The Pyrolysis-Bioenergy-Biochar Pathway to Carbon-Negative Energy

Final Report

May 31, 2019

Research Institutions

Iowa State University

University of California, Berkeley

Indiana University - Purdue University Indianapolis

Investigators

Department of Agronomy, Iowa State University: David A. Laird, Professor; Sotirios Archontoulis, Assistant Professor; Fernando E Miguez, Associate Professor; Natalia Rogovska, Assistant Scientist; Santanu Bakshi, Postdoctoral Research Associate; Chumki Banik, Postdoctoral Research Associate; Rivka B. Fidel, Postdoctoral Research Associate; Deborah M. Aller, Graduate Research Assistant; Hamze Dokoohaki, Graduate Research Assistant; Isaiah L. Huber, Graduate Research Assistant; Garret Lies, Undergraduate Assistant.

Department of Mechanical Engineering, Iowa State University: Robert C. Brown, Distinguished Professor; Mark Mba-Wright, Associate Professor; Wenqin Li, Graduate Research Assistant.

Department of Economics, Iowa State University: Dermot J. Hayes, Professor; Amani E. Elobeid, Lecturer; Wendong Zhang, Assistant Professor; Alejandro Plastina, Assistant Professor; Ryan Goodrich, Graduate Research Assistant; Wendiam Sawadgo, Graduate Research Assistant.

School of Public and Environmental Affairs, Indiana University - Purdue University Indianapolis: Jerome Dumortier, Assistant Professor.

Department of Agricultural & Resource Economics, University of California, Berkeley: David Zilberman, Professor.

Abstract Avoiding irreversible climate change requires >50% reduction in anthropogenic greenhouse gas (GHG) emissions by the year 2050 and the net removal of GHGs from the atmosphere by the end of the 21st century. This challenge is particularly daunting given that energy derived from fossil fuels is at the core of all modern economies and some sectors of the economy, such as transportation, will be almost impossible to completely decarbonize. To address this challenge, we investigated the integrated pyrolysis-bioenergy-biochar platform (PBBP) to determine whether this system can produce economically viable carbon-negative energy products. The PBBP has the potential to be carbon negative because the biomass feedstock used in the PBBP contains carbon, which came from the atmosphere via photosynthesis, and carbon in the biochar co-product has a half life ranging from centuries to millennia when biochar is used as a soil amendment. Major accomplishments during the project include the design and development of a biochar module within the Agricultural Production Systems sIMulator (APSIM), a widely used and publically available cropping systems model. The APSIM Biochar Model provides for the first time a means of systematically investigating complex soil-biochar-crop-climate-management interactions, and critically a means of estimating the agronomic and environmental impacts of soil biochar applications at scales ranging from a single pedon to global. Developing the biochar model required a mechanistic understanding of complex biochar, soil, crop, management, and climate interactions. To this end, we conducted a series of laboratory studies to assess the diversity of biochar physical and chemical properties and a series of laboratory-incubations, greenhouse pot experiments, and agricultural field trials to understand how biochar amendments and biochar properties influence soil processes and plant growth. Key products of this research include quantitative relationships relating the highest pyrolysis temperature and biomass feedstock properties to biochar alkalinity, anion and cation exchange capacity, nitrate and ammonium sorption capacity, and ratios of labile to recalcitrant biochar carbon and nitrogen. We developed a hot water extraction procedure that can be used to rapidly quantify the size and C:N ratios of the labile and recalcitrant biochar fractions, which are critical input parameters for the APSIM Biochar Model. The APSIM Biochar Model was calibrated and validated using results from long-term field trials and shown to accurately predict biochar effects on soil bulk density, soil pH, soil water content, and nitrogen availability across multiple years and soil types. Predictions of biochar impacts on crop growth and grain yields at the field plot scale were less accurate but captured general trends. This is not surprising given the number of environmental and management factors that influence crop yields at the plot scale. To address biochar impacts on crop yields more accurately at regional and national scales, we integrated the APSIM Biochar Model with the pSIMS platform and built a separate stochastic Bayesian network model that predicts the probability of a crop yield response to biochar for the entire U.S. with 10m X 10m resolution. Combined these products will help businesses to site pyrolysis plants by determining where there is a potential market for biochar and by determining the optimum type of biochar for soils and cropping systems at local and regional scales. The biochar industry exists today at relatively small scale. Vanguard pyrolysis plants are targeting value-added biochar products for niche applications, rather than soil applications on production agricultural fields. To enhance the probability of success for these early pyrolysis plants, we investigated and developed protocols for producing zero-valent iron (ZVI) biochar complexes and high anion exchange capacity (AEC) biochars as potential value-added biochar products. The ZVI biochars were shown to be effective for remediating water contaminated with trichloroethylene (and potentially other chlorinated organic compounds) through reductive dehalogenation and arsenic (and potentially other heavy metals) through complexation and precipitation. The high AEC biochars have potential for removal of oxyanions from industrial and agricultural effluents by anion exchange. Techno-economic analysis (TEA) of the PBBP was used to determine the minimum fuel selling price (MFSP) and lifecycle GHG emissions for a 1000 dry ton per day fast pyrolysis plant. Both MFSP and GHG emissions were shown to be strongly dependent on the ash content and O:C ratio of the biomass feedstock. The TEA results indicate a trade-off between economic and environmental benefits based on feedstock selection. The estimated MFSP for 346 different feedstocks ranged from $2.3/gal to $4.8/gal of liquid fuels in the diesel/gasoline range. The TEA demonstrated that the PBBP has the potential to produce carbon negative energy products even when indirect land use and synergistic agronomic and environmental effects of soil biochar applications are discounted. Economic analysis integrated the APSIM mechanistic and the stochastic Bayesian network biochar models with the CARD/FAPRI Agricultural Outlook Model, a general equilibrium macroeconomic model, to predict farmer’s “willingness to pay” to apply biochar on their fields and the resulting impact of the PBBP on CO2-e emissions and nitrate leaching. It was found that biochar applications to areas with high probability of crop yield response in the U.S. could offset a maximum of 2% of the current U.S. anthropogenic CO2-e emissions per year. Scaling the PBBP to achieve much larger offsets of anthropogenic CO2-e emissions is possible but will require policy changes that include incentives for carbon negative energy production and credits for soil carbon sequestration.

Introduction Avoiding irreversible climate change requires >50% reduction in anthropogenic greenhouse gas (GHG) emissions by the year 2050 and the net removal of GHGs from the atmosphere by the end of the 21st century [1]. To address the need to remove GHG from the atmosphere, we are investigating the integrated Pyrolysis-Bioenergy-Biochar Platform (PBBP) for the production of carbon-negative energy (Figure 1). Bio-oil and non-condensable gases produced by fast pyrolysis of biomass are potential sources of liquid transportation fuels, heat, power, bio-asphalt, and other products that can offset fossil fuels [2]. Biochar, the condensed aromatic carbon-rich solid co- product of biomass pyrolysis, is a soil amendment that is effective for sequestering carbon while improving soil quality and reducing leaching of nutrients [3, 4]. The PBBP is potentially carbon negative because the half-life of biochar C in soils is hundreds to thousands of years, depending on the biochar quality. Furthermore, biochar has the potential to increase agricultural productivity, which would have cascading effects, such as decreasing the need for fertilizer and reducing the amount of crop land needed to produce food, which further reduce anthropogenic greenhouse gas emissions. This project was designed to address several of the key challenges limiting industrial scale deployment of the PBBP. One of those challenges is the lack of an ability to predict crop yield response to biochar applications at local, regional, and global scales. Crop yield responses to biochar applications depend on complex interactions among biochar type, soil type, crop genetics, crop management and weather. An ability to predict crop yield responses to biochar applications is necessary for determining the local and regional markets for the biochar co- product of PBBP plants, and hence technoeconomic assessments of the viability of PBBP plants. Furthermore, crop yield responses have feedback effects on regional and global grain markets and land use decisions, which influence GHG emissions. Another key challenge is the lack of an ability to predict direct environmental impacts of soil biochar applications, specifically net impacts on soil greenhouse gas emissions and nutrient leaching. Future policies incentivizing carbon negative systems will require accurate lifecycle assessments for assigning carbon credits. Measurement of carbon sequestration at the field scale would be cost prohibitive for assigning carbon credits, hence mechanistic models are needed. In this project, these challenges were addressed through integrated agroecosystem, technoeconomic, and economic modeling.

Cellulosic biomass

Figure 1: The integrated pyrolysis-bioenergy-biochar platform has the potential to provide carbon negative energy products and multiple ecosystem services.

The goals of our project were: 1) To advance basic understanding of the impacts of biochar on agroecosystems; 2) to assess the technical and economic viability of an integrated pyrolysis- bioenergy-biochar industry in the Upper Mississippi River Basin (UMRB), California, and U.S. Southeast; 3) to assess regional and global impacts of an integrated pyrolysis-bioenergy-biochar industry on indirect land use and net GHG emissions; and 4) to build a foundation for the development of a vanguard economically-viable carbon-negative integrated pyrolysis-bioenergy- biochar industry. The research systematically achieved these goals through the following specific objectives:

1) Develop, parameterize, and validate a biochar module for the Agricultural Production Systems sIMulator (APSIM). 2) Use techno-economic analysis to assess the economic performance of pyrolysis plants producing bioenergy and biochar co-products and use life cycle assessments to determine the net GHG emissions from an integrated pyrolysis-bioenergy-biochar production facility. 3) Quantify the public and private benefits accrued from integrating biochar into pyrolysis-based bioenergy production systems for three case studies. 4) Estimate carbon credits for indirect land use avoidance and compare system production costs. Background The International Panel on Climate Change (IPCC) has indicated that the global economy needs to become carbon negative by the end of the century to avoid catastrophic effects of climate change [1]. Although some progress has been made, it is clear that market forces alone will not compel the U.S. economy, let alone the world economy, to reach the IPCC goal of becoming carbon negative by the end of the century. Large scale governmental intervention in the form of a carbon tax or cap-and-trade system will be needed to meet this goal. In the U.S., near term prospects for such government intervention are low, because “climate change” has become embroiled in partisan politics. However, we assume that eventually, whether due to a change in political power or de-politicization of climate change as a result of increasing frequency of climate related disasters, the U.S. will initiate policies and regulations designed to address climate change. Much of the rest of the world has signed onto the Paris Climate Accords and is moving ahead with decarbonization. This project was designed to help lay the foundation for industrial scale deployment of the PBBP, which has the potential to make a significant contribution towards the IPCC goal of de-carbonizing the global economy. Scope of the problem: Total U.S. GHG emissions are approximately 6,457 Million Metric Tons of CO2 equivalent (CO2-e) per year [5]. Of this total, approximately 29% are from transportation, 22% from industry, 12% from commercial and residential, and 9% from agriculture. Most of the GHG emissions from the transportation, industry, commercial and residential sectors of the economy come from the burning of petroleum and natural gas for the production of heat and power. While steady improvements in energy use efficiency are reducing GHG emissions from these sources, the only way to eliminate these GHG emissions is to replace the fossil fuel energy used in these sectors with low carbon electricity. Currently about 28% of U.S. GHG emissions comes from electricity generation, which is dominated by natural gas, coal, and nuclear (35%, 27% and 19% of the total, respectively) generation capacity [5, 6]. Only 17% of the electricity generated in the U.S. today comes from renewable sources, with hydro, wind, and solar contributing 7%, 6%, and 1.6% of the total, respectively [6]. However, a historic transition is underway, as the percentage of U.S. electricity generated by coal has declined by 40% in the last 10 years; meanwhile the share of electricity generated from natural gas and renewables is increasing rapidly. Indeed, wind generation capacity is increasing by about 30% per year and solar generation capacity is increasing at an even faster rate. The growth in renewable electricity is occurring because technology advances have reduced the cost of renewable electricity below that of electricity produced from coal and at a level that is now competitive with natural gas. The rapid growth in renewable electricity generation demonstrates that it is possible to de- carbonize the electricity sector of the U.S. economy. Decarbonizing the industry, commercial, and residential sectors is also at least theoretically possible through a massive growth in renewable electricity generating capacity and by switching from fossil fuel to electricity in these sectors. Decarbonizing the transportation sector, however, is much more problematic. Electric vehicles and electrically powered mass transit have the potential to reduce the dependence of the transportation sector on fossil fuels. However, electrification is not practical for many forms of transportation such as jet aircraft, large trucks, agricultural equipment, earth moving equipment, and marine shipping, because batteries simply do not have high enough energy density for these applications. Hence, it is clear that our economy will require liquid transportation fuels for the foreseeable future, and that producing liquid fuels that do not on net contribute GHG to the atmosphere is a critical technology challenge. Carbon negative systems: The UNEP Emissions Gap Report [7] indicated that the world will need to remove about 20 Gt of CO2 from the atmosphere every year by the year 2100 to avoid a climate trajectory that surpasses 2 °C warming. This means that development of CO2 removal technologies, otherwise known as carbon negative technologies, that can be deployed at very large scale is a second critical technology challenge. The most prominent carbon negative technology is Bioenergy Carbon Capture and Sequestration (BECCS), where biomass is burned directly or co-fired with fossil fuel to generate electricity and the emitted CO2 is captured and geologically sequestered. Although the technology needed for BECCS exists today and is used at an industrial scale for tertiary oil and gas recovery, there has been little progress towards implementing BECCS technology to reduce CO2 emissions from power plants and other point sources. The lack of progress is due to three key reasons: First, the capture and geologic sequestration of CO2 from power plants is fundamentally a waste cleanup and disposal operation that substantially reduces the efficiency of power plants while adding no ancillary economic value [8]. Second, BECCS power plants need to be very large facilities (1000 to 2000 Mg feedstock per day) to capture economies of scale. However, the logistics and expense of biomass harvest, storage, and transport are increasingly problematic as scale increases. This is so because biomass production is dispersed across the landscape and biomass is a bulky-low density material that is expensive to transport, and difficult to store safely in bulk. And third, the safety and liability issues associated with transporting and geologically sequestering large quantities of CO2 are significant. Although BECCS is featured prominently in The Emissions Gap Report [7] produced by the United Nations Environment Programme in 2017 and a recent report from the National Academy of Science [9], there is skepticism about the feasibility of large scale BECCS systems for the reasons mentioned above. Furthermore, the critical need for renewable liquid transportation fuels and the second law of thermodynamics suggests that the finite supply of biomass resources would be better used to directly produce liquid biofuels rather than being combusted to produce electricity and CO2 followed by the use of electricity to reduce CO2 and H2O to produce liquid fuels. The Pyrolysis Bioenergy-Biochar Platform (PBBP also called BioEnergy-BioChar Systems; BEBCS) is an alternative to BECCS that addresses many of the problems with BECCS systems. A 2016 paper published in Science [10] compared the economic prospects for PBBP with BECCS and concluded that PBBP is a more economical means of sequestering carbon when carbon prices are below $1000 Mg-1 C. BECCS systems are able to sequester a larger fraction of the feedstock C than PBBP systems, but the geologically sequestration of CO2 is expensive and generates no economic value. PBBP systems achieve carbon negative status when the biochar co-product is applied to agricultural soils, a process which adds economic value by enhancing soil quality and agricultural productivity. BECCS systems need to be very large scale, which increases the costs and logistical problems associated with biomass feedstock harvest, storage and transport. By contrast, distributed processing of biomass to bio-oil and biochar co-products in medium scale PBBP plants reduces feedstock transportation costs and logistical problems. Finally, the primary energy product coming from BECCS systems is electricity, which can be produced by other renewable technologies; whereas PBBP systems produce high-value liquid fuels, which are critically needed to displace fossil fuels in the transportation sector of our economy. Current state of the Biochar and PBBP industries: The petroleum industry is a mature large scale industry upon which the U.S. economy is highly dependent. Under current U.S. economic policy the environmental costs associated with fossil fuels are externalized and the environmental benefits associated with the PBBP are discounted. Thus without a serious climate change mitigation policy it will be impossible for biofuels generated through the PBBP to compete with fossil fuels on a large scale. By contrast, the biochar industry is a small but growing industry in the U.S.; biochar entrepreneurs are primarily targeting high-value niche markets in horticulture, organic agriculture, land reclamation and environmental remediation [11]. Most biochar production facilities use slow pyrolysis or gasification and are either not capturing the bioenergy co-products or are producing process heat and/or electricity rather than liquid biofuel products. The current biochar industry is also primarily using feedstocks that can be obtained for little or no cost, such as orchard trimmings and bark beetle killed tress harvested to suppress fire in the intermountain west. China is emerging as a global leader in biochar research (Figure 2) and is likely to be the first country with large-scale commercialization of PBBP. China has severe air quality problems stemming in part from the widespread practice of burning crop residues. China also has severe soil contamination problems, a critical need to improve nutrient and water use efficiency in agricultural production, and a rapidly growing demand for both food and energy. All of these issues can be partially Figure 2: Number of refereed publications listed in addressed through PBBP [12]. the Web of Science database identified by the topic Technology: Various biorenewable word “biochar”. Blue indicates the number of energy production technologies have publications originating from the U.S., orange from been investigated in order to reduce China, and gray from the rest of the world. Web of the country’s dependence on foreign Science accessed 4-30-19. energy, enhance energy security, utilize excess agricultural resources and mitigate environmental concerns [13]. Biorenewable resources can be converted into bioenergy, transportation fuels, chemicals and fibers through biochemical and thermochemical conversion technologies. Here we focus on pyrolysis, the thermochemical conversion of biomass feedstocks in the absence of oxygen into various gas (non-condensable gases), liquid (bio-oil), and solid (biochar) co-products. Although it has a relatively low energy density, the non-condensable gas can be combusted to generate heat and/or power. Bio-oil, a viscous and dark-brown fluid with water content of 15-20%, can be refined to produce liquid fuels and other products [14]. Biochar is used as a soil amendment because it has the potential to sequester carbon, improve soil quality, and increase plant productivity. Pyrolysis technologies are broadly categorized based on different reaction conditions, as fast pyrolysis, slow pyrolysis, catalytic pyrolysis, and autothermal fast pyrolysis. Fast pyrolysis is a rapid (~2s) thermochemical conversion technology operating at moderate temperatures (typically ~ 500°C) [14]. Fast pyrolysis systems are optimized for the production of bio-oil. Production of bio-oil is maximized (up to 75 wt %) under fast pyrolysis temperatures of around 500°C. Slow Pyrolysis has a lower heating rate of 0.1-1°C/s compared to fast pyrolysis (10-200°C/s) [14], and higher yield of biochar around 25-35 wt % [15]. Raw bio-oil has several undesirable properties, such as high water content, high viscosity, high ash content, high oxygen content and high corrosiveness [14], which make it less competitive relative to petroleum. Several studies, which have investigated bio-oil upgrading to biofuels, focused mainly on hydrotreating, hydrocracking, and fluid catalytic cracking (FCC) [16]. Catalytic pyrolysis is a combination of fast pyrolysis and catalytic upgrading processes. Biomass is decomposed and hydrotreated in the same reactor in the presence of catalysts or the pyrolysis vapor can be catalytically upgraded to less reactive compounds subsequently [17-19]. Therefore, it can be grouped as in-situ and ex-situ catalytic pyrolysis, depending on whether catalytic upgrading takes place within or following the pyrolysis reactor. The recent development of stage fractionation technology has greatly facilitated the upgrading of bio-oil to commercially viable products. Stage fractionation is essentially in-situ staged condensation of pyrolysis volatiles to separate high boiling point, medium boiling point and low boiling point fractions into separate streams [20]. The high boiling point fraction consists of phenolic oligomers and anhydrosugars, which can be later separated based on water solubility. The medium boiling point fraction consists of phenolic monomers and the low boiling point fraction consists of light oxygenates and water. The phenolic oligomers and monomers once stabilized by hydrogenation are essentially a biocrude that can be refined in the same way as petroleum to produce liquid transportation fuels and other products. The anhydrosugars, primarily levoglucosan, can be fermented to produce ethanol, biobutanol and other products. The aqueous light oxygenate fraction, primarily water and acetic acid, has lower intrinsic value but some applications are under development. A second recent major technology advance is the development of autothermal fast pyrolysis [21-24]. During autothermal fast pyrolysis a small amount of oxygen is injected into the pyrolysis reactor to provide heat for the pyrolysis decomposition reactions by oxidizing a small fraction of the biomass. Conventional fast pyrolysis is endothermic and hence requires external heating, which is problematic because heat transfer is typically the limiting factor for scaling of conventional fast pyrolysis. Autothermal fast pyrolysis, by contrast, is slightly exothermic and hence does not require external heating. Recent research has shown that most of the heat generated during autothermal fast pyrolysis comes from oxidation of non-condensable gases and to a lesser extent the biochar with little effect on bio-oil yields [24]. The development of autothermal fast pyrolysis has greatly enhanced the ability to scale fast pyrolysis reactors while reducing both the capital and operating costs of PBBP plants [21, 22, 25]. Viability of the PBBP: Technoeconomic analyses (TEA) of fast pyrolysis of biomass to produce different products have been conducted for several years. Wright et al. [2] evaluated the minimum fuel selling price (MFSP) of converting corn stover to naphtha- and diesel-range stock fuel via fast pyrolysis as $3.09/gal and $2.11/gal for the hydrogen production and hydrogen purchase scenarios, respectively. The capital cost of the nth plant for the hydrogen production scenario was estimated as $287 million, while for the hydrogen purchase scenario capital cost was assessed as $200 million. Pacific Northwest National Laboratory (PNNL) has done a TEA study to evaluate the economic cost of the fast pyrolysis conversion of lignocellulosic biomass to hydrocarbon fuels [26]. They reported a MFSP as $3.34/gal for gasoline blendstock and the capital investment for the whole pyrolysis and upgrading process as $700 million. A study of producing biofuels, biochemical and hydrocarbon chemicals via fast pyrolysis of red oak compared the economic performance among different products scenarios [27]. The MPSPs for biofuel production, biochemical production and hydrocarbon chemicals production have been estimated as $3.09/gal, $433.7/MT and $773.5/MT, respectively. Dang et al. [28] investigated the economic and environmental performance of producing ultra-low carbon emissions by co- firing bio-oil with coal. The results from this study suggest that a minimum carbon price of $67.4 ± 13 per metric ton of CO2-e could make the bio-oil co-firing fuel commercially viable and competitive. None of these TEA studies have considered the effects of recent technology advances such as the development of autothermal fast pyrolysis. Value of the biochar co-product: Production, characteristics and potential applications for biochar from various feedstocks have been widely studied. Biochar derived from six waste biomass sources: pine sawdust, paunch grass, broiler litter, sewage sludge, dewatered pond sludge and dissolved air-floatation sludge were recently analyzed and compared [29]. All six kinds of biochar contained low levels of heavy metals, which indicate their feasibility of being used as soil amendments. Biochar from pine sawdust was recommended due to its high surface area, high aromaticity, high carbon content, and low ash content. Biochar produced from commercially cultivated seaweed has also been investigated [30]. Seaweed biochar contains low carbon content and surface area, but high yield, essential mineral nutrients (N, P, and K), and exchangeable cations. The pH of seaweed biochars ranges from 7 to 11. The study concluded that blending seaweed biochar with lignocellulosic biochar might have the most potential to improve the soil quality. Another study [31] compared the profitability of biochar production via slow and fast pyrolysis. Producing biochar via slow pyrolysis is not profitable although it produces more biochar compared to fast pyrolysis. The profitability depends not only on the biochar yields, but also the value of the energy products. The two main products of slow pyrolysis are pyrolysis gas (64% of dry biomass) and biochar (36%), while fast pyrolysis mainly produces bio-oil (53%) and biochar (26%). This study estimated capital costs for slow pyrolysis and fast pyrolysis as $132M and $200M, respectively. Minimum product selling price for biofuel and biochar were estimated as $2.68/gal and $346/t, respectively, for the fast pyrolysis case. Agronomic value of biochar: Much of the current understanding of physical and chemical properties of biochar and the impact of biochar applications on soil properties, environmental outcomes, and crop productivity was summarized in the 2015 edition of Biochar for Environmental Management, edited by J. Lehmann and S. Joseph [32]. In brief, agronomic research has shown that soil biochar amendments commonly increase agricultural production on marginal and degraded lands but may have little or no crop yield benefit on well-managed, high- quality soils [33-36]. Laboratory and field research in soil science has identified some of the mechanisms by which biochar enhances soil quality, and hence crop productivity. For example, biochar has been shown to reduce soil bulk density and increase soil porosity, both of which improve the rooting environment and hence stimulate plant growth [37, 38]. Biochar has also been shown to increase nutrient- and water-holding capacity of most soils and especially sandy, low-organic matter, and otherwise degraded soils [32, 37, 39]. The literature also shows that soil biochar amendments are effective means of sequestering carbon and reducing N2O emissions, although the nature of interactions between biochar and biogenic soil organic matter (soil humus) remains controversial [40-42]. Most of the scientific literature has reported positive agronomic and environmental outcomes from soil applications of biochar; however, there are also reports finding no effect and even adverse outcomes from the use of biochar [33]. Understanding and predicting positive and negative outcomes resulting from soil biochar applications is made difficult by the diversity of biochar physical and chemical properties, changes in the properties of biochar that occur with time (biochar aging), and complex soil-biochar-crop-climate- management interactions. Results

