Evolution of the Global Forage Products Trade Network and Implications for China’s Import Security

Abstract

Growing global supply chain uncertainties significantly threaten China’s forage import security. The evolving characteristics of the global forage trade network directly impact the stability of China’s supply. This study constructs a directed, weighted trade network based on global forage products trade data (2000–2024). Using complex network analysis methods, it systematically analyzes the network’s topological structure and evolutionary patterns, with a focus on their impact on China’s import security. The study addresses the following questions: What evolutionary patterns does the global forage trade network exhibit in terms of its topological structure? How does the evolution of this network impact the import security of forage products in China, specifically regarding supply chain stability and risk resilience? The research findings indicate the following: (1) From 2000 to 2024, the total volume of global forage products trade increased by 48.17%, primarily driven by forage products excluding alfalfa meal and pellets, which accounted for an average of 82.04% of volume annually. Additionally, the number of participating countries grew by 21.95%. (2) The global forage products trade network follows a power–law distribution, characterized by increasing network density, a clustering coefficient that initially declines and then rises, and a shortening of the average path length. (3) The core structure of the global forage products trade network shows an evolutionary trend of diffusion from core nodes in North America, Oceania, and Asia to multiple core nodes, including those in North America, Oceania, Europe, Africa, and Asia. (4) China’s forage products trade network displays distinct phase characteristics; however, imports face significant risks from high supply chain dependency and exposure to international price fluctuations. Based on these conclusions, it is recommended that China actively expands trade relations with potential product-exporting countries in Africa, encouraging enterprises to “go global.” Additionally, China should establish a three-dimensional supply chain security system, comprising maritime, land, and storage components, to enhance risk resistance and import safety.

1. Introduction

Forage products are a vital food source for herbivorous livestock. The global trade in forage products plays a crucial role in meeting the needs of herbivorous livestock across countries with varying resource endowments. It is estimated that by 2030, an additional 3.2 billion tons of forage will be required to support an increase of 830 million ruminants [1,2]. However, natural conditions, the international trade environment, and policy changes significantly influence the global forage products trade. Extreme weather events such as El Niño, droughts, and floods have caused declines in crop yields. The intensification of land use conflicts in Western Europe has led to grassland degradation [3]. Additionally, trade disputes between nations have prompted adjustments in tariff policies; the Red Sea crisis has sharply increased shipping costs [4]; and the Russia–Ukraine conflict has driven up fertilizer prices [5]. Collectively, these factors pose significant challenges to the global forage products trade, impacting supply stability and market dynamics [6]. China, as a major livestock producer, has experienced a rapid increase in forage consumption in recent years, making it one of the leading countries in forage demand [7]. Since 2015, a series of policy documents have provided clear objectives and guidance for China’s forage industry. These include the Central No. 1 Document (2015–2024), the 2022 “14th Five-Year Plan for the Development of the Forage Industry [8],” and the 2023 “Opinions on Implementing Grain-Saving Actions in Animal Husbandry [9].” The planned production of 98 million tons of high-quality forage and a 7% reduction in feed consumption underscore strategic efforts to promote rapid industry growth. In the short term, imports remain an important means of addressing the shortage of high-quality forage in China. However, supply chain disruptions may arise due to the current uncertain international situation and the escalation of the China–U.S. trade conflict. Analyzing the evolving trends of the global forage trade network and mitigating import risks for China is therefore of great significance for maintaining a stable supply of forage products in the country.

Research on trade networks has been relatively extensive in specific global industries or for certain product categories, such as grain [10], pork [11], and palm oil [12]. However, there has been comparatively little research on the trade networks of forage products. As the importance of these products in national food security and related policy initiatives becomes more prominent, this area of research is gradually gaining attention. The academic community has conducted multidimensional studies on the global trade network of forage products focusing on aspects such as trade market structure, trade network characteristics, and risks associated with import dependence. In terms of market structure, global forage trade exhibits a highly concentrated monopoly pattern on the supply side, dominated long-term by the United States and Australia. Meanwhile, China’s surging demand as a core importer is reshaping the trade landscape [13,14]. Regarding trade network characteristics, the global alfalfa trade network displays a coexistence of decentralization and clustering, with a stable position of core trading countries [15]. The United States occupies a key position in the global alfalfa trade network due to its advantages in quality standards, marketing strategies, and logistics systems [16]. Concerning import dependence risks, the United States holds significant market power in the Chinese forage market [17], with China’s alfalfa imports experiencing exponential growth [18] and gradually reducing its high dependence on U.S. imports [19]. China has relatively low dependence on alfalfa imports from Spain, with sufficient remaining potential for trade expansion [20]. However, issues of import concentration and trade vulnerability remain prominent [21]. South Korea faces similar challenges as domestic high-quality forage can only meet 43% of its cattle feed demand [22]. Japan’s livestock industry remains highly reliant on imported forage products [23]. In response to import dependence, various countries have adopted different strategies. Japan has proposed developing forage resources in mountainous and hilly areas and reclaiming abandoned agricultural land for forage cultivation [23]. The South Korean government promotes planting forage in fallow rice fields [22]. China has suggested expanding markets through the “Belt and Road” initiative [24], establishing high-quality alfalfa production bases [25], developing regionally appropriate grain-forage rotation models [26], and promoting the oat grass industry [27].

Overall, the aforementioned studies produced valuable insights into global forage products trade networks. However, several areas require further in-depth investigation. First, most existing research primarily focuses on alfalfa product. However, as exported forage products have diversified in both variety and form, a critical gap remains: the lack of a comprehensive analysis of their global spatial patterns. Second, regarding the import security of China, the world’s second-largest number of existing studies mainly emphasize enhancing domestic forage yield and quality, with relatively few addressing how to ensure the safety and stability of China’s forage imports. In response, this paper presents a comprehensive study of the global forage products trade network using complex network theory. It reveals the characteristics of two major categories of forage products trade networks and analyzes China’s position and potential risks within them. Therefore, it provides a scientific basis for China to formulate relevant trade policies.

2. Materials and Methods

2.1. Research Methods

(1)

Network Matrix Construction

Based on complex network theory, each country or region involved in the trade of forage products is represented as a node within the forage products trade network. A network matrix is then constructed, as shown in Equation (1).

$$A_{ij}^t=\left[\begin{array}{cccc}{a}_{11}& a_{12}& ...& a_{1n}\\ a_{21}& a_{22}& ...& a_{2n}\\ ...& ...& ...& ...\\ a_{n1}& a_{n2}& ...& a_{nn}\end{array}\right]$$

(1)

In Equation (1), $A_{ij}^t$ represents an undirected, unweighted trade network for year t. $n$ represents the number of nodes in the network, and the element $a_{\mathit{ij}}$ in matrix $A^t$ represents whether a trade relationship exists between country $i$ and country $j$ in year $t$. Specifically, $a_{\mathit{ij}}$ = 1 if there is a trade relationship between country $i$ and country $j$, and $a_{\mathit{ij} }$= 0 if no such relationship exists. When $i=j$, $a_{ij}=0$.

