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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Using high-throughput amplicon sequencing to determine diet of generalist lady beetles in agricultural landscapes

Determining feeding relationships is central to understanding biological control potential in the field. However, methods to differentiate actual (or realized) feeding relationships from potential feeding relationships is lacking especially for small, generalist predators such as lady beetles. In this study, we used DNA metabarcoding approaches to characterize actual feeding relationships of lady beetles (Coccinellidae) in the field and validated our methods with a lab study. We first asked whether high-throughput amplicon sequencing (HTS) can characterize diets of lady beetles ranging from monotypic diets to diverse diet mixtures in the lab. We then examined whether diet composition and breadth of lady beetles collected from different habitat types in southern WI varied between monocultures of soybean and corn, diverse tallgrass prairie, and urban habitats. Lastly, we asked whether different body or tissue types (partial-body versus whole-body specimens) would change the likelihood of prey detection for both studies. In our controlled lab study, we found that HTS can accurately assess diet composition and diet breadth for lady beetle populations, but at the individual level, HTS has limitation for individuals feeding on more than three species of prey at any given time. In our field study, we documented lower prey richness and diet breadth in corn compared to soybean and grassland, and greater prey DNA in regurgitants than in whole-body specimens. Finally, we found that reduced diet diversity was associated with an increased prevalence of intraguild predation, but that habitat and prey diversity are not necessarily correlated. The prey detection rates (proportion of specimens with prey DNA) in our study were comparable to other studies (55% in the lab study, 33% in field study) and varied with diet composition and habitat type. Furthermore, the relatively low detection rates suggest that many consumer individuals would need to be assayed to fully assess diet diversity, especially in diverse systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

How profitable is switchgrass in Illinois, USA? An economic definition of marginal land

Decisions regarding the conversion of land from an existing crop to bioenergy crops are critical for the sustainable production of both food and fuels. This study seeks to establish criteria for delineating land as “economically marginal”, and thus suited for growing switchgrass. In this case study of an Illinois agricultural field, the profitability of switchgrass, with farmgate prices of $\$44$ Mg –1 , $\$66$ Mg –1 , or $\$88$ Mg –1 , was compared to corn and soybean crop prices. Further, the study also evaluates the profitability of switchgrass when replacing corn-based yield estimates from the Soil Productivity Index (SPI) of Illinois. Based on a dry-matter yield of 10.45 Mg ha –1 , switchgrass can compete with soybeans only at the high price of $\$88$ Mg –1 , but depending on location, can compete with corn at $\$66$ Mg –1 . Across Illinois, at $\$88$ ha –1 , all Illinois land with SPI < 100% and 95% of land under SPI class C (SPI 100–116) is profitable under switchgrass. Switchgrass may not be profitable relative to corn grown in the SPI class A (SPI > 133) and only 7% of class B (SPI 117–132). Our results show that land with drainage and erosion limitations is economically marginal when corn and soybean yields are low, and the farmgate price for switchgrass is greater than $\$66$ Mg –1 . However, this may not be possible on land where switchgrass is replacing frequent soybean rotations (corn–soybean ratio ≤ 1). Land used to produce only soybeans may only be marginal at the farmgate price of $\$88$ Mg –1 . Further studies need to be conducted to identify how much land can be converted to switchgrass without harming corn production.

09 BIOMASS FUELS↗
DOE OSTI2021

Grassland-to-cropland conversion increased soil, nutrient, and carbon losses in the US Midwest between 2008 and 2016

After decades of declining cropland area, the United States (US) experienced a reversal in land use/land cover change in recent years, with substantial grassland conversion to cropland in the US Midwest. Although previous studies estimated soil carbon (C) loss due to cropland expansion, other important environmental indicators, such as soil erosion and nutrient loss, remain largely unquantified. In this study, we simulated the environmental impacts from the conversion of grassland to corn and soybeans for 12 US Midwestern states using the EPIC (Environmental Policy Integrated Climate) model. Between 2008 and 2016, over 2 Mha of grassland were converted to crop production in these states, with much less cropland concomitantly abandoned or retired from production. The net grassland-cropland conversion increased annual soil erosion by 7.9%, nitrogen (N) loss by 3.7%, and soil organic carbon loss by 5.6% relative to that of existing cropland, despite an associated increase in cropland area of only 2.5%. Notably, the above estimates represent the scenario of converting unmanaged grassland to tilled corn and soybeans, and impacts varied depending upon crop type and tillage regime. Corn and soybeans are dominant biofuel feedstocks, yet the grassland conversion and subsequent environmental impacts simulated in this study are likely not attributable solely to biofuel-driven land use change since other factors also contribute to corn and soybean prices and land use decisions. Nevertheless, our results suggest grassland conversion in the Upper Midwest has resulted in substantial degradation of soil quality, with implications for air and water quality as well. Additional conservation measures are likely necessary to counterbalance the impacts, particularly in areas with high rates of grassland conversion (e.g. the Dakotas, southern Iowa).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

