Location: Plant Physiology and Genetics Research2019 Annual Report
The objectives of the plan concentrate on utilizing advanced phenomic and genomic approaches to genetically improve cotton, oilseed crops, bioenergy and industrial crops and expand their use for food, feed, fuel, and fiber production for United States agricultural sectors and global use. To reach that goal our specific objectives are: Objective 1: Use existing and newly developed field-based phenotyping methods to evaluate cotton, oilseeds, and other industrial and biofuel crops, and utilize the results to enable effective use of high-througput phenotyping (HTP) methodology for crop genetic improvement and management. Sub-objective 1A: Field-based evaluation of cotton using high-throughput phenotyping and conventional methods for germplasm improvement and crop management. Sub-objective 1B: Field-based phenotypic evaluations for biofuel crop camelina using high-throughput and traditional phenotyping technologies for traits related to drought stress. Sub-objective 1C: Use high-throughput and traditional phenotyping strategies to identify soybean germplasm with abiotic stress tolerance traits. Sub-objective 1D: Phenotypic characterization of USDA guayule collection under abiotic stress conditions and Arizona growing conditions using traditional and high-throughput phenotyping technologies. Objective 2: Utilize various new and conventional genetic approaches to identify genes and associated molecular markers conditioning abiotic stress tolerance in arid environments, and determine relationships with important agronomic traits. Sub-objective 2A: Identify molecular markers associated with genes involved in temporal patterns with abiotic stress tolerance and agronomic traits in cotton using high-throughput phenotyping. Sub-objective 2B: Identifying alleles/genes and associated molecular markers conditioning yield and abiotic stress tolerance and related traits in bioenergy crop, camelina. Sub-objective 2C: Identify genes/alleles and associated molecular markers conditioning yield and abiotic stress tolerance in soybean.
The objectives of the plan will be carried out using various high through-put phenotyping (HTP) approaches used to identify and improve cotton, camelina, soybean and guayule crops with increased tolerance to abiotic stress and stable productivity. For each crop, a genetic population/diversity panel will be planted under well-watered (WW) and water-limited treatments, based on agronomic recommendations of each crop, in replicated design over several years. The HTP data will be collected on a weekly basis throughout the growing season using HTP platforms that use electronic sensors to measure crop height, canopy multi-spectral reflectances and canopy temperature. In addition to HTP measurements, morphological, physiological and agronomic traits including plant height, lodging score, and flowering date will be collected during the growing season. At physiological maturities, plots will be harvested and seed/lint yield will be determined. Oil and leaf wax contents and compositions will be quantified using standard gas chromatography analysis. For guayule, rubber and resin will be determined using an Ion chromatography system. Traits will be analyzed using MIXED model in statistical analysis software (SAS) software, where water treatments, different environments and accessions will be considered as fixed effects and replicates will be the random effect. Differences among lines within each water treatment will be determined with a Bonferroni adjustment for multiplicity test. G×E interaction analysis will be conducted for recorded traits where water treatments, replicates, environments, and accessions will be considered as random effects. Quantitative trait loci (QTL)/alleles/genes associated with complex traits like heat and drought stress tolerances will also be identified. Cotton recombinant inbred line (RIL) population and camelina and soybean diversity panels will be genotyped using Genotyping-by-Sequencing technology. Genome-Wide Association Studies (GWAS) and QTL analyses will be used to identify molecular markers that are associated with and controlling the dynamic changes in plant growth under stress conditions, crop productivity traits and stability and oil and wax content and quality (Objective 1). Best linear unbiased predictors (BLUPs) of each phenotypic trait will be determined using mixed model of SAS software. GWAS analyses will be conducted using the trait analysis by association, evolution and linkage (TASSEL) package. To find the best model that is able to detect the associations between phenotypic traits and single nucleotide polymorphism (SNP) markers, and reduce the number of false-positive associations, the Mixed Linear Models (MLM) approach of TASSEL will be used. Candidate genes from multiple GWAS analyses will be identified from genomic intervals in the reference genome assemblies. In cotton, QTL analyses will be conducted using the inclusive composite interval mapping (ICIM) program.
