LI-COR Biosciences (United States)

Dissolved inorganic carbon (DIC) - including aqueous CO2, carbonic acid, bicarbonate, and carbonate - is often the largest pool of carbon in aquatic systems. Biogeochemical processes result in exchanges of carbon between the various DIC components and may act to move carbon into or out of the DIC pool. The isotopic composition of carbon is a product of both its source and mass-dependent fractionation as carbon changes form through the processes acting on it. Consequently, measurement of the stable carbon isotope composition of DIC is a valuable tool for understanding biogeochemical processes in aquatic systems. However, differences in isotopic composition are small, and separating source contributions requires precise measurement.Measurement of DIC can be done by conversion to CO2 in the presence of a strong acid and quantification of liberated CO2 by gas analysis. To determine isotopic composition of the liberated CO2 (δ13C) historical methods used isotope ratio mass spectrometry (IRMS). More recently, tunable diode laser absorption spectrometry (TDLAS) based gas analyzers have been adopted for these measurements but have continued to base methodological considerations on those developed for IRMS. While IRMS and TDLAS can both be used to determine δ13C, there are fundamental differences in the technology, which should be considered during application. In particular, this has meant δ13C - DIC measurements have been unable to take full advantage of TDLAS performance characteristics. Here we describe methodological advancements from integration of a TDLAS (LI-7825 carbon isotope analyzer) with a DIC measurement system (LI-5370A), that include changes to the pneumatic and analytical approach used in the DIC system. Pneumatic modifications allow the TDLAS to operate at an independent flow rate from the DIC system and serve to manipulate the residence time for CO2 along the flow path. We describe use of a non-CO2 free carrier gas, which allows the DIC measurement to take full advantage of analyzer precision and minimize errors intrinsic to δ13C as determined by TDLAS. We present data demonstrating measurement precision over a range of conditions and show that under similar conditions, these methodological changes result in precision exceeding that published previously for TDLAS-DIC measurements.

Continental-scale research infrastructures and flux networks, alongside smaller networks and individual sites, directly measure evaporative water loss, as well as heat, CO2, CH4, and other gas exchange between the surface and the atmosphere. Over four decades, such flux stations covered 2100+ stationary measurement points and various campaign sites.Despite such major advantages as extremely high-resolution, real-time results, continuous and direct nature of flux measurements, their applications are only now and rather slowly entering fields beyond academia due to the perceived method complexity, actual complexity and cost of traditional instrumentation and site operation, lack of broad geographic data coverage, and absence of a comprehensive approach focused on direct flux measurements specifically tailored for bringing immediate societal benefits.This presentation continues to address these challenges by simplifying explanations, offering detailed guides for practicing the method, presenting the latest lower-cost simple automated instrumentation and novel computing tools, facilitating peer-to-peer cross-sharing to increase data coverage and reduce station setup costs, and providing professional services for experiment design and execution. One example of the most recent developments is the 2025 publication of three new plain-language guides/protocols on direct dMRV/aMRV/MMRV (Figure below). These aim to fundamentally change carbon markets by providing a direct, defensible, traceable, repeatable, real-time, evidence-based approach to quantify sequestration and emission in application beyond academia.The ultimate goal of this presentation is to ignite discussions on utilizing flux measurements for practical decision-making applications to benefit society and to identify current needs, ideas, and examples for leveraging flux data in everyday decision contexts.

SummaryNatural genetic variation in photosynthesis and photoprotection within crop germplasm represents an untapped resource for crop improvement.Sorghum bicolor(sorghum) is one of the world’s most widely grown crops, yet the genetic basis of photoprotection in sorghum is not well understood.This study examined genetic variation in non-photochemical quenching traits by screening a field-grown panel of 861 genetically diverse natural sorghum accessions across two years.Broad-sense heritability ranged between 0.3 to 0.65 across different chlorophyll fluorescence parameters. A combination of genome- and transcriptome-wide (GWAS and TWAS) identification of genetic correlates with the observed trait variation uncovered a complex genetic architecture of many significant small-effect loci. An ensemble approach based on GWAS and TWAS results and the covariance between different fluorescence parameters was used to identify 110 unique candidate genes.The resulting high-confidence candidates reveal novel genetic associations with photoprotection and offer resources for further genetic studies and crop genomic improvement efforts.