Trade networks are classified as directed or undirected based on their directionality, and as weighted or unweighted networks based on the presence of weights. The global forage products trade network is a directed, weighted network, as represented in Equation (2).

$$W_{ij}^t=\left[\begin{array}{cccc}{w}_{11}& w_{12}& ...& w_{1n}\\ w_{21}& w_{22}& ...& w_{2n}\\ ...& ...& ...& ...\\ w_{n1}& w_{n2}& ...& w_{nn}\end{array}\right]$$

(2)

In the equation, $W_{ij}^t$ represents a directed weighted network for year t. $n$ represents the number of nodes in the network. The element $w_{ij}$ in the $W^t$ matrix represents the trade volume between country $i$ and country $j$ in year $t$, where $w_{ij}=0$, when $i=j$. Using the total import and export trade volumes between any two countries as edge weights, an undirected weighted network is constructed. The symmetric adjacency matrix is shown in Equation (3).

$$V_{ij}^t=\left[\begin{array}{cccc}{v}_{11}& v_{12}& ...& v_{1n}\\ v_{21}& v_{22}& ...& v_{2n}\\ ...& ...& ...& ...\\ v_{n1}& v_{n2}& ...& v_{nn}\end{array}\right]$$

(3)

In the equation, $V_{ij}^t$ represents the undirected weighted network for year t. $n$ represents the number of nodes in the network. The element $v_{ij}$ in the $V^t$ matrix represents the trade volume between country $i$ and country $j$ in year $t$, where $w_{ij}=0$, when $i=j$. $v_{ij}$=$v_{ji}$=$w_{ij}$+$w_{ji}$.

(2)

Trade Network Analysis Indicators

Based on the construction of the network matrix and the directed weighted network, this study employs the following indicators to analyze the global forage products trade network. First, metrics such as node degree, node strength, degree distribution, and cumulative degree distribution are used to examine trade connections between countries and their positions within the network. Then, global characteristic indicators—including network density, average path length, and average clustering coefficient—are applied to assess the overall properties of the global forage products trade network.

①

Node degree

This study uses the node degree indicator to quantify the closeness of direct trade connections between countries for forage products, aligning with the research objective of analyzing trade relationships between nations. This indicator has been applied in studies of global food trade networks [10] and effectively represents the strength of trade associations between countries.

Node degree refers to the number of trading partners directly connected to a node within the trade network and serves as a key indicator for measuring the scale of that node’s trade connections [28]. In a directed network, node degree is divided into out-degree and in-degree based on the direction of flow. The sum of these two is called the degree, as expressed in Equations (4)–(6).

$$K_i^{out}=\sum _{j=1}^n{a}_{ij}$$

(4)

$$K_i^{in}=\sum _{j=1}^n{a}_{ji}$$

(5)

$$K_i=K_i^{in}+K_i^{out}$$

(6)

Among them, $K_i^{out}$ represents the out-degree and $a_{ij}$ represents whether there is an export trade relationship from country $i$ to country $j$. $K_i^{in}$ represents the in-degree and $a_{ji}$ represents whether there is an import trade relationship from country $j$ to country $i$. $K_i$ represents the degree. $n$ represents the total number of nodes in the network.

②

Node strength

This study uses the node strength indicator to measure the actual trade volume and influence of countries within the global trade network of forage products. This indicator has been employed in a previous study [29] to identify key countries and analyze their impact.

Node strength reflects the magnitude of forage trade flow. Directed networks are subdivided into export strength and import strength [30]. See Equations (7)–(9).

$$S_i^{out}=\sum _{j=1}^n{a}_{ij}{w}_{ij}$$

(7)

$$S_i^{in}=\sum _{j=1}^n{a}_{ji}{w}_{ji}$$

(8)

$$S_i=S_i^{in}+S_i^{out}$$

(9)

In this context, $S_i^{out}$ represents export intensity, $a_{ij}$ represents whether there is an export trade relationship from country $i$ to country $j$, and $w_{ij}$ represents the trade volume of forage products exported from country $i$ to country $j$. $S_i^{in}$ represents import intensity, $a_{ji}$ represents whether there is an import trade relationship from country $j$ to country $i$, and $w_{ji}$ represents the trade volume of forage products imported by country $i$ to country $j$. $S_i$ represents node intensity.

③

Degree Distribution and Cumulative Degree Distribution

This study utilizes degree distribution and cumulative degree distribution metrics to identify core nodes and peripheral structures within the trade network, aligning with the research objective of determining core nodes and their roles. These metrics have been validated in the analysis of the global food trade network, effectively revealing the uneven distribution of node degrees and the dominant position of core nodes [10].

Degree distribution illustrates the probability distribution of the number of trading partners for nodes within the trade network. This relationship is expressed in Equation (10).

$$P_K={\displaystyle \frac{n_k}{k}}$$

(10)

Among them, $P_k$ represents the degree distribution, $n$ represents a node in the network, k represents the degree, and $n_k$ represents the number of nodes.

If the degree distribution follows a power–law relationship, the network is said to exhibit scale-free characteristics. This is characterized by the coexistence of a large number of low-degree nodes and a very small number of high-degree hub nodes [31], resulting in a highly uneven distribution of node degrees. In such networks, the few nodes with high degrees are called “hubs”, which play a crucial role in scale-free networks [32]. This relationship is shown in Equation (11).

$$p\left(k\right)=k^{-r}$$

(11)

Here, $p\left(k\right)$ represents the probability of node degree $k$ in the network, $k$ represents the node degree, and $r$ represents the power–law relationship exponent.

Cumulative degree distribution. The cumulative degree distribution represents the sum of all degree distribution probabilities in the network for degree values greater than $k_0$, and its expression is shown in Equation (12).

$$P(k\ge k_0)=\sum _{k=k_0}^{k_{max}}p\left(k\right)$$

(12)

wherein $p(k \ge k_0)$ represents the segment where the cumulative distribution is greater than or equal to $k_0$, $p\left(k\right)$ represents the probability of node degree $k$ in the network, $k_0$ represents an artificially set critical value of degree, and $k_{\mathit{max}}$ represents the maximum value of node degree in the trade network.

④

Network Density

This study selects the network density indicator because it quantifies the overall cohesion of the global forage products trade network, aligning with the research objective of analyzing the network’s characteristics. This indicator effectively reflects the global characteristics of trade associations [12], confirming its suitability for similar studies.

Network density refers to the ratio of actual trade connections to all possible trade connections. It serves as an indicator for measuring the closeness of connections between various nodes in the network [33], as expressed in Equation (13).

$$P={\displaystyle \frac{M}{n(n-1)}}$$

(13)

Here, $P$ represents the trade network density and $M$ represents the actual number of existing links within the trade network, with a value range of [0, 1]. A higher network density represents a more tightly interconnected trade network.

⑤

Average Clustering Coefficient

This study uses the average clustering coefficient to assess the regional agglomeration and grouping characteristics of the global forage products trade network, aligning with the research objectives. This metric has also been applied in analyzing the overall characteristics of trade networks in studies of the global palm oil trade [12].

