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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

Dynamics of carbon dioxide exchange of a wheat community grown in a semi-closed environment

A wheat (Triticum aestivum Yecora Rojo) community was grown in the semi-closed conditions of the NASA/KSC Biomass Production Chamber (BPC). Experiments were conducted to determine whole community carbon dioxide exchange rates as influenced by growth and development, carbon dioxide concentration, time within the photoperiod, irradiance, and temperature. Plants were grown at a population of about 1500 per sq meter using a 20 hour light/4 hour dark daily regime. Light was supplied by HPS vapor lamps and irradiance was maintained in the range of 590 to 675 mu mol per sq meter. The temperature regime was 20 C light/16 C dark and nutrients were supplied hydroponically as a thin film. Fractional interception of PPF by the community increased rapidly during growth reaching a maximum of 0.96, 24 days after planting. This time corresponded to canopy closure and maximum rates of net photosynthesis (NP). Net daily CO2 utilization rates were calculated to day 48 and a 4th order regression equation integrated to obtain total moles of CO2 fixed by the community. This procedure may be useful for monitoring and prediction of biomass yields in a closed ecology life support system (CELSS).

Corey, Kenneth A.↗
DOE OSTI2022

A Macroalgal Cultivation Modeling System (MACMODS): Evaluating the Role of Physical-Biological Coupling on Nutrients and Farm Yield

Offshore aquaculture has the potential to expand the macroalgal industry. However, moving into deeper waters requires suspended structures that will present novel farm-environment interactions. Here, we present a computational modeling framework, the Macroalgal Cultivation Modeling System (MACMODS), to explore within-farm modifications to light, seawater flow, and nutrient fields across time and space scales relevant to macroalgae. A regional ocean model informs the site-specific setting, the Santa Barbara Channel in the Southern California Bight. A fine-scale hydrodynamic model predicts modified flows and turbulent mixing within the farm. A spatially resolved macroalgal growth model, parameterized for giant kelp, Macrocystis pyrifera , predicts kelp biomass. Key findings from model integration are that regional ocean conditions set overall farm performance, while fine-scale within-farm circulation and nutrient delivery are important to resolve variation in within-farm macroalgal performance. Therefore, we conclude that models resolving within-farm dynamics can provide benefit to farmers with insight on how farm design and regional ocean conditions interact to influence overall yield. Here, the presence of repeating longlines aligned with the mean current generate flow diversions around the farm as well as attached Langmuir circulations and increased turbulence intensity. These flow-induced phenomena lead to less biomass in the interior portion of the farm relative to the edges. We also find that there is an effluent “footprint” that extends as much as 20 km beyond the farm. In this regard, MACMODS can be used to not only evaluate farm design and cultivation practices that maximize yield but also explore interactions between the farm and ecosystem in order to minimize impacts.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1994

Factors affecting the estimate of primary production from space

Remote sensing of primary production in the euphotic zone has been based mostly on visible-band and water-leaving radiance measured with the coastal zone color scanner. There are some robust, simple relationships for calculating integral production based on surface measurements, but they also require knowledge for photoadaptive parameters such as maximum photosynthesis which currently cannot be obtained from spave. A 17,000-station data set is used to show that space-based estimates of maximum photosynthesis could improve predictions of psi, the water column light utiliztion index, which is an important term in many primary productivity models. Temperature is also examined as a factor for predicting hydrographic structure and primary production. A simple model is used to relate temperature and maximum photosynthesis; the model incorporates (1) the positive relationship between maximum photosynthesis and temperature and (2) the strongly negative relationship between temperature and nitrate in the ocean (which directly affects maximum growth rates via nitrogen limitation). Since these two factors relate to carbon and nitrogen, 'balanced carbon/nitrogen assimilation' was calculated using the Redfield ratio, It is expected that the relationship between maximum balanced carbon assimilation versus temperature is concave-down, with the peak dependent on nitrate uptake kinetics, temperature-nitrate relationships,a nd the carbon chlorophyll ration. These predictions were compared with the sea truth data. The minimum turnover time for nitrate was also calculated using this approach. Lastly, sea surface temperature gradients were used to predict the slope of isotherms (a proxy for the slope of isopycnals in many waters). Sea truth data show that at size scales of several hundred kilometers, surface temperature gradients can provide information on the slope of isotherms in the top 200 m of the water column. This is directly relevant to the supply of nutrients into the surface mixed layer, which is useful for predicting integral biomass and primary production.

