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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Impact of vertical and seasonal variation in leaf traits on simulating soybean canopy photosynthesis via 1D and 3D modeling

Accurate modeling of photosynthesis is crucial for predicting crop productivity and quantifying the carbon cycle in agroecosystems. Leaf traits are essential inputs for modeling canopy photosynthesis. Yet, many existing models still use fixed plant functional type (PTF)-based values to parameterize leaf traits under a big-leaf or two-big-leaf assumption, neglecting their vertical profiles and seasonal changes. This simplification may introduce significant uncertainties in estimating gross primary productivity (GPP). In this study, we simulated soybean GPP and tested the effects of vertical and seasonal variation in three key leaf photosynthetic traits: the maximum carboxylation rate at 25 °C (Vcmax 25 ), leaf chlorophyll content (LCC), and leaf mass per area (LMA) in the 1D-SCOPE and 3D-Helios models. Weekly field measurements were conducted during the growing season of 2024 to support the simulation. We designed ten leaf trait parameterization schemes by incorporating different combinations of vertical profiles and seasonal changes, while assuming homogeneous canopy architecture in both models. Our results revealed that Vcmax 25 vertical and seasonal variation had the strongest influence on simulated GPP in both 1D and 3D models, while LCC and LMA effects were minimal. Particularly, the scheme with an empirically parameterized Vcmax 25 profile achieved comparable performance to the scheme with the measured Vcmax 25 profile. Both 1D-SCOPE and 3D-Helios accurately modeled GPP (SCOPE: R 2 = 0.87, Bias = 0.55 µmol m⁻² s⁻¹; Helios: R 2 = 0.9, Bias = 0.22 µmol m⁻² s⁻¹) under the most complex scheme, and their responses to vertical and seasonal variation in leaf traits were consistent, demonstrating the robustness of our findings. Based on our findings, we propose a scalable framework for parameterizing leaf traits to improve GPP simulations. This study contributes to improving the representation of leaf trait dynamics in canopy-level photosynthesis models, potentially enhancing our ability to predict crop productivity and understand agroecosystem carbon dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Interactive effects of precipitation, temperature, and grazing pressure on the net ecosystem carbon balance of grazing lands across the continental United States

Grazing lands cover approximately one-third of the contiguous United States, support much of the nation's beef production, and are an important component of the U.S. terrestrial carbon budget. In this study, we quantified net ecosystem carbon balance (NECB), the net status of grazing lands as a carbon sink (C-sink) or source (C-source) and a key determinant of soil health and productivity. Our primary objective was to synthesize multiple years of annual NECB across heterogeneous grazing lands across the continental U.S and evaluate annual NECB against physical drivers (mean annual precipitation (MAP), mean annual temperature (MAT), vegetation, and moisture condition) and management practices (grazing pressure index (GPI) and fertilization history). We hypothesized that (1) NECB is higher in mesic and fertilized grasslands; (2) NECB increases with MAP and MAT but decreases with GPI; and (3) interactive effects exist among MAP, MAT, and GPI. Using carbon fluxes measured by eddy covariance towers and methane emissions including both enteric methane and manure derived from stocking rates across seven USDA Long-term Agroecosystem Research Network sites, we found: (1) grazing lands were a C-sink or carbon neutral at most sites; (2) vegetation type, moisture conditions, or fertilization had no significant effect on NECB; (3) NECB increased with MAP and MAT, but decreased with a higher GPI; and (4) MAT had a significant positive effect on NECB when MAP exceeded 750 mm (greater water availability). The effect of GPI on NECB was significantly negative when MAP was below 1000 mm, significantly negative when MAT < 12 °C and significantly positive when MAT > 16 °C. Thus, most grazing lands in our study acted as C-sinks unless water deficit, low temperature, or heavy grazing were interactively present. Understanding how climate and management influence NECB of grazing lands is key to maintaining resilient agroecosystems that secure beef production and sustain rural prosperity.

