مجله اقتصاد و توسعه کشاورزی
سال سی‌ام شماره 2 (پیاپی 31، تابستان 1395)

Poultry is an important commodity for household consumption. In recent years, price fluctuation for this commodity has caused an uncertain condition for consumers and poultry prices over the past two years has changed a lot. This has caused many changes and uncertainty in a purchase decision. Analysis of changes and volatility modeling can be a great help to predict the poultry prices and great facilities in creating appropriate policies in future. The prices of staples such as poultry consumption basket is highly variable because much of the protein is necessary for daily energy are supplied in this way to households. So when the price of chicken which has been changed over the past two years and has always been in the press and media attention, has been selected in this study. Fluctuations in price of chicken have caused a surge in consumer expectations and contributed in volatility of chicken price.
In this study ARCH models have been used for daily price of poultry of Iran’s market and this was investigated for2012-13and2013-14.BecauseARCH models can model the impact of heterogeneous variance over time in time series data then the variance of time series, which is limited in time, has no time limit. Many time series are more complex than a linear patterns, thus, non-linear models are of particular importance in Economic Sciences and Econometrics. Accordingly, Engle presented that ARCH model can model the heterogeneous variance components of the error term. That is a disturbing element and modeling can help to examine and explore the relationship between the components can be found disturbing. Basically, these models fit the data to a cluster and periodic oscillations with high volatility and low volatility associated with the period. In this study, we used several different models like ARCH, GARCH, IGARCH, and TGARCH. The distribution of the error term of the model also followt-student distribution. This study shows that the heterogeneous variance exists in error term and indicated by LM-test.
Results showed that stationary test of the poultry price has a unit root and is stationary with one lag difference, and thus the price of poultry was used in the study by one lag difference. Main results showed that ARCH is the best model for fluctuation prediction. Moreover, news has asymmetric effect on poultry price fluctuation and good news has a stronger effect on poultry price fluctuation than bad news and leverage effect doesnot existin poultry price. Moreover current fluctuation does not transmit to future. One of the main assumptions of time series models is constant variance in estimated coefficients. If this assumption has not been, the estimated coefficients for the correlation between the serial data would be biased and results in wrong interpretation. The results showed that ARCH effects existed in error terms of poultry price and so the ARCH family with student t distribution should be used. Normality test of error term and exam of heterogeneous variance needed and lack of attention to its cause false conclusion. Result showed that ARCH models have good predictive power and ARMA models are less efficient than ARCH models. It shows that non-linear predictions are better than linear prediction. According to the results that student distribution should be used as target distribution in estimated patterns.
Huge need for poultry, require the creation of infrastructure to response to demands. Results showed that change in poultry price volatility over time, may intensifies at anytime. The asymmetric effect of good and bad news in poultry price leading to consumer's reaction. The good news had significant effects on the poultry market and created positive change in the poultry price, but the bad news did not result insignificant effects. In fact, because the poultry product in the household portfolio is essential, it should not fluctuate. When poultry imports decline, as well as the dependence of the poultry price to world prices declines therefore lower fluctuations of world prices are transmitted to domestic prices. Expanding poultry farms, cold storage and balance the raw materials market, would lead to less fluctuations in the poultry price industry and can be effective.

Continuous fluctuations in the prices of agricultural commodities have a significant effect on the situation of countries, especially developing countries in the world. In the short term, the impact of price shocks on imports of agricultural commodities and balance of payments and foreign exchange reserves is considerable in developing countries and it has negative effects on social security programs in these countries. In the long- run, these fluctuation effects on specialization of resources in production and development in agricultural sector delays. If price volatility to rise out of the market principals, increases in commodity prices in special times and for special products translates to bubble pick. Friedman’s theory on efficient markets underlines that, given rational behavior and rational expectations, the price of an asset will always reflect market fundamentals. Therefore, the main question is whether the forces of demand and supply, or explosive behavior (bubbles) generate price volatility mutations. If price volatility is due to market demand and supply conditions, a series of policies, including reducing demand or increase in supply can be effective to reduce volatility. However, if price fluctuations are for reasons beyond market factors, changes in supply or demand fluctuations may be more severe and worsen the situation of the producer and the consumer. Hence, it is important, especially for policy makers to determine that whether the market factors or factors outside of market cause fluctuations in the market.
