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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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At least 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

XVis: Visualization for the Extreme-Scale Scientific-Computation Ecosystem (Year-end report FY16 Q4)

The XVis project brings together the key elements of research to enable scientific discovery at extreme scale. Scientific computing will no longer be purely about how fast computations can be performed. Energy constraints, processor changes, and I/O limitations necessitate significant changes in both the software applications used in scientific computation and the ways in which scientists use them. Components for modeling, simulation, analysis, and visualization must work together in a computational ecosystem, rather than working independently as they have in the past. This project provides the necessary research and infrastructure for scientific discovery in this new computational ecosystem by addressing four interlocking challenges: emerging processor technology, in situ integration, usability, and proxy analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

The Kokkos Ecosystem [Brief]

In 2016/2017, the field of High-Performance Computing (HPC) entered a new era driven by fundamental physics challenges to produce ever more energy and cost-efficient processors. Since the convergence on the Message-Passing Interface (MPI) standard in the mid-1990s, application developers enjoyed a seemingly static view of the underlying machine — that of a distributed collection of homogeneous nodes executing in collaboration. However, after almost two decades of dominance, the sole use of MPI to derive parallelism acted as a limiter to improved future performance. While MPI is widely expected to continue to function as the basic mechanism for communication between compute nodes for the immediate future, additional parallelism is required on the computing node itself if high performance and efficiency goals are to be realized. When reviewing the architectures of the top HPC systems today, the change in paradigm is clear: the compute nodes of the leading machines in the world are either powered by many-core chips with a few dozen cores each, or use heterogeneous designs, where traditional CPUs marshal work to massively parallel compute accelerators which has as many as 200,000 processing threads in flight simultaneously. Complicating matters further for application developers, each processor vendor has its own preferred way of writing code for their architecture.The Kokkos EcoSystem was released by Sandia in 2017 to address this new era in HPC system design by providing a vendor independent performance portable programming system for scientific, engineering, and mathematical software applications written in the C++ programming language. Using Kokkos, application developers can be more productive because they will not have to create and maintain separate versions of their software for each architecture, nor will they have to be experts in each architecture's peculiar requirements. Instead, they will have a single method of programming for the diverse set of modern HPC architectures. While Kokkos started in 2011 as a programming model only, it soon became clear that complex applications needed more. It is also critical to have a portable mathematical functions and developers need tools to debug their applications, gain insight into the performance characteristics of their codes and tune algorithm performance parameters through automated processes. The Kokkos EcoSystem addresses those needs through its three main components: the Kokkos Core programming model, the Kokkos Kernels math library, and the Kokkos Tools project.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

High-frequency and Vertical Variations of Soil Moisture and their Impact on Ecosystem Dynamics

The vertical distribution of subsurface moisture is a major control on ecosystem dynamics and climate. The Mediterranean climate of California, with wet winters and dry summers, provides a testbed for understanding how transpiration is sustained through extended dry periods. The project developed and tested a new subsurface hydrology model that includes a parameterization of stochastic hydraulic conductivity to capture fracture flow, a diagnostic model of rooting profile that depends on the subsurface hydrology, and a representation of hydraulic redistribution by plant roots. A sensitivity study shows that it is plants with rooting depths >8 m that could access the subsurface moisture. The insight is applied to understand differential tree mortality during the 2012-2016 drought in California. The analysis shows that greater mortality trees on plutonic rocks than on sedimentary rocks. The study demonstrates, for the first time, the importance of lithology for the distribution and health of ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Software Resilience using Kokkos Ecosystem

Due to the cost of hardware failures within mission critical and scientific applications, it is necessary for software to provide a mechanism to prevent or recover from interruptions. The Kokkos ecosystem is a programming environment that provides performance and portability to many applications that run on DOE supercomputers as well as smaller scale systems. These applications require a higher level of service due to the cost associated with each simulation or the critical nature of the mission. Software resilience enables an application of manage hardware failures reducing the cost of an interruption. Two different resilience methodologies have been added to the Kokkos ecosystem: checkpointing has been added for restart capabilities and a resilient execution model has been added to account for failures in compute devices. The design and implementation of each of these additions are described, and appropriate examples are included for end users.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