Goal 1: To advance basic understanding of the impacts of biochar on agroecosystems; Specific objective 1: Develop, parameterize, and validate a biochar module for the Agricultural Production Systems sIMulator (APSIM). The development of an economically viable pyrolysis-biochar-bioenergy industry that produces carbon negative biochar and bioenergy co-products will require an ability to predict a priori the agronomic and environmental impacts of biochar applications to agricultural soils. This is so, because the economic viability of a specific pyrolysis plant will depend heavily on the local availability of biomass feedstock and markets for both the bioenergy and biochar co- products. Biocrude (hydrotreated bio-oil), because it is a high energy density product, can be shipped in bulk to distant refineries. By contrast, biochar is a low density material that is expensive to ship; hence it will most likely be applied to soils that are close to the pyrolysis plant. Therefore, knowing how crops grown in the vicinity of a pyrolysis plant will respond to different types and amounts of biochar is critical information for designing pyrolysis plants that produce optimized biochar products for that region. And, knowing crop yield responses to biochar for crops grown in the vicinity of a proposed pyrolysis plant is critical information for assessing the value for the biochar co-product and the size of the biochar market for the proposed plant. Furthermore, an ability to predict the impact of soil biochar applications on soil greenhouse gas emissions, long-term soil carbon sequestration, and the leaching of nutrients from agricultural fields is critical needed for the design and implementation of policies intended to mitigate climate change and reduce nutrient pollution of surface and ground water. To address these needs we proposed to develop a biochar model that can be used to predict agronomic and environmental impacts of biochar applications. However, before such a model could be developed and parameterized it was essential to conduct basic research to advance our understanding of the mechanisms by which biochar interacts with and influences soil properties and processes and ultimately the influence of biochar amendments on crop growth and productivity. The following published papers document progress made in fulfilling this goal:

Lawrinenko, M. and D.A. Laird. 2015. Anion exchange Capacity of Biochar. Green Chemistry, 17:9, 4628-4636 (2015). DOI:10.1039/C5GC00828J. The anion exchange capacity (AEC) of Table 1: Anion exchange capacity of biochars biochar strongly influences the retention, produced from three feedstocks at 500 and 700 movement, and use efficiency of anionic °C HTT. Analyses performed in triplicate and nutrients, such as nitrate, phosphate, and data is presented as average (standard deviation). sulfate in soils. An ability to predict which biochars have significant levels of AEC and pH 4 pH 6 pH 8 which biochars have little or no AEC is Feedstock HTT (°C) (cmol kg−1) (cmol kg−1) (cmol kg−1) therefore important for being able to model Albumin 500 14.7 (1.05) 2.45 (0.464) 1.65 (0.948) Albumin 700 15.5 (1.71) 5.95 (1.10) 2.32 (0.881) the effects of biochar amendments on Alfalfa 500 10.9 (2.46) 3.1 (0.28) 0.94 (0.34) availability to crops, nutrient leaching, Alfalfa 700 25.8 (4.08) 9.6 (1.07) 2.1 (0.87) nutrient use efficiency, and ultimately crop Cellulose 500 7.8 (1.94) 2.6 (0.21) 0.60 (0.37) response to biochar applications. Little is Cellulose 700 24.2 (5.94) 18.1 (8.66) 4.1 (0.18) known about the nature of anion exchange Maize Stover 500 17.5 (5.81) 3.8 (0.66) 1.0 (0.21) sites on biochar surfaces and what production Maize Stover 700 27.8 (9.10) 13.8 (4.22) 7.2 (1.39) conditions promote AEC in biochar. We determined that the AEC of biochars produced from four feedstocks (maize stover, cellulose, alfalfa meal, and albumin) ranged from 0.602 to 27.76 cmol kg−1 and increased with decreasing pH (p < 0.0001) and peak pyrolysis temperature (Table 1). A cellulose biochar, composed almost entirely of C, H, and O, exhibited significant AEC at pH 8 suggesting that pH independent O containing functional groups contribute AEC. Fourier transform infrared spectroscopy revealed a prominent 1590 cm−1 band, which we attribute in part to C–O+ stretching of oxonium heterocycles. Both the C1s and O1s X-ray photoelectron (XPS) spectra of the biochars provide additional evidence for oxonium heterocycles. The N1s XPS spectra of albumin biochars indicated the presence of pyridinic groups. We conclude that oxonium functional groups contribute pH independent AEC and that both pyridinic functional groups and non-specific proton adsorption by condensed aromatic rings contribute pH dependent AEC to biochars. The study demonstrates that peak pyrolysis temperature and soil pH are the critical parameters needed for estimating biochar AEC.

Lawrinenko, M., D.A. Laird, R.L. Johnson, and D. Jing. 2016. Accelerated aging of biochars; impact on anion exchange capacity. Carbon. 10:217-227. Doi: 10.1016/j.carbon.2016.02.096 Understanding the stability of biochar anion exchange capacity (AEC) and by what mechanisms AEC changes as biochar ages and weathers in soil environments is critical for assessing the long-term impact of biochar amendments on the movement, retention, and bioavailabilty of anionic nutrients in soils. The goal of this study was to investigate chemical changes that occur during ageing of biochar in neutral or alkaline soils and to assess the impact of biochar ageing on AEC. To simulate and accelerate ageing, biochars were oxidized in alkaline hydrogen peroxide for 4 months. Biochars produced at a highest treatment temperature (HTT) of 700 °C exhibit greater condensed aromatic character than biochars produced at 500 °C HTT; and changed by a concerted 4 plus 2 π electron mechanism yielding endoperoxide surface structures (Figure 3). By contrast, the 500 °C biochars dominantly oxidized by a 2 plus 2 π electron pathway, which led to the formation of more carbonyl and hydroxyl groups on surfaces of the biochars. Anion exchange capacity of most biochars declined with ageing; exhibiting a mean decrease of 54%. Biochars produced at a HTT of 700 °C had higher AEC values and exhibited greater resistance to loss of AEC during the ageing treatments in contrast to 500 °C HTT biochars. The Figure 3: Formation of endoperoxide observed decline in AEC during ageing is partly functional groups on surfaces of attributed to the reduction of oxonium heterocycles biochar. to pyran structures due to nucleophilic attack on, and oxidation of, nonbridging α-C by OH- groups (Figure 4). Hetero O+ atoms that are adjacent to two bridging aromatic C atoms, which are resistant to nucleophilic attack, are recalcitrant. Thus the 700 °C pyrolysis temperature yielded biochars with more condensed aromatic C that were more likely to have Figure 4: Nucleophilic attack O+ groups with two adjacent bridging aromatic C resulting in simultaneous reduction of atoms and hence be recalcitrant to nucleophilic oxonium heterocycle and oxidation of attack and loss of AEC. Other possible mechanisms an exposed α-C. for the observed loss of biochar AEC during the ageing treatments include the loss of heterocyclic N in pyridinium structures and a decrease in the number of aromatic sites which may abstract protons. Both of these process, however, are highly pH dependent and believed to contribute little to biochar AEC at pH 6. The results of this study demonstrate that AEC of low termperature biochars is unstable and expected to degrade rapidly expecially in alkaline soils. By contrast, the AEC of biochars produced at high temperature is stable and may persiste for extended periods of time in soils.

Fidel, R.B., D.A Laird, M.L. Thompson, and M. Lawrinenko. 2017. Characterization and quantification of biochar alkalinity. Chemosphere 167:367-373. DOI:10.1016/j.chemosphere.2016.09.151 Many biochars are alkaline, which Table 2: Biochar abbreviation, feedstock, means that biochars function as soil liming pyrolysis temperature, process type, and biochar agents raising the pH of acidic soils. The pH (in H2O and 1 M NaCl) expressed as means of liming ability, or ‘calcium carbonate two replicates (±0.1). equivalent’, is an important chemical Biochar Feedstock T (°C) Process pH (H2O) pH (NaCl) property of a biochar, because it influences CE5s Cellulose 500 Slow pyrolysis 6.4 6.4 soil pH and by extension soil nutrient RO5f Red Oak 500 Fast pyrolysis 7.1 7.3 cycling and crop productivity. However, a CS5f Corn stover 500 Fast pyrolysis 8.4 7.6 lack of knowledge regarding the nature of CS3s Corn stover 300 Slow pyrolysis 7.3 6.7 biochar alkalis has hindered understanding CS5s Corn stover 500 Slow pyrolysis 10.1 9.2 of pH-sensitive biochar-soil interactions. In CS6s Corn stover 600 Slow pyrolysis 10.3 9.3 this study, we investigate the nature of HW5s Hardwood ~500 Slow pyrolysis 7.9 8.2 biochar alkalinity and presented a suite of MW6g Mixed wood ~600 Gasification 8.8 8.6 analytical methods for quantification of T = highest pyrolysis treatment temperature. biochar alkalinity. We identified four forms of biochar alkalinity (low-pKa structural, other organic, carbonate, and other inorganic alkalinity) and quantified them in eight biochars (Table 2, Figure 5). These biochars, derived from four feedstocks (cellulose, red oak, mixed wood, and corn stover) and produced at three temperatures (300, 500 and 600 °C), demonstrated a wide range of total alkalinity and diversity of alkali distributions. Biochars produced from cellulose, corn stover and wood feedstocks had significant low-pKa organic structural (0.03 - 0.34 meq g-1), other organic (0 - 0.92 meq g-1), carbonate (0.02 - Figure 5: Biochar structural (conjugate acids 1.5 meq g-1), and other inorganic (0 - 0.26 meq g-1) 5 ≤ pKa ≤ 6.4), other organic, carbonate - 2- alkalinities. All four categories of biochar (HCO3 + CO3 ), and other inorganic (non- alkalinity contributed to total biochar alkalinity and carbonate) alkalinity in meq per gram of are therefore relevant to pH-sensitive soil biochar. Error bars represent the standard processes. Total biochar alkalinity was strongly deviation of the mean (n = 3). correlated with base cation concentration, but biochar alkalinity was not a simple function of elemental composition, soluble ash, fixed carbon (FC), or volatile matter (VM) content. Variability in the impact of pyrolysis conditions and feedstock on biochar alkalinity and alkali composition suggest that biochar alkalinity may arise from complex interactions during production that are difficult to predict solely from pyrolysis parameters. A lack of consistent correlations between VM, FC or ash content and total alkalinity or alkali composition suggests that proximate analysis may be of little use for assessing biochar alkalinity. Acid-soluble base cation concentration was an accurate proxy for total alkalinity among the biochars studied here, but this approximation of total alkalinity alone does not provide insight into alkali composition or the contribution of biochar to soil pH buffering capacity or CEC. The diversity of biochar alkalinity indicates a potential to develop biochars tailored to specific agricultural and environmental applications, including the amelioration of soil acidity and buffering the pH of composts and soil-less potting media.

Bakshi, S., D.M. Aller, D.A. Laird, and R. Chintala. 2016. Comparison of the Physical and Chemical Properties of Laboratory- and Field-Aged Biochars. Journal of Environmental Quality 45:1627-1634. doi:10.2134/jeq2016.02.0062. The long-term impact of biochar on soil properties and agronomic outcomes is influenced by changes in the physical and chemical properties of biochars that occur with time (aging) in soil environments. Fresh biochars, however, are often used in studies because aged biochars are generally unavailable. Therefore, a need exists to develop a method for rapid aging of biochars in the laboratory. The objectives of this study were to compare the physicochemical properties of fresh, laboratory-aged (LA), and field-aged (FA) (≥3 yr) biochars and to assess the appropriateness of a laboratory aging procedure that combines acidification, oxidation, and incubations as a mimic to field aging in neutral or acidic soil environments. Twenty-two biochars produced by fast and slow pyrolysis, and gasification techniques from five different biomass feedstocks (hardwood, corn stover, soybean stover, macadamia nut shells, and switchgrass) were studied. In general, both laboratory and field aging caused similar increases in ash-free volatile matter (% w/w), cation and anion exchange capacities, specific surface area, and modifications in oxygen containing surface functional groups of the biochars. However, ash content increased for FA (18–195%) and decreased for LA (22–74%) biochars, and pH decreased to a greater extent for LA (2.8–6.7 units) than for FA (1.6–3.8 units) biochars. New contributions to science from this study include advances in understanding of biochar aging, demonstration that VM/FC and H/Corg ratios are complimentary indices of biochar stability (Figures 6 and 7), and the Figure 6: Relationship between volatile development of protocols for rapid matter/fixed C (VM/FC) ratios for fresh and aged laboratory aging of biochars. Laboratory biochars. Diamonds indicate laboratory-aged aging of biochars, through a combination of (LA) biochars; squares indicate field-aged (FA) acid hydrolysis, oxidation, and adsorption of biochars. Results for the macadamia nut shells DOC, mimicked many, but not all, of the (MNS) biochar were excluded from the physical and chemical changes that occurred regression analysis. Error bars indicate SE. during ≥3 yr of natural field aging of biochars. Acidification and leaching of soluble alkali and alkaline earth metal salts and carbonates occurred during both laboratory and field aging, although complete removal of carbonates did not occur under field conditions. Leaching or mineralization of labile pyrogenic organic compounds and adsorption of biogenic DOC on biochar surfaces occurred during both field and laboratory aging. Hardwood biochars are less Figure 7: Relationship between volatile subject to oxidation than the herbaceous matter/fixed C (VM/FC) and H/organic C biochars, and the extent of oxidation (H/Corg) (mol/mol) ratios for biochars produced during aging increases for gasification by slow pyrolysis, fast pyrolysis, and gasification biochars < slow pyrolysis biochars < fast techniques (included are fresh, laboratory-aged, pyrolysis biochars. The VM/FC and and field-aged biochars). Error bars indicate SE. H/Corg ratios are complementary indices of biochar stability. The laboratory aging procedure, in general, mimicked the aging processes observed in the field; however, we recommend using a less aggressive acid treatment in the future. The results demonstrate that changes occurring during the aging of biochars are significant, and hence experimental results obtained using fresh biochars may not be relevant for predicting the long-term impact of biochars on soil properties and crop growth responses. The results also demonstrate that the proposed laboratory aging procedure is effective for predicting the direction of changes in biochar properties on field aging.

Aller, D., S. Bakshi, D.A. Laird. 2017. Modified method for proximate analysis of biochars. Journal of Analytical and Applied Pyrolysis. 124:335-342. doi.org/10.1016/j.jaap.2017.01.012 Proximate analysis is widely used to determine moisture, volatile matter (VM), fixed carbon (FC) and ash content of biochars, and these parameters are key inputs for assessing biochar quality in agroecosystem models. The original ASTM D1762-84 proximate analysis method was developed to assess quality of hardwood charcoal for use as fuel. For the ASTM method, samples are initially sieved to 850 μm, then moisture content is determined as the percent mass loss on heating air-dry samples to 105◦C in an air atmosphere, VM as percent mass lost between 105◦C and 950◦C, and percent ash as mass remaining after combustion at 750◦C for 6 h. During VM determination the ASTM method prescribes a process of Figure 8: Percent volatile matter (VM) of ◦ preheating the furnace to 950 C, preheating diverse biochars determined by the ASTM and the samples in crucibles (lids-on) by placing modified proximate analysis methods. Error them on the outer edge of the furnace with bars represent one standard error of the mean. the door open for two minutes at 300◦C, then moving them a little farther into the furnace with the door open for three minutes at 500◦C, and finally placing the crucibles at the back of the furnace with the door closed for six minutes. We consider the ASTM procedure to be problematic because the samples may be exposed to O2 during both the moisture and VM determination steps. Furthermore, the ASTM VM procedure is not well controlled, as heating is not necessarily the same in all furnaces when the door is open, and moving samples around in a hot furnace limits the number of samples that can be analyzed at the same time, and is potentially hazardous. Therefore, we developed a modified proximate analysis method designed to address these concerns and standardize the procedure for assessing the quality of diverse biochars. To do so, 6 un- weathered (Fresh-1), 6 laboratory- weathered (LW), 5 un-weathered but stored (Fresh-2), and 5 field-weathered (FW) biochars were investigated. The results demonstrated the importance of using an inert gas purge during VM Figure 9: Percent fixed carbon (FC) on a dry determination. Substantial differences ash free basis for diverse biochars as determined in %VM and % FC were observed for by the ASTM and modified methods. Error bars 16 of the 22 biochars between the two represent one standard error of the mean. methods (Figures 8 and 9). Estimates of %VM obtained with the ASTM method were significantly higher for all of the Fresh-1 and LW biochars except for LW HS1and LW SS relative to the Modified method. We determined that a N2 purge is necessary during both moisture and VM determination to avoid errors associated with sample oxidation. We assessed a range of boundary temperatures (350–950◦C) for separating VM and FC, and determined that 800◦C is the minimum temperature required to distinguish between VM and FC in biochars. Furthermore, correlation between VM/FC and molar H/Corg ratios suggests that VM/FC ratios are a useful measure of biochar stability. Use of the proposed modified method is encouraged to reduce variance in analytical results among studies.

Fidel, R.B., D.A. Laird, K.A. Spokas. 2018. Sorption of ammonium and nitrate to biochars is electrostatic and pH-dependent. Scientific Reports 8 (1):17627. The capacity of biochar to adsorb mineral forms of nitrogen (N) influences the entire N cycle − including NO3 leaching, N2O emissions, NH3 volatilization, N mineralization, N immobilization, plant availability of N, and ultimately crop productivity. However, the capacity + − of different types of biochars to adsorb NH4 and NO3 is variable and not well understood. In + − this study, we compared NH4 and NO3 sorption rates to acid-washed biochars produced from red oak (Quercus rubra) and corn stover (Zea mays) at three pyrolysis temperatures (400, 500 and 600 °C) and a range of solution pHs (3.5–7.5) (Figure 10) . Additionally, we investigated + − 2+ − sorption mechanisms by quantification of NH4 and NO3 sorption, as well as Ca and Cl + displacement for corn stover biochars. Solution pH curves showed that NH4 sorption was maximized (0.7–0.8 mg N g−1) with low pyrolysis temperature (400 °C) biochar at near neutral pH (7.0–7.5), − whereas NO3 sorption was maximized (1.4–1.5 mg N g−1) with high pyrolysis temperatures (600 °C) and low pH + - (3.5–4). The Langmuir Figure 10: Sorption of NH4 (left) and NO3 (right) on acid- (r2 = 0.90–1.00) and washed red oak biochars produced at 400°C, 500°C and 600°C Freundlich (r2 = 0.81– (RO4s, RO5s, and RO6s, respectively) from 10 mg N L−1 0.97) models were good solutions adjusted to various pHs using Ca(OH)2. + predictors for both NH4 − + − 2+ − (pH 7) and NO3 (pH 3.7) sorption isotherms. Lastly, NH4 and NO3 displaced Ca and Cl , respectively, from previously CaCl2-saturated corn stover biochars. Results from the pH curves, Langmuir isotherms, and cation displacement curves all support the predominance of ion exchange mechanisms. Our results demonstrate the importance of solution pH and chemical + − composition in influencing NH4 and NO3 sorption capacities of biochar.

Banik, C., M. Lawrinenko, S. Bakshi and D.A. Laird. 2018. Impact of pyrolysis temperature and feedstock on surface charge and functional group chemistry of biochars. J. Env. Qual. 47:452- 461. The capacity of biochars to adsorb ionic contaminants is strongly influenced by biochar surface chemistry. We studied the effects of biomass feedstock type, pyrolysis temperature, reaction media pH, and AlCl3 pre-pyrolysis feedstock treatments on biochar anion exchange capacity (AEC), cation exchange capacity (CEC), point of zero net charge (PZNC), and point of zero salt effect (PZSE). We used the relationship between PZNC and PZSE to probe biochar surfaces for the presence of unstable (hydrolyzable) surface charge functional groups (Figure 11). The results indicate that biochars produced at ≤500°C have high CECs and low AEC, PZSE, and PZNC values due to the dominance of negative surface charge Figure 11: Relationship between point of zero salt arising from carboxylate and phenolate effect (PZSE) and point of zero net charge (PZNC) functional groups. Biochars produced at for the biochars produced at different pyrolysis ≥700°C have low CEC and high AEC, temperature from different feedstocks. PZSE, and PZNC values, consistent with a dominance of positive surface charge arising from nonhydrolyzable bridging oxonium (oxygen heterocycles) groups. However, biochars produced at moderate temperatures (500–700°C) have high PZSE and low PZNC values, indicating the presence of nonbridging oxonium groups, which are rapidly degraded under alkaline conditions − by OH attack on the oxonium α-C. Biochars treated with AlCl3 have high AEC, PZSE, and PZNC values due to variably charged aluminol groups on biochar surfaces. The results provide support for the presence of both hydrolyzable and nonhydrolyzable oxonium groups on biochar surfaces. They also demonstrate that biochars produced at high pyrolysis temperatures (>700°C) or those receiving pre-pyrolysis treatments with AlCl3 are optimized for anionic contaminant adsorption, whereas biochars produced at low pyrolysis temperatures (400°C) are optimized for cationic contaminant adsorption.

