Fluctuation Impact of Main Food Crops Production to Total Grain Yield

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1 doi: / Fluctuation Impact of Main Food Crops Production to Total Grain Yield Xuanzuo Lin Northeast Agricultural University, Harbin , Heilongjiang, China Huiping Liu* Northeast Agricultural University, Harbin , Heilongjiang, China *Corresponding author( Abstract The main purpose of this article is to research on rule of fluctuation of main food crops production to that of total grain yield. We use the covariance of panel data, unit root test of correlation coefficient, Johanson cointegration test, and VAR model to find the truth. We found a drastic change in grain production with a cycle of fluctuation of 4.09 years. Grain production has the greatest impact on maize production, then rice production, soybean production at last. While, grain production can be greatly influenced by maize production, then rice production, soybean production at last. We made a study of the relationship between the fluctuation of grain crops production and that of total grain yield. The study the relationship of fluctuation between the main grain crops production and total grain yield, and draw a reliable conclusion. Key words: Grain Yield; Fluctuation Cycle; ADF Test; Co-integration Test; VAR Model. 1. INTRODUCTION Food security has always been the hot issue focused by each country. It is the basis of economic development, social stability, self-reliance and self-improvement for one country. How is the overall condition of total grain production fluctuation? What is the cycle of fluctuation? Whether the fluctuation of main food crops production has an impact on grain yield or not, and what is the degree of such impact? Which crop has the greatest impact on grain yield? In this article, the dynamic analysis of measurement model is conducted to research on the interaction relationship between main food crops production and grain yield, so as to find the answer to the abovementioned questions. Many scholars have made a lot of research in respect of grain production and price fluctuation. Brian (2011) pointed out that grain has storability because of its physical property, thereby grain price can be influenced easily and change as the change in its available supply quantity. Frank and Chris (2015) put forward that how to make an accurate prediction of change in grain price as well as conduct early warning of abnormal status of grain price are two major contents regarding food security monitoring. According to GARCH (1, 1) model analysis research, Gilbert & Morgan (2012) believed international grain price shown a great fluctuation in the year of 2007, 2008 and ZHOU De & Dieter Koemle (2015) found that due to the intervention of various policies in the domestic market of China, the market price for grain had been distorted. International market price has a delayed effect on domestic market price for grain. However, Christopher L. Gilbert (2012) thought the reason for the increase in global grain price in lies in: On one hand, the relationship between grain and energy market because the grain is taken as bio-fuel recently. One the other hand the export restriction policies adopted by food exporting countries. By utilizing monthly data (in IMF Database) of grain price in Africa regions (in southern Sahara) from January 2003 to December 2010 and GARCH (1, 1) model analysis, Nicholas Minot (2014) concluded that: The grain price shows an obvious rising trend in only 7 regions of total 67 regions the grain price are stable in two thirds of regions, while in 17 regions, the grain price does not increase but fall. Some scholars such as Siddique (2014) said the fluctuation of grain price include two parts: Predictable part and unpredictable part. In the context of economic globalization, inadequate in investment for agriculture, impact of production changes and demand, supply response capacity, variation of plantation area, climate change, the rising price of energy and chemical fertilizer, instability of import condition and exchange rates, all these are factors that affect the fluctuation of grain price. Debin Tian analyzed the fluctuation condition of grain yield by multiple linear regression analysis and found that grain sowing area, per unit area yield have a significant impact on grain yield. Xuanzuo Lin study on the interaction relationship between the forest carbon sinks and economic growth, the empirical results indicated that forest carbon sinks has a significant pushing on economic growth. The said study are mainly take an concentration on: factors that influence the fluctuation of grain yield; the reason for the fluctuation of regional grain price; research on grain price conduction; Few 193