An Unmanned Aerial System (UAS) for concurrent measurements of solar-induced chlorophyll fluorescence and hyperspectral reflectance toward improving crop monitoring

Unmanned aerial system (UAS)-based remote sensing can provide high spatial- and temporal-resolution crop monitoring for precision management. Existing crop monitoring UAS primarily use conventional techniques such as multi-spectral broadband vegetation indices (VIs) to track canopy structure/biomass. Recently developed lightweight hyperspectral sensors can track crop physiology and performance via complementary signals such as solar-induced chlorophyll fluorescence (SIF) and photochemical reflectance index (PRI) that can be derived from hyperspectral reflectance. However, few existing UAS can acquire high-quality SIF and hyperspectral reflectance. Here, we designed a novel UAS that simultaneously captures far-red SIF and hyperspectral reflectance, using a single bifurcated fiber and motorized arm to measure downwelling and upwelling irradiance. The UAS was tested over (1) a heterogeneous corn field with spatially varying crop yield potential and (2) a set of soybean and corn plots under differing nutrient treatments. Our UAS can maintain stability under low-to-moderate winds (2–3 m/s) with <0.4 m geolocation accuracy, <0.1 m altitude drift, and <1.5° error of the fiber optic from nadir position during scanning. Weekly seasonal campaigns reveal that SIF outperforms conventional VIs such as the normalized difference vegetation index (NDVI) for distinguishing plots with different crop yield potential, especially after canopy closure when NDVI tends to saturate. The UAS can capture the diurnal dynamics of SIF and PRI for both corn and soybean. At the seasonal scale, SIF acquired from the UAS correlates well with that from a fixed SIF tower system ( R 2 = 0.81), which measured the same target albeit with different footprints, demonstrating the capability of UAS in characterizing the seasonal progression of crop activity. In conclusion, our newly developed UAS provides high-quality SIF and hyperspectral reflectance, facilitating mechanistic understanding of the physiological control on photosynthesis dynamics. Our findings also imply that this UAS could enable extension of fixed tower-based systems to monitor multiple targets from diurnal to seasonal scales and improve monitoring of heterogeneous fields.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Coupling localized Noah-MP-Crop model with the WRF model improved dynamic crop growth simulation across Northeast China

Croplands play a critical role in regulating the energy and moisture exchanges between the land surface and atmosphere. However, the interactions between cropland and climate are usually poorly represented due to a lack of detailed representation in crop types and field management. Here, we coupled the Noah-MP-Crop model with the state-of-the-art Weather Research and Forecasting (WRF) model to explore and evaluate the crop growth dynamics in response to climate variations across Northeast China. The default parameters of the crop model were not exactly suitable for the agricultural ecosystems in Northeast China. The detailed cropland distribution, and crop phenology parameters including growing degree days (GDD) and planting (harvesting) date were first created using multi-source remote sensing products and reanalysis data, and was then successfully used to simulate the growth and yield for corn and soybean and associated energy exchanges. We also optimized and calibrated other crop parameters using the time-series of the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface products. The modified crop model substantially improved the simulation of crop growth, plant physiology, and biomass accumulation for both corn and soybean. Coupling the localized dynamic crop model into the WRF led to considerable decreases in the simulated mean-absolute-errors (MAEs) and biases of the leaf area index, evapotranspiration, and gross primary production compared with the MODIS observed values. Compared with the statistical yield from each province, the modified crop model underestimated the corn yield from 11.1% to 48.6%, whereas overestimated the soybean yield from 16.5% to 162.6%.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Quantifying carbon budget, crop yields and their responses to environmental variability using the ecosys model for U.S. Midwestern agroecosystems

