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Article

Effects of Human–Elephant Conflict and Wildlife Damage Compensation on Farm Households’ Farmland Transfer-Out and Abandonment

by
Junfeng Chen
1,†,
Jie Yang
2,†,
Yi Xie
3 and
Yi Zheng
2,*
1
School of Agricultural Economics and Rural Development, Renmin University of China, Beijing 100872, China
2
School of Economics and Management, Fujian Agriculture and Forestry University, Fuzhou 350002, China
3
School of Economics and Management, Beijing Forestry University, Beijing 100083, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agriculture 2026, 16(6), 666; https://doi.org/10.3390/agriculture16060666
Submission received: 7 February 2026 / Revised: 7 March 2026 / Accepted: 13 March 2026 / Published: 14 March 2026
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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Abstract

In areas with frequent wildlife activity, coordinating biodiversity conservation with agricultural production is a critical issue for achieving agricultural sustainability. This study uses farm household survey data collected in 2022 from Asian elephant distribution areas in Yunnan Province, China. It systematically evaluates the effects of Human–Elephant Conflict (HEC) and the wildlife damage compensation policy on farm households’ farmland use behavior. Focusing on farmland adjustment behavior under the context of biodiversity conservation, we develop an analytical framework of “HEC–policy intervention–farm household farmland use behavior.” Using survey data from 1276 farm households, we examine the effects of HEC on farmland transfer-out and farmland abandonment. We also analyze the moderating role of the wildlife damage compensation policy. In addition, we explore the heterogeneity between areas inside and outside nature reserves. The results show that: (1) HEC significantly increase the likelihood of farmland transfer-out and farmland abandonment among farm households; (2) the wildlife damage compensation policy partially mitigates the positive effects of HEC on farmland transfer-out and farmland abandonment; and (3) the effects of HEC on farmland transfer-out and farmland abandonment are more pronounced for farm households outside nature reserves. The wildlife damage compensation policy shows a stronger inhibitory effect on farmland transfer-out inside nature reserves. In contrast, it has a stronger inhibitory effect on farmland abandonment outside nature reserves. From the perspective of farmland use, this study reveals how HEC and policy intervention influence farm households’ farmland allocation behavior. It also provides empirical evidence for improving wildlife damage compensation mechanisms. In addition, the findings help promote synergy between agricultural sustainability and biodiversity conservation.

1. Introduction

Wildlife plays a vital role in maintaining the balance of natural ecosystems, and its conservation has become a central issue in global environmental governance [1,2]. However, in agricultural landscapes with frequent wildlife activity, biodiversity conservation often interacts with agricultural production and may influence farm households’ production decisions [3,4]. The Asian elephant (Elephas maximus) is one of the largest terrestrial mammals in Asia and serves as a flagship and umbrella species [5]. Asian elephant is listed as an endangered species by the International Union for Conservation of Nature (IUCN). Since 1988, China has listed the Asian elephant as one of the most strictly protected wildlife species. The population of Asian elephants has increased from about 180 individuals in the 1980s to more than 300 at present [6]. However, with habitat loss and the expansion of human activities, the range of the Asian elephant has increasingly overlapped with rural production areas [7,8,9]. Farmland has become an important area for the migration and foraging of Asian elephants. As a result, Human–Elephant Conflict (HEC) has become increasingly prominent [10,11,12,13]. The Asian elephant affects farm households’ farmland use by trampling farmland and feeding on crops [14,15,16]. According to official statistics from the Yunnan Province, between 2011 and 2018, a total of 14,340 farm households experienced crop losses due to HEC, with total economic losses amounting to 3.23 million USD [7,17].
Frequent HEC increases the uncertainty of agricultural production. It may also change farm households’ expectations of returns and risks from farmland, thereby influencing their land-use decisions [18,19,20]. When agricultural production risks increase, farm households may adjust their land-use strategies to reduce risk exposure, such as transferring out farmland or abandoning cultivation [20,21,22,23,24]. To mitigate the losses caused by HEC, China has gradually established a wildlife damage compensation system. This policy provides economic compensation to farm households affected by HEC. Through fiscal transfer payments, it partly offsets farmers’ losses and may influence their agricultural decisions and farmland use behavior. In theory, HEC acts as an exogenous risk shock. It increases the cost of farmland management and strengthens farm households’ expectations of agricultural production risks [25,26]. As a result, farm households may adjust their land-use strategies. For example, they may transfer out farmland to obtain relatively stable rental income. They may also abandon cultivation when farming can no longer generate positive returns [27]. At the same time, the wildlife damage compensation policy helps reduce farmers’ losses and stabilize their income expectations. This policy can lower agricultural production risks to some extent and influence farm households’ decisions to continue farming their land [28,29]. Their relationship is shown in Figure 1.
Existing studies on HEC have largely focused on ecological and geographical perspectives. They mainly examine changes in the behavioral patterns of Asian elephants, as well as the temporal and spatial characteristics of conflict events. These studies provide technical support for nature reserve planning and for monitoring the activities of Asian elephants [12,30,31,32]. A consistent finding in the literature is that farmland and its surrounding areas constitute hotspots of HEC [33,34]. In China, responses to HEC have mainly relied on the construction of fences and the implementation of the wildlife damage compensation policy [35]. Empirical studies on the mechanisms through which HEC affects farm households’ farmland use and the role of policy interventions remain limited. Existing studies mainly examine the determinants of farmland transfer-out and abandonment. These factors include resource endowment, individual characteristics, expected agricultural returns, labor structure changes, non-farm employment opportunities, property rights institutions, and market conditions [36,37,38,39,40]. Under the context of biodiversity conservation, HEC represents an uncertain exogenous shock. It may influence farm households’ land-use decisions. Yet, it has rarely been incorporated into analytical frameworks explaining farmland transfer-out and farmland abandonment behaviors. At the same time, the wildlife damage compensation policy is an important institutional instrument for mitigating HEC and reducing production risks faced by farm households. However, empirical evidence based on micro-level data remains limited. In particular, the mechanisms through which economic compensation influences farm households’ agricultural production decisions are still not well understood.
Furthermore, HEC occurs not only inside nature reserves, but also frequently in surrounding areas and agricultural regions outside reserves [30,41]. Differences exist between areas inside and outside nature reserves in terms of land-use regulation, protection intensity, and policy implementation environments. As a result, farm households may respond differently to HEC and wildlife damage compensation policies across space [11,42,43,44,45]. However, existing studies have paid limited attention to such spatial heterogeneity under different governance contexts.
Based on this background, this study uses micro-level survey data from 1276 farm households in HEC areas of Yunnan Province, China. It examines the effects of HEC on farm households’ farmland transfer-out and farmland abandonment behavior. Specifically, the study focuses on the following aspects: to (1) analyze the effects of HEC on farm households’ farmland transfer-out and farmland abandonment; (2) test the mechanisms through which wildlife damage compensation mitigates the effects of HEC on farmland transfer-out and farmland abandonment; and (3) examine the heterogeneity in the effects of HEC and wildlife damage compensation on farmland transfer-out and farmland abandonment between areas inside and outside nature reserves. This study reveals the mechanisms through which HEC and policy intervention shape farm households’ farmland allocation behavior. It provides empirical evidence for improving the wildlife damage compensation policy and mitigating HEC. The findings also contribute to promoting a synergy between agricultural production and biodiversity conservation.
The remainder of this paper is organized as follows. Section 2 introduces the study area, data sources, and variable measurements. Section 3 presents the empirical results. Section 4 discusses the findings and research limitations. Section 5 provides the conclusions and policy implications.