2 articles are discuss the research on the relationship between the fluctuation of grain crops production and that of total grain yield. In this article, we use relevant data of grain yield, rice, maize, soybean and wheat from1949 to 2014 and in accordance with modern economic cycle theory, based on covariance of panel data, unit root test of correlation coefficient, Johanson co-integration test, and VAR model, to study the relationship of fluctuation between the main grain crops production and total grain yield, and draw a reliable conclusion. 2. INDEX SELECTION AND DATA PROCESSING Province from 1949 to 2014 is obtained from National Bureau of Statistics. In order to reduce the fluctuation, eliminate the heteroscedasticity may appear in the data, we take natural logarithm of the output data for grain, rice, maize, soybean, wheat, and mark they with five symbols ZCL, SD, YM, DD, XM respectively. LnZCL log( ZCL ) ; LnSD log( SD ) ; LnYM log( YM ) ; LnDD log( DD ) ; LnXM log( XM ) LnZCL LnSD LnYM LnDD LnXM Figure 1. General variation of total grain yield as well as four major grain crops output from 1949 to From the above chart, it is observed that in the four major grain crops from 1949 to 2014: the variation trend of maize production is similar to that of total grain yield, particularly after 2004; the variation trend of rice production is match with that of total grain yield after The degree of fluctuation of rice production is drastic before 1986 with a frequent fluctuation in a short term; the variation trend of soybean production is similar to that of total grain yield before 2005, and shows a short-term fluctuation after 2005; the variation trend of wheat production is similar to that of total grain yield before 1998, but this shows a decline trend after VAR METHOD IMPROVEMENT BASE ON THE SMOOTH EMPIRICAL LIKELIHOOD 3.1. Empirical Likelihood Method of Quantile Estimates Suppose X1, X 2..., X n as a set of random samples from the unknown distribution function F( X ), and the density function of f ( X ) As to the given 0 1 q, we define q quantile as 1 ( ) inf : ( ) F q x F X q, and construct the 1 confidence interval of 0 F ( q ). We use the method of Lagrangian multiplier to calculate the maximum value subject to constraint, and the logarithm empirical likelihood ratio can be obtained as follows: 194

3 F ( ) 1 ( ) n Fn l( ) 2log R( ) 2 n Fn ( 0 )log (1 Fn ( 0)) log q 1 q (1) It can be seen that log R ( 0 ) is a jump function with jumping observed value, which is only able to take 2 the finite number value to make it meet ; it is approximate but not very accurate, so the smooth kernel density estimation function is used to replace F n ( 0), which is a more reasonable choice. The logarithm empirical likelihood ratio after the adjustment is of: ( 0 ) F n 1 F n ( 0 ) 0 0 n 0 n 0 l( ) 2log R( ) 2 n F ( ) log (1 F ( ))log q 1 q (2) 1 ( ) n x X i F n x G( ) (3) n i1 h 3.2. Calculation Process According to the empirical likelihood estimation method, we can calculate the quantile interval of samples, and what we mainly apply is the determined value of quantile, which needs to design one kind of method to calculate the quantile value in accordance with the quantile interval. Because the empirical likelihood method has the bootsroop characteristics, we can use the sampling method similar to the bootsroop method to carry out the non-parametric estimation. (1) Suppose from the population distribution F, then according to the observed value, and finally construct the empirical likelihood function and confidence interval in accordance with the foregoing method. (2) Conduct the simple sampling from, calculate the quantile, and observe has the confidence interval or not; if it exists, the quantile shall be reserved, otherwise the simple sampling would be conducted again to calculate the quantile. (3) Repeat the procedures in (1) and (2), and calculate the quantile of p in number. (4) Stipulate the mean value statistics of, serving as the estimation of the quantile. 4. ANALYSIS OF CYCLE OF FLUCTUATION OF TOTAL GRAIN YIELD IN HEILONGJIANG PROVINCE In accordance with the cycle fluctuation theory, a complete cycle shall be like this: start from wave trough (peak), it rise (fall) to wave peak (trough), then it go back to wave trough (peak). The cycle shall have obvious wave trough and peak. Duration of wave trough and peak with year as unit time regarding time series shall be more than 2 year. In addition, based on the actual requirement, the amplitude error has to greater than 5%. A complete fluctuation cycle shall be formed according to the morphological characteristic of trough peak - trough. Therefore, the fluctuation of grain yield in Heilongjiang Province from 1949 to 2014 could be divided into 21 cycles percent Figure 2.The chance in growth rate of link relative ratio of total grain yield from 1949 to year 195

