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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

AmeriFlux US-UR8 Yellow Jacket - UCRB

This is the AmeriFlux version of the carbon flux data for the site US-UR8 Yellow Jacket - UCRB. Site Description - This site is located in Yellow Jacket, Colorado, on a moderate slope. The area is irrigated using a center pivot system, with alfalfa being the dominant vegetation. The region experiences a temperate climate, characterized by cool to mild temperatures throughout the year and substantial annual precipitation, creating favorable conditions for agricultural activities.

Neale, C. U. [Daugherty Water for Food Global Inst↗
NASA NTRS2019

Water-Saving Agriculture Can Deliver Deep Water Cuts for China

China is working hard to reconcile growing demands for freshwater with already oversubscribed renewable water resources. However, the knowledge essential for setting and achieving the intended water consumption cuts remains limited. Here we show that on-farm water management interventions such as improved irrigation and soil management practices for maize cultivation can lead to substantial water consumption reductions, by a simulated total of 28-46% (7-14 billion m3/year) nationally, with or without the impacts of climate change. The water consumption cut is equivalent to 16-31% of the ultimate capacity of the South-North Water Transfer Project. Much of the reduction is achievable at the populous and water-stressed North China Plain and Northeast China. Meanwhile, the interventions can increase maize production by an estimated 7-15%, meeting 22-28% of demand increase projected for 2050. The water management and food production improvements obtained are crucial for achieving multiple Sustainable Development Goals (SDGs) related to water, land, and food in China and far beyond.

Guorui Huang↗
NASA NTRS2020

Remote monitoring of agricultural systems using NDVI time series and machine learning methods: a tool for an adaptive agricultural policy

This study aims to provide accurate information about changes in agricultural systems (AS) using phenological metrics derived from the NDVI time series. Use of such information could help land managers optimize land use choices and monitor the status of agricultural lands, under a variety of environmental and socioeconomic conditions. For this purpose, the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data were used to derive phenological metrics over the Oum Er-Rbia basin (central Morocco). Random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN) classifiers were explored and compared on their ability to classify AS classes over the study area. Four main AS classes have been considered: (1) irrigated annual crop (IAC), (2) irrigated perennial crop (IPC), (3) rainfed area (RA), and (4) fallow (FA). By comparing the accuracy of the three classifiers, the RF method showed the best performance with an overall accuracy of 0.97 and kappa coefficient of 0.96.The RF method was then chosen to examine time variations in AS over a 16-year period (2000–2016). The AS main variations were detected and evaluated for the four AS classes. These variations have been found to be linked well with other indicators of local agricultural land management, as well as the historical agricultural drought changes over the study area. Overall, the results present a tool for decision makers to improve agricultural management and provide a different perspective in understanding the spatiotemporal dynamics of agricultural systems.

Youssef Lebrini↗
DOE OSTI2020

Design Consideration for Solar Powered Wastewater Treatment Facility for Agriculture and Potable Usage on Acoma Pueblo Reservation

Environmental stewardship has long been a guiding principle among indigenous peoples. Acoma Pueblo, located approximately 45 miles west of Albuquerque, New Mexico, shares these values. They are exploring how to incorporate these values into future development projects. As such, Acoma recently created a wastewater treatment facility to provide irrigation water to nearby crops. Treatment facilities like this further sustainable practices and benefit the community but may draw power from the grid. This report looks at design considerations for possible photovoltaic utilization at the site to offset current power and monetary costs. Lessons learned from a larger, municipal wastewater treatment center will be applied with special considerations for Acoma Pueblo's values and unique wastewater treatment facility configuration.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

AmeriFlux FLUXNET-1F US-Tw3 Twitchell Alfalfa

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-Tw3 Twitchell Alfalfa. This is the FLUXNET version of the carbon flux data for the site US-Tw3 Twitchell Alfalfa produced by applying the standard ONEFlux (1F) software. Site Description - The Twitchell Alfalfa site is an alfalfa field owned by the state of California and leased to third parties for farming. The tower was installed on May 24, 2013. This site and the surrounding region are part of the San Joaquin - Sacramento River Delta drained beginning in the 1850's and subsequently used for agriculture. The field has been alfalfa for X years…., Crop rotation occurs every 5-6 years. The site is harvested by mowing and bailing several times per year. The field is fallow typically between November and February. The site is irrigated by periodically-flooded ditches surrounding the field. The site is irrigated by raising, and subsequently lowering the water table??

