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 Agricultural Science; Vol. 7, No. 1; 2025 
ISSN 2690-5396   E-ISSN 2690-4799 

https://doi.org/10.30560/as.v7n1p29 

29               Published by IDEAS SPREAD 
 

Climate Perception and Adaptation Decision of Apple Growers in 
Guanzhong Plain Region Based on Structural Equation Model 

Zhou Lei1 

1 College of Geography and Environment, Baoji University of Arts and Sciences, Baoji, China 

Correspondence: Zhou Lei, College of Geography and Environment, Baoji University of Arts and Sciences, Baoji, 
China. E-mail:  

 

Received: February 15, 2025    Accepted: March 6, 2025   Online Published: March 12, 2025 

 

The research is financed by Baoji University of Arts and Sciences Graduate Innovative Research Project (Project 
number: YJSCX23YB53). 

 

Abstract 

It is important to clarify the influence path of farmers' climate change perception and adaptation behavior decision-
making for the further effective implementation of rural revitalization strategy. Based on structural equation model, 
SPSS and Amos software were used to import survey data, and then the influence path of climate change perception 
and adaptation behavior of apple farmers in the study area was discussed. The results showed that: (1) The 
individual characteristics of farmers had a significant positive effect on their objective adaptation ability, but had 
a negative effect on their climate change perception ability and household economic characteristics. (2) The family 
characteristics of farmers had a negative and significant effect on objective adaptation. (3) Farmers' climate 
perception had a significant positive effect on their objective adaptation ability. The indicators of each potential 
variable can reflect itself well and have significant representativeness. Finally, according to the test results of 
structural equation model, some effective suggestions are put forward for individual, family and perception. 

Keywords: structural equation model, Guanzhong plain, apple farmers, climate perception, adaptive behavior 

1. Introduction 

Climate change has had a profound impact on all walks of life. Among them, farmers, as micro-subjects of 
agricultural production, have the strongest perception and sensitivity to climate change (Ren et al., 2015). In order 
to reduce the adverse effects of climate change, farmers often take a series of measures to cope with it. Perception 
is the premise of adaptive decision-making, and accurate perception can help people better adopt appropriate 
measures to deal with meteorological disasters and minimize losses. It is crucial for the agricultural sector to 
formulate effective policies on agriculture and agriculture and for farmers to adopt effective strategies to identify 
the influencing factors of climate change perception and identify the influencing paths of "perception-
adaptation"(Luo et al., 2017). At present, the academic community has carried out a wealth of studies on climate 
change and adaptation behavior, mainly focusing on climate suitability assessment, climate perception, and 
influencing factors of adaptation behavior (Song et al., 2024; Song et al., 2022; Wang et al., 2021). This provides 
experience for the study of this paper. This paper takes Guanzhong Plain as a research area and uses structural 
equation model to clarify the relationship between attribute, perception and adaptation based on questionnaire 
survey data of apple growers. 

2. Study Area Profiles and Data Sources  

2.1 Profile of The Study Area 

In this paper, Guanzhong Plain is selected as the research region, which is located in the middle of Shaanxi 
Province, starting from Baoji in the west, Tongguan in the winter solstice, reaching the Loess Plateau of northern 
Shaanxi in the north and the Qinling mountain system in the south. Guanzhong Plain is flat with fertile soil, formed 
by the alluvial action of Weihe River and rich in water resources. The climate is a temperate monsoon climate with 
four distinct seasons. Here, agricultural production conditions are superior, The land type is mainly cultivated land, 
mainly planting apple, wheat, corn and other crops. It is one of the important agricultural production areas in China. 
The agricultural development of Guanzhong region is related to the economic and social development of Shaanxi 
Province, and its ecosystem service function plays a crucial role in the ecological environment and agricultural 



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production of the whole Weihe River basin(Ren et al., 2015).According to its natural resource endowment 
advantages, the fruit industry in this region is very developed, especially the apple industry with excellent quality 
and high market recognition, forming a complete industrial chain.Among them, Qianyang apple has been sent into 
space four times, taking the lead in introducing foreign high-quality seedlings and working technology, and the 
unique Qianyang model of dwarfization cultivation has become a national model. Under the background of climate 
change, it is of urgent practical significance to study and analyze the influence path of the perception and adaptation 
behavior of apple growers to climate change in Guanzhong Plain for the improvement and upgrading of 
agricultural industry and the further implementation of the national rural revitalization strategy. 

