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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Soil Water Potentials (1990-2018) from a calibrated ELM-FATES, and rooting depth analyses scripts, PA-BCI, Panama

This dataset has ELM-FATES simulation outputs and scripts that accompany and reproduce the manuscript, Chitra-Tarak et al. (2021) Hydraulically-vulnerable trees survive on deep-water access during droughts in a tropical forest. New Phytologist. https: //doi.org/10.1111/nph.17464”. This dataset is organized into four parts: (1) source code for the model, Energy Exascale Earth System Land Model-Functionally Assembled Advanced Terrestrial simulator (ELM-FATES) (2) R scripts associated with ELM-FATES simulations: to prepare files for parameter ensemble simulation runs, calibrate the model with observed hydrological fluxes observed at BCI, re-run the model with tuned parameters and obtain long-term soil matric potentials by depth (3) ELM-FATES hydrological simulation outputs at PA-BCI, Panama (1990-2018, daily), and (4) R scripts for inverse rooting depth modeling and post-processing: prepare datasets and inversely model tree rooting depths at BCI, test hypotheses about the relationship of rooting depths with plant hydraulic traits and drought-induced mortality, and reproduce the manuscript itself. n.b. 1. Data needed to run R scripts in (2) and (4) and thus to reproduce the manuscript are sourced from various public datasets. Refer to the data availability section of the accompanying manuscript. 2. This dataset version (prepared on May 12, 2021) matches the revised and published version of the accompanying manuscript (published May 16, 2021) replacing a previous version. In the publication, this dataset is cited as follows: Chitra-Tarak R, Xu C, Aguilar S, Anderson-Teixeira K, Chambers J, Detto M, Faybishenko B, Fisher R, Knox R, Koven C et al. 2020. Soil water potentials (1990–2018) from a calibrated ELM-FATES, and rooting depth analyses scripts, PA-BCI, Panama. 2.0. NGEE Tropics Data Collection. doi: 10.15486/ngt/1696806

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Deeply Rooted: Evaluating Plant Rooting Depth as a Means for Enhanced Soil Carbon Sequestration (Full Technical Report)

Soils store three times as much carbon (C) as the atmosphere, but are not at capacity, and enhanced soil C storage is considered an essential strategy to mitigate rising atmospheric CO 2 levels. Agricultural soils have experienced substantial C loss in the past century due to poor agricultural practices and erosion. A shift towards deep-rooting crops and low-impact soil management could potentially increase long-term sequestration of C fixed by plants and stored in their root tissues, particularly for crops that have naturally deep root systems (>1 meter). A substantial amount of the CO 2 taken up by plants is allocated to their root systems, and because C deposited in deep soil layers has a longer residence time (up to millennia, in contrast to C deposited in topsoils), C increases at depth may have better long-term C sequestration potential than topsoils. However, the accrual, turnover, and stabilization of C in subsoils is a critical knowledge gap. We investigated a deeply rooted plant, switchgrass (Panicum virgatum), as a means of increasing carbon stocks in marginal and agricultural soils. We hypothesized that deep (>30 cm) SOC stocks would be greater under bioenergy crops relative to stocks under shallow-rooted conventional crop cover. To test this hypothesis, we compared soil depth profiles beneath deeply rooted switchgrass (cultivated for 4-30 years) and paired shallowrooted annual controls. We studied 12 field sites, 3 that were collected in 2018 before the start of the project as part a Department of Energy (DOE) Sustainable Biofuels study, and 9 that we collected in 2019 on a national field sampling campaign across the eastern US. In our publication from the 2018 study, which was written in collaboration with the LLNL Soil Microbiome Scientific Focus Area (SFA), we found that C stocks increased under switchgrass, but that the increases were dependent on soil texture. In the 2019 study, we found that carbon accrual tended to occur most consistently in low C soil in the southern US, which could indicate that perennial grasses may be a viable strategy to increase SOC in marginals soils in this region. We have published two studies from the 2019 sampling campaign thus far, we measured microbial growth parameters that will aid in modeling subsoil carbon cycling, and we found that switchgrass appears to move water upward in the soil profile and could promote drought tolerance, which a phenomenon commonly performed by trees known as ‘hydraulic redistribution;’ our study is the first to show this can occur in deep-rooted grasses. Finally, we published a modeling paper in collaboration with the LLNL Soil Microbiome SFA, where we found that poorly crystalline minerals are abundant and strongly correlated with organic C in geographically limited zones with enhanced weathering rates. Our results will inform technological development in the agricultural carbon sequestration sector as well as future negative emissions policies.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2021

