مجله اقتصاد و توسعه کشاورزی
سال سی و سوم شماره 1 (پیاپی 42، بهار 1398)

Broiler poultry units are among the most important units in the agricultural sector. In most developing countries, there are a lot of investments in these units. By examining the state of industry in the last few years, which has been declining, it seems that there is no efficient and optimal utilization of these units. Due to lack of proper management, appropriate financial programs and financial operations control in chicken production and main inputs price fluctuation, poultry farms do not have the necessary financial efficiency. These issues have reduced the financial efficiency of production units in the poultry industry. Consequently, the competitiveness of products of these units has decreased compared to their own competitors. In this regard, the review of financial efficiency can provide a general picture of the financial situation and performance of these units and can instruct managers and financial planners in making appropriate decisions. Accordingly, present study tries to review the financial efficiency of poultry farms in Tabriz County which has a special place in supplying protein of country and East Azerbaijan province, Iran. In this study, financial ratios and data envelopment analysis (DEA) approaches were used to evaluate financial efficiency. DEA is a nonparametric method in operations research and economics for the estimation of production frontiers. It has been used to empirically measure productive efficiency of decision making units (or DMUs). Although DEA has a strong link to production theory in economics, the tool is also used for benchmarking in operations management, where a set of measures is selected to benchmark the performance of manufacturing and service operations. In the circumstance of benchmarking, the efficient DMUs, as defined by DEA, may not necessarily form a “production frontier”, but rather lead to a “best-practice frontier. Non-parametric approaches have the benefit of not assuming a particular functional form/shape for the frontier, however they do not provide a general link (equation) relating output and input. There are also parametric approaches which are used for the estimation of production frontiers. These require that the shape of the frontier be guessed beforehand by specifying a particular function relating output to input. One can also combine the relative strengths from each of these approaches in a hybrid method where the frontier units are first identified by DEA and then a smooth surface is fitted to these. This allows a best-practice relationship between multiple outputs and multiple inputs to be estimated. Data envelopment analysis (DEA) is a linear programming methodology to measure the efficiency of multiple decision-making units (DMUs) when the production process presents a structure of multiple inputs and outputs. The statistical population of this research was 45 poultry farms in Tabriz County which were surveyed by census. By studying the economic characteristics of poultry farms in Tabriz it is revealed that the minimum and maximum capacity of studied poultry units is 6000 and 100,000 pieces, respectively; with an average capacity of 30,000 pieces. According to the results, mean production of units is 66 tons at each period and about 56 percent of units are producing below the average production. Results indicate that the average of financial efficiency of poultry farms is 63.66 percent. Also, among 45 studied units, only 5 of them have 100 percent efficiency and rest of them suffer from lack of this important factor. Therefore, there is a gap of about 36 percent to maximize financial efficiency (100 percent). Results reveal that the units which their efficiency are more that 70 percent, have higher production capacity than the other ones. Economic and logical use of inputs, especially nutrient inputs and energy will have a significant impact on reducing the costs of the poultry farms and increasing their profit. Furthermore, the existence of inflationary situation in economy and the volatility of prices, including the price of inputs for poultry units, have led to a significant increase in production costs in poultry farms; as a result stabilizing the price of the main inputs can prepare a suitable base for regular planning and enhance financial efficiency of units. One of the problems with this type of studies is the lack of financial information of economic units. Given that the results of this type of studies can be applied to better planning of their economic units, therefore establishing and maintaining financial information by economic units will help to better and accurately conduct these kind of studies.

Agricultural products market in Iran is facing structural problems with non-competitive and inefficient conditions for trade of agricultural products, which leads to high price fluctuations for these products. Future markets as one of the risk sharing strategies would shift price risk to brokers and intermediaries. So, future markets are considered as one of the best tools for reducing agricultural risk. Designing and implementing future contracts is time-consuming and costly. Therefore, in order to succeed in setting up such contracts, it is essential to pay attention to several main issues consisting selecting the correct commodity for exchange, determining the optimal specification of the future contracts of agricultural products, and the way of decision making and preferences of market participants. Despite the precise design of future contracts, future markets may fail after commencing due to lack of access for farmers, to use these tools. The purpose of this study is to predict future market acceptance by rice farmers in Sari.
