محاسبة کارآیی اقتصادی- زیست‌‏محیطی تولید محصول جو بر مبنای ردپای آب

Introduction: Due to the increase in population and the need to provide food and preserve the environment, the importance of the water crisis has increased in the years leading to the third millennium and the beginning of the 21^st century. The reduction of underground and surface water resources and the destruction of the environment and water ecosystems are signs of the water crisis in Iran and the world. On the other hand, the production of agricultural products is associated with the creation of environmental effects, especially water and soil pollution (due to the use of pesticides and as a result, the loss of environmental balance). Therefore, there is a need for policy-makers to have indicators in the field of the effects of agricultural activities on natural resources and the environment, so that they can measure the economic and environmental effects. The water footprint index was first introduced by Hoekstra and Hung in 2002; and over recent years, it has been widely used by experts in different parts of the world. The water footprint is a multidimensional indicator that shows the volume of water consumed by the type of water source and the volume of polluted water by the type of pollutant. All components of the total water footprint are determined by time and place. The water footprint consists of three components, including blue water footprint, green water footprint and gray water footprint. Therefore, given the importance of sustainability of water resources, in this study, by considering the components of water footprint as inputs in the production function, the economic-environmental efficiency was estimated using the Stochastic Frontier Approach (SFA) for the production of barley in Iran; specifically, Water Footprint- Stochastic Frontier Approach (WF-SFA) framework was used to analyze the economic-environmental efficiency of barley production. Thus, in this study, in order to estimate the environmental effects, the water footprint index was used and the economic-environmental efficiency of barley production was estimated. For this purpose, the components of barley water footprint were calculated in the provinces producing this product. Then, the economic-environmental efficiency of barley production in the provinces of Iran was calculated.
Materials and Methods: In the first step, in order to calculate the water footprint, meteorological data was collected for the cities that had the highest level of barley cultivation in each province. This information included average wind speed (m/s), maximum temperature (c), minimum temperature (c), average temperature, 24-hour precipitation (mm), maximum relative humidity (%), minimum relative humidity (%), average relative humidity (%), sunny hours and daily radiation amount. The total water footprint during crop growth (WF) is the sum of blue, green and gray water components. After calculating the components of barely water footprint in the provinces of the country following Battese and Coelli (1995), an SFA model was created for barely production. In the year t, for the i province, the basic stochastic frontier production 

where y_it is the product obtainable from input X_it (subtraction of inputs) and β is the vector of unknown parameters; in addition, v_it represents random errors that are assumed to have a normal distribution with zero mean and variance σ_v² and is distributed independently of u_it.
In this study, in order to calculate the economic-environmental efficiency using the stochastic frontier production function, the appropriate production function form was first selected. The forms of the production function examined in this study are Cobb-Douglas and Translog. After choosing the appropriate production function and model type, the stochastic frontier production function was estimated by the maximum likelihood method as well as the economic-environmental efficiency of barley production was estimated.
Results and Discussion: By examining different barley producing provinces, it could be seen that the highest amount of total water footprint was related to the provinces of Ilam, Kerman, Sistan and Baluchistan and South Khorasan and the lowest amount was related to the provinces of Tehran, Golestan, Qazvin and Ardabil. The total amount of water footprint for most provinces was in the range of 3 to 4 thousand cubic meters. Also, the results showed that the barley water footprint in Iran did not follow a very specific geographical pattern, but in tropical and low-rainfall provinces such as Sistan and Baluchistan, the water footprint was higher and the main difference between the water footprints of the provinces was due to the blue water footprint. The results of the estimation of the stochastic frontier production function of barley production using the variable efficiency model over time showed that the combined input variables, blue water footprint and green water footprint had a significant effect on the production of this product. In this regard, the positive coefficient of the combined input variable (X1) and the green water footprint (X4) indicated that assuming the constancy of other conditions, a 1% increase in the amount of each of these variables would lead to an increase of about 1% in the production of barley among the provinces. These findings indicated the low level of accumulation of combined inputs in the production of barley due to the low production rate of this product and also the low amount of precipitation in the country. The labor force variable (X2) was not statistically significant; and the coefficient of this variable in the estimated production function indicated that the labor force had a very small positive effect on the efficiency of barley production. Therefore, due to the very low contribution of labor in barley production, it is expected that the amount of barley production in Iran will increase with the increase in the degree of mechanization.
 
Conclusions: The final results showed that the provinces of Ilam, Qom, and Isfahan had the lowest economic-environmental efficiency, and the provinces of Kohgiluyeh and Boyer Ahmad, Kermanshah, and Kurdistan had the highest economic-environmental efficiency in barley production, respectively. The overall average economic and environmental efficiency of wheat production was estimated at 0.94. Furthermore, the results of the estimation of the inefficiency model showed that the economic and environmental efficiency of barley production was higher for regions with higher per capita GDP and more rainfall. In other words, the variables of per capita GDP and annual rainfall negatively affect the economic and environmental inefficiency of barely. So, it is suggested to evaluate the effects of human and social capital on the economic-environmental efficiency of agricultural production in future studies. It is also suggested to follow the following methods to reduce pollution and preserve the environment: changing production methods, more efficient management and the use of superior technologies by ineffective provinces (for example, new irrigation methods to reduce the water footprint), the use of green fertilizers and so-called low-risk chemical fertilizers approved by international organizations to reduce the gray water footprint as one of the factors that reduce the efficiency and biological control instead of using pesticides (to reduce the gray water footprint) to eliminate pests. On the other hand, encouraging incentives and punishments are also very effective for farmers. The government should think of incentive measures to encourage efficient sectors; for example, it can give the priority of using resources with lower prices to farmers who produce less environmental pollution by using fewer chemical fertilizers and pesticides. Paying subsidies to efficient producers is also effective. On the other hand, environmental regulations should be set for producers. Establishing a tax on undesirable products to increase the motivation of producers and farmers to use environmentally friendly methods and techniques is also very effective.