Abstract
<jats:p>Nowadays, the development of m-polar fuzzy set (mPFS)-based aggregation operators is important for the evaluation of Climate-Smart Agriculture (CSA) strategies because they can provide a robust framework to decrease uncertainty (based on multi-dimensional parameters) in the information that is taken from real-life climate-related decision-making issues. This study's goal is to use Maclaurin Symmetric Mean (MSM) operations to determine the aggregation operators (AGOPs) in mPF information environment. In this chapter, the MSM operations are utilized to develop some new averaging mPFS-based AGOPs for modelling multiple poles in agricultural practices under climate variability. The proposed AGOPs consist of the mPF-MSM (mPFMSM), mPF weighted MSM (mPFWMSM), and mPF ordered weighted MSM (mPFOWMSM) AGOPs. Further, a real-world application predicting the effect of climate change on different crops at a specific site in Pakistan by applying the proposed framework based on mPFMSM and mPFWMSM operators for the aggregation of the obtained data from relevant experts.</jats:p>