Deeply Rooted: Evaluating Plant Rooting Depth as a Means for Enhanced Soil Carbon Sequestration (Abbreviated Final Report)

Soils store three times as much carbon (C) as the atmosphere, but are not at capacity, and enhanced soil C storage is considered an essential strategy to mitigate rising atmospheric CO 2 levels. Agricultural soils have experienced substantial C loss in the past century due to poor agricultural practices and erosion. A shift towards deep-rooting crops and low-impact soil management could potentially increase long-term sequestration of C fixed by plants and stored in their root tissues, particularly for crops that have naturally deep root systems (>1 meter). A substantial amount of the CO 2 taken up by plants is allocated to their root systems, and because C deposited in deep soil layers has a longer residence time (up to millennia, in contrast to C deposited in topsoils), C increases at depth may have better long-term C sequestration potential than topsoils. However, the accrual, turnover, and stabilization of C in subsoils is a critical knowledge gap.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

How deep should we go to understand roots at the top of the world?

Harsh environmental conditions and the short summers of northern, high-latitude biomes impose unique constraints on the plants that live in the arctic tundra and the boreal forest. To escape the harsh aboveground environment, plants in these habitats often allocate a large portion of their biomass belowground to facilitate nutrient acquisition. In turn, the proximity of living plant roots to vast stores of sequestered soil carbon in these biomes means that shifts in rooting depth distribution and the size of the root–soil interface could significantly contribute to ongoing climate change. Indeed, plant ‘priming’ of rhizosphere decomposition via root exudation, particularly from shallowly distributed roots, can lead to losses of carbon from tundra soils. While we have a hard-won understanding of the distribution of plant communities across the arctic tundra and the boreal forest from direct field observations scaled to the landscape level using climate-informed mapping techniques (i.e. the Circumpolar Arctic Vegetation Map (CAVM); Walker et al. , 2005), these vegetation maps are only the tip of the iceberg (Iversen et al. , 2015). Root form and function remain hidden beneath the land surface. In an article recently published in New Phytologist , Blume-Werry et al. (2023, 10.1111/nph.18998) asked whether rooting depth distribution, and ensuing carbon emissions, could be inferred from commonly used vegetation mapping classifications across the pan-Arctic. An important question to guide our understanding, mapping, and prediction of belowground characteristics and ecosystem feedbacks at the top of the world. Unfortunately, they found that the answer was ‘not quite’. While rooting depth distribution varied demonstrably, in turn causing substantial changes in modeled carbon emissions via rhizosphere ‘priming’, variation across rooting depth profiles did not correspond with vegetation mapping classes. If we are unable to predict belowground rooting depth distributions across large spatial scales by leveraging aboveground vegetation community distributions, how then should belowground researchers proceed?

