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Article

Input Behavior of Farmer Production Factors in the Range of Asian Elephant Distribution: Survey Data from 1264 Households in Yunnan Province, China

by
Beimeng Liu
†,
Yuchen Du
†,
Mengyuan Zhao
and
Yi Xie
*
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.
Diversity 2023, 15(11), 1147; https://doi.org/10.3390/d15111147
Submission received: 19 October 2023 / Revised: 15 November 2023 / Accepted: 17 November 2023 / Published: 18 November 2023
(This article belongs to the Special Issue Human-Wildlife Conflicts)
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Abstract

:
This article, based on the sustainable livelihood framework and survey data from 1264 households in Xishuangbanna Dai Autonomous Prefecture, Puer City, and Lincang City in Yunnan Province, China, analyzes the impact mechanism of livelihood capital on the production input behavior of farmers affected by Asian elephant damage and the moderating effect of Asian elephant damage on this process using ordinary least squares (OLS) models. The study finds the following: (1) Asian elephant damage has a significant negative effect on farmers’ production input, meaning that as the severity of Asian elephant damage increases, farmers reduce their input into agricultural production factors. (2) Livelihood capital has a significant positive effect on farmers’ production input, and both the increment and stock of livelihood capital promote an increase in farmers’ production input. (3) Asian elephant damage strengthens the influence of livelihood capital on farmers’ inputs of agricultural production factors. Based on these findings, four recommendations are proposed: emphasizing the cultivation and enhancement of farmers’ livelihood capital, improving strategies for managing and preventing wildlife damage, optimizing the economic compensation mechanism for human–wildlife conflicts, and adhering to sustainable development and resource allocation. These recommendations aim to enhance wildlife conservation and management policies, strengthen farmers’ risk-coping capabilities, and ensure the sustainability of agricultural production and livelihoods.

1. Introduction

Human–wildlife conflict (HWC) is a global phenomenon occurring under the constraints of limited natural resources, impacting the livelihoods of millions of people worldwide [1]. In some countries, especially developing nations rich in wildlife, escalating HWC has posed severe threats to ecosystems, food security, social governance, and the sustainable development of farmers’ livelihoods [2]. Not only does HWC impede wildlife conservation, but it also results in significant economic or welfare losses to humans [3,4]. Among various key “problem animals” are elephants [5]. Like other wildlife conservation conflicts, the crop damage caused by elephants is complex, severe, widespread, and often a protracted challenge to subsistence farmers [6]. For example, in October 2020, approximately 8000 farmers in northeastern Nigeria were displaced due to crop destruction by approximately 250 elephants [7]. From 2017 to 2020 in China, damage caused by Asian elephants to approximately 2.43 million hectares of crops incurred a direct economic loss of CNY 15.38 billion [8]. Data from the Xishuangbanna National Nature Reserve Administration in China reveal that, since the 1990s, Asian elephants have destroyed over 50,000 tons of grain, resulting in economic losses exceeding CNY 200 million [9]. These data all demonstrate the significant impact of HWC on sustainable agricultural livelihoods [10,11]. Therefore, researching how farmers, as the fundamental organizational units in agricultural production [12], adjust their production behaviors against the backdrop of severe HWC and high natural risk levels forms the foundation and key to further research aimed at enhancing agricultural production capacity, elevating the sustainable development of farmers’ livelihoods, and managing human–wildlife relationships adeptly.
The impacts of HWC, including direct negative impacts and indirect behavioral impacts, have always been a focal point in academic circles. Direct negative impacts of wildlife damage on human lives encompass crop destruction, livestock killing, property damage, and human attacks [13]. The indirect impacts of HWC primarily manifest as adjustments in production input behaviors. After experiencing wildlife damage, farmers, facing this uncertainty of risk, often engage in self-adjustment behaviors [14]. For example, previous studies have noted that to mitigate the losses inflicted upon their households by wildlife damage, farmers autonomously modify their ways of living and production [9], including erecting electric fences, altering cropping patterns and production inputs, increasing the cultivation area of less vulnerable crops [15], and in some cases, even resorting to abandoning severely damaged lands [16]. However, for farmers possessing different livelihood capital, the production input behaviors they can adjust vary [17]. Research has found that five types of livelihood capital—human capital, financial capital, material capital, natural capital, and social capital—all impact farmers’ production behaviors [18,19]. For instance, farmers with more natural capital often tend to rely on agricultural income, while those with more financial and social capital are more willing to engage in non-agricultural industries to generate additional income [20]; production input behaviors are also constrained by factors related to family and arable land [21]. Summarizing the above research reveals that, in the current global context, where wildlife damage is becoming increasingly severe, existing studies rarely focus on the impacts wildlife damage have on the production input behaviors of farmers under heterogeneous livelihood capital conditions, necessitating further exploration of this issue.
In summary, based on existing research findings, there are several areas in current HWC research that warrant improvement: Firstly, most existing studies related to HWC primarily focus on establishing wildlife control mechanisms, the impact of HWC on the conservation willingness of people [22], and the optimization mechanisms of public liability insurance for HWC [23,24], with fewer studies pertaining to the impact of HWC on farmers’ production factor input behaviors. Secondly, existing research regarding the impact of HWC on farmers’ production input behaviors lacks studies into the heterogeneity of farmers’ livelihood capital and does not take into consideration the modulating effect that incidents involving Asian elephants exert on different livelihood capital farmers in adjusting agricultural production inputs.

