Search NASA⌕ Search

SEARCH · Search NASA

Results for “wheat simulation models”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

101 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS1984

Comprehensive Understanding for Vegetated Scene Radiance Relationships

Directional reflectance distributions spanning the entire existent hemisphere were measured in two field studies; one using a Mark III 3-band radiometer and one using the rapid scanning bidirectional field instrument called PARABOLA. Surfaces measured included corn, soybeans, bare soils, grass lawn, orchard grass, alfalfa, cotton row crops, plowed field, annual grassland, stipa grass, hard wheat, salt plain shrubland, and irrigated wheat. Analysis of field data showed unique reflectance distributions ranging from bare soil to complete vegetation canopies. Physical mechanisms causing these trends were proposed. A 3-D model was developed and is unique in that it predicts: (1) the directional spectral reflectance factors as a function of the sensor's azimuth and zenith angles and the sensor's position above the canopy; (2) the spectral absorption as a function of location within the scene; and (3) the directional spectral radiance as a function of the sensor's location within the scene. Initial verification of the model as applied to a soybean row crop showed that the simulated directional data corresponded relatively well in gross trends to the measured data. The model was expanded to include the anisotropic scattering properties of leaves as a function of the leaf orientation distribution in both the zenith and azimuth angle modes.

Kimes, D. S.↗
NASA NTRS2000

Refinements to SSiB with an Emphasis on Snow-Physics: Evaluation and Validation Using GSWP and Valdai Data

Refinements to the snow-physics scheme of SSiB (Simplified Simple Biosphere Model) are described and evaluated. The upgrades include a partial redesign of the conceptual architecture to better simulate the diurnal temperature of the snow surface. For a deep snowpack, there are two separate prognostic temperature snow layers - the top layer responds to diurnal fluctuations in the surface forcing, while the deep layer exhibits a slowly varying response. In addition, the use of a very deep soil temperature and a treatment of snow aging with its influence on snow density is parameterized and evaluated. The upgraded snow scheme produces better timing of snow melt in GSWP-style simulations using ISLSCP Initiative I data for 1987-1988 in the Russian Wheat Belt region. To simulate more realistic runoff in regions with high orographic variability, additional improvements are made to SSiB's soil hydrology. These improvements include an orography-based surface runoff scheme as well as interaction with a water table below SSiB's three soil layers. The addition of these parameterizations further help to simulate more realistic runoff and accompanying prognostic soil moisture fields in the GSWP-style simulations. In intercomparisons of the performance of the new snow-physics SSiB with its earlier versions using an 18-year single-site dataset from Valdai Russia, the version of SSiB described in this paper again produces the earliest onset of snow melt. Soil moisture and deep soil temperatures also compare favorably with observations.

Mocko, David M.↗
NASA NTRS2017

Integrating Water Supply Constraints into Irrigated Agricultural Simulations of California

Simulations of irrigated croplands generally lack key interactions between water demand from plants and water supply from irrigation systems. We coupled the Water Evaluation and Planning system (WEAP) and Decision Support System for Agrotechnology Transfer (DSSAT) to link regional water supplies and management with field-level water demand and crop growth. WEAP-DSSAT was deployed and evaluated over Yolo County in California for corn, rice, and wheat. WEAP-DSSAT is able to reproduce the results of DSSAT under well-watered conditions and reasonably simulate observed mean yields, but has difficulty capturing yield interannual variability. Constraining irrigation supply to surface water alone reduces yields for all three crops during the 1987-1992 drought. Corn yields are reduced proportionally with water allocation, rice yield reductions are more binary based on sufficient water for flooding, and wheat yields are least sensitive to irrigation constraints as winter wheat is grown during the wet season.