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

DEEPER: An Intergrated Platform for Deeper Roots

Crops with deeper roots would have multiple benefits, including better drought tolerance, reduced requirement for nitrogen fertilizer, and better sequestration of atmospheric CO 2 . DEEPER is an integrated platform of phenomic, genomic, and in silico technologies to generate maize lines with deeper roots. DEEPER is: LEADER (Leaf Elemental Accumulation from Deep Roots) is a breakthrough technology to nondestructively measure rooting depth by using the plant itself as a sensor. LEADER uses handheld X-ray Fluorescence spectrometry to quantify foliar accumulation of elements that are differentially distributed in the soil profile. LEADER is nondestructive and is orders of magnitude cheaper, faster, and more precise than any competing assay of rooting depth in the field. LEADER is able to distinguish deep-rooted from shallow-rooted maize lines in the field without the need for costly and noisy soil coring. RootRobot/DIRT3D, to automatically phenotype root architecture in any field, combining RootRobot, a mechatronics platform to excavate, clean, section, and image mature root crowns, with DIRT3D, software to quantify architectural traits in 3D. Anatomics, a high-throughput platform to phenotype root anatomy, combining LAT 2.0, a technology for 3D imaging of root anatomy and composition, with RootScan3D, software to automatically extract 3D anatomical and cell wall composition metrics from LAT 2.0 output. Using this platform we discovered two novel root traits, parenchyma cell wall thickness and multiseriate cortical sclerenchyma, that improve rooting depth and drought tolerance in maize and wheat. OpenSimRoot/Deep, software to simulate root interaction with hard subsoils. Using this platform we discovered novel concepts regarding how to increase crop rooting depth by modulating how individual root axes respond to hard soil. DeepGenes, a toolkit of genes, parent lines, and genomic selection strategies to enable breeding hybrids with deeper roots. We discovered 3 novel root genes that increase rooting depth in maize and wheat. DEEPER discovered novel root phenotypes for deeper rooting, and delivered validated ideotypes for deeper-rooted maize; novel technologies to rapidly assess root depth, root architecture and anatomy in field-grown plants; novel software tools for root modeling and 3D image analysis of root architecture and anatomy; and validated genes and genomic selection models to deploy traits for deeper rooting in maize breeding. Each DEEPER technology is transformative in its own right, and exceeds existing technologies. They are mutually synergistic, deployable for field-grown plants, and are ready for application. The phenotyping and modeling technologies are readily applicable to many crops, and genetic leads in maize may have utility in other grasses. Taken as a whole they represent a transformative platform to develop deeper-rooted crops, with greater drought tolerance, reduced fertilizer requirement, and greater carbon sequestration.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Effects of Slope Aspect and Rainfall on Belowground Deep Fine Root Traits and Aboveground Tree Height

The vertical root distribution and rooting depth are the main belowground plant functional traits used to indicate drought resistance in arid and semiarid regions. The effects of the slope aspect on the aboveground traits are visible but not the belowground deep root traits. We aimed to investigate the fine root traits of the locust tree ( Robinia pseudoacacia L.) planted on southerly and northerly aspects, and the variations in the rooting depth in regions with different rainfall, as well as assessing how deep rooting, might affect the response to drought in a loess region. We selected three study sites with different rainfall amounts, with six sampling plots at each site (three each with southerly and northerly aspects). Soil core samples were collected down to the depth where no roots were present. The locust trees tended to develop deeper fine roots rather than greater heights. The tree height and diameter were greater for locust trees on northerly aspects, whereas trees on southerly aspects had significantly deeper rooting depths. Fine root traits (root length, root area, and root dry weight density) were higher in the southerly aspect for both Changwu and Ansai, but lower in Suide. The ratio of the root front depth tree height ranged from 1.04 to 3.17, which was higher on southerly than northerly aspects, and it increased as the rainfall decreased. Locust tree growth traits (belowground fine root and aboveground tree height) were positively correlated with the mean annual rainfall. The soil moisture content of the topsoil decreased as the rainfall decreased, but the pattern varied in the deep layer. Our results suggest that the variations in the belowground rooting depth under different slope aspects may be related to plant survival strategies. The vertical extension of the rooting depth and tree height may be key functional traits that determine plant growth in drought-prone regions.

