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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Seismoacoustic Analysis of the Large Surface Explosion Coupling Experiment Using a Large- N Seismic Array

Seismoacoustic signals at local distance (<~10 km) are widely used as important constraints on source parameters for near-surface events, yet the seismoacoustic wave generation and energy partitioning are not fully understood. Spatially dense sensors could provide observations in high resolution to capture the full wavefield for better understanding wave propagation and improving source estimation. Recently, spatially dense observations of the local seismoacoustic wavefield produced by a pair of 1-ton surface explosions have been recorded using a large-N seismic array. This large-N array consists of 446 geophones and covers an area of approximately 2×2.5 km 2 . The two surface explosions occur at the same location but at different times with different atmospheric conditions. Both seismic and air–ground coupled acoustic waves from the two surface explosions are well observed. Analyses of signals recorded by the large-N seismic array show different acoustic wave speed and amplitude for the two explosions. A strong spatial variability in acoustic wave speed and amplitude for each explosion is also observed. Finally, the observations suggest the important role of local atmosphere state on wave propagation and source estimation and demonstrate how the use of a large-N capability can improve characterization of the propagation medium and source.

58 GEOSCIENCES↗
DOE OSTI2022

Monitoring Operational States of a Nuclear Reactor Using Seismoacoustic Signatures and Machine Learning

Monitoring nuclear reactors is an important safety and security task with growing requirements. We explore the possibility of using seismic and acoustic data for inferring the power level of an operating reactor. Continuous data recorded at a single seismoacoustic station that is located about 50 m away from a research reactor was visualized and analyzed. The data show a clear correlation between seismoacoustic features and reactor main operational states. We designed a workflow that includes two machine learning (ML) models to classify the reactor operational states (OFF, transition, and ON) and estimate reactor power levels (10%, 30%, 50%, 70%, and 90%). We applied and compared five ML algorithms for the reactor OFF-transition-ON and four approaches for the power level classification. We also compared the performance of ML models trained with seismic-only, acoustic-only, and both types of data. Five-fold cross validations were implemented to assure a thorough evaluation of the model performances. Additionally, the results show the extreme boosting gradient algorithm worked best for the first model, whereas random forests performed best for the second model. Combining seismic and acoustic data leads to better performance than using a single type of data. Seismic data contributed more than acoustic data for both models. We reached an accuracy of 0.98 for reactor OFF and ON. The accuracies for the transition state and power levels are less optimal with a minimum accuracy of 0.66. However, our results suggest seismic and acoustic data contain useful information about the transition state as well as power levels. Seismic and acoustic data could be integrated with other observations to improve monitoring performance.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

The Missing Correlation Between the Potential Rate Impacts of Rooftop Solar and the Timing of State Net Metering Policy Revisions

Data supporting the article “The Missing Correlation Between the Potential Rate Impacts of Rooftop Solar and the Timing of State Net Metering Policy Revisions” (https://www.nlr.gov/docs/fy25osti/93543.pdf). Residential solar photovoltaic (PV) output in most states is credited at the retail electricity rate, a policy commonly known as net metering. Twelve states have replaced net metering with alternative rate structures that reduce PV adopter bill savings. Proponents of these revisions argue that net metering increases the electricity rates of customers without PV. Here, we analyze the degree to which the timelines of net metering revisions have correlated with potential electricity rate impacts. We estimate that potential rate impacts at the end of 2023 were less than 1% of typical customer bills in 37 of 44 states that have offered net metering. There are no statistically significant differences in average or median estimated rate impacts between states that have and have not revised net metering. Nine of the states that had revised net metering did so when estimated impacts were less than 1% of typical customer bills. Many states have retained net metering into higher PV deployment levels with increased risk of potential rate impacts. Only two states-California and Hawaii-retained net metering beyond estimated rate impacts of 5%, and both have revised net metering. These findings do not suggest a clear, consistent link between net metering revision timelines and potential rate impacts. The timing and nature of net metering revisions are ultimately policy decisions based on state-level priorities and considerations.

