Statistical Analysis on the Factors Affecting 

Agricultural Landowners’ Willingness to 

Enroll in a Tree Planting Program

Taeyoung Kim and Christian Langpap

DEPAR TMENT OF AGRICULT URE

United States Department of Agriculture

Forest 

Service

Pacific Northwest 

Research Station

Research Paper

PNW-RP-604 

September

2015
Authors

Taeyoung Kim is an assistant professor, Department of Food and Resource 

Economics (Institute of Agriculture & Life Science), Gyeongsang National 

University, #452-321, 501, Jinju-daero, Jinju-si, Gyeongsangnam-do, 660-701, 

Republic of Korea ; and Christian Langpap is an associate professor, Department 

of Applied Economics, Oregon State University, 240E Ballard Extension Hall, 

Corvallis, OR 97331-3601. This work was performed under PNW agreement 

10-JV-11261955-064.

Cover photograph: Pine plantation and pond, by Chris Schnepf, University of 

Idaho, Bugwood.org.

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Abstract

Kim, Taeyoung; Langpap, Christian. 2015. Statistical analysis on the factors 

affecting agricultural landowners’ willingness to enroll in a tree planting program. Res. Pap. PNW-RP-604. Portland, OR: U.S. Department of Agriculture, 

Forest Service, Pacific Northwest Research Station. 34 p.

This report provides a statistical analysis of the data collected from two survey 

regions of the United States, the Pacific Northwest and the Southeast. The survey 

asked about individual agricultural landowners’ characteristics, characteristics of 

their land, and the landowners’ willingness to enroll in a tree planting program 

under incentive payments for carbon sequestration. From this statistical analysis, 

we found both similar and contrasting features between the two survey regions. 

The Southeast is more responsive to incentive payment for carbon sequestration 

than the Pacific Northwest, and the variables that are highly correlated with the 

willingness to enroll in a tree planting program are somewhat different between 

the two regions. 

Keywords: Agricultural landowner survey, afforestation, carbon sequestration, incentive payments, statistical analysis, tree planting.

Contents

1 Introduction

2 Survey Description

3 Statistical Analysis of Survey Results

3 Landowners’ Demographic Characteristics and Attributes

11 Land Characteristics

18 Other Attributes Related to Land Use and Management

21 Landowners’ Response to Incentives for Planting Trees and Correlations 

With Other Factors

21 Proposed Incentive Payment Scheme and Questionnaire Design

22 Landowners’ Willingness to Plant Trees for Carbon Sequestration

29 Conclusion

29 Acknowledgments

30 Literature Cited

33 Appendix: Description of Incentive Scheme

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Statistical Analysis on the Factors Affecting Agricultural Landowners’ Willingness to Enroll in a Tree Planting Program

Introduction

The forest sector plays an important role in mitigating greenhouse gas emissions by 

transferring carbon from the atmosphere through the process of photosynthesis of 

standing live trees and in other forest ecosystem components such as the understory 

and soil (USDA 2011). Afforestation—tree planting on lands previously not in 

forest—has often been suggested as a possible strategy to increase carbon sequestration (e.g., Adams et al. 1999, Moulton and Richards 1990), and has relatively 

larger potential for carbon sequestration than other land use choices (Gorte 2009). 

Afforestation of crop or pasture land can sequester 2.2 to 9.5 metric tons of carbon 

dioxide equivalent per acre per year (Mt CO2eq. ac-1 y-1) (US EPA 2005) and 2.7 to 

7.7 Mt CO2 eq. ac-1 y-1 (Lewandrowski et al. 2004), respectively. 

Much of the literature that examines afforestation of agricultural land has 

focused on estimating the costs of carbon sequestration and has shown that afforestation is a relatively low-cost measure for mitigating CO2 emissions. Sectoral model 

approaches such as the U.S. Regional Agricultural Sector Model (USMP) and 

The Forest and Agricultural Sector Optimization Model (FASOM) have explicitly 

modeled the links between agricultural land, forest land, and timber markets, and 

examined the potential for offsetting changes in land use (from forest to agriculture) 

resulting from price feedbacks (Adams et al. 1993, Alig et al. 1997, Lewandrowski 

2004). Alternatively, Plantinga (1997), Plantinga et al. (1999), Stavins (1999), 

Newell and Stavins (2000), and Lubowski et al. (2006) used econometric models to 

calculate the opportunity costs of afforestation, which account for additional factors 

affecting land enrollment decisions such as the cost of acquiring skills and nonmarket benefits. 

An individual landowner who is a utility maximizer might consider not only 

expected net returns, but also various additional factors when making land use 

decisions. However, most previous studies are not based on surveys of individual 

landowners. Hence, a lack of information about individual landowners’ characteristics and land characteristics has been an important obstacle to understanding the 

key factors affecting individual landowners’ land use decisions. 

To overcome this obstacle, we conducted a survey to obtain individual landowners’ characteristics (demographic characteristics and owner-specific attributes) 

and land characteristics, including spatial attributes. A stated-preference approach 

based on a survey allows us to examine the various factors affecting landowners’ 

afforestation decisions as a complement to revealed preference studies. 

The objectives of this report are to conduct a statistical analysis to understand 

the key factors affecting landowners’ tree planting program participation, measure 

the potential extent of participation in a tree planting program for carbon 
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RESEARCH PAPER PNW-RP-604

sequestration, and conduct a comparison study to identify differences in relevant 

characteristics between the two survey regions. 

The rest of this report comprises a detailed survey description, a statistical 

analysis of survey results, landowners’ response to incentives for planting trees, and 

correlations with factors affecting landowners’ land use decisions, and conclusions. 

Survey Description

The main purpose of the survey is to generate data to conduct an empirical analysis of landowners’ responses to an afforestation incentives program. The survey 

gathered data on factors affecting landowners’ decisions regarding use of their 

land, including program participation. Survey respondents were asked a series of 

questions about their demographic characteristics, the types and areas of lands they 

own, annual net returns and level of productivity of their lands, the spatial characteristics of agricultural lands they own, reasons for owning their agricultural lands, 

their understanding and attitudes about the importance of environmental services 

provided by their lands, and their willingness to enroll in response to incentives for 

tree planting to sequester carbon. 

Two regions, the Pacific Northwest (PNW) and the Southeast (SE),1 were 

selected to conduct the survey and conduct a comparative analysis, because it has 

been shown that the PNW is relatively less responsive to incentive payments for 

afforestation than the SE (Alig et al. 2010, Lewandrowski et al. 2004). 

Agricultural landowners’ mailing lists for both regions were provided by 

county tax assessor’s offices, except for six counties in Georgia, for which data 

were purchased from qPublic.net, which manages counties’ geographic information 

system (GIS), parcel, and tax data. One thousand landowners out of 20,443 and 

14,104 candidate agricultural landowners for the PNW and SE, respectively, were 

randomly chosen to participate in the survey. A draft of the survey was reviewed by 

a group of experts (USDA Forest Service and Oregon State University Department 

of Statistics, Department of Applied Economics, and College of Forestry). An inperson pretest was conducted with a group of agricultural landowners to design the 

questionnaires and to establish an appropriate bid range of carbon prices. Based on 

the in-person discussions and expert reviews, a total of 42 questions were written, 

including an open-ended question asking recipients’ willingness to participate in an 

1 The specific counties selected for the survey are (1) Pacific Northwest region: Benton, 

Jefferson, Columbia, Lane, Polk, Coos, Crook, Deschutes, Douglas, Josephine, Lake, 

Marion, Linn, and Clatsop in western Oregon, and Grays Harbor, Pierce, Whatcom, San 

Juan, Clallam, Jefferson, Skamania, and Kitsap in western Washington; (2) Southeast: 

Alleghany, Ashe, Buncombe, Swain, and Wilkes in western North Carolina, and Bartow, 

Gillmer, Harbersham, Stephens, Catoosa, Walker, Banks, Fannin, Lumpkin, Murray, 

Rabun, and Union in northern Georgia.
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Statistical Analysis on the Factors Affecting Agricultural Landowners’ Willingness to Enroll in a Tree Planting Program

incentives program. We provided a flyer to help respondents understand the detailed 

incentive scheme. 

