Changes in Implicit Prices of Prairie Pothole Habitat

Changes in Implicit Prices of Prairie PotholeHabitat

Chad Lawley

Assistant Professor, Department of Agribusiness and Agricultural Economics, Universityof Manitoba, 377-66 Dafoe Road, Winnipeg, MB, Canada R3T 2N2 (phone: 204-474-9397;

fax: 204-261-7251;e-mail: [email protected]).

I estimate changes in agricultural land value discounts due to prairie pothole habitat. The implicit pricesof pothole habitat acreage are estimated from a series of hedonic models using Manitoba agriculturalland transaction data from 1990 to 2009. I find that the discount on wetland acreage increased by atleast 40%, suggesting that significant unanticipated increases in the benefits of converting wetlandsemerged over the course of the study period. I also estimate a series of quantile regression hedonicmodels. The quantile regression models indicate that the land value discounts on prairie pothole acreageas a percent of per acre sales prices are constant across the land value distribution. These results haveimplications for the design of habitat conservation programs, particularly those involving long-termagreements between landowners and conservation agencies.

Dans la presente etude, j’ai evalue les variations des rabais sur les terres agricoles en raison del’habitat dans les fondrieres des Prairies. J’ai estime les prix implicites des superficies occupees parles fondrieres a l’aide d’une serie de modeles hedonistes comprenant des donnees sur les transactionsde terres agricoles effectuees au Manitoba au cours de la periode de 1990 a 2009. Les resultats demon etude montrent que les rabais accordes sur les superficies de terres humides ont augmente d’aumoins 40%, ce qui autorise a penser que les avantages lies a la conversion des terres humides ontcru de facon significative et imprevue au cours de la periode d’etude. J’ai egalement estime une seriede modeles hedonistes par le biais de regressions quantiles. Ces modeles ont montre que les rabaissur les superficies occupees par les fondrieres des Prairies, lesquels sont determines en fonction d’unpourcentage du prix de vente a l’acre, etaient constants dans la repartition de la valeur des terres.Ces resultats ont des repercussions sur la conception des programmes de conservation des habitats,en particulier ceux qui comportent des ententes a long terme entre des proprietaires fonciers et desorganismes de conservation.

INTRODUCTION

Conversion of prairie potholes and associated upland acreage is a persistent feature of theagricultural landscape in the Canadian Prairie Provinces and the U.S. Northern Plains.1

Recent estimates suggest that more than half of prairie pothole acreage present in NorthAmerica at the time of European settlement has been lost or degraded (Euliss et al 2006;Dahl and Watmough 2007). Approximately 5% of remaining western Canadian wetlandacreage was lost between 1985 and 2001 (Watmough and Schmoll 2007). Conversion of

1 In this paper prairie pothole habitat refers to prairie potholes (depressional wetlands) and theirsurrounding grasslands.

Canadian Journal of Agricultural Economics 62 (2014) 171–190

DOI: 10.1111/cjag.12022

171

172 CANADIAN JOURNAL OF AGRICULTURAL ECONOMICS

prairie pothole habitat is expected to continue due to a number of factors.2 First, thebenefits of converting habitat have tended to increase over time as a result of recentincreases in the relative returns on annual crops, the adoption of larger farm machinery,and agronomic advances that have improved the productivity of annual crops on marginalland (GAO 2007; Gelso et al 2008).3 Second, the cost of converting pothole habitat haslikely decreased over time due to advances in and greater availability of drainage andclearing equipment, adoption of geographic information system (GIS) technology, andan expanded rural drainage network.

Prairie pothole habitat constrains the use of land in annual crop production and istherefore expected, on average, to reduce land values. The land value discount reflectsthe present discounted value of the long-run expected benefits or costs of converting thehabitat to a more profitable use. Land value discounts might change over time for a coupleof reasons. First, unanticipated changes in agricultural markets, adoption of improvedinputs including farm machinery, seed, and chemicals, and changes in precipitation levelscorresponding to wet/dry cycles might shift long-run expectations about the benefitsand costs of converting habitat. Second, habitat conversion alters the composition ofremaining habitat acreage and, if the extent of conversion is significant, the average landvalue discount may change.

In this paper, I estimate implicit prices of prairie pothole habitat based on a series ofhedonic land value models using land transaction data spanning a 20-year period. Thisis the first study to document the extent to which agricultural land value discounts dueto habitat acreage have changed over time. The hedonic regression estimates shed lighton the average discount land sellers are willing to accept for land with prairie potholeacreage.4 They also document the premium land sellers demand, and buyers are willingto pay, for higher productivity soil. I also investigate the extent to which discounts onhabitat acreage vary across land values using a series of quantile regressions.

I use a spatially explicit data set that documents bare agricultural land sales inthe southwestern portion of Manitoba from 1990 to 2009. This data set documents theper acre sales price of parcels, the quarter sections in the sale, and the extent of arableacreage, wetlands, bush/pasture, and native hay on each quarter section. I combine theland transactions data set with a quarter section-level soil productivity index compiled bythe Manitoba Agricultural Services Corporation (MASC; the provincial agency respon-sible for administering Manitoba government sponsored crop insurance and agriculturallending). I also control for geographic factors such as distances to cities, grain elevators,

2 A recent study in southeastern Saskatchewan suggests that roughly 50% of existing wetlands maybe drained over the next 20 years (Cortus et al 2011). Rashford et al (2011) provide evidence (basedon Census Agricultural Region level data for census years 1981–2006) suggesting regions of westernCanada will lose between 5% and 20% of existing pasture to cultivation by 2011.3 Technological developments that increase the productivity of annual crops on marginal land mightinclude the use of hardier seed varieties, precision input application technology, and reduced tillagecultivation.4 The discount will depend on the expected use of the land after the sale. Wetlands and theirassociated grasslands have value to livestock producers and to recreational users, whereas they area constraint if the surrounding land is used for annual crop production. The hedonic estimates inthis paper capture the average land value discount due to prairie pothole acreage.

IMPLICIT PRICES OF PRAIRIE POTHOLE HABITAT 173

the latitude and longitude of sales parcels, and MASC crop insurance risk zones catego-rized according to spatially correlated risk factors that affect agricultural production.

I find that the absolute premium on high and medium productivity soil (relative tolow productivity soil) has increased over time, but the premium on high productivity soilas a percent of land values has remained constant. I also find that the discount on habitatacreage (relative to arable acreage) has tended to increase over time, both in absolute andpercentage terms. The conservative estimates presented in this paper suggest that the landvalue discount due to wetland acreage has increased by 160% in absolute terms and by40% when expressed as a percentage of land values. These results provide strong evidencethat unanticipated benefits of conversion emerged through the course of the study period,and at a minimum, uncertainty about the future benefits of converting habitat increased.

The remainder of the paper proceeds as follows. In the next section the empiricalmodel is outlined including a detailed description of the interpretation of the estimatedimplicit prices. The data are presented next, followed by presentation of the results andpolicy implications. The final section presents concluding remarks.

