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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Exploring 3D leaf anatomical traits for C 4 photosynthesis: chloroplast and plasmodesmata pit field size in maize and sugarcane

Summary Volume and surface area of chloroplasts and surface area of plasmodesmata pit fields are presented for two C 4 species, maize and sugarcane, with respect to cell surface area and cell volume. Serial block face scanning electron microscopy (SBF‐SEM) and confocal laser scanning microscopy with the Airyscan system (LSM) were used. Chloroplast size estimates were much faster and easier using LSM than with SBF‐SEM; however, the results were more variable than SBF‐SEM. Mesophyll cells were lobed where chloroplasts were located, facilitating cell‐to‐cell connections while allowing for greater intercellular airspace exposure. Bundle sheath cells were cylindrical with chloroplasts arranged centrifugally. Chloroplasts occupied c. 30–50% of mesophyll cell volume, and 60–70% of bundle sheath cell volume. Roughly 2–3% of each cell surface area was covered by plasmodesmata pit fields for both bundle sheath and mesophyll cells. This work will aid future research to develop SBF‐SEM methodologies with the aim to better understand the effect of cell structure on C 4 photosynthesis.

3D leaf imaging↗
DOE OSTI2021

Root angle in maize influences nitrogen capture and is regulated by calcineurin B-like protein (CBL)-interacting serine/threonine-protein kinase 15 ( ZmCIPK15 )

Crops with reduced nutrient and water requirements are urgently needed in global agriculture. Root growth angle plays an important role in nutrient and water acquisition. A maize diversity panel of 481 genotypes was screened for variation in root angle employing a high-throughput field phenotyping platform. Genome-wide association mapping identified several single nucleotide polymorphisms (SNPs) associated with root angle, including one located in the root expressed CBL-interacting serine/threonine-protein kinase 15 (ZmCIPK15) gene (LOC100285495). Reverse genetic studies validated the functional importance of ZmCIPK15, causing a approximately 10° change in root angle in specific nodal positions. A steeper root growth angle improved nitrogen capture in silico and in the field. OpenSimRoot simulations predicted at 40 days of growth that this change in angle would improve nitrogen uptake by 11% and plant biomass by 4% in low nitrogen conditions. In field studies under suboptimal N availability, the cipk15 mutant with steeper growth angles had 18% greater shoot biomass and 29% greater shoot nitrogen accumulation compared to the wild type after 70 days of growth. We propose that a steeper root growth angle modulated by ZmCIPK15 will facilitate efforts to develop new crop varieties with optimal root architecture for improved performance under edaphic stress.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Soil penetration by maize roots is negatively related to ethylene‐induced thickening

Abstract Radial expansion is a classic response of roots to a mechanical impedance that has generally been assumed to aid penetration. We analysed the response of maize nodal roots to impedance to test the hypothesis that radial expansion is not related to the ability of roots to cross a compacted soil layer. Genotypes varied in their ability to cross the compacted layer, and those with a steeper approach to the compacted layer or less radial expansion in the compacted layer were more likely to cross the layer and achieve greater depth. Root radial expansion was due to cortical cell size expansion, while cortical cell file number remained constant. Genotypes and nodal root classes that exhibited radial expansion in the compacted soil layer generally also thickened in response to exogenous ethylene in hydroponic culture, that is, radial expansion in response to ethylene was correlated with the thickening response to impedance in soil. We propose that ethylene insensitive roots, that is, those that do not thicken and can overcome impedance, have a competitive advantage under mechanically impeded conditions as they can maintain their elongation rates. We suggest that prolonged exposure to ethylene could function as a stop signal for axial root growth.

Vanhees, Dorien J.↗
DOE OSTI2020

A temporal hierarchy underpins the transcription factor–DNA interactome of the maize UPR

Adverse environmental conditions reduce crop productivity and often increase the load of unfolded or misfolded proteins in the endoplasmic reticulum (ER). This potentially lethal condition, known as ER stress, is buffered by the unfolded protein response (UPR), a set of signaling pathways designed to either recover ER functionality or ignite programmed cell death. Despite the biological significance of the UPR to the life of the organism, the regulatory transcriptional landscape underpinning ER stress management is largely unmapped, especially in crops. To fill this significant knowledge gap, we performed a large–scale systems–level analysis of the protein–DNA interaction (PDI) network in maize (Zea mays). Using 23 promoter fragments of six UPR marker genes in a high–throughput enhanced yeast one–hybrid assay, we identified a highly interconnected network of 262 transcription factors (TFs) associated with significant biological traits and 831 PDIs underlying the UPR. We established a temporal hierarchy of TF binding to gene promoters within the same family as well as across different families of TFs. Cistrome analysis revealed the dynamic activities of a variety of cis–regulatory elements (CREs) in ER stress–responsive gene promoters. By integrating the cistrome results into a TF network analysis, we mapped a subnetwork of TFs associated with a CRE that may contribute to UPR management. Lastly, we validated the role of a predicted network hub gene using the Arabidopsis system. The PDIs, TF networks, and CREs identified in our work are foundational resources for understanding transcription–regulatory mechanisms in the stress responses and crop improvement.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Carbon Flux Phenology from the Sky: Evaluation for Maize and Soybean

