1. Introduction
China is the world’s largest vegetable producer and consumer, with a large import and export volume of vegetables. According to data from 2017, China’s vegetable output was 5.44 million tons, ranking first among the major vegetable producing countries in the world. With a large population base, China has naturally become the largest vegetable consumer (Data source:
http://www.stats.gov.cn/, accessed on 1 February 2022). China has a large import and export volume of vegetables. Since China’s reform and opening up, the Chinese vegetable industry has developed rapidly and its production scale has continued to expand [
1]. From the supply side, in 2019, the vegetable planting area was more than 300 million acres, and the annual production exceeds 72 million tons, with foreign exports exceeding 11 million tons, creating huge foreign exchange income. From the demand side, the national vegetable consumption reached 709.8954 million tons in 2019. Compared with the average annual compound growth rate of 0.46% of the population, the compound growth rate of vegetable consumption reached 2.06%. Affected by many factors, such as increased costs, extreme weather, information asymmetry and market environment, vegetable price volatility is increasing (national vegetable industry development plan 2015–2020). In 2019, typhoon “lichima” caused a severe rainstorm in Shouguang City, Shandong Province, which affected many vegetables, and the prices of some vegetables in Beijing increased by as much as 30%. The frequent and violent fluctuation of vegetable prices not only brings huge losses to producers and consumers but also puts pressure on the government’s market regulation. In the complex domestic and international environment, it increases the uncertainty of the vegetable market, which also makes it difficult to study the influencing factors of vegetable price fluctuation in complex and uncertain environments. This is the significance of this paper, in order to find the infection source of vegetable price fluctuation in complex and uncertain environments.
Regarding the sources of disturbances in vegetable price fluctuations, domestic and foreign scholars have mainly studied three aspects: demand, supply, and circulation. From the demand side, Gilbert studied the impact of demand growth, currency expansion and exchange rate changes on agricultural product prices [
2]. Using the ARCH and GARCH models, the inherent logic of the fluctuation of vegetable prices and the money supply was found [
3]. Some scholars have found that people prefer fresh vegetables, therefore, the fluctuation of vegetable prices will be affected by their quality [
4]. The reason is that vegetables are perishable, which also leads to the rapid transmission of their decline, which directly leads to changes in vegetable supply [
5].
In addition to demand factors, supply-related factors may also lead to the volatility of vegetable prices, and scholars have conducted studies from the perspective of the vegetable supply chain [
6], of which production cost is the most important factor [
7]. Additionally, the quality of fertilizers, pesticides, seeds and other production materials can constitute a source of production risk for agricultural products. In addition, through the research on the policy reform of supply chain regulations in fresh fruit and vegetable market in Turkey, scholars found that after reducing the number of middlemen, the wholesale price of fresh vegetables was effectively reduced [
8]. In addition, the supply level is also related to the export system between importing countries, and seasonal tariff quotas also lead to fluctuations in vegetable prices [
9].
Risks may also arise at the circulation level, with many scholars focusing on the transmission of vegetable prices at the wholesale and marketing levels [
10]. Transaction costs are an important source of risk in fresh produce markets, and capital market shocks can amplify risks following a reduction in the production of storage-resistant vegetables; Ward, R. found that wholesale prices fluctuate first, followed by changes in retail and point-of-shipment prices [
11]. There are also studies showing that online public opinion will have a potential impact on vegetable price fluctuations [
12], and media hype and government macro-control will aggravate vegetable price fluctuations [
13]; in addition, scholars have made a knowledge map of vegetable price research at home and abroad by combing a large number of vegetable price literature [
14].
