Abstract
The study aims to improve the economic income of pig breeding industry under environmental regulation and control the environmental pollution caused by pig breeding. Long short-term memory (LSTM) neural network combined with environmental regulation is proposed to forecast the price of live pigs, to reduce the cost of environmental pollution control and improve the production efficiency of pig breeding. Primarily, analyses are made on the industrial structure and pollution of pigs in China, and studies are carried out on the inevitability of large-scale and intensive pig breeding. Then, pig breeding and environmental pollution are coordinated under the environmental regulation. From the perspective of green total factor productivity, calculation is made on the profit of pig breeding and the cost of environmental pollution control. Next, the LSTM neural network is used to predict the price of live pigs, thus effectively controlling the scale of pig breeding and making timely decisions that conform to market rules. The results show that with the increase of feed and land prices, the advantages of large-scale pig breeding gradually become prominent, which leads to the small- and medium-sized scale farmers withdrawing from the market. Compared with other similar models, the designed model can better simulate the future trend of hog price, of which the prediction accuracy is over 80%. When combined with environmental regulations, the prediction accuracy of the model for different data sets reaches 83%, so the designed model can better predict the changing trend of the price of live pigs, thus improving the production efficiency of large-scale pig farmers.
Highlights
Pig breeding industry is an important part of China’s rural economic system
The intensive and large-scale pig breeding model poses a challenge to the carrying capacity of the natural environment, which becomes an important agricultural pollution source. erefore, in the process of agricultural economic development, the government began to pay attention to the sustainable development of pig breeding industry and formulated relevant environmental regulation policy system to centrally control the environmental pollution caused by large-scale pig breeding in rural areas
Results of support vector regression algorithm (SVR) algorithm and convolutional neural networks (CNN) show an upward trend of pork price, while the results of the Long short-term memory (LSTM) algorithm are relatively stable. e reason may be that LSTM algorithm can retain the correlation information in the historical data of pig price, while other algorithms can only retain the short-term information in the iterative process, which affects the prediction results of the model. e model based on LSTM neural network can predict the pork price well
Summary
Pig breeding industry is an important part of China’s rural economic system. With the rapid increase of people’s demand for pork in recent years, pig breeding industry has developed rapidly [1]. Ere are many research works on green total factor productivity of pig industry breeding, combined with the research of China’s macroeconomic development and environmental regulation measurement [7]. Studies are released on the relationship between green total factor productivity of pigs and environmental regulation by using panel data combined with environmental control cost, aimed at looking for the inflection point of the optimal agricultural environmental regulation intensity under different pig breeding scales. From these results, the most suitable pig breeding scale can be obtained to realize the green and sustainable development of pig breeding industry and further improve farmers’ income
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