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Appendix for The Impact of Land-Use Change on Ecosystem Services, Biodiversity and Returns to Landowners: A Case Study in the State of Minnesota by Stephen Polasky, Erik Nelson, Derric Pennington, and Kris A. Johnson
1. Land use / land cover (LULC) change maps
We create six maps of 1992 to 2001 land use / land cover (LULC) change in Minnesota at the grid cell level (cell size = 30 x 30 m). All LULC change maps use the same 1992 LULC pattern (Fry et al. 2009); however, the 2001 LULC pattern for each map differs. For the baseline LULC change map, the 2001 LULC pattern is the observed pattern (Fry et al. 2009). The other LULC change maps assume alternative 2001 LULC patterns (see below for a through explanation of the 5 alternative scenarios).
On each LULC change map each grid cell is assigned a two-digit classification of LULC change. The first digit represents the LULC category in the grid cell in 1992 and the second the LULC category in the grid cell in 2001. For example, if 4 represents forest and 6 agriculture than a grid cell with a 46 was in forest cover in 1992 and agricultural cover in 2001. In the discussion below we index the first digit with j and the second digit with k. The definitions of the LULC categories (Anderson Level 1 class codes) are given in Table 1.
Table 1. LULC Class definitions from the NLCD 1992/2001 retrofit change product used in the scenarios for Minnesota (from http://www.mrlc.gov/faq.php).
Code
Anderson Level 1 Class
Descriptions
1
Open water
All areas of open water, generally with less than 25% vegetation or soil cover.
2
Urban
Includes developed open spaces with a mixture of some constructed materials, but mostly vegetation in the form of lawn grasses such as large-lot single-family housing units, parks, golf courses, and vegetation planted in developed settings for recreation, erosion control, or aesthetic purposes. Also included are lands of low, medium, and high intensity with a mixture of constructed materials and vegetation, such as single-family housing units, multifamily housing units, and areas of retail, commercial, and industrial uses.
3
Barren
Areas of bedrock, pavement, scarps, talus, slides, glacial debris, strip mines, gravel pits, and other accumulations of earthen material. Generally, vegetation accounts for less than 15% of total cover.
4
Forest
Areas dominated by trees generally taller than 5 meters, and greater than 20% of total vegetation cover. Includes deciduous forest, evergreen forest, and mixed forest.
5
Grassland/Shrub
Includes grassland areas dominated by gramminoid or herbaceous vegetation and shrub/scrub areas dominated by shrubs less than 5 meters tall with shrub canopy typically greater than 20% of total vegetation, including true shrubs, young trees in an early successional stage, or trees stunted due to harsh environmental conditions. Management techniques that associate soil, water, and forage-vegetation resources are more suitable for rangeland management than are practices generally used in managing pastureland. Some rangelands have been or may be seeded to introduced or domesticated plant species.
6
Agriculture
Includes cultivated crops and pasture/hay Cultivated crops are described as areas used for the production of annual crops, such as corn, soybeans, vegetables, tobacco, and cotton, and also perennial woody crops such as orchards and vineyards. This class also includes all actively tilled land. Pasture/Hay is described as grasses, legumes, or grass-legume mixtures planted for livestock grazing or the production of seed or hay crops, typically on a perennial cycle.
7
Wetlands
Includes woody wetlands and herbaceous wetlands Areas where forest or shrubland vegetation accounts for greater than 20 percent of vegetative cover and the soil or substrate is periodically saturated with or covered with water. This class also includes areas where perennial herbaceous vegetation accounts for greater than 80 percent of vegetative cover and the soil or substrate is periodically saturated with or covered with water.
1A. Baseline LULC change
A summary of statewide LULC change on the baseline change map is presented in Table 2. Area is measured in acres.
Table 2: Baseline 1992 to 2001 LULC change
k
Agriculture
Barren
Forest
Grassland / Shrub
Open Water
Urban
Wetlands
1992 Totals
j
Agriculture
24,120,804
2,021
97,638
32,365
86,360
43,362
110,464
24,493,015
Barren
120
63,541
1,458
182
3,807
31
897
70,036
Forest
89,799
6,689
14,393,111
19,130
14,044
22,424
112,967
14,658,164
Grassland / Shrub
36,275
149
88,885
2,093,448
382
9,278
21,756
2,250,173
Open Water
12,487
3,127
21,627
3,287
3,032,070
742
25,059
3,098,400
Urban
6,523
13
2,189
661
3,134
2,656,976
4,360
2,673,857
Wetlands
71,394
557
167,702
23,349
17,577
6,966
6,453,303
6,740,849
2001 Totals
24,337,402
76,098
14,772,610
2,172,424
3,157,374
2,739,779
6,728,806
1B. Alternative LULC change scenarios
We created 5 different LULC change maps where the 2001 LULC pattern on each deviated from the observed pattern. In the first two alternative scenarios we prevented some observed changes from occurring, namely in the no agricultural expansion scenario no new agricultural area was established in Minnesota between 1992 and 2001 and in the no urban expansion scenario no new urban land was established in Minnesota between 1992 and 2001. In these scenarios, grid cells that converted to the LULC of interest on the baseline change map remained in their 1992 LULC. Statewide LULC summary statistics for these two scenarios as compared to the baseline are given in Table 3. Area is measured in acres.
Table 3: No agricultural and no urban expansion scenarios
Area of 1992 LULC that was not lost to agriculture or urban use by 2001
Scenario
Agriculture
Barren
Forest
Grassland / Shrub
Open Water
Urban
Wetlands
No agricultural expansion
NA
120
89,799
36,275
12,487
6,523
71,394
No urban expansion
43,362
31
22,424
9,278
742
NA
6,966
We also generated two scenarios in which agriculture and forest area expanded compared to the baseline pattern of change. In the agricultural expansion scenario, all grid cells in land capability classes (LCCs) 1 and 2 (land with the most agriculturally productive soils and gentlest slopes; (USDA-NRCS 2009), which were not in agriculture as of 2001 on the baseline map were placed into agriculture as of 2001. Grid cells of LCCs of 1 and 2 in urban area and open water as of 1992 and 2001 and located in Cook County were the only exceptions to this rule (no LCC data is available for Cook County, an area almost devoid of agricultural land use). Further, all observed agricultural land abandonment from 1992 to 2001 in areas with LCCs of 1 or 2 was prevented and retained as agriculture in 2001. All other land-use change dynamics in this scenario are given by the baseline LULC change map. Statewide LULC change summary statistics for the agricultural expansion scenario are given in Table 4. Area is measured in acres.
Table 4: Agricultural Expansion Scenario
k
Agriculture
Barren
Forest
Grassland / Shrub
Open Water
Urban
Wetlands
1992 Totals
j
Agriculture
24,199,399
1,163
81,592
27,103
73,007
24,427
88,669
24,495,361
Barren
6,759
56,956
1,458
182
3,775
31
878
70,040
Forest
2,099,674
6,270
12,403,626
15,736
12,812
18,345
101,719
14,658,184
Grassland / Shrub
629,998
143
78,273
1,515,360
359
6,884
19,357
2,250,374
Open Water
19,540
3,030
18,442
2,364
3,031,033
544
22,416
3,097,369
Urban
7,907
13
1,768
433
2,771
2,657,021
3,988
2,673,901
Wetlands
715,553
492
153,330
21,291
15,851
5,814
5,828,567
6,740,898
2001 Totals
27,678,830
68,069
12,738,490
1,582,469
3,139,609
2,713,065
6,065,594
Note: The state area for the LULC change map for this scenario is 1,633 acres greater than for the baseline LULC change map. Slight spatial differences in the digital maps of LULC change and LCC used in this scenario generated a scenario LULC map that does not exactly match the extent of the baseline scenario map.
In the forestry expansion scenario, all non-forested areas as of 2001 on the baseline change map with a forest productivity index (FPI) of 70 or greater (on a scale of 0 to 100) in the counties of Roseau, Lake of the Woods, Beltrami, Clearwater, Becker, Hubbard, Cass, Morrison, Mille Lacs, Kanabec, Aitkin, Itasca, Carlton, and parts of St. Louis were put into forest land use as of 2001 (FPI measures the suitability of land for managed forest growth and, like LCC data, comes from USDA-NRCS 2009). These counties produce a majority of the states timber products (personal conversation, Grant Domke). As in the agricultural expansion scenario, grid cells with FPI of 70 or greater classified as urban area and open water in 1992 and in 2001 retained their observed 2001 cover. Further, in this scenario, all observed forest abandonment from 1992 to 2001 in the select counties in grid cells with a FPI 70 were prevented and retained as forest cover in 2001. Land-use change dynamics in areas outside these counties or in areas in these counties but with a FPI < 70 are given by the baseline LULC change map. Statewide LULC change summary statistics for the forestry expansion scenario are presented in Table 5. Area is measured in acres.