Material and In the present study, by applying the generalized version of the sup augmented Dickey-Fuller (GSADF) test, bubble behavior identified. For this test, time series data from 2002 to 2013 that collected on a monthly basis, was used. Study variables include the chicken and beef prices. Since in the internal investigations, discussing for speculative bubbles in commodities in agricultural products not addressed, it is important to address this issue. These products, select from this perspective that these goods regularly have price volatility over the past years (mainly increase) and therefore, faced with mass and low production. As a result, consumers sometimes have not access to a sufficient quantity of these goods or the supply too much in some periods and vice versa.
Our findings suggest that food commodities exhibited bubble behavior during mentioned years. These mutations of explosive behavior are, in general, short-lived. Assessing trends in the price of chicken show those four cycles of bubble behavior have seen for this product. In the meat market, there are important reasons and factors behind these fluctuations. Fluctuations in the price of inputs, especially after subside targeting in Iran in 2010, increasing the general level of prices and inflation, consumer cross-sectional volatility (changes in demand) and low ability to store the meat for a long time, not full replacement of fresh poultry meat to freeze and finally and the most important factor the elimination of subsidies for agricultural inputs in recent years are the important factors that affect price and production volatility in recent years in Iran meat industry. For the price of beef, just two short-term and long-term bubbles appear in the studied period. Rising meat prices due to the rising cost of inputs such as animal fodder and a variety of animal feed, animal drugs, high cost of packaging, transportation problems, related to subsidize livestock inputs, labor and the total inflation are the most important factors for price fluctuations in beef meat.It should be noted that price fluctuations in the price of chicken meat are more and more severe than the price of red meat. The reason for more fluctuations in the price of chicken meat is that per capita consumption of chicken meat is larger than red meat and the level of price for this meat is lower than red meat. Therefore, customers can buy it even when the price of chicken meat increases from its mean. The price of red meat is about 4 to 5 times of chicken meat and therefore volatility in the price of red meat can considerably affect on real income and consumption by consumers.
Considering the results and given the importance and influence of external factors on the price volatility of food commodities, we propose the strategies for reducing price volatility mutations and thus reduce the bubble behavior, such that it can be mentioned to apply the appropriate policy of import and export to control the market fluctuations and prices. It is recommended that the daily and monthly prices of major agricultural products such as meat, scrutinized and assessed carefully in order to determine the real parts of price fluctuations for better policy considerations.

Stock shortage is one of the development impasses in developing countries and trough it the agriculture sector has faced with the most limitation. The share of Iran’s agricultural sector from total investments after the Islamic revolution (1979) has been just 5.5 percent. This fact causes low efficiency in Iran’s agriculture sector. For instance per each 1 cubic meter of water in Iran’s agriculture sector, less that 1 kilogram dry food produced and each Iranian farmer achieves less annual income and has less mechanization in comparison with similar countries in Iran’s 1404 perspective document. Therefore, it is clear that increasing investment in agriculture sector, optimize the budget allocation for this sector is mandatory however has not been adequately and scientifically revised until now. Thus, in this research optimum budget allocation of Iran- Khorasan Razavi province agriculture sector was modeled.
In order to model the optimum budget allocation of Khorasan Razavi province’s agriculture sector at first optimum budget allocation between agriculture programs was modeled with compounding three indexes: 1. Analyzing the priorities of Khorasan Razavi province’s agriculture sector experts with the application of Analytical Hierarchy Process (AHP), 2. The average share of agriculture sector programs from 4th country’s development program for Khorasan Razavi province’s agriculture sector, and 3.The average share of agriculture sector programs from 5th country’s development program for Khorasan Razavi province’s agriculture sector. Then, using Delphi technique potential indexes of each program was determined. After that, determined potential indexes were weighted using Analytical Hierarchy Process (AHP) and finally, using numerical taxonomy model to optimize allocation of the program’s budget between cities based on two scenarios. Required data, also was gathered from the budget and planning office of Khorasan Razavi’s Jahad Keshavarzi organization during 2006-2015. They were collected through distributed binary comparison questionnaires related to AHP model between Khorasan Razavi’s agricultural experts in 2015 and distributed questionnaires related to Delphi technique between Khorasan Razavi’s agricultural experts in 2015. Indeed, Super decision and Taxonomy software were applied to analyze the gathered data.
