Cropland Cash Rental Rates in the
Upper Mississippi River Basin
Lyubov A. Kurkalova, Christopher Burkart, and Silvia Secchi
Technical Report 04-TR 47
October 2004
Center for Agricultural and Rural Development
Iowa State University
Ames, Iowa 50011-1070
www.card.iastate.edu
Lyubov Kurkalova is an associate scientist in the Resource and Environmental Policy Division at
the Center for Agricultural and Rural Development (CARD); Christopher Burkart is a research
assistant in the Department of Economics; and Silvia Secchi is an assistant scientist in the
Resource and Environmental Policy Division at CARD, Iowa State University.
The authors thank Arathi Bhaskar for competent research assistance and Todd Campbell for
computer support.
This paper is available online on the CARD Web site: www.card.iastate.edu. Permission is
granted to reproduce this information with appropriate attribution to the authors.
For questions or comments about the contents of this paper, please contact Lyubov Kurkalova,
560A Heady Hall, Iowa State University, Ames, IA 50011-1070; Ph: 515-294-7695; Fax: 515-2946336; E-mail: lyubov@iastate.edu.
Iowa State University does not discriminate on the basis of race, color, age, religion, national origin, sexual
orientation, sex, marital status, disability, or status as a U.S. Vietnam Era Veteran. Any persons having
inquiries concerning this may contact the Director of Equal Opportunity and Diversity, 1350 Beardshear Hall,
515-294-7612.
Abstract
The report documents the creation of estimates for cropland cash rental rates in the
Upper Mississippi River Basin in 1997. Although the basic data come from disparate
sources, we employ a unifying estimation procedure based on the presumption that the
cropland cash rental rate is an increasing function of corn yield potential. The rates are
estimated at some 42,000 National Resources Inventory data points representing cropland
in Illinois, Indiana, Iowa, Michigan, Minnesota, Missouri, South Dakota, and Wisconsin.
Keywords: cropland cash rental rates, land retirement, Upper Mississippi River Basin.
CROPLAND CASH RENTAL RATES IN THE
UPPER MISSISSIPPI RIVER BASIN
Introduction
This document provides a technical overview of the creation of estimates for
cropland cash rental rates in the Upper Mississippi River Basin (UMRB) in 1997. The
purpose of constructing the estimates is to provide the capability to model agricultural
land retirement decisions following the approach of Smith (1995), who measures the
opportunity cost of land retirement using cropland cash rental rates. The ultimate unit for
which the rate is predicted is a “point” in the 1997 National Resources Inventory (NRI)
data set (Nusser and Goebel 1997). We construct the estimates at some 42,000 NRI
points representing cropland in Illinois, Indiana, Iowa, Michigan, Minnesota, Missouri,
South Dakota, and Wisconsin.
The basic data for construction of the estimates have been compiled from disparate
sources. The analysis for which this eventually is intended is watershed based, but
because of the nature of the underlying data, the estimates had to be generated using
administrative boundaries such as counties and crop reporting districts. Although the
fundamental concept is similar, each data source (all state-specific) requires a unique
procedure because of the wide range in available spatial resolution.
In the remaining sections of the report, we first outline the general procedure and
then present the details on estimation for each state separately. The report concludes with
the summaries of the estimates for the UMRB.
Procedure
Under the assumption that the cropland cash rental rate is a monotonic function of
corn yield potential, we estimate piecewise linear functions, which express the per acre
cash rental rate as the function of the corn yield estimate, and use the functions to
estimate the cash rental rate of every NRI point (USDA-NRCS 1997) in the study. The
2 / Kurkalova, Burkart, and Secchi
functions, referred to as rental rate functions, are estimated separately for each of the
states and, where possible, for sub-state geographical units (multi-county districts or
counties) to better represent spatial heterogeneity in the opportunity costs and to account
for possible rent differences that may exist because of non-agricultural land uses.
To estimate the corn yield potential of each NRI point, we use EPIC (Williams
1990), a physical processes simulation model, to simulate 30 years of corn-soybean
rotation under normal weather conditions. The 15-year average of the predicted corn
yield is used as the measure of the corn yield potential of the point.
Illinois
Source of Cash Rental Rate Data and Productivity—Rental Rate Relationship.
Moody, Hornbaker, and DeBlock (2000) provide low and high cash rents,
rA low , rA high , rB low , rB high , rC low , rC high , for Class A, B, and C soils for six Illinois regions,
hereafter referred to as lease survey regions (LSRs). The class of the soil can be
determined by the optimum productivity index (PI) (Olson and Lang 2000)
(http://research.nres.uiuc.edu/soilproductivity/, accessed July 2004). Soils with a PI from
133 to 147 are in Class A, from 117 to 132 are in Class B, and from 100 to 116 are in
Class C.
Details on Estimation of the Rental Rate Functions. The rental rate functions are
LSR-specific. The 1999 cash rents from Moody, Hornbaker, and DeBlock (2000) are
deflated to 1997 dollars using the deflator 1.018 ($1999/$1997) computed from the data
in USDA-NASS (1999). Because the rent ranges for the classes reported in Moody
Hornbaker and DeBlock overlapped by as much as $10, the upper class low rate and the
adjacent lower class upper rate (e.g., the Class A low rate and Class B upper rate) were
averaged to provide the piecewise linear function break points. Thus, we obtained
rBA ≡ ( rB high + rA low ) / 2 and rCB ≡ ( rC high + rB low ) / 2 .
To evaluate the percentages of the soils of different classes in the LSRs, we first
obtain information on the PI by soil type from Olson and Lang (2000). Next we use the
STATSGO database (USDA-NRCS 2004) to allocate soil types within the LSRs. Then
we calculate the percentage of agricultural land area by soil class (A, B, C, non-prime
agricultural land) within each LSR (Table 1).
Cropland Cash Rental Rates in the Upper Mississippi River Basin / 3
To assign the rental rate to each point in an LSR, we begin by rank-ordering all the
points in the LSR by the corn yield potential from the lowest to the highest and assigning
to the non-prime, C, B, and A land class based on the percentages of the total LSR
cropland in these four categories. Thus, we obtain LSR-specific low and high corn yield
potential for the non-prime, C, B, and A classes: ymin and yC low for non-prime land,
yC low and yCB for Class C land, yCB and y BA for Class B land, y BA and ymax for Class A
land (Figure 1). The endpoint cash rental rates, rC low , rCB , rBA , and rA high , are assigned to
the endpoints of the C, B, and A classes, that is, to yC low , yCB , y BA , and ymax ,
respectively. The resulting four points are connected by linear pieces, and the left-most
piece is extended leftward to cover the non-prime land (Figure 1). Thus, the rental rate
functions are given by the following equations:
b11 + b21 ( y − yC low ) / ( yCB − yC low ) , if y ≤ yCB ,

