Robert P. Anex

Production, transportation and milling costs of sweet sorghum as a feedstock for centralized bioethanol production in the upper Midwest.

Sweet sorghum has been identified as a possible ethanol feedstock because of its biomass yield and high concentration of readily fermentable sugars. It has found limited use, however, because of poor post-harvest storage characteristics and short harvest window in cooler climates. Previous research (Bennett, A.S., Anex, R.P., 2008. Farm-gate production costs of sweet sorghum as a bioethanol feedstock. Transactions of the ASABE 51(2), 603-613) indicates that fermentable carbohydrates (FC) can be produced at less expense from sweet sorghum than from corn grain. Previous research, however, did not include costs associated with off-farm transportation, storage, or capital costs associated with milling and energy recovery equipment that are required to provide FC suitable for biological conversion. This study includes these additional costs and reevaluates sweet sorghum as a biocommodity feedstock. A total of eight harvest-transport-processing options are modeled, including 4-row self-propelled and 2-row tractor-pulled forage harvesters, two different modes of in-field transport, fresh processing, on-farm ensilage and at-plant ensilage. Monte Carlo simulation and sensitivity analysis are used to account for system variability and compare scenarios. Transportation costs are found to be significant ranging from

Functional group and fertilization affect the composition and bioenergy yields of prairie plants

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Trade-offs among agronomic, energetic, and environmental performance characteristics of corn and prairie bioenergy cropping systems

71 Mg (-1) FC, with highest costs associated with at-plant ensilage scenarios. Economies of scale benefit larger milling equipment and boiler systems reducing FC costs by more than 50% when increasing annual plant capacity from 37.9 to 379 million liters. Ensiled storage of high moisture sweet sorghum in bunkers can lead to significant losses of FC (>20%) and result in systems with net FC costs well above those of corn-derived FC. Despite relatively high transport costs, seasonal, fresh processed sweet sorghum is found to produce FC at costs competitive with corn grain derived FC.

Effect of Ammonia Soaking Pretreatment and Enzyme Addition on Biochemical Methane Potential of Switchgrass

Prairies used for bioenergy production have potential to generate marketable products while enhancing environmental quality, but little is known about how prairie species composition and nutrient management affect the suitability of prairie biomass for bioenergy production. We determined how functional‐group identity and nitrogen fertilization affected feedstock characteristics and estimated bioenergy yields of prairie plants, and compared those prairie characteristics to that of corn stover. We tested our objectives with a field experiment that was set up as a 5 × 2 incomplete factorial design with C3 grasses, C4 grasses, legumes, and multi‐functional‐group mixtures grown with and without nitrogen fertilizer; a fertilized corn treatment was also included. We determined cell wall, hemicellulose, cellulose, and ash concentrations; ethanol conversion ratios; gross caloric ratios; aboveground biomass production; ethanol yields; and energy yields for all treatments. Prairie functional‐group identity affected the biomass feedstock characteristics, whereas nitrogen fertilization did not. Functional group and fertilization had a strong effect on aboveground biomass production, which was the major predictor of ethanol and energy yields. C4 grasses, especially when fertilized, had among the most favorable bioenergy characteristics with high estimated ethanol conversion ratios and nongrain biomass production, relatively high gross caloric ratios, and low ash concentrations. The bioenergy characteristics of corn stover, from an annual C4 grass, were similar to those of the biomass of perennial C4 grasses. Both functional‐group composition and nitrogen fertility management were found to be important in optimizing bioenergy production from prairies.

Implications of biofuel policy-driven land cover change for rainfall erosivity and soil erosion in the United States

Cellulosic bioenergy production provides opportunities to utilize a range of cropping systems that can enhance the multifunctionality of agricultural landscapes. In a 9‐ha field experiment located on fertile land in Boone County, IA, USA, we directly compared a corn‐soybean rotation harvested for grain, continuous corn harvested for grain and stover, continuous corn harvested for grain and stover with a rye cover crop, newly reconstructed prairie harvested for biomass and fertilized with nitrogen, and unfertilized newly reconstructed prairie harvested for biomass. Comparisons were made using four performance indicators: harvestable yield, net energy balance (NEB), root production, and nutrient balances. We found trade‐offs among systems in terms of the measured performance indicators. Continuous corn systems were the highest yielding, averaging 13 Mg ha−1 of harvested biomass (grain plus stover), whereas fertilized and unfertilized prairies produced the least harvested biomass at 8.8 and 6.5 Mg ha−1, respectively. Mean NEBs were highest in continuous corn systems at 45.1 GJ ha−1, intermediate in the corn‐soybean rotation at 28.6 GJ ha−1, and lowest in fertilized and unfertilized prairies at 11.4 and 10.5 GJ ha−1, respectively. Concomitant with the high yields of the continuous corn systems were the large nutrient requirements of these systems compared to the prairie systems. Continuous corn with rye required three times more nitrogen inputs than fertilized prairie. Root production, on the other hand, was on average seven times greater in the prairie systems than the annual crop systems. On highly fertile soils, corn‐based cropping systems are likely to play an important role in maintaining the high productivity of agricultural landscapes, but alternative cropping systems, such as prairies used for bioenergy production, can produce substantial yield, require minimal externally derived inputs, and can be incorporated into the landscape at strategic locations to maximize the production of other ecosystem services.