EMPIRICAL FRAMEWORK

A number of previous studies use hedonic models of agricultural land values based onparcel-level observations, with a focus on backing out the implicit prices of site char-acteristics such as drainage and soil quality (notable examples include Miranowski andHammes 1984; Palmquist and Danielson 1989; Xu et al 1993; Tsoodle et al 2006). Thehedonic studies to date have not examined changes in the implicit prices of land charac-teristics over time.

I estimate the following cross-sectional hedonic regressions for five four-year intervalsbetween 1990 and 2009

pit = αt Qit + βt Xit + uit (1)

log(pit) = αt Qit + βt Xit + uit (2)

where t indexes the four-year intervals (1990–93, 1994–97, 1998–2001, 2002–05, and2006–09) and i indexes individual sales parcels. The dependent variable is the real peracre sales price in 2002 CAD, denoted p. The vector Q includes parcel characteristicsof interest including high and medium productivity soil indicators (low productivity soilis the omitted category), the percentage of each sale that is in prairie pothole habitat,categorized into wetland, bush/pasture, and native hay acreage, and the mean latitudeand mean longitude of each sale parcel. The vector X is a set of control variables describedin the Data section, including year and MASC crop insurance risk area dummy variables.The standard errors from the estimates are clustered by MASC risk areas to adjust thestandard errors for unobserved attributes common within the risk areas.5

Estimating the hedonic model separately for each four-year interval generates fiveestimates of the implicit premiums on high and medium productivity soil, the implicit

5 See Pope (2008) for an application using residential real estate transactions in North Carolina,where standard errors are clustered using U.S. Census tracts and block groups.


discounts on prairie pothole habitat, and the premium/discount due to the latitude andlongitude of sales parcels.6 I assess the statistical significance of changes in the implicitprices of these characteristics relative to 1990–93, which is the base period.

I also estimate a series of quantile regression models for each period based onEquation (2) for the 25th, 50th, and 75th percentiles of the land value distribution. Thequantile regressions provide estimates of the implicit prices of habitat acreage across arepresentative set of points in the land value distribution. The estimated implicit prices atthe different quantiles will differ if the benefits of conversion vary systematically acrossland values. The quantile regressions are used to evaluate whether or not temporal changesin implicit prices are concentrated in one part of the land value distribution.

Interpretation of Implicit PricesInterpretation of the implicit prices of immutable characteristics such as soil productivityis straightforward. Soil texture, slope, and salinity are fixed characteristics of land and,all else equal, sales parcels with higher productivity soil should sell for a premium. Inter-preting the implicit price of habitat is less straightforward because landowners can chooseto convert or to maintain habitat. As a result, the discount on habitat acreage dependson the relative magnitudes of the long run expected costs and benefits of converting.If the present discounted net benefit of converting habitat is negative, then the implicitdiscount due to the habitat on that parcel captures the expected benefits of converting.Alternatively, if the present discounted net benefit of converting habitat is positive, thenthe implicit discount captures the cost of converting.7 In the absence of accurate infor-mation on the net benefits of converting individual patches of habitat, estimates of theimplicit discounts on habitat acreage will capture a weighted average of (1) the benefitsof converting habitat acreage not at risk of conversion and (2) the costs of convertinghabitat acreage at risk of conversion.

To illustrate, Figure 1 depicts the expected net benefit of converting existing habitatwithin a given region, where the expected net benefit of converting is the present valueof the long run benefits of conversion minus the present value of the initial costs ofconversion and long run maintenance costs. Total existing habitat within the region is thedistance from 0 to E along the horizontal axis. Existing habitat is ordered such that thepresent value of the expected net benefit of converting habitat is increasing from left toright. The net benefit of converting habitat to the left of a is negative, while the net benefitof converting habitat to the right of a is positive.

The benefits and costs of converting two habitat parcels, labeled Z and Y, are plottedin Figure 1. These habitat parcels will have different impacts on agricultural land values.

6 A couple of recent studies have examined temporal changes in implicit prices in hedonic models.Costa and Kahn (2004) conduct a hedonic study of wage-risk compensating differentials using U.S.Census data from 1940, 1950, 1960, 1970, and 1980. They show that the value of life in the UnitedStates tended to increase from 1940 to 1980. McMillen (2008) estimates somewhat parsimonioushedonic models of house prices in Chicago to study changes in the distribution of house prices soldin 1995 and 2005.7 For example, Palmquist and Danielson (1989) suggest that estimates of the implicit prices oferosion will reflect the present value of future productivity losses when erosion control measuresare not worthwhile and the present value costs of erosion control in cases when it is worthwhile toinvest in erosion control measures.


Expected net benefit of converting existing habitat

0

c1

b1

$

Existing habitat

b2

c2

Z Ya E

Figure 1. Benefits, costs, and implicit prices of existing habitat

First, note that the net benefit of converting habitat parcel Z is negative. In this casecurrent and future landowners will not find it worthwhile to convert the habitat parcel—the habitat is not at risk of conversion—and the impact of the habitat on the sales value ofthe parcel is equal to the (unrealized) benefit of converting the habitat, b1. The expectednet benefit of converting parcel Y is positive, suggesting that current or future landownerswill find it worthwhile to convert the habitat at some point in time—the habitat is at riskof conversion.8 The impact of habitat Y on the sales value of the land parcel it is locatedwithin is therefore equal to the cost of converting the habitat, c2.

Hedonic estimates of the implicit discount on habitat acreage are the weighted averageof the benefit of converting habitat to the left of a, and the cost of converting habitat to theright of a, based on those parcels that sold. If no habitat is at risk of conversion (i.e., nohabitat is to the right of a) the estimated implicit discount on habitat acreage will capturethe average private benefit of converting habitat. Examination of Figure 1 suggests thatif the majority of habitat is not at risk of conversion then the hedonic point estimate ofthe implicit discount on habitat acreage is a lower bound estimate of the average privatebenefit of converting habitat (or the private cost of restoring habitat).

The analysis in this paper examines how the implicit discount on habitat acreagehas changed over time. It is commonly thought that the benefits of converting habitat

8 Land conversion deemed worthwhile by expected net present value criteria may not occur instan-taneously for a number of reasons. The current landowner may not find it worthwhile to converthabitat, whereas potential future landowners bidding on the land may find it worthwhile to convert.The costs of converting habitat are sunk and the benefits are uncertain, implying there is value inretaining the option to convert (for an application of real options to land-use conversion betweenagriculture and forestry, see Schatzki 2003). Finally, time constraints may make it difficult forproducers to convert habitat when it would be worthwhile to do so (Cortus et al 2011).


have increased over time due to greater demand for arable land, increased nuisance costsdue to the adoption of larger farm machinery, and adoption of crop varieties that arebetter suited to production on marginal lands. Also, the costs of converting habitat havelikely fallen due to increased access to drainage and clearing equipment, adoption of GIStechnology, and expansion of rural drainage networks.9 Changes in the implicit discounton habitat acreage are a function of the rate of change in the benefits of conversion andthe rate of change in the costs of conversion. If the costs of conversion are constant orfalling over time, then the change in the estimated discount on habitat acreage is a lowerbound estimate of the change in the average benefits of converting.10