Table 5: Forestry Expansion Scenario
k
Agriculture
Barren
Forest
Grassland / Shrub
Open Water
Urban
Wetlands
1992 Totals
j
Agriculture
23,647,274
2,005
572,513
29,806
85,912
42,490
106,354
24,486,353
Barren
120
63,179
1,820
182
3,806
31
897
70,036
Forest
79,742
6,578
14,403,799
17,613
13,492
20,806
107,135
14,649,166
Grassland / Shrub
26,944
143
170,584
2,023,350
361
7,466
20,227
2,249,075
Open Water
12,355
3,108
22,111
3,232
3,029,195
708
24,789
3,095,498
Urban
6,521
13
2,195
658
3,134
2,654,935
4,359
2,671,815
Wetlands
67,081
534
262,688
22,354
17,063
6,635
6,359,639
6,735,994
2001 Totals
23,840,038
75,560
15,435,711
2,097,196
3,152,962
2,733,070
6,623,399
53,957,937
Note: The state area on this LULC change map is 26,555 acres less than it is on the baseline LULC change map. Slight spatial differences in the digital maps of LULC and forest productivity index generated a scenario LULC map that does not exactly match the extent of the baseline scenario map.
Finally, we generated a land conservation scenario whereby land within a 100 m buffer of all Minnesota River Basin streams (MN DNR 2009a) and some agricultural lands throughout the rest of the state were restored to natural cover by 2001. First, if a grid cell located within the stream buffer was designated agriculture use in 1992 and 2001 under the baseline change scenario then it was restored to its pre-settlement vegetation type as of 2001 based on a pre-settlement vegetation map (MN DNR 2009b). The potential restored natural covers include restored open water, restored forest, restored grassland, restored wetland, and unknown restored cover. Second, all buffer grid cell transitions from non-urban use to agriculture use between 1992 and 2001 in the baseline scenario were prevented; instead, these grid cells were assigned restored versions of their 1992 cover (except for grid cells with barren cover in 1992; these grid cells were assigned their pre-settlement vegetation). Otherwise, each non-urban and non-agriculture grid cell in the buffer was placed in a restored version of its baseline scenario 2001 LULC type (e.g., if a grid cell in the buffer had a 2001 cover of grassland on the baseline LULC change map then it became restored grassland in 2001 on the conservation scenario LULC change map). The one exception to this rule was buffer grid cells with barren land; barren is not a restored cover. Therefore, barren grid cells in the buffer on the baseline scenario in 2001 were assigned their pre-settlement vegetation cover in the conservation scenario. Buffer grid cells in urban use in 2001 on the baseline map remained urban in this scenario.
For areas outside the stream buffer, we attempted to convert all agriculture use grid cells located on low agriculturally productive soils, LCC categories 5 through 8, as of 1992 and 2001 on the baseline change map to their pre-settlement vegetation types as of 2001. Further, if any grid cell in LCC categories 5 through 8 on the baseline change map was converted to agriculture use between 1992 and 2001 then its conversion was blocked; instead, for this scenario these grid cells retained their 1992 LULC. Land-use change dynamics in all other areas are given by the baseline LULC change map.
We use the word attempted in the paragraph above because our conversion process in areas outside the buffer area was not perfect. The LCC raster grids we used in this scenario did not perfectly align spatially with the baseline LULC change map. Therefore, we ended up converting some agricultural land in LCCs 1 through 4 to their natural land covers and not converting some agricultural land in LCCs 5 through 8 to their natural land covers. Throughout the state of Minnesota agriculture use on land with LCCs of 5 and 6 under this scenario was reduced by 60% versus baseline change and agriculture use on land with LCCs of 7 and 8 was reduced by 55% versus baseline change. Conversely, this scenario reduced the amount of agriculture use on land with LCCs of 1 and 2 and 3 and 4 by 5% and 9% versus baseline change, respectively. Some of this reduction in agriculture land use on grid cells with LCCs of 1 through 4 was due to universal conservation within the stream buffer. However, some of the agricultural land loss in high quality soils can be attributed to the misalignment of soil and LULC maps.
In this scenario the index k expands to 8 (restored open water), 9 (restored forest), 10 (restored grassland / shrub), and 11 (restored wetlands). The area in restored unknown was randomly assigned a restored land-use type on the final conservation land-use change map (i.e., it was given a k value between 8 and 11).
Statewide LULC change summary statistics for the conservation scenario are presented in Table 6. Area is measured in acres.
Table 6: Conservation Scenario
K
Agriculture
Barren
Forest
Grassland / Shrub
Open Water
Urban
Wetlands
j
Agriculture
22,092,135
1,992
97,122
32,194
83,740
43,359
108,258
Barren
109
62,705
1,458
182
3,807
31
897
Forest
74,648
6,685
14,339,576
19,114
14,012
22,424
112,673
Grassland / Shrub
32,478
149
88,885
2,045,431
381
9,278
21,756
Open Water
8,491
3,102
21,248
3,052
2,988,954
742
24,340
Urban
4,947
13
2,087
640
3,084
2,658,414
4,159
Wetlands
58,868
555
167,453
23,324
17,411
6,962
6,309,118
2001 Totals
22,271,676
75,202
14,717,829
2,123,936
3,111,387
2,741,209
6,581,201
Table 6: Conservation Scenario (continued)
k
Restored Forest
Restored Grassland / Shrub
Restored Open Water
Restored Wetlands
Restored Unknown
1992 Totals
j
Agriculture
732,994
910,611
16,614
372,291
980
24,492,290
Barren
175
506
15
149
0
70,034
Forest
50,890
16
29
272
0
14,640,340
Grassland / Shrub
0
51,703
0
0
0
2,250,060
Open Water
373
237
18,322
667
0
3,069,528
Urban
102
21
47
201
0
2,673,715
Wetlands
22
19
127
119,430
0
6,703,289
2001 Totals
784,557
963,114
35,154
493,010
980
53,899,255
Note: The state area on this LULC change map is 85,236 less than it is on the baseline LULC change map. Slight spatial differences in the in the digital maps of LULC, LCC types, and the pre-settlement vegetation map generated a scenario change map that does not exactly match the extent of the baseline scenario map.
2. Carbon storage and sequestration
2a. Calculating land management area in each county
For each county in Minnesota under each scenario we determined the amount of acreage involved in each LULC transition jk. Some of these transitions involved land publically managed for conservation purposes (land in land stewardship categories 1 or 2 on the Minnesota Stewardship map; see MN DNR 2000). We assumed publically conserved land could be involved in forest to forest, forest to grassland/shrub, forest to wetlands, grassland/shrub to forest, grassland/shrub to grassland/shrub, grassland/shrub to wetlands, wetlands to forest, wetlands to grassland/shrub, and wetlands to wetlands transitions (restored land is not part of this classification and is discussed below). To allocate each countys publically conserved land across these transition types in county i, PubPAijk, we use the following formula,
(1)
where PubPAi is the publically conserved area in county i according to the Minnesota Stewardship map, Aijk is the area of county i that transitions from LULC j to LULC k form 1992 to 2001 under a scenario, and N is the set of LULC transitions listed above that can involve publically conserved land. PubPAijk = 0 for all LULC transitions jk that cannot involve publically conserved land. Therefore, the area of working land in county i involved in transition jk from 1992 to 2001, PrivAijk, under all scenarios except for the conservation scenario is given by,
(2)
Let RestAijk indicate the area in county i that transitioned from j to one of the restored land uses k (8 11). We assume that no publically conserved area was involved in these transitions. Therefore, in the conservation scenario PrivAijk for each j is given by,
(3)
(4)
(5)
(6)
(7)
(8)
(9)
The fraction of publically conserved land in each county i is given in Table 7.
