Mapper
S1 ________ Current crop and pasture land with MP ≤ P50
S2 ________ Current crop and pasture land with MP ≤ P25
S3 ________ S1 + current grass and shrub land with P25 < MP < P50
S4 ________ S2 + current grass and shrub land with P10 < MP < P25
Economic ___ Current crop and pasture land with potential profitability < 0
Here P10, P25 and P50 are the 10th, 25th and 50th percentile of crop MP value
Citation:
Yang, P., Zhao, Q., Cai, X. Dec. 10, 2019. “Land productivity and land availability for growing bioenergy crop in the Contiguous U.S. Center for Advanced Bioenergy and Bioproducts Innovation (CABBI).” University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4584681_V1
Assessing Marginal Land Availability Based on Land Use Change Information in the Contiguous United States
Data are from the paper: Jiang, C., Guan, K., Khanna, M., Chen, L., Peng, J. July 23, 2021. “Assessing Marginal Land Availability Based on Land Use Change Information in the Contiguous United States.” Environmental Science & Technology. DOI: 10.1021/acs.est.1c02236.
Dataset citation: Jiang, C., Guan, K., Khanna, M., Chen, L., Peng, J. Sept. 7, 2022. Data from “Assessing Marginal Land Availability Based on Land Use Change Information in the Contiguous United States.” University of Illinois Urbana-Champaign. DOI: 10.13012/B2IDB-6395937_V1.
Marginal Land
Using land use history, land productivity and land vulnerability datasets, potential marginal land was identified. The definition of marginal land used here is based on the premise that economically marginal land is at the borderline of profitability and tends to convert its use between crop and non-crop due to changes in commodity price, growing conditions, policy and other factors. Spatial resolution is 30m.
Permanent Cropland/Grassland
Permanent cropland and grassland were identified using the NASS Cropland Data Layer. These pixels were always classfied the same during the 2008-2015 period. The data have a spatial resolution of 30m.
Land Productivity
Land productivity was derived using the National Commondity Crop Productivity Index. Values range from 0-1. The data have a spatial resolution of 30m.
Contacts:
Kaiyu Guan, kaiyug@illinois.edu; Madhu Khanna, khanna1@illinois.edu
Assessing Marginal Land Availability Based on Land Use Change Information in the Contiguous United States
Data are from the paper: Jiang, C., Guan, K., Khanna, M., Chen, L., Peng, J. July 23, 2021. “Assessing Marginal Land Availability Based on Land Use Change Information in the Contiguous United States.” Environmental Science & Technology. DOI: 10.1021/acs.est.1c02236.
Dataset citation: Jiang, C., Guan, K., Khanna, M., Chen, L., Peng, J. Sept. 7, 2022. Data from “Assessing Marginal Land Availability Based on Land Use Change Information in the Contiguous United States.” University of Illinois Urbana-Champaign. DOI: 10.13012/B2IDB-6395937_V1.
Marginal Land
Using land use history, land productivity and land vulnerability datasets, potential marginal land was identified. The definition of marginal land used here is based on the premise that economically marginal land is at the borderline of profitability and tends to convert its use between crop and non-crop due to changes in commodity price, growing conditions, policy and other factors. Spatial resolution is 30m.
Permanent Cropland/Grassland
Permanent cropland and grassland were identified using the NASS Cropland Data Layer. These pixels were always classfied the same during the 2008-2015 period. The data have a spatial resolution of 30m.
Land Productivity
Land productivity was derived using the National Commondity Crop Productivity Index. Values range from 0-1. The data have a spatial resolution of 30m.
Contacts:
Kaiyu Guan, kaiyug@illinois.edu; Madhu Khanna, khanna1@illinois.edu
1. Dataset Name: Yields and Soil Carbon for Row Crops and Energy Crops
2. Creator: Elena Blanc-Betes
3. Creator's Contact Information (email): mblanc7@illinois.edu
4. Creation Date or Dataset Time Span: 2020-01-08
5. Dataset Description:
File Format: Excel spreadsheet (.xlsx, .csv)
Variables: FIPS: county fips identification number State: state name County: county name Commodity: crop name Rotation method: rotation method used for crop production. continuous corn(cc), corn-soybean rotation(cs), soybean-corn rotation(sc) landtype: type of land under crop production. Cropland(crop) or CRP land(crp) grasstype: the type of future land-use status. Remain as crp land(crp), converted to miscanthus(misc), converted to switchgrass(sg) Residue removal rate: the rate of crop residue removed from fields. No removal(0%), 30% removal rate(30%), 50% removal rate(50%) Tillage method: the tillage option chosen for crop production. Conventional tillage(ct) or Conservation/No tillage (nt) Yields: yield for row crops. Unit/acre Mature yield: average yields for mature energy crops. Mg/ha Corn stover yield: the amount of corn stover collected from fields. Mg/acre.
