1. Land Use / Land Cover (LULC) Change Maps

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). Anderson Level Code Descriptions 1 Class 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

1 Anderson Level Code Descriptions 1 Class 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 Grassland Open 1992 Agriculture Barren Forest Urban Wetlands / Shrub Water Totals 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 / 36,275 149 88,885 2,093,448 382 9,278 21,756 2,250,173 j Shrub 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

2 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 Grassland / Open Scenario Agriculture Barren Forest Urban Wetlands Shrub Water 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 Grassland Open Agriculture Barren Forest Urban Wetlands 1992 Totals / Shrub Water 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 / 629,998 143 78,273 1,515,360 359 6,884 19,357 2,250,374 Shrub 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.

3 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 state’s 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 Grassland Open 1992 Agriculture Barren Forest Urban Wetlands / Shrub Water 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 / 26,944 143 170,584 2,023,350 361 7,466 20,227 2,249,075 Shrub Open 12,355 3,108 22,111 3,232 3,029,195 708 24,789 3,095,498 Water 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

4 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 Grassland / Open Agriculture Barren Forest Shrub 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

5 Table 6: Conservation Scenario (continued) k Restored Restored Restored Restored Restored Grassland / 1992 Totals Forest Open Water Wetlands Unknown Shrub 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 / 0 51,703 0 0 0 2,250,060 j Shrub 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 county’s 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,

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,

6 (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 Perc County Percentage County Percentage County enta ge 0.66 27001 2.78% 27059 0.00% 27117 % 3.25 27003 2.88% 27061 16.53% 27119 % 3.54 27005 7.58% 27063 1.18% 27121 % 7.35 27007 7.25% 27065 0.00% 27123 % 0.09 27009 0.09% 27067 2.83% 27125 % 0.13 27011 4.01% 27069 2.65% 27127 % 0.47 27013 0.70% 27071 4.04% 27129 % 0.74 27015 0.56% 27073 3.47% 27131 % 0.56 27017 1.52% 27075 59.12% 27133 % 0.80 27019 2.94% 27077 1.37% 27135 % 23.1 27021 19.54% 27079 0.33% 27137 8% 5.22 27023 0.59% 27081 0.49% 27139 % 11.5 27025 2.37% 27083 1.12% 27141 0% 0.38 27027 3.18% 27085 0.54% 27143 % 1.41 27029 3.93% 27087 1.72% 27145 %

7 0.36 27031 69.91% 27089 5.45% 27147 % 2.69 27033 0.95% 27091 0.11% 27149 % 1.44 27035 0.73% 27093 1.20% 27151 % 0.20 27037 2.30% 27095 2.42% 27153 % 1.05 27039 0.09% 27097 2.45% 27155 % 3.67 27041 2.42% 27099 0.11% 27157 % 0.14 27043 0.11% 27101 0.54% 27159 % 0.00 27045 0.77% 27103 0.33% 27161 % 2.07 27047 0.75% 27105 0.16% 27163 % 0.00 27049 0.98% 27107 0.66% 27165 % 0.87 27051 2.81% 27109 0.35% 27167 % 2.76 27053 7.24% 27111 2.20% 27169 % 1.40 27055 4.19% 27113 0.00% 27171 % 0.76 27057 0.69% 27115 4.56% 27173 %

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 i’s 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

8 Lake States (NLS) region (Minnesota’s 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 NLS’s 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 j’s 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 SOC N of LULC Mg acre-1 Notes Source estimates Mean (SD) Equilibrium Slobodian et al. 2002, Wetland – 50.10 3 achieved at 75 Bedard-Haughn et al. 2006, prairie pothole (18.25) years. Euliss et al. 2006

9 SOC N of LULC Mg acre-1 Notes Source estimates Mean (SD) Equilibrium Wetland – 530.15 1 achieved at 2000 Gorham 1991 peatland years. Frank et al. 1995, Zan et al. 2001, Frank et al. 2002, Equilibrium 39.98 Coleman et al. 2004, Al- Grassland 12 achieved at 50 (16.23) Kaisi et al. 2005, Liebig et years. al. 2005, McLauchlan et al. 2006, Omonode et al. 2007 Bauer et al. 1987, Hansen and Strong 1993, Frank et al. 1995, Biondini et al. 1998, Schuman et al. 1999, Equilibrium Yang and Wander 1999, achieved at 20 Yang and Kay 2001, years. Corn and Halvorson et al. 2002, Paul 29.18 soybean rotation et al. 2003, DeGryze et al. Agriculture 41 using conventional 2004, Al-Kaisi et al. 2005, (8.58) agricultural Liebig et al. 2005, Puget practices and and Lal 2005, Russell et al. average fertilizer 2005, Euliss et al. 2006, applications. Venterea et al. 2006, Gál et al. 2007, Kucharik 2007, Morris et al. 2007, Omonode et al. 2007, Franzluebbers et al. 2009 Equilibrium Urban 33.47 1 achieved at 50 Fissore et al. in press years. 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 Biomass N of LULC Mg acre-1 Notes Source estimates Mean (SD) Wetland – n/a n/a prairie pothole

10 Biomass N of LULC Mg acre-1 Notes Source estimates Mean (SD) Wetland – n/a n/a peatland Risser et al. 1981, Equilibrium achieved Bransby et al. 1998, at 50 years. Oesterheld et al. 1999, 4.09 Belowground biomass Grassland 10 Zan et al. 2001, Baer (0.77) is the only source of et al. 2002, Tilman et biomass carbon al. 2006, Nelson et al. considered. 2009 Equilibrium achieved at 20 years. Belowground biomass is the only source of 1.94 biomass carbon Schuman et al. 1999, Agriculture 6 considered. Pastures (0.93) IPCC 2006 are continuously grazed at 2 head per hectare. Hayfields assumed to be 50% of natural grassland. Urban 7.00 1 Equilibrium achieved Fissore et al in press at 50 years.