Chamberlain, Samuel D↗
NASA NTRS1994

Short term responses of nitrogen trace gas emissions to nitrogen fertilization in tropical sugar cane: Variations due to soils and management practices

Nitrogen (N) fertilization of agricultural systems is thought to be a major source of the increase in atmospheric N2O; NO emissions from soils have also been shown to increase due to N fertilization. While N fertilizer use is increasing rapidly in the developing world and in the tropics, nearly all of our information on gas emissions is derived from studies of temperate zone agriculture. Using chambers, we measured fluxes of N2O and NO following urea fertilization in tropical sugar cane systems growing on a variety of soil types in the Hawaiian Islands, USA. On the island of Maui, where urea is applied in irrigation lines and soils are mollisols and inceptisols, N2O fluxes were elevated for a week or less following fertilization; maximum average fluxes were typically less than 30 ng cm(exp -2)/ h. NO fluxes were often an order of magnitude less than N2O. Together, N2O and NO represented from 0.01 - 0.5% of the applied N. In fields on the island of Hawaii, where urea is broadcast on the surface and soils are andisols, N2O fluxes were similar in magnitude to Maui but remained elevated for much longer periods after fertilization. NO emissions were 2-5 times higher than N2O through most of the sampling periods. Together the gases loss represented approximately 1. 1 - 3% of the applied N. Laboratory studies indicate that denitrification is a critical source of N2O in Maui, but that nitrification is more important in Hawaii. Experimental studies suggest that differences in the pattern of N2O/NO and the processes producing them are a result of both carbon availability and placement of fertilizer, and that the more information-intensive fertilizer management practice results in lower emissions.

Matson, P. A.↗
NASA NTRS2016

A Satellite Data-Driven, Client-Server Decision Support Application for Agricultural Water Resources Management

Water cycle extremes such as droughts and floods present a challenge for water managers and for policy makers responsible for the administration of water supplies in agricultural regions. In addition to the inherent uncertainties associated with forecasting extreme weather events, water planners need to anticipate water demands and water user behavior in a typical circumstances. This requires the use decision support systems capable of simulating agricultural water demand with the latest available data. Unfortunately, managers from local and regional agencies often use different datasets of variable quality, which complicates coordinated action. In previous work we have demonstrated novel methodologies to use satellite-based observational technologies, in conjunction with hydro-economic models and state of the art data assimilation methods, to enable robust regional assessment and prediction of drought impacts on agricultural production, water resources, and land allocation. These methods create an opportunity for new, cost-effective analysis tools to support policy and decision-making over large spatial extents. The methods can be driven with information from existing satellite-derived operational products, such as the Satellite Irrigation Management Support system (SIMS) operational over California, the Cropland Data Layer (CDL), and using a modified light-use efficiency algorithm to retrieve crop yield from the synergistic use of MODIS and Landsat imagery. Here we present an integration of this modeling framework in a client-server architecture based on the Hydra platform. Assimilation and processing of resource intensive remote sensing data, as well as hydrologic and other ancillary information occur on the server side. This information is processed and summarized as attributes in water demand nodes that are part of a vector description of the water distribution network. With this architecture, our decision support system becomes a light weight 'app' that connects to the server to retrieve the latest information regarding water demands, land use, yields and hydrologic information required to run different management scenarios. Furthermore, this architecture ensures all agencies and teams involved in water management use the same, up-to-date information in their simulations.

