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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS1983

Separability of agricultural crops with airborne scatterometry

Backscattering measurements were acquired with airborne scatterometers over a site in Cass County, North Dakota on four days in the 1981 crop growing season. Data were acquired at three frequencies (L-, C- and Ku-bands), two polarizations (like and cross) and ten incidence angles (5 degrees to 50 degrees in 5 degree steps). Crop separability is studied in an hierarchical fashion. A two-class separability measure is defined, which compares within-class to between-class variability, to determine crop separability. The scatterometer channels with the best potential for crop separability are determined, based on this separability measure. Higher frequencies are more useful for discriminating small grains, while lower frequencies tend to separate non-small grains better. Some crops are more separable when row direction is taken into account. The effect of pixel purity is to increase the separability between all crops while not changing the order of useful scatterometer channels. Crude estimates of separability errors are calculated based on these analyses. These results are useful in selecting the parameters of active microwave systems in agricultural remote sensing.

Mehta, N. C.↗
NASA NTRS1984

Benchmark data on the separability among crops in the southern San Joaquin Valley of California

Landsat MSS data were input to a discriminant analysis of 21 crops on each of eight dates in 1979 using a total of 4,142 fields in southern Fresno County, California. The 21 crops, which together account for over 70 percent of the agricultural acreage in the southern San Joaquin Valley, were analyzed to quantify the spectral separability, defined as omission error, between all pairs of crops. On each date the fields were segregated into six groups based on the mean value of the MSS7/MSS5 ratio, which is correlated with green biomass. Discriminant analysis was run on each group on each date. The resulting contingency tables offer information that can be profitably used in conjunction with crop calendars to pick the best dates for a classification. The tables show expected percent correct classification and error rates for all the crops. The patterns in the contingency tables show that the percent correct classification for crops generally increases with the amount of greenness in the fields being classified. However, there are exceptions to this general rule, notably grain.

Morse, A.↗
NASA NTRS1986

Early warning and crop condition assessment research

The Early Warning Crop Condition Assessment Project of AgRISTARS was a multiagency and multidisciplinary effort. Its mission and objectives were centered around development and testing of remote-sensing techniques that enhance operational methodologies for global crop-condition assessments. The project developed crop stress indicators models that provide data filter and alert capabilities for monitoring global agricultural conditions. The project developed a technique for using NOAA-n satellite advanced very-high-resolution radiometer (AVHRR) data for operational crop-condition assessments. This technology was transferred to the Foreign Agricultural Service of the USDA. The project developed a U.S. Great Plains data base that contains various meteorological parameters and vegetative index numbers (VIN) derived from AVHRR satellite data. It developed cloud screening techniques and scan angle correction models for AVHRR data. It also developed technology for using remotely acquired thermal data for crop water stress indicator modeling. The project provided basic technology including spectral characteristics of soils, water, stressed and nonstressed crop and range vegetation, solar zenith angle, and atmospheric and canopy structure effects.

Boatwright, G. O.↗
NASA NTRS1990

AVHRR monitoring of U.S. crops during the 1988 drought

Effects of the 1988 drought on crops in the U.S. Corn Belt were assessed and monitored by the Foreign Crop Condition Assessment Division (FCCAD), U.S. Department of Agriculture. The primary data were vegetation index numbers (VINs), each of which was calculated as an average vegetation index of a geographically referenced cell of AVHRR pixels. Using VINs, the FCCAD was able to detect the existence of drought early in the season, monitor changing conditions, and provide objective assessments of the drought's extent and severity. Field observations confirmed the image analyses, and underlined the importance of the timing of extreme weather events with respect to crop stages for interpreting VINs. The analyses were conducted in an operational environment, providing a unique test of the AVHRR data for large area, near real-time crop monitoring. Because large area, operational remote sensing of crops is quite different from traditional, controlled, small plot research studies, more work is needed to link the two; this would improve crop assessment capabilities.

Teng, William L.↗
NASA NTRS1992

Crop growth and associated life support for a lunar farm

Supporting human life on a lunar base will require growing many different food crops. This paper investigates the growth dynamics of four crops (wheat, soybeans, potatoes, and lettuce) for general similarities and differences, along with associated material flows of the gases, liquids, and solids in a lunar farm. The human dietary requirements are compared with the protein, carbohydrate, and lipid contents of these hydroponically grown, high-productivity crops to derive a lunar farm diet. A simple and general analytical model is used to calculate the mass fluxes of CO2, H2O, HNO3, and O2 during the life cycle of each of the four crops. The resulting farm crop areas and corresponding biomass production rates are given. One significant conclusion of this study is that there is a 'lipid problem' associated with the incorporation of these four crops into a viable diet.

