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All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Midwest Food Security & Agriculture II: Leveraging NASA Earth Observations to Analyze and Display Crop Phenology Data and Weather Conditions to Support Expansion of Small Grain Crops in the Midwest

Agriculture in the Midwest is dominated by monoculture systems that strip the soil of nutrients, decrease yields, and worsen water quality. Crop diversification and cultivating small grains is economically and ecologically advantageous, but limited in practice due to a lack of data about small-grain crop performance as well as monoculture-favoring insurance coverage, economic incentives, and sociocultural traditions. Practical Farmers of Iowa (PFI) strives to create more sustainable agriculture and supports Midwestern farmers in adding small grains into their crop rotations. The DEVELOP team partnered with the USDA Agricultural Research Service and the USA National Phenology Network to assist PFI in applying NASA Earth observation data to track small-grain crop performance. This project developed a Google Earth Engine user interface that enables PFI farmers to compare satellite-assessed vegetation production and climatic conditions of their farm with other PFI farmers. The team assessed the crop performance through Normalized Difference Vegetation Index (NDVI) values derived from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI). Additionally, the team created a regression model to analyze the predictive properties of vegetation indices and crop yield, ultimately establishing a relationship with mean NDVI from the early growing season. PFI farmers can utilize these products to make informed decisions about their crop production as well as advocate for more expansive insurance policies to protect small-grain crops.

Sophie Barrowman↗
DOE OSTI2017

Soil depth and crop determinants of bacterial communities under ten biofuel cropping systems

Biofuel-cropping systems, projected for large land areas, can potentially change their soil microbiome and the ecosystem services they catalyze. Here, we determined the bacterial community composition and relevant soil properties for samples collected after 6 crop years at 0–10 cm, 10–25 cm, 25–50 cm, and 50–100 cm under corn, switchgrass, Miscanthus, and restored prairie, as well as 0–10 cm under six additional candidate biofuel crops in replicate side-by side plots. Deep sequencing of the 16S rRNA-V4 region established that soil bacterial communities were significantly differentiated by depth as determined by proportional OTU abundance and composition, UniFrac distance, and taxonomic and indicator analyses. The cropping system significantly impacted bacterial community composition within the top three layers, with corn and switchgrass communities the most different within the 0–25 cm and 25–50 cm depths, respectively. The effects of crop type and depth co-mingled, likely attributed to differences in rooting depth and biomass among crops. Individual phyla demonstrated varying patterns with depth, with significant proportional decreases of Proteobacteria, Actinobacteria, Planctomycetes, and Bacteroidetes but proportional increases of Firmicutes from shallow to deep soils. The Acidobacteria, Verrucomicrobia, and Chloroflexi peaked in abundance in the middle layers, whereas Thaumarchaeota decreased in abundance. Importantly, some classes within the Acidobacteria, Verrucomicrobia, and Firmicutes followed contrasting patterns with depth suggesting that they have different ecological specializations. Poplar, followed by soils with perennial crops contained the most C in the surface soils, with data indicating that these differences will become more pronounced with time.

09 BIOMASS FUELS↗
DOE OSTI2018

Future crop yields and water productivity changes for Nebraska rainfed and irrigated crops

In this study, we assessed future rainfed and irrigated crop yield and water productivity changes in Nebraska across multiple climate and emission scenarios using an empirical modeling approach. We found rainfed crops showed slightly increased crop water productivity while irrigated crops showed no change or decreased water productivity. Contrary to U.S.-wide studies reporting declines in crop yields, we projected Nebraska crop yields to increase overall with greatest increases in current rainfed fields due to combined effects from maximum and minimum temperatures. However, the increased rainfed yields are not sufficient to fully close the gap between rainfed and irrigated yields.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Alley cropping affects perennial bioenergy crop root distribution, carbon, and nutrient stocks

