中国养猪业生产波动分析与预测预警研究
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摘要
我国是世界生猪生产大国,同时,我国也是猪肉的消费大国。养猪业的稳定发展对促进整个畜牧业、农业的发展有着重要意义,在整个国民经济中也占有非常重要的地位。在2000-2011年间,我国生猪价格发生了三次大幅度的波动,对CPI上升起了领头和推波助澜的作用。CPI增长原因78%的是食品价格,食品价格中猪肉涨幅达59.8%[11。由此可见,生猪生产波动不仅影响宏观经济运行,也影响了社会经济的稳定发展。
本文首先对我国的生猪生产情况进行了简要的介绍和分析。养猪户数大量减少,但散养的养殖场(户)仍占绝对多数,在95%以上。2003年我国生猪规模化程度仅为30%左右,经过几年的生猪生产结构的调整,2010年生猪规模化程度已接近70%。虽然规模化程度有了大幅度的提高,但我国的生猪养殖水平与国际养殖水平相比,仍有较大提升空间。
本文研究内容如下：
(1)鉴于研究我国生猪生产波动的研究主要采用单指标的研究方法,本文突破以往的采用单指标的研究方法,采用出栏量、存栏量和能繁母猪存栏量三个指标。生猪生产三个指标之间并不是孤立的,而是存在内在的联系,更能准确反映生猪生产的实际情况。
(2)采用H-P滤波法对三个指标分解出波动成分,并研究其波动周期。存栏量的波动与能繁母猪存栏量波动具有相同的波动规律,出栏量与存栏量的谷底发生时间基本上是一致的。经格兰杰因果关系分析发现：出栏量波动是存栏量波动、能繁母猪存栏量波动的格兰杰因果关系；存栏量波动、能繁母猪存栏量波动是出栏量波动的格兰杰因果关系,并确定各指标之间的影响方向及影响程度。
(3)本文对生产成本构成和收益进行了分析。经格兰杰因果关系检验,生产成本中主要成本波动与出栏量波动、存栏量波动、能繁母猪存栏量波动存在着以下关系：①散养、规模精饲料费波动与出栏量的波动存在互为格兰杰因果关系：散养、规模仔猪费波动与出栏量波动存在互为格兰杰因果关系。散养死亡损失费波动与出栏量波动只存在单向的格兰杰因果关系。在动态模型中散养精饲料、散养死亡损失费波动对出栏量影响显著。②散养和规模精饲料费波动、散养仔猪费波动、规模死亡损失费波动、规模雇工费波动与存栏量存在互为格兰杰因果关系；规模仔猪费波动、散养医疗防疫费波动、规模死亡损失费波动与存栏量波动只存在单向的格兰杰因果关系。在动态模型中,散养和规模的精饲料费、散养死亡损失费对存栏量影响显著。③散养和规模仔猪费波动、规模死亡损失费波动与能繁母猪存栏量存在互为格兰杰因果关系；散养和规模卫生防疫费波动、散养死亡损失费波动、规模医疗防疫费波动与能繁母猪存栏量波动存在单向格兰杰因果关系。在能繁母猪存栏量动态模型中散养仔猪费和医疗防疫费、规模仔猪费对能繁母猪存栏量影响显著。④生猪生产收益波动对生猪生产波动的影响分析发现,不管是散养还是规模生猪收益波动与生猪的出栏量波动不存在格兰杰因果关系：散养和规模的生猪收益波动与存栏量的波动存在双向格兰杰因果关系；散养和规模收益波动与能繁母猪存栏量波动存在双向格兰杰因果关系。在三个动态模型中只有规模收益对能繁母猪存栏量影响显著。
(4)公共卫生事件对我国的生猪生产波动的影响,从中长期来看影响不大,存在短期的影响,且影响是局部性的。生猪的疫情对生猪的影响较大,且影响时间较长。国家的生猪生产政策对生猪生产的影响还没有明确的结论,不同的学者对生猪生产政策意见不一。通过系统动力学对生猪疫情、生猪生产政策无法量化分析的一些因素进行了仿真分析。仿真结果发现,生猪在没有延迟的情况下,生猪的供给可以很好的适应市场需求的变化,不会出现生猪出栏量的大幅波动的情况。这说明,生猪生产过程的延迟是生猪出栏量的波动的根源之一。生猪生产过程的延迟使生猪出栏量波动变得频繁。发生重大疫情也是生猪生产的波动的原因,发生重大疫情时改变了能繁母猪存栏量的趋势,生猪存栏量波动频率虽然没有改变,但改变了波动幅度。利用系统动力学模型对无法量化分析的能繁母猪补贴、冻肉储备政策进行了仿真分析。
(5)本文比较BP人工神经网络法和支持向量机方法对生猪出栏量、存栏量及能繁母猪存栏量预测的效果,确定支持向量机方法建立生猪出栏量预测模型,而BP人工神经网络法适合对存栏量及能繁母猪存栏量预测。并对2013年出栏量、存栏量、能繁母猪存栏量走势进行预测。基于上述研究,在本文勾勒的生猪产量风险预警理论的基本框架内,运用定量的预警方法,对2013年和2014年生猪生产风险进行了预警。
本文研究创新点：1)在方法上,采用格兰杰因果关系检验模型分析生猪物质与服务费用、人工成本、生猪生产收益对生猪生产波动影响的格兰杰因果关系,以及各因素之间波动影响方向及波动幅度,采用动态面板数据模型进行实证分析。对于无法量化分析的影响因素采用系统动力学进行仿真分析。2)在内容上,打破衡量生猪生产波动采用单一指标的衡量方式,采用出栏量、存栏量、能繁母猪存栏量三个指标来全面衡量我国养猪业生产的波动。基于支持向量机模型、BP人工神经网络模型对我国养猪业的生产进行预测、在预测的基础上对养猪业生产波动进行了预警研究。
本文基于相关分析提出了养猪业生产波动调控的政策建议。从加强畜牧信息网络平台建设、完善与健全防疫体系、加强饲料产业的建设、适度提高散养的规模化程度、实时推出生猪期货和加强各级政府部门间宏观调控措施的协调六个方面提出了一些政策建议。
