Impact of imported dairy products on China’s raw milk production

Abstract

With the continuous opening up of China’s dairy market to foreign countries, dairy products import volume continues to grow rapidly. The structural vector autoregressive model (SVAR) was used in this article to analyze the impact of dairy product imports on China’s raw milk production from 1996 to 2017. It is found that, dairy product import volume has a positive impact on China’s raw milk production, and negative impact on the liquid dairy product; and mainly negative impacts on the cost control variables in the short term. The price of corn has a stronger impact on the raw milk production compared with that of the soybean meal prices and crude oil price; the impact of Domestic raw milk demand on raw milk production fluctuates frequently in the short term, and has a positive impact on the diary export. Based on this, this article believes that adjusting the milk industry policy, optimizing the dairy products import structure and the dairy cows’ source structure, and advocating scientific feeding can effectively alleviate the impact caused by dairy products import.

Keywords

Dairy import raw milk production shock effect Structure vector autoregressive model (SVAR)

1 Introduction

As one of the important economic sectors of the animal husbandry industry, raw milk production not only has to meet consumers’ growing health consumption needs, but also is the main source of herders’ income. Therefore, there is no substitute for the stability of raw milk production in terms of keeping the effectiveness of the dairy market and ensuring the income of herdsmen. After China joined the WTO, the average import tariff of dairy products has continued to drop, and even zero tariffs have been gradually imposed on imported dairy products from Australia, New Zealand and other countries; the “melamine” incident has severely damaged consumers’ confidence in domestic dairy products; However, China’s raw milk does not have comparative advantages in terms of production cost control and breeding cow quality. Therefore, compared with developed countries’ dairy industry, China’s dairy products do not have competitive advantages in terms of price and quality [3], which ultimately leads to the sustained growth trend of dairy products import.

The surge in dairy products import directly impacts the market share of domestic dairy products, which in turn reduces the demand for domestic raw milk. From 1996 to 2015, China’s raw milk production increased from 6.294 million tons to 37.547 million tons, of which raw milk production in 2016-2017 was 36.022 million tons and 35.453 million tons; China’s dairy product imports continued to increase from 77, 300 tons in 1996 to 2,174,200 tons in 2017, and the cumulative total imports were 15.661,800 tons, of which the cumulative total imports of dry dairy products were 13.0255 million tons, accounting for 83.17%. And the total import volume of liquid dairy products was 2,636,300 tons, accounting for 16.83%. Based on the above comparison, it is believed that China’s dairy product imports will continue to grow, but China’s raw milk production has fallen rapidly after reaching its peak in 2015.

Imported dairy products have an obvious substitution effect on China’s raw milk production, but China’s total imports of dairy products are still growing rapidly. The existing research on China’s dairy product trade mainly focuses on two areas. One is to simulate and analyze the impact of China’s imported dairy products from New Zealand and Australia on China’s dairy product production and consumer welfare. The research methods are mainly based on GTAP and CGE models. The second is to study the impact of dairy product price fluctuations on the price of dairy products in China. The research methods are mainly VAR model and GARCH model. Most of the existing studies ignore the impact of dairy product imports on China’s raw milk production, and the data sample size used is small, so it is impossible to study the impact of dairy product imports on China’s dairy industry as a whole. Therefore, this paper uses the data on the actual import volume of dairy products in China, and no longer sticks to the use of small sample data for model analysis; sets China’s raw milk production as the dependent variable, the dairy product import volume as the main independent variable, and uses cost, demand, currency, etc. Control variables to analyze the impact of domestic and foreign factors on China’s raw milk production.

2 Theoretical analysis and research hypothesis

With the rapid development of globalization and the deepening of China’s opening up, the impact of external shocks caused by trade flows on China’s raw milk production has become more and more obvious. Whether the surge in dairy imports threatens China’s raw milk production has become the focus of attention from all walks of life; external impacts are not limited to trade, but also raw milk production costs and raw milk demand also have varying degrees of impact on China’s raw milk production.

(1) The impact of imported dairy products on the production of raw milk.

Dairy products can be subdivided into six categories: liquid milk, milk powder, yogurt, whey, butter, and cheese; in terms of their physical form, liquid milk and yogurt can be classified as liquid dairy products; milk powder, whey, butter, Cheese can be divided into dry dairy products. From 1996 to 2017, China’s raw milk production increased from 6.294 million tons to 35.453 million tons; China’s dairy product imports continued to increase from 77,300 tons to 2,174,200 tons. And the total import volume of dry dairy import is 13.0255 million tons, accounting for 83.17%, while the total import volume of liquid dairy products is 2,636,300 tons, accounting for 16.83%. Due to length limitation, this article only discusses the impact of total dairy product imports and liquid dairy product imports on China’s raw milk production. Based on above information, the author makes the following hypotheses:

H1: The total import volume of dairy products is the main external shock factor for the fluctuation of China’s raw milk production;

H2: The import volume of liquid dairy products is the secondly shock factor for the fluctuation of China’s raw milk production;

(2) The impact of cost promotion on raw milk production. This section sets the raw milk production cost factor as a control variable. In 2004, the Institute of Information of the Chinese Academy of Agricultural Sciences investigated dairy farms and found that feed costs accounted for 73.03% of the total cost of dairy farming in China, of which concentrated feed costs accounted for 53.85% and coarse feed costs approximately 18.07%; In the concentrated feeding, corn and soybean meal account for an absolute proportion. Among them, corn and soybean meal account for 74% of the concentrated feeding in the dairy farming area. Affected by international events, natural disasters and other factors, the price of corn and soybean meal in China continues to rise [9, 14], the cost of concentrated feed required for raw milk production has risen accordingly. International crude oil price fluctuations affect the production of raw milk by affecting the development of biomass energy, the cost of agricultural machinery fuel consumption, the cost of feed, the cost of international trade and transportation, and the development of biomass energy [16].

H3: Corn prices and soybean meal prices are the main cost impact factors for changes in China’s raw milk production;

H4: International crude oil prices also have an impact on China’s raw milk production.

(3) The impact of demand-pull on the production of raw milk. This section sets the demand for raw milk as a control variable. The rapid development of China’s economy has driven the rapid increase in the level of national per capita disposable income, the liberalization of the birth policy, the high population aging, and the “nutritious breakfast plan” and other domestic demand factors have led to a rapid increase in the demand for dairy products; although China’s dairy products has no comparative advantage in aspects such as international market share or revealed comparative advantage [1, 8], but there is still a certain export each year.

