Forestry, Faculty of
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Forests 15 (6): 970 (2024)
Cai, Qi
Sun, Bowen
Zhang, Xufeng
Bo, Wenjing
Wang, Guangyu
Zhou, Zefeng
With more and more loss caused by forest biological disasters (FBDs) in China, forest farmers, as one of the most important stakeholders, are participating in the control. In this study, the ordinary least squares model, simultaneous equation model, and propensity score matching method were used with the data from 818 surveys conducted in the typical FBD outbreak provinces, to reveal the FBD control behaviors of forest farmers and their differences between western and eastern regions of China. The results indicated the following. (1) Household factors: forest farmers could timely take control measures. An increase of 1 ha in the area of occurrence would increase the control measures by 3.26 ha. However, the control measures can only reduce 50% of the economic loss caused by FBDs and cannot effectively control the spread trend of FBDs. There are issues, including an insufficient and old labor force, insufficient technology support, and low consciousness of ecology protection. (2) External factors: forest farmers would increase control when the temperature rises, and reduce control when rainfall increases. After village committees unify organizing the control, their participation enthusiasm would increase, which would have a substitution relationship with the household investment. (3) Regional difference: the eastern region of China has higher figures than the western in terms of outbreak area, economic losses, control measures, and pesticide cost. If the western forest farmers have the control funds as the eastern forest farmers have, and the eastern forest farmers have the control intensity as the western forest farmers have, the overall FBD control effect would be better.
Citation: Cai, Q.; Sun, B.; Zhang, X.;Bo, W.; Wang, G.; Zhou, Z. ForestBiological Disaster Control Behaviorsof Forest Farmers and Their SpatialHeterogeneity in China. Forests 2024,15, 970. https://doi.org/10.3390/f15060970Received: 27 March 2024Revised: 26 May 2024Accepted: 30 May 2024Published: 31 May 2024Copyright: © 2024 by the authors.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).ArticleForest Biological Disaster Control Behaviors of Forest Farmersand Their Spatial Heterogeneity in ChinaQi Cai 1 , Bowen Sun 2, Xufeng Zhang 3,*, Wenjing Bo 1, Guangyu Wang 4 and Zefeng Zhou 5,*1 Institute of Ecological Conservation and Restoration, Chinese Academy of Forestry, Beijing 100091, China;caiqilinda1010@126.com (Q.C.); bowenjing@caf.ac.cn (W.B.)2 School of Economics and Management, Tsinghua University, Beijing 100084, China; bowensun@hotmail.com3 School of Economics and Management, Beijing University of Technology, Beijing 100124, China4 Faculty of Forestry, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada;guangyu.wang@ubc.ca5 Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China* Correspondence: xufengzhang@bjut.edu.cn (X.Z.); zefengzhou2022@126.com (Z.Z.)Abstract: With more and more loss caused by forest biological disasters (FBDs) in China, forestfarmers, as one of the most important stakeholders, are participating in the control. In this study,the ordinary least squares model, simultaneous equation model, and propensity score matchingmethod were used with the data from 818 surveys conducted in the typical FBD outbreak provinces,to reveal the FBD control behaviors of forest farmers and their differences between western andeastern regions of China. The results indicated the following. (1) Household factors: forest farmerscould timely take control measures. An increase of 1 ha in the area of occurrence would increase thecontrol measures by 3.26 ha. However, the control measures can only reduce 50% of the economic losscaused by FBDs and cannot effectively control the spread trend of FBDs. There are issues, includingan insufficient and old labor force, insufficient technology support, and low consciousness of ecologyprotection. (2) External factors: forest farmers would increase control when the temperature rises,and reduce control when rainfall increases. After village committees unify organizing the control,their participation enthusiasm would increase, which would have a substitution relationship withthe household investment. (3) Regional difference: the eastern region of China has higher figuresthan the western in terms of outbreak area, economic losses, control measures, and pesticide cost. Ifthe western forest farmers have the control funds as the eastern forest farmers have, and the easternforest farmers have the control intensity as the western forest farmers have, the overall FBD controleffect would be better.Keywords: forest biological disasters (forest diseases, pests, rodent or hare infestations, and harmfulplants); control behaviors; forest farmers; spatial heterogeneity; propensity score matching1. IntroductionForest biological disasters (FBDs) are situations where the outbreak scale of diseases,pests, rodent or hare infestations, and harmful plants exceeds the natural resistance of theforest ecosystem, causing losses in ecosystem services and other economic outputs [1–3].Across the world, invasive insects cost a minimum of US$ (hereafter “$”) 70.0 billionper year, while associated health costs exceed $6.9 billion per year [4,5]. Along with theglobal ecological environment degradation, climate changes, international trade carryinginvasive organisms, and China’s large area of planted forests with weak resistance tobiological disasters, the scale of domestic FBDs continues to expand in China [6]. Theaverage growth rate has been 1.3% per year in the past decade, and the outbreak area ofFBDs reached 12.55 million ha in 2021. Among them, diseases accounted for 22.68%, insectpests accounted for 61.87%, rabbit disasters accounted for 13.91%, and harmful plantsaccounted for 1.51% [7]. The average economic loss caused by FBDs exceeds $21.54 billionForests 2024, 15, 970. https://doi.org/10.3390/f15060970 https://www.mdpi.com/journal/forestsForests 2024, 15, 970 2 of 18per year [8]. There are more than 8000 species of forest pests in China, of which more than300 are serious threats. Among them, pine wood nematode (Bursaphelenchus xylophilus),American white moth (Hyphantria cunea), forestry rodent (hare) infestations, and SouthAmerican Climber (Mikania micrantha) are the key threats [9,10]. In 2021, the outbreak areaof Bursaphelenchus xylophilus disease alone reached 1.72 million ha, causing economic lossesof around $3 billion. Mikania micrantha disease alone caused ecological losses of up to$123.08 million in the Pearl River Delta region, while the losses caused by Hyphantria cuneaand forestry rodent (hare) infestations were as high as $32.76 billion and $95.37 billion,respectively [11–13].According to the Forest Law of China, the control of FBDs follows the guideline of“whoever manages is responsible for control [14].” Collective forest land accounts for 61.34%of the national forest area and is mainly managed by individual farmers or cooperativeor shareholding organizations, while forest farmers are the important part [15,16]. Whileforest farmers are encouraged to participate in forestry activities, they also shoulder theresponsibility of FBD control [17,18]. Because FBDs spread, the untimely control behaviorsof forest farmers can lead to an expansion of the outbreak area, leading to economic loss [19].In addition, focusing only on their own economic interest, forest farmers are more likelyto use highly toxic but low-cost pesticides [20,21]. This kind of behavior could causenon-point source pollution and endanger ecological environmental safety. Even if pestsdevelop antibodies to pesticides, pesticides could also lead to ineffective input from forestfarmers and endanger the health of humans and animals [22,23]. Therefore, it is urgent tounderstand the status quo of forest farmers’ control behaviors using the integrated methodof survey study and empirical analysis.Thus far, research on the participation of forest farmers in FBD control has beenconducted mainly focusing on three aspects. (1) The first is about the participation andwillingness to pay of forest