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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Long-term variability of root production in bioenergy crops from ingrowth core measurements

Long-term determination of root biomass production upon land-use conversion to biofuel crops is rare. To assess land-use legacy influences on belowground biomass accumulation, we converted 22-year-old Conservation Reserve Program (CRP) grasslands and 50+-year-old agricultural (AGR) lands to corn (C), switchgrass (Sw) and restored prairie (Pr) biofuel crops. We maintained one CRP grassland as a reference (Ref). We hypothesized that land-use history and crop type have significant effects on root density, with perennial crops on CRP grasslands having a higher root biomass productivity, while corn grown on former agricultural lands produce the lowest root biomass. Methods The ingrowth core method was used to determine in situ ingrowth root biomass, alongside measurements of aboveground net primary productivity (ANPP). Ancillary measurements, including air temperature, growing season length and precipitation were used to examine their influences on root biomass production. Important Findings Root biomass productivity was the highest in unconverted CRP grassland (1716 g m-2 yr-1) and lowest in corn fields (526 g m-2 yr-1). All perennial sites converted from CRP and AGR lands had lower root biomass and ANPP in the first year of planting but peaked in 2011 for switchgrass and a year later for restored prairies. Ecosystem stability was higher in restored prairies (AGR-Pr: 4.3 ± 0.11; CRP-Pr: 4.1 ± 0.10), with all monocultures exhibiting a lower stability. Root biomass production was positively related to ANPP (R2 = 0.40). Overall, attention should be given to root biomass accumulation in large-scale biofuel production as it is a major source of carbon sequestration.

Environmental Sciences & Ecology↗
DOE OSTI2023

A functional trait framework for integrating nitrogen‐fixing cover crops into short‐rotation woody crop systems

Abstract Developing approaches to simultaneously maximize short‐rotation woody crop (SRWC) productivity while minimizing footprints associated with intensive management is imperative to profitable and sustainable bioenergy production systems. Intercropping nitrogen (N)‐fixing cover crops in SRWC systems is an overlooked approach to sustainably intensify SRWC production by increasing N availability using less environmentally costly inputs. Here, we discuss how functional traits (e.g., seasonal activity, lifespan, leaf habit, soil exploration) of cover crops and SRWCs may interact through space and time influencing access to light, water, and nutrients to provide a framework for successful integration of cover crops into SRWCs. Next, we summarize the literature on intercropping forest plantations with N‐fixing cover crops to identity research gaps and outline future research needs and opportunities. And then, using empirical N demand and productivity data from SRWCs and cover crop N inputs from the literature, we illustrate how SRWC leaf habit (conifer evergreens and deciduous hardwoods) would influence successful integration of cover crops and potential N fixation. We estimate that integrating cover crops into SRWCs could supply 27% and 72% of the N demand across a 10‐year rotation for an evergreen and a deciduous hardwood, respectively. These figures suggest these integrated SRWC systems may approach a virtual minimal external N input when other biogeochemical cycles are considered. The guiding principles presented here are grounded in ecological theory and provide a framework for sustainable intensification of forest production.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Data for Clumping Index Estimation With 30°-tilted Cameras in Row Crops: Evaluation of Methods and Segment Size Effects

The clumping index (CI) quantifies the spatial distribution of foliage elements and is essential for accurately estimating the plant area index (PAI), canopy radiative transfer, and photosynthesis. Traditionally, the finite-length averaging method (LX), the gap size distribution method (CC), and a combined approach of CC and LX (CLX) have been applied to instruments like TRAC and digital hemispherical photography to estimate CI. However, a comprehensive evaluation of these methods in row crops remains limited, especially regarding the influence of segment size on CI. Meanwhile, digital cameras offer a cost-effective and user-friendly solution for canopy measurements in row crops, yet their application in this context remains underexplored. In this study, we employed a new approach using a 30°-tilted digital camera to estimate CI in corn and soybean fields, applying the LX, CC, and CLX methods. We systematically assessed the performance of these three methods by combining field measurements in real-world fields with simulations using the LESS 3D radiative transfer model. Our results showed that CLX applied to the whole image and 45° segment offered accurate estimation of CI (bias within ±0.1, RMSE < 0.2) and PAI (bias within ±0.4, RMSE < 1) in real-world fields and LESS simulations. The accuracy of the LX method was highly sensitive to segment size, with the best performance observed at the 15° segment (PAI bias within ±0.4). In contrast, the CC method remained stable across different segment sizes, and its performance was generally comparable to that of LX, except at the 15° segment. Across view zenith angles, CI derived from CC generally showed a continuous increase, while those from LX and CLX followed a rising trend at small zenith angles but began to decline at 68°, likely due to an increasing proportion of no-gap segments. Seasonally, LX tended to show decreasing CI during early growth stages but increased as the canopy matured, whereas CC and CLX showed gradually increasing CI before plateauing at peak PAI. The 30°-tilted camera effectively captured CI variations across different angles and growth stages, making it a practical and robust instrument for row crop canopy structure analysis. Applying these CI methods to digital cameras offers a low-cost and accessible CI estimation alternative, improving canopy structure monitoring accuracy in row crops.