4 It is observed from the above chart that the fluctuation of grain yield in Heilongjiang Province has the following features: To begin with, the amplitude of fluctuation is relatively large, with an average of 37.78%. The cycle in has the amplitude of fluctuation: 79.78%. It is followed by that of , the amplitude is 77.04%. 20 cycles have amplitude of fluctuation over 10%, namely strong fluctuation cycle, they account for 95.23% of the total cycles. All of these indicate a dramatic change in the grain yield in Heilongjiang Province. Secondly, most are mid-term fluctuations. From 21 cycles of fluctuation, the fluctuation is occur once in each 3 to 7 years, with an average cycle of 4.09 years, among which has a largest cycle of 7 years. The cycle length distribution of the other 20 cycles is: 8 cycles with a length of 3 years; 6 cycles with a length of 4 years; 5 cycles with a length of 5 years; 1 cycle with a length of 6 years; At last, most are classical fluctuation. There are 2 natures of fluctuation: The fluctuation with a negative value of the wave trough is called classical fluctuation. The fluctuation with a positive value of the wave trough is called growing fluctuation. The number of classical fluctuation is 15, which account for 71.42% of the total cycles; the number of growing fluctuation is 6, which make up 28.58% of the total cycles. 5. EMPIRICAL RESEARCHES ON THE IMPACT OF FLUCTUATION OF FOUR MAIN GRAIN CROPS PRODUCTION ON TOTAL GRAIN YIELD 5.1. Analysis of Covariance and Its Correlation Analysis covariance, concerning how to adjust influential effect of concomitant variable to dependent variable, is a statistical approach for a higher degree of effective analysis of experimental treatment effect. It is also an approach for comprehensive analysis of variance and analysis of regression so as to conduct statistical control for the experiment. Correlation analysis studies whether there is dependence relationship among different phenomena or not. It also studies the direction and degree of correlation for specific dependence relationship, and is the statistical approach for analyzing correlativity between random variables. See bellow covariance and coefficient of correlation calculated by the software Evies8.0:With the advancement in networking and multimedia technologies enables the distribution and sharing of multimedia content widely. In the meantime, piracy becomes increasingly rampant as the customers can easily duplicate and redistribute the received multimedia content to a large audience. Table 1. Covariance and coefficient of correlation for output of grain and four main crops. LnZCL LnSD LnYM LnDD LnXM Covariance Coefficient of correlation It is known from Table 1 that the covariance of rice is the biggest of all; it is followed by maize, soybean and wheat, where the covariance of wheat has a relative bigger gap compared to the other three crops. As for coefficient of correlation, maize has the biggest value of all; it is followed by rice, soybean and wheat. The coefficient of correlation of wheat is which shows a relative bigger gap compared to the other three crops. Combine the analysis result of Table 1, this text only conduct a study on the interactional relationship between the total grain yield and the four main grain corps: rice, maize, as well as soybean Co-integration Test Time series LnZCL, LnSD, LnYM, LnDD are reposeful integrated series of first order. To avoid spurious regression, Johanson co-integration test is required to conduct on time series of grain yield and each crop. Specific results as follows: Table 2. Johanson co-integration test Hypothesize d Eigenvalue Trace tatistic Critical Value Prob None * At most 1 * At most 2 * At most 3 * Remark: * stands for 5% under significance level. From the test result, original hypothesis of co-integration relationship does not exist among rejected variables above 5% of significance level. Co-integration relationship exists between each other. The following results are estimated by OLS regression: 196

5 ln ZCL ln YM ln SD ln DD (4) it 2 R DW F From the abovementioned results, the grain yield as well as the estimation of long-term equilibrium relationship among three major grain crops can give a fine explanation to the economic reality. The estimation expression demonstrate that if the production of maize, rice and soybean changes 1% respectively, then the total grain yield will change 0.512%, 0.238% and 0.087% accordingly. Maize production has the greatest impact on grain yield; rice production has less impact on grain yield, soybean production at last VAR Model In accordance with research summary and test of pre-period theories, it is determined that there is interrelation between grain yield and three main grain crops. Eviews8.0 is used for parameters estimation of the above model. All the reciprocals of root module are in the same unit circle, so the conclusion of Lag phase II shall be chosen. Put the estimated results of VAR model in Lag phase II with matrix form: D(LNZCL) D(LNZCL) D(LNYM) D(LNYM) D(LNSD) D(LNSD) D(LNDD) D(LNDD) t