farmers. Stallman et al. (2015) explored the willingness of229 farmers in Missouri, USA, to cooperate in pest control, and found that 91% of farmersare willing to cooperate [24]. Sheremet et al. (2017) analyzed the willingness of farmers toparticipate in pest control in Finland through a choice experiment method and found thathouseholds are willing to pay £14.9 per hectare for the control measures taken in commer-cial forests [25]. Zhu et al. (2015) analyzed the data of farmers in Zhenjiang Province, China,and found that 66.23% of the farmers are willing to participate in control by professionalcompanies [26]. Han et al. (2019) found that the larger the area of commercial forests,the greater the demand for FBDs’ control from farmers [27]. The greater the labor forcein the household, the higher willingness they have to accept the control services to keepthe quality of their commercial forests. (2) The second is about the support policies forforest farmers in FBD control. Jones et al. (2010) found that forest farmers can reduce lossesby $16 million through technical training and control information early-warning systemsbased on a pesticide database [28]. In this way, forest farmers could be informed in advanceof FBD outbreak trends and suitable prevention measures, as the environmental externalitygenerated by participating in FBD control is higher than forest farmers’ economic benefits.Hanley et al. (2012) proposed the development of an ecological compensation mecha-nism to encourage farmers to participate in forest protection through tax benefits, whichcan alleviate the economic burden of forest farmers caused by the spillover effect [29,30].(3) The third aspect is about the forms of forest farmer participation in FBD control. Ayresand Lombardero (2017) constructed couplings between forest, forest management, andsocioeconomic systems to explore the pest control cooperative model to increase the com-mercialization level of government services so that forest managers are encouraged toparticipate in FBD control with government support [31]. Adinoyi (2014) proposed that,for state-owned forest land, governments should finance the formation of socialized pestcontrol (including forest insurance, control companies, a control specialized team, etc.), andindividual forest farmers should be responsible for FBD control [32]. Liao et al. (2022) usedan endogenous transformation model and found that control companies can significantlyimprove the input of production factors from forest farmers, and there is a complementaryForests 2024, 15, 970 3 of 18effect between capital input and labor input [33]. Moreover, forest farmers need to increasetheir capital and labor input to match the requirements of socialized services.Through the literature review, it can be concluded that the status quota of farmers’participation in FBD control mainly focuses on participation willingness, participationmethods, and willingness to pay. Overall, forest farmers are willing to participate in controlto reduce the economic losses of their commercial forests. With government support, theirwillingness to participate would increase. Furthermore, the pest control cooperative modeland control companies are developing trends of forest farmer participation in FBD control.However, from a theoretical perspective, there is a relatively limited in-depth analysis ofinfluencing factors and effects of forest farmers’ control behaviors. Moreover, althoughChina’s forestry development plays a big role in global ecological development, it is verylimited to study the control behaviors of forest farmers in China at both the national andregional scales.Therefore, the research objectives of this study are: (1) to analyze the impact of house-hold and external factors on FBD control behaviors of forest farmers using developed OLSand SEM models; (2) to analyze the spatial heterogeneity (between the eastern and westernregions) of forest farmers’ control behaviors using the propensity score matching method.2. Study Area and Method2.1. Study AreaThe study area is the mainland of China. The concept of the “Hu Line”, a well-known demographic-geographic line in China, was introduced to divide the country intotwo regions: western and eastern [34–36]. The western region is sparsely populated andthe economic development is relatively lagging, while the eastern region is relativelyeconomically developed with a dense population [37,38]. The differences in population,environment, and economy between the western and eastern regions of China, includingthe differences in forest farmers’ cognition, natural conditions, government support, etc.,may result in regional differences in the perception of FBD outbreaks and the resultinglosses, the behaviors of measures taken, the investment, etc. Thus, two representativeprovinces were selected from both the western and eastern regions, and a comparativeanalysis was conducted for the spatial heterogeneity of control behaviors of forest farmers.As shown in Figure 1, the colors from dark to light represent the outbreak areas ofFBDs by province. In the western region, the provinces of Xinjiang and Inner Mongoliawere selected for the highest area of FBDs. Both are also the main epidemic areas forrodents (hares). In the east, Shandong Province, with a high outbreak area, was chosen.Shandong is the main epidemic area for Hyphantria cunea, while Bursaphelenchus xylophilushas also occurred frequently there in recent years. Guangdong Province was selected due toits high incidence of Bursaphelenchus xylophilus. It is also a major epidemic area for Mikaniamicrantha [39]. This research mainly focuses on the outbreak of overall FBDs in all collectiveforest land owned by farmers.2.2. Data CollectionA questionnaire survey was conducted in the provinces of Shandong, Guangdong,Inner Mongolia, and Xinjiang from January to December 2019. Specifically, the question-naire was developed based on previous studies by Ke and Wen (2014) and Duan andWen (2016), as well as the authors’ experience and expertise [40,41]. Interviews were thenconducted with province-, prefecture-, and county-level officials to identify typical commu-nities/villages with representative FBD occurrence and control measures [42]. Five typesof forest management models, including management by a regular individual household,management by large households, management by joint households, management bycooperatives, and management by village groups, were investigated. The interviews wereconducted by research personnel based on the questionnaire content, asking questions ofthe forest managers and filling in the questionnaire based on the answers. After nearlyone year of questionnaire surveys, 20 villages from 10 counties of five cities in Shandong,Forests 2024, 15, 970 4 of 1820 villages from 10 counties of five cities in Guangdong, 20 villages from 10 counties offive cities in Inner Mongolia, and 18 villages from six counties of two cities in Xinjiang,were surveyed. Overall, 818 of 890 questionnaires were effective, with a validity rate of91.91%. Among them, a total of 437 sets of data were obtained from the eastern region,where Guangdong and Shandong are located, and 381 sets of data were obtained from thewestern region, where Xinjiang and Inner Mongolia are located. In the form of an interview,researchers ask forest operators questions according to the questionnaire content, and theresearchers fill in the questionnaire according to the answer content.Forests 2024, 15, x FOR PEER REVIEW 4 of 20 Figure 1. The study area. Note: the yellow dots on the map indicate the locations where we con-ducted the survey study. 