2.2 Questionnaire Survey and Data Sources 

The questionnaire survey in this paper adopts the investigation mode of online and offline integration, with offline 
as the main and online as the auxiliary. The offline survey is conducted by a combination of random sampling 
questionnaire and semi-structured interview, while the online survey is conducted by sending electronic 
questionnaires to the target subjects through computers, mobile phones and other devices.The survey was 
conducted in October and November 2024.The survey sites were Fenggeling Town in Chencang District of Baoji 
City and Nanzhai Town, Zhangjiayuan Town, Caobi Town, Chengguan Town, Cuijiatou Town and Shuigou Town 
in Guanzhong area of Shaanxi Province. A number of villages in each town were randomly selected for 
questionnaire survey and case interview. As can be seen from Table 1, the survey contents include: (1) household 
attribute data of growers. It is composed of demographic attribute data (gender, age, educational background) and 
socio-economic characteristic data (income, distance from town, planting area, crop type). (2) Perceived data of 
climate change in recent 10 years, including temperature and precipitation (winter and summer), floods, pests and 
diseases, etc. (3) Objective adaptation to climate change (technical behavior=1~7 kinds; economic behavior=1~4 
kinds; migratory behavior=1~2 kinds). 

 

Table 1. Sample description Statistics and variable description 

Variate Serial 
Number 

Assignment Specification Mean 
Value 

Standard 
Deviation 

Individual  Gender XB 1=Male; 2=Female 1.23 0.42 

Age NL 1=Under 18 years old; 2=18 to 25 years old; 
3=26 to 30 years old; 4=31 to 40 years old; 
5=41 to 50 years old; 6=51 to 60; 7=Over 61 
years old 

5.88 1.29 

Education 
Background 

XL 1=Junior high school and below; 2=High 
school/technical secondary school; 3=Junior 
college; 4=Undergraduate college; 
5=Postgraduate and above 

1.51 0.71 

 Number of 
farmers 

WNRS 1=1 or 2 people; 2=3 or 4 people; 3=5 people 
and above 

1.31 0.61 

Family Family income JTSR 1=10, 000 to 50, 000 yuan; 2=60, 000 to 100, 
000 yuan; 3=more than 100, 000 yuan 

2.15 0.41 

Distance from 
town 

JZJL 1=0~5km; 2=6~10km; 3=11~15km; 
4=16~20km; 5=20km 以上 

1.86 1.04 

Planting area ZZMJ 1=Less than 5 acres; 2=6 to 10 acres; 3=11 to 
15 acres; 4=16~20 acres; 5=Over 21 acres 

2.23 1.12 

Crop type ZWZL 1=One kind; 2=Two kinds; 3=Three kinds; 
4=Four kinds 

1.79 0.75 

Climate 
perception 

The average 
temperature 
(summer) 

QWXJ 1=Decrease a lot; 2=Decrease some; 
3=Basically unchanged; 4=Increase some; 
5=Increase a lot 

3.65 1.39 

The average 
temperature 
(winter) 

QWDJ 1=Decrease a lot; 2=Decrease some; 
3=Basically unchanged; 4=Increase some; 
5=Increase a lot 

3.40 1.12 



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Precipitation 
(Summer) 

JSXJ 1=Decrease a lot; 2=Decrease some; 
3=Basically unchanged; 4=Increase some; 
5=Increase a lot 

3.67 1.38 

Precipitation 
(Winter) 

JSDJ 1=Decrease a lot; 2=Decrease some; 
3=Basically unchanged; 4=Increase some; 
5=Increase a lot 

3.49 1.07 

flood HL 1=Decrease a lot; 2=Decrease some; 
3=Basically unchanged; 4=Increase some; 
5=Increase a lot 

3.51 1.02 

Diseases and 
insect pests 

BCH 1=Decrease a lot; 2=Decrease some; 
3=Basically unchanged; 4=Increase some; 
5=Increase a lot 