Lihui, Ma↗
NASA NTRS2012

Incorporating Plant Phenology Dynamics in a Biophysical Canopy Model

The Multi-Layer Canopy Model (MLCan) is a vegetation model created to capture plant responses to environmental change. Themodel vertically resolves carbon uptake, water vapor and energy exchange at each canopy level by coupling photosynthesis, stomatal conductance and leaf energy balance. The model is forced by incoming shortwave and longwave radiation, as well as near-surface meteorological conditions. The original formulation of MLCan utilized canopy structural traits derived from observations. This project aims to incorporate a plant phenology scheme within MLCan allowing these structural traits to vary dynamically. In the plant phenology scheme implemented here, plant growth is dependent on environmental conditions such as air temperature and soil moisture. The scheme includes functionality that models plant germination, growth, and senescence. These growth stages dictate the variation in six different vegetative carbon pools: storage, leaves, stem, coarse roots, fine roots, and reproductive. The magnitudes of these carbon pools determine land surface parameters such as leaf area index, canopy height, rooting depth and root water uptake capacity. Coupling this phenology scheme with MLCan allows for a more flexible representation of the structure and function of vegetation as it responds to changing environmental conditions.

environmental changes↗
DOE OSTI2024

Tree water uptake patterns across the globe

Plant water uptake from the soil is a crucial element of the global hydrological cycle and essential for vegetation drought resilience. Yet, knowledge of how the distribution of water uptake depth (WUD) varies across species, climates, and seasons is scarce relative to our knowledge of aboveground plant functions. With a global literature review, we found that average WUD varied more among biomes than plant functional types (i.e. deciduous/evergreen broadleaves and conifers), illustrating the importance of the hydroclimate, especially precipitation seasonality, on WUD. By combining records of rooting depth with WUD, we observed a consistently deeper maximum rooting depth than WUD with the largest differences in arid regions – indicating that deep taproots act as lifelines while not contributing to the majority of water uptake. The most ubiquitous observation across the literature was that woody plants switch water sources to soil layers with the highest water availability within short timescales. Hence, seasonal shifts to deep soil layers occur across the globe when shallow soils are drying out, allowing continued transpiration and hydraulic safety. While there are still significant gaps in our understanding of WUD, the consistency across global ecosystems allows integration of existing knowledge into the next generation of vegetation process models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Root water uptake of biofuel crops revealed by coupled electrical resistivity and soil water content measurements

Abstract Biofuel crops, including annuals such as maize ( Zea mays L.), soybean [ Glycine max (L.) Merr.], and canola ( Brassica napus L.), as well as high‐biomass perennial grasses such as miscanthus ( Miscanthus × giganteus J.M. Greef & Deuter ex Hodkinson & Renvoiz), are candidates for sustainable alternative energy sources. However, large‐scale conversion of croplands to perennial biofuel crops could have substantial impacts on regional water, nutrient, and C cycles due to the longer growing seasons and differences in rooting systems compared with most annual crops. However, due to the limited tools available to nondestructively study the spatiotemporal patterns of root water uptake in situ at field scales, these differences in crop water use are not well known. Geophysical imaging tools such as electrical resistivity (ER) reveal changes in water content in the soil profile. In this study, we demonstrate the use of a novel coupled hydrogeophysical approach with both time domain reflectometry soil water content and ER measurements to compare root water uptake and soil properties of an annual crop rotation with the perennial grass miscanthus, across three growing seasons (2009–2011) in southwest Michigan, USA. We estimated maximum root depths to be between 1.2 and 2.2 m, with the vertical distribution of roots being notably deeper in 2009 relative to 2010 and 2011, likely due to the drought conditions during that first year. Modeled cumulative ET of both crops was underestimated (2–34%) relative to estimates obtained from soil water drawdown in prior studies but was found to be greater in the perennial grass than the annual crops, despite shallower modeled rooting depths in 2010 and 2011.

