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NASA NTRSDate not supplied

Impact of Space Radiation on Plants: from Arabidopsis thaliana to Crops

One of the major concerns for long-term exploration missions beyond the Earth’s magnetosphere is radiation risk, primarily from solar particle events (SPE) and galactic cosmic rays (GCR). With the goal of manned Mars exploration, the production of fresh food during long-duration space missions provides critical nutritional supplementation and may also benefit astronauts’ behavioral health. However, the effects of space radiation on plants and plant propagules have not been sufficiently investigated and characterized. In this study, we evaluated the effect of simulated GCR (using dry seeds) or SPE (using hydrated seeds) on seeds of Arabidopsis, Mizuna mustard, ‘Outredgeous’ red romaine lettuce, and ‘Red Robin’ dwarf tomato. Seeds were exposed to various doses of simulated space radiation scenarios, either acutely or at a low dose rate (LDR), using the NASA Space Radiation Laboratory (NSRL) facility at Brookhaven National Lab (BNL). Exposure to simulated GCR or SPE at the levels tested had no significant impact on the germination rate in Arabidopsis and crop seeds; however, GCR reduced the viability of lettuce and tomato seeds. Overall, the morphological changes of the seedlings cultured from irradiated seeds were dose- and ion quality- dependent, with heavier ions causing more severe damage. These changes ranged from cotyledon deformation, shortened root length, smaller seedling size, and other signs of stress, depending on the seed types. Both 40 and 80 cGy (LDR) exposures of GCR or SPE significantly affected tomato early seedling development, delayed tomato fruiting, and reduced the total yield of tomato. Altered nutritional values were also found in edible biomass, especially for the GCR 80 cGy (LDR) groups. For both GCR and SPE, 40 cGy showed some effects, but to a much lesser extent compared with 80 cGy, which can be considered as the “maximum permissible exposure” for the seed types we evaluated in this study. The impact of space radiation on seeds potentially affects the ability of plants to adapt to other environmental stresses (e.g. microgravity, water stress, and hardware constraints) as well as susceptibility to plant diseases, which need to be furtherly investigated. This research is funded by NASA’s Human Research Program.

J T Richards↗
NASA NTRSDate not supplied

Impact of Space Radiation on Plant Seeds: from Arabidopsis thaliana to Crops

One of the major concerns for long-term exploration missions beyond the Earth’s magnetosphere is radiation risk, primarily from solar particle events (SPE) and galactic cosmic rays (GCR). With the goal of manned Mars exploration, the production of fresh food during long-duration space missions provides critical nutritional supplementation and may also benefit astronauts’ behavioral health. However, the effects of space radiation on plants and plant propagules have not been sufficiently investigated and characterized. In this study, we evaluated the effect of simulated GCR (using dry seeds) or SPE (using hydrated seeds) on seeds of Arabidopsis, Mizuna mustard, ‘Outredgeous’ red romaine lettuce, and ‘Red Robin’ dwarf tomato. Seeds were exposed to various doses of simulated space radiation scenarios, either acutely or at a low dose rate (LDR), using the NASA Space Radiation Laboratory (NSRL) facility at Brookhaven National Lab (BNL). Exposure to simulated GCR or SPE at the levels tested had no significant impact on the germination rate in Arabidopsis and crop seeds; however, GCR reduced the viability of lettuce and tomato seeds. Overall, the morphological changes of the seedlings cultured from irradiated seeds were dose- and ion quality- dependent, with heavier ions causing more severe damage. These changes ranged from cotyledon deformation, shortened root length, smaller seedling size, and other signs of stress, depending on the seed types. Both 40 and 80 cGy (LDR) exposures of GCR or SPE significantly affected tomato early seedling development, delayed tomato fruiting, and reduced the total yield of tomato. Altered nutritional values were also found in edible biomass, especially for the GCR 80 cGy (LDR) groups. For both GCR and SPE, 40 cGy showed some effects, but to a much lesser extent compared with 80 cGy, which can be considered as the “maximum permissible exposure” for the seed types we evaluated in this study. The impact of space radiation on seeds potentially affects the ability of plants to adapt to other environmental stresses (e.g. microgravity, water stress, and hardware constraints) as well as susceptibility to plant diseases, which need to be furtherly investigated. This research is funded by NASA’s Human Research Program.

