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NASA NTRS2015

A Framework for the Cross-Sectoral Integration of Multi-Model Impact Projections: Land Use Decisions Under Climate Impacts Uncertainties

Climate change and its impacts already pose considerable challenges for societies that will further increase with global warming (IPCC, 2014a, b). Uncertainties of the climatic response to greenhouse gas emissions include the potential passing of large-scale tipping points (e.g. Lenton et al., 2008; Levermann et al., 2012; Schellnhuber, 2010) and changes in extreme meteorological events (Field et al., 2012) with complex impacts on societies (Hallegatte et al., 2013). Thus climate change mitigation is considered a necessary societal response for avoiding uncontrollable impacts (Conference of the Parties, 2010). On the other hand, large-scale climate change mitigation itself implies fundamental changes in, for example, the global energy system. The associated challenges come on top of others that derive from equally important ethical imperatives like the fulfilment of increasing food demand that may draw on the same resources. For example, ensuring food security for a growing population may require an expansion of cropland, thereby reducing natural carbon sinks or the area available for bio-energy production. So far, available studies addressing this problem have relied on individual impact models, ignoring uncertainty in crop model and biome model projections. Here, we propose a probabilistic decision framework that allows for an evaluation of agricultural management and mitigation options in a multi-impactmodel setting. Based on simulations generated within the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), we outline how cross-sectorally consistent multi-model impact simulations could be used to generate the information required for robust decision making. Using an illustrative future land use pattern, we discuss the trade-off between potential gains in crop production and associated losses in natural carbon sinks in the new multiple crop- and biome-model setting. In addition, crop and water model simulations are combined to explore irrigation increases as one possible measure of agricultural intensification that could limit the expansion of cropland required in response to climate change and growing food demand. This example shows that current impact model uncertainties pose an important challenge to long-term mitigation planning and must not be ignored in long-term strategic decision making

farmlands↗
NASA NTRS2015

GeneLab: A Systems Biology Platform for Spaceflight Omics Data

NASA's mission includes expanding our understanding of biological systems to improve life on Earth and to enable long-duration human exploration of space. Resources to support large numbers of spaceflight investigations are limited. NASA's GeneLab project is maximizing the science output from these experiments by: (1) developing a unique public bioinformatics database that includes space bioscience relevant "omics" data (genomics, transcriptomics, proteomics, and metabolomics) and experimental metadata; (2) partnering with NASA-funded flight experiments through bio-sample sharing or sample augmentation to expedite omics data input to the GeneLab database; and (3) developing community-driven reference flight experiments. The first database, GeneLab Data System Version 1.0, went online in April 2015. V1.0 contains numerous flight datasets and has search and download capabilities. Version 2.0 will be released in 2016 and will link to analytic tools. In 2015 Genelab partnered with two Biological Research in Canisters experiments (BBRIC-19 and BRIC-20) which examine responses of Arabidopsis thaliana to spaceflight. GeneLab also partnered with Rodent Research-1 (RR1), the maiden flight to test the newly developed rodent habitat. GeneLab developed protocols for maxiumum yield of RNA, DNA and protein from precious RR-1 tissues harvested and preserved during the SpaceX-4 mission, as well as from tissues from mice that were frozen intact during spaceflight and later dissected. GeneLab is establishing partnerships with at least three planned flights for 2016. Organism-specific nationwide Science Definition Teams (SDTs) will define future GeneLab dedicated missions and ensure the broader scientific impact of the GeneLab missions. GeneLab ensures prompt release and open access to all high-throughput omics data from spaceflight and ground-based simulations of microgravity and radiation. Overall, GeneLab will facilitate the generation and query of parallel multi-omics data, and deep curation of metadata for integrative analysis, allowing researchers to uncover cellular networks as observed in systems biology platforms. Consequently, the scientific community will have access to a more complete picture of functional and regulatory networks responsive to the spaceflight environment.. Analysis of GeneLab data will contribute fundamental knowledge of how the space environment affects biological systems, and enable emerging terrestrial benefits resulting from mitigation strategies to prevent effects observed during exposure to space. As a result, open access to the data will foster new hypothesis-driven research for future spaceflight studies spanning basic science to translational science.

proteomics↗
NASA NTRS2016

Thinking Outside of the Blue Marble: Novel Ocean Applications Using the VIIRS Sensor