2. Materials and Methods

2.1. Data Source

This study utilizes data acquired through a survey of farming households, with data sourced from areas in Yunnan Province (Xishuangbanna Prefecture, Pu’er City, and Lincang City) frequently visited by Asian elephants in 2022, encompassing 6 counties (districts), 21 townships, 43 administrative villages, and 84 natural villages. The survey team was composed of faculty and graduate students from the authors’ institution, as well as staff from the nature reserve management bureau, adopting a one-on-one structured questionnaire interview approach. Furthermore, to circumvent language communication barriers, local nature reserve staff and village heads accompanied the team throughout the research process. The survey content included information on village overviews, individual and family socio-economic statuses of farmers, farmer cognition of wildlife, wildlife damage and control measures, and wildlife damage compensation, among others. The survey adhered to the principles of multi-stage stratified random sampling. Firstly, taking into account the distribution of wildlife resources, wildlife damage, and wildlife damage compensation, a method combining typical sampling and stratified random sampling was utilized, selecting 6 counties (districts) from 3 prefecture-level cities (prefectures). Secondly, considering various townships’ economic development, population characteristics, geographic locations, and crop planting situations, 2–5 townships were randomly selected from each county (district). Subsequently, approximately 2 administrative villages were randomly selected from each township and, on average, 2 natural villages were randomly chosen from each administrative village. Lastly, based on household registration numbers, approximately 10 farming households were randomly selected from each natural village (some villages may have slightly more random households), with survey subjects being the household heads or family members familiar with the circumstances. In total, 1290 survey questionnaires were obtained. To strictly ensure the quality of the survey, after inspecting and eliminating invalid questionnaires due to the omission of key information and outliers, 1264 effective questionnaires remained, achieving a 98% effectiveness rate.

2.2. Research Hypotheses

2.2.1. The Impact of Damage Caused by Asian Elephants on the Input of Production Factors

Damage caused by Asian elephants negatively impacts the yield and quality of crops farmed by households, subsequently reducing anticipated revenues [25]. Rational households affected by these incidents often take measures to avoid increased sunk costs and minimize the impact. For example, interviews revealed that approximately 67% affected households opt to reduce the planting area for the next season or decrease the input of fertilizers, pesticides, and labor, and in severe cases, even abandon the severely affected land to reduce production costs and losses. Moreover, as the severity of damage caused by Asian elephant increases, households are willing to invest fewer production elements. Hence, Hypothesis 1 is proposed:
H1. 
Damage caused by Asian elephants has a negative impact on the input of production factors of farming households, meaning that the higher the degree of damage, the less investment there is in production factor inputs.

2.2.2. The Impact of Household Livelihood Capital on the Input of Production Factors

The Sustainable Livelihood Analysis Framework, developed by the UK Department for International Development, categorizes livelihood capital into five aspects: human, natural, physical, financial, and social capital [26]. The heterogeneity of household livelihood capital significantly influences its input into production factors [27]. The impact of livelihood capital on livelihood strategies mainly affects the choice of livelihood strategies through changes in the stock and reorganization of livelihood capital [28]. Therefore, households with large stocks and a diverse reorganization of livelihood capital will increase production inputs [29]. Thus, Hypothesis 2 is introduced:
H2. 
Livelihood capital has a positive impact on the input of production factors.
Using variables such as labor age, health status, educational level, and labor proportion, this study measures the impact of human capital on the input of production factors. The age of labor directly impacts agricultural production. Compared with a young labor force, older labor households, to some extent, reflect “weak quality” labor, impacting input [30]. The health status of households affects labor quality, and labor proportion affects labor quantity, directly manifesting in production [31]. Higher health levels and larger labor proportions reduce the necessity for excessive capital input and decrease the demand for hiring workers [32]. The variation in educational levels is also a crucial factor causing differential behavior in agricultural production [33]. Therefore, Hypothesis 2a is proposed:
H2a. 
Human capital has a negative impact on the input of production factors.
Natural capital, essentially a collective term for livelihood resources and related services [34], is measured using arable land area, family-contracted land area, and arable land quality. Studies indicate that the larger a family’s arable land, the more production elements are needed [35]. If the land quality is high, the return on crops planted will also be high, making households more willing to invest more production elements to expect higher returns [36]. Thus, Hypothesis 2b is proposed:
H2b. 
Natural capital has a positive impact on the input of production factors.
Physical capital constitutes an essential material foundation of the livelihood capital of rural households [37]. In this study, we considered the amount of agricultural production equipment and the number of tools owned by the household, the area of their homestead land, and the quality and the construction year of their house as research variables. Agricultural production tools and equipment are indispensable elements in farming; the quantity and sophistication of such tools have a direct impact on the production factor input by the households [38]. The higher the number and the more complex the machinery and transportation tools a household owns, the higher the labor skill required for their operation, thus necessitating the household to invest more labor resources [39]. Based on this, we propose Hypothesis 2c:
H2c. 
Physical capital has a positive impact on the input of production factors.
Financial capital is primarily gauged by a family’s economic standing, encompassing overall household income, financial conditions, the proportion of agricultural income to total income, and borrowing capacity [40]. This paper measures a household’s financial capital by considering the total amount of government subsidies received, the presence of loans and borrowings, and the ease or difficulty of borrowing. For rural households, financial institutions are more inclined to lend to those with diversified livelihoods, reflecting the agricultural production capabilities of the household [41]. Consequently, the higher probability a household has of obtaining loans, the greater the likelihood they will increase their agricultural production factor inputs [42]. Based on this, we propose Hypothesis 2d:
H2d. 
Financial capital has a positive impact on the input of production factors.
Social capital is an intangible asset possessed by an individual [43], reflecting the social relationships and networking abilities of rural households. In China, scholars often use “relationship” resources to measure social capital [44]. Huangchen and others believe that social capital significantly alters the behavior of rural households [45]. To explore how social capital influences the production factor input behavior of rural households, we mainly consider the number of relatives and friends in the village, the frequency with which the household receives assistance from other villagers, their participation frequency in village collective affairs (such as village representative meetings and elections), and annual household communication expenditure as research variables. Studies indicate that households with more friends and relatives and frequent help from others tend to receive more information and financial assistance, making them more “generous” in production investments [46]. Based on this, we propose Hypothesis 2e:
H2e. 
Social capital has a positive impact on the input of production factors.