XVis: Visualization for the Extreme-Scale Scientific Computation Ecosystem. Final Report

Scientific computing is no longer be purely about how fast computations can be performed. Energy constraints, processor changes, and I/0 limitations necessitate significant changes in both the software applications used in scientific computation and the ways in which scientists use them. Components for modeling, simulation, analysis, and visualization must work together in a computational ecosystem, rather than working independently as they have in the past. The XVis project provides the necessary research and infrastructure for scientific discovery in this new computational ecosystem by addressing four interlocking challenges: emerging processor technology, in situ integration, usability, and proxy analysis. This report reviews the accomplishments of the XVis project to prepare scientific visualization for Exascale computing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Plant and shrub tissue decompositions in changing environments after six-years in a Peatland ecosystem

SPRUCE, Spruce and Peatland Responses Under Changing Environments, is a ten-year long ecosystem manipulation that tests the responses of a high-carbon peatland ecosystem to warming and elevated CO2. This experiment is located a Northern Minnesota peatland. Here we studied how plant tissue decomposition is affected by the climate treatments at SPRUCE. Six different litter types were placed into mesh bags and deployed into the peatland. The samples were then removed after six years, and we analyzed those samples in the laboratory. Analysis of these samples consisted of removing foreign growth, drying each sample, weighing each litter bag, grinding into a fine powder, and finally sending each sample off for phosphorus, carbon, and nitrogen analysis. Although we expected to observe effects of warming on decomposition rates, with faster decomposition in the warmer treatments, we only observed faster decomposition in the warmer plots in a few litter types.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

OLCF’s Advanced Computing Ecosystem (ACE): FY25 Update for Ongoing Efforts

The advent of widespread use of artificial intelligence (AI) and machine learning (ML) models in science, coupled with fast data production rates of scientific instruments strain the traditional batch-oriented high-performance computing (HPC) environment. As scientific exploration continues to require more data and faster processing and analysis, new emerging technologies and capabilities to enable cross-facility and time-sensitive workflows are required for seamless integration of HPC and experimental facilities. The Advanced Computing Ecosystem (ACE) is a strategic initiative within the Oak Ridge Leadership Computing Facility (OLCF) established in 2024 to support the development of cutting-edge technologies to advance computational research and infrastructure at OLCF and across the Department of Energy (DOE). Several DOE initiatives are spearheading the evolution of the scientific landscape by blurring facility boundaries and connecting the user facilities to advance scientific capabilities and ensure energy dominance. The DOE Integrated Research Infrastructure (IRI) program is one example that is laying a foundation to support complex cross-facility workflows. The IRI program aims to integrate diverse computational resources, data infrastructures, and scientific instruments to facilitate collaboration and accelerate scientific discovery. The Interconnected Science Ecosystem (INTERSECT) initiative at Oak Ridge National Laboratory (ORNL) is another example that aims to revolutionize scientific research through AI-driven, interconnected autonomous laboratories and research facilities. Finally, the American Science Cloud (AmSC), recently announced in the “One Big Beautiful Bill”, aims to leverage prior infrastructure efforts of the IRI and automation and AI efforts of INTERSECT (and others) to build a federated, AI-augmented AmSC platform to unify the DOE’s computing, experimental, and data resources to catalyze scientific innovation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

SPRUCE Deep Peat Heating (DPH) to Whole Ecosystem Warming (WEW) Metagenomes for Peat Samples Collected June 2016

This data set provides links to the results of metagenomic analyses of 44 peat samples collected on 13 June 2016 from SPRUCE experiment treatment and ambient plots. Experimental plots had received approximately 24 months of belowground warming (deep peat heating (DPH), Hanson et al. 2015) with the last 9 of those months including air warming for implementation of whole ecosystems warming (WEW – Hanson et al. 2016). The SPRUCE experiment site is located in an ombrotrophic bog forest in northern Minnesota, 40 km north of Grand Rapids, in the USDA Forest Service Marcell Experimental Forest (MEF). WEW Metagenomes: Data from these metagenomes are archived in the U.S. Department of Energy Joint Genome Institute (DOE JGI) Integrated Microbial Genomes (IMG) system and are available at the accession numbers provided below (Table 2) and in the accompanying inventory file. The easiest way to find results on IMG is at this link, https://img.jgi.doe.gov/cgi-bin/m/main.cgi, and then enter “June2016WEW” as a search term in the “Quick Genome Search:” box at the top of the page. This work was done in collaboration between ORNL, USFS and the DOE Joint Genome Institute (JGI) study that is targeting understanding the microbial metagenomes of “fungal, bacterial, and archaeal communities mediating C cycling and trace gas flux in peatland ecosystems subject to climate change” (Proposal ID: 1445).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

