Plant-soil interactions and stand decline in alfalfa: mechanisms and mitigation strategies
Bibliographic record
Abstract
In agricultural systems, effects of plants on soil microbial communities have been demonstrated to feedback over time and impact plant growth and productivity through plant-soil feedback (PSF). When negative, PSF results in productivity decline, limiting alfalfa (Medicago sativa) production. Unlike negative PSF, positive PSF promotes plant growth and improves productivity. Despite this, we do not fully understand the mechanisms of PSF and are thus limited in our strategies to mitigate productivity decline. Using the plant-soil feedback framework, we collected vegetation and soil samples from alfalfa stands grown to mixture (alfalfa-grass) and monoculture at stand ages 1 to 6 years old, near Saskatoon, SK. These soils were used in a completely randomized experimental design to inoculate 4 alfalfa varieties, viz. 2010, Foothold, 3010, and Spyder, and 5 other forage species, viz. Onobrychis viciifolia, Trifolium pratense, Vicia americana, Elymus lanceolatus, and Agropyron cristatum, of which traits depicting root economic spectrum and symbiosis were sampled. Additionally, next-generation amplicon sequencing was used to identify amplicon sequence variants (ASVs) of soil bacteria, oomycetes, and arbuscular mycorrhizal and other fungi in the inoculum associated with PSF. Field conditions including plant diversity, soil phosphorus, soil texture, weed abundance, and fiber content of focal crop mediated how plants condition soil microbial communities. These conditioning effects altered the relative composition of soil mutualists, plant-growth promoting microbes, saprotrophs and pathogens, all of which affected PSF. These PSFs, however, differed depending on the variety and crop species identity due to differences in how these plant types interacted with the soil microbiome. This allowed me to identify more than 30 soil microbial taxa that promoted positive or negative PSF, although the important taxa were rarely consistent among varieties or species. Root trait expressions for high resource conservative strategies and symbioses with mutualists lead to more positive PSF while the opposite traits (more resource acquisitive strategies and reduced symbioses) lead to more negative PSF. These root traits, however, varied among the species and to a smaller extent among the varieties, indicating that some crop species and cultivars can resist soil biotic stress under certain field conditions, and thus alleviate stand decline. This plant-soil feedback approach will be useful in trait-based selection during pasture rejuvenation and cultivar development for resistance to soil biotic stress. The resources provided in this study will therefore enhance sustainable management of productivity decline in agroecosystems.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".