DATA

The data used in this analysis is drawn from the prairie pothole region of southwesternManitoba, as indicated in Figure 2. Data were collected from a number of sources.Arms-length bare land transaction data were obtained from the provincial assessor withManitoba Local Government, the provincial department responsible for assessing ruralproperty and maintaining land transaction records. The transaction data documents thequarter sections involved in the sale, the date the sale occurred, and the sale price. Theper acre sale price is calculated as the sale price divided by total acreage in the sale. Thetransaction data also documents land use within each quarter section, including cultivatedand noncultivated acreage in slough, brush, hay, pasture, waterways, and other uses suchas roads and ditches.11

Quarter section-level soil productivity indices as assessed by the MASC are usedto control for agricultural capability. Soil survey reports conducted in the 1930s andlong-term average yields for each soil type form the basis of the MASC productivityindices. Soil productivity indices range from A to J, where A denotes the most productiveland and J denotes the least productive land. The productivity index is a composite ofmultiple factors, including soil texture, organic matter, water-holding capacity, topsoildepth, salinity, soil erosion, topography, and stoniness. I classify A, B, and C as highproductivity soil, D, E, and F as medium productivity soil, and G to J as low productivitysoil.

I control for spatially correlated unobservables using a large number of geographiccontrol variables. I include the mean longitude and latitude (in meters, based on thecentroids of each quarter section) of each sales parcel to control for unobserved geographicfactors that might influence the agricultural productivity of a sales parcel. Crop insurancerisk zones categorized by MASC are used to control for spatially correlated risk factors

9 It is also possible that conversion costs for some habitat, such as wetlands, has increased due tolack of maintenance of local area drainage network infrastructure and increased drainage activityon upstream acreage.10 As habitat is converted over time, the average cost of converting the remaining at-risk habitat willalso change. Since habitat is converted according to the net benefit of conversion, the remaininghabitat may have a higher or a lower average cost of conversion when compared to recently convertedhabitat.11 Slough and combinations of slough, hay, and bush are grouped together as wetlands in thisanalysis. Bush/pasture and native hay are also separate categories, comprising the grasslands orvegetated areas surrounding prairie potholes.


Note: Study region is the gridded area. The grid indicates townships.

Figure 2. Study area

such as drought, frost, excess heat, excess moisture, and risk of insect, disease, and weedinfestation. These risk zones are used in combination with the soil productivity indiceswhen setting crop insurance premiums and coverage levels. I also include the averagedistance of each sale parcel to the nearest large elevator (defined as having an annualcapacity in excess of 25,000 tons), as well as average distances to the cities of Brandonand Portage la Prairie, the two largest urban centers in the study area. Agricultural landvalue is expected to decrease as the distance to a major grain elevator increases. Distancesto urban centers of Brandon and Portage la Prairie are included to control for urbandevelopment pressure and the value of access to urban amenities.

Finally, year dummy variables are included to control for year specific unobservedfactors influencing land prices that are common across the study region, includingmacroeconomic factors, such as interest rates, exchange rates, and crop and livestockprices.


Table 1. Summary statistics

1990–93 1994–97 1998–2001 2002–05 2006–09

Mean SD Mean SD Mean SD Mean SD Mean SD

Per acre sale price 281.34 178.30 329.82 213.77 408.50 323.49 460.46 360.84 560.17 485.22Wetland 12.77 17.91 12.64 17.48 11.81 16.23 11.62 16.69 11.36 17.13Bush/pasture 9.32 23.20 8.48 21.74 9.23 21.98 8.99 22.50 11.31 24.69Native hay 9.03 22.16 8.39 21.50 9.21 21.51 7.89 19.81 8.72 20.99Other acreage 4.63 13.11 4.45 12.91 3.75 11.92 2.96 10.00 2.92 10.73High prod soil 21.93 40.77 22.84 41.52 20.70 39.79 22.03 40.91 21.98 40.59Med prod soil 41.55 48.33 43.66 48.55 44.50 48.67 46.56 48.78 42.83 48.21Mean latitude 5514.39 55.08 5517.70 55.49 5513.66 54.85 5515.32 55.87 5518.46 55.27Mean longitude 432.00 62.83 429.87 63.13 427.29 64.36 431.65 65.26 435.38 64.45log(dist to elevator) 0.010 0.001 0.010 0.001 0.010 0.001 0.010 0.001 0.010 0.001log(dist to Brandon) 0.011 0.001 0.011 0.001 0.011 0.001 0.011 0.001 0.011 0.001log(dist to Portage) 0.012 0.001 0.012 0.001 0.012 0.001 0.012 0.001 0.012 0.001log(elevation) 6.10 0.20 6.11 0.21 6.11 0.20 6.10 0.21 6.08 0.21Total sale acres 209.56 130.35 219.73 143.13 227.23 157.14 233.37 163.59 234.99 168.84Observations 2,471 2,609 2,044 1,987 1,791

Notes: Per acre sale price is in 2002 CAD. High productivity soil and medium productivity soilare percent of sale acreage (low productivity soil is the omitted category). Wetland, bush/pasture,native hay, and other acreage are percent of sale acreage (cultivated acreage is the omitted category).Mean latitude and longitude are measured in kilometers. Distances and elevation are measured inmeters.

Descriptive statistics are presented in Table 1. The real per acre sale price (in 2002CAD) roughly doubled from 1990–93 to 2006–09. Just over 20% of the parcels soldin the study region have high productivity soils and approximately 40% have mediumproductivity soils. More than 11% of the land area of each sale parcel is in wetland.The wetland share tended to decrease over time, perhaps reflecting wetland drainage, atendency for parcels with less wetland acreage to sell in the later part of the study period,or a combination of the two. Acreage in bush and pasture is approximately 10% whileacreage in native hay tends to be just under 9%. Roughly 20% of land area sold is allocatedto native vegetative cover. There appears to be a trend towards larger average total salesacres, starting at 210 acres in the 1990–93 period and increasing to 235 as of 2006–09. Asexpected, the geographic features of sales parcels capturing factors such as distance tocities and elevators, and mean latitude and longitude do not vary substantially over time.

RESULTS

Ordinary Least SquaresResults from the ordinary least squares regressions for each year as specified in Equations(1) and (2) are presented in Tables 2 and 3, respectively. The explanatory variables in thelinear models explain between 27% and 43% of the variation in per acre sales values.Overall, the fit of the semi-log models is somewhat better; they explain 43–56% of thevariation in the log of per acre sales price. Although not reported in the tables, both


Tab

le2.

Lin

ear

leas

tsq

uare

she

doni

cm

odel

1990

–93

1994

–97

1998

–200

120

02–0

520

06–0

9

Coe

f.St

d.E

rr.

Coe

f.St

d.E

rr.

Coe

f.St

d.E

rr.

Coe

f.St

d.E

rr.

Coe

f.E

rr.

Wet

land

−2.

730.

52**

*−

3.22

0.47

***

−4.

560.

85**

*−

4.93

1.47

***

−7.

091.

94**

*B

ush/

past

ure

−1.

970.

46**

*−

1.97

0.62

**−

2.75

0.53

***

−3.

400.

