Agricultural productivity measurement with dynamic data envelopment analysis

In solving multi-period efficiency problems, classical data envelopment analysis (DEA), which measures the efficiency of decision units separately for each period, does not include intermediates and inter-period connections in the model. The gap-based dynamic DEA analyzes the overall effectiveness of the decision units for all periods by including the transition between periods in the model. Dynamic DEA (DDEA) takes into account the inter-period input/output dependency in calculating the efficiency levels of economic decision units, such as the agricultural sector, depending on time. The purpose of the dynamic DEA model used in this study is to measure the dynamic efficiency values of the decision-making units by combining the undirected model with the input and output-oriented efficiency in a single objective function by providing the time connection between the intermediate product and the periods. In this study, the evaluation of agricultural productivity covering the periods of 2016-2017 and 2018 with DDEA was carried out on the basis of European Union (EU) countries. Indicators for agricultural efficiency analysis of countries were selected based on a literature review; two normal inputs (agricultural labor force and land), one unwanted input (chemical fertilizer), one normal output (agricultural output), one unwanted output (CO2), and one link variable (public agricultural expenditure) were determined as model variables. Gap-based static data envelopment analysis (SBM) models and void-based DDEA models were solved with the Microsoft Excel Solver. As a result of the analysis, it has been determined that the agricultural efficiency scores of 12 EU countries are equal to 1 according to both models, and these countries are agriculturally efficient.