14 SOLAR ENERGY↗
DOE OSTI2021

Nucleon momentum fraction, helicity and transversity from 2+1-flavor lattice QCD

A detailed analysis of the systematic uncertainties in the calculation of the isovector momentum fraction, ( x ) u - d , helicity moment, ( x ) Δ u - Δ d , and the transversity moment, ( x ) δu - δd , of the nucleon is presented using high-statistics data on seven ensembles of gauge configurations generated by the JLab/W&M/LANL/MIT collaborations using 2 + 1-flavors of dynamical Wilson-clover quarks. The much higher statistics have facilitated better control over all systematics compared to previous lattice calculations. The least understood systematic — excited-state contamination — is quantified by studying the variation of the results as a function of different estimates of the mass gap of the first excited state, obtained from two- and three-point correlation functions, and as a function of the pion mass M π . The final results are obtained using a simultaneous fit in the lattice spacing a , pion mass M π and the finite volume parameter M π L keeping leading order corrections. The data show no significant dependence on the lattice spacing and some evidence for finite-volume corrections. Our final results, in the $ \overline{\mathrm{MS}} $ scheme at 2 GeV, are ( x ) u - d = 0 . 155(17)(20), ( x ) Δ u - Δ d = 0 . 183(14)(20) and ( x ) δu - δd = 0 . 220(18)(20), where the first error is the overall analysis uncertainty assuming excited-state contributions have been removed, and the second is an additional systematic uncertainty due to possible residual excited-state contributions. These results are consistent with phenomenological global fit values.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Continued Development and Advanced Testing of DPC Filler Cements (on FY22 R&D and Demonstration Activities) (Progress Report)

Commercial generation of energy by nuclear power plants in the United States (U.S.) has produced thousands of metric tons of spent nuclear fuel (SNF), the disposal of which is the responsibility of the U.S. Department of Energy (DOE). Utilities typically utilize the practice of storing this SNF in dual-purpose canisters (DPCs). DPCs were designed, licensed, and loaded to meet Nuclear Regulatory Commission (NRC) requirements that preclude the possibility of a criticality event during SNF storage and transport, but were not designed or loaded to preclude the possibility of a criticality event during the regulated post-closure period following disposal, which could be up to 1,000,000 years (Price, 2019). There are several options being investigated that could facilitate the disposal of SNF stored in DPCs in a geologic repository (Hardin et al., 2015; SNL 2020b; SNL 2021b). These include: (1) repackage the SNF into canisters that are designed to prevent criticality during the regulated post-closure period following disposal, but with an increased disposal cost estimated at approximately $\$$20B in United States dollars (USD) (Freeze et al., 2019); (2) analysis of the probability and consequences of criticality from the direct disposal of DPCs during a 1,000,000-year post-closure period in several geologic disposal media (Price, 2019); and (3) filling the void space of a DPC with a material before its disposal that significantly limits the potential for criticality over the post-closure regulatory period. This report further investigates the third option, filling DPC already containing SNF with a material to limit the potential for criticality over the post-closure regulatory period.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

A multi-criteria land suitability assessment of field allocation decisions for switchgrass

With the goal of evaluating biomass resource availability in certain regions in the United States, the Department of Energy, Department of Agriculture, Environmental Protection Agency, national laboratories, and Forest Service research laboratories, together with academic and industry collaborators, performed a resource analysis study, 2016 Billion-Ton Report: Advancing Domestic Resources for a Thriving Bioeconomy, Volume 1: Economic Availability of Feedstocks, using POLYSYS model to estimate biomass potential. The study provided county- and state-level biomass potential projections, with biomass feedstock production estimates and suitability assessment of lands acreage likely to transition to energy crops. For this analysis, we estimate biomass supply by year 2028, more specifically, lands acreage transition to switchgrass in the Midwest of the United States, across 50 counties in 3 states (Nebraska, Kansas, and Colorado). We propose a multi-criteria site suitability framework, to determine the potential acreage and production for switchgrass, at the field level. Site selection criteria span agronomic and ecosystem services such as previous land cover, crop rotation, soil water holding capacity, crop productivity, field efficiency, topography, soil organic matter, soil leaching propensity, distance to surface water bodies, and soil erosivity. Efficient agricultural field selection for dedicated energy crops field allocation is critical to evaluate biomass resource availability, and it is our position that estimation at the field level would provide a more accurate and useful estimation to support an emerging bioeconomy. Using field suitability scores and county-level assessments, we model land allocation decision for energy crop in the future, estimating the spatial distribution of dynamics of energy crop adoption with priority to fields scoring the highest. Additionally, compared with POLYSYS results from the report mentioned above, our method shows good performance overall, with about 2% and 1.2% of difference in switchgrass acreage allocation and crop production, respectively.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Economics of Crop Rotations With and Without Carinata for Sustainable Aviation Fuel Production in the SE United States