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,

11 (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,

Ri= AB i R ABFaust + OH i R OHFaust + JPR i JPFaust . (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 Rotation Time Rotation Time County Carbon County Biomass (Ri) (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

12 Rotation Time Rotation Time County Carbon County Biomass (Ri) (Ri) 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 i’s typical forest mix that, on average, has 55-year old trees and carbon storage in i’s typical forest mix that, on average, has 45-year old trees, divided by 10:

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 k’s 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 k’s 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

13 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 k’s 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,

14 (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 scenario’s 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. Agriculture Urban Primary Secondary Light Habitat LULC area area roads roads roads 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 0.70 0.80 0.80 0.60 0.40 0.85 ownership* Forest – private ownership 0.70 0.80 0.80 0.60 0.40 0.95 w/ easement* Forest – 0.70 0.80 0.80 0.60 0.40 1.00 public

15 Agriculture Urban Primary Secondary Light Habitat LULC area area roads roads roads Suitability ownership* Grassland – private 0.60 0.70 0.70 0.50 0.40 0.85 ownership* Grassland – private ownership 0.60 0.70 0.70 0.50 0.40 0.95 w/ easement* Grassland – public 0.60 0.70 0.70 0.50 0.40 1.00 ownership* Agriculture – private 0.00 0.50 0.50 0.40 0.40 0.20 ownership* Agriculture – private ownership 0.00 0.50 0.50 0.40 0.40 0.30 w/ easement* Agriculture – public 0.00 0.50 0.50 0.40 0.40 0.20 ownership* Wetland – private 0.60 0.80 0.80 0.60 0.40 0.85 ownership* Wetland – private ownership 0.60 0.70 0.70 0.50 0.40 0.95 w/ easement* Wetland – public 0.60 0.80 0.80 0.60 0.40 1.00 ownership* 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. Agricult Urban Primary Secondary Light Habitat LULC ure area area roads roads roads Suitability Open water 0.00 0.00 0.00 0.00 0.00 0.00

16 Agricult Urban Primary Secondary Light Habitat LULC ure area area roads roads roads Suitability 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 0.70 0.80 0.80 0.60 0.40 0.90 ownership* Forest – private ownership w/ 0.70 0.80 0.80 0.60 0.40 0.95 easement* Forest – public 0.70 0.80 0.80 0.60 0.40 1.00 ownership* Grassland – private 0.60 0.70 0.70 0.50 0.40 0.10 ownership* Grassland – private ownership w/ 0.60 0.70 0.70 0.50 0.40 0.10 easement* Grassland – public 0.60 0.70 0.70 0.50 0.40 0.10 ownership* Agriculture – 0.00 0.50 0.50 0.40 0.40 0.05 private ownership* Agriculture – private ownership 0.00 0.50 0.50 0.40 0.40 0.05 w/ easement* Agriculture – public 0.00 0.50 0.50 0.40 0.40 0.05 ownership* Wetland – private 0.60 0.80 0.80 0.60 0.40 0.50 ownership* Wetland – private ownership w/ 0.60 0.80 0.80 0.60 0.40 0.50 easement* Wetland – public 0.60 0.80 0.80 0.60 0.40 0.50 ownership* 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. Agriculture Urban Primary Secondary Light Habitat LULC area area roads roads roads 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 0.00 0.00 0.00 00.00 0.00 0.00

17 Agriculture Urban Primary Secondary Light Habitat LULC area area roads roads roads Suitability ownership* Forest – private ownership w/ 0.00 0.00 0.00 0.00 0.00 0.00 easement* Forest – public 0.00 0.00 0.00 0.00 0.00 0.00 ownership* Grassland – private 0.50 0.70 0.50 0.40 0.30 0.90 ownership* Grassland – private 0.50 0.70 0.50 0.40 0.30 0.95 ownership w/ easement* Grassland – public 0.50 0.70 0.50 0.40 0.30 1.00 ownership* Agriculture – private 0.30 0.50 0.50 0.40 0.40 0.30 ownership* Agriculture – private 0.30 0.50 0.50 0.40 0.40 0.50 ownership w/ easement* Agriculture – public 0.30 0.50 0.50 0.40 0.40 0.50 ownership* Wetland – private 0.60 0.80 0.80 0.60 0.40 0.40 ownership* Wetland – private 0.60 0.80 0.80 0.60 0.40 0.20 ownership w/ easement* Wetland – public 0.60 0.80 0.80 0.60 0.40 0.20 ownership* 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

18 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 threat’s 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 Maximum effective distance of Weight source degradation source (km) 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 threat’s 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 threat’s 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,

骣骣2.99 i=exp - d rxy琪琪 xy 桫桫dr max (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 habitat’s sensitivity to threats is based on general principles from landscape ecology (e.g., Lindenmayer et al. 2008). 19 Therefore, the total threat level in grid cell x with LULC or habitat type j is given by Dxj,

R Y D= w r ib S xj邋r=1 y = 1 r y rxy x jr (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,

Qxj= H j(100 - D xj ) (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 crop’s 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 m’s 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