Agriculture; Irrigation; Water Resources Managemen↗
NASA NTRS1993

The dynamics of hydroponic crops for simulation studies of the CELSS initial reference configurations

During the past several years, the NASA Program in Controlled Ecological Life Support Systems (CELSS) has continued apace with crop research and logistic, technological, and scientific strides. These include the CELSS Test Facility planned for the space station and its prototype Engineering Development Unit, soon to be active at Ames Research Center (as well as the advanced crop growth research chamber at Ames); the large environmental growth chambers and the planned human test bed facility at Johnson Space Center; the NSCORT at Purdue with new candidate crops and diverse research into the CELSS components; the gas exchange data for soy, potatoes, and wheat from Kennedy Space Center (KSC); and the high-precision gas exchange data for wheat from Utah State University (USU). All these developments, taken together, speak to the need for crop modeling as a means to connect the findings of the crop physiologists with the engineers designing the system. A need also exists for crop modeling to analyze and predict the gas exchange data from the various locations to maximize the scientific yield from the experiments. One fruitful approach employs what has been called the 'energy cascade'. Useful as a basis for CELSS crop growth experimental design, the energy cascade as a generic modeling approach for CELSS crops is a featured accomplishment in this report. The energy cascade is a major tool for linking CELSS crop experiments to the system design. The energy cascade presented here can help collaborations between modelers and crop experimenters to develop the most fruitful experiments for pushing the limits of crop productivity. Furthermore, crop models using the energy cascade provide a natural means to compare, feature for feature, the crop growth components between different CELSS experiments, for example, at Utah State University and Kennedy Space Center.

Volk, Tyler↗
DOE OSTI2021

Too Good to Be True? Evaluation of Colonoscopy Sensitivity Assumptions Used in Policy Models

Background: Models can help guide colorectal cancer screening policy. Although models are carefully calibrated and validated, there is less scrutiny of assumptions about test performance. Methods: We examined the validity of the CRC-SPIN model and colonoscopy sensitivity assumptions. Standard sensitivity assumptions, consistent with published decision analyses, assume sensitivity equal to 0.75 for diminutive adenomas (<6 mm), 0.85 for small adenomas (6–10 mm), 0.95 for large adenomas (≥10 mm), and 0.95 for preclinical cancer. We also selected adenoma sensitivity that resulted in more accurate predictions. Targets were drawn from the Wheat Bran Fiber study. In the study, we examined how well the model predicted outcomes measured over a three-year follow-up period, including the number of adenomas detected, the size of the largest adenoma detected, and incident colorectal cancer. Results: Using standard sensitivity assumptions, the model predicted adenoma prevalence that was too low (42.5% versus 48.9% observed, with 95% confidence interval 45.3%–50.7%) and detection of too few large adenomas (5.1% versus 14.% observed, with 95% confidence interval 11.8%–17.4%). Predictions were close to targets when we set sensitivities to 0.20 for diminutive adenomas, 0.60 for small adenomas, 0.80 for 10- to 20-mm adenomas, and 0.98 for adenomas 20 mm and larger. Conclusions: Colonoscopy may be less accurate than currently assumed, especially for diminutive adenomas. Alternatively, the CRC-SPIN model may not accurately simulate onset and progression of adenomas in higher-risk populations. Impact: Misspecification of either colonoscopy sensitivity or disease progression in high-risk populations may affect the predicted effectiveness of colorectal cancer screening. When possible, decision analyses used to inform policy should address these uncertainties.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Isolation of Methane Enriched Bacterial Communities and Application as Wheat Biofertilizer under Drought Conditions: An Environmental Contribution

The search for methanotrophs as plant-growth-promoting rhizobacteria (PGPR) presents an important contribution to mitigating the impact of global warming by restoring the natural soil potential for consuming methane while benefiting plants during droughts. Our in silico simulations suggest that water, produced as a byproduct of methane oxidation, can satisfy the cell growth requirement. In addition to water, methanotrophs can produce metabolites that stimulate plant growth. Considering this, we proposed that applying methanotrophs as PGPR can alleviate the effect of droughts on crops, while stimulating atmospheric methane consumption. In this work, we isolated a series of methanotrophic communities from the rhizospheres of different crops, including Italian sweet pepper and zucchini, using an atmosphere enriched with pure methane gas, to determine their potential for alleviating drought stress in wheat plants. Subsequently, 23 strains of nonmethanotrophic bacteria present in the methanotrophic communities were isolated and characterized. We then analyzed the contribution of the methane-consuming consortia to the improvement of plant growth under drought conditions, showing that some communities contributed to increases in the wheat plants’ lengths and weights, with statistically significant differences according to ANOVA models. Furthermore, we found that the presence of methane gas can further stimulate the plant–microbe interactions, resulting in larger plants and higher drought tolerance.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2018