Kuhl, Alexandria S.↗
DOE OSTI2021

An integrated framework of plant form and function: the belowground perspective

Summary Plant trait variation drives plant function, community composition and ecosystem processes. However, our current understanding of trait variation disproportionately relies on aboveground observations. Here we integrate root traits into the global framework of plant form and function. We developed and tested an overarching conceptual framework that integrates two recently identified root trait gradients with a well‐established aboveground plant trait framework. We confronted our novel framework with published relationships between above‐ and belowground trait analogues and with multivariate analyses of above‐ and belowground traits of 2510 species. Our traits represent the leaf and root conservation gradients (specific leaf area, leaf and root nitrogen concentration, and root tissue density), the root collaboration gradient (root diameter and specific root length) and the plant size gradient (plant height and rooting depth). We found that an integrated, whole‐plant trait space required as much as four axes. The two main axes represented the fast–slow ‘conservation’ gradient on which leaf and fine‐root traits were well aligned, and the ‘collaboration’ gradient in roots. The two additional axes were separate, orthogonal plant size axes for height and rooting depth. This perspective on the multidimensional nature of plant trait variation better encompasses plant function and influence on the surrounding environment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Root genetics in the field to understand drought adaptation and carbon sequestration (Final Scientific/Technical Report)

For all crop plants, roots play a critical role in growth. Roots anchor the plants, and are the primary site of nutrient and water uptake. Roots are also the main source of C to soil in the form of root tissues and exudates, and thus greatly influence SOM stocks. To perform these functions, primary roots extend into soil, producing a network of branching roots of characteristic form, known as its root system architecture (RSA). RSA varies among species, and among varieties within a species that are adapted to different environments. Root traits are major targets for the second green revolution because of their potential to improve crop productivity, increase drought tolerance and nutrient acquisition, and increase C capture of soil. Improving the quality of roots in maize will be particularly valuable, since this crop is planted on over 92 million acres annually in the US. The future sustainability of agricultural systems relies on their ability to enhance soil organic matter (SOM) storage and reduce GHG emissions, while maintaining or enhancing productivity. This program had two components, Sensors and Models. For the first component, we designed and built a high-throughput phenotyping platform for root pulling of maize plants. This eliminated the physical labor of manually pulling up plants and reduced the number of personnel required down to one. The standardized pulling mechanism allowed recording force curves during the pulling process, providing additional information. We validated that the maximum force for pulling the root system was well-correlated with the root system mass and provided root crowns for further RSA analysis. These root crowns identified significant correlations with 2D root area and root depth, along with 3D root volume, total root length and number of root tips. We then used this system for field-based studies in maize on the genetics of root system architecture and its relation to nitrogen-use efficiency (NUE), including using lines relevant to the Corteva breeding program. Varieties were also evaluated at Corteva sites in the cornbelt and Danforth farm in Missouri, to establish responses across sites. From these studies we have identified genetic loci associated with root traits and created mutant lines for these loci and correlations of root traits with NUE. For the Models component, we worked to incorporate root and soil characteristics into the MEMS 2.0 soil and ecosystem biogeochemical model. Existing soil C models, such as Century, are unable to represent specific root trait interactions with the soil environment and therefore to accurately forecast the potential C sequestration benefits of root breeding under different climatic and soil type conditions. We have developed the MEMS 2.0 ecosystem biogeochemical model to improve quantification of farm-scale soil carbon and greenhouse gas emissions. The new knowledge and large datasets produced by this project will be used to develop and drive an innovative model capable of forecasting the impacts on soil C stocks and nutrient dynamics. An innovation was to use the empirical data from the field studies (in 1, above) to model genetic variation in nitrogen use efficiencies and soil C input. Our work demonstrated that maize root-derived C rapidly replaces existing soil C and after 3 years of continuous maize, up to 20% of soil organic C in the topsoil (0-15cm) and 3% in the subsoil (15-30cm) was contributed by maize. However, this contribution did not entirely represent a net increase. Root C contribution to soil was affected by maize genetics. We have analyzed soils derived from the CSU field trials for C and N stocks, in the different soil physical fractions represented by the MEMS model, using both physical fractionation with elemental analyses, and Fourier transformed infrared spectroscopy. Data will be used to link crop nitrogen use efficiencies with soil C sequestration and provide data to bridge the field trials with the model development, for verification of model predictions. The project had a number of successful outcomes: we have used the new phenotyping platform to identify new genetic loci that can enhance root phenotypes; we have partnered with multiple maize seed companies phenotype varieties in their breeding programs; we have developed the MEMS model that can help inform industry on the potential for carbon sequestration in the agricultural sector, and which is now available at the CSU Soil Carbon Solutions Center for use.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Integrating New Arctic Plant Functional Types in a Land Surface Model Using Above- and Belowground Field Observations: Modeling Archive

