Factors Affecting Financial Losses Caused by Wild Boars in Ningxia, China

Abstract

There is a need to reduce human–wildlife conflicts in the area around Liupanshan Nature Reserve in Ningxia Hui Autonomous Region, China. This study investigated the financial losses caused by wild boar and their causes. A questionnaire investigation (n = 135) and a field test were conducted, which included 108 sample lines and 97 infrared cameras. A principal component analysis and generalised linear model was used to analyse the importance of the effect of the factors on wild boar damage. Based on an estimate of 17,049 wild boars in the study area, we found that in the agricultural land owned by the residents, the boar density of each county and distance from the village to the nature reserve were the most significant factors that affected crop damage. Then, financial losses in spring, summer, and autumn had a moderate effect on financial loss, and the crop type had the lowest effect. We recommend reducing the wild boar population by increasing leisure hunting and the number of leopards. Additionally, a focus on farmland protection is a practical way to prevent wild boar invasions. Meanwhile, it is also necessary to conduct long-term monitoring of wild boar population status and manage the relationship between the government, research teams, and local people to more efficiently and comprehensively reduce conflicts between humans and wild boars.

1. Introduction

The harmonious coexistence of humans and nature requires financial environmental protection work and the cooperation and assistance of local residents [1]. It is of utmost importance to determine how to carry out protection work without negatively affecting, and better enhancing, the local economy. Recently, with the significant improvement in the economic environment, various nature reserves and their surrounding environments in China have been greatly improved, alongside their wildlife populations [2,3]. However, this has resulted in the intensification of human–wildlife conflicts (HWCs) in multiple areas. With the expansion of human activity, the environment continues to be transformed, resulting in profound consequences for HWC worldwide [4]. The economy is the priority for developing countries, especially for those residents in remote and impoverished areas where farming and livestock-rearing form an integral part of the local economy [5,6]. Additionally, some of the most serious HWCs occur in urban areas [7]. The financial losses caused by HWCs can lead to income losses.

In China, the proportion of agricultural and related products that contribute to the gross domestic product is high, and statements from farmers show that the activity area and wild boar population are increasing [8], which may affect HWCs. In the southern part of the Ningxia Autonomous Region, which focuses on agricultural development, this proportion is likely to be even greater [9]. The Liupanshan Mountain Region in southern Ningxia includes Yuanzhou District, Jingyuan County, Pengyang County, Longde County, and Xiji County, most of which are located in remote areas around the Liupanshan Nature Reserve. The protected area is composed of the Liupanshan Mountains and is an excellent case study for HWCs.

The Liupanshan Nature Reserve was promoted to a national-level nature reserve in 1988 and is an important water source in northwest China. The Liupanshan Nature Reserve is rich in bird and mammal diversity [10], which inevitably leads to HWCs in its surrounding areas. Recently, the area around Liupanshan Mountain has suffered severe damage due to HWCs. The main form of HWC is the destruction of farmland, and the main culprit animal is the wild boar (Sus scrofa) [11], which is a widespread mammal that can cause serious economic damage to farmers [12,13]. Due to the consistently high number of wild boars, residents around the Liupanshan Nature Reserve often suffer financial losses due to the destruction that is caused by wild boars descending the mountain, resulting in severe crop damage [14]. In addition, wild boars are also carriers of African swine fever, which poses a disease threat to the lives of local residents [15].

In previous studies on HWC around the local area, there was a lack of research on population status, but the population status of wild animals is closely related to the occurrence of HWCs [16]. The focus of previous research in local areas was only on the issue of human–animal conflict itself, while the frequency and scale of HWC may not linearly increase with the increase in the number of wild animals. The real mechanism is likely more complex and is driven by a combination of multiple factors. Thus, it is necessary to combine population surveys with research on HWCs.

