Search NASA⌕ Search

SEARCH · Search NASA

Results for “row crops”

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.

120 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

The Interactions Between Shading and Organic Fertilizer Application on Dry-farmed Tomato Grown Between Photovoltaic Panels

Agrivoltaic systems are mixed systems of solar photovoltaic (PV) panels and agricultural production, where shade from the panels can result in lower evapotranspiration for crops, which is of particular interest for dryland agriculture. Dry-farmed tomato (Solanum lycopersicum) production in the Willamette Valley of Oregon has lower total yields and higher rates of blossom-end rot (BER) than irrigated tomato production, resulting in reduced marketable yields. To determine how dry-farmed ‘Early Girl’ tomato performed in an agrivoltaics system, a trial was conducted at the Valley Creek Solar Project (Salem, OR, USA) in 2020, using three different amendment treatments and three levels of shading from the panels. Amendment treatments were 0N (receiving no fertilizer), 84N (receiving 84 kg·ha −1 N), and 168N (receiving 168 kg·ha −1 N), applied as processed chicken manure. Plants were estimated to receive an irradiance factor of 30%, 76%, and 89% for full-shade, partial-shade, and full-sun treatments respectively. There was an interaction between amendment treatments and shading treatments in their effects on unblemished yield (yield of fruit without BER or sunscald). The optimum fertilizer application for full-shade and partial-shade rows was 84N, the optimum for full-sun rows was 0N. Fertilizing these rows at these rates resulted in an unblemished yield for the aisle of 11.1 t·ha −1 , which was lower than unblemished yields reported in previous experiments and trials in open fields. However, these results are from a single location and a single year, and other solar sites may behave more similar to open-field conditions. Shading from the panels increased average fruit weight and decreased incidence of BER and sunscald, suggesting that crops were less drought stressed. This resulted in similar unblemished yields for the full-shade and full-sun plots at 84N and 168N. Applying fertilizer resulted in higher total yields, smaller average fruit weight, increased BER incidence, and decreased sunscald incidence. The results suggest a possible synergy between dry-farmed tomato production and agrivoltaics, although several concerns remain, including difficulties managing the vegetation under panels, rules restricting PVs on high-value agricultural soils, and the possibility of soil compaction during PV installation.

14 SOLAR ENERGY↗
DOE OSTI2021

Bioenergy sorghum’s deep roots: A key to sustainable biomass production on annual cropland

Abstract Bioenergy sorghum has high biomass yield potential, drought resilience, good nitrogen use efficiency, and a root system that contributes to the accumulation of soil organic carbon. In this study, field grown bioenergy sorghum root systems were analyzed during the growing season to characterize their depth, biomass, morphology, anatomy, and gene expression profiles. Bioenergy sorghum roots grew continuously during a 155‐day growing season producing ~175 nodal roots, accumulating ~7 Mg of dry biomass per hectare, and reaching >2 m deep in the soil profile. Nodal roots within 20 cm of the stem were 1–5 mm in diameter, whereas roots deeper in soil profiles were enriched in lateral roots with small diameters (~30–500 µm) enabling growth through soil macropores. In older field‐grown plants, roots with intact endodermal, vascular and inner root tissues were surrounded by degraded or aerenchyma‐filled epidermal and cortical cell layers. Transcriptome analysis of nodal, surface, and deep roots identified >2,500 differentially expressed genes involved in root growth, transport, adaptation, defense, and AMF–root interaction. Deep roots (180–240 cm) differentially expressed genes that regulate lateral root growth. Surface roots (0–20 cm) located mid‐row differentially expressed genes involved in nitrate transport, whereas ammonium transport genes were expressed in surface and deep roots and genes involved in phosphate transport were expressed in nodal, surface, and deep roots. Overall, bioenergy sorghum's long growing season enables root systems to grow deeper and accumulate more biomass than annual grain crops such as maize, attributes that could help restore annual cropland soil organic carbon levels and improve soil productivity. Deep roots active in nutrient transport are positioned to take‐up fertilizer leached deep into soil profiles mitigating potential nutrient run‐off. Bioenergy sorghum's large and deep root system is a key to sustainable production of biofuels, biopower, and bioproducts on annual cropland.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

U.S. Agrivoltaics Irradiance Database

This is a foundational data set for research and deployment of agrivoltaics, which is the co-location of agriculture and solar power plants on the same land. This irradiance and shading dataset can be utilized to determine the suitability of agrivoltaics configurations for a given region and crop-type. The data is hourly, 4x4 km resolution across the contiguous United States and Hawaii. It is calculated from the National Solar Radiation Database sites, using the System Advisor Model (SAM) to simulate the shading patterns for 10 common agrivoltaics configurations. Sunlight availability data is reported for 10 locations on the ground between adjacent rows of solar panels, as well as averaged across areas of interest such as the average irradiance in the edge-to-edge open area or across 3-6 planting beds. Other available metrics include the input meteorological data from the NSRDB (e.g. global horizontal irradiance, wind speed, etc.) and estimates for comparing energy and agricultural characteristic across the 10 configurations, including power output per acre or per kW installed capacity and farmable land area per acre.