J T Richards↗
NASA NTRSDate not supplied

Impact of Space Radiation on Plant Seeds: from Arabidopsis thaliana to Crops

One of the major concerns for long-term exploration missions beyond the Earth’s magnetosphere is radiation risk, primarily from solar particle events (SPE) and galactic cosmic rays (GCR). With the goal of manned Mars exploration, the production of fresh food during long-duration space missions provides critical nutritional supplementation and may also benefit astronauts’ behavioral health. However, the effects of space radiation on plants and plant propagules have not been sufficiently investigated and characterized. This is the final report of our project to summarize the findings of the effect of space radiation exposure on plant seeds from morphometrics, nutritional values, to molecular machenisms. In this study, we evaluated the effect of simulated GCR (using dry seeds) or SPE (using hydrated seeds) on seeds of Arabidopsis, Mizuna mustard, ‘Outredgeous’ red romaine lettuce, and ‘Red Robin’ dwarf tomato. Seeds were exposed to various doses of simulated space radiation scenarios, either acutely or at a low dose rate (LDR), using the NASA Space Radiation Laboratory (NSRL) facility at Brookhaven National Lab (BNL). Exposure to simulated GCR or SPE at the levels tested had no significant impact on the germination rate in Arabidopsis and crop seeds; however, GCR reduced the viability of lettuce and tomato seeds. Overall, the morphological changes of the seedlings cultured from irradiated seeds were dose- and ion quality- dependent, with heavier ions causing more severe damage. These changes ranged from cotyledon deformation, shortened root length, smaller seedling size, and other signs of stress, depending on the seed types. Both 40 and 80 cGy (LDR) exposures of GCR or SPE significantly affected tomato early seedling development, delayed tomato fruiting, and reduced the total yield of tomato. Altered nutritional values were also found in edible biomass, especially for the GCR 80 cGy (LDR) groups. Underlying mechanisms were furtherly evaluated using transcriptomic analysis. For both GCR and SPE, 40 cGy showed some effects, but to a much lesser extent compared with 80 cGy, which can be considered as the “maximum permissible exposure” for the seed types we evaluated in this study. The impact of space radiation on seeds potentially affects the ability of plants to adapt to other environmental stresses (e.g. microgravity, water stress, and hardware constraints) as well as susceptibility to plant diseases, which need to be furtherly investigated. This research is funded by NASA’s Human Research Program.

