The effect of weeds on soil arbuscular mycorrhizal fungi and agronomic traits in spring wheat (Triticum aestivum L.) under organic management in Canada
Bibliographic record
Abstract
Kubota, H., Quideau, S. A., Hucl, P. J. and Spaner, D. M. 2015. The effect of weeds on soil arbuscular mycorrhizal fungi and agronomic traits in spring wheat (Triticum aestivum L.) under organic management in Canada. Can. J. Plant Sci. 95: 615-627. Understanding the influence of weeds in agroecosystems may aid in developing efficient and sustainable organic wheat production systems. We examined the effect of weeds on soil microbial communities and the performance of spring wheat (Triticum aestivum L.) under organic management in Edmonton, AB, Canada. We grew 13 Canadian spring wheat cultivars in organically managed hand-weeded less-weedy and weedy treatments in 2010 and 2011. The less-weedy treatment exhibited greater grain yield and tillers per square meter, while kernel weight, test weight, days to maturity, plant height, grain P and protein content were not altered by weed treatment. Canada Western Red Spring (CWRS) wheat cultivars CDC Go and CDC Kernen were the most yield-stable because they minimized fertile tiller reduction in response to weed pressure (10 and 13% reduction, respectively, compared with the average reduction of 20%). Other cultivars exhibited yield stability through increased kernel weight. The contribution of arbuscular mycorrhizal fungi (AMF) to the total phospholipid fatty acid increased in both treatments; however, the rate of this increase was greater in the weedy treatment than the less-weedy treatment (from 2.9 to 3.9%, from 2.8 to 3.1%, respectively). Weed dry biomass was positively correlated with AMF% in the less-weedy treatment only. Organic systems tend to be weedier than conventional systems. We found that weeds are important determinants of AMF proliferation in soil. In addition, choosing wheat cultivars that maintain important yield components under severe weed stress is one strategy to maximize yields in organic systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".