Spatiotemporal expansion and methane emissions of rice-crayfish farming systems in Jianghan Plain, China

文献类型： 外文期刊

第一作者： Wei, Haodong

作者： Wei, Haodong;Zhang, Xinyu;You, Liangzhi;Cai, Zhiwen;Meng, Ke;Yang, Jingya;Cao, Junjun;Wu, Hao;Hu, Qiong;You, Liangzhi

作者机构：

关键词： Rice -crayfish farming system; Spatiotemporal expansion; CH4 emission mitigation; Landsat satellite data; DNDC model

期刊名称：AGRICULTURAL AND FOREST METEOROLOGY （ 影响因子：6.2； 五年影响因子：6.9 ）

ISSN： 0168-1923

年卷期： 2024 年 347 卷

页码：

收录情况： SCI

摘要： The rice -crayfish field (i.e., RCF), a recently emerged rice cultivation pattern, has experienced remarkable growth in China over the last decade due to its significant socioeconomic advantages. However, the impacts of expanding RCF areas on the regional -scale ecological environment, particularly concerning methane (CH4) emissions, remain unclear. A major obstacle in addressing this knowledge gap is the absence of accurate and upto-date spatial distribution information on RCF across years. Here, we selected Jianghan Plain which has the largest RCF area in China as the study area. First, we developed a phenology-based identification algorithm using Landsat-7/8 satellite data, which considered the distinctive flooding signatures of RCF during the rice fallow periods, to identify RCF at the regional scale. Second, we employed the DeNitrification-DeComposition (DNDC) model to simulate the CH4 fluxes of various rice cropping systems, including RCF, rice monoculture (RM), ricerapeseed rotation (RR), and rice -wheat rotation (RW). Finally, the effects of RCF expansion during 2014-2019 on regional CH4 emissions were analyzed by comparing six scenarios that simulated the conversion of different rice cropping systems to RCF. Results showed the phenology-based algorithm performed well in extracting RCFs, achieving an overall accuracy >92 % for all years based on 1025 RCF and 2096 non-RCF validation samples. RCF generated the least CH4 flux, followed by RM, RR, and RW. Moreover, shifting from traditional rice cropping systems to RCF reduced CH4 emissions across all cases, with mitigation rates ranging from 4.82 % to 21.85 %, indicating RCF's substantial CH4 mitigation potential. These findings significantly improve our understanding of the ecological effects of RCF cultivation, which is critical for advancing land use planning and decision -making for sustainable agricultural development in China. Our presented evaluation method of integrating the remote sensing mapping algorithm and DNDC model can be easily generalized for other crop types in other regions.

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