Balch, W. M.↗
NASA NTRS2014

Uncertainties in Predicting Rice Yield by Current Crop Models Under a Wide Range of Climatic Conditions

Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We evaluated 13 rice models against multi-year experimental yield data at four sites with diverse climatic conditions in Asia and examined whether different modeling approaches on major physiological processes attribute to the uncertainties of prediction to field measured yields and to the uncertainties of sensitivity to changes in temperature and CO2 concentration [CO2]. We also examined whether a use of an ensemble of crop models can reduce the uncertainties. Individual models did not consistently reproduce both experimental and regional yields well, and uncertainty was larger at the warmest and coolest sites. The variation in yield projections was larger among crop models than variation resulting from 16 global climate model-based scenarios. However, the mean of predictions of all crop models reproduced experimental data, with an uncertainty of less than 10 percent of measured yields. Using an ensemble of eight models calibrated only for phenology or five models calibrated in detail resulted in the uncertainty equivalent to that of the measured yield in well-controlled agronomic field experiments. Sensitivity analysis indicates the necessity to improve the accuracy in predicting both biomass and harvest index in response to increasing [CO2] and temperature.

crop-model ensembles↗
NASA NTRS1985

Remote sensing of wetland biomass and productivity for global biosystem research

The study of ecosystem function and the contributions of particular ecosystems to global elemental cycling will require data collection from orbital platforms. Presently used ground sampling techniques are not adequate for large area vegetation assessments. The research was directed toward an understanding of the relationship between spectral radiance and plant canopy biomass. Changes in the architecture of the canopy alters the observed spectral radiance patterns. The green components of a plant canopy interacts most strongly with incident radiation and usually reflect edaphic conditions as they relate to plant vigor. Plant biomass serves as an excellent integrator of the environmental factors encountered by the plant and can be used as an indicator for predicting soil redox, nutrient status, microbial activity, etc. The use was examined of TM spectral data to discriminate biomass classes and, in turn, to relate the biomass classes to the chemical status of the soil. Several preliminary studies have indicated that solar angle can be used to improve biomass predictions. This important observation also suggests that latitudinal extrapolation of the biomass predicting models will be possible if selected characteristics of the incident radiation are known.

Hardisky, M. A.↗
DOE OSTI2024

Getting allometry right at the Oak Ridge free‐air CO 2 enrichment experiment: Old problems and new opportunities for global change experiments

Societal Impact Statement Free‐air CO 2 enrichment (FACE) experiments provide essential data on forest responses to increasing atmospheric CO 2 for evaluations of climate change impacts on humanity. Understanding and reducing the uncertainty in the experimental results is critical to ensure scientific and public confidence in the models and policy initiatives that derive therefrom. One source of uncertainty is the estimation of tree biomass using mathematical relationships between biomass and easily obtained and non‐destructive measurements (allometry). We evaluated the robustness of the allometric relationships established at the beginning of a FACE experiment and discuss the challenges and opportunities for the new generation of FACE experiments. Summary Long‐term field experiments to elucidate forest responses to rising atmospheric CO 2 concentration require allometric equations to estimate tree biomass from non‐destructive measurements of tree size. We analyzed whether the allometric equations established at the beginning of a free‐air CO 2 enrichment (FACE) experiment in a Liquidambar styraciflua plantation were still valid at the end of the 12 year experiment. Aboveground woody biomass was initially predicted by an equation that included bole diameter, taper, and height, assuming that including taper and height as predictors would accommodate changes in tree structure that might occur over time and in response to elevated CO 2 . At the conclusion of the FACE experiment, we harvested 23 trees, measured dimensions and dry mass of boles and branches, and extracted and measured the woody root mass of 10 trees. Although 10 of the harvested trees were larger than the trees used to establish the allometric relationship, measured aboveground woody biomass was well predicted by the original allometry. The initial linear equation between bole basal area and woody root biomass underestimated final root biomass by 28%, but root biomass was just 21% of total wood mass, and errors in aboveground and belowground estimates were offsetting. The allometry established at the beginning of the experiment provided valid predictions of tree biomass throughout the experiment. New allometric approaches using terrestrial laser scanning should reduce an important source of uncertainty in decade‐long forest experiments and in assessments of centuries‐long forest biomass accretion used in evaluating carbon offsets and climate mitigation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Multi-site Milling Strategy Reveals Significant Variation in Biomass Composition of Switchgrass ( Panicum virgatum ) Grown at Ten Locations