This Modeling Archive is in support of an NGEE Arctic publication "Integrating New Arctic Plant Functional Types in a Land Surface Model Using Above- and Belowground Field Observations". We used field observations of biomass and leaf traits (Salmon et al., 2019b) across a gradient of plant communities on the Seward Peninsula in northwest Alaska (Breen et al., 2020; Iversen et al., 2017) to replace the original ELM configuration with nine, arctic-specific PFTs. Original arctic PFTs in the model were (1) broadleaf deciduous boreal shrub and (2) C3 arctic grass. New PFTs were (1) lichen, (2) bryophyte, (3) evergreen dwarf shrub, (4) deciduous dwarf shrub, (5) deciduous low shrub, (6) deciduous low to tall shrub, (7) alder shrub, (8) forb, and (9) graminoid. New PFTs were parameterized and evaluated using site-level trait and biomass measurements (Salmon et al., 2019b), root C:N ratios and rooting depth patterns (Salmon et al., 2019c), and root biomass (Salmon et al., 2020). This archive contains forcing data and model output from ELM simulations conducted at the Kougarok Hillslope site (Kougarok Road Mile Marker 64). Simulations were conducted for the Kougarok Hillslope site using meteorological driving data from the Scenarios Network for Alaska and Arctic Planning (SNAP) downscaled climate projection dataset using NCAR-CCSM forcing (Bieniek et al., 2020 dataset; Walsh et al., 2018). The archive includes three versions of model simulations representing a progression from the original model configuration to an Arctic-specific configuration (see Output section below). Simulation 1 used grid cell data from a global E3SM configuration, including the fractional area of E3SM PFTs assigned to the grid cell containing the Kougarok Hillslope site in global simulations. This simulation did not distinguish between different plant communities on the Kougarok Hillslope, but instead used a single point simulation to represent the entire area. Simulation 2 used default E3SM PFT definitions combined with adjusted depth to bedrock for plant communities with shallow rocky layers to represent the role of abiotic soil factors in driving site differences (based on depth measurements of Iversen et al., 2019a), and adjusted the relative areas of the broadleaf deciduous boreal shrub and C3 arctic grass PFTs to reflect the observed spatial coverage of shrub and non-shrub PFTs across Kougarok Hillslope plant communities. Simulation 3 used the new arctic PFT definitions and parameterizations based on the vegetation types present at the site, along with the adjusted soil depths used in Simulation 2. Included are *.pdf, *.nc (NetCDF), *tar.gz, *.py, and *.bash files. We would like to thank Mary's Igloo Native Corporation for allowing us to perform this research on their land and giving us the opportunity to share this research with their community. Thanks to Shawn Serbin and Alistair Rogers for helpful discussions. Holly Vander Stel, Joanne Childs, and Stan Wullschleger helped with collection of field data. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Hydraulically‐vulnerable trees survive on deep‐water access during droughts in a tropical forest