r = b12 + b22 ( y − yCB ) / ( y BA − yCB ) , if yCB < y ≤ y BA ,

b13 + b23 ( y − y BA ) / ( ymax − y BA ) , if y > y BA ,
where the parameters b11 , b12 , b12 , b22 , b13 , b23 vary by LSR; they are reported in Table 2.
Indiana
Source of Cash Rental Rate Data and Productivity—Rental Rate Relationship.
Atkinson, Miller, and Cook (1997) provide data on cropland cash rental rates ($139/acre,
$107/acre, and $78/acre) together with representative corn yields (153 bu/acre, 120
bu/acre, and 89 bu/acre) for top, average, and poor quality land in the northern region of
the state (the only area relevant to the UMRB region).
Details on Estimation of the Rental Rate Functions. The rental rate function passes
through the three data points available and thus has two pieces. It is given by the formula
 r = −5.258 + 0.935 y , if y ≤ 120,

 r = −9.364 + 0.970 y , if y > 120.
Before applying the function for prediction, the EPIC-provided yields were scaled so that
the area-weighted average yield for the northern region is within one bushel of 131.8
4 / Kurkalova, Burkart, and Secchi
bu/acre, the 1997 average yield reported by USDA-NASS (1997) for this region (see
Missouri section for rationale).
Iowa
Source of Cash Rental Rate Data and Productivity—Rental Rate Relationship. ISU
Extension (1997) provides county-level data on cropland cash rental rates for low-,
average-, and high-quality land, together with the percentages of total county cropland in
these three categories.
Details on Estimation of the Rental Rate Functions. Rental rate functions are county-
specific. For each county, we begin by rank-ordering all the points by the corn yield
potential from the lowest to the highest and assigning to the low-, medium-, and highquality class based on the percentages of the total county cropland in these three
categories (Figure 2). That is, the yield potential y1 is determined as follows. Rank-order
the points by corn yield potential in ascending order and choose the parcels from the
beginning of the list until the area in these parcels is greater or equal to the area of lowquality land. Then y1 is the yield potential of the last point chosen. Yield potential y2 is
chosen similarly: continue choosing parcels from the list until the area in these parcels is
greater or equal to the sum of the areas of low- and medium-quality land. The midpoints
of the classes, ylow =
1
1
1
( ymin + y1 ) , ymed = ( y1 + y2 ) , and yhigh = ( y2 + ymax ) , are
2
2
2
assigned the corresponding cash rental rates reported in ISU Extension, rlow , rmed , and rhigh ,
respectively. The endpoints of the yield distribution in the county, ymin and ymax , are
assigned rental rate values 20 percent lower than the low-quality land rental rate, 0.8rlow ,
and 20 percent higher than the high-quality land rental rate, 1.2 rhigh , respectively. The
resulting five points (three midpoints of the corresponding classes and two endpoints) are
connected by linear pieces to form the piecewise linear cash rental rate function. Since by
construction the corn yield potential of any point in the county falls between ymin and
ymax , the resulting function allows estimation of the cash rental rate for any point in the
county. Thus, the final rental rate functions had four linear pieces given by equations
Cropland Cash Rental Rates in the Upper Mississippi River Basin / 5
 b11 + b21 ( y − ymin ) / ( ylow − ymin ) ,

 b12 + b22 ( y − ylow ) / ( ymed − ylow ) ,
r=
 b13 + b23 ( y − ymed ) / ( yhigh − ymed ) ,

b14 + b24 ( y − yhigh ) / ( ymax − yhigh ) ,
if
ymin ≤ y < ylow ,
if
ylow ≤ y < ymed ,
if
ymed ≤ y < yhigh ,
if
yhigh ≤ y < ymax ,
where the parameters b11 , b21 , b12 , b22 , b13 , b23 , b14 , b24 vary by county; they are provided in
Table 3. Table 4 provides the proportions of agricultural land in the low-, medium-, and
high-quality land classes.
Michigan
The UMRB overlaps with only three Michigan counties. For the points located in
Berrien County (FIPS = 26021), we used the Indiana rental rate function scaled by EPICpredicted yields to obtain a county-average yield is of 117 bu/acre (USDA-NASS 1997).
Gogebic County (FIPS = 26053) and Iron County (FIPS = 26071) in the Upper Peninsula
bordering Wisconsin contain only 27 observations and were excluded from the sample.
Minnesota
Source of Cash Rental Rate Data and Productivity—Rental Rate Relationship. The
University of Minnesota’s Center for Farm Financial Management makes available a
Minnesota land economics dataset at http://www.cffm.umn.edu/landeconomics/landdata/
(University of Minnesota, n.d.). This set contains 1997 soil rental rate data for the full
spectrum of soil types in each county: six specific rates reported for each county, each
associated with a variety of soils. In addition, the total acreage of soils associated with a
particular rate is available.
Details on Estimation of the Rental Rate Functions. Rental rate functions are county
specific. In each county, NRI points are ranked by corn yield potential and then assigned
to a rate range based on their productivity and the proportion of total land in each rate
category. Each rate range has an accompanying range of yields; the mean of the yield
range is assigned the rate for that range. The resulting points provide the basis for
construction of a piecewise linear yield/rate function that assigns a rate to each NRI point
based on its yield potential, location (county), and soil rental rate category. The
functional relation of rates to yield ranges and rates is
6 / Kurkalova, Burkart, and Secchi
2 ( b2 − b1 ) 