While planning for future space-borne sensors will increase the quality, quantity, and duration of ocean observations in the years to come, efforts to extend the limits of sensors currently in orbit can help shed light on future scientific gains as well as associated uncertainties. Here, we present several applications that are unique to the polar orbiting Visual Infrared Imaging Radiometer Suite (VIIRS), each of which challenge the threshold capabilities of the sensor and provide lessons for future missions. For instance, while moderate resolution polar orbiters typically have a one day revisit time, we are able to obtain multiple looks of the same area by focusing on the extreme zenith angles where orbital views overlap, and pair these observations with those from other sensors to create pseudo-geostationary data sets. Or, by exploiting high spatial resolution (imaging) channels and analyzing patterns of synoptic covariance across the visible spectrum, we can obtain higher spatial resolution bio-optical products. Alternatively, non-traditional products can illuminate important biological interactions in the ocean, such as the use of the Day-Night-Band to provide some quantification of phototactic behavior of marine life along light polluted beaches, as well as track the location of marine fishing vessel fleets along ocean fronts. In this talk, we explore ways to take full advantage of the capabilities of existing sensors in order to maximize insights for future missions.

ocean color↗
NASA NTRS2016

Synthetic Astrobiology

Synthetic biology - the design and construction of new biological parts and systems and the redesign of existing ones for useful purposes - has the potential to transform fields from pharmaceuticals to fuels. Our lab has focused on the potential of synthetic biology to revolutionize all three major parts of astrobiology: Where do we come from? Where are we going? and Are we alone? For the first and third, synthetic biology is allowing us to answer whether the evolutionary narrative that has played out on planet earth is likely to have been unique or universal. For example, in our lab we are re-evolving the biosynthetic pathways of amino acids in order to understand potential capabilities of an early organism with a limited repertoire of amino acids and developing techniques for the recovery of metals from spent electronics on other planetary bodies. And what about the limits for life? Can we create organisms that expand the envelope for life? In the future synthetic biology will play an increasing role in human activities both on earth, in fields as diverse as human health and the industrial production of novel bio-composites. Beyond earth, we will rely increasingly on biologically-provided life support, as we have throughout our evolutionary history. In order to do this, the field will build on two of the great contributions of astrobiology: studies of the origin of life and life in extreme environments.

synthetic biology↗
NASA NTRS2016

Advanced Plant Habitat (APH)

The Advanced Plant Habitat (APH) hardware will be a large growth volume plant habitat, capable of hosting multigenerational studies, in which environmental variables (e.g., temperature, relative humidity, carbon dioxide level light intensity and spectral quality) can be tracked and controlled in support of whole plant physiological testing and Bio-regenerative Life Support System investigations.

Plant↗
NASA NTRS2016

Initial Technology Assessment for the Large UV-Optical-Infrared (LUVOIR) Mission Concept Study

The NASA Astrophysics Divisions 30-Year Roadmap prioritized a future large-aperture space telescope operating in the ultra-violet-optical-infrared wavelength regime. The Association of Universities for Research in Astronomy envisioned a similar observatory, the High Definition Space Telescope. And a multi-institution group also studied the Advanced Technology Large Aperture Space Telescope. In all three cases, a broad science case is outlined, combining general astrophysics with the search for bio-signatures via direct-imaging and spectroscopic characterization of habitable exo-planets. We present an initial technology assessment that enables such an observatory that is currently being studied for the 2020 Decadal Survey by the Large UV-Optical Infrared (LUVOIR) surveyor Science and Technology Definition Team. We present here the technology prioritization for the 2016 technology cycle and define the required technology capabilities and current state-of-the-art performance. Current, planned, and recommended technology development efforts are also reported.

large aperture↗
NASA NTRS2016

Robots for Astrobiology!

The search for life and its study is known as astrobiology. Conducting that search on other planets in our Solar System is a major goal of NASA and other space agencies, and a driving passion of the community of scientists and engineers around the world. We practice for that search in many ways, from exploring and studying extreme environments on Earth, to developing robots to go to other planets and help us look for any possible life that may be there or may have been there in the past. The unique challenges of space exploration make collaborations between robots and humans essential. The products of those collaborations will be novel and driven by the features of wholly new environments. For space and planetary environments that are intolerable for humans or where humans present an unacceptable risk to possible biologically sensitive sites, autonomous robots or telepresence offer excellent choices. The search for life signs on Mars fits within this category, especially in advance of human landed missions there, but also as assistants and tools once humans reach the Red Planet. For planetary destinations where we do not envision humans ever going in person, like bitterly cold icy moons, or ocean worlds with thick ice roofs that essentially make them planetary-sized ice caves, we will rely on robots alone to visit those environments for us and enable us to explore and understand any life that we may find there. Current generation robots are not quite ready for some of the tasks that we need them to do, so there are many opportunities for roboticists of the future to advance novel types of mobility, autonomy, and bio-inspired robotic designs to help us accomplish our astrobiological goals. We see an exciting partnership between robotics and astrobiology continually strengthening as we jointly pursue the quest to find extraterrestrial life.