SPRUCE Radiocarbon analyses quantify peat carbon losses with increasing temperature in a whole ecosystem warming experiment, 2014-2020

This data set contains three *.csv data files with measurements of organic and inorganic carbon fractions and radiocarbon fractions in peat porewater profiles. Measurements of carbon fractions from enclosure drainage outflows are also included. Porewater profiles were collected up to four times annually from 2014 to 2020 with a piezometer and outflow samples were collected up to eight times annually in 2016 and 2017. Porewater organic and inorganic fractions were measured using a Finnigan Mat Delta V Isotope Ratio Mass Spectrometer and a Shimadzu Total Organic Carbon. Radiocarbon analyses were conducted at Lawrence Livermore National Laboratory (LLNL). The peat soils were subjected to deep peat heating (DPH) beginning in June of 2014 followed by whole ecosystem warming (WEW) in August of 2015 (Hanson et al. 2017). The experimental work was conducted in a Picea mariana [black spruce] – Sphagnum spp. bog forest in northern Minnesota, 40 km north of Grand Rapids, in the USDA Forest Service Marcell Experimental Forest (MEF).These changes in these inorganic and organic carbon fractions in response to whole ecosystem warming may alter decomposition and microbial communities, as well as overall soil carbon storage.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

SPRUCE Sphagnum Phytobiome Responses to Whole Ecosystem Warming and Elevated Atmospheric CO2 in July, 2017-2021

This dataset reports the carbon (C) and nitrogen (N) isotopic composition of Sphagnum moss tissue collected from the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experimental site within the Marcell Experimental Forest in northern Minnesota, USA. Tissues were collected inside the SPRUCE experimental study plot, where air and peat warming are combined in whole-ecosystem warming treatments, from 2019-2021. Additionally, rates of nitrogen fixation and methane oxidation measured for Sphagnum from 2017, 2019, and 2021. Sphagnum tissue % water content was measured at all timepoints. This dataset was used to conduct analyses within Petro et al (2023). Sphagnum C and N concentrations and 13C and 15N isotopic natural abundance were determined for three replicate Sphagnum tissue samples collected from inside the SPRUCE enclosures in July 2019-2021. Elemental and isotope analyses were conducted at the University of Georgia – Center for Applied Isotope Studies (CAIS; https://cais.uga.edu/). Plant elemental analysis was performed by the micro-Dumas method, while isotopic natural abundance was measured by isotope ratio mass spectrometry. 13C natural abundance is expressed as the per mille (‰) deviation from the Pee Dee Belemnite standard (PDB) 13C:12C ratio (δ13C), while 15N natural abundance is expressed as the ‰ deviation from the N2 atmospheric 15N:14N ratio (δ15N). Rates of nitrogen fixation and methane oxidation were measured using serum bottle incubations performed with Sphagnum collected inside the SPRUCE enclosures in July 2017, 2019, and 2021. Rates were calculated according to the amount of 15N-N2 or 13C-CH4 (methane) incorporated into the Sphagnum tissue or incubation headspace over the 48-hour intion period. In 2017, incubations were performed in growth chambers set to the temperature of the experimental plots during Sphagnum sampling. In 2019 and 2021, the incubations were performed directly inside the experimental plots. The 13C and 15N isotopic natural abundance data and rate measurements presented in this dataset provide insight into the impacts of whole-ecosystem warming and elevated atmospheric carbon dioxide (CO2) on N and C cycling within the Sphagnum phytobiome. This dataset contains two data files in comma-separate values (*.csv) format. Additional metadata are provided: two data dictionaries and a file-level metadata file in comma-separate values (.csv) format and a user guide in PDF (*.pdf) format. Additional datasets on NH4-N availability, porewater concentrations of CH4 and CO2, and Sphagnum groundcover within the SPRUCE experimental plots are available. These are outlined in the Related Datasets section in the user guide.

