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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Spatial Variations in Crop Growing Seasons Pivotal to Reproduce Global Fluctuations in Maize and Wheat Yields

Testing our understanding of crop yield responses to weather fluctuations at global scale is notoriously hampered by limited information about underlying management conditions, such as cultivar selection or fertilizer application. Here, we demonstrate that accounting for observed spatial variations in growing seasons increases the variance in reported national maize and wheat yield anomalies that can be explained by process-based model simulations from 34 to 58% and 47 to 54% across the 10 most weather-sensitive main producers, respectively. For maize, the increase in explanatory power is similar to the increase achieved by accounting for water stress, as compared to simulations assuming perfect water supply in both rainfed and irrigated agriculture. Representing water availability constraints in irrigation is of second-order importance. We improve the model’s explanatory power by better representing crops’ exposure to observed weather conditions, without modifying the weather response itself. This growing season adjustment now allows for a close reproduction of heat wave and drought impacts on crop yields.

Maize↗
DOE OSTI2024

A mixture of grass–legume cover crop species may ameliorate water stress in a changing climate

Climate change models predict increasing precipitation variability in the mid-latitude regions of Earth, generating a need to reduce the negative impacts of these changes on crop production. Despite considerable research on how cover crops support agriculture in a changing climate, understanding is limited of how climate change influences the growth of cover crops. We investigated the early development of two common cover crop species—crimson clover (Trifolium incarnatum) and rye (Secale cereale)—and hypothesized that growing them in the mixture would ameliorate stress from drought or waterlogging. This hypothesis was tested in a 25-day greenhouse experiment, where the two factors (species number and water stress) were fully crossed in randomized blocks, and plant responses were quantified through survival, growth rate, biomass production and root morphology. Water stress negatively influenced the early growth of these two species in contrasting ways: crimson clover was susceptible to drought while rye performed poorly under waterlogging. Per-plant biomass in rye was always greater in mixture than in monoculture, while per-plant biomass of crimson clover was greater in mixture under drought. Both species grew longer roots in mixture than in monoculture under drought, and total biomass of mixtures did not differ significantly from the more-productive monoculture (rye) in any water condition. In the face of increasingly variable precipitation, growing crimson clover and rye together has potential to ameliorate water stress, a possibility that should be further investigated in field experiments.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1977

Estimated winter wheat yield from crop growth predicted by LANDSAT

An evapotranspiration and growth model for winter wheat is reported. The inputs are daily solar radiation, maximum temperature, minimum temperature, precipitation/irrigation and leaf area index. The meteorological data were obtained from National Weather Service while LAI was obtained from LANDSAT multispectral scanner. The output provides daily estimates of potential evapotranspiration, transpiration, evaporation, soil moisture (50 cm depth), percentage depletion, net photosynthesis and dry matter production. Winter wheat yields are correlated with transpiration and dry matter accumulation.

Kanemasu, E. T.↗
NASA NTRS2010

Using Leaf Chlorophyll to Parameterize Light-Use-Efficiency Within a Thermal-Based Carbon, Water and Energy Exchange Model