a2 + a1 
 a1 + 3a − 4a + a  y − 2  , if y ≤ y1 ,

3
2
1 


2 ( b3 − b2 ) 
a3 + a 2 
 a2 +
y−
 , if y1 ≤ y < y2 ,
3a4 − 4a3 + a2 
2 


2 ( b4 − b3 ) 
a4 + a3 
r =  a3 +
y−
 , if y2 ≤ y < y3 ,
3a5 − 4a4 + a3 
2 


2 ( b5 − b4 ) 
a5 + a4 
 a4 +
y−
 , if y3 ≤ y < y4 ,
3a6 − 4a5 + a4 
2 


2 ( b6 − b5 ) 
a6 + a5 
 a5 +
y−
 , if y ≥ y4 .
2 
3a7 − 4a6 + a5 

where
y j = a j +1 +
a j + 2 − a j +1
2
for
j = 1,..., 4.
The values of the function parameters appear in Tables 5 and 6, and a graphical
representation of the procedure can be found in Figure 3. Zero values for the yield range
parameters simply indicate that the county has insufficient points in a particular range;
this can occur because of the spacing of yields. This happens when a single observation
represents a large proportion of land in the county that has one or more small rate classes.
However, this is not an issue in practice, as the zero value indicates that no observations
exist in that range, that is, the parameter value is not used in any calculations.
Missouri
Source of Cash Rental Rate Data and Productivity—Rental Rate Relationship. Plain
and White (2003) provide estimates on how 2003 cash rental rates depend on corn yield
for the whole state of Missouri. MASS (2003) provides average non-irrigated cropland
cash rent per acre, by crop reporting district (CRD), for the years 1997 and 2003.
Details on Estimation of the Rental Rate Functions. Rental rate functions are CRD-
specific. Plain and White (2003) provide statewide average 2003 rental rates r1 - r6
corresponding to six ranges of corn yield (<100, 100-120, 121-139, 140-149, 150-159,
and >159 bu/acre). We use MASS (2003) data to deflate the six 2003 average rental rates
into 1997 dollars, separately by CRD (the rates of rent growth vary from 1.13
$2003/$1997 for South Central CRD to 1.44 $2003/$1997 for North Central CRD).
Cropland Cash Rental Rates in the Upper Mississippi River Basin / 7
To construct the CRD-specific rental rate functions, we assigned the corresponding
average 1997 rental rates to the midpoints of the yield ranges (or “representative” points
for the open end ranges), ( y1 ,..., y6 ) = (90, 110, 130, 145, 155, 170). The resulting six
points were connected by linear pieces. The first and the last linear pieces were extended
to the left and to the right, respectively. It turned out that the third and the fourth linear
pieces had identical equations. Thus, the final rental rate functions had four linear pieces
given by equations
 r = a01 + a11 y ,
r = a + a y,

02
12

 r = a03 + a13 y ,
 r = a04 + a14 y ,
if y ≤ 110,
if 110 < y ≤ 130,
if 130 < y ≤ 155,
if y > 155,
where the parameters aij , i = 0,1, j = 1,..., 4 , vary by CRD; they are reported in Table 7.
For each parcel in a CRD, the cash rental rates were predicted using the constructed
function, and then the average rental rate was computed with the NRI expansion factors
used as weights. If the predicted average rate was lower (higher) than the one reported in
MASS (2003), then the EPIC-predicted corn yield was scaled up (down) to have the
predicted rental rate average within one dollar of the one reported. The rationale behind
this scaling is that the EPIC-predicted corn yield potential represents a long-term average
yield which may differ from the 2003 realized yield.
South Dakota
Source of Cash Rental Rate Data and Productivity—Rental Rate Relationship.
SDASS (1997) reports 1997 minimum, maximum, and average cropland cash rent by
county.
Details on Estimation of the Rental Rate Functions. Only six counties have cropland
acreage within the UMRB (FIPS = 46011, 46029, 46039, 46051, 46091, 46109). The
rental rate functions are county-specific; each of them consists of two linear pieces. The
minimum of the predicted crop yield potential, ymin , was assigned the minimum of the
cash rent reported, rmin , and the maximum crop yield potential, ymax , was assigned the
maximum cash rent reported, rmax . The median crop yield potential, ymed , was initially
8 / Kurkalova, Burkart, and Secchi
assigned the average rental rate in the county, ravg . If the predicted average county rate
was lower (higher) than the one reported, the assignment of the median yield potential
was adjusted upward (downward) so that the predicted county-average rental rate was
within one bushel of that reported in SDASS (1997). More specifically, the rental rate
functions have the form
 r = rmin + ( y − ymin )( a − rmin ) / ( ymed − ymin ) , if y ≤ ymed ,