3.49 0.96 

Objective 
adaptation 

Climate access 
TQTJ 

1=One kind; 2=Two kinds; 3=Three kinds; 
4=Four kinds; 5=Five kinds 

3.75 0.70 

Reason for action 
CQYY 

1=One kind; 2=Two kinds; 3=Three kinds; 
4=Four kinds; 5=Five kinds 

3.26 0.72 

Assumption 
problem 

CSWT 
1=One kind; 2=Two kinds; 3=Three kinds; 
4=Four kinds; 5=Five kinds; 6=Six kinds 

3.66 0.91 

Decision 
influencing 
factors 

XSYS 
1=One kind; 2=Two kinds; 3=Three kinds; 
4=Four kinds; 5=Five kinds;6=Six 
kinds;7=Seven kinds; 8=Eight kinds 

4.65 1.42 

Type of behavior 
CSSL 

1=One kind; 2=Two kinds; 3=Three kinds; and 
so on until 13= Thirteen kinds 

7.21 2.00 

 

A total of 300 paper and electronic questionnaires were distributed this time. After eliminating invalid 
questionnaires such as those with incorrect or incomplete answers(Zhang et al., 2018), 265 valid questionnaires 
were finally collected, with an effective rate of 88.33%. Among the surveyed farmers, the proportion of males was 
the highest, at 76.60%, while that of females was only 23.40%. The age group of 51 to 60 years old had the largest 
proportion, at 41.13%, followed by those over 61 years old, accounting for 36.23%. The proportion of those with 
junior high school education was the highest, at 61.13%, followed by those with high school or technical secondary 
school education, accounting for 26.79%. Regarding household income, the proportion of those with an income 
of 60, 000 to 100, 000 yuan was the largest, at 80.76%. In terms of distance from the town, the proportions of those 
within 0 to 5 kilometers and 6 to 10 kilometers were 45.28% and 36.60% respectively. The number of farmers 
with a planting area of 6 to 10 acres was the largest, accounting for 58.11%. Besides growing apples, many farmers 
also grow crops such as wheat and corn. The types of crops grown are diverse, and the proportion of those growing 
two types of crops is 40.00%. 

According to a random sampling survey, over 80% of the farmers believe that the average temperatures in summer 
and winter have changed, but the extent of the change varies. 66.42% of the farmers think that the average 
temperature in summer has increased (including a slight increase and a significant increase, the same below), while 
22.27% of the farmers hold the opposite view. As for the average temperature in winter, only 58.12% of the farmers 
believe that it has increased, and 25.29% think it has decreased (including a slight decrease and a significant 
decrease, the same below). In the farmers' view, the temperature in summer has generally increased, while in 
winter, there seems to be a trend of getting colder. Regarding the perception of changes in precipitation, the 
proportions of farmers who think precipitation has increased, remained unchanged, and decreased in summer are 
58.12%, 22.64%, and 19.24% respectively; in winter, the proportions are 53.21%, 32.08%, and 14.71% 
respectively. The perception of changes in precipitation is not significant, with the proportion of farmers who think 
precipitation has increased being the largest, and it is higher in summer than in winter. In terms of the perception 
of changes in natural disasters, the proportion of farmers who think that floods have increased is the highest, at 
61.89%. The proportion of farmers who think that the occurrence of pests and diseases has slightly increased is 
the highest, at 60.00%. The farmers' ability to obtain information and objectively adapt needs to be improved. In 
terms of the choice of adaptation measures, the top three measures are all technical behaviors, indicating that 
farmers tend to use scientific and technological means to solve problems encountered in agricultural production. 



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Economic behaviors have a lower proportion of adopters because when taking such measures, they need to 
consider the cost and effect of the measures as well as the family's economic conditions. However, the proportion 
is not low. Few farmers choose to migrate, indicating that agricultural production holds an important position in 
the local development. 