The survey was designed and conducted following Dillman’s (1978, 2007) 

survey design method. The final sets of survey questionnaires were mailed out on 

January 14, 2011, with a personalized cover letter, university letterhead, a flyer 

explaining the incentive program, and a $2 bill as a token of appreciation for survey 

participation.2 A followup postcard reminder was mailed out a week after the first 

mailing, and a third reminder with a replacement survey questionnaire was mailed 

out a month after the first mailing. The final response rates for the survey are 47 

percent for the PNW region and 27 percent for the SE region. 

Finally, a followup phone survey of a sample of nonrespondents for the PNW 

region and a mail survey for both regions were conducted to assess and control for 

selection bias induced by nonresponses. Out of 100 nonrespondents (around 20 

percent of nonrespondents) contacted in the PNW region, 27 answered the followup 

phone survey. Additionally, 26 out of 100 returned a followup mail survey in the 

PNW region, and 38 out of 150 returned a followup mail survey in the SE region. 

Statistical Analysis of Survey Results

The survey questionnaire included questions on (1) general knowledge of climate 

change; (2) characteristics of agricultural lands owned or rented; (3) current activities, and past and future tree planting preferences; (4) response to incentives for 

tree planting to sequester carbon; and (5) demographic characteristics and other 

attributes that might affect landowners’ willingness to accept tree planting. We start 

by providing a summary of landowners’ demographic characteristics. We focus on 

describing implications based on the entire sample and provide detailed summary 

statistics tables (see tables 1 and 2). 

Landowners’ Demographic Characteristics and Attributes

This section provides a statistical description of survey respondents’ demographic 

characteristics and attributes such as occupation and retirement status. Detailed 

summary statistics of respondents’ demographic characteristics are provided in 

table 1 for the PNW and table 2 for the SE. Landowners’ demographic characteristics were collected to ensure that we reached an adequate cross section of agricultural landowners with this survey. The information will be used to describe how 

landowners’ demographic characteristics and their attributes affect landowners’ 

willingness to participate in a tree planting program. 

2 Many studies have shown that prepaid monetary incentives can achieve higher response 

rates (e.g., Brennan 1992, Little and Hubbard 1988, James and Bollstein 1990, Salant and 

Dillman 1994).
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RESEARCH PAPER PNW-RP-604

Table 1—Summary statistics of surveyed data for the Pacific Northwest region

Cropland Grassland Total

Variables Mean Std. dev. Mean Std. dev. Mean Std. dev.

Demographic characteristics and owners’ attributes:

 Age 60.246 12.296 60.728 12.831 60.520 12.590

 Education (1 if elementary school) 0.011 0.103 0.016 0.127 0.014 0.117

 Education (1 if middle school) 0.027 0.162 0.028 0.167 0.028 0.164

 Education (1 if high school) 0.289 0.454 0.297 0.458 0.293 0.456

 Education (1 if college) 0.107 0.310 0.110 0.313 0.109 0.311

 Education (1 if university) 0.294 0.457 0.313 0.465 0.305 0.461

 Education (1 if graduate school) 0.273 0.447 0.236 0.425 0.252 0.435

 Household income (1 if less than $20 K) 0.059 0.236 0.045 0.207 0.051 0.220

 Household income (1 if $20 to $40 K) 0.203 0.403 0.183 0.387 0.192 0.394

 Household income (1 if $40 to $60 K) 0.171 0.378 0.175 0.381 0.173 0.379

 Household income (1 if $60 to $80 K) 0.139 0.347 0.183 0.387 0.164 0.371

 Household income (1 if $80 to $100 K) 0.123 0.329 0.183 0.387 0.157 0.364

 Household income (1 if $100 K or more) 0.305 0.462 0.232 0.423 0.263 0.441

 Gender (1 if male) 0.701 0.459 0.675 0.469 0.686 0.465

 Occupation (1 if farmer) 0.246 0.432 0.130 0.337 0.180 0.385

 Retired (1 if retired) 0.380 0.487 0.427 0.496 0.406 0.492

 Member of NGOa (1 if member) 0.160 0.368 0.146 0.354 0.152 0.360

 Member of agricultural organization (1 if member) 0.337 0.474 0.252 0.435 0.289 0.454

 Refer neighbors decision (1 if it does) 0.813 0.391 0.793 0.406 0.801 0.399

 Knowledge about climate change 0.973 0.162 0.972 0.167 0.972 0.164

 Knowledge about carbon sequestration 0.877 0.329 0.907 0.292 0.894 0.308

Land characteristics: 

 Total land owned (acres) — — — — 137.048 492.263

 Total land owned in region (acres) — — — — 106.591 404.017

 Total land rented in region (acres) — — — — 38.309 226.836

 Total agricultural land owned (acres) 81.582 288.677 40.526 136.778 58.257 216.575

 Total agricultural land rented (acres) 50.791 256.836 3.598 22.946 23.979 171.020

 Number of unconnected parcels 2.037 3.861 1.642 2.311 1.813 3.080

 Acquisition method (1 if bought) 0.455 0.499 0.346 0.477 0.393 0.489

 Acquisition method (1 if inherited) 0.834 0.373 0.907 0.292 0.875 0.331

 Owned year 23.197 15.799 22.227 14.988 22.646 15.333

 Ownership (1 if individual owner) 0.679 0.468 0.744 0.437 0.716 0.451

 Expect development (1 if does) 0.080 0.272 0.081 0.274 0.081 0.273

 Annual farm profit/acre 285.042 432.792 149.397 243.958 213.539 352.215

 Proportion of high-productivity land (%) 46.789 45.852 27.203 38.818 35.662 43.059

 Proportion of low-productivity land (%) 14.415 31.009 33.224 43.070 25.101 39.408

 Proportion of not-in-use land (%) 11.598 25.370 18.103 32.758 15.294 29.935
5

Statistical Analysis on the Factors Affecting Agricultural Landowners’ Willingness to Enroll in a Tree Planting Program

Table 1—Summary statistics of surveyed data for the Pacific Northwest region (continued)

Cropland Grassland Total

Variables Mean Std. dev. Mean Std. dev. Mean Std. dev.

Spatial and physical characteristics: 

 Close to home–resident owner (1 if “yes”) 0.893 0.310 0.907 0.292 0.901 0.299

 Close to city (1 if “yes”) 0.203 0.403 0.142 0.350 0.169 0.375

 Close to highway (1 if “yes”) 0.604 0.490 0.561 0.497 0.580 0.494

 Adjacent to forest (1 if “yes”) 0.348 0.477 0.431 0.496 0.395 0.489

 Adjacent to agricultural land (1 if “yes”) 0.840 0.368 0.809 0.394 0.822 0.383

 Adjacent to river (1 if “yes”) 0.257 0.438 0.215 0.412 0.233 0.423

 Adjacent to conservation area (1 if “yes”) 0.139 0.347 0.126 0.333 0.132 0.338

 Close to fire hazard (1 if “yes”) 0.160 0.368 0.191 0.394 0.178 0.383

 Steep slope area (1 if “yes”) 0.214 0.411 0.211 0.409 0.212 0.410

Reasons for owning properties 

 (importance score from 1 to 5):

 For selling farm product 3.882 1.290 3.390 1.446 3.603 1.401

 For investment 3.102 1.350 3.126 1.354 3.115 1.351

 For heir 3.283 1.485 3.260 1.486 3.270 1.484

 For protecting nature 3.519 1.228 3.654 1.191 3.596 1.208

 For amenity 4.433 1.057 4.654 0.812 4.559 0.931

Environmental services 

 (importance score from 1 to 5):

 For preventing soil erosion 4.246 1.156 4.163 1.146 4.199 1.150

 For improving water quality 4.166 1.168 4.220 1.092 4.196 1.125

 For providing wildlife habitat 4.032 1.177 4.093 1.169 4.067 1.172

 For carbon sequestration 3.428 1.398 3.541 1.384 3.492 1.390

Other attributes: 

 Conservation farming (1 if “yes”) 0.326 0.470 0.224 0.417 0.268 0.443

 Own forest (1 if “yes”) 0.406 0.492 0.415 0.494 0.411 0.493

 Past afforestation (1 if “yes”) 0.321 0.468 0.325 0.469 0.323 0.468

 Future afforestation plan (1 if “yes”) 0.321 0.468 0.362 0.481 0.344 0.476

Administrative organization: 

 NGO 0.446 0.500 0.417 0.495 0.430 0.496

 Government agencies 0.283 0.453 0.287 0.454 0.285 0.453

 Carbon market 0.054 0.228 0.104 0.307 0.082 0.275

 Private company 0.228 0.422 0.191 0.395 0.208 0.407

— = not applicable. 

