DOI: 10.5061/dryad.fbg79cp3v
title: Climate change mitigation potential of widespread cover crop adoption in U.S.

description: This geospatial dataset represents climate change mitigation benefits from
 widespread cover crop adoption on U.S. cropland. We simulated changes in
 soil organic carbon stocks and nitrous oxide fluxes over a 20-year period
 for baseline cover crop adoption rates (derived from historical adoption
 rates) and a high cover crop adoption (80%) scenario in the continental
 U.S. Data were generated using the DayCent ecosystem model driven by
 cropping histories in the USDA National Resources Inventory (NRI) and
 associated agricultural management data. Here we present the mean and
 standard deviation of annual soil organic carbon stock changes and nitrous
 oxide fluxes for both baseline and high cover crop adoption scenarios on a
 county level.
description: We compared a high (80%) cover crop (CC) adoption scenario with
 the most current CC adoption rates in each region (NASS, 2017) and
 projected the 20-year soil organic carbon (SOC) stock change and
 N<sub>2</sub>O flux for each scenario. The DayCent
 biogeochemical model was used to simulate the effect of CC on 132,319
 survey locations included in the National Resources Inventory (NRI), a
 program that monitors land use in the United States and cumulatively
 represent 94.1 Mha of cropland in the country. Either crimson clover
 (<em>Trifolium incarnatum</em> L.), cereal rye
 (<em>Secale cereale</em> L.), or radish (<em>Raphanus
 sativus</em>) CC were simulated depending on regional CC species
 preferences and compatibility with the crop rotation and management
 specific to each NRI location. A Monte Carlo approach adapted from Ogle et
 al. (2010, 2023) was used to quantify uncertainty associated with
 management input data and error in model parameters. We
 aggregated average annual SOC stock change and N<sub>2</sub>O
 flux for the baseline and high adoption scenarios at the county-level for
 each Monte Carlo iteration. We present the uncertainty as the standard
 deviation from 1000 iterations. We also present total cropland area and
 cropland with newly adopted cover crops at the start of the study for each
 county. Data are presented in a shapefile format with associated maps for
 visualization.
description: # Climate change mitigation potential of widespread cover crop adoption in
 U.S.
 [https://doi.org/10.5061/dryad.fbg79cp3v](https://doi.org/10.5061/dryad.fbg79cp3v) This geospatial dataset represents climate change mitigation benefits from widespread cover crop adoption on U.S. cropland. We simulated changes in soil organic carbon stocks and nitrous oxide fluxes over a 20-year period for baseline cover crop adoption rates (derived from historical adoption rates) and a high cover crop adoption (80%) scenario in the continental U.S. Data were generated using the DayCent ecosystem model driven by cropping histories in the USDA National Resources Inventory (NRI) and associated agricultural management data. Here we present the mean and standard deviation of annual soil organic carbon stock changes and nitrous oxide fluxes for both baseline and high cover crop adoption scenarios on a county level. ## Description of the data and file structure We compared a high (80%) cover crop (CC) adoption scenario with the most current CC adoption rates in each region (NASS, 2017) and projected the 20-year soil organic carbon (SOC) stock change and N2O flux for each scenario. The DayCent biogeochemical model was used to simulate the effect of CC on 132,319 survey locations included in the National Resources Inventory (NRI), a program that monitors land use in the United States and cumulatively represent 94.1 Mha of cropland in the country. Either crimson clover (*Trifolium incarnatum* L.), cereal rye (*Secale cereale* L.), or radish (*Raphanus sativus*) CC were simulated depending on regional CC species preferences and compatibility with the crop rotation and management specific to each NRI location. A Monte Carlo approach adapted from Ogle et al. (2010, 2023) was used to quantify uncertainty associated with management input data and error in model parameters. We aggregated average annual SOC stock change and N2O flux for the baseline and high adoption scenarios at the county-level for each Monte Carlo iteration. We present the uncertainty as the standard deviation from 1000 iterations. We also present total cropland area and cropland with newly adopted cover crops at the start of the study for each county. Data are presented in a shapefile format with associated maps for visualization. Files & Metadata 1. CCMitigationPotentialData.shp: Shape file which contains county polygons and associated data. The North American 1983 Datum is used. Data fields include: * FIPS_code: Identifying Federal Information Processing Standard (FIPS) code for the U.S. county * C_Mn_CC: Mean annual change in SOC stock under the CC75 scenario (t CO2-eq ha-1) * C_Mn_B: Mean annual change in SOC stock under the baseline scenario (t CO2-eq ha-1) * C_Mn_D: Mean difference in annual change in SOC stock between the CC75 and baseline scenarios (t CO2-eq ha-1). Positive values represent additional SOC accrual due to CC. * C_sd_CC: Standard deviation of annual change in SOC stock under the CC75 scenario across 1000 Monte Carlo simulations (t CO2-eq ha-1). * C_sd_B: Standard deviation of annual change in SOC stock under the baseline scenario across 1000 Monte Carlo simulations (t CO2-eq ha-1) * C_sd_D: Standard deviation of the difference in annual change in SOC stock between the CC75 and baseline scenarios (t CO2-eq ha-1) * N_Mn_CC: Mean annual N2O flux under the CC75 scenario (t CO2-eq ha-1) * N_Mn_B: Mean annual N2O flux under the baseline scenario (t CO2-eq ha-1) * N_Mn_D: Mean difference in annual N2O flux between the CC75 and baseline scenarios (t CO2-eq ha-1). Negative values represent mitigated N2O emissions due to CC. * N_sd_CC: Standard deviation of annual N2O flux under the CC75 scenario across 1000 Monte Carlo simulations (t CO2-eq ha-1). * N_sd_B: Standard deviation of annual N2O flux under the baseline scenario across 1000 Monte Carlo simulations (t CO2-eq ha-1). * N_sd_D: Standard deviation of the difference annual N2O flux between the CC75 and baseline scenarios (t CO2-eq ha-1). * TotCrop: Total cropland included in our analysis within each county (hectares). * NwAdop: Total cropland which adopted CC at the start of the study (hectares). 2. PNG figures which map all data fields contained in CCMitigationPotentialData.shp. Figure names correspond to column names described above.