Agricultural↗
DOE OSTI2021

Validation of the Community Land Model Version 5 over the Contiguous United States (CONUS) using in situ and remote sensing data sets

The Community Land Model (CLM) is an effective tool to simulate the biophysical and biogeochemical processes and their interactions with the atmosphere. Although CLM Version 5 (CLM5) constitutes various updates in these processes, its performance in simulating energy, water and carbon cycles over the Contiguous United States (CONUS) at scales which land surface changes and hydrometeorological and hydroclimatological applications are more locally relevant is yet to be assessed. In this study, we conducted three simulations at 0.125? during 1979-2018 over the CONUS using different configurations of CLM, namely CLM5-biogeochemistry (CLM5BGC), CLM4.5BGC, and CLM5-satellite phenology (CLM5SP). We validated and compared their simulations against multiple remote-sensed and in-situ datasets. Overall, the parametric and structural updates (e.g., carbon cost for nitrogen uptake, variable soil thickness, dry surface layer) in CLM5 improve its ability in capturing terrestrial biogeochemical dynamics. The low evapotranspiration in CLM5BGC is associated with biases in simulating vegetation phenological characteristics rather than soil water limitations. The mismatch between CLM5BGC-simulated peak leaf area index and reference data can be attributed to CLM5BGC's inability in simulating phenology of trees and grasses. The differences between CLM-simulated irrigation and reference estimates can be attributed to differences between processes represented in models and in reality, and uncertainties in input and validation datasets. Evaluation against observations at small catchments suggest that hydrologic parameters needed to be calibrated to improve simulations of runoff, especially subsurface runoff. Additional efforts are needed to incorporate spatially-distributed plant phenology and physiology parameters and regional-specific agricultural management practices (e.g., planting, harvest).

Cheng, Yanyan↗
DOE OSTI2021

Assessing Adaptive Irrigation Impacts on Water Scarcity in Nonstationary Environments—A Multi‐Agent Reinforcement Learning Approach

Abstract One major challenge in water resource management is to balance the uncertain and nonstationary water demands and supplies caused by the changing anthropogenic and hydroclimate conditions. To address this issue, we developed a reinforcement learning agent‐based modeling (RL‐ABM) framework where agents (agriculture water users) are able to learn and adjust water demands based on their interactions with the water systems. The intelligent agents are created by a reinforcement learning algorithm adapted from the Q‐learning algorithm. We illustrated this framework in a case study where the RL‐ABM is two‐way coupled with the Colorado River Simulation System (CRSS), a long‐term planning model used for the administration of the Colorado River Basin, for assessing agriculture water uses impacts on water scarcity. Seventy‐eight intelligent agents are simulated, which can be grouped into three categories based on their parameter values: the “aggressive” (swift actions; low regrets), the “forward‐looking conservative” (mild actions; high regrets; fast learning), and the “myopic conservative” (mild actions; median regrets; slow learning). The ABM‐CRSS results showed that the major reservoirs in the Upper Colorado Basin might experience more frequent water shortages due to the increasing water uses compared to the original CRSS results. If the drought continues, the case study also demonstrates that agents can learn and adjust their demands.

Hung, Fengwei↗
DOE OSTI2022

Coffee supply chain planning under climate change

The growing demand but uncertain supply makes the sustainability of the coffee industry a shared concern for all participants along the coffee supply chain. This study proposed a decision-making model that comprises the cultivation management, including shade management and annual agriculture management, and the supply chain logistics. A two-stage stochastic program is presented and used within a rolling horizon scheme that periodically updates input data information to deal with uncertainty associated with future climate scenarios. The program minimizes the total expected cost of the entire supply chain of arabica coffee. The study applied the model to the real case study of arabica coffee bean supply to the U.S. market, trying to answer whether arabica coffee supply can meet the U.S. demand from 2022 to 2050 and how to best mitigate any shortage through corporate-farmer partnerships. The results show that the coffee supply will have a 3% shortage in the future; medium-level shade management and more irrigation and fertilization are possible mitigation strategies. These results demonstrate the need for all participants to adopt suitable technologies for the sustainability of global coffee supply chains together.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2015

NASA UAV Airborne Science Capabilities in Support of Water Resource Management

This workshop presentation focuses on potential uses of unmanned aircraft observations in support of water resource management and agriculture. The presentation will provide an overview of NASA Airborne Science capabilities with an emphasis on past UAV missions to provide context on accomplishments as well as technical challenges. I will also focus on recent NASA Ames efforts to assist in irrigation management and invasive species management using airborne and satellite datasets.