China is the leading country of pig production and pork consumption in the world. Stable development of the pig industry is very significant to promote the development of the animal husbandry, agriculture, also occupies a very important position in the national economy as a whole. In the period of2000-2011, three were sharp fluctuation of pig price in China, which forced up CPI. Growth of CPI,78%is due to the price of food, while pork price rose to59.8%(Jiang Hongyun). Thus, pig production fluctuation not only affects the macroeconomic operation, but also affects the social stability and economic development.
This paper first introduces and analyzes China's pig production conditions briefly. The number of pig raising households reduced greatly, but backyard farms (households) still accounted for an absolute majority, about more than95%. China's pig scale is only about30%in2003, after several years of pig production structure adjustment, but pig scale has been close to70%in2010. Although the degree of the scale has been greatly improved, China's pig breeding level, compared with international breeding level, there is still much room for improvement.
The research contents of this paper are as follows:
(1) Study on pig production fluctuation in China mainly adopted the research method of single index in the past. This paper makes a breakthrough in study method of single index, adopting three indexes, namely, amount of slaughter, amount of livestock on hand and amount of breeding sow livestock on hand. Three indexes are not isolated, and have the intrinsic relation. Thus, it can more accurately reflect the actual situation of pig production.
(2) Using the H-P filter method (Hodrick-Prescott Filter), decomposing the volatility components of the three indicators, and studying its fluctuation cycle. Fluctuation of amount of livestock on hand and fluctuation of amount of breeding sow livestock on hand have the same fluctuations:the bottom time of the amount of slaughter and livestock on hand is basically identical. Using the Grainger causality analysis, we find that fluctuation of slaughter is Grainger causal relationship of fluctuation of livestock on hand and fluctuation of breeding sows livestock on hand; fluctuation of livestock on hand and fluctuation of breeding sows livestock on hand are Grainger causal relationship of fluctuation of the fluctuation of slaughter, and determining the influence direction and the influence degree among indexes.