Table 7. Percentage of county area that is publically conserved
County
Percentage
County
Percentage
County
Percentage
27001
2.78%
27059
0.00%
27117
0.66%
27003
2.88%
27061
16.53%
27119
3.25%
27005
7.58%
27063
1.18%
27121
3.54%
27007
7.25%
27065
0.00%
27123
7.35%
27009
0.09%
27067
2.83%
27125
0.09%
27011
4.01%
27069
2.65%
27127
0.13%
27013
0.70%
27071
4.04%
27129
0.47%
27015
0.56%
27073
3.47%
27131
0.74%
27017
1.52%
27075
59.12%
27133
0.56%
27019
2.94%
27077
1.37%
27135
0.80%
27021
19.54%
27079
0.33%
27137
23.18%
27023
0.59%
27081
0.49%
27139
5.22%
27025
2.37%
27083
1.12%
27141
11.50%
27027
3.18%
27085
0.54%
27143
0.38%
27029
3.93%
27087
1.72%
27145
1.41%
27031
69.91%
27089
5.45%
27147
0.36%
27033
0.95%
27091
0.11%
27149
2.69%
27035
0.73%
27093
1.20%
27151
1.44%
27037
2.30%
27095
2.42%
27153
0.20%
27039
0.09%
27097
2.45%
27155
1.05%
27041
2.42%
27099
0.11%
27157
3.67%
27043
0.11%
27101
0.54%
27159
0.14%
27045
0.77%
27103
0.33%
27161
0.00%
27047
0.75%
27105
0.16%
27163
2.07%
27049
0.98%
27107
0.66%
27165
0.00%
27051
2.81%
27109
0.35%
27167
0.87%
27053
7.24%
27111
2.20%
27169
2.76%
27055
4.19%
27113
0.00%
27171
1.40%
27057
0.69%
27115
4.56%
27173
0.76%
2b. Carbon storage in 1992
The carbon model accounts for carbon stored in above- and below-ground biomass and in the soil (in forests above ground biomass carbon includes the carbon in deadwood, understory, and the forest floor). Total carbon storage in 1992 on PubPAijk where j = 4 (forest) is determined by assuming that the entire PubPAijk area is covered by a forest mix typically found in county i that, on average, has 45-year old trees. The typical forest mix in a county is found with US Forest Inventory Analysis (FIA). The three types of forest in Minnesota according to the FIA are Aspen-Birch, Oak-Hickory, and White-Red-Jack Pine and therefore, the carbon stored in county is publically protected forests as of 1992, PubPCi4k, is given by the relative mix of these forests in i,
(10)
where, ABi, OHi, and JPi are the fraction of forest area in Aspen-Birch, Oak-Hickory, and White-Red-Jack Pine, respectively, in county i according to the FIA, BAB45 and SAB45 gives biomass carbon and SOC, respectively, on an acre of a 45-year old Aspen-Birch stand in the Northern Lake States (NLS) region (Minnesotas region), BOH45 and SOH45 gives biomass carbon and SOC, respectively, on an acre of a 45-year old Oak-Hickory stand in the NLS region, and BJP45 and SJP45 gives the biomass carbon and SOC, respectively, on an acre of a 45-year old White-Red-Jack Pine stand in the NLS region. Forest stand carbon storage numbers come from Smith et al. (2006)s reforestation tables for the NLS (as opposed to NLSs afforestation tables). At 45 years of age, these forest types are not in storage equilibrium and sequester carbon as they continue to age. See Table 8 for values of for each county i.
Table 8: Metric tons of carbon stored in an acre of publically conserved forest by county as of 1992.
County
Carbon
County
Carbon
County
Carbon
County
Carbon
27001
88.27
27043
83.88
27089
88.43
27131
73.50
27003
73.50
27045
75.20
27091
73.50
27135
89.45
27005
87.62
27047
73.50
27095
84.65
27137
89.62
27007
89.44
27049
78.34
27097
82.96
27139
81.50
27009
81.26
27051
73.50
27099
73.50
27141
76.14
27013
73.50
27053
73.50
27107
87.68
27143
73.50
27015
73.50
27055
74.80
27109
74.35
27145
74.55
27017
88.85
27057
90.04
27111
82.97
27147
73.50
27021
88.49
27059
81.11
27113
85.77
27153
81.99
27025
79.79
27061
89.36
27115
88.26
27157
75.30
27027
76.57
27065
84.71
27119
84.49
27159
89.21
27029
89.07
27069
88.58
27121
73.50
27161
73.50
27031
89.55
27071
89.36
27125
89.00
27169
73.70
27035
85.26
27075
89.38
27127
73.50
27171
79.31
27037
75.98
27077
89.66
27129
73.50
27173
73.50
27039
73.50
Note: These values reflect the areal mix of forest type found in each county according to the US FIA. For unlisted counties we use the average of all reported county-level data.
We assume all other land-use grid cells, including those in working forests, have attained their LULC js biomass and SOC storage steady-state levels or equilibrium as of 1992. Per acre equilibrium levels for all non-working forest LULC types and their sources are listed in Tables 9 and 10.
Table 9. Metric tons of stored soil organic carbon (SOC) per acre within the first meter of the soil profile by LULC type
LULC
SOC
Mg acre-1
Mean (SD)
N of estimates
Notes
Source
Wetland prairie pothole
50.10 (18.25)
3
Equilibrium achieved at 75 years.
Slobodian et al. 2002, Bedard-Haughn et al. 2006, Euliss et al. 2006
Wetland peatland
530.15
1
Equilibrium achieved at 2000 years.
Gorham 1991
Grassland
39.98 (16.23)
12
Equilibrium achieved at 50 years.
Frank et al. 1995, Zan et al. 2001, Frank et al. 2002, Coleman et al. 2004, Al-Kaisi et al. 2005, Liebig et al. 2005, McLauchlan et al. 2006, Omonode et al. 2007
Agriculture
29.18
(8.58)
41
Equilibrium achieved at 20 years. Corn and soybean rotation using conventional agricultural practices and average fertilizer applications.
Bauer et al. 1987, Hansen and Strong 1993, Frank et al. 1995, Biondini et al. 1998, Schuman et al. 1999, Yang and Wander 1999, Yang and Kay 2001, Halvorson et al. 2002, Paul et al. 2003, DeGryze et al. 2004, Al-Kaisi et al. 2005, Liebig et al. 2005, Puget and Lal 2005, Russell et al. 2005, Euliss et al. 2006, Venterea et al. 2006, Gl et al. 2007, Kucharik 2007, Morris et al. 2007, Omonode et al. 2007, Franzluebbers et al. 2009
Urban
33.47
1
Equilibrium achieved at 50 years.
Fissore et al. in press
Note: Different types of wetlands have different carbon storage potential. In the northern part of the state wetlands are typically peatlands with very high carbon storage in their soils (Gorham 1991). Based on a state map of peatlands from the Minnesota Department of Natural Resources, we assumed that wetlands in Aitkin, Beltrami, Carlton, Cass, Itasca, Koochiching, Lake, Roseau, and St. Louis Counties were peatlands. Wetlands in all other counties were assumed to be regular wetlands or prairie potholes, which have a lower SOC storage value.
Table 10. Metric tons of stored biomass carbon per acre by LULC type
LULC
Biomass
Mg acre-1
Mean (SD)
N of estimates
Notes
Source
Wetland prairie pothole
n/a
n/a
Wetland peatland
n/a
n/a
Grassland
4.09
(0.77)
10
Equilibrium achieved at 50 years. Belowground biomass is the only source of biomass carbon considered.
Risser et al. 1981, Bransby et al. 1998, Oesterheld et al. 1999, Zan et al. 2001, Baer et al. 2002, Tilman et al. 2006, Nelson et al. 2009
Agriculture
1.94
(0.93)
6
Equilibrium achieved at 20 years. Belowground biomass is the only source of biomass carbon considered. Pastures are continuously grazed at 2 head per hectare. Hayfields assumed to be 50% of natural grassland.
Schuman et al. 1999, IPCC 2006
Urban
7.00
1
Equilibrium achieved at 50 years.
Fissore et al in press
To find carbon storage levels in working forest area in a county in 1992 we use the following formula,
(11)
where, BABFaust and SABFaust gives the average metric tons of biomass carbon and SOC stored in an acre of an even-age stand of managed Aspen-Birch with a rotation time of 60 years in the Northern Lake States (NLS) region, BOHFaust and SOHFaust gives the average biomass carbon and SOC stored in an acre of an even-age stand of managed Oak-Hickory with a rotation time of 30 years in the NLS region, and BJPFaust and SJPaust gives the average biomass carbon and SOC stored in an acre of a even-age stand of managed White Red Jack Pone with a rotation time of 30 years in the NLS region. These rotation times are given by the forest-type specific Faustmann volume estimate that comes with the FIA data. The Faustmann volume indicates the economically optimal volume at which to cut a tree stand. According to Smith et al. (2006) the Fuastmann volume for an Aspen-Birch stand in the NLS region, 1,688 cubic feet per acre, is achieved after approximately 60 years of growth, the Fuastmann volume for Oak-Hickory in the NLS region, 443 cubic feet per acre, is achieved after approximately 30 years of growth, and the Fuastmann volume for White-Red-Jack Pine in the NLS region, 824 cubic feet per acre, is achieved after approximately 30 years of growth. Thus, BxFaust and SxFaust are given with the following,
(12)
(13)
where, RxFaust is the Faustmann rotation time for forest type x and Bxz and Sxz is the metric tons of biomass carbon and SOC stored in an acre of forest type x of age z (Smith et al. 2006). Average overall rotation time in a county is given by,
. (14)
See Table 11 for estimates of by county.