LTAR↗
DOE OSTI2022

The nitrogen gap in soil health concepts and fertility measurements

Soil nitrogen (N) often limits productivity in agroecosystems, prompting fertilizer applications that increase crop yields but can degrade the environment. Nitrogen's dual role in both productivity and environmental quality should center it in soil health frameworks. We use recent evidence to argue that N availability is an emergent property of the integrated soil biogeochemical system and is strongly influenced by plant traits and their interactions with microbes and minerals. Building upon this, we theorize that the sources of plant and microbial N shift across soil health gradients, from inorganic N dependence in ecologically simple systems with poor soil health to a highly networked supply of organic N in healthy soils; ergo, investments in soil health should increase ecological complexity and the pathways by which plants can access N, leading to more resilient nutrient supplies and yields in a variable climate. However, current N assessment methods derive from a historical emphasis on inorganic N pool sizes and are unable to capture the shifting drivers of N availability across soil health gradients. We highlight the need to better understand the plant-microbial-mineral interactions that regulate bioavailable N as a first step to improving our ability to measure it. We conclude it will be necessary to harness agroecosystem complexity, account for plant and microbial drivers, and carefully integrate external N inputs into soils' internal N network to expand the routes by which N from organic pools can be made bioavailable. Finally, by emphasizing N in soil health concepts, we argue that researchers can accelerate advances in N use efficiency and resiliency.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Molecular and Dual-Isotopic Profiling of the Microbial Controls on Nitrogen Leaching in Agricultural Soils under Managed Aquifer Recharge

Nitrate (NO 3 – ) leaching is a serious health and ecological concern in global agroecosystems, particularly those under the application of agricultural-managed aquifer recharge (Ag-MAR); however, there is an absence of information on microbial controls affecting NO 3 – leaching outcomes. We combine natural dual isotopes of NO 3 – ( 15 N/ 14 N and 18 O/ 16 O) with metagenomics, quantitative polymerase chain reaction (PCR), and a threshold indicator taxa analysis (TITAN) to investigate the activities, taxon profiles, and environmental controls of soil microbiome associated with NO 3 – leaching at different depths from Californian vineyards under Ag-MAR application. The isotopic signatures demonstrated a significant priming effect (P < 0.01) of Ag-MAR on denitrification activities in the topsoil (0–10 cm), with a 12–25-fold increase of 15 N–NO 3 – and 18 O–NO 3 – after the first 24 h of flooding, followed by a sharp decrease in the enrichment of both isotopes with ~80% decline in denitrification activities thereafter. In contrast, deeper soils (60–100 cm) showed minimal or no denitrification activities over the course of Ag-MAR application, thus resulting in 10–20-fold of residual NO 3 – being leached. Metagenomic profiling and laboratory microcosm demonstrated that both nitrifying and denitrifying groups, responsible for controlling NO 3 – leaching, decreased in abundance and potential activity rates with soil depth. TITAN suggested that Nitrosocosmicus and Bradyrhizobium, as the major nitrifier and denitrifier, had the highest and lowest tipping points with regard to the NO 3 – changes (P < 0.05), respectively. Overall, our study provides new insight into specific depth limitations of microbial controls on soil NO 3 – leaching in agroecosystems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

From Depletion to Restoration: Lessons From Long‐Term Monitoring of Carbon Gains and Losses in Cropping Systems

As global atmospheric CO 2 rapidly approaches a key tipping point, there is an urgent need to implement strategies to reverse this pattern. A generally accepted understanding of carbon (C) in agricultural fields includes: (H1) substantial C loss occurs when natural vegetation is converted to crops, (H2) soils typically reach a steady-state C concentration under contemporary practices, and (H3) improved management or crop selection can enhance soil C stocks over time. Significant variability exists, but studies consistently show large C losses from agricultural ecosystems, supporting H1. Although steady-state C levels (H2) are commonly assumed, measuring C gains or losses in mature agroecosystems is challenging. Efforts to increase soil C storage (H3) have limited data due to the diversity of potential practices, compounded by substantial variability in soil C measurements. Here, long-term (7–17 year) ecosystem C flux data from diverse cropping systems revealed that conventionally tilled annual row crops (maize and soybean) act as significant long-term atmospheric C sources, challenging H2. Furthermore, conservation tillage practices reduced C losses compared with conventional tillage but showed minimal evidence for long-term ecosystem C storage, even after 20+ years. This indicates that no-till practices reduce C losses but imply that no soil C is added, challenging H3. By contrast, perennial Miscanthus × giganteus, Panicum virgatum, and restored tallgrass prairie systems store C at the ecosystem scale more effectively than minimally tilled annual row crops. Analysis over multiple years demonstrates significant ecosystem C storage with perennial crops, varying by species, starting in the first year of transition. These findings, although focused on one region, suggest that the assumptions of steady-state C levels and increased storage from conservation practices do not universally apply and that significant changes to agroecosystems are required to increase C storage.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Data for Soil Oxygen Dynamics: A Key Mediator of Tile Drainage Impacts on Coupled Hydrological, Biogeochemical, and Crop Systems