To achieve this goal, the positive Mathematical Programming Model (PMP) is used in the simulation of the traditional and future market within the framework of the GAMS software. All required data were derived from statistics provided by the Ministry of Agriculture and Statistical Center of Iran during 2000 to 2015. The objective function of the model was a calibrated objective function which maximizes the actual quantities of farmers' production. But it should be noted that in solving this model, it was assumed that when the future market is launched the decision of the producers regarding the amount of production is not affected and only a part of their product will be offered in the future market, rather than in the traditional market, with the aim of reducing the price risk. Since this assumption does not validate   in the actual operating conditions and it is expected that the producers' decision-making process would also be affected after entering the futures market and trading in this market. The results of simulation of the traditional market showed that the real average of the production, consumption, and net exports respectively were about 1.1663*10+5, 24074.390 and 1.4071*10+5 tone and the total profit of the producers of these products was about 3.7928*10+8 million Rials..
Based on the results of simulation of future market, the real average of the production, Consumption and Net exports equals about 1.4349*10+5, 26199.05 and 1.1729*10+5 tone respectively and the total profit of the producers of these products is about 3.7958*10+8 million Rials. Thus it is expected that after commencing future market for agricultural products, 44% of all rice farmers would sell their product using future contracts.
Therefore producers' decisions are not affected by the level of production and only a part of their product would be offered in the future market instead of the traditional market with the aim of reducing the price risk. In addition to comparing this market to the traditional market, the launch of the future market will increase the production, consumption and net exports about 1.9, 8.8 and 5.6 percent respectively. Due to the strategic condition of the rice product and the suitability of this product to enter the future market, it should be noted that in the process of optimal design of future contracts, without paying attention to all dimensions for launching the upcoming market, this market will not be successful. Therefore, in this study determination of the amount of participation by rice farmers before launching a successful future market for rice crops has been considered. The first stage were simulating conditions before the launch of the future market, named traditional market conditions of rice, and the average real values of production, consumption, net exports and total profit of the producers of this product were estimated in Sari city. Subsequently, with the goal of reducing the price risk, the conditions after launch of the future market were simulated that represent about half of rice producers will be participanting in the upcoming market. Base on the results of this study, it is suggested that the launch of futures markets and transfering process to the Agricultural Commodity Exchange would need cultural and extension courses to understand the benefits of entering this market.

Dairy products, especially milk, play an important role in healthy food basket of Iranian households. The long-standing increase in the consumption of this food has been a policy of the government. In recent years, government efforts to increase per capita consumption of dairy products and milk have led to an increase in demand for these products; with the per capita consumption of milk in 2016 equals 27.1 liters. Studies indicate that price of milk does not have impressive impact on milk consumption. For example, Cyprus, with very high price for milk, is one of the leading countries in milk consumption. Therefore, it seems that other factors have contributed to increase of demand for milk in the countries. One of the factors is general advertising of milk that promotes milk consumption among households. Today, advertising is an essential element in introducing goods and products and serving the needs of human life. Advertising should be used to boost economic movement by identifying the product. The role of advertising can be examined in a chain of influence from firm level to market level for sales and product demands. Therefore, because of the milk consumption importance, governments, spend money on advertising in order to increase its consumption. This policy (increasing demand by general milk advertising) has been prevalent in the world for many years and has been the main task of governments, for example, in the United Kingdom in 2015 about 12.4 million pounds or in the United States between years of 2000 to 2001, an average of 25.1 million dollars annually been spent on general advertising of milk. This approach can also be applied to government policies in Iran.  Given that general advertising is costly, the key question is whether general advertising in milk industry can affect the demand for milk (from the consumer's point of view) and, as a result, the profit of milk industry (from the perspective of producers). Although the results of assessment about the effect of advertising on consumption and profits of firms producing basic goods, including milk, have been reported in other studies, but the reliability of estimates has not been studied. This is basically due to the unknown type of statistical distribution of the effect of advertising.  