2. Materials and Methods

2.1. Study Area

The study area covers the main regions with active Asian elephant populations in China, including Xishuangbanna Prefecture, Pu’er City, and Lincang City in the Yunnan Province (Figure 2). These areas are recognized as important ecological function zones in China, characterized by diverse flora and fauna and rich biodiversity, and are often referred to as a “kingdom of animals and plants.” Yunnan Province is the only region in China where the Asian elephant is distributed [46]. Xishuangbanna Prefecture hosts the largest Asian elephant population, and was historically the only habitat of Asian elephants in China; Pu’er City has become a new habitat in recent years due to the expansion of elephant groups, while Lincang City is home to a smaller elephant population [39]. The spatial distribution map of the study area was generated using ArcGIS 10.8 (Esri, Redlands, CA, USA).
At the same time, Yunnan Province was among the first regions in China to pilot the wildlife damage compensation policy. After HEC occurs, farm households can apply for financial compensation under relevant regulations. This compensation helps offset losses to farmland cultivation caused by HEC [5,29,47].

2.2. Data Collection

The data were obtained from a questionnaire survey of farm households conducted by the research team in the study area. The survey team consisted of faculty members and graduate students from the research group, as well as staff from local nature reserves. A pilot survey was initiated in July 2021, followed by a one-month formal survey conducted between July and August 2022. Through face-to-face interviews and structured questionnaires, the survey collected detailed information on HEC, wildlife damage compensation, and the socioeconomic characteristics of farm households in Yunnan Province. The sampling procedure followed a multi-stage stratified random sampling strategy. First, considering the distribution of Asian elephants, eight counties (districts) in three prefecture-level cities (prefectures) were selected. Second, based on recommendations from local wildlife authorities and considering the severity of HEC and local agricultural conditions, we first identified administrative villages located within the main activity range of Asian elephants. These villages served as the sampling frame for the subsequent survey. On this basis, 4–10 administrative villages were randomly selected in each county (district) as survey samples to ensure the coverage of areas with different levels of HEC and agricultural production conditions. Finally, about 20 farm households were randomly selected in each administrative village. The respondents were the household head or the primary production decision-maker. To ensure data quality, questionnaires with missing key information or abnormal values were removed. A total of 1276 valid farm household samples were obtained. This sample size is comparable to, or larger than, those used in similar farm household surveys in rural China and is sufficient for subsequent empirical analysis [6,7].