Tile drainage removes excess water and is an essential, widely adopted management practice to enhance crop productivity in the US Midwest and throughout the world. Tile drainage has been shown to significantly change hydrological and biogeochemical cycles by lowering the water table and reducing the residence time of soil water, although examining the complex interactions and feedbacks in an integrated hydrology–biogeochemistry–crop system remains elusive. Oxygen dynamics are critical to unraveling these interactions and have been ignored or oversimplified in existing models. Understanding these impacts is essential, particularly so because tile drainage has been highlighted as an adaptation under projected wetter springs and drier summers in the changing climate in the US Midwest. We used the ecosys model that uniquely incorporates first-principle soil oxygen dynamics and crop oxygen uptake mechanisms to quantify the impacts of tile drainage on hydrological and biogeochemical cycles and crop growth in corn–soybean rotation fields. The model was validated with data from a multi-treatment, multi-year experiment in Washington, IA. The relative root mean square error (rRMSE) for the corn and soybean yield in validation is 5.66 % and 12.57 %, respectively. The Pearson coefficient (r) of the monthly tile flow during the growing season is 0.78. Plant oxygen stress turns out as an emergent property of the equilibrium between the soil oxygen supply and biological demand. The impact of tile drainage on the system is achieved through a series of coupled feedback mechanisms. The model results show that tile drainage reduces the soil water content and enhances soil oxygenation. It additionally increases the subsurface discharge and elevates inorganic nitrogen leaching, with seasonal variations influenced by climate and crop phenology. The improved aerobic condition alleviates crop oxygen stress during wet springs, thereby promoting crop root growth during the early growth stage. The development of greater root density, in turn, mitigates water stress during dry summers, leading to an overall increase in the crop yield by ∼6 %. These functions indicate the potential of tile drainage in bolstering crop resilience to climate change and the use of this modeling tool for large-scale assessments of tile drainage. The model reveals the underlying causal mechanisms that drive the agroecosystem response to drainage on the coupled hydrology, biogeochemistry, and crop system dynamics.

Modeling↗
DOE OSTI2022

Integrated Modeling of U.S. Agricultural Soil Emissions of Reactive Nitrogen and Associated Impacts on Air Pollution, Health, and Climate

Agricultural soils are leading sources of reactive nitrogen (Nr) species including nitrogen oxides (NO x ), ammonia (NH 3 ), and nitrous oxide (N 2 O). Here, the propensity of NO x and NH 3 to generate ozone and fine particulate matter (PM) and associated impacts on health are highly variable, whereas the climate impacts of long-lived N 2 O are independent of emission timing and location. However, these impacts have rarely been compared on a spatially-resolved, monetized basis. In this study, we update the nitrogen scheme in an agroecosystem model to simulate the agricultural emissions of Nr from across the United States. We then apply a reduced-form air pollution health effects model to assess air quality impacts from NO x and NH 3 and a social cost of N 2 O to assess the climate impacts. Assuming an $\$$8.2 million Value of a Statistical Life (VSL) and a $\$$13,100/ton social cost of N 2 O, the air quality impacts are a factor of ~7 to 15 times as large as the climate impacts in heavily populated coastal regions, whereas the ratios are closer to 2.5 in sparsely populated regions. Our results show that air pollution, health, and climate should be considered jointly in future assessments of how farming practices affect Nr emissions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Field‐scale analysis of miscanthus production indicates climate change may increase the opportunity for water quality improvement in a key Iowa watershed

