N. Ashktorab; M. Zibaei
Abstract
Introduction: 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. ...
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Introduction: 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 identifiedMaterials and Methods: 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.Results and Discussion: 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.Conclusion: 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.
N. Ashktorab; Gh. Layani; Gh.R. Soltani
Abstract
Introduction: The area under cultivation and yield of crops are affected by various factors, some of which are controllable and some others are uncontrollable. Controllable factors are divided into two types of price and non-price factors. Among the price factors, prices of agricultural products and ...
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Introduction: The area under cultivation and yield of crops are affected by various factors, some of which are controllable and some others are uncontrollable. Controllable factors are divided into two types of price and non-price factors. Among the price factors, prices of agricultural products and inputs play an important role in expanding the cultivation area. Uncontrollable factors also have great effects on increasing the cultivation area of agricultural products. Two of the most important factors that affect yield are weather and climate conditions. The agricultural sector that is one of the sectors that is most vulnerable to climate changes has often been used for political debates and research projects. In the agricultural sector, cereal and especially maize, have a special place in the world both in production and in the area under cultivation. Therefore, given the importance of this product, investigating the effects of climate changes on cultivation area and yield of maize needs careful examination.
Material and Method: Panel data in econometrics has many advantages over using cross-sectional data and time series. The Hausman test is used to determine the fixed and random effects in the panel data. Also panel data unit root tests will be necessary. In this study, several price and non-price factors are considered:
(1) Cit = f(Wit, RPit-1, Rit, Tit, Cit-1)
where Cit: maize cultivation area in province i in year t
Wit : wheat irrigated area and dry area in province i in year t
RPit-1: relative imposed price of maize and wheat in t-1.
Rit : rainfall in province i in year t
Tit : temperature in province i in year t
Cit-1: maize cultivation area in province i in year t-1
(2) Cit = α1 Wit + α2 RPit-1+ α3Rit+ α4 Tit+ α5 Cit-1+ uit
In addition, in this study the Ricardian model was used to examine the impact of climate change on maize yield.
(3)
: Yield per hectare of maize in province i in year t
: Temperature in province i in year t
: Rainfall in province i in year t
: Latitude and height above sea level, respectively.
The data used in this study were for the provinces of Fars, Khuzestan, Kerman, Kermanshah and Elam for the period 1993-2011.
Results and Discussion: According to Table 1, all variables are significant at the one percent level of confidence. Therefore, all of the variables are stationary.
Table 1- Results of stationary test for variables
variables Levin-Lin & chow stat. Pesaran& Shin stat. Stationary state
Cultivation area of maize 2.51*** ***1.13 I(0)
Cultivation area of Irrigated wheat 2.45*** 2.89*** I(0)
Cultivation area of Dry wheat 5.02*** 3.76*** I(0)
rainfall 8.74*** 6.09*** I(0)
Temperature 5.78*** 3.40*** I(0)
Relative imposed price of maize to wheat 2.74*** 1.36*** I(0)
Source: Research calculations
Based on the results shown in Table 2, all variables are significant. The highest and the lowest estimated coefficient is for the relative imposed price of maize to wheat (6.68) and cultivation area of dry wheat (0.01).
Table2- Results of the factors affecting the maize cultivation area in selected provinces
Variables coefficient Standard deviation T-statistics
Constant -2.49* 1.23 -2.03
Imposed price ratio with a lag 6.68*** 1.32 5.06
Cultivation area of Irrigated wheat -0.42* 0.22 -1.88
Cultivation area of Dry wheat -0.01* 0.006 -1.98
Cultivation area of maize with a lag 0.66*** 0.06 10.28
Rainfall 0.17*** 0.06 2.86
R-squared 0.97 Durbin-Watson stat 1.84
Adjusted R-squared 0.96 F-statistic 184.5
Source: Research calculations
Table 3 indicates the results of the Ricardian model by using the panel data method. R2 in this model is equal to 86 % and it shows that %86 of the variation of maize yield is explained by variables. According to the results,the rainfall altitude, the rainfall height above sea, the square of rainfall and latitude have significant effects on maize yield.
Table 3- Results of climatic factors on maize yield in selected provinces
Variables coefficient Standard deviation T-statistics
Constant 12.73*** 2.55 4.99
Temperature -0.11 0.20 -0.52
Rainfall 0.002*** 0.0002 7.32
Height above sea level -0.0004*** 0.0001 -3.51
Square of temperature 0.001 0.005 0.17
Square of rainfall -2.62*** 2.45 -10.71
Latitude -0.07*** 0.02 -3.05
R-squared 0.86 Durbin-Watson stat 1.59
Adjusted R-squared 0.81 F-statistic 5.15
Source: Research calculations
Conclusions: In this study, the factors affecting maize cultivation area and yield as a plant that uses a lot of water in Fars, Khuzestan, Kerman, Kermanshah and Elam provinces were investigated. The results showed that non-price factors such as rainfall and temperature have a significant impact on the cultivation area of maize. Due to the emphasis placed on the policy of self-sufficiency in wheat, irrigated and dry cultivation area of wheat and imposed price of wheat, mentioned by Garshasbi et al., have a significant impact on the cultivation area of maize in the selected provinces. The results indicated that according to specific climatic conditions in these provinces, irrigated wheat can be a proper alternative product for maize. Due to Vaseghi and Esmaeili, climate changes could have adverse effects on maize yield and can lead to a reduction of maize cultivation area. Due to the inevitability of global warming, further investigation of this issue is very important.
Keywords: Maize, Rainfall, Temperature, Yield, Cultivation area, Ricardian model, Panel data method
