Monitoring and classifying cropland productivity degradation to support implementing land degradation neutrality: The case of China

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Mainstreaming the land degradation neutrality (LDN) framework in national and local policy systems is critical to addressing global land degradation. However, existing land productivity assessment limits the generalization of LDN in regions with complex geographical backgrounds. To support integrating productivity balance measures into the “Requisition-Compensation Balance of Cropland” policy in China, we applied a novel cropland productivity monitoring and classification system in a case area, Yangtze River Economic Belt (YREB) in China, including four agricultural zones (i.e., large-scale, smallholder, hilly, and suburban agricultural zones), based the most active days (MAD) assessment method and the quadratic regression model. The results show that although the average productivity of YREB cropland increased from 2001 to 2017, ∼20% of the croplands are in the significant degradation hotspot areas; There is clear spatial variation in the productivity degradation in different cropping systems (i.e., single- and double-crops) and “watershed gradient” and “urban-rural gradient” are two main degradation spatial patterns; Eight major degrading types (e.g., “no degradation,” “degradation reversed,” and “continuous degradation”) are identified and the “early stage of degradation” accounts for the highest proportion. Meanwhile, the “long-term” and “accelerated degradation” of productivity in the second season of double-crops are also threats hard to ignore. These findings suggest that YREB's cropland productivity degradation process is multidimensional and complex, especially when linked to natural and human drivers. Nevertheless, this paper can support China's LDN decision-making by helping answer the key questions (i.e., where is the degradation, what is the degeneration type, and how to neutralize the degradation?) and recommending targeted neutralization strategies for the agricultural zones under various geographical backgrounds. These findings can also inform LDN policy development in the wider global region.