H5: The impact of domestic dairy product demand on raw milk production is stronger than that of dairy product exports.

(4) Other factors. This section sets dairy cow inventory and the purchase price of raw milk as control variables. According to calculations, it is believed that China’s dairy cow inventory level has continued to increase at an annual growth rate of 5.87%; however, in the actual raw milk output, the total raw milk output in 2017 has fallen to the level in 2008, and the dairy cow inventory at this time has also been significantly reduced; That is, the increase in dairy cow inventory cannot offset the decline in raw milk output caused by the decrease of dairy cow inventory;

H6: The dairy cow inventory will affect the stability of China’s raw milk production;

H7: The increase in the purchase price of raw milk will stimulate the growth of China’s raw milk output.

3 Research methods and data sources

(1) Structural vector autoregressive model

The basic form of VAR is shown in formula (1): $K_{t} = φ_{0} + φ_{1} y_{t - 1} + φ_{2} K_{t - 2} + \dots + φ_{p} x_{t - p} + ɛ_{t}$ (1)

Among them, φis the n*1 dimensional constant term; φ_i (i = 1, 2, 3 … , p) is the n * n-dimensional regression parameter; ɛ_t is the random disturbance term, and K_t is the column vector of the index variable at t.

The structural vector autoregressive model (later referred as SVAR) adds the simultaneous influence relationship among variables, and makes the research results more accurate by imposing constraints. It imposes constraint on the variables at the same time to solve the simultaneous equation bias problem, and then reflect the current influence between the variables, and then consider real economic problems to explain in detail the impact of random disturbance items on endogenous variables. The SVAR model expression is as follows: $A_{0} K_{t} = φ_{1} K_{t - 1} + φ_{2} K_{t - 2} + \dots + φ_{p} K_{t - p} + ɛ_{t}$ (2)

Among them, K_t is the endogenous variable, A₀ is the coefficient matrix, p is the lag order, and ɛ_t is the error vector. Here, A₀ is reversible by default, multiply both sides of the equation by $A_{0}^{- 1}$ to get the reduced VAR form:

$\begin{matrix} K_{t} = & A_{0}^{- 1} φ_{1} K_{t - 1} + A_{0}^{- 1} φ_{2} K_{t - 2} + \dots \\ + A_{0}^{- 1} φ_{p} K_{t - p} + A_{0}^{- 1} ɛ_{t} \end{matrix}$ (3)

By comparing the basic forms of VAR and SVAR models, it is found that the uncertainty of the SVAR model has n² more uncertainty than that of the VAR model. Therefore, n² must set limitation. In order to reduce the influence of uncertain factors, assume that A₀ is the lower triangular matrix, add $\frac{n (n - 1)}{2}$ restrictions, and the A₀ matrix will be as follows: $A_{0} = [\begin{matrix} \begin{matrix} 1 \\ a_{21} \end{matrix} \\ ⋮ \\ a_{n 1} \end{matrix} \begin{matrix} \begin{matrix} 0 \\ 1 \end{matrix} \\ ⋮ \\ a_{n 2} \end{matrix} \begin{matrix} \begin{matrix} \dots \\ \dots \end{matrix} \\ ⋱ \\ \dots \end{matrix} \begin{matrix} \begin{matrix} 0 \\ 0 \end{matrix} \\ ⋮ \\ 1 \end{matrix}]$

In this part of the empirical study, AB type is used for short-term constraints to create a SVAR, and an empirical test of the impact of the research indicators through impulse response analysis and variance decomposition.

(2) Variable selection and data processing

a. Variable selection

Based on the above analysis of the external impacts on China’s raw milk production, the dependent variable of this empirical analysis is China’s raw milk production; the independent variables are total dairy product imports and import volume of liquid dairy products; cost control variables are corn price, soybean meal price, international crude oil price; demand control variables are domestic demand for raw milk and dairy product export volume; other control variables are dairy cow inventory and raw milk purchase price.

b. Data source and processing

In view of the availability of data, the empirical part of this study uses data from 1996 to 2017. In terms of data processing, the CPI index issued by the National Bureau of Statistics of the previous year = 100 is used to deflate the prices of corn, soybean meal, and raw milk; to eliminate the impact of prices; given that the data used in this part of the empirical analysis is annual data, and there is no need to consider seasonal factors here;

In order to eliminate the heteroscedasticity in the time series and improve the robustness of the model, the variables used in this part are processed logarithmically. The results after taking the logarithm of each variable are shown in Table 1. It can be seen that the average value of China’s raw milk production is relatively large, followed by dairy cow inventory; while the difference between the standard deviation of imported liquid dairy products is large, and the standard of domestic raw milk demand is relatively small.

Table 1

Statistics and description of variables after taking the logarithm

Variables	Variable description	Mean	Standard deviation
Dependent variable	China Raw Milk Output(lnyield)	7.43	0.83
Main independent variable	Total imports of dairy products (lnimport)	3.49	1.20
	Import volume of liquid dairy products (lnlimport)	0.68	1.72
Control variable	Corn Price(lnpmaize)	0.42	0.42
	Soybean meal price(lnpsoybean)	1.08	0.30
	Texas Light Crude Oil Prices (lnwti)	3.69	0.65
	Domestic raw milk demand (lngdp)	–2.35	0.23
	China’s dairy exports (lnexport)	1.47	0.40
	Cow inventory (lncow)	6.70	0.58
	Raw milk purchase price (lnpraw)	0.82	0.41

Data source: Collected from the dairy industry related yearbooks and databases

4 Empirical analysis results

4.1 The impact of total dairy product import volume on the content of raw milk in China

(1) Stationarity test

First, the stationarity test of the variables is carried out to eliminate the influence of the unit root on the measurement results. The test results are shown in Table 2. Lnpmaize rejects the null hypothesis at the 5% significance level and is a stationary series; while lngdp, lnwti, lnimport, lnsoybean, lncow, lnpraw, lnyield, and lnexport accept the null hypothesis at the 10% significance level, and is an unsteady time series, so its first-order difference, Dlngdp, Dlnwti, Dlnimport, Dlnsoybean, Dlncow, Dlnpraw, and Dlnexport reject the null hypothesis that there is a unit root, and it is a stationary time series; while lnyield rejects the null hypothesis after the second-order difference, that is, D (lnyield, 2) is a stationary time series.