Bakshi, S., C. Banik, D.A. Laird. 2018. Quantification and characterization of chemically-and thermally labile and recalcitrant biochar fractions. Chemosphere 194 (2018) 247-255. doi.org/10.1016/j.chemosphere.2017.11.151 The C:N ratio of biochar labile fraction is an important parameter used in the APSIM biochar agroecosystem model for assessing biochar C stability and N cycling in soils. However, no prior methods for determining the C:N ratio of the labile fraction have been published. Furthermore, the relative viability of chemical and thermal methods for quantifying the labile fraction has not been previously compared. Here we compared chemically and thermally labile fractions for nine biochars produced from five biomass feedstocks using four production techniques. Biochar fractionation methods included proximate analysis, hot water extraction, acid and base extractions (0.05 M, 0.5 M, 1 M, 2 M, 3 M, and 6 M of either H2SO4 or NaOH), and oxidation with 15% H2O2 and 0.33 M KMnO4 (pH 7.2). The amount of C extracted, oxidized, or volatilized from the biochars by the various treatments ranged from 0.05 to 76% of total C. The KMnO4 treatment was the most effective, removing on average 51.8% of total C from the studied biochars. The second and third most effective treatments were the 0.05 M base and hot water treatments, which removed on average 35.9 and 28.4% of total C from the biochars, respectively. Least effective was the 80 thermal treatment (proximate HW 70 analysis), which removed on average only 10.3% of total C from the 60 biochars. A trend of decreasing C 50 removal with increasing base concentration was observed for all 40 samples except the HA biochar. By 30 C extracted

N extracted contrast, the effect of acid strength on (% ) extracted N or C 20 the amount of extractable C was inconsistent. The amount of C 10 oxidized with KMnO4 was 0 0 10 20 30 40 50 60 70 80 significantly correlated with the mass extracted (% ) amount of C extracted with hot 2 Figure 12: Relationship between the percent C and N water (R = 0.687; P < 0.05) but not and the percent mass extracted from biochars using with the amount of C removed by the hot water (HW) extraction protocol. any of the other treatments (P >

0.05). No significant correlation (P > 0.05) was found between the amount of C volatilized by the thermal treatment and the amount of C extracted or oxidized by any other treatments. In general, fast pyrolysis biochars had more chemically extractable or oxidizable C than the slow pyrolysis biochars; however, no consistent pattern was evident for C removed among feedstocks. These results indicate that the various hot water, acid, base, oxidation, and thermal treatments differentially removed C from the various biochars. We determined that chemical treatments other than hot water cause secondary reactions, which result in the addition of O and/or H to the biochar residues and preclude accurate determination of the mass of the labile biochar fraction by extraction and oxidation procedures. By contrast, the percent of total C extracted by the hot water treatment was strongly correlated with the mass loss (Figure 12), indicating that the hot water extraction method is not adversely affected by addition reactions. Regardless of extraction method, C:N ratios can be accurately determined for chemically labile biochar fractions, because estimates of C and N in labile and recalcitrant fractions are not adversely affected by addition reactions. ‘Fixed C’ in biochar as estimated by proximate analysis is selectively removed by various chemical extractants, suggesting that fixed C determined by proximate analysis is not equivalent to chemically recalcitrant biochar C. The C:N ratios of the labile fraction of biochars are generally lower than the C:N ratios of the whole biochar; and the labile fraction of herbaceous biochars are lower than the C:N ratios of the labile fraction of hardwood biochars. Results indicate that herbaceous biochars may be a source of N fertility while hardwood biochars may immobilize N through mineralization of the labile fraction during the first few years after biochar application to soils. Based on the results of this study we recommend the use of the hot water extraction procedure for quantifying the relative masses of recalcitrant and labile biochar C and determining the C:N ratios of these fractions, because the hot water extraction is robust, reproducible and does not suffer from addition reactions which preclude the use of the various chemical extraction and oxidation procedures.

Rogovska, N., D.A. Laird, and D.L. Karlen. 2016. Corn and Soil Response to Biochar Application and Stover Harvest. Field Crops Research, 187, 96-106. DOI:10.1016/j.fcr.2015.12.013 Soil biochar amendments are hypothesized to help mitigate the negative effects of harvesting crop residues for bioenergy production on soil quality. This study quantified effects of a onetime biochar application on crop yields and changes in soil properties at a Midwestern site where corn (Zea mays) stover is being harvested. The long-term experiment was established in Boone County, Iowa by application of 0, 9.8, and 18.4 Mg ha−1 of <0.64 cm hardwood biochar, in the fall of 2007 (Control, C1, and C2 treatments, respectively). Soil properties were measured in summer 2010, three years after the biochar application and following two stover harvests (0, 50 and 90% of above ground residue). Fertilization rates were adjusted to account for removal of nutrients with residue. Corn and biomass yields were measured for the 2008–2012 crop years.∼ Biochar∼ applications increased total carbon (TC) of soils by about 0.5% and soil pH by about 0.5 pH units but had no significant effect on total nitrogen (TN), soil test nutrient concentrations, bulk density (BD), aggregate stability or effective cation exchange capacity (ECEC). Residue removal had no significant effect on TC, TN, ECEC, or soil test nutrients except for Mehlich III extractable Zn and S concentrations. The 90% residue removal treatment significantly reduced wet aggregate mean weight diameter by 13% compared to no residue harvest. During the first five years of the study, the 50 and 90% residue removal treatments significantly increased corn grain yields by 1.4 Mg ha−1, averaged across all biochar treatments (Figure 13). When analyzed on an annual basis, biochar had no significant effect on grain yield, but when averaged across years, the 18.4 Mg ha−1 biochar application rate significantly increased yields by 0.49 Mg ha−1 for the zero residue removal treatment but not for the 50% and 90% residue removal treatments. Residue removal increased yield to a greater extent during years of stress due to either excess moisture (2008 and 2010) or drought (2012). There was no yield effect in 2011, a Figure 13: Relationship between the percent year with favorable climatic conditions. C and N and the percent mass extracted from Grain yield boosts in response to residue biochars using the hot water (HW) extraction removal can be attributed to greater nutrient protocol. availability from enhanced fertilizer inputs, greater soil organic matter mineralization/decreased N immobilization rates and possibly due to absence of negative effects of allelopathy on early season growth. No yield boost associated with residue removal or biochar applications was observed in 2011, a year with near optimum weather conditions. Yield differences between residue removal treatments reappeared in 2012, a drought year, only for the Control (no biochar) treatment as biochar amended soils most likely held more plant available water. During the first five years of the experiment, residue removal had generally positive effects on corn yields. Soil quality parameters measured two years after continuous residue removal did not show significant changes mostly due to short period of time and the ability of Mollisols to buffer against changes in physicochemical characteristics. The magnitude of yield increase due to residue removal, up to 3.7 Mg ha−1, may be an attractive option for farmers who considering selling agricultural residue to local biorefineries. However, this short term economic gain may lead to long term loss if residue harvest results in depletion of SOM. Biochar application can potentially mitigate this degradation by increasing SOC content, however; biochar C, is qualitatively different from the biogenic C supplied by crop residues and is not expected to be as effective as residue in stabilizing soil structure and will not protect soils from erosion. Thus some residue should be left on the surface even for soils that receive high doses of biochar.

Aller, D., R. Mazur, K. Moore, R. Hintz, D. Laird, and R. Horton. 2017. Biochar Age and Crop Rotation Impacts on Soil Quality. Soil Science Society of America Journal. 81:1157-1167. Corn residue removed from Midwestern farms is a large potential source of biomass for cellulosic bioenergy production in the U.S. However, long-term harvesting of biomass may lead to the degradation of soil quality unless management practices that compensate for the removal of biomass are used. In this study, biochar amendments and long-term crop rotations that include triticale and switchgrass with corn and soybeans were hypothesized to reduce the negative effects of biomass harvesting on soil quality. The soil samples used in this study were collected from long-term rotation plots (LTRPs), which were established in central Iowa in 2006 to investigate the sustainability of diverse bioenergy cropping systems. The LTRPs are located on the Sorenson Research Farm, part of the Iowa State University Agronomy and Agricultural Engineering Research Farms in Boone County, IA. The dominant soil series at the site are Webster (Fine-loamy, mixed, superactive, mesic Typic Endoaquoll), Clarion (Fineloamy, mixed, superactive, mesic Typic Hapludoll), and less than 1% of Nicollet (Fine-loamy, mixed, superactive, mesic Aquic Hapludoll). The study site is comprised of 112 whole plots. Sixteen of the whole plots are in continuous switchgrass, and these are not split into subplots. The other 96 whole plots are split into 192 subplots, with biochar applied on one-half of each split plot. Hence, we have 16 continuous switchgrass whole plots plus 4 crop rotations with 6 phases of each rotation and 2 biochar subplot treatments and 4 replications for a total number of 208 plots. Whole plot dimensions are 9.1 m by 9.1 m with subplots having dimensions of 4.6 m by 9.1 m. The study included five different crop rotations: continuous corn (Rot. 1), alternating corn–soybean (Rot. 2), corn–soybean–triticale/soybean– corn–soybean–triticale/soybean (Rot. 3), corn–corn–corn/switchgrass–switchgrass–switchgrass– switchgrass (Rot. 4), and continuous switchgrass (Rot. 5). Rotations were in a 6-yr cycle with all phases of each rotation present every year in four replicate blocks. Since establishment, residue management at the site has been 100% removal of aboveground biomass from all plots containing corn, switchgrass, and triticale.

Figure 14: Gravity drained soil volumetric 3 -3 Figure 15: Total soil C by rotation water content (cm cm ) by rotation, measured in 2006 and in 2016 for biochar measured in 2014 for the biochar (bc) and (bc) and no-biochar (no-bc) treatments. no-biochar (no bc) treatments. Values are Values are averages of four field replicates averages of four field replicates with with standard error bars. Different letters standard error bars. Different letters indicate significant differences between indicate significant differences between biochar and no-biochar treatments within a biochar and no-biochar treatments within a rotation (P < 0.05). rotation (P < 0.05).

Chemical breakthrough curves, measured for intact soil cores indicate that crop rotations that include switchgrass or triticale increased both retardation and dispersivity relative to conventional rotations and biochar amendments decreased dispersivity relative to controls (Table 3). Across all crop rotations, there was an increase in total soil C and N, soil C/N ratio, pH and gravity drained water content, and a decrease in bulk density for soils treated with biochar relative to no-biochar controls (Figures 14 and 15). No significant effect of biochar age on soil physical properties was measured in 2014 but significant increases with biochar age were found for total soil C and N in 2016, suggesting a synergistic interaction (negative priming). Continuous switchgrass stands were found to build soil organic C and N, increase retention of plant available P and K, and lower bulk density relative to the continuous corn cropping system. The results suggested that soil biochar amendments and crop rotations that included switchgrass helped mitigate some of the adverse effects of biomass harvesting on soil quality.

Table 3: Physical and chemical properties measured in 2014. Values are the average of four replicates with standard error and are grouped by crop rotation and biochar. Only no biochar plots were used in comparing crop rotations. Biochar effect (yes, no) does not include Rot. 5 data. Different letters indicate significance between factors within a group (P < 0.05). Crop rotation Biochar treatment‡ Property† Rot 1 Rot 2 Rot 3 Rot 4 Rot 5 Yes No Log K s, cm d-1 0.221b (0.156) 0.770ab (0.266) 1.096a (0.196) 1.229a (0.221) 0.881ab (0.315) 0.894a (0.123) 0.829a (0.110) R 0.963b (0.045) 1.137ab (0.178) 1.135ab (0.080) 1.546a (0.206) 1.214ab (0.267) 1.034a (0.038) 1.194a (0.071) λ, cm 6.118b (0.800) 13.617ab (6.509) 11.278ab (2.966) 25.134a (6.131) 6.602b (1.026) 7.609a (1.963) 13.804a (2.283) VWC, cm3 cm-3 0.412a (0.004) 0.416a (0.007) 0.413a (0.005) 0.402a (0.006) 0.417a (0.009) 0.445a (0.004) 0.410b (0.003) BD, g cm-3 1.511a (0.011) 1.479ab (0.020) 1.482ab (0.015) 1.512a (0.010) 1.430b (0.027) — — † K s, saturated hydraulic conductivity; R , retardation, l, dispersivity; VWC, volumetric water content; BD, bulk density; Rot 1, continuous corn; Rot 2, alternating corn–soybean; Rot 3, corn–soybean–triticale/soybean–corn–soybean–triticale/soybean; Rot 4, corn–corn–corn/switchgrass- switchgrass–switchgrass–switchgrass; Rot 5, continuous switchgrass. ‡ Combined effects of biochar and tillage, which are confounded in the 2014 data set. Our findings indicate that crop rotation, biochar, and biochar age all had an impact on several, but not all, of the soil chemical and physical properties measured in this study (Table 3). In general, crop rotations containing switchgrass (Rot. 4 and 5) had a positive impact on a greater number of soil quality indicators compared to the conventional cropping systems (Rot. 1 and 2) and a conventional rotation with a cover crop (Rot. 3). Continuous switchgrass contributed to the accumulation of soil C and N, increased retention of P and K, and decreased soil bulk density, but with a possible tradeoff from biomass harvesting leading to lower soil pH. Biochar increased volumetric water content total soil C and N, soil C/N ratio, pH, and plant available K, while decreasing soil bulk density and solute dispersivity. The evidence from the 2016 sampling that total soil C and N continued to increase with biochar age implied a synergistic interaction (negative priming). This finding emphasized the critical importance of including biochar age as a factor in future biochar field studies and the need for more field studies that evaluate longer term, > 4 yr, biochar aging effects on soil quality. The interaction between biochar and crop rotations indicated the complexity of soil quality responses to biochar and the possibility of further synergistic interactions by integrating biochar amendments with management systems that include annual cover crops or perennial biomass crops to enhance C sequestration and sustainability of biomass harvesting. Overall, results supported our stated hypotheses, that the sustainability of bioenergy cropping systems could be enhanced with the incorporation of biochar amendments and alternative crop rotations into the Midwestern landscape.

Aller, D., S. Rathke, D. Laird, R. Cruse, and J. Hatfield. 2017. Impacts of fresh and aged biochars on plant available water and water use efficiency. Geoderma 307:114-121. http://dx.doi.org/10.1016/j.geoderma.2017.08.007 The ability of soils to hold sufficient plant available water (PAW) between rainfall events is critical to crop productivity. Most studies indicate that biochar amendments decrease soil bulk density and increase soil water retention. However, limited knowledge exists regarding biochars ability to influence PAW and water use efficiency (WUE), and even less is known about the effects of aged biochars on PAW and WUE. This greenhouse study investigated the influence of six fresh and six aged biochars on PAW and WUE for three soils of contrasting texture. PAW and WUE were assessed by growing maize in repacked soil columns (1 kg soil). Plant and water data were collected from the V1 growth stage until the plants died of water stress. Relative to the controls, both fresh and aged biochars increased soil moisture retention in the clay loam soil, had no impact in a silt loam soil, and had variable effects in a sandy loam soil (Figure 16). Final biomass weight increased with the addition of fresh biochar in the sandy loam and silt loam soils soil. Both fresh and aged biochars decreased PAW in the clay loam soil and had no impact on PAW in the silt loam soil (Figure 17). Fresh biochar increased PAW, while aged biochar had no effect on PAW for the sandy loam soil. WUE decreased in response to both fresh and aged and decreased in the clay loam soil, while aged biochar increased biomass weight in the silt loam biochars in the clay loam soil and was variable for the other two soils (Table 4).

Table 4: Soil and plant properties measured from each soil column. Values are the average of four replicates with standard errors. Different letters indicate significance between biochar (BC) ages (Fresh BC and Aged BC) and the control by soil type (P < 0.05).

Figure 16: Effect of fresh and aged biochar Figure 17: Water retention curves for on soil water content over time during plant biochar age and control treatments within growth within each soil type. Inset bar each soil type. Inset bar graphs are the graphs are the calculated PAW (difference calculated PAW (difference between between weight after 24 h (FC) and weight −0.33 and −15 bars) separated by biochar at final weighing) separated by biochar age age within each soil type. within each soil type.

Aged biochars did not have the same impact on soil-water relations as the equivalent fresh biochars. Results indicated that all laboratory aged biochars were more hydrophilic than their fresh counterparts and the relative degree of biochar hydrophobicity decreased further following a drying-wetting-drying treatment. Furthermore, we found significant interactions effects of soil type, biochar type, and biochar age on WUE and PAW. Overall, the influence of fresh and aged biochars on the soil and plant properties measured in this study were highly variable and differed by biochar type; resulting in positive, negative, or neutral effects depending on soil type and the response variable being measured. Biochar has a potential role in the global effort to improve water management in rainfed agriculture, but biochar applications must be made strategically. Attention must be given to what biomass feedstock and what pyrolysis conditions are used to produce the biochar, and to the soil - biochar interactions. Furthermore, our results indicate that results obtained with fresh biochars may not be predictive of long-term effects, because aged biochars may have different impacts on soil water relations than fresh biochars.

Fidel, R.B. D.A. Laird, and T.B. Parkin. 2017. Impact of six lignocellulosic biochars on C and N dynamics of two contrasting soils. GCB Bioenergy. DOI: 10.1111/gcbb.12414 Both soil and biochar properties are known to influence greenhouse gas emissions from biochar-amended soils, but poor understanding of underlying mechanisms challenges prediction and modeling. Here, we examine the effect of six lignocellulosic biochars produced from the pyrolysis of corn stover and wood feedstocks on CO2 and N2O emissions from soils collected from two bioenergy cropping systems. Six biochars made from four different feedstocks at three different temperatures were used for this study. Slow pyrolysis biochars made from corn stover were pyrolyzed in a N2-purged muffle furnace for ~1 h at 300, 500, and 600 °C highest heating temperature (CS3s, CS5s, and CS6s, respectively). A mixed wood gasification biochar (MW6g) was obtained from ICM Inc. and hardwood slow pyrolysis biochar (HW5s) was obtained from Royal Oak (http://royal- oak.com/; #10 sieve size). Fast pyrolysis corn stover biochar, CS5f, was obtained from the Center for Sustainable Energy Technologies (CSET) at Iowa State University. All other biochars were ground to <0.50 mm to minimize the influence of particle size. Two soils of contrasting textures and parent materials, Soil A and Soil B, were collected from two research field sites in Iowa. Soil A contained 15% sand, 80% silt, and 5% clay, whereas Soil B contained 48% sand, 42% silt, and 10% clay. Soil A contained a similar amount of total C (1.9%) as Soil B (1.6%); however, only Soil A effervesced upon exposure to hydrochloric acid. The soil samples were collected from the top 5 cm following corn harvest at both sites, refrigerated for one month, and sieved (<4 mm) before use. Any visible plant residues remaining after sieving were removed by hand. Biochar samples (0.05 g) were mixed with 10 g (oven-dry equivalent weight) samples of field-moist soils A and B in 150-mL serum vials in quadruplicate. Controls, used to assess the -1 influence of biochar IC, were prepared by mixing 0, 0.5 and1.0 mg g of CaCO3 with each soil; these control treatments are designated C0, C1, and C2, respectively. Controls, used to assess the mineralization of biochar LOC, were prepared by mixing biochars with a 50/50 mixture of silt and sand-sized quartz. These ‘quartz controls’ additionally received 0.5 mL of a microbial inoculant. The soil samples and quartz controls were equilibrated at 20 °C for 50 days, during which time soil samples were maintained at field moisture content and the quartz controls were maintained at field capacity (-1/3 bar matric potential). After the 50-day equilibration period, corn stover (ground <0.5 mm) was mixed into the soil samples at 0.5% (wt%), and fertilizer -1 solution (87, 42, and 54 mg kg of N, P, and K, respectively) was added as NH4NO3 and K2HPO4 to soil samples and quartz controls. The quartz controls also received an additional 0.5 mL of microbial inoculant at this time. The samples were then incubated for 140 days following fertilization, during which time moisture was maintained at -1/3 bar equivalent. CO2 and N2O emissions were quantified on days 0, 1, 6, 8, 13, 15, 20, 36, 43, 49, 64, 71, 78, 83, 97, 113, 127, and 140 during the post fertilization incubation.

Figure 18: Final pH of soils and quartz Figure 19: DOC in 2 M KCl extracts of controls at the end of the 190-day soils (C0 = control) that were incubated with incubation. Dotted line shows the initial pH either CaCO3 (C1 and C2) or biochar. Error of 6.3. Error bars indicate ±SD. bars indicate ±SD.

Figure 21: Total N2O-N emissions over 8 Figure 20: Total CO -C emissions during 2 days following fertilization, in μg N per g of the 140-day post-fertilization period. Error soil (±SE) (N O emissions were negligible bars indicate ±SE. 2 after 8 days).

+ When amended to Soil A, biochars consistently decreased KCl-extractable DOC, NH4 , and - NO3 . The observed decrease in DOC could be partially explained by the biochars’ liming effect, as DOC tended to decrease with increase in soil pH among biochar amended Soil A samples (Figures 18 and 19). Effects of biochar on total accumulated CO2-C emissions were minimal (<0.45 mg C g-1 soil; <10% of biochar C), consistent with mineralization and hydrolysis of small labile organic and inorganic C fractions in the studied biochars (Figure 20). Comparisons of soil CO2 emissions with emissions from microbially inoculated quartz–biochar mixtures (‘quartz controls’) provide evidence of soil and biochar-specific negative priming. Five of six biochar amendments suppressed N2O emissions from at least one soil (Figure 21), and the magnitude of N2O emissions suppression varied with respect to both biochar and soil types. Biochar - amendments consistently decreased final soil NO3 concentrations, while contrasting effects on + pH, NH4 , and DOC highlighted the potential for formation of anaerobic microsites in biochar- amended soils and consequential shifts in the soil redox environment. Thus, results implicated both reduced substrate availability and redox shifts as potential factors contributing to N2O emission suppression. More research is needed to confirm these mechanisms, but overall our results suggest that soil biochar amendments commonly reduce N2O emissions and have little effect on CO2 emissions beyond the mineralization and/or hydrolysis of labile biochar C fractions. Considering the large C credit for the biochar C, we conclude that biochar amendments can reduce greenhouse gas emissions and enhance the climate change mitigation potential of bioenergy cropping systems. The results support the hypotheses that (i) biochar IC contributes to short-term CO2 emissions, (ii) biochars do not significantly accelerate mineralization of biogenic SOC when biochar IC and LOC are accounted for, and (iii) biochars reduce N2O emissions by altering the soil redox environment and/or reducing available OC and inorganic N substrates. We did not observe any evidence for the influence of biochar carbonates on N2O emissions. No single acid- or base-soluble biochar OC fraction was able to consistently serve as a proxy for mineralizable biochar OC; however, results showed a clear, soil specific influence of biochar IC and OC on - CO2 emitted. Consistent reductions in extractable NO3 with biochar amendment support - previous studies positing NO3 sorption to biochar as a mechanism for reducing N2O emissions, while contrasting responses of other soil properties to biochar amendment highlight the ability of biochars to influence microsite redox status in a context specific manner. Thus, a convergence of evidence suggests that sorption of substrates to biochar and changes in the soil redox environment are key mechanisms driving biochars’ effects on CO2 and N2O emissions – and that the dominant mechanisms likely vary by soil.

