fungal communities in the soil to potentially confer additional benefits to soybeans (Glycine max (L.) Merr.) in rotation after canola (Brassica napus). Since indigenous AM fungi are already adapted to local conditions and possess a more diverse community than the typical commercial inoculants, it was hypothesized that augmenting indigenous AM fungi may serve as a viable substitute for chemical fertilizers. The objective was to evaluate the capacity of indigenous AM fungi to confer additional benefits to crops under varying levels of fertilizer. It was hypothesized the indigenous AM fungi would maintain or improve soybean growth metrics with decreasing levels of fertilizer use to a higher degree when compared to commercial inoculant. This study took place at three field sites in northern Ontario, Canada where it is common to see a canola-soybean crop rotation. Canola, a non-mycorrhizal crop, was first planted in the 2021 field season. Based on the literature, it was assumed that a non-mycorrhizal crop would have a detrimental effect on AM fungi. Concurrently, during this season, sorghum-sudangrass (Sorghum × drummondii), a highly mycotrophic plant, was planted in a small adjacent plot with the objective of increasing the abundance of indigenous AM fungi in the soil. Rhizosphere soil from the sorghum-sudangrass plot was subsequently collected and used as indigenous inoculant in the main experiment the following year. In 2022, soybeans were planted where canola had grown previously. This study used a randomized split plot experimental design with fertilizer as the main treatment (at 0, 50, and 100% the recommended rate according to provincial guidelines) and AM fungal inoculant (commercial, indigenous, and no additions) as the subplot treatment with 4 replicates for a total of 36 experimental plots at each site. Root colonization and biomass were assessed at V2 and R2 while apparent harvest index, yield, protein and oil content, and spore counts were assessed after the soybeans reached physiological maturity. Overall, no evidence was found that the treatments (fertilizer and AM fungal inoculant) had a statistically significant effect on AM fungal root colonization, biomass, apparent harvest index, yield, protein content, or oil content of soybeans and the hypothesis was rejected. The findings suggest that the benefits of AM fungi are context dependent. In soils with relatively moderate nutrient content, there may not be a significant advantage to introducing additional AM fungal inputs. Additionally, these results indicate that conventional agricultural practices may not be consistently detrimental to AM fungal inoculum potential. Furthermore, canola and soybean farmers in northern Ontario can direct their resources towards promoting sustainable practices and maintaining soil health, rather than relying on AM fungal inoculants, to overall facilitate a healthy microbial community and increase the robustness of the agroecosystem.
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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 | 0.003 | 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) | 0.001 | 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".