Table 11: Metric tons of carbon stored in an acre of working forests by county in 1992
County
Carbon
Rotation Time (Ri)
County
Biomass
Rotation Time (Ri)
27001
78.68
57.91
27087
76.38
55.23
27003
54.26
30.00
27089
79.34
58.90
27005
75.62
53.26
27091
54.26
30.00
27007
78.20
55.96
27095
72.99
51.58
27009
60.20
32.76
27097
69.15
46.58
27013
54.26
30.00
27099
54.26
30.00
27015
54.26
30.00
27107
78.09
57.45
27017
79.37
58.53
27109
55.70
31.65
27021
76.42
53.78
27111
69.57
47.30
27025
64.84
42.18
27113
74.88
53.75
27027
59.42
35.94
27115
77.91
56.59
27029
78.51
56.84
27119
72.73
51.27
27031
78.98
57.19
27121
54.26
30.00
27035
71.61
48.61
27125
80.30
60.00
27037
58.43
34.80
27127
54.26
30.00
27039
54.26
30.00
27129
54.26
30.00
27043
71.69
50.08
27131
54.26
30.00
27045
57.12
33.29
27135
78.43
56.34
27047
54.26
30.00
27137
78.82
56.85
27049
62.40
39.37
27139
67.70
45.48
27051
54.26
30.00
27141
56.72
31.70
27053
54.26
30.00
27143
54.26
30.00
27055
56.44
32.51
27145
56.02
32.03
27057
75.01
49.87
27147
54.26
30.00
27059
64.69
40.67
27153
67.94
45.43
27061
78.70
56.89
27157
57.29
33.49
27065
73.10
51.70
27159
70.16
42.30
27069
79.59
59.18
27161
54.26
30.00
27071
79.43
58.15
27169
54.60
30.39
27075
79.40
58.09
27171
64.03
41.25
27077
78.72
56.64
27173
54.26
30.00
Note: These values reflect the areal mix of forest type found in each county according to the US FIA. For unlisted counties we use the use the average of all reported county-level data.
2c. Annual carbon sequestration from 1992 to 2001
Given our 1992 steady-state assumptions for all j except j = 4 (forest) on publically conserved land, any land-use transition jj (the grid cell does not change land-use between 1992 and 2001) other than j = 4 on publically conserved land generated no annual carbon sequestration between 1992 and 2001. Namely,
(15)
(16)
where, andindicate the annual metric tons of carbon sequestered on publically conserved working land, respectively, with land-use transition jj in county i. Further, any grid cell transition from a land type to its restored type except for j = 4 to k = 9 generated no annual carbon sequestration between 1992 and 2001. Namely,
(17)
(18)
(19)
For jj transitions where j = 4 on publically conserved land in county i, the annual carbon sequestration rate is equal to the difference between carbon storage in is typical forest mix that, on average, has 55-year old trees and carbon storage in is typical forest mix that, on average, has 45-year old trees, divided by 10:
(20)
We divide by 10 because sequestration occurs over the entire 10 year time period. We ignore the SOC pool because it is already in equilibrium inacross all i as of 1992 (Smith et al. 2006).
For transitions on publically conserved land where j = 4 and k 4 and k < 8 we assume the transition occurs in 1996 and therefore, the annual carbon sequestration rate is given by,
(21)
where, TBk indicates the number of years we assume it takes LULC k to reach its biomass carbon storage equilibrium, TSk indicates the number of years we assume it takes LULC k to reach its SOC storage equilibrium, and Bk and Sk indicates ks per acre equilibrium biomass carbon and SOC storage values (see Tables 8 and 9 for TBk, TSk, Bk, and Sk values). The ratios 5/TBk and 5/TSk indicate the portion of ks equilibrium storage levels that has been reached as of 2001, 5 years after the transition. For example, we assume newly established grassland requires 50 years to reach its equilibrium SOC content. Therefore, a grid cell that transitioned to grassland 5 years prior to 2001 is 5 / 50 = 1 /10 to its new equilibrium SOC content given the prior equilibrium SOC content on the grid cell. We divide biomass sequestration by 10 because the forest biomass is sequestering carbon from 1992 to 1996 and then ks biomass is sequestering from 1996 to 2001; in other words, there is annual flux across all 10 years in the biomass pool of land transitioning from j = 4 to k 4 and k < 8 on publically conserved land. We divide total SOC sequestration by 5 because sequestration in the soil only begins to take place once land use changes; according to Smith et al. (2006), the SOC pool in all forests in the NLS is in equilibrium after 45 years of stand growth.
For transitions on working land and transitions to restored land where j = 4 and k 4 or 9 we assume the transition occurs in 1996 and therefore, the annual carbon sequestration rate is given by,
(22)
(23)
In this case we divide biomass and soil sequestration by 5 because carbon flux in the biomass and SOC pools only begins at the time of transition.
For all transitions on publically conserved land where j 4 and k = 4 and all transitions on working land with j 4 to restored land with k = 9, the annual sequestration rate is given by
(24)
(25)
In this case we assume that a 95-year old forest is its equilibrium age and thus multiply storage at 95 years by 5 / 95 to determine storage 5 years after transition. We divide biomass and SOC sequestration by 5 because a carbon flux only begins at the time of transition.
For all transitions on working land where j 4 and k = 4, the annual sequestration rate is given by,
(26)
In this case we divide biomass and soil sequestration by 5 because a carbon flux only begins at the time of transition.
For all transitions on publically conserved, working land, and to restored land where j 4 and k 4 or 9, the annual sequestration rate is given by,
(27)
(28)
(29)
In this case we divide biomass and soil sequestration by 5 because a terrestrial carbon flux only begins at the time of transition.
For all transitions on publically conserved land where j = 4 and k = 9, annual sequestration is given by,
(30)
In this case we use half of the sequestration associated with forest maturation in county i from an average age of 45 to 55 to proxy for the sequestration generated by a working forest in equilibrium that begins a transition to a restored forest as of 1997 (thus multiplication by 0.5). We divide biomass and soil sequestration by 5 because a carbon flux only begins at the time of transition.
3. Biodiversity Conservation Model: Habitat Extent and Quality
For each of the six scenarios we measure the quality and spatial extent of habitat for three species groups: all terrestrial species, forest-interior-breeding songbirds, and grassland-breeding songbirds. We combine information on a scenarios LULC pattern, the spatial pattern of species habitat, and the spatial pattern of designated threats to produce a map of available habitat and its relative quality for the state of Minnesota.
First, for each species group we assign a habitat suitability score to each LULC type ranging from 0 to 1, with non-habitat scored as 0 and the most suitable habitat scored as 1, with marginal habitat scored in between. For example, grassland songbirds may prefer native prairie habitat above all other habitat types (habitat suitability = 1), but will also make use of a managed hayfield (habitat suitability = 0.5). For this study we scored habitat differently based on its level of state and federal protection. We used the Minnesota Department of Natural Resources GAP data on stewardship for the state: code 1 and 2 are publicly protected lands, code 3 is land under an easement, and code 4 private lands (MN DNR 2000). We assume the habitat quality potential of a LULC increases with the level of protection. See the last column of Tables 12-14 for information on habitat suitability scores of LULC types for general terrestrial biodiversity, forest breeding birds, and grassland breeding birds.
Table 12. Sensitivity to degradation sources and habitat suitability weights each LULC type for General Terrestrial Biodiversity. Higher numbers indicate more sensitivity or more suitable habitat.
LULC
Agriculture area
Urban area
Primary roads
Secondary roads
Light roads
Habitat Suitability
Open water
0.00
0.00
0.00
0.00
0.00
0.00
Urban
0.00
0.00
0.00
0.00
0.00
0.00
Barren
0.00
0.00
0.00
0.00
0.00
0.00
Forest private ownership*
0.70
0.80
0.80
0.60
0.40
0.85
Forest private ownership w/ easement*
0.70
0.80
0.80
0.60
0.40
0.95
Forest public ownership*
0.70
0.80
0.80
0.60
0.40
1.00
Grassland private ownership*
0.60
0.70
0.70
0.50
0.40
0.85
Grassland private ownership w/ easement*
0.60
0.70
0.70
0.50
0.40
0.95
Grassland public ownership*
0.60
0.70
0.70
0.50
0.40
1.00
Agriculture private ownership*
0.00
0.50
0.50
0.40
0.40
0.20
Agriculture private ownership w/ easement*
0.00
0.50
0.50
0.40
0.40
0.30
Agriculture public ownership*
0.00
0.50
0.50
0.40
0.40
0.20
Wetland private ownership*
0.60
0.80
0.80
0.60
0.40
0.85
Wetland private ownership w/ easement*
0.60
0.70
0.70
0.50
0.40
0.95
Wetland public ownership*
0.60
0.80
0.80
0.60
0.40
1.00
Note: The asterisks denote natural lands that are managed for a variety of economic, environmental, and recreational uses. We subdivided forest, grassland, agricultural, and wetlands based on conservation management codes from the GAP Stewardship database containing land ownership information for the entire state of Minnesota (MNDNR 2000).
Table 13. Sensitivity to degradation sources and habitat suitability weights each LULC type for Breeding Forest Interior Songbirds. Higher numbers indicate more sensitivity or more suitable habitat.