Influence of Load Profile on Biomechanics of the Squat and Deadlift

Long duration space travel will expose astronauts to extended periods of reduced gravity. Since gravity is not present to assist loading, astronauts will use resistive and aerobic exercise regimes for the duration of the space flight to counteract the effect reduced gravity has on the body. Astronauts will exercise on a flywheel based device on the second Orion Exploration Mission (EM2). The effect that the flywheel load profile has on biomechanics is unknown when compared to free weights or a simulated free weight profile. The purpose of this evaluation is to compare the differences in lower body kinematics and kinetics between the flywheel and free weight profile. Test subjects were instrumented with reflective markers for motion capture data collection while exercising on the Device for Aerobic and Resistive Training (DART) prototype developed by TDA Research, Inc. of Wheat Ridge, CO. Exercises performed were either the squat while wearing a harness or deadlift while grasping a T-bar, both of which interfaced with the DART through a rope cable. The DART, a motorized device controlled via software, is capable of producing different load profiles. Profiles include simulated free weights with adjustable eccentric overload capability and flywheel. Test variables included the applied device load and the load profile, both set through the DART software interface. Motion capture data was collected with a 12 camera system (Smart-DX, BTS Bioengineering, Brooklyn, NY). Bilateral ground reaction force data were collected with force plates (P6000, BTS Bioengineering). DART cable force was recorded through an internal load cell. Data was collected from a total of four test subjects. The three repetition maximum was determined for each test subject for both squat and deadlift. High, medium and low loads were determined based on this maximum. The test subject performed five repetitions of each exercise at each load and each test trial was repeated twice. Cadence was controlled during exercising. Biomechanical data were used to drive the models utilizing the OpenSim software platform. Subject specific models were scaled to match the anthropometrics of the test subjects and used to estimate the peak joint angle, joint range of motion, the peak joint moment and the joint moment impulse of the lower extremity joints and the lumbar joint.These are preliminary results because the data analysis is ongoing. There was a lot of inter-subject variability, however, in general, there was a greater peak lumbar flexion angle for the flywheel squat exercise. There was a trend towards a greater range of motion at the hip for the squat exercise with free weight. There was also a greater hip extension, lumbar extension and ankle plantar flexion impulse moment during the squat exercise with free weight. During the deadlift exercise, the peak knee flexion angle and peak knee extension moment were greater when using the free weight profile. For deadlift, the hip extension, lumbar extension and ankle plantar flexion impulse moment tended to be greater with the free weight profile. Overall, the kinematic and kinetic outcomes for the flywheel profile were either statistically the same as free weight profile in many cases, significantly reduced in a few cases, and rarely higher.