D(LNZCL) D(LNYM) D(LNSD) D(LNDD)) t2 (5) It is observed from the estimated results of equation that the total output in lag phase I have impact on three main crops, namely 1.434, 0.557, respectively. Lag phase II have a negative impact on it with a relatively smaller parameter, this is indicate that if grain yield increase 1%, the output of the three main grain crops will increase 1.434%, 0.557% and 0.041% respectively in the next year. Grain yield has the greatest impact on maize, and then is rice, soybean at last. The output of the three main crops in lag phase II would have a big impact on total grain yield, with the parameter values of 0.268, 0.037, and respectively. Lag phase I has a smaller impact than lag phase II. Maize production has the greatest impact on total grain yield. If the maize production rise 1%, the total grain yield would rise 0.268% in the next two years. 6. CONCLUSIONS (1) From the overall variation trend from 1949 to 2014, it is observed that the variation trend of maize is similar to that of total yield, particularly after The variation trend of rice production is match with that of total grain yield after The variation trend of soybean production is similar to that of total grain yield before The variation trend of wheat production is similar to that of total grain yield before (2) As for amplitude of fluctuation, the amplitude of fluctuation of grain output is relatively large, with average amplitude of 37.78%. The maximum amplitude is 79.78%. 20 cycles have amplitude of fluctuation over 10%, namely strong fluctuation cycle, they account for 95.23% of the total cycles. Grain yield changes radically. (3) In terms of cycle of fluctuation, most are mid-term fluctuations. From 21 cycles of fluctuation, the fluctuation occurs once in each 3 to 7 years, with an average cycle of 4.09 years, where the largest cycle is 7 years. The cycle length of the other 20 cycles as follow: 8 cycles with a length of 3 years; 6 cycles with a length of 4 years; 5 cycles with a length of 5 years; 1 cycle with a length of 6 years. (4) With respect to the natures of fluctuation, most of cycles are in classical fluctuation: 15, which account for 71.42% of the total cycles; the number of growing fluctuation is 6, which make up 28.58% of the total cycles. (5) From the results of covariance and correlation analysis, rice has the largest covariance, then maize, soybean and wheat. As for coefficient of correlation: Wheat has the largest value, then maize, soybean and wheat. (6) As for the co-integration test result, the grain yield as well as the estimation of long-term equilibrium relationship among three major grain crops can give a fine explanation to the economic reality. If the production of maize, rice and soybean changes 1% respectively, then the total grain yield will change 0.512%, 0.238% and 197

6 0.087% accordingly. Maize production has the greatest impact on grain yield among the three crops; rice production take the second place regarding its impact on grain yield, soybean production at last. (7) In terms of the results of VAR model, if the grain yield increases by 1%, the three main crops yield will increase by1.434%, 0.557%, and 0.041% respectively. Grain yield has the greatest impact on maize, rice followed, and soybean as last. Maize production has the greatest impact on grain yield among the three crops. If the maize production rise 1%, the total grain yield would rise 0.268% in the next two years. ACKNOWLEDGEMENTS This work was supported by This work was supported by the course construction project of graduate student in Northeast Agricultural University (NEAU2015-ykc015) and the project of humanities and social science in Heilongjiang province education department( ). REFERENCES Brian D. Wright (2011) The Economics of Grain Price Volatility, Applied Economic Perspectives and Policy, 33(1), pp Christopher L. Gilbert. (2012) International agreements to manage food price volatility, Global Food Security, 1(2), pp Debin Tian, Cuixia Li (2015) Influence Factors of Grain Yield Fluctuation Based on Multiple Linear Regression Analysis, Metallurgical and Mining Industry, 7(8), pp Frank Davenportl, Chris Funk (2015) Using time series structural characteristics to analyze grain prices in food insecure countries, Food Security, 7(5), pp Nicholas Minotr (2014) Food price volatility in sub-saharan Africa: Has it really increased, Food Policy, 45(3), pp Siddique Ahmed, Chamhuri Siwar, Basri Abdul Talib, Norshamliza Chamhuri, Rabiul Islam (2014) Tackling Food Price Volatility: The Challenge of The Days to Come, UMK Procedia, 1(D24), pp Xuanzuo Lin, Huiping Liu (2016) Model and Simulation of Interaction Relationship between Forest Carbon Sinks and Economic Growth, Rev. Téc. Ing. Univ. Zulia, 39(5), pp Zhou De, Dieter KoemIe (2015) Price transmission in hog and feed markets of China, Journal of Integrative Agriculture, 14 (6), pp