Abstract The Raccoon River Basin is the primary source for drinking water in Iowa's largest city and plays a major role in the Mississippi River Basin's high nutrient exports. Future climate change may have major impacts on the biological, physiological, and agronomic processes imposing a threat to ecosystem services. Efforts to reduce nitrogen (N) loads within this basin have included local litigation and the implementation of the Iowa Nutrient Reduction Strategy, which suggest incorporating bioenergy crops (i.e., miscanthus) within the current corn–soybean landscape to reach a 41% reduction in nitrate loads. This study focuses on simulating N export for historical and future land use scenarios by using an agroecosystem model (Agro‐IBIS) and a hydrology model (THMB) at the 500‐m resolution, similar to the scale of agricultural fields. Model simulations are driven by CMIP5 climate data for historical, mid‐century, and late‐century under the RCP 4.5 and 8.5 warming projections. Using recent crop profit analyses for the state of Iowa, profitability maps were generated and nitrogen leaching thresholds were used to determine where miscanthus should replace corn–soybean area to maximize reductions in N pollution. Our results show that miscanthus inclusion on low profit and high N leaching areas can result in a 4% reduction of N loss under current climate conditions and may reduce N loss by 21%–26% under future climate conditions, implying that water quality has the potential continue to improve under future climate conditions when strategically implemented conservation practices are included in future farm management plans.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Approximately 15% of Miscanthus yield is lost at current commercial cutting heights in Iowa

Various works have quantitatively characterized the effects of environmental and management factors on Miscanthus x giganteus Greef et Deu (mxg) yield and, therefore, anticipated land requirement per unit production. However, little work has addressed the effects of cutting height, which may significantly contribute to the difference between the standing aboveground biomass at harvest (i.e., biological yield) and harvested yield. This study quantitatively characterized the effect of cutting height using a replicated nitrogen trial of a 5-year-old mxg stand in southeast Iowa and related this information to observations of cutting height in nearby commercial fields. Nitrogen fertilizer did not significantly change the relationship of the stem segment mass to length, and overall, a 1-cm stem segment contributes 0.5% of the total stem biomass within the bottom 44 cm of the stem. This results in an average harvest loss of 15% of the aboveground standing biomass when cutting at 30 cm, typically seen in commercial mxg fields in eastern Iowa. Cutting height should be considered when accurately predicting commercial mxg harvest yields and changes in soil organic carbon in a commercial mxg agroecosystem.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Manganese concentration influences nitrogen cycling in agricultural soil

Manganese (Mn) can modulate nitrogen (N) transformations in soil, yet its role in agroecosystems remains understudied. We conducted a 51-day microcosm incubation with agricultural soils differing in long-term N history (N 0 , no added N; N 1 , added 225 kg N ha −1 for a duration of 27 years) and amended with soluble Mn at 0 (M 0 ), 50 (M 1 ), or 250 (M 2 ) mg kg −1 and Glyceria striata (Lam.) residue. In N 1 soils, Mn additions (both M 1 and M 2 ) lowered total mineral N by 25 % relative to N 1 M 0 at day 51 and reduced 51-day cumulative N₂O by 32 % (N 1 M 1 ) and 42 % (N 1 M 2 ) vs. N 1 M 0 , whereas effects in N 0 were negligible. Mn also depressed ammonia-oxidizing bacterial amoA gene transcripts at day 15 in N 1 M 2 vs. N 1 M 0 (2.5 fold change). This reduction was likely due to increased N loss via complete denitrification to N₂ through microbial pathways such as nitrate/nitrite-dependent manganese oxidation (NDMO), where bacteria directly used the added Mn 2+ to reduce nitrate (NO 3 − ) and nitrite (NO 2 − ) to N 2 or as Mn dependent-ammonia oxidation (Mnammox) where bacteria oxidized ammonium (NH 4 + ) to N 2 , using Mn oxides as electron acceptors that formed from the oxidation of the added Mn. Other contributing mechanisms may include Mn-induced N immobilization, toxicity, and changes in the microbial community. These mechanistic results indicate that background Mn availability and redox dynamics can shape nitrification–denitrification pathways under N-rich conditions. In conclusion, we highlight how native Mn pools and redox state may help explain observed variability in N losses and greenhouse gas production across agricultural soils.