Results of budget allocation of Khorasan Razavi province’s agriculture sector using three mentioned indexes showed that between 8 programs, P1 and P6 have the most and least share, respectively. The results of the Delphi technique for determining potential indexes of between cities budget allocation of agriculture sector programs indicated that totally there are 62 indexes. Findings of between cities budget allocation of agriculture sector programs showed that for budget allocation of P1 based on 1 and 2 scenarios, Kalat and Davarzan cities have the most and least share, respectively and vice versa. For budget allocation of P2 based on 1 and 2 scenarios, Bardaskan and Kalat cities have the most and least share, respectively and vice versa. For budget allocation of P3 based on 1 and 2 scenarios, Mashhad and Joghatai cities have the most and least share, respectively and vice versa. For budget allocation of P4 based on 1 and 2 scenarios, Jovein and Torghabe Shandiz cities have the most and least share, respectively and vice versa. For budget allocation of P5 based on 1 and 2 scenarios, Chenaran and Neishabour cities have the most and least share, respectively and vice versa. For budget allocation of P6 based on 1 and 2 scenarios, Mashhad and Khoushab cities have the most and least share, respectively and vice versa. For budget allocation of P7 based on 1 and 2 scenarios, Neishabour and Saleh Abad cities have the most and least share, respectively and vice versa. Finally, for budget allocation of P8 based on 1 and 2 scenarios, Neishabour and Khoushab cities have the most and least share, respectively and vice versa.
The study concludes that the agriculture sector budget of Khorasan Razavi Province’s has not been allocated optimally. Therefore, paying attention to this fact that agriculture sector budget allocation which carried out previously between various programs, have been provided different instructions for opposite ideas always caused to challenge between beneficiary groups. This study provided a scientific and comprehensive model for budget allocation of agriculture sector between programs and cities using agriculture experts, and can be suggested to governors and Jahad Keshavarzi organizations to apply the results.

Date is as one of the important items of the agricultural production in Iran as Iran's share of global production was 14.1% in 2012 and rank of production increased to second in the world too. In recent years, price uncertainty in date market has increased due to changes in government policies on date prices pattern, from a guaranteed buying pattern to negotiated price pattern. According to the importance of this industry in the country and issues that are always in marketing and market making of agricultural products in developing countries, This paper seeks to measure marketing margins in the industry due to the product's market power and price volatility, and to achieve this goal, the main idea of this paper based on the study Brorsen et al (11). This paper provides a conceptual and empirical framework for analyzing marketing margins in a date market facing output price uncertainty in Iran. Present study evaluated marketing margins into component reflecting the marginal cost of the processing industry, oligopoly price distortions, and an output risk component. The empirical finding is that, while marketing margin is about 33%, the coefficient for oligopoly is more than the coefficient for oligopsony. In the other words, there is an asymmetric monopoly power among buyers and sellers, Also the estimated coefficient for price risk based on exponential GARCH approach indicates, this factor would affect the marketing margin about 7 percent, if all other factors remain constant.

The growth of agricultural production and natural resources are from primary objectives of any political system, because this section has a vital role in providing food security. According to the production theories, production growth will come from two sources; more use of production factors within the framework of existing technologies and second, with using more advanced and more efficient production methods and effective use of production factors. In fact, the second one is tied to the concept of productivity. New and efficient technologies are the most important factor for productivity growth. Technology was enhanced through internal and external sources. External sources include spillover technology from developed countries into another country. Empirical evidence on the impact of spillover technology on productivity growth of indigenous producer is vague. One perspective proposes that FDI, technology transferred from developed countries has positive effects on developing countries and another perspective is against it. This paper explores the role of technological spillover on total factor productivity (TFP) growth in agricultural sectors of Iran uses time series data during 1971-2011.
