Fernando E. Miguez

Assessing potential of biochar for increasing water-holding capacity of sandy soils

Increasing the water‐holding capacity of sandy soils will help improve efficiency of water use in agricultural production, and may be critical for providing enough energy and food for an increasing global population. We hypothesized that addition of biochar will increase the water‐holding capacity of a sandy loam soil, and that the depth of biochar incorporation will influence the rate of biochar surface oxidation in the amended soils. Hardwood fast pyrolysis biochar was mixed with soil (0%, 3%, and 6% w/w) and placed into columns in either the bottom 11.4 cm or the top 11.4 cm to simulate deep banding in rows (DBR) and uniform topsoil mixing (UTM) applications, respectively. Four sets of 18 columns were incubated at 30 °C and 80% RH. Every 7 days, 150 mL of 0.001 M calcium chloride solution was added to the columns to produce leaching. Sets of columns were harvested after 1, 15, 29, and 91 days. Addition of biochar increased the gravity‐drained water content 23% relative to the control. Bulk density of the control soils increased with incubation time (from 1.41 to 1.45 g cm−3), whereas bulk density of biochar‐treated soils was up to 9% less than the control and remained constant throughout the incubation period. Biochar did not affect the CEC of the soil. The results suggest that biochar added to sandy loam soil increases water‐holding capacity and might increase water available for crop use.

Modeling miscanthus in the Soil and Water Assessment Tool (SWAT) to simulate its water quality effects as a bioenergy crop

There is increasing interest in perennial grasses as a renewable source of bioenergy and feedstock for second-generation cellulosic biofuels. The primary objective of this study is to estimate the potential effects on riverine nitrate load of cultivating Miscanthus x giganteus in place of conventional crops. In this study, the Soil and Water Assessment Tool (SWAT) is used to model miscanthus growth and streamwater quality in the Salt Creek watershed in Illinois. SWAT has a built-in crop growth component, but, as miscanthus is relatively new as a potentially commercial crop, data on the SWAT crop growth parameters for the crop are lacking. This leads to the second objective of this study, which is to estimate those parameters to facilitate the modeling of miscanthus in SWAT. Results show a decrease in nitrate load that depends on the percent land use change to miscanthus and the amount of nitrogen fertilizer applied to the miscanthus. Specifically, assuming a nitrogen fertilization rate for miscanthus of 90 kg-N/ha, a 10%, 25%, and 50% land use change to miscanthus will lead to decreases in nitrate load of about 6.4%, 16.5%, and 29.6% at the watershed outlet, respectively. Likewise, nitrate load may be reduced by lowering the fertilizer application rate, but not proportionately. When fertilization drops from 90 to 30 kg-N/ha the difference in nitrate load decrease is less than 1% when 10% of the watershed is miscanthus and less than 6% when 50% of the watershed is miscanthus. It is also found that the nitrate load decrease from converting less than half the watershed to miscanthus from corn and soybean in 1:1 rotation surpasses that from converting the whole watershed to just soybean.

Modeling spatial and dynamic variation in growth, yield, and yield stability of the bioenergy crops Miscanthus × giganteus and Panicum virgatum across the conterminous United States

C4 perennial grasses are being considered as environmentally and economically sustainable high yielding bioenergy feedstocks. Temporal and spatial variation in yield across the conterminious United States is uncertain due to the limited number of field trials. Here, we use a semi‐mechanistic dynamic crop growth and production model to explore the potential of Miscanthus × giganteus (Greef et. Deu.) and Panicum virgatum L. across the conterminous United States. By running the model for 32 years (1979–2010), we were able to estimate dry biomass production and stability. The maximum rainfed simulated end‐of‐growth‐season harvestable biomass for M. × giganteus was ca. 40 Mg ha−1 and ca. 20 Mg ha−1 for P. virgatum. In addition, regions of the southeastern United States were identified as promising due to their high potential production and stability and their relative advantage when compared with county‐level maize biomass production. Regional and temporal variation was most strongly influenced by precipitation and soil water holding capacity. Miscanthus × giganteus was on average 2.2 times more productive than P. virgatum for locations where yields were ≥10 Mg ha−1. The predictive ability of the model for P. virgatum was tested with 30 previously published studies covering the eastern half of the United States and resulted in an index of agreement of 0.71 and a mean bias of only −0.62 Mg ha−1 showing that, on average, the model tended to only slightly overestimate productivity. This study provides with potential production and variability which can be used for regional assessment of the suitability of dedicated bioenergy crops.