Recently, residents have complained about wild boars, and effective prevention and control methods are needed. The population growth rates of wild boar may exceed 2.0, which is very high among ungulates [17,18,19]. Methods such as banishing, scarecrows, and primitive fences cannot effectively prevent the invasion of wild boars. Additionally, due to the religious beliefs of the local people, wild boars cannot be consumed, so there is a lack of motivation for hunting [20]. The improvement in the ecological environment has led to an increase in the population of wild boars, but there is a need to solve the problem of HWC. Thus, the population status of wild boars, the financial losses caused by the wild boar invasion of farmland, the related factors such as boar density and distance, the reduction in the financial losses caused by wild boars, and the methods for reducing the population of wild boars need to be investigated. As a result, this study aims to investigate the relationship between the population status of wild boars and the economic losses they cause to surrounding areas.

2. Materials and Methods

2.1. Study Area

Liupanshan Nature Reserve is located in the southern part of Ningxia Hui Autonomous Region at 35°15′–35°41′ N, 106°09′–106°30′ E, with a total area of 90,071 km². This study was conducted within the area around the Liupanshan Nature Reserve, including Yuanzhou District, Xiji County, Jingyuan County, Longde County, and Pengyang County (

Table 1. Study area details concerning the geography, climate, and human population.

Location	Area	Urban Population	Rural Population	Average Temperature	Average Rainfall
Yuanzhou District	3501 km2	476.7k (58.21%)	199.2k (41.79%)	4–15 °C	631.7 mm
Xiji County	4000 km2	70.27k (14.82%)	403.8k (85.18%)	2–15 °C	686.4 mm
Pengyang County	3238 km2	66.10k (26.85%)	180.1k (73.15%)	3–17 °C	463.5 mm
Longde County	1268 km2	41.74k (38.14%)	67.7k (61.86%)	2–14 °C	760.0 mm
Jingyuan County	1443 km2	31.00k (36.05%)	55.0k (63.95%)	3–14 °C	745.2 mm

, Figure 1).

2.2. Data Collection

This study required data on the population status of the wild boars and economic damage that is caused by conflicts between humans and boars, and the data were obtained using two methods: field research that included sample line investigation along with infrared camera installed, and questionnaire investigation (Table S1).

The field research and questionnaire investigation were conducted from February to July 2023, in which a total of 810.847 km of 108 sample lines and 97 infrared cameras were installed, and 135 respondents were randomly interviewed in 15 villages in all the counties. Figure 1 shows that the study area was 10,527.2 km². Traces, faeces and entities of wild boars were recorded during the investigation of the sample lines (Figure 2).

The footprints that were left by wild boar individuals were identified as wild boar group based on the following method: whether there was a difference in the step distance (distance from the front to back footprints) of the footprint chain. The footprint chains of multiple individuals crossing the transect that were separated by less than 30 m from each other were considered to be a wild boar herd [21]. To avoid duplicate recording of footprints that were left by the same individual, the investigators moved horizontally and in parallel and maintained contact at all times. If a footprint chain was found to cross multiple spline lines horizontally, it was considered to be footprints left by the same individual [22]. If a wild boar herd footprint chain was identified, the number of individuals in the wild boar herd was calculated based on the number of footprint chains after each individual was identified. The freshness of the footprints was determined based on the duration of the snowfall/rainfall and the discovery of faeces. The surface of the fresh faeces from the wild boars in autumn and winter was smooth, and the surface of old faeces had cracks.

Fieldwork in the sample lines was used to determine the density of the wild boars [23], and the data captured by infrared cameras were used to confirm the existence of wild boars in that particular region [24]. Another function of deploying infrared cameras was to serve as a data foundation for future research on the population status, animal behaviour, and action routes of wildlife in the region. A questionnaire gathered data on the amount of agricultural land that was owned by the respondent, the crop type, and the number of mu (1 mu = 666.67 m²) lost by each household for main crops such as maize and potato in four seasons, and financial losses were determined by the local selling prices in 2023 [25]. For factors affecting crop damage, related details, such as the crop type, agricultural land owned, financial losses in each season, density of wild boars in the area, and distance from the nearest boundary of the Natural Reserve, were recorded.