The average clustering coefficient is the mean value of the clustering coefficients of all nodes, which characterizes the agglomeration level of the trade network [34]. Its representation is presented in Equation (14).

$$C={\displaystyle \frac{1}{n}}\sum _{i=1}^n{\displaystyle \frac{m_i}{e_i(e_i-1)}}$$

(14)

where $C$ represents the average clustering coefficient, $n$ represents the total number of nodes in the network, $m_i$ represents the number of edges actually existing between the neighbors of node $i$, and $e_i$ represents the degree of node $i$. Its value range is [0, 1], and a larger value implies stronger network cohesion.

⑥

Average Path Length

This study uses the average path length indicator to measure the average shortest path distance between nodes, reflecting the overall accessibility of the global forage products trade network [35] and aligning with the research objectives. This indicator effectively characterizes the global structural properties of trade networks [12]. Its formula is presented in Equation (15).

$$L={\displaystyle \frac{1}{n(n-1)}}\sum _{i\ge j}^n{d}_{ij}$$

(15)

where $L$ represents the average path length, $n$ represents the total number of nodes in the network, and $d_{ij}$ represents the shortest path between node $i$ and node $j$ in the forage trade network.

(3)

Import Risk Indicators

In the analysis of import risks for pasture products, this article uses the Herfindahl–Hirschman Index (HHI) to represent the import concentration of the importing country or region [36], as shown in Equation (16).

$$HH{H}_i=\sum _{i=1}^N\left({\displaystyle \frac{w_i}{w}}\right)$$

(16)

$w_i$ represents the import volume of the $i$-th country among the source countries of forage products imports. $w$ represents the total import volume of forage products. The magnitude of the Herfindahl–Hirschman Index (HHI) reflects the degree of product concentration, typically ranging from 0 to 1. A higher index indicates greater import concentration for a country or region. The classification criteria are as follows: an HHI between 0.1 and 0.4 indicates a low level of concentration, while an HHI of 0.4 or higher indicates a high level of concentration.

2.2. Data Sources

In this study, forage products include Alfalfa Meal and Pellets (HS121410) (AM&P) and Forage Products Excluding Alfalfa Meal and Pellets (HS121490) (forage products excl. AM&P) (

Table 1. Major categories of forage products in global forage products trade.

HS Code	Categories of Forage Products
121410	Alfalfa meal and pellets
121490	Swedes, mangolds, fodder roots, hay, lucerne “alfalfa”, clover, sainfoin, forage kale, lupines, vetches and similar forage products, whether or not in the form of pellets (excl. lucerne “alfalfa” meal and pellets)

). The time span covers the years 2000 to 2024, encompassing import and export trade data of forage products from nearly 130 countries or regions. Data on China include the Chinese mainland, Hong Kong, and Macao regions. Data from 2000 to 2023 are sourced from the United Nations Comtrade Database (UN Comtrade). Since UN Comtrade’s 2024 data are incomplete and Trade Map’s are more current and comprehensive, we sourced the 2024 data from Trade Map. The global trade data for forage products contains a small number of very low values. This suggests that some trade network connections between countries are quite marginal. Therefore, this paper sets a threshold of 100 US dollars and ignores trade relationships below this value. According to calculations, the ignored trade relationships account for less than 6% of the total global trade, indicating that their impact on the global forage products trade network is minimal.

This study adheres to the HS code standards of UN Comtrade, selecting 121410 (AM&P) and 121490 (forage products excl. AM&P) as the core categories. As the largest and most representative product categories in the global forage trade, both have continuous and comprehensive data, effectively reflecting the overall trade patterns. However, we acknowledge that aggregating multiple forage varieties under code 121490 may obscure differences between varieties. This limitation will be addressed in the text, and we recommend conducting more detailed research in the future as more granular data becomes available.

3. Empirical Tests and Analysis of Results

To test the sensitivity of the research conclusions to the network construction methods and to ensure the accuracy, robustness, and generalizability of the results, this study conducted a robustness analysis. The specific method involves using both directed and undirected weighted approaches to recalculate and systematically compare the topological structure of the trade network from 2000 to 2024. The key indicators for comparison include network density, average clustering coefficient, average path length, node degree, and node strength.

A systematic comparison of global topological indicators was performed on directed weighted networks and undirected weighted networks spanning from 2000 to 2024. The analysis revealed that, due to inherent differences in construction methods, the two network types differ quantitatively. The undirected network exhibits higher network density and average clustering coefficient, along with shorter average path lengths. However, the core evolutionary patterns identified by both networks are highly consistent. Specifically, network density has consistently increased, average path length has steadily decreased, and the average clustering coefficient has followed a common trend of initially decreasing and then increasing. These findings demonstrate the robustness of the research conclusions regardless of the network construction methods (

Table 2. Comparison of topological indicators of directed and undirected weighted networks for forage products excluding alfalfa meal and pellets, and alfalfa meal and pellets from 2000 to 2024.

	Network Type	Global Metrics	2000	2004	2008	2012	2016	2020	2024
Forage products excl. AM&P	Directed weighted network	Network density	0.03	0.04	0.03	0.04	0.04	0.05	0.06
		Average clustering coefficient	0.30	0.33	0.30	0.36	0.38	0.37	0.40
		Average path length	2.96	3.03	3.02	2.88	2.78	2.69	2.57
	Undirected weighted network	Network density	0.05	0.06	0.06	0.07	0.06	0.08	0.09
		Average clustering coefficient	0.43	0.49	0.46	0.56	0.54	0.52	0.52
		Average path length	2.78	2.60	2.62	2.49	2.56	2.41	2.39
AM&P	Directed weighted network	Network density	0.02	0.02	0.03	0.03	0.03	0.03	0.04
		Average clustering coefficient	0.28	0.29	0.28	0.31	0.34	0.32	0.35
		Average path length	3.10	3.09	2.99	2.99	2.89	2.91	3.01
	Undirected weighted network	Network density	0.04	0.04	0.05	0.06	0.05	0.05	0.07
		Average clustering coefficient	0.43	0.42	0.44	0.50	0.44	0.42	0.49
		Average path length	3.15	3.00	2.84	2.75	2.79	2.72	2.65

Note: “forage products excl. AM&P” refers to Forage Products Excluding Alfalfa Meal and Pellets (HS121490), “AM&P” refers to Alfalfa Meal and Pellets (HS121410). Number of nodes in the forage products excluding alfalfa meal and pellets network: 107–151; Number of nodes in the alfalfa meal and pellets network: 93–125; Topological indicators are dimensionless. Data sources: Calculated based on the UN Comtrade and Trade Map.

).

The consistency of node centrality metric rankings, calculated from both directed weighted and undirected weighted networks, was quantitatively analyzed using Spearman’s rank correlation coefficient. The analysis is based on the global forage products trade network, with the number of nodes (countries/regions) varying each year (2000: N = 83; 2010: N = 88; 2020: N = 76; 2024: N = 70). The rankings for node degree (based on the number of connections) and node strength (weighted by trade volume) were compared. The resulting correlation coefficients are dimensionless values ranging from −1 to 1, where values closer to 1 indicate greater ranking consistency. The results indicate that all correlation coefficients are significant at the p < 0.001 level. Our analysis shows that the node centrality rankings from both methods are robust, and we cannot attribute this consistency to random factors. Although there are theoretical differences between the “directed” and “undirected” methods, the conclusions regarding the core nodes of the global grassland trade network are highly consistent. The main research findings derived from directed weighted networks are robust and have not been significantly biased by the choice of a single method (

Table 3. Robustness test for node centrality rankings under different network construction methods in 2000, 2010, 2020, and 2024.

