Exploring the Feasibility of Enhancing Indigenous Arbuscular Mycorrhizal Fungi Communities for Sustainable Soybean Production in northern Ontario
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».