Resources Management↗
DOE OSTI2024

Further adoption of conservation tillage can increase maize yields in the western US Corn Belt

Conservation tillage can reduce soil erosion, increase soil health, and decrease labor and fuel input costs. Despite these benefits, potential yield impacts remain an important concern for farmers considering adoption. Previous research suggests that conservation tillage is likely to have the largest yield benefits in more arid conditions, but a lack of field-level analyses across climatic, management and soil conditions limits confidence in such predictions. Satellite imagery provides the opportunity to monitor agricultural lands at sub-field resolution across large spatial scales and wide environmental gradients. Here we investigate the maize yield impacts of conservation tillage in the semi-arid western US Corn Belt, using sub-field resolution datasets on tillage practices and crop yields derived from satellite data spanning four states (Nebraska, Kansas, South Dakota, and North Dakota) between 2008 and 2020. On these datasets, we estimate heterogenous yield outcomes for several thousand maize fields across gradients in climate, soil quality and irrigation status by using a causal forests analysis, an adaptation of the random forests machine-learning algorithm for causal inference on observational data. We find that long-term adoption of conservation tillage increased rainfed maize yields by an average of 9.9% in the region. Impacts on irrigated yields were small and not statistically significant. These results, along with an analysis of variables related to greater than average yield benefits, indicate that improved water infiltration and retention are the primary reasons for conservation tillage benefits. Despite yield benefits, many fields estimated to see increased yields under long term low till have not adopted the practice. Therefore, we identify specific counties likely to benefit most from increased levels of adoption. Our results strengthen the understanding of the impacts of conservation agriculture on crop yields and help define environments and counties most likely to benefit from conservation tillage.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Energy balance in the DSSAT-CSM-CROPGRO model

One potential way to improve crop growth models is for the models to predict energy balance and evapotranspiration (ET) from first principles, thus serving as a check on “engineered” ET methodology. In this paper, we present new implementations and the results of an energy balance model (EBL) developed by Jagtap and Jones (1989) and then implemented in DSSAT’s CROPGRO (CG-EBL) model by Pickering et al. (1995) as a linked energy balance-photosynthesis model that has not been field-tested until now. The energy balance code computes evapotranspiration and other energy balance components, as well as a canopy air temperature, based on three sources (sunlit leaves, shaded leaves, soil surface). Model performance was evaluated with measured biomass and energy fluxes from two sites in Nebraska, namely, the US-Ne2 irrigated maize-soybean rotation field and the US-Ne3 rainfed maize-soybean rotation field, which are part of the Ameriflux eddy covariance network (htt ps://ameriflux.lbl.gov/sites). After implementing new aerodynamic resistances and the stomatal conductance model of the Ball–Berry–Leuning, crop growth, evapotranspiration and soil temperature were simulated well by the EBL model. The EBL improved ET predictions slightly over the often-used FAO56 method [Penman–Monteith (Allen et al., 1998)] for 4 of the 5 years evaluated for both irrigated and rainfed conditions. Further, a significant improvement was achieved using EBL for the simulation of soil temperature at the various depths compared to STEMP, the original subroutine in DSSAT for simulating soil temperature. Compared to the other available DSSAT methods, the EBL explicitly simulates the impacts of crop morphology, physiology and management on the crop’s environment and energy and mass exchange, which in turn directly affect the water use and irrigation requirements, phenology, photosynthesis, growth, sterility, and yield of the crop.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2005

Shortwave Radiation

Accurate shortwave radiation data is critical to evapotranspiration (ET) models used for developing irrigation schedules to optimize crop production while saving water, minimizing fertilizer, herbicide, and pesticide applications, reducing soil erosion, and protecting surface and ground water quality. Low cost silicon cell pyranometers have proven to be sufficiently accurate and robust for widespread use in agricultural applications under unobstructed daylight conditions. More expensive thermopile pyranometers are required for use as calibration standards and measurements under light with unique spectral properties (electric lights, under vegetation, in greenhouses and growth chambers). Routine cleaning, leveling, and annual calibration checks will help to ensure the integrity of long-term data.