Comparison of Centrality Metrics	2000	2010	2020	2024
Directed out-strength vs. undirected strength	0.72 **	0.738 **	0.649 **	0.511 **
Directed out-degree vs. undirected degree	0.825 **	0.914 **	0.86 **	0.866 **

Note: ** indicates a significance level of p < 0.001. Data sources: Calculated based on the UN Comtrade and Trade Map.

).

3.1. Evolution of the Global Forage Products Trade Network

Analysis on Changes in Global Forage Products Trade Volume and Number of Trading Countries

The global trade volume of forage products has generally shown a significant upward trend, increasing by 48.17% from 2000 to 2024. By establishing the equation $y_i=\alpha +\beta x_i+{\epsilon }_i$, where $y_i$ represents the global trade volume of forage products, $\mathsf{\alpha }$ and $\mathsf{\beta }$ are constants, and ${\epsilon }_i$ is a random disturbance term. The F-test for the model was conducted (F = 47.882, p < 0.05), and the regression coefficient for the year was significantly positive (β = 44.335, t = 6.92, p < 0.01). This indicates that the upward trend in trade volume over the years is statistically significant and not due to random fluctuations. This growth is primarily driven by the sustained expansion in global demand for livestock products, which in turn increases the demand for forage products and further stimulates the growth of their trade volume. Among these, the trade volume of forage products excl. AM&P contributes the most, accounting for the largest share of total forage trade, with an average annual proportion of 82.04%. In 2024, the total trade volume of forage products excl. AM&P increased by 66.96% compared to 2000. This growth is mainly because forage products excl. AM&P are a crucial food source for herbivorous livestock. The rapid development of animal husbandry in Asia and the Middle East has led to a surge in forage products excl. AM&P demand. Conversely, the trade volume of AM&P shows a downward trend, decreasing by 10.92% in 2024 compared to 2000. Its share of total forage trade has declined from 20.64% to 12.41%. Due to the need for cold chains or constant-humidity storage, AM&P incurs high costs and has low added value, making it difficult to offset cross-border losses. As a result, the proportion of AM&P in total trade volume has decreased annually (Figure 1).

The total number of trading countries shows a significant upward trend, with a 21.95% increase in 2024 compared to 2000. The following equation is established, $y_j=\alpha +\beta x_j+{\epsilon }_j$, where $y_j$ represents the global trade volume of forage products, $\mathsf{\alpha }$ and $\mathsf{\beta }$ are constants, and ${\epsilon }_j$ is a random disturbance term. The F-test for the model was conducted (F = 35.245, p < 0.05), and the regression coefficient for the year was significantly positive (β = 0.951, p < 0.01). This trend, indicating that the number of trading countries is increasing year by year, is statistically significant and not due to random fluctuations. The proportion of trading partners compared to the total number of potential trading countries increased from 65% in 2000 to 78% in 2024. This reflects a rise in the number of countries actively engaged in forage trade, an expansion of the global forage trade network, and improved overall connectivity. Specifically, the number of importing and exporting countries (or regions) for forage products increased by 21.55% and 7.69%, respectively, in 2024 compared to 2000. This trend indicates a gradual expansion in global demand for forage products. Driven by this demand, the number of exporting countries has steadily grown, allowing forage products to reach broader regions through the trade network. Among the two product categories, the number of exporting countries (or regions) is fewer than the number of importing countries (or regions). The supply side is dominated by a small group of countries. Due to natural endowments and production technology limitations, only a few economies have a competitive advantage in forage exports, resulting in a low-competition export market. In contrast, the demand side encompasses a wider range of countries, and expansion of multilateral demand continues to intensify the competition among importers. The number of importing and exporting countries of forage products excl. AM&P is higher than for AM&P, indicating that the production and consumption of forage products excl. AM&P are more widespread. Due to processing or usage restrictions, AM&P has a narrower range of trade-participating countries (see

Table 4. Statistics on the number of trade partners and import-export countries (regions) for global forage and related products from 2000 to 2024. Unit: piece.

Product Categories	Year	2000	2004	2008	2012	2016	2020	2024
Forage product	The number of actual trading partners	123	133	137	137	148	141	150
	The total number of potential trading countries	189	191	192	193	193	193	193
	The proportion of the number of trading partners	65%	70%	71%	71%	77%	73%	78%
AM&P	Importing countries/regions	92	101	105	102	116	114	110
	Exporting countries/regions	39	52	56	53	54	56	50
Forage products excl. AM&P	Importing countries/regions	113	116	131	136	140	137	138
	Exporting countries/regions	79	80	86	79	83	83	77

Data sources: UN Comtrade, Official Website of the United Nations, Trade Map Database.

).

3.2. Analysis on the Characteristics of the Global Forage Products Trade Network

Node Degree Characteristics

Using 2000 and 2024 as the initial and terminal years of the study period, the comparative analysis of data effectively reveals the long-term characteristics of the forage products trade network. As shown in

Table 5. Distribution of node degrees in the global forage products trade network in 2000 and 2024 Unit: piece.

Year	Node Degree	AM&P	Forage Products Excl. AM&P
2000	Average value	4.94	8.19
	Top three countries	Australia 30	Australia 61
		U.S. 26	Germany 49
		Italy 26	Italy 45
2024	Average value	7.97	13.69
	Top three countries	The Netherlands 74	The Netherlands 83
		Italy 51	Germany 82
		Spain 44	USA 76

Data Sources: Compiled based on the UN Comtrade and Trade Map.

, the average node degree for the two major categories of forage products increased in both periods. The node degree for AM&P grew by 3.03, representing a 61.34% increase, while forage products excl. AM&P increased by 5.5, a 67.16% rise. This indicates that the global forage trade network has become denser, with significantly enhanced connectivity. Among these, the average node degree for forage products excl. AM&P experienced the most substantial change. This reflects a broader circulation range and makes it the most strongly connected category within the trade network, thereby underscoring its central role in global forage trade. Conversely, AM&P exhibited the smallest change in node degree, with a relatively narrow circulation scope attributable to its inherent product characteristics, resulting in notably lower network coverage. Between 2000 and 2024, the global forage trade landscape underwent significant transformation. The core hubs of the global forage trade network shifted from traditional leading countries such as Australia and the United States to European nations including the Netherlands, Spain, and France. This shift is driven by increased demand—resulting from the intensification of European livestock industries, the facilitation of intra-EU trade policies, and advantages in port logistics—which marks a transition of the global forage trade center toward Europe.