J T Richards↗
NASA NTRSDate not supplied

Impact of Space Radiation on Plant Seeds: from Arabidopsis thaliana to Crops

One of the major concerns for long-term exploration missions beyond the Earth’s magnetosphere is radiation risk, primarily from solar particle events (SPE) and galactic cosmic rays (GCR). With the goal of manned Mars exploration, the production of fresh food during long-duration space missions provides critical nutritional supplementation and may also benefit astronauts’ behavioral health. However, the effects of space radiation on plants and plant propagules have not been sufficiently investigated and characterized. This is the final report of our project to summarize the findings of the effect of space radiation exposure on plant seeds from morphometrics, nutritiaonal values, to molecular machenisms. In this study, we evaluated the effect of simulated GCR (using dry seeds) or SPE (using hydrated seeds) on seeds of Arabidopsis, Mizuna mustard, ‘Outredgeous’ red romaine lettuce, and ‘Red Robin’ dwarf tomato. Seeds were exposed to various doses of simulated space radiation scenarios, either acutely or at a low dose rate (LDR), using the NASA Space Radiation Laboratory (NSRL) facility at Brookhaven National Lab (BNL). Exposure to simulated GCR or SPE at the levels tested had no significant impact on the germination rate in Arabidopsis and crop seeds; however, GCR reduced the viability of lettuce and tomato seeds. Overall, the morphological changes of the seedlings cultured from irradiated seeds were dose- and ion quality- dependent, with heavier ions causing more severe damage. These changes ranged from cotyledon deformation, shortened root length, smaller seedling size, and other signs of stress, depending on the seed types. Both 40 and 80 cGy (LDR) exposures of GCR or SPE significantly affected tomato early seedling development, delayed tomato fruiting, and reduced the total yield of tomato. Altered nutritional values were also found in edible biomass, especially for the GCR 80 cGy (LDR) groups. Underlying mechanisms were furtherly evaluated using transcriptomic analysis. For both GCR and SPE, 40 cGy showed some effects, but to a much lesser extent compared with 80 cGy, which can be considered as the “maximum permissible exposure” for the seed types we evaluated in this study. The impact of space radiation on seeds potentially affects the ability of plants to adapt to other environmental stresses (e.g. microgravity, water stress, and hardware constraints) as well as susceptibility to plant diseases, which need to be furtherly investigated. This research is funded by NASA’s Human Research Program.

J.T. Richards↗
NASA NTRS2021

Assessing within-Field Corn and Soybean Yield Variability from WorldView-3, Planet, Sentinel-2, and Landsat 8 Satellite Imagery

Crop yield monitoring is an important component in agricultural assessment. Multispectral remote sensing instruments onboard space-borne platforms such as Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) have shown to be useful for efficiently generating timely and synoptic information on the yield status of crops across regional levels. However, the coarse spatial resolution data inherent to these sensors provides little utility at the management level. Recent satellite imagery collection advances toward finer spatial resolution (down to 1 m) alongside increased observational cadence (near daily) implies information on crops obtainable at field and within-field scales to support farming needs is now possible. To test this premise, we focus on assessing the efficiency of multiple satellite sensors, namely WorldView-3, Planet/Dove-Classic, Sentinel-2, and Landsat 8 (through Harmonized Landsat Sentinel-2 (HLS)), and investigate their spatial, spectral (surface reflectance (SR) and vegetation indices (VIs)), and temporal characteristics to estimate corn and soybean yields at sub-field scales within study sites in the US state of Iowa. Precision yield data as referenced to combine harvesters’ GPS systems were used for validation. We show that imagery spatial resolution of 3 m is critical to explaining 100% of the within-field yield variability for corn and soybean. Our simulation results show that moving to coarser resolution data of 10 m, 20 m, and 30 m reduced the explained variability to 86%, 72%, and 59%, respectively. We show that the most important spectral bands explaining yield variability were green (0.560 µm), red-edge (0.726 µm), and near-infrared (NIR - 0.865 µm). Furthermore, the high temporal frequency of Planet and a combination of Sentinel-2/Landsat 8 (HLS) data allowed for optimal date selection for yield map generation. Overall, we observed mixed performance of satellite-derived models with the coefficient of determination (R^2) varying from 0.21 to 0.88 (averaging 0.56) for the 30 m HLS and from 0.09 to 0.77 (averaging 0.30) for 3 m Planet. R^2 was lower for fields with higher yields, suggesting saturation of the satellite-collected reflectance features in those cases. Therefore, other biophysical variables, such as soil moisture and evapotranspiration, at similar fine spatial resolutions are likely needed alongside the optical imagery to fully explain the yields.

agriculture; yield; within-field; corn; soybean; r↗
NASA NTRS2018

Comparisons of Two Spatial Implementations of a Crop Model Using Remotely Sensed Observations over Southeastern United States