2.2. Data Collection A questionnaire survey was conducted in the provinces of Shandong, Guangdong, Inner Mongolia, and Xinjiang from January to December 2019. Specifically, the question-naire was developed based on previous studies by Ke and Wen (2014) and Duan and Wen (2016), as well as the authors’ experience and expertise [40,41]. Interviews were then con-ducted with province-, prefecture-, and county-level officials to identify typical commu-nities/villages with representative FBD occurrence and control measures [42]. Five types of forest management models, including management by a regular individual household, management by large households, management by joint households, management by co-operatives, and management by village groups, were investigated. The interviews were conducted by research personnel based on the questionnaire content, asking questions of the forest managers and filling in the questionnaire based on the answers. After nearly one year of questionnaire surveys, 20 villages from 10 counties of five cities in Shandong, 20 villages from 10 counties of five cities in Guangdong, 20 villages from 10 counties of five cities in Inner Mongolia, and 18 villages from six counties of two cities in Xinjiang, were surveyed. Overall, 818 of 890 questionnaires were effective, with a validity rate of 91.91%. Among them, a total of 437 sets of data were obtained from the eastern region, where Guangdong and Shandong are located, and 381 sets of data were obtained from the western region, where Xinjiang and Inner Mongolia are located. In the form of an inter-view, researchers ask forest operators questions according to the questionnaire content, and the researchers fill in the questionnaire according to the answer content. The details of the questionnaire are discussed below. 2.3. Indicators The questionnaire consisted of three parts: basic information about forest farmers, their perception of FBDs, and their perception of FBD control measures. For each part, the specific indicators selected are shown in Table 1. Figure 1. The study area. Note: the yellow dots on the map indicate the locations where we conductedthe survey study.The details of the questionnaire are discussed below.2.3. IndicatorsThe questi aire consist d of three parts: basic information about forest farmers,their perception of FBDs, and their perception of FBD control easures. For each part, thespecific indicators selected are shown in Table 1.In the part on basic information and control behaviors of interviewees, we describedthe basic information of the interviewed forest managers, referencing the related litera-t re [43–45]. Descriptive statistics were used to describe the gender, age, education level,health status, family siz , management form, women’s equ lity status, total householdincome, and income from forestry, to understand the household characteristics. Statisticalanalysis was used to investigate the perceptions of forest farmers regarding the impact ofFBD, ways to acquire control knowledge, the entity to take control measures, preferredsupport for better control, sources of pesticide acquisition, and the impact of these ontheir daily lives. This was done to gain a g neral understand ng of forest farmers’ basicattitudes towards control measures, as well as the impact of FBDs and correspondingcontrol measures on their livelihoods [46].Forests 2024, 15, 970 5 of 18Table 1. Contents and specific indicators of the forest farmers’ control behavior questionnaire.Contents IndicatorsBasic information about forest farmersGender, age, years of education, health, family size,management models, equality status of women, totalhousehold income, and total forestry income.Perception of FBDsThe identification of the FBDs, loss types caused byFBDs, the impact of pesticides on life, the way toobtain control knowledge, the preferred way to getsupport, the entity to take the control measures forcommercial forest and public welfare forests, and thesource of pesticide acquisition.Perception of FBD controlControl area, pesticide input, labor day input,proportion of forestry income in the overallhousehold income, area of planted forests, outbreakarea, economic loss, control knowledge cognition,temperature-rise perception, rainfall-increaseperception, and perception of the organizationalability of village committee for FBD control.Forest farmers’ control behavior not only depends on the personal characteristics of theinterviewed individuals but is mainly influenced by the overall situation of the household,such as the amount of labor, livelihood assets, etc. [47]. Based on the Cobb–Douglas (C–D)production function, labor and capital are important factors affecting output [48]. Forestfarmers’ control behavior meets the input–output relationship, and the control behavioradopted by forest farmers is an important output variable in the function [16,49]. Theinput of labor hours is used as an indicator for labor in the control, and the amount ofcapital invested in pesticide purchases is the capital variable in the C–D function [50].Therefore, we chose the control area, input of labor days, and pesticide purchasing costs asthe indicators to be analyzed.The income that forest farmers receive from forestry production is their main moti-vation to take control measures to mitigate the loss of FBDs [51]. The larger the area ofthe monoculture plantation, the higher the risk of the occurrence of FBDs and the relatedloss [52–54]. Therefore, the factors of the proportion of forestry income in the overallhousehold income, the area of planted forest, the area of FBDs, and the amount of economicloss are selected. In addition, forest farmers’ awareness of the control could affect theadoption of specific control measures and enthusiasm for the control of forest farmers [55].Therefore, the indicator of the forest farmers’ knowledge about FBD control is selected inthe questionnaire.In addition to household-related factors, external factors such as climate conditions andpolicies may affect the control behaviors of forest farmers [56–58]. Therefore, the indicatorsof climate and rainfall increase perception are selected. The government organizes forestfarmers to participate in control efforts, distributes ecological pesticides, and hires controlorganizations to improve the popularity of forest farmers’ ecological control [33]. In China,the village committee is a basic government organization that directly deals with forestfarmers [59,60]. Therefore, the ability of the village committee to organize control efforts isselected to analyze the impact of external factors on their control behaviors.2.4. Analytical MethodsIn this study, the ordinary least square method (OLS) was first employed and thenupdated to a simultaneous equation model (SEM) overcoming the endogenous problemsof OLS, to analyze the influencing factors of forest farmers’ control behaviors. Second,a propensity score matching model (PSM) was developed to further analyze the spatialheterogeneity of forest farmers’ control behaviors between the eastern and western regions.The details are as follows.Forests 2024, 15, 970 6 of 182.4.1. OLS ModelThe OLS model was first developed to preliminarily analyze the influencing factorsof the control behaviors of forest farmers. OLS, the most commonly used estimationmethod for single-equation linear regression models, minimizes the error sum of squareswith respect to the regression parameters [61]. OLS models are based on the assumptionthat the relationship between the study variable and explanatory variables is linear, atleast approximately, and the error term has a zero mean and constant variance and isuncorrelated and normally distributed [62]. According to the C–D production function,the control area was used as the output variable of the control behaviors, while the otherindicators were the independent variables. Specifically, the OLS model was developedas below.CAi = ω0 +ω1PCi +ω2 IDi +ω3POi +ω4CKi +ω5VCi +ω6PAi+ω7TIi +ω8RIi +ω9FIi +ω10ESi + uiwhere ω is the coefficient, µ is the random error, CA is the control area, PC is the pesticidecost, ID is input days for the control work, PO is the outbreak area of FBDs, CK is controlknowledge, VC is the ability of the village committee organizing the FBD control, PA is thearea of planted forests, TI is the perception of temperature increment, RI is the perception ofrainfall increment, FI is the percentage of forestry income in the overall household income,and ES is the economic loss caused by FBDs. Of note, TI, RI, and VC are the forest farmers’perceptions of the degree of increment or strength. The perception response is a Likertscale divided into five points, with 5 points for the strongest perception and 1 point for theweakest one.2.4.2. SEMThe control area, investment, and working hours are part of the control measures.Regarding the control area, it could