As one of the major agricultural production areas in the world, the United States (U.S.) Midwest plays a vital role in the global food supply and agricultural ecosystem services. Although significant efforts have been made in modeling the carbon cycle dynamics over this area, large uncertainty still exists in the previous simulations in terms of reproducing individual components of the carbon cycle and their responses to environmental variability. Here we evaluated the performance of an advanced agroecosystem model, ecosys, in simulating carbon budgets over the U.S. Midwest, considering both the magnitude of carbon flux/yield and its response to environmental (climate and soil) variability. We conducted model simulations and evaluations at 7 cropland eddy-covariance sites as well as over 293 counties of Illinois, Indiana, and Iowa in the U.S. Midwest. The site-level simulations showed that ecosys captured both the magnitude and seasonal patterns of carbon fluxes (i.e., net ecosystem carbon exchange (NEE), ecosystem gross primary production (GPP), and ecosystem respiration (Reco)), leaf area index (LAI), and dynamic plant carbon allocation processes, with R2 equal to 0.92, 0.87, 0.87, and 0.78 for GPP, NEE, Reco, and LAI, respectively across all the sites compared with the observations. For regional scale simulations, ecosys reproduced the spatial distribution and interannual variability of corn and soybean yields with the constraints of observed yields and a new remotely sensed GPP product, with R2 of multi-year averaged simulated and observed yield equal 0.83 and 0.80 for corn and soybean, respectively. The simulated responses of carbon cycle dynamics to environmental variability were consistent with that from the empirical observations at both site and regional scales. Furthermore, our results demonstrated the applicability of ecosys in simulating the carbon cycle and soil carbon dynamics of the U.S. Midwestern agroecosystems under different climate and soil conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Chemical Imaging of Fine Mode Atmospheric Particles Collected from a Research Aircraft over Agricultural Fields

Vegetation and crops in agricultural areas are a significant source of both biogenic secondary organic aerosols and of directly emitted primary biological atmospheric particles (PBAPs) composed of plant fragments/debris, fungal spores, pollen, and bacteria among others. Seasonal crop harvesting significantly intensifies regional PBAP emissions and aerosolizes soil organic matter and dust particulates. In this paper, the atmospheric contributions and impact of PBAPs have been insufficiently investigated due to limited real-time observations and ambiguity in their detection by existing measurement techniques. We collected ambient particles on-board an instrumented Beechcraft BE76 Duchess research aircraft flying above actively harvested agriculture fields of corn and soybean crops. In this work, particle samples were analyzed using complementary chemical imaging techniques, including computer-controlled scanning electron microscopy coupled with elemental microanalysis to describe particle-type population and scanning transmission X-ray microscopy to provide quantitative metrics on particle internal heterogeneity. We show the particle-class contribution is consistent between corn and soybean harvesting across altitudes of 100–300 m AGL. The contribution of PBAPs in the size range of 0.15–1.25 μm is estimated to be 10–12% of ~39,000 analyzed particles. Determining viscosity with X-ray microanalysis has shown that the fine-mode organic particles collected are viscous/semisolid (10 2 –10 12 Pa s) while the majority of PBAP fragments are solid (>10 12 Pa s). The observation of submicrometer, solid carbonaceous fragments of biogenic origin has implications for the regional cloud condensation nuclei and ice nuclei budget. Therefore, seasonal harvesting of crops may play an important, yet unrecognized, role in regional cloud formation and climate.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Dynamic urban land extensification is projected to lead to imbalances in the global land-carbon equilibrium

Abstract Human-Earth System Models and Integrated Assessment Models used to explore the land-atmosphere implications of future land-use transitions generally lack dynamic representation of urban lands. Here, we conduct an experiment incorporating dynamic urbanization in a multisector model framework. We integrate projected dynamic non-urban lands from a multisector model with projected dynamic urban lands from 2015 to 2100 at 1-km resolution to examine 1 st -order implications to the land system, crop production, and net primary production that can arise from the competition over land resources. By 2100, future urban extensification could displace 0.1 to 1.4 million km 2 of agriculture lands, leading to 22 to 310 Mt of compromised corn, rice, soybean, and wheat production. When considering increased corn production required to meet demands by 2100, urban extensification could cut increases in yields by half. Losses in net primary production from displaced forest, grassland, and croplands ranged from 0.24 to 2.24 Gt C yr −1 , potentially increasing land emissions by 1.19 to 6.59 Gt CO 2 yr −1 . Although these estimates do not consider adaptive responses, 1 st -order experiments can elucidate the individual role of sub-sectors that would otherwise be masked by model complexity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Data for Clumping Index Estimation With 30°-tilted Cameras in Row Crops: Evaluation of Methods and Segment Size Effects