Global food security is one of the most pressing issues of the current century, particularly for developing nations. Agricultural simulation models can be a key component in testing new technologies, seeds and cultivars etc. However, inaccurate input information, model related errors and the mode of implementation can also add to model uncertainties. In this study, the crop model is implemented in two separate fashions: a)gridded (GriDSSAT model) and b) using random spatial ensembles (RHEAS model). This is done in the Southeastern US to evaluate and understand the modelperformance over a region data availabilities. Once the model performance is assessed, multiple satellite based earth observation parameters such as soil moisture, vegetation index etc. can be assimilated into crop models to reduce input and model related uncertainties particularly in data limited regions. In this study, the National Agricultural Statistical Services (NASS) reported yield data at county levels are used for comparison andvalidation purposes. The GriDSSAT model estimation of corn yields in comparison with the reported NASS yields showed an overall RMSD of nearly 3720 (kg/ha) whereas RMSD for the RHEAS model implementation was 3550 (kg/ha). Overall the GriDSSAT model had negative bias of nearly 2400 kg/ha (except for 2013) while RHEAS had a slight positive bias of 400 kg/ha (approx.).

SERVIR↗
NASA NTRSDate not supplied

Effects of Supplemental Far-Red Light on Leafy Green Crops for Space

The use of plants to provide food and eventual bioregenerative life support has been studied for nearly 50 years. A logical starting point for early missions like the International Space Station (ISS) is to grow leafy greens to supplement the crew’s diet of packaged foods. In an attempt to expand the list of potential crops, NASA conducted ground studies with eight leafy greens: ‘Dragoon’ lettuce, ‘Extra Dwarf’ pak choi, shungiku, ‘Barese’ Swiss chard, ‘Red Russian’ kale, ‘Toscano’ kale, ‘Amara’ mustard, and ‘Outredgeous’ lettuce, which has been used in prior ground and flight tests with the Veggie Plant Chamber. Plants were grown for 28 days under 320μmol m(exp -2)s(exp -1) PPFD from LED lights, 3000 ppm CO2, and 23 C to simulate an environment similar to the Veggie Plant Chamber aboard ISS. Half of the plants were given ~7 μmol m(exp -2)s(exp -1) and the other half, ~23μmol m(exp -2)s(exp -1) of supplemental far-red (735 nm). Supplemental far-red light resulted in increased fresh mass yields for some species but not all. This could be due to the relative small amount of far-red photons even in the supplemental treatment. ‘Extra Dwarf’ pak choi and ‘Dragoon’ lettuce produced the highest yields (70-80 g FM/plant) under both lighting regimes. A more consistent response to supplemental far-red light was increased plant canopy cover and increased shoot heights, which may be a consideration for volume constrained systems in space.

LaShelle E Spencer↗
NASA NTRS2024

Evaluating Differences Among Crop Models in Simulating Soybean in-Season Growth

Crop models are useful tools for simulating agricultural systems that require continued model development and testing to increase their robustness and improve how they describe our current understanding of processes. Coordinated and “blind” evaluation of multiple models using same protocols and experimental datasets provides unique opportunities to further improve models and enhance their reliability. For soybean [Glycine max (L.) Merr.], there has been limited coordinated multi-model evaluations for the simulation of in-season plant growth dynamics. We evaluated ten dynamic soybean crop models for their simulation of in-season plant growth using data from five experiments conducted in Argentina, Brazil, France, and USA. We evaluated models after a Blind (using only phenology data) and a Full calibration (with in-season and end-of-season variables). Calibration reduced model uncertainty by reducing standard bias for the simulation of in-season variables (biomass, leaf, pod, and stem weights, and leaf area index, LAI). However, we found that most models had difficulty in reproducing leaf growth dynamics, with normalized root mean squared error (nRMSE) of 56% for leaf weight and 43% for LAI (across locations and models after Full calibration). Models with different levels of complexity and experience were capable of simulating final seed yield at maturity with reasonable accuracy (nRMSE of 8–31% after Full calibration). However, the nRMSE for pod weight (of 17–64% after Full calibration) was two-fold larger than that of seed yield. Moreover, the models differed in how they simulated timing from sowing to beginning seed growth (47–93 days) and effective seed filling period (18–54 days), owing to model structural differences in defining the reproductive developmental stages. Overall, we identified the following processes that can benefit from further model improvement: leaf expansion and senescence, reproductive phenology, and partitioning to reproductive growth. Simulation of pod wall tissue and individual seed cohorts is another aspect that many models currently lack. Model improvement can benefit from high-temporal resolution experimental datasets that concurrently account for phenology, plant growth, and partitioning. Further, we recommend collecting reproductive phenology in the field consistent with actual dry matter allocation to organs in the models and collecting multiple observations of seed and pod weight to aid model improvement for simulation of seed growth and yield formation.