Bioenergy crop models: Descriptions, data requirements and future challenges

Field studies that address the production of lignocellulosic biomass as a source of renewable energy provide critical data for the development of bioenergy crop models. A literature survey revealed that 14 models have been used for simulating bioenergy crops including herbaceous and woody bioenergy crops, and for crassulacean acid metabolism (CAM) crops. These models simulate field‐scale production of biomass for switchgrass (ALMANAC, EPIC, and Agro‐BGC), miscanthus (MISCANFOR, MISCANMOD, and WIMOVAC), sugarcane (APSIM, AUSCANE, and CANEGRO), and poplar and willow (SECRETS and 3PG). Two models are adaptations of dynamic global vegetation models and simulate biomass yields of miscanthus and sugarcane at regional scales (Agro‐IBIS and LPJmL). Although it lacks the complexity of other bioenergy crop models, the environmental productivity index (EPI) is the only model used to estimate biomass production of CAM (Agave and Opuntia) plants. Except for the EPI model, all models include representations of leaf area dynamics, phenology, radiation interception and utilization, biomass production, and partitioning of biomass to roots and shoots. A few models simulate soil water, nutrient, and carbon cycle dynamics, making them especially useful for assessing the environmental consequences (e.g., erosion and nutrient losses) associated with the large‐scale deployment of bioenergy crops. The rapid increase in use of models for energy crop simulation is encouraging; however, detailed information on the influence of climate, soils, and crop management practices on biomass production is scarce. Thus considerable work remains regarding the parameterization and validation of process‐based models for bioenergy crops; generation and distribution of high‐quality field data for model development and validation; and implementation of an integrated framework for efficient, high‐resolution simulations of biomass production for use in planning sustainable bioenergy systems.

Miscanthus × giganteus productivity: the effects of management in different environments.

Miscanthus × giganteus is a C4 perennial grass that shows great potential as a high‐yielding biomass crop. Scant research has been published that reports M. × giganteus growth and biomass yields in different environments in the United States. This study investigated the establishment success, plant growth, and dry biomass yield of M. × giganteus during its first three seasons at four locations (Urbana, IL; Lexington, KY; Mead, NE; Adelphia, NJ) in the United States. Three nitrogen rates (0, 60, and 120 kg ha−1) were applied at each location each year. Good survival of M. × giganteus during its first winter was observed at KY, NE, and NJ (79–100%), and poor survival at IL (25%), due to late planting and cold winter temperatures. Site soil conditions, and growing‐season precipitation and temperature had the greatest impact on dry biomass yield between season 2 (2009) and season 3 (2010). Ideal 2010 weather conditions at NE resulted in significant yield increases (P < 0.0001) of 15.6–27.4 Mg ha−1 from 2009 to 2010. Small yield increases in KY of 17.1 Mg ha−1 in 2009 to 19.0 Mg ha−1 in 2010 could be attributed to excessive spring rain and hot dry conditions late in the growing season. Average M. ×giganteus biomass yields in NJ decreased from 16.9 to 9.7 Mg ha−1 between 2009 and 2010 and were related to hot dry weather, and poor soil conditions. Season 3 yields were positively correlated with end‐of‐season plant height ( ρ̂=0.91 ) and tiller density ( ρ̂=0.76 ). Nitrogen fertilization had no significant effect on plant height, tiller density, or dry biomass yield at any of the sites during 2009 or 2010.

Do cover crops increase or decrease nitrous oxide emissions? A meta-analysis

There are many environmental benefits to incorporating cover crops into crop rotations, such as their potential to decrease soil erosion, reduce nitrate (NO3) leaching, and increase soil organic matter. Some of these benefits impact other agroecosystem processes, such as greenhouse gas emissions. In particular, there is not a consensus in the literature regarding the effect of cover crops on nitrous oxide (N2O) emissions. Compared to site-specific studies, meta-analysis can provide a more general investigation into these effects. Twenty-six peer-reviewed articles including 106 observations of cover crop effects on N2O emissions from the soil surface were analyzed according to their response ratio, the natural log of the N2O flux with a cover crop divided by the N2O flux without a cover crop (LRR). Forty percent of the observations had negative LRRs, indicating a cover crop treatment which decreased N2O, while 60% had positive LRRs indicating a cover crop treatment which increased N2O. There was a significant interaction between N rate and the type of cover crop where legumes had higher LRRs at lower N rates than nonlegume species. When cover crop residues were incorporated into the soil, LRRs were significantly higher than those where residue was not incorporated. Geographies with higher total precipitation and variability in precipitation tended to produce higher LRRs. Finally, data points measured during cover crop decomposition had large positive LRRs and were larger than those measured when the cover crop was alive. In contrast, those data points measuring for a full year had LRRs close to zero, indicating that there was a balance between periods when cover crops increased N2O and periods when cover crops decreased emissions. Therefore, N2O measurements over the entire year may be needed to determine the net effect of cover crops on N2O. The data included in this meta-analysis indicate some overarching crop management practices that reduce direct N2O emissions from the soil surface, such as no soil incorporation of residues and use of non-legume cover crop species. However, our results demonstrate that cover crops do not always reduce direct N2O emissions from the soil surface in the short term and that more work is needed to understand the full global warming potential of cover crop management.

