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At least 217 records · Page 12

Overview and highlights of Early Warning and Crop Condition Assessment project

Work of the Early Warning and Crop Condition Assessment (EW/CCA) project, one of eight projects in the Agriculture and Resources Inventory Surveys Through Aerospace Remote Sensing (AgRISTARS), is reviewed. Its mission, to develop and test remote sensing techniques that enhance operational methodologies for crop condition assessment, was in response to initiatives issued by the Secretary of Agriculture. Meteorologically driven crop stress indicator models have been developed or modified for wheat, maize, grain sorghum, and soybeans. These models provide early warning alerts of potential or actual crop stresses due to water deficits, adverse temperatures, and water excess that could delay planting or harvesting operations. Recommendations are given for future research involving vegetative index numbers and the NOAA and Landsat satellites.

Boatwright, G. O.↗

Signature-extendable technology - Global space-based crop recognition

The use of signature-extendable technology to improve the efficiency of machine processing of remotely sensed data is examined. Temporal profile technology is employed to automatically recognize crops; the technique uses the Kauth and Thomas (1976) transform of Landsat, multidata, and parameters derived from a model of each crop's greenness-time trajectory. The basic characteristics of temporal profile technology and the U.S. based labeling algorithm are described. Consideration is given to signature extension, signature-extendable spaces, and signature-extendable features. The greenness and brightness parameters used in temporal profile technology are derived. The signature extendability of the parameters is evaluated by applying them to the analysis of corn and soybean crops in the U.S. and Argentina. It is noted that the technique is an affordable and efficient method for deriving data on crops on a global basis.

Hall, Forrest G.↗

The commercial use of satellite data to monitor the potato crop in the Columbia Basin

The imaging of potato crops with satellites is described and evaluated in terms of the commercial application of the remotely sensed data. The identification and analysis of the crops is accomplished with multiple images acquired from the Landsat MSS and TM systems. The data are processed on a PC with image-procesing software which produces images of the seven 1024 x 1024 pixel windows which are subdivided into 21 512 x 512 pixel windows. Maximization of imaged data throughout the year aids in the identification of crop types by IR reflectance. The classification techniques involve the use of six or seven spectral classes for particular image dates. Comparisons with ground-truth data show good agreement; for example, potato fields are identified correctly 90 percent of the time. Acreage estimates and crop-condition assessments can be made from satellite data and used for corrective agricultural action.

Waddington, George R., Jr.↗

Quinoa: An emerging new crop with potential for CELSS

Chenopodium quinoa is being considered as a new crop for the Controlled Ecological Life Support System (CELSS) because of its high protein values (12 - 18%) and unique amino acid composition. Lysine, and essential amino acid that is deficient in many grain crops, is found in quinoa approaching Food and Agriculture Organization of the United Nations (FAO) standards set for humans. This 'new' crop, rich in protein and with desirable proportions of important amino acids, may provide greater versatility in meeting the needs of humans on long-term space missions. Initially, the cultivars CO407 x ISLUGA, CO407 Heat Tolerant Population 1, and Real' (a Bolivian variety) were examined. The first cultivar showed the most promise in greenhouse studies. When grown hydroponically in the greenhouse, with no attempt to maximize productivity, this cultivar produced 202 g m(exp -2) with a harvest index of 37%. None of the cultivars were greater than 70 cm in height. Initial results indicate that quinoa could be an excellent crop for CELSS because of the high concentration of protein, ease of use, versatility in preparation, and potential for greatly increased yields in controlled environments.

Schlick, Greg↗

Developing a Graphical User Interface for the ALSS Crop Planning Tool

The goal of my project was to create a graphical user interface for a prototype crop scheduler. The crop scheduler was developed by Dr. Jorge Leon and Laura Whitaker for the ALSS (Advanced Life Support System) program. The addition of a system-independent graphical user interface to the crop planning tool will make the application more accessible to a wider range of users and enhance its value as an analysis, design, and planning tool. My presentation will demonstrate the form and functionality of this interface. This graphical user interface allows users to edit system parameters stored in the file system. Data on the interaction of the crew, crops, and waste processing system with the available system resources is organized and labeled. Program output, which is stored in the file system, is also presented to the user in performance-time plots and organized charts. The menu system is designed to guide the user through analysis and decision making tasks, providing some help if necessary. The Java programming language was used to develop this interface in hopes of providing portability and remote operation.

