SEARCH · Engineering Papers
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.
This data set includes modeled (AgroIBIS-VSF) output of crop yield for miscanthus and a corn/soybean rotation in .nc format. Input includes the precipitation scenarios (.csv) used to generate the runs, and supplemental observed yield monitor data (.csv) from the research area of interest.
The belowground microbiomes have many beneficial functions that assist plant growth, including nutrient cycling, acquisition and transport, as well as alleviation of stresses caused by nutrient limitations such as nitrogen (N). Here we analyzed the root endosphere, rhizosphere and soil bacterial communities of seven sweet sorghum genotypes differing in sensitivity to N-stress. Sorghum genotypes were grown in fields with no (low-N) or sufficient (high-N) N. The dry shoot weight ratio (low-N/high-N) was used to determine N-stress sensitivity. Our hypothesis was that genotypes tolerant and sensitive to N-stress select distinct bacterial communities. The endosphere and rhizosphere bacterial community structure were significantly different between the N-stress sensitive and tolerant genotypes in the high-N field, but not in the low-N field. However, significant changes in the relative abundance of specific bacterial taxa were observed in both fields. Streptomyces, a bacterial genus known to alleviate plant abiotic stresses, was enriched in the endosphere and rhizosphere of the tolerant genotypes in the low-N field. Our study indicates that sweet sorghum genotypes tolerant to N-stress select taxa that can potentially mitigate the N-stress, suggesting that the interactions between N-stress tolerant lines and the root-associated microbiome might be vital for coping with N-stress.
The file contains Switchgrass and M. × giganteus long-term yield data were collected from multiple long-term experiments along Michigan and Wisconsin, USA.
For sustainable biomass production of Miscanthus × giganteus (hereafter miscanthus), understanding the impact of stand age and nitrogen (N) fertilization on biomass yield is crucial. This study investigated the effects of varying N fertilization rates (0, 56, 112, and 168 kg N ha-1) on yield components (tiller height, density, and weight) and their correlations with end-of-season biomass yield in miscanthus. We also explored end-of-season biomass yield prediction using in-season traits (canopy height, leaf area index (LAI), and leaf chlorophyll content (LCC)). The study was conducted at two sites in Illinois: a previously unfertilized 10-year-old miscanthus research stand at Urbana and a 16-year-old commercial stand at Pesotum with a history of annual 56N application. Results from 2018-2021 in Urbana and 2020-2021 in Pesotum showed increased biomass yields with N fertilization, varying by rate, year, and location. Biomass yield in Pesotum peaked at 56N, while in Urbana, it increased significantly at 112 kg N ha-1. Biomass yield was strongly correlated with tiller height and weight measured at Urbana across N rates. Morphological traits measured every 2-3 weeks during the 2020 and 2021 growing seasons showed that canopy height was the strongest single predictor of miscanthus biomass yield, followed by LCC. Mid-August to September measurements of these traits were the best predictors of biomass yield. Multiple regressions involving the canopy height and LCC further improved yield predictions. We conclude that while N enhances biomass yields of aging miscanthus, the optimum rate depends on the site, environmental conditions, and management.
This repository contains call source code and input files that are part of the DayCent_CABBI BNI model experiments described in the manuscript below. Hartman et al. 2022. In Silico Evaluation of Plant Nitrification Suppression Effects on Agroecosystem Nitrogen Loss. Ecosphere . In Review.
An increase in the demand for and production of vegetables in the winter, mainly in northern and Siberian regions, inevitably leads to mass building of structures for growing plants under completely artificial conditions. An industrial lighting technology is required whose main parameters (spectrum, irradiance, photoperiod) should be assigned carefully and should uniquely determine, along with other important characteristics of the artificial climate, the productivity of the plant-production facility. The most widespread crops grown in our country under indoor conditions are cucumber and tomato plants, which account for more than 98% of the area in greenhouses. These plants are good prospects for growing completely under intense artificial lighting conditions (photocultures). Optimization of the main parameters of optical radiation when growing these plants is the most important task of achieving their profitable production. At present, considerable experience has been gained in studying the dependence of productivity of cucumber and tomato communities on irradiation conditions. Fundamental studies of the Agrophysical Research Institute of the Russian Academy of Sciences, Timiryazev Institute of Plant Physiology of the Russian Academy of Sciences, Timiryazev Agricultural Academy, and other institutes create a good basis for a detailed study of the given problem. Commercial sources of radiation substantially differing in spectral characteristics in the region of photosynthetically active radiation (PAR) were used in the studies.