A semimechanistic model predicting the growth and production of the bioenergy crop Miscanthus×giganteus: description, parameterization and validation

Biomass based bioenergy is promoted as a major sustainable energy source which can simultaneously decrease net greenhouse gas emissions. Miscanthus×giganteus (M.×giganteus), a C4 perennial grass with high nitrogen, water, and light use efficiencies, is regarded as a promising energy crop for biomass production. Mathematical models which can accurately predict M.×giganteus biomass production potential under different conditions are critical to evaluate the feasibility of its production in different environments. Although previous models based on light‐conversion efficiency have been shown to provide good predictions of yield, they cannot easily be used in assessing the value of physiological trait improvement or ecosystem processes. Here, we described in detail the physical and physiological processes of a previously published generic mechanistic eco‐physiological model, WIMOVAC, adapted and parameterized for M.×giganteus. Parameterized for one location in England, the model was able to realistically predict daily field diurnal photosynthesis and seasonal biomass at a range of other sites from European studies. The model provides a framework that will allow incorporation of further mechanistic information as it is developed for this new crop.

Evaluation of synergistic antioxidant potential of complex mixtures using oxygen radical absorbance capacity (ORAC) and electron paramagnetic resonance (EPR)

Previous research has demonstrated that certain combinations of compounds result in a decrease in toxic or pro-oxidative effects, previously noted when compounds were administered singly. Thus, there is a need to study many complex interactions further. Two in vitro techniques [electron paramagnetic resonance (EPR) and oxygen radical absorbance capacity (ORAC) assays] were used in this study to assess pro- and antioxidant capacity and synergistic potential of various compounds. Rutin, p-coumaric acid, abscisic acid, ascorbic acid, and a sugar solution were evaluated individually at various concentrations and in all 26 possible combinations at concentrations found in certain foods (honey or papaya), both before and after simulated digestion. EPR results indicated sugar-containing combinations provided significantly higher antioxidant capacity; those combinations containing sugars and ascorbic acid demonstrated synergistic potential. The ORAC assay suggested additive effects, with some combinations having synergistic potential, although fewer combinations were significantly synergistic after digestion. Finally, ascorbic acid, caffeic acid, quercetin, and urate were evaluated at serum-achievable levels. EPR analysis did not demonstrate additive or synergistic potential, although ORAC analysis did, principally in combinations containing ascorbic acid.

Multivariate analysis and visualization of soil quality data for no-till systems

To evidence the multidimensionality of the soil quality concept, we propose the use of data visualization as a tool for exploratory data analyses, model building, and diagnostics. Our objective was to establish the best edaphic indicators for assessing soil quality in four no-till systems with regard to functioning as a medium for crop production and nutrient cycling across two Illinois locations. The compared situations were no-till corn-soybean rotations including either winter fallowing (C/S) or cover crops of rye (Secale cereale; C-R/S-R), hairy vetch (Vicia villosa; C-R/S-V), or their mixture (C-R/S-VR). The dataset included the variables bulk density (BD), penetration resistance (PR), water aggregate stability (WAS), soil reaction (pH), and the contents of soil organic matter (SOM), total nitrogen (TN), soil nitrates (NO(3)-N), and available phosphorus (P). Interactive data visualization along with canonical discriminant analysis (CDA) allowed us to show that WAS, BD, and the contents of P, TN, and SOM have the greatest potential as soil quality indicators in no-till systems in Illinois. It was more difficult to discriminate among WCC rotations than to separate these from C/S, considerably inflating the error rate associated with CDA. We predict that observations of no-till C/S will be classified correctly 51% of the time, while observations of no-till WCC rotations will be classified correctly 74% of the time. High error rates in CDA underscore the complexity of no-till systems and the need in this area for more long-term studies with larger datasets to increase accuracy to acceptable levels.

A spatial modeling framework to evaluate domestic biofuel-induced potential land use changes and emissions.

We present a novel bottom-up approach to estimate biofuel-induced land-use change (LUC) and resulting CO2 emissions in the U.S. from 2010 to 2022, based on a consistent methodology across four essential components: land availability, land suitability, LUC decision-making, and induced CO2 emissions. Using high-resolution geospatial data and modeling, we construct probabilistic assessments of county-, state-, and national-level LUC and emissions for macroeconomic scenarios. We use the Cropland Data Layer and the Protected Areas Database to characterize availability of land for biofuel crop cultivation, and the CERES-Maize and BioCro biophysical crop growth models to estimate the suitability (yield potential) of available lands for biofuel crops. For LUC decision-making, we use a county-level stochastic partial-equilibrium modeling framework and consider five scenarios involving annual ethanol production scaling to 15, 22, and 29 BG, respectively, in 2022, with corn providing feedstock for the first 15 BG and the remainder coming from one of two dedicated energy crops. Finally, we derive high-resolution above-ground carbon factors from the National Biomass and Carbon Data set to estimate emissions from each LUC pathway. Based on these inputs, we obtain estimates for average total LUC emissions of 6.1, 2.2, 1.0, 2.2, and 2.4 gCO2e/MJ for Corn-15 Billion gallons (BG), Miscanthus × giganteus (MxG)-7 BG, Switchgrass (SG)-7 BG, MxG-14 BG, and SG-14 BG scenarios, respectively.