Quality and dynamics of soil organic matter in a typical Chernozem of Ukraine under different long-term tillage systems
Bibliographic record
Abstract
Kravchenko, Y., Rogovska, N., Petrenko, L., Zhang, X., Song, C. and Chen, Y. 2012. Quality and dynamics of soil organic matter in a typical Chernozem of Ukraine under different long-term tillage systems. Can. J. Soil Sci. 92: 429–438. Tillage has been reported to induce changes in soil organic matter (SOM) concentrations and quality. Conversion of plow-tillage to minimum till and no-till (NT) farming enhances the SOM pool. Enrichment of the SOM pool is essential for maintaining fertility of Chernozems, advancing food security, and improving the environment. The main objective of this study was to examine the effect of different tillage systems on the SOM concentration, its quality and dynamics including CO 2 assimilation by heterotrophic bacteria and humus characteristics – the carbon (C) concentration in humic substances and the labile soil organic C fraction (SOCL) extracted with 0.1 N NaOH – as well as the molecular masses, spectroscopic parameters and physiological effects of humic acids on germinating pea (Pisum sativum L.) seeds. Our study was conducted on a long-term experimental site on a Haplick Chernozem in the Poltava region of Ukraine over a 10-yr period from 1996 to 2006. Results indicate that conversion from conventional to reduced soil tillage systems increased SOM concentrations in 0- to 10-cm soil layer and led to the accumulation of C in fulvic acids and humins. No significant differences in SOM storage in the 0- to 100-cm layer were observed among tillage systems. However, reduced tillage systems had a higher proportion of SOCL, a lower ratio of C in humic acids/C in fulvic acids and more humic acids with molecular masses from 110 to 2000 kDa. Our study demonstrated that the quality and dynamics of SOM are closely related to soil tillage practices.
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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".