2.3. Data Analysis

• The population density of the wild boars on each sample line was calculated using the following formula:

  $$D={\displaystyle \frac{\pi x}{2sl}}$$

  where x is the total number of fresh footprint chains of the wild boars, s is the length of the total sample line, and l is the average daily active distance [26] (search for keywords such as “Wild boar” and “daily moving distance”, corresponding articles and data, and obtain the daily activity distance of wild boars in the corresponding area). The daily activity distance of the wild boars was determined using relevant articles and data.
• The formula for calculating the average density of the wild boars in the entire survey area was:

  $$\overline{D}={\displaystyle \frac{1}{n}}{\displaystyle \sum _{i=1}^n{D}_i}$$

  where $\overline{D}$ is the average density of the wild boars on all the survey lines, n is the number of sample lines, and D_i is the distribution density of the wild boars on the ith sample line.
• The population of the wild boars in the survey area was calculated using the following formula:

  $$N=\overline{D}\times A$$

  where N represents the population size of the wild boars, and A represents the total area of the survey area.

A geographic information system (GIS) in ArcMap 10.8 was used to display the study area and for designating the sample lines. The density of the wild boars was determined by the traces, faeces, and entities identified in the sample line. The distance was the length between the investigated village and the boundary of the nearest Natural Reserve of the Liupanshan Mountain range.

Subsequently, a principal component analysis (PCA) and generalised linear model (GLM) were used to analyse the specific effects of the various parameters on the economic damage that was caused by wild boars.

To verify the financial losses that were caused by wild boars and affected by different factors, a PCA was used to analysis crop damage. We included eight factors, including crop type, land owned, winter, spring, summer, and autumn losses, boar density, and distance, as follows:

PCA_CropDamage = a_1i(Crop Type) + a_2i(Land Owned) + a_3i(Winter) + a_4i(Spring) + a_5i(Summer) + a_6i(Autumn) + a_7i(Density) + a_8i(Distance)

(1)

To determine the impact of the number of wild boars, wild boar density, distance from the villages to the protected areas, and land area on the degree of damage caused by the wild boars, logistic regression and a generalised linear model were used:

Destroy Degree = glm (Boar Numbers + Boar Density + Distance + Land Owned) = “binominal”

(2)

2.4. Model Selection

The selection of the optimal model, namely the generalised linear model, was based on the Akaike information criterion (AIC). To determine the influential factors, we used analysis of variance tables. Additionally, the relationship between the significant factors and the response variables was shown by effect plots [20]. The significance level was set at p < 0.05, and analyses were performed using the program R version 4.10.

3. Results

3.1. Wild Boar Status

A total of 1352 wild boar traces were recorded in this survey. It was estimated that the total number of wild boars in the survey area was 17,049, with an average density of 2.4924 individuals/km².

3.2. Livelihood System in the Study Area

The main income of the local residents was recorded as farming and livestock farming. On average, farmers owned 12.6 mu of land, with the vast majority of farmers planting crops, such as maize and potatoes. The local selling price in 2023 for maize was 3205 CNY/mu and 1830 CNY/mu for potatoes. The crops that were destroyed by the wild boars were all maize and potatoes. The livestock that were raised included goats, sheep, and cattle. In addition to farming and raising livestock, there were also a small number of businessmen and civil servants. But still, farming is the main source of local residents’ income, and wild boars have had a significant impact on their main source of income.

3.3. Crop Damage and Financial Losses

The vast majority of the surveyed residents’ farmland was affected by the wild boars, resulting in an average annual loss of USD 2518 per household. Among the damaged crops, 85.2% were maize and 14.8% were potatoes. Average financial losses per household of each district/county in each season are presented in Figure 3, and severe damage rate in each district/county is presented in Figure 4.

The distribution of the eight factors that were considered to affect the economic losses due to crop damage is presented in

Table 2. Wild boar status in the different counties, which includes the recorded number (n = 1352), distribution area, population, and density.

Location	Number of Boars	Distribution Area (km2)	Inferred Population	Boar Density (n/km2)
Yuanzhou District	406	1667	4488	2.691
Xiji County	13	1988	2576	1.296
Pengyang County	43	1543	3205	2.077
Longde County	262	1004	3091	3.078
Jingyuan County	628	1111	3689	3.320

,

Table 3. Agricultural damages and financial losses of different type of crops. The average loss is calculated based on the number of damaged farmlands and the local crop price per mu of land.