Chlorophylls absorb photosynthetically active radiation and thus function as vital pigments for photosynthesis, which makes leaf chlorophyll content (C(sub ab) useful for monitoring vegetation productivity and an important indicator of the overall plant physiological condition. This study investigates the utility of integrating remotely sensed estimates of C(sub ab) into a thermal-based Two-Source Energy Balance (TSEB) model that estimates land-surface CO2 and energy fluxes using an analytical, light-use-efficiency (LUE) based model of canopy resistance. The LUE model component computes canopy-scale carbon assimilation and transpiration fluxes and incorporates LUE modifications from a nominal (species-dependent) value (LUE(sub n)) in response to short term variations in environmental conditions, However LUE(sub n) may need adjustment on a daily timescale to accommodate changes in plant phenology, physiological condition and nutrient status. Day to day variations in LUE(sub n) were assessed for a heterogeneous corn crop field in Maryland, U,S.A. through model calibration with eddy covariance CO2 flux tower observations. The optimized daily LUE(sub n) values were then compared to estimates of C(sub ab) integrated from gridded maps of chlorophyll content weighted over the tower flux source area. The time continuous maps of daily C(sub ab) over the study field were generated by focusing in-situ measurements with retrievals generated with an integrated radiative transfer modeling tool (accurate to within +/-10%) using at-sensor radiances in green, red and near-infrared wavelengths acquired with an aircraft imaging system. The resultant daily changes in C(sub ab) within the tower flux source area generally correlated well with corresponding changes in daily calibrated LUE(sub n) derived from the tower flux data, and hourly water, energy and carbon flux estimation accuracies from TSEB were significantly improved when using C(sub ab) for delineating spatio-temporal variations in LUE(sub n). The results demonstrate the synergy between thermal infrared and shortwave reflective wavebands in producing valuable remote sensing data for operational monitoring of carbon and water fluxes.

Houlborg, Rasmus↗
NASA NTRS1981

A meteorologically driven grain sorghum stress indicator model

A grain sorghum soil moisture and temperature stress model is described. It was developed to serve as a meteorological data filter to alert commodity analysts to potential stress conditions and crop phenology in selected grain sorghum production areas. The model also identifies optimum conditions on a daily basis and planting/harvest problems associated with poor tractability.

Taylor, T. W.↗
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↗
NASA NTRS1982

Thermal infrared research: Where are we now?

The use of infrared temperatures in agriculture and hydrology is based on the energy balance equation which is used to estimate evapotranspiration and crop stress over small areas within a field as well as large areas. For its full utilization, this measurement must be combined with other spectral data collected at a time resolution sufficient to detect changes in the agricultural or hydrological systems and at a spatial resolution with enough detail to sample within individual fields. The most stringent requirement is that the data be readily available to the user. The spatial resolution necessary for IR measurements to be incorporated into evapotranspiration models to accurately estimate field and regional transpiration or measure crop stress; methods to estimate crop stress and yield over large areas and different cultivars within a species; the temporal resolution adequate for detecting crop stress or inclusion in evapotranspiration models; and ancillary parameters for estimating thermal IR measurements must be investigated.

Hatfield, J. L.↗
NASA NTRS2015

The Agricultural Model Intercomparison and Improvement Project (AgMIP) Town Hall

AgMIP (www.agmip.org) is an international community of climate, crop, livestock, economics, and IT experts working to further the development and application of multi-model, multi-scale, multi-disciplinary agricultural models that can inform policy and decision makers around the world. This meeting will engage the AGU community by providing a brief overview of AgMIP, in particular its new plans for a Coordinated Global and Regional Assessment of climate change impacts on agriculture and food security for AR6. This Town Hall will help identify opportunities for participants to become involved in AgMIP and its 30+ activities.

farm crops↗
NASA NTRS1979

Feature extraction applied to agricultural crops as seen by LANDSAT

The physical interpretation of the spectral-temporal structure of LANDSAT data can be conveniently described in terms of a graphic descriptive model called the Tassled Cap. This model has been a source of development not only in crop-related feature extraction, but also for data screening and for haze effects correction. Following its qualitative description and an indication of its applications, the model is used to analyze several feature extraction algorithms.