In this study, the effect of advertising is estimated by analyzing the statistical significance of general advertising and its reliability. Also, with the simultaneous review of the effect of general advertising on industry profits and retail demand in a multi-market equilibrium model that considers advertising effects from farm to retail, the results are more reliable. So, the Significance of model of general advertising on milk industry profit in a multi-market equilibrium is examined. The purpose of this study is to investigate the significant role of general advertisement on Iran’s milk retail demand and industry profits in a multi-market equilibrium in 2016. Results indicated that by increasing the price elasticity of demand for processed milk at retail level, the level of advertising significance statistically declined and by increasing in input prices for raw milk production at farm level (forage, straw, wheat, barley, etc.), in most own price elasticities scenarios of demand, the level of advertising significance statistically declined, but by increasing the optimal advertisement budget (advertising intensity index), the level of advertising significance statistically increases, but when the markets are considered as multi equilibrium, the interval of milk general advertising significances declines for the minimum and maximum advertising intensity indicator. However, the more the market moves towards non-competitiveness, the significance level of processed milk general advertising declines. Also, results show that the average of the F statistics in competitive multi- market of milk is 1.519 to 10.657. This value for the competitive multi- market of dairy is 3.032 3.692 and for the non-competitive market is 0.981-3.414. Namely, with a less competitive market, general advertising effect declines significantly. On average, in different scenarios of the input price at farm level, general advertising effect varies significantly from 2.937 to 3.414.  The mean of milk advertising significant level in all scenarios is 3.883. Therefore, in all considered scenarios, milk advertising has a significant effect on consumer demand and the profit of the milk industry.  The effect of processed milk general advertising at retail level on the price of raw milk at farm level shows that the effect of advertising on farm prices decreases with increasing price elasticity. Also, by increasing the index of optimal advertising intensity, the effect of advertising on farm prices also decreases. The more the market goes to non-competitive, the effect of general advertising on the price increases at farm level. The average advertising effectiveness (advertising budget) is found to be at farm level price of 0.009226. According to this study results, the effect of advertising on industry profits is significant enough to justify advertising programs. This issue is examined by considering the statistical distribution of the effect of advertising on industry profits as a function of the statistical distribution of the effect of advertising on consumption. The results showed that the F-statistic for examining the effect of advertising on industrial profits was equal to the F-statistic associated with the reduced form of advertising effect on product prices. While the size of the effect of advertising on profits increased with the effect of advertising on demand, there was no significant correlation between the effect of demand and the significant effect of profit. One of the most important results of this study is that the statistical significance of advertising in demand function is neither necessary nor sufficient for the statistical significance of the effect of advertising in the reduced forms of price and profit functions of the industry. Therefore, it is suggested that in the study of the effectiveness of variables, the effects of different levels of marketing, the assumption of the type of market are considered. Also, in order to increase the influence of general advertising on milk consumption, decision makers can implement polices to reduce the prices of inputs and increase the intensity of advertising. Also, policies that make the milk market more competitive (at various levels of marketing) can be beneficial.

Poverty is prevalent in majority of the world's nations. If a country is not on the path of eliminating or reducing poverty, it will not be on the path of growth and development. Undoubtedly, the first step in planning of anti-poverty and reducing inequality for development of society is a good recognizing of poor people and poverty situations. To do so, it is necessary to define and use poverty indicators for measuring poverty, which can reveal the different aspects of poverty. Indeed, more precisely identifying the number and grade of households below the poverty line in different locations can help policymakers toward better planning. According to the statistics provided by the Statistical Centre of Iran, in recent years, due to the low level of income of villagers, it has been emphasized more on poverty issue in the rural sector than the urban sector, and the size and severity of poverty in rural areas have been more tangible. Income inequalities (using the Gini coefficient) have always been higher in rural areas than in urban areas. So in this regard, the main purpose of this study is to "survey the rural poverty indices and its affecting factors in rural areas of Iran". One of the most important concerns of governments is awareness of poverty and inequality in society to take action to improve the status of poverty and distribution of income. In this regard, it is important to determine the poverty line and poverty measurement indicators. In this study, poverty indicators were categorized into two categories of "Classical indicators", including "Head Quant Ratio", "Poverty Gap index", "Income Gap Ratio", "Kakwani Index", "Kakwani - Sen index", "Sen Index" and "Modern indicators", including "Gini coefficient index", "Atkinson index", "Araar Index", "DER Index", "Foster - Wolfon index", and "Steban Grading Ray Index". It should be noted that the classification of indices is done to simplify the subject, although the basis of this classification is the observance of the two "monotonicity axiom" and "transfer axiom" by the indexes, that is, the use or absence of polarization in the distribution of income (and inequality). The polarization distribution of a variable is the degree of distribution be distributed around a polar group. Many papers and studies have examined the difference between indices of inequality and polarized indicators and concluded that, in general, phenomena such as "middle class disappeared" or "boundary grouping" cannot be estimated by indices of inequality such as "Gini coefficient index". These topics require more robust and complete indicators that can also analyze the polarity of the data. the aim of this research is examining the situation of poverty and distribution of income “inequality”. Indicators of poverty measurement include modern and classical indicators then  the impact of some macroeconomics variables on poverty  has been investigated using data of income plan for households in rural areas in years 1987-2016, with Stata software and DASP package