20 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 s = 1 s = 2 s = 3 s = 4 s = 1 s = 2 s = 3 s = 4 s = 1 s = 2 s = 3 s = 4 County (LCC 1 (LCC 3 (LCC 5 (LCC 7 (LCC 1 (LCC 3 (LCC 5 (LCC 7 (LCC 1 (LCC 3 (LCC 5 (LCC 7 and 2) and 4) and 6) and 8 and 2) and 4) and 6) and 8 and 2) and 4) and 6) 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

21 Alfalfa Hay (Short Tons / Acre) Corn Silage (Short Tons / Acre) Oats (Bushels / Acre) LCC Group LCC Group LCC Group s = 1 s = 2 s = 3 s = 4 s = 1 s = 2 s = 3 s = 4 s = 1 s = 2 s = 3 s = 4 County (LCC 1 (LCC 3 (LCC 5 (LCC 7 (LCC 1 (LCC 3 (LCC 5 (LCC 7 (LCC 1 (LCC 3 (LCC 5 (LCC 7 and 2) and 4) and 6) and 8 and 2) and 4) and 6) and 8 and 2) and 4) and 6) and 8 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

22 Alfalfa Hay (Short Tons / Acre) Corn Silage (Short Tons / Acre) Oats (Bushels / Acre) LCC Group LCC Group LCC Group s = 1 s = 2 s = 3 s = 4 s = 1 s = 2 s = 3 s = 4 s = 1 s = 2 s = 3 s = 4 County (LCC 1 (LCC 3 (LCC 5 (LCC 7 (LCC 1 (LCC 3 (LCC 5 (LCC 7 (LCC 1 (LCC 3 (LCC 5 (LCC 7 and 2) and 4) and 6) and 8 and 2) and 4) and 6) and 8 and 2) and 4) and 6) and 8 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 s = 2 s = 1 s = 2 s = 1 s = 2 s = 3 s = 4 s = 1 s = 3 s = 4 s = 3 s = 4 (LCC (LCC (LCC County (LCC 1 (LCC 3 (LCC 5 (LCC 7 (LCC 1 (LCC 5 (LCC 7 (LCC 5 (LCC 7 3 and 1 and 3 and and 2) and 4) and 6) and 8 and 2) and 6) and 8 and 6) and 8 4) 2) 4) 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

23 Pasture (AUM / Acre) Corn (Bushels / Acre) Soybeans (Bushels / Acre) LCC Group LCC Group LCC Group s = 2 s = 1 s = 2 s = 1 s = 2 s = 3 s = 4 s = 1 s = 3 s = 4 s = 3 s = 4 (LCC (LCC (LCC County (LCC 1 (LCC 3 (LCC 5 (LCC 7 (LCC 1 (LCC 5 (LCC 7 (LCC 5 (LCC 7 3 and 1 and 3 and and 2) and 4) and 6) and 8 and 2) and 6) and 8 and 6) and 8 4) 2) 4) 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

24 Pasture (AUM / Acre) Corn (Bushels / Acre) Soybeans (Bushels / Acre) LCC Group LCC Group LCC Group s = 2 s = 1 s = 2 s = 1 s = 2 s = 3 s = 4 s = 1 s = 3 s = 4 s = 3 s = 4 (LCC (LCC (LCC County (LCC 1 (LCC 3 (LCC 5 (LCC 7 (LCC 1 (LCC 5 (LCC 7 (LCC 5 (LCC 7 3 and 1 and 3 and and 2) and 4) and 6) and 8 and 2) and 6) and 8 and 6) and 8 4) 2) 4) 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 s = 1 s = 2 s = 3 s = 4 s = 1 s = 2 s = 3 s = 4 (LCC 1 (LCC 3 (LCC 5 (LCC 7 (LCC 1 (LCC 3 (LCC 5 (LCC 7 County and 2) and 4) and 6) and 8 and 2) and 4) and 6) 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 25 Barley (Bushels / Acre) Wheat (Bushels / Acre) LCC Group LCC Group s = 1 s = 2 s = 3 s = 4 s = 1 s = 2 s = 3 s = 4 (LCC 1 (LCC 3 (LCC 5 (LCC 7 (LCC 1 (LCC 3 (LCC 5 (LCC 7 County and 2) and 4) and 6) and 8 and 2) and 4) and 6) and 8 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

26 Barley (Bushels / Acre) Wheat (Bushels / Acre) LCC Group LCC Group s = 1 s = 2 s = 3 s = 4 s = 1 s = 2 s = 3 s = 4 (LCC 1 (LCC 3 (LCC 5 (LCC 7 (LCC 1 (LCC 3 (LCC 5 (LCC 7 County and 2) and 4) and 6) and 8 and 2) and 4) and 6) and 8 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- Average 1997- Rate of Yield 1996 State-Wide 2001 State-Wide Improvement Yield Yield 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 county’s 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

27 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 are allocated across the county’s LCC group category distribution. In all regions but the northwest, oats is the proxy for other crops. In the northwest, barley is the proxy for the other crops.

We used the Farm Financial Database (http://www.finbin.umn.edu/) and regional censuses of Minnesota agriculture (Farm Business Management 1992-2001) to construct a time series of regional prices and production costs (exclusive of land rent) for each crop type from 1992 to 2001 (Table 19). Minnesota has 6 agriculture regions. We found the average price per yield unit and average production cost per acre for each crop in each region for the period 1992 to 1996 and 1997 to 2001. We used the 1992 to 1996 average values for 1992 (p92im and c92im) and 1997 to 2001 average values for 2001 (p01im and c01im). All prices and costs were denominated in 1992 constant dollars. Each county was assigned crop prices and production costs according to its regional affiliation. The values for p92im and p01im are given in Table 18. The values for c92im and c01im are given in Table 19.