Robotics↗
NASA NTRS2015

Synthetic Astrobiology

Synthetic biology - the design and construction of new biological parts and systems and the redesign of existing ones for useful purposes - has the potential to transform fields from pharmaceuticals to fuels. Our lab has focused on the potential of synthetic biology to revolutionize all three major parts of astrobiology: Where do we come from? Where are we going? and Are we alone? For the first and third, synthetic biology is allowing us to answer whether the evolutionary narrative that has played out on planet earth is likely to have been unique or universal. For example, in our lab we are re-evolving the biosynthetic pathways of amino acids in order to understand potential capabilities of an early organism with a limited repertoire of amino acids and developing techniques for the recovery of metals from spent electronics on other planetary bodies. In the future synthetic biology will play an increasing role in human activities both on earth, in fields as diverse as human health and the industrial production of novel bio-composites. Beyond earth, we will rely increasingly on biologically-provided life support, as we have throughout our evolutionary history. In order to do this, the field will build on two of the great contributions of astrobiology: studies of the origin of life and life in extreme environments.

Potential Fields↗
NASA NTRS2016

Ozone Gardens for the Citizen Scientist

NASA Langley partnered with the Virginia Living Museum and two schools to create ozone bio-indicator gardens for citizen scientists of all ages. The garden at the Marshall Learning Center is part of a community vegetable garden designed to teach young children where food comes from and pollution in their area, since most of the children have asthma. The Mt. Carmel garden is located at a K-8 school. Different ozone sensitive and ozone tolerant species are growing and being monitored for leaf injury. In addition, CairClip ozone monitors were placed in the gardens and data are compared to ozone levels at the NASA Langley Chemistry and Physics Atmospheric Boundary Layer Experiment (CAPABLE) site in Hampton, VA. Leaf observations and plant measurements are made two to three times a week throughout the growing season.

Pippin, Margaret↗
NASA NTRS2016

The Formation of Nucleobases from the Irradiation of Purine in Astophysical Ices and Comparisons with Meteorites.

N-heterocycles have been identified in meteorites and their extraterrestrial origins are suggested by isotopic ratio measurements. Although small N- heterocycles have not been detected in the interstellar medium (ISM), recent experiments in our lab have shown that the irradiation of the aromatic molecules like benzene (C6H6) and naphthalene (C10H8) in mixed molecular ices leads to the formation of O- and N-heterocyclic molecules. Among the class of N-heterocycles are the nucleobases, which are of astrobiological interest because they are the information bearing units of DNA and RNA. Nucleobases have been detected in meteorites [3-5], with isotopic signatures that are also consistent with an extraterrestrial origin. Three of the biologically relevant nucleobases (uracil, cytosine, and guanine) have a pyrimidine core structure while the remaining two (adenine and guanine) possess a purine core. Previous experiments in our lab have demonstrated that all of the bio-logical nucleobases (and numerous other molecules) with a pyrimidine core structure can be produced by irradiating pyrimidine in mixed molecular ices of several compositions [6-8]. In this work, we study the formation of purine-based molecules, including the nucleobases adenine, and guanine, from the ultraviolet (UV) irradiation of purine in ices consisting mixtures of H2O and NH3 at low temperature. The experiments are designed to simulate the astrophysical conditions under which these species may be formed in dense molecular clouds, protoplanetary disks, or on the surfaces of icy bodies in planetary systems.

