Logging and mill residues are currently the largest sources of woody biomass for bioenergy in the US, but short-rotation woody crops (SRWCs) are expected to become a larger contributor to biomass production, primarily on lands marginal for food production. However, there are very few studies on the environmental effects of SRWCs, and most have been conducted at stand rather than at watershed scales. In this manuscript, we review the potential environmental effects of SRWCs relative to current forestry or agricultural practices and best management practices (BMPs) in the southeast US and identify priorities and constraints for monitoring and modeling these effects. Plot-scale field studies and a watershed-scale modeling study found improved water quality with SRWCs compared to agricultural crops. Further, a recent watershed-scale experiment suggests that conventional forestry BMPs are sufficient to protect water quality from SRWC silvicultural activities, but the duration of these studies is short with respect to travel times of groundwater transporting nitrate to streams. While the effects of SRWC production on carbon (C) and water budgets depend on both soil properties and previous land management, woody crops will typically sequester more C when compared with agricultural crops. The overall C offset by SRWCs will depend on a variety of management practices, the number of rotations, and climate. Effects of SRWCs on biodiversity, especially aquatic organisms, are not well studied, but a meta-analysis found that bird and mammal biodiversity is lower in SRWC stands than unmanaged forests. Long-term (i.e., over multiple rotations) water quality, water use, C dynamics, and soil quality studies are needed, as are larger-scale (i.e., landscape scale) biodiversity studies, to evaluate the potential effects of SRWC production. Such research should couple field measurement and modeling approaches due to the temporal (i.e., multiple rotations) and spatial (i.e., heterogeneous landscape) scaling issues involved with SRWC production.
soil organic carbon
Agricultural residues have been identified as a significant potential resource for bioenergy production, but serious questions remain about the sustainability of harvesting residues. Agricultural residues play an important role in limiting soil erosion from wind and water and in maintaining soil organic carbon. Because of this, multiple factors must be considered when assessing sustainable residue harvest limits. Validated and accepted modeling tools for assessing these impacts include the Revised Universal Soil Loss Equation Version 2 (RUSLE2), the Wind Erosion Prediction System (WEPS), and the Soil Conditioning Index. Currently, these models do not work together as a single integrated model. Rather, use of these models requires manual interaction and data transfer. As a result, it is currently not feasible to use these computational tools to perform detailed sustainable agricultural residue availability assessments across large spatial domains or to consider a broad range of land management practices. This paper presents an integrated modeling strategy that couples existing datasets with the RUSLE2 water erosion, WEPS wind erosion, and Soil Conditioning Index soil carbon modeling tools to create a single integrated residue removal modeling system. This enables the exploration of the detailed sustainable residue harvest scenarios needed to establish sustainable residue availability. Using this computational tool, an assessment study of residue availability for the state of Iowa was performed. This study included all soil types in the state of Iowa, four representative crop rotation schemes, variable crop yields, three tillage management methods, and five residue removal methods. The key conclusions of this study are that under current management practices and crop yields nearly 26.5 million Mg of agricultural residue are sustainably accessible in the state of Iowa, and that through the adoption of no till practices residue removal could sustainably approach 40 million Mg. However, when considering the economics and logistics of residue harvest, yields below 2.25 Mg ha−1 are generally considered to not be viable for a commercial bioenergy system. Applying this constraint, the total agricultural residue resource available in Iowa under current management practices is 19 million Mg. Previously published results have shown residue availability from 22 million Mg to over 50 million Mg in Iowa.