be affected by other indicators. Specifically, the propor-tion of forestry income in the household income could directly affect the pesticide purchaseof forest farmers, hence indirectly affecting the control area [33,63]. The area of plantedforests is the direct factor leading to an increase in the FBD outbreak area; as a result,forest farmers would take control measures [64]. Meanwhile, according to the study byCai et al. (2019), there is a mutual causal relationship between control measures, outbreakarea of FBDs, and economic loss [63]. As the OLS model has endogenous issues, we furtheremployed the SEM model to overcome the issues and improve the regression efficiency ofthe model [42,65].Combined with a two-stage least squares method with seemingly unrelated regression,the method of three-stage least squares is the most commonly used estimation in SEM [65] toestablish four partial equations of control area, pesticide cost, outbreak area, and economicloss. These four indicators are endogenous variables, and the others are correspondingcontrol variables. Thus, in the function of control area, the coefficient of pesticide costmeans the efficiency of the control funds, and pest outbreak area means the timeliness ofcontrol measures, and the factors of ID, CK, VC, TI, and RI could also influence the controlmeasures. In the function of pesticide cost, the coefficient of control measures taken areameans the timeliness of the control fund investment, and the factors of FI, CK, and VCcould also influence the pesticide cost input willingness of farmers. In the function ofpest outbreak area, the coefficient of control area means the efficiency of control measures,and the plantation area also would influence the pest outbreak trends. In the function ofeconomic losses, the pest outbreak area would increase the losses, and the control measurescould reduce the losses.The specific formula was finally determined after repeated testing of the endogeneity,stability, and significance of each index, where α, β, λ, and θ represent the correspondingcoefficients, respectively. µ are error terms. The details are as follows:CAi = α0 + α1PCi + α2 IDi + α3POi + α4CKi + α5VCi + α6TIi + α7RIi + u1iForests 2024, 15, 970 7 of 18PCi = λ0 + λ1FIi + λ2CAi + λ3CKi + λ4VCi + u2iPOi = β0 + β1PAi + β2CAi + u3iESi = θ0 + θ1CAi + θ2POi + µ4i2.4.3. PSM ModelAs forest farmers are affected by different external factors, such as education environ-ment, natural environment, and economic development status, they would have regionaldifferences in the perception of FBD outbreak and its control. Therefore, the PSM methodwas selected to analyze the perception differences in FBD outbreak area, economic losses,control area, and pesticide cost, after excluding other external factors, if the farmers in theeastern region are in the environment of farmers in the western region.Specifically, Shandong and Guangdong in the eastern region are assigned as 1 and setas the treatment group; Inner Mongolia and Xinjiang in the western region were assignedas 0 and set as the control group. The factors of control area, outbreak area, pesticidecost, and economic losses are used as matching dependent variables, and the selectedcontrol variables are consistent with the dependent variables of each regression model inthe SEM equation.As it is not possible to obtain the cognitive status of forest farmers in the eastern regionif they live in the western region, it is necessary to construct a counterfactual framework;that is, given a set of covariates, to score each farmer, calculate the probability of enteringthe treatment group, and record it as the tendency score. In fact, it is the dimensionalityreduction projected on the disposal dimension of a set of covariables that can match thefarmers on the east side with those on the west side in multiple dimensions [66]. Therefore,that makes the distribution of the covariates of the two paired farmers the same; only oneis assigned to the eastern group and the other to the western group. This is equivalent to arandom experiment. After all samples are matched, the difference in the outcome variablebetween the paired sample groups is calculated to obtain an average treatment effect [63].The specific formula is as follows:ATT = E(Yi − Y0|P = 1) = E(Yi|P = 1)− E(R0|P = 1)where P is the region of the farmer, (P = 1 means the farmer is located in the eastern region,P = 0 in the western region), and Yi represents the output variables of outbreak area, controlarea, pesticide cost, and economic losses in the eastern region. Y0 represents the outputvariables of outbreak area, control area, pesticide cost, and economic losses in the westregion. To account for unobservable E(Y0|P = 1) variables, the control group in the westernregion and the treatment group in the eastern region were used to compare treatment effectsand standard errors, etc. Mahalanobis matching, K-nearest neighbor matching, caliper-neighbor matching, radius matching, kernel matching, and local linear regression matchingare used to estimate and compare with the coefficients before matching.3. Results3.1. The Status Quo of Control Behaviors3.1.1. Sample Demographics of the IntervieweesThe sample demographics of the interviewees are shown in Table 2. Most of themare healthy men aged 46 to 60 (with an average age of 52) and have an elementary schooleducation of around 5 years. The family size is mostly between 3 and 5 (59.78%), and 73.59%of families state that males and females are equal. The main forms of forestry managementare single households (55.38%), followed by joint households (17.24%), shared cooperation(11.37%), village group management (10.24%), and forestry cooperatives (5.99%). Theannual household income is mostly between $2001 and $5000, with an average of $9546.There are 11 large-scale forestry households with annual forestry income exceeding $20,000,while the group with forestry income below $500 accounts for the highest proportionForests 2024, 15, 970 8 of 18(77.87%), with an average overall household income of $1518.57. Overall, the forestrymanagement of collective forests is still dominated by single-household managementwith lower forestry income, while the education level and family livelihood welfare arerelatively low.Table 2. Sample Demographics of the Interviewees.Indicator Amounts Proportion (%) Mean Standard DeviationGenderMale = 1 717 87.650.91 0.28Female = 0 101 12.35AgeUnder 35 117 14.351.93 12.3035 to 45 years old 152 18.5846 to 60 years old 358 43.77over 60 years old 191 23.35Years of education6 years and below (elementary school) 519 63.455.09 4.216–9 years (middle school) 212 25.9210–12 years (high school) 75 9.1713+ years (college) 12 1.47Family size2 people or less 212 25.923.70 1.633–5 people 489 59.786 people or more 117 14.3Health degreeSerious illness = 1 35 4.283.88 1.18Mild illness = 2 98 11.98Generally healthy = 3 121 14.79Healthier = 4 249 30.44Very healthy = 5 315 38.51Operation formSingle household = 1 453 55.381.35 0.97Joint household = 2 141 17.24Shared cooperation = 3 93 11.37Village group management = 4 82 10.24Forestry cooperative = 5 49 5.99Status of WomenLower than men = 1 145 17.731.90 0.46Equality = 2 602 73.59Higher than men = 3 71 8.68Annual household incomeBelow $2000 211 25.799545.60 51,172.37$2001–5000 234 28.61$5001–10,000 189 23.11$10,001–20,000 124 15.16Over $20,001 60 7.33Annual forestry incomeBelow $500 637 77.871518.57 16,808.78$501–2000 112 13.69$2001–20,000 58 7.09$20,001 11 1.34Note: 1 US dollar is converted to 6.5 Chinese yuan.3.1.2. The Descriptive Statistics of Control BehaviorsIn terms of the household, as shown in Table 3. The average proportion of forestryincome to total household income is 9%, and the average area of planted forests is 1.76 ha.The average forest area affected by FBDs is 0.68 ha per year, resulting in an annual averageloss of nearly $153.8. The average area for taking control measures is 0.94 ha per year, withan average annual investment of $75.9 in pesticides and 5.5 days of labor. The perception ofthe increase in rainfall is slightly stronger than that of temperature rise, and the respondentshave a relatively good understanding of FBD control. They also believe that the villagecommittee has relatively strong