Understanding the DayCent model

This article presents the biochemical methane potential (BMP) results from the anaerobic digestion (AD) of switchgrass. Triplicate BMP assays were performed on: untreated switchgrass, aqueous ammonia soaking (AAS) pretreated switchgrass (soaked in 29.5% reagent-grade aqueous ammonia at 5 L kg-1 switchgrass for 5 d), and AAS-pretreated switchgrass plus cellulytic enzymes at 12.5, 25, 62.5, and 125 filter paper units (FPU) enzyme g-1 volatile solids (VS). Biogas production and biogas methane content were measured daily in all treatments for 21 d. Both biogas and corrected methane production varied significantly among treatments, especially during the first 7 d of the BMP period. Total methane production at 21 d was corrected for enzyme degradation, and methane yields ranged from 0.15 to 0.36 m3 CH4 kg-1 VS. We compared the corrected energy yield of biogas from switchgrass to prior reports of the energy yield of ethanol from switchgrass via simultaneous saccharification and fermentation (SSF). The AD of AAS-pretreated switchgrass at the highest enzyme loading rates resulted in a 120% increase in energy extracted as compared to AAS-pretreated switchgrass converted to ethanol via SSF. Overall, the addition of enzymes to AAS-pretreated switchgrass greatly accelerated the rate of methane production over the untreated switchgrass and AAS-pretreated switchgrass without enzymes. However, the process economics are not clear, and additional work is needed to determine whether pretreating switchgrass with aqueous ammonia and/or enzymes before AD is economically advantageous.

What does it take to detect a change in soil carbon stock? A regional comparison of minimum detectable difference and experiment duration in the north central United States

Large‐scale conversion of traditional agricultural cropping systems to biofuel cropping systems is predicted to have significant impact on the hydrologic cycle. Changes in the hydrologic cycle lead to changes in rainfall and its erosive power, and consequently soil erosion that will have onsite impacts on soil quality and crop productivity, and offsite impacts on water quality and quantity. We examine regional change in rainfall erosivity and soil erosion resulting from biofuel policy‐induced land use/land cover (LULC) change. Regional climate is simulated under current and biofuel LULC scenarios for the period 1979–2004 using the Weather Research Forecast (WRF) model coupled to the NOAH land surface model. The magnitude of change in rainfall erosivity under the biofuel scenario is 1.5–3 times higher than the change in total annual rainfall. Over most of the conterminous United States (~56%), the magnitude of the change in erosivity is between −2.5% and +2.5%. A decrease in erosivity of magnitude 2.5–10% is predicted over 23% of the area, whereas an increase of the same magnitude is predicted over 14% of the area. Corresponding to the changes in rainfall erosivity and crop cover, a decrease in soil loss is predicted over 60% of the area under the biofuel scenario. In Kansas and Oklahoma, the states in which a large fraction of land area is planted with switchgrass under the biofuel scenario, soil loss is estimated to decrease 12% relative to the baseline. This reduction in soil loss is due more to changes in the crop cover factor than changes in rainfall or rainfall erosivity. This indicates that the changes in LULC, due to future cellulosic biofuel feedstock production, can have significant implications for regional soil and water resources in the United States and we recommend detailed investigation of the trade‐offs between land use and management options.