In 2019, the aviation sector in the United States emitted 255 million metric tons of carbon dioxide (CO 2 ) emissions, i.e., about five percent of the total domestic CO 2 emissions from the energy sector. The sustainable aviation fuel (SAF) derived from carinata (Brassica carinata) could reduce CO 2 emissions of the aviation sector in the United States. Therefore, it is important to estimate changes in farm economics with and without carinata for ascertaining its production feasibility. In this context, the current study first assesses a combination of 12 popular rotations of corn, cotton, peanut, and soybean with winter crops of winter wheat and carinata in South Georgia over 4 years. Then, the net present values (NPVs) of 292 feasible cropping systems over 4 years are calculated. Finally, this study develops a risk model for ascertaining the probability distributions of NPVs for selected cropping systems subject to uncertainties related to prices and yields of summer and winter crops. Carinata in the corn-corn-soybean rotation has the highest NPV ($\$$2,996/ha). The least risky rotation is cotton-cotton-peanut, with a 58.9% probability of a positive NPV. Carinata can decrease the risk level of crop rotations by 8.1%, only if a contract price of $\$$440.9/t is offered. Therefore, a risk averse, risk neutral, or risk acceptant farmer can potentially include carinata in the rotation. Overall, carinata would increase the profitability of farm operations and decrease risk in the SE United States, and therefore, a high likelihood exists, that farmers would adopt it for meeting the growing demand for SAF in the United States.

09 BIOMASS FUELS↗
DOE OSTI2023

Quantification of Physical and Numerical Mixing in a Coastal Ocean Model Using Salinity Variance Budgets

Numerical mixing, the spurious mixing primarily generated by the discretization of advection, is often significant in estuarine and coastal models due to sharp, energetic fronts. In this study, we compare on- and offline estimates of numerical mixing in a submesoscale-resolving realistic simulation of the ocean state over the Texas-Louisiana continental shelf. While offline estimates of numerical mixing differ from online estimates, offline methods may be the only analysis available. This study offers insight into the differences between the on- and offline methods. We use two methods to estimate numerical mixing offline, based on salinity squared $s^2$ and volume-mean salinity variance $s^{\prime^2}$. Numerical mixing estimated from the $s^{\prime^2}$ budget is generally within 60\% of the magnitude for the online method but captures the temporal variability well. However, the $s^2$ budget compares poorly due to larger truncation errors associated with the tendency and advection terms, which can be reduced by increasing the model output frequency. We also investigate the effects of horizontal resolution on numerical mixing using a two-way nested grid. The volume-integrated numerical mixing constitutes 57\% of the bulk physical mixing -- the mixing prescribed by the turbulence closure scheme -- in the coarse model and may exceed the physical mixing by half an order of magnitude. We find that numerical mixing is reduced by 35\% on average in the nested model, less than expected based on scaling of the numerical mixing for an upwind advection scheme, likely due to new dynamical processes that emerge in the nested simulation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Quantifying Uncertainty in All-to-All Estimates of Space Object Conjunction Probabilities using U-Statistics

Predicting space object conjunctions is inherently probabilistic due to initial state and orbit model uncertainty. A commonly considered Monte Carlo estimator of the conjunction probability is the ’all-to-all’ estimator. Given independent random samples of the trajectories of both objects, the estimator is the percentage of all pairs of trajectories that result in a conjunction. Intuitively, the all-to-all estimator is the best possible estimator of the conjunction probability since it considers all pairs of Monte Carlo samples. However, its distribution is not available in closed-form, which limits its use in practice and makes this intuition difficult to make rigorous. In this paper, the all-to-all estimator is identified as a U-statistic, which implies that it has several favorable properties. Specifically, the estimator is the minimum variance unbiased estimator of the conjunction probability and is asymptotically Gaussian distributed. An approximate confidence interval for the conjunction probability is obtained from an estimate of the asymptotic Gaussian distribution. We show how to efficiently compute the confidence interval and demonstrate that the interval has the nominal coverage level. The confidence intervals are also seen to be narrower than those based on the commonly-used each-to-each estimator. Furthermore, the all-to-all estimator is shown to allow different Monte Carlo sample sizes, whereas the each-to-each estimator requires equal sample sizes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Simultaneous estimation of multiple eigenvalues with short-depth quantum circuit on early fault-tolerant quantum computers

