مجله اقتصاد و توسعه کشاورزی
سال سی و یکم شماره 3 (پیاپی 36، پاییز 1396)

There is a strong need to influence both speed and direction of innovation and technological change in agriculture. An Agricultural Technological Innovation System (ATIS) is a collaborative arrangement bringing together several organizations working toward innovational-technological, managerial, organizational, and institutional change in agriculture. The purpose of this study was to identify and explain functional implicationsaffecting the development of the agricultural technological innovation system.
This research paradigm was in the form of exploratory phase mixed method with qualitative to quantitative data connection approach. The statistical sample of the qualitative section, consists key members of the nanotechnology and biotechnology committees of Ministry of Agriculture-jihad (35 people) which 12 persons were interviewed by using purposive sampling technique, and quantitative section of research consists the members of nanotechnology and biotechnology of agricultural sector committees (117 people), which were all enumerated. To achieve the reliability and validity of the qualitative section of research three-dimensional vision technique wasused. And at the quantitative section, Cronbach's alpha test and opinions of subject experts were used. Cronbach’s alpha and ordinal theta were 0.78 and 0.89, respectively.
After the discovery of 30 subsidiary elements in form of 7 main factors achieved from a qualitative section of research using Atlas.ti5.2 software (research-development implications, Institutional-Structural implications, Legal-policy implications, business-marketing implications, innovational-technological implications, credit- financial implications, extensional - educational implications), finally, at the quantitative section of research using SPSS, the total variance explaining by the identified factors, measured. According to the results of exploratory factor analysis, research – developmentimplications, explained the 17.23% of the total variance the functional implications of agricultural technological innovation system development. This research finding was consistent with researchers results of Abdi and Hassanzadeh (2), Farshad et al. (10) and Sharifzadeh et al. (24). Legal - policy implicationswas the second factor with 15.58% of the explained variance the implications of the development of agricultural technological innovation system. This research finding was consistent with the results of researches were carried out by Temel et al.(25), Bergek et al. (4), Hekkert and Negro (12), Abdi and Hasanzadeh (2), Meigounpoory et al. (19), Farshadet al. (10) and Sharifzadeh et al. (24).Business -marketing implicationsis composed of variables likerisk decrease on investment in agricultural businesses, Price balance of agricultural products (increase or decrease cost of production because of the lack of proper cultivation, increase cost of production inputs and tensions in the global rate of products) and scientific management in the agricultural production farmlands with 12.32 percent of explained variance the implications of agricultural technological innovation development as the third important factor was classified. This section of findings was in agreement with research’s results of Temel et al. (25), Cristina and Patarapong (8), Negro et al. (21), Momeni and Alizadeh (20), Abdi and Hasanzadeh (2) and Meigounpoory et al. (19). Institutional - structural implications werethe fourth factor of the implications of development of agricultural technological innovation, which explained 10.68 percent of total variance. This finding of the study was consistent with the results of research Cristina and Patarapong (8), Negro et al. (21), Momeni and Alizadeh (20), Meigounpoory et al. (19), Farshad et al. (10), Sharifzadeh et al.(24 ). Innovative - technical implications as the fifth factor of the implications of agricultural technological innovation development with important variables like proportionment between technological innovation and the needs of farmers demand and the predicate the impact of events on technological innovations such as climate change explained 8.21% of the total variance that this finding was consistent with research results of Farshad et al. (10), Sharifzadeh et al. (24).Extensional - educational implicationswereclassified as the last of the implications to technological innovation system for agricultural development that this finding matches with the results of Hekkert and Negro (21) and Momeni and Alizadeh (20).
The results showed that 7 of the mentioned factors, explained at about 78.35% of the total variance of functional implications affecting the development of agricultural technological innovation system. To verify the structure validity of a questionnaire and fitness of model to measurement the functional implicationsimportance of agricultural technological innovation system development, collected data analyzed by the software of LISRELwin8.8 through confirmatory factor analysis that the results of various fitness indicators showed that the model is based on an acceptable level (df/x2=1.785, Goodness of Fit Index=0.93, Adjusted Goodness of Fit Index=0.96, Comparative Fit Index=0.93, Incremental Fit Index=0.95, Root Mean square Residual=0.06, Root Mean Square Error of Approximation=0.056).