Agricultural Model Intercomparison and Improvement↗
NASA NTRS2021

Climate Change Impacts and Adaptation for Dryland Farming Systems in Zimbabwe: A Stakeholder-Driven Integrated Multi-Model Assessment

Decision makers need accurate information to address climate variability and change and accelerate transformation to sustainability. A stakeholder-driven, science-based multi-model approach has been developed and used by the Agricultural Model Intercomparison and Improvement Project (AgMIP) to generate actionable information for adaptation planning processes. For a range of mid-century climate projections—likely to be hotter, drier, and more variable—contrasting future socio-economic scenarios (Representative Agricultural Pathways, RAPs) were co-developed with stakeholders to portray a sustainable development scenario and a rapid economic growth pathway. The unique characteristic of this application is the integration of a multi-modeling approach with stakeholder engagement to co-develop scenarios and adaptation strategies. Distribution of outcomes were simulated with climate, crop, livestock, and economic impact assessment models for smallholder crop livestock farmers in a typical dryland agro-ecological zone in Zimbabwe, characterized by low and erratic rainfall and nutrient depleted soils. Results showed that in Nkayi District, Western Zimbabwe, climate change would threaten most of the farms, and, in particular, those with large cattle herds due to feed shortages. Adaptation strategies that showed the most promise included diversification using legume production, soil fertility improvement, and investment in conducive market environments. The switch to more legumes in the farming systems reduced the vulnerability of the very poor as well as the more resourced farmers. Overall, the sustainable development scenario consistently addressed institutional failures and motivated productivity-enhancing, environmentally sound technologies and inclusive development approaches. This yielded more favorable outcomes than investment in quick economic wins from commercializing agriculture.

Climate change adaptation↗
NASA NTRS2012

Landsat Science: 40 Years of Innovation and Opportunity

Landsat satellites have provided unparalleled Earth-observing data for nearly 40 years, allowing scientists to describe, monitor and model the global environment during a period of time that has seen dramatic changes in population growth, land use, and climate. The success of the Landsat program can be attributed to well-designed instrument specifications, astute engineering, comprehensive global acquisition and calibration strategies, and innovative scientists who have developed analytical techniques and applications to address a wide range of needs at local to global scales (e.g., crop production, water resource management, human health and environmental quality, urbanization, deforestation and biodiversity). Early Landsat contributions included inventories of natural resources and land cover classification maps, which were initially prepared by a visual interpretation of Landsat imagery. Over time, advances in computer technology facilitated the development of sophisticated image processing algorithms and complex ecosystem modeling, enabling scientists to create accurate, reproducible, and more realistic simulations of biogeochemical processes (e.g., plant production and ecosystem dynamics). Today, the Landsat data archive is freely available for download through the USGS, creating new opportunities for scientists to generate global image datasets, develop new change detection algorithms, and provide products in support of operational programs such as Reducing Emissions from Deforestation and Forest Degradation in Developing Countries (REDD). In particular, the use of dense (approximately annual) time series to characterize both rapid and progressive landscape change has yielded new insights into how the land environment is responding to anthropogenic and natural pressures. The launch of the Landsat Data Continuity Mission (LDCM) satellite in 2012 will continue to propel innovative Landsat science.