delta 13 carbon↗
DOE OSTI2021

Revisiting “Additional Carbon”: Tracking Atmosphere–Ecosystem Carbon Exchange to Establish Mitigation and Negative Emissions From Bio-Based Systems

Climate stabilization plans rely heavily on advanced bioenergy and bioproducts for substitution of fossil-based energy sources and materials, and increasingly, for negative emissions via the direct sequestration of biogenic carbon. Yet, there remain persistent, largely unresolved critiques of bioenergy assessment methodology, particularly in the areas of land use and biogenic carbon accounting. The concept of “additional carbon” calls for evaluating the climate performance of bio-based systems by whether feedstock production creates measurable new local agro-ecosystem uptake of carbon from the atmosphere. This concept is challenging to operationalize for first-generation biofuels, and has largely been advanced as a negative critique. However, carbon additionality is more straightforward to establish—and less critical to overall system mitigation performance—in advanced bioenergy systems. In this Perspective, I review the additional carbon critique, and why it is analytically challenging to address in first-generation biofuel systems based on conventional food crops with large existing markets. Next, I make a case that carbon additionality (1) is more readily achievable with cellulosic feedstocks, (2) is more directly observable for dedicated biomass crops, and (3) is not a strict requirement for achieving net mitigation in carbon-negative bio-based systems. I end by discussing how centering atmosphere–ecosystem carbon exchanges in bio-based system assessment could create new opportunities for enterprise-scale performance monitoring and verification, augmenting and diversifying the current reliance on model-based life-cycle assessment approaches.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Editorial: Forest Rhizosphere Interactions: Cascading Consequences for Ecosystem-Level Carbon and Nutrient Cycling

Mycorrhizal fungimine organicmatter and subsequently transfer plant-available nutrients to roots. While there is disagreement considering the role of mycorrhizal fungi in ecosystem carbon (C) and nutrient cycling relative to factors such as climate (Cotton, 2018; Bennett and Classen, 2020), soil nutrient availability (Brzostek et al., 2015; Frey, 2019), or host plant community composition (Read and Perez-Moreno, 2003), it is now clear that mycorrhizal fungal symbionts control a substantial proportion of C and nutrient flow belowground (Steidinger et al., 2019). Yet, determining the relative importance of arbuscular mycorrhizal (AM) and ectomycorrhizal (EM) fungi to these processes is still an open field of inquiry in ecosystemecology (Averill et al., 2019a) and earth system modeling (Sulman et al., 2019). At large scales, ecosystems dominated by EM fungi are thought to store more soil C per unit nitrogen (N) (Averill et al., 2014), have slower N cycles (Phillips et al., 2013), and respond more strongly to global change drivers such as N-deposition (Jo et al., 2019). However, it is unclear how generalizable these patterns are across sites, and plant and mycorrhizal taxa. If site or species-dependent relationships exist between mycorrhizal plants, fungi, and soil biogeochemical cycles, forecasting hot spots and hot moments of mycorrhizal fungal activities and their consistency across space and time would be evenmore challenging (Averill et al., 2019b; Kivlin and Hawkes, 2020).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Assessment of Mesophotic Coral Ecosystem Connectivity for Proposed Expansion of a Marine Sanctuary in the Northwest Gulf of Mexico: Larval Dynamics