File Naming Convention Description: Yield for corn and soybean-[Month]-[Year].xlsx
Spatial Resolution: County
Creation Information: Yields for corn,soybean and energy crops are simulated and transfered from the Daycent group. Yields for other row crops are collected from USDA-NASS quick stats database(https://www.nass.usda.gov/Quick_Stats/).
DayCent Gridded Yield Data
Data are from the paper: Fan, X., Khanna, M., Lee, Y., Kent, J., Shi, R., Guest, J.S., Lee, D.K. June 20, 2024. "Spatially Varying Costs of GHG Abatement with Alternative Cellulosic Feedstocks for Sustainable Aviation Fuels." Environmental Science and Technology. DOI: 10.1021/acs.est.4c01949.
Data citation: Coming Soon
Description: Yields were simulated for the four feedstocks whle considering spatial variations in growing conditions across the U.S. and variability in weather. Thirty different weather inputs were used, and land cover conditions and management assumptions described in Tables S11 and S12 in the supplemental information.
Contacts:
Madhu Khanna, khanna1@illinois.edu
AgroIBIS Gridded Yield Data
Data are from the paper: Coming soon
Description of method coming soon.
Contacts:
Theo Hartman, tmihart@illinois.edu; Bryan Petersen, bryan20@iastate.edu; Andy VanLoocke, andyvanl@iastate.edu
CABBI Simulation Results for "Assessing the Returns to Land and Greenhouse Gas Savings from Producing Energy Crops on Conservation Reserve Program Land"
Data are from the paper: Chen, L., Blanc-Betes, E., Hudiburg, T.W., Hellerstein, D., Wallander, S., DeLucia, E.H., Khanna, M. Jan. 7, 2021. “Assessing the Returns to Land and Greenhouse Gas Savings from Producing Energy Crops on Conservation Reserve Program Land.” Environmental Science & Technology. DOI: 10.1021/acs.est.0c06133.
Dataset citation: Chen, L., Blanc-Betes, E., Hudiburg, T.W., Hellerstein, D., Wallander, S., DeLucia, E.H., Khanna, M. Sept. 7, 2020. “BEPAM Model Code and CABBI Simulation Results for ‘Assessing the Returns to Land and Greenhouse Gas Savings from Producing Energy Crops on Conservation Reserve Program Land.’” University of Illinois at Urbana-Champaign. DOI: 10.13012/B2IDB-2224392_V2
Annual Soil Carbon Loss
DayCent simulated average annual soil carbon sequestration by miscanthus and switchgrass on CRP land over the period, 2016-2030. Positive values indicate a net increase in soil carbon stocks while the negative values indicate a decrease in soil carbon stocks.
CRP Spatial Information
This section contains the data showing the spatial distribution of CRP land under grass cover practices enrolled in the eastern region. The average CRP annual land rental payment received by landowners in 2016 for each county may also be displayed in this section.
CRP Breakeven Cost
BEPAM calculated values indicating the breakeven cost at both 1 Mg DW of the crop on cropland and 1 ha of the crop on cropland.
CABBI Data Access
Contacts:
Luoye Chen, luoyec2@illinois.edu; Madhu Khanna, khanna1@illinois.edu; Elena Blanc-Betes, mblanc7@illinois.edu
Original Scenarios
BAU = original baseline scenario, not currently used in paper 1
RFS1 = new baseline based off of RFS1 levels, not RFS2
RFSco = corn ethanol only scenario
RFS = full corn + cellulosic ethanol with miscanthus/switchgrass
Sensitivity Analyses
RFN = no additional nitrogen added for stover removal
RFScstover = full corn + cellulosic ethanol but all cellulosic comes from corn stover, not perennials
RFSperennial = full corn + cellulosic ethanol but all cellulosic comes from perenials, NO corn stover
Citation:
Ferin, K.M., Chen, L., Zhong, J., Acquah, S., Heaton, E.A., Khanna, M., VanLoocke, A. Jan. 12, 2021. “Water Quality Effects of Economically Viable Land Use Change in the Mississippi River Basin under the Renewable Fuel Standard.” Environmental Science & Technology. DOI: 10.1021/acs.est.0c04358.
Data Location:
Ferin, K.M., Chen, L., Zhong, J., Acquah, S., Heaton, E.A., Khanna, M., VanLoocke, A. Feb. 21, 2021. “Simulated Land Allocation, Nitrogen Use, and Nitrogen Loss in the Mississippi Atchafalaya River Basin for Various RFS2 (Renewable Fuel Standard) Policy Scenarios.” University of Illinois at Urbana-Champaign. DOI: 10.13012/B2IDB-3388479_V1
Model scenarios are in the CABBI Model Repository in the Ferin_et_al_2021_Nleach folder. See this document for instructions to access.