 r = a + ( y − ymed )( rmax − a ) / ( ymax − ymed ) , if y > ymed ,
where the parameter a is chosen so that the predicted area-weighted average rental rate is
equal to ravg which is reported in SDASS (1997). The county-specific data used in
estimation is provided in Table 8.
Wisconsin
Source of Cash Rental Rate Data and Productivity—Rental Rate Relationship.
WASS (2001) provides average 2001 cropland rental rates by CRD.
Details on Estimation of the Rental Rate Functions. The WASS (2001) rental rates
are deflated to 1997 dollars using the deflator 1.2 ($2001/$1997) computed from the data
in USDA-NASS (1999, 2004). Since no information on the productivity–rental rate
relationship was found for the state, we utilized the rental rate functions estimated for
neighboring states in prediction. Specifically, we used the functions estimated for Illinois
for the southern part of the state, Southwest, South Central, and Southeast CRDs, and the
functions estimated for Minnesota for the rest of the state. In particular, the rental rate
function estimated for the Northwest LSR of Illinois was used for estimation of the cash
rental rates in the Illinois Southwest and South Central CRDs. The rental rate function
estimated for the Northeast LSR of Illinois was used for estimation of the rental rates in
the Illinois Southeast CRD. The predictions were then calibrated to match the available
CRD-average rental rates.
Summary
A regional summary of the results can be seen in Tables 9 and 10 as well as in
Figures 5 and 6. Figure 5 shows average rental rates by eight-digit Hydrologic Unit Code
(HUC); this is the scale at which the eventual analysis is to be done. Figure 6 shows
Cropland Cash Rental Rates in the Upper Mississippi River Basin / 9
average rental rates by county. Note in Figure 6 that, thanks to the use of NRI-pointspecific characteristics in the creation of the data, there is a smooth transition across state
lines despite the differences in source information.
Tables
TABLE 1. Illinois: Proportions of agricultural land in various land classes
Region
Percent Class A
Percent Class B
Percent Class C
Percent non-prime
East
Central
0.452
0.343
0.175
0.030
Northeast
0.223
0.403
0.361
0.013
Northwest
0.339
0.310
0.335
0.016
South
0.003
0.166
0.505
0.325
South
Central
0.085
0.222
0.475
0.218
West
Central
0.346
0.108
0.526
0.020
TABLE 2. Illinois: Rental rate function parameters
LSR
Northwest
Northeast
West Central
East Central
South Central
South
b11
78.6
72.7
91.4
83.5
88.4
44.2
b21
40.8
32.9
24.6
32.4
35.9
17.7
b12
119.4
105.6
115.9
115.9
124.3
61.9
b22
23.1
22.6
26.5
26.5
30.5
11.3
b13
142.4
128.2
142.4
142.4
154.7
73.2
b23
30.5
42.7
38.3
38.3
31.9
17.2
TABLE 3. Iowa: Rental rate function parameters
County
FIPS
19001
19003
19005
19007
19009
19011
19013
19015
19017
19019
19021
19023
19025
19027
19029
b11
61.231
72.000
72.727
49.000
76.000
82.737
87.455
75.886
77.257
85.000
82.444
81.000
83.273
91.700
71.500
b21
15.308
18.000
18.182
12.250
19.000
20.684
21.864
18.971
19.314
21.250
20.611
20.250
20.818
22.925
17.875
b12
76.538
90.000
90.909
61.250
95.000
103.421
109.318
94.857
96.571
106.250
103.056
101.250
104.091
114.625
89.375
b22
19.533
21.000
18.409
15.194
25.714
26.579
30.000
23.310
24.276
18.750
26.736
26.500
21.992
19.063
30.938
b13
96.071
111.000
109.318
76.444
120.714
130.000
139.318
118.167
120.848
125.000
129.792
127.750
126.083
133.688
120.313
b23
24.107
11.000
20.000
20.778
20.714
20.643
23.818
22.521
23.152
25.818
24.792
23.750
26.644
16.938
22.813
b14
120.179
122.000
129.318
97.222
141.429
150.643
163.136
140.688
144.000
150.818
154.583
151.500
152.727
150.625
143.125
b24
24.036
24.400
25.864
19.444
28.286
30.129
32.627
28.138
28.800
30.164
30.917
30.300
30.545
30.125
28.625
Cropland Cash Rental Rates in the Upper Mississippi River Basin / 11
TABLE 3. Continued
County
FIPS
19031
19033
19035
19037
19039
19041
19043
19045
19047
19049
19051
19053
19055
19057
19059
19061
19063
19065
19067
19069
19071
19073
19075
19077
19079
19081
19083
19085
19087
19089
19091
19093
19095
19097
19099
19101
19103
19105
19107
19109
19111
19113
19115
19117
b11
86.190
81.723
74.833
72.308
51.429
75.857
76.500