Thepoultry industry, as sub-sectors of the agricultural sector,isone of the economic activities as considered risky and play a significant role in the public life of our community. The poultry industryin Iran has a bilateral relationship with global markets because on the one hand is exporters of agriculture production and on the other hand is a major importer of inputs such a cornandsoybean. So in terms of high transactions volume, poultry industriesare influenced by international prices and volatilities. Crude oil is one of the most important commodities in the global economyand in Iran has a comparative advantage that is seen as a strategic resource. A significant portion of Iran's revenue is from oil exports account and crude oil price. Therefore, oil prices in the world is an important factor that affecting the ability of our import volume. Recent observations show that the volatility and uncertainty in oil prices are transmitted through exchange rate (USD America) to real economic markets, other markets, the exchanges and the domestic agricultural and food products markets. This articleseems clearly impressive after Iraq-USA war in 2002 and the Global Financial Crisis in 2005. So in this paper, we try to analysis correlation between oil prices, exchange rates and the price of poultry inputs for the two periods, before the Global Financial Crisis and Iraq-USA war (1995-2004) and after that (2005-2014).
Material and Theperiods ofstudy are pre and after the Iraq-USA war and the Global Financial Crisis. Our monthly data collected from the Central Bank of Iran, Animal Support Company since 1995 to 2014. For the purpose of this paper, we used Vine Copula-MGARCH approaches. Before everything at first, we controlled the stationary and seasonal unitroots behavior in data with ADF,KPSS and HEGY stationary and seasonal tests.After that for analysis the correlation of prices, we used MGARCH models for modeling volatilities and collecting the residual of equations. Because of the limitation in linear correlation coefficients and the advantages of copulas for modeling and analysis correlation, we used copula approaches for this sector. At first, we modeledvolatilities with kind of MGARCH models such as CCC and DCC GARCHes and after that for collection pure residuals we must eliminate the past effect of each variable or in other means we can tell, using ARMA with MGARCHmodel can give us residuals that have not any effect of past behaviors in variables.
The results of the ADF unit-root test has indicatedthat all variables are not stationary and accumulated from the first stages. Similarly, the KPSS unit root test has shownsuch as ADF test results. Based on these tests our variables are not stationary and in two periods of study and the first stage of a difference,they are accumulated. Seasonal unit root test or "HEGY test" results also showed there arenot seasonal behaviors in two periods of variables. After these tests for modeling volatilities at first, we needed to detect ARCH behaviors in variables. Because of that, we controlled ARCH effect behaviors in variables and for this aim, we use an ARCH-LM test. Detecting ARCH behaviors help us to use the kind of MGARCH models for modeling volatilities. Our results indicate that CCC and DCC models with ARMA model have flexibility for modeling. So after that examination, we have collected the residuals of equations and collected the residuals of each equationin ARIMA-CCC-MGARCH model. We calculated the correlation of oil price, exchange rate and input prices with kind of Vine-Copulas. Results of R-Vine, C-Vine and D-Vine models indicated that the correlation between oil price and exchange rate are different in two periods, as the positive correlation of oil and exchange rate, in the first period, change to a negative correlation in the second periods. Correlation of oil price and input pricesin second time are more than beforethe crisis. Clarke and Voung tests for choosing Vine models indicate that R-Vine models for after and before period are the best.
Based on R-Vine models our results indicated that correlation between oil price and input prices are more than before the crisis and this is not a suitable situation for Iran's industries. At last, we offer that, using oil incomes forincreased infrastructures of input productions it may be better than importing inputs.