Table 18: Average crop price by state region for the periods 1992-1996 and 1997-2002 (all values are expressed in 1992 dollars) 1992-1996 South South South North North West east central west west east Corn for Grain (per bushel) $2.09 $2.10 $2.09 $2.15 $2.21 $2.09 Corn for Silage (per short $16.46 $17.90 $14.43 $15.90 $16.31 $17.47 ton) Soybeans (per bushel) $5.47 $5.43 $5.38 $5.48 $5.61 $5.45 Wheat (per bushel) $3.23 $3.39 $3.29 $3.42 $3.61 $3.38 Alfalfa Hay (per short ton) $78.10 $78.51 $67.06 $73.25 $52.55 $74.61 Oats (per bushel) $1.28 $1.28 $1.31 $1.26 $1.29 $1.31 Barley (per bushel) $1.91 $1.94 $1.94 $1.86 $2.17 $1.81 Pasture (per animal unit $4.40 $4.05 $3.70 $4.05 $4.05 $4.05 month (AUM)) 1997-2001 Corn for Grain (per bushel) $1.58 $1.57 $1.57 $1.57 $1.59 $1.57 Corn for Silage (per short $15.07 $14.64 $14.50 $14.74 $15.17 $16.06 ton) Soybeans (per bushel) $4.47 $4.49 $4.52 $4.45 $4.34 $4.43 Wheat (per bushel) $2.73 $2.60 $2.59 $2.62 $2.77 $2.66 Alfalfa Hay (per short ton) $69.84 $73.75 $63.08 $65.87 $50.00 $64.31 Oats (per bushel) $0.98 $0.98 $1.06 $0.85 $1.05 $0.97 Barley (per bushel) $1.29 $1.56 $1.53 $1.46 $1.52 $1.27 Pasture (per AUM) $8.34 $6.47 $4.59 $6.47 $6.47 $6.47

28 Table 19: Average crop production cost per acre (less land rental costs) by state region for the periods 1992-1996 and 1997-2002 (all values are expressed in 1992 dollars) 1992-1996 South South North North South west West east central west east Corn for Grain $215.73 $215.16 $191.66 $182.19 $146.68 $183.38 Corn for Silage $213.14 $246.49 $201.70 $189.57 $151.32 $161.98 Soybeans $136.28 $132.58 $123.75 $116.26 $102.69 $136.38 Wheat $99.14 $95.33 $106.01 $96.67 $95.51 $90.97 Alfalfa Hay $157.87 $185.14 $128.31 $137.03 $100.43 $122.77 Oats $82.70 $81.36 $78.17 $78.71 $71.55 $80.40 Barley $87.81 $78.63 $78.63 $60.98 $84.40 $81.31 Pasture $33.95 $25.96 $17.96 $25.96 $25.96 $25.96 1997-2001 Corn for Grain $216.79 $210.02 $185.21 $190.89 $163.08 $184.68 Corn for Silage $252.41 $241.62 $194.99 $218.73 $159.07 $192.04 Soybeans $138.92 $133.25 $120.69 $119.55 $104.15 $132.44 Wheat $109.93 $114.17 $86.92 $99.90 $103.80 $102.87 Alfalfa Hay $163.89 $159.68 $128.71 $150.07 $103.17 $123.59 Oats $87.60 $90.64 $76.68 $88.36 $74.27 $75.87 Barley $95.17 $103.27 $74.37 $93.07 $89.53 $87.48 Pasture $23.65 $18.11 $12.56 $18.11 $18.11 $18.11

5. Forestry and Urban Returns

Estimated returns to forestry and urban development on the landscape were modeled using data from Lubowski (2002) and Lubowski et al. (2006, 2008; see Table 20). Lubowski (2002) and Lubowski et al. (2006, 2008) found average per acre county-level net returns to commercial forestry and urban development for 1992 and 2002. Here we use 2002 data as a proxy for net returns in 2001. We multiplied county i’s 1992 per acre net forestry returns by a scenario’s RestAi4k and PrivAi4k and then summed across the two values to determine county i’s 1992 net forestry returns for that scenario. Similarly, we multiplied county i’s 2002 per acre net forestry returns by a scenario’s PrivAij4 to determine county i’s 2001 net forestry returns for that scenario. Likewise, for urban values we multiplied county i’s 1992 per acre net urban returns by a scenario’s RestAi2k and PrivAi2k and then summed across the two values to determine county i’s 1992 net urban returns for that scenario. Finally, we multiplied county i’s 2002 per acre net urban returns by a scenario’s PrivAij2 to determine county i’s 2001 net urban returns for that scenario.

Table 20: Average per acre net returns to managed forestry and urban land use from Lubowski (2002) and Lubowski et al. (2006, 2008) (all values are expressed in 1992 dollars; 1992 = 100 and 2002 = 0.78).