capabilities to organize FBD control. In terms of regionaldifferences, the proportion of forestry income and the area of planted forests are higher inthe western region than in the east. However, the perception of the affected area, economiclosses, area of taking control measures, pesticide use, and time spent on control, as well asthe perception of the increase in temperature and rainfall, knowledge of FBD control, andevaluation of the village committee’s ability to organize FBD control, are much lower in thewestern region than in the eastern region.Forests 2024, 15, 970 9 of 18Table 3. The descriptive statistics of control behaviors.Indicator Mean StandardDeviation Mean (Eastern Region) Mean (Western Region)Proportion of forestry income (FI) Forestry income to totalhousehold income (%) 0.09 0.21 0.08 0.09Plantation area (PA) Continuous variable (ha) 1.76 5.92 0.62 2.04Outbreak area of FBDs (FO) Continuous variable (ha) 0.68 3.23 1.00 0.32Economic losses induced byFBDs (ES) Continuous variable (US dollar) 153.77 874.64 249.19 44.32Area with control measures (CA) Continuous variable (ha) 0.94 5.73 1.29 0.53Pesticide cost (PC) Continuous variable (US dollar) 75.93 381.46 105.43 42.10Labor days for control work (ID) Continuous variable (days) 5.47 15.53 7.26 3.42The perception of temperatureincrease (TI) 5–1 is very large–very small 2.89 1.11 2.97 2.80The perception of rainfallincrease (RI) 5–1 is very large–very small 3.17 1.19 3.43 2.87Understanding of controlknowledge (CK)5–1 is know very well–veryignorant of 3.32 0.98 3.68 2.99The ability of village committee toorganize control work (VC) 5–1 is very strong–very weak 4.03 0.99 4.14 3.91Note: Forestry income includes income from forestry operations and subsidies related to forestry; householdincome includes wage income, operating income (planting, forestry, gathering, sideline, breeding, and self-employment), subsidies, capital income, and other incomes.Forest farmers believe that FBDs mainly damage forest timber (80.8%) while havinglittle impact on house damage, and human and animal infection. The knowledge of FBDcontrol is mainly learned through life experience (43.2%), government-hosted technicaltraining (41.3%), and introduction when purchasing pesticides (30.5%). The pesticidesare mainly acquired from self-purchasing (68.7%). They believe that the application ofpesticides pollutes the environment (54.4%) and increases the cost of FBD control (39.7%).Furthermore, 48.6% of respondents think that commercial forests should control againstFBDs by themselves, while 50.8% of farmers believe that public-welfare forests should becontrolled by the local forestry department because forest farmers cannot directly obtaineconomic benefits from public-welfare forests. They hope to receive support through finan-cial subsidies (41.4%), government organizations (36.8%), ecological pesticide acquisition(29.4%), and technical training (27.6%).Regarding the FBD type, rat and rabbit disasters, aphids, red spiders (Kanazawaspider mites), and economic crop diseases frequently occur in Xinjiang, and pesticidessuch as propargite, abamectin, and lime sulfur are usually used. Rat/rabbit disasters,sea-buckthorn moth (Holcocerus hippophaeclus), Hyphantria cunea, cryptorrhynchus lapathi(Phytoecdysteroid), Kanazawa spider mites, etc. are always found in forestland in Inner Mon-golia, while the main pesticides used are diflubenzuron, matrine, cypermethrin, pyrethroid,etc. Shandong forest farmers are mostly faced with Hyphantria cunea, Bursaphelenchusxylophilus, rotten disease, altosis sulcus (Eucryptorrhynchus brandti), alternaria leaf spot, anddrosophila, while pesticides such as diflubenzuron, methylurea, dichlorvos, and imida-cloprid are mainly used. Guangdong forest farmers are mainly faced with threats fromMikania micrantha, Bursaphelenchus xylophilus, and yellow bamboo locust (Ceracris kiangsu),etc., while they mainly use diflubenzuron, dichlorvos, dimethoate and high-efficiencychlorolipin (Beta-cypermethrin). Among these provinces, dichlorvos, dimethoate, and cyper-methrin are pesticides with restrictions, and unreasonable application would lead to seriousnon-point source pollution of pesticides at the rural level.Therefore, in terms of the impact of pesticides on farmers’ lives, 56.22% and 49.38% ofthe respondents from the eastern and western regions, respectively, believed that the use ofpesticides would damage the environment and cause air and water pollution, increasingthe cost of household input (52.21% in the east, 31.12% in the west). Additionally, moreof the west side believes that pesticides would reduce food safety (19.09%) than the eastside (11.24%). Moreover, a small number of forest farmers believe that pesticides wouldaffect the quality and sales of forest products (10.84% in the east, 6.22% in the west), makethemselves or their families sick (9.64% in the east, 4.15% in the west), and even causelivestock to get sick and other hazards (4.02% in the east, 0.83% in the west).Forests 2024, 15, 970 10 of 183.2. The Analysis of Control BehaviorsIn order to have a clearer understanding of the impact of funds, outbreak, and loss, aswell as plantation area, climate perception, labor input, and the organizational ability ofvillage committees, on forest farmers’ prevention and control behaviors, the OLS model wasfirst used. First, the collinearity test was conducted on the model. The VIF coefficient wasbetween 1.14 and 1.94, with an average of 1.15; thus, there was no collinearity issue. Second,the heteroskedasticity issue was tested using the Breusch–Pagan and Cook–Weisberg tests.As a result, the p-value is 0.001, at the 1% significance level to refuse the hypothesis ofconstant variance, so we can conclude that there is no heteroscedasticity issue in the model.The regression results are shown in Table 4.Table 4. Analysis of control behaviors and the influencing factors.OLS SEMVariables ControlAreaStd.Err.ControlAreaStd.Err.PesticideCostStd.Err.PestAreaStd.Err.EconomicLossesStd.Err.Plantation area (PA) −0.02 0.01(−1.55) −0.010.01(−1.02)The perception of temperature increase (TI) −0.04 0.06(−0.59)0.13**0.07(1.99)The perception of rainfall increase (RI) −0.02 0.06(−0.39)−0.56***0.16(−3.52)Area with control measures (CA) 191.46***28.98(6.61)1.11***0.08(13.13)−751.31*400.62(−1.88)Proportion of forestry income (FI) 0.43*0.26(1.65)267.67***75.10(3.56)Outbreak area of FBDs (FO) 0.94***0.04(22.79)3.26***0.86(3.78)1354.14***364.52(3.71)Economic losses induced by FBDs (ES) 0.00004 0.00(0.57)Pesticide cost (PC) 0.0003 0.00(1.39) −0.0030.00(−1.55)Labor days for control work (ID) −0.003 0.004(−0.58)−0.08***0.02(−3.52)Understanding of control knowledge (CK) 0.005 0.04(0.13) −0.010.04(−0.31) 2.588.64(0.30)The organizational control ability of thevillage committee (VC) 0.070.06(1.14)0.33***0.08(4.23)−26.18*14.91(−1.76)constant −0.03 0.34(−0.09) −0.510.34(−1.49) 44.9981.40(0.55)−0.21***0.08(−2.62) −29.34109.11(−0.27)p-value *** *** *** *** ***Note: The signs of “***” “**” “*” represent the significance at the 1%, 5%, and 10% levels, respectively. Thenumbers within the brackets are the statistical z value.Overall, the model is significant at the 1% significance level. However, only two vari-ables of eleven passed the significance test in the OLS model, which verifies the hypothesisthat there is an endogenous issue in this simple OLS model. Furthermore, the controlmeasures taken would also have an impact on the capital investment and its outbreak,and there may be an endogeneity issue between the variables, which affects regressionefficiency. Hence, to overcome this issue, the SEM model was developed. As a result, all ofthe four sub-models of control area, pesticide cost, FBD outbreak area, and economic loss,which are endogenous variables, passed the significance test (1%).Specifically, in the sub-model of the control measures taken area, the perceived increasein temperature and rainfall passed the significance test. Moreover, for each additionalunit of temperature and rainfall, the control area would increase by 0.13 ha and decreaseby 0.56 ha, respectively. In addition, the outbreak area (ha), input labor days, and abilityof community committees to organize control also passed the significance test. For