LULC
Agriculture area
Urban area
Primary roads
Secondary roads
Light roads
Habitat Suitability
Open water
0.00
0.00
0.00
0.00
0.00
0.00
Urban
0.00
0.00
0.00
0.00
0.00
0.00
Barren
0.00
0.00
0.00
0.00
0.00
0.00
Forest private ownership*
0.70
0.80
0.80
0.60
0.40
0.90
Forest private ownership w/ easement*
0.70
0.80
0.80
0.60
0.40
0.95
Forest public ownership*
0.70
0.80
0.80
0.60
0.40
1.00
Grassland private ownership*
0.60
0.70
0.70
0.50
0.40
0.10
Grassland private ownership w/ easement*
0.60
0.70
0.70
0.50
0.40
0.10
Grassland public ownership*
0.60
0.70
0.70
0.50
0.40
0.10
Agriculture private ownership*
0.00
0.50
0.50
0.40
0.40
0.05
Agriculture private ownership w/ easement*
0.00
0.50
0.50
0.40
0.40
0.05
Agriculture public ownership*
0.00
0.50
0.50
0.40
0.40
0.05
Wetland private ownership*
0.60
0.80
0.80
0.60
0.40
0.50
Wetland private ownership w/ easement*
0.60
0.80
0.80
0.60
0.40
0.50
Wetland public ownership*
0.60
0.80
0.80
0.60
0.40
0.50
Note: The asterisks denote natural lands that are managed for a variety of economic, environmental, and recreational uses. We subdivided forest, grassland, agricultural, and wetlands based on conservation management codes from the GAP Stewardship database containing land ownership information for the entire state of Minnesota (MNDNR 2000).
Table 14. Sensitivity to degradation sources and habitat suitability weights each LULC type for Breeding Grassland Songbirds. Higher numbers indicate more sensitivity or more suitable habitat.
LULC
Agriculture area
Urban area
Primary roads
Secondary roads
Light roads
Habitat Suitability
Open water
0.00
0.00
0.00
0.00
0.00
0.00
Urban
0.00
0.00
0.00
0.00
0.00
0.00
Barren
0.00
0.00
0.00
0.00
0.00
0.00
Forest private ownership*
0.00
0.00
0.00
00.00
0.00
0.00
Forest private ownership w/ easement*
0.00
0.00
0.00
0.00
0.00
0.00
Forest public ownership*
0.00
0.00
0.00
0.00
0.00
0.00
Grassland private ownership*
0.50
0.70
0.50
0.40
0.30
0.90
Grassland private ownership w/ easement*
0.50
0.70
0.50
0.40
0.30
0.95
Grassland public ownership*
0.50
0.70
0.50
0.40
0.30
1.00
Agriculture private ownership*
0.30
0.50
0.50
0.40
0.40
0.30
Agriculture private ownership w/ easement*
0.30
0.50
0.50
0.40
0.40
0.50
Agriculture public ownership*
0.30
0.50
0.50
0.40
0.40
0.50
Wetland private ownership*
0.60
0.80
0.80
0.60
0.40
0.40
Wetland private ownership w/ easement*
0.60
0.80
0.80
0.60
0.40
0.20
Wetland public ownership*
0.60
0.80
0.80
0.60
0.40
0.20
Note: The asterisks denote natural lands that are managed for a variety of economic, environmental, and recreational uses. We subdivided forest, grassland, agricultural, and wetlands based on conservation management codes from the GAP Stewardship database containing land ownership information for the entire state of Minnesota (MNDNR 2000).
Second, we evaluate the impact of threats, which can degrade and reduce habitat quality in a grid cell either directly (e.g., habitat loss) or indirectly (e.g., edge effects from habitat fragmentation). Designated threats for this study include urban and agricultural areas, and primary, secondary, and tertiary or light roads. Urban and agriculture areas were quantified directly from the scenario LULC map while roads were evaluated using a statewide road layer (MN DOT 2009). The impact of threats is mediated by three factors.
The first factor we determine is the relative impact of each threat on a habitat grid cell. Because some threats are more damaging for all habitats, we assign a relative impact score to all threats (see Table 15). A threats weight, wr, indicates the relative negative impact of a threat. For example, if urban grid cell has a weight of 1 and road cell a weight of 0.5 then the urban area causes twice the degradation, all else equal.
Table 15. Weights and effective distances for degradation sources
Degradation source
Maximum effective distance of degradation source (km)
Weight
Agriculture area
4.0
0.8
Urban area
5.0
1.0
Primary roads
3.0
0.8
Secondary roads
2.0
0.7
Light roads
1.0
0.5
Second, we assign a threat-mitigating factor represented as the distance between the grid cell and the threat and the impact of the threat across space. If a grid cell is within the assigned impact distance of a particular threat then the grid cell is within the threats degradation zone. In general, the severity of a threat on habitat quality decreases as distance from the habitat grid cell to the threat increases, so that grid cells that are proximate to a threat will experience higher degradation or lower habitat quality. We use an exponential distance-decay rate to describe how a threats impact diminishes over space. For example, if the maximum distance of a threat is set at 1 km, the impact of the threat will decline by ~ 50% when a habitat pixel is 200 m from the defined threat. The impact of threat ry on habitat in grid cell x, given by irxy, is normalized by the maximum effective distance of threat r, drmax, and is represented by the following equation,
(31)
where, dxy is the distance between grid cell x and the source of threat r, grid cell y.
Third, we determine the relative sensitivity of a habitat type in a grid cell to all threats and is the final input used to generate the total degradation level a grid cell. Let Sjr[0,1] indicate the sensitivity of habitat type j to degradation source r where values closer to 1 indicate greater sensitivity to a threat. See Tables 12-14 for all Sjr values for all functional groups. For example, a forest habitat patch may suffer more degradation from an adjacent pasture (more sensitive) than a grassland habitat patch (lower sensitivity). The model assumes the more sensitive a habitat type is to a threat, the more degradation to that habitat will be caused by that degradation source. A habitats sensitivity to threats is based on general principles from landscape ecology (e.g., Lindenmayer et al. 2008).
Therefore, the total threat level in grid cell x with LULC or habitat type j is given by Dxj,
(32)
where, y indexes all grid cells on the landscape (including x). If Sjr = 0 then Dxj is not a function of threat r.
We calculate the quality of habitat in parcel x of LULC j by Qxj where,
(33)
Therefore, when Qxj = 100 the quality of habitat in grid cell x is at its maximum.
For each of the three measures of biodiversity (general terrestrial biodiversity, grassland songbirds and forest songbirds), we give a habitat quality landscape score for each scenario, which is an aggregate of all grid cell-level habitat quality scores on the landscape under each scenario.
4. Agriculture Model
We created a per acre yield function for each crop type in each county as a function of soil type and technology change using observed data on soil-yield relationships (USDA-NRCS 2009). We then multiplied crop area stratified by soil type in a county in 1992 and 2001 for a scenario by the yield function for that crop and soil type to generate expected county-level production of each crop for both years under the scenario. To generate a county-level estimate of net revenue from agriculture for 1992 and 2001 we multiplied each crops county-level production by county-level price for the crop, subtracted county-level production costs for the crop, and summed across all county-level crop net return values.
Specifically, let Agit indicate the net value of agricultural production in county i in year t = 1992 (92) or 2001 (01)
(34)
(35)
where m = 1,,M indexes crops, s = 1,,5 indexes the group of LCCs 1 and 2 (s = 1), 3 and 4 (s = 2), 5 and 6 (s = 3), 7 and 8 (s = 4), and unknown LCC (s = 5), Ai6kms indicates the acres in county i in agricultural land use in 1992 (j = 6) in LCC group s that is being used to produce crop m, Ai6k =, Aij6ms indicates the area in county i in agricultural land use in 2001 (k = 6) in LCC group s that is being used to produce crop m, Aij6 =, ptim indicates the price of a unit of ms yield in county i in year t (e.g., dollars per bushel of corn in 1992), ctim indicates the per acre cost of producing m on an acre of land in county i in year t, Yims indicates the per acre yield of crop m on LCC group s in county i, and is the observed state-wide rate of yield improvement in crop m from 1992 to 2001.
The yield function for crop m in county i on LCC group s for groups s = 1,,4 was determined by averaging across all observed non-irrigated yields of m in i on LCC category group s (USDA-NRCS 2009). Yield on unknown LCC type (s = 5) is given by,
(36)
where = 1 if there is one or more observed yields of m on s in the USDA-NRCS (2009) database and equals 0 otherwise. We determined county-level yields for corn, corn silage, soybeans, alfalfa hay, pasture, oats, barley, and spring wheat in each i on each s. See Table 16 for crop yields by county and soil LCC group.