2.3. Variable Selection

The dependent variables are the farm households’ behaviors of farmland transfer-out and farmland abandonment. HEC primarily affects farmland production activities undertaken by farm households. Farmland transfer-out or abandonment means that farm households no longer continue cultivating their lands. This may pose potential challenges to agricultural sustainability. Considering the characteristics of HEC, existing literature, and field survey evidence, this study selects farmland transfer-out and farmland abandonment among farm households as the dependent variables [48,49].
The core explanatory variables in this study are HEC and wildlife damage compensation, with wildlife damage compensation also serving as a moderating variable. HEC in this study refers to cases where an Asian elephant tramples farmland or feeds on crops. Wildlife damage compensation is a policy implemented in China that provides economic compensation to citizens for personal injuries or property losses caused by wildlife. In this study, it specifically refers to economic compensation provided to farm households for losses caused by Asian elephants trampling farmland or raiding crops. HEC mainly manifest as the trampling and feeding on food crops and cash crops cultivated on farmland by farm households. Accordingly, the severity of HEC is measured using the monetary value of crop losses incurred by farm households due to HEC, calculated as the total loss value of food crops and cash crops cultivated on farmland. The loss amount is based on self-reported data from farm households. Although some reporting bias may exist, face-to-face interviews were used to help respondents recall their losses, which improves the accuracy of the data. This measurement approach has been widely used in related studies [14,25]. In addition, given the current implementation of the wildlife damage compensation policy, wildlife damage compensation programs have been established throughout the study area. After experiencing HEC, farm households are eligible to apply for wildlife damage compensation in accordance with relevant regulations. If a farm household received wildlife damage compensation, the variable is assigned a value of 1; otherwise, it is assigned a value of 0.
In addition, farmland use behaviors may be related to farm households’ overall resource endowments. Therefore, several control variables are included to ensure the rigor and robustness of the analysis. These controls capture key dimensions of household resources, including natural, physical, human, financial, and social capital [50,51,52,53]. Detailed definitions of the variables and descriptive statistics are reported in Appendix A (Table A1).

2.4. Statistical Analysis

2.4.1. Model of the Effects of HEC on Farm Households’ Farmland Transfer-Out and Abandonment

This study first specifies an econometric regression model to examine the effects of HEC on farm households’ farmland use behavior. Farmland transfer-out and farmland abandonment are typical discrete choice outcomes for farm households. Their values take either 0 or 1. Therefore, the dependent variable is binary. For such binary choice problems, a binary choice model is more appropriate for estimation. If ordinary least squares (OLS) is used, the predicted probabilities may fall outside the [ 0 , 1 ] interval and heteroscedasticity may occur. The binary logistic model transforms probabilities using the logistic distribution function. This ensures that predicted probabilities remain within the [ 0 , 1 ] interval. For this reason, it has been widely used in studies of farm household behavioral decisions [31]. Based on this, this study uses a binary logistic model to estimate the effects of HEC and wildlife damage compensation on farmland transfer-out and farmland abandonment. A probit model is further used for robustness tests. The model is specified as follows:
P n = F α + ∑ i = 1 n β i x i = 1 1 + e x p [ − ( α + ∑ i = 1 n β i x i ) ]
By transforming Equation (1) into logarithmic form, the linear specification of the regression model is expressed as follows:
ln P n 1 − P n = α + ∑ i = 1 n β i x i
In Equations (1) and (2), n denotes the number of observations. P n represents the probability that the nth farm household engages in farmland transfer-out or farmland abandonment. α   is the constant term, and β i   denotes the regression coefficient associated with HEC x i . After estimating the effects of HEC on farm households’ farmland transfer-out and farmland abandonment using the logistic model, we first examine the magnitude and direction of the estimated coefficients and then analyze the corresponding marginal effects based on the marginal effects results.

2.4.2. Moderating Effect Model of Wildlife Damage Compensation

This study examines whether the wildlife damage compensation policy influences farm households’ farmland use behavior. It also analyzes whether the policy moderates the effects of HEC on farmland transfer-out and farmland abandonment. Based on these relationships, both a direct (linear) effect model and a moderating effect model are constructed [54]. Hierarchical regression analysis is then conducted to test the proposed hypotheses [55]. The specific model specifications are as follows.
In the first step, to examine the effects of wildlife damage compensation on farmland transfer-out and farmland abandonment among farm households, the first-order term of wildlife damage compensation is introduced into the baseline model (2), and the direct (linear) effect model is specified as follows:
L o g i t C h a n g e i = α i + β 1 H E C i + β 2 C o m p e n s a t i o n i + γ C V i + ε i
In the second step, to analyze the moderating role of wildlife damage compensation in the effects of HEC on farmland transfer-out and farmland abandonment among farm households, the independent variable and the moderating variable are first mean-centered following standard moderation analysis procedures [32]. An interaction term between HEC and wildlife damage compensation is then constructed and incorporated into model (3) to specify the moderating effect model as follows:
L o g i t C h a n g e i = α i + β 1 H E C i + β 2 C o m p e n s a t i o n i + β 3 H E C i × C o m p e n s a t i o n i + γ C V i + ε i
In Equations (3) and (4), H E C i represents Human–Elephant Conflict, and C o m p e n s a t i o n i denotes wildlife damage compensation. If the R 2 of Equation (4) is significantly higher than that of Equations (2) and (3), or if the interaction term between HEC and wildlife damage compensation ( H E C i × C o m p e n s a t i o n i ) is statistically significant, it indicates that wildlife damage compensation has a significant moderating effect on the impacts of HEC on farmland transfer-out and farmland abandonment among farm households [56]. The empirical analysis was conducted using Stata 17 (StataCorp LLC, College Station, TX, USA).

