JOURNAL OF AGRICULTURAL ECONOMICS RESEARCH
Year:2021 | Volume:12 | Issue:4|Papers Count:12

The purpose of this research was investigating the effecting factors on the adoption of agricultural supplemental insurance by Alborz Province farms. The type of research is descriptive-correlation that is done through survey method. Statistical population of research are farmers in the villages of Alborz province that throught Krejcy & Morgan table and clustered sampling was selected 317 people totally random. Main tool of this research is a questionnaire that it`s validity is obtained by using panel of experts and masters in the agricultural managment field. The reliability of the questionnaire is done through pre-test and by Cronbach's alpha coefficient, that was acceptable. The results of analysis regression are indicated annual agricultural income, awareness of insurance conditions, land ownership, having a non-agricultural job, level of education and riskiness well explain for 68. 1% changes in adoption of agricultural supplemental insurance.

The aim of this study is evaluate the effect of household characteristics on demand for major food groups that make the urban and rural household food basket in Iran. For this purpose, we was used the raw data of Statistical Center of Iran as. Then pseudo-panel data was made using cross-sectional household survey data as average of total observation on each province as cohort during 1384-93. Finally, household demand were estimated with QUAIDS model and household characteristics and location variables estimated separately for rural and urban areas of Iran. Results indicate that food demand pattern is different in urban and rural. Also the effect of household characteristics and regional variables on food group's demand in rural and urban areas shows that householder education, household size, age, gender and region of residence have a significant effect on demand. So in implementation of any policy that aimed improving household food security should be based on regional and availability of food and household characteristics.

The most effective climatic variables were identified using Fayoucan's optimization algorithm. In order to test the effect of climate variables on yield and date risk, for each of these areas, the random production function Jost and pop was estimated. The results of temperature and precipitation indexes were for hot and humid regions (0. 45 and 0. 66), and hot and dry regions (3. 04 and 0. 18), which indicate regional impact. Annual climate change has caused bad weather for farmers. Annual temperature changes and irregular rainfall changes over the past years, as well as climate change and warming in the future, could lead to serious dangers in reducing crop yields in agriculture. Regarding the unforeseen changes in temperature and precipitation in warm, dry, warm and humid areas, it is recommended that, in order to reduce the risk of yield for dates in the areas, such methods as the use of tolerant cultivars of dates in comparison with temperature and rainfall changes in the regions are recommended.

The demographic structure, as in other parts of the world, in Iran is changing. These changes are associated with a reduction in the share of children and an increase in the share of the elderly of the total population. The United Nations has predicted that in the years 2015-2030, the population of the elderly will double in Iran. Accordingly, the main objective of this study is to investigate the effect of demographic structure on carbon dioxide emissions in Iran during the period from 1350 to 1394. In this study, based on the results of variables stationary, the Autoregressive Distributed Lag (ARDL) approach was applied. The results of the research indicate that the two demographic (15-64 years) and (0-14 years) periods have a significant impact on CO2 per capita emissions, while the aging population (65 years and older) has a lower effect on CO2 emissions.

Since agricultural activities are inherently risky and farmers have to make decisions in such situations, their decision-making in a context of risk depends on their attitude to risk. In other words, different attitudes, cause people making different decisions in similar situations. In this study, it is tried to determine the attitude of farmers in Rasht county about risk based on four inputs of nitrate and phosphate fertilizers, poison and labour using the Safety First Model. The required data was obtained by filling out questionnaires and face to face interview with 221 rice farmers in year1396 using two-stage cluster sampling method. The results of the study indicate that all of rice farmers are intermediate risk averse towards the use of nitrate fertilizers (urea), phosphate fertilizer and poison (herbicide). This type of attitude can lead to the use of more fertilizers and poisons which result in unhealthy crops and environmental pollution. But rice farmers have different attitudes in terms of labour input, with almost 83% of them are risk averse and only about 17% are risk prefer.

Due to the importance of production and consumption of milk and dairy products, this study with purpose of investigation the consumers’ preferences and its affecting factors have been done as the nested logit model in the Sari city in 2018. Sample size was selected based on simple random sampling method and 275 questionnaires have been collected. The results show that yogurt, milk and cheese have the most preferences among the dairy products and consumers have more tendencies to low fat dairy products than full fat. The study results of affecting factors on selecting of each the diverse dairy products and individual preferences revealed that price and family cost decrease the probability of dairy products choosing and age, education and attention to sport variables increase its probability. Marketing mix variables (p4) were also considered as factors influencing the selection of dairy products. Marketing managers, especially in dairy market by using of these results can improve their products based on consumers tend and preferences.