Table 2
Root test results of each variable in ADF unit

Variable ADF statistics (c,t,q) 5% critical value P value Conclusion

Lngdp –1.53 (c,0,0) –2.99 0.50 Unstable

Dlngdp –4.14 (c,0,1) –2.99 0.00 Stable

Lnwti –1.21 (c,0,0) –2.99 0.65 Unstable

Dlnwti –4.58 (c,0,1) –2.99 0.001 Stable

Lnimport –0.64 (c,0,0) –2.99 0.84 Unstable

Dlnimport –6.43 (c,0,1) –2.99 ≤0.00 Stable

Lnpmaize –3.03 (c,0,0) –2.99 0.05 Stable

Lnpsoybean –2.43 (c,0,0) –2.99 0.14 Unstable

Dlnpsoybean –4.36 (c,0,1) –2.99 0.00 Stable

Lncow –1.39 (c,0,0) –2.99 0.57 Unstable

Dlncow –2.78 (c,0,1) –2.99 0.08 Stable

Lnpraw –2.17 (c,0,0) –3.00 0.22 Unstable

Dlnpraw –6.12 (c,0,1) –3.00 ≤0.00 Stable

Lnyield –2.08 (c,0,0) –2.99 0.25 Unstable

D(yield,2) –5.50 (c,0,1) –3.00 ≤0.00 Stable

Lnexport –2.01 (c,0,0) –2.99 0.28 Unstable

Dlnexport –4.66 (c,0,1) –3.00 0.00 Stable

Variable	ADF statistics	(c,t,q)	5% critical value	P value	Conclusion
Lngdp	–1.53	(c,0,0)	–2.99	0.50	Unstable
Dlngdp	–4.14	(c,0,1)	–2.99	0.00	Stable
Lnwti	–1.21	(c,0,0)	–2.99	0.65	Unstable
Dlnwti	–4.58	(c,0,1)	–2.99	0.001	Stable
Lnimport	–0.64	(c,0,0)	–2.99	0.84	Unstable
Dlnimport	–6.43	(c,0,1)	–2.99	≤0.00	Stable
Lnpmaize	–3.03	(c,0,0)	–2.99	0.05	Stable
Lnpsoybean	–2.43	(c,0,0)	–2.99	0.14	Unstable
Dlnpsoybean	–4.36	(c,0,1)	–2.99	0.00	Stable
Lncow	–1.39	(c,0,0)	–2.99	0.57	Unstable
Dlncow	–2.78	(c,0,1)	–2.99	0.08	Stable
Lnpraw	–2.17	(c,0,0)	–3.00	0.22	Unstable
Dlnpraw	–6.12	(c,0,1)	–3.00	≤0.00	Stable
Lnyield	–2.08	(c,0,0)	–2.99	0.25	Unstable
D(yield,2)	–5.50	(c,0,1)	–3.00	≤0.00	Stable
Lnexport	–2.01	(c,0,0)	–2.99	0.28	Unstable
Dlnexport	–4.66	(c,0,1)	–3.00	0.00	Stable

Note: c in (c, t, q) represents a constant term, t represents a trend term, and q represents a lag order.

4.2 Model lag order

First, since series in the SVAR model are stationary variables and do not obey the same order single integration at all time, and there is no co-integration relationship among the variables; secondly, considering the stability of the SVAR model, the non-stationary time series are differentiated, that is, using time-series variables such as Dlngdp, Dlnwti, Dlnimport, Lnpmaize, Dlnpsoybean, Dlncow, Dlnpraw, D (lnyield, 2), Dlnexport, etc., the SVAR model of the impact of the total amount of dairy imports on the fluctuation of raw milk production in China is established.

Determine the optimal lag order. According to the minimum criteria of LR, FPE, AIC, SC, and HQ, the optimal lag order of each variable of the SVAR model is considered to be 1. At the same time, considering that this part of the empirical study uses annual data, the sample space is limited, and multiple control variables are added. Therefore, the optimal lag order of each variable should not be too large, and the optimal lag order of the SVAR model of the impact of total dairy product imports on China’s raw milk production is 1, which is in line with theory and practice (Table 3).

Table 3
Test results of optimal lag order

Lag LR FPE AIC SC HQ

0 NA 6.32E-16 –9.46 –9.01* –9.35

1 135.17* 3.86e-17* –12.81* –8.37 –11.69*

Lag	LR	FPE	AIC	SC	HQ
0	NA	6.32E-16	–9.46	–9.01*	–9.35
1	135.17*	3.86e-17*	–12.81*	–8.37	–11.69*

The unit circle is used to test the stability of the SVAR model. The specific results are shown in Fig. 1. It is found that the reciprocal of the AR polynomial characteristic roots of the SVAR model with lag 1 order are all less than 1, that is, located in the unit circle. Therefore, the model meets the requirements for stability.

Fig.1

The stationarity test results of the SVAR model.

4.3 Impulse response function and variance decomposition results

(1) Impulse response function

Based on the SVAR model of the impact of the total dairy product import on China’s raw milk production, an impulse response function with an observation period of 24 periods can be set to obtain variable pairs such as Dlnimport, Dlngdp, Dlnwti, Lnpmaize, Dlnpsoybean, Dlncow, Dlnpraw, Dlnexport, etc. The external shock effect caused by China’s raw milk production D (lnyield, 2), the specific shock direction and degree of shock caused by each variable are shown in Fig. 2:

Fig. 2

The impulse response of China’s raw milk production to total dairy imports and other external impacts.

First, when domestic raw milk demand (Dlngdp) imposes a one-unit impact on raw milk production, the current response of China’s raw milk production is –0.014; the fourth phase of lagging raw milk production’s response to raw milk demand increases slowly to the zero axis, gradually return to zero after the 9th period, reaching a stable stage. The impact caused by domestic demand for raw milk fluctuates drastically in the short term. With consumption concepts such as “healthy consumption”, “nutritious breakfast”, after “a cup of milk a day makes Chinese people strong” have been widely recognized, and consumers have increased their demand for dairy products. However, the frequent occurrence of dairy product safety incidents in China has also caused the demand for domestic raw milk to decrease again in the short term. In the long run, with the improvement of the quality of domestic raw milk and dairy products, consumers’ demand for domestic raw milk has returned to rationality. Increase the consumption of domestic raw milk and its finished products.