(3) This paper analyzes the production cost structure and benefits structure. Using Granger causality test, we find that fluctuation of the main cost, fluctuation of slaughter, fluctuation of livestock on hand, fluctuation of breeding sow livestock on hand have the following relationship: fluctuation of backyard and scale fine feed fees and fluctuation of slaughter have mutual Grainger causal relationship; fluctuation of backyard and piglet fees scale and fluctuation of slaughter have mutual Grainger causal relationship. Fluctuation of backyard death damages and fluctuation of slaughter have only a one-way Grainger causal relationship. In dynamic panel data model, fluctuations of backyard fine feed fees and backyard death loss fees have the most obvious influence on the amount of slaughter. Fluctuation of backyard and scale fine feed costs, fluctuation of backyard piglet fees, fluctuation of scale death loss fees, fluctuation of scale employment costs and amount of livestock on hand have mutual Grainger causal relationship; fluctuation of scale piglet fees, fluctuation of backyard medical treatment fees, fluctuation of scale death loss fees and fluctuation of amount of livestock have only one-way Grainger causal relationship. In the dynamic model, backyard and scale fine feed costs and backyard death loss fees have obvious effect on amount of livestock on hand. Fluctuation of backyard and scale piglet fees, fluctuation of scale death loss fees and amount of breeding sows livestock on hand have mutual Grainger causal relation; fluctuation of backyard and scale health and epidemic prevention costs, fluctuation of backyard death loss costs, fluctuation of scale medical and epidemic prevention costs and fluctuation of breeding sows have one-way Grainger causal relation. In the dynamic model of amount of breeding sows livestock on hand, backyard piglet fees, medical and epidemic prevention costs and scale piglet fees have obvious influence on the amount of breeding sows livestock on hand. By analyzing the effect of fluctuation of pig production yield on fluctuation of pig production, we find that backyard and scale pig yields have not Grainger causal relation with the amount of pig slaughter; fluctuation of backyard and scale pig yields have two-way Grainger causal relation with the amount of livestock on hand; fluctuation of backyard and scale yields have mutual Grainger causal relation with the amount of breeding sows livestock on hand. In the three dynamic models only returns to scale has a significant effect on the amount of breeding sows livestock.
(4) Public health events have little obvious impact China's pig production fluctuation from the long view, while there is short-term effect, and the effect is localized. Effect of the epidemic on the pig production is larger, and the time of impact is very long. Influence of national policy on pig production has no still clear conclusion, and different scholars disagree on pig production policy. Using the system dynamics, we simulate and analyze some factors such as pig diseases and pig production policy that can't be quantized. The simulation results reveal that, under the conditions of pig production without delay, pig supply can be very good to adapt to the change of market demand, not cause sharp fluctuations in the amount of pig slaughter. This shows that, the process delay of pig production is one of the causes of the fluctuations of the amount of pig slaughter. Process delays of pig production make the amount of pig slaughter become frequent. The occurrence of major epidemic is also cause of pig production fluctuation. The occurrence of major epidemic changes the trend of breeding sows livestock on hand. Fluctuation frequency of pig livestock on hand does not change, but the amplitude of fluctuation has changed. Using the system dynamics model, we simulate the subsidies to breeding sows and frozen meat reserve policy which can't be quantized.
(5) Compared with the effect of BP artificial neural network method (BP artificial neural networks (ANN)) and support vector machine method (support vector machine (SVM)) on amount of pig slaughter, amount of pig livestock on hand and amount of breeding sows livestock on hand, we determine to adopt the method of support vector machines to establish pig slaughter prediction model, while the BP manual neural network method is useful for prediction of amount of pig livestock on hand and amount of breeding sows on hand. We predict the trend of amount of pig slaughter, amount of pig livestock on hand and amount of breeding sows livestock on hand in2013. Based on the above research, with the basic framework of pig production risk early-warning theory, this paper forecasts the risk of pig in2013and in2014by using quantitative warning method.
The innovation points of this research:1. In the method, using the Grainger causality test model, analyzing pig material and service costs, artificial costs, the Grainger causal relationship between pig production yield and pig production fluctuation, the direction and amplitude of fluctuation among, using dynamic panel data model for empirical analysis, using system dynamics simulation analysis for the factors which can't be quantized.2. In content, breaking through the measure of pig production fluctuation with the single index, adopting the three indicators such as amount of pig slaughter, amount of pig livestock on hand, amount of breeding sows livestock on hand to measure the fluctuation of pig production in China. Based on support vector machine model production and BP artificial neural network model, we predict China's pig production, and conduct the early warning system research to the fluctuation of pig production based on the forecast.