Cell wall composition influences biomass use as a forage and as a feedstock for biofuel and chemical conversion. To examine the influence of environment on composition of switchgrass (Panicum virgatum L.), we utilized a multi-environment experiment consisting of clones of switchgrass genotypes grown at up to ten locations in the continental US. We tested the influence of different milling treatments on biomass composition trait predictions via near-infrared reflectance spectroscopy (NIRS). We found that most compositional trait predictions (29/34) were significantly different (P < 0.05) when a single lot of biomass was subjected to disparate milling treatments, i.e., knife milling vs. knife milling with an additional cyclone milling. Further, depending on the plant material tested, three to eight compositional trait predictions vary (P < 0.05) when identical biomass was knife milled at different sites followed by cyclone milling at a single site, including for traits such as Klason lignin, nitrogen, and carbon. In some cases, variation due to milling site exceeded environmentally induced compositional variation of a single switchgrass genotype grown at different sites. From these observations, we recommend a protocol with two sequential millings that decouples growth environment from a particular mill. Utilizing this approach, we found that 46/46 biomass composition traits from the warm season herbaceous forage and switchgrass bioethanol NIRS equations vary significantly (P < 0.001) in clones of a switchgrass genotype (WBC) grown at ten sites, with the growth site representing the largest average source of variation (41%). This multi-site milling approach can be used to examine environmental and gene-by-environment influences on composition with the goal of optimizing cell wall composition in different environments for biomass utilization.

09 BIOMASS FUELS↗
NASA NTRS2020

To What Extent Biomass Burning Aerosols Impact South America Seasonal Climate Predictions?

We applied the Goddard Earth Observing System for subseasonal to seasonal climate prediction to assess the impact of inclusion biomass burning (BB) aerosols over South America (SA) during the austral winter. We also evaluated the model sensitivity to the BB emissions prescription using no emissions, monthly climatological, and daily emissions. Each hindcast consisted of four members running from June to November of each year between 2000 and 2015. Our results indicated that interactive BB aerosols improve the seasonal climate prediction performance over SA. More realistic daily based emissions significantly further improve the performance in comparison with the climatological ones. Therefore, improvements in the BB emissions representation are urged to represent the aerosol impacts on seasonal climate prediction performance adequately.