Second, when the total dairy product imports (Dlnimport) impose a one-unit impact on China’s raw milk production, the current response of China’s raw milk production is 0.009; after the third period, it quickly changes to a negative impact, which is –0.009; After the 12th period, it gradually returned to the zero axis. On the whole, in the short term, total dairy product import volume stimulate the growth of China’s raw milk production, but in the long run, the impact of total dairy product import volume on China’s total raw milk production is mainly negative. The reasons are as follows: The production cycle is long, and it takes about two years for cows from cubs to lactation. Therefore, the increase in the total amount of dairy products imported in the short term will not immediately reduce the production of raw milk; Second, the total imported dairy products will have a long-term substitution effect on domestic raw milk. Dairy companies import a large amount of industrial bulk powder for the production of reconstituted milk and infant milk powder; therefore, the long-term increase in the total imported dairy products will lead to the direct reduce of milk companies’ demand for domestic raw milk, leading to a reduction in raw milk production in China. However, it is worth noting that the results of the impulse response function show that the negative impact of total dairy product imports on China’s raw milk production is not strong. Fluctuations in raw milk production are more due to internal factors [15 , 20].

Third, when the price of corn (Lnpmaize) imposes a one-unit shock on China’s raw milk production, the current response of China’s raw milk production is zero, and the shock effect of the first period reaches the maximum point, which is –0.033; after the seventh period, the price of corn mainly has a positive impact on China’s raw milk production and this impact tends to the zero axis. The impact of the price of soybean meal (Dlnpsoybean) on China’s raw milk production was mainly a negative impact before the first seven periods, and the impact of soybean meal prices after the ninth period gradually tended to zero [7]. In the short term, both corn prices and soybean meal prices will have a negative impact on China’s raw milk production. As corn and soybean meal are important production materials for raw milk, the continuous increase in the prices of both will increase feed cost. At the same time, the purchase price of raw milk in China varies. The year the production cost is even close to the purchase cost, that is, China’s raw milk production is faced with the double threaten of high costs and low purchase prices. Some dairy farmers withdraw from raw milk production, resulting in a reduction in production [12]. However, in the long run, the impact of corn price and soybean meal price on raw milk production is slowly attenuating. The reason may be that the corn price and soybean meal price do not have the conditions for long-term and continuous rise; in dairy cow feed, the ratio of corn and soybean meal is about 4:1, and the price of corn fluctuates much higher than the price of soybean meal, so the impact of corn price on raw milk production is greater than that of soybean meal; third, although the short-term internal scattered households withdraw from raw milk production due to money loss, but in the long run, the limitations of smallholder production have caused dairy farmers to re-enter raw milk production activities.

Fourth, when the dairy cow inventory (Dlncow) has a one-unit impact on China’s raw milk production, the current response of China’s raw milk production is 0.015, that is, the dairy cow inventory has a positive impact on raw milk production; gradually after the third period, this impact goes towards the zero axis, the driving effect on the production of raw milk is attenuated. In the short term, the impact of dairy cow inventory on China’s raw milk production is mainly positive, that is, the increase in dairy cow inventory will stimulate the growth of raw milk production [10]. This is in line with the situation of current raw milk production in China. Although China’s dairy cow yield is maintaining a growth rate of nearly 5% every year, compared with developed countries in the dairy industry, China’s dairy cow yield in 2016 was only one-third that of Denmark and one-fourth that of Israel. Therefore, thus in a short term the increase in raw milk production in China still depends on dairy cow inventory; in the long run, dairy cow inventory will not be able to promote the steady growth of raw milk production for a long time.

Fifth, when the raw milk purchase price (Dlnpraw) has a one-unit impact on China’s raw milk production, the then response of China’s raw milk production is 0.003; the second response is 0.004; from the sixth period onwards the raw milk output’s impact on the price gradually tends to the zero axis. On the whole, the purchase price of raw milk has a positive impact on the production of raw milk, which is consistent with the research hypothesis and also in line with the actual situation of raw milk production, that is, the increase in milk prices will increase the enthusiasm of dairy farmers in breeding [19].

(2) Variance decomposition results

In the SVAR model, the impulse response function describes the impact of each endogenous variable in the model on itself and other endogenous variables, and the variance decomposition is usually used to measure the degree of influence of different variables on the same variable. Then the contribution rate of each explanatory variable to the explained variable is analyzed. Here, the dairy product import volume is the main explanatory variable, and other external impacts are the control variables, and the external impacts suffered by China’s raw milk production are subjected to variance decomposition [11]. The results of the variance decomposition are shown in Table 4. Raw milk production, total dairy product imports, and corn prices account for relatively high proportions in the variance decomposition of China’s raw milk production.

Table 4

Decomposition of SVAR model forecast variance of total dairy product imports— — each factor accounts for the percentage of forecast variance

Period	Standard deviation	GDP growth rate	International crude oil prices	Imports volume of dairy products	Corn price	Soybean meal price	Dairy cow inventory	Raw milk price	Raw milk output	Dairy products export volume
1	0.112	6.452	7.984	2.914	0.002	0.067	7.640	0.349	74.594	0.000
2	0.138	7.241	5.379	27.036	17.259	0.932	3.799	0.483	37.850	0.019
3	0.149	11.555	5.485	25.965	16.478	0.895	3.504	0.482	35.532	0.103
4	0.152	11.470	5.535	25.770	16.420	1.082	3.478	0.500	35.275	0.470
5	0.156	11.258	5.622	26.053	16.622	1.163	3.447	0.489	34.886	0.460
6	0.157	11.226	5.624	25.990	16.586	1.222	3.452	0.490	34.922	0.488
7	0.157	11.359	5.594	25.951	16.727	1.217	3.434	0.487	34.733	0.497
8	0.157	11.368	5.595	25.931	16.750	1.229	3.433	0.488	34.707	0.498
9	0.158	11.380	5.590	25.922	16.788	1.228	3.430	0.488	34.671	0.502
10	0.158	11.385	5.590	25.917	16.790	1.229	3.430	0.488	34.667	0.505
11	0.158	11.392	5.588	25.913	16.797	1.230	3.429	0.488	34.657	0.506
12	0.158	11.393	5.588	25.911	16.799	1.230	3.429	0.488	34.655	0.506
13	0.158	11.394	5.588	25.911	16.800	1.230	3.429	0.488	34.653	0.506
14	0.158	11.395	5.588	25.910	16.800	1.230	3.429	0.488	34.653	0.506
. . .	. . .	. . .	. . .	. . .	. . .	. . .	. . .	. . .	. . .	. . .
24	0.158	11.395	5.588	25.910	16.801	1.230	3.429	0.488	34.652	0.506

Note: The order of impact is in accordance with Dlngdp, Dlnwti, Dlnimport, Lnpmaize, Dlnpsoybean, Dlncow, Dlnpraw, D(Lnyield,2), Dlnexport.