We introduce a multi-modal, multi-level quantum complex exponential least squares (MM-QCELS) method to simultaneously estimate multiple eigenvalues of a quantum Hamiltonian on early fault-tolerant quantum computers. Our theoretical analysis demonstrates that the algorithm exhibits Heisenberg-limited scaling in terms of circuit depth and total cost. Notably, the proposed quantum circuit utilizes just one ancilla qubit, and with appropriate initial state conditions, it achieves significantly shorter circuit depths compared to circuits based on quantum phase estimation (QPE). Numerical results suggest that compared to QPE, the circuit depth can be reduced by around two orders of magnitude under several settings for estimating ground-state and excited-state energies of certain quantum systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Aqueous Bromide Discharges from U.S. Coal-Fired Power Plants: Points of Origin, Concentration Ranges, and Effluent Treatment Costs

Bromide discharges from coal-fired power plants have received increased attention from regulatory bodies due to their contribution to the formation of disinfection byproducts (DBPs) in downstream drinking water treatment plants. Here, this paper characterizes the relative contributions of bromide from coal feedstocks and bromine-based mercury control processes, estimates the distribution of bromide concentrations at 85 active coal-fired power plants across the United States (U.S.) with wet flue gas desulfurization units, and estimates the cost of bromide removal from wastewater discharge using year 2020 data. Bromide discharges are estimated at the plant level using a combination of the reported coal rank and composition combusted, estimates of bromide addition in mercury control techniques under multiple halogen addition scenarios, and the estimated flue gas desulfurization (FGD) wastewater flow rate. The median, simulated plant-level estimation of total FGD wastewater flow is 18.3 gallons/min at a bromide concentration of 319 mg/L, equivalent to ~11.6 tonnes/year of bromide discharges to the environment. Next, we evaluated the expected cost of employing the best available technology (BAT) to control bromide discharges in FGD wastewater to prevent contributions to DBP formation. Treatment would need to remove more than 99.8% of bromide to reach the 0.2 mg/L voluntary incentive program (VIP) limit. The total cost of treatment depends on whether disposal is on- or off-site; the average costs for all plants combined come to an average of $110 million ($95.2/kgal) in 2021 U.S. dollars for on-site disposal, or $134 million ($115/kgal) for off-site disposal.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Decoding Golden Eagle Movement Behavior from High-Resolution, Variable-Rate Telemetry Data Through Bayesian Filtering

The recent advances in animal tracking technology have enabled the collection of a vast amount of in situ data regarding the movement of wildlife at high spatiotemporal resolution. These data are usually available at variable time resolutions and contains noise (error) originating from GPS fixes. Decoding movement characteristics, particularly of flying animals, from telemetry data while handling these factors is a challenging yet important task for conservation purposes. Typically, this task is broken into two subtasks: resampling, and model calibration. The resampling subtask converts the variable rate positional data into a constant time interval data, while the model calibration subtask uses the resampled data to tune time-invariant parameters of the proposed models. For telemetry data at high temporal resolutions (order of 1 second), it is very challenging to decouple noise from actual movements using interpolation-based resampling techniques. Any errors introduced during resampling can significantly alter the the calibration and prediction attributes of the movement model. We address this problem through a unified Bayesian state-space framework that can handle both the resampling and calibration tasks in a single step. In addition, we use the speed and heading of the bird from telemetry data to regularize the position information of the bird. We use a Kalman filtering approach to include these nonlinearly related motion parameters within the state space framework. We cross-validated to quantify how this inclusion affects the model performance in estimating true bird movements. The relationship between the true state of the bird and environmental and topographical covariates is then represented parametrically. These parameters are then tuned using stochastic sampling strategies like Markov Chain Monte Carlo (MCMC). We use the telemetry data collected from golden eagles in the western USA to demonstrate the applicability of this approach to build a predictive, probabilistic movement model. Our preliminary results show that this approach provides improved predictive performance in terms of capturing higher-order motion parameters such as angular and horizontal accelerations, which may have simpler and more direct relationships with environmental covariates than corresponding speeds. In this talk, we will demonstrate how this state-space approach benefits the prediction capabilities of a movement model in simulating golden eagle paths through a wind power plant in Wyoming given certain atmospheric conditions. The model outcomes are aimed at informing mitigation strategies that can minimize the potential for collisions of golden eagles with wind turbines.