Carbon flux phenology is widely used to understand carbon flux dynamics and surface exchange processes. Vegetation phenology has been widely evaluated by remote sensors; however, very few studies have evaluated the use of vegetation phenology for identifying carbon flux phenology. Currently available techniques to derive net ecosystem exchange (NEE) from a satellite image use a single generic modeling subgroup for agricultural crops. But, carbon flux phenological processes vary highly with crop types and land management practices; this paper reexamines this assumption. Presented in this paper are an evaluation of ground-truth remotely sensed vegetation indices with in situ NEE measurements and an identification of vegetation indices for estimating carbon flux phenology metrics by crop type. Results show that the performance of different vegetation indices as an indicator of phenology varies with crop type, particularly when identifying the start of a season and the peak of a season. Finally, maize fields require vegetation indices that make use of the near-infrared and red reflectance bands, while soybean fields require those making use of the shortwave infrared (IR) and near-IR bands. In summary, the study identifies how to best utilize remote sensing technology as a crop specific measurement tool.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Transcripts and genomic intervals associated with variation in metabolite abundance in maize leaves under field conditions

Abstract Plants exhibit extensive environment-dependent intraspecific metabolic variation, which likely plays a role in determining variation in whole plant phenotypes. However, much of the work seeking to use natural variation to link genes and transcript’s impacts on plant metabolism has employed data from controlled environments. Here, we generated and analyzed data on the variation in the abundance of 26 metabolites across 660 maize inbred lines under field conditions. We employ these data and previously published transcript and whole plant phenotype data reported for the same field experiment to identify both genomic intervals (through genome-wide association studies (GWAS)) and transcripts (using both transcriptome-wide association studies (TWAS) and an explainable artificial intelligence (AI) approach based on random forest (RF)) associated with variation in metabolite abundance. Both genome-wide association and random forest-based methods identified substantial numbers of significant associations including genes with plausible links to the metabolites they are associated with. In contrast, the transcriptome-wide association identified only six significant associations. In three cases, genetic markers associated with metabolic variation in our study colocalized with markers linked to variation in non-metabolic traits scored in the same experiment. We speculate that the poor performance of transcriptome-wide association studies in identifying transcript-metabolite associations may reflect a high prevalence of non-linear interactions between transcripts and metabolites and/or a bias towards rare transcripts playing a large role in determining intraspecific metabolic variation.

Mathivanan, Ramesh Kanna↗
DOE OSTI2021

Overexpression of a Sugarcane BAHD Acyltransferase Alters Hydroxycinnamate Content in Maize Cell Wall

The purification of hydroxycinnamic acids [ p -coumaric acid ( p CA) and ferulic acid (FA)] from grass cell walls requires high-cost processes. Feedstocks with increased levels of one hydroxycinnamate in preference to the other are therefore highly desirable. We identified and conducted expression analysis for nine BAHD acyltransferase ScAts genes from sugarcane. The high conservation of AT10 proteins, together with their similar gene expression patterns, supported a similar role in distinct grasses. Overexpression of ScAT10 in maize resulted in up to 75% increase in total p CA content. Mild hydrolysis and derivatization followed by reductive cleavage (DFRC) analysis showed that p CA increase was restricted to the hemicellulosic portion of the cell wall. Furthermore, total FA content was reduced up to 88%, resulting in a 10-fold increase in the p CA/FA ratio. Thus, we functionally characterized a sugarcane gene involved in p CA content on hemicelluloses and generated a C4 plant that is promising for valorizing p CA production in biorefineries.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Identification of a Fusarium ear rot resistance gene in maize by QTL mapping and RNA sequencing