Bioengineering↗
NASA NTRS2011

NASA Tech Briefs, May 2011

Topics covered include: 1) Method to Estimate the Dissolved Air Content in Hydraulic Fluid; 2) Method for Measuring Collimator-Pointing Sensitivity to Temperature Changes; 3) High-Temperature Thermometer Using Cr-Doped GdAlO3 Broadband Luminescence; 4)Metrology Arrangement for Measuring the Positions of Mirrors of a Submillimeter Telescope; 5) On-Wafer S-Parameter Measurements in the 325-508-GHz Band; 6) Reconfigurable Microwave Phase Delay Element for Frequency Reference and Phase-Shifter Applications; 7) High-Speed Isolation Board for Flight Hardware Testing; 8) High-Throughput, Adaptive FFT Architecture for FPGA-Based Spaceborne Data Processors; 9) 3D Orbit Visualization for Earth-Observing Missions; 10) MaROS: Web Visualization of Mars Orbiting and Landed Assets; 11) RAPID: Collaborative Commanding and Monitoring of Lunar Assets; 12) Image Segmentation, Registration, Compression, and Matching; 13) Image Calibration; 14) Rapid ISS Power Availability Simulator; 15) A Method of Strengthening Composite/Metal Joints; 16) Pre-Finishing of SiC for Optical Applications; 17) Optimization of Indium Bump Morphology for Improved Flip Chip Devices; 18) Measuring Moisture Levels in Graphite Epoxy Composite Sandwich Structures; 19) Marshall Convergent Spray Formulation Improvement for High Temperatures; 20) Real-Time Deposition Monitor for Ultrathin Conductive Films; 21) Optimized Li-Ion Electrolytes Containing Triphenyl Phosphate as a Flame-Retardant Additive; 22) Radiation-Resistant Hybrid Lotus Effect for Achieving Photoelectrocatalytic Self-Cleaning Anticontamination Coatings; 23) Improved, Low-Stress Economical Submerged Pipeline; 24) Optical Fiber Array Assemblies for Space Flight on the Lunar Reconnaissance Orbiter; 25) Local Leak Detection and Health Monitoring of Pressurized Tanks; 26) Dielectric Covered Planar Antennas at Submillimeter Wavelengths for Terahertz Imaging; 27) Automated Cryocooler Monitor and Control System; 28) Broadband Achromatic Phase Shifter for a Nulling Interferometer; 29) Super Dwarf Wheat for Growth in Confined Spaces; 30) Fine Guidance Sensing for Coronagraphic Observatories; 31) Single-Antenna Temperature- and Humidity-Sounding Microwave Receiver; 32) Multi-Wavelength, Multi-Beam, and Polarization-Sensitive Laser Transmitter for Surface Mapping; 33) Optical Communications Link to Airborne Transceiver; 34) Ascent Heating Thermal Analysis on Spacecraft Adaptor Fairings; 35) Entanglement in Self-Supervised Dynamics; 36) Prioritized LT Codes; 37) Fast Image Texture Classification Using Decision Trees; 38) Constraint Embedding Technique for Multibody System Dynamics; 39) Improved Systematic Pointing Error Model for the DSN Antennas; 40) Observability and Estimation of Distributed Space Systems via Local Information-Exchange Networks; 41) More-Accurate Model of Flows in Rocket Injectors; 42) In-Orbit Instrument-Pointing Calibration Using the Moon as a Target; 43) Reliability of Ceramic Column Grid Array Interconnect Packages Under Extreme Temperatures; 44) Six Degrees-of-Freedom Ascent Control for Small-Body Touch and Go; and 45) Optical-Path-Difference Linear Mechanism for the Panchromatic Fourier Transform Spectrometer.

Source record↗
DOE OSTI2022

Higher than expected N 2 O emissions from soybean crops in the Pampas Region of Argentina: Estimates from DayCent simulations and field measurements

In developing countries, agriculture generally represents a large fraction of GHG emissions reported in National Inventories, and emissions are typically estimated using Tier 1 IPCC guidelines. However, field data and locally adapted simulation models can improve the accuracy of IPCC estimations. In this report we aimed to quantify anthropogenic N2O emissions from croplands of Argentina through field measurements, model simulations and IPCC guidelines. Here we measured N 2 O emissions and their controlling factors in 62 plots of the Pampas Region with corn, soybean and wheat/soybean crops and in unmanaged grasslands. We accounted for gross emissions from crops and background emissions from unmanaged grasslands to calculate net anthropogenic emissions from crops as the difference between them. We calibrated and evaluated the DayCent model and then simulated different weather and management scenarios. Finally, we applied IPCC guidelines to estimate anthropogenic N 2 O emissions at the same plots. The DayCent model accurately simulated annual N 2 O emission for all crops as compared to measured data (RMSE = 1.4 g N ha -1 day -1 ). Measured and simulated emissions in soybean crops were higher than in corn and wheat/soybean crops. Gross N 2 O emissions ranged from 1.4 to 5.1 kg N ha -1 yr -1 for current environmental (soil and weather) and management (crops and fertilizer doses) conditions. Background emissions ranged between 1.1 and 1.3 kg N ha -1 yr -1 , and therefore net anthropogenic emissions ranged from 0.3 to 4.0 kg N ha-1 yr -1 . IPCC Tier 1 emission factors underestimated N2O releases from soybean, that were on average 4.87 times greater when estimated with DayCent and observations (0.53 vs 2.47 and 2.69 kg N ha -1 yr -1 , respectively). On the contrary, IPCC estimates for corn and wheat/soybean crops were similar to modeled and measured values. Our results suggest that N 2 O emissions from the vast 15 million ha of soybean croplands in the Pampas Region may be substantially underestimated.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

