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Reports given by Soviet specialists at a meeting of Socialist countries on remote sensing of the earth using aerospace methods are presented.
We review the current state of agricultural systems science, focusing in particular on the capabilities and limitations of agricultural systems models. We discuss the state of models relative to five different Use Cases spanning field, farm, landscape, regional, and global spatial scales and engaging questions in past, current, and future time periods. Contributions from multiple disciplines have made major advances relevant to a wide range of agricultural system model applications at various spatial and temporal scales. Although current agricultural systems models have features that are needed for the Use Cases, we found that all of them have limitations and need to be improved. We identified common limitations across all Use Cases, namely 1) a scarcity of data for developing, evaluating, and applying agricultural system models and 2) inadequate knowledge systems that effectively communicate model results to society. We argue that these limitations are greater obstacles to progress than gaps in conceptual theory or available methods for using system models. New initiatives on open data show promise for addressing the data problem, but there also needs to be a cultural change among agricultural researchers to ensure that data for addressing the range of Use Cases are available for future model improvements and applications. We conclude that multiple platforms and multiple models are needed for model applications for different purposes. The Use Cases provide a useful framework for considering capabilities and limitations of existing models and data.
Ideal three-dimensional imaging of complex samples made up of micron-scale structures extending over mm to cm, such as biological tissues, requires both wide field of view and high resolution. For existing optics and detectors used for micro-CT (computed tomography) imaging, sub-micron pixel resolution can only be achieved for fields of view of <2 mm. This article presents a unique detector system with a 6 mm field-of-view image circle and 0.5 µm pixel size that can be used in micro-CT units utilizing both synchrotron and commercial X-ray sources. A resolution-test pattern with linear microstructures and whole adult Daphnia magna were imaged at beamline 8.3.2 of the Berkeley Advanced Light Source. Volumes of 10000 × 10000 × 7096 isotropic 0.5 µm voxels were reconstructed over a 5.0 mm × 3.5 mm field of view. Measurements in the projection domain confirmed a 0.90 µm measured spatial resolution that is largely Nyquist-limited. This unprecedented combination of field of view and resolution dramatically reduces the need for sectional scans and computational stitching for large samples, ultimately offering the means to elucidate changes in tissue and cellular morphology in the context of larger, whole, intact model organisms and specimens. This system is also anticipated to benefit micro-CT imaging in materials science, microelectronics, agricultural science and biomedical engineering.
Bibliography with abstracts of remote sensing in agriculture
The Microbial Design for a Developing Bioeconomy workshop report is one of a four-part Frontier Science for the Bioeconomy series hosted by the DOE Biological and Environmental Research program’s Biological Systems Science Division. The series defines frontiers of plant science, agriculture, synthetic biology, biobased materials, and environmental microbiome science that will unlock the promise of an innovative, resilient U.S. bioeconomy.
In rural Alaskan communities personal health is threatened by energy costs and limited access to clean water, wastewater management, and adequate nutrition. Fuel-‐based energy systems are significant factors in determining local accessibility to clean water, sanitation and food. Increasing fuel costs induce a scarcity of access and impact residents' health. The University of Alaska Fairbanks (UAF) School of Natural Resources and Agricultural Sciences (SNRAS), NASA's Ames Research Center, and USDA Agricultural Research Service (ARS) have joined forces to develop high‐efficiency, low‐energy consuming techniques for water treatment and food production in rural circumpolar communities. Methods intended for exploration of space and establishment of settlements on the Moon or Mars will ultimately benefit Earth's communities in the circumpolar north. The initial phase of collaboration is completed. Researchers from NASA Ames Research Center and SNRAS, funded by the USDA‐ARS, tested a simple, reliable, low-energy sewage treatment system to recycle wastewater for use in food production and other reuse options in communities. The system extracted up to 70% of the water from sewage and rejected up to 92% of ions in the sewage with no carryover of toxic effects. Biological testing showed that plant growth using recovered water in the nutrient solution was equivalent to that using high‐purity distilled water. With successful demonstration that the low energy consuming wastewater treatment system can provide safe water for communities and food production, the team is ready to move forward to a full‐scale production testbed. The SNRAS/NASA team (including Alaska students) will design a prototype to match water processing rates and food production to meet rural community sanitation needs and nutritional preferences. This system would be operated in Fairbanks at the University of Alaska through SNRAS. Long‐term performance will be validated and operational needs of the system determined. The testbed will be a part of the university education and operator training program. The "Forgotten Alaska" has long awaited this technology to augment the traditional