3. Results

3.1. Effects of HEC on Farm Households’ Farmland Use Behavior

HEC, control variables, and all explanatory variables are sequentially incorporated into the models for estimation. The regression results are reported in Table 1. The results indicate that HEC has a positive effect on farmland transfer-out and farmland abandonment among farm households at the 1% significance level. A 1% increase in the severity of HEC increases the probability of farmland transfer-out by 1.8 percentage points and farmland abandonment by 0.90 percentage points. HEC has a stronger effect on farmland transfer-out.

3.2. Robustness Checks

Two robustness checks are conducted. First, the probit model is used as an alternative to the Logit model to test the robustness of the results. As reported in Table 2, HEC exhibits significantly positive effects on both farmland transfer-out and farmland abandonment at the 1% significance level. Second, the original explanatory variable is replaced with an alternative indicator capturing whether Asian elephants entered a farm household’s farmland. The variable is assigned a value of 1 if Asian elephants entered the farmland, and 0 otherwise. The effects of HEC on farmland transfer-out and farmland abandonment are re-estimated. The robustness results presented in Table 3 show that HEC have significant effects on farmland transfer-out and farmland abandonment at the 1% and 5% significance levels, respectively. Overall, the baseline empirical results pass the robustness checks, further confirming that HEC promotes farmland transfer-out and farmland abandonment among farm households.

3.3. Moderating Effects of Wildlife Damage Compensation on Farm Households’ Farmland Use Behavior

The regression results examining the effects of wildlife damage compensation and the interaction term on farmland transfer-out among farm households are presented in Table 4. The results show that the interaction term between HEC and wildlife damage compensation has a significantly negative effect on both farmland transfer-out and farmland abandonment at the 1% significance level. A one-percentage-point increase in the probability of receiving wildlife damage compensation reduces the probability of farmland transfer-out by 5.30 percentage points and farmland abandonment by 1.80 percentage points. These findings suggest that wildlife damage compensation mitigates the positive effect of HEC on farmland transfer-out and abandonment, thereby helping stabilize farmland cultivation.

3.4. Heterogeneity Analysis by Nature Reserves

3.4.1. Heterogeneity Analysis of the Effects of HEC and Wildlife Damage Compensation on Farm Households’ Farmland Transfer-Out

The heterogeneity regression results are reported in Table 5. The results indicate that HEC has a significant effect on farmland transfer-out among farm households outside nature reserves, with a marginal effect of 0.018. Wildlife damage compensation also shows a moderating effect on farmland transfer-out both inside and outside nature reserves. This moderating effect is stronger for farm households located within nature reserves.

3.4.2. Heterogeneity Analysis of the Effects of HEC and Wildlife Damage Compensation on Farm Households’ Farmland Abandonment

The heterogeneity regression results are reported in Table 6. The results show that HEC has a significant effect on farmland abandonment among farm households outside nature reserves, with a marginal effect of 0.008. In addition, wildlife damage compensation exhibits a moderating effect on farmland abandonment only for farm households located outside nature reserves.

