جستجوی مقالات مرتبط با کلیدواژه "water footprint" در نشریات گروه "اقتصاد کشاورزی"

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 21st 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. 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 yit is the product obtainable from input Xit (subtraction of inputs) and β is the vector of unknown parameters; in addition, vit represents random errors that are assumed to have a normal distribution with zero mean and variance σv2 and is distributed independently of uit.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. 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.  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.

Economic Ecological Efficiency is defined as the efficiency of using environmental resources to meet human needs. This concept can be considered as a suitable criterion for evaluating the sustainability of production and its economic efficiency. Due to the fact that the production of agricultural products is associated with environmental effects and the highest amount of water consumption is used to produce agricultural products on a global scale, so in this study, in order to investigate the environmental effects of wheat production, water index was used. For this purpose, first the traces of wheat water in the provinces of the country during the period 2000-2011 were calculated and then the economic-ecological efficiency of wheat production was estimated. The results of water footprint calculation showed that the provinces of Gilan, South Khorasan, Semnan and Sistan and Baluchestan have the average total of more water footprint in wheat production. Also, the average water, green and gray water footprint in the country during the study period was equal to 2625.7, 428.1 and 594.1 cubic meters per ton. The results of estimating the economic-environmental efficiency also showed that among the studied variables, compositional input and green water footprint have the most positive effect on improving the production value of wheat. The results also show that the provinces of East Azerbaijan, North Khorasan and Khorasan Razavi have the lowest average efficiency and the provinces of Gilan, Sistan and Baluchestan, Mazandaran and Ilam have the highest economic-ecological efficiency of wheat production, respectively. The average total economic-ecological efficiency of wheat production was estimated to be 0.84. Also, the results of estimating the inefficiency model showed that the economic-ecological efficiency of crop production is higher for areas with per capita GDP and higher rainfall

Achieving food and water security is one of the most important goals of policymakers in different countries. Water scarcity in Iran could be one of the major food security challenges in the future. For this purpose, climatic zoning was performed using the Domarten method. After calculating the water requirement using CROPWAT software; the amount of virtual water, water footprint, water productivity, volume of water consumed by each crop in each zone and the optimal food gap in different climates were calculated. According to the results; The per capita water footprint of the agricultural sector in hyper-arid, desert arid, semi-arid, Mediterranean and humid climates and Iran is 1611.97, 1228.09, 665.83, 884.01, 600.21 and 998.20cubic meters, respectively. In addition, the intensity of water consumption within the 5 climatic zones and Iran is 64.89, 88.03, 63.19, 41.01, 56.38 and 65.48 %, respectively. Net virtual water imports for each zone show that desert arid and humid zones are exporters of virtual water and hyper-arid, semi-arid and Mediterranean zones are importers of virtual water. The results show a contradiction between the realization of water security and food security in terms of quantity. The results showed that if the goal is to establish water security; by increasing the net import of virtual water (in the case of high-consumption products with low water efficiency), the intensity of pressure on domestic water resources in each area could be reduced. But if the goal is self-sufficiency; If the current situation does not change (crop composition in cropping pattern, yield and irrigation efficiency in each zone), more pressure will be put on water resources. Therefore, in order to achieve the goals of self-sufficiency and water security coefficients at the same time; It is recommended to increase the yield, improve the irrigation efficiency, formulate a suitable cultivation pattern and allocate water resources according to water productivity and its yield in each zone.

Red meat plays an important role in providing animal protein of Iranian households. However, its supply has experienced some challenges stemming from exchange rate fluctuations, smuggling livestock to the neighboring countries, and importing frozen meat financed by subsidized currency. In addition, there has been significant emphasis on self-sufficiency and providing the red meat domestically. On the other hand, based on Falcon Mark index, Iran has encountered water tension, the situation that may be getting worse as self-sufficiency is more focused. Regarding the problem, this study aimed at investigating different scenarios of domestically-produced and import of red meat with due consideration of simultaneously minimizing the costs of red meat and water use. For this purpose, multiobjective programing was applied using data for 2000-2018 in which minimization of spending for domestic consumption and water footprint embodied in domestic production was included. The selected scenarios were different combinations of the weights for minimization objectives. The results showed that depending on the weights assigned to the objectives, providing 30-40 percent of domestic need from domestically produced red meat was recommended while the remaining need might be provided via imports. The findings revealed that recommended combination for domestically-produced and imported red meat could reduce the spending on domestic need and water footprint significantly, resulting in 63 percent and 42 percent reduction in water use and cost spending, respectively; in addition, greenhouse gas emissions would be reduced. These results are the result of importing sheep meat from Australia or cattle meat with bones from Brazil. In spite of a significant reduction in the objectives’ values, the selected countries for import revealed a low diversity.