CORPSE model with litter decomposition parameters derived from the LIDET dataset

This is a version of the CORPSE model (Carbon, Organisms, Rhizosphere and Protection in the Soil Environment, Sulman et al. 2014) that uses litter decomposition parameters derived from a modified Monte Carlo simulation using the LIDET litter decomposition dataset (Long-term Intersite Decomposition Experiment Team, Harmon 2013). The code also includes the Baseline parameters, and the eight other best parameter sets identified in a modified Monte Carlo simulation. Related publication:Juice, S.M., Ridgeway, J.R., Hartman, M.D., Parton, W.J., Berardi, D.M., Sulman, B.N., Allen, K.E., & Brzostek, E.R. Reparameterizing litter decomposition using a simplified Monte Carlo method improves litter decay simulated by a microbial model and alters bioenergy soil carbon estimates. Description of files:The folder "Input Files" contains one folder for each LIDET site with data necessary to run the model. Note that "(site)" in the filenames below indicates where the LIDET site code appears (see Table 1 for site codes). Data streams include: CORPSE_full_spinup_litter.csv, CORPSE_full_spinup_rhizo.csv, CORPSE_full_spinup_bulk.csv, litterbag_init_100g_6spp.csv: initial C and N (kg C or N/m2) pool values for each soil layer, the litterbag_init_100_6spp.csv file is for the litterbag layer and is the same file for all sites. All initial C and N files have the same columns (Column - Description - Units) uFastC - Unprotected fast decomposing carbon - kg carbon/m2 uSlowC - Unprotected slow decomposing carbon - kg carbon/m2 uNecroC - Unprotected necromass carbon - kg carbon/m2 pFastC - Protected fast decomposing carbon - kg carbon/m2 pSlowC - Protected slow decomposing carbon - kg carbon/m2 pNecroC - Protected necromass carbon - kg carbon/m2 livingMicrobeC - Carbon in living microbial biomass - kg carbon/m2 uFastN - Unprotected fast decomposing nitrogen - kg nitrogen/m2 uSlowN - Unprotected slow decomposing nitrogen - kg nitrogen/m2 uNecroN - Unprotected necromass nitrogen - kg nitrogen/m2 pFastN - Protected fast decomposing nitrogen - kg nitrogen/m2 pSlowN - Protected slow decomposing nitrogen - kg nitrogen/m2 pNecroN - Protected necromass nitrogen - kg nitrogen/m2 inorganicN - Inorganic nitrogen - kg nitrogen/m2 CO2 - Carbon in carbon dioxide - kg carbon/m2 livingMicrobeN - Nitrogen in living microbial biomass - kg nitrogen/m2 soilT (site) DOY274start.csv: Average daily soil temperature (oC) interpolated from previously calculated monthly values used in DayCent LIDET simulations (Bonan et al., 2013). soilT (site) DOY274start.csv: Average daily soil volumetric water content (VWC) scalar interpolated from previously calculated monthly values used in DayCent LIDET simulations (Bonan et al., 2013). litter production.csv: Average daily litter production values for each site, data sources listed in Table S3 of related publication. litter (site) CN.csv: C:N ratio for each species from LIDET dataset (Table 2, Harmon 2013). (site).csv: Table indicating number of observations for each species decomposed at each site. Instructions: Save the model code ("CORPSE_LIDET.R") and "Input Files" folder in the same folder. Also make a folder for the model output (e.g., "results_Baseline") in the same folder. Set the working directory (setwd) in the model code to the folder with the files saved in step #1. Select the parameter set to use for the litter and litterbag compartments, comment out all other parameter sets. Run code. Output will be saved in the folder made in step 1. Output destination can be changed as necessary in code section called "Running the model." Table 1 LIDET sites and site codes used in model files. Site Code - Site AND - H.J. Andrews Experimental Forest BNZ - Bonanza Creek Experimental Forest BSF - Blodgett Research Forest CDR - Cedar Creek Natural History Area CPR - Central Plains Experimental Range HBR - Hubbard Brook Experimental Forest HFR - Harvard Forest JUN - Juneau KBS - Kellogg Biological Station KNZ - Konza Prairie Research Natural Area NWT - Niwot Ridge/Green Lakes Valley OLY - Olympic National Park OLY Conifer forest SEV - Sevilleta National Wildlife Refuge SMR - Santa Margarita Ecological Reserve UFL - University of Florida VCR - Virginia Coast Reserve Table 2 LIDET species and species codes used in model files (6 common species). Species - Species Code Sugar maple (Acer saccharum) - ACSA Drypetes (Drypetes glauca) - DRGL Red pine (Pinus resinosa) - PIRE Chestnut oak (Quercus prinus) - QUPR Western redcedar (Thuja plicata) - THPL Wheat (Triticum aestivum) - TRAE References:Bonan, G. B., Hartman, M. D., Parton, W. J., & Wieder, W. R. (2013). Evaluating litter decomposition in earth system models with long-term litterbag experiments: an example using the Community Land Model version 4 (CLM4). Global Change Biology, 19(3), 957-974. https://doi.org/https://doi.org/10.1111/gcb.12031 Harmon, M. (2013). LTER Intersite Fine Litter Decomposition Experiment (LIDET), 1990 to 2002. Long-Term Ecological Research. Forest Science Data Bank, Corvallis, OR. [Data set]. Accessed http://andlter.forestry.oregonstate.edu/data/abstract.aspx?dbcode=TD023. https://doi.org/10.6073/pasta/f35f56bea52d78b6a1ecf1952b4889c5. Sulman, B. N., Phillips, R. P., Oishi, A. C., Shevliakova, E., & Pacala, S. W. (2014). Microbe-driven turnover offsets mineral-mediated storage of soil carbon under elevated CO2. Nature Climate Change, 4, 1099 - 1102. https://doi.org/10.1038/nclimate2436