4. Discussion

This study incorporates HEC as an exogenous environmental risk and the wildlife damage compensation policy as an institutional arrangement into the analysis of farm households’ farmland use behavior. From the perspectives of risk shocks and policy intervention, this study examines how environmental disturbances and policy responses jointly shape farmland use behavior under the context of HEC. It provides a new perspective for coordinating agricultural sustainability and biodiversity conservation.
This study shows that HEC, by damaging farmland and crops, significantly promote farmland transfer-out and farmland abandonment, indicating that HEC have become an important exogenous risk factor shaping farmland use behavior. In the sample, 74.29% of farm households experienced HEC, highlighting the widespread nature of HEC in areas where humans and elephants coexist. Under persistent disturbance from Asian elephants, farm households tend to balance agricultural inputs and expected returns and adopt risk-averse farmland use strategies [57]. In particular, farm households are more likely to transfer out farmland to reduce direct production risks; when farmland transfer-out is constrained, they may be forced to choose farmland abandonment instead [58,59]. These findings suggest that HEC not only directly disrupt agricultural production processes, but also alter farm households’ risk expectations, thereby reshaping patterns of agricultural farmland use [19,44].
The results indicate that the wildlife damage compensation policy plays a significant moderating role in the effects of HEC on farmland transfer-out and farmland abandonment. The policy provides economic compensation for crop losses and farmland management losses suffered by farm households. This helps offset the direct economic shocks caused by HEC. As a result, it reduces the uncertainty of returns from continued agricultural production. In terms of the mechanism, the compensation policy has a direct financial effect. It may also send a policy signal that the government is committed to addressing HEC. This signaling effect helps stabilize farm households’ expectations about agricultural production [26,60]. When compensation is delivered in a timely and stable manner, farm households are more likely to maintain confidence in continued agricultural production. This reduces their incentives to transfer out farmland or abandon cultivation due to risk uncertainty [61]. Therefore, the compensation policy not only provides financial protection by covering part of the actual losses but also stabilizes farmers’ risk expectations as a psychological stabilizer. These two effects together influence farm households’ land-use decisions. However, relying solely on ex post compensation may lead to diminishing policy effects. Different compensation ratios may lead to different behavioral responses from farm households. If the compensation level is insufficient to cover long-term production risks, the policy may only provide short-term relief and may not fundamentally stabilize farm households’ farmland use decisions [62]. Field surveys also show that compensation covers only about 30% of crop losses on farmland. This indicates that the compensation policy is still insufficient to some extent. Therefore, wildlife damage compensation after HEC should be complemented by preventive measures. A combined system of “ex ante prevention + ex post compensation” should be established. This would shift the policy approach from passive compensation to proactive risk management [63].
Heterogeneity analysis shows that HEC has a stronger effect on farmland transfer-out and farmland abandonment among farm households outside nature reserves. This suggests that wildlife shocks are more likely to drive adjustments in land-use decisions in areas outside nature reserves. On the one hand, non-farm employment opportunities are relatively more abundant outside nature reserves. Farm households are less dependent on agricultural income. When facing wildlife-related risks, they are more likely to adjust farmland use to reduce production uncertainty. On the other hand, land transfer markets are more active outside nature reserves. When agricultural returns decline, farm households can more easily reallocate land resources by transferring out farmland or exiting farming [30,37].
The moderating effect of wildlife damage compensation on the relationship between HEC and farmland transfer-out is stronger among farm households inside nature reserves, while the effect is relatively weaker outside the reserves. This result may be related to stronger constraints in local land transfer markets and higher HEC risks inside nature reserves [45,53]. Nature reserves usually implement stricter land-use regulations and ecological protection policies. As a result, farmland transfer faces stronger institutional constraints and higher transaction costs [36,61]. In addition, the probability of farm households experiencing HEC is higher inside nature reserves than outside. In our sample, the proportions are 78.25% and 72.64%, respectively. This indicates that farm households inside nature reserves face higher uncertainty and potential loss risks. Under such conditions, potential tenants are more sensitive to farmland management risks. This reduces the demand for farmland transfer inside nature reserves [4,64]. With both high risks and restricted land transfer markets, farm households have fewer opportunities to exit agricultural production through land transfer [46,47]. Therefore, when wildlife damage compensation can partly offset the losses caused by HEC, farm households are more likely to continue holding their farmland [65]. As a result, the compensation policy shows a stronger moderating effect on reducing farmland transfer-out inside nature reserves.
By contrast, the wildlife damage compensation policy significantly moderates the effect of HEC on farmland abandonment among farm households outside nature reserves. This moderating effect is not statistically significant for farm households within nature reserves. This difference may be related to variations in the cost–benefit structure of farmland management across different spatial contexts. Within nature reserves, Asian elephant activity is more frequent and land-use regulations are more restrictive, which limits opportunities for farmland transfer-out. When farm households are unable to exit agricultural production through land transfer, they face higher risks and expected losses from continued cultivation [45,66]. Under such conditions, ex post compensation payments may be insufficient to cover long-term cultivation costs. They may also fail to offset the uncertainty associated with repeated damage. The compensation may not substantially improve the expected returns from continued farming. As a result, the compensation policy is less effective in reducing the probability of farmland abandonment within nature reserves [67]. In contrast, outside nature reserves, HEC are relatively less severe and land-use constraints are weaker. In these areas, the compensation policy can more effectively offset crop losses and reduce perceived cultivation risks, thereby influencing farm households’ decisions on whether to continue farming. Consequently, wildlife damage compensation plays a more pronounced role in mitigating farmland abandonment among farm households outside nature reserves [13,42].
Our findings are broadly consistent with some conclusions in international HEC research. For example, studies in Africa and South Asia show that HEC can alter farm households’ land-use decisions by increasing agricultural production risks [63]. In these regions, HEC mitigation often involves multiple measures. These include electric fencing, habitat improvement, population management, and community participation. Such measures aim to reduce agricultural risks and alleviate conflict pressure [68,69,70]. Some studies also propose the use of Payments for Ecosystem Services (PES) or tourism revenue sharing. These mechanisms can compensate farmers for wildlife-related agricultural risks and help reduce government costs in HEC management [63,71,72]. However, compared with these regions, HEC governance in China relies more on government fiscal expenditure to compensate losses. This has formed a governance approach characterized by a “wildlife shock–policy intervention” framework. The wildlife damage compensation mechanism established in Yunnan has partly mitigated the impact of HEC on agricultural production. However, its effectiveness is constrained by the long-term sustainability of compensation funding. Therefore, while improving the compensation system, future policies could also consider introducing PES mechanisms to further enhance the effectiveness of HEC governance.
This study has some limitations. First, wildlife damage compensation is measured as a binary variable. It only indicates whether farm households receive compensation. It does not capture the relationship between the compensation amount and the actual losses. Future studies could use indicators such as the ratio of compensation received to total losses. Second, this study mainly examines the effects of HEC on farmland transfer-out and farmland abandonment. However, HEC may also affect other agricultural production activities. These aspects deserve further exploration in future research.