89.077
79.600
75.867
54.857
51.429
81.581
77.333
69.714
77.556
78.400
80.000
72.571
79.418
67.429
86.333
93.733
75.500
80.571
80.800
81.664
75.257
70.400
64.615
85.500
78.667
79.000
77.556
82.873
76.622
74.286
78.857
78.857
87.077
70.400
76.966
73.333
49.000
b21
21.548
20.431
18.708
18.077
12.857
18.964
19.125
22.269
19.900
18.967
13.714
12.857
20.395
19.333
17.429
19.389
19.600
20.000
18.143
19.855
16.857
21.583
23.433
18.875
20.143
20.200
20.416
18.814
17.600
16.154
21.375
19.667
19.750
19.389
20.718
19.156
18.571
19.714
19.714
21.769
17.600
19.241
18.333
12.250
b12
107.738
102.154
93.542
90.385
64.286
94.821
95.625
111.346
99.500
94.833
68.571
64.286
101.976
96.667
87.143
96.944
98.000
100.000
90.714
99.273
84.286
107.917
117.167
94.375
100.714
101.000
102.080
94.071
88.000
80.769
106.875
98.333
98.750
96.944
103.591
95.778
92.857
98.571
98.571
108.846
88.000
96.207
91.667
61.250
b22
27.914
20.692
24.167
20.865
21.786
26.250
28.264
27.692
24.500
18.000
13.571
21.786
24.387
28.750
19.732
26.667
17.000
22.692
26.429
20.686
20.071
19.583
25.000
19.847
25.223
28.250
21.759
23.500
28.300
19.846
20.268
26.667
20.000
26.667
22.045
19.167
25.893
28.214
20.714
20.154
28.300
23.849
30.833
15.194
b13
135.652
122.846
117.708
111.250
86.071
121.071
123.889
139.038
124.000
112.833
82.143
86.071
126.364
125.417
106.875
123.611
115.000
122.692
117.143
119.958
104.357
127.500
142.167
114.222
125.938
129.250
123.839
117.571
116.300
100.615
127.143
125.000
118.750
123.611
125.636
114.944
118.750
126.786
119.286
129.000
116.300
120.056
122.500
76.444
b23
24.802
10.615
20.625
18.558
18.929
22.857
26.411
32.390
24.000
17.323
18.214
18.929
25.114
29.583
15.982
30.278
20.000
24.441
28.571
25.269
22.786
16.042
21.833
21.611
18.646
21.750
21.732
28.857
27.000
14.615
18.482
35.500
18.125
30.278
21.818
24.222
27.750
24.643
24.643
13.857
27.000
22.319
25.556
20.778
b14
160.455
133.462
138.333
129.808
105.000
143.929
150.300
171.429
148.000
130.156
100.357
105.000
151.477
155.000
122.857
153.889
135.000
147.133
145.714
145.227
127.143
143.542
164.000
135.833
144.583
151.000
145.571
146.429
143.300
115.231
145.625
160.500
136.875
153.889
147.455
139.167
146.500
151.429
143.929
142.857
143.300
142.375
148.056
97.222
b24
32.091
26.692
27.667
25.962
21.000
28.786
30.060
34.286
29.600
26.031
20.071
21.000
30.295
31.000
24.571
30.778
27.000
29.427
29.143
29.045
25.429
28.708
32.800
27.167
28.917
30.200
29.114
29.286
28.660
23.046
29.125
32.100
27.375
30.778
29.491
27.833
29.300
30.286
28.786
28.571
28.660
28.475
29.611
19.444
12 / Kurkalova, Burkart, and Secchi
TABLE 3. Continued
County
FIPS
19119
19121
19123
19125
19127
19129
19131
19133
19135
19137
19139
19141
19143
19145
19147
19149
19151
19153
19155
19157
19159
19161
19163
19165
19167
19169
19171
19173
19175
19177
19179
19181
19183
19185
19187
19189
19191
19193
19195
19197
b11
71.692
61.455
72.000
64.444
80.700
67.333
75.111
68.233
49.000
67.333
70.556
81.714
72.471
59.333
80.889
70.429
85.333
77.433
73.143
80.686
53.000
82.293
88.571
73.200
82.000
81.569
83.556
52.800
73.714
54.000
76.622
61.455
83.714
51.333
82.000
76.571
76.000
65.000
72.646
84.941
b21
17.923
15.364
18.000
16.111
20.175
16.833
18.778
17.058
12.250
16.833
17.639
20.429
18.118
14.833
20.222
17.607
21.333
19.358
18.286
20.171
13.250
20.573
22.143
18.300
20.500
20.392
20.889
13.200
18.429
13.500
19.156
15.364
20.929
12.833
20.500
19.143
19.000
16.250
18.162
21.235
b12
89.615
76.818
90.000
80.556
100.875
84.167
93.889
85.292
61.250
84.167
88.194
102.143
90.588
74.167
101.111
88.036
106.667
96.792
91.429
100.857
66.250
102.867
110.714
91.500
102.500
101.962
104.444
66.000
92.143
67.500
95.778
76.818
104.643
64.167
102.500
95.714
95.000
81.250
90.808
106.176
b22
21.808
24.396
33.571
29.444
21.125
18.750
22.222
24.708
15.194
18.750
28.591
19.635
19.265
22.500
19.589
16.611
21.167
22.583
21.000
22.071
15.417
25.383
30.536
17.500
24.000
22.427
25.101
21.000
21.857
22.000
19.167
24.396
21.786
15.083
20.556
20.348
22.857
24.750
19.346
21.046
b13
111.423
101.214
123.571
110.000
122.000
102.917
116.111
110.000
76.444
102.917
116.786
121.778
109.853
96.667
120.700
104.647
127.833
119.375
112.429
122.929
81.667