In coral reef ecosystems, mesophotic coral habitat ( >30m to the end of the photic zone) are extensions of shallow reefs and contribute to the persistence of coral reef populations. In the North West Gulf of Mexico (NW GOM), the Flower Garden Banks National Marine Sanctuary (FGBNMS) is an isolated reef ecosystem comprising contiguous shallow and mesophotic reefs habitats on two central banks along the margin of the continental shelf. A future expansion of the sanctuary is proposed to include additional mesophotic banks and aims at building a network of protected areas in the NW GOM to ensure the persistence of the coral reef populations inhabiting the sanctuary. To evaluate the feasibility of this expansion and investigate the overall dynamics of coral species in the region, we studied the patterns of larval connectivity of Montastraea cavernosa, a common depth generalist coral species, using a larval dispersal modeling approach. Our results highlighted larval exports from the NW GOM banks to the northeastern and southwestern GOM, larval connectivity between all banks investigated in this study, and the potential for exporting larvae from mesophotic to shallower reefs. Our study associated with Studivan and Voss (2018; associate manuscript) demonstrates the relevance of combining modeling and genetic methods to consider both demographic and genetic timescales for the evaluation of the connectivity dynamics of marine populations. In the case of the NW GOM, both studies support the future management plan for expanding FGBNMS.

Garavelli, Lysel↗
DOE OSTI2016

Warming Alters Expressions of Microbial Functional Genes Important to Ecosystem Functioning

Soil microbial communities play critical roles in ecosystem functioning and are likely altered by climate warming. However, so far, little is known about effects of warming on microbial functional gene expressions. Here, we applied functional gene array (GeoChip 3.0) to analyze cDNA reversely transcribed from total RNA to assess expressed functional genes in active soil microbial communities after nine years of experimental warming in a tallgrass prairie. Our results showed that warming significantly altered the community wide gene expressions. Specifically, expressed genes for degrading more recalcitrant carbon were stimulated by warming, likely linked to the plant community shift toward more C 4 species under warming and to decrease the long-term soil carbon stability. In addition, warming changed expressed genes in labile C degradation and N cycling in different directions (increase and decrease), possibly reflecting the dynamics of labile C and available N pools during sampling. However, the average abundances of expressed genes in phosphorus and sulfur cycling were all increased by warming, implying a stable trend of accelerated P and S processes which might be a mechanism to sustain higher plant growth. Furthermore, the expressed gene composition was closely related to both dynamic (e.g., soil moisture) and stable environmental attributes (e.g., C 4 leaf C or N content), indicating that RNA analyses could also capture certain stable trends in the long-term treatment. Overall, this study revealed the importance of elucidating functional gene expressions of soil microbial community in enhancing our understanding of ecosystem responses to warming.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

A Real-Time Magnetoencephalography Brain-Computer Interface Using Interactive 3D Visualization and the Hadoop Ecosystem

Ecumenically, the fastest growing segment of Big Data is human biology-related data and the annual data creation is on the order of zetabytes. The implications are global across industries, of which the treatment of brain related illnesses and trauma could see the most significant and immediate effects. The next generation of health care IT and sensory devices are acquiring and storing massive amounts of patient related data. An innovative Brain-Computer Interface (BCI) for interactive 3D visualization is presented utilizing the Hadoop Ecosystem for data analysis and storage. The BCI is an implementation of Bayesian factor analysis algorithms that can distinguish distinct thought actions using magneto encephalographic (MEG) brain signals. We have collected data on five subjects yielding 90% positive performance in MEG mid- and post-movement activity. We describe a driver that substitutes the actions of the BCI as mouse button presses for real-time use in visual simulations. This process has been added into a flight visualization demonstration. By thinking left or right, the user experiences the aircraft turning in the chosen direction. The driver components of the BCI can be compiled into any software and substitute a user’s intent for specific keyboard strikes or mouse button presses. The BCI’s data analytics OPEN ACCESS Brain. Sci. 2015, 5 420 of a subject’s MEG brainwaves and flight visualization performance are stored and analyzed using the Hadoop Ecosystem as a quick retrieval data warehouse.