29 1992 2002 County FIPS Code Managed Forestry Urban Managed Forestry Urban 27001 -$0.31 $1,429.82 $0.20 $2,487.74 27003 $2.36 $2,699.53 $8.59 $4,246.26 27005 $0.89 $1,331.21 $2.00 $2,316.17 27007 $0.79 $1,331.21 $1.27 $2,316.17 27009 $4.71 $2,415.37 $9.15 $3,799.28 27011 $0.83 $1,676.34 $2.57 $2,916.66 27013 $2.36 $1,528.43 $8.59 $2,659.30 27015 $2.36 $1,528.43 $8.59 $2,659.30 27017 -$0.27 $1,429.82 $0.07 $2,487.74 27019 $0.83 $3,196.82 $2.57 $5,028.46 27021 $1.08 $1,331.21 $1.97 $2,316.17 27023 $0.83 $1,676.34 $2.57 $2,916.66 27025 $1.13 $2,415.37 $4.89 $3,799.28 27027 $1.76 $2,415.37 $6.78 $3,799.28 27029 $0.36 $1,232.60 $0.82 $2,144.60 27031 $0.45 $1,429.82 $0.78 $2,487.74 27033 $0.83 $1,232.60 $2.57 $2,144.60 27035 $1.37 $1,331.21 $3.42 $2,316.17 27037 $1.88 $3,196.82 $7.13 $5,028.46 27039 $2.36 $1,873.56 $8.59 $3,259.79 27041 $0.83 $1,331.21 $2.57 $2,316.17 27043 $0.33 $1,676.34 $2.49 $2,916.66 27045 $2.03 $1,676.34 $7.59 $2,916.66 27047 $2.36 $1,676.34 $8.59 $2,916.66 27049 $1.41 $1,873.56 $5.74 $3,259.79 27051 $2.36 $1,331.21 $8.59 $2,316.17 27053 $2.36 $3,504.18 $8.59 $5,511.93 27055 $2.11 $1,676.34 $7.82 $2,916.66 27057 $2.95 $1,331.21 $3.95 $2,316.17 27059 $2.15 $2,415.37 $5.81 $3,799.28 27061 $0.46 $1,429.82 $0.86 $2,487.74 27063 $0.83 $1,232.60 $2.57 $2,144.60 27065 $0.17 $1,232.60 $2.00 $2,144.60 27067 $0.83 $1,676.34 $2.57 $2,916.66 27069 -$0.59 $1,232.60 -$0.27 $2,144.60 27071 $0.06 $1,429.82 $0.34 $2,487.74 27073 $0.83 $1,676.34 $2.57 $2,916.66 27075 $0.09 $1,429.82 $0.37 $2,487.74 27077 $0.66 $1,429.82 $1.04 $2,487.74 27079 $0.83 $1,873.56 $2.57 $3,259.79 27081 $0.83 $1,232.60 $2.57 $2,144.60 27083 $0.83 $1,232.60 $2.57 $2,144.60 27085 $0.83 $1,676.34 $2.57 $2,916.66

30 1992 2002 County FIPS Code Managed Forestry Urban Managed Forestry Urban 27087 -$0.03 $1,232.60 $1.02 $2,144.60 27089 -$0.56 $1,232.60 -$0.18 $2,144.60 27091 $2.36 $1,676.34 $8.59 $2,916.66 27093 $0.83 $1,676.34 $2.57 $2,916.66 27095 $0.18 $1,232.60 $2.04 $2,144.60 27097 $1.06 $1,232.60 $3.75 $2,144.60 27099 $2.36 $1,676.34 $8.59 $2,916.66 27101 $0.83 $1,232.60 $2.57 $2,144.60 27103 $0.83 $1,528.43 $2.57 $2,659.30 27105 $0.83 $1,232.60 $2.57 $2,144.60 27107 -$0.41 $1,232.60 $0.26 $2,144.60 27109 $2.19 $3,196.82 $8.08 $5,028.46 27111 $0.83 $1,331.21 $3.45 $2,316.17 27113 -$0.04 $1,232.60 $1.38 $2,144.60 27115 $0.10 $1,232.60 $0.75 $2,144.60 27117 $0.83 $1,232.60 $2.57 $2,144.60 27119 $0.21 $1,232.60 $2.13 $2,144.60 27121 $2.36 $1,331.21 $8.59 $2,316.17 27123 $0.83 $3,845.06 $2.57 $6,048.12 27125 -$0.67 $1,232.60 -$0.52 $2,144.60 27127 $2.36 $1,232.60 $8.59 $2,144.60 27129 $2.36 $1,676.34 $8.59 $2,916.66 27131 $2.36 $1,873.56 $8.59 $3,259.79 27133 $0.83 $1,232.60 $2.57 $2,144.60 27135 $0.67 $1,232.60 $1.12 $2,144.60 27137 $0.58 $2,060.17 $0.94 $3,240.57 27139 $0.80 $3,196.82 $3.89 $5,028.46 27141 $2.92 $2,415.37 $8.46 $3,799.28 27143 $2.36 $1,528.43 $8.59 $2,659.30 27145 $2.16 $2,415.37 $7.97 $3,799.28 27147 $2.36 $1,873.56 $8.59 $3,259.79 27149 $0.83 $1,331.21 $2.57 $2,316.17 27151 $0.83 $1,676.34 $2.57 $2,916.66 27153 $1.02 $1,232.60 $4.02 $2,144.60 27155 $0.83 $1,676.34 $2.57 $2,916.66 27157 $2.01 $1,676.34 $7.53 $2,916.66 27159 $5.00 $1,232.60 $6.93 $2,144.60 27161 $2.36 $1,873.56 $8.59 $3,259.79 27163 $0.83 $3,196.82 $2.57 $5,028.46 27165 $0.83 $1,528.43 $2.57 $2,659.30 27167 $0.83 $1,676.34 $2.57 $2,916.66 27169 $2.32 $1,676.34 $8.47 $2,916.66

31 1992 2002 County FIPS Code Managed Forestry Urban Managed Forestry Urban 27171 $1.22 $2,415.37 $5.17 $3,799.28 27173 $2.36 $1,676.34 $8.59 $2,916.66

6. Water Quality and Yield Models

The following model descriptions are adapted from Tallis et al 2010. For each scenario we determined water yield and total phosphorous loadings for the Minnesota River Basin. First, we model water yield, which approximates the absolute annual water yield across the basin, and is calculated as the difference between precipitation and actual evapotranspiration on each grid cell. We used maps of 30-year mean annual precipitation and reference evapotranspiration (adapted from data provided by the Minnesota State Climatology Office), soil depth and plant available water content (USDA-NRCS 2009), as well as data on the coefficients of rooting depth (Schenk and Jackson, 2002) and evapotranspiration (adapted from Allen et al. 1998) for each LULC type (See Table 21).