This study quantified root biomass distribution; root accrual of C, N, P, and K; and changes in soil organic carbon (SOC) associated with alley-cropped switchgrass (Panicum virgatum L.), prairie cordgrass (Spartina pectinata Bosc ex Link), intermediate wheatgrass [Thinopyrum intermedium (Host) Barkworth and Dewey ‘Rush’], and a native polyculture planted between rows of ‘NM6’ poplar (Populus maximowiczii × P. nigra) and ‘Fish Creek’ willow (Salix purpurea) at two Minnesota sites (Empire and Granada). After 4 yr since establishment, SOC declined at each site but was not influenced by species selection. NM6 poplar–prairie cordgrass systems had among the highest root biomass, C, and nutrient accrual, with up to 16.3 Mg root biomass, 7.0 Mg C, 175 kg N, 31 kg P, and 97 kg K ha -1 . Fine roots were the largest fraction of belowground biomass, although course roots were also a large fraction for poplar and prairie cordgrass. Tree roots extended to 6 m into the crop alley, although 85–89% were within 1 m of tree rows, depending on tree species. Crop fine root biomass was reduced up to 67% at 1 m from tree rows and 20% at 3.5 m and was up to 142% greater in willow than in poplar alleys. Total root C was predominated by poplars at Empire regardless of herbaceous crop type, whereas the proportions of tree and crop root C varied by crop at Granada. These results suggest that prairie cordgrass is well suited to alley cropping and that, due to the competitive ability of poplars, productivity, C sequestration, and nutrient accrual may be greater in willow systems in the long term.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2019

Crop Readiness Level (CRL): A Scale to Track Progression of Crop Testing for Space

The development of engineering technologies and hardware for aerospace applications is often tracked on a 1-9 scale of readiness or TRL, with a "1" representing very basic or fundamental principles, and a "9" being flight tested, functional hardware. Preparing to grow crops for supplemental food and eventual life support contributions on space missions faces similar challenges. Nearly 20 years ago, the concept of a "crop readiness level" was suggested at a bioregenerative life support conference held at Kennedy Space Center, but there was little follow up to this. We propose to revive this concept to track the preparation and testing of different crop species for eventual use in the unique environment of space. For the sake of uniformity, we recommend a 1-9 scale, with a "1" being just the identification of a potential crop, followed by some basic horticultural testing, cultivars trials, then testing growth and yield under various controlled environments, progression to more space-like environments and hardware, understanding the nutritional, organoleptic, and food safety aspects of the crop, initial testing in space, and a final stage of growing the crop for food in space ("9"). We attempted to make the scaling logical and progressive, but our main goal is to initiate a dialogue in the space, plant research community to develop a scale for assessing crop readiness.

Romeyn, M. W.↗
NASA NTRS2015

The AgMIP GRIDded Crop Modeling Initiative (AgGRID) and the Global Gridded Crop Model Intercomparison (GGCMI)

Climate change is a significant risk for agricultural production. Even under optimistic scenarios for climate mitigation action, present-day agricultural areas are likely to face significant increases in temperatures in the coming decades, in addition to changes in precipitation, cloud cover, and the frequency and duration of extreme heat, drought, and flood events (IPCC, 2013). These factors will affect the agricultural system at the global scale by impacting cultivation regimes, prices, trade, and food security (Nelson et al., 2014a). Global-scale evaluation of crop productivity is a major challenge for climate impact and adaptation assessment. Rigorous global assessments that are able to inform planning and policy will benefit from consistent use of models, input data, and assumptions across regions and time that use mutually agreed protocols designed by the modeling community. To ensure this consistency, large-scale assessments are typically performed on uniform spatial grids, with spatial resolution of typically 10 to 50 km, over specified time-periods. Many distinct crop models and model types have been applied on the global scale to assess productivity and climate impacts, often with very different results (Rosenzweig et al., 2014). These models are based to a large extent on field-scale crop process or ecosystems models and they typically require resolved data on weather, environmental, and farm management conditions that are lacking in many regions (Bondeau et al., 2007; Drewniak et al., 2013; Elliott et al., 2014b; Gueneau et al., 2012; Jones et al., 2003; Liu et al., 2007; M¨uller and Robertson, 2014; Van den Hoof et al., 2011;Waha et al., 2012; Xiong et al., 2014). Due to data limitations, the requirements of consistency, and the computational and practical limitations of running models on a large scale, a variety of simplifying assumptions must generally be made regarding prevailing management strategies on the grid scale in both the baseline and future periods. Implementation differences in these and other modeling choices contribute to significant variation among global-scale crop model assessments in addition to differences in crop model implementations that also cause large differences in site-specific crop modeling (Asseng et al., 2013; Bassu et al., 2014).

climate↗
DOE OSTI2017

Crop yield response to climate change varies with crop spatial distribution pattern