In this study, Kendrick model was used to calculate total factor productivity. After calculating productivity, affective factors on it, were examined through ARDL model. The aim of this study is examination of technology spillover on the productivity of agricultural sectors. The degree of technology diffusion grows with increase in technology distance between the hosts and the foreign countries. The greater the technology distance, the more difficult it becomes for developing countries to boost independent innovation. To calculate the index of technology spillover, commercial partners should be considered that are more advanced in science and technology than Iran. For this purpose, commercial partners in this study are Group of Eight developed countries (D8) plus China. Technology spillover indexes are thus decomposed into two components: Imports of capital goods and foreign direct investment (FDI). Index of capital goods imports measures imports of capital goods from major commercial partners of Iran (D8 countries and china). Foreign direct investment (FDI) represents the share of foreign capital in agricultural sectors. Therefore, the empirical analysis of the technology spillover on the productivity of agricultural sectors is based on theatrical framework and ARDL model.
According to the results of the ARDL model, technological spillover effect on the TFP of agricultural sector, in long-term shows that technology spillover has a positive and significant effect on agricultural productivity from both channels of capital goods import and foreign direct investment (FDI). But in short-term spillover variable from a capital goods import channel is not significant. This indicates that absorption of the technology from imports of capital goods channel do not perform very well and probably low-tech and without affecting on TFP of agriculture sector is imported. But in long-term technology spillover from imports channels also had a positive impact on total factor productivity of the agricultural sector. In fact, high technologies in long-term can be absorbed through the imports and influenced on TFP. According to thesis study results, the estimated coefficient of error correction term is equal to 0.53.This means that in each period, 53 percent of imbalances of agricultural TFP will be resolved. The average speed of upward of adjustment reflects the fact that in Iran economy, deviations and imbalances have arisen in the agricultural sector TFP caused by technology spillovers shocks, move very fast towards long-run equilibrium.
The purpose of this paper is to advance the knowledge for a key question with evident implications for economic policy: What is the importance of international technology spillover transmitted through trade and FDI for the TFP growth in the agricultural sector of Iran? For this purpose, we have set out from the modeling initially based on a theoretical framework, which is modified by introducing two fundamental channels. Thus, we have included the capital goods imports and foreign direct investment as factors capable of influencing TFP, both directly and indirectly: improving the capacity of absorption of foreign technology. In fact, we have included an explicit measure of international technology spillover which combines the technological capacity of the rest of the country and the weight of the imports that are made from each one of them. The different specifications of the model are estimated using the ARDL method and the period is that from 1971 to 2011. The results achieved reveal, first, the existence of international technology spillover which have had a favorable impact on the TFP growth of the agricultural sector of Iran. Secondly, the paper also provides additional evidence that supports the role of imports and FDI as a channel of transmission of such spillover. This result therefore provided new evidence to the positive influence of FDI on productivity, suggesting, that the higher the technological capacity of the trading partners, the greater this influence will be. In addition, we obtained a very high relationship between FDI shares and the effect of technology spillovers on agricultural productivity.

Agricultural activities increasingly use water, fertilizers and pesticides, which may generate negative impacts on environment. Nowadays, nitrogen leaching from agricultural lands is a widespread global problem. Therefore, alternative land management practices such as nutrient management (rate, method and time of application), tillage operations (conservation and no-tillage), and irrigation management are routinely used to reduce non-point source pollution and improve water quality. In fact, a number of studies have illustrated the positive effects of best management practices (BMPs) on water and nutrient losses. The objective of this paper is to develop a bio-economic model and introducing the policy instrument for reducing nitrate from irrigation and drainage Dorudzan. We aim to identify ‘‘win–win’’ opportunities for improving farm profitability and reducing nitrate leaching.