2.2.3. The Impact of Damage Caused by Asian Elephants on the Path of Livelihood Capital Affecting Production Input

Damage caused by Asian elephants also affects the correlation between household livelihood capital and production input. Specifically, when Asian elephant damage occurs, it impacts all five livelihood capitals: human, natural, physical, social, and financial [47]. When the livelihood capitals of households change, they will reallocate their input into production elements based on their capital [48]. Therefore, Hypothesis 3 is suggested:
H3. 
Damage caused by Asian elephants can intensify the impact of livelihood capital on the input of production factors.

2.3. Analytical Framework

In this study, an evaluation framework for adjustments in production input behaviors among farming households with varying livelihood capital levels under wildlife-induced incidents was constructed, based on the DFID Sustainable Livelihood Framework (Figure 1, where H represents human capital, N represents natural capital, P represents physical capital, F represents financial capital, and S represents social capital.). Under the risk posed by wildlife damage, farming households, acting as rational economic entities, adjust their production input behaviors due to profit uncertainty, thereby aiming to maximize profits or minimize losses [49]. The severity of wildlife damage can also have varying impacts on the adjustment of farming households’ production input behaviors. Furthermore, under this operational mechanism, the adjustments in production input behaviors undertaken by households will also differ due to the heterogeneity of livelihood capitals.

3. Model Design and Variable Selection

3.1. Model Design

3.1.1. Benchmark Model

Combining the characteristics of farm households in the actual survey area, in the measurement of household livelihood capital, according to the subjective weighting method, it is believed that the five types of livelihood capital are equally important; therefore, a weight of 0.2 can be assigned to each type of livelihood capital [50]. The entropy method is employed to assign weights to the specific aforementioned indicators. The entropy method can effectively avoid the subjective influence brought by artificial weighting. Essentially, it determines the weight according to the degree of dispersion of indicator data, granting higher weights to indicators with a greater degree of dispersion [51]. According to the calculation results of the entropy method, the weights of each indicator are as shown in Table 1.
The ordinary least squares (OLS) model was adopted to analyze the impact of livelihood capital on the input of production factors in farm households. The OLS model formula is as follows:
Y = x 0 + ∑ i = 1 n γ i L C i + ε
where Y represents the production factor input of the farm household, x 0 is the constant term, γ i is the coefficient of the livelihood capital variable to be estimated, L C i represents various livelihood capitals, and ε is a random error term.

3.1.2. Moderating Effect Model

The moderating effect is an important concept in social science research and is also a crucial method used by researchers to explore the relationships among multiple variables [52]. The quantity of livelihood capital owned by a farm household will influence the amount of agricultural production factor inputs. Typically, incidents involving Asian elephants can cause damage to crops. To prevent further expanding the impact of destruction caused by Asian elephants on their own livelihoods, farm households might choose to reduce agricultural production factor inputs to lower their own agricultural production costs. This implies that incidents involving Asian elephants may enhance the linkage between household livelihood capital and agricultural production factors. The specific model for the moderating effect is as follows:
Y = i 0 + α X + β Z + γ X Z + ε i i = 1 , 2 , 3 ⋯ , n
where Y represents the production factor input of the farm household, i 0 is the constant term, α is the coefficient to be estimated for the total livelihood capital, X represents the total livelihood capital owned by the farm household, β is the coefficient to be estimated for the severity of damage caused by Asian elephants, Z represents the severity of damage caused by Asian elephants, γ is the coefficient to be estimated for the interaction term, X Z is the interaction term between the farm household’s livelihood capital and Asian elephant damage, and ε i is the random error term.

