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Incorporating undesirable outputs in the operational assessments through the integration of Life Cycle Assessment (LCA) and Data Envelopment Analysis (DEA) has received great attention recently. There are many studies throughout literature that apply various methods to integrate LCA and DEA. In this case study, the six most common approaches were employed to assess the winter wheat cropping system in Poland. These six methods were: a) ignoring undesirable outputs, b) treating undesirables as inputs to the DEA model, c) data transformation, d) impact rate, e) ratio model, and f) slack based measurement DEA with undesirable outputs. The environmental impact of wheat production was assessed by determining its carbon footprint (CF). The mean CF equalled 0.45 kg CO2eq per kg wheat grain (ranging from 0.25 to 0.67). According to the model comparison results, a slack based measurement DEA with undesirable outputs could better reflect the performance of undesirable outputs, and was selected as the most appropriate method to maximize the efficiency of winter wheat production while minimizing undesirable outputs. The advantage of applying the slack based model with undesirable outputs was that the targets presented by this model were based on existing efficient farms, as opposed to theoretical results; thus achieving these targets are feasible. The average efficiency score equalled 0.43, whereby few farms were classified as efficient farms. The results of the proposed integrated model showed a high reduction potential for mineral fertilizers (up to 595 kg ha−1 y−1), seed (up to 37 kg ha−1 y−1), and fuel (up to 75 L ha−1 y−1) in winter wheat farms. These results help farmers to obtain a realistic and reliable usage pattern for inputs in a winter wheat production system, whereby the greatest production can be achieved in conjunction with the lowest possible environmental impact.
Pishgar-Komleh et al. (Wed,) studied this question.