The linkage between crop yield and climate variability has been confirmed in numerous studies using statistical approaches. A crucial assumption in these studies is that crop spatial distribution pattern is constant over time. Here, we explore how changes in county-level corn spatial distribution pattern modulate the response of its yields to climate change at the state level over the Contiguous United States. Our results show that corn yield response to climate change varies with crop spatial distribution pattern, with distinct impacts on the magnitude and even the direction at the state level. Corn yield is predicted to decrease by 20~40% by 2050s when considering crop spatial distribution pattern changes, which is 6~12% less than the estimates with fixed cropping pattern. The beneficial effects are mainly achieved by reducing the negative impacts of daily maximum temperature and strengthening the positive impacts of precipitation. Our results indicate that previous empirical studies could be biased in assessing climate change impacts by ignoring the changes in crop spatial distribution pattern. As a result, this has great implications for understanding the increasing debates on whether climate change will be a net gain or loss for regional agriculture.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1978

A comprehensive data processing plan for crop calendar MSS signature development from satellite imagery: Crop identification using vegetation phenology

The author has identified the following significant results. The phenological method of crop identification involves the creation of crop signatures which characterize multispectral observations as phenological growth states. The phenological signature models spectral reflectance explicitly as a function of crop maturity rather than as a function of date. A correspondence of time to growth state is established which minimizes the smallest difference between the given multispectral multitemporal vector and a category mean vector. The application of the method to the identification of winter wheat and corn shows (1) the method is capable of discriminating crop type with about the same degree of accuracy as more traditional classifiers; (2) the use of LANDSAT observations on two or more dates yields better results than the use of a single observation; and (3) some potential is demonstrated for labeling the degree of maturity of the crop, as well as the crop type.

Hlavka, C. A.↗
DOE OSTI2017

Assessing wild bees in perennial bioenergy landscapes: effects of bioenergy crop composition, landscape configuration, and bioenergy crop area

Wild bee populations are currently under threat, which has led to recent efforts to increase pollinator habitat in North America. Simultaneously, U.S. federal energy policies are beginning to encourage perennial bioenergy cropping (PBC) systems, which have the potential to support native bees. Our objective was to explore the potentially interactive effects of crop composition, total PBC area, and PBC patches in different landscape configurations. Using a spatially-explicit modeling approach, the Lonsdorf model, we simulated the impacts of three perennial bioenergy crops (PBC: willow, switchgrass, and prairie), three scenarios with different total PBC area (11.7%, 23.5% and 28.8% of agricultural land converted to PBC) and two types of landscape configurations (PBC in clustered landscape patterns that represent realistic future configurations or in dispersed neutral landscape models) on a nest abundance index in an Illinois landscape. Our modeling results suggest that crop composition and PBC area are particularly important for bee nest abundance, whereas landscape configuration is associated with bee nest abundance at the local scale but less so at the regional scale. Moreover, strategies to enhance wild bee habitat should therefore emphasize the crop composition and amount of PBC.

09 BIOMASS FUELS↗
DOE OSTI2021

An assessment of the harvesting and fuel performance of a single-pass cut-and-chip harvester in commercial-scale short-rotation poplar crops as influenced by crop and weather conditions

There is a need for data on commercial-scale harvesting operations in hybrid poplar short rotation crops to better understand costs and logistics, especially for modeling large scale biorefineries. An investigation was conducted on the in-field performance of a single-pass cut-and-chip harvester operating in commercial fields for over 370 individual wagon loads representing a range of crop and field conditions. Mean material capacity peaked at 70 Mg h -1 in dry conditions in lower biomass (<60 Mg ha -1 ), but was only 28 Mg h -1 during wet conditions and often higher standing biomass. Wet ground conditions require the harvester to divert additional power to maintaining forward movement, which results in decreased material capacity and increased fuel consumption. Crops with higher standing biomass had taller trees that do not always feed as smoothly into the harvester, slowing forward progress and lowering material capacity. Mean crop specific fuel consumption, L of fuel per Mg of biomass processed, generally decreased as standing biomass increased. When standing biomass was above 40 Mg ha -1 mean crop specific fuel consumption (FCC) was 1.69 L Mg -1 in dry conditions and 3.98 L Mg -1 in wet conditions, but when standing biomass was below 40 Mg ha-1 FCC increased drastically (as high as 5 L Mg -1 ) because the harvester is putting more effort into forward speed instead of material processing. Developing relationships between material capacity and fuel consumption based on standing biomass and ground conditions at representative scales are essential for conducting environmental and economic analyses of these systems.