An integrated biophysical-economic model was developed with five components. The first component is based on a process-based biophysical model (SWAT) that simulates the key processes of crop growth within the water and nitrogen cycles. The second component is a meta-model, which is used to link the biophysical model with the economic model. The meta-model employs regression techniques to replace the original simulation model and allow the application of simulation model results in an economic model of farm production. Crop yield is the key link between the two models. A quadratic function (Eq. 1), which allows estimation of the effect of increasing input levels and diminishing marginal returns, particularly when a wide range of inputs need to be incorporated, was used in this study.
(1)
Where, Y, W and N are crop yield, water and nitrogen fertilizer application, respectively.
The third component is a simple farm economic model, which is used to predict farmers’ response behavior under different policy scenarios, and to determine when and how much to irrigate and fertilize, and the farm gross margin that results (Eq. 2).
(2)
Where, GM, , , and C are the farm gross margin (rial/ha), water price (rial/m3), nitrogen fertilizer price (rial/kg), crop price (rial/kg) and other costs, respectively.
The fourth component is a nitrate leaching function which is a function of water and nitrogen fertilizer application (Eq. 3).
(3)
The fifth component is assessment of policy incentives. Regulators have at their disposal a portfolio of policy instruments (such as water pricing or tax on nitrogen fertilizer application) that can be used to influence nitrate leaching.
For running SWAT model, the data for estimating the wheat, barley, corn and rice yield production functions were generated by running the biophysical model hundreds of times with a different range of amount of water and nitrogen applications. The total number of scenarios of combinations of amounts of irrigation and nitrogen applications was about 96, 72, 88 and 84 for wheat, barley, corn and rice, respectively. Then, the yield production function was estimated with the multiple non-linear regression technique in the Eviews software. For model simulation, the economic model was formulated based on the crop production function obtained from the multiple non-linear regression analysis in the SWAT running stage and generate the farm gross margins. The effects of increasing water prices on nitrate leaching were simulated with the biophysical model.
The values of the coefficients from the multiple nonlinear regressions are shown in Table 1.
Table 1-Results from multiple non-linear regressions
Rice corn Barely Wheat Parameters
-6.011*** -11.335** -2.64*** 0.978***
(-6.252) (-2.075) (-4.073) (5.125)
0.0221** 0.047*** 0.0288*** 0.00098***
(8.17) (2.68) (7.00) (11.342)
-1.13*10-5*** -3.21*10-5*** -4.31*10-5*** -1.38*10-5***
(-7.726) (-2.478) (-6.657) (-12.985)
0.000158*** - 0.008*** 0.01***
(6.272) - (5.835) (31.407)
-3.23*10-6*** -4.88*10-6*** -3.24*10-5*** -1.22*10-5***
(-6.378) (-6.815) (-10.422) (-42.378)
- 9.15*10-6*** 2.19*10-5*** 5.89*10-6***
- (7.766) (5.563) (11.27)
0.99 0.98 0.955 0.994 R2
***,** significant in 1% and 5% , Numbers in parentheses ret-statistics
The results for water and nitrogen inputs, crop yield, farm gross margin and nitrate leaching under agronomic optimum, economic optimum and bio-economic optimum, also are shown in Table 2.
Table 2- Comparison between agronomic optimum, economic optimum and bio-economic optimum
Gross margin Nitrate leaching crop yield nitrogen fertilizer Irrigation* Scenario crop
(rial/ha) (kg/ha) (kg/ha) (kg/ha) (m3/ha)
26743460 56.81 5913.2 523 11721.5 Agronomic optimum wheat
26995120 49.83 5882.9 486.4 10727 economic optimum
26354880 37.99 5723.4 408.59 9824.5 bio-economic optimum
16314000 15.08 4203.5 262 10020 Agronomic optimum barely
16416560 14.71 4105.8 245.8 9637.75 economic optimum
16416560 14.71 4105.8 245.8 9637.75 bio-economic optimum
44180040 101.31 9009 750 20000 Agronomic optimum corn
44364700 89.57 8988 684 20000 economic optimum
40805580 37.9 8390 394 20000 bio-economic optimum
66876590 51.98 5020 244.58 24450 Agronomic optimum rice
67111950 42.43 5000 209.91 23918.14 economic optimum
67098590 38 4900 197.2 23918.14 bio-economic optimum
*irrigation efficiency 40%
The results show that there are win-win opportunities for improving farm profitability and reducing nitrate leaching by moving from current status to economic optimum. But from this point onwards, reduction in nitrate leaching is not achievable without profit penalties. In other word, to move from economic optimum to bio-economic optimum point, there is a trade-off relationship between farm profitability and groundwater quality protection. Also results of policy assessment showed that for wheat-water pricing and for corn and rice, because of high elasticity of yields of these crops to water application, tax on nitrogen application are cost effective policies.