First, as the main source of external shocks, the total import volume of dairy products (Dlnimport), its contribution rate in the variance decomposition is second only to the raw milk production itself, and is the second factor affecting raw milk production [18]. The contribution rate of dairy product imports on China’s raw milk production in the first period was only 2.914%; the second period’s contribution rate rose rapidly to 27.036%; starting from the 14th period, the total dairy product imports are in the variance forecast with the contribution rate being stable at 25.910%. The forecast variance decomposition shows that among all external shock factors, China’s total dairy product import volume contribute the most to the fluctuation of raw milk production, which is consistent with the original hypothesis.

Second, as the explained variable, the raw milk production (D(Lnyield, 2) has the largest contribution to itself, in the first period being 74.594%; and in the second period dropped rapidly to only 37.85%; Its 13th period’s variance contribution rate was 34.653%, and there was no subsequent decrease, which remained and stabilized at 34.653%. Although the contribution of raw milk production to itself has decayed over time, it is still the main contributor to fluctuations in China’s raw milk production. It is not easy to obtain market supply and demand information in time, and the status of production and revenue in the previous period is still an important basis for dairy farmers to make production decisions.

Third, among the cost control variables, the contribution rate of corn price (Lnpmaize) to China’s raw milk production was 0.002 in the first period; this rose rapidly to 17.259% in the second period; After the 14th period, the impact of the price of corn on China’s raw milk production remains at 16.800%. International crude oil prices (Dlnwti) contributed the most to China’s raw milk production in the first period, which was 7.984%; starting from the 11th period, it remained at 5.588%. Soybean meal prices (Dlnpsoybean) contributed only 0.067% to China’s raw milk production in the first period; starting from the 11th period, the contribution of soybean meal prices has always remained at 1.230%. A horizontal comparison of the contribution of corn prices, soybean meal prices, and international crude oil prices on the impact of the China’s raw milk production, it is found that the contribution of domestic corn prices to the production of raw milk is much higher than that of international crude oil prices and domestic soybean meal prices is weakest [13]. This conclusion is consistent with the research hypothesis. There are two reasons for the difference in the contribution of different cost impact factors. One is that the consumption of corn in dairy cow feed is about 4 times that of soybean meal. Therefore, the cost of corn has a high share in the cost of raw milk production compared with the price of soybean meal; the second is that the international crude oil price affects the price of feed by affecting the production cost of corn and soybean meal, which is then transmitted to the production of raw milk [21].

Fourth, in terms of the contribution of demand impact factors to the changes in China’s raw milk production, domestic raw milk demand (Dlngdp) has the most significant contribution to China’s raw milk production. The impact of domestic raw milk demand on China’s raw milk production is not obvious at the beginning. And it reached a peak of 11.555% at the third stage; starting from the fourteenth period, the contribution of raw milk demand on China’s raw milk production remained stable. The contribution of China’s dairy exports (Dlnexport) to the production of raw milk is relatively small. Starting from the 11th period, the contribution of dairy exports has remained stable at 0.506%.

Fifth, in terms of the contribution of other factors, the dairy cow inventory (Lncow) contributes more to the changes in China’s raw milk production than raw milk prices (lnpraw). The current contribution of dairy cow inventory to China’s raw milk production was 7.640%; the current contribution of raw milk prices to China’s raw milk production fluctuations was 0.349%. The dairy cow inventory has a large contribution to China’s raw milk production, which is in line with the fact that the Chinese dairy cow yields is normal and the raw milk production output depends on the increase or decrease of dairy cow inventory [2, 4]; the raw milk purchase price makes a small contribution to the raw milk production. The reason for this phenomenon may be the weak premium ability of dairy farmers.

4.4 Empirical results of the impact of liquid dairy product import volume on China’s raw milk production

(1) Stationarity test

Stationarity test. The test results of ADF unit roots are shown in Table 5, except that lnpmaize rejects the null hypothesis at the 5% significance level, which is a stationary sequence; lngdp, lnwti, lnpsoybean, lncow, lnpraw, and lnexport are not stationary at the 10% significance level time series, after the first-order difference, Dlngdp, Dlnwti, Dlnpsoybean, Dlncow, Dlnpraw, and Dlnexport are stable time series at the 1% and 10% significance levels; lnlimport and lnyield are unstable time series at the 10% significance level, After two-order difference between the two, D (lnlimport, 2) and D (Lnyield, 2) are stationary time series.

Table 5
Unit root test results of each variable in the SVAR shock model of the import volume of liquid dairy products