The clumping index (CI) quantifies the spatial distribution of foliage elements and is essential for accurately estimating the plant area index (PAI), canopy radiative transfer, and photosynthesis. Traditionally, the finite-length averaging method (LX), the gap size distribution method (CC), and a combined approach of CC and LX (CLX) have been applied to instruments like TRAC and digital hemispherical photography to estimate CI. However, a comprehensive evaluation of these methods in row crops remains limited, especially regarding the influence of segment size on CI. Meanwhile, digital cameras offer a cost-effective and user-friendly solution for canopy measurements in row crops, yet their application in this context remains underexplored. In this study, we employed a new approach using a 30°-tilted digital camera to estimate CI in corn and soybean fields, applying the LX, CC, and CLX methods. We systematically assessed the performance of these three methods by combining field measurements in real-world fields with simulations using the LESS 3D radiative transfer model. Our results showed that CLX applied to the whole image and 45° segment offered accurate estimation of CI (bias within ±0.1, RMSE < 0.2) and PAI (bias within ±0.4, RMSE < 1) in real-world fields and LESS simulations. The accuracy of the LX method was highly sensitive to segment size, with the best performance observed at the 15° segment (PAI bias within ±0.4). In contrast, the CC method remained stable across different segment sizes, and its performance was generally comparable to that of LX, except at the 15° segment. Across view zenith angles, CI derived from CC generally showed a continuous increase, while those from LX and CLX followed a rising trend at small zenith angles but began to decline at 68°, likely due to an increasing proportion of no-gap segments. Seasonally, LX tended to show decreasing CI during early growth stages but increased as the canopy matured, whereas CC and CLX showed gradually increasing CI before plateauing at peak PAI. The 30°-tilted camera effectively captured CI variations across different angles and growth stages, making it a practical and robust instrument for row crop canopy structure analysis. Applying these CI methods to digital cameras offers a low-cost and accessible CI estimation alternative, improving canopy structure monitoring accuracy in row crops.

Modeling↗
DOE OSTI2022

Cross-scale sensing of field-level crop residue cover: Integrating field photos, airborne hyperspectral imaging, and satellite data

Conservation tillage practices can bring benefits to agricultural sustainability. Accurate spatial and temporal resolved information of field-scale crop residue cover, which reflects tillage intensity, is highly valuable for evaluating the outcomes of government conservation programs and voluntary ecosystem service markets, as well as facilitating agroecosystem modeling to quantify cropland biogeochemical processes. Remote sensing has the potential to cost-effectively detect crop residue cover, however, existing regional-scale studies were limited by insufficient ground truth data, scale mismatch between coarse satellite pixels and ground data, and the lack of key spectral data for detecting crop residues. Therefore, this study developed an innovative cross-sensing framework to integrate proximal sensing, airborne hyperspectral imaging, and satellite Earth Observation through deep learning to quantify field-level crop residue cover fractions at the regional scale. Specifically, we have collected intensive ground orthographic photos and conducted airborne hyperspectral surveys at corn and soybean fields of Champaign and nearby counties in Illinois, the heartland of the U.S. Corn Belt. Through semi-automatic labeling aided by ResNet-50 and superpixel image segmentation, we obtained 6719 records of ground residue fractions. With these ground data, we developed the 1-dimensional convolution neural network (CNN) model using airborne hyperspectral reflectance, which has 0.5m spatial resolution and 3–5 nm spectral resolution from 400 to 2400 nm, to predict residue fractions. By applying the CNN model to airborne pixels, we augmented “ground truth” data of crop residues and further combined them with Harmonized Landsat and Sentinel-2 (HLS) satellite data to quantify regional residue fractions at 30 m resolution. Results show that airborne hyperspectral imagery with CNN can accurately detect residue fractions (R 2 = 0.82, relative RMSE = 11.73%) to effectively generate quasi “ground truth” data to support satellite upscaling to all fields. With independent ground data for testing, we found that the ground-airborne-satellite integrative framework achieved better predictions in estimating crop residue cover (R 2 = 0.67, relative RMSE = 17.53%) than the conventional ground-satellite upscaling (R 2 = 0.22, relative RMSE = 32.09%). Here we also found that the shortwave infrared wavelengths, particularly 2100–2300 nm, are vital for predicting crop residue cover. Sentinel-2 and Landsat-8 data have a comparable capability to track residue fractions due to similar shortwave infrared wavelengths. This study highlights the high accuracy of hyperspectral imaging to detect agroecosystem tillage management practices and the advantages of cross-scale sensing to cost-effectively integrate multi-source data to quantify field-level agroecosystem variables across scales.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Can upscaling ground nadir SIF to eddy covariance footprint improve the relationship between SIF and GPP in croplands?