Fidel, R.B., D.A. Laird, and T.B. Parkin. 2017. Impact of biochar organic and inorganic C on soil CO2 and N2O emissions. J. Env. Quality 46:505-513. doi:10.2134/jeq2016.09.0369. https://dl.sciencesocieties.org/publications/jeq/pdfs/46/3/505 Biochar has been shown to influence soil CO2 and N2O emissions following application to soil, but the presence of carbonates in biochars has largely confounded efforts to differentiate among labile and recalcitrant C pools in biochar and establish their timeframe of influence. Understanding the mechanism, magnitude, and duration of biochar C pools’ influence on C and N dynamics is imperative to successful implementation of biochar for C sequestration. Here we therefore aim to assess biochar organic and inorganic C pool impacts on CO2 and N2O emissions from soil amended with two untreated biochars, inorganic carbon (as Na2CO3), acid (HCl) and bicarbonate (NaHCO3) extracts of the biochars, and acid and bicarbonate/acid-washed biochars during a 140-d soil incubation. We hypothesized that (i) both biochar labile organic carbon (LOC) and inorganic carbon (IC) pools contribute significantly to short-term (<1 mo) CO2 + - emissions from biochar-amended soil, (ii) biochars will influence the size of soil NH4 and NO3 pools, and (iii) changes in soil inorganic N pools will affect soil N2O emissions. Biochar treatments did not affect N2O emissions (Table 5), but biochar treatments clearly influenced soil CO2 production (Table 6) and soil inorganic N concentrations, indicating that + − Table 5: Soil pH, NH4 -N, and NO3 –N of soil measured following 140-d incubation period, in wt% ± SD (n = 5); cumulative N2O-N emissions measured during the first 9 d following fertilization (± SE; n = 5) (after 9 d, N2O emissions were not significantly greater than zero).†

+ − Table 6: Soil pH, NH4 -N, and NO3 –N of soil measured following 140-d incubation period, in wt% ± SD (n = 5); cumulative N2O-N emissions measured during the first 9 d following fertilization (± SE; n = 5) (after 9 d, N2O emissions were not significantly greater than zero).†

biochar IC, LOC, and recalcitrant C pools differentially affect soil C and N dynamics (including soil respiration, carbonate hydrolysis, and N transformations). Most notably, IC pool impacts on CO2 production were largely restricted to the first ≤48 h of the incubation, whereas LOC pool impacts were evident throughout the 140-d incubation. Additionally, the observed liming effects of all biochar treatments (significantly higher pHs relative to controls) emphasize the need for controls that distinguish biochar LOC pool effects from pH effects on CO2 emissions. Both HCl and NaHCO3 solutions solubilized significant quantities of biologically labile OC; we therefore caution that acid washing to remove IC may also remove significant quantities of LOC and confound attempts to quantify biochar IC impacts by difference. The nonadditive relationships among CO2 produced by soil treated with biochar extracts and washed biochars observed here suggest that soluble biochar LOC and IC pools can be stabilized by chemical and/or physical interaction with the recalcitrant biochar matrix. The untreated biochars examined here have previously been shown to not have significant interactions with native SOC and/or corn stover OC; however, this may not be the case in soils with different SOC pool sizes and compositions (or soils receiving different residues). Therefore, special attention should be given to biochar OC × native SOC interactions that may occur in other contexts involving various biochars, soils, and fertilizers.

Fidel, R.B., D.A. Laird, T.B. Parkin. 2019. Effect of Biochar on Soil Greenhouse Gas Emissions at the Laboratory and Field Scales. Soil Systems (in Press). Biochar application to soil has been proposed as a means for reducing soil greenhouse gas emissions and mitigating climate change. The effects, however, of interactions between biochar, moisture and temperature on soil CO2 and N2O emissions, remain poorly understood. Furthermore, the applicability of lab-scale observations to field conditions in diverse agroecosystems remains uncertain. Here we investigate the impact of a mixed wood gasification biochar on CO2 and N2O emissions from loess-derived soils using: (1) controlled laboratory incubations at three moisture (27, 31 and 35%) and three temperature (10, 20 and 30°C) levels, and (2) a field study with four cropping systems (continuous corn (CC), switchgrass (SG), low diversity grass mix (LD), and high diversity grass-forb mix (HD)). In the field study, cropping systems had a highly significant effect on soil CO2 emissions (CO2 emissions decreased in the following order SG>LD=HD>CC), however, biochar had no significant effect on soil CO2 emissions (Figure 22). The lack of effect of biochar on soil CO2 emissions measured 3 years after the biochar amendment, supports previous observations that the impact of biochar on CO2 emissions is predominately Figure 22: Average daily CO2 emissions for each restricted to the short term (<1 month). cropping system in the field study with and without By contrast, biochar reduced N2O biochar (CC = continuous corn, SG = switchgrass, emissions from soils under continuous LD = low diversity grass mix, HD = high diversity no-till corn cultivation by an average of grass and forb mix; 0 = control, 1 = biochar- 27% (Figure 23); this magnitude of amended). suppression is consistent with average reduction of 28% reported for field studies by Cayuela et al., [43]. In the laboratory experiment, biochar reduced N2O emissions at 20°C with 27% and 31% moisture, but reductions in N2O emissions measured for other temperatures and moisture levels were not were not significant. Differences in N2O emissions results between lab-scale and field-scale studies may be due to differences in study design, such as temperature and moisture fluctuations that occur under field but not laboratory conditions, differences in biological inputs, differences in fertilizer type and distribution in the soil, differences in physical or biochemical properties between the soil stored and sieved in the lab for incubation and field soils, and differences in properties between the fresh biochar added in the incubation compared with the aged biochar present in the field study. The biochar in the field study had been weathering in the soil for 2.5 years before the study began. Generally, biochar aging in soils or other aqueous environments is associated with an increase in oxygen functional groups, and a decrease in soluble biochar components such as salts, carbonates, and low molecular weight organic compounds. Cumulative cropping system N2O emissions were, highest for continuous corn and tended to decrease with increasing plant species diversity and decreasing fertilizer application rate (HD≤LD

Laird, D.A., J.M. Novak, H.P. Collins, J.A. Ippolito, D.L. Karlen, R.D. Lentz , K.R. Sistani, K. Spokas, R.S. Van Pelt. 2017. Multi-year and multi-location soil quality and crop biomass yield responses to hardwood fast pyrolysis biochar. Geoderma. 289: 46-53. Biochar can remediate degraded soils and maintain or improve soil health, but specific and predictable effects on soil properties and crop productivity are unknown because of complex interactions associated with weather patterns, inherent soil characteristics, site-specific crop and soil management practices, and the source, production characteristics, and amount of biochar applied. This multi-location field study was designed and conducted to determine if consistent response patterns could be elucidated by controlling the type and amount of biochar applied,

Figure 24: Trends with time after treatments were imposed in differences between post-treatment and pre-treatment surface (0–15 cm) SOC values for the (A) Ames Iowa (IA), Kimberly Idaho (ID), St. Paul Minnesota (MN), and Prosser Washington (WA) locations and (B) for the Bowling Green Kentucky (KY) location. depth of incorporation, and soil/crop management practices as much as possible for six U.S. locations. When averaged for five reporting locations, biochar or biochar plus manure bio+man) treatments significantly (P < 0.001) increased surface (0–15 cm) soil organic carbon (SOC) levels by 48 or 47%, respectively, relative to control treatments (Figure 24). The SOC levels for the manure only treatment were not significantly different from the Figure 25: Box plot showing control normalized total controls. No other measured soil above ground biomass yields for all treatments for properties showed significant individual locations (averaging across years) for the Ames biochar or biochar × manure Iowa (IA), Kimberly Idaho (ID), Bowling Green interactions, even though Kentucky (KY), St. Paul Minnesota (MN), Big Springs applying manure significantly Texas (TX), and Prosser Washington (WA) locations. increased extractable K, Mg, Na, and P levels. Analysis of three or four years of pooled biomass yield data from the six locations showed a significant location effect (P < 0.001), but treatment effects were not significant. However, dividing annual plot yields by the average for all control plots at each location created a dataset of relative yields that showed a significant location × treatment interaction and higher normalized yields (36%) due to biochar (P=0.017) at one of the six locations (Figure 25). There was no evidence of a synergistic interaction between manure and the biochar used in this study on either soil quality or crop yields. This conclusion, however, does not preclude synergistic interactions when other types of biochar, manures, and different crops, soils, management systems and climates are considered. We also hypothesized that complex soil × climate ×biochar interactions would result in different soil quality and crop yield responses in different locations. The results of this study generally support this hypothesis. Across all locations, biochar amendments were effective for increasing SOC levels and our results indicate that the effect of biochar on SOC persisted for at least 1500 days under field conditions. Therefore, our multi- location field trials support a growing body of evidence that biochar amendments are an effective means for soil C sequestration. Our results also suggest that positive crop yield responses to biochar applications are possible, but are only anticipated when specific soil quality problems are limiting crop productivity. Overall, we conclude that hardwood biochar produced by fast pyrolysis can be an effective soil amendment for increasing SOC levels within a broad range of temperate soils, but crop yield responses should be anticipated only when specific soil quality problems limit productivity.

Dokoohaki, H., F. Miguez, D.A. Laird, R. Horton and A. Basso. 2017. Assessing the biochar effects on selected physical properties of a sandy soil: An analytical approach. Communications in Soil Science and Plant Analysis. 48:1387-1398. doi.org/10.1080/00103624.2017.1358742. Biochar application to soils is a promising practice with agronomic and environmental benefits. Our analysis is based on an experiment in which soil columns were incubated with three biochar application rates (0%, 3% and 6% w/w), two application methods and three replications. Soil water retention curves (SWRCs) were determined at three sampling times, 15, 29 and 91 days, after the start of the incubation. The van Genuchten (VG) model was fitted to all SWRCs and then used to estimate the pore-size distribution (PSD). Standard deviation (SD), skewness and mode (D) were calculated to interpret the geometry of PSDs. The Dexter “S- index” and saturated hydraulic conductivity (Ks) were also estimated using the VG model parameters. Statistical analysis was performed for all the parameters using a linear mixed model. Air-dried soil mixed with 0% (control), 3% and 6% (w/w) biochar were packed into PVC columns (18-cm-long 7.7 cm id). End caps with a 3-mm hole for drainage were attached. The columns were filled with 100 g of coarse sand (4–7 mm) and a total oven dry mass of 994 g of soil plus biochar or soil only (controls). Two biochar application methods were used in this study in order to simulate subsurface, deepbanding in rows (DBR) and uniform topsoil mixing (UTM) applications. The biochar mixed with soil was applied either in the top 11.4 cm or at the bottom 11.4 cm of the soil column. An additional 5 cm of soil without biochar was added either at the top of the DBR columns or at the bottom of the UTM columns. A total of 72 columns (two biochar application methods, three biochar rates, four sampling dates and three replications) were randomly distributed on two square tables and incubated at 30°C and 80% RH in a dark room. At each of the four sampling times (1, 15, 29 and 91 days after the start of the incubation), 18 soil columns (two biochar application methods, three biochar rates and three replications) were split into subsamples and analyzed for various physical and chemical properties. SWRCs were modeled using mean VG equation coefficients for all treatments at each sampling time (Figure 26). All biochar treatments had higher water content values near saturation and a steeper slope at the SWRC inflection point compared with the control for the 29 and 91 day sampling dates, but not the 15 day sampling date. Because the shape of an SWRC is determined by the structural pores rather than the matrix pores, the observed changes in the shape of SWRCs imply that the biochar amendments caused a change in the soil structure. Increased water content at lower tensions indicates that biochar additions increased total porosity. Higher water retention by soils receiving biochar amendments relative to the control is similar to the effect of an increase in SOM content, which increases the specific surface area and decreases the soil bulk density. Relative to controls, all biochar treatments showed increased porosity, water content at both saturation and field capacity and improved soil physical quality. The biochar-amended soils had a change in PSD with smaller most frequent pore diameters and alpha parameters and larger S- index, total porosity and higher saturated, field capacity and residual water contents compared to control soils without biochar. A key novel finding is that differences in soil pore geometry parameters obtained by fitting the measured SWRCs to the VG equation provided quantitative evidence of the shift in PSD to smaller average pore sizes, reducing macro- in favor of meso- and micro-porosity and hence enhancing soil water retention. This improvement can help biochar- amended sandy soils retain more plant available water for crop use and reduce leaching losses of water and nutrients compared with non-amended sandy soils.

Figure 26: Effects of biochar treatments and sampling time on the measured soil water retention curves (top) and the estimated pore-size distributions (bottom). Specific objective 1: Develop, parameterize, and validate a biochar module for the Agricultural Production Systems sIMulator. Developing a viable biochar model for agroecosystems is a daunting task given the complexity of soil-biochar-crop-management-weather interactions. While it is impossible to model the full complexity of soil-agroecosystems it is possible for a model to capture the major processes and to predict outcomes within acceptable uncertainty limits. The Agricultural Production Systems sIMulator (APSIM) is an advance agroecosystem model that is able to predict crop yields and various environmental outcomes across diverse soil types, cropping systems and climates (https://www.apsim.info/). The following papers document the development and validation of a biochar module within the Agricultural Production Systems sIMulator (APSIM) agroecosystem model framework.

Archontoulis, S.V., I. Huber, F.E. Miguez, P.J. Thorburn, and D.A. Laird. 2016. A model for mechanistic and system assessments of biochar effects on soils and crops and trade-offs. GCB Bioenergy. doi: 10.1111/gcbb.12314. We developed a biochar model within the Agricultural Production Systems sIMulator (APSIM) software that integrates biochar knowledge and enables simulation of biochar effects within cropping systems. The model has algorithms that mechanistically connect biochar to soil organic carbon (SOC), soil water, bulk density (BD), pH, cation exchange capacity, and organic and mineral nitrogen. Soil moisture (SW)–temperature–nitrogen limitations on the rate of biochar decomposition were included as well as biochar-induced priming effect on SOC mineralization. The model has 10 parameters that capture the diversity of biochar types, 15 Figure 27: A simplified diagram illustrating parameters that address biochar-soil APSIM model. Everything in red was interactions and 4 constants. The range of modified by the biochar module. Boxes are values and their sensitivity is reported. state variables, solid arrows are rate variables To develop the biochar model, we and indicate material flow, broken arrows synthesized biochar information and indicate information flow, and circles are programmed the biochar model within intermediate variables. Driving variables for Agricultural Production Systems sIMulator the system are indicted by the green circles at (APSIM), a worldwide leading agricultural the top. RES, surface residue; FOM, fresh systems modeling platform, which is organic matter; BIOM, microbial pool; frequently updated, and freely available HUM, humic pool; INERT, inert pool; SAT, (www.apsim.info). APSIM is a field scale saturation point; DUL and LL, drained upper model that operates on a daily time step. Its and lower limits; KL, ability of the roots to modular design allows users to select a crop take up water; CEC, cation exchange from among 30 currently available crop capacity; and BD, soil bulk density. models or a combination of crops in rotation, cultivars, soils, climates, and management practices. In turn the model provides outputs for numerous soil-plant variables; including crop growth processes, soil water, soil temperature, nitrogen and carbon cycling, GHG emissions and residue dynamics. The major processes simulated by the model and their feedback mechanisms are shown in Figure 27. We used the model to investigate long-term (30 years) biochar effects on U.S. maize and Australia wheat in various soils. Results indicated that the effect of biochar was the largest in a sandy soil (Australian wheat) and the smallest in clay loam soil (U.S. maize). Table 6: List of parameters in the biochar model, with their definitions, units and range of values found in the literature. BC1, BC2, BC3 are hypothetical biochar types used for sensitivity analysis and WC is a woodchip biochar used for model evaluation.

We classified the biochar model parameters in three major categories (Table 6): (i) those that are highly dependent on biochar type (production method X feedstock source X production temperature) and management (application amount, incorporation depth); (ii) those that are determined by soil and biochar interactions (effects on soil hydrology, priming); and (iii) finally those that are assumed to be constants in the model. All of these parameters are important and affect various soil processes and properties differently. Parameters for the first category can be extracted from various literature resources or measured as outlined above or can be taken from the Biochar Engineering web tool (http://spark.rstudio.com/veromora/BiocharEng/) or other biochar databases (http://biochar.ucdavis.edu/). For the second and third category, literature information is incomplete and specific experiments are needed to fill modeling knowledge gaps. Figure 28 shows the goodness of fit of the biochar model to several soil-plant variables obtained under field conditions [36]. The calibrated biochar parameter values used in this simulation are shown in Table 9. Overall, the model matched experimental observations with a mean relative absolute error from -0.4% to 13.1%, which is very good. The prediction of soil BD and pH were the most accurate, while the ability of the model to predict more complex variables such as SOC was lower.

Figure 28: Biochar model predictions of corn grain and stover yield, soil bulk density (BD), soil pH, SOC (soil organic carbon) and volumetric soil moisture vs. experimental observations from Rogovska et al. [36]. The biochar was applied in November 2010 (see parameter values in Table 6) and the measurements were obtained in 2012. Overall results from the sensitivity analysis showed that the effect of biochar was the largest in a sandy soil (Australian wheat) and the smallest in clay loam soil (U.S. maize). On average across cropping systems and soils the order of sensitivity and the magnitude of the response of biochar to various soil-plant processes was (from high to low): SOC (11% to 86%) > N2O emissions (-10% to 43%) > plant available water content (0.6% to 12.9%) > BD (-6.5% to - 1.7%) > pH (-0.8% to 6.3%) > net N mineralization (-19% to 10%) > CO2 emissions (-2.0% to 4.3%) > water filled pore space (-3.7% to 3.4%) > grain yield (-3.3% to 1.8%) > biomass (-1.6% to 1.4%). Our analysis showed that biochar has a larger impact on environmental outcomes than agricultural production in temperate regions. The mechanistic model has the potential to optimize biochar application strategies to enhance environmental and agronomic outcomes but more work is needed to fill knowledge gaps identified in this work.

Dokoohaki, H. F.E. Miguez, S. Archontoulis, D. Laird. 2018. Use of inverse modelling and Bayesian optimization for investigating the effect of biochar on soil hydrological properties Agricultural Water Management 208: 268-274. Physical properties of biochar such as small particle size and high porosity can modify soil properties and help to improve soil water dynamics. However, there has been no consistent long-term measurements of change in soil physical properties due to biochar application under real field conditions. In this study, we use a unique dataset of soil water content measurements in a corn-soybean cropping system (with and without biochar) for two years. Soil water content was measured every 30 min at 4 different depths and with 3 replications in corn plots. The effect of biochar was expected to be the difference between the physical soil properties of the two treatments. The APSIM model, a process-oriented crop model, was employed in order to find the physical properties of biochar and no-biochar treatments by using inverse modeling. First, a global sensitivity analysis was carried out to find the most sensitive inputs for the APSIM model for soil water simulation. Then the Metropolis-Hasting algorithm was used to inversely estimate the APSIM soil input properties using the measured soil moisture data. Results of the sensitivity analysis showed that the drainage upper limit (DUL) was the most sensitive soil property followed by saturated hydraulic conductivity (KS), saturated water content (SAT), maximum rate of plant water uptake (KL), maximum depth of surface storage (MAXPOND), lower limit volumetric water content (LL15) and lower limit for plant water uptake (LL). The difference between the posterior distributions (with and without biochar) showed an increase in DUL by approximately 10%. No considerable change was noted in LL15, MAXPOND and KS whereas SAT and LL showed a slight increase and decrease in biochar treatment respectively compared to no-biochar.

Figure 29: Daily mean and one standard deviation of soil water content for the 10 cm soil depth in 2013 and 2014 in corn plots. The light lines indicate no biochar and the dark lines indicate the biochar treatment.

Figure 30: Daily mean and one standard deviation of soil water content for the 10 cm soil depth observed and simulated for the biochar (bottom) and no-biochar (top) corn plots. The results demonstrate that the biochar application resulted in improved soil physical properties, which resulted in greater soil water retention in corn plots under field conditions for over two years (Figure 29). Our results also demonstrate that calibrated APSIM model is able to accurately predict the effect of biochar and no-biochar treatments on daily soil moisture levels across the two years of the study (Figure 30). The biochar treatments on APSIM’s parameters was quantified. Biochar caused an increase in DUL and KL along with a slight increase in SAT while no significant change in LL15, KS and MAXPOND due to the biochar treatments was observed. Increase in the capacity of the soil for holding water resulted in higher water uptake with lower LL for the biochar treatment. A key novel component in this study was the integration of field-level measurements with a modeling component for estimation of more realistic soil properties as a result of biochar application.

Goal 2: Assess the technical and economic viability of an integrated pyrolysis-bioenergy-biochar industry in the Upper Mississippi River Basin (UMRB), California, and U.S. Southeast; Specific objective 2: Use techno-economic analysis to assess the economic performance of pyrolysis plants producing bioenergy and biochar co-products and use life cycle assessments to determine the net GHG emissions from an integrated pyrolysis-bioenergy-biochar production facility. Many different configurations of thermochemical biomass conversion plants are possible. The engineering challenge is to design and build plants that leverage locally available biomass feedstocks and produce bioenergy and biochar products that are appropriate for the local and regional markets. Such plants must be scaled appropriately and ultimately be economically viable. The following papers document techno-economic analyses of potential pyrolysis- biochar-bioenergy plant configurations:

Li, W., Ghosh, A., Bbosa, D., Brown, R., and Wright, M. M. 2018. Comparative techno- economic, uncertainty and life cycle analysis of lignocellulosic biomass solvent liquefaction and sugar fermentation to ethanol. ACS Sustainable Chemistry & Engineering, 6(12), 16515–16524. This study compares the use of three low boiling point polar aprotic solvents, tetrahydrofuran (THF), acetone, and 1,4-dioxane, for extracting biomass sugars in ethanol biorefineries (Figure 31). The techno-economic analysis employs experimental data to build a chemical process model and estimate capital and operating costs of a commercial-scale biorefinery. The biomass solvent liquefaction in a 2000 metric tonne per day sugar fermentation biorefinery yields ethanol at minimum fuel-selling prices (MFSP) of $2.98 to $4.06 per gallon. THF achieves the lowest MFSP. Uncertainty analysis indicates that solvent to biomass ratio, glucose and xylose yields, feedstock price, and capital costs are the primary drivers of the ethanol MFSP. The 10% to 90% percentile for THF-based ethanol MFSP are estimated to be $2.31 and $3.21 per gallon. Life cycle analysis shows that with a lower solvent to biomass ratio as suggested by previous studies, our process could achieve GHG emissions reduction of 25% and 45% for THF and acetone cases, respectively. Further optimization of the process could lead to significant reductions in ethanol costs, commercialization risks, and GHG emissions.

Figure 31: Process flow diagram of THF solvent liquefaction for ethanol production.

The significance of this study is that green solvents can provide an alternative to enzymatic hydrolysis for recovering biomass sugars in lignocellulosic biorefineries. The proper polar aprotic solvent could reduce biomass pretreatment costs if target sugar yields, solvent to biomass ratios, and recovery rates can be achieved.