Julliana L. M. Freire↗
NASA NTRS2011

Forest Biomass Mapping From Lidar and Radar Synergies

The use of lidar and radar instruments to measure forest structure attributes such as height and biomass at global scales is being considered for a future Earth Observation satellite mission, DESDynI (Deformation, Ecosystem Structure, and Dynamics of Ice). Large footprint lidar makes a direct measurement of the heights of scatterers in the illuminated footprint and can yield accurate information about the vertical profile of the canopy within lidar footprint samples. Synthetic Aperture Radar (SAR) is known to sense the canopy volume, especially at longer wavelengths and provides image data. Methods for biomass mapping by a combination of lidar sampling and radar mapping need to be developed. In this study, several issues in this respect were investigated using aircraft borne lidar and SAR data in Howland, Maine, USA. The stepwise regression selected the height indices rh50 and rh75 of the Laser Vegetation Imaging Sensor (LVIS) data for predicting field measured biomass with a R(exp 2) of 0.71 and RMSE of 31.33 Mg/ha. The above-ground biomass map generated from this regression model was considered to represent the true biomass of the area and used as a reference map since no better biomass map exists for the area. Random samples were taken from the biomass map and the correlation between the sampled biomass and co-located SAR signature was studied. The best models were used to extend the biomass from lidar samples into all forested areas in the study area, which mimics a procedure that could be used for the future DESDYnI Mission. It was found that depending on the data types used (quad-pol or dual-pol) the SAR data can predict the lidar biomass samples with R2 of 0.63-0.71, RMSE of 32.0-28.2 Mg/ha up to biomass levels of 200-250 Mg/ha. The mean biomass of the study area calculated from the biomass maps generated by lidar- SAR synergy 63 was within 10% of the reference biomass map derived from LVIS data. The results from this study are preliminary, but do show the potential of the combined use of lidar samples and radar imagery for forest biomass mapping. Various issues regarding lidar/radar data synergies for biomass mapping are discussed in the paper.

Sun, Guoqing↗
DOE OSTI2025

Reduced soil diazotroph diversity decreases nitrogen fixation rates, but depends on land management

Soil diazotrophs convert atmospheric nitrogen into plant-available ammonium through free-living nitrogen fixation (FLNF). This sustainable nitrogen source can reduce our dependence on synthetic fertilizer inputs in conventional agricultural systems. However, we know little about the effect of diazotroph diversity on FLNF, especially given that FLNF is intermediate within the broad-narrow functional spectrum. Here, we determined how management-mediated shifts in diazotroph diversity would impact their ecosystem function (FLNF) by quantifying diazotroph diversity across a long-term management gradient during and after the growing season. In addition to field observations, we leveraged the same management gradient to manipulate diversity in soil microcosms via chloroform fumigation exposure. In the field, diazotroph diversity was significantly higher after the growing season, and the biologically-based annual cropping system harbored the highest diazotroph diversity. However, perennial cropping systems maintained the highest FLNF despite lower diazotroph diversity, and both soil moisture and temperature were stronger predictors of FLNF. Based on these results, integrating diverse perennial crops into agricultural landscape could result in greater N from FLNF, particularly at the end of the growing season. When we reduced biodiversity in a manipulation experiment, the diversity-FLNF association was stronger than in the field experiment, suggesting that FLNF communities are not as functionally redundant as taxonomically ‘broad’ ecosystem functions. Here, the strength of diversity-FLNF correlation varied by previous land management. Diazotroph diversity better predicted FLNF in annual and forest soil microcosms, and microbial biomass carbon better predicted FLNF in perennial soil microcosms. Taken together, our results show that while diazotroph diversity influences FLNF, especially under extreme environmental disturbances, abiotic factors like soil moisture and temperature are stronger constraints on FLNF in the field.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2019

Importance of Tree- and Species-Level Interactions With Wildfire, Climate, and Soils in Interior Alaska: Implications for Forest Change Under A Warming Climate