calculating classical indicators which includes: "Head Quant Ratio", "Poverty Gap", "Income Gap Ratio", "Kakwan index", "Severity Poverty of Sen", "Sen Poverty index" and modern indicators which includes: "Gini index", "Atkinson index", "Arrar index", "DER index", "Foster - Wolfson index, "Steban Gradin Ray index. In order to investigate the factors affecting poverty, firstly, between two linear and logarithmic functional forms, logarithmic forms are used as the appropriate form for running regression. In addition, White's powerful variance has been used to solve the heterogeneity variance problem. The results of regression estimation for factors affecting poverty indicators show that economic growth, fertility rate, unemployment, household size, time trend, rural population, agricultural producer price index, net capital agriculture and agricultural value added affect all six modern indicators of poverty. The result of indicators using income plan data for households in rural areas  for years 1987-2016, with Stata 14.0 software  and DASP 3.0 package shows that poverty increased in years 1987-1992 and 2012-2016 but decreased in 1993 till 2011. Reducing unemployment, controlling inflation and controlling the growth rate of rural populations are important factors affecting poverty. Increasing agricultural producers' prices will improve rural incomes and improve poverty, as well as increase in agricultural investment and increase in agricultural value added, which can have a major impact on rural welfare and poverty. It is also recommended that supportive policies based on spatial and regional differences should be developed and applied based on the difference in income deciles.

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.

Rapid growth in the world population would substantially exacerbate pressure on all resources particularly water resources and consequently would cause difficulties in global food security. In addition, degradation of water resources is one of the greatest environmental challenges facing almost all countries around the world including Iran. In Iran, the situation is even worse as it is located in a dried and low precipitation region. Thus, before Iran reaches at an irrevocable point, it needs to revisit its development policies. In fact, what is considered as a strategic principle in the path of sustainable development is the balance between the development policies and the state of the existing country's natural resources base, specially the water resources. Thus, in order to manage optimal usage of water resources and to coordinate farm land utilization policies and water resource availability in different provinces, information on water security situation in terms of physical, social and economic factors are necessary. The present study seeks to specify the status of water security in provinces of Iran using water poverty index. Given that water security is a multidimensional concept and it is not possible to use one variable to represent its different dimensions, the indicator method is typically used to evaluate this concept. In the present study, the Poverty Index is utilized to measure water security in different provinces of Iran. This index consists of five main water related components including: Resources Accessibility, Capacity, Consumption, and Environment. These components in turns are determined by various variables such as the volume of groundwater resources and annual surface water per person, the variation of rainfall in a 10-year period, number of household having  access to public water pipeline, percentage of population having access to urban wastewater collection and disposal services, percentage of population covered by the social security services, literacy rates in the population over the age of 6, rate of participation, GDP at constant prices, employment rate in non-agricultural activities, annual water usages, percentage of irrigated land, amount of fertilizer and pesticides distributed annually, and percentage of protected areas under the management of the Environmental Protection Agency. These variables are first standardized using minimum-maximum method Then, an index for each of the five components are computed. Next, an index of water poverty is calculated for each province by aggregating all five components. At the end, based on the index of water poverty all provinces are classified into Water Unsafe, Lower safe, Moderate safe, Upper safe, and Full Safe provinces. Results revealed that, five provinces, including Sistan va Baluchestan, Qom, Kerman, Hormozgan and Golestan were the most insecure provinces based on the calculated water poverty index. These regions are facing a severe water crisis. Two provinces, including Tehran and Gilan, had lower safe water security. Also, five provinces, consisting of East Azerbaijan, Zanjan, Semnan, Kermanshah and Lorestan faced upper safe situation, while five provinces, including Bushehr, Chahar Mahaal va Bakhtiari, Kohgiluyeh va Boyer-Ahmad, Kurdistan and Markazi had full Safe of water security. Other provinces were ranked in moderate safe status in Iran. The correlation between Water Poverty Index (WPI) and its components indicates that all components are positively and significantly correlated with the Water Poverty Index, except for the capacity item. The magnitudes of the calculated correlation coefficients in this study were 0.459, 0.628, 0.776 and 0.518, respectively for resources accessibility, capacity, consumption, and environment components. The consumption item has the strongest relationship with the Water Poverty Index. Consequently, in order to improve water security, it is recommended that policy makers give priority to this item.   Given that the roots of existing water poverty in different provinces were not the same, it is suggested that water policy makers and planners take into consideration the province-specific factors for setting up the planes aiming to prevent more water insecurity in Iran. From this point of view, the WPI results can be used to prioritize the provinces and understand the roots of water insecurity in each of the provinces. Providing water security or water poverty map for Iran is essential for having a clear understanding of water security situation in different regions in Iran and is recommended. Finally, information provided by WPI can be used in efficient management of water resources in different provinces and at national level.