Li, W., Qi Dang, R.C Brown, D. Laird, M.M Wright. 2017. The impacts of biomass properties on pyrolysis yields, economic and environmental performance of the pyrolysis-bioenergy-biochar platform to carbon negative energy. Bioresource Technology. 241:959-968. doi.org/10.1016/j.biortech.2017.06.049 This study evaluated the impact of biomass properties on pyrolysis product yields, and the economic and environmental performance of the pyrolysis-biochar-bioenergy platform. We developed and applied a fast pyrolysis, feedstock-sensitive, regression-based chemical process model to 346 different feedstocks, which were grouped into five types: woody, stalk/cob/ear, grass/plant, organic residue/product and husk/shell/pit. The results show that biomass ash content (0.3–7.7 wt%) increases biochar yield from 0.13 to 0.16 kg/kg of biomass, and decreases biofuel yields from 87.3 to 40.7 gallons per tonne. Higher O/C ratio (0.88–1.12) in biomass decreases biochar yield and increases biofuel yields within the same ash content level. Higher ash content of biomass increases minimum fuel selling price (MFSP), while higher O/C ratio of biomass decreases MFSP within the same ash content level. The impact of ash and O/C ratio of biomass on GHG emissions are not consistent for all feedstocks. Figure 32 shows the impact of biomass ash content on MFSP, and Figure 33 compares the GHG of various feedstocks.

Figure 32: The impact of biomass ash Figure 33: Comparison of feedstock content on the minimum fuel-selling price choice on greenhouse gas emissions of (MFSP) biofuel production The significance of these findings is that the MFSP of pyrolysis biofuel production is strongly related to the ash content of the feedstock. Feedstocks with low ash content achieve the lowest MFSP due to higher biofuel production yields. Furthermore, ash content impacts overall GHG emissions. Average feedstock ash content varies, and a comparison of their GHG emissions indicates that low ash biomass such as organic residue achieve much lower GHG emissions than high ash-content biomass such as grasses. Pretreatment methods such as ash passivation developed at Iowa State University can mitigate the impacts of high ash content.

Li, W., Dumortier, J., Dokoohaki, H., Miguez, F.E., Brown, R.C., Laird, D., Wright, M.M. 2019. Regional techno-economic and life cycle analysis of the pyrolysis-bioenergy-biochar platform for carbon-negative energy. (in review by Biofuels, Bioproducts, and Biorefining). This study investigates the sensitivity of the minimum fuel selling price (MFSP) and greenhouse gas emissions (GHG) for a 2000 Mg day-1 integrated pyrolysis-bioenergy-biochar platform with respect to the biorefinery location. The regional techno-economic and life cycle analysis is evaluated in three U.S. counties using representative crops: Rice in Glenn County (California), corn in Hamilton County (Iowa), and peanuts in Jackson County (Florida). We evaluate the biochar selling price considering crop yield increases of 0.6%, 2.9%, and 10% after biochar application over 20 years in Glenn County (California), Hamilton County (Iowa), and Jackson County (Florida) respectively. The biochar prices are calculated under low and high commodity prices to determine upper and lower bounds. Jackson County (Florida) has the most economically beneficial scenario with an average MFSP of $1.55/gal of biofuel produced while Hamilton County (Iowa) has the highest average MFSP of $3.82/gal. The life cycle analysis shows a high potential for carbon-negative energy production for this platform with up to 64% of the 304 cases of feedstock could produce biofuel with negative GHG emissions. Figures 34 and 35 show the Minimum Fuel-Selling Price and Greenhouse Gas emissions for biofuel produced from the pyrolysis biorefinery.

Figure 34: Mean minimum fuel-selling price (MFSP) with standard errors for two

different biochar price scenarios in Hamilton (IA), Glenn (CA) and Jackson (FL)

Figure 35: The impact of biomass properties (O/C and ash) on GHG emissions The significance of these results is that they indicate that the pyrolysis-bioenergy-biochar platform has the potential for economically producing bioenergy products while sequestering carbon. Biochar’s ability to increase agricultural productivity provides an additional revenue source for pyrolysis biorefineries. This incentive is sensitive to facility location and feedstock properties suggesting that biorefineries will need to consider these parameters in optimizing their deployment location.

Goal 3: Assess regional and global impacts of an integrated pyrolysis-bioenergy-biochar industry on indirect land use and net GHG emissions; Specific objective 3: Quantify the public and private benefits accrued from integrating biochar into pyrolysis-based bioenergy production systems for three case studies. One approach for assessing regional and global impacts of an integrated pyrolysis-bioenrgy- biochar industry on indirect land-use and GHG emissions is to first scale the APSIM Biochar Model to make regional, national and ultimately global predictions of the impact of biochar on crop production and carbon sequestration and then to integrate the APSIM Biochar Model with macro-economic models. In the this section we report on three papers: The first paper documents the coupling of the APSIM Biochar Model with an economic model to predict net private and net global public benefits for Midwestern continuous corn and corn-soybean cropping systems. The second paper documents an economic analysis using the CARD/FAPRI Model of farmer’s willingness to pay for applying biochar on agricultural fields in the U.S. Midwest, Southeast and California. And, the final paper documents the recent coupling of the APSIM Biochar Model with the pSIMS modeling platform, which allows regional and global scaling. In the future, this new capability will allow the APSIM Biochar Model to be coupled with macroeconomic models and to be used to make regional and global assessments of the potential impact of various PBBP scenarios on energy production, crop productivity, carbon sequestration, GHG emissions, and nitrate leaching.

Aller, D., S. Archontoulis, W. Zhang, W. Sawadgo, D. Laird, K. Moore. 2018. Long term biochar effects on corn yield, soil quality and profitability in the US Midwest. Field Crops Research 227:30-40. Corn production in the U.S. Midwest has the potential to generate a large amount of crop residue that can be used as feedstock for bioenergy production. However, unconstrained harvesting of crop residues is associated with long-term declines in soil quality and ultimately crop productivity. Biochar applications are hypothesized to mitigate many of the negative effects of crop residue removal but data and economic analyses to support decision making are lacking. To explore sustainable and profitable practices for residue harvesting in central Iowa we used 11 years of soil, crop yield, and management data to calibrate the Agricultural Production Systems sIMulator (APSIM) biochar model (Figure 36). We then used the model to evaluate how different biochar types and application rates impact productivity and environmental performance of conventional corn and corn-soybean cropping systems in Iowa under different N fertilizer application rates and residue harvesting scenarios. A cost-benefit analysis was also employed to identify the economically optimal biochar application rate from both producer and societal perspectives. We used the calibrated model and historical weather data to simulate the impact of two biochar types, four biochar application rates, three residue removal rates, and three nitrogen fertilizer rates on corn yields (Figure 37), nitrate leaching (Figure 38) and SOC levels (Figure 39) over a 32-year period. Overall the changes in average corn yields attributable to biochar were small over the 32- year simulation period and across all scenarios. The largest simulated effect was a yield decline of 2.6% for the CC system with application of 90 Mg ha−1 high C biochar, 0% residue removal, and a fertilization rate of 75 kg N ha−1, which would put the Figure 36: Impact of biochar applied in 2012, 2013, and 2014 corn crop under considerable N on corn yields for a continuous corn cropping system. stress. The 2.6% yield loss equates −1 Measured values (black diamonds) and predicted yields (blue to a total of 6112 kg ha (97 bu −1 −1 line) are averages for all plots with biochar (left side) and ac ) or an average of 191 kg ha −1 −1 −1 without biochar (right side). Error bars and shaded blue areas yr (3 bu ac yr ) over the 32- indicate standard deviations. year simulation period. Although the overall effect of biochar on corn yields was small, the model indicated a complex cropping system by N fertilization rate by biochar application rate by residue removal rate interaction. Simulated corn yields for the continuous corn and corn-soybean cropping systems showed different trends for residue removal rates for the 75 and 150 kg N ha−1 fertilization rate scenarios (Figure 38). For the continuous corn system, the 75 kg N ha-1 fertilization Figure 37: Differences in average corn yields (kg ha−1) rate without residue removal between biochar and no-biochar control treatments for the 32 resulted in the largest yield decline year simulation (1985–2016) in continuous corn and corn- followed by the 90% and then 50% soybean cropping systems under different N fertilization and residue removal rates, regardless of residue management scenarios and applications of low/high C biochar application rate. At the content and C:N ratio biochars applied in 1985. same fertilization rate but for the corn-soybean rotation system, a residue removal rate of 50% had the largest yield decline followed by the 90% and then 0% residue removal rates, regardless of biochar application rate. At the 150 kg N ha−1 fertilization rate corn yields decreased for all biochar application and residue removal rates compared to the no-biochar control for the continuous corn system. Whereas in the corn-soybean rotation system corn yields increased at the Figure 38: Average difference in NO3 leaching for the 32 year 0% and 50% residue removal simulation (1985–2016) between biochar and no-biochar rates and decreased at the control treatments for the continuous corn and corn-soybean 90% residue removal rate cropping systems under different N fertilization and residue regardless of biochar management scenarios and when low/high C content and C:N application rate. These ratio biochars were applied in 1985. differences are attributed primarily to biochar and crop residue effects on N immobilization/ mineralization with a lesser effect due to soil water. Specifically, the model predicted that biochar applications would decrease N availability to the crop while the labile C in biochar is being decomposed due to N immobilization. The effect of biochar treatments on corn yields was positive at the 225 kg N ha−1 fertilization rate regardless of biochar application and residue Figure 39: Average change in total soil organic carbon removal rates. At the 225 kg N levels for the 32 year simulation (1985–2016) in ha−1 fertilization rate, N continuous corn and corn-soybean cropping systems under availability is no longer limiting different N fertilization and residue management scenarios to crop growth and hence the and when low/high C content and C:N ratio biochars were positive aspects of biochar on soil applied in 1985. quality boost yields. − The simulations showed similar patterns for NO3 leaching as were observed for corn yields (Figure 38). Simulation results indicate that application of both the high and low C biochars − decreased NO3 leaching through the root zone (NO3 leached below 1.5 m) for all N fertilization − and residue removal rates and for both cropping systems. The percent reduction in NO3 leaching attributed to the biochar treatments relative to the no-biochar controls increased with the − N fertilization rate and the amount of biochar applied. The maximum impact on NO3 leaching for both cropping systems was a reduction of about 10% when the low C biochar was applied and a nearly 20% reduction when the high C biochar was applied. This difference is attributed to the C:N ratio of the biochar and the availability of biochar C for N immobilization. − −1 Residue removal decreased NO3 leaching at the 75 and 150 kg ha N fertilization rates, but − −1 increased NO3 leaching at the 225 kg ha N fertilization rate. These differences are attributed to N being limiting to the crop at the low and medium fertilization rates, so as more residue is − removed the availability of N increases, enhancing root growth, and reducing NO3 leaching. Whereas, at the high fertilization rate, N is not limiting plant growth, thus as residue removal − increases, N immobilization decreases, and NO3 leaching increases. The 32-year simulation indicates that biochar applications, biochar type, and residue removal rate have large impacts on soil organic carbon (SOC) levels while cropping system, and N fertilization rate have relatively small effects on SOC levels (Figure 39). There was a direct relationship between biochar application rate and the increase in SOC content at the end of the 32-year simulation. For the no-biochar control scenarios, SOC levels increased over time for the 0% residue removal treatment but decreased over time for the 50% and 90% residue removal treatments. For the scenarios with 22, 45, and 90 Mg ha−1 biochar application rates, as percent residue removal increased, the percent increase in SOC levels decreased but were higher than the initial SOC levels because of the biochar C. When the low C biochar was applied, the simulations indicate a smaller increase in SOC levels over 32 years relative to the simulations using the high C biochar. This observation is attributed to the lower C content and C:N ratio of the low C biochar. The cost-benefit analysis revealed that public benefits, evaluated from decreased nitrate leaching and increased soil carbon levels, significantly outweighed the private revenue accrued from crop yield gains, and that a biochar application rate of 22 Mg ha−1 was more cost-effective (per ton) compared to higher biochar rates (Figure 40). Overall, this study found that applying biochar once at a rate of 22 Mg ha−1 allows for the sustainable annual removal of 50% of corn residue for 32 years, is profitable for farmers Figure 40: Net private and net global public benefits even with minimal impact on grain for the continuous corn and corn-soybean cropping yield, and beneficial to society through systems for different biochar application rates, 0% reduced nitrate leaching and increased residue removal, and when a high C biochar (blue soil organic carbon levels. bars) and low C biochar (red bars) is applied. Values The findings from this study are relative to the baseline scenarios of no biochar, 0% residue removal, and 225 kg N ha−1 and 150 kg N demonstrated that over a 32-year period −1 biochar applications can eliminate ha for the continuous corn and corn-soybean negative effects of residue harvesting cropping systems, respectively. on soil quality while at the same time reducing nitrate leaching, increasing soil organic carbon, and not impacting corn yields. Specifically, at least 50% of the residue can be removed annually for 32 years if a one-time application of 22 Mg ha−1 biochar is made. The simulations revealed that corn yields are most affected by the amount of N applied and not by the addition of biochar − or residue removed. The opposite was observed for NO3 leaching, with increasing rates of − biochar leading to the greatest reduction in NO3 leaching. Biochar applications are also effective for building soil organic carbon even under increasing rates of residue removal. This finding in particular, could have important positive implications for the U.S. bioenergy industry while improving the sustainability of agricultural systems. The costbenefit analysis revealed that the public benefits that result from applying biochar coupled with the ability to harvest more residue, significantly outweighed the private benefits. Biochar applications are an economically viable option in Iowa when at least 50% of the residue is harvested for sale; which can be done in an environmentally sustainable way.

Dumortier, J., H. Dokoohaki, A. Elobeid, D.J. Hayes, D. Laird, and F. Miguez. 2019. Biochar- induced yield increases and its effects on global carbon emissions and land-use. Renewable & Sustainable Energy Reviews (under review) Treating agricultural soils with biochar is of interest for three reasons: First, higher yields can potentially reduce the agricultural land required for crop production and hence, can decrease carbon emissions from avoided indirect land-use change. We show that a higher yield in the U.S. does indeed reduce global land requirements. Second, it can improve the profitability of farming and enhance rural welfare depending on the expected yield increase and the prices of crops and biochar. Third, the combined production of biochar and bio-oil, which can be upgraded into liquid transportation fuels, can lead to carbon-negative energy production. There are currently no financial incentives to reduce carbon emissions or to increase soil carbon sequestration in the U.S. and thus, potential yield improvements are the only economic incentive for farmers to apply biochar at this time. We quantify the expected yield and return increase as well as the supply of biochar feedstock (i.e., corn stover, switchgrass, and forest logging residues) in the United States. We then combine the results with a global agricultural outlook model to calculate the effects of increased crop yields in the U.S. on global land-use and emissions as well as the potential carbon credit payment to farmers. Carbon credits would lower the cost to farmers for biochar application. The basis for our analysis of biochar-induced yield improvements in the U.S. is the soil- and location-specific estimates of expected yield increase presented in Dokoohaki et al. (2019). We also derive the supply curve for corn stover delivered to a biofuel plant. This is needed to understand the economic viability of the biochar bio-oil system. The expected yield increase obtained from the agronomic model is translated into farmers’ willingness-to-pay (WTP) for biochar (in $ Mg−1). The WTP is the per metric ton of biochar difference between the net present value from crop production with and without biochar application. We calculate the expected additional revenue due to biochar-induced higher yields over 20 years under low and high commodity prices for six crops, i.e., corn, peanuts, rice, sorghum, soybeans, and wheat. States that rank high in terms of expected yield increase may have lower crop yields resulting in a lower WTP for biochar because the additional yield — although high in terms of percent increase — does not translate into much additional revenue. For example, North Carolina and South Carolina have large percentage yield increases but are outperformed by Alabama, Georgia, and Florida in terms of WTP (Figure 41).

-1 Figure 41: Median WTP $ Mg for slow pyrolysis corn stover biochar.

In general, the highest WTP is observed in states along the Eastern seaboard. Also, eastern Mississippi and northeastern Louisiana have large areas in which farmers have a high WTP for biochar. Although farmers in Alabama, Georgia, and Florida would have a high WTP for biochar, the total area on which biochar would be applied is very small because those states only have small areas of the six crops that are covered in this analysis. To better determine the percentage of crop area covered at various WTP values, we performed a kernel density estimation as well as calculating a cumulative distribution function (Figure 42). The kernel density estimation of the willingness-to-pay for all six crops shows that for biochar prices of $238-$265 Mg−1, 5% of crop area would be covered under low commodity prices. Under high commodity prices, biochar prices of $665-$747 Mg−1 would result in the same coverage. The lower bound of the price range for a given commodity price level is biochar obtained from switchgrass and the upper bound is for biochar produced from forest residues. To obtain a cropland coverage of 25%, biochar prices need to decrease to $146-$169 Mg−1 and $395-$453 Mg−1 under the low and high commodity price scenarios, respectively. Those numbers illustrate the importance of commodity prices which introduces 2.7-fold differences between the upper and lower bound for WTP for a given crop yield and biochar feedstock. Note that our WTP measure is based only on the expected yield increase over a 20 year period and does not take into account any change in management practice. Under the assumption that the costs associated with applying biochar to the field and any change in management practice are relatively comparable across the study area (e.g., national, state, and county), our results still indicate areas that are more likely to apply biochar. In that case, the WTP has to be corrected to take the additional cost of implementation into account which will likely decrease the WTP (assuming that other inputs are not reduced). Figure 42: Kernel density estimation of willingness-to-pay (2017 $ Mg−1) for low and high commodity prices. We quantify the maximum annual area coverable with biochar knowing that not all cropland receives biochar due to low profitability. The 2017 total area of the six crops evaluated in this study was 88.63 million hectares and we assume an application rate of 5 Mg ha−1. Corn stover, switchgrass, and forest logging residues are used as biochar feedstock. Currently, only biochar from logging residues is produced on a commercial scale and thus, a biomass price needs to be present to incentivize farmers to supply biomass for biochar production. There is significant uncertainty around biomass production from corn stover and switchgrass in the United States. To capture the wide range or parameter values, we differentiate our scenarios along four dimensions: (1) biomass production cost (high/low), (2) biomass price, (3) adoption of switchgrass, and (4) tillage. The 2011 BTS provides sustainable corn stover removal rates under reduced- and no-tillage but we also use revised removal rates from the 2016 BTS. Under high biomass production cost and a biomass price of $55 Mg−1, little biomass is available and the maximum coverage is 0.4% of cropland using the revised removal coefficients from the 2016 BTS. At the price of 55 $ Mg−1 and low biomass production cost, an annual coverage of 12% can be achieved assuming that farmers remove corn stover at a rate consistent with the 2011 BTS no-tillage removal coefficients. Increasing the biomass price to $110 Mg−1 results in a large variation of the coverage ranging from 1.7 (1.91%) to 28.0 (31.7%) million hectares. The production of biomass is very sensitive with respect to the biomass production cost and the assumed removal coefficients. In the previous sections, we focused on additional revenue for U.S. farmers resulting from applying biochar to soils. To evaluate the effect of higher commodity yields in the U.S. on global GHG emissions, we use the CARD/FAPRI agricultural outlook model to quantify the land-use effects of 1% higher yields for corn, soybeans, and wheat in the United States. With the higher yields, the global land use is estimated to be only 0.06% lower compared to the baseline. The difference in area is approximately 0.47 million hectares. There are significant declines in crop area in Brazil (0.30%), Russia (0.16%), and Mexico (0.12%) whereas the reduction in the U.S. is only 0.06%. The reduction in Brazil is important due to the large areas that are situated in carbon rich environments. In Russia, there is a significant decrease in area for oats (-0.11%) and wheat (-0.26%). Wheat accounts for over 58% of agricultural area modeled for Russia in the CARD/FAPRI Model. The GHG emissions in the CARD/FAPRI Model are calculated for minimum, mean, and maximum carbon coefficients. The reductions in emissions range from 25.09 to 68.56 Tg of CO2-e. The emission savings are slightly higher, i.e., 30.16 to 86.88 Tg of CO2-e, if only cropland is considered. The higher yields results in a decrease in commodity prices and an expansion of the livestock sector which subsequently increases the amount of pasture used and hence, carbon emissions. To quantify how those emission savings translate into carbon payment for farmers in the U.S., we proceed as follows: Assuming that farmer’s with the highest willingness-to-pay are the first to apply biochar, we calculate the minimum crop area necessary to achieve a yield increase by 1% at the national level. Depending on the biochar feedstock and commodity prices, this area ranges from 9.27 to 10.53 million hectares. If we assume that the carbon payments — from the reduction in emissions — would be distributed equally to farmers irrespective of their individual yield increase, then for every one dollar increase in carbon price −1 (measured in CO2-e), farmers would receive between $2.40 and $9.40 ha . For example, −1 assuming a low carbon price of $10 Mg CO2-e farmers would receive between $24 and $94 ha−1. Hence, carbon credit scheme could significantly increase the willingness-to-pay for biochar. The Supply Curve for Corn Stover as an input in Bio-Energy Production. One motivation for this research is to calculate the delivered price of corn stover to biofuel production facilities. This cost is crucial to understanding the economic viability of the bio-oil bio-char production system. The literature on the availably of cellulosic feedstock, including U.S. Department of Energy [44- 45], focuses on whether sufficient cellulosic feedstocks will be physically available to meet the mandate. Common assumptions in this literature are 100% participation in supplying available feedstocks and homogenous producers. However, firms with industry experience in cellulosic biomass markets report that quantity supplied from producers is not equal to physical availability of feedstock [46-47], which further suggests the supply curve for cellulosic feedstock will be different than that for corn ethanol and other dedicated feedstocks. The supply chain connecting biofuel processing firms and suppliers of biomass is evolving, and processors face a choice in the collection and pricing strategies they will be employed to procure biomass. One option is to pay a single price for biomass collected field-side (processor collection). Another is to pay a single price for biomass at the plant gate (supplier delivery). The literature in this area is relatively young, but there is a sense that the evolution of contracting and pricing structures will dictate the industry's success, and ultimately the costs of producing biofuels from dedicated and non-dedicated energy crops. We examine the collection and pricing choices for a cost-minimizing cellulosic biofuel processor, who initially has monopsony power in feedstock procurement in their collection area. We derive optimal prices, total expenditures on feedstocks, and the collection areas required to meet a processor's fixed input needs. Numerical simulation based on corn stover for biomass is used to illustrate optimal pricing and the extent of biomass collection areas for different procurement and pricing strategies. We use these findings to calculate the rates at which collection costs increase for a monopsonistic stover processor constrained to a defined procurement area, as might emerge as the industry moves toward commercialization. The derived marginal cost curve for a monopsonistic processor of stover is compared with the marginal cost curve across alternative feedstocks.