Fusarium ear rot (FER) caused by Fusarium verticillioides is a prevalent maize disease. To comprehensively characterize the genetic basis of the natural variation in FER resistance, a recombinant inbred line (RIL) population was used to map quantitative trait loci (QTL) for FER resistance. A total of 17 QTL were identified by linkage mapping in eight environments. These QTL were located on six chromosomes and explained 3.88–15.62% of the total phenotypic variation. Moreover, qFER1.03 had the strongest effect and accounted for 4.98–15.62% of the phenotypic variation according to analyses of multiple environments involving best linear unbiased predictions. The chromosome segment substitution lines (CSSLs) derived from a cross between Qi319 (donor parent) and Ye478 (recurrent parent) were used to verify the contribution of qFER1.03 to FER resistance. The line CL171, which harbored an introgressed qFER1.03 , was significantly resistant to FER. Further fine mapping of qFER1.03 revealed that the resistance QTL was linked to insertion/deletion markers InDel 8 and InDel 2, with physical distances of 43.55 Mb and 43.76 Mb, respectively. Additionally, qFER1.03 differed from the previous resistance QTL on chromosome 1. There were three annotated genes in this region. On the basis of the RNA-seq data, which revealed the genes differentially expressed between the FER-resistant Qi319 and susceptible Ye478, GRMZM2G017792 (MPK3) was preliminarily identified as a candidate gene in the qFER1.03 region. The Pr-CMV-VIGS system was used to decrease the GRMZM2G017792 expression level in CL171 by 34–57%, which led to a significant decrease in FER resistance. Using RIL and CSSL populations combined with RNA-seq and Pr-CMV-VIGS, the candidate gene can be dissected effectively, which provided important gene resource for breeding FER-resistant varieties.

Xia, Yusheng↗
DOE OSTI2023

Row selection in remote sensing from four-row plots of maize and sorghum based on repeatability and predictive modeling

Remote sensing enables the rapid assessment of many traits that provide valuable information to plant breeders throughout the growing season to improve genetic gain. These traits are often extracted from remote sensing data on a row segment (rows within a plot) basis enabling the quantitative assessment of any row-wise subset of plants in a plot, rather than a few individual representative plants, as is commonly done in field-based phenotyping. Nevertheless, which rows to include in analysis is still a matter of debate. The objective of this experiment was to evaluate row selection and plot trimming in field trials conducted using four-row plots with remote sensing traits extracted from RGB (red-green-blue), LiDAR (light detection and ranging), and VNIR (visible near infrared) hyperspectral data. Uncrewed aerial vehicle flights were conducted throughout the growing seasons of 2018 to 2021 with data collected on three years of a sorghum experiment and two years of a maize experiment. Traits were extracted from each plot based on all four row segments (RS) (RS1234), inner rows (RS23), outer rows (RS14), and individual rows (RS1, RS2, RS3, and RS4). Plot end trimming of 40 cm was an additional factor tested. Repeatability and predictive modeling of end-season yield were used to evaluate performance of these methodologies. Plot trimming was never shown to result in significantly different outcomes from non-trimmed plots. Significant differences were often observed based on differences in row selection. Plots with more row segments were often favorable for increasing repeatability, and excluding outer rows improved predictive modeling. These results support long-standing principles of experimental design in agronomy and should be considered in breeding programs that incorporate remote sensing.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Detection of Outliers in LiDAR Data Acquired by Multiple Platforms over Sorghum and Maize

High-resolution point cloud data acquired with a laser scanner from any platform contain random noise and outliers. Therefore, outlier detection in LiDAR data is often necessary prior to analysis. Applications in agriculture are particularly challenging, as there is typically no prior knowledge of the statistical distribution of points, plant complexity, and local point densities, which are crop-dependent. The goals of this study were first to investigate approaches to minimize the impact of outliers on LiDAR acquired over agricultural row crops, and specifically for sorghum and maize breeding experiments, by an unmanned aerial vehicle (UAV) and a wheel-based ground platform; second, to evaluate the impact of existing outliers in the datasets on leaf area index (LAI) prediction using LiDAR data. Two methods were investigated to detect and remove the outliers from the plant datasets. The first was based on surface fitting to noisy point cloud data via normal and curvature estimation in a local neighborhood. The second utilized the PointCleanNet deep learning framework. Both methods were applied to individual plants and field-based datasets. To evaluate the method, an F-score was calculated for synthetic data in the controlled conditions, and LAI, the variable being predicted, was computed both before and after outlier removal for both scenarios. Results indicate that the deep learning method for outlier detection is more robust than the geometric approach to changes in point densities, level of noise, and shapes. The prediction of LAI was also improved for the wheel-based vehicle data based on the coefficient of determination (R2) and the root mean squared error (RMSE) of the residuals before and after the removal of outliers.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Capturing Maize Stand Heterogeneity Across Yield-Stability Zones Using Unmanned Aerial Vehicles (UAV)