Crop Type	Number of Farmers That Were Affected	%	Average Loss USD	Loss in CNY
Maize	115	85.2	2710	19,592
Potato	20	14.8	1414	10,227

and

Table 4. Financial loss (in CNY) associated with crop damage (n = 135) due to the wild boars that was used in the principal component analysis.

	Mean	Median	SD	Maximum
Winter	274	0	274	6410
Spring	4629	3205	4629	38,460
Summer	3822	3205	3288	16,025
Autumn	9479	6410	8505	48,075

. The PCA of the factors affecting crop damage is presented as a bi-plot in Figure 5 for the first two principal components. The first component, Dim1, described 33.3% of the total variation, while the second component, Dim2, described 18.2% of the total variation. The intensity of the orange colour indicates the significance of each factor in both the PCAs. The most significant features were ”boar density”, “land owned”, and “distance”. The factors “spring loss”, “summer loss”, and “autumn loss” played a moderate role in the financial loss, and ”winter loss” and “crop type” contributed the least to the crop damage.

3.4. Damage Degree and Affecting Factors

The effects of the factors, such as the boar number, boar density, distance, and land owned, on the damage degree due to the wild boars were investigated by fitting the GLM (with an AIC of 156.03;

Table 5. The effects of the significant factors on the degree of crop damage caused by the wild boars. The estimate is the generalised linear model-based effects as the log-transformed odds ratio (standard error). The significance of each level was compared with another level (called the damage degree) and the p-values are presented, which were further supported by the z-values.

	Odds Ratio	Estimate	Standard Error	z-Value	p-Value
(Intercept)	0.91	−2.39	1.63	−1.46	0.14
Boar Number	1	0	0	−1.83	0.07
Boar Density	9.27	2.23	0.68	3.28	<0.01
Distance	1.08	0.75	0.02	3.14	<0.01
Land Owned	0.90	−0.10	0.03	−3.10	<0.01

, Figure 6), which revealed that the damage degree was significantly influenced by all of the above factors. The level of damage degree was divided into severe damage, coded as 1 (damage degree > 50%), and light damage, coded as 0 (damage degree < 50%).

The GLM result showed that a boar density increase of 0.1/km² resulted in a chance of severe damage that was 0.927 times higher, with a p-value of <0.01. With an increase of one wild boar, the chances of severe damage were 0.999 times less, with a p-value of 0.05. With a 1 km increase in the distance between the village and the natural reserve, the chances of severe damage were 1.078 times more, with a p-value of <0.01. Lastly, with an increase of one mu of agricultural land that the farmer owned, the chances of severe damage were 0.901 times less, with a p-value of <0.01. The trends are shown in Figure 5.

4. Discussion

After an 810.847 km-long field sampling survey from February to July 2023 and trace detection of wild boars, it was found that there were sufficient numbers of wild boars in Yuanzhou District, Xiji County, Pengyang County, Longde County, and Jingyuan County in the Liupanshan area to cause crop damage to villages around the nature reserve. The highest density of wild boars was 3.320/km², with an average of 2.492/km².

As a common pest animal in the Liupanshan area of Ningxia, the financial losses caused by wild boars for the local residents were high. The respondents suffered an average annual loss of USD 2518 due to damage to crops caused by wild boars. In addition, due to previous laws prohibiting the hunting of wild boars [27], which is the traditional method of reducing the wild boar population [28], local religions prohibiting contact with wild boars, and the scarcity of the common leopard population in the region [29], the number of wild boars is not being significantly reduced. It is worth noting that the same amount of hunting caused by hunters in previous eras hit the wild boar, whose reproduction rate is quite high, much less hard than other carnivores (e.g., wolves and leopards). Overhunting decimated the populations of other carnivores, while wild boars were not so affected. During the subsequent period of ecological protection, nature reserves were prioritised and wildlife populations recovered, but this was just the right opportunity for wild boars to proliferate. As it happens, the reserves are surrounded by farmland, and a large part of the wild boar’s diet comes from human-grown grains. Therefore, wild boars are rampant in the Liupanshan area, often entering the surrounding rural areas to destroy farmland.