The water yield model is based on the Budyko curve, developed by Zhang et al. (2001), and annual average precipitation. We determine annual water yield (Yjx) for each grid cell on the landscape (indexed by x = 1,2,…,X) as follows:

where, AETxj is the annual actual evapotranspiration on grid cell x with LULC j and Px is the average annual precipitation on grid cell x. The evapotranspiration partition of the water balance, , is an approximation of the Budyko curve (Zhang et al. 2001).

(40) where, is the Budyko Dryness index on a grid cell x with LULC j, which is the ratio of potential evapotranspiration to precipitation (Budyko 1974). is an annualized ratio of plant accessible water storage to expected precipitation.

(41) where, AWCx is the volumetric plant available water content measured in mm and is estimated as the difference between field capacity and wilting point. AWCx is defined by soil texture and effective soil depth, which establishes the amount of water capacity in the soil that is available for use by a plant. Z is the Zhang constant that presents the seasonal rainfall distribution. Finally, with Rxj is calculated by the following,

(42) where, ETox is the reference evapotranspiration on grid cell x and kxj is the plant evapotranspiration coefficient associated with the LULC j on pixel x. ETox represents an index

32 of climatic demand while kxj is largely determined by a grid cell’s vegetative characteristics (Allen et al. 1998).

Second, we determine the quantity of phosphorous retained by each grid cell in the basin using information on nutrient loadings based on export coefficients and filtering characteristics of each LULC (see Table 21; Reckhow et al. 1980), the water yield output noted above, and a Digital Elevation Model (EROS Center 1996). Adjusted Loading Value for grid cell x, ALVx, is calculated by the following equation:

(43) where, polx is the export coefficient at grid cell x and HSSx is the Hydrologic Sensitivity Score for grid cell x and is calculated as:

where, is the mean runoff index for the basin, and x is the runoff index for grid cell x and is calculated by the following:

(45) where , is the sum water yield of all grid cells along the water flow path above and including grid cell x.

Once we determine ALVx, we then estimate how much of the load is retained by each grid cell downstream of a neighboring cell, as surface runoff moves phosphorous across the landscape and towards the mouth of the Minnesota River. Using a Geographic Information System, we model the route of surface water down flowpaths as determined by the slope of a grid cell. Each grid cell downstream is allowed to retain phosphorous based on its land-use type. Finally, the model aggregates the phosphorous loading that reaches the stream from each grid cell to determine the total loading for the entire basin.

Table 21. Estimates for nutrient loading, evapotranspiration, rooting depth, available water capacity, and vegetation filtering. Nutrien t Evapotranspiratio Rooting Capacit Vegetation LULC loading n depth y filtering Open water 1 1 1 1 0 Urban 2 1100 1 1 0 Barren 3 50 1 1 10 Forest 4 90 1000 104 60 Grassland / 100 750 91 50 Shrub 5 Agriculture 6 929 500 64 5 Wetland 7 1 400 1 60