3.2. Variable Selection

3.2.1. Dependent Variable

This study used the amount of farm household production factor inputs to represent adjustments in their production input behavior. The production factor input of farm households includes two parts: capital factor input and labor factor input [53]. Capital factor inputs encompass various cost inputs throughout the entire agricultural production process (such as purchasing seeds, seedlings, pesticides, fertilizers, fodder, and feed; bagging and mulching; using agricultural machinery; and irrigation costs). Labor factor inputs include self-used labor and labor hired by the farm household throughout the entire production process. Ultimately, the actual purchase prices of capital factor inputs and hired labor costs are used to convert these two types of inputs into monetary value per acre. The total amount of production factor input was used as the dependent variable.

3.2.2. Core Independent Variables

The core independent variables selected in this study were the severity of damage caused by Asian elephants and the farm household’s livelihood capital. Based on the Sustainable Livelihoods Analysis Framework developed by the UK Department for International Development, livelihood capital was divided into five aspects in this study: human capital, natural capital, physical capital, financial capital, and social capital. Adjusting existing indicators according to the ecological environment, natural resource endowment, cultural customs, religious beliefs, and the uniqueness of farmers’ livelihoods in Yunnan Province, a measurement system suitable for studying farmers’ livelihood capital in the context of damage caused by Asian elephants was formed. Specific indicators and their descriptions are shown in Table 1.

4. Results

4.1. Examining the Impact of Damage Caused by Asian Elephants on Farmers’ Inputs of Production Factors

In exploring the impact of damage caused by Asian elephants on farmers’ production input behavior, the actual amount lost due to damage caused by Asian elephants was initially compared with the annual income of the farmer’s family in order to gauge the severity of the incident in relation to the Asian elephant. This led to the establishment of the proportion of actual damage amounts to total income, categorized into five levels of severity, denoted as 1, 2, 3, 4, and 5, following an even distribution from 0% to 100% severity.
Employing the total agricultural production input as the independent variable, and the severity of the damage caused by Asian elephants in addition to the total livelihood capital of the farmers as dependent variables, regression analysis was performed, with the results illustrated in Table 2.
The regression outcomes denote that, at a 5% significance level, damage caused by Asian elephants exerted a notable influence on farmers’ production input; a negative regression coefficient indicated that the higher the severity of the damage caused by Asian elephants, the lesser the input into agricultural production. This outcome may reflect that, when confronting a high risk of Asian elephant damage, farmers opt to mitigate potential loss risks by reducing inputs or adjusting production input behaviors. In summary, H1 is validated.

4.2. Heterogeneous Analysis of the Impact of Household Livelihood Capital on Agricultural Production Input Behavior

4.2.1. Heterogeneous Analysis of the Impact of Household Livelihood Capital on Total Agricultural Production Inputs

Table 3 reports the regression results of the impact of household livelihood capital on agricultural production input. Household livelihood capital has a significant positive impact on production input at the 10% level, validating H2. One possible reason for this is that households with richer livelihood capital might enhance their ability to increase agricultural production factors more than households with less livelihood capital, thereby being able to input more agricultural production factors.
To clarify how each of the five livelihood capitals separately affects the input behavior of agricultural production factors, in this study, human capital, natural capital, physical capital, financial capital, and social capital were individually regressed, while controlling for a potential influencing factor: gender. The regression results are shown in Table 3.
Results regarding human capital showed a negative correlation with total production factor inputs at the 10% significance level. This means that when households possess more human capital, their total production factor inputs are actually less. One interpretation is that households with greater human capital may rely more on technology and efficient agricultural production methods, thereby achieving relatively high yields with fewer input factors. Moreover, enhanced human capital might also come with optimized labor allocation by the household, reducing total inputs.
Results regarding natural capital, at the 1% significance level, show a positive correlation with total production factor inputs. This suggests that households with more natural capital tend to input more production factors, possibly because abundant natural resources and land conditions provide more opportunities for agricultural production, encouraging the larger-scale and more diversified input of production factors.
The impacts of physical and financial capital on production factor inputs did not pass the significance test. We theorize that the possible reason for this is that the selected measurement indicators for physical capital—quantity of production equipment and tools, homestead area, housing quality, and construction year—have very little variability and thereby do not significantly influence the variation in production element input behavior. The regression results for financial capital also did not pass the significance test; we suspect the possible reason for this is that households might prefer to use financial capital for other purposes, such as household living expenses and education, instead of investing all of it in agricultural production.
Results regarding social capital, at the 1% significance level, also show a positive correlation with total production element inputs. This indicates that households with more social capital tend to input more production elements. Social capital includes relationships and resources in social networks, which may provide them with more information, technology, and support, promoting greater production element input. The gender control variable did not have a significant impact on the empirical results, meaning that, after considering other factors, gender does not have a noticeable impact on the total production element inputs of households.