Decomposition↗
DOE OSTI2020

Electrical monitoring of saline tracers to reveal subsurface flow pathways in a flat ditch-drained field

Time-lapse electrical resistivity imaging (ERI) combined with salt tracers can enhance our understanding of subsurface flow and transport in ditch-drained agroecosystems. In a nearly level field adjacent to a drainage ditch, we installed 192 electrodes in a 72-m2 plot along with five hydrometric stations to enable time-lapse ERI monitoring during storms. Immediately prior to a wintertime rainstorm, we applied 5 kg of KBr to a shallow trench located roughly 11 m upgradient from the ditch. Antecedent conditions prior to the storm were wet, assuring a hydrologically connected state that favored tracer dissolution and movement in the subsurface. We monitored the evolution of the tracer plume with ERI over a seven-day period. Four-dimensional (4D) delineation of precipitation and tracer transport using time-lapse ERI showed that the tracer plume moved preferentially downward through a discontinuity in the argillic horizon within the first 57 hrs. Over the following 79 hrs, the tracer plume exhibited steady, lateral movement towards the drainage ditch within a more permeable sandy horizon situated just below the argillic. A spatial moment analysis of logarithmic changes in conductivity in the sand layer revealed seepage velocities ranging from 0.0027 to 0.0482 m d-1; these values were positively correlated with hydraulic gradients, suggesting laminar flow processes typical of sandy aquifers. Increasing groundwater velocities with hydraulic gradients suggest that solute transfers from groundwater to ditch are likely to be faster in storms, especially during wet periods. Even so, predominantly slow lateral transport within the sandy horizon, even during storms, indicates that critical source areas of pollutant loss are likely within a few meters of the ditch.

Robinson, Judith L.↗
DOE OSTI2022

Airborne hyperspectral imaging of cover crops through radiative transfer process-guided machine learning

Cover cropping between cash crop growing seasons is a multifunctional conservation practice. Timely and accurate monitoring of cover crop traits, notably aboveground biomass and nutrient content, is beneficial to agricultural stakeholders to improve management and understand outcomes. Currently, there is a scarcity of spatially and temporally resolved information for assessing cover crop growth. Remote sensing has a high potential to fill this need, but conventional empirical regression operated with coarse-resolution multispectral data has large uncertainties. Therefore, this study utilized airborne hyperspectral imaging techniques and developed new process-guided machine learning approaches (PGML) for cover crop monitoring. Specifically, we deployed an airborne hyperspectral system covering visible to shortwave-infrared wavelengths (400–2400 nm) to acquire high spatial (0.5 m) and spectral (3–5 nm) resolution reflectance over 23 cover crop fields across Central Illinois in March and April of 2021. Airborne hyperspectral surface reflectance with high spectral and spatial resolution can be well matched with field data to quantify cover crop traits. Furthermore, the PGML models were pre-trained by synthetic data from soil-vegetation radiative transfer modeling (one million records), and then fine-tuned with field data of cover crop biomass and nutrient content. Results show that airborne hyperspectral data with PGML can achieve high accuracy to predict cover crop aboveground biomass (R 2 = 0.72, relative RMSE = 15.16%) and nitrogen content (R 2 = 0.69, relative RMSE = 16.59%) through leave-one-field-out cross-validation. Unlike the pure data-driven approach (e.g., partial least-squares regression), PGML incorporated radiative transfer knowledge and obtained higher predictive performance with fewer field data. Meanwhile, with field data for model fine-tuning, PGML predicted biomass more accurately than the inversion of radiative transfer models. Here we also found that the red edge has a high contribution in quantifying aboveground biomass and nitrogen content, followed by green and shortwave spectra. This study demonstrated the first attempt of utilizing hyperspectral remote sensing to accurately quantify cover crop traits. We highlight the strength of PGML in exploiting sensing data to quantify ecosystem variables to advance agroecosystem monitoring for sustainable agricultural management.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Multifunctional landscapes for dedicated bioenergy crops lead to low-carbon market-competitive biofuels

