Historical (1981-2005) vs. Projected (2031-’55) Yields. Each year’s crop yields are calculated as an average of all counties in North and South Dakota. Hashed representations of projected yields are from RCP 4.5 emissions scenario from seven GCMs, namely CESM (Community Earth System Model), CNRM (Center National de Recherches Météorologiques (France)), GFDL (Geophysical Fluid Dynamics Laboratory), GISS (Goddard Institute of Space Studies), HADGEM (Hadley Global Environment Model), IPSL (Institut Pierre-Simon Laplace (France)) and MIROC (Model for Interdisciplinary Research on Climate). Median projection in a given year is calculated by taking the median yield value of the yield projections from each of seven climate model outputs in each county and then taking the average across counties. We restrict spring wheat and alfalfa yield forecasts to zero for years in which these are projected to be negative values.

Land use change ranges in each panel are in acres per thousand county acres. The white colored counties represent missing yields for at least one crop in all years.

2006 Land Use in the Dakotas (Cropland Data Layer, USDA NASS). The color legend represents various land use types in the region.

The impact of climate change on land conversion was assessed by projecting the land-use model under GCM hindcast and forecast climatic conditions. For each GCM, we projected future row crop proportions under 40 years of average hindcast conditions (i.e., assuming historical climate prevails into the future), and under 20 year of average hindcast conditions followed by 20 years of average forecast conditions. We used twenty years of forecast conditions to avoid assuming that climate changes projected for mid-century would have occurred immediately. The 20-20 assumption approximates a linear transition from the historic to future climate and is not limiting since the probabilities will converge given sufficient years under a given climate regime. Projected climate change impacts differed in direction and spatial pattern between GCMs (Fig. S3). Projected changes in the proportion of the landscape planted in row crops were generally small (mean change = 0.01), but included increases of over 0.25 of the landscape and declines of over 0.3 of the landscape (Fig. S3). Conversion probabilities responded to precipitation as expected, with an increase in precipitation associated with higher probabilities that grass converts to crops and lower probabilities that crops convert to grass.

The USGS Forecasting Scenarios of Land-use Change (FORE-SCE) model was used to produce an agricultural biofuel scenarios for the Northern Glaciated Plains, from 2012 to 2030. The modeling used parcel data from the USDA's Common Land Unit (CLU) data set to represent real, contiguous ownership and land management units. A Monte Carlo approach was used to create 50 unique replicates of potential landscape conditions in the future, based on a agricultural scenario from the U.S. Department of Energy's Billion Ton Update. The data are spatially explicit, covering the entire Northern Glaciated Plains ecoregions (an EPA Level III ecoregion), with a spatial resolution of 30-meters and 22 unique land-cover classes (including common crop types in the region). Files included are 1) the starting land cover data set for 2012, 2) 50 Monte Carlo replicates of 2030 land cover, and 3) supporting metadata. data represent 50 individual Monte Carlo runs for the 2012 to 2030 biofuel scenario in the Northern Glaciated Plains ecoregion,

The USGS Forecasting Scenarios of Land-use Change (FORE-SCE) model was used to produce an agricultural biofuel scenarios for the Northern Glaciated Plains, from 2012 to 2030. The modeling used parcel data from the USDA's Common Land Unit (CLU) data set to represent real, contiguous ownership and land management units. A Monte Carlo approach was used to create 50 unique replicates of potential landscape conditions in the future, based on a agricultural scenario from the U.S. Department of Energy's Billion Ton Update. The data are spatially explicit, covering the entire Northern Glaciated Plains ecoregions (an EPA Level III ecoregion), with a spatial resolution of 30-meters and 22 unique land-cover classes (including common crop types in the region). Files included are 1) the starting land cover data set for 2012, 2) 50 Monte Carlo replicates of 2030 land cover, and 3) supporting metadata. data represent 50 individual Monte Carlo runs for the 2012 to 2030 biofuel scenario in the Northern Glaciated Plains ecoregion,

The purpose of this project was to estimate and map the probability that grassland converts to cropland in the northern plains and prairie region given potential climate change. This region provides critical breeding and migratory habitat for waterfowl and other wetland-dependent species, and is also a highly productive agricultural region. Generally, the regional effects projected by climate models are increasing temperatures and more variable precipitation, which could provide incentives for private landowners to convert native and managed grassland to intensive cropland. Conversion of grassland to cropland can result in habitat loss for dependent species and the degradation of a range of ecosystem services. If climate change alters the spatial distribution of both agricultural land use and suitable habitat, land managers and conservationists may need to alter efforts to offset the negative consequences of combined climate and land-use change on habitats and dependent species. The land-use change projections associated with this report provide information for such management efforts.

The purpose of this project was to estimate and map the probability that grassland converts to cropland in the northern plains and prairie region given potential climate change. This region provides critical breeding and migratory habitat for waterfowl and other wetland-dependent species, and is also a highly productive agricultural region. Generally, the regional effects projected by climate models are increasing temperatures and more variable precipitation, which could provide incentives for private landowners to convert native and managed grassland to intensive cropland. Conversion of grassland to cropland can result in habitat loss for dependent species and the degradation of a range of ecosystem services. If climate change alters the spatial distribution of both agricultural land use and suitable habitat, land managers and conservationists may need to alter efforts to offset the negative consequences of combined climate and land-use change on habitats and dependent species. The land-use change projections associated with this report provide information for such management efforts.

This dataset contains tabular data and scripts used to analyze and produce figures for the manuscript Martin et al. entitled "Tracking cropland transitions: a comparative analysis of U.S. land cover change data."