128.250
141.250
109.000
126.500
124.389
129.545
87.000
114.000
89.500
114.944
101.214
126.429
79.250
123.056
116.063
117.857
106.000
110.154
127.222
b23
22.154
24.940
30.000
23.889
23.625
17.083
22.778
29.583
20.778
17.083
32.242
20.097
21.618
18.333
14.500
15.753
19.667
17.917
22.571
23.905
22.500
20.868
28.592
20.000
15.643
16.433
22.121
26.000
21.000
28.625
24.222
24.940
23.571
18.750
19.313
26.080
23.571
30.250
17.668
19.861
b14
133.577
126.154
153.571
133.889
145.625
120.000
138.889
139.583
97.222
120.000
149.028
141.875
131.471
115.000
135.200
120.400
147.500
137.292
135.000
146.833
104.167
149.118
169.842
129.000
142.143
140.821
151.667
113.000
135.000
118.125
139.167
126.154
150.000
98.000
142.368
142.143
141.429
136.250
127.821
147.083
b24
26.715
25.231
30.714
26.778
29.125
24.000
27.778
27.917
19.444
24.000
29.806
28.375
26.294
23.000
27.040
24.080
29.500
27.458
27.000
29.367
20.833
29.824
33.968
25.800
28.429
28.164
30.333
22.600
27.000
23.625
27.833
25.231
30.000
19.600
28.474
28.429
28.286
27.250
25.564
29.417
Cropland Cash Rental Rates in the Upper Mississippi River Basin / 13
TABLE 4. Iowa: Proportions of agricultural land in various land quality classes
County FIPS
19001
19003
19005
19007
19009
19011
19013
19015
19017
19019
19021
High Quality
0.23
0.20
0.24
0.21
0.27
0.35
0.39
0.45
0.54
0.30
0.40
Medium Quality
0.43
0.42
0.52
0.48
0.42
0.43
0.40
0.35
0.28
0.50
0.40
Low Quality
0.34
0.38
0.24
0.31
0.31
0.22
0.21
0.20
0.18
0.20
0.20
TABLE 5. Minnesota: Rental rate function parameters, yield cutoff points
County FIPS
27001
27003
27005
27007
27009
27011
27013
27015
27017
27019
27021
27023
27025
27029
27033
27035
27037
27039
27041
27043
27045
27047
27049
27051
27053
27055
27057
27059
a1
4.34
3.32
3.99
3.82
4.47
3.31
4.77
2.75
3.26
3.47
4.43
4.06
3.53
4.13
1.64
3.26
2.81
4.11
3.95
5.68
3.37
3.89
2.98
3.36
4.17
4.55
3.87
2.97
a2
5.14
6.88
6.23
7.65
5.51
4.65
6.85
5.59
5.75
6.22
5.07
4.58
6.96
5.42
4.48
5.2
5.15
5.5
5.04
7.88
7.67
6.14
6.28
5.02
5.36
8.11
4.25
6.82
a3
6.47
7.05
6.63
7.66
6.55
4.77
7.24
6.05
6.2
6.72
6.13
4.79
7.22
6.14
4.77
5.33
5.32
5.95
5.73
8.09
7.82
6.75
6.5
5.34
6.22
8.44
5.31
6.83
a4
7.42
8.31
6.76
7.89
6.56
5.29
7.76
6.58
7.2
7.1
7.11
5.66
8.06
6.23
4.96
5.41
5.48
6.68
5.85
9.51
8.49
7.55
7.27
5.62
6.63
8.9
5.93
6.89
a5
7.45
8.42
6.92
8.09
6.74
5.75
8.48
6.86
8.13
7.54
7.41
7.12
8.22
6.56
5.22
6.77
6.34
8.08
5.88
10.17
9.42
8.72
8.34
6.01
6.97
10.27
7.06
7.24
a6
8.35
9.04
6.97
8.25
7.36
6.28
9.28
7.01
8.71
7.56
8.81
7.82
8.66
7.44
6.62
7.05
7.35
8.64
5.97
10.7
9.82
9.58
9.47
6.48
8.34
11.38
8.3
8.35
a7
10.14
9.71
9.23
11.66
9.22
6.87
11.31
9.36
9.27
10.32
8.81
9.15
11.45
8.34
7.2
8.16
9.85
9.52
7.76
11.94
11.32
12.94
10.24
7.14
9.18
11.38
8.8
10.17
14 / Kurkalova, Burkart, and Secchi
TABLE 5. Continued
County FIPS
27061
27063
27065
27067
27073
27079
27081
27083
27085
27091
27093
27095
27097
27099
27101
27103
27105
27109
27111
27115
27117
27121
27127
27129
27131
27137
27139
27141
27143
27145
27147
27149
27151
27153
27155
27157
27159
27161
27163
27165
27169
27171
27173
a1
3.26
3.55
2.67
2.68
2.19
4.33
1.76
2.82
5.08
4.61
3.63
2.82
3.44
5.21
2.24
4.68
3.5
2.62
2.38
2.94
2.32
3.45
2.96
4.43
3.59
5.65
4.18
2.96
4.03
2.61
1.85
3.22
3.02
3.19
3.51
3
2.52
5.4
3.16
3.88
3.09
4.39
2
a2
6.67
4.66
5.58
4.95
3.54
5.99
1.9
3.94
5.75
5.78
5.93
6.64
6.14
7.8
4.15
6.68
4.37
5.28
3.85
6.44
2.55
4.75
4.59
6.02
6.9
6.91
6.97
5.22
6.04
5.39
5.51
4.64
3.57
5.35
4.05
6.99
4.92
6.66
4.55
6.39
6.65
5.98
3.35
a3
0
5.5
6.53
5.21
3.88
6.22
2.89
4.25
5.92
6.59
5.99
6.82
6.5
8.72
4.81
6.79
5.58
5.81
4.25
6.98
2.68
5.6
4.8
6.16
7.49
0
7.66
5.25
6.11
5.57
6.54
5.14
4.06
5.65
4.22
7.24
5.15
6.91
4.75
8.85
7.27
7.8
3.77
a4
6.79
6.51
6.98
5.61
4.37
6.54
3.07
4.65
6.14
9.03
6.39
7.18
6.72
9.43
4.89
7.41
6.08
6.37
4.6
7.95
3.06
5.84
4.93
6.72
7.82
0
8.27
8.72
6.76
5.91
0
5.85
5.27
5.65
4.94
7.95
0
6.97
5.18
9.4
7.53
8.42
4.08
a5
6.99
7.74
7.03
6.14
4.39
7.08
4.48
5.6
6.66
9.48
6.73
7.57
7.11
10.02
5.29
7.99
6.92
7.39
5.04
8.16
4.66
5.99
6.54
7.42
8.67
7.59
8.72
9.34
7.17
5.98
7.34
6.44
5.58
5.74
6.27
8.2
5.33
7.29
5.84
9.73
7.83
9.37
4.85
a6
7.29
8.15
7.46
7.45
5.27
7.54
5.11
6.41
6.85
9.79