14 SOLAR ENERGY↗
DOE OSTI2022

Impacts of corn intercropping with soybean, peanut and millet through different planting patterns on population dynamics and community diversity of insects under fertilizer reduction

Corn is one of the key grain crops in China and the excessive use of chemical fertilizers and pesticides seriously damages the ecological environment in fields. To explore a more scientific and reasonable way to plant corn and simultaneously reduce the overuse of chemical fertilizers and pesticides, the impact of corn intercropping with soybean, peanut, and millet, respectively, through five planting patterns, including three intercropping patterns (2 corn rows to 2, 3 and 4 rows of soybean/peanut or 2, 4 and 6 millet rows, respectively) and two monoculture patterns of corn and soybean, peanut or millet under normal (600 kg/ha) and reduced (375 kg/ha) levels of NPK (N:P 2 O 5 :K 2 O = 15:15:15) fertilization on the population abundance and community diversity of insects, leaf nutrients, and induced plant hormones jasmonic acid (JA) and salicylic acid (SA) was studied in 2018 and 2019. The results showed that the insect community indexes of the species number ( S ), the diversity index ( H ), and the uniformity index ( E ) generally increased under intercropping and were significantly higher than those under corn monoculture. The prevalence of Asian corn borer ( Ostrinia furnacalis ) on the intercropping corn plants decreased by based on the average of seven surveys per year for each treatment 2.9 to 17 heads per 30 plants compared with that on the monoculture corn plants. The number of natural enemy insect species on corn plants under intercropping was significantly higher than that under corn monoculture. That is, intercropping may decrease the population of Asian corn borers by increasing S , H , E , and natural enemy insect species (NEI). Moreover, intercropping type and fertilizer level significantly affected corn leaf nutrient contents. Compared with the normal fertilizer level, fertilizer reduction significantly reduced the foliar contents of amino acids, soluble protein, and soluble sugar in corn plants. In addition, corn–soybean and corn–peanut intercropping significantly increased the three nutrient contents in corn leaves compared with corn monoculture. In terms of corn nutrients, intercropping could compensate for the effects of fertilizer reduction. The foliar content of JA in corn–soybean intercropping was significantly higher than in corn monoculture. Under corn–soybean and corn–peanut intercropping, SA was significantly lower than under corn monoculture. Overall, intercropping, not fertilizer reduction, can significantly increase insect community diversity while reducing the population abundances of the key insect pest species on corn plants. Intercropping reduced the SA content, increased amino acids and thus reduced the susceptibility of corn to the pest insects.

Li, Likun↗
DOE OSTI2026

Data for Yield from Iowa’s first commercial miscanthus fields: implications of spatial variability for productivity and sustainability beyond research plots

