A Spatial Modeling Framework to Evaluate Domestic Biofuel-Induced Potential Land Use Changes and Emissions

We present a novel bottom-up approach to estimate biofuel-induced land-use change (LUC) and resulting CO₂ emissions in the U.S. from 2010 to 2022, based on a consistent methodology across four essential components: land availability, land suitability, LUC decision-making, and induced CO₂ emissions. Using high-resolution geospatial data and modeling, we construct probabilistic assessments of county-, state-, and national-level LUC and emissions for macroeconomic scenarios. We use the Cropland Data Layer and the Protected Areas Database to characterize availability of land for biofuel crop cultivation, and the CERES-Maize and BioCro biophysical crop growth models to estimate the suitability (yield potential) of available lands for biofuel crops. For LUC decisionmaking, we use a county-level stochastic partial-equilibrium modeling framework and consider five scenarios involving annual ethanol production scaling to 15, 22, and 29 BG, respectively, in 2022, with corn providing feedstock for the first 15 BG and the remainder coming from one of two dedicated energy crops. Finally, we derive high-resolution above-ground carbon factors from the National Biomass and Carbon Data set to estimate emissions from each LUC pathway. Based on these inputs, we obtain estimates for average total LUC emissions of 6.1, 2.2, 1.0, 2.2, and 2.4 gCO2e/MJ for Corn-15 Billion gallons (BG), <i>Miscanthus × giganteus</i> (MxG)-7 BG, Switchgrass (SG)-7 BG, MxG-14 BG, and SG-14 BG scenarios, respectively

Yield dent (see text) for maize under actual (a) conditions and differences in yield dent for uWN-actual (b), uW-actual (c), and uN-actual (d) for all grid cells with at least 100ha maize cropland [38] and a minimum yield of 0.5 tDM ha^-1.

<p>Yield dent (see text) for maize under actual (a) conditions and differences in yield dent for uWN-actual (b), uW-actual (c), and uN-actual (d) for all grid cells with at least 100ha maize cropland [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0198748#pone.0198748.ref038" target="_blank">38</a>] and a minimum yield of 0.5 tDM ha^-1.</p

CV of global maize, wheat, rice, and soybean productivity (%) over 28 years (1981–2008) of the 10 individual GGCMs, their ensemble median and FAO statistics [39].

<p>Data are shown for actual, unlimited (uWN), unlimited nutrients (uN) and unlimited water (uW) conditions and have been detrended prior to computing CVs. FAO data is only available for actual conditions. For better readability, the lowest CVs per model (rows) are colored green, highest are colored orange.</p

GGCMs participating in the study, model type and key references, as well as nutrients considered in crop model simulations (N: nitrogen, P: phosphorus, K: potassium).

<p>GGCMs participating in the study, model type and key references, as well as nutrients considered in crop model simulations (N: nitrogen, P: phosphorus, K: potassium).</p

Global distribution of relative (%) temporal yield variability per production system (actual, uW, uN, uWN) and GGCM per grid cell for maize.

<p>Colored bars show the interquartile range of yield CVs across all grid cells with at least 100ha maize cropland [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0198748#pone.0198748.ref038" target="_blank">38</a>] and a minimum yield of 0.5 tDM ha^-1. Black lines within the bars show the median, dashed whiskers extend to the maximum value with 1.5 times the interquartile range and values outside this range are classified as outliers and depicted as dots. Yield CV of more than 100% are not shown.</p

Changes in CV from purely rainfed to fully irrigated systems with current nitrogen (uW-rf).

<p>The CV can increase in regions where different growing seasons are specified for irrigated and rainfed systems [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0198748#pone.0198748.ref015" target="_blank">15</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0198748#pone.0198748.ref038" target="_blank">38</a>]. Maps show data of the GGCM ensemble median for all grid cells with at least 100ha maize cropland [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0198748#pone.0198748.ref038" target="_blank">38</a>] and a minimum yield of 0.5 tDM ha^-1.</p

Fig. S1 from Coordinating AgMIP data and models across global and regional scales for 1.5°C and 2.0°C assessments

Climate scenarios for rainfed maize growing season under the 2.0 °C climate realization across 5 HAPPI GCMs. a-e show changes in temperature and f-j show changes in precipitation. Grid cells with less than 10 ha of maize not shown

Fig. S3 from Coordinating AgMIP data and models across global and regional scales for 1.5°C and 2.0°C assessments

Difference between 2.0 °C and 1.5 °C on rainfed maize with (left) and without (right) CO₂ effects for three global gridded crop models (pDSSAT, GEPIC, LPJmL)

Fig. S2 from Coordinating AgMIP data and models across global and regional scales for 1.5°C and 2.0°C assessments

Projected rainfed maize yield change (compared to HAPPI 2006-2015 current period). Columns show different global gridded crop models (pDSSAT, GEPIC, LPJmL), rows show five HAPPI GCMs that provided driving climate projections. Grid cells with less than 10 ha of maize not shown