09 BIOMASS FUELS↗
DOE OSTI2018

Perennialization and Cover Cropping Mitigate Soil Carbon Loss from Residue Harvesting

While the US Midwest is expected to serve as a primary feedstock source for cellulosic biofuel production, the impacts of residue harvesting on soil organic carbon (SOC) may greatly limit sustainable production capacity. However, viable feedstock production could be realized through adoption of management practices and cropping systems that offset residue‐harvest‐induced SOC losses. Sequestration of SOC can be enhanced by increasing the duration of crop soil cover through cover or double cropping or cultivation of dedicated perennials. However, assessing the efficacy of such options across sites and over long periods is experimentally challenging. Hence, we use the Environmental Productivity Integrated Climate (EPIC) model to provide such an assessment. Model‐data integration was used to calibrate and evaluate model suitability, which exhibited reasonable effectiveness through R 2 of 0.97 and 0.63 for SOC stock and yield, respectively. Long‐term simulations indicate considerable capacity for offsetting SOC loss. Incorporating rye ( Secale cereal L.) into continuous corn ( Zea mays L.) and corn–soybean [ Glycine max (L.) Merr.] systems offset the SOC losses induced by harvesting 21.2 and 38.3% of available stover, respectively. Similarly, converting 20.4% of corn–soybean land to miscanthus ( Miscanthus × giganteus J.M. Greef & Deuter ex Hodkinson & Renvoize) or 27.5% of land to switchgrass ( Panicum virgatum L.) offset the SOC impacts of harvesting 60% of stover from the remaining corn–soybean lands. These responses indicate that adoption of such measures would sizably affect the life cycle consequences of residue‐derived biofuels and expand estimates of sustainable cellulosic feedstock production capacity from the US Midwest. Core Ideas EPIC suitably assessed SOC under annual, cover, double, and perennial crops. Perennial cultivation offset the SOC losses of considerable residue harvesting. Rye double crops offset sizable residue‐induced SOC losses. Adoption could greatly expand Midwestern sustainable biofuel production capacity.

Jones, Curtis D.↗
DOE OSTI2018

Automatic Responses of Crop Stocks and Policies Buffer Climate Change Effects on Crop Markets and Price Volatility

Climate change has the potential to affect crop prices and price volatility. However, the economic models used in prior assessments largely do not include known, automatic, stabilizing factors. Crop storage can stabilize prices and U.S. crop policy tends to provide support that moves opposite prices. We quantify effects of circa 2050 climate forcing on the inter-annual variability of U.S. Corn Belt corn and soybean yields using statistical crop models and climate scenarios from regional and global climate models. Climate change generally reduces mean yields and increases the inter-annual variability of yields in the Midwestern U.S. Using these yield impacts and an economic model with automatic market stabilizers, we find only modest increases in price volatility. Although individual producers and states are negatively affected by the yield reductions, the aggregate effect for all corn and soybean producer returns can be positive because of price increases. Moreover, agricultural policies based on price levels or revenue variation offset some of the impacts of market variation on farm income. Our results differ from other recent results and temper concerns that increasing climate instability necessarily translates to greater uncertainty about agricultural commodity uses, including as food and biofuels, in the near future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Evaluation of the Uncertainty in Satellite-Based Crop State Variable Retrievals Due to Site and Growth Stage Specific Factors and Their Potential in Coupling with Crop Growth Models

Coupling crop growth models and remote sensing provides the potential to improve our understanding of the genotype x environment x management (G × E × M) variability of crop growth on a global scale. Unfortunately, the uncertainty in the relationship between the satellite measurements and the crop state variables across different sites and growth stages makes it difficult to perform the coupling. In this study, we evaluate the effects of this uncertainty with MODIS data at the Mead, Nebraska Ameriflux sites (US-Ne1, US-Ne2, and US-Ne3) and accurate, collocated Hybrid-Maize (HM) simulations of leaf area index (LAI) and canopy light use efficiency (LUE Canopy ). The simulations are used to both explore the sensitivity of the satellite-estimated genotype × management (G × M) parameters to the satellite retrieval regression coefficients and to quantify the amount of uncertainty attributable to site and growth stage specific factors. Additional ground-truth datasets of LAI and LUE Canopy are used to validate the analysis. The results show that uncertainty in the LAI/satellite measurement regression coefficients lead to large uncertainty in the G × M parameters retrievable from satellites. In addition to traditional leave-one-site-out regression analysis, the regression coefficient uncertainty is assessed by evaluating the retrieval performance of the temporal change in LAI and LUE Canopy . The weekly change in LAI is shown to be retrievable with a correlation coefficient absolute value (|r|) of 0.70 and root-mean square error (RMSE) value of 0.4, which is significantly better than the performance expected if the uncertainty was caused by random error rather than secondary effects caused by site and growth stage specific factors (an expected |r| value of 0.36 and RMSE value of 1.46 assuming random error). As a result, this study highlights the importance of accounting for site and growth stage specific factors in remote sensing retrievals for future work developing methods coupling remote sensing with crop growth models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Crop‐associated virus infection in a native perennial grass: reduction in plant fitness and dynamic patterns of virus detection

