كليدواژه :
انتقال قيمت , ايران , كشاورزي , هزينه توليد , هزينه توليد
چكيده فارسي :
ﻫﺪف از اﯾﻦ ﭘﮋوﻫﺶ، ﺑﺮرﺳﯽ ﺗﺎﺛﯿﺮ ﺷﻮكﻫﺎي ﻗﯿﻤﺘﯽ ﻧﻬﺎدهﻫﺎي ﻣﻮرد اﺳﺘﻔﺎده در ﺗﻮﻟﯿﺪ ذرت داﻧﻪ اي، ﺑﺮ ﻗﯿﻤﺖ اﯾﻦ ﻣﺤﺼﻮﻻﺳﺖ. ﺑﺮاي اﯾـﻦ ﻣﻨﻈـﻮر از داده ﻫﺎي ﻣﺮﺑﻮط ﺑﻪ ﻗﯿﻤﺖ ﻧﻬﺎدهﻫﺎي ﻣﻮرد اﺳﺘﻔﺎده در ﺗﻮﻟﯿﺪ ذرت از ﺟﻤﻠﻪ زﻣﯿﻦ، ﮐﻮد، ﻧﯿﺮويﮐﺎر، ﺑﺬر و ﺳـﻢ و ﻫﻤـﯿﻦ ﻃـﻮر ﻗﯿﻤـﺖ ﻣﺤﺼـﻮل ذرت از ﺳـﺎل زراﻋﯽ1378-79 ﺗﺎ 1390-91 ﺑﺮاي 16 اﺳﺘﺎن ﮐﺸﻮر ﮐﻪ ﺗﻮﻟﯿﺪﮐﻨﻨﺪه 93/5 درﺻﺪ از ذرت ﺗﻮﻟﯿﺪي ﮐﺸﻮر ﻣﯽﺑﺎﺷﻨﺪ، از روش ﺧﻮد رﮔﺮﺳﯿﻮن ﺑﺮداري ﺑﺎ ﮐﺎرﺑﺮد داده ﻫﺎي ﺗﺎﺑﻠﻮﯾﯽ ﺑﺮاي ﺗﺠﺰﯾﻪ و ﺗﺤﻠﯿﻞ داده ﻫﺎ اﺳﺘﻔﺎده ﺷﺪ. ﻧﺘﺎﯾﺞ ﺣﺎﮐﯽ از آن اﺳﺖ ﮐﻪ ﻧﻮﺳﺎن ﻗﯿﻤﺖ ذرت ﺗﺎ ﺣﺪي ﻣﺘﺎﺛﺮ از ﻗﯿﻤﺖ ﻋﻮاﻣﻞ ﺗﻮﻟﯿﺪ اﺳﺖ.63% از ﺗﻐﯿﯿﺮات رﺷﺪ ﻗﯿﻤﺖ ذرت ﻣﺮﺑﻮط ﺑﻪ ﻣﻘﺎدﯾﺮ ﮔﺬﺷﺘﻪ ﺧﻮد ﻣﺘﻐﯿﺮ، 4/8 درﺻﺪ ﻣﺮﺑﻮط ﺑﻪ ﺗﻐﯿﯿﺮ در رﺷﺪ اﺟﺎره زﻣﯿﻦ، 24 درﺻﺪ ﻣﺮﺑﻮط ﺑﻪ ﺗﻐﯿﯿﺮ در رﺷﺪ ﻗﯿﻤﺖ ﮐﻮد ﺷﯿﻤﯿﺎﯾﯽ؛ 5/1 درﺻﺪ ﻣﺮﺑﻮط ﺑﻪ رﺷﺪ ﺗﻐﯿﯿﺮ در رﺷﺪ ﻗﯿﻤﺖ ﺑﺬر )ﺑﺎ ﺗﻮﺟﻪ ﺑﻪ ﻧﺘﺎﯾﺞ ﺗﺨﻤﯿﻦ ﻧﺤﻮه اﺛﺮ ﮔﺬاري ﻣﻌﮑﻮس اﺳﺖ(، و 1/4درﺻﺪ ﻣﺮﺑـﻮط ﺑـﻪ ﺗﻐﯿﯿـﺮ در رﺷﺪ ﻗﯿﻤﺖ ﺳﻤﻮم ﮐﺸﺎورزي اﺳﺖ.ﺑﺎ ﺗﻮﺟﻪ ﺑﻪ ارﺗﺒﺎط ﺑﯿﻦ ﻗﯿﻤﺖ ﻧﻬﺎده ﻫﺎ و ﺳﺘﺎده ذرت، ﺳﺎﻣﺎن دﻫﯽ ﺑﺎزارﺗﻘﺎﺿﺎي ﻧﻬﺎده ﻫﺎ و ﺗﺎﻣﯿﻦ و ﺗﻮزﯾﻊ ﺑﻪ ﻣﻮﻗـﻊ آن ﻫـﺎ در ﻬﺖ ﮐﻨﺘﺮل ﻧﻮﺳﺎﻧﺎت و ﺷﻮك ﻫﺎي ﻗﯿﻤﺘﯽ در ﺑﺎزار ﻧﻬﺎده ﻣﯽ ﺗﻮاﻧﺪ در ﺗﺜﺒﯿﺖ ﻗﯿﻤﺖ ﻣﺤﺼﻮل ذرت ﻣﻮﺛﺮ ﺑﺎﺷﺪ
چكيده لاتين :
Introduction
One of the characteristics of agricultural products in Iran is continuous price fluctuations. There are many factors that can lead to fluctuations in prices in the agricultural sector. The important of these factors are the seasonal and cyclical changes in supply, the shift from global prices to the domestic market of agricultural products, the shifting of the volatility of inputs into products and the fluctuations caused by the general trend of prices The aim of this study is to evaluate the impact of price shock inputs that are used in the production of corn, on the price of the corn crop. Studies in Iran are further on the transfer of the farm to the retail price and the transmission of price shocks from the inputs market to product market has not been established. To examine this matter, VAR model with panel data was used.
Materials and Methods
For this purpose, we used data on the prices of inputs used in corn production, including land, fertilizer, labor, seed, and pesticides, as well as the price of corn since 1999 to 2012 year, for 16 provinces in Iran. That these provinces produce 93.5 percent of total corn Produced in the country. In order to analyze the data model is specified in the form of PVAR (Vector Auto Regression with Panel Data). Time-series vector auto regression (VAR) models originated in the macro econometric literature as an alternative to multivariate simultaneous equation models. All variables in a VAR system are typically treated as endogenous, although identifying restrictions based on theoretical models or on statistical procedures may be imposed to disentangle the impact of exogenous shocks on the system. To specify the model input price shock impacts the price of corn. The model introduced by Kilian was used as the basis model. According to the study, the effect of input price shocks on the price of corn models was modified.