The Space Biotechnology and Commercial Applications grant was funded by NASA's Kennedy Space Center in FY 2002 to provide dedicated biotechnology and agricultural research focused on the regeneration of space flight environments with direct parallels in Earth-based applications for solving problems in the environment, advances in agricultural science, and other human support issues amenable to targeted biotechnology solutions. This grant had three project areas, each with multiple tasks. They are: 1) Space Agriculture and Biotechnology Research and Education, 2) Integrated Smart Nanosensors for Space Biotechnology Applications, and 3) Commercial Applications. The Space Agriculture and Biotechnology Research and Education (SABRE) Center emphasized the fundamental biology of organisms involved in space flight applications, including those involved in advanced life support environments because of their critical role in the long-term exploration of space. The SABRE Center supports research at the University of Florida and at the Space Life Sciences Laboratory (SLSL) at the Kennedy Space Center. The Integrated Smart Nanosensors for Space Biotechnology Applications component focused on developing and applying sensor technologies to space environments and agricultural systems. The research activities in nanosensors were coordinated with the SABRE portions of this grant and with the research sponsored by the NASA Environmental Systems Commercial Space Technology Center located in the Department of Environmental Engineering Sciences. Initial sensor efforts have focused on air and water quality monitoring essential to humans for living and working permanently in space, an important goal identified in NASA's strategic plan. The closed environment of a spacecraft or planetary base accentuates cause and effect relationships and environmental impacts. The limited available air and water resources emphasize the need for reuse, recycling, and system monitoring. It is essential to collect real-time information from these systems to ensure crew safety. This new class of nanosensors will be critical to monitoring the space flight environment in future NASA space systems. The Commercial Applications component of this program pursued industry partnerships to develop products for terrestrial use of NASA sponsored technologies, and in turn to stimulate growth in the biotechnology industry. For technologies demonstrating near term commercial potential, the objective is to include industry partners on or about the time of proof of concept that will not only co-invest in the technology but also take the resultant technology to the commercial market.
This dataset contains processed data from a combination of survey flux chambers and long-term automated flux chambers. Soil flux measurements were conducted from June 2023 through December 2025 in a mature longleaf pine forest in Georgetown, SC. Soil respiration measurements were conducted approximately biweekly for two and a half years, before and after a root exclusion that took place on May 5, 2024. Processed, QAQC’d data for the treatment (root exclusion) and control (roots intact) before and after the root exclusion can be found in the file: 1_DATA_ESS_DOE_HR_RS_HB2_QAQC_Survey_Data_20260223.csv. Two multiday deployments were also conducted prior to the root exclusion using long-term automated chambers to continuously monitor greenhouse gas soil efflux. Processed, QAQC’d data for both long-term deployments can be found in the file: 2_DATA_ESS_DOE_HR_RS_HB2_QAQC_Longterm_Data_20260209.csv. Raw and working data files (.json, .81x, & .82z format) from LI-COR equipment are included for reference and can be accessed using SoilFluxPro software. CSV metadata files describe the raw data and modifications made using SoilFluxPro v5 and Matlab R2024b, as well as formatting and units for processed CSVs. Matlab code is included for reading in the processed CSVs, with sample figures comparing treatment and control. This research was performed as part of the project: “Improving models of stand and watershed carbon and water fluxes with more accurate representations of soil-plant-water dynamics in southern pine ecosystems”, which examines in part the effects hydraulic redistribution on soil efflux of carbon dioxide, water vapor and methane, as well as soil moisture and temperature in a southern pine ecosystem with sandy soils and high water table.
With growing populations and pressing environmental problems, future economies will be increasingly plant-based. Now is the time to reimagine plant science as a critical component of fundamental science, agriculture, environmental stewardship, energy, technology and healthcare. This effort requires a conceptual and technological framework to identify and map all cell types, and to comprehensively annotate the localization and organization of molecules at cellular and tissue levels. This framework, called the Plant Cell Atlas (PCA), will be critical for understanding and engineering plant development, physiology and environmental responses. A workshop was convened to discuss the purpose and utility of such an initiative, resulting in a roadmap that acknowledges the current knowledge gaps and technical challenges, and underscores how the PCA initiative can help to overcome them.
Drought severely affects global food production. Sorghum is a typical drought-resistant model crop. Based on RNA-seq data for Sorghum with multiple time points and the gray correlation coefficient, this paper firstly selects candidate genes via mean variance test and constructs weighted gene differential co-expression networks (WGDCNs); then, based on guilt-by-rewiring principle, the WGDCNs and the hidden Markov random field model, drought-responsive crucial genes are identified for five developmental stages respectively. Enrichment and sequence alignment analysis reveal that the screened genes may play critical functional roles in drought responsiveness. A multilayer differential co-expression network for the screened genes reveals that Sorghum is very sensitive to pre-flowering drought. Furthermore, a crucial gene regulatory module is established, which regulates drought responsiveness via plant hormone signal transduction, MAPK cascades, and transcriptional regulations. The proposed method can well excavate crucial genes through RNA-seq data, which have implications in breeding of new varieties with improved drought tolerance.