The shift in this core hub further confirms Europe’s prominent position in the global forage trade. From a regional distribution perspective, Europe, North America, and Oceania serve as central nodes in the global forage products trade, exhibiting the highest node degrees across various forage categories. Following these are Asia, South America, and Africa (Figure 2). Developed countries in Europe and North America display high node degrees within the trade network, driven by their resource endowments [37], advanced agricultural technologies [38], and highly specialized division of labor [39]. Asia predominantly functions as an importer of forage products with limited import channels [22], mainly importing dried alfalfa and forage products excl. AM&P varieties [14]. South America exhibits a relatively limited export scale, concentrated trading partners, and a lack of bidirectional and regional interactions. These factors result in its forage trade network having fewer and weaker connections than Asia’s, which benefits from broad import sources [40]. Africa’s livestock industry is primarily small-scale and characterized by low productivity [41], which suppresses demand for commercial forage. Severe climatic conditions in Africa have resulted in an acute shortage of high-quality feed [42]. The industry chain and supporting infrastructure are underdeveloped, leading to minimal participation in international trade and the lowest node degree. However, Africa’s node degree increased by a factor of 1.98 from 2000 to 2024, with South Africa demonstrating particularly notable growth, where the node degree rose from 11 to 39. This growth is attributable to South Africa’s abundant forage resources and regional conditions conducive to alfalfa hay production. The country’s annual output exceeds 1.5 million tons, with exports averaging between 140,000 and 260,000 tons annually. These figures underscore South Africa’s significant potential for forage production and export [43].

3.3. Analysis of Influencing Factors for Changes in Node Degree

Policies and climatic conditions jointly influence the degree centrality patterns in the global grassland trade network. Policy Impact. The Common Agricultural Policy (CAP) of the European Union significantly promotes the production and trade of grassland products within the region by providing production subsidies and unified quality standards. On the one hand, direct subsidies reduce production costs, expand the scale of grassland cultivation, and increase trade supply in Europe. On the other hand, unified standards eliminate internal barriers, strengthen trade connections among member states, and enhance Europe’s overall degree centrality, gradually establishing it as a global center for grassland trade. Simultaneously, China’s policies of relaxing imports and reducing tariffs create additional export opportunities for European countries such as Spain and Italy, thereby reinforcing Europe’s core position externally. Climate Impact. The climate in Europe and North America is mild and humid, which is conducive to high forage yields, laying the foundation for extensive trade connections and maintaining high network connectivity. In contrast, the Middle East is arid and receives little rainfall, limiting large-scale production and resulting in heavy reliance on imports. This provides a stable market for Europe and the United States, consolidating their dominant positions in the forage trade network.

Degree Distribution and Cumulative Degree Distribution Characteristics

The cumulative degree distribution of the global forage products trade network was modeled using a power function based on the relevant degree distribution values. This analysis yielded power–law exponent values for different types of forage products across various years (

Table 6. Fitting functions for the cumulative degree distribution in the global forage products trade network in 2000, 2010, 2020, and 2024.

Product Categories	2000		2010		2020		2024
	−r ***	R2	−r ***	R2	−r ***	R2	−r ***	R2
AM&P	−8.129	0.969	−0.762	0.946	−0.711	0.948	−0.667	0.945
Forage products excl. AM&P	−0.652	0.940	−0.593	0.938	−0.522	0.907	−0.528	0.912

Note: −r denotes the power–law exponent of the fitting function, R² denotes the coefficient of determination of the fitting function, *** denotes significance at the 1% level.

). The power–law exponent (−r) characterizes the imbalance within the forage trade network structure. The coefficient of determination (R²) measures the goodness of fit between the power function and the cumulative degree distribution of the trade network. For all forage products, the R² values exceed 0.9, indicating an excellent fit and confirming that the cumulative degree distribution follows a to the power–law pattern. Having passed the statistical significance test at the 1% level, this further verifies that the fitting results are neither random nor coincidental. The fitted power–law exponent (−r) for other types of hay is relatively smaller, suggesting lower market concentration and greater market resilience. This implies a more balanced participation of various countries in trade. Conversely, the absolute value of the exponent (−r) for AM&P is relatively larger, indicating higher market concentration and weaker market resilience. Nodes in the network tend to trade with nodes that have a high degree.

The overall cumulative degree distribution of the global forage products trade network exhibits a power–law characteristic (Figure 3). In 2024, 3% of the national nodes have degrees exceeding 70, with a small number of highly connected “hub” countries dominating the network through extensive trade links. Conversely, a large number of countries have low node degrees, participating in only limited trade activities. By 2024, 86.84% of the national nodes have degrees concentrated between 1 and 30, indicating a “core-periphery” structure. The node degrees of core hub countries show a significant upward trend; in 2000, their degrees were primarily between 30 and 60, whereas by 2024, they predominantly range from 70 to 83. The average node degree of these core hubs has increased by approximately 35.1, reflecting diversification in the trade structure of forage products, an expansion of trade scope, and an increasingly evident globalization trend.

The analysis of the cumulative degree distribution curves for AM&P and forage products excl. AM&P types in Figure 3 reveals distinct characteristics of their trade networks. The AM&P curve from 2000 to 2024 shows a continuous outward expansion, indicating increased participation by more countries and a shift from a “few hubs monopolizing” pattern toward a “multi-entity collaboration” model. In contrast, the curves for forage products excl. AM&P types exhibit an initial expansion followed by contraction. Specifically, the outward expansion from 2000 to 2020 reflects a more dispersed trade distribution. The inward convergence of the curve in 2024 overlaps with that of 2020. This pattern indicates a reduction in the number of trade participants, which reflects short-term fluctuations caused by factors such as abnormal climate and changes in trade policies.

3.4. Evolution Trend of the Global Forage Products Trade Network

3.4.1. Temporal Evolutionary Trend

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Increased Network Density. From 2000 to 2024, the global trade network density of various forage products has shown an upward trend (Figure 4). The integration of trade networks has improved, resulting in closer trade relations among nations and the formation of a more cohesive network structure. Notably, the density of the forage products excl. AM&P trade network has continuously increased, with an 88.74% rise in 2024 compared to 2000, driven by accumulated trade relationships that expand network coverage and connectivity. The trade network density of the AM&P (Main Supply and Market Partners) fluctuated but trended upward, increasing by 75% in 2024 compared to 2000. This growth reflects a rising market demand for high-quality forage and deepening trade cooperation, which continuously strengthens the network’s cohesion.

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Growth in Clustering Coefficient. From 2000 to 2024, the average clustering coefficient of the global forage products trade network initially decreased and then increased overall. From 2000 to 2008, it fluctuated and declined, followed by an upward trend from 2009 to 2024 (Figure 4). Specifically, the clustering coefficient for forage products excl. AM&P exhibited a fluctuating decline from 2000 to 2008, which disrupted the stability of small clusters and weakened local clustering. However, from 2009 to 2024, it increased from 0.38 to 0.4, indicating tighter connections among neighboring countries and the formation of more small-scale trade clusters, thereby enhancing local clustering. The clustering coefficient for AM&P fluctuated sharply between 0.2 and 0.3 from 2000 to 2014, reflecting a high dependence on core hub countries; these hubs significantly influenced “small-circle trade”. From 2015 to 2024, it showed a fluctuating upward trend, with the emergence of new trade clusters and the restoration of local agglomeration.

(3)

Decrease in Average Path Length. As the number of participating countries and regions in the global forage products trade network increased, the average path length exhibited a declining trend, indicating an overall improvement in transmission efficiency and a deepening of network globalization (Figure 4). Different categories of forage products showed variations in average path length. The AM&P network, characterized by a lower node degree and sparser connections, often requires traversing more intermediary nodes for indirect links, resulting in a longer average path length ranging from 2.7 to 3.2. In contrast, the average path length for forage products excl. AM&P is shorter, between 2.5 and 3.0, primarily due to a higher number of participating economies and denser connections among nodes, which naturally shorten the paths.