CABBI Simulation Results for "Assessing the Returns to Land and Greenhouse Gas Savings from Producing Energy Crops on Conservation Reserve Program Land"
Data are from the paper: Chen, L., Blanc-Betes, E., Hudiburg, T.W., Hellerstein, D., Wallander, S., DeLucia, E.H., Khanna, M. Jan. 7, 2021. “Assessing the Returns to Land and Greenhouse Gas Savings from Producing Energy Crops on Conservation Reserve Program Land.” Environmental Science & Technology. DOI: 10.1021/acs.est.0c06133.
Dataset citation: Chen, L., Blanc-Betes, E., Hudiburg, T.W., Hellerstein, D., Wallander, S., DeLucia, E.H., Khanna, M. Sept. 7, 2020. “BEPAM Model Code and CABBI Simulation Results for ‘Assessing the Returns to Land and Greenhouse Gas Savings from Producing Energy Crops on Conservation Reserve Program Land.’” University of Illinois at Urbana-Champaign. DOI: 10.13012/B2IDB-2224392_V2
Annual Soil Carbon Loss
DayCent simulated average annual soil carbon sequestration by miscanthus and switchgrass on CRP land over the period, 2016-2030. Positive values indicate a net increase in soil carbon stocks while the negative values indicate a decrease in soil carbon stocks.
CRP Spatial Information
This section contains the data showing the spatial distribution of CRP land under grass cover practices enrolled in the eastern region. The average CRP annual land rental payment received by landowners in 2016 for each county may also be displayed in this section.
CRP Breakeven Cost
BEPAM calculated values indicating the breakeven cost at both 1 Mg DW of the crop on cropland and 1 ha of the crop on cropland.
CABBI Data Access
Contacts:
Luoye Chen, luoyec2@illinois.edu; Madhu Khanna, khanna1@illinois.edu; Elena Blanc-Betes, mblanc7@illinois.edu
CABBI Simulation Results for "Assessing the Returns to Land and Greenhouse Gas Savings from Producing Energy Crops on Conservation Reserve Program Land"
Data are from the paper: Chen, L., Blanc-Betes, E., Hudiburg, T.W., Hellerstein, D., Wallander, S., DeLucia, E.H., Khanna, M. Jan. 7, 2021. “Assessing the Returns to Land and Greenhouse Gas Savings from Producing Energy Crops on Conservation Reserve Program Land.” Environmental Science & Technology. DOI: 10.1021/acs.est.0c06133.
Dataset citation: Chen, L., Blanc-Betes, E., Hudiburg, T.W., Hellerstein, D., Wallander, S., DeLucia, E.H., Khanna, M. Sept. 7, 2020. “BEPAM Model Code and CABBI Simulation Results for ‘Assessing the Returns to Land and Greenhouse Gas Savings from Producing Energy Crops on Conservation Reserve Program Land.’” University of Illinois at Urbana-Champaign. DOI: 10.13012/B2IDB-2224392_V2
Annual Soil Carbon Loss
DayCent simulated average annual soil carbon sequestration by miscanthus and switchgrass on CRP land over the period, 2016-2030. Positive values indicate a net increase in soil carbon stocks while the negative values indicate a decrease in soil carbon stocks.
CRP Spatial Information
This section contains the data showing the spatial distribution of CRP land under grass cover practices enrolled in the eastern region. The average CRP annual land rental payment received by landowners in 2016 for each county may also be displayed in this section.
CRP Breakeven Cost
BEPAM calculated values indicating the breakeven cost at both 1 Mg DW of the crop on cropland and 1 ha of the crop on cropland.
CABBI Data Access
Contacts:
Luoye Chen, luoyec2@illinois.edu; Madhu Khanna, khanna1@illinois.edu; Elena Blanc-Betes, mblanc7@illinois.edu
Original Scenarios
BAU = original baseline scenario, not currently used in paper 1
RFS1 = new baseline based off of RFS1 levels, not RFS2
RFSco = corn ethanol only scenario
RFS = full corn + cellulosic ethanol with miscanthus/switchgrass
Sensitivity Analyses
RFN = no additional nitrogen added for stover removal
RFScstover = full corn + cellulosic ethanol but all cellulosic comes from corn stover, not perennials
RFSperennial = full corn + cellulosic ethanol but all cellulosic comes from perenials, NO corn stover
Citation:
Ferin, K.M., Chen, L., Zhong, J., Acquah, S., Heaton, E.A., Khanna, M., VanLoocke, A. Jan. 12, 2021. “Water Quality Effects of Economically Viable Land Use Change in the Mississippi River Basin under the Renewable Fuel Standard.” Environmental Science & Technology. DOI: 10.1021/acs.est.0c04358.