Soil trace gas flux rates derived from closed-transient chamber-based techniques rely on estimating the rate of change in gas concentrations prior to disturbance by the chamber. Several regression methods are available for estimating this rate of change, all of which require selecting a fitting window over which to apply the regression. Window selection has historically been subjective and reliant on expert knowledge, using information from only small subset of measurements to fit to a larger dataset. This somewhat arbitrary approach overlooks the influence of individual chamber and sampling location properties on the development of turbulence, as well as the unique transport characteristics of different trace gases. Here, we describe an empirical method for selecting the fitting window for an exponential regression model, based upon iterative analysis of within-chamber gas mixing dynamics. The method operates at a batch scale, incorporating data from every observation to determine the appropriate start and stop times for the fitting window of the larger dataset, using SoilFluxPro 5.2’s JavaScript console.Chamber-based estimates of the stable carbon isotope ratio (δ13C) of soil-respired CO2, which utilize the Keeling mixing model, are also sensitive to the chosen fitting window; however no clear empirical approach for window optimization has been proposed. We also present a potential model-based approach to reduce uncertainty in Keeling model-derived δ13C estimates, offering a comprehensive analysis using low-magnitude soil gas flux data from a desert ecosystem. Together these fitting window optimization strategies enhance the robustness of soil gas flux and δ13C estimates. 

The Internet of the Environment (IoE) represents a subset of the Internet of Things (IoT) where connected environmental monitoring sensors, that utilize edge computing and embedded power and communications, report on the state of the environment to improve scientific understanding via large-scale monitoring. The IoE approach offers several notable advantages compared to traditional methods:It facilitates measurements in almost any location, including large-scale deployments and remote, hard-to-reach areas. It supports consistent measurement techniques that are scalable from field to landscape to continental levels, all while reducing operational costs. It leads to cost savings and minimizes data loss through enhanced operations, utilizing predictive maintenance powered by machine learning. It integrates various external datasets into a unified cloud platform, enabling automated post-processing and advanced data analytics. This presentation covers the integration of an eddy covariance (EC) sensor with an IoE Module, transforming it into a node within the IoE system. Other sensors, measuring different environmental parameters, can serve as additional nodes.Once the data from these nodes is transmitted to the cloud, it can be utilized for automated quality assurance/quality control, gap filling, forecasting, accumulation, and spatial extrapolation, leveraging external datasets and statistical/machine learning tools.This integration enables users to construct dynamic virtual networks of real measurements from various research sites and nodes, facilitating scientific research and fostering collaboration between institutions and groups.An illustrative example of this approach is presented through a network of actual evapotranspiration EC sensors, which collect flux data across diverse irrigated and rain-fed agricultural landscapes for immediate societal applications.

Continental-scale research infrastructures and flux networks (e.g., AmeriFlux, AsiaFlux, ChinaFlux, ICOS, NEON, OzFlux), as well as numerous smaller GHG flux networks, and individual sites, measure CO2, CH4, and other GHG exchange, as well as water vapor fluxes (evapotranspiration, ET) between ecosystem and atmosphere.After four decades of academic use, the flux stations covered over 2100 stationary measurement locations, and numerous campaigns’ locations. Most measurements were used for process-level ecological and hydrological studies and long-term climate and ecosystem modeling.Such measurements use ultra-high-resolution methodology and state-of-the-art hardware vastly superior to typical monitoring-grade methods and equipment deployed outside academia for a wide range of non-academic decision-making applications. However, despite providing exceptional ways to measure GHG emissions and ET, these are very rarely utilized outside academia.The key reasons for such lack of utilization are:The perceived complexity of the method - can be resolved via simple-language explanations and a detailed guide described in the presentation The lack of data in the specific ecosystem or area and the associated expenses required to establish a new station - can be significantly reduced and often resolved by a peer-to-peer cross-sharing concept outlined in the presentation The absence of a robust overall approach to using flux measurements for immediate societal benefits - can be resolved by adopting an approach currently used by automated weather stations (AWS) feeding and tuning remotes sensing products and resulting in weather modeling and forecasting (see Figure below) The ultimate goal of this presentation is to ignite and provide a base for a discussion regarding the latest needs, ideas, and examples of the use of flux measurements for practical ‘everyday’ decision-making applications benefiting society. The new Community of Practice “Carbon Dew” is also introduced to address some of these pressing needs.