This dataset contains biomass yield measurements and associated vegetation index data collected from commercial Miscanthus × giganteus fields in eastern Iowa during the 2022–2023 growing seasons. The data support the analyses presented in the article: “Yield From Iowa's First Commercial Miscanthus Fields: Implications of Spatial Variability for Productivity and Sustainability Beyond Research Plots.” We collected 105 ground-truth biomass samples from four mature commercial fields (>4 years old) covering 92.81 ha. Samples were taken from 3 m² quadrats that were hand-harvested in alignment with commercial harvest timing. Stem biomass (excluding leaves) was weighed, moisture-corrected, and converted to dry-matter yield expressed in Mg DM ha⁻¹. Sampling locations were selected to capture spatial variability visible in aerial imagery and were recorded using RTK GPS. Each biomass observation was paired with vegetation indices derived from high-resolution PlanetScope satellite imagery (3 m resolution). Images were acquired throughout the growing season, and indices were calculated to evaluate their ability to predict end-of-season biomass yield. Statistical and machine learning approaches were used to identify key predictors, and a linear regression model based on end-of-July Green Normalized Difference Vegetation Index (GNDVI) was developed and evaluated. This repository includes the data used in that modeling workflow. Management practices, economic data, full imagery time series, and additional methodological details are described in the associated publication and are not included here. The dataset consists of three comma-separated value (CSV) files: 1. Combine_Groundtruth_Yield_VI_22_23.csv This file contains ground-truth biomass yield measurements and associated key vegetation index values collected during the 2022 and 2023 growing seasons. Rows: 105 observations Columns: Year — Year of observation (2022 or 2023) Field — Field location identifier Sample_number — Unique sample identifier GNDVI_End_Jul — Green Normalized Difference Vegetation Index calculated at end of July GNDVI_End_Aug — Green Normalized Difference Vegetation Index calculated at end of August NDRE_End_Aug — Normalized Difference Red Edge index calculated at end of August Biomass_Stem_Yield_MgDM/ha — Measured stem biomass yield (megagrams dry matter per hectare) 2. trainData_GNDVI.csv This file contains the subset of observations used to train the predictive relationship between July GNDVI and biomass yield. Rows: 76 observations Columns: Unnamed: 0 — Row index retained from the original data processing workflow GNDVI_End_Jul — GNDVI at end of July Stem_Yield_MgDM/ha — Observed stem biomass yield (Mg DM ha⁻¹) 3. testData_GNDVI.csv This file contains the test dataset used to evaluate model performance. Rows: 29 observations Columns: Unnamed: 0 — Row index retained from the original data processing workflow GNDVI_End_Jul — GNDVI at end of July Predicted_Yield_MgDM/ha — Model-predicted stem biomass yield (Mg DM ha⁻¹) Observed_Yield_MgDM/ha — Measured stem biomass yield (Mg DM ha⁻¹)

Potential yield, yield gap, in-field management, y↗
DOE OSTI2021

New Orthophoto Generation Strategies from UAV and Ground Remote Sensing Platforms for High-Throughput Phenotyping

Remote sensing platforms have become an effective data acquisition tool for digital agriculture. Imaging sensors onboard unmanned aerial vehicles (UAVs) and tractors are providing unprecedented high-geometric-resolution data for several crop phenotyping activities (e.g., canopy cover estimation, plant localization, and flowering date identification). Among potential products, orthophotos play an important role in agricultural management. Traditional orthophoto generation strategies suffer from several artifacts (e.g., double mapping, excessive pixilation, and seamline distortions). The above problems are more pronounced when dealing with mid- to late-season imagery, which is often used for establishing flowering date (e.g., tassel and panicle detection for maize and sorghum crops, respectively). In response to these challenges, this paper introduces new strategies for generating orthophotos that are conducive to the straightforward detection of tassels and panicles. The orthophoto generation strategies are valid for both frame and push-broom imaging systems. The target function of these strategies is striking a balance between the improved visual appearance of tassels/panicles and their geolocation accuracy. The new strategies are based on generating a smooth digital surface model (DSM) that maintains the geolocation quality along the plant rows while reducing double mapping and pixilation artifacts. Moreover, seamline control strategies are applied to avoid having seamline distortions at locations where the tassels and panicles are expected. The quality of generated orthophotos is evaluated through visual inspection as well as quantitative assessment of the degree of similarity between the generated orthophotos and original images. Several experimental results from both UAV and ground platforms show that the proposed strategies do improve the visual quality of derived orthophotos while maintaining the geolocation accuracy at tassel/panicle locations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Data for Miscanthus giganteus Biolistic Transformation Using the Visible RUBY Red Marker Gene to Monitor Transformation Efficiency

Miscanthus × giganteus ( M × g ) is a high- yielding perennial C4 bioenergy crop, but genetic improvement by breeding is constrained by triploid sterility and clonal propagation. Improving genetic transformation methods for M × g would provide opportunities for advantageous trait introgression. Use of an easy to phenotype reporter gene is a promising strategy to improve transformation processes and efficiency. This study presents an efficient novel method for biolistic transformation of inflorescence- derived callus in M × g and demonstrates its efficacy using RUBY, a betalain-based noninvasive reporter that is visible throughout the transformation process. RUBY expression ( Zea mays codon optimized) was visible from callus stage through plantlet development into maturity. RUBY expressing independently transformed plants were confirmed by hygromycin phosphotransferase ELISA and by genomic PCR demonstrating that the RUBY phenotype is sufficient for screening transformants. The Zea mays codon optimized hygromycin selection marker was driven by previously established promoters for Miscanthus, ZmUBI and 2×35S, while the RUBY gene expression was controlled by a known Zea mays C4 promoter, Brachypodium UBI10, newly employed in Miscanthus. The construct containing the 2×35S promoter for hygromycin had a 15.1% transformation efficiency while the ZmUBI promoter had a 20.5% transformation efficiency. This study provides a novel, highly efficient protocol for successful biolistic transformation of M × g for stable expression. This study also demonstrates that RUBY expression can be used as a convenient and powerful monitor of transformation in ongoing and future work to engineer M × g into an improved bioproduct feedstock. **NOTE: in "TableS2_ProtocolComparison.csv", the data from row 665 to 971 should be removed.

Gene Editing↗
NASA NTRS1982

Crop identification with multifrequency, multipolarization, and multiangle radars

In a corn and soybeans test site in Webster County, Iowa, airborne radar scatterometers were used on August 19 and September 10, 1980, to investigate the backscattering properties of these crops at wavelengths of 2.3, 6.3, and 19 cm (Ku-, C-, and L-band, respectively). Both horizontal transmit-horizontal receiver (HH) polarization and horizontal transmit-vertical receive (HV) polarization combinations were used at L- and C-band. Only the VV polarization combination was available at Ku-band. Measurements were obtained at 10 angles of observation from 5 to 50 deg in steps of 5 deg (referenced to the nadir). Excellent separation between corn and soybeans was achieved when either C-band HV at 50 deg or when a defined depolarization factor was used at C-band. Good separation existed at L-band also using either L-band HH at 50 deg or an L-band polarization factor. Significant row direction effects were observed for all HH data near 10 deg. Significant effects of surface soil moisture were observed for all configurations at L-band and C-band.

Paris, J. F.↗
NASA NTRS2017

Estimating the Relative Water Content of Leaves in a Cotton Canopy

Remotely sensing plant canopy water status remains a long-term goal of remote sensing research. Established approaches to estimating canopy water status the Crop Water Stress Index, the Water Deficit Index and the Equivalent Water Thickness involve measurements in the thermal or reflective infrared. Here we report plant water status estimates based upon analysis of polarized visible imagery of a cotton canopy measured by ground Multi-Spectral Polarization Imager (MSPI). Such estimators potentially provide access to the plant hydrological photochemistry that manifests scattering and absorption effects in the visible spectral region.Twice during one day, +- 3 hours from solar noon, we collected polarized imagery and relative water content data on a cotton test plot located at the Arid Land Agricultural Research Center, United States Department of Agriculture, Maricopa, AZ. The test plot, a small portion of a large cotton field, contained stressed plants ready for irrigation. The evening prior to data collection we irrigated several rows of plants within the test plot. Thus, ground MSPI imagery from both morning and afternoon included cotton plants with a range of water statuses. Data analysis includes classifying the polarized imagery into sunlit reflecting, sunlit transmitting, shaded foliage and bare soil. We estimate the leaf surface reflection and interior reflection based upon the per pixel polarization and sunview directions. We compare our cotton results with our prior polarization results for corn and soybean leaves measured in the lab and corn leaves measured in the field.

Cotton Canopy↗
DOE OSTI2025

Data for Identifying the best high-biomass sorghum hybrids based on biomass yield potential and feedstock quality affected by nitrogen fertility management under various environments

Data were collected from agronomy fields in Urbana and Ewing, IL, during the 2022 and 2023 growing seasons. The dataset includes dry biomass yield, nitrogen, phosphorus, and potassium concentrations and removals, and chemical composition elements (cellulose, hemicellulose, lignin, and soluble fractions) for 13 high-biomass sorghum hybrids. data_sharing.xlsx contains 20 columns and 104 rows. Below is the explanation of all variables in the file: Year: 2022; 2023 Location: Urbana, IL; Ewing, IL N rate (kg-N/ha): 0; 112 Hybrid #: H1-H13 Pedigree: Pedigree for 13 hybrids Dry biomass yield (Mg/ha): Aboveground dry biomass yield N (g/kg): Nitrogen concentration in plant tissue P (g/kg): Phosphorus concentration in plant tissue K (g/kg): Potassium concentration in plant tissue N (kg/ha): Nitrogen removal by aboveground biomass P (kg/ha): Phosphorus removal by aboveground biomass K (kg/ha): Potassium removal by aboveground biomass Cellulose (g/kg): Cellulose concentration in plant tissue Hemicellulose (g/kg): Hemicellulose concentration in plant tissue Lignin (g/kg): Lignin concentration in plant tissue Soluble (g/kg): Soluble concentration in plant tissue Cellulose (Mg/ha): Cellulose content in aboveground biomass Hemicellulose (Mg/ha): Hemicellulose content in aboveground biomass Lignin (Mg/ha): Lignin content in aboveground biomass Soluble (Mg/ha): Soluble content in aboveground biomass

