AI-Enabled Strategies for Managing Microplastic Risk in Agroecosystems

The continuous increase in microplastic (MP) pollution poses significant risks to human health and environmental sustainability, especially in agroecosystems. This study focused on identifying and managing MP risk to crops in agricultural soils in China, which is among the world’s largest consumers of plastic. Via the use of 3243 site-year field observations, we developed intelligent agriculture models to predict MP-related crop risks and identify key drivers, such as climate, livestock density, and fertilizer application, other than the use of agricultural plastic film. Rice was most sensitive to MPs, with an average risk quotient (RQ; unitless) of (3.76 ± 1.95), which is 2.19 and 1.93 times greater than those of maize and wheat, respectively. Climate factors are closely related to livestock density and agricultural management practices, potentially exacerbating MP risk under future conditions. Optimizing livestock density and fertilizer use levels reduced MP risk by 20.9%, 22.9%, and 20.3% and increased crop yields by 9.0%, 6.0%, and 5.6% for maize, rice, and wheat, respectively. Despite limitations related to model uncertainty and policy implementation, the proposed intelligent agriculture model provides a comprehensive basis and potential solutions for assessing and managing MP risk to crops.