Bayesian methods↗
DOE OSTI2023

Laisk measurements in the nonsteady state: Tests in plants exposed to warming and variable CO2 concentrations

Abstract Light respiration (RL) is an important component of plant carbon balance and a key parameter in photosynthesis models. RL is often measured using the Laisk method, a gas exchange technique that is traditionally employed under steady-state conditions. However, a nonsteady-state dynamic assimilation technique (DAT) may allow for more rapid Laisk measurements. In 2 studies, we examined the efficacy of DAT for estimating RL and the parameter Ci* (the intercellular CO2 concentration where Rubisco's oxygenation velocity is twice its carboxylation velocity), which is also derived from the Laisk technique. In the first study, we compared DAT and steady-state RL and Ci* estimates in paper birch (Betula papyrifera) growing under control and elevated temperature and CO2 concentrations. In the second, we compared DAT-estimated RL and Ci* in hybrid poplar (Populus nigra L. × P. maximowiczii A. Henry “NM6”) exposed to high or low CO2 concentration pre-treatments. The DAT and steady-state methods provided similar RL estimates in B. papyrifera, and we found little acclimation of RL to temperature or CO2; however, Ci* was higher when measured with DAT compared to steady-state methods. These Ci* differences were amplified by the high or low CO2 pre-treatments. We propose that changes in the export of glycine from photorespiration may explain these apparent differences in Ci*.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Optimal sensing on an asymmetric exceptional surface

We study the connection between exceptional points (EPs) and optimal parameter estimation, in a simple system consisting of two counterpropagating traveling wave modes in a microring resonator. The unknown parameter to be estimated is the strength of a perturbing cross-coupling between the two modes. Partially reflecting the output of one mode into the other creates a non-Hermitian Hamiltonian that exhibits a family of EPs, creating an exceptional surface (ES). We use a fully quantum treatment of field inputs and noise sources to obtain a quantitative bound on the estimation error by calculating the quantum Fisher information (QFI) in the output fields, whose inverse gives the Cramér-Rao lower bound on the mean-squared error of any unbiased estimator. We determine the bounds for two input states, namely, a semiclassical coherent state and a highly nonclassical NOON state. We find that the QFI is enhanced in the presence of an EP for both of these input states and that both states can saturate the Cramér-Rao bound. We then identify idealized yet experimentally feasible measurements that achieve the minimum bound for these two input states. We also investigate how the QFI changes for parameter values that do not lie on the ES, finding that these can have a larger QFI, suggesting alternative routes to optimize the parameter estimation for this problem.

Exceptional points↗
DOE OSTI2020

Leptonic scalars at the LHC

We explore the collider prospects of neutrino non-standard interaction with a Standard Model (SM) gauge-singlet leptonic scalar &straightphi; carrying two units of lepton-number-charge. These leptonic scalars are forbidden from interacting with the SM fermions at the renormalizable level and, if one allows for higher-dimensional operators, couple predominantly to SM neutrinos. For masses at or below the electroweak scale, &straightphi; decays exclusively into neutrinos. Its characteristic production signature at hadron collider experiments like the LHC would be via the vector boson fusion process and leads to same-sign dileptons, two forward jets in opposite hemispheres, and missing transverse energy, i.e., $$ pp\to {\mathrm{\ell}}_{\alpha}^{\pm }{\mathrm{\ell}}_{\beta}^{\pm } jj+{E}_T^{\mathrm{miss}}\left(\alpha, \beta =e,\mu, \tau \right) $$ pp → ℓ α ± ℓ β ± jj + E T miss α β = e μ τ . Exploiting the final states of electrons and muons, we estimate, for the first time, the sensitivity of the LHC to these lepton-number-charged scalars. We show that the LHC sensitivity is largely complementary to that of low-energy precision measurements of the decays of charged leptons, charged mesons, W , Z and the SM Higgs boson, as well as the neutrino beam experiments like MINOS, and searches for neutrino self-interactions at IceCube and in cosmological observations. For &straightphi; mass larger than roughly 10 GeV, our projected LHC sensitivity would surpass all existing bounds.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Minimum entropy filtering for a single output non-Gaussian stochastic system using state transformation

This paper presents a novel filter design for the single-output stochastic non-linear systems subjected to non-Gaussian noises and the proposed assumptions. Based on a state transformation, the unmeasurable states of the systems can be estimated where non-linear terms in the systems have been eliminated. It has been shown that the estimation error is linearly dynamical regarding to the presented vector-valued filter gain which can be optimised by minimising the entropy-based performance criterion. In addition, the convergence of the presented algorithm is analysed in mean-square sense and a numerical example is given to verify the effectiveness of the presented filtering algorithm. Meanwhile, the extended Kalman filter, unscented particle filter and minimum entropy filter are given for the comparisons of the filtering performance. Following the presented framework, some extensions of the presented filtering algorithm are discussed to indicate the flexibility of the filter design. The contribution of this paper can be summarised as establishing a novel minimum entropy filtering framework which consists of model transformation, entropy optimisation and convergence analysis.