The UN Climate Change Conference in Glasgow spawned the enhancement and updating of many nations’ climate pledges. Previous research has investigated the effects of these pledges on limiting planetary warming, but their spatially explicit effects on land use/cover are unknown. Here, we linked the Glasgow pledges and the spatially explicit responses of the Tibetan Plateau’s land systems. We found that while fulfilling global climate pledges may not significantly affect the global shares of forestland, grassland/pasture, shrubland, and cropland, it needs a 9.4% increase in the forest area of the Tibetan Plateau. This need is an area 11.4 times the increase of the plateau’s forest in the 2010s, or greater than the size of Belgium. The new forest comes mainly from the medium-density grassland in the Yangtze River basin, calling for more proactive environmental management for the headwaters area of this longest river in Asia.
Solving complex real-world grand challenge problems requires in-depth collaboration of researchers from multiple disciplines. Such collaboration often involves harnessing multiscale and multi-dimensional data and combining models from different fields to simulate systems. However, the progress on this front has been limited mainly due to significant gaps in domain knowledge and tools that are typically employed in silos of the domains. Researchers from different fields face considerable barriers to understanding and reusing each other’s data/models in order to collaborate effectively. For example, in solving the global sustainability problems, researchers from hydrology, climate science, agriculture, and economics need to run their respective models to study different components of the global and local food, energy and water systems while, at the same time, need to interact with other researchers and integrate the results of one model with another. Developing this kind of model coupling workflow calls for (1) a large amount of data being processed and exchanged across domains and organizations, (2) identifying and processing the output of one model to make it ready for integration into another model, (3) controlling the workflow dynamically so that it runs until a certain convergence condition or other criteria is met, and (4) close collaboration among the modelers to explore, tune, and test the configuration and data transformation needed to link the models. We have developed C3F, a flexible collaborative model coupling framework to help researchers accelerate their model integration and linking efforts by leveraging advanced cyberinfrastructure such as high-performance computing and virtual containers. In this paper, we describe our experience and lessons learned in developing this cyberinfrastructure solution to support the linking of Water Balance Model (WBM) and SIMPLE-G agricultural economic model in an NSF funded INFEWS project and a DOE-funded Program on Coupled Human and Earth Systems (PCHES) to study the implications of groundwater scarcity for food-energy-water systems. The C3F model coupling framework can be extended to facilitate other model linkages as well.
Changes in soil organic carbon (SOC) and nitrogen (SON) are strongly affected by land management, but few long-term comparative studies have surveyed changes throughout the whole soil profile. We quantified 25-year SOC and SON changes to 1 m in 10 replicate ecosystems at an Upper Midwest, USA site. We compared four annual cropping systems in maize ( Zea mays )-soybean ( Glycine max)-winter wheat ( Triticum aestivum ) rotations, each managed differently (Conventional, No-till, Reduced input, and Biologically based); in three managed perennial systems (hybrid Poplar ( Populus × euramericana ), Alfalfa ( Medicago sativa ), and Conifer ( Pinus spp.); and in three successional systems (Early, Mid- and Late succession undergoing a gradual change in species composition and structure over time).
Cull sows are a unique population on swine farms, often representing poor producing or compromised animals, and even though recent studies have reported that the microbiome is associated with susceptibility to diseases, the microbiome of the cull sow population has not been explored. The main objective of this study was to investigate whether there were differences in fecal and upper respiratory tract microbiota composition for groups of sows of different health status (healthy, cull, and compromised/ clinical sows) and from different farms (1 to 6). Six swine farms were visited once. Thirty individual fecal samples and nasal swabs were obtained at each farm and pooled by five across health status and farm. Samples underwent 16S rRNA gene amplicon sequencing and nasal and fecal microbiota were analyzed using QIIME2 v.2021.4. Overall, the diversity of the nasal microbiota was lower than the fecal microbiota (p < 0.01). No significant differences were found in fecal or nasal alpha diversity by sow’s health status or by farm. Furthermore, there were significant differences in nasal microbial composition by farm and health status (PERMANOVA, p < 0.05), and in fecal microbiota by farm (PERMANOVA, p < 0.05), but not by health status. Lastly, at the L7 level, there was one differentially abundant taxa across farms for each nasal and fecal pooled samples. This study provided baseline information for nasal and fecal microbiota of sows under field conditions, and results suggest that farm of origin can affect microbial diversity and composition. Furthermore, sow’s health status may have an impact on the nasal microbiota composition.