Variables ADF statistics (c,t,q) 5% critical value P value Conclusion

Lngdp –1.534 (c,0,0) –2.986 0.500 Stable

Dlngdp –4.135 (c,0,1) –2.992 0.004 Unstable

Lnwti –1.209 (c,0,0) –2.986 0.654 Stable

Dlnwti –4.575 (c,0,1) –2.992 0.001 Unstable

Lnlimport –0.416 (c,0,0) –2.992 0.891 Stable

D(lnlimport,2) –5.994 (c,0,2) –2.998 ≤0.001 Stable

Lnpmaize –3.034 (c,0,0) –2.986 0.045 Unstable

Lndoupo –2.433 (c,0,0) –2.992 0.144 Stable

Dlndoupo –4.363 (c,0,1) –2.992 0.002 Unstable

Lncow –1.392 (c,0,0) –2.992 0.569 Stable

Dlncow –2.778 (c,0,1) –2.992 0.076 Unstable

Lnpraw –2.170 (c,0,0) –2.998 0.222 Stable

Dlnpraw –6.117 (c,0,1) –2.998 ≤0.001 Unstable

Lnyield –2.081 (c,0,0) –2.992 0.253 Stable

Dlnyield –5.499 (c,0,2) –2.998 ≤0.001 Unstable

Lnexport –2.012 (c,0,0) –2.986 0.280 Stable

Dlnexport –4.663 (c,0,1) –2.998 0.001 Stable

Variables	ADF statistics	(c,t,q)	5% critical value	P value	Conclusion
Lngdp	–1.534	(c,0,0)	–2.986	0.500	Stable
Dlngdp	–4.135	(c,0,1)	–2.992	0.004	Unstable
Lnwti	–1.209	(c,0,0)	–2.986	0.654	Stable
Dlnwti	–4.575	(c,0,1)	–2.992	0.001	Unstable
Lnlimport	–0.416	(c,0,0)	–2.992	0.891	Stable
D(lnlimport,2)	–5.994	(c,0,2)	–2.998	≤0.001	Stable
Lnpmaize	–3.034	(c,0,0)	–2.986	0.045	Unstable
Lndoupo	–2.433	(c,0,0)	–2.992	0.144	Stable
Dlndoupo	–4.363	(c,0,1)	–2.992	0.002	Unstable
Lncow	–1.392	(c,0,0)	–2.992	0.569	Stable
Dlncow	–2.778	(c,0,1)	–2.992	0.076	Unstable
Lnpraw	–2.170	(c,0,0)	–2.998	0.222	Stable
Dlnpraw	–6.117	(c,0,1)	–2.998	≤0.001	Unstable
Lnyield	–2.081	(c,0,0)	–2.992	0.253	Stable
Dlnyield	–5.499	(c,0,2)	–2.998	≤0.001	Unstable
Lnexport	–2.012	(c,0,0)	–2.986	0.280	Stable
Dlnexport	–4.663	(c,0,1)	–2.998	0.001	Stable

Note: (c, t, q) respectively represent constant term, trend term and lag term.

(2) Model lag order

The optimal lag order is determined according to the minimum criteria of AIC, SC, and HQ,and specific results are shown in Table 6. It can be seen that the optimal lag order of the SVAR model for the impact of liquid dairy product imports on China’s raw milk production is order 1. As the empirical analysis uses annual data, the sample space is limited; at the same time, there are several control variables for reasonable modeling. Therefore, the lag order is too large to make the SVAR model lose freedom and affect the accuracy of the empirical results; The optimal lag order is set to order 1 to meet the theoretical requirements and actual production conditions.

Table 6

Test results of optimal lag order

Lag	LogL	LR	FPE	AIC	SC	HQ
0	99.217	NA	0.000	–7.845	–7.400642*	–7.733
1	216.409	132.478*	2.38e-16*	–10.99207*	–6.549	–9.874611*

The unit circle test is used to verify the stability of the SVAR model in which the import volume of liquid dairy products has an impact on the production of raw milk in China. The test results are shown in Fig. 3. The reciprocal of the AR characteristic polynomial roots of the 1-order lagging liquid dairy product import volume impact model SVAR are all less than 1, that is, all are located in the unit circle, so the model meets the requirements for stability.

Fig. 3

The stationarity test result of the SVAR model.

4.5 Impulse response function and variance decomposition

(1) Impulse response function

First, when the domestic raw milk demand (Dlngdp) imposes a one-unit impact on China’s raw milk production, the current response of China’s raw milk production is –0.015; but in the subsequent third period, raw milk production’s response toward raw milk demand’s impact turned negative again, being –0.017; starting from the ninth period, the response of China’s raw milk production gradually tended to the zero axis and remained stable.

Second, the current impact of international crude oil prices (Dlnwti) on China’s raw milk production is –0.008; it turned into a positive impact in the sixth period, and the impact of international crude oil prices on China’s raw milk production gradually stabilized after the ninth period.

Third, when the import volume of liquid dairy products (D (lnlimport, 2)) imposes a positive impact of one standard deviation unit on China’s raw milk production, the current response of China’s raw milk production is –0.006; starting from the fourth period, The response of China’s raw milk to the import of liquid dairy products is basically stable below the zero axis, that is, liquid dairy products have a negative impact on China’s raw milk production. This conclusion is consistent with the original hypothesis.

Fourth, the impact of the price of corn (lnpmaize) on China’s raw milk production shows a strong negative effect in the first and second phases, that is, in the short term, changes in the price of corn led to a reduction in the production of raw milk in China; At the beginning of the third period, the response of raw milk production to corn prices gradually turned into a positive direction. When the price of soybean meal (Dlnpsoybean) caused a one-unit impact on China’s raw milk production, raw milk production responded negatively to it during the first six periods. Starting from the seventh period, the impact of soybean meal prices gradually turned positive (Fig. 4).

Fig. 4

The impulse response of China’s raw milk production to the impact of liquid dairy products import volume and impacts of other external factors.

Fifth, the production of raw milk (D (lnyield, 2)) is most affected by its own shock in the short term with the current shock response being 0.032. Starting from the third period, the response of raw milk production to its own shock is infinitely close to the zero axis which is towards stable.

Sixth, the current impact of dairy cow inventory (Dlncow) on China’s raw milk is 0.032; after the third period, the impact of dairy cow inventory on raw milk production gradually disappears. The reason for the short-term impact of dairy cattle inventory on raw milk production is that the inventory continues to fluctuate in the short-term, and some individual years even incidents of large-scale “cow killing” was occurred, which severely dampens the enthusiasm of dairy farmers in production and thus the raw milk production growth cannot be stimulated in the long term.

In summary, in the SVAR model where the import of liquid dairy products has an impact on China’s raw milk production, the empirical analysis results of the impact of control variables, including the demand for dairy products, international crude oil prices, corn prices, soybean meal prices, raw milk production, dairy cow inventory, raw milk purchase prices, and dairy product export volume, on China’s raw milk production are consistent with the original hypothesis. However, after comparison, it is found that the import volume of liquid dairy products and the total volume of dairy products have different impacts on China’s raw milk production. The impact of domestic dairy product imports on China’s raw milk production is mainly positive, but the impact of liquid milk import volume on China’s raw milk production is mainly negative, and the impact of total dairy product imports on China’s raw milk production is much higher than that of imported liquid dairy products. There are two reasons for the above difference. One is that the cumulative imports of liquid dairy products accounted for 16.75% of the total imports of dairy products from 1996 to 2017, that is, the impact caused by the imports of liquid dairy products is less than the impact of the total imports of dairy products; the second is that the output of liquid dairy products in China in 2017 was 26,916,600 tons. If the conversion ratio between raw milk and liquid dairy products is 1:1, it is equivalent to consuming 2,700 tons of raw milk, while China’s production of raw milk in 2017 was 3545 10,000 tons, that is, the raw milk consumed by liquid dairy products accounts for 75.93% of China’s total raw milk production. While in 2017, the import volume of dry dairy products was 1,472,500 tons, accounting for 67.73% of China’s total dairy product import volume. Therefore, the production of dairy products in China is dominated by liquid dairy products, supplemented by dry dairy products, while imports of dairy products are based on dry dairy products and supplemented by liquid dairy products. The production and import characteristics of dairy products determine that if China imports more liquid dairy products, it will severely reduce the demand for domestic raw milk for the production of liquid dairy products. The continued growth of liquid diary import will have a negative impact on China’s raw milk production; Relatively speaking, only a small part of domestic raw milk is used to produce dry dairy products. Therefore, the increase in the total import volume of dairy products will have a positive impact on China’s raw milk production in the short term.

