Soil Carbon Mineralization and Aggregate Distribution in Various Tillage Practices of Rice–Wheat Cropping System: A Field and Laboratory Study
JOURNAL OF SOIL SCIENCE AND PLANT NUTRITION(2024)
College of Agronomy
Abstract
Different tillage and residue management practices can strongly impact soil structure stability and soil organic carbon (SOC) sequestration. However, the detailed information about the aggregate stability, SOC protection, and mineralization within aggregates are still lacking. Using aggregate fractionation with laboratory incubation, we investigated aggregate-associated SOC, soil structural stability, and SOC mineralization in rice–wheat rotation under different tillage treatments: CT0 (puddled rice, conventional wheat − residue); CTR (puddled rice, conventional wheat + residue); NT0 (direct rice seeding, zero-tilled wheat − residue); and NTR (direct rice seeding, zero-tilled wheat + residue). NTR significantly enhanced the large macro-aggregate fraction (> 2 mm) at the 0–45 cm soil layer and macro-aggregate-associated SOC at the 0–15 cm soil layer. However, CTR enhanced the macro-aggregate-associated SOC at the 15–30 cm layer. Notably, the mean weight diameter ( 8
MoreTranslated text
Key words
Carbon mineralization,Aggregate distribution,Tillage,Residue returning,Rice–wheat rotation
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined