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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
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↗
DOE OSTI2025

The impact of climate change on Korea’s agricultural sector under the national self-sufficiency policy

Evolving environmental conditions due to climate change have brought about changes in agriculture, which is required for human life as both a source of food and income. International trade can act as a buffer against potential negative impacts of climate change on crop yields, but recent years have seen breakdowns in global trade, including export bans to improve domestic food security. For countries that rely heavily on imported food, governments may institute policies to protect their agricultural industry from changes in climate-induced crop yield changes and other countries’ potential trade restrictions. This study assesses the individual and combined effects of climate impacts and food self-sufficiency policies in Korea, which is highly dependent on imports. We use the Global Change Analysis Model (GCAM), a global integrated assessment model, to explore (1) the direct impact of climate change on Korea’s agricultural yields, (2) the full impacts of global climate change on agricultural production, including trade-induced changes due to yield changes in other regions, (3) the impacts of food self-sufficiency policy, and (4) the interactive impact of climate change and self-sufficiency policies. We find that, in Korea, the direct impact of climate change on agricultural yields would be overshadowed by the impact of global climate change due to changing trade patterns. Second, global climate change leads to a rise (rice and wheat) or a decline (soybeans) in Korean producer revenues, while simultaneously raising consumer expenditures on both staples and non-staples. Third, implementing self-sufficiency policies for wheat and soybeans in Korea boosts the nation’s producer revenues, in conjunction with the effects of climate change, at the cost of additional increases in consumer expenditures for both staples and non-staples.

Science & Technology - Other Topics↗
DOE OSTI2024

AmeriFlux AR-Bal Balcarce BA

This is the AmeriFlux version of the carbon flux data for the site AR-Bal Balcarce BA. Site Description - The study was carried out in a 19-ha rainfed soybean plot at the Unidad Integrada Balcarce, located southeast of Buenos Aires, Argentina. The site is located in an environment of gentle hills at 130 m.a.s.l. Its soil was classified as a typical Argiudoll, with a loamy clayey texture up to 0.30 m and between 0.80 to 1.10 m depth, and a clayey texture between 0.30 and 0.80 m depth. The slope was 1:50 and oriented NE to SW. A caliche layer was found at 1 to 1.2 m depth thus the water table was considered beneath that level. The soybean variety used was DM 3810, Maturity Group III with an indeterminate growing habit. It was sown on November 21, 2012, and emerged on December 1. The crop archive had a maximum height of 0.75 m.

Gassmann, Maria Isabel↗
DOE OSTI2025

AmeriFlux FLUXNET-1F AR-CCa Carlos Casares agriculture

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site AR-CCa Carlos Casares agriculture. This is the FLUXNET version of the carbon flux data for the site AR-CCa Carlos Casares agriculture produced by applying the standard ONEFlux (1F) software. Site Description - This tower is located in a private agricultural production field. The owner follow the typical rotation of the region: corn / soybean / wheat-soybean

Posse, Gabriela [INTA]↗
DOE OSTI2025

AmeriFlux FLUXNET-1F AR-Bal Balcarce BA

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site AR-Bal Balcarce BA. This is the FLUXNET version of the carbon flux data for the site AR-Bal Balcarce BA produced by applying the standard ONEFlux (1F) software. Site Description - The study was carried out in a 19-ha rainfed soybean plot at the Unidad Integrada Balcarce, located southeast of Buenos Aires, Argentina. The site is located in an environment of gentle hills at 130 m.a.s.l. Its soil was classified as a typical Argiudoll, with a loamy clayey texture up to 0.30 m and between 0.80 to 1.10 m depth, and a clayey texture between 0.30 and 0.80 m depth. The slope was 1:50 and oriented NE to SW. A caliche layer was found at 1 to 1.2 m depth thus the water table was considered beneath that level. The soybean variety used was DM 3810, Maturity Group III with an indeterminate growing habit. It was sown on November 21, 2012, and emerged on December 1. The crop archive had a maximum height of 0.75 m.

Gassmann, Maria Isabel [Universidad de Buenos Aire↗
DOE OSTI2025

AmeriFlux US-IAC Iowa State University SE tower

This is the AmeriFlux version of the carbon flux data for the site US-IAC Iowa State University SE tower. Site Description - This is a 4 ha (200 m x 200 m) sorghum-corn-soybean rotation established at the Sustainable Advanced Bioeconomy Research (SABR) farm at Iowa State University. The site has a long history of conventional row-cropping, predominantly corn-soy rotations. In the immediately preceding growing season, soybeans were grown at the SABR farm.

(Rojda), Guler Aslan Sungur↗
DOE OSTI2026

AmeriFlux US-CLN Coles Farm North

This is the AmeriFlux version of the carbon flux data for the site US-CLN Coles Farm North. Site Description - The land was given to Iowa State University in 1974, and was a corn-soybean rotation with conventional tillage operation until 2015. In 2016, the management was moved to strip tillage corn-soybean rotation with cover crops. The field is located east of Williams, IA.

Neale, Christopher [Daugherty Water for Food Insti↗
DOE OSTI2026

AmeriFlux US-CLS Coles Farm South

This is the AmeriFlux version of the carbon flux data for the site US-CLS Coles Farm South. Site Description - The land was given to Iowa State University in 1974, and was a corn-soybean rotation with conventional tillage operation until 2015. In 2016, the management was moved to strip tillage corn-soybean rotation with cover crops. The field is located east of Williams, IA.

Prueger, John H. [National Laboratory for Agricult↗
DOE OSTI2026

AmeriFlux FLUXNET-1F US-IAC Iowa State University SE tower

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-IAC Iowa State University SE tower. This is the FLUXNET version of the carbon flux data for the site US-IAC Iowa State University SE tower produced by applying the standard ONEFlux (1F) software. Site Description - This is a 4 ha (200 m x 200 m) sorghum-corn-soybean rotation established at the Sustainable Advanced Bioeconomy Research (SABR) farm at Iowa State University. The site has a long history of conventional row-cropping, predominantly corn-soy rotations. In the immediately preceding growing season, soybeans were grown at the SABR farm.

(Rojda), Guler Aslan Sungur↗
DOE OSTI2025

Investigations on the Thermal Stability and Kinetics of Biolubricants Synthesized from Different Types of Vegetable Oils

Petroleum-based lubricants raise environmental concerns due to their non-biodegradability and toxicity, whereas biobased lubricants underperform owing to low thermal stability. This study examined and compared three vegetable oils, along with their chemically modified versions, to better understand their suitability as biolubricants. High oleic soybean oil (HOSOY), regular soybean oil (RSOY), and waste cooking oil (WCO) were subjected to chemical modification, where isopropyl groups were attached to the fatty acid chains of the oils to produce branched oils, i.e., b-HOSOY, b-RSOY, and b-WCO. The detailed kinetic study of each regular and modified sample was investigated using thermogravimetric analysis. The kinetic parameters, such as the activation energies, reaction rate, and pre-exponential factor, were generated via Friedman methods. The differential thermal gravimetric (DTG) analysis showed low volatilization at the onset temperature in each modified oil as compared with the unmodified samples under an oxidative environment. Furthermore, the comparative kinetic studies demonstrated the enhanced thermoxidative stability of the modified products relative to their unaltered counterparts. Among the tested oils, the b-RSOY showed an average activation energy of 325 kJ/mol, followed by the b-WCO: 300 kJ/mol and the b-HOSOY: 251 kJ/mol, indicating the most stable modified product under an oxidative environment. For all the samples, the pre-exponential factors were in good agreement with the activation energies, which validates that finding the pre-exponential components is crucial to the kinetic analysis.