McClay, Wilbert↗
DOE OSTI2018

Using Near-Infrared-Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree–Grass Ecosystems

Tree–grass ecosystems are widely distributed. However, their phenology has not yet been fully characterized. The technique of repeated digital photographs for plant phenology monitoring (hereafter referred as PhenoCam) provide opportunities for long-term monitoring of plant phenology, and extracting phenological transition dates (PTDs, e.g., start of the growing season). Here, we aim to evaluate the utility of near-infrared-enabled PhenoCam for monitoring the phenology of structure (i.e., greenness) and physiology (i.e., gross primary productivity—GPP) at four tree–grass Mediterranean sites. We computed four vegetation indexes (VIs) from PhenoCams: (1) green chromatic coordinates (GCC), (2) normalized difference vegetation index (CamNDVI), (3) near-infrared reflectance of vegetation index (CamNIRv), and (4) ratio vegetation index (CamRVI). GPP is derived from eddy covariance flux tower measurement. Then, we extracted PTDs and their uncertainty from different VIs and GPP. The consistency between structural (VIs) and physiological (GPP) phenology was then evaluated. CamNIRv is best at representing the PTDs of GPP during the Green-up period, while CamNDVI is best during the Dry-down period. Moreover, CamNIRv outperforms the other VIs in tracking growing season length of GPP. In summary, the results show it is promising to track structural and physiology phenology of seasonally dry Mediterranean ecosystem using near-infrared-enabled PhenoCam. We suggest using multiple VIs to better represent the variation of GPP.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Novel Protocol for Acute In Situ Ecotoxicity Test Using Native Crustaceans Applied to Groundwater Ecosystems

Current standardized laboratory test protocols use model species that have limitations to accurately assess native species responses to stressors. We developed and tested a novel acute in situ protocol for testing field-collected organisms. We used Asellus aquaticus and NaCl as a reference toxicant to test for the effects of location (laboratory vs. in situ), medium (synthetic vs. field water), substrate (presence vs. absence), and protocol replicability. We further tested the protocol using groundwater-adapted isopods: Proasellus assaforensis for the effect of location, P. cavaticus of medium and P.lusitanicus of substrate. Our results showed that A.aquaticus’ lethality obtained with the novel acute in situ protocol did not significantly differ from those from laboratory testing. However, laboratory tested P.assaforensis showed a higher sensitivity, suggesting that its acclimation to laboratory conditions might have pernicious effects. A. aquaticus and P. cavaticus showed a higher mortality using synthetic medium in situ and under laboratory conditions, which overestimated the stressor’s effect. Besides, substrate use had no significant effect. The novel acute in situ protocol allows the use of native species under realistic scenarios. It is particularly well adapted for assessing the risk of groundwater ecosystems but it can be applied to a wide range of ecosystems.

Castaño-Sánchez, Andrea↗
DOE OSTI2026

Data from: Coupled machine learning-ecosystem ensemble models substantially improve predictions of nitrous oxide (N 2 O) fluxes from US croplands

Nitrous oxide (N₂O) is a potent and persistent greenhouse gas, with rising atmospheric concentrations driven in part by inefficient use of synthetic nitrogen (N) fertilizers in agriculture. Predicting soil N₂O emissions is challenging due to high spatial and temporal variability arising from complex soil biogeochemical processes. Process-based ecosystem models and standalone machine learning (ML) approaches without extensive site-specific calibration often miss high emission episodes. Here, we show how an Ensemble Modeling System (EMS) based on outputs from an ensemble of ecosystem models coupled to an ensemble of ML models can improve predictions and understanding of N2O fluxes from US cropland. Trained and validated on approximately 12,000 N2O chamber measurements at 17 U.S. Midwest sites (six crops, 35 management practices), the EMS accurately predicted daily fluxes of N2O at both training (R² = 0.84, RMSE = 16.4 g N ha⁻¹ d⁻¹) and held-out testing sites (R² = 0.84, RMSE = 6.2 g N ha⁻¹ d⁻¹). Analyses identified six dominant N₂O drivers: soil organic carbon (SOC), NH₄⁺, NO₃⁻, water-filled pore space (WFPS), soil temperature, and biomass production. Wet, warm soils produced large N₂O peaks only with sufficient SOC and mineral N; in low-SOC soils, fluxes remained low. Incorporating these drivers into process-based models might significantly improve their predictive capacity. The EMS demonstrates a strong potential to predict N₂O fluxes at unseen sites, enabling more reliable regional inventories, improved gap-filling where measurements are sparse, and enhanced understanding of mechanisms to advance targeted mitigation strategies in food, feed, and bioenergy crops.

agricultural sciences↗