Corn’s (Zea mays L.) stover is a potential nonfood, herbaceous bioenergy feedstock. A vital aspect of utilizing stover for bioenergy production is to establish sustainable harvest criteria that avoid exacerbating soil erosion or degrading soil organic carbon (SOC) levels. Our goal is to empirically estimate the minimum residue return rate required to sustain SOC levels at numerous locations and to identify which macroscale factors affect empirical estimates. Minimum residue return rate is conceptually useful, but only if the study is of long enough duration and a relationship between the rate of residue returned and the change in SOC can be measured. About one third of the Corn Stover Regional Partnership team (Team) sites met these criteria with a minimum residue return rate of 3.9 ± 2.18 Mg stover ha−1 yr−1, n = 6. Based on the Team and published corn-based data (n = 35), minimum residue return rate was 6.38 ± 2.19 Mg stover ha−1 yr−1, while including data from other cropping systems (n = 49), the rate averaged 5.74 ± 2.36 Mg residue ha−1 yr−1. In broad general terms, keeping about 6 Mg residue ha−1 yr−1 maybe a useful generic rate as a point of discussion; however, these analyses refute that a generic rate represents a universal target on which to base harvest recommendations at a given site. Empirical data are needed to calibrate, validate, and refine process-based models so that valid sustainable harvest rate guidelines are provided to producers, industry, and action agencies.
Corn (Zea mays L.) stover is a potential bioenergy feedstock, but little is known about the impacts of reducing stover return on yield and soil quality in the Northern US Corn Belt. Our study objectives were to measure the impact of three stover return rates (Full (~7.8 Mg ha−1 yr−1), Moderate (~3.8 Mg ha−1 yr−1) or Low (~1.5 Mg ha yr−1) Return) on corn and soybean (Glycine max. L [Merr.]) yields and on soil dynamic properties on a chisel-tilled (Chisel) field, and well- (NT1995) or newly- (NT2005) established no-till managed fields. Stover return rate did not affect corn and soybean yields except under NT1995 where Low Return (2.88 Mg ha−1) reduced yields compared with Full and Moderate Return (3.13 Mg ha−1). In NT1995 at 0–5 cm depth, particulate organic matter in Full Return and Moderate Return (14.3 g kg−1) exceeded Low Return (11.3 g kg−1). In NT2005, acid phosphatase activity was reduced about 20% in Low Return compared to Full Return. Also the Low Return had an increase in erodible-sized dry aggregates at the soil surface compared to Full Return. Three or fewer cycles of stover treatments revealed little evidence for short-term impacts on crop yield, but detected subtle soil changes that indicate repeated harvests may have negative consequences if stover removed.
Net benefits of bioenergy crops, including maize and perennial grasses such as switchgrass, are a function of several factors including the soil organic carbon (SOC) sequestered by these crops. Life cycle assessments (LCA) for bioenergy crops have been conducted using models in which SOC information is usually from the top 30 to 40 cm. Information on the effects of crop management practices on SOC has been limited so LCA models have largely not included any management practice effects. In the first 9 years of a long-term C sequestration study in eastern Nebraska, USA, switchgrass and maize with best management practices had average annual increases in SOC per hectare that exceed 2 Mg C year−1 (7.3 Mg CO2 year−1) for the 0 to 150 soil depth. For both switchgrass and maize, over 50 % of the increase in SOC was below the 30 cm depth. SOC sequestration by switchgrass was twofold to fourfold greater than that used in models to date which also assumed no SOC sequestration by maize. The results indicate that N fertilizer rates and harvest management regimes can affect the magnitude of SOC sequestration. The use of uniform soil C effects for bioenergy crops from sampling depths of 30 to 40 cm across agro-ecoregions for large scale LCA is questionable.