From the existing literature, domestic and foreign scholars have done some research on the interference sources of vegetable price fluctuation, which provides a certain reference for this study, but there are some deficiencies. The research on the risk sources of vegetable price fluctuation is generally considered from a single factor, such as supply, demand and weather. However, the environment affecting vegetable price fluctuation is often uncertain, complex and diverse. Firstly, most scholars consider the single variables that affect vegetable prices, such as quality factors, seasonal replacement factors, natural disaster factors, etc., when studying vegetable price fluctuations from a single perspective they often ignore the influence of other factors and variables, resulting in the lack of scientificity of the results; secondly, even if some studies involve several factors, their standards are different, which makes the verification results lack unity and unconvincing. Then, few scholars study the influencing factors of vegetable price fluctuation in extremely uncertain environments; finally, some studies use the form of a questionnaire, which has great subjectivity. Based on this, this paper aims to solve two problems: one is to select vegetable price interference sources by using an objective quantitative method which can adapt to the chaotic nature; second, to take the vegetable price under complex and uncertain environment as the research object and establish the early warning index system of vegetable price risk. Therefore, different from the traditional expert consultation method or literature review method, the data source of this paper is to collect the text data of network platforms, industry consultation websites and academic literature websites, extract the subject words by LDA method, select the initial interference source of vegetable price fluctuation, and then code and sort through grounded theory to establish the interference source index system of vegetable price fluctuation. The key influencing factors are selected by the concept lattice weighted group method.
4. Model Calculation Results and Analysis
4.1. Keyword Extraction Based on LDA Topic Model
(1) LDA topic modeling results analysis. The LDA topic model is an unsupervised machine learning algorithm that can be used to mine the implicit topic information in large-scale document sets. The number of topics T is a very important parameter in LDA, and the selection of T is directly related to the extraction and analysis of topics afterward. When the number of topics is small, the information between topics is more scattered and cannot extract information effectively; when the number of topics is large, the degree of correlation between topics will increase and the topics have a high overlap. It has been shown that the model quality is better for
α = 50/T and
β = 0.01. The LDA model tends to assign few words to each topic and prefers to describe the document with fewer topics. In general, the number of topics in a text set is related to the size of the text set, the larger the size of the text set, the higher the number of topics [
17].
In this paper, we used the degree of confusion to select the number of topics. The number of topics selected ranges from 1 to 20 for iteration. The results are shown in
Figure 2.
From the calculation results, it can be seen that as the number of topics increases, the perplexity shows a trend of first decreasing and then increasing, and the perplexity reaches the lowest value when the number of topics is taken to 9. Therefore, the model performance reaches the optimum when the number of topics is 9. Afterward, the probability vector distribution matrix of about nine topics can be obtained by training the LDA model with the collected corpus, and the training results are also visualized to obtain
Figure 3. From
Figure 3, we can see that the nine themes obtained from the training have a high degree of similarity and overlap, such as “reduction” and “drop”, “price increase” and “mark up”, “production and sales” and “sales”, etc. We can find the central corpus by further clustering to extract the theme words with a high degree of relevance, so as to reduce the redundancy of the data.
(2) Analysis of DBSCAN clustering results. The calculation results of evaluation indexes are CH = 45,747.1824, DB = 0.84441, SC = 0.6934. According to the analysis of characteristic words extracted from the core corpus of cluster 1, the topic is mainly related to the factors affecting the planting, circulation and holidays of vegetable prices. The topic of cluster 2 is mainly related to the cost and transportation factors affecting vegetable prices. The topic words of cluster 3 mainly include vegetable production and consumer income. The topic of Cluster 4 mainly includes vegetable varieties and substitutes. The topic of Cluster 5 mainly includes labor force, planting area, CPI, economy and other factors. The topic of the central corpus of cluster 6 is the weather. Therefore, the weather is one of the important factors affecting vegetable prices, and there are many reports and concerns on the Internet.
4.2. Construction of Vegetable Price Fluctuation Index System Based on Grounded Theory
Through the keywords obtained earlier, nouns or noun phrases are extracted from them as feature words, and the feature words are coded and analyzed and modeled by using the rooting theory [
25].
(1) Open coding. This process performs open coding of the collected information with feature words as concept words, and the open coding is completed with two key steps, a conceptualization step, and a categorization step, in accordance with the principles of objective science.
Through constant comparative analysis, 32 categories were summarized.
(2) Spindle coding. In the open coding results, 32 categories were integrated and grouped together to obtain five main categories and corresponding 27 sub-categories. The sales cost, transportation cost, profit, pesticide, sowing area, technology level, resource, variety, industry chain, processing, infrastructure, and vegetable production were grouped into the supply category, the festival, car, substitute, network environment, population size, vegetable consumption, price index, and people’s livelihood were grouped into the demand category, the weather, month, and geographic location were grouped into the natural environment category, the government policy and market economy were grouped into the economic policy environment category, and vegetable price as vegetable price category. The spindle coding results are shown in
Table 4.