Despite the new equipment capabilities, uneven crop stands are still common occurrences in crop fields, mainly due to spatial heterogeneity in soil conditions, seedling mortality due to herbivore predation and disease, or human error. Non-uniform plant stands may reduce grain yield in crops like maize. Thus, detecting signs of variability in crop stand density early in the season provides critical information for management decisions and crop yield forecasts. Processing techniques applied on images captured by unmanned aerial vehicles (UAVs) has been used successfully to identify crop rows and estimate stand density and, most recently, to estimate plant-to-plant interval distance. Here, we further test and apply an image processing algorithm on UAV images collected from yield-stability zones in a commercial crop field. Our objective was to implement the algorithm to compare variation of plant-spacing intervals to test whether yield differences within these zones are related to differences in crop stand characteristics. Our analysis indicates that the algorithm can be reliably used to estimate plant counts (precision >95% and recall >97%) and plant distance interval (R2 ~0.9 and relative error <10%). Analysis of the collected data indicated that plant spacing variability differences were small among plots with large yield differences, suggesting that it was not a major cause of yield variability across zones with distinct yield history. This analysis provides an example of how plant-detection algorithms can be applied to improve the understanding of patterns of spatial and temporal yield variability.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Effect of Biomass Water Dynamics in Cosmic-Ray Neutron Sensor Observations: A Long-Term Analysis of Maize–Soybean Rotation in Nebraska

Precise soil water content (SWC) measurement is crucial for effective water resource management. This study utilizes the Cosmic-Ray Neutron Sensor (CRNS) for area-averaged SWC measurements, emphasizing the need to consider all hydrogen sources, including time-variable plant biomass and water content. Near Mead, Nebraska, three field sites (CSP1, CSP2, and CSP3) growing a maize–soybean rotation were monitored for 5 (CSP1 and CSP2) and 13 (CSP3) years. Data collection included destructive biomass water equivalent (BWE) biweekly sampling, epithermal neutron counts, atmospheric meteorological variables, and point-scale SWC from a sparse time domain reflectometry (TDR) network (four locations and five depths). In 2023, dense gravimetric SWC surveys were collected eight (CSP1 and CSP2) and nine (CSP3) times over the growing season (April to October). The N0 parameter exhibited a linear relationship with BWE, suggesting that a straightforward vegetation correction factor may be suitable (fb). Results from the 2023 gravimetric surveys and long-term TDR data indicated a neutron count rate reduction of about 1% for every 1 kg m−2 (or mm of water) increase in BWE. This reduction factor aligns with existing shorter-term row crop studies but nearly doubles the value previously reported for forests. This long-term study contributes insights into the vegetation correction factor for CRNS, helping resolve a long-standing issue within the CRNS community.

Chemistry↗
DOE OSTI2019

Data from: Multiple maize reference genomes impact the identification of variants by GWAS in a diverse inbred panel

Use of a single reference genome for genome-wide association studies (GWAS) limits the gene space represented to that of a single accession. This limitation can complicate identification and characterization of genes located within presence/absence variations (PAVs). In this study, we present the draft de novo genome assembly of PHJ89, an Oh43-type inbred line. Using three separate reference genome assemblies (B73, PH207, and PHJ89) that represent the predominant germplasm groups of maize, we generated three separate whole-seedling gene expression profile and single nucleotide polymorphism (SNP) matrices from a panel of 942 diverse inbred lines. We identified 34,447 (B73), 39,672 (PH207), and 37,436 (PHJ89) transcripts that are not present in the respective reference genome assembly. GWAS was conducted in the 942 inbred panel using both the SNP and expression data values to map sugarcane mosaic virus (SCMV) resistance. Highlighting the impact of alternative reference genomes in gene discovery, GWAS results for SCMV resistance using expression values as a surrogate measure of PAV resulted in robust detection of the physical location of a known resistance gene when using the B73 reference that contains the gene, but not when using the PH207 reference. This study provides the valuable resource of the Oh43-type PHJ89 genome assembly as well as SNP and expression data for 942 individuals generated using three different reference genomes.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1972

Spectral characteristics of normal and nutrient-deficient maize leaves

Reflectance, transmittance and absorbance spectra of normal and six types of mineral-deficient (N,P,K,S,Mg and Ca) maize (Zea mays L.) leaves were analyzed at 30 selected wavelengths along the electromagnetic spectrum from 500 to 2600 nm. Chlorophyll content and percent leaf moisture were also determined. Leaf thermograms were obtained for normal, N- and S- deficient leaves. The results of the analysis of variance showed significant differences in reflectance, transmittance and absorbance in the visible wavelengths among leaf numbers 3, 4, and 5, among the seven nutrient treatments, and among the interactions of leaves and treatments. In the reflective infrared wavelengths only treatments produced significant differences. The chlorophyll content of leaves was reduced in all deficiencies in comparison to controls. Percent moisture was increased in S-, Mg- and N- deficiencies. Positive correlation (r = 0.707) between moisture content and percent absorption at both 1450 and 1930 nm were obtained. Polynomial regression analysis of leaf thickness and leaf moisture content showed that these two variables were significantly and directly related (r = 0.894).

Al-Abbas, A. H.↗