Cook, Bruce D.↗
NASA NTRS2001

Fractal dimension analysis of weight-bearing bones of rats during skeletal unloading

Fractal analysis was used to quantify changes in trabecular bone induced through the use of a rat tail-suspension model to simulate microgravity-induced osteopenia. Fractal dimensions were estimated from digitized radiographs obtained from tail-suspended and ambulatory rats. Fifty 4-month-old male Sprague-Dawley rats were divided into groups of 24 ambulatory (control) and 26 suspended (test) animals. Rats of both groups were killed after periods of 1, 4, and 8 weeks. Femurs and tibiae were removed and radiographed with standard intraoral films and digitized using a flatbed scanner. Square regions of interest were cropped at proximal, middle, and distal areas of each bone. Fractal dimensions were estimated from slopes of regression lines fitted to circularly averaged plots of log power vs. log spatial frequency. The results showed that the computed fractal dimensions were significantly greater for images of trabecular bones from tail-suspended groups than for ambulatory groups (p < 0.01) at 1 week. Periods between 1 and 4 weeks likewise yielded significantly different estimates (p < 0.05), consistent with an increase in bone loss. In the tibiae, the proximal regions of the suspended group produced significantly greater fractal dimensions than other regions (p < 0.05), which suggests they were more susceptible to unloading. The data are consistent with other studies demonstrating osteopenia in microgravity environments and the regional response to skeletal unloading. Thus, fractal analysis could be a useful technique to evaluate the structural changes of bone.

Non-NASA Center↗
NASA NTRS2019

Dwarf Tomato and Pepper Cultivars for Space Crops

Several dwarf tomato and pepper varieties were evaluated under ISS-simulated growth conditions (22ºC, 50% RH, 1500 ppm CO2, and 300 μmol m(exp -2) s(exp -1) of light for 16 h per day) with the goal of selecting those with the best growth, nutrition, and organoleptic potential for use in a pick and eat salad crop system on ISS and future exploration flights. Testing included six cultivars of tomato (Red Robin, Scarlet Sweet ’N’ Neat, Tiny Tim, Mohamed, Patio Princess, and Tumbler) and six cultivars of pepper (Red Skin, Fruit Basket, Cajun Belle, Chablis, Sweet Pickle, and Pompeii). Plants were grown to an age sufficient to produce fruit (70 to 106 days for tomato and 109 days for pepper). Tomato fruits were harvested when they showed full red color, beginning ca. 70-days age and then at weekly intervals thereafter, while peppers were grown until numerous fruits showed color and all fruits (green and colored) were harvested once at the end of the test. Plant sizes, yields, and nutritional attributes were measured and used to down-select to three cultivars for each species. In particular, we were interested in cultivars that were short (dwarf) but still produced high yields. Nutritional data included elemental (Ca, Mg, Fe, and K) composition, vitamin K, phenolics, lycopene, anthocyanin, lutein, and zeaxanthin. The three down-selected cultivars for each species were evaluated for sensory attributes, including overall acceptability, appearance, color intensity aroma, flavor and texture. The combined data were compared and given weighting factors to rank the cultivars as potential candidates for testing in space. For tomato, the ranking was 1) cv. Mohamed, 2) cv. Red Robin, and 3) cv. Sweet N’ Neat. For pepper, the ranking was 1) cv. Pompeii, 2) cv. Red Skin, and 3) cv. Fruit Basket. These rankings are somewhat subjective but provide a good starting point for conducting higher fidelity testing with these crops (e.g., testing with LED lighting similar to the Veggie plant unit), and ultimately conducting flight experiments.