7.19
8.34
7.13
10.78
5.36
8.47
7.8
8.24
5.39
8.61
6.11
6.3
6.66
7.77
8.91
8.53
8.92
9.36
7.31
6.05
7.74
6.67
6.46
5.78
6.51
9.3
5.41
8.14
6.62
10.44
10.09
9.41
5.08
a7
9.22
8.64
9.85
9.79
7.58
10.84
6.25
7.55
8.81
11.65
9.56
10.78
8.81
11.73
7.16
10.66
9.13
9.32
7.45
10.34
6.96
7.61
8.07
9.42
11.48
11.31
10.91
9.55
9.31
10.62
10.19
7.98
7.15
9.46
6.77
9.62
9.46
10.56
9.13
11.39
11.04
11.23
6.09
Cropland Cash Rental Rates in the Upper Mississippi River Basin / 15
TABLE 6. Minnesota: Rental rate function parameters
County FIPS
27001
27003
27005
27007
27009
27011
27013
27015
27017
27019
27021
27023
27025
27029
27033
27035
27037
27039
27041
27043
27045
27047
27049
27051
27053
27055
27057
27059
27061
27063
27065
27067
27073
27079
27081
27083
27085
27091
27093
27095
27097
27099
27101
27103
b1
16
25
36
25
25
44
76
68
12
73
13
68
28
23
68
13
55
69
36
82
64
73
76
51
41
75
26
26
15
71
20
60
58
73
48
63
73
80
55
17
25
69
66
76
b2
18
28
40
28
28
49
86
79
14
82
17
76
32
26
77
17
64
77
40
92
72
82
86
58
46
85
30
30
17
81
22
68
65
80
54
70
82
90
61
19
28
78
75
86
b3
20
31
45
31
31
54
95
89
15
91
20
85
35
29
86
20
72
86
45
102
80
91
95
65
51
95
33
33
19
90
25
75
72
82
60
78
91
100
68
21
30
87
83
95
b4
22
34
49
34
34
60
105
100
17
100
23
93
39
32
94
23
81
94
49
112
88
100
105
71
56
105
37
37
21
100
27
83
75
92
66
86
100
110
74
23
33
95
91
105
b5
24
37
54
37
37
65
114
110
18
109
25
102
42
35
103
25
89
103
54
122
96
109
114
78
61
115
40
40
23
109
30
90
80
102
72
94
109
120
81
25
36
104
95
114
b6
26
40
58
40
40
70
124
121
20
118
27
110
46
38
112
27
98
111
58
133
104
118
124
85
66
125
44
44
25
119
32
98
87
112
78
102
118
130
87
27
39
113
99
124
16 / Kurkalova, Burkart, and Secchi
TABLE 6. Continued
County FIPS
27105
27109
27111
27115
27117
27121
27127
27129
27131
27137
27139
27141
27143
27145
27147
27149
27151
27153
27155
27157
27159
27161
27163
27165
27169
27171
27173
b1
63
56
26
20
53
45
68
73
74
14
73
20
76
44
71
57
65
23
48
70
23
76
41
90
63
48
60
b2
70
64
30
23
59
51
76
82
84
16
82
22
86
50
80
64
73
26
54
79
26
86
46
101
73
54
68
b3
78
72
33
26
66
57
85
91
93
18
91
25
95
55
86
71
81
29
60
89
29
95
51
108
81
60
75
b4
86
80
34
28
72
63
93
100
103
20
100
27
105
61
89
78
89
32
66
96
32
105
56
119
91
66
83
b5
94
88
37
31
79
69
102
109
112
22
109
30
114
66
98
85
97
35
72
105
35
114
61
130
99
72
90
b6
102
96
38
34
86
75
110
118
122
24
118
32
123
72
107
92
105
38
78
114
38
124
66
134
109
78
98
TABLE 7. Missouri: Rental rate function parameters
Crop Reporting
District
Northwest
North Central
Northeast
West Central
Central
East Central
Southwest
South Central
Southeast
a01
32.625
31.200
36.118
36.316
36.610
38.571
39.255
40.000
39.107
a11
a02
0.145
0.139
0.161
0.161
0.163
0.171
0.174
0.178
0.174
-3.262
-3.120
-3.612
-3.632
-3.661
-3.857
-3.926
-4.000
-3.911
a12
a03
a13
a04
a14
0.471
0.451
0.522
0.525
0.529
0.557
0.567
0.578
0.565
39.150
37.440
43.342
43.579
43.932
46.286
47.106
48.000
46.929
0.145
0.139
0.161
0.161
0.163
0.171
0.174
0.178
0.174
-5.800
-5.547
-6.421
-6.456
-6.508
-6.857
-6.979
-7.111
-6.952
0.435
0.416
0.482
0.484
0.488
0.514
0.523
0.533
0.521
Cropland Cash Rental Rates in the Upper Mississippi River Basin / 17
TABLE 8. South Dakota: Data for rental rate function estimation
County name
Brookings
Codington
Deuel
Grant
Marshall
Roberts
FIPS
46011
46029
46039
46051
46091
46109
Min rent, rmin
28
25
30
20
25
20
Max rent, rmax
85
55
70
67
60
75
Average rent, ravg
49.5
39.8
50.2
48.6
38.6
48.5
Source: SDASS 1997.
TABLE 9. Average rental rates by state
State
Illinois
Indiana
Iowa
Michigan
Minnesota
Missouri
South Dakota
Wisconsin
Average Rental Rate
115.0964385
128.1830088
123.4889401
81.80714286
84.87055572
60.53107564
49.92083333
80.87981283
TABLE 10. Average rental rates by four-digit watershed
Four-digit HUC
0701
0702
0703
0704
0705
0706
0707
0708
0709
0710
0711
0712
0713
0714
Average Rental Rate
52.45
89.42
37.49
83.73
41.52
122.61
74.69
125.48
121.08
112.51
67.80
116.72
128.32
78.20
Figures
Rental
Rate
rAhigh
rBA
rCB
rlow
ymin yClow
Non- Class C
prime
yCB
Class B
ymax
y BA
Class A
FIGURE 1. Illinois: Estimation of cash rental rate function
Yield
Potential
Cropland Cash Rental Rates in the Upper Mississippi River Basin / 19