84**

*−

4.81

1.25

***

Nat

ive

hay

−1.

480.

43**

*−

1.60

0.35

***

−1.

610.

48**

*−

2.79

0.76

***

−3.

270.

66**

*O

ther

acre

age

−1.

340.

51**

−1.

490.

62**

−1.

390.

68*

−2.

931.

44*

−3.

752.

98H

igh

prod

soil

1.55

0.53

**1.

630.

41**

*2.

350.

82**

2.57

0.73

***

2.83

0.99

**M

edpr

odso

il0.

560.

12**

*0.

610.

15**

*1.

270.

44**

0.83

0.40

*0.

870.

35**

Mea

nla

titu

de−

0.36

0.38

−0.

270.

40−

0.19

0.43

0.21

0.39

0.89

0.89

Mea

nlo

ngit

ude

−0.

550.

82−

0.37

0.76

−0.

031.

15−

0.47

1.15

1.40

2.06

log(

dist

toel

evat

or)

−121

430

743

9488

1524

525

063

2642

774

0251

930

4598

361

688

log(

dist

toB

rand

on)

−238

2138

623

−569

6051

095

−105

404

7721

7−7

6958

9961

4−1

7463

915

6950

log(

dist

toPo

rtag

e)−1

4174

559

633*

*−1

4474

051

682*

*−2

3281

274

316*

*−4

2339

089

824*

**−4

0559

411

5429

***

log(

elev

atio

n)17

013

711

812

889

149

254

159

395

337

Tota

lsal

eac

res

−0.

100.

03**

−0.

150.

03**

*−

0.13

0.03

***

−0.

130.

06*

−0.

150.

05**

R2

0.43

0.34

0.27

0.40

0.41

Obs

erva

tion

s2,

471

2,60

92,

044

1,98

71,

791

Not

es:C

oeff

icie

ntsa

refr

oman

ordi

nary

leas

tsqu

ares

regr

essi

onw

here

the

depe

nden

tvar

iabl

eis

the

pera

cre

sale

spri

ce(2

002

CA

D)a

ndad

diti

onal

inde

pend

ent

vari

able

sin

clud

eM

ASC

risk

zone

dum

my

vari

able

san

dye

ardu

mm

yva

riab

les.

Rob

ust

stan

dard

erro

rsad

just

edfo

rcl

uste

ring

byM

ASC

risk

zone

sar

ere

port

ed.T

hesy

mbo

ls*,

**,a

nd**

*on

the

stan

dard

erro

rsin

dica

teth

atth

eco

effi

cien

tis

stat

isti

cally

diff

eren

tfr

om0

atth

e10

,5,a

nd1%

leve

l,re

spec

tive

ly.


Tab

le3.

Sem

i-lo

gle

ast

squa

res

hedo

nic

mod

el

1990

–93

1994

–97

1998

–01

2002

–05

2006

–09

Coe

f.St

d.E

rr.

Coe

f.St

d.E

rr.

Coe

f.St

d.E

rr.

Coe

f.St

d.E

rr.

Coe

f.St

d.E

rr.

Wet

land

−0.

0105

0.00

11**

*−

0.01

050.

0011

***

−0.

0115

0.00

14**

*−

0.01

150.

0021

***

−0.

0146

0.00

16**

*B

ush/

past

ure

−0.

0084

0.00

13**

*−

0.00

750.

0014

***

−0.

0076

0.00

13**

*−

0.00

850.

0008

***

−0.

0090

0.00

11**

*N

ativ

eha

y−

0.00

590.

0013

***

−0.

0060

0.00

09**

*−

0.00

480.

0008

***

−0.

0067

0.00

11**

*−

0.00

760.

0009

***

Oth

erac

reag

e−

0.00

490.

0015

**−

0.00

480.

0015

**−

0.00

370.

0011

**−

0.00

650.

0021

**−

0.00

820.

0037

*H

igh

prod

soil

0.00

460.

0010

***

0.00

440.

0007

***

0.00

430.

0013

**0.

0041

0.00

10**

*0.

0037

0.00

13**

Med

prod

soil

0.00

250.

0004

***

0.00

210.

0005

***

0.00

260.

0009

**0.

0017

0.00

06**

0.00

170.

0007

**M

ean

lati

tude

−0.

0022

0.00

09**

−0.

0017

0.00

09*

−0.

0009

0.00

09−

0.00

060.

0008

0.00

040.

0010

Mea

nlo

ngit

ude

−0.

0022

0.00

19−

0.00

130.

0017

−0.

0008

0.00

24−

0.00

030.

0022

0.00

220.

0029

log(

dist

toel

evat

or)

−13

.68

82.2

418

.76

33.2

017

.58

42.1

912

.12

62.9

729

.74

64.4

4lo

g(di

stto

Bra

ndon

)−

89.0

711

2.84

−15

8.25

104.

71−

180.

5611

0.63

−14

9.30

123.

77−

162.

6316

5.23

log(

dist

toPo

rtag

e)−

406.

7515

9.21

**−

369.

2211

8.74

**−

372.

7714

2.32

**−

569.

0914

5.35

***

−40

4.28

188.

27*

log(

elev

atio

n)0.

3996

0.27

770.

3706

0.22

540.

1842

0.22

600.

3174

0.21

300.

3791

0.39

23To

tals

ale

acre

s−

0.00

020.

0001

**−

0.00

030.

0001

***

−0.

0002

0.00

01**

−0.

0002

0.00

01**

−0.

0002

0.00

01**

*R

20.

510.

480.

430.

510.

56O

bser

vati

ons

2,47

12,

609

2,04

41,

987

1,79

1

Not

es:C

oeff

icie

nts

are

from

anor

dina

ryle

ast

squa

res

regr

essi

onw

here

the

depe

nden

tva

riab

leis

the

loga

rith

mof

the

per

acre

sale

spr

ice

(200

2C

AD

)and

addi

tion

alin

depe

nden

tvar

iabl

esin

clud

eM

ASC

risk

zone

dum

my

vari

able

sand

year

dum

my

vari

able

s.R

obus

tsta

ndar

der

rors

adju

sted

for

clus

teri

ngby

MA

SCri

skzo

nes

are

repo

rted

.T

hesy

mbo

ls*,

**,

and

***

onth

est

anda

rder

rors

indi

cate

that

the

coef

fici

ent

isst

atis

tica

llydi

ffer

ent

from

0at

the

10,5

,and

1%le

vel,

resp

ecti

vely

.


models include controls for MASC crop insurance risk zones and year dummies. Jointtests that the year dummy variables (within each four-year interval) are identical produceF-statistics ranging from 3.9 to 22.3, which correspond to p-values less than 0.01. Thissuggests that the year dummy variables are not identical and are picking up temporaltrends within the four-year intervals common to all sales parcels. The crop insurance riskzones are included to control for spatially correlated unobservables that might influenceland values. Joint tests that the coefficients on the eight crop insurance risk zone dummyvariables are identical yield F-statistics ranging from 12.1 to 28.9, suggesting that the riskzones are picking up important spatially correlated determinants of land values.

