Soil aggregate and organic carbon contents with different surface mulching under dryland farming system
Bibliographic record
Abstract
[Objectives]Study on the effect of different mulching treatments on soil aggregates and their relationships with soil organic carbon,will help selecting the sustainable mulching measures for the loess hilly region of China.[Methods]The field experiment was carried out in 2008.Four treatments were included as control without mulching treatment(CK),straw mulching at a rate of 9000 kg/hm2(M1),straw mulching at a rate of 4500 kg/hm2(M2) and plastic film mulching(PM) during winter wheat growing season.Soil samples were collected at layers of 0-10 cm and 10-20 cm in 2014 after crop harvest,soil aggregates were separated into 5mm,5-2 mm,2-1 mm,1-0.5 mm,0.5-0.25 mm and 0.25 mm and were determined by using dry sieving method and wet sieving method.The mean weight diameter(MWD) and geometric mean diameter(GMD) were calculated and the content of soil organic carbon was also measured.[Results]1) Compared with CK,soil mechanical-stable macroaggregates content was increased by straw mulching,the increase in the 0-10 cm soil layer was 5.1%in M1 and 2.0%in M2 treatment,respectively.Both straw and plastic film mulching improved the macroaggregate contents in 10-20 cm soil layer,with significant increase of 7.6%,4.3%and 3.1%in M1,M2 and PM,respectively.2) Compared with CK,the contents of water-stable macroaggregates(0.25 mm) in M1 and M2 were increased by 6.8%and 5.0%in 0-10 cm soil layer,respectively,mulching with plastic film had no significant effect.3) All the straw mulching and plastic film mulching treatments improved the values of MWD and GMD in 0-10 cm soil layer significantly.Compared with CK,the soil MWD was increased by 47.8%,24.7%and 24.6%,and the GMD was by 48.9%,34.8%and 31.6%in M1,M2 and PM treatments.4) Soil organic carbon content was increased by straw mulching.Soil organic carbon content was in turn 11.9%and 6.3%higher in 0-10 cm layer,and 4.2%and 4.5%higher in 10-20 cm layer in M1 and M2 than in CK,respectively.Plastic film mulching had no effect on soil organic carbon sequestration.Soil organic carbon content was significantly correlated with 0.25 mm size water-stable aggregates content at 0-10 cm depth.[Conclusions]In the perspective of soil structure,mulching with crop straw rather than with plastic film could improve soil structures and soil quality through increasing the quantity of water-stable macroaggregate and total soil organic carbon content.Straw mulching at a rate of 9000 kg/hm2 is superior to other rate and can be regarded as a reasonable cultivation practice in the Loess Plateau.
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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 | 0.000 | 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".