Juice, Stephanie↗
DOE OSTI2025

Agriculture’s Potential Regional Economic Contributions to the United States Economy When Supplying Feedstock to the Bio-Economy

The economic impact of obtaining biomass could become significant to U.S. rural economies via the establishment of a bioeconomy. In 2023, the Bioenergy Technologies Office (BETO) and Oak Ridge National Laboratory provided a road map to obtain over a billion tons of biomass for conversion to bioenergy and other products. Using information from this roadmap, this study estimates the potential positive and negative economic impacts that occur because of land use change, along with increased technological advances. This is achieved by using the input–output model, IMPLAN, and impacting 179 Bureau of Economic Analysis regions in the conterminous United States. Biomass included in the analysis comprises dedicated energy crops, crop residues, and forest residues. The analysis found that managing pastures more intensively could result in releasing land to produce dedicated energy crops on 30.8 million hectares, resulting in the production of 361 million metric tons of biomass. This, coupled with crop residues from barley, corn, oats, sorghum, and wheat (162 million metric tons), plus forest residues (41 million metric tons), provide 564 million dry metric tons of biomass. Assuming the price for biomass in 2023 dollars was USD 77 per dry metric-ton, this additional production results in an economic benefit for the nation of USD 619 billion, an increase from the Business As Is scenario (Baseline) of almost USD 100 billion per year, assuming a mature biomass industry. An additional 700,000 jobs are required to grow, harvest/collect, and transport the biomass material from the land.

ForSEAM↗