Given the rural revitalization strategy in the new era, agricultural development is faced with the dual constraints of resources and the environment. Promoting the green development of agriculture is one of the important missions to solve major social issues in the new era. The implementation goal of the carbon emission trading system is to achieve a win-win situation between carbon emission reduction and green development. To evaluate the effectiveness of the carbon emission trading system on agricultural enterprises, this paper uses a double-difference model to analyze the policy effect and mechanism research path of the impact of the carbon emission trading system on the total factor productivity of agricultural enterprises. The results based on the panel data of listed agricultural companies from 2010 to 2020 show that (1) carbon emission trading rights have significantly improved the total factor productivity of agricultural enterprises; (2) green innovation in carbon emission trading rights have an impact on the total factor productivity of agricultural enterprises; and (3) heterogeneity analysis shows that the effect of carbon emission trading rights on the total factor productivity of agricultural enterprises mainly exists in large-scale, nonstate-owned, high-debt enterprises, enterprises in the eastern region, and enterprises with government subsidies. Therefore, in the future, China should continue to implement the current carbon emission trading rights system in air pollution control, and at the same time, it needs to be supplemented by government intervention and other means for long-term governance. In conclusion, the study provides a reference value for promoting the realization of the long-term goal of “low carbon” and “high quality” green development of agricultural economy and for making reasonable and effective behavioral decisions for the survival and development of enterprises.

The green production behavior of pig farmers is the basis for high-quality development in animal husbandry. In order to solve the problem of poor green production behaviors in small- and medium-sized pig farmers, it is necessary to analyze the influencing factors and how they interact with each other. The Rational Peasant Theory and Prospect Theory were used in this paper to analyze the occurrence motivation of the green production behaviors of small- and medium-sized pig farmers. The Logit model and the ISM analysis method were used to test the influencing factors and their mechanisms. This was conducted using data from a study of 747 small- and medium-sized pig farmers in Henan Province. The results show that the green production behaviors of small- and medium-sized pig farmers are motivated by internal expected return, affected by the monitoring pressure from external stakeholders and limited by their own resource capacity; the influencing factors of different green production behaviors are different, and there are more influencing factors of scientific disease control, standardized management and waste recycling than of rational feeding. The following shows how the influencing factors on pig farmers’ green production behavior interact with one another: level of education → external pressure, farming conditions and operating characteristics → cognition of return → green production behavior (i.e., cognition of return is the direct factor; external pressure, farming conditions and operating characteristics are indirect factors; and level of education is the underlying factor). Some measures should be implemented to promote green production behaviors, such as the continuation of the support for green production, the strengthening of supervision and publicity, the increasing of investment in technology and equipment, and the improving of the green production literacy of farmers. In conclusion, this paper deepens the understanding of the mechanism of green production behaviors of small- and medium-sized pig farmers, and provides the theoretical basis and concrete measures for the government and for pig farmers.

The pig industry is the pillar industry of animal husbandry in China, and epidemics can lead to drastic changes in pig supply, affecting the healthy development of the pig industry and residents’ quality of life. This study analyzed the mechanism of the effect of swine epidemics on nonlinear shocks to pig supply, and monthly data on pig supply from January 2012 to June 2020 were applied to study the threshold effect of swine epidemics on pig stock and slaughter in China empirically, using the index of swine epidemics’ width (ISEW) as the threshold variable. The results of this study were as follows: (1) The influence of the ISEW over 7 months on pig stock in China was divided into two ranges, and the pig stock did not change significantly when the ISEW was less than 0.25. Swine epidemics had a significantly negative impact on the pig stock when the ISEW was larger than 0.25. (2) The influence of the ISEW over 8 months on pig slaughter was also divided into two ranges. When the ISEW was less than 0.33, epidemics had a positive and significant effect on pig slaughter, while epidemics had a marked negative impact on pig slaughter when the ISEW was greater than 0.33. Based on these conclusions, this study proposed relevant measures for the prevention and control of swine epidemics.

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.

Introduction: The outbreak of African swine fever (ASF) has led to a sharp increase in the price of hogs, accelerating the capitalization process of the hog industry and causing China’s hog industry to enter a “super hog cycle,” which has attracted much attention. Methods: Based on this, to clarify the mechanism of ASF on the capitalization of hog farming and enrich the relevant theoretical research, this paper uses panel data from 30 provinces in China from 2013 to 2023, and adopts the difference-in-differences model (DID) and the moderation effect model to analyze the impact of the epidemic outbreak on the capitalization of pig farming, providing a new perspective for exploring the influencing factors of hog farming capitalization. Results and discussion: The research found that ASF significantly accelerated the capitalization process of hog farming. This conclusion remained valid after undergoing a series of robustness tests. Moreover, the improvement of industrial agglomeration degree and the strengthening of government support would have a significant positive regulatory effect on it. Heterogeneity analysis revealed that in regions with high agricultural capital stock and non-feed-producing areas, the positive impact of ASF on the capitalization process of hog farming was more significant. Based on this, the article proposed policy suggestions such as strengthening the epidemic prevention and control system, preventing excessive expansion of hog production capacity, coordinating efforts through multiple channels to enhance the capitalization level of the hog industry, and promoting the high-quality development of the hog industry.

Introduction: The cyclical fluctuations characterized by drastic ups and downs in raw milk prices have long been one of the bottlenecks restricting the high-quality development of the Chinese dairy industry.Methods: To clarify the formation rule of the cyclical fluctuations of raw milk prices in China, this study deeply analyzed the internal mechanism of the periodic fluctuations in raw milk prices from a microscopic perspective. Utilizing price index data from November 2007 to August 2024, we used a two-threshold mechanism transfer model to research the transformation mechanism and duration of the periodic fluctuations in raw milk prices.Results and discussion: The findings indicated that raw milk price fluctuations exhibit nonlinear mechanism transformations at two critical thresholds: a price index of 107.74 (which reflects an approximate increase of 8% after adjusting for seasonal and pricing factors) and a change rate in the price index of 0.016 (approximately 0). A complete price fluctuation cycle of raw milk comprises four phases: the low-price uptrend phase, the high-price uptrend phase, the high-price downtrend phase, and the low-price downtrend phase. Among these phases, the duration of the upward phases is generally longer than that of the downward phases; the duration of the high-price uptrend phase and the low-price downtrend phase exceeds that of the low-price uptrend phase and the high-price downtrend phase, respectively. Hence, relevant policy suggestions are put forward from four main aspects: strengthening source governance, improving the regulatory mechanism, building an early warning system, and establishing a purchase and storage system.