Volk, Tyler↗
NASA NTRS1992

The dynamics of hydroponic crops for simulation studies of the CELSS initial reference configurations

The goal of this research is to develop a progressive series of mathematical models for the CELSS hydroponic crops. These models will systematize the experimental findings from the crop researchers in the CELSS Program into a form useful to investigate system-level considerations, for example, dynamic studies of the CELSS Initial Reference Configurations. The crop models will organize data from different crops into a common modeling framework. This is the fifth semiannual report for this project. The following topics are discussed: (1) use of field crop models to explore phasic control of CELSS crops for optimizing yield; (2) seminar presented at Purdue CELSS NSCORT; and (3) paper submitted on analysis of bioprocessing of inedible plant materials.

Volk, Tyler↗
NASA NTRS1992

Regenerative Life Support Systems Test Bed performance - Lettuce crop characterization

Two crops of lettuce (Lactuca sativa cv. Waldmann's Green) were grown in the Regenerative Life Support Systems (RLSS) Test Bed at NASA's Johnson Space Center. The RLSS Test Bed is an atmospherically closed, controlled environment facility for the evaluation of regenerative life support systems using higher plants. The chamber encloses 10.6 m 2 of growth area under cool-white fluorescent lamps. Lettuce was double seeded in 480 pots, each containing about 250 cm 3 of calcined-clay substrate. Each pot was irrigated with half-strength Hoagland's nutrient solution at an average total applied amount of 2.5 and 1.8 liters pot -1 , respectively, over each of the two 30-day crop tests. Average environmental and cultural conditions during both tests were 23°C air temperature, 72% relative humidity, 1000 ppm carbon dioxide (CO 2 ), 16h light/8h dark photoperiod, and 356 μmol m -2 s -1 photosynthetic photon flux. After 30 days from seeding, average yield (g pot -1 ) for the first and second crop tests were 72 and 49 edible (shoot) fresh mass, 3.65 and 2.56 edible dry mass, and 0.44 and 0.45 inedible (root) dry mass, respectively. Averaging over both crop tests, the test bed met the requirements of 2.1 person-days of oxygen production, 2.4 person days of CO 2 removal and 129 person days of potential potable water production. The satisfactory growth of both crops demonstrates the potential of lettuce for air revitalization and water production in a controlled ecological life support system. Differences in crop performance resulting from engineering system effects provides evidence of the resilience of biological systems.

Life support systems↗
NASA NTRS1997

Selection and Characterization of Vegetable Crop Cultivars for use in Advanced Life Support Systems

Cultivar evaluation for controlled environments is a lengthy and multifaceted activity. The chapters of this thesis cover eight steps preparatory to yield trials, and the final step of cultivar selection after data are collected. The steps are as follows: 1. Examination of the literature on the crop and crop cultivars to assess the state of knowledge. 2. Selection of standard cultivars with which to explore crop response to major growth factors and determine set points for screening and, later, production. 3. Determination of practical growing techniques for the crop in controlled environments. 4. Design of experiments for determination of crop responses to the major growth factors, with particular emphasis on photoperiod, daily light integral and air temperature. 5. Developing a way of measuring yield appropriate to the crop type by sampling through the harvest period and calculating a productivity function. 6. Narrowing down the pool of cultivars and breeding lines according to a set of criteria and breeding history. 7. Determination of environmental set points for cultivar evaluation through calculating production cost as a function of set points and size of target facility. 8. Design of screening and yield trial experiments emphasizing efficient use of space. 9. Final evaluation of cultivars after data collection, in terms of production cost and value to the consumer. For each of the steps, relevant issues are addressed. In selecting standards to determine set points for screening, set points that optimize cost of production for the standards may not be applicable to all cultivars. Production of uniform and equivalent- sized seedlings is considered as a means of countering possible differences in seed vigor. Issues of spacing and re-spacing are also discussed.

