With the power of deep learning, super-resolution (SR) methods enjoy a dramatic boost of performance. However, they usually have a large model size and high computational complexity, which hinders the application in devices with limited memory and computing power. Some lightweight SR methods solve this issue by directly designing shallower architectures, but it will affect SR performance. In this paper, we propose the dual feature aggregation strategy (DFA). It enhances the feature utilization via feature reuse, which largely improves the representation ability while only introducing marginal computational cost. Thus, a smaller model could achieve better cost-effectiveness with DFA. Specifically, DFA consists of local and global feature aggregation modules (LAM and GAM). They work together to further fuse hierarchical features adaptively along the channel and spatial dimensions. Extensive experiments suggest that the proposed network performs favorably against the state-of-the-art SR methods in terms of visual quality, memory footprint, and computational complexity.