The boreal zone of Alaska is dominated by interactions between disturbances, vegetation, and soils. These interactions are likely to change in the future through increasing permafrost thaw, more frequent and intense wildfires, and vegetation change from drought and competition. We utilize an individual tree-based vegetation model, the University of Virginia Forest Model Enhanced (UVAFME), to estimate current and future forest conditions across sites within interior Alaska. We updated UVAFME for application within interior Alaska, including improved simulation of permafrost dynamics, litter decay, nutrient dynamics, fire mortality, and post-fire regrowth. Following these updates, UVAFME output on species-specific biomass and stem density was comparable to inventory measurements at various forest types within interior Alaska. We then simulated forest response to climate change at specific inventory locations and across the Tanana Valley River Basin on a 2 × 2 km2 grid. We derived projected temperature and precipitation from a five-model average taken from the CMIP5 archive under the RCP 4.5 and 8.5 scenarios. Results suggest that climate change and the concomitant impacts on wildfire and permafrost dynamics will result in overall decreases in biomass (particularly for spruce (Picea spp.)) within the interior Tanana Valley, despite increases in quaking aspen (Populus tremuloides) biomass, and a resulting shift towards higher deciduous fraction. Simulation results also predict increases in biomass at cold, wet locations and at high elevations, and decreases in biomass in dry locations, under both moderate (RCP 4.5) and extreme (RCP 8.5) climate change scenarios. These simulations demonstrate that a highly detailed, species interactive model can be used across a large region within Alaska to investigate interactions between vegetation, climate, wildfire, and permafrost. The vegetation changes predicted here have the capacity to feed back to broader scale climate-forest interactions in the North American boreal forest, a region which contributes significantly to the global carbon and energy budgets.

forest modeling↗
NASA NTRS1983

Remote sensing investigations of wetland biomass and productivity for global biosystems research

Monitoring biomass of wetlands ecosystems can provide information on net primary production and on the chemical and physical status of wetland soils relative to anaerobic microbial transformation of key elements. Multispectral remote sensing techniques successfully estimated macrophytic biomass in wetlands systems. Regression models developed from ground spectral data for predicting Spartina alterniflora biomass over an entire growing season include seasonal variations in biomass density and illumination intensity. An independent set of biomass and spectral data were collected and the standing crop biomass and net primary productivity were estimated. The improved spatial, radiometric and spectral resolution of th LANDSAT-4 Thematic Mapper over the LANDSAT MSS can greatly enhance multispectral techniques for estimating wetlands biomass over large areas. These techniques can provide the biomass data necessary for global ecology studies.

Harkisky, M.↗
NASA NTRS1987

New dimension analyses with error analysis for quaking aspen and black spruce

Dimension analysis for black spruce in wetland stands and trembling aspen are reported, including new approaches in error analysis. Biomass estimates for sacrificed trees have standard errors of 1 to 3%; standard errors for leaf areas are 10 to 20%. Bole biomass estimation accounts for most of the error for biomass, while estimation of branch characteristics and area/weight ratios accounts for the leaf area error. Error analysis provides insight for cost effective design of future analyses. Predictive equations for biomass and leaf area, with empirically derived estimators of prediction error, are given. Systematic prediction errors for small aspen trees and for leaf area of spruce from different site-types suggest a need for different predictive models within species. Predictive equations are compared with published equations; significant differences may be due to species responses to regional or site differences. Proportional contributions of component biomass in aspen change in ways related to tree size and stand development. Spruce maintains comparatively constant proportions with size, but shows changes corresponding to site. This suggests greater morphological plasticity of aspen and significance for spruce of nutrient conditions.

Woods, K. D.↗
DOE OSTI2020

Carbon Isotope Composition and the NDVI as Phenotyping Approaches for Drought Adaptation in Durum Wheat: Beyond Trait Selection

High-throughput phenotyping platforms provide valuable opportunities to investigate biomass and drought-adaptive traits. We explored the capacity of traits associated with drought adaptation such as aerial measurements of the Normalized Difference Vegetation Index (NDVI) and carbon isotope composition (δ13C) determined at the leaf level to predict genetic variation in biomass. A panel of 248 elite durum wheat accessions was grown at the Maricopa Phenotyping platform (US) under well-watered conditions until anthesis, and then irrigation was stopped and plot biomass was harvested about three weeks later. Globally, the δ13C values increased from the first to the second sampling date, in keeping with the imposition of progressive water stress. Additionally, δ13C was negatively correlated with final biomass, and the correlation increased at the second sampling, suggesting that accessions with lower water-use efficiency maintained better water status and, thus, performed better. Flowering time affected NDVI predictions of biomass, revealing the importance of developmental stage when measuring the NDVI and the effect that phenology has on its accuracy when monitoring genotypic adaptation to specific environments. The results indicate that in addition to choosing the optimal phenotypic traits, the time at which they are assessed, and avoiding a wide genotypic range in phenology is crucial.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Optimizing resource allocation in Miscanthus breeding via sparse testing designs for genomic prediction