Std. dev. = standard deviation.

a NGO = nongovernmental organization.
6

RESEARCH PAPER PNW-RP-604

Table 2—Summary statistics of surveyed data for the Southeast region

Cropland Grassland Total

Variables Mean Std. dev. Mean Std. dev. Mean Std. dev.

Demographic characteristics and owners’ attributes:

 Age 64.123 10.784 62.298 11.513 62.935 11.274

 Education (1 if elementary school) 0.025 0.156 0.020 0.140 0.022 0.146

 Education (1 if middle school) 0.012 0.111 0.020 0.140 0.017 0.130

 Education (1 if high school) 0.333 0.474 0.311 0.465 0.319 0.467

 Education (1 if college) 0.136 0.345 0.205 0.405 0.181 0.386

 Education (1 if university) 0.247 0.434 0.238 0.428 0.241 0.429

 Education (1 if graduate school) 0.247 0.434 0.205 0.405 0.220 0.415

 Household income (1 if less than $20 K) 0.111 0.316 0.106 0.309 0.108 0.311

 Household income (1 if $20 to $40 K) 0.148 0.357 0.185 0.390 0.172 0.379

 Household income (1 if $40 to $60 K) 0.111 0.316 0.119 0.325 0.116 0.321

 Household income (1 if $60 to $80 K) 0.160 0.369 0.152 0.361 0.155 0.363

 Household income (1 if $80 to $100 K) 0.111 0.316 0.126 0.333 0.121 0.326

 Household income (1 if $100 K or more) 0.358 0.482 0.311 0.465 0.328 0.470

 Gender (1 if male) 0.815 0.391 0.795 0.405 0.802 0.400

 Occupation (1 if farmer) 0.185 0.391 0.139 0.347 0.155 0.363

 Retired (1 if retired) 0.494 0.503 0.497 0.540 0.496 0.526

 Member of NGOa (1 if member) 0.247 0.434 0.219 0.415 0.228 0.421

 Member of agricultural organization (1 if member) 0.370 0.486 0.305 0.462 0.328 0.470

 Refer neighbors decision (1 if it does) 0.877 0.331 0.861 0.347 0.866 0.341

 Knowledge about climate change 0.926 0.264 0.940 0.238 0.935 0.246

 Knowledge about carbon sequestration 0.840 0.369 0.854 0.354 0.849 0.359

Land characteristics:

 Total land owned (acres) — — — — 115.767 155.984

 Total land owned in region (acres) — — — — 109.580 152.531

 Total land rented in region (acres) — — — — 15.328 50.177

 Total agricultural land owned (acres) 34.667 42.237 43.187 68.109 40.213 60.387

 Total agricultural land rented (acres) 10.123 36.311 7.113 26.711 8.164 30.365

 Number of unconnected parcels 1.966 1.514 2.220 3.569 2.131 3.013

 Acquisition method (1 if bought) 0.679 0.470 0.530 0.501 0.582 0.494

 Acquisition method (1 if inherited) 0.457 0.501 0.298 0.459 0.353 0.479

 Owned year 26.559 15.482 24.460 15.151 25.193 15.267

 Ownership (1 if individual owner) 0.728 0.448 0.748 0.435 0.741 0.439

 Expect development (1 if does) 0.123 0.331 0.086 0.281 0.099 0.299

 Annual farm profit/acre (1 if it is) 205.050 318.508 112.247 153.362 144.648 228.848

 Proportion of high-productivity land 36.906 45.435 25.819 40.955 29.690 42.803

 Proportion of low-productivity land 11.200 27.897 10.497 27.306 10.743 27.455

 Proportion of not-in-use land 12.433 31.382 23.221 38.365 19.454 36.379
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Statistical Analysis on the Factors Affecting Agricultural Landowners’ Willingness to Enroll in a Tree Planting Program

Table 2—Summary statistics of surveyed data for the Southeast region (continued)

Cropland Grassland Total

Variables Mean Std. dev. Mean Std. dev. Mean Std. dev.

Spatial and physical characteristics: 

 Close to home - resident owner (1 if “yes”) 0.679 0.470 0.742 0.439 0.720 0.450

 Close to city (1 if “yes”) 0.111 0.316 0.113 0.317 0.112 0.316

 Close to highway (1 if “yes”) 0.815 0.391 0.748 0.435 0.772 0.421

 Adjacent to forest (1 if “yes”) 0.691 0.465 0.642 0.481 0.659 0.475

 Adjacent to agricultural land (1“yes”) 0.741 0.441 0.722 0.450 0.728 0.446

 Adjacent to river (1 if 'yes') 0.284 0.454 0.152 0.361 0.198 0.400

 Adjacent to conservation area (1 if “yes”) 0.222 0.418 0.225 0.419 0.224 0.418

 Close to fire hazard (1 if “yes”) 0.086 0.283 0.119 0.325 0.108 0.311

 Steep slope area (1 if '“yes”) 0.469 0.502 0.483 0.501 0.478 0.501

Reasons for owning properties

 (importance score from 1 to 5):

 For selling farm product 3.753 1.462 3.490 1.595 3.582 1.552

 For investment 3.062 1.426 3.079 1.468 3.073 1.450

 For heir 4.296 0.993 4.159 1.120 4.207 1.077

 For protecting nature 4.160 1.066 4.225 0.974 4.203 1.005

 For amenity 4.370 1.167 4.530 0.944 4.474 1.027

Environmental services

 (importance score from 1 to 5):

 For preventing soil erosion 4.543 0.881 4.715 0.647 4.655 0.740

 For improving water quality 4.531 1.038 4.603 0.857 4.578 0.923

 For providing wildlife habitat 4.173 1.202 4.364 1.003 4.297 1.078

 For carbon sequestration 3.605 1.385 3.887 1.186 3.789 1.263

 Other attributes: 

 Conservation farming (1 if “yes”) 0.259 0.441 0.166 0.373 0.198 0.400

 Own forest (1 if “yes”) 0.778 0.418 0.728 0.446 0.746 0.436

 Past afforestation (1 if “yes”) 0.407 0.494 0.219 0.415 0.284 0.452

 Future afforestation plan (1 if “yes') 0.235 0.426 0.185 0.390 0.203 0.403

Administrative organization:

 NGO 0.346 0.485 0.302 0.463 0.315 0.467

 Government agencies 0.308 0.471 0.365 0.485 0.348 0.479

 Carbon market 0.000 0.000 0.016 0.126 0.011 0.106

 Private company 0.192 0.402 0.190 0.396 0.191 0.395

— = not applicable.

Std. dev. = standard deviation.

a NGO = nongovernmental organization. 
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RESEARCH PAPER PNW-RP-604

Age, education, and household income— 

Age has traditionally been considered one of the main variables that might affect 

landowners’ land use decisions. Several studies have shown that age has a positive 

correlation with adoption of soil conservation practices (Ervin and Ervin 1982), 

while it has a negative correlation on harvesting and investment on silvicultural 

activities (Beach et al. 2005). However, some studies argue that age does not 

significantly affect timber harvest behavior and participation in a forestry cost-share 

program (Dennis 1989, Nagubadi et al. 1996). Figure 1 presents the distribution of 

respondents with respect to age group, which shows that 19.6 percent of agricultural 

landowners in the PNW region and 30.6 percent in the SE region are in the 65-yearor-older group. Survey participants’ average age was 60.5 in the PNW region, 

compared to 62.9 in the SE region (see tables 1 and 2). 