5. Conclusions

This study finds that HEC significantly promotes farmland transfer-out and farmland abandonment among farm households. As a result, agricultural production activities are suppressed to some extent. Wildlife damage compensation can mitigate this negative impact and help stabilize agricultural production. However, the stabilizing effect remains limited because the compensation level is relatively low. Further heterogeneity analysis shows that the compensation policy has a stronger effect in reducing farmland transfer-out inside nature reserves. In contrast, it has a stronger effect in reducing farmland abandonment outside nature reserves. This reflects the important role of differences in land transfer constraints and agricultural risk structures under different spatial governance contexts in shaping farm households’ land-use decisions. Based on these findings, the following policy recommendations are proposed.
First, strengthen risk prevention mechanisms for HEC and shift governance from ex post compensation to ex ante prevention and risk management. For example, early warning and monitoring systems can be established, habitat management for Asian elephants can be improved, and protective facilities such as electric fences can be built in high-risk areas. These measures can reduce the probability of Asian elephants entering farmland and mitigate agricultural production risks at the source.
Second, improve the wildlife damage compensation system and establish a tiered compensation system. Differentiated compensation standards should be set according to the risk level of Asian elephant activity and the opportunity costs of farmlands in different regions. In addition, compensation should be delivered in a timely manner to stabilize farm households’ expectations for agricultural production.
Third, introduce Payments for Ecosystem Services (PES) mechanisms to strengthen incentives for farm households to participate in biodiversity conservation. This can be achieved through community participation and tourism revenue sharing. For example, government funds or ecotourism revenues could be used as funding sources. These funds could provide long-term compensation to farm households. The compensation would target households that continue farming or participate in habitat protection in Asian elephant corridor areas. Such measures can help mitigate HEC while promoting the coordinated development of agricultural production and biodiversity conservation.

Author Contributions

Conceptualization, J.C., Y.X. and Y.Z.; methodology, J.C. and Y.X.; investigation, J.C. and Y.X.; writing—original draft preparation, J.C. and J.Y.; writing—review and editing, J.C. and J.Y.; supervision, J.Y. and Y.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the China Postdoctoral Science Foundation, grant number 2025M772463; the National Social Science Foundation of China, grant number 2023BGL177; the Fujian Research Center for Xi Jinping Thought on Socialism with Chinese Characteristics for a New Era, grant number FJ2023XZB004; and the Science and Technology Innovation Special Foundation of Fujian Agriculture and Forestry University, grant number KCX23F35A.

Institutional Review Board Statement

This study does not fall within the scope of medical research, involves no animal experimentation, and entails no human subjects. All data were collected via questionnaire surveys. Our research adheres to the ethical principles outlined in the Declaration of Helsinki. Renmin University of China, Beijing Forestry University, and Fujian Agriculture and Forestry University informed us that our research does not require ethical approval. As all participants voluntarily took part in the survey, the collected information was strictly confidential and anonymized, and no personal privacy was infringed upon nor ethical issues raised, rendering ethical approval unnecessary. Our research process strictly adhered to the guidelines and regulations of Renmin University of China, Beijing Forestry University, and Fujian Agriculture and Forestry University. No non-routine procedures were employed in any human-involved research. Before all interviews, the content of the study was explained to the interviewees and informed consent was obtained.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

