Search NASA⌕ Search

SEARCH · Search NASA

Results for “corn farming”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1985

Sun-view angle effects on reflectance factors of corn canopies

The effects of sun and view angles on reflectance factors of corn (Zea mays L.) canopies ranging from the six leaf stage to harvest maturity were studied on the Purdue University Agronomy Farm by a multiband radiometer. The two methods of acquiring spectral data, the truck system and the tower systrem, are described. The analysis of the spectral data is presented in three parts: solar angle effects on reflectance factors viewed at nadir; solar angle effects on reflectance factors viewed at a fixed sun angle; and both sun and view angles effect on reflectance factors. The analysis revealed that for nadir-viewed reflectance factors there is a strong solar angle dependence in all spectral bands for canopies with low leaf area index. Reflectance factors observed from the sun angle at different view azimuth angles showed that the position of the sensor relative to the sun is important in determining angular reflectance characteristics. For both sun and view angles, reflectance factors are maximized when the sensor view direction is towards the sun.

Ranson, K. J.↗
DOE OSTI2021

Soil carbon sequestration potential and the identification of hotspots in the Eastern Corn Belt of the United States

Soil carbon sequestration is a significant CO 2 mitigation strategy but precise assessments of sequestration require spatially explicit modeling of potential changes in soil organic carbon (SOC) in response to soil, climate, land condition and management interactions. We assessed the SOC sequestration potential of the Eastern Corn Belt (ECB) in the USA (Indiana, Kentucky, Maryland, Michigan, Ohio, Pennsylvania, and West Virginia) in response to the adoption of conservation farming practices and land use change (pasture and forestation) using the SOCRATES model. Input data was provided through an intersection of the STATSGO, National Land Cover Database and a PRISM climate surface. At the end of the 20th century, the 15.3 Mha of cropped soils in the ECB contained 632 Tg C, an estimated reduction of 52% since the introduction of agriculture in the mid-1800s. Complete adoption of no-tillage practices on Prime Cropland would potentially recover 147 Tg SOC over 20 years, whereas a continuation of conventional tillage would produce a loss of 35 Tg SOC. Sequestration hotspots (> 500 Gg increase in SOC) under no-tillage cover 2.3 Mha providing 28 Tg C over 20 years. The conversion of Marginal (non-Prime) agricultural lands to Forests would yield an additional 13 Tg C in SOC and 381 Tg C in aboveground biomass. The rehabilitation of Minelands to Forest would yield an additional 4 Tg C in SOC and 42 Tg C in biomass. Here, opportunities to sequester C in the ECB via tillage and reforestation are substantial and should be incorporated into regional and national climate change mitigation strategies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

FieLDS

Field Landscape Decision Support (FieLDS) is a decision support tool that assists agricultural producers and farm managers in selecting parts of the field that can be converted to bioenergy plants, like perennial grasses, from the conventional use, like corn or soybean growth. That conversion of parts of the field is done based on the specific landscape properties and the agricultural producer’s priorities. The decision support tool generates a map of the field layout and spatially marks where to place each plant based on the spatial input from the user. Also, the user selects which priorities (sustainability indicators) are most relevant to them in their decision-making. By allowing the user to set the weights among the priorities, the tool empowers the user to generate field designs specific to their needs instead of imposing the developer’s set of priorities, and increases the transparency of the decision process.

Vazhnik, Veronika↗
NASA NTRS2001

Nutrient Stress Detection in Corn Using Neural Networks and AVIRIS Hyperspectral Imagery

AVIRIS image cube data has been processed for the detection of nutrient stress in corn by both known, ratio-type algorithms and by trained neural networks. The USDA Shelton, NE, ARS Variable Rate Nitrogen Application (VRAT) experimental farm was the site used in the study. Upon application of ANOVA and Dunnett multiple comparsion tests on the outcome of both the neural network processing and the ratio-type algorithm results, it was found that the neural network methodology provides a better overall capability to separate nutrient stressed crops from in-field controls.