(2) Variance decomposition

First, the import volume of liquid dairy products (D (lnlimport)) ranks sixth in the variance decomposition. The current contribution of liquid dairy products imports to the changes in China’s raw milk production volume is 1.059%; after the 14th period, the contribution of liquid milk to raw milk production has stabilized at 2.931%.

Second, the contribution of raw milk production (D (Lnyield, 2)) to its own volatility is still relatively large. In the variance decomposition, the current contribution of raw milk production to itself was 45.223%; then the contribution of raw milk production to itself slowly dropped to 24.135% in the 15th period, and remained stable for a long time.

Third, among the cost control variables, the price of corn (lnpmaize) accounts for much higher percent than the price of soybean meal (Dlnpsoybean) and the price of international crude oil (Dlnwti). The current contribution of corn price to raw milk production is 3.513%; the current contribution of soybean meal price to raw milk production is 4.926%; and the international crude oil price contributes relatively little to the fluctuation of China’s raw milk production, the contribution being 2.462%. Compared with the variance decomposition results of the SVAR model, which has an impact on China’s raw milk production caused by the total imports of dairy products, the price of corn is still the most important cost factor affecting China’s raw milk production; the importance of the contribution of soybean meal prices and international crude oil prices to raw milk production is converted to each other, but the conversion of the contribution of the two is purely a change in the mathematical sense, and will not change the actual impact of the two on China’s raw milk production (Table 7).

Table 7

SVAR model prediction variance decomposition of total liquid dairy products imports-the percentage of each factor in the prediction variance

Period	Standard deviation	GDP growth rate	International crude oil prices	Imports volume of dairy products	Corn price	Soybean meal price	Dairy cow inventory	Raw milk price	Raw milk output	Dairy products export volume
1	0.112	7.204	2.462	1.059	3.513	4.926	35.611	0.003	45.223	0.000
2	0.138	7.291	1.812	1.244	40.853	2.379	18.120	2.100	25.305	0.896
3	0.148	11.150	2.084	1.294	37.810	3.227	16.660	1.927	25.023	0.827
4	0.152	10.857	2.041	2.812	36.842	3.407	16.242	1.886	24.420	1.492
5	0.155	10.625	2.034	2.758	37.180	3.768	15.969	1.848	24.344	1.475
6	0.157	10.569	2.023	2.879	36.991	3.769	15.967	1.838	24.452	1.512
7	0.157	10.747	2.028	2.864	37.090	3.747	15.867	1.828	24.299	1.530
8	0.157	10.796	2.020	2.921	37.146	3.759	15.810	1.821	24.199	1.528
9	0.157	10.813	2.021	2.924	37.164	3.762	15.792	1.819	24.169	1.535
10	0.157	10.833	2.020	2.927	37.164	3.763	15.781	1.819	24.153	1.541
11	0.157	10.845	2.020	2.928	37.161	3.766	15.776	1.818	24.144	1.542
12	0.157	10.850	2.020	2.929	37.162	3.766	15.773	1.818	24.138	1.542
13	0.157	10.852	2.020	2.930	37.162	3.767	15.772	1.818	24.136	1.543
14	0.157	10.853	2.020	2.931	37.162	3.767	15.771	1.818	24.135	1.543
15	0.157	10.854	2.019	2.931	37.163	3.767	15.771	1.818	24.135	1.543
24	0.157	10.855	2.019	2.931	37.163	3.767	15.771	1.818	24.134	1.543

Fourth, the contribution of domestic raw milk demand (Dlngdp) to changes in raw milk production is much higher than that of China’s dairy exports (Dlnexport). The current contribution of China’s raw milk demand to the changes in China’s raw milk production is 7.204%; the contribution of dairy product exports to the change in China’s raw milk production has never been high, and its current contribution rate is zero. China’s raw milk production mainly meets internal demand, and the annual export volume of dairy products is mostly maintained at about 25,000 tons. Therefore, the impact of dairy product exports on the fluctuation of China’s raw milk production is less than internal demand.

Fifth, among other control variables, the contribution of dairy cow inventory (Dlncow) to fluctuations in China’s raw milk production is higher than that of raw milk purchase prices (Dlnpraw). The current contribution of dairy cow inventory to China’s raw milk production is 35.611%; the purchase price of raw milk has a relatively small contribution to changes in raw milk production, which remains at 1.818% for a long time.

A comprehensive comparison of the variance decomposition results of the SVAR model of the impact of liquid dairy product imports and total dairy product imports on China’s raw milk production reveals that raw milk production has a relatively large contribution to the fluctuations in raw milk production in China. This is because for quite a long time China’s raw milk producers mainly operate in scattered, small-scale operations, and have obvious small-scale farmers’ economic characteristics. That is, the raw milk production decision in this period mainly depends on the profitability of raw milk in the previous period. In the above two SVAR models, the contribution of other control variables to the raw milk production has changed. The main reason for the above changes is that the total import volume of dairy products and the import volume of liquid dairy products are significantly different in value when the variance is decomposed. And the values of the remaining control variables have not changed, and the order of the variables in the two SVAR models have not changed. Therefore, the total import volume of dairy products and the value of the import volume of liquid dairy products indirectly lead to the changes of the contribution of the control variables to the production of raw milk in China. The changes, which are only changes in the mathematical sense, will not change the objective facts that various variables have an impact on China’s raw milk production.