Kauth, R. J.↗
DOE OSTI2023

Root phenotypes for improved nitrogen capture

Abstract Background Suboptimal nitrogen availability is a primary constraint for crop production in low-input agroecosystems, while nitrogen fertilization is a primary contributor to the energy, economic, and environmental costs of crop production in high-input agroecosystems. In this article we consider avenues to develop crops with improved nitrogen capture and reduced requirement for nitrogen fertilizer. Scope Intraspecific variation for an array of root phenotypes has been associated with improved nitrogen capture in cereal crops, including architectural phenotypes that colocalize root foraging with nitrogen availability in the soil; anatomical phenotypes that reduce the metabolic costs of soil exploration, improve penetration of hard soil, and exploit the rhizosphere; subcellular phenotypes that reduce the nitrogen requirement of plant tissue; molecular phenotypes exhibiting optimized nitrate uptake kinetics; and rhizosphere phenotypes that optimize associations with the rhizosphere microbiome. For each of these topics we provide examples of root phenotypes which merit attention as potential selection targets for crop improvement. Several cross-cutting issues are addressed including the importance of soil hydrology and impedance, phenotypic plasticity, integrated phenotypes, in silico modeling, and breeding strategies using high throughput phenotyping for co-optimization of multiple phenes. Conclusions Substantial phenotypic variation exists in crop germplasm for an array of root phenotypes that improve nitrogen capture. Although this topic merits greater research attention than it currently receives, we have adequate understanding and tools to develop crops with improved nitrogen capture. Root phenotypes are underutilized yet attractive breeding targets for the development of the nitrogen efficient crops urgently needed in global agriculture.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Phosphorus availability and leaching losses in annual and perennial cropping systems in an upper US Midwest landscape

Abstract Excessive phosphorus (P) applications to croplands can contribute to eutrophication of surface waters through surface runoff and subsurface (leaching) losses. We analyzed leaching losses of total dissolved P (TDP) from no-till corn, hybrid poplar ( Populus nigra X P. maximowiczii ), switchgrass ( Panicum virgatum ), miscanthus ( Miscanthus giganteus ), native grasses, and restored prairie, all planted in 2008 on former cropland in Michigan, USA. All crops except corn (13 kg P ha −1 year −1 ) were grown without P fertilization. Biomass was harvested at the end of each growing season except for poplar. Soil water at 1.2 m depth was sampled weekly to biweekly for TDP determination during March–November 2009–2016 using tension lysimeters. Soil test P (0–25 cm depth) was measured every autumn. Soil water TDP concentrations were usually below levels where eutrophication of surface waters is frequently observed (> 0.02 mg L −1 ) but often higher than in deep groundwater or nearby streams and lakes. Rates of P leaching, estimated from measured concentrations and modeled drainage, did not differ statistically among cropping systems across years; 7-year cropping system means ranged from 0.035 to 0.072 kg P ha −1 year −1 with large interannual variation. Leached P was positively related to STP, which decreased over the 7 years in all systems. These results indicate that both P-fertilized and unfertilized cropping systems may leach legacy P from past cropland management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Soil-related developments of the Biome-BGCMuSo v6.2 terrestrial ecosystem model

Terrestrial biogeochemical models are essential tools to quantify climate–carbon cycle feedback and plant–soil relations from local to global scale. In this study, a theoretical basis is provided for the latest version of the Biome-BGCMuSo biogeochemical model (version 6.2). Biome-BGCMuSo is a branch of the original Biome-BGC model with a large number of developments and structural changes. Earlier model versions performed poorly in terms of soil water content (SWC) dynamics in different environments. Moreover, lack of detailed nitrogen cycle representation was a major limitation of the model. Since problems associated with these internal drivers might influence the final results and parameter estimation, additional structural improvements were necessary. In this paper the improved soil hydrology as well as the soil carbon and nitrogen cycle calculation methods are described in detail. Capabilities of the Biome-BGCMuSo v6.2 model are demonstrated via case studies focusing on soil hydrology, soil nitrogen cycle, and soil organic carbon content estimation. Soil-hydrology-related results are compared to observation data from an experimental lysimeter station. The results indicate improved performance for Biome-BGCMuSo v6.2 compared to v4.0 (explained variance increased from 0.121 to 0.8 for SWC and from 0.084 to 0.46 for soil evaporation; bias changed from -0.047 to -0.007 m 3 m -3 for SWC and from -0.68 to -0.2 mm d -1 for soil evaporation). Simulations related to nitrogen balance and soil CO 2 efflux were evaluated based on observations made in a long-term field experiment under crop rotation. The results indicated that the model is able to provide realistic nitrate content estimation for the topsoil. Soil nitrous oxide (N 2 O) efflux and soil respiration simulations were also realistic, with overall correspondence with the observations (for the N 2 O efflux simulation bias was between -0.13 and -0.1 mgNm -2 d -1 , and normalized root mean squared error (NRMSE) was 32.4 %–37.6 %; for CO 2 efflux simulations bias was 0.04–0.17 gCm -2 d -1 , while NRMSE was 34.1 %–40.1 %). Sensitivity analysis and optimization of the decomposition scheme are presented to support practical application of the model. The improved version of Biome-BGCMuSo has the ability to provide more realistic soil hydrology representation as well as nitrification and denitrification process estimation, which represents a major milestone.