Attention tothe firmssustainability dimensions including social, environmental, and economic responsibilities haveincreased due to their unsustainable business models. Accordingly, studies have shown that innovation is a key to achieve sustainability dimensions and applying Innovation System (IS) approach has recommended as an assessment tool for sustainable innovation. The purpose of this system is to decrease the pressure on the environment and public resources. It also helps policymakers determine the processes and components of the system which intervention makes positive changes to them. Hence, in the face of challenges such as resources limitation, climate change effects, and increasing population in the dairy industry, the importance of innovation has been raised for competitiveness and economic, environmental, and social promotion. So that, it leads to the sustainable, ethical, accessible, safe, and nutritious productions. In this case, innovation with the aim of profitability and in a sustainable and environmental manner is one of the most important challenges facing the dairy industry. Sincethe innovation system is an effective and continuous motivator for sustainable technological innovation and the institutional environment has a great influence on technological innovation; current study has outlined the institution-sustainability matrix of the Iranian dairy industry innovation system because some studies show that this industry has been unstable in recent years (for instance: lack of attention into the regional differences in the policy-making of dairy industry and heterogeneity and insufficient development in line with indices of dairy industry development).
In terms of research goals, we applied a qualitative-exploratory study. In-depth and semi-structured interviews (with dairy key informants (n=26) and subject-matter specialists (n=20)) also helped in the data collectionstage. In this regard, the snowball sampling technique supported this research in identification of the individual samples that were in the bestposition in the dairy industry in terms of organizational status, management experiences, technical knowledge and executive aspect of this industry. Content analysis technique and Atlas. This software (for coding and classification of the concepts) wasused in order to attain research goals. In this way, we attempted to answer two research questions which include:1. What are the most important recent innovations of the Iranian dairy industry IS to achieve economic, social, and environmental sustainability?
2. How are the sustainability type and situation of IS institutions which have contributed tothese innovations presentation?
Therefore, the frequencies mentioned for each sustainable innovation (including economic, social, and environmental dimensions) were calculated. Then, institutional actors of dairy innovation system and their involved subsystems which played a role in these innovations network were determined and their sustainability matrix was drawn to show the status of each institution in terms of its relation to the sustainability dimensions. Respondents explained also toward their attitude related to the sustainability or instability of these institutions.
Our findings explain that innovations ineconomic dimension are at maximum (25 sub-categories) while social (14 sub-categories), and environmental (7 sub-categories) innovations are in the lower situation. This issue represents the fact that despite the emphasis on triple dimensions of sustainable innovations in recent years, and especially in the international community; they havent found their original position in the Iranian dairy industry. According to the plentiful and negative impacts of dairy industry waste tothe environment, pay attention to the environmental innovation codes, in comparison with the other codes, has not had much importance in this industry. Although environmental innovations havethe lowest frequency, they have a great importance in promoting the sustainability of the Iranian dairy industry so that, the two first ranks of this industry recent innovations are allocated to the environmental type.Our analysis also shows that institutional sustainability of the Iranian dairy industry IS is much less than the expected situation and responses indicated that there are a variety of instabilities in the macro and micro institutions/organizations of this industry.
According to the role of systemic and sustainable innovation toward achieving sustainability goals, innovation system policy making of an Iranian dairy industry requires a comprehensive view to all dimensions of sustainability, including economic, social, and environmental. Additionally, instabilities have been considered in terms of social and economic dimensions, and environmental instability of the system less mentioned. So, environmental sustainability is a newer debate that should be seriously participated in the innovation policy and sustainability of the dairy industry. Finally, this study has suggested that institutional sustainability dimensions and capacities call for more investigation in the future researchers.

Measuring changes in economic welfare have been known as one of the practical economic issues. So that, this study aimed to calculate the welfare changes resulting from the change in the price of rice in Mazandaran province and is the first study that done using the food groups’ details and household’s data for estimates demand functionin the country. For this purpose, welfare microeconomic theory and compensating variation (CV) criteria and household income and expenditure data of Mazandaran province in 2014 wereused.