The weathering of silicate rocks and minerals, an important concern of geologists and geochemists for many years, traditionally has been approached from strictly physical and chemical points of view. Biological effects were either unrecognized, ignored, or were mentioned in passing to account for such phenomena as the accumulation of organic matter in sediments or the generation of reducing environments. A major exception occurred in soil science where agricultural scientists, studying the factors important in the development of soils and their ability to nourish and sustain various crops, laid the foundation for much of what is known of the biological breakdown of silicate rocks and minerals. The advent of the space age accelerated the realization that many environmental problems and geo- chemical processes on Earth can only be understood in terms of ecosystems. This in turn, spurred renewed interest and activity among modem biologists, geologists and soil scientists attempting to unravel the intimate relations between biology and the weathering of silicate rocks and minerals of the earth surface.
The weathering of silicate rocks and minerals, an important concern of geologists and geochemists for many years, traditionally has been approached from strictly physical and chemical points of view. Biological effects were either unrecognized, ignored, or were mentioned in passing to account for such phenomena as the accumulation of organic matter in sediments or the generation of reducing environments. A major exception occurred in soil science where agricultural scientists, studying the factors important in the development of soils and their ability to nourish and sustain various crops, laid the foundation for much of what is known of the biological breakdown of silicate rocks and minerals. The advent of the space age accelerated the realization that many environmental problems and geochemical processes on Earth can only be understood in terms of ecosystems. This in turn, spurred renewed interest and activity among modem biologists, geologists and soil scientists attempting to unravel the intimate relations between biology and the weathering of silicate rocks and minerals of the earth's surface.
Airborne observations using lightweight camera systems are desirable for a variety of applications. This system was contemplated as a method to provide a simple remote sensing aerial platform. Kites have been successfully employed for aerial observations, but have historically required natural wind or towing to become airborne. This new method negates this requirement, and widens the applicability of kites for carrying instrumentation. Applicability is primarily limited by the space available on the ground for launching. The innovation is a method for launching kites in low-wind or no-wind conditions. This method will enable instrumentation to be carried aloft using simple (or complex) kite-based systems, to obtain observations from an aerial perspective. This technique will provide access to altitudes of 100 meters or more over any area normally suited for kite flying. The duration of any observation is dependent on wind strength; however, the initial altitude is relatively independent. The system does not require any electrical or combustion-based elements. This technology was developed to augment local-scale airborne measurement capabilities suitable for Earth science research, agricultural productivity, and environmental observations. The method represents an extension of techniques often used in aeronautical applications for launching fixed-wing aircraft, such as sailplanes, using mechanical means not incorporated in the aircraft itself. The innovation consists of an elastic cord (for propulsive force), a tether extension (optional, for additional height), and the kite (instrumentation optional). Operation of the system is accomplished by fixing the elastic cord to ground (or equivalent), attaching the cord with/or without a tether extension to the kite, tensioning the system to store energy, and releasing the kite. The kite will climb until energy is dissipated.
The “science-softCon UV/Vis+ Photochemistry Database” (www.photochemistry.org) is a large and comprehensive collection of EUV-VUV-UV–Vis-NIR spectral data and other photochemical information assembled from published peer-reviewed papers. The database contains photochemical data including absorption, fluorescence, photoelectron, and circular and linear dichroism spectra, as well as quantum yields and photolysis related data that are critically needed in many scientific disciplines. This manuscript gives an outline regarding the structure and content of the “science-softCon UV/Vis+ Photochemistry Database”. The accurate and reliable molecular level information provided in this database is fundamental in nature and helps in proceeding further to understand photon, electron and ion induced chemistry of molecules of interest not only in spectroscopy, astrochemistry, astrophysics, Earth and planetary sciences, environmental chemistry, plasma physics, combustion chemistry but also in applied fields such as medical diagnostics, pharmaceutical sciences, biochemistry, agriculture, and catalysis. In order to illustrate this, we illustrate the use of the UV/Vis+ Photochemistry Database in four different fields of scientific endeavor.
Explore the source record for details and available documents.
Most business-as-usual scenarios for farming under changing climate regimes project that the agriculture sector will be significantly impacted from increased temperatures and shifting precipitation patterns. Perhaps ironically, agricultural production contributes substantially to the problem with yearly greenhouse gas (GHG) emissions of about 11% of total anthropogenic GHG emissions, not including land use change. It is partly because of this tension that Climate Smart Agriculture (CSA) has attracted interest given its promise to increase agricultural productivity under a changing climate while reducing emissions. Considerable resources have been mobilized to promote CSA globally even though the potential effects of its widespread adoption have not yet been studied. Here we show that a subset of agronomic practices that are often included under the rubric of CSA can contribute to increasing agricultural production under unfavorable climate regimes while contributing to the reduction of GHG. However, for CSA to make a significant impact important investments and coordination are required and its principles must be implemented widely across the entire sector.