Table 16: Average Crop Yields by Land Classification Category (LCC) Group and County
Alfalfa Hay (Short Tons / Acre)
Corn Silage (Short Tons / Acre)
Oats (Bushels / Acre)
LCC Group
LCC Group
LCC Group
County
s = 1
(LCC 1 and 2)
s = 2
(LCC 3 and 4)
s = 3
(LCC 5 and 6)
s = 4 (LCC 7 and 8
s = 1
(LCC 1 and 2)
s = 2
(LCC 3 and 4)
s = 3
(LCC 5 and 6)
s = 4 (LCC 7 and 8
s = 1
(LCC 1 and 2)
s = 2
(LCC 3 and 4)
s = 3
(LCC 5 and 6)
s = 4 (LCC 7 and 8
27001
3.76
3.01
2.28
0.00
12.16
8.48
5.00
0.00
54.16
41.29
30.00
0.00
27003
4.34
3.14
2.42
0.00
0.00
0.00
0.00
0.00
81.15
60.58
49.33
0.00
27005
4.88
3.22
2.27
1.87
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
27007
4.36
3.20
2.19
0.00
11.96
7.54
10.00
0.00
74.29
55.94
34.17
0.00
27009
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
27011
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
27013
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
86.83
72.35
55.00
0.00
27015
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
83.65
69.68
59.00
0.00
27017
0.00
0.00
0.00
0.00
13.15
11.44
0.00
0.00
70.00
65.50
0.00
0.00
27019
4.23
3.42
2.82
2.50
0.00
0.00
0.00
0.00
80.52
64.05
52.00
48.00
27021
0.00
4.00
0.00
0.00
12.75
9.31
0.00
0.00
77.81
56.98
42.67
0.00
27023
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
78.58
69.63
0.00
0.00
27025
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
76.25
61.17
49.22
70.00
27027
4.67
3.33
2.05
2.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
27029
4.52
3.36
2.44
1.55
0.00
0.00
0.00
0.00
85.96
58.88
18.00
0.00
27033
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
71.00
0.00
0.00
0.00
27037
4.10
3.29
2.51
0.00
0.00
0.00
0.00
0.00
81.15
62.74
50.60
0.00
27039
5.02
4.25
2.47
0.00
0.00
0.00
0.00
0.00
82.13
69.00
40.00
0.00
27041
4.49
3.20
1.47
0.00
16.80
14.50
7.80
0.00
69.97
50.68
26.33
0.00
27043
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
87.91
75.25
54.00
0.00
27045
0.00
0.00
2.50
0.00
0.00
0.00
0.00
0.00
82.76
69.18
48.00
42.00
27047
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
82.80
71.88
48.00
0.00
27049
5.14
3.82
2.47
2.50
0.00
0.00
0.00
0.00
84.11
62.24
39.00
0.00
27051
4.60
3.41
1.67
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
27053
3.99
3.18
2.50
0.00
0.00
0.00
0.00
0.00
78.54
61.93
45.33
0.00
27055
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
86.03
67.64
63.25
0.00
27057
0.00
0.00
0.00
0.00
8.31
5.63
3.50
0.00
78.82
51.18
28.33
0.00
27059
0.00
0.00
0.00
0.00
14.34
10.07
0.00
0.00
76.60
55.10
28.00
0.00
27061
4.01
2.64
0.00
0.00
13.22
9.50
0.00
0.00
75.83
53.89
40.00
0.00
27063
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
85.30
68.72
0.00
0.00
27065
4.15
3.11
2.55
0.00
21.38
16.40
0.00
0.00
79.85
61.18
50.00
0.00
27067
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
82.06
65.84
0.00
0.00
27069
4.51
3.50
0.00
0.00
0.00
0.00
0.00
0.00
97.50
52.50
0.00
0.00
27073
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
77.18
61.88
0.00
0.00
27077
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
78.71
54.71
17.50
0.00
27079
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
86.71
73.50
48.00
0.00
27081
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
80.10
63.83
0.00
0.00
27083
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
79.16
65.27
0.00
0.00
27085
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
85.53
71.43
0.00
0.00
27087
4.42
3.12
1.53
0.00
13.90
9.54
0.00
0.00
99.17
66.67
0.00
0.00
27089
4.29
3.29
0.00
0.00
10.75
8.23
0.00
0.00
100.00
70.28
0.00
0.00
27091
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
85.71
72.48
0.00
0.00
27093
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
83.17
70.51
62.00
0.00
27095
4.13
3.00
2.30
0.00
21.17
15.88
0.00
0.00
78.50
58.83
47.75
0.00
27097
3.94
2.71
2.30
0.00
18.43
13.21
0.00
0.00
72.14
54.17
0.00
0.00
27099
3.50
0.00
0.00
0.00
15.00
15.00
0.00
0.00
80.53
69.37
45.00
0.00
27101
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
79.00
63.04
0.00
0.00
27103
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
85.21
71.58
0.00
0.00
27105
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
83.47
66.31
0.00
0.00
27107
4.65
3.45
1.60
0.00
14.50
10.33
0.00
0.00
115.00
99.81
0.00
0.00
27109
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
82.71
68.68
39.14
0.00
27111
4.86
3.79
1.69
1.90
15.04
13.06
10.50
0.00
69.15
54.01
27.87
0.00
27113
4.83
3.87
0.00
0.00
0.00
0.00
0.00
0.00
95.56
67.83
0.00
0.00
27117
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
77.75
62.00
0.00
0.00
27119
4.27
3.04
1.93
1.60
13.07
9.82
0.00
0.00
81.20
58.27
32.00
0.00
27121
4.63
3.51
1.69
0.00
17.08
15.34
10.95
0.00
72.00
55.50
28.00
0.00
27123
4.19
3.15
2.56
0.00
0.00
0.00
0.00
0.00
80.42
62.03
46.21
0.00
27125
4.86
3.67
0.00
0.00
0.00
0.00
0.00
0.00
96.94
65.45
0.00
0.00
27127
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
82.33
80.50
0.00
0.00
27129
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
85.39
71.21
0.00
0.00
27131
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
82.39
68.47
50.33
0.00
27133
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
76.97
64.54
0.00
0.00
27135
3.51
3.53
0.00
0.00
0.00
0.00
0.00
0.00
81.97
55.53
0.00
0.00
27137
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
57.28
52.15
35.00
0.00
27139
4.31
3.41
2.84
3.00
0.00
0.00
0.00
0.00
79.10
64.65
52.92
0.00
27141
4.23
3.19
2.40
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
27143
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
86.66
76.82
0.00
0.00
27145
4.40
3.36
2.25
0.00
0.00
0.00
0.00
0.00
77.82
61.67
46.00
0.00
27147
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
81.48
68.72
56.00
30.00
27149
4.66
3.41
1.60
0.00
17.34
15.32
0.00
0.00
0.00
0.00
0.00
0.00
27151
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
76.77
68.71
0.00
0.00
27153
3.49
2.50
1.91
0.00
15.19
10.38
5.50
0.00
83.69
62.10
39.33
0.00
27155
4.79
3.31
0.00
0.00
16.96
11.71
0.00
0.00
0.00
0.00
0.00
0.00
27157
5.57
4.43
2.50
0.00
0.00
0.00
0.00
0.00
87.37
67.10
44.50
0.00
27159
2.87
2.18
1.60
1.20
43.67
9.28
0.00
0.00
71.67
48.60
30.00
0.00
27161
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
86.50
73.29
0.00
0.00
27163
4.05
3.13
2.52
0.00
0.00
0.00
0.00
0.00
78.48
61.14
46.67
0.00
27165
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
85.93
75.52
0.00
0.00
27167
4.93
3.85
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
27169
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
88.94
74.26
57.75
44.00
27171
4.16
3.26
2.55
0.00
0.00
0.00
0.00
0.00
80.80
62.66
49.14
0.00
27173
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
78.38
65.80
0.00
0.00
Table 16 (cont.): Average Crop Yields by LCC Group and County
Pasture (AUM / Acre)