3.4.2. Spatial Evolutionary Trend

We constructed a directed weighted network map of global forage products trade for the years 2000, 2010, 2020, and 2024 using Gephi’s (v0.10.1) visualization techniques (Figure 5). In the diagram, nodes represent individual countries involved in the trade of forage products. The size and color intensity of each node visually indicate the country’s level of trade participation within the network. The width and color intensity of the edges represent the volume of bilateral trade flows, while arrows explicitly indicate the direction of forage products trade.

From 2000 to 2024, the global forage products trade network exhibited a diffusion pattern that originated from core nodes in North America, Oceania, and Asia and expanded toward Europe, Africa, and other regions. This spatial evolution is detailed in

Table 7. Top 10 countries and regions by node degree and node strength in 2000, 2010, 2020, and 2024.

2000		2010		2020		2024
Node degree	Node strength	Node degree	Node strength	Node degree	Node strength	Node degree	Node strength
Australia	Japan	Germany	USA	Italy	USA	Italy	USA
USA	Australia	USA	Japan	Spain	Japan	Spain	Japan
Germany	USA	Italy	Australia	Germany	China	USA	South Korea
France	South Korea	Australia	South Korea	Belgium	UAE	The Netherlands	China
Italy	Spain	Belgium	UAE	USA	South Korea	Germany	Australia
The Netherlands	France	France	Spain	The Netherlands	Australia	Belgium	Canada
Canada	Saudi Arabia	Spain	Canada	France	Spain	France	Spain
Spain	Italy	The Netherlands	France	Poland	Saudi Arabia	Poland	Saudi Arabia
UK	The Netherlands	Poland	China	UK	Italy	UK	UAE
Belgium	UAE	Canada	Germany	Australia	Canada	Canada	The Netherlands

Note: UK is the abbreviation for the United Kingdom, and UAE is the abbreviation for the United Arab Emirates. Data sources: Same as above.

. In 2000, Japan, Australia, and the United States occupied central positions due to their extensive forage products trade relations. By 2010, the number of key trading nations had increased, with the core network extending to include the US, Japan, Australia, South Korea, and the United Arab Emirates. In this network, the US and Australia primarily served as exporters, while Japan, South Korea, and the UAE were importers. In 2020, China emerged as the world’s second-largest forage products importer, while the US remained a major exporter. By 2024, the network’s core nodes had diversified further. Structurally, the network expanded to include new major exporters such as Romania, Egypt, and South Africa, and importers like Switzerland and Vietnam. Meanwhile, China maintained its position as the second-largest importer (

Table 8. Top 10 countries by in-degree strength and out-degree strength of forage products in 2024. Unit: tons.

	2024
Countries	USA	Australia	Spain	Canada	France	Italy	The Netherlands	Romania	Saudi Arabia	Egypt
Node out strength	4,090,787	1,434,881	739,404	661,159	383,630	284,391	220,391	191,175	157,773	103,720
Countries	Japan	China	South Korea	Saudi Arabia	UAE	USA	The Netherlands	Switzerland	Canada	Germany
Node out strength	1,855,832	1,343,952	1,195,665	863,816	529,292	353,610	276,991	192,375	139,106	111,352

Note: UAE is the abbreviation for the United Arab Emirates. Data sources: Same as above.

). Notably, two trade connections (with trade volume data based on the exporter’s statistical standards) exhibited significant changes over the years: the Australia–Japan link and the US–China link. The trade volume of the Australia–Japan connection decreased by 70.29% from 2000 to 2024, indicating a diversification of Japan’s forage products import sources and reduced dependency on Australia. Conversely, the US–China trade volume increased by 298.63 times, reflecting China’s transition from a primary forage exporter to a major importer, with its import sources becoming more concentrated. The number of trade links exceeding 200,000 tons in volume grew from nine in 2000 to sixteen in 2024, signifying both an increase in the number of inter-node connections and a substantial rise in their weights. This trend illustrates a growing diversification within the global forage trade network.

3.5. Evolution of China’s Forage Products Trade Network and Import Security Risks

Based on the characteristic analysis of the global forage trade network, the evolutionary trajectory of China as a key node warrants special attention. Between 2000 and 2024, China evolved from a secondary participant to the second-largest core importer. While this growth has promoted the development of the global forage products trade, it has also introduced new supply chain risks due to the high concentration of import sources. The following analysis will specifically examine the evolution of China’s forage products trade network and provide an in-depth exploration of its import security risks.

3.5.1. Evolution of China’s Forage Products Trade Network

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Analysis of Changes in Trade Volume and Number of Trading Countries for China’s Forage Products

The trade volume of China’s forage products exhibits distinct phase-specific characteristics, characterized by a fluctuating trend that shifts from low to high levels (Figure 6). The period from 2000 to 2009 represents a low trade volume phase, driven predominantly by exports. This phase was characterized by an average annual trade volume of only 74,900 tons and a peak of just 113,900 tons. In contrast, the period from 2010 to 2024 marks a high trade volume phase. The average annual trade volume reached 1,260,675 tons—an increase of 15.83 times compared to the previous phase. This growth was accompanied by a shift from a net export to a net import status. Specifically, between 2010 and 2022, trade volume rose from 253,500 tons to 1,982,800 tons, representing a 6.82-fold increase. However, in 2024, trade volume declined to 1,348,572 tons, a 31.99% decrease from 2022, yet it remained substantially higher than during the initial phase.

The number of trade partners involved in China’s forage products exports and imports exhibits a trend of initial growth followed by a decline (Figure 6). From 2000 to 2009, the number of trading partners fluctuated at a high level, averaging around 24, and displayed a clear inverted U-shaped pattern. During this period, the number steadily increased from 2000 to 2004, with an average annual growth rate of 5.7%, reaching a peak of 30 trading countries in 2004. From 2004 to 2009, the number declined by an average of 10.2% annually, decreasing to 18 trading partners, though still maintaining a relatively high level. The period from 2010 to 2024 reflects a phase of steady development, with the number gradually increasing from 18 to 22. However, the average number of trading partners during this phase declined to 19—a 20.83% reduction from the previous period and a significant drop compared to earlier years.

(2)

Characteristics of China’s Forage Products Trade Network

The engagement of China’s forage products trade network demonstrates a trend of “absolute growth but relative decline” (

Table 9. Comparison of the average node degree in China’s trade network of forage products with the global one in 2000 and 2024. Unit: piece.

Year	Node Degree	AM&P	Forage Products Excl. AM&P
2000	Global average	4.94	8.19
	China	5	26
2024	Global average	7.97	13.69
	China	9	19

Data sources: Same as above.

). In 2000, the node degrees for AM&P and forage products excl. AM&P were 5 and 26, respectively, ranking 13th and 20th globally. By 2024, these values increased to 7 and 19, but their rankings dropped to 30th and 40th. This shift indicates that, despite the rapid expansion of the global forage products trade network, China has maintained and increased its absolute number of connections. However, the growth rate of trade partners in other countries has been more pronounced, resulting in a relative decline in China’s ranking. In both 2000 and 2024, China’s node degrees for these two forage product categories exceeded the global average. This demonstrates that China maintained a relatively high number of trading partners and broader trade coverage, establishing the country as a more active participant within the global trade network.