According to the viewpoint of institutionalist economists, by moving in the direction of improving the quality of governance, inclusive economic and political institutions are formed, leading to sustainable growth of various sectors of the economy and ultimately development. One of these key sectors is agriculture and food markets. Accordingly, the aim of this study is to investigate the effect of good governance quality on agricultural sector growth in selected developing countries during the period 1996-2016 by using the Bayesian Vector Autoregressive (BVAR). The study area includes the countries of the Economic Cooperation Organization (ECO), the Shanghai Cooperation Organization, the South American Common Market (Mercosur), the Economic Community of West African States (ECOWAS). Studies have shown that the growth of the agricultural sector is generally affected by good governance indicators in the long run and with varying intensity. In Iran, government effectiveness and quality of regulation are the most influential indicators of governance, and political stability and lack of violence, corruption control, rule of law, and critique and accountability are at a later stage.

Increasing the price of energy carriers, due to their contribution to production costs, directly affects the production price index of different sectors of the economy and reduces the welfare of the society. The rising trend of global prices for energy and food suppliers in recent years has made economists interested in examining the relationship between these two variables. In this study the effects of energy prices on the food price index was investigated by using of Atou-Regressive Distributed Lag model at the years of 1980-2016. Also, the error correction term, ECM (-1) was estimated in the estimated model of 0. 76, which indicates that in e The results showed that in the long run, with the rise in the price of energy carriers and because of the manufacturer's alignment with the rise in the price of energy carriers and the effect of other sub-sectors that have been affected by this rise in prices, and they affect the amount of production, the price of food increases dramatically.

Based on Dutch disease, it is predicted that due to increased oil incomes, its consequences may be reflected in all other economic sectors including traditional sector. The aim of this study is to examine the impact of oil price fluctuations on some variables in agricultural sector. Data used for the study include production, exports, and pistachio and dates prices during years 1994-2014. data concerning agricultural sector was collected from website of agricultural – jihad ministry and Iran’ s statistical centre as well as Central Bank site during years 1994-2014. As results of two ARIMAX and neural network models showed that, the order of impact intensity of oil price fluctuations is the same on each variable in both models. But in neural network, the direction of impact of oil price fluctuations on variables has taken place more precisely so that a reverse relationship between oil price and two variables of production and exports which have led to Dutch disease phenomenon is clearly shown. Therefore, the neural network model is more accurate than the Arimax model.

Bank diversification is one of the important results of the globalization of the banking industry and the creation of competition in this industry. This paper empirically examines the impact of diversification on the valuation of banks in the Iranian stock market. Therefore, the relationship between banking diversification and valuation in the stock market with the data of accepted banks in Tehran Stock Exchange during the period of 2010-2016 is considered. In this paper, Tobin Q is used for the stock market valuation index and the share of non interest earnings for the Bank's diversification index. The results of data analysis using the data panel method show that banking diversification has a negative effect on the banks' valuation in the stock market; thus, the diversification of the bank due to entering of banks to new business, creates potential risks and reduces the bank's valuation in the stock market. Hence, the policy of diversification in the agricultural bank should be taken in such a way that it does not expose the agricultural bank to greater risk.

The aim of this study was to calculate inputs' productivity and production elasticity in industrial farms in Chaharmahal and Bakhtaran province. The random sampling method is simple and the sample size is determined based on the Cochran formula of 86 dairy farms. Data were collected using a questionnaire and a census questionnaire. MIDAS regression model is used to analyze the data. To measure the traction and productivity of production institutions, the transcendental production function, as well as the fit of the model and the variables are meaningful, were used, respectively, from the F and t statistics. The results of this study indicate that the final productivity of the last breeder, labor force, drug and health services and consumption of concentrate in the tested dairy farms is positive, but the final consumption of fuel and fodder and crude oil is negative. Also, for 26. 4% of dairy farms, the total factor productivity is less than one, and these dairy farms are at a disadvantage.

Economic of medium-sized businesses is one of the potential areas for increasing employment, especially for the vulnerable. This study aimed to identify the main dimensions and components of economic empowerment of clients supported by Imam Khomeini Relief Committee. The research method was survey and the data were collected through reference to existing documents and questionnaires. The sample size is 384 designs based on Cochran model. Research questions have also been inferentially analyzed using the structural equation method. The conceptual model of the research is presented by examining the existing models of empowerment and economic priorities in the field of agriculture and the usual methods in the process of empowerment and employment. The main dimensions of the model include preparation and preparation for project implementation, job creation, job stabilization, self-sufficiency and exit support, all path coefficients are significant and acceptable variance explained and structures are above 0. 5. Is. Therefore, the model variables have a predictive capability of over 30%