Results and Discussion
If the shock rise in the price of pesticide, fertilizer, and land to occur each of these shocks separately will increase the price of corn, the effect of these shocks has been moved to later periods. Fertilizers price shocks are long-term and neutralization of this process is very slow. However the impact of price shocks of agricultural pesticides and the land quickly neutralized and this index converges whit corn price. Shock rise in corn prices over a period, Makes the corn prices generally decline in the next period and increase the area under cultivation in the following year, with increasing the area under cultivation may take advantage of economies of scale provided and on the other hand, there is the possibility of commuting buying with more inputs. The effect of all impulses can be said after ten periods, will be neutralized. Only shock that after ten periods are not neutral and it is still its effects visible as the shock of the price of fertilizer. The results showed that the corn price volatility is partly influenced by the price of production inputs.63% of the corn price growth changes related to changing values of its past, 8/4% due to changes in land rental growth, 24% of the change in the growth of the price of fertilizer; 1.5% growth related to changes in the price of seed growth (according to the results of the effect is reversed), and 4.1% related to changes in the growth of prices of agricultural pesticides.
Conclusions
Time of purchase of chemical fertilizers is during planting (phosphate fertilizers) and retention period product (Nitrogen fertilizers).In this sense, it seems that the price of fertilizer and how to increase or decrease is one of the important variables and decision-making (after the price trend of maize) in the formation of corn prices on the market. The ownership structure of the agricultural sector is in such a way that most farmers have their land ownership. The cost of renting land is in the form of implicit costs and not obvious and its role in decision-making between variables is low. The cost of agricultural pesticides in the entire cost of production is not significant and the results show that its role in the formation of corn price changes is small. Organization of market demand and supply and timely distribution of inputs for control market price fluctuations and shocks on inputs market can be effective in stabilizing the price of corn.
