Exploring the emergence and changing dynamics of a new integrated rice-crawfish farming system in China

Abstract

Crop-aquaculture systems are widely adopted around the world as they can provide high protein and energy outputs per unit of land and raise farm incomes, particularly for smallholder farmers. Recently, a new crop-aquaculture system, which combines rice production with crawfish breeding (integrated rice-crawfish farming), has emerged and rapidly expanded in China. However, the spatial extent and temporal dynamics of this integrated farming system largely remain unclear, which prohibits rigorous impact assessments to support its sustainable development. Here we use time series of Landsat satellite data, for the first time, to explore the emergence and the changing dynamics of this rice-crawfish farming system for the period of 2013-2021 in five provinces of China, where 90% of the global crawfish are produced. The total area of rice-crawfish farming in these five provinces increased steadily from 0.11 Mha in 2013 to 0.70 Mha in 2019, then sharply contracted by a third in 2020 and rebounded in 2021. Rice-crawfish system located primarily in low-elevation plain areas with abundant water resources. More concentrated rice-crawfish distribution is observed in Jianghan Plain, and regions around Dongting Lake and Poyang Lake. The spatial distribution of rice-crawfish cultivation experienced considerable expansion towards the east and north from 2013 to 2021, with the largest expansion found in Jiangsu and Anhui after 2018. Over 6% of counties experienced notable area increases of more than 60 km2 from 2017 and 2019, but 20% of counties have decreased from 2019 to 2021. Irrigated cropland is the largest contributor to rice-crawfish expansion with a contribution of 56%, followed by water bodies (25%) and rainfed cropland (13%). The spatial and temporal information provided in this study helps to understand the evolution of rice-crawfish cultivation in China and facilitates more efficient management of land resources under the rapid development of this farming system.

Freshwater Aquaculture Mapping in “Home of Chinese Crawfish” by Using a Hierarchical Classification Framework and Sentinel-1/2 Data

“…The fine classification of inland aquaculture areas has significant and far-reaching implications for understanding the spatial distribution and production allocation of different types of inland aquaculture, as well as for achieving efficient management and sustainable development. Previous studies have primarily focused on satellite image extraction for individual aquaculture types, leaving a gap in research that simultaneously extracts multiple types of inland aquaculture areas [4][5][6]8,19,53]. For instance, Cai et al [8] proposed the RAUNet deep learning method and utilized high-quality GF-2 images covering the entire Qianjiang City for rice-crawfish field extraction.…”

“…Within inland freshwater aquaculture regions, intricately woven into diverse landscape patterns, a notable shift has emerged from singular aquaculture models (e.g., aquaculture ponds) towards a collaborative development paradigm that integrates both crops and aquaculture (e.g., rice-crawfish fields) [4]. This strategic transition aims to optimize yields within the constraints of limited arable land resources while ensuring food security.…”

“…This strategic transition aims to optimize yields within the constraints of limited arable land resources while ensuring food security. Previous studies primarily focused on single extracting either aquaculture ponds or rice-crawfish fields using remote sensing satellite imagery [4][5][6][7][8][9]. Within inland aquaculture areas, a myriad of aquaculture models may have targeted impacts on water resource security, biodiversity conservation, disease risks, and resource sustainability [10].…”

“…Zeng et al [21], utilizing an SVM classification method based on geometric features, proposed a contour-based water segment regularity measurement method, which evaluates the zero-curvature portions of the boundaries, effectively distinguishing aquaculture ponds from natural water bodies. Wei et al [4] utilized the CART decision tree algorithm along with the Simple Non-Iterative Clustering algorithm (SNIC) to identify rice-crawfish pixels during fallow and transplanting periods in rice-crawfish fields. Xia et al [18] utilized the RF and 255 spectral-temporal features derived from 15 GF-6 tiles to assess the potential of GF-6 data in identifying crop types.…”

Information Acquisition Ability and Farmers’ Herd Behavior in Rice–Crayfish Coculture System Adoption

“…The data for this study were obtained from a field survey on the development of the RCS conducted by a group from Huazhong Agricultural University in July 2019. In China, the RCS is currently being vigorously promoted and developed mainly in the plains of the middle and lower reaches of the Yangtze River [56]. Therefore, Hubei and Jiangsu provinces were selected as the survey areas for this study.…”