4.2.2. Heterogeneous Analysis of the Influence of Household Livelihood Capital on Capital and Labor Input Behaviors

In conventional economics, the theory of production function factors classifies farmers’ production factor inputs into capital and labor inputs. To test the level of influence of these two under the impact of farmers’ livelihood capital, regression analysis was performed on the influence of five livelihood capitals on capital and labor inputs. The analysis results are presented subsequently.
Heterogeneous analyses of the impact of livelihood capital on capital input behavior are as follows. Firstly, human capital and capital inputs exhibited a negative correlation, significant at the 5% level. This implies that individuals or households with more human capital tend to invest less in capital inputs. Those with abundant human capital—potentially possessing more skills, knowledge, and experience—may utilize existing resources more efficiently, achieving comparatively better results with less capital input.
Secondly, a positive correlation, significant at the 1% level, was observed between natural capital and capital input. This indicates that individuals or households with more natural capital tend to invest more in capital inputs. Abundant natural resources may provide more opportunities for agricultural production, encouraging increased capital input to realize larger-scale and higher-yield agricultural production.
Thirdly, the impact of physical capital on capital input did not pass the significance test. One possible explanation for this might be that the selected capital inputs—such as seeds, saplings, pesticides, fertilizers, feed, bags, mulch, rented agricultural vehicles, and irrigation investment—are minimally affected by the physical capital owned by households, making the regression results not significant. Another possible reason might be that physical capital has strong substitutability with human and natural capital and households might opt to increase investment in the other two types of capital when physical capital is lacking.
Fourthly, the impact of financial capital on capital input demonstrated a positive correlation, significant at the 5% level, meaning that households with more financial capital have a stronger capital input capacity and tend to invest more.
Fifthly, the influence of social capital on capital input also showed a positive correlation and was significant at the 1% level. This suggests that individuals or households with more social capital tend to invest more in capital inputs. Social capital might provide more information, technology, and support, encouraging increased capital input to achieve better agricultural production outcomes.
In general, households with higher levels of human capital tend to invest less in capital inputs, while those with more natural, financial, and social capital are inclined to allocate more resources to capital inputs.
Heterogeneous analyses of the impact of livelihood capital on labor input behavior are as follows. Firstly, the results for human capital showed a negative correlation with total labor input at the 10% significance level. This means that entities with less human capital often invest more in labor input, perhaps tending to employ more labor. Those with more human capital might utilize their own labor more efficiently, reducing the need for additional labor.
Secondly, the results related to natural capital indicated a positive correlation with total labor input, significant at the 1% level. This signifies that individuals or households with more natural capital often input more labor. Rich natural capital can offer more opportunities for agricultural production, prompting an increase in labor input.
Thirdly, the results for physical capital indicated a positive correlation with total labor input, significant at the 10% level. This implies that those with more physical capital tend to invest more in labor input. Physical capital includes production tools, equipment, and agricultural machinery. Having more physical capital might enable more efficient agricultural production, thus increasing labor input.
Fourthly, the results concerning financial capital revealed a negative correlation with total labor input, significant at the 1% level. This suggests that households with more financial capital often invest less in labor input. This could be because financial capital may offer more opportunities for financing and investment, increasing the likelihood of households engaging in non-agricultural employment and thereby reducing labor input in agriculture.
Fifthly, the results for social capital showed a positive correlation with total labor input, significant at the 1% level. This means that entities with more social capital often input more labor. The more social capital famers have, the greater the likelihood of obtaining more labor resources.
In conclusion, households with higher levels of human and financial capital tend to invest less in labor inputs, whereas households with more natural, physical, and social capital may allocate more resources to labor inputs.
In summary, through a comprehensive regression of production factors and separate regressions for different production factor inputs of households, Hypotheses 2 and 2a–2e of this study have all been validated.

4.3. Examination of the Impact of Damage Caused by Asian Elephants on the Path of Livelihood Capital’s Impact on Factor Inputs into Production

Incidents involving Asian elephants might modulate the influence of livelihood capital on factor input behavior; therefore, we further regressed the interaction of damage caused by Asian elephants with household livelihood capital on the input of household production factors. The results are displayed in Table 4.
The results indicate that incidents involving Asian elephants impact the correlation between household livelihood capital and household factor inputs in production, with a positive coefficient, implying that incidents with Asian elephants enhance the effect of household livelihood capital on its factor input amounts.
There are several reasons that might explain these findings. Firstly, Asian elephant incidents often cause severe damage to agricultural production and livelihood capital, such as destroying crops and demolishing farmers’ homes and property. Farmers need to invest additional resources to deal with the losses brought about by these incidents; thus, it might result in a reduction in livelihood capital. Such loss might limit farmers’ further capabilities to invest in production factors. Secondly, economic pressure and resource scarcity might arise. Faced with the economic loss triggered by damage caused by Asian elephants, farmers might experience increased economic pressure, leading to resource scarcity. Under these circumstances, farmers might have to reduce input into production factors, such as cutting down on the purchase of fertilizers, pesticides, or seeds, in order to lower costs and save funds. Thirdly, uncertainty and risk avoidance associated with incidents involving Asian elephants might make farmers more cautious and risk-averse, possibly reducing their input into production factors to avoid further losses. Furthermore, lack of adaptability is also an issue. Some farmers might lack the ability to adapt to Asian elephant incidents, being unable to take effective countermeasures, leading to a decline in output and a reduction in capital. When facing disaster risks, households might focus more on basic needs for food and warmth instead of investing more resources to increase output. Moreover, the absence of policies and insufficient support is noteworthy. The current government has not provided adequate policy and financial compensation for damage caused by Asian elephants, rendering farmers unable to effectively cope with the disaster. Policy absence may limit farmers’ motivation to increase input into production factors, thus affecting the relationship between livelihood capital and input into production factors. In summary, H3 is validated.