This paper presents policy proposal of pig production fluctuation regulation based on correlation analysis. From strengthening the construction of animal husbandry information network platform, perfecting and improving epidemic prevention system, strengthening the construction of feed industry, moderately improving the scale degree of backyard, issuing pig futures in a real time and strengthening the coordination of the macro-control measures of government departments at all levels, this paper puts forward some policy suggestions in six aspects.
本文首先对我国的生猪生产情况进行了简要的介绍和分析。养猪户数大量减少,但散养的养殖场(户)仍占绝对多数,在95%以上。2003年我国生猪规模化程度仅为30%左右,经过几年的生猪生产结构的调整,2010年生猪规模化程度已接近70%。虽然规模化程度有了大幅度的提高,但我国的生猪养殖水平与国际养殖水平相比,仍有较大提升空间。
本文研究内容如下：
(1)鉴于研究我国生猪生产波动的研究主要采用单指标的研究方法,本文突破以往的采用单指标的研究方法,采用出栏量、存栏量和能繁母猪存栏量三个指标。生猪生产三个指标之间并不是孤立的,而是存在内在的联系,更能准确反映生猪生产的实际情况。
(2)采用H-P滤波法对三个指标分解出波动成分,并研究其波动周期。存栏量的波动与能繁母猪存栏量波动具有相同的波动规律,出栏量与存栏量的谷底发生时间基本上是一致的。经格兰杰因果关系分析发现：出栏量波动是存栏量波动、能繁母猪存栏量波动的格兰杰因果关系；存栏量波动、能繁母猪存栏量波动是出栏量波动的格兰杰因果关系,并确定各指标之间的影响方向及影响程度。
(3)本文对生产成本构成和收益进行了分析。经格兰杰因果关系检验,生产成本中主要成本波动与出栏量波动、存栏量波动、能繁母猪存栏量波动存在着以下关系：①散养、规模精饲料费波动与出栏量的波动存在互为格兰杰因果关系：散养、规模仔猪费波动与出栏量波动存在互为格兰杰因果关系。散养死亡损失费波动与出栏量波动只存在单向的格兰杰因果关系。在动态模型中散养精饲料、散养死亡损失费波动对出栏量影响显著。②散养和规模精饲料费波动、散养仔猪费波动、规模死亡损失费波动、规模雇工费波动与存栏量存在互为格兰杰因果关系；规模仔猪费波动、散养医疗防疫费波动、规模死亡损失费波动与存栏量波动只存在单向的格兰杰因果关系。在动态模型中,散养和规模的精饲料费、散养死亡损失费对存栏量影响显著。③散养和规模仔猪费波动、规模死亡损失费波动与能繁母猪存栏量存在互为格兰杰因果关系；散养和规模卫生防疫费波动、散养死亡损失费波动、规模医疗防疫费波动与能繁母猪存栏量波动存在单向格兰杰因果关系。在能繁母猪存栏量动态模型中散养仔猪费和医疗防疫费、规模仔猪费对能繁母猪存栏量影响显著。④生猪生产收益波动对生猪生产波动的影响分析发现,不管是散养还是规模生猪收益波动与生猪的出栏量波动不存在格兰杰因果关系：散养和规模的生猪收益波动与存栏量的波动存在双向格兰杰因果关系；散养和规模收益波动与能繁母猪存栏量波动存在双向格兰杰因果关系。在三个动态模型中只有规模收益对能繁母猪存栏量影响显著。
(4)公共卫生事件对我国的生猪生产波动的影响,从中长期来看影响不大,存在短期的影响,且影响是局部性的。生猪的疫情对生猪的影响较大,且影响时间较长。国家的生猪生产政策对生猪生产的影响还没有明确的结论,不同的学者对生猪生产政策意见不一。通过系统动力学对生猪疫情、生猪生产政策无法量化分析的一些因素进行了仿真分析。仿真结果发现,生猪在没有延迟的情况下,生猪的供给可以很好的适应市场需求的变化,不会出现生猪出栏量的大幅波动的情况。这说明,生猪生产过程的延迟是生猪出栏量的波动的根源之一。生猪生产过程的延迟使生猪出栏量波动变得频繁。发生重大疫情也是生猪生产的波动的原因,发生重大疫情时改变了能繁母猪存栏量的趋势,生猪存栏量波动频率虽然没有改变,但改变了波动幅度。利用系统动力学模型对无法量化分析的能繁母猪补贴、冻肉储备政策进行了仿真分析。
(5)本文比较BP人工神经网络法和支持向量机方法对生猪出栏量、存栏量及能繁母猪存栏量预测的效果,确定支持向量机方法建立生猪出栏量预测模型,而BP人工神经网络法适合对存栏量及能繁母猪存栏量预测。并对2013年出栏量、存栏量、能繁母猪存栏量走势进行预测。基于上述研究,在本文勾勒的生猪产量风险预警理论的基本框架内,运用定量的预警方法,对2013年和2014年生猪生产风险进行了预警。
本文研究创新点：1)在方法上,采用格兰杰因果关系检验模型分析生猪物质与服务费用、人工成本、生猪生产收益对生猪生产波动影响的格兰杰因果关系,以及各因素之间波动影响方向及波动幅度,采用动态面板数据模型进行实证分析。对于无法量化分析的影响因素采用系统动力学进行仿真分析。2)在内容上,打破衡量生猪生产波动采用单一指标的衡量方式,采用出栏量、存栏量、能繁母猪存栏量三个指标来全面衡量我国养猪业生产的波动。基于支持向量机模型、BP人工神经网络模型对我国养猪业的生产进行预测、在预测的基础上对养猪业生产波动进行了预警研究。
本文基于相关分析提出了养猪业生产波动调控的政策建议。从加强畜牧信息网络平台建设、完善与健全防疫体系、加强饲料产业的建设、适度提高散养的规模化程度、实时推出生猪期货和加强各级政府部门间宏观调控措施的协调六个方面提出了一些政策建议。