Agriculture is a fundamental societal activity, characterized by many different landscapes, crops, markets, and participants. Food, agricultural, and biofuels products are central to the daily life of all citizens, though most do not recognize the fragility of the environment that brings forth this abundance. As is noted in the 2012 report from the President's Council of Advisors on Science and Technology, Agricultural Preparedness and the United States Agricultural Research Enterprise (PCAST, 2012) the food and agricultural system faces constant challenges in: Managing new pests, pathogens, and invasive plants. Increasing the efficiency of water use. Growing food in a changing climate. Reducing the environmental footprint of agriculture. Managing the production of bioenergy. Producing safe and nutritious food. Assisting with global food security and maintaining abundant yields. Science Breakthroughs 2030 was organized to identify the most compelling research directions in food and agriculture, in particular those empowered by the application of insights and tools from disciplines of science and engineering not typically associated with food and agricultural research. A committee appointed by the Chairman of the National Research Council explored ideas for research directions with input from the scientific community, with the objective of producing a report describing ambitious and achievable scientific pathways to address major problems and create new opportunities in food and agriculture. Following numerous meetings, a jamboree, and town hall, the appointed committee prepared a report that has subsequently become a reference for federal agencies supporting research in the food and agricultural space. It highlights five key areas for research investment with broad application across food and agriculture: integrated systems research; sensor development; data mining and information sciences, genomics; and the microbiome.
Tile drainage removes excess water and is an essential, widely adopted management practice to enhance crop productivity in the US Midwest and throughout the world. Tile drainage has been shown to significantly change hydrological and biogeochemical cycles by lowering the water table and reducing the residence time of soil water, although examining the complex interactions and feedbacks in an integrated hydrology–biogeochemistry–crop system remains elusive. Oxygen dynamics are critical to unraveling these interactions and have been ignored or oversimplified in existing models. Understanding these impacts is essential, particularly so because tile drainage has been highlighted as an adaptation under projected wetter springs and drier summers in the changing climate in the US Midwest. We used the ecosys model that uniquely incorporates first-principle soil oxygen dynamics and crop oxygen uptake mechanisms to quantify the impacts of tile drainage on hydrological and biogeochemical cycles and crop growth in corn–soybean rotation fields. The model was validated with data from a multi-treatment, multi-year experiment in Washington, IA. The relative root mean square error (rRMSE) for the corn and soybean yield in validation is 5.66 % and 12.57 %, respectively. The Pearson coefficient (r) of the monthly tile flow during the growing season is 0.78. Plant oxygen stress turns out as an emergent property of the equilibrium between the soil oxygen supply and biological demand. The impact of tile drainage on the system is achieved through a series of coupled feedback mechanisms. The model results show that tile drainage reduces the soil water content and enhances soil oxygenation. It additionally increases the subsurface discharge and elevates inorganic nitrogen leaching, with seasonal variations influenced by climate and crop phenology. The improved aerobic condition alleviates crop oxygen stress during wet springs, thereby promoting crop root growth during the early growth stage. The development of greater root density, in turn, mitigates water stress during dry summers, leading to an overall increase in the crop yield by ∼6 %. These functions indicate the potential of tile drainage in bolstering crop resilience to climate change and the use of this modeling tool for large-scale assessments of tile drainage. The model reveals the underlying causal mechanisms that drive the agroecosystem response to drainage on the coupled hydrology, biogeochemistry, and crop system dynamics.
CRISPR/Cas9 based genome editing has advanced our understanding of a myriad of important biological phenomena. Important challenges to multiplex genome editing in maize include assembly of large complex DNA constructs, few genotypes with efficient transformation systems, and costly/labor-intensive genotyping methods. Here we present an approach for multiplex CRISPR/Cas9 genome editing system that delivers a single compact DNA construct via biolistics to Type I embryogenic calli, followed by a novel efficient genotyping assay to identify desirable editing outcomes. We first demonstrate the creation of heritable mutations at multiple target sites within the same gene. Next, we successfully created individual and stacked mutations for multiple members of a gene family. Genome sequencing found off-target mutations are rare. Multiplex genome editing was achieved for both the highly transformable inbred line H99 and Illinois Low Protein1 (ILP1), a genotype where transformation has not previously been reported. In addition to screening transformation events for deletion alleles by PCR, we also designed PCR assays that selectively amplify deletion or insertion of a single nucleotide, the most common outcome from DNA repair of CRISPR/Cas9 breaks by non-homologous end-joining. The Indel-Selective PCR (IS-PCR) method enabled rapid tracking of multiple edited alleles in progeny populations. The ‘end to end’ pipeline presented here for multiplexed CRISPR/Cas9 mutagenesis can be applied to accelerate maize functional genomics in a broader diversity of genetic backgrounds.