3. Research Hypothesis and Research Method 

3.1 Research Hypothesis  

Through literature search on topics such as "climate change", "perception", and "adaptation", it was found that the 
earliest research in this field in China appeared in 2002, which was later than that in foreign countries. Since 2008, 
the number of studies has gradually increased. The early research was relatively simple, mainly focusing on climate 
perception and adaptation. Later, it gradually diversified, covering aspects such as risk perception of climate 
change, influencing factors, and adaptation efficacy. (Song et al., 2024; Song et al., 2022; Wang et al., 2021). 
Existing studies have shown that farmers' adaptation decisions to climate change are the result of the joint effect 
of individual internal factors and external environmental factors, including gender, age, knowledge reserves, 
experience, information acquisition, weather changes, social economy, and policy systems, etc. In the theoretical 
model, these factors can be attributed to the personal characteristics of farmers, family economic characteristics, 
climate perception, and the objective adaptation ability of farmers, all of which play an important role in farmers' 
climate perception and adaptation perception. Individual characteristics belong to the category of population 
characteristics, usually including gender, age, education level, etc. Family economic characteristics have the dual 
characteristics of population characteristics and social economic characteristics, usually including family size, 
income, transportation distance, occupation category, etc. Climate perception refers to the degree of cognition of 
climate change, including temperature and precipitation, various meteorological disasters, etc. Objective 
adaptation ability can be defined as the adaptation ability composed of natural, information, economic, technical, 
management, and institutional resources, and is reasonably allocated in the adaptation decision-making process to 
achieve an effective response, manifested as the effectiveness of these resources for adaptation. Based on this, this 
paper proposes the following hypotheses and constructs a path model of the effects of individual characteristics, 
family characteristics, and climate perception on the objective adaptation of farmers (see Figure 1). The selection 
and assignment of variables are shown in the above table. Due to the small degree of discrimination between 
indicators, the model fitting effect is poor. Based on professional knowledge, the correlation relationships between 
variables are debugged and the direction of effect is selected to make the model fit the observed data, which is 
statistically significant and consistent with theoretical knowledge. 

Assumption H1: The individual characteristics of farmers influence their perception ability regarding climate 
change.  

Assumption H2: The individual characteristics of farmers affect their objective adaptability.  

Assumption H3: The individual characteristics of farmers influence the changes in their family and socio-economic 
characteristics.  

Assumption H4: The family characteristics of farmers affect their perception ability regarding climate change.  

Assumption H5: The family characteristics of farmers influence their objective adaptability.  

Assumption H6: The perception ability of farmers regarding climate change affects their objective adaptability. 

 

 
Figure 1. Model of the influence mechanism of farmers' climate perception and adaptation behavior 

(Hypothetical model) 

 



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3.2 Research Methods 

In order to clarify the above-mentioned action paths, this study established a sample database in SPSS software, 
and then imported it into Amos software to construct a structural equation model (SEM) for analysis. This model 
is based on factor analysis and linear regression methods, and is used to represent the relationships among complex 
variables. Structural equation models can be divided into measurement models and structural models. The 
measurement model describes the relationship between indicators and latent variables, while the structural model 
describes the relationship between latent variables (Li et al., 2019). Indicators are observable variables, while latent 
variables cannot be directly observed and can only be inferred from indicators. Its analytical formula is: 

x Λ ε α                                         (1) 

y Λ η β                                         (2) 

η Α Β ξ                                      (3) 

Equation (1) and (2) represent the measurement relationship, and equation (3) represents the structural relationship. 
x and y are exogenous and endogenous indicators, and α and β are errors in x and y measurements.Λ  is the factor 
load matrix of x index on ε latent variable.Λ  is the factor load matrix of y index on η latent variable.η is an 
endogenous latent variable.ξ is an exogenous latent variable.Α is the relationship between endogenous latent 
variables.Βis the influence of exogenous variables on endogenous variables (Li et al., 2019).ξ is the error term in 
the model that fails to explain the part of the equation. 

4. Research Results and Analysis 

4.1 Reliability and Validity Test Results 

This study employed SPSS software to conduct reliability tests on the scale data. The results indicated that the 
Cronbach's α coefficient value was 0.704, suggesting that the reliability of this questionnaire was good and 
acceptable. Due to the influence of the dimensional differences of the data variables, the standardized Cronbach's 
α coefficient was slightly lower than the non-standardized value, at 0.684. However, the two values were close, 
which further confirmed the consistency and stability of the indicator data. For the validity test of the data, the 
KMO test results showed that the KMO value was 0.75, which was within the acceptable range and the numerical 
suitability was good. At the same time, the Bartlett's sphericity test results indicated that the significance P value 
was 0.000***, which rejected the null hypothesis. This result further supported the rationality of factor analysis. 