As indicated in Tables 2 and 3, habitat acreage reduces per acre sales values. In1998–2001 a 1% increase in wetland acreage reduced the per acre sales price by 1.15%.Based on the estimates in Table 2, a parcel with a 20 acre wetland (roughly the averagesize among 1998–2001 sales) on a 160 acre parcel sold at a $9,120 discount relative toan otherwise identical sales parcel with no wetland acreage. In 1998–2001 parcels withbush/pasture acreage also sold at a discount. Expressed as an elasticity, a 1% increase inbush/pasture acreage reduced per acre sales values by 0.76%. From Table 2, this translatesinto a discount of approximately $4,125 for a sales parcel with 15 acres of bush/pasturecompared to a sales parcel with no bush/pasture acreage. Finally, native hay imposesthe lowest discount on sales values: in 1998–2001 a 1% increase in native hay acreagereduced per acre sales values by 0.48%. The relatively low discount on native hay suggeststhat the returns on native hay acreage are higher than the returns on bush/pasture andwetland acreage. Further, the costs of conversion for native hay land are generally lowerthan conversion costs of bush and wetlands.

A number of recent papers examine the net benefits and costs of wetland drainageand upland conversion. Cortus et al (2011) present a number of farm-level simulationestimates of the net benefit of draining wetlands in Saskatchewan across a number ofrepresentative farms that vary by size of farm operation. Their results suggest the averageannual net benefit of converting wetlands that are at risk of conversion ranges from$129 per acre to $189 per acre of wetland drained (converted to 2002 CAD). Based onfarmer survey data from two regions of Saskatchewan, Yu and Belcher (2011) find thatthe average annual payment farmers would be willing to accept to conserve riparianvegetation ranges from $29 per acre to $31 per acre.

Gelso et al (2008) estimate the total benefit of converting wetlands (the cost ofretaining) based on a stated preference survey of landowners in Kansas. The Gelsoet al study (2008) accounts for a variety of wetland characteristics including number ofwetlands, frequency of hydration, and dispersion. Their base estimate suggests that a 1%increase in wetland coverage increases the annual rental discount of the land (the annualbenefit of draining the wetland) by $0.80 per acre (converted from 2008 USD to 2002CAD). Gelso et al (2008) find that the various characteristics of wetlands as described intheir survey instrument can affect the benefits of drainage in a nonlinear and irregularfashion.

I take an alternative, but complementary, revealed preference approach to gener-ate lower bound estimates of the benefits of habitat conversion (or the costs of habitatrestoration). Due to data limitations, the analysis in this paper ignores heterogeneityin wetland characteristics such as frequency of hydration and dispersion. The estimatespresented in this paper imply the lower bound on the annualized value of the average


benefits of reducing wetland acreage by 1% is approximately $0.71 per acre (assuminga 10% discount rate) or $0.35 per acre (assuming a 5% discount rate) in the 2006–09 period. Although not directly comparable, the lower bound estimates for Mani-toba are below the benefits of wetland drainage in Kansas as presented in Gelso et al(2008).

Consistent with expectations, Tables 2 and 3 indicate that parcels with higher pro-ductivity soil tend to sell at a premium. At the midpoint of the study—the 1998–2001period—a 1% increase in high productivity soil acreage increased the sales price by 0.43%and a 1% increase in medium productivity soil increased the sales price by 0.26%. Bothof these elasticity estimates are statistically different from zero. To put this in perspective,the results in Table 2 indicate that a 160 acre sales parcel with high productivity soil soldfor an average premium of $37,600 over an equivalent parcel with low productivity soiland an average premium of $20,320 over a parcel with medium productivity soil.

Greater distance to Portage la Prairie tends to reduce land sales values. Portagela Prairie is located within 100 km of Winnipeg, the major urban center in Manitoba.This result is therefore likely the result of moderate urban development pressure, greaterdemand for recreational services from the land, and the benefits of convenient access tothe urban amenities provided by Winnipeg and Portage la Prairie. Distance to Brandonand major grain elevators do not have a statistically significant impact on land valuesand sales parcels at higher elevation do not sell for a premium. Finally, per acre salesprices tend to be lower on larger parcels, a result that is consistent with previous hedonicagricultural land value research (Featherstone et al 1993; Xu et al 1993; Tsoodle et al2006).

Individually, mean latitude and longitude tend to have an insignificant impact onland values in the linear model. In the semi-log hedonic model presented in Table 3 theresults suggest that parcels sold at a higher latitude tend to sell at a discount in the earlierperiods in the study. By the end of the study the mean latitude has little effect on salesvalues. This may be a function of adaptive measures taken to combat the relatively shortergrowing season in the northern portion of the province, including adoption of new seedvarieties or increased canola acreage.12 It may also reflect changing expectations aboutthe climatic conditions in the northern relative to the southern portion of the study region.After controlling for MASC risk zones and distance from Portage la Prairie (which is onthe eastern fringe of the study region) longitude does not have a statistically significantimpact on agricultural land values.

Changes in Implicit PricesChanges in the implicit prices of sales parcel characteristics are of primary interest in thispaper. Tests of the statistical significance of changes in the implicit prices in each period,relative to the 1990–93 period, are presented in Table 4. I conduct these tests by poolingobservations from the 1990–93 period with each of the later periods. I include interactionterms between the independent variables and dummy variables for the later periods. Thestatistical test of the significance of the change in implicit prices across periods is based

12 The frost-free period in the northern portion of the study region ranges from 95 to 105 days,compared to the frost-free period in the southern portion which ranges from 105 to 115 days. Thefrost-free period in the eastern portion of the study region can be between 115 and 125 days.


Table 4. Changes in implicit prices relative to 1990–93 (Semi-log model)

Robust clustered Robust standardDifference standard errors errors

1990–93 to 1994–97Wetland − 0.0001 0.0009 0.0008Bush/pasture 0.0009 0.0013 0.0008Native hay − 0.0001 0.0007 0.0007High productivity soil − 0.0003 0.0005 0.0004Medium productivity soil − 0.0004 0.0004 0.0004Latitude 0.0004 0.0006 0.0006Longitude 0.0010 0.0006 0.0007

1990–93 to 1998–2001Wetland − 0.0011 0.0011 0.0010Bush/pasture 0.0008 0.0011 0.0008Native hay 0.0011 0.0010 0.0008High productivity soil − 0.0003 0.0007 0.0006Medium productivity soil 0.0001 0.0007 0.0004Latitude 0.0012 0.0007 0.0006**Longitude 0.0014 0.0006* 0.0008*

1990–93 to 2002–05Wetland − 0.0010 0.0017 0.0011Bush/pasture − 0.0001 0.0014 0.0008Native hay − 0.0008 0.0006 0.0007High productivity soil 0.0005 0.0008 0.0006Medium productivity soil − 0.0008 0.0005 0.0004*Latitude 0.0016 0.0012 0.0006***Longitude 0.0020 0.0004*** 0.0008**

1990–93 to 2006–09Wetland − 0.0042 0.0012*** 0.0009***Bush/pasture − 0.0006 0.0020 0.0008Native hay − 0.0018 0.0010 0.0009**High productivity soil − 0.0009 0.0007 0.0006Medium productivity soil − 0.0007 0.0006 0.0005Latitude 0.0025 0.0010** 0.0007***Longitude 0.0045 0.0014** 0.0009***