NIR spectroscopy is a rapid and accurate green technology for high-throughput biomass characterization, including sorghum ( Sorghum bicolor ), a promising energy crop for the biofuel industry. This study assessed the influence of particle size on NIR spectroscopic analysis (wavelength range: 867–2535 nm) of sorghum biomass composition. Grown under field conditions, a total of 113 types of genetically diverse sorghum accessions were dried, ground, and sieved (<250, 250–600, 600–850, and > 850 µm particle size) for developing partial least square regression (PLSR) prediction models for moisture, ash, extractive, glucan, xylan, acid-soluble lignin (ASL), acid-insoluble lignin (AIL), and total lignin (ASL + AIL). Overall, smaller particle sizes provided better model performance, while no single particle size provided the best performance for all the selected components. With only 9 selected bands and 4 latent variables (LVs), the best PLSR model was obtained for moisture with particle size of 600–850 µm with the square root of the coefficient of determination (R) of 0.85, the ratio of prediction to deviation (RPD) of 2.2, and the root mean square error (RMSE) of 0.46 % in external validation. Similar model performances were also obtained for ash, extractive, glucan, and xylan. This study showed that size reduction could effectively improve NIR spectroscopic analysis for lipid-producing sorghum biomass for the biofuel industry.
The clumping index (CI) quantifies the spatial distribution of foliage elements and is essential for accurately estimating the plant area index (PAI), canopy radiative transfer, and photosynthesis. Traditionally, the finite-length averaging method (LX), the gap size distribution method (CC), and a combined approach of CC and LX (CLX) have been applied to instruments like TRAC and digital hemispherical photography to estimate CI. However, a comprehensive evaluation of these methods in row crops remains limited, especially regarding the influence of segment size on CI. Meanwhile, digital cameras offer a cost-effective and user-friendly solution for canopy measurements in row crops, yet their application in this context remains underexplored. In this study, we employed a new approach using a 30°-tilted digital camera to estimate CI in corn and soybean fields, applying the LX, CC, and CLX methods. We systematically assessed the performance of these three methods by combining field measurements in real-world fields with simulations using the LESS 3D radiative transfer model. Our results showed that CLX applied to the whole image and 45° segment offered accurate estimation of CI (bias within ±0.1, RMSE < 0.2) and PAI (bias within ±0.4, RMSE < 1) in real-world fields and LESS simulations. The accuracy of the LX method was highly sensitive to segment size, with the best performance observed at the 15° segment (PAI bias within ±0.4). In contrast, the CC method remained stable across different segment sizes, and its performance was generally comparable to that of LX, except at the 15° segment. Across view zenith angles, CI derived from CC generally showed a continuous increase, while those from LX and CLX followed a rising trend at small zenith angles but began to decline at 68°, likely due to an increasing proportion of no-gap segments. Seasonally, LX tended to show decreasing CI during early growth stages but increased as the canopy matured, whereas CC and CLX showed gradually increasing CI before plateauing at peak PAI. The 30°-tilted camera effectively captured CI variations across different angles and growth stages, making it a practical and robust instrument for row crop canopy structure analysis. Applying these CI methods to digital cameras offers a low-cost and accessible CI estimation alternative, improving canopy structure monitoring accuracy in row crops.
Accurate modeling of photosynthesis is crucial for predicting crop productivity and quantifying the carbon cycle in agroecosystems. Leaf traits are essential inputs for modeling canopy photosynthesis. Yet, many existing models still use fixed plant functional type (PTF)-based values to parameterize leaf traits under a big-leaf or two-big-leaf assumption, neglecting their vertical profiles and seasonal changes. This simplification may introduce significant uncertainties in estimating gross primary productivity (GPP). In this study, we simulated soybean GPP and tested the effects of vertical and seasonal variation in three key leaf photosynthetic traits: the maximum carboxylation rate at 25 °C (Vcmax25), leaf chlorophyll content (LCC), and leaf mass per area (LMA) in the 1D-SCOPE and 3D-Helios models. Weekly field measurements were conducted during the growing season of 2024 to support the simulation. We designed ten leaf trait parameterization schemes by incorporating different combinations of vertical profiles and seasonal changes, while assuming homogeneous canopy architecture in both models. Our results revealed that Vcmax25 vertical and seasonal variation had the strongest influence on simulated GPP in both 1D and 3D models, while LCC and LMA effects were minimal. Particularly, the scheme with an empirically parameterized Vcmax25 profile achieved comparable performance to the scheme with the measured Vcmax25 profile. Both 1D-SCOPE and 3D-Helios accurately modeled GPP (SCOPE: R2 = 0.87, Bias = 0.55 µmol m⁻² s⁻¹; Helios: R2 = 0.9, Bias = 0.22 µmol m⁻² s⁻¹) under the most complex scheme, and their responses to vertical and seasonal variation in leaf traits were consistent, demonstrating the robustness of our findings. Based on our findings, we propose a scalable framework for parameterizing leaf traits to improve GPP simulations. This study contributes to improving the representation of leaf trait dynamics in canopy-level photosynthesis models, potentially enhancing our ability to predict crop productivity and understand agroecosystem carbon dynamics.