4.2 Confirmatory Factor Analysis Results 

This study employed Amos software to conduct confirmatory factor analysis (CFA) on the indicator data to 
characterize the degree of association between test items and variable factors. It should be noted that during the 
measurement process, the first item of the same variable factor would be regarded as the reference item, and thus 
its significance (p-value) would not be displayed. In the individual characteristic factor, XB was not shown as the 
reference variable, while the standardized loading coefficient of NL was 0.743, indicating a significant positive 
contribution of this variable to individual characteristics, with a p-value < 0.01, and XL presented a significant 
negative loading, with a p-value < 0.01. In the family characteristic factor, WNRS was used as the reference 
variable, and it showed significance based on the levels of JTSR, JZJL, ZZMJ, and ZWZL. The standardized 
loading coefficients ranged from 0.583 to 0.737, and the p-values were all less than 0.01, indicating that these 
variables had a strong and significant positive influence on the family characteristic factor. In the climate 
perception factor, QWXJ was used as the reference, and QWDJ, JSXJ, JSDJ, and BCH variables all presented 
significant positive loading, with loading coefficients ranging from 0.681 to 0.918, and p-values all less than 0.01, 
indicating that these variables had a significant positive contribution to the climate perception factor in terms of 
statistics. In terms of objective adaptation, TQTJ was used as the reference variable, and CQYY, CSWT, XSYS, 
and CSSL variables had positive loading coefficients, but the standard loading coefficients were relatively low and 
had a large distinction (ranging from 0.489 to 0.822), with p-values all less than 0.01, indicating that their influence 
intensity was not as strong as the variables in the climate perception factor, but their contribution to the objective 
adaptation factor was still not negligible. Based on these variables, it can be considered that each measurement 
indicator shows a reasonable and effective presentation on the corresponding factor. 

4.3 Structural Equation Model Fitting Results 

This study employed Amos software to conduct the goodness-of-fit test for the structural equation model based on 
the questionnaire data. Referring to relevant studies (Li et al., 2015), the chi-square degree of freedom ratio, 
RMSEA (Root Mean Square Error of Approximation), and CFI (Comparative Fit Index) were selected as the 
indicators for measuring the overall adaptability of the model. The chi-square degree of freedom ratio was 2.4, 



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which was less than 3, indicating a good fit. The RMSEA was 0.073, which was less than 0.1, and the CFI was 
0.917, meeting the general requirement of CFI ≥ 0.9. The model fit was good. The measurement indicators of 
latent variables were all significant at the 1% level, indicating that each indicator could well reflect the latent 
variables and had significant representativeness. 

4.4 Structural Equation Model Hypothesis Test Results 

The test results of the structural model were obtained through Amos software. The results showed that the path 
coefficient of the H4 hypothesis was not within the range of (-1, +1) and should be discarded. Besides, the other 
paths (H1-H3, H5-H6) were all verified. Individual characteristics have a negative impact on family characteristics 
(β = -0.021, P < 0.01***), and climate perception (β = -0.014, P < 0.01***), indicating that different individual 
characteristics may lead to significant differences in family economic characteristics and climate perception, and 
this difference is highly statistically significant. Individual characteristics have a significant positive impact on 
objective adaptation (β = 0.68, P < 0.01***), meaning that factors such as age, gender, and education level of 
individuals can enhance their adaptability to climate change, emphasizing the importance of individual differences. 
Family characteristics have a negative and significant impact on objective adaptation (β = -0.518, P < 0.029**), 
indicating that the adoption of adaptation behaviors is constrained by factors such as the number of family laborers, 
income, land resources, and geographical location. Climate perception positively and significantly affects 
objective adaptation (β = 0.447, P < 0.032**), suggesting that growers have perceived climate change and this 
perception is likely to be transformed into positive adaptation behaviors. 

 

 
Figure 2. Schematic diagram of structural equation model 

Note. *** and ** represent 1% and 5% significance levels respectively, and dotted lines indicate that this path is 
invalid. 