anadditional unit of each of them, the control area would increase by 3.26 ha, decrease by0.08 ha, and increase by 0.33 ha, respectively.In the sub-model of investment to pesticide purchase, the control area, the percentageof forestry income in household income, and the organizational ability of communitycommittees passed the significance test. The percentage of forestry income in householdincome can greatly stimulate forest farmers to purchase pesticides. With every 1% increase,the investment of forest farmers to purchase pesticides would increase by 267.7 US dollars.The control cost per hectare would be 191.5 US dollars. The organization of communitycommittees would reduce the willingness of forest farmers to purchase pesticides individu-Forests 2024, 15, 970 11 of 18ally. With a one-unit increment of the organizational ability of the community committees,the individual investment would decrease by 26.18 US dollars.In the sub-model of the FBD outbreak area, the CA passed the significance test. Withthe 1-ha increment of the control area, the FO would increase by 1.11 ha. This indicates theemployed control measures mitigate the occurrence but they still cannot totally control theoutbreak area of FBDs.In the sub-model of economic loss resulting from FBDs, both the variables of FOand CA passed the significance test. For a 1-ha increase in FO, the economic loss wouldincrease by 1354.1 US dollars. However, for a 1-ha increase in CA, the economic loss woulddecrease by 751.3 dollars. This indicated that the employed control measures can reducethe economic loss resulting from FBDs to some degree.3.3. The Spatial Heterogeneity AnalysisIn order to analyze the spatial heterogeneity in control behaviors and economic-lossperception of forest farmers, the PSM method was used to explore the differences in controlmeasures, capital investment, FBD outbreak area, and economic losses between both sidesof the “Hu Line” while removing the differences of external conditions such as nature andenvironment. PSM could more clearly correct the influence of regional deviation on controlbehavior. By taking different matching methods, the maximum loss was 19 samples inthe experimental group and 11 samples in the control group, with 766–818 valid matchingsamples remaining. This indicates the matching effect is good. In terms of the balancingtest, the values of pseudo R2 and LR chi2 after matching were smaller than those beforematching. This indicates that the model after matching was more stable.Specifically, in the sub-model of the FBD outbreak area, as shown in Table 5. All resultspassed the t-test and significance test. Before matching, forest farmers in the eastern regionbelieved that the average outbreak area of their forest land was 0.62 ha per year, while thisfigure was 0.36 ha in the western region. However, after matching to exclude the differencein external conditions between both sides, forest farmers in the eastern region would thinkthat their actual outbreak areas would be reduced to 0.47–0.6 ha, while forest farmers inthe western region would think this would be about 0.17 ha. This result indicates that theoutbreak risk of FBDs in the eastern region is higher than in the western region before andafter matching. After matching, the outbreak area in both regions decreases, while theoutbreak area in the eastern region is still larger than in the western region.In terms of economic loss, all results passed the t-test and significance test. Beforematching, the forest farmers in the eastern region believed that the average annual losswould be 249.2 dollars, while those in the western region believed that would be 44.3 dollars.However, after matching to exclude the difference in the control area and outbreak areabetween both regions, the forest farmers in the eastern region would believe that theeconomic loss is reduced to 191.6–246.2 US dollars, while those in the western region wouldbelieve that the economic loss is increased to 80 US dollars.In terms of the control area, the caliper-neighbor and radius-matching methods passedthe significance test, as shown in Table 6. Before matching, forest farmers in the easternregion believed that the average control area per year was 1.11 ha, while those in thewestern region thought it was 0.59 ha. The control area of the eastern region is larger thanin the western region. However, after matching to exclude the difference in outbreak area,investment, labor input, perception of the increment of the temperature and precipitation,control knowledge, and organizational ability of the community committees, the forestfarmers in the eastern region believe that the control area would be reduced to 0.46 ha,while those in the western region think the control area would be around 0.8 ha. This resultindicates that, after matching, the control area of the eastern region would be decreased,and the western control area would be increased and even larger than in the eastern region.Forests 2024, 15, 970 12 of 18Table 5. Matching quality indicators and PSM results of FBD outbreak area and the induced eco-nomic losses.FBD Outbreak Area Economic LossesMatching Method Ps R2 LR chi2 ATT Diff. S.E. t-Test Ps R2 LR chi2 ATT Diff. S.E. t-TestBefore 0.03 21.95 0.62 0.26 0.13 1.97 ** 0.013 14.69 249.19 204.86 60.92 3.36 ***Mahalanobis 0.00 0.25 0.60 0.40 0.17 2.37 ** 0.001 0.81 246.16 166.64 58.29 2.86 ***K-nearest neighbor 0.01 4.31 0.60 0.43 0.14 3.00 *** 0.001 0.67 246.16 158.04 60.68 2.60 ***Caliper-neighbor 0.002 2.05 0.47 0.31 0.14 2.26 ** 0.003 3.26 191.57 110.91 43.62 2.54 **Radius 0.002 1.41 0.47 0.30 0.12 2.44 ** 0.009 10.04 191.57 134.74 42.21 3.19 ***Kernel 0.003 2.38 0.60 0.35 0.13 2.64 *** 0.00 0.20 246.16 170.29 59.20 2.88 ***Local linear regression 0.00 0.25 0.60 0.40 0.17 2.35 ** 0.001 0.81 246.16 166.33 58.29 2.85 ***Note: The signs of “***” “**” indicate statistical significance levels at 1% and 5%, respectively.Table 6. Matching quality indicators and PSM results of control area and pesticide cost.Control Area Pesticide CostMatching Method Ps R2 LR chi2 ATT Diff. S.E. t-Test Ps R2 LR chi2 ATT Diff. S.E. t-TestBefore 0.13 120.16 1.11 0.52 0.27 1.92 * 0.08 75.08 99.16 51.80 25.81 2.01 **Mahalanobis 0.01 12.58 0.62 −0.08 0.19 −0.39 0.01 8.68 77.46 56.68 16.95 3.34 ***K-nearest neighbor 0.01 9.34 0.62 −0.08 0.21 −0.39 0.003 3.08 77.46 42.25 35.28 1.20Caliper-neighbor 0.01 11.73 0.46 −0.31 0.20 −1.54 * 0.003 2.81 76.07 41.16 35.37 1.16Radius 0.01 7.91 0.46 −0.37 0.19 −1.96 ** 0.002 1.69 76.07 38.20 31.74 1.20Kernel 0.003 2.51 0.62 −0.18 0.19 −0.97 0.01 5.52 77.46 46.04 29.79 1.55Local linear regression 0.01 12.58 0.62 −0.14 0.19 −0.70 0.01 8.68 77.46 31.77 16.96 1.87 **, **, and *** indicate statistical significance levels at 10, 5, and 1%, respectively.In terms of the investment in pesticide purchase, before the matching, the forestfarmers in the eastern region spent 99.2 dollars per year, while those in the western regionspent 47.4 dollars per year. However, after matching to exclude the difference in controlarea, the percentage of forestry income, control knowledge, and the organizational abilityof the community committees, the investment of the forest farmers in the eastern regionwould be reduced to 77.46 dollars, being 31.7–56.7 dollars more than in the western region.This result indicated that the control investment of the eastern region is higher than that ofthe western region before and after matching, and after matching, the investment from thefarmers in both regions would be reduced.4. DiscussionIn this paper, a survey study was conducted to understand FBD outbreak, control,and economic loss from the perspective of forest farmers. Furthermore, the OLS and SEMmodels were developed based on the Cobb–Douglas production function to investigatethe influencing factors of the control behaviors of forest farmers in this paper. Overall, thisstudy has a marginal contribution to understanding the control behaviors of forest farmersand their spatial heterogeneity in China, which is informative for the decision-makingprocess regarding how to develop policy instruments to motivate the control behaviors offorest farmers.