Astrochemistry↗
NASA NTRS2013

Advanced Exploration Systems (AES) Logistics Reduction and Repurposing Project: Advanced Clothing Ground Study Final Report

All human space missions require significant logistical mass and volume that will become an excessive burden for long duration missions beyond low Earth orbit. The goal of the Advanced Exploration Systems (AES) Logistics Reduction & Repurposing (LRR) project is to bring new ideas and technologies that will enable human presence in farther regions of space. The LRR project has five tasks: 1) Advanced Clothing System (ACS) to reduce clothing mass and volume, 2) Logistics to Living (L2L) to repurpose existing cargo, 3) Heat Melt Compactor (HMC) to reprocess materials in space, 4) Trash to Gas (TTG) to extract useful gases from trash, and 5) Systems Engineering and Integration (SE&I) to integrate these logistical components. The current International Space Station (ISS) crew wardrobe has already evolved not only to reduce some of the logistical burden but also to address crew preference. The ACS task is to find ways to further reduce this logistical burden while examining human response to different types of clothes. The ACS task has been broken into a series of studies on length of wear of various garments: 1) three small studies conducted through other NASA projects (MMSEV, DSH, HI-SEAS) focusing on length of wear of garments treated with an antimicrobial finish; 2) a ground study, which is the subject of this report, addressing both length of wear and subject perception of various types of garments worn during aerobic exercise; and 3) an ISS study replicating the ground study, and including every day clothing to collect information on perception in reduced gravity in which humans experience physiological changes. The goal of the ground study is first to measure how long people can wear the same exercise garment, depending on the type of fabric and the presence of antimicrobial treatment, and second to learn why. Human factors considerations included in the study consist of the Institutional Review Board approval, test protocol and participants' training, and a web-based data collection questionnaire. Cardiovascular exercise was chosen as the activity in this experiment for its profuse sweating effect and because it is considered a more severe treatment applied to the clothes than every-day usage. Study garments were exercise T-shirts and shorts purchased from various vendors. Fabric construction, fabric composition, and finishing treatment were defined as the key variables. The study was divided into three balanced experiments: a cotton-polyester-wool (CPW) T-shirts study with 61 participants, a polyester-modacrylic-polyester/cocona (PMC) T-shirts study with 40 participants, and a shorts study with 70 participants. In the CPW study, the T-shirts were made of 100% cotton, or of 100% polyester or of 100% wool, and categorized into open and tight knit constructions. In the PMC study, the T-shirts were made of 100% polyester, or of 82% modacrylic, or of 95% polyester with 5% cocona fiber, without construction distinction. The shorts were made either of 100% cotton or of 100% polyester, and were knitted or woven. Some garments were treated with Bio-Protect 500 antimicrobial finish according to the experimental design. The data collected from the questionnaire included garment identification, level of exertion, duration of exercise session, number of exercise sessions, an ordinal preference scale for nine sensory elements, and reason for retiring a used garment. From the analysis of the combined CPW and PMC shirt studies, there are statistically significant differences among the mean lifetimes of various types of shirts. The exercise shirts with the longest mean lifetimes are untreated wool (600 minutes), treated cotton (526 minutes), and untreated modacrylic (515 minutes). From the combined CPW and PMC shirt studies, the most preferred material was untreated open-knit wool, which is one of the two materials that jointly were worn the longest, untreated wool, both open-knit and tight-knit. For the CP shorts study, there were no statistically significant differences in mean lifetimes of the exercise shorts at the 5% significance level due to the treatment combinations. There was therefore no justification to examine differences among levels of main effects or interactions. The preference for shorts was in this order: untreated woven polyester, untreated knitted polyester, untreated woven cotton, and treated knitted cotton. The nine preference scales were tabulated to determine the preference responses at the end of those exercise periods which were prior to the period when a garment was retired and a new garment was started. The assumption is that an unfavorable assessment of a garment leads to its retirement. The scent scale response was predominantly unfavorable at the end of the exercise period immediately prior to the exercise period when a new garment was started.

Byrne, Vicky↗
NASA NTRS2016

Self-Healing Composite of Thermoset Polymer and Programmed Super Contraction Fibers

A composition comprising thermoset polymer, shape memory polymer to facilitate macro scale damage closure, and a thermoplastic polymer for molecular scale healing is disclosed; the composition has the ability to resolve structural defects by a bio-mimetic close-then heal process. In use, the shape memory polymer serves to bring surfaces of a structural defect into approximation, whereafter use of the thermoplastic polymer for molecular scale healing allowed for movement of the thermoplastic polymer into the defect and thus obtain molecular scale healing. The thermoplastic can be fibers, particles or spheres which are used by heating to a level at or above the thermoplastic's melting point, then cooling of the composition below the melting temperature of the thermoplastic. Compositions of the invention have the ability to not only close macroscopic defects, but also to do so repeatedly even if another wound/damage occurs in a previously healed/repaired area.

