Soil Microbial Biomass and Its Relationship With Yields of Irrigated Wheat Under Long-term Conservation Management
Bibliographic record
Abstract
Relating soil microbial properties to crop productivity is important to appreciate the value of soil microbial activities in sustainable agriculture. Over a 10-year period, we evaluated the effects of conservation (CONS) management practices on soil microbial biomass carbon (MBC). The CONS practices included addition of composted cattle manure; reduced tillage; diverse crop rotations that comprised wheat (Triticum aestivum L.), potato (Solanum tuberosum L.), dry bean (Phaseolus vulgaris L.), and sugar beet (Beta vulgaris L.); and use of cover crops. The CONS management was applied to 3- to 5-year irrigated crop rotations and compared with conventional (CONV) management systems that did not have any of the CONS practices. Continuous wheat was also included. We then related MBC to wheat yields. Averaged over the 10-year period, CONS management overall increased MBC in wheat rhizosphere and bulk soil by 18% and 34%, respectively. When rotations of the same length were compared, CONS management in 3-year rotations increased rhizosphere MBC by 18% and bulk soil MBC by 30%; the corresponding increases in 4-year rotations were 13% and 36%. Regressions between soil MBC and wheat yields were quadratic, with MBC in wheat rhizosphere associated with increasing wheat yields up to 720 mg C kg−1 soil. The corresponding value for MBC in bulk soil was 645 mg C kg−1 soil. These effects were related to the compost and crop C inputs to the soil, which impacted soil organic C contents. Therefore, CONS management resulted in a cycle of high MBC and high wheat yields.
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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) | 0.000 | 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 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".