The Pythagorean fuzzy set is an extension of the traditional fuzzy set which offers an effective approach to addressing the complex uncertainty in decision-analysis processes, making it applicable across a broad spectrum of applications. Given the limitations of available facts, decision experts often face challenges in precisely defining the grades of belongingness (BG) and non-belongingness (NG) using crisp values. In such situations, opting for interval-valued belongingness and non-belongingness grades proves to be a suitable choice. This work uses the notion of power aggregation operators through Schweizer and Sklar (SS) operations to build aggregation operators for interval-valued Pythagorean fuzzy (IVPF) sets. This paper introduces several aggregation operators within the IVPF framework, such as the IVPF SS power weighted average operator, the IVPF SS power geometric operator, and the IVPF SS power average operator. The existence of SS t-norms and t-conorms in the IVPF framework for addressing multi-attribute decision-making problems gives the generated operators the ability to make the information aggregation approach more adaptable compared to other current ones. The application of the proposed approach holds the potential to enhance crop yield, optimize resource utilization, and contribute to the overall sustainability of agriculture. Additionally, sensitivity and comparative analyses are provided to clarify the stability and dependability of the results acquired through this approach.