China is the leading country of pig production and pork consumption in the world. Stable development of the pig industry is very significant to promote the development of the animal husbandry, agriculture, also occupies a very important position in the national economy as a whole. In the period of2000-2011, three were sharp fluctuation of pig price in China, which forced up CPI. Growth of CPI,78%is due to the price of food, while pork price rose to59.8%(Jiang Hongyun). Thus, pig production fluctuation not only affects the macroeconomic operation, but also affects the social stability and economic development.
This paper first introduces and analyzes China's pig production conditions briefly. The number of pig raising households reduced greatly, but backyard farms (households) still accounted for an absolute majority, about more than95%. China's pig scale is only about30%in2003, after several years of pig production structure adjustment, but pig scale has been close to70%in2010. Although the degree of the scale has been greatly improved, China's pig breeding level, compared with international breeding level, there is still much room for improvement.
The research contents of this paper are as follows:
(1) Study on pig production fluctuation in China mainly adopted the research method of single index in the past. This paper makes a breakthrough in study method of single index, adopting three indexes, namely, amount of slaughter, amount of livestock on hand and amount of breeding sow livestock on hand. Three indexes are not isolated, and have the intrinsic relation. Thus, it can more accurately reflect the actual situation of pig production.
(2) Using the H-P filter method (Hodrick-Prescott Filter), decomposing the volatility components of the three indicators, and studying its fluctuation cycle. Fluctuation of amount of livestock on hand and fluctuation of amount of breeding sow livestock on hand have the same fluctuations:the bottom time of the amount of slaughter and livestock on hand is basically identical. Using the Grainger causality analysis, we find that fluctuation of slaughter is Grainger causal relationship of fluctuation of livestock on hand and fluctuation of breeding sows livestock on hand; fluctuation of livestock on hand and fluctuation of breeding sows livestock on hand are Grainger causal relationship of fluctuation of the fluctuation of slaughter, and determining the influence direction and the influence degree among indexes.
