Ecosystem model parameterization and adaptation for sustainable cellulosic biofuel landscape design

Abstract

Renewable fuel standards in the US and elsewhere mandate the production of large quantities of cellulosic biofuels with low greenhouse gas (GHG) footprints, a requirement which will likely entail extensive cultivation of dedicated bioenergy feedstock crops on marginal agricultural lands. Performance data for such systems is sparse, and non-linear interactions between the feedstock species, agronomic management intensity, and underlying soil and land characteristics complicate the development of sustainable landscape design strategies for low-impact commercial-scale feedstock production. Process-based ecosystem models are valuable for extrapolating field trial results and making predictions of productivity and associated environmental impacts that integrate the effects of spatially variable environmental factors across diverse production landscapes. However, there are few examples of ecosystem model parameterization against field trials on both prime and marginal lands or of conducting landscape-scale analyses at sufficient resolution to capture interactions between soil type, land use, and management intensity. In this work we used a data-diverse, multi-criteria approach to parameterize and validate the DayCent biogeochemistry model for upland and lowland switchgrass using data on yields, soil carbon changes, and soil nitrous oxide emissions from US field trials spanning a range of climates, soil types, and management conditions. We then conducted a high-resolution case study analysis of a real-world cellulosic biofuel landscape in Kansas in order to estimate feedstock production potential and associated direct biogenic GHG emissions footprint. Our results suggest that switchgrass yields and emissions balance can vary greatly across a landscape large enough to supply a biorefinery in response to variations in soil type and land-use history, but that within a given land base both of these performance factors can be widely modulated by changing management intensity. This in turn implies a large sustainable cellulosic biofuel landscape design space within which a system can be optimized to meet economic or environmental objectives.

Biofuel production and soil GHG emissions after land‐use change to switchgrass and giant reed in the U.S. Southeast

“…Calibration of the DAYCENT model for switchgrass (lowland ecotype) has already been achieved and has been evaluated for both Unites States and Europe environments in our previous work (Field et al., ; Nocentini, Di Virgilio, & Monti, ). To obtain the parameterization of the model for giant reed, besides using field data from our own long‐term experiments in North Italy (Alexopoulou et al., ; Monti & Zegada‐Lizarazu, ), a literature research has been carried out to select those studies which reported significant information on giant reed's aboveground and below‐ground C pools.…”

“…To simulate the mature phase, the prdx value was set lower (0.225) to reduce the yield capacity of giant reed. The sfnxmx (1) parameter was set slightly higher than 0 (Field et al., ), only to simulate switchgrass and giant reed capacity to achieve considerable yields without N fertilization (Alexopoulou et al., ; Monti & Zegada‐Lizarazu, ).…”

“…Unfortunately, very few studies reported the root biomass of giant reed, which, however, seems to reach values significantly over 10 Mg/ha, both in fine‐ (Monti & Zatta, ) and sandy‐textured soils (Nassi o Di Nasso et al., ), once the crop is established. While switchgrass model calibration was evaluated for soil N 2 O emissions (Field et al., ; r = .54), no data are currently present in the literature about soil N 2 O emissions in giant reed. Nevertheless, biomass N content was considered during model parameterization (Kering et al., ; Nassi o Di Nasso et al., ), which helped to deliver more reliable model outcomes on N 2 O emissions.…”

Consensus, uncertainties and challenges for perennial bioenergy crops and land use

“…Ecosystem process‐based models have been applied for spatially explicit bioenergy sustainability assessment for more than a decade (Sheehan et al ., ). Modern high‐power computing enables ecosystem model application at the requisite fine spatial scales (Nichols et al ., ) either through thousands of independent runs of ‘point’ models, for example DayCent (Davis et al ., ; Yu et al ., ; Field et al ., ) and EPIC (Zhang et al ., ; Gelfand et al ., ) or using ‘network’ models (e.g. SWAT; Wu et al ., ; Gramig et al ., ) that consider lateral hydrological or biogeochemical flows between networks of thousands of nodes.…”

Greenhouse gas budget of a poplar bioenergy plantation in Belgium: CO2 uptake outweighs CH4 and N2O emissions

“…The environmental drivers of N 2 O and CH 4 emissions are largely unknown, and observations of the fluxes of these gases produce varying results (Harris et al, ). Former land use, site‐specific soil properties and climate conditions influence GHG emissions from SRCs (Field, Marx, Easter, Adler, & Paustian, ; Whitaker et al, ). Site management, that is, the use of fertilizer, irrigation, and length of the rotation period, also influences the GHG balance (Carter et al, ; Díaz‐Pinés et al, ).…”