Testing the efficacy of a microbial inoculant to increase cold tolerance in corn
Bibliographic record
Abstract
Climate change is increasing the severity of abiotic stresses that can reduce plant growth and crop yield. Furthermore, modern agricultural practices have degraded soil microbiota, thus limiting their ability to alleviate abiotic stress in plants. Due to climate change, irregular temperature events, such as periods of cold during spring planting or fall harvest, may reduce crop yield and even render it unfit for market. Annually, in the province of Quebec and throughout Canada, low temperature stress causes significant losses of staple crops such as corn. It has been shown that inoculation with a microbial consortium can improve soil microbial diversity and reduce the effect of abiotic stresses such as high temperature and salinization on crops. However, there has been limited research on their efficacy in alleviating low temperature stress. While there are many agricultural biostimulants available, none are specifically targeted for cold stress. This project aimed to test the efficacy of an existing commercial bacterial inoculant to promote the growth of corn under low temperature stress. The expected mode of action for this to be achieved is phosphorus and calcium solubilization, and iron chelation. The temperatures tested were 25 °C as the optimal temperature, and 20 °C and 15 °C as the low temperatures. The ability of the consortium to alleviate cold stress was assessed through a series of germination and biomass accumulation experiments done in a highly controlled environment along with plant tissue nutrient analysis. The response of the rhizosphere to low temperatures and the addition of the microbial inoculant was characterized by community profiling using 16S rRNA sequencing data. The results showed that there was no significant effect of the consortia on germination rate or early plant growth. There was also no effect on plant height throughout the V0 to V4 growth stages for the range of temperatures tested, although there was a detectable yet insignificant increase in biomass at the V4 stage for the treatment group at the optimal temperature. This suggests that the inoculant may have slightly promoted plant growth under ideal conditions. The nutrient analysis showed a higher concentration of phosphorous, calcium, and metals such as iron and zinc in the plant tissue of the treatment group at the optimal temperature, further supporting this finding. The results of the rhizosphere microbiome community profiling showed that the addition of the inoculant resulted in no detectable effect on community structure at any of the temperatures tested, although the temperature differences resulted in significantly different microbial communities. Overall, the inoculant seemed to have a positive yet statistically insignificant effect on plant growth at the optimal temperature tested only
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".