In support of Objective 1, research was conducted to improve the high-throughput phenotyping (HTP) data processing pipeline and phenotyping system stability. System validations were used to identify limitations and pursue improvements of data quality for subsequent project scale-up. A long-term solution was proposed for an adaptive HTP system with plug-n-play multi-modal sensors including multispectral cameras, infrared (IR) thermometers, and range finders to measure the biomass, temperature, and height of the plant canopy, respectively. All sensors and controllers have been tested in the lab and prepared for outdoor testing in the field to validate their performance and stability. A Mini LiDAR sensor array was also developed to more accurately measure plant height. To ensure the fast and wide coverage required for field phenotyping, an unmanned aerial vehicle (UAV) was evaluated and modified to include a 6-band multispectral instrument including a thermal band. A future HTP platform design that features drone networking was initiated to automate the field mapping simultaneously under the same weather/lighting conditions. The addition of the HTP drone network with ground sensor stations will promote advanced interpretations of the phenotypic data through multi-dimensional phenotyping maps via an in-field distributed Wireless Sensor Network (WSN). Sub-Objective 1A focuses on field-based evaluation of cotton using traditional and HTP methods for the analysis. The first cotton trial being evaluated is comprised of a population developed by ARS researchers in College Station, Texas, and Florence, South Carolina, which was received in 2016 and evaluated for yield and fiber quality traits in multi-year, multi-location trials in Texas, South Carolina, and Maricopa, Arizona. The 2017 data were used to select 20 lines with increased fiber quality and yield. In 2018 the 20 lines were further evaluated in replicated field trials at each of the three locations. This year, the 20 lines were planted again at each location in a randomized complete block design with four replicates at each location. In Maricopa, the lines will be assessed for heat tolerance using weekly HTP techniques and complementary, traditional measurements including soil moisture, growth stage, flowering date, and pollen sterility. After harvest, yield and fiber quality will be assessed. This year will be the final evaluation of these lines, and a germplasm release is anticipated. The second cotton trial being assessed is comprised of the Regional Breeders Testing Network (RBTN) population(s). In 2017 and 2018 the population(s) were evaluated for differences in chlorophyll content using both a traditional hand-punch method and an HTP method utilizing spectral reflectance from proximal sensors. After evaluation, lines with significant differences in chlorophyll content were identified using the traditional method but results did not correlate with the HTP method. It was determined the HTP method was not accurate enough for cotton genetic diversity trials at this time. The improvement of the HTP method for leaf chlorophyll content is now being addressed in the related project 2020-21000-013-00D, "Analysis and Quantification of G x E x M Interactions for Sustainable Crop Production" under Sub-objective 2A. The 2019 RBTN population will instead be evaluated for radiant use efficiency (RUE). This trait is under evaluation in the related project 2020-11000-013-00D for biomass and grain sorghum under Sub-objectives 3A and 3B. In support of Sub-objective 1B to characterize the phenotypic variation in soybean populations, we are optimizing the agronomic protocols of growing soybean under Arizona semi-arid conditions. Soybean cultivars belonging to different maturity groups were planted in replicated trials on three planting dates during April, May and June in Maricopa, Arizona. Results indicated that planting date had a significant effect on soybean growth and productivity, where soybean planting in April and May survived through the summer with set pods and acceptable quality of the harvested seed. Late planting in mid-June resulted in severe chlorosis and eventual abortion of seed set and overall negative impacts on seed quality. The results also revealed that soybean cultivars belonging to maturity group (MG) four, five and six had good adaptability for growing in Arizona conditions. Sub-objective 2C focuses on characterizing the phenotypic variation of camelina grown under field stress conditions, including drought and heat stresses. A spring Camelina sativa diversity panel was planted in Maricopa during the fall season under well-watered and water-deficit conditions in replicated trials. HTP data were collected on a weekly basis throughout the growing season using a Lee Avenger phenotyping tractor. In addition to HTP measurements, traditional morphological traits, including flowering time and plant height, were measured. At physiological maturity, plots were harvested for seed yield and seed weight was determined. Seed samples were analyzed for oil content and fatty acid compositions and glucosinolates using Near-Infrared Spectroscopy (NIRS). Preliminarily results indicated significant effects for water stress treatments as well as genotypes for the studied traits, where high phenotypic variations among genotypes and the variable effects of drought stress conditions on camelina were observed. Some genotypes showed high stability when grown under stressed conditions. To understand the phenotypic and genetic variations in traits related to rubber and resin production in guayule, the USDA guayule collection, including improved breeding germplasm and wild types collected from Northern Mexico and Southern Texas deserts, were grown in replicated field trials in Maricopa, Arizona, under two irrigation regimes. Morphological traits, including plant height, canopy volume and perimeter, number of main branches, stem thickness and leaf traits, were measured for one-year old shrubs. Initial results revealed variations in those traits among guayule accessions under stress and non-stress conditions. The growth vigor of guayule accessions was reduced in response to drought stress (reduced irrigation) compared to those grown under well-watered conditions. Sub-objective 2A focuses on identifying molecular markers associated with abiotic stress tolerance in cotton using an HTP approach. In 2017, germplasm from the Regional Breeders’ Testing Network population was identified with reduced canopy temperatures under drought, an indication of drought tolerance. These lines and others were evaluated for leaf wax and cutin monomer content and composition from greenhouse-grown plants. These traits are potentially associated with drought tolerance by conserving water loss, which can result in reduced canopy temperatures. Significant differences in leaf wax and cutin were identified and a publication has been submitted. These lines were used to develop a recombinant inbred line mapping population. The F2:F3 seed were increased in the greenhouse, bolls were harvested, and 60 F3:F4 seed from each cross were planted in the field this year. The F3:F4 lines will be evaluated for flowering date and maturity to facilitate next year’s experimental design, in which lines will be evaluated for drought tolerance.