(1) In terms of control behaviors, outbreak area and economic loss would significantlyaffect the control behaviors of forest farmers, which is consistent with what is stated inthe previous study: “if forest farmers realize the area of FBDs is larger and the result-ing economic losses are more severe, they would be more conscious of taking controlmeasures [53,55]”.For an increase of 1 ha in the occurrence area, the control area would increase by3.26 ha, and for an increase of 1 ha in the control area, it would reduce the loss of economicvalue by $751.31. However, the existing control measures cannot completely stop thespread/outbreak trend of FBDs. Increasing FBDs by 1 ha would still cause an economicloss of $1354.14. These findings are generally consistent with the conclusion that nationalcontrol is “timely but inefficient” given by [63]. In addition, based on the survey, it wasrevealed that, in control activities, single-household control is still dominant (55.38%),with the issues of lack of funds, technical support, and labor input negatively affectingcontrol efficiency.Forests 2024, 15, 970 13 of 18Moreover, it was indicated that there are issues of lacking labor forces and input laborhours. According to the survey of forest farmers, most household sizes are around 4 people,and most of the people who play the major role in taking control measures are 50+ yearsold and not well educated. This is consistent with the current situation in the rural areasof China. With rapid urbanization, most young laborers move to urban areas [36]. Asa result, there is a shortage of young people in rural areas to engage in production andmanagement activities.The investment is relatively timely, with an average of 191 dollars per hectare for con-trol. Households whose main income source is from forestry would increase the pesticideinput by $267.67. This conclusion is very consistent with the statement that “if the house-holds’ livelihood mainly relies on forestry income, they are more conscious of adoptingmanagement measures to obtain higher income” in the previous study [51]. However, theaverage income from forestry is just around 1500 dollars per year for forest farmers. Ofthe respondents, 54.4% agreed that pesticides cause environmental pollution, and 39.7%thought they would increase the economic cost; 68.7% of them purchase pesticides ontheir own, and regarding their knowledge regarding pesticides, 43.2% gained it from lifeexperience, and 30.5% from business introduction. As a result, considering the limitedincome, forest farmers usually use cheap but high-pollution chemical pesticides despitebeing aware of the environmental concerns of these pesticides, resulting in non-point sourcepollution of pesticides.(2) The weather conditions have influences on control behaviors. The perceived climatewarming stimulates control behaviors, while the perceived precipitation increase reducesthe willingness to take control measures. According to Rupert et al. (2017), precipitationand temperature are two important indicators of climate change [67]. Forest farmersperceive the increase in temperature and then predict that FBDs will increase based ontheir experience. As a result, they would enhance the control behaviors for FBDs. Theincreased precipitation would negatively affect the survival of pests to inhibit the outbreakof biological disasters to a certain extent, and it would dilute pesticides and negativelyaffect the control effect. So, when precipitation increases, forest farmers would reduce theirwillingness to take control measures [68].(3) Forest farmers think that the organization of the community committee in FBDs’control is helpful. According to the analysis of the SEM model, the control measuresof forest farmers would increase by 0.33 ha if the organization capacity of the villagecommittee increased by one unit. However, it has a substitution relationship with thewillingness of forest farmers to purchase pesticides. That increase would lead to a decreasein the fund input from the forest farmers by $26.18. The survey indicated that 41.4% offorest farmers hope the government can provide funds, organization and distribution ofecological pesticides, technical training (27.6%), and other forms of support. In order toalleviate the issues of forest land abandonment and the lack of labor and control technologycaused by urbanization [50], community committees organize forest farmers to outsourcecontrol work to professional companies to improve the control effect [33]. Under such theorganization of the committees, forest farmers’ enthusiasm for control would increase byleveraging the scale economy. Ecological compensation funds from the government arecommonly used funding sources by the community committee to organize control work,which can reduce the amount of forest farmers’ investment in purchasing pesticides [60].However, such an organization being formed by community committees is still in thesmall-scale pilot stage in China, and further policy support still needs to be developed.(4) As for the spatial heterogeneity of control behaviors, the values of the eastern regionare higher than in the western region, both before and after matching in terms of FBDoutbreak area, economic loss, and pesticide cost. The reasons could be more internationaltrade transportation carrying invasive species such as Hyphantria cunea and Bursaphelenchusxylophilus in the eastern region [69], and the more suitable weather and conditions (moisterand hotter) for pests [70]. Therefore, the eastern forest farmers thought the FBD outbreakarea was larger. Additionally, the forests in the eastern region are mainly for economicForests 2024, 15, 970 14 of 18benefits, under programs such as the Development of Fast-growing and High-yield TimberPlantation Bases, which are aimed at relieving domestic timber demand pressure and betterprotecting natural forests [6]. These forestlands mostly plant single-stand tree species withweak resistance to disasters, making the control work more difficult [64]. Thus, the easternforest farmers thought the economic losses were more serious in the eastern region thanin the western region. Eastern forest farmers have a higher education level and are moresensitive to forest biological disasters [71], hence paying more attention and investing morefunds, technologies, and control measures to reduce economic losses [35].(5) The regional perception bias of control behaviors shows that, after matching, theFBD outbreak area, pesticide cost, economic losses, and control area perceived by theeastern farmers would be slightly reduced if the eastern farmers were in the environmentof the western forest farmers, as the western region has a large area and sparse population,and large-scale forestlands such as the Three-North (west-north-northeast China) networksof the shelterbelt program offer more ecological services. Thus, if the eastern forest farmerswere in the western farmers’ conditions, they would reduce their economic loss perception.In addition, the economic development in the western region is lagging, and the fragileecological environment overlaps with economic poverty [35,72]. As a result, eastern forestfarmers would reduce their pesticide costs and control technology investment if they wereunder the western conditions. Therefore, all indicators in the eastern region would declineafter matching.After matching, in the western region, FBD outbreak area and pesticide cost wouldslightly decrease, while economic losses and control area would slightly increase. Thecontrol area is higher than in the eastern region if the difference between both regions isexcluded (in other words, the western farmers have the same conditions as the easternfarmers). The reason for this is, firstly, that the arid western region with less precipitation,multiple plateaus, and mountains is not conducive to the reproduction and survival offorest pests [36]. Secondly, the most serious FBDs in the western region have strongregularity, and the main challenge is the lack of sufficient funds. Thus, the major controlwork in the western region is to control disasters within a reasonable range and preventlarge-scale disasters [64]. Meanwhile, farmers in the eastern region, with the more difficultcontrol work, need higher control technology and investment funds, as they need to controlinvasive species