Summary To understand the eco‐evolutionary significance of plant viruses in nature, we must (i) quantify the effects of infection on plant fitness and (ii) recognize that native plants are increasingly exposed to crop‐associated viruses. Studies of perennials are particularly needed: most of our knowledge of plant‐virus interactions is from annuals, yet long‐lived species dominate landscapes. Here we used aster models for life‐history analysis and longitudinal measures of plant virus status to evaluate multi‐year consequences of crop virus infection in a native perennial. We used Barley yellow dwarf virus acquired from wheat to inoculate seedlings of P anicum virgatum L. (switchgrass), a North American prairie grass. We grew inoculated and mock‐inoculated individuals of two ecotypes for 3 years in the field. We measured plant size, infection status and fitness components. Aster modelling provided integrated multi‐year measures of fitness. Crop virus inoculation reduced multi‐year native plant fitness by 30% over 2 years despite generally asymptomatic infection and evidence of resistance. This reduction was greater than predicted from individual fitness components or most size measures. Ecotypes differed in response, with the lowland ecotype experiencing higher apparent recovery from infection. Virus treatment in the upland ecotype delayed flowering phenology and reduced seed filling. Synthesis . Our use of field experimentation, surveys of plant infection status and aster modelling demonstrates a rigorous and broadly applicable approach for quantifying the effects of viruses and other microbes on multi‐year plant fitness. We found that a crop virus had negative multi‐year effects on native plant fitness even after infection was no longer detected. Viruses may have substantial effects on native vegetation with domestication of landscapes and agricultural expansion.

Alexander, Helen M.↗
NASA NTRS1981

Evaluation of spring wheat and barley crop calender models for the 1979 crop year

During the Large Area Crop Inventory Experiment, spring wheat planting date and crop development stage estimates based on historical normals were improved by the use of the Feyerherm planting date and Robertson spring wheat crop calendar models. The Supporting Research Crop Calendar Project element modified the Robertson model to reduce bias at cardinal growth stages within the growing season. These models were tested in 1980 along with a state-of-the-art barley model (Williams) against a ground-truth data set from 49 calendar year 1979 segments in the U.S. Great Plains spring wheat and barley region.

Nazare, C. V.↗
NASA NTRS1984

Automatic corn-soybean classification using Landsat MSS data. I - Near-harvest crop proportion estimation. II - Early season crop proportion estimation

The techniques used initially for the identification of cultivated crops from Landsat imagery depended greatly on the iterpretation of film products by a human analyst. This approach was not very effective and objective. Since 1978, new methods for crop identification are being developed. Badhwar et al. (1982) showed that multitemporal-multispectral data could be reduced to a simple feature space of alpha and beta and that these features would separate corn and soybean very well. However, there are disadvantages related to the use of alpha and beta parameters. The present investigation is concerned with a suitable method for extracting the required features. Attention is given to a profile model for crop discrimination, corn-soybean separation using profile parameters, and an automatic labeling (target recognition) method. The developed technique is extended to obtain a procedure which makes it possible to estimate the crop proportion of corn and soybean from Landsat data early in the growing season.

Badhwar, G. D.↗
NASA NTRS1996

Recycling crop residues for use in recirculating hydroponic crop production

As part of bioregenerative life support feasibility testing by NASA, crop residues are being used to resupply elemental nutrients to recirculating hydroponic crop production systems. Methods for recovering nutrients from crop residues have evolved from water soaking (leaching) to rapid aerobic bioreactor processing. Leaching residues recovered the majority of elements but it also recovered significant amounts of soluble organics. The high organic content of leachates was detrimental to plant growth. Aerobic bioreactor processing reduced the organic content ten-fold, which reduced or eliminated phytotoxic effects. Wheat and potato production studies were successful using effluents from reactors having with 8- to 1-day retention times. Aerobic bioreactor effluents supplied at least half of the crops elemental mass needs in these studies. Descriptions of leachate and effluent mineral content, biomass productivity, microbial activity, and nutrient budgets for potato and wheat are presented.

NASA Center KSC↗