Due to fossil fuels overuse, land use change, global population growth and the development of industrial activity to meet the welfare and demands of the global community, global climate has undergone gradual, but drastic, changes in the post-industrial revolution era mainly manifested in the rise of mean temperature, more frequent extreme climatic events like floods, hails, tropical storms, heat and cold waves, rising sea level, polar ice melting, droughts and etc. Climate change is a mix of dominant and lasting atmospheric characteristics of a geographic area over time and is often based on such variables as temperature, precipitation, humidity, wind, solar radiation, and number of sunny days, sea level temperature, and the thickness of ice layers at sea. Climate of a region is dedictated by a set of these factors in the long run, as well as other local characteristics such as the length of growing season and the intensity of floods whose change influences how people live and harm different sectors including agriculture and environment. Present study aimed to explore the impact of climate change on total agronomical production in 10 agro-ecological zones of Iran. Present study employed panel data econometrics to explore the impact of climate change (mean precipitation, temperature, evapotranspiration rate, relative humidity, wind speed) on total agronomical production over the 1985-2015 periods. The panel data set included the observations related to multiple sectors and have been collected at different times. Panel data has been used when it is impossible to use just time series data or cross-sectional data. They contain more information in addition they are more diverse and have less multicollinearity between variables, so they are more efficient. In analysis of the combined data firstly should consider a certain section (e.g. country, region or province) and then focus on the attributes of the variables related to all N sections over a certain period, T. The number of data is not required to be equal over the sections (unbalanced panel model) and it is also possible to have variables that are constant in a certain section over the studied time period. In a panel data model, the variables are measured sequentially both among the sections of the statistical population and over time. In panel data, before proceeding to model estimation, we should firstly recognize whether panel or pool model is appropriate for the estimation and statistical inference. To do this, we first integrate whole data as pool to estimate the model and calculate the sum of residual squares. Then, the model is estimated as a panel model with different y-intercepts for a certain section and the residual squares are again summed. Finally, F-statistic is applied as the following equation to test the constructed model. The data were collected from Iran Meteorological Organization and Ministry of Jihad-e Agriculture. After checking the stationarity of the data, panel model with random effects has been estimated. The results showed that total agronomical production has been influenced by temperature, evapotranspiration, and wind speed at 0.05 level and precipitation at 0.10 level. The impacts of global warming can be currently observed across the world. Agricultural sector is especially vulnerable to climate change so the rise in average seasonal temperature would shorten the growth period of most crops, causing the loss of their yields. Climate changes, such as the change in temperature, precipitation, pest and disease outbreaks adversely influence food production systems, decrease harvest and jeopardize food security. As predicted for Iran, it is expected that occurrence of climate change – which is characterized by rainfall decline, rise in temperature, and increase in the occurrence of extreme weather conditions – have harmful consequences for the agronomical production. Climate change imposes remarkable economic costs on agronomical producers. The producers may have to either stop growing these crops or adapt to climate change as far as possible. In most cases, it is not economically feasible for producers to apply adaptive methods and the majority of their life aspects are potentially influenced. According to the results, it is recommended to use water pricing policies in agricultural sector that motivate farmers to use modern irrigation technologies and low irrigation-resistant cultivars, alter planting pattern towards crops with higher water use efficiency, therefore plan for and grant financial facilities, such as crop insurance, in order to prepare agronomical farmers for climate change.