freshwater resources which are essential for human life, sustainable livelihood, food security and conservation of ecosystem appear to be under increasing pressure from population growth, socio-economic development and climate changes. The largest consumer of water is agricultural sector. Hence improving productivity in agricultural sector and reducing agricultural water use hold the key to tacking water scarcity.  But over the past decades, it has been argued that international trade of agricultural crops from wet-countries to arid and semi-arid countries is one possible path to mitigate water shortage. The trade of commodities, which water has been used in their production, is generally referred to as virtual water trade. Often the terms "virtual water" and "water footprint" are usually used synonymously, while there are significant differences. The water footprint concept, however, has a wider application. In fact, the water footprint of a product is an empirical indicator of how much water is consumed, when and where, measured over the whole supply chain of the product. In other words, the water footprint is a multidimensional indicator, showing volumes but also making explicit the type of water use and the location and timing of water use. For products containing virtual water, trade is a means of transferring water resources between regions and also this virtual water trade network among provinces has a large share of domestic trade. in current study, in order to determine the inter-provincial virtual water trade network of the country, water footprint of wheat, barley and maize and also the amount of excess supply and excess demand of selected products has been calculated using data over 1395 in each province.  Therefore virtual water trade network of each product has been obtained in different provinces of the country. Then, using the transportation model, exporting provinces has been specified and the amount of exports of different products for minimizing shipping costs has been identified In order to determine the excess supply and excess demand of the selected products in each province, firstly water footprint of each product was calculated for each province, then virtual water trade network of each product was identified and by using the transportation model, export route was determined.
The green, blue, gray and white water footprints of studied crops were estimated following the calculation frameworks of Hoekstra and Chapagain (2008) and Hoekstra et al. (2009), and modifications proposed by Ababaei and Ramezani Etedali (2014). By calculating water footprint components for different plains, their mean values were obtained for each province and then the obtained water footprint components in both irrigated and dryland were aggregated together for each product.
In this part of the study, due to lack of access to information and statistics of the amount of exports and imports between Iran’s provinces, at first per capita consumption of each Iranian person was obtained for each product. Then, total consumption of each province was obtained from the province's population by per capita consumption of each product. In order to calculate the excess demand or excess supply of each province, the total production of each province was deducted from the total consumption of each province. Finally, virtual water trade of each product in each province was acquired from water footprint in excess supply or demand.
Finally, the purpose of current study is to provide a minimum cost model for a virtual trade network from production centers to consumer centers. In the transportation model used here, the objective function is to minimize the total transportation costs between all selected agricultural production centers and consumption centers. The constraint (1) indicates that the amount of exchangeable product in each province is more than or equal to the product demanded by the province. Constraint (2) ensures that the product delivered between two centers is less than or equal to the capacity of the center. In constraint (3), the total demand for products is considered to be equal to the total amount of exchanged product. Constraint (4) provides for the positive value of exchanged items between supply and demand centers. Based on the results of this study, the provinces of Azarbaijan sharghi, Azarbaijan gharbi, Ardebil, Ilam, Khuzestan, Zanjan, Fars, Qazvin, Kurdistan, Kermanshah, Golestan, Lorestan, Markazi and Hamedan are the wheat suppliers and so are the exporter. The provinces of Isfahan, Bushehr, Tehran, Chaharmahal and Bakhtiari, Khorasan, Semnan, Sistan and Baluchestan, Qom, Kerman, Kohgiluyeh and Boyer Ahmad, Gilan, Mazandaran, Hormozgan and Yazd have exceeded demand for wheat thus they are importer. Among all provinces of the country, Tehran has the highest wheat consumption, due to the fact that the population of this province is about 13 million (Iran's capital of history, 1395). Kayani (2018) has shown that Tehran province is the largest importer of agricultural products and virtual water in the country. According to the results of the study, after ten years, mentioned province remains the importer. Among the provinces where surplus wheat has been supplied, Golestan province has the largest wheat exports up to 1.1 million tons, and by exporting this product about 2846.6 million cubic meters of water has been exported. Based on the results, Tehran province is the destination of export of Azarbaijan sharghi, Azarbaijan gharbi, Ardebil, Ilam, Zanjan, Qazvin, Kurdistan, Kermanshah, Markazi and Hamedan provinces. In addition to the province of Tehran, Ardebil, Ilam and Markazi provinces have to export 492, 188 and 182 thousand tons of wheat to the provinces of Gilan, Isfahan and Qom, in order to minimize transportation costs.
Following the results, Ardebil, Ilam, Khorasan, Semnan, Qazvin, Kermanshah, Golestan, Lorestan, Markazi and Hamedan provinces have excess supply of barley in the country, while the provinces of Azarbaijan sharghi, Azarbaijan gharbi, Isfahan, Bushehr, Tehran, Chaharmahal va Bakhtiari, Khuzestan, Zanjan, Sistan va Baluchestan, Fars, Qom, Kurdistan, Kerman, Kohgiluyeh va Boyer Ahmad, Gilan, Mazandaran, Hormozgan and Yazd have excess demand for barley in the country. Kermanshah and Hamedan provinces are the largest exporters of barley, which export about 690 and 666 million cubic meters of virtual water through exports of barley to other provinces and foreign countries. The rainfall of these two provinces is about 475 and 334 millimeters and footprint of water production in both provinces is 2833 and 4568 m 3 / ton. Barley import of Tehran province has taken place from Semnan, Qazvin, and Kermanshah, Golestan, Lorestan, Markazi and Hamedan provinces with a mean distance of 324 kilometers. Considering that Tehran is the largest importer of barley in the country, it is justifiable that all provinces that are located near that province are the export bases..
The province of Tehran is the largest consumer and importer of maize in the country and since Tehran is not maize producer, it is not possible to calculate the water footprint. On the other hand, the province of Ilam is the smallest consumer of maize and is the sole exporter of this product which exports 21 million cubic meters of virtual water along with this product. The provinces of Khuzestan, Kermanshah and Fars are the largest maize producers in Iran, which respectively produce 351, 152, 123 thousand tons in 2016. The province of Ilam export to the provinces of Tehran and Khorasan, respectively, 634 and 99 thousand tons of maize, the cost of maize supplies is minimized. Excess demand from other provinces of the country has also been provided from imports of other countries. Comparison of the results of this study, based on the statistics of 2016, and the Kayani study (2018), which was carried out in 2006, showed no significant changes in water resources management. Modifying the agricultural cropping pattern and correcting the pattern of consumption in line with the water footprint of agricultural products can be useful in improving the situation of the country's water resources in the long run. Determining the pattern of agricultural trade based on water footprint production of these products and the volume of virtual exports and imports of each product in each province could have a significant effect on reducing water losses in provinces of Iran.