It is also argued that income is negatively correlated with timber harvest 

(Beach et al. 2005; Dennis 1989, 1990), and positively correlated with silvicultural 

management activities (Beach et al. 2005). However, several studies have found that 

both income and education do not significantly affect forest owners’ management 

decisions (Dennis 1989, Langpap 2006). Farm- or forest-related occupations are 

positively and significantly correlated with timber harvest (Beach et al. 2005). The 

fraction of landowners in the sample whose highest education level is a bachelor’s 

degree is 55.9 percent in the PNW region and 46.6 percent in the SE region (see fig. 

2), which is high relative to 24 percent for the general population (Butler 2008).

The distribution of household income level in figure 3 shows that 27.5 and 32.3 

percent of agricultural landowners in the PNW and SE region, respectively, have 

annual household income of at least $100,000. This proportion is considerably 

higher than that for the general population (12 percent) (Butler 2008). 

Figure 1—Distribution of age group in the Pacific Northwest (PNW) and Southeast (SE). 

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

25-34 35-44 45-54 55-64 65-74 75+

PNW

SE

Percentage of respondents

Age group
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Statistical Analysis on the Factors Affecting Agricultural Landowners’ Willingness to Enroll in a Tree Planting Program

Gender, occupation, retirement, and memberships—

The distribution by gender shows that 68.6 percent of survey respondents in the 

PNW and 80.2 percent in the SE region are male. Around 18 and 15 percent of agricultural landowners in the PNW and SE regions, respectively, have a farm-related 

occupation. Additionally, 40.6 percent of landowners in the PNW and 49.6 percent 

in the SE are retirees, compared to 49 percent of family forest owners (Butler 2008). 

The percentage of respondents that are members of a nongovernmental organization (NGO) is 15.2 and 22.8 percent in the PNW and SE, respectively, and 28.9 and 

32.8 percent of them are members of an agricultural association (see tables 1 and 2). 

More than 80 percent of landowners (80.1 percent in the PNW and 86.6 percent in 

the SE) who completed the survey questionnaire refer to other neighbors’ land use 

and management decisions for their own management and land use decisions.

0

5

10

15

20

25

30

35

Elementary Middle High College Bachelor's Graduate

PNW

SE

Percentage of respondents

Education level

Figure 2—Distribution of education group in the Pacific Northwest (PNW) and Southeast (SE). 

Figure 3—Distribution of household income level in the Pacific Northwest (PNW) and Southeast (SE). 

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

Less than $20K $20K - $40K $40K - $60K $60K - $80K $80K - $100K $100K or more

PNW

SE

Percentage of respondents

Household income level (dollars)
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RESEARCH PAPER PNW-RP-604

Sources of information— 

Figure 4 shows that receiving information from other landowners (70.9 percent) is 

the preferred source of information, followed by offline publications in the PNW 

and online media (Internet, TV, and radio) in the SE. Obtaining information from 

membership organizations is the least preferred method in both regions, possibly 

because not all landowners have a membership. These results are mostly consistent 

with Butler (2008). 

Figure 4—Proportion of information sources level in the Pacific Northwest (PNW) and Southeast (SE). 

0

10

20

30

40

50

60

70

80

Offline

publications

Internet,

TV/radio

Other

landowners

Organization

membership

Local/federal

agencies

PNW

SE

Percentage of respondents

Information sources

General knowledge of climate change—

At the beginning of the survey, we asked whether landowners have basic knowledge about climate change, global warming, and carbon sequestration. We 

expected that the landowners who do not have knowledge about climate change 

and carbon sequestration might have difficulty answering the questionnaire, 

which might affect their willingness to participate in a tree planting program 

for carbon sequestration. Through these questions, we also tried to remind the 

survey respondents that tree planting can contribute to carbon sequestration 

and climate change mitigation. As shown in figure 5, 97.2 and 93.5 percent of 

respondents in PNW and SE region, respectively, have knowledge about climate 

change and global warming, and 89.4 and 84.9 percent of respondents in PNW 

and SE, respectively, have knowledge about a positive relationship between 

carbon sequestration and tree planting. We note that a relatively smaller proportion of landowners have knowledge about carbon sequestration through tree 

planting compared to knowledge about climate change. 
11

Statistical Analysis on the Factors Affecting Agricultural Landowners’ Willingness to Enroll in a Tree Planting Program

Land Characteristics 

An important aspect of designing an incentives program for farm landowners is 

learning about their land and how it is used. Therefore, landowners were asked to 

answer questions about characteristics of their lands. 

Size of lands owned and rented—

The size of lands managed by landowners can be a factor affecting their decision on 

participation in a tree planting program. Figure 6 presents average size of land (i.e., 

total land, agricultural land) owned/rented by survey respondents (see tables 1 and 

2 for more details). The average size of total lands owned by survey respondents is 

137 and 115.8 ac (55.4 and 46.9 ha) in the PNW and SE, respectively. The average 

size of agricultural land owned within the survey region is 58.3 and 40.2 ac (23.6 

and 16.3 ha) in the PNW and SE, respectively. In the PNW, the average size of 

cropland is greater than that of grassland, while the reverse is true in the SE. 

Figure 7 shows the distribution of agricultural land across size classes. In the 

PNW and SE, respectively, 54.1 and 46 percent of landowners owned less than 30 

ac (12.1 ha), while 11.1 and 14.6 percent of them owned at least 100 ac (40.5 ha).

Number of unconnected parcels—

We asked respondents about the number of unconnected parcels they own, because 

we expect that more unconnected parcels would allow landowners to allocate 

parts of their land to a tree planting program more easily. Moreover, the number of 

unconnected parcels might be correlated with the size of agricultural lands. Figure 

8 shows that most landowners (70.7 percent in the PNW and 57.3 percent in the SE) 

have one parcel, 15 percent (PNW) and 19.8 percent (SE) have two parcels, and 1.8 

percent (PNW) and 3.9 percent (SE) of landowners have more than six parcels. 

75.0 80.0 85.0 90.0 95.0 100.0

Knowledge about carbon

sequestration

Knowledge about climate change

PNW

SE

Percentage of respondents

Figure 5—General knowledge about climate change and carbon sequestration in the Pacific Northwest 

(PNW) and Southeast (SE).
12

RESEARCH PAPER PNW-RP-604

0 50 100 150

Total grassland rented

Total cropland rented

Total grassland owned

Total cropland owned

Total agricultural land rented in region

Total agricultural land owned in region

Total acreage of land rented in region

Total acreage of land owned in region

Total acreage of land owned

PNW

SE

Size of lands (ac)

Ownership and category

Figure 6—Size of lands owned and rented in the Pacific Northwest (PNW) and Southeast (SE).

Figure 7—Distribution of agricultural land across size classes in the Pacific Northwest (PNW) 

and Southeast (SE).

0

5

10

15

20

25

30

35

Less than 10 10-29 30-49 50-69 70-99 100 and more

PNW

SE

Percentage of respondents

Size classes (ac)
13

Statistical Analysis on the Factors Affecting Agricultural Landowners’ Willingness to Enroll in a Tree Planting Program

Figure 9 shows average number of parcels across six different size classes. As 

we expected, as agricultural land size increases the number of parcels increases as 

well. The average number of parcels is 1.8 in the PNW and 2.1 in the SE (see tables 

1 and 2).

Figure 8—Distribution of number of unconnected parcels in the Pacific Northwest (PNW) 

and Southeast (SE). 

Figure 9—Average number of unconnected parcels across size classes in the Pacific Northwest (PNW) 

and Southeast (SE). 

