We transformed a data envelopment analysis (DEA) optimization model into a robust second-order cone equivalent to immunize against output perturbation in an uncertainty set. The robust DEA framework was then used to assess the effect of a wood hardening treatment using methyl methacrylate (MMA) on selected hybrid poplar clones. Because the performance of MMA-hardened hybrid poplar clones varies across clones, ranking hardened clones is crucial for developing hardening treatments for specific industrial applications. The numerical results demonstrate that the hardening treatment can be optimized by applying the proposed DEA framework to select the best hybrid poplar clone types and the optimal amount of impregnated chemicals.