Langhans, Robert W.↗
NASA NTRS2000

Matching Crew Diet and Crop Food Production in BIO-Plex

This paper matches the BIO-Plex crop food production to the crew diet requirements. The expected average calorie requirement for BIO-Plex is 2,975 Calories per crewmember per day, for a randomly selected crew with a typical level of physical activity. The range of 2,550 to 3,400 Calories will cover about two-thirds of all crews. The exact calorie requirement will depend on the gender composition, individual weights, exercise, and work effort of the selected crew. The expected average crewmember calorie requirement can be met by 430 grams of carbohydrate, 100 grams of fat, and 90 grams of protein per crewmember per day, for a total of 620 grams. Some fat can replaced by carbohydrate. Each crewmember requires only 2 grams of vitamins and minerals per day. Only unusually restricted diets may lack essential nutrients. The Advanced Life Support (ALS) consensus is that BIO-Plex should grow wheat, potato, and soybean, and maybe sweet potato or peanut, and maybe lettuce and tomato. The BIO-Plex Biomass Production System food production and the external food supply must be matched to the crew diet requirement for calories and nutritional balance. The crop production and external supply specifications can each be varied as long as their sum matches the required diet specification. We have wide flexibility in choosing the crops and resupply. We can easily grow one-half the crew calories in one BIO-Plex Biomass Production Chamber (BPC) if we grow only the most productive crops (wheat, potato, and sweet potato) and it we achieve nominal crop productivity. If we assume higher productivity we can grow a wider variety of crops. If we grow one-half of the crew calories, externally supplied foods can easily provide the other half of the calories and balance the diet. We can not grow 95 percent of the crew calories in two BPCs at nominal productivity while growing a balanced diet. We produce maximum calories by growing wheat, potato, and peanut.

Jones, Harry↗
NASA NTRS2002

Application of Thermal Infrared Remote Sensing for Quantitative Evaluation of Crop Characteristics

Evidence suggests that thermal infrared emittance (TIR) at the field-scale is largely a function of the integrated crop/soil moisture continuum. Because soil moisture dynamics largely determine crop yields in non-irrigated farming (85 % of Alabama farms are non-irrigated), TIR may be an effective method of mapping within field crop yield variability, and possibly, absolute yields. The ability to map yield variability at juvenile growth stages can lead to improved soil fertility and pest management, as well as facilitating the development of economic forecasting. Researchers at GHCC/MSFC/NASA and Auburn University are currently investigating the role of TIR in site-specific agriculture. Site-specific agriculture (SSA), or precision farming, is a method of crop production in which zones and soils within a field are delineated and managed according to their unique properties. The goal of SSA is to improve farm profits and reduce environmental impacts through targeted agrochemical applications. The foundation of SSA depends upon the spatial and temporal characterization of soil and crop properties through the creation of management zones. Management zones can be delineated using: 1) remote sensing (RS) data, 2) conventional soil testing and soil mapping, and 3) yield mapping. Portions of this research have concentrated on using remote sensing data to map yield variability in corn (Zea mays L.) and soybean (Glycine max L.) crops. Remote sensing data have been collected for several fields in the Tennessee Valley region at various crop growth stages during the last four growing seasons. Preliminary results of this study will be presented.

Shaw, J.↗
NASA NTRS1990

Space Data for Crop Management

CROPIX, Inc., formed in 1984 by Frank Lamb, president of the Eastern Oregon Farming Company, monitors primarily potato crops in a 20,000 square mile area of northern Oregon and central Washington. Potatoes are a high value specialty crop that can be more profitable to the farmer if he has advance knowledge of market conditions, knows when to harvest, and when to take it to market. By processing and collecting data collected by the NASA-developed Landsat Earth Resources survey satellites, Lamb is able to provide accurate information on crop acreage and conditions on a more timely basis than the routine estimates by the USDA. CROPIX uses Landsat data to make acreage estimates of crops, and to calculate a field-by-field vegetative index number. CROPIX then distributes to its customers a booklet containing color-coded maps, an inventory of crops, plus data and graphs on crop conditions and other valuable information.

Source record↗
NASA NTRS2002

Advanced Life Support Food Subsystem Salad Crop Requirements

As the National Aeronautics and Space Administration (NASA) begins to look towards longer duration space flights, the importance of fresh foods and varied menu choices increases. Long duration space missions require development of both a Transit Food System and a Lunar or Planetary Food System. These two systems are intrinsically different since the first one will be utilized in the transit vehicle in microgravity conditions while the second will be used in conditions of partial gravity (hypogravity). The Transit Food System will consist of prepackaged food of extended shelf life. Microgravity imposes significant limitations on the ability of the crew to handle food and allows only for minimal processing. Salad crops will be available for the planetary mission. Supplementing the transit food system with salad crops is also being considered. These crops will include carrots, tomatoes, lettuce, radish, spinach, chard, cabbage, and onion. The crops will be incorporated in the menu along with the prepackaged food. The fresh tasting salad crops will provide variety, texture, and color in the menu. This variety should provide increased psychological benefit. Preliminary studies on spinach, tomatoes, and bok choy have been completed. Sensory and analytical tests, including color and moisture were conducted on the chamber grown crops and compared to store bought spinach, tomatoes, and bok choy. Preliminary studies of the appropriate serving sizes and number of servings per week have also been conducted.

