Managing arbuscular mycorrhizal fungi in cropping systems
Bibliographic record
Abstract
Market globalization, demographic pressure, and environmental degradation have led us to reconsider many of our current agricultural systems. The heavy use of chemical inputs, including fertilizers and pesticides, has resulted in pollution, decreased biodiversity in intensively-farmed regions, degradation of fragile agro-ecosystems, and prohibitive costs for many farmers. Low input sustainable cropping systems should replace conventional agriculture, but this requires a more comprehensive understanding of the biological interactions within agro-ecosystems. Mycorrhizal fungi appear to be the most important telluric organisms to consider. Mycorrhizae, which result from a symbiosis between these fungi and plant roots, are directly involved in plant mineral nutrition, the control of plant pathogens, and drought tolerance. Most horticultural and crop plants are symbiotic with arbuscular mycorrhizal fungi. Mycorrhizal literature is abundant, showing that stimulation of plant growth can be mainly attributed to improved phosphorous nutrition. Although the mycorrhizal potential of its symbiosis to improve crop production is widely recognized, it is not implemented in agricultural systems. There is an urgent need to improve and widely apply analytical methods to evaluate characteristics such as, relative field mycorrhizal dependency, soil mycorrhizal infectivity, and mycorrhizal receptivity of soil. Decreased use of fertilizers, pesticides, and tillage will favour arbuscular mycorrhizal fungi. However, shifting from one system to a more sustainable one is not easy since all components of the cropping system are closely linked. Different cases, from actual agricultural practices in different countries, are analyzed to highlight situations in which mycorrhizae might or might not play a role in developing more sustainable agriculture. Key words: Cropping systems, mycorrhizae, sustainability, technical itineraries, rotation
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".