Huber, I., D. Laird, R. Martinez-Feria, and S.V. Archontoulis. 2017. Environment impacts of large scale biochar applications through spatial modeling. American Geophysical Union, New Orleans, 11-15 Dec. 2017. (Manuscript in preparation). Corn production in the U.S. Midwest has the potential to generate a large amount of crop residue for bioenergy production. However, unconstrained harvesting of crop residues is associated with a long term decline in soil quality and environmental benefits. Biochar applications can mitigate many of the negative effects of residue removal but regional scale analysis to support decision making are lacking. The objectives of this study are: 1) to develop an integrated system to predict impacts of biochar applications at regional scales, and 2) to investigate, regional variation in the long-term effects of biochar application and residue removal practices on maize productivity, soil organic carbon levels, and nitrate leaching across the U.S. Midwest. We coupled the APSIM biochar model (version 7.9; Holzworth et al., [48]; Archontoulis et al., [49]) within the pSIMS platform (version 2.0; Elliot et al., [50]) to simulate four scenarios: • Baseline: continuous corn over 30 years under no-till • Scenario I: Baseline plus a single biochar application of 12 Mg ha-1 • Scenario II: Baseline plus 50% corn residue removal every year • Scenario III: Baseline plus biochar plus residue removal (scenarios I and II) Compared to the baseline simulations, the biochar scenario resulted in 8.9% higher SOC levels (0-30 cm depth), the residue removal scenario in 5.4% lower SOC levels and the combined biochar and residue removal scenario in 3.6% higher regional average SOC levels after 20 years (Figure 43a). Compared to the baseline, a single biochar application increased average soil water holding capacity and porosity in the topsoil (0-30 cm) by 1.4% and 3.1%, respectively. The residue removal scenario slightly decreased porosity and soil water holding capacity. The water field pore space (ratio of soil water to saturation) decreased by 1.3% under both biochar and residue removal scenarios for different reasons; increased porosity in the biochar scenario and decreased soil water in the residue removal scenario. Compared to the baseline, a single biochar application decreased N losses to leaching (below 120 cm soil depth) and denitrification by 1.7% on average across the 3 states and 20 years of simulations. Residue removal decreased N losses by 19.4% and residue removal plus biochar application together by 21.2% compared to the baseline. The reasons are: a) temporary immobilization of inorganic N caused by biochar decomposition, and b) the decline in SOC under residue removal practices decrease the production of inorganic N via soil N mineralization (Figure 43b). The majority of N loss reduction derived from reduction in N leaching and not from the denitrification. Residue removal decreased N losses more than the biochar because of the increased loss of soil water to − evaporation and runoff that decreased soil water loss to drainage, the driver of NO3 leaching. Crop Yields: A single biochar application on average increased corn yields by 0.48, 0.65 and 0.53% in Iowa, Illinois and Indiana, respectively (Figure 43c). Assuming a corn price of 4 $/bushel this yield increase is translated to an additional gross revenue of 40, 49 and 21 million $ per year for Iowa, Illinois and Indiana, respectively. Across the three states, the annual residue removal reduced yield by an average value of 0.75% that is 64 million $ loss per year. The single biochar application offset 45% of this decline, which means that addition of biochar enhances the long term sustainability of crop residue harvesting. Future cost-benefit analyses should account for the price of harvesting/selling 31 million Mg per year of residue across the three states.

Figure 43: Regional scale model based assessments of production and environmental impacts of biochar application; A) effect on SOC, B) effect on nitrogen loss, C) effect on maize yields compared to the baseline scenario.

Cumulative effects on soil carbon sequestration and nitrate leaching indicate that a one-time biochar application is able to fully compensate for the negative effects of residue removal on soil organic carbon levels while retaining the positive effects of residue harvesting on reduced nitrate leaching compared to the baseline scenario through the 20 year simulation (Figure 44).

Figure 44: (A) Carbon sequestration 20 years after applying biochar and/or residue removal scenarios compared to the baseline, and (B) cumulative N losses over 20 years of applying the different scenarios compared to the baseline.

Goal 4: Build a foundation for the development of a vanguard economically-viable carbon- negative integrated pyrolysis-bioenergy-biochar industry. Specific objective 4: Quantify the public and private benefits accrued from integrating biochar into pyrolysis-based bioenergy production systems for three case studies. Soil biochar applications have been shown to be effective for sequestering carbon in soils, reducing N2O emissions from soils under row crop production, and for reducing leaching of nutrients from agricultural soils. However, under the current economic and regulatory systems, these environmental benefits for society are discounted. The only incentive for farmers to actually apply biochar to their soils is a crop yield increase. Crop yield responses to biochar applications show considerable variation due to complex soil-crop-biochar-management-weather interactions. A recent analysis of 507 crop yield differences between biochar-treated and no- biochar controls found significant positive crop yield responses for 21.6% and significant negative crop yield responses for 3.5% of the trials; the rest were not significant [51]. Therefore, it is difficult for farmers to justify large scale use of biochar on their fields given the high level of uncertainty that they will obtain a positive crop yield response. Thus, a major barrier to the expansion of the biochar industry in the U.S. is the lack of knowledge about where biochar will have a positive impact on crop yields at the farm and regional scales. Our approach to meeting Goal 4 is threefold: First, we have combined agronomic and environmental modeling of biochar with economic modeling to identify regions in the U.S. having the highest probability for a crop yield response to biochar applications and hence regions where pyrolysis-biochar-bioenergy plants might find a market for the sale of the biochar co- product. Second, we have reviewed the growth of biofuel industry to date and consider barriers and opportunities for growth of second generation biofuels, including the PBBP. Third, we have investigated several value-added biochar products that are potential entry products for vanguard biochar-bioenergy companies in the absence of policy and programs that incentivize carbon sequestration and the production of carbon negative bioenergy and biochar co-products. Dokoohaki, H., F. Miguez, D. Laird, J. Dumortier. 2019. Where should we apply biochar? Environmental Research Letters. https://doi.org/10.1088/1748-9326/aafcf0 Biochar is a low-density organic by-product of thermochemical conversion of biomass that is being evaluated as a soil amendment. Biochar can be used to sequester carbon in the soil and also has the potential to boost crop yields when it is used to improve yield-limiting soil properties. So far, complex interactions among biochars, soils, crops and climates have made it difficult to assess the agronomic potential of biochar and challenging to answer the question: ’where should we apply biochar to get the maximum benefit from its application?’ We addressed this challenge by developing an extensive informatics workflow for processing and analyzing crop yield response data as well as a large spatial-scale modeling platform. We built a database of raw data for 17 variables from 103 refereed publications that included 1260 observations. The selected studies examined the effect of biochar applications on crop production (grain/biomass). Given that absolute yield is not readily comparable among studies, we used the Response Ratio (RR) as the target variable. The RR is defined as:

A positive RR indicates a positive yield response to biochar application, whereas a RR of 0 shows no change from control treatment. This variable also can be easily transformed back to Relative Increase (RI) using:

Soil Organic Carbon (SOC), sand, silt, clay content, CEC and soil pH were extracted from all studies to explain both chemical and physical properties of soil. Biochar carbon, nitrogen, ash content, pH, C:N ratio, highest pyrolysis temperature, feedstock and thermochemical process were also variables extracted to account for both the feedstock properties and pyrolysis process in assessing biochar type. Biochar feedstock was classified into ‘woody’, ‘non-woody’ and ‘manure’, while pyrolysis type was characterized as ‘fast’ and ‘slow’. Along with soil and biochar properties, latitude, N content and biochar application rates were incorporated into our model in order to predict the RR, the target variable. A Bayesian belief Network (BN) was used for modeling the yield response to biochar applications. The BN model included both qualitative and quantitative components. The qualitative component is a graphical model which represents how the variables are statistically dependent on each other; nodes indicate variables and arcs show dependencies. The quantitative component is the conditional probability distribution of a node xi (specified in the graphical model) on its parents pa(xi). Taking into account the conditional independence assumption (Markov condition), the joint distribution over all the variables xi, i = 1, . . . , n is equal to the product of conditional distributions defined for each variable: When new evidence is introduced, it propagates through the BN and the posterior probabilities are computed. This is called an inference and it allows for detecting the change in the probabilities of some variables given a value for other variables. In the current study, a hybrid BN model (a model that includes both discrete and continuous variables) was developed within the R environment and using the “Bayes Server” software. The final design of the model’s structure resulted in 84 parameters. The heterogeneity among crop species and other key drivers of yield that are not included in the model, convinced us to use the probability of yield increase (p(RR>0)) instead of directly using estimated mean of RR; this inherently accounts for the variability around the average estimate of RR. Thereby, the estimated mean and variance were used to estimate p(RR>0) and identify places with high probability of yield increase. We setup a repeated cross validation procedure with 250 iterations for training and testing our BN. For each iteration, the model was trained with 80% of the observations in the dataset and tested against the remaining 20% of observations. Efficiency (EF) and Mean Absolute Difference (MAD) were used to compare the performance of these models as follows:

Where, Ym is modeled, YO is observed, Y¯O is the mean of the observed and n is the number of the observations. EF varies between −1 to 1; EF=1 represent the perfect match between modeled and observed data, whereas EF<0 indicates an unsatisfactory model performance. The estimated grand mean for yield Response Ratio (RR) to biochar average 12% increase for all studies in our database; we also observed a large variability in RR, ranging from -24.4% to 98%, and the InterQuartile Range(IQR) ranging from 0 to 21%. Among all soil properties, clay content, SOC, pH and CEC showed a significant negative correlation with RR, while sand and silt content were positively correlated with RR. Yield response was invariant with nitrogen application rate, biochar ash content and biochar pH. The highest pyrolysis temperature, biochar N and C/N ratio showed a significant negative correlation to RR, and higher biochar C content was significantly correlated with higher RR. A linear model analysis revealed a minor (not significant) association between feedstock and crop type with RR while no direct association was found between thermochemical technology and RR. The spatial distribution of probability of yield increase resulting from biochar application is shown in Figure 45. Regions known to have a high soil quality (e.g. Des Moines lobe in north central Iowa) showed a low probability of having a yield increase under all biochar scenarios. Our model indicates a high chance of a positive RR in areas with highly weathered soils (e.g. Eastern half of San Joaquin valley). Yield response was predicted to be most negative in areas with very high SOC, CEC or soil pH such as those found in north Texas and Minnesota (Figure 45). We estimated the total area where a >75% probability of yield increase is expected for each scenario as well as areas where this is expected to be <25%. This helped us identify the most and least responsive regions to biochar applications across the U.S. Total high probability areas for biochar application range from 8.4% to 30% of total cropland in the U.S. The WS15 (Biochar produced from hardwood biomass and applied at 15 Mg ha−1) was the scenario with the largest high probability area with 39.7 Mha, whereas, SF5 (Biochar produced from soybean stover biomass and applied at 5 Mg ha−1) resulted in the lowest high probability area with 11.2 Mha.

Figure 45: Estimated probability of yield increase for WS15 (left). Areas with more than 75% chance of yield increase for WS15 (right).

We selected the Central Valley of California to assess the model’s response to different soil properties under the CS15 scenario. The Central Valley is an agricultural region drained by the Sacramento and San Joaquin rivers, about 82 Km wide, and it extends 600km northwest from the Tehachapi Mountains to Redding. The southern part of the valley, also known as San Joaquin valley, is well-known for having highly variable alluvial soils ranging from very acidic-low organic matter soils to very alkaline-high organic matter soils. The BN model did surprisingly well capturing the essence of our general understanding of crop yield response to biochar on high and low-quality soils. Yield response was the weakest in west side of the San Joaquin valley, which is dominated by soils with high clay, pH, CEC and organic matter (Figure 46). On the other hand, the old, highly weathered soils on the east side of the valley, which have low organic matter, CEC and pH, showed the greatest response to biochar applications. The model’s response was not attributed to just one variable, as all soil properties contributed to the estimation of yield response (Figure 46). The total area with high probability of yield response to biochar application varied from 0.01 Mha for SF5 to 0.6 Mha for WS15, which is approximately 1% and 15% of total cropland in the Central Valley, respectively.

Figure 46: Spatial variation of soil properties and probability of a yield increase for WS15 scenario in the Central valley of California. SOM: Soil Organic Matter; CEC: Cation Exchange Capacity; P(RR>0):Probability of finding positive yield response to biochar application.

There are various economic aspects of biochar application such as expected revenue from applying biochar, cost of biochar application, and commodity price effects of biochar-induced yield increases in the long-run, or potential revenue from the provision of environmental services (e.g., increased carbon sequestration and/or reduction in nitrogen use). For this case study, we focus only on the increase in revenue at the county level, the long-run effects on commodity prices, and the availability of biochar feedstock. The differences in the expected yield increase between the 5 Mg ha−1 and the 15 Mg ha−1 application rate is not sufficient to justify the higher application rate and thus, we focus on the lower rate. To determine farmers’ willingness to pay for a ton of biochar, it is necessary to know the increase in revenue. Figure 47 shows the expected increase in revenue per hectare per ton of biochar applied for the scenarios CS5 (corn stover biochar at 5 Mg ha−1). Most of the revenue gains are observed in the southeast for corn and the Mid-Atlantic States for wheat. Note that those areas do not coincide with large corn and wheat acreage. The average increase in revenue across counties in Illinois, Indiana, and Iowa is $17.30 or $216 over 20 years with a 5% discount rate. For wheat and soybeans the revenue gains are more moderate compared to corn suggesting that the best use of biochar is its application in the Southeastern parts of the U.S. for highest returns.

Figure 47: Per year increase in revenue ($ ha−1) per ton of biochar applied for the CS5 scenario based on long-term (2022) price projections for corn, soybeans, and wheat.

If biochar is applied on a large scale on agricultural soils in the U.S., there will be long-term commodity price effects because supply is increasing thereby decreasing prices. The decrease in commodity prices from the expected yield increase is about 7.5% and 7.6% for soybeans and corn, respectively and 6.3% for wheat based on an economic simulation model for U.S. land-use change. Over all our Bayesian network model was trained using the data collected from 103 published studies reporting yield response to biochar. Our results showed an average 12% increase in crop yield from all the studies with a large variability ranging from -24.4% to 98%. Soil clay content, pH, cation exchange capacity and organic carbon appeared to be strong predictors of crop yield response to biochar. We also found that biochar carbon, nitrogen, and highest pyrolysis temperature significantly influence the yield response to biochar. Our large spatial-scale modeling revealed that from 8.4% to 30% of all croplands in the U.S, is expected to show a positive response to biochar application depending on the parent material and production engineering. It was found that biochar application to the areas with high probability of crop yield response just in the U.S. could offset a maximum of 2% of the current anthropogenic carbon emissions per year.

Khanna, M., D. Rajagopal, and D. Zilberman. 2019. Lessons Learnt From a Decade of Experience with Biofuels: Comparing Hype with Evidence. The Review of Environmental Economics and Policy. (invited paper under review). Since 2005, biofuel mandates, subsidies, and import tariffs have driven the U.S. to more than double its biofuel production and to overtake Brazil as the largest producer of ethanol. Multiple concerns have been raised about the unintended consequences of this growth. We review theoretical and empirical strains of literature on the impacts of biofuel on grain commodity prices, greenhouse gas emissions, liquid transportation fuel use and land use change. The experience with first-generation biofuels has provided several lessons that can aid the discourse about biofuels and biofuel policies in the future. First, the adverse impact of biofuels on agricultural commodity prices was exacerbated by several other factors, including low crop stocks, high energy prices and growing demand for food. However, over time, agricultural supply responded to higher prices by raising productivity and changing cropping patterns. Second, in the long run, crop acreage is fairly inelastic even with the high crop prices induced by biofuels over the last decade and thus the indirect land use change effect of biofuel can be expected to be small at least in aggregate although the magnitude of this effect may vary spatially. Third, the limited success of supply-side biofuel policies over the last decade also shows the need to design policies that induce demand for biofuels to match the growth in supply potential. Stimulating demand for higher blends requires addressing inefficiencies in the pass- through of the RIN subsidy to retail market, targeting incentives for purchase of higher ethanol- blends based on heterogeneous preferences for ethanol and investments in retail distribution infrastructure for higher ethanol blends. Fourth, technological development can be slow and may require learning-by-doing which makes the case for establishing technology-forcing policies over a long-time period. These policies need to be credible in the assurance they provide for the demand for biofuels in order to induce the capital investment needed for developing the industry. Recent changes in the RFS that have lowered volumetric mandates for ethanol, (particularly cellulosic ethanol) due to demand-side constraints and lack of supply. Miao et al. [52] show conditions under which non-waivable mandates are critical to induce investment in the advanced biofuel industry. If the goal is long term transformation of the fuel market, then enhancing demand for biofuels and enforcing mandates will be a more effective strategy than reducing volumetric targets which introduces uncertainty and discourages investment in biofuels. Unlike first generation biofuels that are based on food crops, second generation biofuels can be produced from crop residues and high yielding dedicated energy crops that can be grown on low quality land and mitigate the conflict with food crop production. They also offer much greater potential to reduce the carbon intensity of transportation fuel. However, policy makers may have overestimated the potential to accelerate their production through policies such as the RFS and tax credits. Policy support over a longer time horizon will be critical to induce their development and the establishment of a retail distribution infrastructure to enable both demand and supply of these biofuels. The application of new genetic engineering technologies, such as CRISPR, may enable breakthroughs that significantly lower their costs of production. Given the large number of options of feedstocks, biofuels and bioproducts, policies that are performance- based (such as a low carbon fuel standard) are more likely to be effective in inducing the development of lower carbon biofuels. Despite recent developments in electric vehicles, the imperative for biofuels as a renewable, low carbon fuel for the transportation sector continues to be strong, particularly in the aviation, long-distance trucking, and marine sectors where batteries face significant technical and economic barriers. Lawrinenko, M., J. (Hans) van Leeuwen, and D.A. Laird. 2017. Sustainable pyrolytic production of zerovalent iron. Sustainable Chemistry & Engineering. 5: 767–773. DOI 10.1021/acssuschemeng.6b02105. Pyrolysis of biorenewable feedstocks and iron oxides is potentially an economical and more sustainable pathway to producing zerovalent iron (ZVI) for environmental rehabilitation. The resulting biochar-zerovalent iron complexes (BC-ZVI) also shows improved remediation kinetics for dehalogenation of trichloroethylene over conventional ZVI. Understanding the transformations of iron to ZVI and the influence of feedstock chemistry on ZVI is critical to the production of BC-ZVI and has not been reported previously. Here we evaluate the effects of highest pyrolysis temperatures (HTTs of 700 and 900 °C) under a 40 mL·min−1 N purge and various biomass feedstocks (cellulose, corn stover, dried distillers’ grain, red oak, and switchgrass) pretreated with FeCl3 as a one-step BC-ZVI production process.

Figure 48: X-ray diffraction patterns of fresh BC-ZVI composites produced from various FeCl3 treated feedstocks at 700 and 900°C. The following mineral phases were identified in the biochars: C = calcium oxide, Z = ZVI, Q = quartz, M = magnetite, S = sylvite, B = barringerite, Sc =schreibersite, F = fayalite, K = K2Fe2P8O24.

Pyrolysis temperature and feedstock composition influenced the Fe mineralogies that formed in biochar during the pyrolysis reaction. Reduction of ferric Fe to ferrous Fe and/or ZVI was achieved to different degrees depending on associated anions, the thermal stability of lattice structures, and overall reducing conditions that were present during pyrolysis. Successful formation of ZVI was achieved for biochars produced from cellulose, corn stover, red oak, and switchgrass pyrolyzed at 900 °C HTT as indicated by the 110, 200, and 211 XRD reflections of ZVI depicted in Figure 48. Magnetite was the dominant form of Fe in biochars produced at 700 °C HTT. However, some evidence of ZVI formation was also observed for biochars produced from cellulose, corn stover, and red oak pyrolyzed at 700 °C HTT. No evidence for ZVI was observed for biochar produced from switchgrass at 700 °C HTT. The XRD results show no evidence of ZVI formation during pyrolysis of FeCl3 treated DDG at either 700 or 900 °C HTT. In general, the XRD results indicate more Fe reduction with greater transformation to ZVI at 900 °C than 700 °C HTT; however, the results also show that feedstock properties influence the formation of ZVI during pyrolysis. Fayalite forms due to coordination of ferrous iron with silicate and forms a solid solution with ZVI. Phase diagrams of iron illustrate the relationships between ZVI and oxides of Fe as a function of temperature, pressure, and O2 fugacity. Wüstite (FeO) is a metastable transition phase of Fe that dominates at high temperatures over a range of O2 fugacity at low pressure. Fayalite and magnetite form in conjunction with ZVI due to the solubility of these mineralogies with the wüstite phase, which forms at high temperatures. Instability of wüstite at low temperature, pressure, and oxygen fugacity drives phase transformation to ZVI and magnetite. Exsolution of fayalite and magnetite phases from solid solution with wüstite occurs during cooling and depends on the stoichiometry and long range crystalline order of wüstite. The relative amounts of fayalite, magnetite, and ZVI that formed in the production of BC-ZVI are thus controlled by Si-content and the abundance of oxidizing gases such as O2 in association with wüstite at high temperatures. The absence of oxidants regulates the stoichiometry of wüstite, which transforms to ZVI and magnetite upon cooling. Thus, low O2 and low Si-content of the feedstock favor a higher yield of ZVI. Biochar derived from DDG was rich in P and K, with control 700 °C HTT biochar containing almost 22.5% P and 55% K by mass. As a result, most of the Fe in the FeCl3 treated DDG biochar was associated with these elements, and there was no evidence of ZVI in either the 700 or 900 °C HTT DDG biochars. Rather the XRD patterns for the 700 °C HTT DDG biochar exhibited evidence for iron carbide (Fe3C), goethite (α-FeO(OH)), potassium iron phosphate (K2Fe2P8O24), and barringerite (Fe2P). Similarly, 900 °C HTT DDG biochar exhibited diffraction evidence for Fe3C, α-FeO(OH), Fe2P, and schreibersite (Fe3P). A transition occurred in heating from 700 to 900 °C. K2Fe2P8O24 which formed in biochar at 700 °C HTT thermally decomposed at higher temperatures yielding Fe3P, potassium oxide (K2O), and sylvite (KCl) in 900 °C HTT DDG biochar. Thus we conclude that pyrolysis at 900 °C effectively reduced Fe to ZVI with most feedstocks; however, the association of silicon (Si) and phosphorus (P) with Fe results in formation of fayalite and Fe phosphates and phosphides, which limited ZVI production efficiency and/or facilitated corrosion of ZVI. Dispersion of ZVI phases on biochar surfaces and association with Si facilitated oxidation of ZVI due to greater accessibility to oxygen and enhanced corrodibility of ZVI in association with fayalite. Feedstocks low in Si and P such as cellulose and red oak yield BC-ZVI suitable for environmental applications.

Lawrinenko, M., D. Jing, C. Banik, D.A. Laird. 2017. Aluminum and iron biomass pretreatment impacts on biochar anion exchange capacity. Carbon 118: 422-430. DOI: 10.1016/j.carbon.2017.03.056 Some biochars have significant anion exchange capacity (AEC) under acidic pH conditions but typically have little or no AEC at neutral to alkaline pHs. We hypothesized that metal oxyhydroxide surface coatings on biochar will increase biochar anion exchange capacity (AEC) at higher pHs due to surface coatings of the high point of zero net charge of metal oxyhydroxides. Biochars were prepared from alfalfa meal, corn stover, and cellulose; which were wetted with FeCl3 or AlCl3 at 1% weight of metal to weight of biomass (500 g) using 1 M stock solutions of these metals. Additional water, up to 1 L, was added to sufficiently wet the biomass, and the resulting slurries were thoroughly mixed to uniformly distribute the metal, dried in a convection oven at 105°C for at least one day and subsequently slow pyrolyzed using a programmable muffle furnace that heated to highest treatment temperatures (HTTs) of 500°C and 700°C under N2 purge. Control biochars were produced from the same untreated biomasses. Chemical analyses were used to quantify AEC and point of zero net charge (PZNC), and spectroscopic (FTIR, XPS, and SEM-EDS) studies provided evidence for the formation of Al-O- C organometallic moieties on biochar surfaces that formed during pyrolysis.