Compensating variation represents the net income that the household must be given to restore themto the utility level they were at before the price change. It is negative after a price increasebecause it is expressed as a central authority expenditure to restore the household to thepre-price change utility level. Estimation of compensating variation needtheestimation of households demand system. In this paper, the parameters of the demand system are estimated by applying nonlinear regression to the system of eight share equations. Parameter estimates provided a clearer understanding of household food consumption behavior in 2014, summarized through income and price elasticity. Parameterestimates provide a theoretically consistent model of household food demand that can be usedto evaluate the welfare implications of food price increases.
Estimates of income elasticity of demand for urban and rural households are presented in Table 4. The income elasticity revealsthat none of the goods are inferior, while the rice and meat are a luxury for urban households. Other groups such as cereals, dairy, oils and fats, fruits and vegetables, other foods and beverages are also essential commodities for urban households. Rice, meat and fruit and vegetable are the luxury goods for rural households, too. The income elasticity of fruits and vegetables, and other foods are close toone for urban households, demonstrating that welfare analysis of price changes need to account for shifts in demandcaused by the income effect of the changes. The elasticityindicate that the income effectcould be large for these commodity groups. Further evidence about these effects will be provided by the compensatedprice elasticity.Compensated own price elasticity, which measure pure substitution effects, are reportedinTable 5 for urban households and Table 6 for rural households. The elasticity of demand for a beverage is large for all householdsand the elasticity of demand forrice is small for all households. These results indicate that households reduce beverage consumption significantly more than rice consumption in response to price increases. Next, consider a 25, 50 and 198 percent increase in the rice price. This price change causes an increase in household expenditure for both urban and rural households by compensating variation. Increasing in households expenditure for rural households has been greater than urban households. According to the results, urban households have seen 0.38 percent increase in their expenditure by 25 percent increase in rice price. 50 and 198 percent Rice price increasing, increase 1.13 and 19.98 percent of urban expenditure accordingly. Rural expenditure increased 1.31, 3.63 and 52.57 percent by increasing 25, 50 and 198 percent in rice price accordingly. Moreover, the comparison between reductions in household welfare in different income groups has shown that household welfare has declined less when levels of income increased.
This study aimed to calculate the welfare changes resulting from the change in the price of rice in Mazandaran province. For this purpose, welfare microeconomic theory and compensating variation (CV) criteria and household income and expenditure data of Mazandaran province in 2014 wereused. Based on the results, with rising rice prices, household welfare of Mazandaran province has fallen. The welfare of rural households has fallen more than the welfare of urban households. The comparison between reductions in household welfare in different income groups has shown that household welfare has declined less when levels of income increased. Therefore, it is necessary to maintain the household welfare of provinces when the rice price rises and support policies must be adopted.

Risk and uncertainty are the main characteristics of agriculture sector and related activities. Risk and uncertainty can affect farmers decision making on output determination, input employment and technology selection. Analysis and understanding the behavior of farmers in risky environment leads to better prediction and evaluation of the result of policies in agriculture sector and therefore helps policymakers to select suitable policies for improving the status of inputs employment in this sector. The aim of this paper is to analyzethe reaction of farmers to the risk of demand uncertainty and its effect on inputs employment in Iran agriculture sector.
Data for variables included in the estimated econometric model in this paper- like interest rate, wage index, number of employees in the agricultural sector, output value, and capital stock weregathered from Iran central bank data center during the period 1974-2012. The augmented dickey fuller test is used to investigate the stationary of variables included in the econometric models of the study. In order to analysis the reaction of farmers to the risk of demand uncertainty and its effect on inputs employment in agriculture sector, two steps were taken as follows: at the first step, a demand prediction model is estimated using a first-order autoregressive process and demand uncertainty in agriculture sector is calculated by the residual of the estimated model. At the second step, the effect of demand uncertainty on capital and labor intensity is tested using Johnson cointegration approach. Schwarz and Quinn's criteria were used to determine the optimal lag numbersin vector autoregressive model. The number of co-integration vectors weredetermined using maximum eigenvalue and trace tests.