0

10

20

30

40

50

60

70

80

1 2 3 4 5 6 and more

PNW

SE

Percentage of respondents

Number of parcels

0

1

2

3

4

5

6

Less than 10 10-29 30-49 50-69 70-99 100 and

more

Average

PNW

SE

Number of parcels

Size classes (number)
14

RESEARCH PAPER PNW-RP-604

Acquisition methods—

How landowners acquired their lands might affect their land use decisions. Landowners were asked whether they bought or inherited their land. Figure 10 shows the proportion of respondents who bought or inherited. The figure shows that 87.5 percent 

of respondents in the PNW own inherited agricultural land, compared to 35.3 percent 

in the SE. The proportion of landowners who bought their land is smaller than the 

proportion who inherited their land in the PNW, but this is reversed in the SE. 

Figure 10—Proportion of agricultural land acquisition methods in the Pacific Northwest (PNW) 

and Southeast (SE).

Figure 11—Distribution of owned years of agricultural lands in the Pacific Northwest (PNW) 

and Southeast (SE).

0.0 20.0 40.0 60.0 80.0 100.0

Inherited

Bought

PNW

SE

Proportion or respondents (%)

Land acquisition

Years of ownership—

Because a large proportion of land is inherited, and the average age of landowners 

in both regions is more than 60 years, we expected that agricultural lands have been 

owned for long periods of time. As shown in figure 11, 53.8 percent of respondents 

in the PNW region and 62.9 percent of respondents in the SE region have owned for 

more than 20 years. On average, survey respondents have owned their property for 

22.6 years in the PNW region and 25.2 in the SE region (see tables 1 and 2).

0

5

10

15

20

25

Less than 10 10-19 20-29 30-39 40-49 50-59 60 and more

PNW

SE

Percentage of respondents

Owned year groups
15

Statistical Analysis on the Factors Affecting Agricultural Landowners’ Willingness to Enroll in a Tree Planting Program

Ownership type—

Ownership type is also considered an important factor for land use decisions. We 

expect that land use change decisions can be made more easily under individual 

ownership than under joint ownership. Tables 1 and 2 show 71.6 and 74.1 percent of 

respondents in the PNW and SE, respectively, have individual ownership. However, 

the fact that individual ownership is the dominant ownership type does not guarantee responsiveness of land use decisions with respect to incentive payments for 

carbon sequestration.

Development pressure—

Development pressure is a well-known attribute affecting land use decisions. We 

asked landowners whether they have any expectation of development within the 

next 10 years. Tables 1 and 2 show 8.1 and 9.9 percent of respondents in the PNW 

and SE, respectively, expect to develop their land. 

Average profits per acre— 

We asked landowners to state their expected annual net returns from agricultural 

lands, because we expected that landowners who owned highly profitable lands 

might be reluctant to convert their lands to another use, and hence might require 

higher compensation than those who own less profitable lands. Figure 12 presents 

the distribution of landowners with respect to seven different ranges of annual 

profits. In the PNW, 46.5 percent and in the SE, 59.5 percent of landowners, own 

agricultural lands yielding less than $100 per acre. The stated average annual profit 

per acre is $213.5 in the PNW and $144.6 in the SE, and cropland profit is greater 

than grassland profit in both regions (see tables 1 and 2). 

Figure 12—Distribution of landowners with respect to agricultural land profits per acre in the Pacific 

Northwest (PNW) and Southeast (SE).

0

10

20

30

40

50

60

70

Less than

100

100-199 200-299 300-399 400-499 500-599 600 and

more

PNW

SE

Percentage of respondents

Prots/acre (dollars)
16

RESEARCH PAPER PNW-RP-604

Productivity of agriculture— 

We also considered the productivity of agricultural lands as one possible factor 

affecting land use decisions. We expect that landowners who owned low-productivity lands might allocate those lands for tree planting at relatively lower compensation. Figure 13 shows the proportion of agricultural lands for three different levels 

of productivity. In both regions, a higher proportion of croplands than grasslands 

are highly productive. On average, low-productivity lands accounted for 25.1 and 

10.7 percent of agricultural lands, and high-productivity lands accounted for 35.7 

and 29.7 percent in the PNW and SE region, respectively. 

Figure 13—Proportion of agricultural lands with respect to different levels of productivities in the Pacific 

Northwest (PNW) and Southeast (SE).

0

10

20

30

40

50

60

Low Mid High Low Mid High Low Mid High

Crop Grass Total

PNW

SE

Percentage of agricultural land

Land not in use—

We also asked landowners how many acres of agricultural lands were not currently 

used for production, because agricultural lands not currently in use might have a 

lower opportunity cost for planting trees. As shown in tables 1 and 2, the average 

size of agricultural land not currently in use is 4.8 ac in the PNW region and 6.8 ac 

in the SE region. 

Spatial and physical characteristics of agricultural land—

The spatial characteristics of agricultural land are important attributes affecting 

land use decisions. However, their effect is not examined in detail in existing 

studies about tree planting because of a lack of information on parcel location 

(Kim 2012). Zhou and Kockelmen (2008) recognized that variables such as central 

business district access and distance to the nearest highway, as well as each parcel’s 

neighborhood attributes, can affect landowner’s land use and management decisions. Development pressure, such as distance from major city, contiguity with 

urban growth boundary, and so on, can affect land use choice (Kristensen et al. 
17

Statistical Analysis on the Factors Affecting Agricultural Landowners’ Willingness to Enroll in a Tree Planting Program

2001, Langpap and Wu 2008, Mansfield et al. 2000). Potential risk of fire can 

affect land use and management decisions as well (Amacher et al. 2005, Konoshima 

et al. 2008). 

Because we do not know the exact location of each parcel, we directly asked 

landowners whether their agricultural lands are close or adjacent to certain areas 

or landmarks such as a home, city boundary, highway, forest land, other agricultural land, river, conservation areas, and fire hazards. We also asked whether their 

agricultural lands have steep slope (more than 15 degrees) areas or not. 

Figure 14 shows the proportion of respondents who answered “yes” to each 

question. The results show that, for the PNW and SE, respectively, 90.1 and 72 

percent of agricultural lands are located within a mile from home, 16.9 and 11.2 

percent are located close to a major city boundary, 58 and 77.2 percent are adjacent to forest land, 82.2 and 72.8 percent are adjacent to other agricultural lands, 

23.3 and 19.8 percent are adjacent to a river, 13.2 and 22.4 percent are adjacent to 

conservation areas, and 17.8 and 10.8 percent are adjacent to fire risk areas. Finally, 

21.2 percent of landowners in the PNW compared to 47.8 percent of landowners in 

the SE own agricultural lands with steep slopes.

Figure 14—Proportion of lands revealed existence of listed spatial and physical characteristics in the 

Pacific Northwest (PNW) and Southeast (SE).

0.0 20.0 40.0 60.0 80.0 100.0

Steep slope

Close to fire risk area

Adjacent to conservation area

Adjacent to river

Adjacent to ag-land

Adjacent to forestland

Close to highway

Close to city boundary

Close to home

PNW

SE

Proportion of lands (%)

Spatial/physical characteristics
18

RESEARCH PAPER PNW-RP-604

Other Attributes Related to Land Use and Management

Reasons for owning agricultural land—

Landowners may have multiple objectives for ownership of their agricultural land, 

and we believe that these various reasons for ownership may affect landowners’ 

land use decisions. To examine how the reasons for owning agricultural land affect 

the landowners’ willingness to plant trees for carbon sequestration, we asked 

landowners to rank the level of importance of their reasons for owning agricultural 

land with a 5-point scale ranging from “not important” to “very important.” We 

categorized five reasons for owning agricultural lands: for selling agricultural products, investment, heirs, protecting nature, and amenities. We expect that land use 

decisions may differ across these reasons. Average scores for cropland, grassland, 

and total lands across regions are described in tables 1 and 2.

Figure 15 shows the distribution of the importance score for each reason. In 

the PNW region, 87.6 percent of agricultural landowners scored at least 4 “for 

amenity,” 53.6 percent “for selling agricultural products,” 50.6 percent “for protecting nature,” 42.6 percent “for heirs,” and 36.5 percent “for investment.” In the SE 

region, 83.6 percent of agricultural landowners scored at least 4 “for amenity,” 71.1 

percent “for heirs,” 71.1 percent “for protecting nature,” 54.4 percent “for selling 

agricultural products,” and 33.6 percent “for investment.” 