Li, Guoqiang↗
NASA NTRS2016

A Flexible Method for Producing F.E.M. Analysis of Bone Using Open-Source Software

This project, performed in support of the NASA GRC Space Academy summer program, sought to develop an open-source workflow methodology that segmented medical image data, created a 3D model from the segmented data, and prepared the model for finite-element analysis. In an initial step, a technological survey evaluated the performance of various existing open-source software that claim to perform these tasks. However, the survey concluded that no single software exhibited the wide array of functionality required for the potential NASA application in the area of bone, muscle and bio fluidic studies. As a result, development of a series of Python scripts provided the bridging mechanism to address the shortcomings of the available open source tools. The implementation of the VTK library provided the most quick and effective means of segmenting regions of interest from the medical images; it allowed for the export of a 3D model by using the marching cubes algorithm to build a surface mesh. To facilitate the development of the model domain from this extracted information required a surface mesh to be processed in the open-source software packages Blender and Gmsh. The Preview program of the FEBio suite proved to be sufficient for volume filling the model with an unstructured mesh and preparing boundaries specifications for finite element analysis. To fully allow FEM modeling, an in house developed Python script allowed assignment of material properties on an element by element basis by performing a weighted interpolation of voxel intensity of the parent medical image correlated to published information of image intensity to material properties, such as ash density. A graphical user interface combined the Python scripts and other software into a user friendly interface. The work using Python scripts provides a potential alternative to expensive commercial software and inadequate, limited open-source freeware programs for the creation of 3D computational models. More work will be needed to validate this approach in creating finite-element models.

gravitational physiology↗
NASA NTRS2016

Coastal High-resolution Observations and Remote Sensing of Ecosystems (C-HORSE)

Coastal benthic marine ecosystems, such as coral reefs, seagrass beds, and kelp forests are highly productive as well as ecologically and commercially important resources. These systems are vulnerable to degraded water quality due to coastal development, terrestrial run-off, and harmful algal blooms. Measurements of these features are important for understanding linkages with land-based sources of pollution and impacts to coastal ecosystems. Challenges for accurate remote sensing of coastal benthic (shallow water) ecosystems and water quality are complicated by atmospheric scattering/absorption (approximately 80+% of the signal), sun glint from the sea surface, and water column scattering (e.g., turbidity). Further, sensor challenges related to signal to noise (SNR) over optically dark targets as well as insufficient radiometric calibration thwart the value of coastal remotely-sensed data. Atmospheric correction of satellite and airborne remotely-sensed radiance data is crucial for deriving accurate water-leaving radiance in coastal waters. C-HORSE seeks to optimize coastal remote sensing measurements by using a novel airborne instrument suite that will bridge calibration, validation, and research capabilities of bio-optical measurements from the sea to the high altitude remote sensing platform. The primary goal of C-HORSE is to facilitate enhanced optical observations of coastal ecosystems using state of the art portable microradiometers with 19 targeted spectral channels and flight planning to optimize measurements further supporting current and future remote sensing missions.

Coasts↗
NASA NTRS2016

Linking Remotely Sensed Aerosol Types to Their Chemical Composition

Aerosol types measured during the Ship-Aircraft Bio-Optical Research (SABOR) experiment are related to GEOS-Chem model chemical composition. The application for this procedure to link model chemical components to aerosol type is desirable for understanding aerosol evolution over time. The Mahalanobis distance (DM) statistic is used to cluster model groupings of five chemical components (organic carbon, black carbon, sea salt, dust and sulfate) in a way analogous to the methods used by Burton et al. [2012] and Russell et al. [2014]. First, model-to-measurement evaluation is performed by collocating vertically resolved aerosol extinction from SABOR High Spectral Resolution LiDAR (HSRL) to the GEOS-Chem nested high-resolution data. Comparisons of modeled-to-measured aerosol extinction are shown to be within 35% +/- 14%. Second, the model chemical components are calculation into five variables to calculate the DM and cluster means and covariances for each HSRL-retrieved aerosol type. The layer variables from the model are aerosol optical depth (AOD) ratios of (i) sea salt and (ii) dust to total AOD, mass ratios of (iii) total carbon (i.e. sum of organic and black carbon) to the sum of total carbon and sulfate (iv) organic carbon to black carbon, and (v) the natural log of the aerosol-to-molecular extinction ratio. Third, the layer variables and at most five out of twenty SABOR flights are used to form the pre-specified clusters for calculating DM and to assign an aerosol type. After determining the pre-specified clusters, model aerosol types are produced for the entire vertically resolved GEOS-Chem nested domain over the United States and the model chemical component distributions relating to each type are recorded. Resulting aerosol types are Dust/Dusty Mix, Maritime, Smoke, Urban and Fresh Smoke (separated into 'dark' and 'light' by a threshold of the organic to black carbon ratio). Model-calculated DM not belonging to a specific type (i.e. not meeting a threshold probability) is termed an outlier and those DM values that can belong to multiple types (i.e. showing weak probability of belonging to a specific cluster) are termed as Overlap. MODIS active fires are overlaid on the model domain to qualitatively evaluate the model-predicted Smoke aerosol types.