Table 7: Effects of pyrolysis temperature, feedstock, pH and metal pre-treatments on specific surface area, anion exchange capacity (AEC), and point of zero net charge (PZNC) of biochars. AEC reported as mean (standard deviation).

Table 7 presents AEC, PZNCs, and specific surface area (SSA) of biochars prepared from alfalfa meal, corn stover, and cellulose. AEC was higher for biochar controls (no metal pretreatments) produced at 700°C than 500°C and AEC of biochar controls increased significantly (p < 0.0001) with decreasing pH. PZNCs of controls also increased with HTT, consistent with greater oxonium heterocycle content and AEC at pH 8. The 700°C HTT biochars produced from Al-treated biomass exhibited greater AEC relative to biochar controls (except 700°C cellulose biochar at pH 6). The AECs of the 700 °C HTT Fe biochars were also higher than the controls at pH 8, but mixed results for these biochars were obtained at pH 4 and 6. PZNC increased for both metal treatments relative to the controls for the 700°C but not the 500°C HTT biochars. Scanning electron microscopy and energy dispersive X-ray (SEM-EDS) elemental mapping were used to investigate the distribution of Fe, Al, O, and exchangeable halides (Cl- or Br-) on surfaces of the metal-treated biochars (Figure 49). Al and O were diffusely distributed on surfaces of the 700°C HTT Al cellulose biochar, although both elements were relatively enriched on the left side of the specimen shown in Figure 49. The light gray shading evident on the left side of the SEM micrograph reflects the greater electron density of Al and O relative to C, the dominant element in the cellulose biochar. The atomic distribution of Cl- indicates the distribution of AEC sites on the biochar surfaces. Cl- is diffusely distributed over all surfaces, but appears slightly more concentrated on the left side of the specimen where Al and O are also concentrated. A bright spot evident in the upper left hand corner of the Al and O elemental maps suggests the presence of a discrete aluminum oxyhydroxide phase. This particle is also clearly evident as a bright spot on the Cl- map, indicating that this particle had higher AEC than other regions in the sample.

Figure 49: SEM micrograph and EDS elemental maps of 700 °C HTT biochar prepared from Al3+-treated (Left) and Fe3+-treated (right) cellulose.

Iron and O were present in both discrete particles and diffuse coatings on surfaces of the 700°C HTT Fe-cellulose biochar (Figure 49). The discrete particles are evidenced by white spots on the SEM micrograph and corresponding bright spots on the Fe and O elemental maps. Cl-, which indicates the distribution of AEC sites, is associated with all biochar surfaces, but is relatively enriched along with Fe and O on a large rectangular biochar particle on the left side of the image. Cl-, however, was not concentrated on the discrete particles suggesting that AEC was not exclusive to the Fe phases. By contrast, Fe was diffusely spread over surfaces of the 700°C HTT Fe alfalfa biochar, suggesting that feedstock properties influence the formation of discrete versus diffuse Fe phases on biochar surfaces during pyrolysis. Pyrolysis of biomass pre-treated with Al or Fe trichlorides yielded various forms of these metals in the resulting biochars. Broad distributions of Al and Fe are observed, with greater dispersion resulting from covalent bonding between these metals and biochar surfaces. Iron 0 presented a range of oxidation states in biochar, yielding Fe , magnetite, and γ-Fe2O3; illustrating a range of reducing conditions during pyrolysis that is related to feedstock. XRD and SEM-EDS analyses demonstrate a heterogeneous distribution of Fe in biochar including large crystalline aggregates and dispersed Fe oxides, while Al was dominantly dispersed as amorphous oxide phases. Despite internal structure of metal phases, their external surfaces are oxides as shown by SEM-EDS and XPS analyses (Figure 49). The distribution and mineralogy of Al and Fe strongly influence biochar surface chemistry and resulting AEC. Multiple mechanisms contribute to biochar AEC. The pH dependent AEC in control biochars is attributed to protonated pyridinium moieties and basal planes of condensed aromatic C, whereas pH-independent AEC is attributed to oxonium heterocycles. Metal oxyhydroxides form by hydration and subsequent reaction of metal oxides with water and thus formed on the Al and Fe oxide phases produced in biochars derived by the metal treatments. For octahedrally coordinated trivalent Al and Fe with six ligands, the formal charge on exposed non-bridging OH ligands is -1/2 while H2O ligands will carry a formal +1/2 charge. At pHs below PZNC, Fe and Al oxyhydroxides become increasingly protonated, accruing positive charge, thereby exhibiting pH-dependent AEC. The coordination number of Al and Fe covalently bonded to biochar surfaces and the relative ionic character of metal-OH/H2O bonds are likely to vary depending on the local chemical environment, which will influence the PZNC and AEC. The study demonstrates the formation of diffuse Al-O-C and Fe-O-C organometallic moieties on biochar surfaces during pyrolysis and that these organometallic moieties increase the AEC of biochar with relatively high points of zero net charge. Such biochars have the potential to remove and retain anionic contaminants form water and to retain and be a slow release source of anionic nutrients in soils. High AEC biochars are a potential value-added biochar product for vanguard PBBP plants.

Lawrinenko, M., Z. Wang, R. Horton, D. Mendivelso-Perez, E. Smith, T. Webster, D.A. Laird, J. (Hans) van Leeuwen. 2017. Macroporous carbon supported zerovalent iron for remediation of trichloroethylene. ACS Sustainable Chemistry & Engineering. 5:1586-1593. DOI: 10.1021/acssuschemeng.6b02375 Groundwater contamination with chlorinated hydrocarbons has become a widespread problem that threatens water quality and human health. Permeable reactive barriers (PRBs), which employ zerovalent iron, are effective for remediation; however, a need exists to reduce the economic and environmental costs associated with constructing PRBs. We present a method to produce zero valent iron supported on macroporous carbon using only lignin and magnetite. Biochars were prepared by slow pyrolysis of lignin (control biochar) or lignin−magnetite mixtures in a stainless steel box contained in a muffle furnace and heated from ambient temperature to 900 °C over 4 h. During pyrolysis and throughout cool-down, samples were −1 purged under 200 mL min N2 gas. Lignin was supplied by Archer Daniels Midland Corporation. This lignin was a coproduct of their Acetosolv process, a modified organosolv process, which is used to extract cellulose from corn stover for fuel-ethanol production. Magnetite was ore grade powder obtained from the Division of Lands & Minerals, Minnesota Department of Natural Resources and was dried in a convection oven prior to use. Lignin was ground in a mortar and pestle. Lignin and magnetite mixtures of 50/50 or 30/70 gravimetric ratios were mixed and immediately pyrolyzed (unpressed sample). Pressed samples were prepared by compression molding of lignin−magnetite mixtures in a 4 in. ID cylindrical aluminum compression mold preheated to 180 °C under 20 ton pressure (about 239 MPa), after which the resulting pellet was pyrolyzed (pressed samples). As pyrolysis yielded large intact solid loaves, resulting biochars were cut and screened between #4 and #12 screens (1.68 to 4.76 mm). This granule fraction was utilized for all analyses. Breakthrough curves of aqueous TCE were measured using stainless steel 22 mm ID × 100 mm columns (Alltech) packed with biochar granules using a modified influent pulse method. The pressed 50/50 BC-ZVI and the biochar control materials were used for this study. Two flow rates were employed, representative of high (12.2 mm min−1) and average (5.6 mm min−1) groundwater pore water velocities. These flow rates are reported as “fast” and “slow” in the results. Milli-Q water was used for preparation of TCE (>99% Alfa-Aesar, Lot# X17A014) solutions and breakthrough curve measurements. Water was delivered by peristaltic pump and one-pore volume of pulses 50 mg L−1 TCE were injected by a programmable syringe pump equipped with a stainless steel syringe. Assay of pulse samples taken prior to and after pulse was averaged to determine influent∼ TCE concentration. Both pumps were calibrated to deliver equal flow rates. Columns were initially purged with five pore volumes of water to thoroughly displace air from biochar prior to breakthrough curve measurements. Column effluent was passed through stainless steel tubing and sequential samples were collected in 1.2 mL gas chromatography (GC) sample vials and immediately sealed without headspace using vial caps equipped with Teflon seals. TCE and degradation byproducts were assayed in accordance with EPA Method 524.218 using a purge and trap GC (Varian CP 3800) equipped with a Tekmar Dorhmann 25 mL purge vessel, Varian Saturn 2200 mass spectrometer, and Restek RTX VMS 60 m x 0.32, 0.32 mm ID GC column. Biochar-ZVI (BC-ZVI) produced by this method exhibits a broad pore size distribution with micrometer sized ZVI phases dispersed throughout a carbon matrix (Figure 50). X-ray diffraction revealed that pyrolysis at 900 °C of a 50/50 lignin−magnetite mixture resulted in almost complete reduction of magnetite to ZVI and that compression molding promotes iron reduction in pyrolysis due to mixing of starting materials. High temperature pyrolysis of lignin yields some graphite in BC-ZVI due to reduction of carbonaceous gases on iron oxides. Breakthrough curves for TCE through a column packed with BC-ZVI and control Figure 50: SEM micrographs of pressed biochar were used to evaluate interactions BC-ZVI and control biochar. Lighter between BC-ZVI and a model chlorinated regions in the 5000× micrographs are Fe hydrocarbon, TCE. The relative concentration phases. maxima in the breakthrough curves of BC-ZVI (Figure 51) were significantly smaller than the relative concentration maxima of the control. Compared to the control biochars, substantially less TCE was transported through the BC-ZVI material, with 96% and 99% removal achieved at the fast and slow flow rates, respectively. As BC-ZVI has little C relative to the control biochar, we can deduce that degradation rather than adsorption is the main cause for the low relative concentrations in the BC-ZVI breakthrough curves. The relative TCE concentrations in the fast-flow breakthrough curve are larger than those in the slow-flow Figure 51: TCE Breakthrough curves of control and BC-ZVI and model parameters. breakthrough curve for both control and BC-ZVI treatments. Fast flow rates reduce the available time for TCE to interact with the biochar granules, which limits adsorption and degradation. At slower flow rates, TCE transport has a larger diffusion component which promotes adsorption and degradation. The results of this study demonstrate that TCE was removed from water as it passed through a column packed with BC-ZVI at flow rates representative of average and high groundwater flow. One dimensional convection−dispersion modeling revealed that adsorption by biochar influences TCE transport and that BC-ZVI facilitated removal of TCE from contaminated water by both adsorption and reductive dehalogenation.

Bakshi, S., C. Banik, S.J. Rathke, D.A. Laird. 2018. Arsenic sorption on zero-valent iron-biochar complexes. Water Research 137:153-163. doi.org/10.1016/j.watres.2018.03.021 Arsenic (As) is toxic to humans and is often found in drinking water in many parts of the world due to the presence of arsenides in aquifer sediments. Various procedures for removal of As from contaminated drinking water (CDW) have been developed, such as precipitation, sorption, ion exchange and membrane separation. However, there is a critical need for low-cost As removal technology that can be deployed in poor and rural communities in India and Bangladesh where As has adversely affected the health of 50 million people. Here we studied the effectiveness of zero-valent iron-biochar (ZVI-biochar) complexes produced by high temperature pyrolysis of biomass and magnetite for removing As5+ from CDW. We used batch equilibrium, batch kinetic, and column leaching studies to investigate As5+ sorption on ZVI-biochars, and X-ray diffraction, scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), and X-ray photoelectron spectroscopy (XPS) to characterize the reaction products and determine reaction mechanisms. Two ZVI-biochars were prepared by first mixing <0.5 mm red oak (RO) and switchgrass (SG) biomass feedstocks with <0.5 mm magnetite ore and the slow pyrolyzing the mixtures at 900°C under a N2 purge. The batch equilibration (48 h) study indicated substantial sorption of As on the ZVI-biochar complexes (4.54 to 15.58 mg g-1) and that As sorption was well described (R2 = 0.98) by the Langmuir isotherm model (Table 8). The results indicate that the sorption maximum was greater for the ZVI-RO than for the ZVI-SG and that sorption maximum increased with decreasing amount of ZVI-biochar complex in the solution. By contrast, the RO and SG biochars (controls no ZVI) yielded maximum As (V) adsorption capacities of 1.42 and 1.15 mg g-1, respectively (data not shown). The RO + magnetite, and SG + magnetite, and magnetite only controls exhibited a similar response to the biochar only controls with maximum As (V) adsorption capacities of <1.6 mg g-1 for all cases. Results for the controls indicate that neither biochar alone nor magnetite alone nor physical mixtures of biochar and magnetite are effective for As (V) sorption under our experimental conditions.

Table 8: Langmuir isotherm and pseudo 2nd order kinetic model parameters for As sorption onto ZVI-biochar complexes. Results of the batch kinetic study showed rapid sorption of As onto ZVI-biochar complexes. The % As removed after 48 h for the 0.05 g and 0.2 g loading treatments was 59.4% and 69.1% for ZVI-RO and 24.6% and 57.8% for the ZVI-SG, respectively. By contrast, controls consisting of biochar alone, magnetite alone and physical mixtures of biochar and magnetite removed only 4-11% of As (V) from the solution after 48 h (data not shown). Only a weak linear relationship was found between the amount of ZVI and the As sorption capacity (R2 = 0.81 for ZVI-RO vs. 0.65 for ZVI-SG). As sorption occurred rapidly during the first 4 h and more slowly thereafter, reaching equilibrium after about 48 h.

Figure 52: Changes in As concentration in leachates (a) and cumulative amount of As sorbed (b) with leaching volume in the column leaching study for ZVI-biochar complexes and pristine biochars. Conditions for the column leaching: influent concentration of As is 1000 mg L-1, influent volume each time is 50 mL.

Results of the column study showed that ZVI-biochar complexes are effective for removing some but not all As from drinking water in a leaching column. The cumulative % As removal decreases with time for all ZVI-biochar and control columns, however, the ZVI-biochar columns were substantially more effective for removing As than the control columns (Figure 52). The ZVI-RO sorbed 44.8% of the added As at the end of the 16 leaching events, whereas the ZVI-SG sorbed 36.3% of the added As. Moreover, the cumulative % As removal for ZVI-RO decreased from 81.8% to 44.8% over the course of 16 leaching events with 50 mg As additions per event, whereas the cumulative % As removal for ZVI-SG decreased from 50.97% to 36.3%. The As concentrations were much lower in leachate from the ZVI-biochar columns compare with leachate from the biochar-only columns. The concentration of As in leachates from the ZVI-RO and ZVI-SG columns reached 0.76 and 0.74 mg L-1, respectively, after 16 leaching events (800 mL), whereas As concentrations in leachate from the pristine RO and SG biochar columns reached 0.9 mg L-1 after the 2nd leaching event. For control columns, the RO and SG biochars initially removed only 12.5% and 10.6% of As, however the removal % decreased to 2.72 and 2.2%, respectively, at the end of 16 leaching events. The RO + magnetite, SG + magnetite and magnetite only columns (data not shown) showed a similar response to biochar control columns. Overall after addition of 800 mg of As in 16 column leaching events, 1 g of ZVI-RO removed 358.5 mg of As (44.8% efficient) and 1 g of ZVI-SG removed 290.98 mg of As (36.3% efficient); whereas the RO and SG controls (biochar only) removed only 20.6 and 16.13 mg of As, respectively. Figure 53: SEM-EDS analysis of ZVI-RO (a) and ZVI-SG (b) after batch equilibration of 0.05 g ZVI-biochar complexes with 25 mg L-1 of As solution for 48 h.

The SEM-EDS analysis was done for samples of the ZVI-RO and ZVI-SG complexes (Figure 53a and 53b, respectively) recovered after the 48 h batch equilibration study to investigate the distribution of C, O, Fe, Si and As. The light gray color on the right side of the ZVI-RO SEM image (Point 1 in Figure 53a) indicates an area of high electron density and the EDS analysis of this region indicates 70% Fe, 23% O and 6% C (EDX data are given as atom%) with negligible As. We infer that light gray region represented by point 1 is a residual ZVI particle, perhaps with a surface oxyhydroxide coating. The stoichiometry of Point 1 may be contrasted with that of Point 2 which has 51% O, 26% Fe, 16.7% C and 2.76% As. The stoichiometry of Point 2 is consistent with an FeOOH phase, which neoformed as ZVI was being oxidized. The light gray electron dense region represented by Point 2 appears to be crust or surface coating that formed on top of a dark porous biochar particle evident at the bottom center of the SEM micrograph. Point 3 in the upper left corner of the SEM in Figure 53a has a stoichiometry of 50% O, 18.6% Si, 8.7% Fe, and 11.8% C, which suggests that it is a mixture of silicate phases, possibly quartz and/or fayalite. While As is diffusely present on all surfaces it is clearly concentrated in the newly formed FeOOH phase (Point 2) and is not significantly associated with the ZVI or silicate rich phases. Figure 53b shows a similar SEM-EDS analysis for a sample of the ZVI-SG complexes recovered from the batch equilibration experiment. Point 1 in Figure 53b has a stoichiometry (54.8% Fe and 40.8% O) which is roughly equivalent with Wustite (FeO) and contains negligible As. While Points 2 (50.9% O and 31.3% Fe) and 3 (56.5% O and 28.0% Fe) have stoichiometry's more consistent with FeOOH and contain 2.6 and 3.9% As, respectively. Thus, the SEM-EDS data for both ZVI-RO and ZVI-SG indicate that As is selectively adsorbed in the newly formed FeOOH phase and not on ZVI, FeO, or Fe/Si phases. XRD analysis of the original ZVI-RO and ZVI-SG biochar complexes show only small residual magnetite peaks and prominent α-Fe (ZVI) peaks, indicating that much of the Fe in the magnetite was reduce to ZVI during pyrolysis (Figure 54). The XRD pattern for the original ZVI-SG biochar also has small peaks for wustite (FeO), while the XRD peaks for the original 2+ ZVI-SG biochar has peaks for both fayalite (Fe 2(SiO4)) and wustite (FeO). There was no evidence of either fayalite or wustite in the XRD patterns of the magnetite ore, hence these phases must have formed during pyrolysis. The 44.9° 2Θ α-Fe peak for the ZVI-SG biochar is about half as intense as the 44.9° 2Θ peak in the XRD pattern for the ZVI-RO biochar. This result indicates that a greater fraction of the magnetite Fe was recrystallized as fayalite and wustite relative to α-Fe during pyrolysis of the high ash switchgrass; whereas a greater fraction of the magnetite Fe was recrystallized as α-Fe during pyrolysis of the low ash red oak biomass. Thus, the results show that feedstock properties influence the Fe mineralogy and the production of ZVI during the co-pyrolysis of biomass feedstocks with magnetite iron ore. Much of the α-Fe in the ZVI-biochar complexes was transformed into other phases during the batch equilibration and column leaching studies as evidenced by the decrease in intensity of the 44.9° 2Θ α-Fe XRD peaks (Figure 54). The XRD pattern for samples collected from the ZVI-RO biochar columns after leaching show prominent peaks for goethite (α-FeOOH), akaganeite (β-FeOOH), wustite, and magnetite; while the XRD patterns for the ZVI-RO biochar samples collected from the batch equilibrium study indicate the presence of goethite and lepidocrocite (γ-FeOOH), wustite, and magnetite. For the ZVI-SG biochar, XRD patterns for samples from the column study show peaks for goethite, fayalite, wustite, and magnetite, while XRD patterns for samples from the batch study show peaks for lepidocrocite, fayalite, wustite, and magnetite. The results indicate that the iron oxyhydroxide phases formed during the column leaching and batch equilibration studies were influenced by both feedstock and conditions during the oxidation reactions.

Figure 54: XRD patterns for the ZVI-RO (a) and ZVI-SG (b) complexes. Shown are XRD patterns for the “original” (as prepared) ZVI-biochar samples, and XRD patterns for samples collected after the “batch” equilibration and “column” leaching studies.

Overall our study demonstrates that ZVI-biochar complexes produced by high temperature pyrolysis of low ash biomass feedstocks and magnetite are effective for removing As5+ from contaminated drinking water in the studied pH range (pH~7-7.5) and in presence of competing ions. As is removed from water by two mechanisms in the presence of ZVI: a) Simultaneous reduction of As5+ to As3+ and oxidation of Fe0 to Fe3+ and the co-precipitation of As3+ and Fe3+ leading to the formation of Fe1-x(Asx)OOH onto biochar surfaces. And b) adsorption of As on preexisting FeOOH mineral surfaces followed by intra-particle diffusion. The co-precipitation (batch sorption and kinetics study here) mechanism is more rapid than surface adsorption and intraparticle diffusion mechanism. As is dominantly sorbed by FeOOH minerals (goethite, akaganeite, and lepidocrocite) and only weakly sorbed by wustite, fayalite, and ZVI in ZVI biochars. The production of ZVI-biochar using a high ash biomass (here SG) leads to greater formation of fayalite and wustite at the expense of ZVI and hence biochar-ZVI complexes that are less effective for removal of As than using a low ash biomass feedstock (here RO).

Conclusions The Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) concludes that most scenarios for limiting global warming to 2°C require large scale deployment of technology that removes CO2 from the atmosphere during the second half of this century [1]. BioEnergy with Carbon Capture and Storage (BECCS) is listed prominently as an option to reach this objective along with other carbon negative technologies including afforestation, enhancing wetlands, soil conservation, accelerated rock weathering, direct air capture of CO2 with geologic sequestration, increasing ocean alkalinity and soil biochar applications in The Emissions Gap Report [7] produced by the United Nations Environment Programme in 2017. The Emissions Gap Report lists emission reduction potential for biochar as 0.2 Gt-CO2-e per year by 2030; the report, however, considers biochar production and sequestration potential as separate from bioenergy production. The Emissions Gap Report indicates that traditional BECCS has an estimated potential to reduce emissions by 2 and 18 Gt-CO2-e per year. More recently the Negative Emissions Technologies and Reliable Sequestration report published by the U.S. National Academy of Sciences in 2018 recognizes that soil biochar applications can be both independent of, and in conjunction with, bioenergy production and thus considers the PBBP to be a form of BECCS [9]. The upper limit for both traditional BECCS and PBBP is set by the supply of sustainably harvestable biomass. The CO2 capture efficiency for traditional BECCS is higher than that of PBBP because all CO2 emissions from the combustion of biomass are ideally captured and sequestered with BECCS. Hence, BECCS CO2-e emissions are limited to that generated during plant construction and biomass harvesting and transport. By contrast, biomass C is assumed to be partitioned between CO2 emitted during the pyrolysis process, liquid fuels, which will eventually be burned releasing CO2, and biochar which sequesters the C in soils. Hence, traditional BECCS has the advantage of sequestering a larger fraction of harvested biomass C. While PBBP has the advantage of enhancing soil quality and thereby increasing the amount of biomass that can be sustainably harvested and of generating liquid fuels, which are critically needed for the transportation sector of the economy. Laird et al. [53] estimated the global potential of the PBBP to be between 3.2 and 6.6 Gt CO2-e per year, which is partitioned between liquid biofuels offsetting fossil fuels and soil biochar C sequestration. This estimate is similar in magnitude to the 2010 estimates by Woolf et al. [10], and the 2 to 18 Gt-CO2-e per year emissions reduction potential attributed to traditional BECCS in the The Emissions Gap Report [7]. Although there is a potential for competition between traditional BECCS and PBBP for biomass feedstock there are also opportunities for synergisms by deploying and integrating both systems. This project has significantly advanced PBBP towards industrial deployment. We have done so by building tools to predict crop and environmental responses to soil biochar applications, and to identify where in the U.S. there is a high probability of crop yield response to biochar applications. Our research has made possible for the first time regional, national and even global scale assessments of PBBP scenario impacts, including indirect land use effects, on net C sequestration, GHG emissions, crop yields, and nutrient leaching. We have provided techno- economic assessments of various fast pyrolysis plants scenarios and we have developed low-cost methods of producing value-added biochar products, which may help the biochar industry during early stages of development. Ultimately, however, the scaling of the PBBP will be difficult as long as bioenergy products must compete with petroleum products while the environmental costs of using fossil fuels are externalized and the environmental benefits of using PBBP products are discounted. When policies and regulation are eventually implemented that significantly incentivize the use of low carbon and carbon negative fuels and soil carbon sequestration, the PBBP will be in a position to scale rapidly as a low-cost option for removing CO2-C from the atmosphere.