4.4. Robustness Check

To verify the robustness of the regression results presented above, the model underwent multicollinearity testing and residual analysis to check its compliance with regression assumptions. The results show that the highest variance inflation factor (VIF) was 1.004, which is within a reasonable range, indicating that there are no serious multicollinearity issues. Simultaneously, the residuals adhered to a normal distribution, satisfying the ordinary least squares (OLS) model’s assumption of normally distributed errors. All of the above results indicate that the model’s estimated results possess high robustness.

5. Discussion

Amidst the increasingly frequent real-world conflicts between humans and wildlife, studying how households adjust their behavior according to their own livelihood capital to effectively deal with risks has positive significance for safeguarding the vital interests of farmers and achieving sustainability in agriculture. On the one hand, it assists households in optimizing allocation decisions when facing risks, enhancing risk adaptability; on the other hand, it contributes to better realizing harmonious coexistence between humans and nature.
Empirical results indicate that for households in communities surrounding natural conservation areas, considering their livelihood capital is essential to sustaining their livelihood strategies. This is consistent with contemporary conclusions regarding households maintaining sustainable livelihood outcomes [54,55], demonstrating that the study’s conclusions have certain scientific and rational bases. Moreover, historical research on the impact of household livelihood capital on the process of production factor inputs often neglects to separately discuss labor and capital factor inputs. Similarly to research conducted by numerous scholars, this study shows that household livelihood capital has a significant positive impact on production factor inputs [56], and apart from human and financial capital, the impact of the remaining livelihood capital on labor and capital factor inputs is basically the same. This conclusion aids in enriching research on the impact of livelihood capital of households in communities surrounding China’s natural conservation areas on labor and capital factor inputs.
In addition, for households in communities surrounding conservation areas, the risks from wildlife damage cause uncertainty in their livelihood outcomes. These risks are heterogeneous; different levels of wildlife damage bring varied degrees of natural risk to households, similarly to the results of research performed by scholars such as Pérez E. (2006) and Xu Jianying (2016) [57,58]. The variances brought by different levels of human–wildlife conflict will also affect the results of sustainable livelihoods for households. This study’s findings show that, when Asian elephant incidents occur, households, as rational actors, will reduce production factor inputs considering the results of increased profits and reduced losses. The more severe the damage caused by Asian elephants, the fewer production factor inputs there are for households with less livelihood capital. Reducing agricultural production factor inputs is not conducive to the long-term operation of households and the protection of land resources because most of the crops planted by households in the distribution area of Asian elephants in Yunnan are perennial economic crops. Production factor inputs affect not only the output of the current season, but also the harvest of the following year. If, due to the impacts of a disaster, the due production factor inputs are reduced, this will further impair future income, affecting the sustainability of household livelihood outcomes, and thus, the resilience of a household facing natural risks [59].
However, this study still has several shortcomings. The main purpose of this research was to discuss how, in areas where human–wildlife conflict is severe, the production factor input behaviors of households with different livelihood capital will be affected, and to explore the potential impact pathway of household profit uncertainty caused by human–wildlife conflict on the process of sustainable livelihoods for households. However, the influence of the ecological vulnerability caused by wildlife damage on the production input behavior of households has not been thoroughly discussed. Therefore, subsequent research will continue to focus on the impact of the ecological vulnerability brought about by wildlife conflict on household production factor input choices to further enrich related research on household livelihood strategy choices.

6. Conclusions

Based on 1264 valid household surveys from communities surrounding areas in Yunnan Province where Asian elephants are frequently active, this paper proposes three hypotheses grounded on the sustainable livelihood framework and literature review. To validate these hypotheses, the ordinary least squares (OLS) model was applied to regress the relationship between household livelihood capital and its input into production factors, while also testing the moderating effect of damage caused by Asian elephants, subsequently passing robustness tests. The main research findings are as follows: (1) damage caused by Asian elephants has a significant negative relationship with household production factor inputs, i.e., as the severity of damage increases, households will reduce their input into agricultural production factors; (2) there is a significant positive relationship between household livelihood capital and its input into production factors, i.e., an increase in the increment and stock of household livelihood capital will promote increases in household production factor inputs; (3) incidents caused by Asian elephants will intensify the impact of household livelihood capital on its agricultural production factor input.

7. Recommendations

7.1. Focus on Cultivating and Enhancing Household Livelihood Capital

Research indicates that alleviating household livelihood pressure and improving disaster resilience crucially hinges on enhancing household livelihood capital [60]. The government could implement measures to promote the nurturing and enhancement of household livelihood capital. This includes offering farmer training, education, and skill-enhancement opportunities, propelling the development of rural financial services, and encouraging household entrepreneurship and the development of micro and small enterprises to boost households’ financial capabilities and the efficiency of resource allocation. Concurrently, private enterprises, social enterprises, and NGOs are encouraged to collaborate with rural communities, providing training, technical support, and market access; assisting households in enhancing their livelihood capital; better integrating and optimizing the allocation among various livelihood capitals; and developing sustainable agricultural production models.