Corn (Bushels / Acre)
Soybeans (Bushels / Acre)
LCC Group
LCC Group
LCC Group
County
s = 1
(LCC 1 and 2)
s = 2
(LCC 3 and 4)
s = 3
(LCC 5 and 6)
s = 4 (LCC 7 and 8
s = 1
(LCC 1 and 2)
s = 2
(LCC 3 and 4)
s = 3
(LCC 5 and 6)
s = 4 (LCC 7 and 8
s = 1
(LCC 1 and 2)
s = 2
(LCC 3 and 4)
s = 3
(LCC 5 and 6)
s = 4 (LCC 7 and 8
27001
5.92
5.04
4.77
4.75
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
27003
0.00
0.00
0.00
0.00
148.77
86.75
0.00
0.00
42.46
24.91
0.00
0.00
27005
3.34
2.24
1.62
1.23
126.16
81.79
0.00
0.00
37.65
24.39
0.00
0.00
27007
5.71
4.18
3.03
0.00
111.31
69.19
98.00
0.00
34.28
21.22
30.00
0.00
27009
0.00
0.00
0.00
0.00
125.57
95.87
0.00
0.00
36.29
27.70
0.00
0.00
27011
0.00
0.00
0.00
0.00
139.32
111.13
0.00
0.00
43.50
34.73
0.00
0.00
27013
5.83
4.48
3.85
2.20
181.73
149.51
109.00
0.00
52.92
43.57
31.50
0.00
27015
5.45
4.00
3.13
0.00
157.03
115.50
0.00
0.00
45.73
33.59
0.00
0.00
27017
6.85
5.10
2.50
4.30
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
27019
0.00
0.00
0.00
0.00
156.15
124.76
103.00
0.00
43.81
35.00
29.00
0.00
27021
3.42
2.21
2.19
1.14
115.73
68.01
0.00
0.00
36.08
21.27
0.00
0.00
27023
0.00
0.00
0.00
0.00
153.88
118.62
0.00
0.00
46.31
35.44
0.00
0.00
27025
4.60
2.40
2.58
1.91
142.75
86.00
67.50
0.00
40.88
24.65
19.50
0.00
27027
0.00
0.00
0.00
0.00
130.62
89.36
85.00
0.00
40.33
27.47
26.00
0.00
27029
5.88
4.36
3.09
1.94
105.54
70.91
0.00
0.00
33.27
22.41
0.00
0.00
27033
0.00
0.00
0.00
0.00
161.25
122.21
0.00
0.00
48.39
36.66
0.00
0.00
27037
0.00
0.00
0.00
0.00
160.83
107.48
53.00
0.00
45.22
30.17
15.00
0.00
27039
5.02
4.25
2.47
0.00
172.63
147.00
0.00
0.00
50.97
43.16
0.00
0.00
27041
5.17
3.68
1.67
0.00
137.29
99.76
0.00
0.00
42.45
30.80
0.00
0.00
27043
6.06
4.85
4.95
0.00
192.05
157.00
144.00
0.00
53.60
43.85
40.00
0.00
27045
0.00
0.00
2.10
1.90
177.00
135.64
103.00
0.00
49.29
37.83
29.00
0.00
27047
6.05
4.50
0.00
0.00
181.23
156.39
0.00
0.00
50.52
43.67
0.00
0.00
27049
4.17
1.79
2.20
1.18
171.27
122.69
0.00
0.00
50.20
35.89
0.00
0.00
27051
5.29
3.93
1.90
0.00
142.44
101.95
0.00
0.00
45.00
32.21
0.00
0.00
27053
5.70
0.00
4.53
0.00
152.08
107.18
0.00
0.00
44.25
31.23
0.00
0.00
27055
0.00
0.00
0.00
0.00
186.16
133.21
0.00
0.00
51.91
37.13
0.00
0.00
27057
3.51
2.99
2.11
1.70
112.53
71.98
0.00
0.00
33.35
21.34
0.00
0.00
27059
0.00
0.00
0.00
0.00
131.27
90.59
0.00
0.00
34.93
24.27
0.00
0.00
27061
5.53
4.50
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
27063
5.85
4.50
0.00
0.00
167.52
128.83
0.00
0.00
48.70
37.28
0.00
0.00
27065
6.70
5.95
0.00
0.00
132.57
101.98
0.00
0.00
37.29
28.49
0.00
0.00
27067
5.14
3.99
2.15
3.00
161.34
127.00
0.00
0.00
48.49
38.13
0.00
0.00
27069
0.00
0.00
2.50
0.00
81.72
58.52
0.00
0.00
31.15
22.30
0.00
0.00
27073
5.00
4.60
0.00
0.00
148.96
113.00
0.00
0.00
44.08
33.31
0.00
0.00
27077
5.82
4.09
0.00
0.00
0.00
40.00
0.00
0.00
29.65
17.44
0.00
0.00
27079
5.68
4.47
3.56
0.00
158.42
125.35
0.00
0.00
44.42
35.12
0.00
0.00
27081
5.50
3.70
0.00
0.00
154.87
115.15
0.00
0.00
48.78
36.23
0.00
0.00
27083
4.85
3.65
0.00
0.00
152.32
117.43
0.00
0.00
47.57
36.65
0.00
0.00
27085
5.40
4.12
3.85
0.00
168.21
140.22
0.00
0.00
47.23
39.22
0.00
0.00
27087
5.44
3.96
2.14
0.00
114.63
78.29
0.00
0.00
34.50
23.58
0.00
0.00
27089
3.62
2.11
2.50
0.00
99.93
66.86
0.00
0.00
34.88
23.28
0.00
0.00
27091
5.80
4.50
6.80
0.00
183.78
150.48
141.00
0.00
52.56
43.00
40.00
0.00
27093
5.05
4.01
3.40
2.53
159.69
126.74
104.50
0.00
48.04
38.04
31.50
0.00
27095
0.00
5.50
4.00
0.00
121.25
99.69
0.00
0.00
34.00
27.86
0.00
0.00
27097
3.23
2.23
2.09
1.07
119.48
75.85
0.00
0.00
37.43
23.69
0.00
0.00
27099
5.45
4.63
5.60
0.00
183.27
149.21
0.00
0.00
50.98
41.68
0.00
0.00
27101
4.70
0.00
0.00
0.00
157.18
115.04
0.00
0.00
48.21
35.21
0.00
0.00
27103
5.72
4.19
3.70
0.00
170.96
132.84
0.00
0.00
47.50
36.92
0.00
0.00
27105
6.40
0.00
0.00
0.00
160.61
119.31
0.00
0.00
48.49
36.00
0.00
0.00
27107
3.35
2.30
1.03
0.80
124.05
91.26
0.00
0.00
36.05
26.41
0.00
0.00
27109
0.00
0.00
0.00
0.00
170.25
127.77
28.00
0.00
49.60
37.21
10.00
0.00
27111
3.31
2.27
1.93
1.09
133.46
86.45
53.00
0.00
38.75
25.16
15.00
0.00
27113
5.80
4.70
0.00
0.00
102.11
72.61
0.00
0.00
34.15
24.30
0.00
0.00
27117
0.00
0.00
0.00
0.00
149.37
109.65
0.00
0.00
46.54
34.41
0.00
0.00
27119
5.49
3.86
2.18
1.60
119.87
81.60
0.00
0.00
35.57
24.14
0.00
0.00
27121
5.34
4.03
1.94
0.00
139.50
108.11
0.00
0.00
41.73
32.34
0.00
0.00
27123
0.00
0.00
0.00
0.00
152.75
97.19
73.67
0.00
45.92
29.26
22.33
0.00
27125
5.85
4.50
3.00
0.00
102.75
69.00
0.00
0.00
34.36
23.04
0.00
0.00
27127
5.35
4.60
0.00
0.00
156.45
117.80
90.00
0.00
47.17
35.32
27.00
0.00
27129
5.66
5.69
0.00
0.00
158.05
127.26
0.00
0.00
47.57
38.21
0.00
0.00
27131
0.00
0.00
0.00
0.00
171.98
135.28
0.00
0.00
52.16
41.00
0.00
0.00
27133
0.00
0.00
0.00
0.00
149.08
133.42
0.00
0.00
45.59
40.75
0.00
0.00
27135
0.00
4.50
5.43
6.00
88.33
55.41
0.00
0.00
33.76
21.15
0.00
0.00
27137
5.25
4.42
3.60
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
27139
0.00
0.00
0.00
0.00
161.67
110.14
99.50
0.00
45.33
30.86
27.83
0.00
27141
0.00
0.00
0.00
0.00
140.23
81.37
68.00
0.00
39.69
23.09
19.00
0.00
27143
5.62
4.61
4.20
0.00
171.28
149.59
0.00
0.00
48.03
41.76
0.00
0.00
27145
0.00
0.00
0.00
0.00
152.20
103.02
0.00
0.00
42.30
28.66
0.00
0.00
27147
6.73
5.50
4.48
3.00
165.87
131.81
102.67
0.00
49.24
39.12
30.67
0.00
27149
5.36
3.92
1.90
0.00
142.80
109.92
0.00
0.00
44.02
33.84
0.00
0.00
27151
0.00
5.50
0.00
0.00
152.78
121.43
0.00
0.00
48.11
38.27
0.00
0.00
27153
3.32
2.38
1.88
0.80
131.31
82.45
89.00
0.00
42.58
26.74
29.00
0.00
27155
0.00
0.00
0.00
0.00
142.53
109.14
0.00
0.00
45.61
34.86
0.00
0.00
27157
4.35
0.00
2.20
1.80
176.35
116.85
0.00
0.00
51.33
34.02
0.00
0.00
27159
2.83
2.25
2.85
1.00
122.67
73.08
0.00
0.00
35.33
21.16
0.00
0.00
27161
5.66
4.65
4.45
2.30
182.33
146.79
0.00
0.00
54.12
43.57
0.00
0.00
27163
0.00
0.00
0.00
0.00
147.70
94.45
68.00
0.00
44.42
28.43
20.50
0.00
27165
5.71
4.39
6.00
0.00
170.12
135.24
0.00
0.00
47.33
37.55
0.00
0.00
27167
0.00
0.00
0.00
0.00
133.90
95.78
0.00
0.00
40.02
28.57
0.00
0.00
27169
0.00
0.00
0.00
0.00
178.92
135.88
92.00
0.00
49.92
37.88
26.00
0.00
27171
0.00
0.00
0.00
0.00
158.04
104.80
71.00
0.00
46.53
30.83
21.00
0.00
27173
5.00
4.60
0.00
0.00
155.07
120.70
83.00
0.00
47.63
36.97
25.00
0.00
Table 16 (cont.): Average Crop Yields by LCC Group and County
Barley (Bushels / Acre)
Wheat (Bushels / Acre)
LCC Group
LCC Group
County
s = 1
(LCC 1 and 2)
s = 2
(LCC 3 and 4)
s = 3
(LCC 5 and 6)
s = 4 (LCC 7 and 8
s = 1
(LCC 1 and 2)
s = 2
(LCC 3 and 4)
s = 3
(LCC 5 and 6)
s = 4 (LCC 7 and 8
27001
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
27003
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
27005
69.86
44.91