Spencer, LaShelle E.↗
NASA NTRS1991

Historical evidence for a connection between volcanic eruptions and climate change

The times of historical volcanic aerosol clouds were compared with changes in atmospheric temperatures on regional, hemispheric, and global scales. These involve either a direct comparison of individual significant eruption years with temperature records, or a comparison of eruption years with composited temperature records for several years before and after chosen sets of eruptions. Some studies have challenged the connection between individual eruptions and climate change. Mass and Portman (1989) recently suggested that the volcanic signal was present, but smaller than previously thought. In a study designed to test the idea that eruptions could cause small changes in climate, Hansen and other (1978) chose one of the best monitored eruptions at the time, the 1963 eruption of Agung volcano on the island of Bali. Using a simple radiation-balance model, in which an aerosol cloud in the tropics was simulated, this basic pattern of temperature change in the tropics and subtropics was reproduced. There may be natural limits to the atmospheric effects of any volcanic eruption. Self-limiting physical and chemical effects in eruption clouds were proposed. Model results suggest that aerosol microphysical processes of condensation and coagulation produce larger aerosols as the SO2 injection rate is increased. The key to discovering the greatest effects of volcanoes on short-term climate may be to concentrate on regional temperatures where the effects of volcanic aerosol clouds can be amplified by perturbed atmospheric circulation patterns, especially changes in mid-latitudes where meridional circulation patterns may develop. Such climatic perturbations can be detected in proxy evidence such as decreases in tree-ring widths and frost damage rings in climatically sensitive parts of the world, changes in treelines, weather anomalies such as unusually cold summers, severity of sea-ice in polar and subpolar regions, and poor grain yields and crop failures.

Rampino, Michael R.↗
NASA NTRS1993

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

During the past several years, the NASA Program in Controlled Ecological Life Support Systems (CELSS) has continued apace with crop research and logistic, technological, and scientific strides. These include the CELSS Test Facility planned for the space station and its prototype Engineering Development Unit, soon to be active at Ames Research Center (as well as the advanced crop growth research chamber at Ames); the large environmental growth chambers and the planned human test bed facility at Johnson Space Center; the NSCORT at Purdue with new candidate crops and diverse research into the CELSS components; the gas exchange data for soy, potatoes, and wheat from Kennedy Space Center (KSC); and the high-precision gas exchange data for wheat from Utah State University (USU). All these developments, taken together, speak to the need for crop modeling as a means to connect the findings of the crop physiologists with the engineers designing the system. A need also exists for crop modeling to analyze and predict the gas exchange data from the various locations to maximize the scientific yield from the experiments. One fruitful approach employs what has been called the 'energy cascade'. Useful as a basis for CELSS crop growth experimental design, the energy cascade as a generic modeling approach for CELSS crops is a featured accomplishment in this report. The energy cascade is a major tool for linking CELSS crop experiments to the system design. The energy cascade presented here can help collaborations between modelers and crop experimenters to develop the most fruitful experiments for pushing the limits of crop productivity. Furthermore, crop models using the energy cascade provide a natural means to compare, feature for feature, the crop growth components between different CELSS experiments, for example, at Utah State University and Kennedy Space Center.

Volk, Tyler↗
NASA NTRS2016

Multi-Wheat-Model Ensemble Responses to Interannual Climate Variability

We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981e2010 grain yield, and we evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R2 0.24) was found between the models' sensitivities to interannual temperature variability and their response to long-termwarming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts.

uncertainty↗
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 NTRS2009

The Crop Evaluation Research for Environmental Strategies (CERES) Remote Sensing 2008 Project Activities