environmental adaptability↗
DOE OSTI2025

Integrated GW Farm ABM

This Data Repository includes data used for the integrated groundwater- farm ABM model, raw model output from scenario ensemble, and processed outputs that isolate the groundwater storage depletion outcomes for the 35,000 farm cells. Model Inputs: Farm ABM Inputs: This folder contains the input data used by the integrated groundwater - farm ABM modelling script (Python file) used for the high performance computing (HPC) experiments. The sub-folder "data inputs" contains all of the farm attribute data, while the three files in the folder have the hydrogeological data lookup table (NLDAS Cost Curve Attributes.csv), a lookup table (Theis well function table.csv) for the groundwater cost curve function, and the farm indexes and corresponding NLDAS ids for all of the cells run in this experiment (nldas farms subset final.csv). NLDAS Cost curve hydrogeological data: Hydrogeological data aggregated to 1/8 degree resolution and aligned with the NLDAS grid. Parameters include: water depth below ground surface [meters], subsurface porosity [unitless], aquifer depth from ground surface to aquifer bottom [meters], annual average recharge (USGS: mm, Doll: meters), and three different hydraulic conductivity (K) values (meters/day). The three K values represent the mean value from Gleeson et al. (2018), one standard deviation above the mean from Gleeson et al. (2018), and the de Graaf et al. 2020 modifications to certain lithologies. Additional information about these datasets and their processing are documented in the supplement to Yoon et al. 2025 (in review). Output: Raw outputs: This folder contains a .zip file that has model outputs for the entire scenario ensemble. There is one csv for each farm id, using the format "farm farmid cases.csv". The relationship between the farm id and NLDAS id is defined by the "nldas farms subset final.csv" located in the Farm ABM Inputs folder. Each csv has 625 rows, corresponding to 625 combinations of different scenario parameter values. Each row (scenario) represents the outcome of a 100 year simulation. Columns define scenario settings and summary statistics for each scenario. The first four columns define the scenario settings: "hydro ratio," "econ ratio," "K scenario," and "gamma scenario." The hydro and econ ratios are values passed to the modeling script that influence multipliers for other model parameters, as documented in the supplement to Yoon et al. 2025 (in review). The gamma multiplier is a coefficient multiplier applied to the baseline gamma values (values below 1 represent lower unobserved costs compared to baseline, values above 1 represent higher costs). The K scenario names represent K values of: "low": 0.5 m/d, "int 1": 2.5 m/d, "int 2": 10 m/d, "high": 50 m/d, and "gleeson": mean Gleeson K value. "Perc vol depleted" is the fraction of groundwater depleted at the end of the 100 simulation. Processed Output: Derived depletion outcomes from raw outputs: All of the individual csv files from the Raw outputs were aggregated into a single file that has the scenario settings and fraction depletion "Perc vol depleted" for every farm cell, for every scenario. The other two files define relationships between the farm id, NLDAS id, and local and major aquifer units, used for aquifer-level depletion analysis.

Agent based modeling↗
DOE OSTI2021

Wild bees and natural enemies prefer similar flower species and respond to similar plant traits

Designing wildflower habitats to support beneficial insects providing pollination and pest control services is important for supporting sustainable crop production. It is often desirable to support both groups of insects, making the selection of resource plants for insect conservation programs more challenging. Moreover, the process of selecting resource plants is complicated by the array of possible options in each region, and the need to provide resources over the entire growing season. Identifying traits shared by resource plants that are attractive to both bees and natural enemies can reduce the need to evaluate new plants in each region, by providing a guide for the types of plants expected to be rewarding to these insects. Using insect visitation data collected from replicated common garden plantings of native wildflower and shrub species from the Great Lakes region of the United States, we found a high degree of correlation between the abundance of bees and natural enemies visiting native plant species. These results were used to identify a set of 15 plant species that can provide resources for these insects throughout the summer. Across all tested species, pollen quantity per flower and the week of bloom were positively correlated with some, but not all, taxonomic groupings of beneficial insects. In contrast, floral area was consistently positively associated with visitation of both natural enemies and wild bees. This trait is easy to document and can allow for efficient local testing of potential resource plants, providing a faster path to implementing insect conservation in working landscapes.

54 ENVIRONMENTAL SCIENCES↗