Klassen, Steve↗
NASA NTRS2021

Financial feasibility of water conservation in agriculture

Global water use for food production needs to be reduced to remain within planetary boundaries, yet the financial feasibility of crucial measures to reduce water use is poorly quantified. Here, we introduce a novel method to compare the costs of water conservation measures with the added value that reallocation of water savings might generate if used for expansion of irrigation. Based on detailed water accounting through the use of a high-resolution hydrology-crop model, we modify the traditional cost curve approach with an improved estimation of demand, increasing marginal cost per water conservation measure combination and add a correction to control for impacts on downstream water availability. We apply the method to three major river basins in the Indo-Gangetic plain (Indus, Ganges and Brahmaputra), a major global food producing region but increasingly water stressed. Our analysis shows that at basin level only about 10% (Brahmaputra) to just over 20% (Indus and Ganges) of potential water savings would be realised; the equilibrium price for water is too low to make the majority of water conservation measures cost effective. The associated expansion of irrigated area is moderate, about 7% in the Indus basin, 5% in the Ganges and negligible in the Brahmaputra, but farmers' gross profit increases more substantially, by 11%. Increasing the volumetric cost of irrigation water influences supply and demand in a similar way and has little influence on water reallocation. Controlling for the impact on return flows is important and more than halves the amount of water available for reallocation.

Geography / Land Utilization↗
DOE OSTI2024

The Realistic Potential of Soil Carbon Sequestration in U.S. Croplands for Climate Mitigation

Existing estimates of the climate mitigation potential from cropland carbon sequestration (C-sequestration) are limited because they tend to assume constant rates of soil organic carbon change over all available cropland area, use relatively coarse land delineations, and often fail to adequately consider the agronomic and socioeconomic dimensions of agricultural land use. This results in an inflated estimate of the C-sequestration potential. We address this gap by defining a more appropriate land base for cover cropping in the United States for C-sequestration purposes: stable croplands in annual production systems that can integrate cover cropping without irrigation. Our baseline estimate of this suitable stable cropland area is 32% of current U.S. cropland extent. Even an alternative, less restrictive definition of stability results in a large reduction in area (44% of current U.S. croplands). Focusing cover crop implementation to this constrained land base would increase durability of associated C-sequestration and limit soil carbon loss from land conversion to qualify for carbon-specific incentives. Applying spatially-variable C-sequestration rates from the literature to our baseline area yields a technical potential of 19.4 Tg CO 2 e yr –1 annually, about one-fifth of previous estimates. We also find the cost of realizing about half (10 Tg CO 2 e yr –1 ) of this potential could exceed 100 USD Mg CO 2 e –1 , an order of magnitude higher than previously thought. While our economic analyses suggest that financial incentives are necessary for large-scale adoption of cover cropping in the U.S., they also imply any C-sequestration realized under such incentives is likely to be additional.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Global gridded crop harvested area, production, yield, and monthly physical area data circa 2015

Abstract Here we provide an update to global gridded annual and monthly crop datasets. This new dataset uses the crop categories established by the Global Agro-Ecological Zones (GAEZ) Version 3 model, which is based on the Food and Agricultural Organization of the United Nations (FAO) crop production data. We used publicly available data from the FAOSTAT database as well as GAEZ Version 4 global gridded dataset to generate circa 2015 annual crop harvested area, production, and yields by crop production system (irrigated and rainfed) for 26 crops and crop categories globally at 5-minute resolution. We additionally used available data on crop rotations, cropping intensity, and planting and harvest dates to generate monthly gridded cropland data for physical areas for the 26 crops by production system. These data are in standard georeferenced gridded format, and can be used by any global hydrology, land surface, or other earth system model that requires gridded annual or monthly crop data inputs.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1972

The status of parametric studies in radar agriculture

Outlined is an information system based on the use of remote sensor data and the design, testing, and implementation of interpretation keys for agriculture. The task of crop identification from radar imagery emphasizes dichotomous keys and the effects of frequency, angular and other microwave dependencies of crops for use in discrimination. A mosaic is formulated from imagery and used to study acres in wheat for spread of circular irrigation, spread of crops, and other phenomena.

Morain, S. A.↗