Perchonok, Michele H.↗
NASA NTRS2016

How Universal is the Relationship Between Remotely Sensed Vegetation Indices and Crop Leaf Area Index? A Global Assessment

Leaf Area Index (LAI) is a key variable that bridges remote sensing observations to the quantification of agroecosystem processes. In this study, we assessed the universality of the relationships between crop LAI and remotely sensed Vegetation Indices (VIs). We first compiled a global dataset of 1459 in situ quality-controlled crop LAI measurements and collected Landsat satellite images to derive five different VIs including Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), two versions of the Enhanced Vegetation Index (EVI and EVI2), and Green Chlorophyll Index (CI(sub Green)). Based on this dataset, we developed global LAI-VI relationships for each crop type and VI using symbolic regression and Theil-Sen (TS) robust estimator. Results suggest that the global LAI-VI relationships are statistically significant, crop-specific, and mostly non-linear. These relationships explain more than half of the total variance in ground LAI observations (R2 greater than 0.5), and provide LAI estimates with RMSE below 1.2 m2/m2. Among the five VIs, EVI/EVI2 are the most effective, and the crop-specific LAI-EVI and LAI-EVI2 relationships constructed by TS, are robust when tested by three independent validation datasets of varied spatial scales. While the heterogeneity of agricultural landscapes leads to a diverse set of local LAI-VI relationships, the relationships provided here represent global universality on an average basis, allowing the generation of large-scale spatial-explicit LAI maps. This study contributes to the operationalization of large-area crop modeling and, by extension, has relevance to both fundamental and applied agroecosystem research.

Vegetation Index↗
NASA NTRS2017

Temperature Increase Reduces Global Yields of Major Crops in Four Independent Estimates

Wheat, rice, maize, and soybean provide two-thirds of human caloric intake. Assessing the impact of global temperature increase on production of these crops is therefore critical to maintaining global food supply, but different studies have yielded different results. Here, we investigated the impacts of temperature on yields of the four crops by compiling extensive published results from four analytical methods: global grid-based and local point-based models, statistical regressions, and field-warming experiments. Results from the different methods consistently showed negative temperature impacts on crop yield at the global scale, generally underpinned by similar impacts at country and site scales. Without CO2 fertilization, effective adaptation, and genetic improvement, each degree-Celsius increase in global mean temperature would, on average, reduce global yields of wheat by 6.0%, rice by 3.2%, maize by 7.4%, and soybean by 3.1%. Results are highly heterogeneous across crops and geographical areas, with some positive impact estimates. Multi-method analyses improved the confidence in assessments of future climate impacts on global major crops and suggest crop- and region-specific adaptation strategies to ensure food security for an increasing world population.

Zhao, Chuang↗
NASA NTRS2018

Spatially-Explicit Monitoring of Crop Photosynthetic Capacity Through the Use of Space-Based Chlorophyll Fluorescence Data

Plant functional traits such as photosynthetic capacity are critical parameters for terrestrial biosphere models. However, their spatial and temporal characteristics are still poorly represented. In this study, we used satellite observations of sun-induced fluorescence (SIF) to estimate top-of-canopy photosynthetic capacity (maximum carboxylation rate, V(sub cmax) at a reference temperature of 25 deg C) for crops, which was in turn utilized to simulate regional gross primary production (GPP). We first estimate the key parameter, V(sub cmax), in the widely-used FvCB photosynthesis model using field measurements of CO2 and water fluxes during 2007-2012 at seven crop eddy covariance flux sites over the US Corn Belt. The results showed that satellite far-red SIF retrievals have a stronger link to V(sub cmax) at the seasonal scale (R(squared)=0.70 for C4 and R(squared)=0.63 for C3 crop) as compared with widely-used vegetation indices. We calibrate an empirical model linking V(sub cmax) with SIF that was used to estimate spatially and temporally varying crop V(sub cmax) for the US Corn Belt region. The resulting V(sub cmax) maps are used together with meteorological data from MERRA reanalysis data and vegetation structural parameters derived from the satellite- based spectral reflectance data to constrain the Soil-Canopy Observation of Photosynthesis and Energy (SCOPE) balance model in order to estimate regional crop GPP. Our results show a substantial improvement in the seasonal and spatial patterns of cropland GPP when compared with crop yield inventory data. The evaluation with tall tower atmospheric CO2 measurements further supports our estimation of spatiotemporal V(sub cmax) from space-borne SIF. Considering that SIF has a direct link to photosynthetic activity, our findings highlight the potential to infer regional V(sub cmax) using remotely sensed SIF data and to use this information for a better quantification of regional cropland carbon cycles.