Sarker, Majher I. (ORCID:0000000299509274)↗
DOE OSTI2025

Soil oxygen dynamics: a key mediator of tile drainage impacts on coupled hydrological, biogeochemical, and crop systems

Tile drainage removes excess water and is an essential, widely adopted management practice to enhance crop productivity in the US Midwest and throughout the world. Tile drainage has been shown to significantly change hydrological and biogeochemical cycles by lowering the water table and reducing the residence time of soil water, although examining the complex interactions and feedbacks in an integrated hydrology–biogeochemistry–crop system remains elusive. Oxygen dynamics are critical to unraveling these interactions and have been ignored or oversimplified in existing models. Understanding these impacts is essential, particularly so because tile drainage has been highlighted as an adaptation under projected wetter springs and drier summers in the changing climate in the US Midwest. We used the ecosys model that uniquely incorporates first-principle soil oxygen dynamics and crop oxygen uptake mechanisms to quantify the impacts of tile drainage on hydrological and biogeochemical cycles and crop growth in corn–soybean rotation fields. The model was validated with data from a multi-treatment, multi-year experiment in Washington, IA. The relative root mean square error (rRMSE) for the corn and soybean yield in validation is 5.66 % and 12.57 %, respectively. The Pearson coefficient (r) of the monthly tile flow during the growing season is 0.78. Plant oxygen stress turns out as an emergent property of the equilibrium between the soil oxygen supply and biological demand. The impact of tile drainage on the system is achieved through a series of coupled feedback mechanisms. The model results show that tile drainage reduces the soil water content and enhances soil oxygenation. It additionally increases the subsurface discharge and elevates inorganic nitrogen leaching, with seasonal variations influenced by climate and crop phenology. The improved aerobic condition alleviates crop oxygen stress during wet springs, thereby promoting crop root growth during the early growth stage. The development of greater root density, in turn, mitigates water stress during dry summers, leading to an overall increase in the crop yield by ∼6 %. These functions indicate the potential of tile drainage in bolstering crop resilience to climate change and the use of this modeling tool for large-scale assessments of tile drainage. The model reveals the underlying causal mechanisms that drive the agroecosystem response to drainage on the coupled hydrology, biogeochemistry, and crop system dynamics.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1973

Effective use of ERTS multisensor data in the Great Plains

The author has identified the following significant results. It appears that identification of corn and soybeans as separate classes may not be feasible for the part of the growing season around August 15. It is hoped that at other points in the growing season separation of corn from soybeans will be possible. Visual examination of September 5 imagery indicates that later in the growing season, it is possible. The resolution of the 1:300,000 scale enlargement remained sharp enough to distinguish features important for making soil association maps. The land use pattern (range vs cultivated) was sharp as were the stream bottoms, stream valley sides, and stock ponds. The resolution of the 1:1,000,000 scale enlargement was less sharp on detail but it appears that some features could be quantitatively analyzed. Among these are the hydrologic features present such as stock ponds and the streams. It would appear that the total area of stock pond water on a frame could be measured, and, after ground truth established their average depth, the volume of water stored could be calculated and be monitored.

Myers, V. I.↗
NASA NTRS1973

Crop identification using ERTS imagery

Digital analysis of August 15 ERTS-I imagery for southeastern South Dakota was performed to determine the feasibility of conducting crop surveys from satellites. Selected areas of bands 4, 5, 6, and 7 positive transparencies were converted to digital form utilizing Signal Analysis and Dissemination Equipment (SADE). The optical transmission values were printed out in a spatial format. Visual analysis of the printouts indicated that cultivated areas were readily distinguished from non-cultivated areas in all four bands. Bare soil was easily recognized in all four bands. Corn and soybeans, the two major crops in the area, were treated as separate classes rather than as a single class called row crops. Bands 6 and 7 provided good results in distinguishing between corn and soybeans.