22.60
0.00
41.88
26.69
12.20
0.00
27007
58.21
37.92
22.86
0.00
35.54
23.85
15.00
0.00
27009
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
27011
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
27013
0.00
0.00
0.00
0.00
53.00
48.00
0.00
0.00
27015
0.00
0.00
0.00
0.00
50.51
41.50
35.00
0.00
27017
55.00
46.88
0.00
0.00
40.00
0.00
0.00
0.00
27019
81.37
66.29
54.00
45.00
51.07
41.83
37.20
35.00
27021
0.00
45.00
0.00
0.00
0.00
30.00
0.00
0.00
27023
0.00
0.00
0.00
0.00
54.38
47.69
0.00
0.00
27025
0.00
0.00
0.00
0.00
0.00
35.00
0.00
0.00
27027
0.00
0.00
0.00
0.00
53.88
36.42
22.50
23.00
27029
62.19
42.91
23.00
0.00
40.58
27.63
17.00
0.00
27033
0.00
0.00
0.00
0.00
51.00
0.00
0.00
0.00
27037
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
27039
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
27041
0.00
0.00
0.00
0.00
43.35
32.66
16.33
0.00
27043
0.00
0.00
0.00
0.00
52.55
44.58
32.00
0.00
27045
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
27047
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
27049
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
27051
0.00
0.00
0.00
0.00
46.82
36.89
21.67
0.00
27053
60.14
49.15
0.00
0.00
49.77
40.51
32.33
0.00
27055
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
27057
63.24
40.59
21.67
0.00
0.00
0.00
0.00
0.00
27059
55.00
48.33
15.00
0.00
40.00
33.33
10.00
0.00
27061
60.00
55.00
0.00
0.00
37.86
27.50
0.00
0.00
27063
0.00
0.00
0.00
0.00
55.24
43.63
0.00
0.00
27065
0.00
45.00
0.00
0.00
0.00
30.00
0.00
0.00
27067
0.00
0.00
0.00
0.00
49.66
41.05
0.00
0.00
27069
80.00
35.83
0.00
0.00
40.63
19.17
0.00
0.00
27073
0.00
0.00
0.00
0.00
57.02
45.04
0.00
0.00
27077
55.38
40.00
15.00
0.00
40.07
30.00
5.00
0.00
27079
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
27081
0.00
0.00
0.00
0.00
54.37
43.00
0.00
0.00
27083
0.00
0.00
0.00
0.00
56.00
46.13
0.00
0.00
27085
0.00
0.00
0.00
0.00
48.67
47.33
0.00
0.00
27087
78.67
47.08
0.00
0.00
42.24
27.29
0.00
0.00
27089
85.63
55.56
0.00
0.00
46.00
27.36
0.00
0.00
27091
0.00
0.00
0.00
0.00
50.45
44.11
31.00
0.00
27093
0.00
0.00
0.00
0.00
49.39
42.11
38.50
0.00
27095
0.00
45.00
0.00
0.00
48.50
30.00
0.00
0.00
27097
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
27099
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
27101
0.00
0.00
0.00
0.00
55.45
43.52
0.00
0.00
27103
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
27105
0.00
0.00
0.00
0.00
57.42
43.77
0.00
0.00
27107
96.14
78.33
0.00
0.00
51.59
37.31
0.00
0.00
27109
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
27111
67.00
53.07
27.53
0.00
38.52
28.55
14.73
0.00
27113
80.37
55.22
0.00
0.00
44.26
31.09
0.00
0.00
27117
0.00
0.00
0.00
0.00
54.62
42.82
0.00
0.00
27119
81.98
51.67
22.50
0.00
46.89
28.81
17.50
0.00
27121
0.00
0.00
0.00
0.00
45.60
36.16
19.29
0.00
27123
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
27125
81.39
52.05
0.00
0.00
44.86
29.55
0.00
0.00
27127
0.00
0.00
0.00
0.00
56.50
55.00
0.00
0.00
27129
0.00
0.00
0.00
0.00
51.07
43.06
0.00
0.00
27131
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
27133
0.00
0.00
0.00
0.00
55.36
46.23
0.00
0.00
27135
72.42
43.38
0.00
0.00
42.58
26.45
0.00
0.00
27137
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
27139
0.00
0.00
0.00
0.00
52.20
41.60
35.83
0.00
27141
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
27143
0.00
0.00
0.00
0.00
51.77
46.47
0.00
0.00
27145
77.47
63.51
46.00
0.00
48.12
40.11
29.00
0.00
27147
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
27149
0.00
0.00
0.00
0.00
46.66
35.28
0.00
0.00
27151
0.00
0.00
0.00
0.00
52.81
47.00
0.00
0.00
27153
0.00
0.00
0.00
0.00
43.35
31.81
19.00
0.00
27155
0.00
0.00
0.00
0.00
44.55
30.43
0.00
0.00
27157
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
27159
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
27161
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
27163
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
27165
0.00
0.00
0.00
0.00
51.59
45.33
0.00
0.00
27167
0.00
0.00
0.00
0.00
50.81
36.43
0.00
0.00
27169
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
27171
0.00
0.00
0.00
0.00
51.22
40.28
32.57
0.00
27173
0.00
0.00
0.00
0.00
54.29
44.21
0.00
0.00
We used observed statewide yield trends for the M modeled crops from 1992 to 2001 to account for technological progress in yield through time; in equation (32) (USDA-NASS 2009). See Table 17 for improvements in yields for select crops.
Table 17: Observed rate of yield improvements from 1992 to 2001
Average 1992-1996 State-Wide Yield
Average 1997-2001 State-Wide Yield
Rate of Yield Improvement from 1992 to 2001 ()
Corn For Grain
114 bushels
142 bushels
0.246
Corn For Silage
11.7 short tons
15.4 short tons
0.316
Soybeans
34.7 bushels
40.2 bushels
0.159
Wheat
37 bushels
41 bushels
0.123
Alfalfa Hay
3.36 short tons
3.50 short tons
0.420
Oats
55.8 bushels
62.4 bushels
0.118
Barley
59.4 bushels
54.4 bushels
-0.084
We allocated a countys 1992 and 2001 crop production over its agricultural land (Ai6k and Aij6, respectively) with a priority system that allocates certain crops preferentially to more productive soils. First we determined the number of acres used for each crop m in each county in 1992 and 2001 with data from USDA-NASS (2009) where NASS92im and NASS01im indicate the acres planted in crop m in 1992 and 2001 according to USDA-NASS (2009). All planted acres in county i according to USDA-NASS not in corn (m = 1), soybeans (m = 2), spring wheat (m = 3), alfalfa hay (m = 4), and corn silage (m = 5) are lumped into the other category (m = 6). If and are less then Ai5k and Aij5 respectively, then the remaining agricultural land in the county, Ai6k and Aij6 , is allocated to pasture (m = 7) and all other acreage is allocated according to NASS92im and NASS01im. However, if and are greater then Ai6k and Aij6, respectively, then pasture land in county i in year t is equal to 0 and
(37)
(38)
Next we allocated acreage Ai5km and Aij6m for all M over LCC group categories to obtain Ai6kms and Aij6ms for s = 1,,5. First, we tried to allocate all corn acres in a county to areas with LCC group 1 (s = 1). If there was additional land available in this category then we allocated soybean acres to grid cells with LCC group 1, and then spring wheat, and so on (with the order of preference for the remaining crops being alfalfa hay, other crops, corn silage, and pasture). If there was insufficient acreage in LCC group 1 to accommodate the entire corn crop (or soybeans, spring wheat) then we allocated the residual crop acres to county area in LCC group category 2, etc. In other words, we allocate all crops over the most productive soil remaining according to the preference noted above until all crop acres ar