(3) This paper analyzes the production cost structure and benefits structure. Using Granger causality test, we find that fluctuation of the main cost, fluctuation of slaughter, fluctuation of livestock on hand, fluctuation of breeding sow livestock on hand have the following relationship: fluctuation of backyard and scale fine feed fees and fluctuation of slaughter have mutual Grainger causal relationship; fluctuation of backyard and piglet fees scale and fluctuation of slaughter have mutual Grainger causal relationship. Fluctuation of backyard death damages and fluctuation of slaughter have only a one-way Grainger causal relationship. In dynamic panel data model, fluctuations of backyard fine feed fees and backyard death loss fees have the most obvious influence on the amount of slaughter. Fluctuation of backyard and scale fine feed costs, fluctuation of backyard piglet fees, fluctuation of scale death loss fees, fluctuation of scale employment costs and amount of livestock on hand have mutual Grainger causal relationship; fluctuation of scale piglet fees, fluctuation of backyard medical treatment fees, fluctuation of scale death loss fees and fluctuation of amount of livestock have only one-way Grainger causal relationship. In the dynamic model, backyard and scale fine feed costs and backyard death loss fees have obvious effect on amount of livestock on hand. Fluctuation of backyard and scale piglet fees, fluctuation of scale death loss fees and amount of breeding sows livestock on hand have mutual Grainger causal relation; fluctuation of backyard and scale health and epidemic prevention costs, fluctuation of backyard death loss costs, fluctuation of scale medical and epidemic prevention costs and fluctuation of breeding sows have one-way Grainger causal relation. In the dynamic model of amount of breeding sows livestock on hand, backyard piglet fees, medical and epidemic prevention costs and scale piglet fees have obvious influence on the amount of breeding sows livestock on hand. By analyzing the effect of fluctuation of pig production yield on fluctuation of pig production, we find that backyard and scale pig yields have not Grainger causal relation with the amount of pig slaughter; fluctuation of backyard and scale pig yields have two-way Grainger causal relation with the amount of livestock on hand; fluctuation of backyard and scale yields have mutual Grainger causal relation with the amount of breeding sows livestock on hand. In the three dynamic models only returns to scale has a significant effect on the amount of breeding sows livestock.
(4) Public health events have little obvious impact China's pig production fluctuation from the long view, while there is short-term effect, and the effect is localized. Effect of the epidemic on the pig production is larger, and the time of impact is very long. Influence of national policy on pig production has no still clear conclusion, and different scholars disagree on pig production policy. Using the system dynamics, we simulate and analyze some factors such as pig diseases and pig production policy that can't be quantized. The simulation results reveal that, under the conditions of pig production without delay, pig supply can be very good to adapt to the change of market demand, not cause sharp fluctuations in the amount of pig slaughter. This shows that, the process delay of pig production is one of the causes of the fluctuations of the amount of pig slaughter. Process delays of pig production make the amount of pig slaughter become frequent. The occurrence of major epidemic is also cause of pig production fluctuation. The occurrence of major epidemic changes the trend of breeding sows livestock on hand. Fluctuation frequency of pig livestock on hand does not change, but the amplitude of fluctuation has changed. Using the system dynamics model, we simulate the subsidies to breeding sows and frozen meat reserve policy which can't be quantized.
(5) Compared with the effect of BP artificial neural network method (BP artificial neural networks (ANN)) and support vector machine method (support vector machine (SVM)) on amount of pig slaughter, amount of pig livestock on hand and amount of breeding sows livestock on hand, we determine to adopt the method of support vector machines to establish pig slaughter prediction model, while the BP manual neural network method is useful for prediction of amount of pig livestock on hand and amount of breeding sows on hand. We predict the trend of amount of pig slaughter, amount of pig livestock on hand and amount of breeding sows livestock on hand in2013. Based on the above research, with the basic framework of pig production risk early-warning theory, this paper forecasts the risk of pig in2013and in2014by using quantitative warning method.
The innovation points of this research:1. In the method, using the Grainger causality test model, analyzing pig material and service costs, artificial costs, the Grainger causal relationship between pig production yield and pig production fluctuation, the direction and amplitude of fluctuation among, using dynamic panel data model for empirical analysis, using system dynamics simulation analysis for the factors which can't be quantized.2. In content, breaking through the measure of pig production fluctuation with the single index, adopting the three indicators such as amount of pig slaughter, amount of pig livestock on hand, amount of breeding sows livestock on hand to measure the fluctuation of pig production in China. Based on support vector machine model production and BP artificial neural network model, we predict China's pig production, and conduct the early warning system research to the fluctuation of pig production based on the forecast.
This paper presents policy proposal of pig production fluctuation regulation based on correlation analysis. From strengthening the construction of animal husbandry information network platform, perfecting and improving epidemic prevention system, strengthening the construction of feed industry, moderately improving the scale degree of backyard, issuing pig futures in a real time and strengthening the coordination of the macro-control measures of government departments at all levels, this paper puts forward some policy suggestions in six aspects.
引文
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