(3) Selective coding. The typical relationship structure of the main category is shown in
Table 5.
By analyzing and comparing the five main categories and 27 corresponding subcategories, “vegetable prices” can be used as the core category of this study. Based on this core category, various variables related to the sources of disturbance of vegetable price volatility in a complex and uncertain environment are grouped into a theoretical framework.
The storyline that unfolds is that four aspects: supply, demand, natural environment, and economic policy environment have an impact on vegetable price fluctuations, with supply playing an intermediate role in the process of the natural environment’s impact on vegetable prices and the economic policy environment playing a moderating role in the process of supply’s impact on vegetable prices.
(4) Construction of vegetable price fluctuation index system. This paper constructs a preliminary system of indicators of vegetable price fluctuation disturbance sources under complex uncertainty conditions based on the three-level coding of rooting theory and optimizes the indicator system by using the literature research method and expert consultation method. Drawing on relevant literature and consulting relevant experts and scholars, the indicators that are difficult to measure are split or combined to ensure that the indicators follow the combination of quantitative and qualitative. The index system constructed after optimization is shown in
Table 6.
4.3. Identification of Key Influencing Factors Based on Improved Concept Lattice-Weighted Group DEMATEL
Then, experts in the field of agricultural economics were invited to determine the interaction between the factors, and the evaluation degree of the influence relationship was set as strong = 4, strong = 3, average = 2, weak = 1, and none = 0. The results were clustered by the conceptual lattice-weighted group method. The direct influence matrix of the pooled four experts’ opinions was obtained.
According to the formula in the traditional DEMATEL method , we obtain the standardized influencing factor matrix M. Furthermore, according to the formula , we can obtain the comprehensive influence matrix N.
Finally, we use formula
and formula
to obtain the influence degree
and affected degree
in the vegetable price fluctuation index system. Then, according to the formula
and
, we obtain the centrality
and cause degree
. Since the probability vector distribution matrix based on the LDA theme model objectively reflects the influence proportion of each index in the vegetable price fluctuation system, it is used as the basis for measuring the importance of the index, while the concept lattice weighted group DEMATEL method more comprehensively considers the interaction between each index. Therefore, we calculate the weighted centrality of each indicator according to Formula (4) to ensure the scientific and rigor of the data results. The calculation results are shown in
Table 7.
From the results of the calculation of the impact degree and the impacted degree of each indicator in
Table 7, the indicators with higher influence degree include the level of economic development, the level of social development, the soundness of the vegetable industry chain, vegetable planting area, and the soundness of price regulation policy, which have relatively higher influence degree on other factors and belong to the “origin type” factors. In terms of the degree of being influenced, the indicators with a higher degree of being influenced include vegetable planting area, annual vegetable production, prices of related substitutes, vegetable consumption of rural residents, and cost profitability. These influencing factors are influenced by other factors to a greater extent and belong to the “result type” factors.
The magnitude of centrality and causality of each indicator in the complex uncertainty system reflect the importance of the indicator in the system and the correlation with other indicators, respectively. The relatively important factors among the 23 indicators of disturbance sources of vegetable price fluctuations are annual vegetable production, the soundness of the vegetable industry chain, and the prices of related substitutes. Meanwhile, the factors affecting vegetable price fluctuations can be divided into 11 causal factors (causal degree > 0) and 12 consequential factors (causal degree < 0) according to the size of the causal degree of each indicator. The factors with larger cause factors are social development level, economic development level and soil conditions, which indicate that these factors are not easily influenced by other factors in the system, but can actively influence other factors. The factors with larger outcome factors are annual vegetable production, vegetable planting area, material cost inputs, etc., which are mainly reflected in the supply category, which indicates that supply level factors are more susceptible to other factors in the vegetable price fluctuation system, but have a weaker ability to influence other factors. Therefore, attention should be paid to the differences between the cause-and-effect factors in the implementation of vegetable price regulation.