1. Characterization of genetic diversity in a spring Camelina sativa diversity panel. Camelina sativa, a crop originating from southeastern Europe and southwestern Asia, is showing renewed public interest. Camelina has high potential to be used as a biofuel crop for semi-arid sustainable agricultural systems. To facilitate faster genetic enhancement and efficient breeding progress in camelina, ARS scientists in Maricopa, Arizona, in collaboration with scientists at the University of Nebraska, Lincoln, and the Donald Danforth Plant Science Center in St. Louis, Missouri, used a high-throughput genotyping-by-sequencing technology to explore genetic diversity and adaptation among Camelina sativa accessions and the possibility of utilizing single nucleotide polymorphic markers for genomic analyses and genetic enhancement. The camelina panel showed a high level of genetic diversity that could serve as the basis for developing new camelina cultivars with desirable characteristics such as high yield potential, high oil production and tolerance to abiotic stress, while being adapted to diverse environments.
2. Identification of candidate genes associated with leaf cuticular wax components in Camelina sativa. Camelina is an oilseed crop that shows great potential as a non-food source of biofuel, but the yields of the crop can be greatly diminished in water-limited environments. The cuticle of plant leaves contains a waxy protective layer that has low permeability to water, which directly affects the rate of leaf water loss and thus the susceptibility of plants to drought conditions. In collaboration with scientists at the Donald Danforth Plant Science Center in St. Louis, Missouri, ARS researchers in Maricopa, Arizona, identified candidate genes associated with as many as 50 phenotypic traits related to leaf wax accumulation in camelina and discovered molecular markers near or within those genes that were associated with these traits. These results and the associated molecular markers will benefit plant breeders and can be used as powerful tools in marker-assisted selection programs to breed improved Camelina sativa cultivars with superior abiotic resistance traits.
Abdel-Haleem, H.A., Waltz, Q., Leake, G. 2019. Tolerance of transplanted guayule seedlings to post-emergence herbicides. Industrial Crops and Products. 133:292-294. https://doi.org/10.1016/j.indcrop.2019.03.041.
Luo, Z., Brock, J., Dyer, J.M., Kutchan, T., Augustin, M., Scachtman, D., Ge, Y., Fahlgren, N., Abdel-Haleem, H.A. 2019. Genetic diversity and population structure of a Camelina sativa spring panel. Frontiers in Plant Science. 10:184. https://doi.org/10.3389/fpls.2019.00184.
Luo, Z., Tomasi, P., Fahlgren, N., Abdel-Haleem, H.A. 2019. Genome-wide association study (GWAS) of leaf cuticular wax components in Camelina sativa identifies genetic loci related to intracellular wax transport. Biomed Central (BMC) Plant Biology. 19:187. https://doi.org/10.1186/s12870-019-1776-0.
Thompson, A.L., Thorp, K.R., Conley, M.M., French, A.N., Andrade-Sanchez, P., Pauli, D. 2019. Comparing nadir and multi-angle view sensor technologies for measuring in-field plant height of upland cotton. Remote Sensing of Environment. 11:700-719. https://doi.org/10.3390/rs11060700.