42 ENGINEERING↗
DOE OSTI2023

Validation of Octane Hyperboosting Phenomenon in Prenol and Structurally Related Olefinic Alcohols

Hyperboosting is a recently discovered phenomenon in which the research octane number (RON) of a blend is higher than both the neat blendstock and the neat fuel it was blended into. RON is a measure of a fuel's resistance to knock, and knock is a cause of engine inefficiency. Blends which exhibit hyperboosting are blends in which an overall improvement in engine efficiency may be expected. The first discovery of hyperboosting came from blending experiments in which prenol was blended into several different base fuels. Here, ignition delay time (IDT) measurements taken using a commercially available constant volume combustion chamber called the Advanced Fuel Ignition Delay Analyzer (AFIDA) are presented. The data show that some prenol blends have longer IDTs (lower reactivity) than either neat prenol or the base fuel, providing further evidence of hyperboosting. Additionally, more blending data is presented in which the base fuel is varied, which allows for a better understanding of hyperboosting sensitivity to chemical classes. The data indicate that aromatics may inhibit, and branched alkanes may enhance the magnitude of hyperboosting observed. Enthalpy of vaporization estimates are also given for several molecules which are blended into a 4-component surrogate. These estimates are derived from Equation of State simulations and reveal that there is no observable correlation between hyperboosting and enthalpy of vaporization. Blending data for molecules which share structural similarities with prenol are also presented. Structure property relationships are suggested, in which the double bond motif of prenol seems to play an important role in hyperboosting. This may help to understand hyperboosting and its underlying mechanism. Lastly, blending curves of surrogate blends with prenol experienced hyperboosting under lean (Homogeneous Charged Compression Ignition-HCCI) operating conditions, which validates that hyperboosting is not an artifact of the octane test methods, but inherent to the properties of prenol.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

A multi-product landscape life-cycle assessment approach for evaluating local climate mitigation potential

Increasing demand for land-based climate mitigation requires more efficient management of agricultural landscapes for competing objectives. Here we develop methods for assessing trade-offs and synergies between intensification and carbon-sequestering conservation measures in annual crop production landscapes using the DayCent ecosystem model and the Greenhouse Gases, Regulated Emissions, and Energy Use in Technologies (GREET) life-cycle assessment (LCA) model. We compiled county-scaled crop yields, fertilizer application rates, and tillage intensity for a corn–soybean farming case study landscape in the US state of Iowa. Using DayCent, we estimated a baseline soil organic carbon (SOC) accrual rate of 0.29 Mg C ha -1 y -1 driven by historical increases in crop productivity and reductions in tillage intensity. We then simulated the effects of management interventions targeted toward intensification (stover removal) and SOC sequestration (tillage intensity reduction and winter cover crop addition) individually and in combination. We propose a new multi-product landscape–LCA approach that analyzes marginal changes in corn grain, corn stover, and soybean production from the landscape in terms of their value for biofuel production (corn ethanol, soy biodiesel, and cellulosic ethanol from stover) and associated net displacement of conventional fossil-derived fuel use. This enables us to evaluate both intensification and sequestration effects in common CO 2 -equivalent mitigation units. We also used DayCent-simulated yields under the different land management scenarios to estimate farm-level costs and revenues. Our results show that intensification via collecting 30% of corn stover for biofuel production would increase the total greenhouse gas (GHG) mitigation potential of this landscape by 0.93 Mg CO 2 e ha -1 y -1 and provide $49 ha -1 y -1 of additional net revenue from biomass sales, but would reduce the baseline SOC accumulation rate by approximately 40%. In contrast, integrated approaches that include co-adoption of winter cover cropping and/or tillage intensity reduction would result in increased rates of SOC accumulation above the baseline, achieving simultaneous improvements in both farm profits and the overall GHG mitigation potential of the landscape.

54 ENVIRONMENTAL SCIENCES↗