(3)

Evolutionary Characteristics of China’s Forage Products Trade Network

From 2000 to 2024, China’s sources for forage products imports shifted from North America and Oceania to Africa and Europe, while a significant increase in the number of source countries drove diversification in the import structure. The pattern of forage products imports in China has shown a clear trend toward diversification. As

Table 10. Evolution of the ranking and share of China’s top three import sources of forage products in 2000, 2006, 2012, 2018, and 2024.

	2000		2006		2012		2018		2024
Ranking	country/region	Share/%	country/region	Share/%	country/region	Share/%	country/region	Share/%	country/region	Share/%
1	Canada	44.84	U.S.	48.74	U.S.	84.26	U.S.	67.95	U.S.	69.16
2	U.S.	37.09	Australia	29.32	Australia	11.98	Australia	17.17	Australia	16.79
3	Australia	16.03	Canada	2.44	Spain	2.29	Spain	11.76	Spain	7.82
Total		97.96		80.5		98.53		96.88		93.77

Data sources: Same as above.

shows, in 2000, Canada, the United States, and Australia dominated China’s import sources, supplying a combined 1,080,000 tons of forage products that accounted for 97.96% of total imports. By 2024, the number of core import countries expanded to five. In addition to the original suppliers, the United States and Australia, new sources such as South Africa, Spain, and Italy were added, with total import volume reaching 1,323,174 tons and representing 98.45% of imports. This evolution reveals that China has not only significantly expanded the scale of its forage products imports but has also actively diversified its import sources across regions, thereby establishing a more stable international supply chain system.

3.5.2. Security Risks of Forage Products Imports in China

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Risks associated with China’s forage products trade supply chain dependence. Firstly, import sources remain highly concentrated. China’s 2024 HHI of 0.51 indicates a significant degree of market concentration and reveals potential dependency risks within its supply chain. China’s forage products import primarily rely on a limited number of countries, notably the United States and Australia, with the United States maintaining a dominant position over an extended period. Since China transitioned to a net forage importer in 2009, the U.S. share decreased significantly from 96.74% to 62.42% by 2019, yet it continued to serve as a core supplier. The 2018 China–U.S. trade friction primarily drove this shift, as the imposition of a 25% tariff directly reduced U.S. market share and prompted China to diversify its import channels. Between 2020 and 2024, the U.S. share rebounded slightly from 68.84% to 69.16%, indicating that China’s high dependency on U.S. high-quality forage persists. Secondly, supply chain disruption risks pose significant threats. Internationally, the Red Sea crisis has heightened transit risks in the Suez Canal [3], declining schedule reliability on China–U.S. shipping routes, and the Pacific hurricane season may easily cause supply delays or interruptions [44]. Domestically, the combination of limited port storage capacity for forage—sufficient for only 10 days of turnover—and heavy reliance on road transport increases vulnerability to supply chain breakdowns during frequent extreme weather events.

(2)

Risks related to international market price transmission. As the world’s second-largest forage importer, China imported 1,086,400 tons of alfalfa hay in 2024, with an external dependency exceeding 36.62%. Fluctuations in international market prices directly affect the domestic industry. China’s forage import price fluctuations [14] primarily result from weather-related reductions in forage production in key exporting countries such as the United States [45], which led to a record FOB (Free On Board) price exceeding $450 per ton in 2023. Additionally, tariffs between China and the U.S. [19], along with fluctuations in the RMB exchange rate, have directly contributed to rising domestic alfalfa prices, thereby compressing profit margins for livestock producers.

4. Discussion

4.1. Analysis on the Evolution of the Global Forage Products Trade Network

The analysis concludes that product characteristics primarily enable forage products excl. AM&P types to exhibit the greatest increase in average node degree, indicating both a broader circulation range and the strongest connectivity within the forage products trade network. The forage products excl. AM&P mainly consists of alfalfa. However, as feeding scenarios have diversified, the inclusion of multi-category hay (such as oat grass, ryegrass, etc.) offers broader climate adaptability and compatibility with various feeding situations. Compared with the findings of Long et al. [20], this study expands the range of product categories and highlights the “shock absorber” role of forage products excl. AM&P categories in trade prices. For instance, when the price of Australian oat hay surges, importers can mitigate cost impacts through substitution. This regulatory effect has enhanced the centrality of forage products, excl. AM&P in the forage products trade. Meanwhile, the average clustering coefficient of the trade network shows a trend of initially increasing and then decreasing. Compared with the research by Li et al. [15], this study better reveals the new characteristics of changes in the forage trade network.

From a regional distribution perspective, the global trade center for forage products is gradually shifting toward emerging markets in Asia, South America, and Africa. A combination of increasing demand, cost advantages, and policy incentives primarily drives this transformation. Notably, South Africa’s performance is particularly remarkable, with its node degree (trade connectivity strength) rising from 11 to 39, establishing it as the most promising forage trade hub on the African continent [43]. This suggests that when major forage-producing regions in the Northern Hemisphere experience reduced yields, South Africa’s supply can help mitigate global price shocks.

Two trade routes have exhibited significant changes: the Australia-Japan connection and the United States–China connection. Between 2000 and 2024, trade volume along these routes decreased by 70.29% and increased by 298.63 times, respectively. This stark contrast reveals a structural shift in the global forage products trade landscape. First, changes on the demand side: the decline of Japan’s dairy industry [46], along with the “de-foraging” trend in feed formulation, has rapidly disintegrated the traditional forage supply chain. Conversely, China’s expansion of large-scale dairy operations and its strong demand for high-protein forage have swiftly increased trade volume. Second, changes on the supply side: Australia’s high forage production costs [47] and low logistics efficiency have placed it at a disadvantage compared to the United States in terms of forage costs. As a result, Japan has gradually increased the number of forage-importing countries and diversified its import sources beyond Australia. In contrast, American forage maintains a core position in China’s imports because its relative advantages in both cost and quality sustain its competitiveness [14].

4.2. Analysis on China’s Forage Products Trade Network and Import Security Issues

The trade volume of Chinese forage products and the number of trading partners exhibit distinct temporal patterns. Trade volume initially remains low before rising significantly, while the number of trading partners shows a decreasing trend over time. The substantial improvement in dairy cow productivity primarily drives the initial low and subsequent increase in trade volume, leading to an exponential rise in demand for high-quality forage. Policy initiatives such as the 2012 “Revitalization of the Dairy Industry and Alfalfa Development Action” and the 2020 decision to include forage within the tariff quota management of agricultural imports further support this trend. Conversely, a shift in China’s forage products export profile mainly accounts for the reduction in the number of trading partners—from high to low. Prior to 2009, China primarily exported forage products in small quantities to various Asian countries, resulting in limited total export volume. After 2009, China transitioned to an import-oriented country, with core importing nations narrowing to five key countries the United States, Australia, South Africa, Spain, and Romania. This supply structure comprises three main suppliers (the US, Australia, and Spain) and two regulatory countries (South Africa and Romania). Bulk procurement discounts, enhanced quality control measures, and reduced logistics costs primarily drive this configuration. Despite the expansion of core importing countries, the safety of forage products imports remains a critical concern. In 2024, the United States accounted for 69.16% of single-source imports, surpassing the food security warning threshold. Increasing uncertainties, such as frequent US–China trade conflicts, fragile shipping routes, and recurrent extreme weather events, pose significant risks of import disruptions and soaring prices. Compared to the analyses by Diao and Wang [25], which focus more broadly on China’s forage products import security issues, this assessment emphasizes the heightened risks associated with import dependency and supply chain vulnerabilities.