Switchgrass is a promising feedstock for cellulosic biorefineries, due to its ability to maintain comparatively high biomass yields across a wide range of soil and climatic conditions. However, there is an incomplete understanding of the economic and environmental tradeoffs associated with cultivating switchgrass on low-productivity land for conversion to biofuels. This study surveys prior literature and demonstrates a new integrated assessment framework, including agroecosystem, ecosystem services valuation, technoeconomic, and life-cycle assessment models, to quantify and contextualize the economic and environmental impacts of switchgrass cultivation on marginal land with downstream conversion to biofuels. Monetizing and incorporating the value of ecosystem services, such as improved water quality benefits from nitrate and sediment reductions, climate change mitigation benefits from CO 2 emission reduction, and recreational and pollination benefits from increased biodiversity, the modeled multifunctional landscape reduces the ethanol production cost by 33.3–58.9 cents/$\scriptsize{L}$-gasoline-equivalent ($\$$1.3–2.2/gge). Planting switchgrass in low productivity land improves soil health, resulting in the carbon footprint reduction credit of 12.8–20.2 gCO 2e /MJ. For an improved switchgrass-to-ethanol conversion pathway with the maximum benefits from ecosystem services, the minimum ethanol selling price and carbon footprint of ethanol, respectively, could reach to 31 cents/$\scriptsize{L}$-gasoline-equivalent (47% reduction relative to average gasoline price) and 3 gCO 2e /MJ (97% reduction relative to gasoline). In conclusion, this low carbon renewable ethanol leads to substantial State and/or Federal policy incentives (~$\$$1/$\scriptsize{L}$-gasoline-equivalent) providing a large benefit to biorefinery operators, farmers, and the public as a whole.

09 BIOMASS FUELS↗
DOE OSTI2021

Predicting measures of soil health using the microbiome and supervised machine learning

Soil health encompasses a range of biological, chemical, and physical soil properties that sustain the commercial and ecological value of agroecosystems. Monitoring soil health requires a comprehensive set of diagnostics that can be cost-prohibitive for routine analyses. The soil microbiome provides a rich source of information about soil properties, which can be assayed in a high-throughput, cost-effective way. We evaluated the accuracy of random forest (RF) and support vector machine (SVM) regression and classification models in predicting 12 measures of soil health, tillage status, and soil texture from 16S rRNA gene amplicon data with an operationally relevant sample set. We validated the efficacy of the best performing models against independent datasets and also tested best practices for processing microbiome data for use in machine learning. Soil health metrics could be predicted from microbiome data with the best models achieving a Kappa value of ~0.65, for categorical assessments, and a R2 value of ~0.8, for numerical scores. Biological health ratings were better predicted than chemical or physical ratings. Validation with independent datasets revealed that models had general predictive value for soil properties, including yield. The ecological profiles of several taxa important for model accuracy matched the observed relationships with soil health, including Pyrinomonadaceae, Nitrososphaeraceae, and Candidatus Udeaobacter. Models trained at the highest taxonomic resolution proved most accurate, with losses in accuracy resulting from rarefying, sparsity filtering, and aggregating at higher taxonomic ranks. Furthermore, our study provides the groundwork for developing scalable technology to use microbiome-based diagnostics for the assessment of soil health.

16S rRNA gene↗
DOE OSTI2022

Long-term compost amendment modulates wheat genotype differences in belowground carbon allocation, microbial rhizosphere recruitment and nitrogen acquisition

The implementation of soil health-promoting practices, such as cover cropping and compost application, has important implications for nutrient cycling and management in agroecosystems. At the same time, plant belowground carbon (C) allocation patterns can influence nutrient cycling and availability in soil through changes to the microbial community, but the effects may depend on the crop genotype and management practices in place. We evaluated belowground C allocation patterns using 13 C labeling and root architecture in two genotypes of winter wheat (Triticum aestivum) with different levels of exudation and belowground allocation strategies in soils with contrasting compost amendment legacy (108.7 Mg ha -1 every 2 years over 10 years vs. no compost). We also measured microbial community structure and function in the rhizosphere and quantified uptake of residue-derived N from 15 N-labelled cover crop residues. We found an interactive effect between soil management and genotype, where in the no-compost soil, the high-exudation genotype (Snowmass) increased exudation by over 4-fold, while the low-exudate genotype (Byrd) increased only 2-fold. While we did not observe genotype differences in rhizosphere enzyme activity or dissolved N pools, residue N uptake was 1.8 times greater for Snowmass in the compost-amended soil. There were more rhizosphere microbial taxa associated with the high-exudate genotype (Snowmass); nine bacterial and seven fungal families were indicative of Snowmass, versus one bacterial and four fungal families for Byrd. Our results suggest that the high-exudation strategy can influence the rhizosphere microbial community, and lead to greater short-term residue N uptake in high SOM soil. By directly linking root architecture, exudation, microbial communities, and N mineralization and uptake dynamics, this work demonstrates that plasticity in root C allocation is genotype-specific and influences microbial communities and nutrient cycling depending on the soil health context.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Manganese effects on plant residue decomposition and carbon distribution in soil fractions depend on soil nitrogen availability