Phenotyping high-biomass perennial crops is laborious and the rate of genetic gain in conventional perennial crop breeding programs is typically low. So, it is especially important to identify methods that produce efficiency gains in the breeding process. Miscanthus is a C4 perennial grass with favorable characteristics for producing biomass as a feedstock for biofuels and diverse bio-based products. Increasing biomass yield will increase profitability and environmental benefits, so it is a key target for Miscanthus breeding. In addition, the identification of well-adapted genotypes across a wide range of environmental conditions requires the establishment of multi-environment trials (METs). Sparse testing is a genomic prediction-based strategy that reduces the phenotyping costs in METs by selecting a subset of genotypes to evaluate in a subset of environments and then predicts the performance of the unobserved genotype-environment combinations. A Miscanthus sacchariflorus (MSA) population comprising 336 genotypes observed across three environments was analyzed implementing sparse testing designs. Three prediction models considering main effects (environments, genotypes, genomic) and interaction effects (genotype-by-environment; G×E interaction) were implemented for forecasting dry biomass yield (YDY), total culm (TCM), average internode length (AIL), and culm node number (CNN). Multiple calibration sets based on different compositions and sizes were considered to evaluate performance in terms of the predictive ability (PA) and the mean square error (MSE) for a fixed testing set size. The training set size ranged from 52 to 112 to predict a fixed set of 224 unobserved genotypes across all three environments. The results showed that the model accounting for G×E interaction consistently presented the highest PA and the lowest MSE: for CNN (PA: ~0.77, MSE: ~0.5) and YDY (PA: ~0.70, MSE: ~1.3) while for TCM and AIL these ranged from ~0.28 to 0.41 and ~1.3 to 4.3, respectively. Overall, varying training sets and allocation strategies did not affect PA and MSE, with 52 non-overlapping and 0 overlapping genotypes per environment as the optimal cost-effective allocation framework. This suggests that implementing sparse testing designs could significantly reduce phenotyping costs by fivefold, without compromising PA in breeding programs for perennial crops such as Miscanthus.

Miscanthus sacchariflorus (MSA)↗
DOE OSTI2022

Effect of biomass liquefaction on glucose and xylose prices predicted by National Renewable Energy Laboratory biochemical sugar model

Abstract The National Renewable Energy Laboratory (NREL) published a model in 2017 that enables the minimum selling price of lignocellulosic sugar to be calculated. The model can be modified to suit any biomass feedstock and operational design. In the present case, the model is used to understand the economics of a process configuration that incorporates liquefaction as a preprocessing step in corn‐stover‐fed biorefinery. This study demonstrates a quantitative approach utilizing an existing biorefinery setup to simulate the biomass liquefaction technique while estimating the price of the resulting sugar product. The objective is to understand whether the addition of liquefaction methodology – and the substitution of acid pretreatment – can reduce the cost of lignocellulosic sugars. The reason for setting up a liquefaction unit at the start is to reduce the yield stress of biomass slurry so that the flow remains unrestricted downstream. The liquefaction process is also unique because it uses no chemicals and saves the cost of pretreatment. The pretreatment step is bypassed because the output of liquefaction can be fed into enzyme hydrolysis just after simple cooking. The liquefaction process can be performed in three modes: enzyme, enzyme mimetic, and a combination of enzyme and enzyme mimetic. The results from the BC1707 model indicate the minimum cost for the enzyme liquefaction route. © 2022 The Authors. Biofuels, Bioproducts and Biorefining published by Society of Industrial Chemistry and John Wiley & Sons Ltd.

09 BIOMASS FUELS↗