In the PCA analysis based on the crop destruction, there was no significant correlation between the maize and potato losses, indicating that maize and potatoes are equally attractive to wild boars. Local residents rarely grow vegetables and fruits, but the crops that they grow have a high carbon and water content that could provide considerable digestible energy, such as potatoes and maize [30], which makes them attractive to wild boars and so have become the main source of their diet [31]. The most important factors affecting the financial losses caused by the wild boars were the local density of the wild boars, agricultural land owned by the farmers, and the vertical distance from the villages to the nature reserve areas. In addition, there was also a moderate correlation between the losses that occurred in spring, summer, and autumn, indicating that the financial losses suffered by farmers largely depended on whether they had effectively protected their land from wild boars. Reducing the number of wild boars and taking measures to protect the land are therefore important factors in determining the financial losses caused by wild boars, as the location of villages is fixed.

Notably, the PCA results indicated a strong relationship between the economic losses caused by wild boars and their density, possibly due to the combined constraints of the farmland, suitable habitat, and food on the wild boar density. In terms of foraging, the wild animals could be (1) only eating the natural vegetation in the natural protection area; or (2) eating both crops and vegetation in the protection area. For wild boars, which rely on farmland, the second scenario is more likely [32]. Therefore, it is not only the density of wild boar that is the main factor affecting HWCs, but the area of arable land also indirectly determines the state of the wild boar population, as it is a potential food source. The residents around the protected area are affected by wild animals such as wild boars, which in turn affects the livelihoods of the surrounding residents, resulting in economic losses caused by HWC [33]. In the absence of sufficient intervention, it can lead to the rapid proliferation of the wild boar populations. If the economic losses caused by wild boars are strongly correlated with the density of wild boars, it indicates that the natural population of wild boars has reached the threshold of resource supply in the protected area, leading to a significant increase in the population of wild boars due to the available resources in the farmland. The results are consistent with this, as the population of wild boars largely depended on the crops in the surrounding farmland. According to local residents, wild boars have caused damage to farmland for over a decade, but the most serious damage occurred in the past two years. Thus, unless farmers around the protected area stop planting crops, it is necessary to immediately and effectively reduce the density of the wild boars.

The GLM results indicated that the degree of damage will increase with the density of the local wild boars, the distance of the villages from the edge of the protected area, and decrease in the area of farmland owned by each household and the number of wild boars in the region. The impact of the number of wild boars on the degree of damage was negligible, indicating that the density of the local wild boars had a greater impact on the degree of land damage. The higher the density of the wild boars, the higher the degree of damage to the land, which also supports the fact that the wild boar population is no longer completely dependent on the natural vegetation in Liupanshan Mountains, but has invaded nearby villages on a large scale. The further away the boars were from the village, the more damaged the farmland, possibly because the distance increased the cost of wild boar raids, requiring them to increase their intake every time they invaded the farmland. Alternatively, the wild boars may settle directly near the farmland, which is a source of food for the wild boars, which greatly increases the rate of farmland destruction. Moreover, when compared with forests and farmland, they prefer transitional grassland [20,34]. This indicates that villages that are far from the protected area should increase their protection against wild boars. If these villages are well protected, the degree of damage caused by wild boars and the distance between the villages and the protected area may become positively correlated in the future.

The crops that were planted in the Liupanshan area were mostly maize and potatoes, so there are unlikely to be different losses due to planting different crops in different seasons. Notably, wild boars not only consumed mature crops in summer and autumn but also dug up and ate the seeds of the crops in spring and winter. Most areas had little or no boar invasion during the winter, but destructive behaviour was observed in a small number of villages regardless of the season. In addition, wild boars will repeatedly destroy the same farmland until there are no crops left [35]. This will greatly damage the regeneration capacity of the affected farmland and affect its reuse rate.

The financial losses that were caused by the destruction of wild boars accounted for a significant portion of the average annual income of the farmers in the Liupanshan area, which affected the residents’ lives and led to a very negative attitude toward the wild boars [36].