Several techniques are used to evaluate decision making units in DMUs with a restricted multiplier. DEA is recognized as a methodology widely used to evaluate the relative efficiency of a set of decision-making units (DMUs) involved in a production process. This approach assumes that the input and output data of the different decision making units (DMUs) are measured with precision. Although DEA is a powerful tool to use measure efficiency, there are some restrictions that need to be considered. One important restriction involves the sensitivity of DEA to the specific data under analysis. In this paper, the linear robust optimization framework of Bertsimas and Sim is used to concentrate on the DEA with uncertain data to determine the efficiency of irrigated wheat farms in Neyshabur County.
This paper proposes a linear robust data envelopment analysis (LRDEA) model using imprecise data represented by an uncertainty set. The method is based on the robust optimization approach of Bertsimas and Sim to seek maximization of efficiency under uncertainty (as does the original DEA model). In this approach, it is possible to vary the degree of conservatism to allow for a decision maker to understand the tradeoff between a constraint’s protection and its efficiency. The method incorporates the degree of conservatism in the maximum probability bound for constraint violation. The most significant uncertainties for a DEA model are input and output data that arise from errors. Application of the proposed model (LRDEA) to the case study (Neishabour district irrigated wheat farms) demonstrates the reliability and flexibility of the model. Monte Carlo simulation was implemented to examine the quality of the LRDEA model 100 random numbers were generated for each input and output of DMUs.
In this section, a case study of Neishabour county irrigated wheat farms is presented to illustrate the use of the methodology in this proposal, which consists of 95 DMUs, one input and five outputs. For the input and output data uncertainty, ten given maximums of a constraint’s violation probability were considered with respect to nominal values: 10%, 20%, up to 100% (i.e. we used Γ = 0.10, 0.20, up to1.00). The results show that the Gamma value decreases as the probability of constraint violation increases. The LRDEA model result shows how efficiency declines as the level of conservatism of the solution increases, that is, as the constraint violation probability decreases. According to the method, if all Gammas equal 0, then robust and original DEA models are the same. The most of the difference between the mean of optimal and actual amount of inputs is related to the two inputs of pesticide and cultivation land in both of the DEA and RDEA models. Accordingly, holding participatory extension classes to train farmers to increase yield and optimal use of existing agricultural land with a cooperative of efficient farmers is recommended. Also, the extinction of integrated pest management (IPM) to increasing non-optimal use of pesticide in the study area is proposed. Monte Carlo simulation was implemented to examine the quality of the LRDEA model 100 random numbers were generated for each input and output of DMUs. In the simulation violation probabilities ranging from 0.1 to 1.0 (at a constant the level of ε), percentages of average conformity are quite high. . However, it declines very rapidly as P approaches 0.7.
Evaluating the performance of many activities by a traditional DEA approach requires a precise input and output data. However, input and output data in real-world problems are often imprecise or vague. To deal with imprecise data, this study uses a robust optimization approach as a way to quantify vague data in DEA models. It is shown that the Bertsimas and Sim approach can be a useful tool in DEA models without introducing additional complexity into the problem (we called linear robust data envelopment analysis (LRDEA)). A case study of Neishabour county irrigated wheat farms is presented to illustrate the reliability and flexibility of the proposed model. The problem was solved for a range of given uncertainty and constraint violation probability levels using the GAMS software. This example suggests that our approach identifies the tradeoff between levels of conservatism and efficiency. As a result, efficiency decreases as the constraint violation probability increased. Additionally the LRDEA approach provides both a deterministic guarantee about the efficiency level of the model, as well as a probabilistic guarantee that is valid for all symmetric distributions.