Environmental limitation mapping of potential biomass resources across the conterminous United States

Abstract

Several crops have recently been identified as potential dedicated bioenergy feedstocks for the production of power, fuels, and bioproducts. Despite being identified as early as the 1980s, no systematic work has been undertaken to characterize the spatial distribution of their long-term production potentials in the United states. Such information is a starting point for planners and economic modelers, and there is a need for this spatial information to be developed in a consistent manner for a variety of crops, so that their production potentials can be intercompared to support crop selection decisions. As part of the Sun Grant Regional Feedstock Partnership (RFP), an approach to mapping these potential biomass resources was developed to take advantage of the informational synergy realized when bringing together coordinated field trials, close interaction with expert agronomists, and spatial modeling into a single, collaborative effort. A modeling and mapping system called PRISM-ELM was designed to answer a basic question: How do climate and soil characteristics affect the spatial distribution and long-term production patterns of a given crop? This empirical/mechanistic/biogeographical hybrid model employs a limiting factor approach, where productivity is determined by the most limiting of the factors addressed in submodels that simulate water balance, winter low-temperature response, summer high-temperature response, and soil pH, salinity, and drainage. Yield maps are developed through linear regressions relating soil and climate attributes to reported yield data. The model was parameterized and validated using grain yield data for winter wheat and maize, which served as benchmarks for parameterizing the model for upland and lowland switchgrass, CRP grasses, Miscanthus, biomass sorghum, energycane, willow, and poplar. The resulting maps served as potential production inputs to analyses comparing the viability of biomass crops under various economic scenarios. The modeling and parameterization framework can be expanded to include other biomass crops.

The impact of alternative land and yield assumptions in herbaceous biomass supply modeling: one‐size‐fits‐all resource assessment?

“…Field trial data were used in the empirical model PRISM‐EM to derive initial county‐level energy crop‐yield expectations. BT16 incorporated yield improvements over time that may be achieved with a mix of improved management practices and crop genotypes: base‐case (1% yield improvements per year) and high‐yield scenarios (2%, 3%, and 4% yield improvements per year) .…”

“…Each deployment technology would require the screening of appropriate cultivars for the intended application. As demonstrated in Daly et al ., climatological factors such as precipitation and minimum temperature drive the spatial variability in potential mature yield for herbaceous energy crops. Within each region, potential yield may vary across land types, as would be expected when sorghum is grown on cropland compared to pastureland in scenarios AS2, AS3, AS5, and AS6.…”

Biomass production of herbaceous energy crops in the United States: field trial results and yield potential maps from the multiyear regional feedstock partnership

“…For objective 1, this study discusses empirically derived yield potential as well as certain management practices that affect yield (e.g., cultivar selection, establishment, fertility, and harvest timing). For objective 2, yield potential maps were developed through an iterative process using the PRISM Environmental Limitation Model (PRISM‐ELM) (Daly et al ., ) and based in part on field research data (both small plot and field scale) obtained from Feedstock Partnership trials. In addition, the summarized raw data from these trials are provided as a supplement to this study, and the full dataset is accessible via the Knowledge Discovery Framework (KDF; U.S. Department of Energy Bioenergy KDF/https://www.bioenergykdf.net).…”

Economic comparative advantage of willow biomass in the Northeast USA

“…The yields for the BT16 were derived from modeling activities of the Sun Grant Regional Partnership (RFP), led by South Dakota State University. As reported in Daly et al ., supply estimates of the BT16 include national modeling of eight specific dedicated energy crops, including willow, sorghum, energy cane, eucalyptus, miscanthus, poplar, switchgrass, and pine. For each of the energy crops, a 30 m gridded suitability index (index ranges from 0 to 100) was generated and transformed to actual yields using a curated set of uniformly managed field trials or historical data as described in Lee et al ., Volk et al ., and Daly et al .…”

“…As reported in Daly et al ., supply estimates of the BT16 include national modeling of eight specific dedicated energy crops, including willow, sorghum, energy cane, eucalyptus, miscanthus, poplar, switchgrass, and pine. For each of the energy crops, a 30 m gridded suitability index (index ranges from 0 to 100) was generated and transformed to actual yields using a curated set of uniformly managed field trials or historical data as described in Lee et al ., Volk et al ., and Daly et al . Actual yields represent the ‘best local variety’ of each crop, assuming uniformly applied best management practices, or, in the case of willow and poplar, the top three cultivars.…”