Recent studies have highlighted the critical role of manganese (Mn) in plant litter decomposition and soil organic carbon (C) cycling in forest ecosystems. Long term nitrogen (N) deposition and N fertilization can increase soil acidity and mobilize bioavailable Mn (Mn 2+ ) in soil. However, no studies have examined the interactive effect of N and Mn fertilization on litter decomposition and carbon distribution in agricultural soils, despite agroecosystems being subject to both N and Mn management. We hypothesized that increased soil N and Mn availability would accelerate plant residue decomposition and transfer of its C to mineral-associated organic matter (MAOM), and that the combined effect of Mn and N enrichment would be greater than the individual effect. Here, we conducted a laboratory incubation experiment by adding 13 C-labeled residue of perennial grass Glyceria striata (Lam.) to agricultural soils that had received 225 kg N ha –1 yr –1 for 27 years (N 1 ) and comparable soils that received no N (N 0 ). Before the experiment, these soils also received three levels of dissolved Mn 2+ , designated M 0 (no additional Mn), M 1 (50 mg kg –1 ), or M 2 (250 mg kg –1 ). We measured total CO 2 production as well as distribution of 13 C from the residue into CO 2 , particulate organic matter (POM), MAOM, and dissolved organic carbon (DOC) over a 1-year period. Manganese amendments significantly increased CO 2 production from residue decomposition in the N 1 soil, but no such effect was observed in the N 0 soil. Manganese also accelerated the loss of residue-derived C from POM and DOC, but increased its recovery in MAOM. However, the positive effect of added Mn in decomposition and recovery in MAOM in the presence of N fertilization occurred only during the initial 30-day decomposition period, where M 2 showed a 12% increase in cumulative CO 2 production from residue, 8% increase in POM loss, and 43% increase in recovery of residue C in MAOM compared to M 0 . For M 1 , only CO 2 emission from residue was significantly higher than Mo during this period. At 365 days M 2 showed 8% increase in CO 2 production, 1% increase in POM loss, and 16% increase in recovery of residue C in MAOM compared to M 0 , but none of these were statistically significant (p < 0.05). This study adds to the growing evidence that increasing Mn availability enhances plant litter decomposition. However, the occurrence and magnitude of Mn-induced stimulation of decomposition is context specific. Further investigation with greater temporal resolution, involving a multitude of litter and soil types and including microbial compositional and functional characterization, is recommended to fully elucidate the interactive role of Mn and N on C cycling.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Soil erosion and lateral carbon fluxes from corn stover-derived biofuel

Abstract Crop residues hold promise to alleviate food vs. fuel competition and contribute to biofuel production. However, the impacts of lateral sediment and carbon fluxes caused by residue removal are not fully understood. Here we employ agroecosystem modeling to conservatively estimate lateral sediment and carbon fluxes resulting from partial corn stover removal in the U.S. Midwest. Results show substantial increases in soil erosion resulting from corn stover removal. For example, the area of continuous corn and corn soybean cropping systems exceeding soil erosion tolerance threshold could increase from 1.1 to 13.3% because of 66% corn stover removal. Depending on removal intensity, conservation, and crop rotation, the stover removal-induced increases in eroded soil organic carbon is equivalent to 3.9–12.5 gCO 2 e MJ −1 , which is comparable to other components of the life cycle impacts of corn stover-derived biofuel. Our findings highlight the need to consider the soil erosion and lateral carbon fluxes impacts of corn stover removal in designing supply chains for cellulosic biofuel production.

Science & Technology - Other Topics↗