7.2. Refine Wildlife Conflict Management and Conservation Strategies

Damage caused by Asian elephants negatively impacts household production factor inputs; therefore, the government should adopt more effective and targeted measures to mitigate this negative impact, which might include establishing the more comprehensive planning of wildlife migration paths, for example, implementing protective facilities such as electric fences and barriers to minimize wildlife intrusion into farmlands. Furthermore, households near conservation areas could voluntarily form cooperatives, mutual aid groups, etc., enabling households with different livelihood capitals to assist each other, compensating for each other’s deficiencies in certain livelihood capitals, achieving collective action, information sharing, and maximizing resource allocation, and thereby enhancing households’ collective actions and response capabilities when facing wildlife damage, minimizing its effect as much as possible.

7.3. Optimize the Economic Compensation Mechanism for Human–Wildlife Conflict

Damage caused by Asian elephants leads to uncertainty in household profits, a reduction in factor inputs, and consequently, negative impacts on household livelihoods and agricultural sustainability. Therefore, it is urgent to optimize the current economic compensation mechanisms for human–wildlife conflict, alleviate the economic losses caused to households by wildlife damage, enhance households’ ability to resist risks and boost their confidence in carrying out production input, mitigate the uncertainty of household profits, and promote the sustainable development of agricultural production.

7.4. Adhere to Sustainable Development and Resource Allocation Optimization

Variable livelihood capital influences the behavior of household production factor inputs in various ways; thus, optimizing the allocation of various household livelihood capitals helps households better utilize their own livelihood capitals for production factor input while gaining more benefits. The government could formulate policies to encourage the optimization of household resource allocation and strengthen the training of agricultural production technical knowledge for households. Through the rational allocation of human, material, natural, and financial capital, the sustainable development of agricultural production can be realized.

Author Contributions

Conceptualization, B.L. and Y.D.; methodology, B.L. and Y.D.; software, B.L., Y.D. and M.Z.; validation, B.L., Y.D. and Y.X.; formal analysis, B.L. and Y.D.; investigation, B.L., Y.D. and M.Z.; resources, B.L.; data curation, B.L., Y.D. and M.Z.; writing—original draft preparation, B.L. and Y.D.; writing—review and editing, B.L., Y.D. and M.Z.; visualization, Y.D. and M.Z.; supervision, Y.X.; project administration, Y.X.; funding acquisition, B.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the General Project of the National Social Science Foundation of China “Research on adaptive Governance Model and Mechanism Innovation of Human-Wildlife Conflict in National Parks Based on CAS Theory”, grant number 23BGL177.

Institutional Review Board Statement

Ethical review and approval were waived for this study due to there being no ethics-related issues involved.

Data Availability Statement

Data are not available due to privacy or ethical restrictions.

Conflicts of Interest

The authors declare no conflict of interest.