Aerosol Types↗
NASA NTRS2017

Microbial Monitoring from the Frontlines to Space: Department of Defense Small Business Innovation Research Technology Aboard the International Space Station

The RAZOR (trademark) EX, a quantitative Polymerase Chain Reaction (qPCR) instrument, is a portable, ruggedized unit that was designed for the Department of Defense (DoD) with its reagent chemistries traceable to a Small Business Innovation Research (SBIR) contract beginning in 2002. The PCR instrument's primary function post 9/11 was to enable frontline soldiers and first responders to detect biological threat agents and bioterrorism activities in remote locations to include field environments. With its success for DoD, the instrument has also been employed by other governmental agencies including Department of Homeland Security (DHS). The RAZOR (Trademark) EX underwent stringent testing by the vendor, as well as through the DoD, and was certified in 2005. In addition, the RAZOR (trademark) EX passed DHS security sponsored Stakeholder Panel on Agent Detection Assays (SPADA) rigorous evaluation in 2011. The identification and quantitation of microbial pathogens is necessary both on the ground as well as during spaceflight to maintain the health of astronauts and to prevent biofouling of equipment. Currently, culture-based monitoring technology has been adequate for short-term spaceflight missions but may not be robust enough to meet the requirements for long-duration missions. During a NASA-sponsored workshop in 2011, it was determined that the more traditional culture-based method should be replaced or supplemented with more robust technologies. NASA scientists began investigating innovative molecular technologies for future space exploration and as a result, PCR was recommended. Shortly after, NASA sponsored market research in 2012 to identify and review current, commercial, cutting edge PCR technologies for potential applicability to spaceflight operations. Scientists identified and extensively evaluated three candidate technologies with the potential to function in microgravity. After a thorough voice-of-the-customer trade study and extensive functional and safety evaluations, the RAZOR (trademark) EX PCR instrument(Bio-Fire Defense, Salt Lake City, UT) was selected as the most promising current technology for spaceflight monitoring applications.

Cherie M Oubre↗
NASA NTRS2016

Brief History of Agricultural Systems Modeling

Agricultural systems science generates knowledge that allows researchers to consider complex problems or take informed agricultural decisions. The rich history of this science exemplifies the diversity of systems and scales over which they operate and have been studied. Modeling, an essential tool in agricultural systems science, has been accomplished by scientists from a wide range of disciplines, who have contributed concepts and tools over more than six decades. As agricultural scientists now consider the next generation models, data, and knowledge products needed to meet the increasingly complex systems problems faced by society, it is important to take stock of this history and its lessons to ensure that we avoid re-invention and strive to consider all dimensions of associated challenges. To this end, we summarize here the history of agricultural systems modeling and identify lessons learned that can help guide the design and development of next generation of agricultural system tools and methods. A number of past events combined with overall technological progress in other fields have strongly contributed to the evolution of agricultural system modeling, including development of process-based bio-physical models of crops and livestock, statistical models based on historical observations, and economic optimization and simulation models at household and regional to global scales. Characteristics of agricultural systems models have varied widely depending on the systems involved, their scales, and the wide range of purposes that motivated their development and use by researchers in different disciplines. Recent trends in broader collaboration across institutions, across disciplines, and between the public and private sectors suggest that the stage is set for the major advances in agricultural systems science that are needed for the next generation of models, databases, knowledge products and decision support systems. The lessons from history should be considered to help avoid roadblocks and pitfalls as the community develops this next generation of agricultural systems models.

agricultural systems↗