Publications

Refereed publications: 1. Aller, D., S. Archontoulis, D. Laird. 2019. Soil organic matter and biochar effects on soil water parameters: measurements and modeling. (In preparation for submission). 2. Li, W., Dumortier, J., Dokoohaki, H., Miguez, F.E., Brown, R.C., Laird, D., Wright, M.M. 2019. Regional techno-economic and life cycle analysis of the pyrolysis-bioenergy-biochar platform for carbon-negative energy. (under review by Biofuels, Bioproducts, and Biorefining) 3. Dumortier, J., H. Dokoohaki, A. Elobeid, D.J. Hayes, D. Laird, and F. Miguez. 2019. Biochar-induced yield increases and its effects on global carbon emissions and land-use. (Under review by Renewable & Sustainable Energy Reviews). 4. Barrett, C. Reardon, T., Swinnen, J., and Zilberman. D. 2019. Structural Transformation and Economic Development: Insights from the Agri-food Value Chain Revolution. Journal of Economic Literature (invited paper under review). 5. Khanna, M., Rajagopal, D., and Zilberman, D. 2019. Lessons Learnt From a Decade of Experience with Biofuels: Comparing Hype with Evidence. The Review of Environmental Economics and Policy. (invited paper under review). 6. Dokoohaki, H., F. Miguez, D. Laird, J. Dumortier. 2019. Where should we apply biochar? Environmental Research Letters. (in Press). 7. Fidel, R.B., D.A. Laird, T.B. Parkin. 2019. Effect of Biochar on Soil Greenhouse Gas Emissions at the Laboratory and Field Scales. Soil Systems (in Press). 8. Chao, L., D.J. Hayes, K.L. Jacobs. 2018. Biomass for bioenergy: Optimal collection mechanisms and pricing when feedstock supply does not equal availability. Energy Economics Volume 76:403-410. 9. Aller, D., S. Archontoulis, W. Zhang, W. Sawadgo, D. Laird, K. Moore. 2018. Long term biochar effects on corn yield, soil quality and profitability in the US Midwest. Field Crops Research 227:30-40. 10. Bakshi, S., C. Banik, S.J. Rathke, D.A. Laird. 2018. Arsenic sorption on zero-valent iron-biochar complexes. Water Research 137:153-163. doi.org/10.1016/j.watres.2018.03.021 11. Bakshi, S., C. Banik, D.A. Laird. 2018. Quantification and characterization of chemically-and thermally labile and recalcitrant biochar fractions. Chemosphere 194 (2018) 247-255. doi.org/10.1016/j.chemosphere.2017.11.151 12. Banik, C., M. Lawrinenko, S. Bakshi and D.A. Laird. 2018. Impact of pyrolysis temperature and feedstock on surface charge and functional group chemistry of biochars. J. Env. Qual. 47: 452-461. 13. Li, W., Ghosh, A., Bbosa, D., Brown, R., and Wright, M. M. 2018. Comparative techno-economic, uncertainty and life cycle analysis of lignocellulosic biomass solvent liquefaction and sugar fermentation to ethanol. ACS Sustainable Chemistry & Engineering, 6(12), 16515–16524. 14. Dokoohaki, H. F.E. Miguez, S. Archontoulis, D. Laird. 2018. Use of inverse modelling and Bayesian optimization for investigating the effect of biochar on soil hydrological properties Agricultural Water Management 208: 268-274. 15. Fidel, R.B., D.A. Laird, K.A. Spokas. 2018. Sorption of ammonium and nitrate to biochars is electrostatic and pH-dependent. Scientific reports 8 (1), 17627. 16. Aller, D., R. Mazur, K. Moore, R. Hintz, D. Laird, and R. Horton. 2017. Biochar Age and Crop Rotation Impacts on Soil Quality. Soil Science Society of America Journal. 81:1157-1167. 17. Aller, D., S. Rathke, D. Laird, R. Cruse, and J. Hatfield. 2017. Impacts of fresh and aged biochars on plant available water and water use efficiency. Geoderma 307:114-121. http://dx.doi.org/10.1016/j.geoderma.2017.08.007 18. Li, W., Qi Dang, R.C Brown, D. Laird, M.M Wright. 2017. The impacts of biomass properties on pyrolysis yields, economic and environmental performance of the pyrolysis-bioenergy-biochar platform to carbon negative energy. Bioresource Technology. 241:959-968. doi.org/10.1016/j.biortech.2017.06.049 19. Fidel, R.B., D.A. Laird, and T.B. Parkin. 2017. Impact of biochar organic and inorganic C on soil CO2 and N2O emissions. J. Env. Quality 46:505-513. doi:10.2134/jeq2016.09.0369. https://dl.sciencesocieties.org/publications/jeq/pdfs/46/3/505 20. Dokoohaki, H., F. Miguez, D.A. Laird, R. Horton and A. Basso. 2017. Assessing the biochar effects on selected physical properties of a sandy soil: An analytical approach. Communications in Soil Science and Plant Analysis. 48:1387-1398. doi.org/10.1080/00103624.2017.1358742. 21. Aller, D., S. Bakshi, D.A. Laird. 2017. Modified method for proximate analysis of biochars. Journal of Analytical and Applied Pyrolysis. 124:335-342. doi.org/10.1016/j.jaap.2017.01.012 22. Brewer, C.E. E. Hall, J. Rudisill, K. Schmidt-Rohr, D.A. Laird, R.C. Brown, K. Zygourakis. 2017. Temperature and Reaction Atmosphere Oxygen Effects on Biochar Properties. Environmental Progress & Sustainable Energy. DOI: 10.1002/ep.12503 23. Fidel, R., S. Archontoulis, B. Babcock, R.C. Brown, H. Dokoohaki, D. Hayes, D.A. Laird, F. Miguez, and M.M. Wright. 2017. Commentary on ‘Current economic obstacles to biochar use in agriculture and climate change mitigation’ regarding uncertainty, context-specificity and alternative value sources. Carbon Management. DOI: 10.1080/17583004.2017.1306408 24. Fidel, R.B. D.A. Laird, and T.B. Parkin. 2017. Impact of six lignocellulosic biochars on C and N dynamics of two contrasting soils. GCB Bioenergy. DOI: 10.1111/gcbb.12414 25. Fidel, R.B., D.A Laird, M.L. Thompson, and M. Lawrinenko. 2017. Characterization and quantification of biochar alkalinity. Chemosphere 167:367-373. DOI: 10.1016/j.chemosphere.2016.09.151 26. Graber, E.R., Tsechansky, L., Fidel, R.B., Thompson, M.L., Laird, D.A. 2017. Determining Acidic Groups at Biochar Surfaces via the Boehm Titration. In: Singh, B., Camps-Arbestain, M., Lehmann, J. (Eds). Methods of Biochar Analysis. CSIRO Publishing, Melbourne, Chapter 8. 27. Laird, D.A., J.M. Novak, H.P. Collins, J.A. Ippolito, D.L. Karlen, R.D. Lentz , K.R. Sistani, K. Spokas, R.S. Van Pelt. 2017. Multi-year and multi-location soil quality 1 and crop biomass yield responses to hardwood fast pyrolysis biochar. Geoderma. 289: 46-53. 28. Lawrinenko, M., Z. Wang, R. Horton, D. Mendivelso-Perez, E. Smith, T. Webster, D.A. Laird, J. (Hans) van Leeuwen. 2017. Macroporous carbon supported zerovalent iron for remediation of trichloroethylene. ACS Sustainable Chemistry & Engineering. 5:1586-1593. DOI: 10.1021/acssuschemeng.6b02375 29. Lawrinenko, M., D. Jing, C. Banik, D.A. Laird. 2017. Aluminum and iron biomass pretreatment impacts on biochar anion exchange capacity. Carbon 118: 422-430. DOI: 10.1016/j.carbon.2017.03.056 30. Lawrinenko, M., J. (Hans) van Leeuwen, and D.A. Laird. 2017. Sustainable pyrolytic production of zerovalent iron. Sustainable Chemistry & Engineering. 5: 767–773. DOI 10.1021/acssuschemeng.6b02105 31. Tammeorg, P., A. Bastos, S. Jeffery, F. Rees, J. Kern, E. Graber, M. Ventura, M. Kibblewhite, A. Amaro, A. Budai, C. Cordovil, X. Domene, C. Gardi, J. Horák, C. Kammann, E. Kondrlova, D. Laird, S. Loureiro, M. Martins, P. Panzacchi, M. Prasad, M. Prodana, A. Puga, L. Sas-Paszt, F. Silva, W. Teixeira, G. Delle Vedove, C. Zavalloni, B. Glaser, F. Verheijen. 2017. Biochar in soils: Towards the required level of scientific understanding. 2017. Journal of Environmental Engineering and Landscape Management DOI: 10.3846/16486897.2016. 1239582 32. Archontoulis, S.V., I. Huber, F.E. Miguez, P.J. Thorburn, and D.A. Laird. 2016. A model for mechanistic and system assessments of biochar effects on soils and crops and trade-offs. GCB Bioenergy. doi: 10.1111/gcbb.12314. 33. Bakshi, S., D.M. Aller, D.A. Laird, and R. Chintala. 2016. Comparison of the Physical and Chemical Properties of Laboratory- and Field-Aged Biochars. Journal of Environmental Quality 45:1627-1634. doi:10.2134/jeq2016.02.0062. 34. Lawrinenko, M. and D.A. Laird. 2015. Anion exchange Capacity of Biochar. Green Chemistry, 17:9, 4628- 4636 (2015). DOI:10.1039/C5GC00828J. 35. Lawrinenko, M., D.A. Laird, R.L. Johnson, and D. Jing. 2016. Accelerated aging of biochars; impact on anion exchange capacity. Carbon. 10: 217-227. Doi: 10.1016/j.carbon.2016.02.096 36. Rogovska, N., D.A. Laird, and D.L. Karlen. 2016. Corn and Soil Response to Biochar Application and Stover Harvest. Field Crops Research, 187, 96-106. DOI:10.1016/j.fcr.2015.12.013

Theses and Dissertations 1. Wenqin Li. Mechanical Engineering; Biorenewable Resources and Technology. Ph.D. 2018. Investigating the development of the bioproducts production platform from a techno-economic and environmental perspective. https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=7632&context=etd 2. Hamze Dokoohaki. Crop Production and Physiology. Ph.D. 2018. The promise of Biochar: From lab experiment to national scale impacts. https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=7577&context=etd 3. Deborah Aller. Agronomy and Environmental Sciences. Ph.D. 2017. Scaling understanding of biochar aging impacts on soil water and crop yields. https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=7073&context=etd 4. Michael Lawrinenko. Soil Chemistry and Environmental Science. Ph.D. 2016. Biochar for sustainable advances in agriculture and environmental remediation. http://search.proquest.com/docview/1860241697 5. Rivka Fidel. Soil Science with minor in Sustainable Agricultural. Ph.D. 2015. Dissertation: Biochar

properties and impact on soil CO2 and N2O emissions. http://lib.dr.iastate.edu/etd/14812/

Presentations 1. Laird, D.A. Invited lecture: “Biochars optimized for adsorption of environmental contaminates” International Symposium on Agro-Environmental Quality: Soil Contamination, Food Safety and Sustainable Development. Nov. 2-5, 2018. Nanjing, China. 2. Laird, D.A. Invited Lecture: “Biochar” 10th Annual Biofuels Science and Sustainability Tour. Aug. 14, 2018. Iowa State University, Sorenson Research Farm, Boone Iowa 3. Laird, D.A. Invited lecture: “Biochars optimized for adsorption of environmental contaminates” 4th Conference on Environmental Pollution and Health. May 18-20th, 2018. Nankai University in Tianjin, China. 4. Laird, D.A. Workshop presentation: “Pyrolysis-Bioenergy-Biochar Platform” New Carbon Economy Workshop. April 4, 2018. Iowa State University, Ames, IA. 5. Li, Wenqin, Mark M. Wright “Regional Techno-economic (TEA) and Life Cycle Analysis (LCA) of the Pyrolysis-Bioenergy-Biochar Pathway to Carbon-Negative Energy” American Institute of Chemical Engineers, Pittsburgh PA, August 2018 6. Wenqin Li, Arpa Gosh, Robert C. Brown, Mark Mba Wright “Techno-economic Analysis (TEA) of Ethanol Production from Solvent Liquefaction of Lignocellulosic Biomass” American Institute of Chemical Engineers, San Francisco CA, October 2017 7. Mark Mba Wright “Clean and Economic Carbon Negative Energy” University of Iowa April 19th, 2017 8. Mark Mba Wright “Carbon negative energy via the fast pyrolysis to fuels and bioproducts platform”32nd International Conference on Solid Waste Technology and Management, March 19th, Philadelphia, PA 2017 9. Aller D, Archontoulis SV, Zhang W, Laird DA, Moore K, 2017. Using APSIM to optimize biochar application rates for Midwest corn-bioenergy cropping systems. ASA meeting October 22–25 Tampa, FL. 10. Aller D, Archontoulis SV, Laird DA, 2017. Evaluation of soil water pedotransfer functions for use in the APSIM biochar model. ASA meeting October 22–25 Tampa, FL. 11. Huber I, Laird D, Martinez-Feria R, Archontoulis SV, 2017. Environment impacts of large scale biochar applications through spatial modeling. American Geophysical Union, New Orleans, 11-15 Dec. 2017. 12. Aller, D., Laird, D., Rathke, S. Quantifying Biochar Impacts on Plant Available Water and Water Use Efficiency. ASA, CSA, SSSA Annual Meeting, Phoenix, AZ. Nov. 2016. 13. Aller, D., Fidel, R., Banik, C., Laird, D., Archontoulis, S. Integrated Pyrolysis-Bioenergy-Biochar Platform for Carbon Negative Energy. C3E Women in Clean Energy Symposium: The Role of Women Internationally in Decarbonizing our Energy Future. Stanford, CA. June 2016. 14. Aller, D., D.A. Laird and S. Rathke. 2016. Quantifying Biochar Impacts on Plant Available Water and Water Use Efficiency. Abstract 100170 in ASA, CSSA, and SSSA International Annual Meetings. Nov. 6- 9, Phoenix, AZ. https://scisoc.confex.com/scisoc/2016am/webprogram/Paper100170.html 15. Bakshi, S., S. Rathke, C. Banik and D.A. Laird. 2016. Impacts of Biochar Aging and Soil Type on Soil Nitrogen Dynamics. Abstract 100165 in ASA, CSSA, and SSSA International Annual Meetings. Nov. 6-9, Phoenix, AZ. https://scisoc.confex.com/scisoc/2016am/webprogram/Paper100165.html 16. Bakshi, S., C.i Banik, and D.A. Laird. 2016. Analysis of the Chemically- and Thermally-Labile Biochar Fractions. Abstract 100158 in ASA, CSSA, and SSSA International Annual Meetings. Nov. 6-9, Phoenix, AZ. https://scisoc.confex.com/scisoc/2016am/webprogram/Paper100158.html 17. Banik, C., N. Rogovska, S. Rathke, S. Bakshi, D.A. Laird, C. Bonin and R.B. Mitchell. 2016. Impacts of Biochar and Various Bioenergy Cropping Systems on Plant Available Soil Nutrients. Abstract 101113 in ASA, CSSA, and SSSA International Annual Meetings. Nov. 6-9, Phoenix, AZ. https://scisoc.confex.com/scisoc/2016am/webprogram/Paper101113.html 18. Dokoohaki, H., F. Miguez, D.A. Laird, and S.V. Archontoulis. 2016. Investigating the Effects of Biochar on Soil Water Content By Optimizing the Soil Physical Parameters of Apsim Model. Abstract 379-5 in ASA, CSSA, and SSSA International Annual Meetings. Nov. 6-9, Phoenix, AZ. https://scisoc.confex.com/scisoc/2016am/webprogram/Paper100750.html 19. Fidel, R., D.A. Laird, and K.A. Spokas. 2016. Sorption of Ammonium and Nitrate to Biochars: Mechanisms and Impacts of Production Parameters. Abstract 48-4 in ASA, CSSA, and SSSA International Annual Meetings. Nov. 6-9, Phoenix, AZ. https://scisoc.confex.com/scisoc/2016am/webprogram/Paper100183.html 20. Laird, D.A., S. Archontoulis, R.C. Brown, M. Wright, and D. Hayes. 2016. Innovations at the Epicenter of the Food, Energy, and Water Nexus. Abstract 283-1 in ASA, CSSA, and SSSA International Annual Meetings. Nov. 6-9, Phoenix, AZ. https://scisoc.confex.com/scisoc/2016am/webprogram/Paper101101.html 21. Lawrinenko, M., D.A. Laird, and H. van Leeuwen. 2016. Feedstock and Temperature Influences in the Production of Biochar-Zero-Valent Iron Composites. Abstract 1384-4 in ASA, CSSA, and SSSA International Annual Meetings. Nov. 6-9, Phoenix, AZ. https://scisoc.confex.com/scisoc/2016am/webprogram/Paper101262.html 22. Rogovska, N., D.A. Laird, C.P. Chiou, and L. Bond. 2016. Development of Field Mobile Soil Nitrate Sensor Technology to Facilitate Precision Fertilizer Management. Abstract 100320 in ASA, CSSA, and SSSA International Annual Meetings. Nov. 6-9, Phoenix, AZ. https://scisoc.confex.com/scisoc/2016am/webprogram/Paper100320.html 23. Wenqin Li, Mark Mba Wright "Techno-economic (TEA) and Life Cycle Analysis (LCA) of the Pyrolysis- Bioenergy-Biochar Pathway to Carbon-Negative Energy," 2016 American Institute of Chemical Engineers Annual Meeting. San Fransisco, CA, 11/14/16 24. Wenqin Li, Mark Mba Wright "Techno-economic (TEA) and Life Cycle Analysis (LCA) of the Pyrolysis- Bioenergy-Biochar Pathway to Carbon-Negative Energy," 2016 Symposium on Thermal and Catalytic Sciences for Biofuels and Biobased Products. Chapel Hill, NC, 11/01/16 25. Aller, D., Bakshi, S., and Laird, D.A. Modification of Proximate Analysis Method for Biochars. American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America Joint International Annual Meetings, Minneapolis MN, Nov. 2015. 26. Aller, D., Laird, D.A., Mazur, R., Moore, K.J., and Hintz, R. Influence of Biochar and Diversified Cropping Systems on Soil Physical and Chemical Properties. American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America Joint International Annual Meetings, Minneapolis MN, Nov. 2015. 27. Aller, D., Laird, D.A., Mazur, R., Moore, K.J., and Hintz, R. Influence of Biochar and Diversified Cropping Systems on Soil Physical and Chemical Properties. Soil and Water Conservation Conference, Greensboro North Carolina, July 2015. 28. Bakshi, S., Aller, D., Laird, D.A., and Chintala, R. Comparison of Laboratory- and Field-Aged Biochars for Agronomic Benefits. American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America Joint International Annual Meetings, Minneapolis MN, Nov. 2015. 29. Bakshi, S., Banik, C., and Laird, D.A., Determining C:N Ratios of the Labile Biochar Fraction. American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America Joint International Annual Meetings, Minneapolis MN, Nov. 2015. 30. Banik, C., Rogovska, N., Huang, S., and Laird, D.A. Phosphorous Leaching and Depth Stratification in the Biochar Amended Field Soils. American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America Joint International Annual Meetings, Minneapolis MN, Nov. 2015. 31. Fidel, R., Laird, D.A., and Parkin, T. Context-Dependent Effect of Biochar on Soil Greenhouse Gas Emissions. American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America Joint International Annual Meetings, Minneapolis MN, Nov. 2015. 32. Laird, D.A. Frontiers in Biochar Science. Invited webinar sponsored by the International Biochar Initiative, Jan. 2016. 33. Laird, D.A. The Pyrolysis-Biochar Platform; On the Road to Carbon Negative Energy. Invited Distinguished Lecture of Environmental Science and Policy Program, Michigan State University, East Lansing MI. Oct. 2015. 34. Laird, D.A. Frontiers in Biochar Science. Invited seminar in Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing MI. Oct. 2015. 35. Laird, D.A. Frontiers in Biochar Science. Invited seminar in W. K. Kellogg Biological Station, Michigan State University, Augusta, MI. Oct. 2015. 36. Laird, D.A. Biochar Effects on Nutrient Leaching in Soils. Invited Keynote lecture at EuroClay 2015: Joint meeting of the European Clay Groups Association, The Clay Minerals Society, International Natural Zeolites Association, and the Geological Society, Edinburgh Scotland, July 2015. 37. Laird, D.A., Anderson, C., and Hayes, D. Carbon Farming as a Carbon Negative Technology. AGU Fall Meeting. San Francisco, Dec. 2015. 38. Rogovska, N., Banik, C., Laird, D.A., Tomer, M.D., and Karlen D.L. Corn and Soil Response to Biochar Application and Stover Harvest. American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America Joint International Annual Meetings, Minneapolis MN, Nov. 2015. 39. Archontoulis SV*, Huber I, Miguez FE, Thorburn PJ, Laird DA, 2014. Modeling Biochar Effects on Soils and Crops Using APSIM. ASA, CSSA and SSSA 2014 international annual meeting November 2–5, Long Beach, California.

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Contacts David A. Laird: [email protected] Deborah M. Aller: [email protected] Sotirios Archontoulis: [email protected] Santanu Bakshi: [email protected] Chumki Banik: [email protected] Robert C. Brown: [email protected] Hamze Dokoohaki: [email protected] Jerome Dumortier: [email protected] Amani E. Elobeid: [email protected] Rivka B. Fidel: [email protected] Ryan Goodrich: [email protected] Dermot J. Hayes: [email protected] Isaiah L. Huber: [email protected] Wenqin Li: [email protected] Fernando E. Miguez: [email protected] Mark Mba-Wright: [email protected] Wendiam Sawadgo: [email protected] David Zilberman: [email protected]