Drought↗
NASA NTRS2021

Strong Regional Influence of Climatic Forcing Datasets on Global Crop Model Ensembles

We present results from the Agricultural Model Intercomparison and Improvement Project (AgMIP) Global Gridded Crop Model Intercomparison (GGCMI) Phase I, which aligned 14 global gridded crop models (GGCMs) and 11 climatic forcing datasets (CFDs) in order to understand how the selection of climate data affects simulated historical crop productivity of maize, wheat, rice and soybean. Results show that CFDs demonstrate mean biases and differences in the probability of extreme events, with larger uncertainty around extreme precipitation and in regions where observational data for climate and crop systems are scarce. Countries where simulations correlate highly with reported FAO national production anomalies tend to have high correlations across most CFDs, whose influence we isolate using multi-GGCM ensembles for each CFD. Correlations compare favorably with the climate signal detected in other studies, although production in many countries is not primarily climate-limited (particularly for rice). Bias-adjusted CFDs most often were among the highest model-observation correlations, although all CFDs produced the highest correlation in at least one top-producing country. Analysis of larger multi-CFD-multi-GGCM ensembles (up to 91 members) shows benefits over the use of smaller subset of models in some regions and farming systems, although bigger is not always better. Our analysis suggests that global assessments should prioritize ensembles based on multiple crop models over multiple CFDs as long as a top-performing CFD is utilized for the focus region.

Agricultural Model Intercomparison and Improvement↗
NASA NTRS2022

Global Gridded Crop Models Underestimate Yield Responses to Droughts and Heatwaves

Extreme events can lead to crop yield declines, resulting in financial losses and threats to food security, and the frequency and intensity of such events is projected to increase. As global gridded crop models (GGCMs) are commonly used to assess climate change impacts on agricultural yields, there is a need to understand whether these models are able to reproduce the observed yield declines. We evaluated 13 GGCMs from the Inter-Sectoral Impact Model Intercomparison Project and compared observed and simulated impact of past droughts and heatwaves on yields for four crops (maize, rice, soy, wheat). We found that most models detect but underestimate the impact of droughts and heatwaves on yield. Specifically, the drought signal was detected by 12 of 13 models for maize and all models for wheat, while the heat signal was detected by eleven models for maize and six models for wheat. To investigate whether the difference between simulated and observed yield declines is due to a misrepresentation of simulated exposure to heat or water scarcity (i.e. misrepresentation of growing season), we analysed the relationship between average discrepancies between observed and simulated yield losses, and average simulated exposure to extreme weather conditions across all crop models. We found a positive correlation between simulated exposure to heat and model performance for heatwaves, but found no correlation for droughts. This suggests that there is a systematic underestimation of yield responses to heat and drought and not only a misrepresentation of exposure. Assuming that performance for the past indicates models' capacity to project future yield impacts, models likely underestimate future yield decline from climate change. High-quality temporally and spatially resolved observational data on growing seasons will be highly valuable to further improve crop models' capacity to adequately respond to extreme weather events.

extreme events↗
NASA NTRS1996

Consideration in selecting crops for the human-rated life support system: a Linear Programming model

A Linear Programming model has been constructed which aids in selecting appropriate crops for CELSS (Controlled Environment Life Support System) food production. A team of Controlled Environment Agriculture (CEA) faculty, staff, graduate students and invited experts representing more than a dozen disciplines, provided a wide range of expertise in developing the model and the crop production program. The model incorporates nutritional content and controlled-environment based production yields of carefully chosen crops into a framework where a crop mix can be constructed to suit the astronauts' needs. The crew's nutritional requirements can be adequately satisfied with only a few crops (assuming vitamin mineral supplements are provided) but this will not be satisfactory from a culinary standpoint. This model is flexible enough that taste and variety driven food choices can be built into the model.

NASA Center JSC↗