Sensory plastids are important in plant responses to environmental changes. Previous studies show that MutS HOMOLOG 1 (MSH1) perturbation in sensory plastids induces heritable epigenetic phenotype adjustment. Previously, the PsbP homolog DOMAIN-CONTAINING PROTEIN 3 (PPD3), a protein of unknown function, was postulated to be an interactor with MSH1. This study investigates the relationship of PPD3 with MSH1 and with plant environmental sensing. The ppd3 mutant displays a whole-plant phenotype variably altered in growth rate, flowering time, reactive oxygen species (ROS) modulation and response to salt, with effects on meristem growth. Present in both chloroplasts and sensory plastids, PPD3 colocalized with MSH1 in root tips but not in leaf tissues. The suppression or overexpression of PPD3 affected the plant growth rate and stress tolerance, and led to a heritable, heterogenous 'memory' state with both dwarfed and vigorous growth phenotypes. Gene expression and DNA methylome data sets from PPD3-OX and derived memory states showed enrichment in growth versus defense networks and meristem effects. Our results support a model of sensory plastid influence on nuclear epigenetic behavior and ppd3 as a second trigger, functioning within meristem plastids to recalibrate growth plasticity.

Abstract Natural gas is considered a bridging technology in the energy transition because it produces fewer carbon emissions than coal, for example. However, when leaks exist, methane is released into the atmosphere, leading to a dramatic increase in the carbon footprint of natural gas, as methane is a much stronger greenhouse gas than carbon dioxide. Therefore, we conducted a detailed study of methane emissions from gas‐powered end‐use appliances and then compared their climate impacts with those of electricity‐powered appliances. We used the Munich Oktoberfest as a case study and then extended the study to 25 major natural gas consuming countries. This showed that electricity has been the more climate‐friendly energy source at Oktoberfest since 2005, due to the extensive use of renewable electricity at the festival and the presence of methane emissions, particularly caused by the incomplete combustion and leakages of natural gas in cooking and heating appliances. By contrast, at the global level, our study shows that natural gas still produces lower carbon emissions for end‐user appliances than electricity in 18 of the 25 countries studied. However, as the share of renewable energy in the electricity mix steadily increases in most countries, the carbon footprint of electricity will be lower than that of natural gas in these countries in the near future. These findings from our comparison of the total carbon emissions of electric and gas‐powered end‐use appliances can help inform the debate on how to effectively address climate change.

Despite a rapid rise in NBS development in recent years, the methods for evaluating NBS still have certain gaps. We propose an approach based on a combination of remote sensing data and meteorological variables to reconstruct the spatio-temporal variation of net ecosystem exchange from eddy-covariance stations. Lagrangian particle dispersion model was used for upscaling of satellite images and flux towers. We trained data-driven models based on kernel methods separately for each selected land cover class. The results suggest that the proposed approach to quantifying carbon exchange on a medium-to-large scale by blending eddy covariance flux data with moderate resolution satellite and weather data provides a set of key advantages over previously deployed methods: (1) scalability, achieved via the validation design based on a separate set of eddy covariance stations; (2) high spatial and temporal resolution due to use of the Landsat imagery; (3) robust and accurate predictions due to improved data quality control, advanced machine learning techniques, and rigorous validation. The machine learning models yielded high cross-validation results. Overall we present here globally scaled technology for the land sector based on high resolution remote sensing imagery, meteorological variables, and direct carbon flux measurements of eddy covariance flux stations.

SummaryOur understanding of the physiological response of rice inflorescence (panicle) to environmental stresses is limited by the challenge of accurately determining panicle photosynthetic parameters and their impact on grain yield. This is primarily due to lack of a suitable gas exchange methodology for panicles, as well as non-destructive methods to accurately determine panicle surface area. To address these challenges, we have developed a custom panicle gas exchange cylinder compatible with the LiCor 6800 Infra-red Gas Analyzer. Accurate surface area measurements were determined with a 3D panicle imaging platform to normalize the panicle-level photosynthetic measurements. We observed differential responses in both panicle and flag leaf for two temperate Japonica rice genotypes (accessions, TEJ-1 and TEJ-2) exposed to heat stress during early grain filling. There was a notable divergence in relative photosynthetic contribution of flag leaf and panicles for the genotype tolerant to heat stress (TEJ-2) compared to the less tolerant accession. The novelty of this approach is that it is non-destructive and more accurately determines panicle area and photosynthetic parameters, enabling researchers to monitor temporal changes in panicle physiology during the reproductive development. The method is useful for panicle-level measurements under diverse environmental stresses, and for evaluating genotypic variation for panicle physiology and architecture in other cereals with compact inflorescences.