5 Conclusions and discussion

5.1 Conclusions

(1) The output of raw milk is the main factor of its own fluctuations. In the short term, dairy product import volume will have a positive impact on China’s raw milk production, and the import of liquid dairy products will have a negative impact. In the long run, the impact of liquid dairy product imports and total dairy product imports on raw milk production in China tends to be negative. Therefore, sustained and fast-growing dairy imports have a significant substitution effect on raw milk production in China.

(2) Cost control variables are dominated by negative impacts in the short term. The impact of corn prices on raw milk production is stronger than that of soybean meal price and crude oil price. In the dairy cow feed ratio, the proportion of corn is three times that of soybean meal, so the impact of corn price on raw milk production is greater than that of soybean meal. International crude oil prices will affect corn prices, soybean meal prices, and fuel costs for pastures; therefore, the impact of national raw milk prices on China’s raw milk production is indirect and negative.

(3) The impact of domestic raw milk demand on raw milk production fluctuates frequently in the short term, and the impact of dairy product exports on China’s raw milk production is mainly positive; the impact of dairy cow inventory is relatively small, and raw milk purchase prices mainly have a positive impact in the short term, these results are in line with the laws of economics.

5.2 Discussions

With the improvement of the income level of Chinese consumers, the change of consumption concept, the support of dairy industry policies, the improvement of dairy product marketing strategies, and the advancement of logistics refrigeration technology, the scale of China’s dairy product market has expanded. China has adopted the method of expanding the scale of raw milk production and increasing the import volume of dairy products to meet the market demand of dairy products, but there is evidence shows that the rapid expansion of the scale of raw milk production has led to a decline in production efficiency, resulting in diseconomies of scale [6]; At the same time, the continuous growth of dairy product imports has produced an obvious substitution effect on the demand for raw milk in China, which is why this study analyzes the impact of dairy product imports on China’s raw milk production.

China is the country with the most open dairy market in the world; China’s dairy industry lacks price advantages, quality advantages and technological advantages, which leads to the continuous growth of China’s dairy product imports. Existing research believes that import trade is one of the main ways of technology spillover; and the state of the art in industrial processes management to obtain positive and sustainable effects on production [5]. Through import trade, developing countries can imitate, improve and re-innovate goods from developed countries; That is to say, the technological content of imported products is proportional to their own quality level, dairy products exported to China have quality advantages, price advantages and technological advantages. Therefore, future research can try to explore the impact of the technological content of imported dairy products on the technological progress of raw milk production in China.

Author Contributions

First author Y.B. is the key author and did most of the writing in this contribution; Corresponding author J.W. and L.Z. verified and solidified the argument, edited the text and drafted the conclusions; C.W. gave review suggestions for the manuscript on the whole writing process. All authors have read and agreed to the published version of the manuscript.

Data availability statement

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Conflicts of interest

It is declared by the authors that this article is free of conflict of interest.

Funding statement

No funding was received.

References

B.C.

and Dong

X.X.

, The impact of dairy imports and the strategic choice of China’s dairy industry security-Also on the terms of trade of the domestic agricultural safety net,84-94+, Issues of Agricultural Economics 37(01) (2016), 112;.

Sims

, Macroeconomics and reality, Econometrica 48(1) (1980).

Liu

C.Q.

, Han

and Zhang

Y.H.

, International comparison and development ideas of China’s dairy industry competitiveness, China Rural Economy (07) (2018), 130–144.

Liu

C.Q.

, Han

and Zhang

Y.H.

, International comparison of China’s dairy industry competitiveness and development ideas, China Rural Economy (07) (2018), 130–144.

Abad-Segura

, Morales

M.E.

, Cortés-García

F.J.

et al., Industrial processes management for a sustainable society: Globalresearch analysis, Processes 8(5) (2020), 631.

Fuller

, Huang

, Ma

et al., Got milk? The rapid rise of China’s dairy sector and its future prospects, Food Policy 31(3) (2006), 201–215.

Luo

, The impact of external shocks on the price volatility of Chinese agricultural products: an empirical study based on the SVAR model, Agricultural Technology and Economy(10) (2011), 4–11.

G.C.

Analysis of the effect of China-Australia Free Trade Agreement on China’s dairy products import and export trade, Shandong University, 2018.

G.H.

, A Summary of Domestic Research on “External Shocks, Economic Developments (05) (2009), 75–78.

10.

Cheng

G.Q.

, Hu

B.C.

and Xu

X.G.

, Analysis of the impact of the new round of agricultural product price increases, Management World (01)(2008), 57-62+81+187–188.

11.

Wang

and Luo

, Can a colonial legacy explain the pollution haven hypothesis? A city-level panel analysis, Structural Change and Economic Dynamics 60 (2022), 482–495.

12.

H.L.

and Li

B.L.

, International competitiveness of China’s dairy products and analysis of influencing factors, International Trade Issues(10) (2011), 14–24.

13.

H.M.

and Jiang

X.Y.

, Analysis on the financial factors of China’s bulk agricultural product price fluctuations-An empirical study based on the SVAR model, Agricultural Technology and Economy(02) (2013), 51–58.

14.

Liu

J.Q.

Modern Macroeconomic Shock Theory, Jilin University Press, 2000.

15.

, Investigation on the external shock factors of this round of domestic price fluctuations, Financial Research (06) (2009), 31–43.

16.

Peng

X.F.

, Analysis on the production technology efficiency of raw milk in China, Agricultural Technology and Economy (06) (2008), 23–29.

17.

Peng

X.F.

, The impact of the construction of China-New Zealand Free Trade Zone on the development of China’s dairy industry, International Trade Issues (01) (2009), 54–60.

18.

Wang

X.S.

and Xie

S.X.

, How do international prices of agricultural products affect the prices of Chinese agricultural products? Economic Research 47(03) (2012), 141–153.

19.

Lei

X.T.

, Xu

Q.Y.

, Jin

C.Z.

Nature of property right and the motives for holding cash: Empirical evidence from Chi-nese listed companies, Managerial and Decision Economics (2021). doi: 10.1002/mde.3469.

20.

X.Y.

, He

Z.W.

, Liu Fang

Chen

J.M.

, Prediction of the damage of the establishment of China-Australia Free Trade Zone to China’s dairy industry, Agricultural Outlook 13(07) (2017), 66–73.

21.

Choi

, Wang

, Zhu

and Lai

, Students’ Perception and Expectation towards Pharmacy Education: A Qualitative Study of Pharmacy Students in a Developing Country, Indian Journal of Pharmaceutical Education and Research 55(1) (2021).