Koehlert, Erik↗

Effects of Bioreactor Retention Time on Aerobic Microbial Decomposition of CELSS Crop Residues

The focus of resource recovery research at the KSC-CELSS Breadboard Project has been the evaluation of microbiologically mediated biodegradation of crop residues by manipulation of bioreactor process and environmental variables. We will present results from over 3 years of studies that used laboratory- and breadboard-scale (8 and 120 L working volumes, respectively) aerobic, fed-batch, continuous stirred tank reactors (CSTR) for recovery of carbon and minerals from breadboard grown wheat and white potato residues. The paper will focus on the effects of a key process variable, bioreactor retention time, on response variables indicative of bioreactor performance. The goal is to determine the shortest retention time that is feasible for processing CELSS crop residues, thereby reducing bioreactor volume and weight requirements. Pushing the lower limits of bioreactor retention times will provide useful data for engineers who need to compare biological and physicochemical components. Bioreactor retention times were manipulated to range between 0.25 and 48 days. Results indicate that increases in retention time lead to a 4-fold increase in crop residue biodegradation, as measured by both dry weight losses and CO2 production. A similar overall trend was also observed for crop residue fiber (cellulose and hemicellulose), with a noticeable jump in cellulose degradation between the 5.3 day and 10.7 day retention times. Water-soluble organic compounds (measured as soluble TOC) were appreciably reduced by more than 4-fold at all retention times tested. Results from a study of even shorter retention times (down to 0.25 days), in progress, will also be presented.

Strayer, R. F.↗

Analysis of proteins involved in biodegradation of crop biomass

The biodegradation of crop biomass for re-use in crop production is part of the bioregenerative life support concept proposed by the National Aeronautics and Space Administration (NASA) for long duration, manned space exploration. The current research was conducted in the laboratory to evaluate the use of electrophoretic analysis as a means of rapidly assaying for constitutive and induced proteins associated with the bacterial degradation of crop residue. The proteins involved in crop biomass biodegradation are either constitutive or induced. As a result, effluent and cultures were examined to investigate the potential of using electrophoretic techniques as a means of monitoring the biodegradation process. Protein concentration for optimum banding patterns was determined using the Bio-Rad Protein Assay kit. Four bacterial soil isolates were obtained from the G.W. Carver research Farm at Tuskegee University and used in the decomposition of components of plant biomass. The culture, WDSt3A was inoculated into 500 mL of either Tryptic Soy Broth or Nutrient Broth. Incubation, with shaking of each flask was for 96 hours at 30 C. The cultures consistently gave unique banding patterns under denaturing protein electrophoresis conditions, The associated extracellular enzymes also yielded characteristic banding patterns over a 14-day period, when native electrophoresis techniques were used to examine effluent from batch culture bioreactors. The current study evaluated sample preparation and staining protocols to determine the ease of use, reproducibility and reliability, as well as the potential for automation.

Crawford, Kamau↗

Suitability of Gray Water for Hydroponic Crop Production Following Biological and Physical Chemical and Biological Subsystems

The water present in waste streams from a human habitat must be recycled in Controlled Ecological Life Support Systems (CELSS) to limit resupply needs and attain self-sufficiency. Plants play an important role in providing food, regenerating air, and producing purified water via transpiration. However, we have shown that the surfactants present in hygiene waste water have acute toxic effects on plant growth (Bubenheim et al. 1994; Greene et al., 1994). These phytotoxic affects can be mitigated by allowing the microbial population on the root surface to degrade the surfactant, however, a significant suppression (several days) in crop performance is experienced prior to reaching sub-toxic surfactant levels and plant recovery. An effective alternative is to stabilize the microbial population responsible for degradation of the surfactant on an aerobic bioreactor and process the waste water prior to utilization in the hydroponic solution (Wisniewski and Bubenheim, 1993). A sensitive bioassay indicates that the surfactant phytotoxicity is suppressed by more than 90% within 5 hours of introduction of the gray water to the bioreactor; processing for more than 12 hours degrades more than 99% of the phytotoxin. Vapor Compression Distillation (VCD) is a physical / chemical method for water purification which employees sequential distillation steps to separate water from solids and to volatilize contaminants. The solids from the waste water are concentrated in a brine and the pure product water (70 - 90% of the total waste water volume depending on operating conditions) retains non of the phytotoxic effects. Results of the bioassay were used to guide evaluations of the suitability of recovered gray water following biological and VCD processing for hydroponic lettuce production in controlled environments. Lettuce crops were grown for 28 days with 100% of the input water supplied with recovered water from the biological processor or VCD. When compared with the growth of plants in control hydroponic solution containing pure deionized water, no growth difference could be measured resulting from any of the recovered water treatments. Both biological treatment and VCD offer alternative technology approaches to recovering water from waste streams appropriate for input into a crop production system. A high level of crop performance (food, air, and water production) can be maintained with either processor; selection decisions can be based on other factors regarding system integration.