Ground solar-induced chlorophyll fluorescence (SIF) is important for the mechanistic understanding of the dynamics of vegetation gross primary production (GPP) at fine spatiotemporal scales. However, eddy covariance (EC) observations generally cover larger footprint areas than ground SIF observations (a bare fiber with nadir), and this footprint mismatch between nadir SIF and GPP could complicate the canopy SIF-GPP relationships. Here, we upscaled nadir SIF observations to EC footprint and investigated the change in SIF-GPP relationships after the upscaling in cropland. We included 13 site-years data in our study, with seven site-years corn, four site-years soybeans, and two site-years miscanthus, all located in the US Corn Belt. All sites’ crop nadir SIF observations collected from the automated FluoSpec2 system (a hemispheric-nadir system) were upscaled to the GPP footprint-based SIF using vegetation indices (VIs) calculated from high spatiotemporal satellite reflectance data. We found that SIF-GPP relationships were not substantially changed after upscaling nadir SIF to GPP footprint at our crop sites planted with corn, soybean, and miscanthus, with R 2 change after the upscaling ranging from -0.007 to 0.051 and root mean square error (RMSE) difference from -0.658 to 0.095 umol m –2 s –1 relative to original nadir SIF-GPP relationships across all the site-years. The variation of the SIF-GPP relationship within each species across different site-years was similar between the original nadir SIF and upscaled SIF. Different VIs, EC footprint models, and satellite data led to marginal differences in the SIF-GPP relationships when upscaling nadir SIF to EC footprint. Furthermore, our study provided a methodological framework to correct this spatial mismatch between ground nadir SIF and GPP observations for croplands and potentially for other ecosystems. Our results also demonstrated that the spatial mismatch between ground nadir SIF and GPP might not significantly affect the SIF-GPP relationship in cropland that are largely homogeneous.

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

Designing Iowa Agricultural Landscapes to Improve Environmental Co-Benefits of Bioenergy Production

Cellulosic bioenergy feedstocks are needed to improve carbon (C) management while provisioning biomass for bioproducts and biofuel. The transition to increased cellulosic biomass production can be guided by land management plans designed to improve economic, environmental, and ecological performance. We constructed a sustainability model to compare landscape designs for biofuel production from corn (Zea mays L.) stover and switchgrass (Panicum virgatum L.) in central Iowa, USA. We used the model to compare environmental and socioeconomic outcomes associated with four landscape management strategies, with and without cellulosic biomass markets. We evaluated (1) a fuelshed area containing over 1.2 million ha (3 million acres) of corn and soybean (Glycine max (L.) Merr.) within 80 km (50 miles) of a commercial-scale cellulosic biorefinery in Nevada, Iowa, and (2) the South Fork watershed containing over 72,000 ha (178,000 acres) of these row crops within eight north central Iowa HUC-12 (hydrologic unit code) watersheds. At both landscape scales, we found that it is possible to achieve multiple environmental and socioeconomic benefits concomitantly with cellulosic biomass production by strategically collecting corn stover and converting the 10% of the lowest-profitability row crop land to perennial switchgrass. Potential benefits from landscape design include increased biodiversity, soil and water quality improvements, increased soil carbon sequestration for climate change mitigation, and reduced fertilizer use and cost. Our model results showed that increasing benefits can accrue when complementary conservation practices (e.g., reduced tillage, use of a rye cover crop) are combined and integrated throughout a fuelshed or watershed area. We conclude that ecologically based landscape designs offer valuable insights about costs and benefits of land management alternatives, with relevance for achieving stakeholder goals.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Microscale spatiotemporal patterns of water, soil organic carbon, and enzymes in plant litter detritusphere