Additionally, figure 16 presents the average scores earned for each category of 

reason for owning (see tables 1 and 2 for more details). Among the five different 

reasons for owning agricultural lands, “For amenity” is scored the highest (4.6 for 

the PNW, 4.5 for the SE), and “For investment” is scored the lowest (3.1) in both 

regions. For the remaining three possible reasons, the PNW gives more weight 

to selling agricultural products, while the SE gives more weight to heirs and 

protecting nature. 

Importance of providing environmental services for the public— 

Landowners can contribute to providing environmental services by converting their 

current land use to another use, or adopting more environmentally friendly management practices. There are multiple environmental services that landowners can 

provide, such as preventing soil erosion, improving water quality, providing wildlife 

habitat, and increasing carbon sequestration. We asked landowners to rank the 

importance of providing these environmental services for the public with a 5-point 

scale ranging from “not important” to “very important.” 

As shown in figure 17, among the four different types of environmental 

services, preventing soil erosion and improving water quality earned the highest 

average score (4.2) in the PNW, and preventing soil erosion earned the highest (4.7) 

in the SE. In both regions, preventing soil erosion, improving water quality, and 

providing wildlife habitat earned at least a score of 4, while sequestering carbon 
19

Statistical Analysis on the Factors Affecting Agricultural Landowners’ Willingness to Enroll in a Tree Planting Program

Figure 15—Distribution of degree of importance for reasons for owing agricultural lands in the Pacific Northwest 

(PNW) and Southeast (SE).

Figure 16—Proportion of reasons for owning properties across levels of importance in the Pacific Northwest (PNW) and Southeast (SE).

0

10

20

30

40

50

60

70

80

For selling products For investment For heirs For nature For amenity

1

2

3

4

5

0

10

20

30

40

50

60

70

80

For selling products For investment For heirs For nature For amenity

1

2

3

4

5

PNW

SE

Reasons for owning

Reasons for owning

Distribution of degree of importance Distribution of degree of importance

Score

Score

0 1 2 3 4 5

For amenity

For nature

For heirs

For investment

For selling products

PNW

SE

Degree of importance

Reasons for owning
20

RESEARCH PAPER PNW-RP-604

earned the lowest score (3.5 and 3.8 in the PNW and SE, respectively) suggesting 

that the respondents are relatively unsupportive of sequestering carbon compared 

to other services. This result also suggests that landowners’ participation in a treeplanting program for carbon sequestration might be motivated by other 

environmental services. 

Past experiences, current management, and future plans—

Past and current experiences with conservation farming practices, tree planting, and 

owning forest lands, as well as future plans for tree planting without any incentive 

payments can also be factors affecting land use decisions. 

Figure 18 shows that the proportion of respondents who are conducting conservation farming practices is 26.8 percent in the PNW and 19.8 percent in the SE, 

whereas 6.0 percent and 6.5 percent of respondents in the PNW and SE, respectively, have received conservation payments. A large proportion of respondents 

(41.1 percent in the PNW and 74.6 percent in the SE) previously owned forest lands. 

Around 32 percent of respondents in the PNW and 28 percent of respondents in the 

SE have past experience with tree planting, and 34.4 percent of respondents in the 

PNW and 20.3 percent of respondents in the SE have future plans for tree planting. 

Preferred administrative organization—

The survey respondents who were willing to participate in a tree planting program 

were asked about their preferred type of administrative organization to provide the 

program. As shown in figure 19, among four different types of candidate organizations, a nongovernmental organization (NGO) is most preferred, and a carbon 

market is least preferred in the PNW, while a carbon market is most preferred, and a 

private company that emits large amounts of CO2 is the least preferred in the SE. 

Figure 17—Average scores of importance of providing environmental services for the public in the 

Pacific Northwest (PNW) and Southeast (SE).

0 1 2 3 4 5

Carbon sequestration

Wildlife habitat

Water quality

Soil erosion

PNW

SE

Importance score

Environmental service types
21

Statistical Analysis on the Factors Affecting Agricultural Landowners’ Willingness to Enroll in a Tree Planting Program

Landowners’ Response to Incentives for Planting Trees 

and Correlations With Other Factors

Proposed Incentive Payment Scheme and Questionnaire Design

To examine landowners’ response to incentive payments for carbon sequestration, 

we proposed an incentive scheme that is consistent with components of the USDA 

Conservation Reserve Program (CRP), including a 50 percent cost-share subsidy for 

establishing trees and an annual rental payment for the 15- and 30-year duration of 

the contract. An additional flyer was provided to help respondents’ understanding 

of how annual compensation would be calculated. The description of the incentive 

scheme, enrollment questions, and an additional flyer are provided in the appendix. 

Figure 18—Proportion of respondents who answered “yes” to questions related to experience with 

conservation practices and programs in the Pacific Northwest (PNW) and Southeast (SE).

Figure 19—Proportion of preferred administrative organization for the Pacific Northwest (PNW) and 

Southeast (SE). NGO = nongovernmental organization.

0 20 40 60 80

Future afforestation

Past afforestation

Own forest land

Conservation payment

Conservation farming practices

PNW

SE

Proportion of respondents answering “yes” (%)

0 10 20 30 40 50

Deforest

No harvest

Renew contract

Harvest

Private company

Carbon market

Government agencies

NGO

PNW

SE

Proportion of respondents prefering (%)
22

RESEARCH PAPER PNW-RP-604

Through in-person pretest, expert review, and literature reviews, we chose a 

carbon price ranging from $1 to $150 per metric ton of carbon, with 12 breaks. This 

is consistent with the average of the maximum carbon price in US EPA (2005) and 

in the U.S. Agricultural Sector Model (Lewandrowski et al. 2004). The payment per 

metric ton of carbon is converted to a per-acre payment based on the annual carbon 

sequestration rate over the duration of the contract. Annual carbon accumulation 

rates over the duration of the contract were calculated by using the carbon accumulation table created by Smith et al. (2006). 

We initially asked survey respondents whether they would be willing to participate in a tree planting program. Then, the respondents who stated they would be 

willing to participate were asked to reveal the amount or proportion of acreage they 

would be willing to enroll in the program, given three different levels of per-acre 

annual compensation.

Landowners’ Willingness to Plant Trees for Carbon Sequestration

From the dichotomous choice question to identify the respondents who are willing 

to participate in a tree planting program, we found that 207 out of 433 (47.8 percent) 

valid respondents in the PNW and 89 out of 232 (38.4 percent) valid respondents in 

the SE responded affirmatively. Given the data collected from valid respondents in 

both regions, we conduct a statistical analysis to examine the correlation between 

landowners’ willingness to allocate their agricultural lands for tree planting and 

various factors affecting land use conversion, as well as carbon incentive payments. 

Effect of incentive payments on enrollment—

Figure 20 presents the acreage of agricultural land that landowners are willing to 

allocate for tree planting for different levels of annual compensation (standardized 

to carbon prices) and contract duration in each region. The linear trend line suggests that compensation and willingness to enroll are positively correlated for both 

regions and contract durations at 1 percent significance level. The slopes of the 

trend lines suggest that willingness to participate is more responsive to changes in 

carbon payment in the SE than in the PNW (p = 0.081). Between the two contract 

durations, respondents are more responsive to changes in carbon payments with a 

30-year contract than with a 15-year contract (p = 0.023).