We would like to thank our investigators and the households who participated in our survey.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Variable definitions and descriptive statistics.
Table A1. Variable definitions and descriptive statistics.
VariableDefinitionMeanSDMinMax
Farmland transfer-outWhether households transferred out farmland (0 = No, 1 = Yes)0.2610.43901
Farmland abandonmentWhether households abandoned farmland (0 = No, 1 = Yes)0.0560.22901
HECSeverity of HEC: total monetary value of farmland damage (CNY)5.3173.519012.32
Wildlife damage compensationWhether households received wildlife damage compensation (0 = No, 1 = Yes) 0.5740.49501
Farmland areaLogarithm of total farmland area (mu)2.6321.18405.656
Farmland fertilitySoil fertility of farmland (1 = Very poor, 2 = Poor, 3 = Average, 4 = Good, 5 = Excellent)3.5691.36915
Forest areaLogarithm of total forest area (mu)2.8421.46207.003
Housing areaPer capita housing area (m2/person) = Total housing area / Number of household members3.6140.5972.3035.745
Housing typeType of dwelling structure (1 = Earthen, 2 = Brick-timber, 3 = Brick-concrete, 4 = Steel-concrete)2.8180.61914
Transportation assetsLogarithm of combined value of e-bikes, motorcycles, tricycles, and cars (CNY)10.231.4466.21513.35
AgeAge of the household head in years45.7811.062377
EducationEducational attainment of household head (1 = Primary or below, 2 = Junior secondary, 3 = Senior secondary or vocational, 4 = Associate degree or above)1.5710.72114
HealthSelf-reported health status of household head (1 = Very poor with major illness, 2 = Poor with minor illness, 3 = Fair, 4 = Good, 5 = Excellent)3.9331.01715
Labor forceNumber of working-age laborers in household 2.6361.08219
Credit formalHousehold obtained loans from banks/microfinance (0 = No, 1 = Yes)0.1920.39401
Borrowing informalNumber of relatives/friends able to provide loans: 1 = Very few (≤2), 2 = Few (3–5), 3 = Moderate (6–8), 4 = Many (9–12), 5 = Very many (≥13)2.9141.33415
Village officialsHousehold contains village official (0 = No, 1 = Yes)0.1330.34001
Social networksNumber of frequently contacted relatives/friends: 1 = None (0–2 contacts), 2 = Few (3–5), 3 = Moderate (6–8), 4 = Many (9–11), 5 = Very many (≥12)4.1170.77815
Social participationFrequency of participation in community activities: 1 = Never, 2 = Rarely (<2/year), 3 = Occasionally (3–5/year), 4 = Often (6–10/year), 5 = Regularly (≥11/year)3.6731.11915
Social trustTrust level toward relatives/friends (1 = Very low, 2 = Low, 3 = Moderate, 4 = High, 5 = Very high)4.2670.69615