Understanding biophysical and biochemical processes in detritusphere is critical to quantifying and modeling plant residue decomposition dynamics and subsequent soil organic carbon (C) accrual. The objectives of the study were to explore (i) micro-environmental conditions within the detritusphere formed around decomposing corn and soybean leaves, and (ii) the relationships between moisture distribution, enzyme activity, and C dynamics during decomposition. We assessed spatial and temporal dynamics of moisture distribution using X-ray and neutron computed tomography, and activities of β-glucosidase and chitinase, two enzymes involved in soil C and N processing, using zymography. We used 13 C labeled residue to track residue contribution to atmospheric CO 2 and soil organic C. Moisture redistribution pattern varied depending on the residue type. While the water was immediately absorbed by the corn leaves and maintained afterward, switchgrass leaves absorbed water more slowly and created water-deficient zones within ~1mm from the residue. This initial moisture depletion led to lower chitinase activity and residue-derived CO 2 emissions in switchgrass. In contrast to chitinase, β-glucosidase activity was influenced by a combination of vegetation history and residue type, and it was higher when the origin of the residue matched the vegetation history of the soil. Pore size had an opposite impact on the studied enzymes, supporting the notion that contrasting soil pore architecture can stimulate activities of different enzymes through a selection of dominant enzyme producers. We concluded that the decomposition dynamics of plant residues is not only a simple function of residue chemistry, but rather a combined effect of the vegetation history, in part through its effect on microbial community composition, the plant residue chemical and likely physical characteristics, and the soil pore structure in the detritusphere. Together, they create temporally dynamic micro-environmental conditions influencing decomposition. Specifically, our study demonstrated that the initial micro-environment formulated in detritusphere can play an important role in enzyme activities and consequent C dynamics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Planting miscanthus instead of row crops may increase the productivity and economic performance of farmed potholes

Abstract Climate change projections indicate that precipitation events in the central United States are expected to become more intense, more frequent in the spring, and less frequent in the summer. Such a precipitation shift could adversely impact crop yields, especially in subfield areas known as farmed potholes, which are highly susceptible to flooding and ponding, and crop death is more likely to occur, particularly early in the growing season. This suggests that planting alternative crops, such as more flood tolerant perennials, in these areas may be a more profitable option. Using observations of crop growth and yield along with ponding depth of a specific field and farmed pothole in the central United States, we developed a spatially explicit version of the agroecosystem model Agro‐IBIS to estimate water depth and crop yield. After evaluating the model, we conducted a case study for a specific farmed pothole with a range of future precipitation scenarios with Agro‐IBIS to simulate the effects of contemporary (2002–2016) and future precipitation on a conventional corn/soybean ( Zea mays L. and Glycine max Merr.) rotation and an alternative perennial miscanthus ( Miscanthus × giganteus Greef et Deu.) cropping system. The depth and frequency of ponding increased under most future precipitation scenarios. The corn/soybean rotation had greater total loss (i.e., no yield) on average (>30%) for all scenarios in comparison to miscanthus (<10%). Under one future precipitation scenario with increased spring precipitation, both the corn/soybean rotation and miscanthus simulations showed an increase in yield. A simple budget analysis indicated that it is more profitable to plant miscanthus instead of corn or soybeans where yields in farmed potholes are consistently poor. Our findings show that potholes can be individually modeled, and their influence on yield can be quantified for use in future management decisions dictated by change in climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Process-based modeling of soil nitrous oxide emissions from United States corn fields under different management and climate scenarios coupled with evaluation using regional estimates

Direct emissions of soil nitrous oxide during a growing season (N 2 O gs ) can be quantified with process-based models considering interactions between management, climate, and soil moisture when key data are available. We used an adapted “parameterized CENTURY/DAYCENT-model” ( p CENTURY) calibrated with crop growth and soil organic matter decay coefficients at the county-level for the estimation of N 2 O gs in the United States Corn Belt. Model estimated N 2 O-emissions from corn-based biofuels scenarios considering crop rotation, fertilizer inputs, tillage, and weather were compared against meta-summary of field observations from 55 studies. Both model and meta-summary ranked N 2 O gs -emissions to be corn > wheat > soybean phase while model likely underestimated cover crop N 2 O gs -emissions. The N 2 O gs -emissions and the associated emission factors (EFs) were modeled and summarized to be greater after anhydrous ammonia than urea application and from conventional tilled than non-tilled fields. Modeled and observed N 2 O gs -emissions after organic and inorganic fertilizer amendment did not differ due to high variability associated with the treatments. However, the organic fertilizer associated EFs were greater according to meta-summary data because of N input rates. Regionalized weather scenarios indicate hotspots for N 2 O gs -emissions can occur where crop N uptake is limited during dry years and in eastern states also during normal or wet seasons. The p CENTURY-derived N 2 O gs EFs (0.91 ± 0.19%) for counties investigated were only slightly lower than literature (1.07 ± 0.57%) or Tier-1 (1%) values. Our preliminary evaluation of regional soil moisture estimates showed reasonable agreement between monthly soil moisture estimates and the North American Soil Moisture Dataset during the growing season, but overestimation of soil moisture in winter-spring can influence the estimates of annual N 2 O emissions so future work is needed to calibrate soil moisture-associated model parameters. Our work provided scenario-based estimates of climate and management impacts on soil N 2 O gs -emissions together with valuable spatial insights into EFs that will be improved by more accurate information of fertilizer inputs and more temporally refined model evaluation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Clumping index estimation with 30°-tilted cameras in row crops: Evaluation of methods and segment size effects