33 Source: Reckhow et al 1980, Allen et al. 1998, Schenk and Jackson 2002.

34 References Al-Kaisi, M. M., X. Yin, and M. A. Licht. 2005. Soil carbon and nitrogen changes as affected by tillage system and crop biomass in a corn–soybean rotation. Applied Soil Ecology 30:174-191. Allen, R. G., Pereira, L.S., Raes, D., and Smith, M. 1998. Crop evapotranspiration – guidelines for computing crop water requirements. FAO irrigation and drainage paper 56. url: http://www.fao.org/docrep/X0490E/x0490e00.htm#Contents Baer, S. G., D. J. Kitchen, J. M. Blair, and C. W. Rice. 2002. Changes in ecosystem structure and function along a chronosequence of restored grasslands. Ecological Applications 12:1688-1701. Bauer, A., C. V. Cole, and A. L. Black. 1987. Soil Property Comparisons in Virgin Grasslands Between Grazed and Nongrazed Management Systems. Soil Science Society of America Journal 51:176. Bedard-Haughn, A., F. Jongbloed, J. Akkerman, A. Uijl, E. de Jong, T. Yates, and D. Pennock. 2006. The effects of erosional and management history on soil organic carbon stores in ephemeral wetlands of hummocky agricultural landscapes. Geoderma 135:296-306. Biondini, M., B. Patton, and P. Nyren. 1998. Grazing intensity and ecosystem processes in a northern mixed-grass prairie, USA. Ecological Applications 8:469-479. Budyko, M.I. 1974, Climate and Life, Academic, San Diego, California. Bransby, D. I., S. B. McLaughlin, and D. J. Parrish. 1998. A review of carbon and nitrogen balances in switchgrass grown for energy. Biomass and Bioenergy 14:379-384. Coleman, M., J. Isebrands, D. Tolsted, and V. Tolbert. 2004. Comparing soil carbon of short rotation poplar plantations with agricultural crops and woodlots in North Central United States. Environmental Management 33:299-308. DeGryze, S., J. Six, K. Paustian, S. J. Morris, E. A. Paul, and R. Merckx. 2004. Soil organic carbon pool changes following land-use conversions. Global Change Biology 10:1120- 1132. Dowell, R. C., D. Gibbins, J. L. Rhoads, and S. G. Pallardy. 2009. Biomass production physiology and soil carbon dynamics in short-rotation-grown Populus deltoides and P. deltoides ◊ P. nigra hybrids. Forest Ecology and Management 257:134-142. Euliss, N. H., R. A. Gleason, A. Olness, R. L. McDougal, H. R. Murkin, R. D. Robarts, R. A. Bourbonniere, and B. G. Warner. 2006. North American prairie wetlands are important nonforested land-based carbon storage sites. Science of the Total Environment, The 361:179-188. Farm Business Management, Annual Report for the Southwest, Southcentral, Southeast, West, Northwest, and Northeast Regions. 1992-2001. Minnesota State Colleges and Universities, Minnesota Riverland Community College, University of Minnesota, Division of Agricultural Education. Frank, A. B., M. A. Liebig, and J. D. Hanson. 2002. Soil carbon dioxide fluxes in northern semiarid grasslands. Soil Biology and Biochemistry 34:1235-1241. Frank, A. B., D. L. Tanaka, L. Hofmann, and R. F. Follett. 1995. Soil carbon and nitrogen of Northern Great Plains grasslands as influenced by long-term grazing. Journal of Range Management 48. Franzluebbers, A., J. Stuedemann, and A. USDA. 2009. Soil-profile organic carbon and total nitrogen during 12 years of pasture management in the Southern Piedmont USA.

35 Fry, J.A., Coan, M.J., Homer, C.G., Meyer, D.K., and Wickham, J.D., 2009, Completion of the National Land Cover Database (NLCD) 1992–2001 Land Cover Change Retrofit product: U.S. Geological Survey Open-File Report 2008–1379, 18 p. Gál, A., T. J. Vyn, E. Michéli, E. J. Kladivko, and W. W. McFee. 2007. Soil carbon and nitrogen accumulation with long-term no-till versus moldboard plowing overestimated with tilled- zone sampling depths. Soil & Tillage Research 96:42-51. Grigal, D. F., and W. E. Berguson. 1998. Soil carbon changes associated with short-rotation systems. Biomass and Bioenergy 14:371-377. Gorham, E. 1991. Northern peatlands: role in the carbon cycle and probable responses to climatic warming. Ecological Applications 1:182-195. Halvorson, A., B. Wienhold, and A. Black. 2002. Tillage, Nitrogen, and Cropping System Effects on Soil Carbon Sequestration Contribution from USDA-ARS. The USDA offers its programs to all eligible persons regardless of race, color, age, sex, or national origin, and is an equal opportunity employer. Soil Science Society of America Journal 66:906- 912. Hansen, E., and T. Strong. 1993. Hybrid poplar spacing/productivity relations in short rotation intensive culture plantations. Biomass and Bioenergy 4:255. IPCC. 2006. IPCC Guidelines for National Greenhouse Gas Inventories: Prepared by the National Greenhouse Gas Inventories Programme. Hayama, Japan. Kucharik, C. J. 2007. Impact of Prairie Age and Soil Order on Carbon and Nitrogen Sequestration. Soil Science Society of America Journal 71:430. Liebig, M. A., H. A. Johnson, J. D. Hanson, and A. B. Frank. 2005. Soil carbon under switchgrass stands and cultivated cropland. Biomass and Bioenergy 28:347-354. Lubowski, R.N. 2002. Determinants of Land-Use Transitions in the United States: Econometric Analysis of Changes among the Major Land-Use Categories. Ph.D. Dissertation, Harvard University, Cambridge, MA. Lubowski, R.N., A.J. Plantinga and R.N. Stavins. 2006. Land-use change and carbon sinks: econometric estimation of the carbon sequestration supply function. Journal of Environmental Economics and Management 51: 135-52. Lubowski, R.N., A.J. Plantinga and R.N. Stavins. 2008. What drives land-use change in the United States? A national analysis of landowner decisions. Land Economics 84(4): 529- 550. Lindenmayer, D., R. Hobbs, R. Montague-Drake, J. Alexandra, A. Bennett, M. Burgman, P. Cae, A. Calhoun, V. Cramer and P. Cullen. 2008. A checklist for ecological management of landscapes for conservation. Ecology Letters 11: 78-91. Minnesota Department of Natural Resources (MN DNR). 2000. Public land and mineral ownership in Minnesota: Minnesota Department of Natural Resources, Division of Lands and Minerals. 34 p. Minnesota Department of Natural Resources (MN DNR). 2009a. Statewide stream network shapfile for Minnesota. Accessed December 2009: http://deli.dnr.state.mn.us/metadata/strm_baseln3.html Minnesota Department of Natural Resources (MN DNR). 2000b. Presettlement vegetation of Minnesota. Accessed December 2009: http://deli.dnr.state.mn.us/metadata/pveg_mrschpy1.html