Summary Deep‐water access is arguably the most effective, but under‐studied, mechanism that plants employ to survive during drought. Vulnerability to embolism and hydraulic safety margins can predict mortality risk at given levels of dehydration, but deep‐water access may delay plant dehydration. Here, we tested the role of deep‐water access in enabling survival within a diverse tropical forest community in Panama using a novel data‐model approach. We inversely estimated the effective rooting depth (ERD, as the average depth of water extraction), for 29 canopy species by linking diameter growth dynamics (1990–2015) to vapor pressure deficit, water potentials in the whole‐soil column, and leaf hydraulic vulnerability curves. We validated ERD estimates against existing isotopic data of potential water‐access depths. Across species, deeper ERD was associated with higher maximum stem hydraulic conductivity, greater vulnerability to xylem embolism, narrower safety margins, and lower mortality rates during extreme droughts over 35 years (1981–2015) among evergreen species. Species exposure to water stress declined with deeper ERD indicating that trees compensate for water stress‐related mortality risk through deep‐water access. The role of deep‐water access in mitigating mortality of hydraulically‐vulnerable trees has important implications for our predictive understanding of forest dynamics under current and future climates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Root and xylem anatomy varies with root length, root order, soil depth and environment in intermediate wheatgrass (Kernza®) and alfalfa

Abstract Background and Aims Deep roots (i.e. >1 m depth) are important for crops to access water when the topsoil is dry. Root anatomy and hydraulic conductance play important roles in the uptake of soil water, particularly water located deep in the soil. We investigated whether root and xylem anatomy vary as a function of root type, order and length, or with soil depth in roots of two deep-rooted perennial crops: intermediate wheatgrass [Thinopyrum intermedium (Kernza®)] and alfalfa (Medicago sativa). We linked the expression of these anatomical traits to the plants’ capacity to take up water from deep soil layers. Methods Using laser ablation tomography, we compared the roots of the two crops for cortical area, number and size of metaxylem vessels, and their estimated root axial hydraulic conductance (ERAHCe). The deepest roots investigated were located at soil depths of 2.25 and at 3.5 m in the field and in rhizoboxes, respectively. Anatomical differences were characterized along 1-m-long individual roots, among root types and orders, as well as between environmental conditions. Key Results For both crops, a decrease in the number and diameter, or both, of metaxylem vessels along individual root segments and with soil depth in the field resulted in a decrease in ERAHCe. Alfalfa, with a greater number of metaxylem vessels per root throughout the soil profile and, on average, a 4-fold greater ERAHCe, took up more water from the deep soil layers than intermediate wheatgrass. Root anatomical traits were significantly different across root types, classes and growth conditions. Conclusions Root anatomical traits are important tools for the selection of crops with enhanced exploitation of deep soil water. The development and breeding of perennial crops for improved subsoil exploitation will be aided by greater understanding of root phenotypes linked to deep root growth and activity.

Plant Sciences↗
DOE OSTI2024

Water foraging with dynamic roots in E3SM; The role of roots in terrestrial ecosystem memory on intermediate timescales (Final Technical Report)