Figure 21 shows the relationship between acreage respondents would be willing 

to enroll and the size of the agricultural land they own. In every case, the linear 

trend line suggests that acreage enrolled increases as the size of agricultural land 

increases, while the slopes are different across the contract duration and regions. 
23

Statistical Analysis on the Factors Affecting Agricultural Landowners’ Willingness to Enroll in a Tree Planting Program

However, we need to be careful when estimating landowners’ willingness to 

allocate their land for tree planting when using size of land holdings as a dependent 

variable in an econometric analysis. Because property size ranges from 0 to 1,000 

ac (0 to 404.7 ha) in the PNW and from 0 to 210 ac (84.9 ha) in the SE, inclusion 

of a few large properties may distort estimation results, and thus may overstate 

true willingness to plant trees under a carbon payment program. If this is an issue, 

the proportion of agricultural land enrolled might be a better option as a dependent 

variable, as it allows us to avoid the scale issue. Thus, figure 22 presents the 

Figure 20—Acreage of enrollment with respect to incentive payment in the Pacific Northwest (PNW) and Southeast (SE).

y = 0.1107x - 2.0374

0

100

200

300

400

500

600

700

800

900

1000

0 50 100 150

Acreage

Carbon price ($/metric ton)

y = 0.2055x - 3.5784

0

100

200

300

400

500

600

700

800

900

1000

0 50 100 150

Acreage

Carbon price ($/metric ton)

y = 0.1946x - 1.6071

0

50

100

150

200

250

300

350

400

450

500

0 50 100 150

Acreage

Carbon price ($/metric ton)

y = 0.2656x - 5.7048

0

50

100

150

200

250

300

350

400

450

500

0 50 100 150

Acreage

Carbon price ($/metric ton)

PNW, 15-year PNW, 30-year

SE, 15-year SE, 30-year
24

RESEARCH PAPER PNW-RP-604

0

10

20

30

40

50

Less than

10

10-29 30-49 50-69 70-99 100 and

more

0

10

20

30

40

50

Less than

10

10-29 30-49 50-69 70-99 100 and

more

PNW

SE

Linear (PNW)

Linear (SE)

Average size of land (ac)Average size of land (ac)

15-year 30-year

Size classes Size classes

Figure 21—Average acreage of enrollment with respect to size of agricultural lands in the Pacific Northwest (PNW) and Southeast (SE).

proportion of land enrolled at different levels of incentive payment. While the 

linear trends in figure 22 are similar to those in figure 20, under a 15-year contract, 

respondents in the SE are more responsive than in the PNW, but under a 30-year 

contract, the opposite is true. This may be because the average timber harvest 

rotation period in the SE is shorter than in the PNW (Lewandrowski et al. 2004, 

Smith et al. 2006), and thus the respondents in the SE might prefer to have a shorter 

contract than in the PNW. 

Correlations between proportion of land enrollment and related factors—

Finally, we provide pairwise correlations between proportion of land enrolled 

and other factors presented in table 3.3 The signs are mostly consistent between a 

15-year contract and a 30-year contract in both regions. We describe and highlight 

the variables for which there are significant differences between the two possible 

contract durations. 

Carbon payments are positively and significantly correlated with enrollment 

for both contract durations and in both regions. Additionally, in the PNW, we find 

a positive correlation between enrollment and a high school education, household 

3 The estimation results of empirical econometric analysis using surveyed data can be found 

in chapter 3 of Kim (2012). The primary goal of this analysis is to predict the landowners’ 

willingness to enroll in a tree planting program in response to different levels of incentive 

payments by controlling various factors affecting the landowners’ land use decision.
25

Statistical Analysis on the Factors Affecting Agricultural Landowners’ Willingness to Enroll in a Tree Planting Program

Figure 22—Proportion of enrollment with respect to incentive payment in the Pacific Northwest (PNW) and Southeast (SE).

y = 0.2262x + 3.092

R2 = 0.0789

0

10

20

30

40

50

60

70

80

90

100

0 25 50 75 100 125 150

Carbon price ($/metric ton)

y = 0.3172x + 3.5906

R2 = 0.1266

0

10

20

30

40

50

60

70

80

90

100

0 50 100 150

Carbon price ($/metric ton)

y = 0.3934x + 0.6249

R2 = 0.1726

0

10

20

30

40

50

60

70

80

90

100

0 25 50 75 100 125 150

Carbon price ($/metric ton)

y = 0.316x - 5.6534

R2 = 0.1258

0

10

20

30

40

50

60

70

80

90

100

0 25 50 75 100 125 150

Carbon price ($/metric ton)

PNW, 15-year PNW, 30-year

SE, 15-year SE, 30-year

PercentPercent

Percent

Percent

income between $40,000 and $60,000, a male landowner, knowledge about climate 

change, inherited land, a higher proportion of unused land, owning land to protect 

nature or amenities, and placing importance on providing water quality and carbon 

sequestration. Furthermore, respondents who owned forest land, have experience 

with past afforestation, have a future afforestation plan prefer, NGOs as administrative organizations, or prefer government agencies as administrative organizations 
26

RESEARCH PAPER PNW-RP-604

Table 3—Correlations between proportion of land enrollment and other variables

Pacific Northwest Southeast

Variables 15 year 30 year 15 year 30 year

Carbon payment 0.2545* 0.3617* 0.4101* 0.2682*

Demographic characteristics and owners’ attributes:

 Age 0.0257 0.0267 0.0609 -0.0566

 Education (1 if elementary school) -0.0144 -0.0243 0.0801 0.1404*

 Education (1 if middle school) -0.0182 -0.0593* 0.0801 -0.0787

 Education (1 if high school) 0.0945* 0.0781* 0.0252 -0.0525

 Education (1 if college) -0.0279 -0.0155 -0.1036* -0.0808

 Education (1 if university) -0.0265 0.0074 0.0132 -0.0063

 Education (1 if graduate school) -0.0311 -0.0481 -0.0118 0.0983*

 Household income (1 if less than $20 K) -0.0787* -0.0654 0.0825 -0.0178

 Household income (1 if $20 to $40 K) -0.0037 0.0103 -0.1337* -0.0752

 Household income (1 if $40 to $60 K) 0.0658* 0.0251 -0.0178 0.0013

 Household income (1 if $60 to $80 K) -0.0199 -0.0227 -0.0512 -0.0433

 Household income (1 if $80 to $100 K) 0.0102 -0.0158 -0.0019 -0.0229

 Household income (1 if $100 K or more) -0.0076 0.0335 0.0981* 0.1114*

 Gender (1 if male) 0.0643 0.1286* 0.0019 0.0647

 Occupation (1 if farmer) -0.0869* -0.0707* -0.0317 -0.1143*

 Retired (1 if retired) 0.0308 0.0174 0.0804 0.0525

 Member of NGO (1 if member) 0.0434 0.0207 0.0081 0.0002

 Member of agricultural organization (1 if member) -0.0710* -0.0322 -0.0958* -0.0561

 Refer neighbors decision (1 if it does) -0.0089 -0.0013 0.0254 0.0093

 Knowledge about climate change 0.0838* -0.0233 -0.0123 0.0342

 Knowledge about carbon sequestration -0.0318 -0.0278 0.0967* 0.0583

Land characteristics: 

 Total land owned (acres) -0.0559 0.0083 0.0079 0.0141

 Total land owned in region (acres) -0.0871* -0.0541 -0.0058 0.0158

 Total agricultural land owned (acres) -0.0813* -0.0696* -0.0596 -0.0256

 Number of unconnected parcels -0.0538 -0.0390 0.0441 -0.0487

 Acquisition method (1 if bought) -0.1623* -0.1433* 0.0729 0.0264

 Acquisition method (1 if inherited) 0.0674* 0.1118* -0.1750* -0.0704

 Owned year 0.0081 -0.0021 -0.0045 -0.0425

 Ownership (1 if individual owner) -0.0335 -0.0263 0.0715 0.0582

 Expect development (1 if does) -0.0274 -0.0107 0.1759* 0.0244

 Annual farm profit/acre (%) -0.1075* -0.1282* -0.0272 -0.0251

 Proportion of high productivity land (%) -0.1043* -0.1134* 0.0105 -0.0182

 Proportion of low productivity land (%) 0.0534 0.0311 0.0952* 0.0651

 Proportion of not-in-use land (%) 0.0724* 0.0773* 0.1435* -0.0007

Note: * indicates statistical significance at α = 5 percent.
27

Statistical Analysis on the Factors Affecting Agricultural Landowners’ Willingness to Enroll in a Tree Planting Program

Table 3—Correlations between proportion of land enrollment and other variables (continued)