Notes: Differences are the coefficients from a pooled ordinary least squares regression where thedependent variable is the logarithm of the per acre sales price (2002 CAD) and all independentvariables are interacted with the later time interval dummy variables (1994–97, 1998–2001, 2002–05,and 2006–09). Additional independent variables include high productivity soil, medium productivitysoil, other acreage, log(distance to elevator), log(distance to Brandon), log(distance to Portage laPrairie), log(elevation), total sale acres, mean latitude, mean longitude, and dummy variables forMASC risk zones and years. The symbols *, **, and *** on the standard errors indicate that thecoefficient is statistically different from 0 at the 10, 5, and 1% level, respectively.


on t-statistics on the interaction terms for the characteristics of interest, including soilproductivity, habitat acreage, and longitude and latitude of the sales parcels. Table 4presents robust standard errors adjusted for clustering by MASC risk zones as wellas heteroskedasticity corrected standard errors. Consistent with previous research, theheteroskedasticity corrected standard errors tend to be smaller than the robust standarderrors adjusted for clustering (Costa and Kahn 2004).

A number of interesting results emerge. The absolute discount on habitat acreageincreases for all three categories of habitat. In percentage terms the discount on wetlandacreage increased but appeared to stay the same for bush/pasture acreage. The lowerbound estimate of the increase in the discount on wetland acreage from 1990–93 to 2006–09 is statistically and economically significant, increasing by 40% over the 20 year periodwhen expressed as an elasticity.13 There is weak evidence that the discount on native hayincreased over the course of the study period. In elasticity terms the discount on nativehay acreage increased by 30% although the statistical significance of this estimate is weak.

Note that the point estimates of the wetland discounts have increased relative to1990–93 for all periods prior to 2006–09 (see Table 4), although they are not statisticallysignificant. A number of factors might cause the wetland discount to increase over time,including adoption of increasingly large farm machinery that increases the nuisance costsof wetlands, a sustained increase in precipitation during a wet cycle that increases waterlevels in potholes, as well as agronomic advances that increase annual crop productivityon marginal land. Nonetheless, the change in the discount is most dramatic in the 2006–09 period, corresponding to the recent increases in crop prices. This suggests that theprice surge shifted land buyers and sellers expectations about future prices, increaseduncertainty about future crop prices, or a combination of the two.

Although the absolute premium on high and medium productivity soil increasedbetween 1990–93 and 2006–09, when expressed as a percent of the per acre sales valuethe premiums did not change. It therefore appears as though appreciation in land valuesis constant across land with different productivity ratings. The impact of longitude andlatitude on sales values does change through the study period. As of 1998–2001, parcelsfarther east tend to increase in value relative to western parcels. There is weak evidencethat parcels that are farther north increased in value relative to southern parcels in 1998–2001 and the 2002–05 periods. By 2006–09 there is strong evidence that parcels to thenorth increased in value relative to parcels in the south. As mentioned earlier, this mightreflect increased ability to adapt to the shorter growing season in the north or may reflectanticipated changes in climate.

Quantile RegressionsIt is possible that the land value discounts due to habitat acreage vary at different points ofthe land value distribution. For example, wetland acreage on higher productivity land bestsuited for annual crop production might impose a larger discount than wetland acreage onlower productivity land that is best suited for livestock production and recreational uses.Land values also capture other sources of unobserved heterogeneity that may influence the

13 From Table 3, in 1990–93 the wetland discount is estimated at −1.05%. From Table 4, the changebetween 1990–93 and 2006–09 is −0.42%, which is a 40% increase in the discount ((0.42/1.05)*100= 40%).


habitat acreage discount. If this is the case, the estimated discounts due to wetland acreagewill vary across the land value distribution. Further, changes in the land value discountdue to habitat may have been concentrated in one point of the land value distribution. Iuse a series of quantile regression models to assess whether or not the estimated discountsvary systematically by land values.

Quantile regression results for habitat acreage, based on the log per acre sales pricemodel in Equation (2), are presented in Table 5.14 The quantile regressions are estimatedat three representative land value quantiles: 25%, 50%, and 75%. These estimates are lesssensitive to potential outliers (compared to ordinary least squares estimates) and provideadditional information on changes in the implicit prices of prairie pothole habitat acrossthe distribution of land values.

As displayed in Figure 3, the discount on wetland acreage increases over the courseof the study period for all three percentiles in the land value distribution presented inTable 5. This suggests that the increase in the discount on wetland acreage reported inTable 4 is not concentrated in one part of the land value distribution. Rather, the discountappears to follow roughly the same trend for each of the three representative land valuepercentiles. These results indicate that the land value discount due to habitat acreage canbe based off of land values. The results also suggest that the land value discount does notvary by soil productivity, when expressed as a percent change in per acre sales price.

These estimates can be compared to the one-time payments for wetland restoration inManitoba under the Wetland Restoration Incentive Program (WRIP) established in 2008.The WRIP is delivered by Manitoba Conservation, with cooperation from the ManitobaHabitat Heritage Corporation and Ducks Unlimited Canada. Manitoba Conservationcovers the initial cost of wetland restoration and offers a one-time wetland restorationpayment of $200 per acre of wetland restored. Conservation agencies then place a per-petual conservation easement on the restored wetland, typically at a payment of 80–95%of assessed value. On average, landowners received roughly $370 per acre of restoredwetland.

As of 2006–09 per acre land prices at the, 25th, 50th, and 75th percentiles are $273,$405, and $685, respectively. Landowners are required to restore a minimum of 40 acresin wetland and surrounding upland. Based on the estimated parameters presented inTable 5 and assuming a 50/50 mix of wetland and bush/pasture, the discount on landvalues due to 40 acres of habitat restoration on a quarter section at the 25th percentileworks out to $9,430 (2008 CAD) due to the 20 wetland acres and $6,280 (2008 CAD)due to the 20 bush/pasture acres. Overall, the discount is $393 (2008 CAD) per acre ofhabitat restored. At a payment of roughly $370 per acre, this suggests that the WRIP istargeting land below the 25th percentile per acre land value in this study region.

The results suggest that the benefits of converting habitat (or the costs of restoringhabitat) appear to be a constant percentage of land values. The reserve site selectionliterature often uses land values to proxy the opportunity costs of habitat conservation(examples include Ando et al 1998; Polasky et al 2001; Ando and Mallory 2012). Theresults in this paper suggest that basing estimates of the opportunity costs of habitat

14 Coefficient estimates for all of the independent variables included in the quantile regressions arenot presented in Table 5. The coefficient estimates for the independent variables are similar to thoseobtained from the ordinary least squares regressions presented in Table 3.


Tab

le5.

Sem

i-lo

gqu

anti

lere

gres

sion

Hed

onic

mod

el

1990

–93

1994

–97

1998

–200

120

02–0

520

06–0

9

Coe

f.St

d.E

rr.

Coe

f.St

d.E

rr.

Coe

f.St

d.E

rr.