5. Conclusions and Suggestions
This paper uses web crawlers to obtain 86,166 pieces of text data related to vegetable price fluctuations over a 10-year period, uses the LDA topic model and DBSCAN clustering algorithm for topic analysis and key corpus research, uses the rooting theory qualitative research method for three-level coding, constructs a system of disturbance source indicators affecting vegetable price fluctuations, and finally applies the improved concept lattice. The DEMATEL algorithm with an improved concept lattice-weighted cluster technique was applied to deeply explore the interactions among the factors and select the key influencing factors: annual vegetable production, the soundness of the vegetable industry chain, the price of related substitutes, the soundness of price regulation policy, vegetable planting area, and climatic conditions. Compared with the previous research, the indicators obtained in this paper subdivide the urban and rural dimensions of vegetable consumption, household Engel coefficient and population number of urban and rural residents [
26]. In the dimension of the natural environment, the network of public opinion related to soil conditions and climatic conditions was introduced, and the impact of social media public opinion on vegetable prices was refined [
12,
13]. In the dimension of the economic and social environment from the macro perspective, the connotation of the indicators is extended at the four levels of price regulation and control policies, industrial chains, and economic and social development levels, which are more objective and comprehensive than the previous system, and enrich and improve the risk warning indicators for vegetable price fluctuations.
According to the conclusions of this paper, the following policy recommendations are put forward:
(1) Ensure the steady growth of vegetable production, and achieve a healthy market supply. Ensure adequate quantity, variety and balanced supply of vegetables to prevent drastic fluctuations in vegetable prices Relevant departments should start to solve the production difficulties from the supply side of funds, technology, innovation and other elements in all aspects, promote the rapid development of new production models, and promote the steady increase in vegetable production and the sustainable and healthy development of related industries. At the same time establishing and improving the relevant incentives and support policies to encourage relevant practitioners and R & D personnel in the vegetable industry to actively explore and improve the level of technical planting at the source of vegetable production.
(2) Promote the construction of the whole industrial chain of vegetables. At present, China’s vegetable production and operation has a certain degree of fragmentation, a single-family scattered planting and operation mode leads to vegetable production and circulation and sales cannot be effectively docked. Therefore, the government should promote the scale production of vegetables as well as the model of agricultural super-connection to improve the vegetable industry chain, realize the standardization of the whole industry chain, and promote the high-quality development of the vegetable industry.
(3) Implement and improve the price control policy of agricultural products, using a variety of regulatory instruments to stabilize price fluctuations. At present, due to the special nature of vegetable production and circulation, the market sometimes fails in the development of the vegetable industry thus hindering the development of the vegetable economy. Price authorities can further improve the price control mechanism from the perspective of the construction of the early warning mechanism for vegetable price fluctuations, the improvement of the vegetable price adjustment fund system, and the maintenance of market supervision norms.
(4) Develop a new model of rural land transfer to protect the vegetable planting area. At present, with the increasing level of urbanization in China’s rural areas, a considerable portion of agricultural land has been expropriated by industrial and commercial industries, bringing some resistance to the large-scale production of vegetables. Therefore, the relevant departments around the minimum vegetable planting area should be set by the “red line” and actively introduce other policies to promote the reasonable and healthy transfer of rural land to insure vegetable planting areas around the country, which are important for the maintenance of stable vegetable prices around China.
(5) Strengthen early warning of natural disasters to reduce the adverse effects of climatic conditions in vegetable production. Disaster factors in climatic conditions have an important impact on vegetable cultivation status and total production. Therefore, it is necessary to vigorously develop the facility vegetable industry chain to alleviate the seasonal contradictions of vegetable price fluctuations; encourage the introduction and research and development of new technologies related to bulk vegetable varieties to mitigate the adverse effects of price fluctuations. At the same time to do a good job of meteorological warning, and achieve early prevention, to minimize the risk and loss.
(6) Promote the construction of a vegetable big data service platform to promote the development of the vegetable industry driven by the internet and big data and reduce the uncertainty of vegetable price fluctuation. This paper suggests integrating market data with meteorological data to provide a data basis for the subsequent vegetable price warning and vegetable quality evaluation. At the same time, it provides vegetable price inquiry and analysis services for vegetable industry operators and consumers to reduce the unreasonable allocation of resources caused by the asymmetry of information between market supply and demand and promote the healthy economic development of the vegetable industry.