## 2 Materials and Methods  
The data used in this study were primary and secondary data. Primary data were taken from questionnaires and interviews of GMO and non-GMO companies and rice farmers in Java, Indonesia. Secondary data were taken from online resources. Judgment sampling techniques were used to collect the GMO companies' information in Indonesia. Then, we took one product as a case study to be observed by using the seven issues of sustainability developed by [PERSON] et al. [PERSON] et al. (2015); [PERSON] et al. (2015). In this research, seven issues of sustainability were determined. Biodiversity, atmosphere, materials, and water were selected as issues of the environmental sustainability dimension. Economic performance was selected as an issue of the economic dimension. Then, employment practices and product responsibilities were selected for the social dimension. Data Envelopment Analysis (DEA) is a non-parametric efficiency evaluation method that uses a mathematical planning model to calculate the distance between each Decision Making Unit (DMU) and the production frontier consisting of the best performing DMUs in practice, and further to calculate the efficiency score of each DMU. The term DMU in DEA can represent a variety of units, such as units from factories, departments, and anything that has similar operational characteristics. There are two factors that influence the selection of DMUs, namely: First, the DMUs must be homogeneous units. These units carry out the same tasks, and have the same objective. Second, the inputs and outputs that characterize the performance of the DMUs must be identical, except that they differ only in their magnitude and size. Next, in this study the DEA method was used to measure the Total Factor Productivity (TFP) indicator and the Total Price Recovery (TPR). This research used the Adjusted Profit (AP) integratedindicator for product comparison in conditions of sustainability. The adjusted profit (AP) indicator provides some input-output properties of the agri-food supply chain, contributes to negative production externalities, and provides one unified sustainability performance measure. This research also used the Total Factor Productivity (TFP) indicator and the Total Price Recovery (TPR) indicator to identify the consistent comparison between the adjusted profits from the different products. With that indicator, the variation in total adjusted profits among the production chains could be divided into variations caused by the difference in price (price represents the difference in TPR) and variations caused by the difference in quantity (quantity represents the difference in TFP) for each input, output, and externality [17; 18; 19]. The agri-food supply chain or Decision Making Unit (DMU) for both GMOs and non-GMOs is explained as a set of three stages: z = 1, z = 2, and z = 3, they are agricultural/farming (z = 1), processing (z = 2), and transport to wholesalers/retailers (z = 3). At every stage, vector x indicates inputs, such as services, materials, energy, labor, and capital that are turned into outputs (products), symbolized by vector y. Some environmental and social externalities are generated as a side effect of production, stated by vector b, such as loss of biodiversity, poor working conditions, pollution, and waste. The schematic diagram of an agri-food supply chain in this research is shown in Figure 1.  
The value of AP in agri-food supply chains is positive if the difference between the aggregated outputs and the aggregated inputs shows positive results and, vice versa, when the difference between the aggregated outputs and the aggregated inputs is negative then the agri-food supply chain has a negative adjusted profit (AP) as the externalities are output penalties that decrease the score. The score of the AP value is calculated by Equation (1) below:  
\[AP=p^{\prime}y-r^{\prime}b-w\prime x \tag{1}\]  
Some outputs, inputs, and externalities are aggregated using price vectors \(p\), \(w\), and \(r\).