In recent years, the use of Plant Incorporated Protectant (PIP) corn by American producers has been increasing dramatically. PIP corn contains genetically inserted traits that produce toxins in the plant that provide narrowly targeted protection against specific insect pests. The plant producing t oxms can offer significant reductions in the application of broad -spectrum pesticides that have ecological and human health consequences. PIP corn as a percentage of total corn acreage planted in the US is expected to continue to increase as these protective traits are "stacked" with other desirable traits by seed companies, and producers are seeing considerable increases in corn yield as a result. The introduction of corn as a bio-fuel source for ethanol has increased production by over 6 million hectares in 2007. The United States Environmental Protection Agency (USEPA), which is responsible for the registration of PIP crops under the Federal Insecticide, Fungicide and Rodenticide Act, views the use of PIP corn as positive. Broad spectrum pesticide use has declined since the PIP traits have been introduced. As the agricultural landscape sees a higher percentage of corn acres using the PIP technology, the risk of the targeted insect pest populations developing resistance to the toxins, thereby rendering the in will increase as well. This result would negate the effectiveness of the PIP corn traits and could reduce production of a US field corn crop valued at $33 billion dollars in 2006 and place US food and now energy security at risk. Concerns over insect pest resistance development to PIP traits have led the USEPA to team with NASA and the Institute for Technology Development (ITD) to develop geo-spatial technologies designed to proactively monitor the corn production landscape for insect pest infestation and possible resistance development. USEPA resistance management simulation models are combined with NASA remote sensi ng products to monitor the corn landscape for resistance development. The two agencies have entered into an agreement which could potentially lead to the development of next generation NASA sensors that will more specifically address the requirements of the USEPA's resistance development strategy and offer opportunities to study the ever changing ecosystem complexities. The USEPA/NASA/ITD team has developed a broad research project entitled CERES (Crop Evaluation Research for Environmental Strategies). CERES is a research effort leading to decision support system tools that are designed to integrate multi-resolution NASA remote sensing data products and USEPA geo -spatial models to monitor the potential for insect pest resistance development from the regional to the landscape and then to the field level.

Casas, Joseph C.↗
NASA NTRS2016

Pick and Eat Crop Testing: Dwarf Tomato and Pepper as Candidate Space Crops

Several dwarf tomato and pepper varieties were evaluated under International Space Station (ISS)-simulated growth conditions (22 degrees Centigrade, 50 percent relative humidity, 1500 parts per million CO2, and 300 micromoles per square meter per second of light for 16 hours per day) with the goal of selecting those with the best growth, nutrition, and organoleptic potential for use in a pick and eat salad crop system on ISS and future exploration flights. Testing included six cultivars of tomato (Red Robin, Scarlet Sweet 'N' Neat, Tiny Tim, Mohamed, Patio Princess, and Tumbler) and six cultivars of pepper (Red Skin, Fruit Basket, Cajun Belle, Chablis, Sweet Pickle, and Pompeii). Plants were grown to an age sufficient to produce fruit (up to 106 days for tomato and 109 days for pepper) using Turface (arcillite) potting media with 18-6-8 control-release fertilizer and supplemental nutrient solution beginning around 60-days-age. Tomato fruits were harvested when they showed full red color, beginning around 70-days age and then at weekly intervals thereafter, while peppers were grown until fruits showed color and were harvested twice (first test) and just once at the end of the second test, with the final harvests including colored and green fruit. Plant sizes, yields, and nutritional attributes were measured and used to down-select to three cultivars for each species. In particular, we were interested in cultivars that were short (dwarf) but still produced high yields. Nutritional data included elemental (Ca, Mg, Fe, and K) content, vitamin K, phenolics, lycopene (for tomato), anthocyanin, lutein, and zeaxanthin. The three down-selected cultivars for each species were grown again and the harvested fruit sent to NASA's Johnson Space Center for sensory evaluation, which included overall acceptability, appearance, color intensity, aroma, flavor and texture. The combined data were compared and given weighting factors to rank the cultivars as candidates for testing in space. Weightings gave maximum importance to plant size (smaller being good) and fruit yield (greater yields being good). For tomato, the ranking was 1) cultivar Mohamed and cultivar Red Robin (tied), and 3) cultivar Sweet N' Neat. For pepper, the ranking was 1) cultivar Pompeii, 2) cultivar Red Skin, and 3) cultivar Fruit Basket. These rankings are somewhat subjective but provide a starting point for conducting higher fidelity testing with these crops (e.g., testing with light emitting diode lighting similar to the Veggie plant unit on ISS), and ultimately conducting a flight experiment.

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