Wild boars are more common at the edges of nature reserves, which are closer to the villages, rather than in the core areas of the nature reserve. This is more convenient for foraging from the farmland. Fences have proven quite effective and could be used to deter the boars [37]. The farmland in the Liupanshan area lacks effective protective measures, and the poorer the farmers, the less funds they have for installation and protection. Effective protective measures require more advanced protective barriers, such as wire mesh barriers and electric barriers [38]. Additionally, building tents to guard the farmland during night and placing scaring devices like firecrackers could also be effective [39]. Unfortunately, these methods require significant financial support, such as government subsidies. However, the government is currently unable to provide sufficient subsidies to compensate for the economic losses caused by the wild boars. Moreover, the more losses that are suffered by the residents in the surrounding areas of the nature reserve due to wildlife, the greater the negative impact it will have on the local ecological protection work. For remote areas in developing countries, protecting residents’ incomes is the key to revitalising the overall ecological protection strategy [40]. We suggest that the government should provide subsidies to farmers in the form of building guardrails.

In addition to providing sufficient protection for the farmland, reducing the population of wild boars is also crucial. Due to the increase in the number of wild boars in Europe and the decrease in the number of hunters [41], in Switzerland, 94% of the total deaths were due to hunting between 2004 and 2010 [42]. However, there are also regions, such as Poland, that already have wolves, which have also contributed 12% to the mortality rate of wild boars [43]. Thus, we believe that expanding the population size of the common leopard, which is the top predator in the Liupanshan region, should be effective. However, the effectiveness of this method is limited and may lead to other issues such as the generation of new HWCs. According to the latest policy, wild boars are no longer legally protected as “Three Animals” and can now be legally hunted. Unfortunately, it is not possible to control the population of wild boars solely through casual hunting or the cultivation of hunters. Thus, the more effective measure is the government’s regular organisation of action to eliminate wild boars. Holding hunting activities with a moderate prize may not effectively control the number of wild boars [44], but establishing specialised hunting entertainment programs can attract foreign tourists, which is expected to stimulate the local GDP and could enable regional finance to provide more crop damage compensation for farmers and funding for official hunting activities. Additionally, using supplementary feeding [45] like corn kernels to direct wild boar to stay in the reserve during periods of high wild boar incidence is an effective method. It is achieved by keeping wild boar in their refuges by scattering fodder on forest paths and covering it with soil to keep wild boars in their refuges, which is particularly effective in spring when food resources in the area with wild vegetation are scarce. These could not only have a moderating effect on the financial losses of the villages around the protected area, but could also result in a significant improvement in the populations of other wild animals, such as roe deer (Capreolus pygargus), Chinese serow (Capricornis milneedwardsii), tufted deer (Elaphodus cephalophus), and long-tailed goral (Naemorhedus griseus), which compete with the wild boars.

5. Conclusions

This study indicates that wild boars have a large population in the Liupanshan Nature Reserve and surrounding areas in the southern part of Ningxia Hui Autonomous Region, China, and they have caused HWCs. The main factors affecting the financial losses caused by the wild boars were the local density of the wild boars and the vertical distance from the villages to the nature reserve areas, which was followed by the amount of agricultural land owned by farmers and the damage caused in spring. This indicated that reducing the density of wild boars is an urgent need, and villages far from the protected area should increase their protection against wild boars, especially in spring. The boars’ behaviour of destroying farmland has caused significant financial losses to the local residents. The vast majority of the residents expressed dissatisfaction with the wild boars and claimed that the government did not compensate the farmers for their losses. We suggest reducing the number of wild boar populations and focusing on farmland prevention to reduce the economic losses of the local residents. Effective methods for reducing the number of wild boars could be to increase the number of common leopards in the Liupanshan Nature Reserve and regularly organise official wild boar hunting activities. For key prevention measures, effective protective measures, such as large protective barriers, need to be installed, and this expenditure may require government subsidies. In addition, fiscal revenue can also be increased by promoting leisure hunting in the area, and supplementary feeding could be used to keep wild boars in reserve. It is also necessary to conduct long-term monitoring of the wild boar population status, manage the relationship between the government, research teams, and local people, and encourage the exchange of ideas on preventing and treating HWCs to more efficiently and comprehensively reduce conflicts between humans and wild boars.