Potato tillage method is associated with soil microbial communities, soil chemical properties, and potato yield
Bibliographic record
Abstract
Intensive potato continuous cropping (IPCC) results in low potato yields compared with non-intensive potato continuous cropping (PCC) and potato-maize rotation (PMRC). However, it is still unclear whether the degree of potato continuous cropping obstacle is related to the soil environment formed by the previous crop. To investigate the effect of planting potatoes and planting maize after harvesting the spring potatoes on soil chemical properties and soil microbial community structure, an experiment was carried out in the same origin soil environment over a period of seven years: (a) PCC, i.e., spring planting; (b) IPCC, i.e., autumn and spring planting (IPCC); (c) PMRC, i.e., spring potatoes and summer maize (PMRC), and (d) fallow (CK). We confirmed that the potato yield under PMRC was significantly higher than that under PCC and IPCC. Under IPCC, soil total phosphorus content was significantly higher than other treatments, whereas ammonium nitrogen content was the lowest. Compared with PCC and IPCC, PMRC had a higher ammonium nitrogen content and lower total phosphorus content. The significantly different fungal taxa in IPCC (Glomerellales, Plectosphaerella, Thelebolales) may threaten the health of the plant and positive correlated with soil total phosphorus, while other microbial taxa in PMRC (Bacillales, Polythrincium, Helotiales) can mainly promotes plant nitrogen uptake and protects plants against diseases. The PMRC-promoting taxa were positively correlated with the ammonium nitrogen content and negative correlated with soil total phosphorus content. In summary, the cropping systems might have affected potato yields by changed soil microorganism community structures - especially fungal community structures - and by the chemical properties of the soils that also depends on microbes.
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 | 0.001 | 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".