To analyzethe behavior of farmers in risky situations in terms of input employment, five possibilities or five scenarios were taken into account. First scenario: if the farmer is risk lover, labor is going to be a constant and capital increase. If, however, the farmer is risk-averse, labor is going to be constant and capital decreases. Second scenario: if farmer is risk lover, labor decreases and capital is going to be constant. Though in the case, that farmer is risk-averse, labor increases and capital is going to be constant. Third scenario: if the farmer is risk lover, labor decreases and capital increases. However, in the case, that farmer is risk-averse, labor increases and capital decreases. Fourth scenario: if the farmer is risk lover, the rate of increasein labor is less than the rate of increasein capital. In the case of risk adverse farmer, the rate of increasein labor is more than the rate of increasein capital. Fifth scenario: if farmer is risk lover, the rate of decreasing in labor is more than the rate of increasein capital. In the case of risk-averse farmer, the rate of decreasing in labor is less than the rate of increasein capital. Cointegrationtest based on eigenvalue and trace statistics in this paper confirm the presence of almost two cointegration vectors between the model variables. According to the estimated coefficients of the restricted vectors, there is a negative relationship between demand uncertainty and capital-labor ratioin long run. The coefficient of demand uncertainty in restricted vector is estimated around -0.33. This shows that as demand uncertainty increase 1%, capital- labor decrease 0.33%. These findings reveal that the firms in the agriculture sector are risk-averse and have a negative response todemand uncertainty. Separately estimation of labor and capital demand function indicates that the coefficient of demand uncertainty is respectively obtained around (-0. 14) and (-0.05). In the other words, the negative effect of demand uncertainty on capital formation is larger than the negative effect of demand uncertainty on labor employment. As demand uncertainty goes up in this sector, both labor force and capital decrease. The rate of decreasing in capital, however, is more than the rate of decreasing in labor force in the agricultural sector.
With increasing demand uncertainty in the agricultural sector, labor-intensity of production process goes up and farmers move toward using labor intensive process and technologies. It is inferred that higher level of demand uncertainty leads to debilitatinginvestment process and retard the trend of capital formation and technology development in the agricultural sector. The implication of such conclusion is that as demand uncertainty increases, capital intensity decreases in agriculture sector and production firms tend to use more labor-intensive technologies and process. This reveals the necessity of serious attention to investment and capital formation issue in this sector. Regarding the intensifying the risky environment in this sector, the government is recommended to use suitable promotion and motivation mechanisms to enhance farmers intensively for investment and output improvement.

Uncertainty existence in farmers crop production pulsed on important and necessity of science of risk management in the agricultural sector. The new risk management selects the best tools and techniques to minimize risks and consequences of decisions. Furthermore, determining the nature of the risk of crops yield can provide useful information about how to manage the risk of the agricultural sector. One of the effects of climate change is caused damage in the agricultural sector. Dependence of crops to climate change is caused that climate factors have a determinative role in the occurrence of crops damaged. Performed studies on the economic effects of climate change have shown that climate change has a significant impact on agricultural yield and its production risk. Moreover, climate change influences crop yield and the risk of crop yield. Although several studies have been carried out about the impact of climate change on crops yield in Iran, the effect of climate change on crops yield risk is infrequently considered. Therefore, this article tries to offer a new way for calculating the risk of crops yield using of CVaR in the period 2017-2047 in the zayanderud agricultural system. The innovation of this study can be stated as follows:1) This study used of Value at Risk index, as one of the most important indicators of risk measuring, to measure the risk of crops yield, 2) For calculating of Value at Risk index, different studies are used from a famous probability distribution such as normal distribution, historical data or Monte-Carlo simulation, while in this study tried to calculate VaR index based on the forecasted scenarios of crops yield, and 3) In this study, in order to produce future scenarios of crops yield is used from ANN-PSO combined method for forecasting crops yield.
The method of this study includes the following steps:1) The production of possible scenarios of temperature and precipitation using of AOGCM models: Today, one of the best tools for the production of climate scenarios is Atmosphere-Ocean General Circulation Models (AOGCM).But the main problem in the use of the output of the AOGCM models is the large spatial scale of their computational cells toward the area under study. LARS-WG model is also one of the most famous models to small scale for outputs of AOGCM models. In this study uncertainty related to AOGCM models, is used of for scenarios of all AOGCM models(including A1B, B1 and A2).