58 GEOSCIENCES↗
DOE OSTI2024

Model run output for WM-ABM 2024 HESS Publication

Model output for computational experiments introducing WM-ABM. See associated HESS publication here: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1604/ all_nldas_cdl_data_v3.txt - Cropland Data Layer data aggregated to 1/8 degree resolution for input into the PMP calibration process. Variables include NLDAS ID, crop type, year, the count of pixels in the grid cell of the crop type, CDL crop ID, and GCAM crop ID. 20230115 ABM runs - WM-ABM model outputs for various scenarios described in HESS publication. If multiple versions exist (e.g., v2, v3, etc.) latest version indicates dataset generated for manuscript. The "baseline" run is the primary run, where as the "memXX" runs indicate sensitivity runs testing different parameters of agent memory. Output files starting with "abm_results_" include groundwater irrigated acres and surface water irrigated acres for each farm/agent combination. Other CSV outputs provide summary statistics of various agent and water shortage outcomes, used for generation of figures in the HESS manuscript.

ABM↗
NASA NTRS2012

Customizing NASA's Earth Science Research Products for addressing MENA Water Challenges

As projected by IPCC 2007 report, by the end of this century the Middle East North Mrica (MENA) region is projected to experience an increase of 3 C to 5 C rise in mean temperatures and a 20% decline in precipitation. This poses a serious problem for this geographic zone especially when majority of the hydrological consumption is for the agriculture sector and the remaining amount is for domestic consumption. In late 2011, the World Bank, USAID and NASA have joined hands to establishing integrated, modem, up to date NASA developed capabilities for various countries in the MENA region for addressing water resource issues and adapting to climate change impacts for improved decision making for societal benefits. The main focus of this undertaking is to address the most pressing societal issues which can be modeled and solved by utilizing NASA Earth Science remote sensing data products and hydrological models. The remote sensing data from space is one of the best ways to study such complex issues and further feed into the decision support systems. NASA's fleet of Earth Observing satellites offer a great vantage point from space to look at the globe and provide vital signs necessary to maintain healthy and sustainable ecosystem. NASA has over fifteen satellites and thirty instruments operating on these space borne platforms and generating over 2000 different science products on a daily basis. Some of these products are soil moisture, global precipitation, aerosols, cloud cover, normalized difference vegetation index, land cover/use, ocean altimetry, ocean salinity, sea surface winds, sea surface temperature, ozone and atmospheric gasses, ice and snow measurements, and many more. All of the data products, models and research results are distributed via the Internet freely through out the world. This project will utilize several NASA models such as global Land Data Assimilation System (LDAS) to generate hydrological states and fluxes in near real time. These LDAS products will then be further compared with other NASA satellite observations (MODIS, VIIRS, TRMM, etc.) and other discrete models to compare and optimize evapotranspiration, soil moisture and crop irrigation, drought assessment and water balance. The floods being a critical disaster in many of the MENA countries, NASA's global flood mapping and modeling framework (CREST) will be customized for country specific needs and delivered to the remote sensing organizations for their future use. Training is an important component under this activity and adequate level of training will be offered to build basic capacity to work with NASA provided data products, models for their future use. This paper provides a comprehensive introduction to NASA's Earth Science mission for understanding the behavior of our home Planet, projecting its health for future generations and applying research results solving societal issues.