4.3. Analysis of the Study’s Innovations and Contributions

From Single-Product Analysis to Systematic Characterization of the Global Forage Products Trade Network. Previous studies on forage trade have primarily focused on individual products (e.g., alfalfa hay) [20]. While these studies offer in-depth insights, they lack a systematic understanding of the overall patterns and structural characteristics of the global forage trade. This gap is particularly evident given the increasing diversification of global forage product types and forms. Research adopting a comprehensive global perspective remains relatively scarce. In this study, we constructed a global trade complex network encompassing multiple forage products (e.g., ryegrass, oat grass, alfalfa meal, and pellets). On a global spatial scale, this study reveals the temporal and spatial dynamic evolution of the global forage products trade network, as well as the evolutionary patterns of China’s trade network. This innovative research perspective provides a new paradigm for understanding the mechanisms governing the flow and allocation of global forage resources, addressing existing gaps in macro-system-level research.

Existing studies on China’s forage supply primarily focus on improving forage yield and quality through policy support and technological advancements [48]. However, as one of the world’s largest forage-importing countries, China highly depends on a fragile and volatile global supply chain for its import security. Addressing this gap, the present study adopts a new perspective by systematically evaluating China’s position, dependency, and vulnerability within the global forage products trade network, considering both import security and the global supply chain. This study expands the discussion of China’s forage security from domestic production to international trade. It thereby provides essential decision-making support for developing a national strategy that “takes domestic supply as the cornerstone and diversified imports as the guarantee.

4.4. Limitations of the Study

While this study contributes to the existing body of knowledge, it has certain limitations. Due to data availability constraints, this study could not reveal the distinct trade patterns of various forage subcategories (e.g., oat grass, ryegrass). Future efforts should focus on improving data mining and integration techniques. Subsequent research could uncover the trade patterns of individual forage varieties and analyze the differing driving mechanisms behind node degrees in the trade networks of these varieties.

5. Conclusions and Implications

5.1. Conclusions

From the perspective of complex networks, this paper constructs a trade network using global forage products trade data from 2000 to 2024 and employs Gephi 0.10.1 software to visualize the network’s evolution across four key years (2000, 2010, 2020, and 2024). By systematically analyzing network characteristic indicators, this study reveals the features and evolutionary patterns of the global forage products trade network. It further examines China’s forage products trade network along with the associated import security risks. The main conclusions are as follows:

(1)

The global forage products trade network has shown an upward trend in total trade volume, the number of participating countries, and network connectivity. The total volume of global forage products trade increased by 48.17%, primarily driven by growth in the trade of forage products excl. AM&P. The number of countries involved in forage products trade rose by 14.79%. Network connectivity improved significantly, with the node degrees of AM&P and forage products excl. AM&P increasing by 61.34% and 67.16%, respectively. Europe, North America, and Oceania maintained central roles in trade volume, while Africa’s participation remained consistently minimal.

(2)

The global forage products trade network exhibited characteristic power–law distribution features. From 2000 to 2024, participation in AM&P and forage products excl. AM&P trade showed periods of both expansion and contraction. The node degree distribution among forage products trading countries revealed a “core-periphery” structure. Core hub countries demonstrated a significant upward trend in node degree, with an average increase of approximately 35.1.

(3)

Between 2000 and 2024, the global forage products trade network experienced several key changes. Network density increased, while the average path length decreased. The clustering coefficient, after an initial decline, subsequently rose. Spatially, the network expanded from North America, Oceania, and Asia to include multiple core nodes across North America, Oceania, Europe, Africa, and Asia.

(4)

China’s forage products trade volume and the composition of its trading partners exhibited distinct phased characteristics. The trade volume and number of trading partners followed a pattern of “low before high” and “high before low” pattern. China faced significant risks related to supply chain dependency and the transmission of international forage prices.

5.2. Implications for China’s Forage Import Security

(1)

To enhance China’s risk resilience, it is essential to identify high-quality foreign forage sources as substitutes. China’s forage imports followed a pattern of more in the early stage and less in the later stage. Concurrently, the number of trading partners decreased, leading to a greater concentration of import sources over time. Although origins of China’s forage imports are diversifying, the United States remains the predominant supplier. These observations yield several implications. First, China should prioritize expanding potential production regions along the Belt and Road Initiative, focusing on developing high-quality alfalfa resources in Central Asian countries such as Kazakhstan and Uzbekistan. Second, strengthening trade relations with African nations that have significant forage potential, such as South Africa and Egypt, is crucial. Additionally, establishing long-term procurement agreements with Argentina and Brazil can leverage their off-season production advantages to offset for domestic seasonal shortages.

(2)

Encouraging capable enterprises to expand internationally is vital. The global forage trade network exhibits a stable “core-periphery” structure, with core countries such as the United States and Australia dominating trade flows. As a major importing country, China has experienced sustained growth in forage demand, while long-term contradictions with domestic resource constraints persist. These conclusions support the following implications. Countries like South Africa possess abundant forage resources and high export potential. Therefore, Chinese government agencies should implement policies to support enterprises in establishing forage cultivation and processing facilities in these resource-rich regions of Africa and South America. Such policies could include providing long-term, low-interest loans and tax incentives, along with duty exemptions for domestically produced forage that enterprises re-export. Furthermore, we recommend a three-pronged approach. First, foster overseas industrial alliances to develop integrated planting and processing projects. Second, enhance overseas investment insurance mechanisms to cover political risks and natural disasters. Third, strengthen international cooperation through bilateral agreements to secure long-term land lease rights.

(3)

To ensure supply chain security, China must develop a comprehensive “Maritime + Land Transport + Storage” system. The total volume of global forage trade has continued to grow amid increasingly frequent geopolitical conflicts and market price fluctuations. A single maritime logistics chain faces vulnerability to disruption. China’s imports confront the risk of “international forage price transmission,” and the global forage trade heavily relies on maritime transportation. These factors lead us to several important implications. To establish a safer and more efficient forage products trade supply chain, China should construct a tripartite “Maritime + Land Transport + Storage” framework. The maritime corridor should prioritize dedicated express routes from the United States, South America, and South Africa to China, with specialized port infrastructure providing support. The China–Europe Railway Express can enhance the land transport network through dedicated forage shipments (from Spain to Zhengzhou) and Central Asian rail corridors (from Kazakhstan to Urumqi). Simultaneously, it can contribute to developing cross-border road and rail transportation systems. China should implement a strategic reserve system with a three-tier architecture encompassing national, regional, and enterprise levels. Additionally, developing futures products for risk hedging can help mitigate the transmission of international price fluctuations.