Coe

f.St

d.E

rr.

Coe

f.St

d.E

rr.

25th

perc

enti

leW

etla

nd−

0.01

10.

001*

**−

0.01

00.

001*

**−

0.01

20.

001*

**−

0.01

40.

001*

**−

0.01

50.

001*

**B

ush/

past

ure

−0.

009

0.00

0***

−0.

009

0.00

0***

−0.

008

0.00

1***

−0.

009

0.00

0***

−0.

010

0.00

1***

Nat

ive

hay

−0.

006

0.00

1***

−0.

006

0.00

0***

−0.

005

0.00

1***

−0.

005

0.00

1***

−0.

008

0.00

1***

R2

0.32

0.33

0.28

0.30

0.35

50th

perc

enti

leW

etla

nd−

0.01

10.

001*

**−

0.01

10.

000*

**−

0.01

30.

001*

**−

0.01

30.

001*

**−

0.01

50.

001*

**B

ush/

past

ure

−0.

009

0.00

0***

−0.

009

0.00

0***

−0.

009

0.00

1***

−0.

009

0.00

0***

−0.

009

0.00

1***

Nat

ive

hay

−0.

005

0.00

0***

−0.

007

0.00

0***

−0.

006

0.00

1***

−0.

006

0.00

0***

−0.

008

0.00

1***

R2

0.33

0.33

0.30

0.35

0.39

75th

perc

enti

leW

etla

nd−

0.01

00.

001*

**−

0.01

00.

001*

**−

0.01

20.

001*

**−

0.01

10.

001*

**−

0.01

50.

001*

**B

ush/

past

ure

−0.

008

0.00

0***

−0.

007

0.00

0***

−0.

007

0.00

1***

−0.

009

0.00

1***

−0.

008

0.00

1***

Nat

ive

hay

−0.

005

0.00

0***

−0.

005

0.00

0***

−0.

004

0.00

1***

−0.

006

0.00

1***

−0.

007

0.00

1***

R2

0.35

0.33

0.31

0.37

0.40

Obs

erva

tion

s2,

471

2,60

92,

044

1,98

71,

791

Not

es:

Coe

ffic

ient

sar

efr

omqu

anti

lere

gres

sion

sw

here

the

depe

nden

tva

riab

leis

the

loga

rith

mof

the

per

acre

sale

spr

ice

(200

2C

AD

)an

dad

diti

onal

inde

pend

entv

aria

bles

incl

ude

high

prod

ucti

vity

soil,

med

ium

prod

ucti

vity

soil,

othe

rac

reag

e,lo

g(di

stan

ceto

elev

ator

),lo

g(di

stan

ceto

Bra

ndon

),lo

g(di

stan

ceto

Port

age

laP

rair

ie),

log(

elev

atio

n),t

otal

sale

acre

s,m

ean

lati

tude

,mea

nlo

ngit

ude,

and

dum

my

vari

able

sfo

rM

ASC

risk

zone

san

dye

ars)

.The

sym

bol*

**on

the

stan

dard

erro

rsin

dica

tes

that

the

coef

fici

ent

isst

atis

tica

llydi

ffer

ent

from

0at

the

1%le

vels

.

IMPLICIT PRICES OF PRAIRIE POTHOLE HABITAT 187-1

.5-1

-0.5

90-93 94-97 98-01 02-05 06-09Sale period

25th percentile

-1.5

-0.5

-1

90-93 94-97 98-01 02-05 06-09Sale period

50th percentile

-1.5

-0.5

-1

90-93 94-97 98-01 02-05 06-09Sale period

75th percentile

Native Hay Bush/Pasture Wetland

Figure 3. Semi-log quantile regression estimates of habitat discounts

restoration (the benefits of conversion) on land values is a reasonable approximation.The opportunity costs of habitat retention, on the other hand, depend on the net benefitof converting habitat, thus taking into account both the benefits and costs of conversion.Since the relationship between the net benefits of habitat conversion and land values isunknown, the use of land values as a proxy for opportunity costs of retention may beinappropriate. Further research into the factors determining the cost of converting habitatwould be worthwhile.

POLICY IMPLICATIONS

These results have implications for the design of habitat conservation programs. Habi-tat protection in the prairie pothole region of both the United States and Canada hastended to rely on payments to landowners in exchange for short or long-term agreementsto maintain or to restore habitat.15 Perpetual habitat conservation easements—such asthose signed by the Fish and Wildlife Service (FWS) in the United States and public orprivate conservation agencies in western Canada—have emerged as a prominent formof conservation in the region. Landowners receive a one-time payment as compensationfor signing a permanent easement that removes the right to convert existing habitat.Landowners voluntarily enter into easements and subsequent land buyers can negotiatereductions in the sales prices of encumbered parcels. From the conservation agencies

15 Brown (1976) conducts an early study of the impact of FWS easements on land values in theprairie pothole region of the United States. Shultz and Taff (2004) examine the impact of FWSwetland easements on land values in a tri-county region of North Dakota between 1995 and2001. Shultz and Taff (2004) focus on the impact of wetland easements on land values, providingevidence that noneased permanent wetlands reduce the average sales price by US $161 or a US$0.82 reduction in per acre sales price across the average 197 acre parcel. Lawley and Towe (2013)provide estimates of the impact of habitat conservation easements on land values in Manitoba. Thefocus of these papers is the extent to which retaining the option to convert habitat (the net benefitof converting habitat) is capitalized into land values. These analyses are presumably restricted tohabitat that is at risk of conversion.


perspective, perpetual agreements secure habitat for the long term and, aside from moni-toring and enforcement costs, conservation easements do not require ongoing paymentsto landowners.

Recent experience with the Conservation Reserve Program (CRP)—a temporaryeasement program—demonstrates that re-enrolling habitat after expiry of the 10–15 yearcontracts can be an expensive proposition in a period of increasing crop prices (Streitfeld2008). Hellerstein and Malcom (2011) present evidence suggesting that if crop prices weremaintained at their 2007 levels CRP rental rates would have to increase by 60% to offsetthe long-term impact of higher crop prices. Perpetual agreements, such as conservationeasements on existing habitat, protect conservation agencies from unanticipated futureincreases in the net benefits of converting habitat. However, substantial unanticipatedincreases in the benefits of converting habitat imply that perpetual easements will becomeincreasingly costly to enforce in the future. Further, large unanticipated shocks to the dis-count on wetland acreage estimated in this study suggest that large uncertainty premiumsare built into the current one-time payments. Conservation agencies might realize savingsby signing more habitat acreage into shorter duration contracts that require smaller un-certainty premiums. Of course, protecting habitat for the long term is preferred for thoseecological functions that take a long time to re-establish if lost. Shorter duration con-tracts also require ongoing sources of funding that may not be available to conservationagencies.