Terrestrial ecosystems can show sustained responses to stress events that may last for years after the initial perturbation. This phenomenon is referred to as legacy or memory and it emerges from a set of ecosystem processes that are poorly captured by Earth System and Land Surface Models. Consequently, these models struggle to predict the impact that extreme climate events have on surface energy, carbon and hydrological exchange once a stressor such as drought has relaxed or in response to repeated exposure to stress. In this project, we set out to test how the addition of dynamic root profiles in land surface models impact the capacity for Earth System Models, such as the Department of Energy’s E3SM, to capture realistic legacy effects. Observations have shown that vegetation shifts their root profiles during stress events to forage for water and these altered root profiles may take years to relax back to the initial state. We hypothesized that the alteration of the belowground root structures may be a source of terrestrial ecosystem legacy that is missing from models. To test this idea we undertook three core activities. (1) We developed a global-scale analysis of the impact of dynamic roots on ecosystem legacy building from a recently developed dynamic root module. (2) We developed new root dynamics in the DOE’s Energy Land Model (ELM) that allowed not only root profiles to shift their profile but also simultaneously alter carbon allocation to fine root pools. We then tested the impact of these new dynamics with intensive sensitivity analysis at four long-term AmeriFlux sites. (3) We undertook detailed isotopic analysis of tree rings from these AmeriFlux sites to assess ecophysiological and ecohydrological legacy to stress events that can be used to benchmark the sensitivity experiments with ELM. From these core activities, we report the following key findings. Firstly, on a global scale, the addition of dynamic roots led to chronically water-stressed ecosystems recovering faster to climate stress while wetter ecosystem showed enhanced legacy. This is because across ecosystems, stress events were almost universally associated with water shortages that led to the development of deeper root profiles. These deeper root profiles proved beneficial for recovery from drought stress. While the root dynamics did not universally improve the modeled representation of legacy it showed complex transient dynamics that emerge from the addition of dynamic roots. Secondly, the sensitivity analysis illustrated long term shifts in rooting depths away from the prescribed default profile suggesting that initializing of root profiles could benefit from spin-up simulations that converge on locally optimized root profiles. In addition, by enabling dynamic allocation some of the sites predicted unrealistically low allocation to roots (and high allocation to leaves). While these changes did not dramatically alter modeled gross primary productivity, they illustrated that without more sophisticated root processes in models, there is little penalty to dramatically disinvest in roots. Thirdly, the isotopic analysis of tree rings showed highly distinct legacy responses across species and sites. For example, T. canadensis showed reduced transpiration the year after stress events illustrated by sustained elevated $\delta^{18}$O. In contrast, A saccharum displayed elevated $\delta^{13}$C associated with reduced stomatal conductance in response to the previous years’ stress event. These geochemical signatures of legacy were present despite tree growth returning to normal the year after the stress. The results show the importance of species-level dynamics in legacy that are absent in modeling that assumes common traits within plant functional types. In summary, the work here established new avenues to explore root dynamics in models while illustrating how additional root processes are needed before dynamic carbon allocation can be implemented. Lastly, this project provided mentorship to a postdoctoral fellow, training for an early career scientist and multiple undergraduate students recruited from a minority serving institution.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Phosphorus budgets of intensively managed row crops at a long–term agroecosystem research site in the upper US Midwest

Phosphorus (P) budgets for cropping systems provide insights for keeping soil P at optimal levels for crops while avoiding excess inputs. We quantified 12 years of P inputs (fertilizer and atmospheric deposition) and outputs (harvest and leaching losses) for replicated maize (Zea mays L.)—soybean (Glycine max L.)—wheat (Triticum aestivum) crop rotations under conventional, no-till, reduced input, and biologically based (organic without compost or manure) management systems at the Kellogg Biological Station LTAR site in southwest Michigan. Conventional, no-till, and reduced input systems were fertilized between 13 and 50 kg P ha –1 depending on year. Soil test phosphorus (STP) was measured at 0- to 25-cm depth every autumn. Leached P was measured as dissolved P in the soil solution sampled beneath the rooting depth and combined with modeled percolation. Fertilization and harvest were the predominant P fluxes in the fertilized systems, whereas only harvest dominated P flux in the unfertilized organic system. Leaching losses were minor terms in the budgets, but leachate concentrations were nevertheless close to the range of concern for downstream eutrophication. Over the 12-year study period, the organic system exhibited a negative P balance (–82.0 kg P ha –1 ), coinciding with suboptimal STP levels, suggesting a need for P supplementation. In contrast, the fertilized systems showed positive P balances (mean: 70.1 kg P ha –1 ) with STP levels well above agronomic optima. Results underscore the importance of tailored P management strategies to sustain crop productivity while mitigating environmental impacts.

60 APPLIED LIFE SCIENCES↗