Bubenheim, David L.↗

Crops Models for Varying Environmental Conditions

New variable environment Modified Energy Cascade (MEC) crop models were developed for all the Advanced Life Support (ALS) candidate crops and implemented in SIMULINK. The MEC models are based on the Volk, Bugbee, and Wheeler Energy Cascade (EC) model and are derived from more recent Top-Level Energy Cascade (TLEC) models. The MEC models simulate crop plant responses to day-to-day changes in photosynthetic photon flux, photoperiod, carbon dioxide level, temperature, and relative humidity. The original EC model allows changes in light energy but uses a less accurate linear approximation. The simulation outputs of the new MEC models for constant nominal environmental conditions are very similar to those of earlier EC models that use parameters produced by the TLEC models. There are a few differences. The new MEC models allow setting the time for seed emergence, have realistic exponential canopy growth, and have corrected harvest dates for potato and tomato. The new MEC models indicate that the maximum edible biomass per meter squared per day is produced at the maximum allowed carbon dioxide level, the nominal temperatures, and the maximum light input. Reducing the carbon dioxide level from the maximum to the minimum allowed in the model reduces crop production significantly. Increasing temperature decreases production more than it decreases the time to harvest, so productivity in edible biomass per meter squared per day is greater at nominal than maximum temperatures, The productivity in edible biomass per meter squared per day is greatest at the maximum light energy input allowed in the model, but the edible biomass produced per light energy input unit is lower than at nominal light levels. Reducing light levels increases light and power use efficiency. The MEC models suggest we can adjust the light energy day-to- day to accommodate power shortages or Lise excess power while monitoring and controlling edible biomass production.

Jones, Harry↗

Crop Surveillance Demonstration Using a Near-Daily MODIS Derived Vegetation Index Time Series

Effective response to crop disease outbreaks requires rapid identification and diagnosis of an event. A near-daily vegetation index product, such as a Normalized Difference Vegetation Index (NDVI), at moderate spatial resolution may serve as a good method for monitoring quick-acting diseases. NASA s Moderate Resolution Imaging Spectroradiometer (MODIS) instrument flown on the Terra and Aqua satellites has the temporal, spatial, and spectral properties to make it an excellent coarse-resolution data source for rapid, comprehensive surveillance of agricultural areas. A proof-of-concept wide area crop surveillance system using daily MODIS imagery was developed and tested on a set of San Joaquin cotton fields over a growing season. This area was chosen in part because excellent ground truth data were readily available. Preliminary results indicate that, at least in the southwestern part of the United States, near-daily NDVI products can be generated that show the natural variations in the crops as well as specific crop practices. Various filtering methods were evaluated and compared with standard MOD13 NDVI MODIS products. We observed that specific chemical applications that produce defoliation, which would have been missed using the standard 16-day product, were easily detectable with the filtered daily NDVI products.