Standardized research protocols enable transdisciplinary research of climate variation impacts in corn production systems

The ability of biogeochemical ecosystem models to represent agro-ecosystems depends on their correct integration with field observations. We report simultaneous calibration of 67 DayCent model parameters using multiple observation types through inverse modeling using the PEST parameter estimation software. Parameter estimation reduced the total sum of weighted squared residuals by 56% and improved model fit to crop productivity, soil carbon, volumetric soil water content, soil temperature, N2O, and soil NO 3 - compared to the default simulation. Inverse modeling substantially reduced predictive model error relative to the default model for all model predictions, except for soil NO 3 - and NH 4 + . Post-processing analyses provided insights into parameter-observation relationships based on parameter correlations, sensitivity and identifiability. Inverse modeling tools are shown to be a powerful way to systematize and accelerate the process of biogeochemical model interrogation, improving our understanding of model function and the underlying ecosystem biogeochemical processes that they represent. Several DayCent submodels were calibrated simultaneously using inverse modeling.Parameter estimation reduced DayCent total sum of weighted squared residuals by 56%.Soil temperature and water content are highly informative in DayCent calibration.Parameter estimation is an efficient way to calibrate soil biogeochemical models.Post-estimation analyses provide unique insights into model structure and function.

Corn Grain Drying Using Corn Stover Combustion and CHP Systems

Variability in soil organic carbon (SOC) results from natural and human processes interacting across time and space, and leads to large variation in the minimum difference in SOC that can be detected with a particular experimental design. Here we report a unique comparison of minimum detectable differences (MDDs) in SOC, and the estimated times required to observe those MDDs across the north central United States, calculated for the two most common SOC experiments: (1) a comparison between two treatments, e.g., moldboard plow (MP) and no-tillage (NT), using a randomized complete block design experiment; and (2) a comparison of changes in SOC over time for a particular treatment, e.g., NT, using a randomized complete block design experiment with time as an additional factor. We estimated the duration of the two experiment types required to achieve MDD through simulation of SOC dynamics. Data for the study came from 13 experimental sites located in Iowa, Illinois, Ohio, Michigan, Wisconsin, Missouri, and Minnesota. Soil organic carbon, bulk density, and texture were measured at four soil depths. Minimum detectable differences were calculated with probability of Type I error of 0.05 and probability of Type II error of 0.15. The MDDs in SOC were highly variable across the region and increased with soil depth. At 0 to 10 cm (0 to 3.9 in) soil depth, MDDs with five replications ranged from 1.04 g C kg−1 (0.017 oz C lb−1; 6%) to 7.15 g C kg−1 (0.114 oz C lb−1; 31%) for comparison of two treatments; and from 0.46 g C kg−1 (0.007 oz C lb−1; 3%) to 3.12 g C kg−1 (0.050 oz C lb−1; 13%) for SOC change over time. Large differences were also predicted in the experiment duration required to detect a difference in SOC between MP and NT (from 8 to >100 years with five replications), or a change in SOC over time under NT management (from 11 to 71 years with five replications). At most locations, the time required to detect a change in SOC under NT was shorter than the time required to detect a difference between MP and NT. Minimum detectable difference and experiment duration decreased with the number of replications and were correlated with SOC variability and soil texture of the experimental sites, i.e., they tended to be lower in fine textured soils. Experiment duration was also reduced by increased crop productivity and the amount of residue left on the soil. The relationships and methods described here enable the design of experiments with high power of detecting differences and changes in SOC and enhance our understanding of how management practices influence SOC storage.

A Case Study of Environmental Benefits of Sensor-Based Nitrogen Application in Corn

The important questions about agriculture, climate, and sustainability have become increasingly complex and require a coordinated, multifaceted approach for developing new knowledge and understanding. A multistate, transdisciplinary project was begun in 2011 to study the potential for both mitigation and adaptation of corn-based cropping systems to climate variations. The team is measuring the baseline as well as change of the systems carbon (C), nitrogen (N), and water footprints, crop productivity, and pest pressure in response to existing and novel production practices. Nine states and 11 institutions are participating in the project, necessitating a well thought out approach to coordinating field data collection procedures at 35 research sites. In addition, the collected data must be brought together in a way that can be stored and used by persons not originally involved in the data collection, necessitating robust procedures for linking metadata with the data and clearly delineated rules for use and publication of data from the overall project. In order to improve the ability to compare data across sites and begin to make inferences about soil and cropping system responses to climate across the region, detailed research protocols were developed to standardize the types of measurements taken and the specific details such as depth, time, method, numbers of samples, and minimum data set required from each site. This process required significant time, debate, and commitment of all the investigators involved with field data collection and was also informed by the data needed to run the simulation models and life cycle analyses. Although individual research teams are collecting additional measurements beyond those stated in the standardized protocols, the written protocols are used by the team for the base measurements to be compared across the region. A centralized database was constructed to meet the needs of current researchers on this project as well as for future use for data synthesis and modeling for agricultural, ecosystem, and climate sciences.