The clumping index (CI) quantifies the spatial distribution of foliage elements and is essential for accurately estimating the plant area index (PAI), canopy radiative transfer, and photosynthesis. Traditionally, the finite-length averaging method (LX), the gap size distribution method (CC), and a combined approach of CC and LX (CLX) have been applied to instruments like TRAC and digital hemispherical photography to estimate CI. However, a comprehensive evaluation of these methods in row crops remains limited, especially regarding the influence of segment size on CI. Meanwhile, digital cameras offer a cost-effective and user-friendly solution for canopy measurements in row crops, yet their application in this context remains underexplored. In this study, we employed a new approach using a 30°-tilted digital camera to estimate CI in corn and soybean fields, applying the LX, CC, and CLX methods. We systematically assessed the performance of these three methods by combining field measurements in real-world fields with simulations using the LESS 3D radiative transfer model. Our results showed that CLX applied to the whole image and 45° segment offered accurate estimation of CI (bias within ±0.1, RMSE < 0.2) and PAI (bias within ±0.4, RMSE < 1) in real-world fields and LESS simulations. The accuracy of the LX method was highly sensitive to segment size, with the best performance observed at the 15° segment (PAI bias within ±0.4). In contrast, the CC method remained stable across different segment sizes, and its performance was generally comparable to that of LX, except at the 15° segment. Across view zenith angles, CI derived from CC generally showed a continuous increase, while those from LX and CLX followed a rising trend at small zenith angles but began to decline at 68°, likely due to an increasing proportion of no-gap segments. Seasonally, LX tended to show decreasing CI during early growth stages but increased as the canopy matured, whereas CC and CLX showed gradually increasing CI before plateauing at peak PAI. The 30°-tilted camera effectively captured CI variations across different angles and growth stages, making it a practical and robust instrument for row crop canopy structure analysis. Furthermore, applying these CI methods to digital cameras offers a low-cost and accessible CI estimation alternative, improving canopy structure monitoring accuracy in row crops.

30°-tilted camera↗
DOE OSTI2020

Life-cycle analysis of soybean meal, distiller-dried grains with solubles, and synthetic amino acid-based animal feeds for swine and poultry production

Swine and poultry meat production are two important sectors in the U.S. economy. They play an important role in environmental sustainability because they contribute to the greenhouse gas (GHG) emissions as the result of agricultural and production activities. To improve the sustain ability of swine and poultry meat production, it is important to understand the environmental effects and identify the key drivers of the production activities. In this work, we presented an environmental assessment of the production of swine (i.e., pork) and poultry (i.e., broiler chicken). We conducted a life-cycle analysis of formulating animal feeds using soybean meal, corn, distiller-dried grains with solubles (DDGS), and synthetic amino acids as candidate ingredients to produce swine and poultry. We evaluated GHG emissions, fossil fuel consumption, and water consumption from formulating and utilizing a variety of animal feeds based on these ingredients for swine and poultry production, using an expanded version of the Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET (R)) model. With pork and broiler chicken as the finished products, the functional unit was defined as one kg of live-weight animal at the farm gate. Feed production was the major contributor to the life-cycle GHG emissions (88 % and 91 % of the total GHG emissions for swine and poultry production, respectively) and fossil fuel consumption (79 % and 84 % of the total fossil fuel consumption for swine and poultry process, respectively). Among the four ingredient types, amino acids had the biggest GHG emission footprint; however, DDGS had the largest effect on increasing GHG emissions of swine and poultry production.

animal feed production↗