McKellip, Rodney↗

Estimation of Crop Gross Primary Production (GPP): Fapar(sub Chl) Versus MOD15A2 FPAR

Photosynthesis (PSN) is a pigment level process in which antenna pigments (predominately chlorophylls) in chloroplasts absorb photosynthetically active radiation (PAR) for the photochemical process. PAR absorbed by foliar non-photosynthetic components is not used for PSN. The fraction of PAR absorbed (fAPAR) by a canopy/vegetation (i.e., fAPAR(sub canopy)) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) images, referred to as MOD15A2 FPAR, has been used to compute absorbed PAR (APAR) for PSN (APARPSN) which is utilized to produce the standard MODIS gross primary production (GPP) product, referred to as MOD17A2 GPP. In this study, the fraction of PAR absorbed by chlorophyll throughout the canopy (fAPAR(sub chl)) was retrieved from MODIS images for three AmeriFlux crop fields in Nebraska. There are few studies in the literature that compare the performance of MOD15A2 FPAR versus fAPAR(sub chl) in GPP estimation. In our study MOD15A2 FPAR and the retrieved fAPAR(sub chl) were compared with field fAPAR(sub canopy) and the fraction of PAR absorbed by green leaves of the vegetation (fAPAR(sub green)). MOD15A2 FPAR overestimated field fAPAR(sub canopy) in spring and in fall, and underestimated field fAPAR(sub canopy) in midsummer whereas fAPAR(sub chl) correctly captured the seasonal phenology. The retrieved fAPAR(sub chl) agreed well with field fAPAR(sub green) at early crop growth stage in June, and was less than field fAPAR(sub green) in late July, August and September. GPP estimates with fAPAR(sub chl) and with MOD15A2 FPAR were compared to tower flux GPP. GPP simulated with fAPAR(sub chl) was corroborated with tower flux GPP. Improvements in crop GPP estimation were achieved by replacing MOD15A2 FPAR with fAPAR(sub chl) which also reduced uncertainties of crop GPP estimates by 1.12-2.37 g C m(sup −2) d(sup −1).

GPM↗

Space Crop Production

As astronauts venture farther from Earth, and for longer periods, food will become increasingly critical. Crop production can supplement a packaged diet to provide additional nutrients and variety for astronauts. Testing with the Veggie and Advanced Plant Habitat chambers on the International Space Station is allowing us to understand the impacts of gravity and spaceflight on crop growth and nutritional content, and the importance of plants to astronauts living and working away from our home planet. The space environment presents unique challenges to crop production, and technology and knowledge gaps have been identified. Key gaps include how to: • Identify and demonstrate effective options to provide both water and oxygen to the root zone in microgravity • Understand the deep space radiation impacts on seeds and plants • Investigate the relationship between microbiomes and food safety • Store and handle seeds to ensure they are viable, free of contaminants and long-lived • Identify / develop potential crops suitable for the space environment • Understand automation and human factors • Determine scalability for different concepts and architectures Filling these gaps will help enable future human exploration and move us toward Earth-independence.

Veggie↗

Smart Crop Farming Systems for Artemis Exploration Missions

Space crop production systems that mitigate risks of crew poor performance or illness due to inadequate food and nutrition are needed during manned Artemis exploration missions beyond LEO. Prototype farms must be designed for deployment on ISS and tested in manned platforms: Gateway, lunar habitats, and Mars trans-hab spacecraft in preparation for human missions to Mars. Food production must be optimal and safe for human consumption. Thus, plant growth facilities (i.e. Veggie and APH) can be enhanced with imaging systems (including hyperspectral, multispectral, lidar, and fluorescence imaging systems) for nondestructive monitoring of plant health, stress and assessing food safety. Databases of crop responses to stress obtained during ground studies can be used to develop novel artificial intelligence (AI) algorithms for optimizing crop production (i.e. environmental settings during growth) and for detecting crop indices that ensure food safety. Future farming systems should be sustainable and smart. Novel adaptive AI algorithms requiring limited data sets for calibration are needed for reducing crew intervention during plant cultivation except for maintenance and harvesting events. Eventually, AI driven control systems that include autonomous planting, growing, and harvesting as well as periodic sanitization need evaluation for supplementing crew diets with fresh produce during future Mars exploration missions.