2)The production of scenarios of selected crop yield and available water in the period 2017-2047: The production of scenarios of selected crops yield and available water is performed using of combinedmethod of ANN-PSO.To combine neural network with particles warm optimization algorithm, from particles warm optimization algorithm is instead of training the neural network using gradient-based algorithms.
3) Measuring risk of crops yield using of VaR and CVaR indexes: VaR index is one of the most important criteria to measure downside risk that it determines the maximum amount of expected losses of a variable for a certain time period and specific confidence level. In this study (according to the non-normal distribution of crops yield scenarios) is used on the historical simulation approach.
In the first phase of research methodology, for producing of climate scenarios from daily available stats related to weather stations of Isfahan, Kabuotarabad, Kuohrang, and Daranwere used. Validation results of LARS-WG model showed that this model is well able to simulate changes of climate parameters. Eventually, 44 scenarios of the maximum temperature, minimum temperature and rainfall wereproduced in each studied stations and for each year. The results of the network design using trial and error methods revealed the best forecast combination model obtained with 3 and6 neurons in the input layer and hidden layer of neural network and assuming the initial population of 200, in PSO algorithm. Results of this step showed that ANN-PSO model is well able to forecast crops yield (wheat, barley, maize and alfalfa) and available water. Furthermore, calculating VaR and CvaR criterain confidence level %95 and for future period of 2017-2047, showed that the values of these two criterions for wheat, barley, maize and alfalfa were equal to (4240, 4205), (4062, 4057), (49061,48480) and (10875,10743) kg/ha. The comparison of the values of these two criterions with the values of last period also showed that for all selected crops, VaR and CvaR criterions is bigger in future period toward last period.
The new offered method can calculate the risk of crops yield due to climate change. The more accurate measuring of risk using of new methods such as CVaR can be suitable guidance for policy man to better management of production risk of crops.

The persistence of rice imports while domestic production shows an increase over time has resulted in forming this hypothesis among rice producers in Iran that import of the rice is unjustified. This study is seeking to evaluate this hypothesis.
The relationship between the import of rice and the quantities of domestic production as well as the other theoretically possible factors explaining import over period 1981-2014, including domestic/world market relative price, exchange rate, domestic income, population, tariff rate are investigated using exploratory data analysis (EDA) approach. In addition, the relationship between import and these factors is quantified using ECM econometric methodology. Furthermore, the VAR framework is utilized to specify causality between the above-mentioned variables and quantities of rice imported.
Results from EDA revealed that there is not a clear relationship between the quantities of domestically produced rice and the imported quantities, while such a relationship is shown between per capita crude oil revenue and the quantities of rice imported. In addition, the quantities of imported rice are not related to the domestic/world price ratio. Moreover, EDA shows a decreasing trend in real domestic price of rice. Results from EDA are supported by the co-integration and ECM methodology. The Granger causality between per capita crude oil revenue and the quantities of rice imported which was tested within VAR framework indicates that there is a one way causality from the first variable to the second one. Furthermore, the estimated ECM shows that the effect of per capita crude oil revenue on quantities of imported rice is higher in log relative to the short run. A one-dollar increase in per capita crude oil revenue results in 360 metric tons import of rice in the long run while the same one dollar increase will result in 290 metric tons import of rice in the short run. These results support the hypothesis that import of the rice is an unstructured import which may hurt domestic rice producers. Finally, calculation of the intra industry trade index indicates that intra-industry trade theory cannot explain the increasing trend of rice import in Iran.
Given that the per capita oil revenue is the main determinant of the rice imports, besides the fact that EDA shows a decreasing trend in real domestic price (terms of trade) of rice and reaching below one led to the conclusion that the unjustified import hypothesis is confirmed in Iran. Accordingly, a revise in rice import is suggested. Specifically, decoupling rice import from crude oil revenues and limiting import, using price elasticity information, to keep an increase in the price of this commodity equivalent to the CPI growth rate for domestic producers is suggested.