such as Hyphantria cunea and Bursaphelenchus xylophilus [73]. Thus, if thewestern farmers are in the control difficulty of the eastern region, the farmers would thinkthe actual control area and control funds could be moderately reduced in the westernregion. However, if the western farmers have the same education level and loss assessmentability as the eastern farmers, they would think the economic loss is higher than beforematching. In addition, in the western region, the plantation area owned by forest farmersis higher, and the family is more dependent on forestry income. After matching, under theeastern control standard, the control area of western farmers would actually be larger, andfar larger than that of eastern farmers.5. ConclusionsIn this paper, the control behaviors and their spatial heterogeneity of forest farmersare investigated via the development of the Cobb-Douglas production function-basedeconometric models. The major conclusions are summarized below.First, the impact of household factors on control behaviors: the control behaviors andinvestment for pesticides of forest farmers are relatively timely. Households whose incomesare mainly from forestry are more willing to invest in pesticide purchases, and the controlactivities can reduce the economic loss by around 50%. However, there are still issues. Theoverall control efficiency is low, and the labor input is insufficient. Most of the controlknowledge of forest farmers comes from their life experience and business advertisements,while the awareness of the merits of taking control measures in an environmentally friendlymanner is weak. Thus, in the case of limited income, forest farmers tend to use cheap butefficient chemical pesticides, while resulting in serious pesticide non-point source pollution.Forests 2024, 15, 970 15 of 18Second, the impact of external factors on control behaviors: the perception of thetemperature rise would encourage forest farmers’ control behaviors, while the perceptionof rainfall rise would discourage the control behaviors by forest farmers. The organizationof community committees in FBD control would improve the enthusiasm for the controlwork by forest farmers, while it would weaken the willingness of forest farmers to theinvestment in pesticide purchases.Third, the regional difference of control behaviors: the outbreak area, economic valueloss, prevention, and control area, capital investment measures, temperature and rainfallincrease perception, prevention and control knowledge, and village committee organiza-tional ability evaluation on the east side are all higher than those in the western region.After the PSM matching, it is indicated that the control effect would be better if the controlintensity of forest farmers in the eastern region is as same as that of farmers in the westernregion, and the funding and technology support of forest farmers in the western region isas same as that of farmers in the eastern region.The Suggestions and Study LimitationsThe business model of public (government-led)-private (market-driven)-participation(forest farmers) (PPP) was proposed to improve control efficiency. For example, the villagecommittee as the basement executive organization could manage the collective foreststhrough forestry cooperatives and shareholding systems. Forest farmers could becomeshareholders by inputting forestland or trees. Besides the proportional dividends to forestfarmers, the village committee could use part of the forest income as forest control costsfor hiring forest rangers or control companies. As for the commercial forest controlled byforest farmers, the village committee could improve control knowledge and efficiency byproviding ecological pesticide and technical training.To enrich the allocation of production factors, including labor, funds, technology, etc.,it was proposed that a control warning and monitoring mechanism and control systemat the village level should be established to provide disaster information to farmers aftersummarizing disaster outbreak regulations and meteorological factors. Leveraging themarket encouragement mechanism, control companies could be introduced to select youngand poor farmers as forest rangers to join the control team and receive control skill training.To reduce the economic risks of forest farmers, the compensation for infected trees andforest insurance could be improved. Combining FBDC with rural revitalization policiescould improve the livelihood of farmers and ensure the ecological safety of forestland.To alleviate regional heterogeneity, underdeveloped areas should be taken seriously.The support could be financial subsidies, special regulation concessions, project invest-ments, etc., from central to local governments. To formulate differentiated control measures,more attention should be given to poverty-stricken areas, key ecological protection areas,and demonstration areas. Additionally, to strengthen the emergency prevention andquarantine capacity at the grass-roots level, emergency control material reserves such asemergency control central stations, emergency pharmaceutical and drug storage areas, etc.,should be built at different levels and regions. Under suitable policy adjustments, westernforest farmers should have the same control funds as eastern forest farmers, and easternforest farmers should have the same control efforts and intensities as western forest farmers.Regarding the limitations of this research, the control difficulties and investments aredifferent for different FBDs. For example, the main control measure for Bursaphelenchusxylophilus is cutting and burning epidemic wood, which leads to a big economic loss.However, it is very challenging to include the differentiated control difficulties of differentFBDs in this control behavior study, and there is no literature thus far. Therefore, in thenext step, we would like to quantify the control difficulties of different FBDs to furtherthis study.Forests 2024, 15, 970 16 of 18Author Contributions: Writing—original draft preparation, Q.C. and X.Z.; writing—modification,and editing of the manuscript, X.Z., B.S. and G.W.; writing—reviewing, all authors; formal analysisof data, Q.C. and W.B.; interviews, Q.C. and B.S.; advice and design ideas, X.Z. and Z.Z. All authorshave read and agreed to the published version of the manuscript.Funding: This work is funded by the Center for Biological Disaster Prevention and Control, NationalForestry and Grassland Administration, Quantitative evaluation of control effect of Bursaphelenchusxylophilus (2023-38). Partially funded by China Postdoctoral Science Foundation (2023M730151). Xing-wen County Forestry Bureau, Planning of National Bamboo Modern Bamboo Industry DemonstrationPark (202304054-4281).Data Availability Statement: The data presented in this study are available on request from thefirst author.Acknowledgments: The authors would like to thank Yali Wen for discussions about this paper.Conflicts of Interest: The authors declare that they have no known competing financial interests orpersonal relationships that could have influenced the work reported in this paper.References1. Bradshaw, C.J.; Leroy, B.; Bellard, C.; Roiz, D.; Albert, C.; Fournier, A.; Barbet-Massin, M.; Salles, J.M.; Simard, F.; Courchamp, F.Massive yet grossly underestimated global costs of invasive insects. Nat. Commun. 2016, 7, 12986. [CrossRef] [PubMed]2. Zhao, L.P. Development and application of forest ecosystem health theory in forest ecological construction of China. J. NanjingFor. Univ. (Nat. Sci. Edit.) 2007, 3, 1–7. [CrossRef]3. 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Article
eng
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Vancouver : University of British Columbia Library
Multidisciplinary Digital Publishing Institute
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Faculty
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forest biological disasters (forest diseases, pests, rodent or hare infestations, and harmful plants)
control behaviors
forest farmers
spatial heterogeneity
propensity score matching
Forest Biological Disaster Control Behaviors of Forest Farmers and Their Spatial Heterogeneity in China
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