Soil Fungal Resources in Annual Cropping Systems and Their Potential for Management
Bibliographic record
Abstract
Soil fungi are a critical component of agroecosystems and provide ecological services that impact the production of food and bioproducts. Effective management of fungal resources is essential to optimize the productivity and sustainability of agricultural ecosystems. In this review, we (i) highlight the functional groups of fungi that play key roles in agricultural ecosystems, (ii) examine the influence of agronomic practices on these fungi, and (iii) propose ways to improve the management and contribution of soil fungi to annual cropping systems. Many of these key soil fungal organisms (i.e., arbuscular mycorrhizal fungi and fungal root endophytes) interact directly with plants and are determinants of the efficiency of agroecosystems. In turn, plants largely control rhizosphere fungi through the production of carbon and energy rich compounds and of bioactive phytochemicals, making them a powerful tool for the management of soil fungal diversity in agriculture. The use of crop rotations and selection of optimal plant genotypes can be used to improve soil biodiversity and promote beneficial soil fungi. In addition, other agronomic practices (e.g., no-till, microbial inoculants, and biochemical amendments) can be used to enhance the effect of beneficial fungi and increase the health and productivity of cultivated soils.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".