O Monje↗

The Arya Crop Yield Forecasting Algorithm: Application to the Main Wheat Exporting Countries

Wheat is the most important commodity traded in the international food market. Thus, accurate and timely information on wheat production can help mitigate food price fluctuations. Within the existing operational regional and global scale agricultural monitoring systems that provide information on global crop yield and area forecasts, there are still fundamental gaps: #1. Lack of quantitative Earth Observation (EO) derived crop information, #2. Lack of global but detailed (national or subnational level) and timely crop production forecasts and #3. Lack of information on forecast uncertainties. In this study we present the Agriculture Remotely-sensed Yield Algorithm (ARYA) an EO-based method, advancing the state of EO data application and usage (addressing gap #1) to forecast wheat yield. The algorithm is based on the evolution of the Difference Vegetation Index (DVI) using MODIS data at 1km resolution and the Growing Degree Days (GDD) from reanalysis data. Additionally, we explore how Land Surface Temperature (LST) can be included into the model and whether this parameter adds any value to the model performance when combined with the optical information. ARYA is implemented at the national and subnational level to forecast winter wheat yield in the main wheat exporting countries of US, Russia, Ukraine, France, Germany, Australia and Argentina from 2001 to 2019 (covering over 70% of wheat exports globally) in a timely manner by providing daily forecasts (addressing gap #2). The results show that ARYA provides yield estimations with RMSE’s within 0.3 ± 0.1 t/ha at national level and 0.6 ± 0,1 t/ha at subnational level after Day Of the Year (DOY) 140 (mid May) in the Northern Hemisphere and DOY 280 (beginning of October) in the Southern Hemisphere. This means that ARYA can provide crop yield estimates of wheat yield with 5-15 % error at national and 7-20 % error at subnational level starting from 2 to 2.5 months prior to harvest.

Agriculture↗

Bhutan Agriculture: Developing a Crop Mask for Rice and Creating a Data Collection Protocol Utilizing Remotely Sensed Data in Bhutan

Rice cultivation in Bhutan has been increasingly threatened by deteriorating soil health and outbreaks of diseases and pests associated with the global change in climate patterns. Field surveys, which the national government of Bhutan has relied on to monitor remote agricultural lands, are becoming increasingly overwhelmed by growing threats to agricultural health. To address these concerns, NASA DEVELOP partnered with the Department of Agriculture of Bhutan, the Bhutan Foundation, and the Ugyen Wangchuck Institute of Conservation and Environmental Research (UWICER) and worked to increase the government of Bhutan’s agricultural monitoring capacity. Utilizing Earth observations including Landsat 8 Operational Land Imager (OLI), Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), Shuttle Radar Topography Mission (SRTM), and Planet imagery, the DEVELOP team worked with NASA SERVIR and created a sampling protocol to identify rice plantations and supplement field surveys for more efficient agriculture monitoring. The analysis focused on districts Paro, Punakha, Samtse, Sarpang, Trongsa, Zhemgang, Wangdue Phodrang, and Samdrup Jongkhar in the year 2020 during the period of transplantation (June) to harvesting of rice (November). The team provided the partners with a sampling protocol for integrating NASA Earth observations into their crop monitoring methods, as well as a crop mask for rice identification and to aid crop management. The crop mask for rice was developed using the Random Forest (RF) classifier for the eight districts of Bhutan. Visually, the random forest model has proved to be more accurate and precise than the classification and Regression Tree model. Statistically, the Random Forest model was 91.8% accurate in identifying rice in Bhutan.

Yeshey Seldon↗

Determining Crop Coefficients Using Remote Sensing for the Maipo River Valley Basin in Chile

Agriculture is the primary use of water in the Maipo River Valley of Central Chile, accounting for ~ 75% of the total demand. Assessment of irrigation needs for agricultural production has commonly relied on reference crop coefficients (Kc) derived from geographic and climatic conditions that differ from those of Chile. This work focused on calculating site-specific Kc values tailored to crop production in the water-stressed Maipo River Valley over consecutive growing seasons from 2019 to 2022. Two distinct approaches were implemented, each relying on remotely-sensed NASA Earth observation datasets. The first method estimated Kc values based on the linear relationship with the Normalized Difference Vegetation Index (NDVI) obtained from either Terra Moderate Resolution Imaging Spectroradiometer (MODIS) or Landsat 8 Operational Land Imager (OLI) surface reflectance. The second technique leveraged information from the ISS Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) by computing the ratio between actual crop evapotranspiration and potential evapotranspiration. The former approach best captured vegetation signals of annual crops while the latter appeared best suited for perennials. Overall, this study provides a strong basis and novel way to accurately estimate Kc using remote sensing, with the potential for improved irrigation management and reduction in water consumption.