Estep, Lee↗
DOE OSTI2022

AmeriFlux FLUXNET-1F US-Ro6 Rosemount I18_North

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-Ro6 Rosemount I18_North. This is the FLUXNET version of the carbon flux data for the site US-Ro6 Rosemount I18_North produced by applying the standard ONEFlux (1F) software. Site Description - This tower is located in a farm field farmed in accordance with a conservation type agricultural practice in the region: a corn/soybean/clover (living mulch/cover crop) rotation with chisel plow tillage in the fall following corn harvest and in the spring following soybeans.

Baker, John↗
DOE OSTI2023

Field-scale dynamics of planting dates in the US Corn Belt from 2000 to 2020

Crop planting dates are a dynamic feature of agricultural systems that respond to short- and long-term climate signals, crop and cultivar selection, and technology changes. Planting date records are essential for yield gap analyses, accurate crop modeling, and tracking farmer adaptations to weather and climate change. Although planting dates have high variation at local scales due to heterogeneity in farm resources and decision-making, available long-term data on planting dates is largely restricted to aggregated regional statistics or, at best, satellite-derived datasets with limited spatiotemporal extent and at resolutions unable to distinguish individual fields (> 250 m). Here, we generated retrospective annual field-scale (30 m) planting date maps for both maize and soybeans spanning 2000-2020 across a 12 state region in the United States Corn Belt based on Landsat satellite data and a large ground sample of over 28,000 maize and soybean fields. Using training data from 2015-2020 for model selection, we found that planting date predictions improved with harmonic regression of Landsat data and additional annual weather covariates. The preferred random forests model approximately doubled performance compared to a null model based on state median planting dates, capturing 47% of field-level variation for maize (mean absolute error, MAE = 7.4 days) and 44% for soybeans (MAE = 7.5 days) against held-out ground truth test data for 2008-2014. We also evaluated the full 2000-2020 dataset with state agricultural statistics, finding strong agreement with median planting dates for maize (R 2 = 0.76, MAE = 4.4 days) and slightly lower agreement for soybeans (R 2 = 0.65, MAE = 5.4 days) when aggregated to the state level. We then used this new dataset to analyze environmental determinants of planting dates at a finer-scale than previously possible, controlling for unobserved variation at the sub-state district level. We found that during 2000-2020, each standard deviation increase in rainfall delayed planting by ~ 2.5 days, and fields with higher soil productivity ratings tended to be planted earlier. We did not find meaningful trends over the last two decades in planting dates for maize or soybeans, in contrast to trends towards earlier planting dates late last century and predicted for this period in climate adaptation studies. We hypothesize increases in early season rainfall may have inhibited these shifts towards earlier planting. Remotely sensed planting dates will be a useful tool for yield gap analyses, crop simulation modeling, and ongoing assessment of climate adaptation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

AmeriFlux US-HWB USDA ARS Pasture Sytems and Watershed Management Research Unit- Hawbecker Site

This is the AmeriFlux version of the carbon flux data for the site US-HWB USDA ARS Pasture Sytems and Watershed Management Research Unit- Hawbecker Site. Site Description - Hawbecker farm is owned by Penn State University. The farming that took place was performed by their Farm Operations Division. The ground is rolling terrain, next to wooded areas, the Beef and Sheep Reasearch Farm, The University Airport, and other large fields maintained by Farm Operations. At the time of this collection period, the site housed another Meteorological Site, a Phenocam, and the GraceNet plots. Crop Rotation during period was 2 years Alfalfa, 1 year Corn for grain and 1 year Wheat.

Goslee, Sarah↗
DOE OSTI2022

AmeriFlux FLUXNET-1F US-HWB USDA ARS Pasture Sytems and Watershed Management Research Unit- Hawbecker Site

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-HWB USDA ARS Pasture Sytems and Watershed Management Research Unit- Hawbecker Site. This is the FLUXNET version of the carbon flux data for the site US-HWB USDA ARS Pasture Sytems and Watershed Management Research Unit- Hawbecker Site produced by applying the standard ONEFlux (1F) software. Site Description - Hawbecker farm is owned by Penn State University. The farming that took place was performed by their Farm Operations Division. The ground is rolling terrain, next to wooded areas, the Beef and Sheep Reasearch Farm, The University Airport, and other large fields maintained by Farm Operations. At the time of this collection period, the site housed another Meteorological Site, a Phenocam, and the GraceNet plots. Crop Rotation during period was 2 years Alfalfa, 1 year Corn for grain and 1 year Wheat.