Pacific Northwest Southeast

Variables 15 year 30 year 15 year 30 year

Spatial and physical characteristics: 

 Close to home—resident owner (1 if “yes”) 0.0452 0.0034 -0.0283 -0.0600

 Close to city (1 if “yes”) 0.0289 0.0112 0.0444 0.0391

 Close to highway (1 if “yes”) -0.0053 -0.0033 -0.0501 -0.0561

 Adjacent to forest (1 if “yes”) 0.0520 0.0396 0.0345 0.0240

 Adjacent to agricultural land (1 if “yes”) -0.0619* -0.0039 -0.0577 -0.0251

 Adjacent to river (1 if “yes”) 0.0339 0.0618 -0.0509 0.0830

 Adjacent to conservation area (1 if “yes”) -0.0547 -0.0307 -0.0276 -0.0114

 Close to fire hazard (1 if “yes” -0.0186 -0.0091 0.1928* 0.1356*

 Steep slope area (1 if “yes”) -0.0269 -0.0485 -0.0016 -0.0066

Reasons for owning properties (importance score from 1 to 5):

 For selling farm product -0.1133* -0.1029* -0.0862 -0.0488

 For investment 0.0392 0.0247 0.0219 -0.0601

 For heir 0.0211 0.0770* -0.0907* -0.0457

 For protecting nature 0.1489* 0.0836* 0.0711 0.0480

 For amenity 0.0828* 0.0500 0.0405 0.0224

Environmental services (importance score from 1 to 5): 

 For preventing soil erosion -0.0403 -0.0280 0.1087* 0.0253

 For improving water quality 0.0134 0.0095 0.0515 0.0134

 For providing wildlife habitat 0.0717* 0.0410 0.1070* 0.1445*

 For carbon sequestration 0.1456* 0.0851* 0.1374* 0.0424

Other attributes: 

 Conservation farming (1 if “yes”) -0.0901* -0.1302* 0.0153 -0.0406

 Own forest (1 if “yes”) 0.0916* 0.0585 0.0692 -0.0022

 Past afforestation (1 if “yes”) 0.1232* 0.0735* 0.0467 0.0692

 Future afforestation plan (1 if “yes”) 0.1080* 0.0385 0.0522 0.0577

Administrative organization: 

 NGO 0.0763* 0.0176 0.0382 -0.0093

 Government agencies 0.0961* 0.0831* -0.1190* -0.0079

 Carbon market -0.0042 -0.0331 0.1234* 0.0217

 Private company -0.1398* -0.0603 0.1173* 0.0778

Note: * indicates statistical significance at α = 5 percent.

NGO = nongovernmental organization.
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RESEARCH PAPER PNW-RP-604

are more likely to enroll. Respondents with household income below $20,000, who 

are farmers and members of an agricultural organization, conduct conservation 

farming, and prefer a private company as an administrative organization are less 

likely to enroll. Enrollment is also negatively correlated with total size of land in the 

region, size of agricultural land owned, purchased land, higher levels of farm profit 

per acre and larger proportions of high-productivity land, and adjacency to other 

agricultural lands. 

In the SE, respondents with household income at least $100,000, with knowledge about carbon sequestration, and who prefer either a carbon market or a private 

company as an administrative organization are more likely to enroll. Additionally, 

enrollment is positively correlated with the proportion of low-productivity and 

unused land, proximity to fire hazards, and interest in preventing soil erosion, 

providing wildlife habitat, and sequestering carbon. Respondents who are farmers, have a college education, household income between $20,000 and $40,000, 

are members of an agricultural organization, and prefer government agencies as 

administrative organization are less likely to enroll. Enrollment is also negatively 

correlated with inherited land and interest in bequeathing the land to heirs. 

However, in both region, landowners’ ages and retirement status, membership of NGO, number of unconnected parcels, owned years, spatial or physical 

characteristic variables (except adjacency with other agricultural land in the PNW, 

and except closeness to fire hazard) are not significantly correlated with the proportion of land enrollment. Additionally, in the SE, the variables representing the 

respondents who have been conducting conservation farming, who own forest, who 

experienced afforestation in the past, and who have a plan for future afforestation 

are not significantly correlated with proportion of land enrollment, while they are 

significantly correlated in the PNW.
29

Statistical Analysis on the Factors Affecting Agricultural Landowners’ Willingness to Enroll in a Tree Planting Program

Conclusion

It is important to know individual landowners’ characteristics and land characteristics to estimate landowners’ willingness to participate in a tree planting program 

given incentive payments for carbon sequestration. This statistical analysis of data 

collected through an agricultural landowner survey is motivated by recognizing 

that a lack of information about these characteristics is one of the main obstacles to 

eliciting willingness to participate in a tree planting program. 

This report provides a statistical analysis of the data collected from the two 

survey regions, the Pacific Northwest and the Southeast. The survey asked about 

characteristics of individual agricultural landowners and their land, as well as their 

willingness to enroll in a tree planting program given different levels of incentive 

payments for carbon sequestration. 

This statistical analysis revealed both similar and contrasting features between 

the two survey regions. One key insight is that landowners in the SE are generally 

more responsive to incentive payments for carbon sequestration than landowners in 

the PNW, and that the variables which are highly correlated with the willingness to 

enroll in a tree planting program are somewhat different between the two regions. 

This report helps us understand the key factors affecting landowners’ tree 

planting program participation, measure the potential extent of participation in a 

tree planting program for carbon sequestration, and conduct a comparison study to 

find different characteristics between the two survey regions. 

Acknowledgments

We thank Ralph Alig for his valuable comments, advice, and funding through the 

Goods, Services, and Values Research Program in the USDA Forest Service Pacific 

Northwest Research Station.
30

RESEARCH PAPER PNW-RP-604

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Statistical Analysis on the Factors Affecting Agricultural Landowners’ Willingness to Enroll in a Tree Planting Program

Appendix: Description of Incentive Scheme

Suppose you are offered incentives to plant fast-growing trees on your agricultural lands for 

carbon sequestration. If you agree, you would enter into a contract with a private or 

governmental organization. The contract would have the following conditions and incentive 

scheme: 

Enrollment question 

Step 1: Given the incentives described above, would you be willing to participate in a tree 

planting program for carbon sequestration? 

□ Yes, given the right incentives and conditions ⇒ If ‘yes’, please proceed to next question.

□ No, not under any conditions ⇒ If ‘no’, please go Question ___. 

Step 2: Question asking willingness to allocate agricultural land 

Suppose you are offered a 15 year contract for a tree planting program. 

If the annual compensation level is $ per acre, on how many acres of your agricultural land 

are you willing to plant trees? If ‘none’, please answer ‘0 (zero)’. 

 How about if annual compensation is $ per acre? 

 How about if annual compensation is $ per acre? 

Cropland: ( ) Acres, or ( ) % Grassland: ( ) Acres, or ( ) % 

Cropland: ( ) Acres, or ( ) % Grassland: ( ) Acres, or ( ) % 

Cropland: ( ) Acres, or ( ) % Grassland: ( ) Acres, or ( ) % 
34

RESEARCH PAPER PNW-RP-604

Example of Annual Compensation Calculation 

If you plant Douglas-Fir in the Western Oregon and Western Washington, as the trees are 

growing, you can reduce CO2 from the atmosphere. 

 * Global warming and climate change are being driven by the addition of carbon dioxide (CO2)

and other greenhouse gases. Carbon dioxide is the biggest culprit, producing 60percent of 

the human-enhanced greenhouse effect that leads to global warming (United Nations 

Environmental Program)

The following figure shows annual accumulation of CO2 by planting Douglas-Fir

 

Source: Smith et al. 2006. USDA Forest Service. 

Suppose you plant Douglas-Fir on 100 acres of your agricultural land. 

And suppose the contract payment for CO2 is $10 per ton.

Then your Annual Compensation will be: 

 With a 15-year contract: 100 acresx4.7 tonsx$10 = $4,700

 With a 30-year contract: 100 acresx9.7 tonsx$10 = $9,700
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