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Agriculture 16 00666 g001
Figure 1. Theoretical analytical framework. Note: “+” represents a positive relationship, and “−” represents a negative relationship.
Figure 1. Theoretical analytical framework. Note: “+” represents a positive relationship, and “−” represents a negative relationship.
Agriculture 16 00666 g001
Agriculture 16 00666 g002
Figure 2. Map of the study area.
Figure 2. Map of the study area.
Agriculture 16 00666 g002
Table 1. Baseline regression results of HEC on farm households’ farmland use behavior.
Table 1. Baseline regression results of HEC on farm households’ farmland use behavior.
VariableFarmland Transfer-OutFarmland Abandonment
CoefficientMarginal
Effects		CoefficientMarginal
Effects
HEC0.103 ***0.018 ***0.183 ***0.009 ***
(0.023)(0.004)(0.048)(0.002)
Farmland area−0.035−0.0060.0210.001
(0.060)(0.010)(0.113)(0.006)
Farmland fertility−0.346 ***−0.060 ***−0.510 ***−0.025 ***
(0.055)(0.009)(0.103)(0.005)
Forest area−0.073−0.0130.153 *0.008 *
(0.048)(0.008)(0.092)(0.005)
Housing area0.675 ***0.116 ***−0.170−0.008
(0.122)(0.020)(0.229)(0.011)
Housing type0.197 *0.034 *0.4170.020
(0.118)(0.020)(0.272)(0.013)
Transportation assets0.0590.010−0.188 *−0.009 *
(0.054)(0.009)(0.096)(0.005)
Age−0.009−0.002−0.034 **−0.002 **
(0.007)(0.001)(0.014)(0.001)
Education0.0730.013−0.213−0.010
(0.101)(0.017)(0.204)(0.010)
Health−0.323 ***−0.056 ***−0.352 ***−0.017 ***
(0.073)(0.012)(0.131)(0.007)
Labor force0.137 **0.024 **−0.258 *−0.013 *
(0.067)(0.011)(0.135)(0.007)
Credit formal0.422 **0.073 **−0.594−0.029
(0.187)(0.032)(0.455)(0.022)
Borrowing informal−0.070−0.0120.185 *0.009 *
(0.056)(0.010)(0.103)(0.005)
Village officials−0.081−0.0140.1150.006
(0.208)(0.036)(0.379)(0.019)
Social networks0.1480.026−0.106−0.005
(0.105)(0.018)(0.195)(0.010)
Social participation0.164 **0.028 **0.350 ***0.017 **
(0.068)(0.012)(0.134)(0.007)
Social trust0.255 **0.044 **0.0010.000
(0.107)(0.018)(0.188)(0.009)
Constant−4.749 *** 1.058
(0.950) (1.833)
Number of obs1276127612761276
R20.0929 0.1215
Note: *** p < 0.01, ** p < 0.05, * p < 0.10. The values in parentheses are standard errors.
Table 2. Robustness check using the probit model.
Table 2. Robustness check using the probit model.
VariableFarmland Transfer-OutFarmland Abandonment
CoefficientMarginal EffectsCoefficientMarginal Effects
HEC0.062 ***0.018 ***0.085 ***0.008 ***
(0.014)(0.004)(0.023)(0.002)
Constant−2.763 *** 0.476
(0.552) (0.892)
Control variablesControlledControlledControlledControlled
Number of obs1276127612761276
R20.0932 0.1223
Note: *** p < 0.01. The values in parentheses are standard errors.
Table 3. Robustness check using an alternative explanatory variable.
Table 3. Robustness check using an alternative explanatory variable.
VariableFarmland Transfer-OutFarmland Abandonment
CoefficientMarginal EffectsCoefficientMarginal Effects
HEC0.750 ***0.129 ***1.000 ***0.050 **
(0.185)(0.031)(0.384)(0.019)
Constant−4.950 *** 0.882
(0.953) (1.833)
Control variablesControlledControlledControlledControlled
Number of obs1276127612761276
R20.0909 0.1059
Note: *** p < 0.01, ** p < 0.05. The values in parentheses are standard errors.
Table 4. Regression results of the effects of wildlife damage compensation on farm households’ farmland use behavior.
Table 4. Regression results of the effects of wildlife damage compensation on farm households’ farmland use behavior.
VariableFarmland Transfer-OutFarmland Abandonment
Direct
Compensation Effect		Moderating
Effect		Marginal
Effects		Direct
Compensation Effect		Moderating EffectMarginal
Effects
HEC0.172 ***0.066 *0.011 *0.307 ***0.152 **0.007 **
(0.029)(0.035)(0.006)(0.053)(0.070)(0.003)
Wildlife damage compensation−0.723 ***−0.387 **−0.064 **−1.575 ***−0.850 **−0.039 **
(0.190)(0.192)(0.032)(0.312)(0.344)(0.016)
HEC × Wildlife
damage compensation		 −0.318 ***−0.053 *** −0.395 ***−0.018 ***
(0.062)(0.010) (0.123)(0.006)
Constant−4.784 ***−4.190 *** 0.9172.038
(0.952)(0.971) (1.848)(1.903)
Control
variables		ControlledControlledControlledControlledControlledControlled
Number of obs127612761276127612761276
R20.10280.1204 0.16730.1851
Note: *** p < 0.01, ** p < 0.05, * p < 0.10. The values in parentheses are standard errors.
Table 5. Heterogeneity results of the effects of HEC and wildlife damage compensation on farm households’ farmland transfer-out.
Table 5. Heterogeneity results of the effects of HEC and wildlife damage compensation on farm households’ farmland transfer-out.
VariableWithin Nature ReservesOutside Nature Reserves
CoefficientMarginal
Effects		CoefficientMarginal
Effects
HEC−0.003−0.0000.100 **0.018 **
(0.075)(0.008)(0.044)(0.008)
Wildlife damage compensation−0.819 **−0.087 **−0.228−0.040
(0.386)(0.040)(0.237)(0.041)
HEC × Wildlife damage compensation−0.563 ***−0.060 ***−0.296 ***−0.052 ***
(0.140)(0.014)(0.076)(0.013)
Constant−5.779 ** −3.018 ***
(2.475) (1.113)
Control variablesControlledControlledControlledControlled
Number of obs377377899899
R20.2029 0.1438
Note: *** p < 0.01, ** p < 0.05. The values in parentheses are standard errors.
Table 6. Heterogeneity results of the effects of HEC and wildlife damage compensation on farm households’ farmland abandonment.
Table 6. Heterogeneity results of the effects of HEC and wildlife damage compensation on farm households’ farmland abandonment.
VariableWithin Nature ReservesOutside Nature Reserves
CoefficientMarginal EffectsCoefficientMarginal Effects
HEC0.1170.0060.216 **0.008 **
(0.117)(0.006)(0.093)(0.004)
Wildlife damage compensation−1.153 *−0.060 *−0.586−0.022
(0.626)(0.032)(0.456)(0.017)
HEC × Wildlife damage compensation−0.149−0.008−0.550 ***−0.021 ***
(0.209)(0.011)(0.167)(0.007)
Constant−0.933 4.142 *
(3.574) (2.481)
Control variablesControlledControlledControlledControlled
Number of obs377377899899
R20.2801 0.2465
Note: *** p < 0.01, ** p < 0.05, * p < 0.10. The values in parentheses are standard errors.
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MDPI and ACS Style

Chen, J.; Yang, J.; Xie, Y.; Zheng, Y. Effects of Human–Elephant Conflict and Wildlife Damage Compensation on Farm Households’ Farmland Transfer-Out and Abandonment. Agriculture 2026, 16, 666. https://doi.org/10.3390/agriculture16060666

AMA Style

Chen J, Yang J, Xie Y, Zheng Y. Effects of Human–Elephant Conflict and Wildlife Damage Compensation on Farm Households’ Farmland Transfer-Out and Abandonment. Agriculture. 2026; 16(6):666. https://doi.org/10.3390/agriculture16060666

Chicago/Turabian Style

Chen, Junfeng, Jie Yang, Yi Xie, and Yi Zheng. 2026. "Effects of Human–Elephant Conflict and Wildlife Damage Compensation on Farm Households’ Farmland Transfer-Out and Abandonment" Agriculture 16, no. 6: 666. https://doi.org/10.3390/agriculture16060666

APA Style

Chen, J., Yang, J., Xie, Y., & Zheng, Y. (2026). Effects of Human–Elephant Conflict and Wildlife Damage Compensation on Farm Households’ Farmland Transfer-Out and Abandonment. Agriculture, 16(6), 666. https://doi.org/10.3390/agriculture16060666

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