Goslee, Sarah↗
NASA NTRS1974

The ERTS-1 investigation (ER-600). Volume 1: ERTS-1 agricultural analysis

The Agriculture Analysis Team of the Johnson Space Center conducted a 1-year-long investigation of ERTS-1 multispectral data to evaluate how well features of agricultural importance could be detected, identified, and located; and their areal extent measured. Six study areas were selected in cooperation with the U.S. Department of Agriculture. Two basic analytical approaches were used to meet the objectives. The conventional image interpretation technique revealed that a particular color was an indication of the density of vegetative cover, not an indication of crop classification. Computer-aided techniques were used to classify crop types (i.e., small grains, truck farm crops, grasses, summer fallow) to accuracies as high as 95 percent on large (12 hectares or more) well-defined fields. A further breakdown into crop species (wheat, barley, soybeans, oats, corn) reduced the accuracy to 70 to 80 percent for single-date observations.

Erb, R. B.↗
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↗
DOE OSTI2025

Sustainable aviation fuel from ethanol: Techno-economic analysis and life cycle analysis

Sustainable aviation fuel (SAF) is crucial for improving energy security, enhancing domestic production, and reducing carbon emissions in the aviation sector. Among various SAF production technologies, the ethanol-to-jet (ETJ) pathway is a promising option due to its economic viability and technological maturity. This study integrates a techno-economic analysis (TEA) and a life cycle analysis (LCA) to evaluate emissions reduction strategies for SAF production via the ETJ pathway, considering use of ethanol derived from both corn grain and corn stover. Conventional corn grain-derived ETJ fuel reduces greenhouse gas (GHG) emissions by 22 % compared to fossil jet fuel, with potential reductions of 26 %–96 % when incorporating renewable energy sources, with a 6 %–32 % increase in the minimum fuel selling price (MFSP). Corn stover-derived ETJ achieves a 77 % GHG reduction but with higher MFSPs compared to corn grain ETJ. Carbon capture and storage (CCS without considering the cost for piping and sequestration, only compression) reduces the emissions of corn grain-derived ETJ by up to 32 gCO 2 e/MJ and enables negative emissions for corn stover-derived ETJ, with MFSP increases ranging from 1 % to 22 %. While carbon capture and utilization (CCU) increase ethanol yield by 47 %, it raises MFSPs by 54 % due to high electricity demand. Sustainable farming practices provide only limited carbon intensity (CI) reductions individually but do offer cumulative benefits when combined. These findings highlight the trade-offs between cost and environmental impact, providing insights to optimize SAF production strategies and support aviation sector goals for emissions reduction.

09 BIOMASS FUELS↗
DOE OSTI2022

AmeriFlux US-UC1 LTAR UCB (Upper Chesapeake Bay) EC1

This is the AmeriFlux version of the carbon flux data for the site US-UC1 LTAR UCB (Upper Chesapeake Bay) EC1. Site Description - Upper Chesapeake Bay farm is privately owned. The farming that took place was performed by the Farm Owner. The ground is rolling terrain, next to wooded areas, private resdiences and other large fields maintained by private land owners. At the time of this collection period, the site housed another Eddy Covariance System and a two Phenocams. Crop has been continuous corn with plans to rotate to alfalfa grass mixture.

Goslee, Sarah↗
DOE OSTI2022

AmeriFlux US-UC2 LTAR UCB (Upper Chesapeake Bay) EC2

This is the AmeriFlux version of the carbon flux data for the site US-UC2 LTAR UCB (Upper Chesapeake Bay) EC2. Site Description - Upper Chesapeake Bay farm is privately owned. The farming that took place was performed by the Farm Owner. The ground is rolling terrain, next to wooded areas, private resdiences and other large fields maintained by private land owners. At the time of this collection period, the site housed another Eddy Covariance System and a two Phenocams. Crop has been continuous corn with plans to rotate to alfalfa grass mixture.