To prepare for a 2014 launch of commercial scale cellulosic ethanol production from corn/maize (Zea mays L.) stover, POET-DSM near Emmetsburg, IA has been working with farmers, researchers, and equipment dealers through “Project Liberty” on harvest, transportation, and storage logistics of corn stover for the past several years. Our objective was to evaluate seven stover harvest strategies within a 50-ha (125 acres) site on very deep, moderately well to poorly drained Mollisols, developed in calcareous glacial till. The treatments included the following: conventional grain harvest (no stover harvest), grain plus a second-pass rake and bale stover harvest, and single-pass grain plus cob-only biomass, grain plus vegetative material other than grain [(MOG) consisting of cobs, husks, and upper plant parts], grain plus all vegetative material from the ear shank upward (high cut), and all vegetative material above a 10 cm stubble height (low cut), with a John Deere 9750 STS combine, and grain plus direct baling of MOG with an AgCo harvesting system. Average grain yields were 11.4, 10.1, 9.7, and 9.5 Mg ha−1 for 2008, 2009, 2010, and 2011, respectively. Average stover harvest ranged from 0 to 5.6 Mg ha−1 and increased N, P, and K removal by an average of 11, 1.6, and 15 kg Mg−1, respectively. Grain yield in 2009 showed a significant positive response to higher 2008 stover removal rates, but grain yield was not increased in 2010 or 2011 due to prior-year stover harvest. High field losses caused the direct-bale treatment to have significantly lower grain yield in 2011 because the AgCo system could not pick up the severely lodged crop. We conclude that decreases in grain yield across the 4 years were due more to seasonal weather patterns, spatial variability, and not rotating crops than to stover harvest.
This paper was presented at the 2012 International Congress on Environmental Modelling and Software in Leipzig, Germany on July 15, 2012.
Abstract: Agricultural residues are the largest near term source of cellulosic biomass for bioenergy production, but removing agricultural residues sustainably requires considering the critical roles that residues play in the agronomic system. Determination of sustainable removal rates for agricultural residues has received significant attention and integrated modeling strategies have been built to evaluate sustainable removal rates considering soil erosion and organic matter constraints. However, the current integrated model, comprised of the agronomic models WEPS, RUSLE2, and SCI, does not quantitatively assess the impacts of residue removal on soil organic carbon and long term crop yields. Furthermore, it does not evaluate the impact of residue removal on greenhouse gas emissions, specifically N2O and CO2 gas fluxes from the soil surface. The DAYCENT model simulates several important processes for determining agroecosystem performance. These processes include daily nitrogen gas flux, daily CO2 flux from soil respiration, soil organic carbon and nitrogen, net primary productivity, and daily water and nitrate leaching. Each of these processes is an indicator of sustainability when evaluating emerging cellulosic biomass production systems for bioenergy. This paper couples the DAYCENT model with the existing integrated model to investigate additional environment al impacts of agricultural residue removal. The integrated model is extended to facilitate two - way coupling between DAYC ENT and the existing framework. The extended integrated model, including DAYCENT, is applied to investigate additional environmental impacts from a recent sustainable agricultural residue removal dataset. Results show some differences in sustainable removal rates compared to previous results for a case study county in Iowa , US . The extended integrated model also predict s that long term yields will decrease .32% – 1.43 % under sustainable residue removal management practices.
A global energy crop productivity model that provides geospatially explicit quantitative details on biomass
potential and factors affecting sustainability would be useful, but does not exist now. This study describes a
modeling platform capable of meeting many challenges associated with global-scale agro-ecosystem modeling.
We designed an analytical framework for bioenergy crops consisting of six major components: (i) standardized
natural resources datasets, (ii) global field-trial data and crop management practices, (iii) simulation units and
management scenarios, (iv) model calibration and validation, (v) high-performance computing (HPC) simulation,
and (vi) simulation output processing and analysis. The HPC-Environmental Policy Integrated Climate
(HPC-EPIC) model simulated a perennial bioenergy crop, switchgrass (Panicum virgatum L.), estimating feedstock
production potentials and effects across the globe. This modeling platform can assess soil C sequestration,
net greenhouse gas (GHG) emissions, nonpoint source pollution (e.g., nutrient and pesticide loss), and energy
exchange with the atmosphere. It can be expanded to include additional bioenergy crops (e.g., miscanthus,
energy cane, and agave) and food crops under different management scenarios. The platform and switchgrass
field-trial dataset are available to support global analysis of biomass feedstock production potential and corresponding
metrics of sustainability.