Habib, Shahid↗
NASA NTRS1980

A model of plant canopy polarization

A model for the amount of linearly polarized light reflected by the shiny leaves of grain crops is based on the morphological and phenological characteristics of the plant canopy and upon the Fresnel equations which describe the light reflection process at the smooth boundary separating two dielectrics. The theory used demonstrates that, potentially, measurements of the linearly polarized light from a crop canopy may be used as an additional feature to discriminate between crops such as wheat and barley, two crops which are so spectrally similar that they are misclassified with unacceptable frequency. Examination of the model suggests that, potentially, satellite polarization measurements may be used to monitor crop development stage, leaf water content, leaf area index, hail damage, and certain plant diseases. The information content of these measurements is needed to evaluate the proposed polarization sensor for the satellite-borne multispectral resource sampler.

Vanderbilt, V. C.↗
NASA NTRS2021

Extreme Lows of Wheat Production in Brazil

Wheat production in Brazil is insufficient to meet domestic demand and falls drastically in response to adverse climate events. Multiple, agro-climate-specific regression models, quantifying regional production variability, were combined to estimate national production based on past climate, cropping area, trend-corrected yield, and national commodity prices. Projections with five CMIP6 climate change models suggest extremes of low wheat production historically occurring once every 20 years would become up to 90% frequent by the end of this century, depending on representative concentration pathway, magnified by wheat and in some cases by maize price fluctuations. Similar impacts can be expected for other crops and in other countries. This drastic increase in frequency in extreme low crop production with climate change will threaten Brazil's and many other countries progress toward food security and abolishing hunger.

Brazil↗
NASA NTRS2024

How Can Future Climate Change Affect the Corn Production System in Nebraska, USA?

The increasing temperatures and changing precipitation patterns have affected both crop yields and water requirements challenging agricultural systems in sustaining high crop yields and ensuring water and food security. In the present study, we evaluated the impact of climate change on the corn production under irrigated and rainfed cropping systems in Nebraska considering four climate zones until 2100 as a case study for the US Corn Belt. Additionally, suggests ways to reduce the impact of climate change on corn yield and water resources. Five global circulation model datasets from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) interconnected to Shared Socioeconomic Pathways (SSPs), were selected. Three future periods: near (2020–2039), mid (2040–2069), and far future (2070–2100) under SSP226, SSP370, and SSP585 scenarios were used to predict the climate changes, crop cycle length, crop yield, and crop water requirement. Additionally, we input annual CO2 concentration scenarios for each SSP scenario to model changes from 2015 to 2100 using the CERES-Maize model. Temperatures are forecasted to rise, precipitation to decline, and crop cycle length to decrease, particularly for SSP370 (up to 49% yield decrease) and SSP585 (up to 67% yield decrease) by 2070–2100. Yields are expected to decline across all scenarios and periods for irrigated and rainfed fields due to the reduction in crop cycle length. Crop water demand is projected to increase, with rainfed systems facing heightened impact due to reduced precipitation. Addressing climate change in Nebraska requires actions like developing new corn varieties to longer the crop season, transitioning to efficient irrigation systems, improving irrigation management for water and food security, and reducing the risk of conflicts over its use. These results open possibilities for discussion about the actions that can be taken to cope with climate change and guarantee water and food security in the US Corn Belt.

CMIP6↗
NASA NTRS1983

Comparison of CRD, APU, and state models for Iowa corn and soybeans and North Dakota barley and spring wheat

A comparison was made among the CEAS crop reporting district (CRD), agrophysical unit (APU), and state level multiple regression yield models for corn and soybeans in Iowa and barley and spring wheat in North Dakota. The best predictions were made by the state model for North Dakota spring wheat, by the APU models for barley, by the CRD models for Iowa soybeans, and by APU covariance models for Iowa corn. Because of this lack of consistency of model performance, CRD models would be recommended due to the availability of the data.

French, V.↗