Rental
Rate
1.2 rhigh
rhigh
rmed
rlow
0.8 rlow
ylow
ymin
Lowquality
land
yhigh
ymed
y1
y2
Mediumquality
Yield
Potential
ymax
Highquality
land
FIGURE 2. Iowa: Estimation of cash rental rate function
20 / Kurkalova, Burkart, and Secchi
Rental
Rate
b6
b5
b4
Soil
Rental
b3
Rate
Categories
b2
b1
y0
a1
y1
a2
y2
a3
a4
a5
a6
Land class proportions for Soil Rental Rate Categories
FIGURE 3. Minnesota: Estimation of cash rental rate function
Yield
Potential
y5
y4
y3
a7
Cropland Cash Rental Rates in the Upper Mississippi River Basin / 21
Rental
Rate
rmax
a
rmin
ymin
ymed
ymax
FIGURE 4. South Dakota: Estimation of cash rental rate function
Yield
Potential
22 / Kurkalova, Burkart, and Secchi
FIGURE 5. Rental rates by eight-digit watershed
Cropland Cash Rental Rates in the Upper Mississippi River Basin / 23
FIGURE 6. Rental rates by county
References
Atkinson, J.H., A. Miller, and K. Cook. 1997. “Land Values Rise Again.” Purdue Agricultural Economics
Report, Purdue University. August. http://www.agecon.purdue.edu/extension/pubs/paer/pre_98/
Paer0897.pdf (accessed July 2004).
Iowa State University Extension (ISU Extension). 1997. “Cash Rental Rates for Iowa: 1997 Survey.” Iowa
State University. May.
Missouri Agricultural Statistical Service (MASS). 2003. “Cash Rents and Balance Sheet.” http://agebb.
missouri.edu/mass/farmfact/pdf/p73.pdf (accessed July 2004).
Moody, E.D., R.H. Hornbaker, and M.L. DeBlock. 2000. “The Market for Illinois Farmland in 1999:
Results of the Survey for Farmland Value and Farm Leasing.” Illinois Society of Professional Farm
Managers and Rural Appraisers. http://www.ace.uiuc.edu/farmlab/ (accessed July 2004).
Nusser, S.M., and J.J. Goebel. 1997. “The National Resources Inventory: A Long-Term Multi-Resource
Monitoring Programme.” Environmental and Ecological Statistics 4: 181-204.
Olson, K.R., and J.M. Lang. 2000. “Optimum Crop Productivity Ratings for Illinois Soils.” Bulletin 811,
College of Agricultural, Consumer and Environmental Sciences, Office of Research. University of
Illinois. http://research.nres.uiuc.edu/soilproductivity/Bulletin%20811%20ALL.pdf (accessed July
2004).
Plain, R., and J. White. 2003. “2003 Cash Rental Rates in Missouri.” University of Missouri-Columbia
Extension Publication No. G427, University of Missouri-Columbia. http://muextension.missouri.edu/
explorepdf/agguides/agecon/G00427.pdf (accessed July 2004).
Smith, R. 1995. “The Conservation Reserve Program as a Least-Cost Land Retirement Mechanism.”
American Journal of Agricultural Economics 77(February): 93-105.
South Dakota Agricultural Statistical Service (SDASS). 1997. “South Dakota 1997 County Level Land
Rents and Values.” South Dakota Agricultural Statistical Service, April. http://www.nass.usda.gov/
sd/releases/rent97.pdf (accessed July 2004).
U.S. Department of Agriculture, National Agricultural Statistics Service (USDA-NASS). 1997. Crops
County Data. http://www.nass.usda.gov/indexcounty.htm (accessed July 2004).
———. 1999. “Agricultural Cash Rents.” Agricultural Statistics Board. http://usda.mannlib.cornell.edu/
reports/nassr/other/plr-bb/rent0799.pdf (accessed July 2004).
———. 2004. “Agricultural Land Values and Cash Rents: Final Estimates 1999-2003.” Statistical Bulletin
No. 993.
U.S. Department of Agriculture, National Resources Conservation Service (USDA-NRCS). 1997. National
Resource Inventory: data files. http://www.nrcs.usda.gov/technical/nri/1997 (accessed October 2004).
Cropland Cash Rental Rates in the Upper Mississippi River Basin / 25
———. 2004. Illinois STATSGO Database. http://www.ncgc.nrcs.usda.gov/branch/ssb/products/
statsgo/data/il.html. (accessed July 2004).
University of Minnesota, Center for Farm Financial Management. No date. “USDA Soil Rental Rates.”
http://www.cffm.umn.edu/landeconomics/landdata/ (accessed July 2004).
Williams, J.R. 1990. “The Erosion Productivity Impact Calculator (EPIC) Model: A Case History.”
Philosophical Transactions of the Royal Society of London B, 329: 421-28.
Wisconsin Agricultural Statistical Service (WASS). 2001. “Wisconsin’s Custom Rate Guide.”
http://www.uwex.edu/ces/ag/facstaff/custrate01.pdf (accessed July 2004).