Selection for yield at high planting density has reshaped the leaf canopy of maize, improving photosynthetic productivity in high density settings. Further optimization of canopy architecture may be possible. However, measuring leaf angles, the widely studied component trait of leaf canopy architecture, by hand is a labor and time intensive process. Here, we use multiple, calibrated, 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm. Automatic skeletonization and segmentation of these 3D geometries enable quantification of the angle of each leaf for each plant. The resulting measurements are both heritable and correlated with manually collected leaf angles. This automated and scaleable reconstruction approach was employed to measure leaf-by-leaf angles for a population of 366 sorghum plants at multiple time points, resulting in 971 successful reconstructions and 3,376 leaf angle measurements from individual leaves. A genome wide association study conducted using aggregated leaf angle data identified a known large effect leaf angle gene, several previously identified leaf angle QTL from a sorghum NAM population, and novel signals. Genome wide association studies conducted separately for three individual sorghum leaves identified a number of the same signals, a previously unreported signal shared across multiple leaves, and signals near the sorghum orthologs of two maize genes known to influence leaf angle. Automated measurement of individual leaves and mapping variants associated with leaf angle reduce the barriers to engineering ideal canopy architectures in sorghum and other grain crops.
A lunar base or a network of lunar bases may have highly desirable support functions in a national or international program to explore and settle Mars. In addition, He-3 exported from the Moon could be the basis for providing much of the energy needs of humankind in the twenty-first century. Both technical and managerial issues must be addressed when considering the establishment of a lunar base that can serve the needs of human civilization in space. Many of the technical issues become evident in the consideration of hypothetical scenarios for the activation of a network of lunar bases. Specific and realistic assumptions must be made about the conduct of various types of activities in addition to the general assumptions given above. These activities include landings, crew consumables, power production, crew selection, risk management, habitation, science station placement, base planning, science, agriculture, resource evaluation, readaptation, plant activation and test, storage module landings, resource transport module landings, integrated operations, maintenance, Base 2 activation, and management. The development of scenarios for the activation of a lunar base or network of bases will require close attention to the 'real world' of space operations. That world is defined by the natural environment, available technology, realistic objectives, and common sense.
Cultural resource management consists of research to identify, evaluate, document and assess cultural resources, planning to assist in decision-making, and stewardship to implement the preservation, protection and interpretation of these decisions and plans. One technique that may be useful in cultural resource management archaeology is remote sensing. It is the acquisition of data and derivative information about objects or materials (targets) located on the Earth's surface or in its atmosphere by using sensor mounted on platforms located at a distance from the targets to make measurements on interactions between the targets and electromagnetic radiation. Included in this definition are systems that acquire imagery by photographic methods and digital multispectral sensors. Data collected by digital multispectral sensors on aircraft and satellite platforms play a prominent role in many earth science applications, including land cover mapping, geology, soil science, agriculture, forestry, water resource management, urban and regional planning, and environmental assessments. Inherent in the analysis of remotely sensed data is the use of computer-based image processing techniques. Geographical information systems (GIS), designed for collecting, managing, and analyzing spatial information, are also useful in the analysis of remotely sensed data. A GIS can be used to integrate diverse types of spatially referenced digital data, including remotely sensed and map data. In archaeology, these tools have been used in various ways to aid in cultural resource projects. For example, they have been used to predict the presence of archaeological resources using modern environmental indicators. Remote sensing techniques have also been used to directly detect the presence of unknown sites based on the impact of past occupation on the Earth's surface. Additionally, remote sensing has been used as a mapping tool aimed at delineating the boundaries of a site or mapping previously unknown features. All of these applications are pertinent to the goals of site discovery and assessment in cultural resource management.
The impact of pollen release and downwind concentrations can be evaluated utilizing remote sensing. Previous NASA studies have addressed airborne dust prediction systems PHAiRS (Public Health Applications in Remote Sensing) which have determined that pollen forecasts and simulations are possible. By adapting the deterministic dust model (as an in-line system with the National Weather Service operational forecast model) used in PHAiRS to simulate downwind dispersal of pollen, initializing the model with pollen source regions from MODIS, assessing the results a rapid prototype concept can be produced. We will present the results of our effort to develop a deterministic model for predicting and simulating pollen emission and downwind concentration to study details or phenology and meteorology and their dependencies, and the promise of a credible real time forecast system to support public health and agricultural science and service. Previous studies have been done with PHAiRS research, the use of NASA data, the dust model and the PHAiRS potential to improve public health and environmental services long into the future.