Modeling↗
NASA NTRS2019

Parameterization-Induced Uncertainties and Impacts of Crop Management Harmonization in a Global Gridded Crop Model Ensemble

Global gridded crop models (GGCMs) combine agronomic or plant growth models with gridded spatial input data to estimate spatially explicit crop yields and agricultural externalities at the global scale. Differences in GGCM outputs arise from the use of different biophysical models, setups, and input data. GGCM ensembles are frequently employed to bracket uncertainties in impact studies without investigating the causes of divergence in outputs. This study explores differences in maize yield estimates from five GGCMs based on the public domain field-scale model Environmental Policy Integrated Climate (EPIC) that participate in the AgMIP Global Gridded Crop Model Intercomparison initiative. Albeit using the same crop model, the GGCMs differ in model version, input data, management assumptions, parameterization, and selection of subroutines affecting crop yield estimates via cultivar distributions, soil attributes, and hydrology among others. The analyses reveal inter-annual yield variability and absolute yield levels in the EPIC-based GGCMs to be highly sensitive to soil parameterization and crop management. All GGCMs show an intermediate performance in reproducing reported yields with a higher skill if a static soil profile is assumed or sufficient plant nutrients are supplied. An in-depth comparison of setup domains for two EPIC-based GGCMs shows that GGCM performance and plant stress responses depend substantially on soil parameters and soil process parameterization, i.e. hydrology and nutrient turnover, indicating that these often neglected domains deserve more scrutiny. For agricultural impact assessments, employing a GGCM ensemble with its widely varying assumptions in setups appears the best solution for coping with uncertainties from lack of comprehensive global data on crop management, cultivar distributions and coefficients for agro-environmental processes. However, the underlying assumptions require systematic specifications to cover representative agricultural systems and environmental conditions. Furthermore, the interlinkage of parameter sensitivity from various domains such as soil parameters, nutrient turnover coefficients, and cultivar specifications highlights that global sensitivity analyses and calibration need to be performed in an integrated manner to avoid bias resulting from disregarded core model domains. Finally, relating evaluations of the EPIC-based GGCMs to a wider ensemble based on individual core models shows that structural differences outweigh in general differences in configurations of GGCMs based on the same model, and that the ensemble mean gains higher skill from the inclusion of structurally different GGCMs. Although the members of the wider ensemble herein do not consider crop-soil-management interactions, their sensitivity to nutrient supply indicates that findings for the EPIC-based sub-ensemble will likely become relevant for other GGCMs with the progressing inclusion of such processes.

Folberth, Christian↗
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↗
DOE OSTI2015

Reducing the negative human-health impacts of bioenergy crop emissions through region-specific crop selection

An expected global increase in bioenergy-crop cultivation as an alternative to fossil fuels will have consequences on both global climate and local air quality through changes in biogenic emissions of volatile organic compounds (VOCs). While greenhouse gas emissions may be reduced through the substitution of next-generation bioenergy crops such as eucalyptus, giant reed, and switchgrass for fossil fuels, the choice of species has important ramifications for human health, potentially reducing the benefits of conversion due to increases in ozone (O₃) and fine particulate matter (PM₂̣₅) levels as a result of large changes in biogenic emissions. Using the Community Earth System Model we simulate the conversion of marginal and underutilized croplands worldwide to bioenergy crops under varying future anthropogenic emissions scenarios. A conservative global replacement using high VOC-emitting crop profiles leads to modeled population-weighted O₃ increases of 5–27 ppb in India, 1–9 ppb in China, and 1–6 ppb in the United States, with peak PM₂̣₅ increases of up to 2 μgm⁻³. We present a metric for the regional evaluation of candidate bioenergy crops, as well as results for the application of this metric to four representative emissions profiles using four replacement scales (10–100% maximum estimated available land). Finally, we assess the total health and climate impacts of biogenic emissions, finding that the negative consequences of using high-emitting crops could exceed 50% of the positive benefits of reduced fossil fuel emissions in value.

09 BIOMASS FUELS↗
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.↗