Horton, M. L.↗
NASA NTRS1973

Application of ERTS-1 data to analysis of agricultural crops and forests in Michigan

The results reported are based on analysis of ERTS Frame 1033-15580 collected over southwestern Lower Michigan on August 25, 1972. Major agricultural crops such as corn and soybeans were approaching maturity at this data and forest canopies were dense. Extensive ground truth information was gathered by detailed field study of test strips. This detailed information was supplemented over larger areas by interpretation of RB-57 and C-47 photography and MSS imagery. Recognition processing of ERTS-1 MSS data was carried out on a digital computer. Fields and forest stands were selected as training sets and test areas. Aerial imagery was essential for locating the positions of these selected areas on ERTS digital tapes. The recognition process was successful for each type of vegetation which had a dense green canopy such as forests, corn, and soybeans. Bare soil was also recognizable as a category.

Safir, G. R.↗
NASA NTRS1973

Reflectance model of a plant leaf

A light ray, incident at 5 deg to the normal, is geometrically plotted through the drawing of the cross section of a soybean leaf using Fresnel's Equations and Snell's Law. The optical mediums of the leaf considered for ray tracing are: air, cell sap, chloroplast, and cell wall. The above ray is also drawn through the same leaf cross section considering cell wall and air as the only optical mediums. The values of the reflection and transmission found from ray tracing agree closely with the experimental results obtained using a Beckman DK-2A Spectroreflectometer. Similarly a light ray, incident at about 60 deg to the normal, is drawn through the palisade cells of a soybean leaf to illustrate the pathway of light, incident at an oblique angle, through the palisade cells.

Kumar, R.↗
NASA NTRS1975

Crop identification technology assessment for remote sensing (CITARS). Volume 10: Interpretation of results

The CITARS was an experiment designed to quantitatively evaluate crop identification performance for corn and soybeans in various environments using a well-defined set of automatic data processing (ADP) techniques. Each technique was applied to data acquired to recognize and estimate proportions of corn and soybeans. The CITARS documentation summarizes, interprets, and discusses the crop identification performances obtained using (1) different ADP procedures; (2) a linear versus a quadratic classifier; (3) prior probability information derived from historic data; (4) local versus nonlocal recognition training statistics and the associated use of preprocessing; (5) multitemporal data; (6) classification bias and mixed pixels in proportion estimation; and (7) data with differnt site characteristics, including crop, soil, atmospheric effects, and stages of crop maturity.

Bizzell, R. M.↗
NASA NTRS1976

A distribution benefits model for improved information on worldwide crop production. Volume 2: Application to various crops

ECON's distribution benefits model has been applied to worldwide distribution of corn, rye, oats, barley, soybeans, and sugar, and to domestic distribution of potatoes. The results indicate that a LANDSAT system with thematic mapper might produce benefits to the United States of about $119 million per year, due to more efficient distribution of these commodities. The benefits to the rest of the world have also been calculated, with a breakdown between trade benefits and those associated with internal use patterns. By far the greatest part of the estimated benefits are assigned to corn, with smaller benefits assigned to soybeans and the small grains (rye, oats, and barley).

Andrews, J.↗
NASA NTRS1977

Crop identification and area estimation by computer-aided analysis of Landsat data

This report describes the results of a study involving the use of computer-aided analysis techniques applied to Landsat MSS data for identification and area estimation of winter wheat in Kansas and corn and soybeans in Indiana. Key elements of the approach included use of aerial photography for classifier training, stratification of Landsat data and extension of training statistics to areas without training data, and classification of a systematic sample of pixels from each county. Major results and conclusions are: (1) Landsat data was adequate for accurate identification and area estimation of winter wheat in Kansas, but corn and soybean estimates for Indiana were less accurate; (2) computer-aided analysis techniques can be effectively used to extract crop identification information from Landsat MSS data, and (3) systematic sampling of entire counties made possible by computer classification methods resulted in very precise area estimates at county as well as district and state levels.

Bauer, M. E.↗