Data Location:
Ferin, K.M., Chen, L., Zhong, J., Acquah, S., Heaton, E.A., Khanna, M., VanLoocke, A. Feb. 21, 2021. “Simulated Land Allocation, Nitrogen Use, and Nitrogen Loss in the Mississippi Atchafalaya River Basin for Various RFS2 (Renewable Fuel Standard) Policy Scenarios.” University of Illinois at Urbana-Champaign. DOI: 10.13012/B2IDB-3388479_V1
Model scenarios are in the CABBI Model Repository in the Ferin_et_al_2021_Nleach folder. See this document for instructions to access.
Original Scenarios
BAU = original baseline scenario, not currently used in paper 1
RFS1 = new baseline based off of RFS1 levels, not RFS2
RFSco = corn ethanol only scenario
RFS = full corn + cellulosic ethanol with miscanthus/switchgrass
Sensitivity Analyses
RFN = no additional nitrogen added for stover removal
RFScstover = full corn + cellulosic ethanol but all cellulosic comes from corn stover, not perennials
RFSperennial = full corn + cellulosic ethanol but all cellulosic comes from perenials, NO corn stover
Citation:
Ferin, K.M., Chen, L., Zhong, J., Acquah, S., Heaton, E.A., Khanna, M., VanLoocke, A. Jan. 12, 2021. “Water Quality Effects of Economically Viable Land Use Change in the Mississippi River Basin under the Renewable Fuel Standard.” Environmental Science & Technology. DOI: 10.1021/acs.est.0c04358.
Data Location:
Ferin, K.M., Chen, L., Zhong, J., Acquah, S., Heaton, E.A., Khanna, M., VanLoocke, A. Feb. 21, 2021. “Simulated Land Allocation, Nitrogen Use, and Nitrogen Loss in the Mississippi Atchafalaya River Basin for Various RFS2 (Renewable Fuel Standard) Policy Scenarios.” University of Illinois at Urbana-Champaign. DOI: 10.13012/B2IDB-3388479_V1
Model scenarios are in the CABBI Model Repository in the Ferin_et_al_2021_Nleach folder. See this document for instructions to access.
Code and Data for "Quantifying Uncertainties in Greenhouse Gas Savings and Mitigation Costs with Cellulosic Biofuels"
Data are from the paper: Lee, Y.Y., Khanna, M., Chen, L., Shi, R., Guest, J., Blanc-Betes, E., Jiang, C., Guan, K., Hudiburg, T., DeLucia, E. Nov. 6, 2023. “Quantifying Uncertainties in Greenhouse Gas Savings and Abatement Costs with Cellulosic Biofuels.” European Review of Agricultural Economics. DOI: 10.1093/erae/jbad036.
Dataset citation: Lee, Y.Y., Khanna, M., Chen, L. April 2, 2023. Data from: “Quantifying Uncertainties in Greenhouse Gas Savings and Abatement Costs with Cellulosic Biofuels.” University of Illinois Urbana-Champaign. DOI: 10.13012/B2IDB-4326514_V1.
GHG Emission
GHG emissions from indirect land use change.
Contacts:
Madhu Khanna, khanna1@illinois.edu
Greenhouse Gas Abatement Cost with SAF
Data are from the paper: Fan, X., Khanna, M., Lee, Y., Kent, J., Shi, R., Guest, J.S., Lee, D.K. June 20, 2024. "Spatially Varying Costs of GHG Abatement with Alternative Cellulosic Feedstocks for Sustainable Aviation Fuels." Environmental Science and Technology. DOI: 10.1021/acs.est.4c01949.
Data citation: Coming Soon
Description: GHG abatement cost was calculated from the difference of the wholesale price of conventional jet fuel from the breakeven price of SAF divided by the difference of the carbon intensity of SAF from the carbon intensity of conventional jet fuel.
Contacts:
Madhu Khanna, khanna1@illinois.edu
GRIDMET is a dataset of daily high-spatial resolution (~4-km, 1/24th degree) surface meteorological data covering the contiguous US from 1979-yesterday. These data can provide important inputs for ecological, agricultural, and hydrological models. These data are updated daily.
The 1991-2020 climatologies were obtained from the THREDDS Catalog.
For more information about the dataset, please see visit Climatology Lab's gridMET site or see the citation below:
Abatzoglou, J. T. (2013), Development of gridded surface meteorological data for ecological applications and modelling. Int. J. Climatol., 33: 121–131.
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