Rice-Crayfish Farming Increases Soil Organic Carbon Sequestration by Promoting Soil Aggregate Protection and Microbial Necromass Accumulation DOI

Wanyang Zhang,

Jiaqiong Wu,

Mingshuang Xu

et al.

Published: Jan. 1, 2024

Language: Английский

Ecosystem sustainability of rice and aquatic animal co-culture systems and a synthesis of its underlying mechanisms DOI
Lei Ge,

Yu Sun,

Yujie Li

et al.

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 880, P. 163314 - 163314

Published: April 6, 2023

Language: Английский

Citations

34
Returned straw reduces nitrogen runoff loss by influencing nitrification process through modulating soil C:N of different paddy systems DOI
Shaopeng Wang,

Limei Zhai,

Shufang Guo

et al.

Agriculture Ecosystems & Environment, Journal Year: 2023, Volume and Issue: 354, P. 108438 - 108438

Published: May 16, 2023

Language: Английский

Citations

20
Integrated rice-crayfish farming system does not mitigate the global warming potential during rice season DOI
Lijin Guo, Wei‐Hung Lin, Cougui Cao

et al.

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 867, P. 161520 - 161520

Published: Jan. 13, 2023

Language: Английский

Citations

18
Rice-crayfish farming system promote subsoil microbial residual carbon accumulation and stabilization by mediating microbial metabolism process DOI

Wanyang Zhang,

Yi Song, Shihao Ma

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 946, P. 174188 - 174188

Published: June 24, 2024

Language: Английский

Citations

5
Assessing the nutrient removal performance from rice-crayfish paddy fields by an ecological ditch-wetland system DOI Creative Commons
Jun Yang,

Shihao Tang,

Yiqi Li

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(19), P. e38373 - e38373

Published: Sept. 24, 2024

Language: Английский

Citations

5
Rice-crayfish farming increases soil organic carbon sequestration by promoting aggregate protection and microbial necromass accumulation DOI

Wanyang Zhang,

Jiaqiong Wu,

Mingshuang Xu

et al.

Agriculture Ecosystems & Environment, Journal Year: 2025, Volume and Issue: 383, P. 109519 - 109519

Published: Feb. 1, 2025

Language: Английский

Citations

0
Comprehensive assessment of integrated rice-crayfish farming system as a new paradigm to air-water-food nexus sustainability DOI
Qiaoyu Sun, Benyamin Khoshnevisan, Jianqiang Zhu

et al.

Journal of Cleaner Production, Journal Year: 2022, Volume and Issue: 377, P. 134247 - 134247

Published: Sept. 23, 2022

Language: Английский

Citations

11
Distribution patterns and influential factors of pathogenic bacteria in freshwater aquaculture sediments DOI
Wenxiang Xi, Xun Zhang, Xianbin Zhu

et al.

Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: 31(10), P. 16028 - 16047

Published: Feb. 3, 2024

Language: Английский

Citations

2
Methane emissions sources and impact mechanisms altered by the shift from rice-wheat to rice-crayfish rotation DOI
Shaopeng Wang, Yilin Liu, Fulin Zhang

et al.

Journal of Cleaner Production, Journal Year: 2023, Volume and Issue: 434, P. 139968 - 139968

Published: Nov. 29, 2023

Language: Английский

Citations

5
A Bi-Temporal-Feature-Difference- and Object-Based Method for Mapping Rice-Crayfish Fields in Sihong, China DOI Creative Commons
Siqi Ma, Danyang Wang, Haichao Yang

et al.

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(3), P. 658 - 658

Published: Jan. 22, 2023

Rice-crayfish field (i.e., RCF) distribution mapping is crucial for the adjustment of local crop cultivation structure and agricultural development. The single-temporal images two phenological periods in year were classified separately, then areas where water disappeared identified as RCFs previous studies. However, due to differences segmentation lakes rivers between images, incorrect extraction unavoidable. To solve this problem, a bi-temporal-feature-difference-coupling object-based (BTFDOB) algorithm was proposed order map Sihong County. We mapped by segmenting bi-temporal simultaneously based on method selecting appropriate feature classification features. evaluate applicability, results years obtained using single-temporal- (STOB) compared with BTFDOB method. suggested that spectral showed high importance, which could effectively distinguish from non-RCFs. Our worked well, an overall accuracy (OA) 96.77%. Compared STOB method, OA improved up 2.18% across three data. concentrated low-lying eastern southern regions, scale expanded Sihong. These findings indicate can accurately identify RCFs, providing scientific support dynamic monitoring rational management pattern.

Language: Английский

Citations

4