Rishudh Thadur↗

CESO 22-4: Parabolic and Suborbital Glovebox in Support of Space Crop Production

Space crop production research approaches and technologies can be validated for microgravity using parabolic or suborbital flight opportunities, and this will save considerable risk, time, and money for implementing new strategies in spaceflight. While the durations of microgravity in these tests are insufficient to grow crops, there are numerous examples of the value of microgravity testing for subsystems such as plant water and nutrient delivery, where multiphase fluid flow can be elucidated in short durations, and horticultural operations, such as harvesting and produce sanitation where containment and contamination can be assessed. Containment in any operational test is essential, as crop operations involve fluids and biological samples, which are potential hazards. This project consists of designing, developing, and constructing a parabolic/suborbital glovebox for experiment containment. The design involves modifications and upgrades to an existing glovebox developed at the University of Louisville. The University of Louisville glovebox was used by KSC researchers for space crop parabolic flight tests in 2021, and lessons learned from that testing have driven design modifications and improvements in the KSC-generated glovebox. Requirements were identified, parts were ordered, an operational science glovebox was fabricated, and a detailed materials specification list was generated. Analyses that are required for flight, remain to be performed to meet airworthiness requirements, and that work will have to be conducted in the future before use in flight.

Food Production↗

Pick-and-Eat Space Crop Production Flight Testing on the International Space Station

Growing fresh, nutritious, palatable produce for crew consumption during spaceflight may provide health-promoting, bioavailable nutrients and enhance the astronaut dietary experience as we move toward longer-duration missions. However, requirements to support consistent growth of a variety of high-quality crops under spaceflight environmental conditions remain unclear. This study explores the potential to grow crops for consumption on the International Space Station (ISS) using Veggie, NASA’s vegetable production chamber system. VEG-04A and VEG-04B were two flight tests with ground components conducted with the leafy green crop mizuna mustard. In each location, mizuna was grown in two Veggie units simultaneously, with the chambers set to different red-to-blue-to-green light formulations. Preflight verification testing with various lighting treatments was conducted to down-select two treatments that contributed to the best desirable growth and sensory qualities in mizuna mustard. For the flight tests, one Veggie was programmed as “red-rich” with an average of 270 μmol m-2 s-1 of 630 nm red light, 30 μmol m-2 s-1 of 455 nm blue light, and 30 μmol m-2 s-1 of 530 nm green light. The second Veggie was “blue-rich” with an average of 150 µmol m-2 s-1 of 630 nm red light, 150 µmol m-2 s-1 of 455 nm blue light, and 30 µmol m-2 s-1 of 530 nm green light. Light quality is known to impact plant growth, nutrition, microbiology, and sensory characteristics on Earth, and the Veggie flight tests examined how these impacts might differ in microgravity. VEG-04A, a 35-day growth test with a single harvest, was initiated in June and harvested in July 2019. VEG-04B, a 56-day test with three harvests from the same plants, assessed sustained productivity. Preflight testing for VEG-04B was conducted after the VEG-04A flight test to improve the operations, approaches, and watering requirements, which resulted in better crop establishment in the VEG-04B flight test. VEG-04B was initiated in October 2019 with harvests at four, six, and eight weeks after initiation. At all of the harvests, the astronauts froze half of the edible plant tissue to return to Earth and weighed the remaining half using the Mass Measurement Device (MMD). Weighed samples were then cleaned with produce-sanitizing wipes, and consenting crew members participated in sensory evaluations of the fresh produce. The remaining sanitized produce was available for crew consumption as desired. Frozen flight samples were returned to Earth for chemical and microbial analyses to assess nutritional quality and food safety. Flight-grown mizuna was generally more acceptable to the crew and had higher nutrient levels, although mizuna grown in the ground control performed better in terms of higher yield and lower microbial load. This presentation will focus on results from the nutritional and sensory analyses, including how nutrients identified as key for supplementing the crew diet varied across lighting treatments, harvest approaches, and spaceflight versus ground conditions. It is our hope that these tests on the ISS will help mitigate the risk of an inadequate food supply for long-duration missions by adding fresh vegetables to the crew diet. This study was supported by NASA’s Human Research and Space Biology Programs through the HERO NNJ13ZSA002N-ILSRA grant solicitation.

Jess Bunchek↗