 

5 Conclusion and Suggestion 

5.1 Conclusion 

This study applied the structural equation model to the investigation of apple growers' perception of climate change 
and their adaptation behaviors. This not only broadened the application scope of the structural equation model but 
also provided new ideas for the research on climate change perception and adaptation behaviors in the face of 
meteorological disasters. Based on the test results, this paper found that demographic attributes, socio-economic 
attributes, personal cognitive ability, and perception of climate change all have varying degrees of positive or 
negative influences on adaptation behaviors. The specific influence paths and magnitudes of influence vary 
depending on the research subjects (as clarified in the previous part "Research Results and Analysis", this will not 
be repeated here). This is both consistent with existing research and has innovative findings. For example, during 
the hypothesis testing of the model, the H4 hypothesis was not verified. This is because there is a possibility that 
family characteristics may indirectly affect climate cognition through social and cultural backgrounds, rather than 
directly acting on climate perception factors. In addition, during the process of converting climate perception into 
adaptation behaviors, it is subject to various factors, and the specific transformation process remains to be 
discussed. 

5.2 Suggestion 

Climate change has had a profound impact on agricultural production. Based on research results, personal 
characteristics and climate perception have significant influence on objective adaptation. Strengthening 
comprehensive ability training for individuals and enhancing their overall knowledge level should not be limited 



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to agronomy and seed industry. This should be done to enhance the ability to cope with climate change. Continuing 
to strengthen the publicity of climate change forecasts, diversifying the channels for obtaining weather 
information, and effectively understanding climate change is the key to adaptation decisions. In agricultural 
production, more activities that benefit farmers should be carried out, providing technical guidance, financial 
subsidies, etc., to dispel families' concerns about engaging in agricultural planting. Activities should be carried out 
in terms of crop varieties, human resources, facilities and equipment, and benefiting farmers and agriculture in the 
adaptation process. Through structural equation models, the paths of the role of individuals, families and 
perception on adaptation were analyzed, and targeted suggestions were proposed based on this. However, in actual 
production, climate perception and adaptation decisions are often the result of multiple influences, involving 
psychology, disaster science and other aspects (Cao et al., 2021;Xiao, 2014). In future research, the research 
perspective should be broadened to further explore the interaction relationship between them. 

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Copyrights 

Copyright for this article is retained by the author(s), with first publication rights granted to the journal. 

This is an open-access article distributed under the terms and conditions of the Creative Commons Attribution 
license (http://creativecommons.org/licenses/by/4.0/). 

 

 
















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    /UKR <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>
    /ENU (Use these settings to create Adobe PDF documents best suited for high-quality prepress printing.  Created PDF documents can be opened with Acrobat and Adobe Reader 5.0 and later.)
  >>
  /Namespace [
    (Adobe)
    (Common)
    (1.0)
  ]
  /OtherNamespaces [
    <<
      /AsReaderSpreads false
      /CropImagesToFrames true
      /ErrorControl /WarnAndContinue
      /FlattenerIgnoreSpreadOverrides false
      /IncludeGuidesGrids false
      /IncludeNonPrinting false
      /IncludeSlug false
      /Namespace [
        (Adobe)
        (InDesign)
        (4.0)
      ]
      /OmitPlacedBitmaps false
      /OmitPlacedEPS false
      /OmitPlacedPDF false
      /SimulateOverprint /Legacy
    >>
    <<
      /AddBleedMarks false
      /AddColorBars false
      /AddCropMarks false
      /AddPageInfo false
      /AddRegMarks false
      /ConvertColors /ConvertToCMYK
      /DestinationProfileName ()
      /DestinationProfileSelector /DocumentCMYK
      /Downsample16BitImages true
      /FlattenerPreset <<
        /PresetSelector /MediumResolution
      >>
      /FormElements false
      /GenerateStructure false
      /IncludeBookmarks false
      /IncludeHyperlinks false
      /IncludeInteractive false
      /IncludeLayers false
      /IncludeProfiles false
      /MultimediaHandling /UseObjectSettings
      /Namespace [
        (Adobe)
        (CreativeSuite)
        (2.0)
      ]
      /PDFXOutputIntentProfileSelector /DocumentCMYK
      /PreserveEditing true
      /UntaggedCMYKHandling /LeaveUntagged
      /UntaggedRGBHandling /UseDocumentProfile
      /UseDocumentBleed false
    >>
  ]
>> setdistillerparams
<<
  /HWResolution [2400 2400]
  /PageSize [612.000 792.000]
>> setpagedevice