Goslee, Sarah↗
DOE OSTI2024

AmeriFlux FLUXNET-1F US-UC1 LTAR UCB (Upper Chesapeake Bay) EC1

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-UC1 LTAR UCB (Upper Chesapeake Bay) EC1. This is the FLUXNET version of the carbon flux data for the site US-UC1 LTAR UCB (Upper Chesapeake Bay) EC1 produced by applying the standard ONEFlux (1F) software. Site Description - Upper Chesapeake Bay farm is privately owned. The farming that took place was performed by the Farm Owner. The ground is rolling terrain, next to wooded areas, private resdiences and other large fields maintained by private land owners. At the time of this collection period, the site housed another Eddy Covariance System and a two Phenocams. Crop has been continuous corn with plans to rotate to alfalfa grass mixture.

Goslee, Sarah↗
DOE OSTI2024

AmeriFlux FLUXNET-1F US-UC2 LTAR UCB (Upper Chesapeake Bay) EC2

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-UC2 LTAR UCB (Upper Chesapeake Bay) EC2. This is the FLUXNET version of the carbon flux data for the site US-UC2 LTAR UCB (Upper Chesapeake Bay) EC2 produced by applying the standard ONEFlux (1F) software. Site Description - Upper Chesapeake Bay farm is privately owned. The farming that took place was performed by the Farm Owner. The ground is rolling terrain, next to wooded areas, private resdiences and other large fields maintained by private land owners. At the time of this collection period, the site housed another Eddy Covariance System and a two Phenocams. Crop has been continuous corn with plans to rotate to alfalfa grass mixture.

Goslee, Sarah↗
DOE OSTI2024

Perennializing marginal croplands: going back to the future to mitigate climate change with resilient biobased feedstocks

Managing annual row crops on marginally productive croplands can be environmentally unsustainable and result in variable economic returns. Incorporating perennial bioenergy feedstocks into marginally productive cropland can engender ecosystem services and enhance climate resiliency while also diversifying farm incomes. We use one of the oldest bioenergy-specific field experiments in North America to evaluate economically and environmentally sustainable management practices for growing perennial grasses on marginal cropland. This long-term field trial called 9804 was established in 1998 in eastern Nebraska and compared the productivity and sustainability of corn (Zea mays L.)—both corn grain and corn stover—and switchgrass (Panicum virgatum L.) bioenergy systems under different harvest strategies and nitrogen (N) fertilizer rates. This experiment demonstrated that switchgrass, compared to corn, is a reliable and sustainable bioenergy feedstock. This experiment has been a catalyst for other bioenergy projects which have also expanded our understanding of growing and managing bioenergy feedstocks on marginal cropland. We (1) synthesize research from this long-term experiment and (2) provide perspective concerning both the knowledge gained from this experiment and knowledge gaps and how to fill them as well as the role switchgrass will play in the future of bioenergy.

09 BIOMASS FUELS↗
DOE OSTI2023

AmeriFlux US-RGF Stanislaus County Forage Farm

This is the AmeriFlux version of the carbon flux data for the site US-RGF Stanislaus County Forage Farm. Site Description - Commercially farmed field in Stanislaus County, California. Part of a multi-year, ground-truthing field study under the DOE ARPA-E SMARTFARM project (https://arpa-e.energy.gov/news-and-media/blog-posts/smartfarm-changing-whats-possible-agriculture). Field is approx. 31.3 ha in size (1,300 ft by 2,600 ft), part of a 176+ acre site in several fields with commercial corn-wheat rotation for silage, in combination with manure application.

Schuppenhauer, Michael R.↗
DOE OSTI2024

AmeriFlux FLUXNET-1F US-RGF Stanislaus County Forage Farm

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-RGF Stanislaus County Forage Farm. This is the FLUXNET version of the carbon flux data for the site US-RGF Stanislaus County Forage Farm produced by applying the standard ONEFlux (1F) software. Site Description - Commercially farmed field in Stanislaus County, California. Part of a multi-year, ground-truthing field study under the DOE ARPA-E SMARTFARM project (https://arpa-e.energy.gov/news-and-media/blog-posts/smartfarm-changing-whats-possible-agriculture). Field is approx. 31.3 ha in size (1,300 ft by 2,600 ft), part of a 176+ acre site in several fields with commercial corn-wheat rotation for silage, in combination with manure application.

Schuppenhauer, Michael R.↗