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Diversity 15 01147 g001
Figure 1. Analytical framework for the adjusting of the input behavior of production factor in the event of wild animal incidents (refer to DFID, 2000).
Figure 1. Analytical framework for the adjusting of the input behavior of production factor in the event of wild animal incidents (refer to DFID, 2000).
Diversity 15 01147 g001
Table 1. Measurement indicators and weights.
Table 1. Measurement indicators and weights.
Variable ClassificationVariable NameMetric NameDescription of the IndicatorMeanStandard DeviationWeight
Dependent VariableProduction factor inputCapital factor input Various cost inputs throughout the entire agricultural production process (such as purchasing seeds, seedlings, pesticides, fertilizers, fodder, feed, bagging and mulching, using agricultural machinery, and irrigation costs) (logarithmic)8.97451.1899
Labor factor inputSelf-used labor and labor input hired by the farm household throughout the entire production process (logarithmic)10.11690.7268
The sum of inputs of factors of productionThe sum of the input amount of production factors (logarithmic)10.35190.9569
Interaction variables The sum of livelihood capital and the severity of damage caused by Asian elephants The sum of livelihood capital multiplied by the severity of damage caused by Asian elephants77.87933104.3232
Core Independent VariablesHWCThe severity of damage caused by Asian elephantsThe losses caused by Asian elephant incidents compared with the annual income of farmers10,014.7416,891.47
Human capitalLabor ageAge of household head45.752411.12230.0108
Health status1. Major diseases; 2. Minor illnesses; 3. General; 4. Healthy; 5. Very healthy3.95381.12100.0060
Educational level1. Primary school and below; 2. Junior high school; 3. High school; 4. Technical secondary school; 5. Associate degree; 6. Undergraduate degree; 7. Master’s degree or above1.62770.92640.1077
Labor proportionNumber of adults in the household who can engage in all labor/total number of households0.61350.23920.0105
Natural capitalArable land areaThe area used for farming by households21.412026.61360.0674
Family-contracted land areaHousehold-contracted land area4.729113.82390.1544
Arable land qualitySlope (1. Very steep; 2. Steep; 3. Some slopes; 4. Gentle undulations; 5. Flat)2.43642.34170.0796
Physical capitalThe amount of agricultural production equipment and number of tools owned by the householdThe amount of production machinery and number of transportation vehicles owned by households3.07991.83470.0226
Area of homestead landFloor area of the house and its courtyard (square meters)234.0399201.42980.0280
The quality of their house1. Mud and wood house; 2. Brick and wood house; 3. Brick and concrete house; 4. Reinforced concrete house2.75940.66340.0045
The construction year of their houseActual building year of the house2003.3130116.82050.0004
Financial capitalGovernment subsidiesTotal amount of government subsidies received by villagers (logarithmic)7.06051.20500.1000
The type of income sourceNumber of types of income sources2.83460.78720.0051
Whether to take out a loan0. No; 1. Yes0.42630.49470.1071
Whether to borrow or not0. No; 1. Yes0.12380.42510.2063
Degree of difficulty in borrowingNumber of relatives, friends, and villagers who can borrow money (1. Quite rare; 2. Few; 3. Average; 4. Many; 5. Plenty)2.83461.26940.0395
Social capitalNumber of family and friendsNumber of relatives and friends in the village (1. Quite rare; 2. Few; 3. Average; 4. Many; 5. Plenty)4.32131.02600.0045
Frequency of receiving assistanceThe frequency at which the family has received assistance from other villagers (1. Very low; 2. Low; 3. Average; 4. High; 5. Very high)3.98590.86130.0067
Frequency of participating in collective activitiesThe frequency of participating in collective village affairs (village representative meetings, elections, etc.) (1. Very low; 2. Low; 3. Average; 4. High; 5. Very high)3.75241.08310.0136
Telephone chargesAverage annual household telephone expenses (logarithmic)7.92990.72530.0252
Control Variable Gender0. Female; 1. Male0.74610.4408
Table 2. Regression results of the impact of Asian elephant damage on the input of production factors.
Table 2. Regression results of the impact of Asian elephant damage on the input of production factors.
VariablesProduction Factor Input
Coef.Std. Err.
HWC−0.0714 **0.0324
Constant10.44930.0517
R20.008
Observations1264
Note: ** indicates significant differences at the 5% levels.
Table 3. Regression results of the impact of farmers’ livelihood capital on the input of production factors.
Table 3. Regression results of the impact of farmers’ livelihood capital on the input of production factors.
VariablesProduction Factor InputLabor InputCapital Input
Coef.Std. Err.Coef.Std. Err.Coef.Std. Err.
LC (Livelihood Capital)0.0006 *0.0004
G (Gender)0.01850.0613
Constant10.300.056
R20.0027
Observations1264
H (Human Capital)−0.3312 *0.1999−0.2886 *0.1626−0.5219 **0.2506
N (Natural Capital)0.0658 ***0.00850.0347 ***0.00670.0991 ***0.0106
P (Physical Capital)0.00200.00460.0069 *0.0037−0.00760.0057
F (Financial Capital)0.00010.0001−0.0001 ***0.00010.0002 **0.0001
S (Social Capital)0.0030 ***0.00040.0016 ***0.00030.0026 ***0.0005
G (Gender)0.00460.0586−0.01900.04720.11330.0736
Constant10.14070.156810.08480.12768.80430.1962
R20.100.060.10
Observations126411611264
Note: ***, **, and * indicate significant differences at the 1%, 5%, and 10% levels, respectively.
Table 4. Regression results of the moderating effect of Asian elephant damage on the input of production factors.
Table 4. Regression results of the moderating effect of Asian elephant damage on the input of production factors.
VariablesProduction Factor Input
Coef.Std. Err.
HWC−0.0679 **0.0324
LC0.0006 *0.0004
Interaction variables
HWC × LC 0.0011 *0.0006
Constant10.46480.0659
R20.008
Observations1264
Note: ** and * indicate significant differences at the 5% and 10% levels, respectively.
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Liu, B.; Du, Y.; Zhao, M.; Xie, Y. Input Behavior of Farmer Production Factors in the Range of Asian Elephant Distribution: Survey Data from 1264 Households in Yunnan Province, China. Diversity 2023, 15, 1147. https://doi.org/10.3390/d15111147

AMA Style

Liu B, Du Y, Zhao M, Xie Y. Input Behavior of Farmer Production Factors in the Range of Asian Elephant Distribution: Survey Data from 1264 Households in Yunnan Province, China. Diversity. 2023; 15(11):1147. https://doi.org/10.3390/d15111147

Chicago/Turabian Style

Liu, Beimeng, Yuchen Du, Mengyuan Zhao, and Yi Xie. 2023. "Input Behavior of Farmer Production Factors in the Range of Asian Elephant Distribution: Survey Data from 1264 Households in Yunnan Province, China" Diversity 15, no. 11: 1147. https://doi.org/10.3390/d15111147

APA Style

Liu, B., Du, Y., Zhao, M., & Xie, Y. (2023). Input Behavior of Farmer Production Factors in the Range of Asian Elephant Distribution: Survey Data from 1264 Households in Yunnan Province, China. Diversity, 15(11), 1147. https://doi.org/10.3390/d15111147

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