36 Minnesota Department of Transportation (MN DOT). 2009. Statewide road network shapfile for Minnesota. Accessed December 2009: http://deli.dnr.state.mn.us/metadata.html? id=L300000052102 McLauchlan, K. K., S. E. Hobbie, and W. M. Post. 2006. Conversion From Agriculture To Grassland Builds Soil Organic Matter On Decadal Timescales. Ecological Applications 16:143-153. Morris, S. J., S. Bohm, S. Haile-Mariam, and E. A. Paul. 2007. Evaluation of carbon accrual in afforested agricultural soils. Global Change Biology 13:1145-1156. Nelson, E., G. Mendoza, J. Regetz, S. Polasky, H. Tallis, D. R. Cameron, K. M. A. Chan, G. C. Daily, J. Goldstein, and P. M. Kareiva. 2009. Modeling multiple ecosystem services, biodiversity conservation, commodity production, and tradeoffs at landscape scales. Frontiers in Ecology and the Environment 7:4-11. Oesterheld, M., J. Loreti, M. Semmartin, and J. Paruelo. 1999. Grazing, fire, and climate effects on primary productivity of grasslands and savannas. Ecosystems of the world:287-306. Omonode, R. A., T. J. Vyn, D. R. Smith, P. Hegymegi, and A. Gál. 2007. Soil carbon dioxide and methane fluxes from long-term tillage systems in continuous corn and corn–soybean rotations. Soil & Tillage Research 95:182-195. Paul, K., P. Polglase, and G. Richards. 2003a. Sensitivity analysis of predicted change in soil carbon following afforestation. Ecological Modelling 164:137-152. Puget, P., and R. Lal. 2005. Soil organic carbon and nitrogen in a Mollisol in central Ohio as affected by tillage and land use. Soil & Tillage Research 80:201-213. Reckhow, K. H., M. N. Beaulac, and J. T. Simpson. 1980. Modeling phosphorus loading and lake response under uncertainty: a manual and compilation of export coefficients. EPA/440/5-80/011. U.S. Environmental Protection Agency, Washington, D.C., USA. Risser, P., E. Birney, H. Blocker, S. May, W. Parton, and J. Wiens. 1981. The true prairie ecosystem. Hutchinson Ross Pub. Co.[New York]: distributed world wide by Academic Press, Stroudsburg, Pa. Russell, A., D. Laird, T. Parkin, and A. Mallarino. 2005. Division S-3-Soil Biology & Biochemistry-Impact of Nitrogen Fertilization and Cropping System on Carbon Sequestration in Midwestern Mollisols. Soil Science Society of America Journal 69:413- 422. Schuman, G. E., J. D. Reeder, J. T. Manley, R. H. Hart, and W. A. Manley. 1999. Impact of grazing management on the carbon and nitrogen balance of a mixed-grass rangeland. Ecological Applications 9:65-71. Schenk, H. J. & R.B. Jackson. 2002. The Global Biogeography of roots. Ecological Monographs 72 (3): 311-328 Slobodian, N., K. Van Rees, and D. Pennock. 2002. Cultivation-induced effects on belowground biomass and organic carbon. Soil Science Society of America Journal 66:924-930. Smith, C. M., and D. G. Wachob. 2006. Trends associated with residential development in riparian breeding bird habitat along the Snake River in Jackson Hole, WY, USA: Implications for conservation planning. Biological Conservation 128:431-446. Smith, J. E., L. S. Heath, K. E. Skog, and R. A. Birdsey. 2006. Methods for calculating forest ecosystem and harvested carbon with standard estimates for forest types of the United States. United States Dept. of Agriculture, Forest Service, Northeastern Research Station. Tallis, H.T., Ricketts, T., Nelson, E, Ennaanay, D., Wolny, S, Olwero, N Vigerstol, K., Pennington, D., Mendoza, G.,.Aukema, J., Foster, J., Forrest, J., Cameron, D, Lonsdorf,

37 E., Kennedy, C. 2010. InVEST 1.004 beta User’s Guide. The Natural Capital Project, Stanford. Tilman, D., J. Hill, and C. Lehman. 2006. Carbon-negative biofuels from low-input high- diversity grassland biomass. Science 314:1598-1600. USDA-FSA. 2009. The Farm Service Agency, United States Department of Agriculture. Conservation Reserve Program Statistics. http://www.fsa.usda.gov/FSA/webapp? area=home&subject=copr&topic=rns-css USDA-NASS. 2009. The National Agricultural Statistics Service, United States Department of Agriculture. Quick Stats (ag statistics database). http://www.nass.usda.gov/Data_and_Statistics/Quick_Stats/index.asp USDA-NRSC. 2009. Soil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture. Soil Survey Geographic (SSURGO) Database for Minnesota. http://soildatamart.nrcs.usda.gov. Venterea, R., J. Baker, M. Dolan, and K. Spokas. 2006. Carbon and nitrogen storage are greater under biennial tillage in a Minnesota corn-soybean rotation. Soil Science Society of America Journal 70:1752-1762. Yang, X., and B. Kay. 2001. Rotation and tillage effects on soil organic carbon sequestration in a typic Hapludalf in Southern Ontario. Soil & Tillage Research 59:107-114. Yang, X. M., and M. M. Wander. 1999. Tillage effects on soil organic carbon distribution and storage in a silt loam soil in Illinois. Soil & Tillage Research 52:1-9. Zan, C., J. Fyles, P. Girouard, and R. Samson. 2001. Carbon sequestration in perennial bioenergy, annual corn and uncultivated systems in southern Quebec. Agriculture, Ecosystems and Environment 86:135-144. Zhang, L., Dawes, W.R. & Walker, G.R. 2001, Response of mean annual evapotranspiration to vegetation changes at catchment scale. Water Resources Research 37:701-708.