CONCLUDING REMARKS

The results presented in this paper indicate that the discount on wetland acreage hasincreased by 40% over the past 20 years, consistent with large unanticipated changesin the benefits of converting habitat. The discount on other types of habitat such asbush/pasture and native hay appears to have stayed relatively constant over the sameperiod. This result suggests that conservation easements on wetlands may become morecostly to enforce in the future. The quantile regression results indicate that conservationpayments based on land values reflect heterogeneity in the opportunity costs of restoringhabitat. However, the results do not suggest that payments to maintain habitat can bebased solely on land values. From the landowners perspective, the cost of maintaininghabitat depends on the net benefits of converting, whereas the discount estimated herecan be interpreted as a lower bound estimate of the total benefits of converting. Withoutsome means of assessing the costs of converting habitat, payments based on land valuesmay not be targeting existing habitat with the highest risk of conversion.

Future research into the costs of converting wetlands would permit a more detailedrevealed preference study identifying habitat at risk of conversion. Indeed, revealed pref-erence studies of the drivers of habitat conversion in western Canada are constrainedby the lack of reliable information on land use change at a level of detail finer thanthe Census Agricultural Region.16 Research in this area would benefit from a detailedinventory of prairie pothole and associated upland, documenting quarter section-level

16 Rashford et al (2011) estimate an econometric model of land use change in western Canada,advancing the literature on land use change by considering a large number of possible crop choices.However, data limitations restricted the analysis to the Census Agricultural Region level, thereforesmoothing out much of the spatial variation in land use choices.


characteristics and the changing status of wetlands and uplands over time. A wetlandinventory with sufficient detail would allow researchers to generate spatially explicit esti-mates of the risks of wetland conversion, estimates of the payments needed to maintain orto restore wetlands, or the costs imposed on landowners due to increased government re-strictions on wetland drainage. Spatially explicit information on the private net benefits ofwetland drainage would also facilitate targeted enforcement of possible wetland drainagerestrictions imposed by direct government regulation or habitat conservation easements.

ACKNOWLEDGMENTS

The author thanks the Linking Environment and Agriculture Research Network (LEARN) andthe Social Sciences and Humanities Research Council (SSHRC) of Canada for funding that as-sisted in conducting this research. The author also thanks Derek Brewin and Ryan Cardwell forhelpful comments and suggestions, the Manitoba Provincial Assessor for access to Manitoba landtransactions data, and Brian Miller for GIS research assistance.

REFERENCES

Ando, A., J. Camm, S. Polasky and A. Solow. 1998. Species distributions, land values, and efficientconservation. Science 279: 2126–28.Ando, A. W. and M. L. Mallory. 2012. Optimal portfolio design to reduce climate-related conserva-tion uncertainty in the prairie pothole region. Proceedings of the National Academy of Science 109(17): 6484–89.Brown, R. J. 1976. A study of the impact of the wetlands easement program on agricultural landvalues. Land Economics 52 (4): 509–17.Cortus, B. G., S. R. Jeffrey, J. R. Underschultz and P. C. Boxall. 2011. The economics of wetlanddrainage and retention in Saskatchewan. Canadian Journal of Agricultural Economics 59 (1): 109–26.Costa, D. L., and M. E. Kahn. 2004. Changes in the value of life, 1940–1980. Journal of Risk andUncertainty 29 (2): 159–80.Dahl, T. E. and M. D. Watmough. 2007. Current approaches to wetland status and trends monitoringin prairie Canada and the continental United States of America. Canadian Journal of RemoteSensing 33 (1): S17–S27.Euliss, N. H., R. A. Gleason, A. Olness, R. L. McDougal, H. R. Murkin, R. D. Roberts, R. A.Bourbonniere and B. G. Warner. 2006. North American prairie wetlands are important nonforestedland-based carbon storage sites. Science of the Total Environment 361: 179–88.Featherstone, A. M., B. W. Schurle, S. S. Duncan and K. D. Postier. 1993. Clearance sales in thefarmland market. Journal of Agricultural and Resource Economics 18 (2): 160–74.Gelso, B. R., J. A. Fox and J. M. Peterson. 2008. Farmers’ perceived costs of wetlands: Effects ofwetland size, hydration, and dispersion. American Journal of Agricultural Economics 90 (1): 172–85.Government Accounting Office (GAO). 2007. Prairie pothole region: At the current pace of ac-quisitions the U.S. Fish and Wildlife Service is unlikely to achieve its habitat protection goals formigratory birds. Report no. GAO-07-1093. Washington, DC: GAO, September.Hellerstein, D. and S. Malcolm. 2011. The influence of rising commodity prices on the conservationreserve program. ERR-110. Washington, DC: U.S. Department of Agriculture, Economic ResearchService, February. http://www.ers.usda.gov/media/131209/err110.pdf (accessed June 29, 2012).Lawley, C. and C. Towe. 2013. Capitalized costs of habitat conservation easements. AmericanJournal of Agricultural Economics, forthcoming.McMillen, D. P. 2008. Changes in the distribution of houses prices over time: Structural character-istics, neighborhood, or coefficients. Journal of Urban Economics 64 (3): 573–89.


Miranowski, J. A. and B. D. Hammes. 1984. Implicit prices of soil characteristics for farmland inIowa. American Journal of Agricultural Economics 66 (5): 745–49.Palmquist, R. B. and L. E. Danielson. 1989. A hedonic study of the effects of erosion control anddrainage on farmland values. American Journal of Agricultural Economics 71 (1): 55–62.Polasky, S., J. D. Camm and B. Garber-Yonts. 2001. Selecting biological reserves cost-effectively:An application to terrestrial vertebrate conservation in Oregon. Land Economics 77 (1): 68–78.Pope, J. C. 2008. Do seller disclosures affect property values? Buyer information and the hedonicmodel. Land Economics 84 (4): 551–72.Rashford, B. S., C. T. Bastian and J. G. Cole. 2011. Agricultural land-use change in prairie Canada:Implications for wetland and waterfowl habitat conservation. Canadian Journal of AgriculturalEconomics 59 (2): 185–205.Schatzki, T. 2003. Options, uncertainty and sunk costs: An empirical analysis of land use change.Journal of Environmental Economics and Management 46: 86–105.Shultz, S. D. and S. J. Taff. 2004. Implicit prices of wetland easements in areas of productionagriculture. Land Economics 80 (4): 501–12.Streitfeld, D. 2008. As prices rise, farmers spurn conservation program. The New York Times.http://www.nytimes.com/2008/04/09/business/09conserve.html?pagewanted=all (accessed June29, 2012).Tsoodle, L. J., B. B. Golden and A. M. Featherstone. 2006. Factors influencing Kansas agriculturalfarm land values. Land Economics 82 (1): 124–39.Watmough, M. D. and M. J. Schmoll. 2007. Environment Canada’s Prairie and Northern HabitatMonitoring Program Phase II: Recent habitat trends in the prairie habitat joint venture. CanadianWildlife Service, Environment Canada, Edmonton, Alberta, Technical Report.Xu, F., R. C. Mittelhammer and P. W. Barkley. 1993. Measuring the contributions of site character-istics to the value of agricultural land. Land Economics 69 (4): 356–69.Yu, J. and K. Belcher. 2011. An economic analysis of landowners’ willingness to adopt wetland andriparian conservation management. Canadian Journal of Agricultural Economics 59 (2): 207–22.

Documents

Changes in Implicit Prices of Prairie Pothole Habitat