Location: Plant Physiology and Genetics Research
2018 Annual Report
Objectives
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.
Approach
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.
Progress Report
This report documents progress for this new project which started in June 2018 and continues research from Project 2020-21410-006-00D, “Genetic Improvement and Phenotyping of Cotton, Bioenergy and Other Industrial Crops”. For more information about prior research, please consult the annual report for the previous project.
Objective 1 focuses on the utilization of both traditional and high-throughput phenotyping methods for the analysis of cotton, oilseeds and other industrial and biofuel crops cultivated under heat and drought conditions. Under Sub-objective 1A, previous field based high-throughput phenotyping (FB-HTP) research revealed the need to capture real-time weather data in addition to data from a core set of proximal sensors. The core sensors capture crop height, canopy temperature, and multi-spectral reflectance indices including a normalized difference vegetation index and a leaf chlorophyll index. The new weather sensors capture ambient air temperature, relative humidity, and radiant energy and have been fully incorporated into the core sensor deployment package and corresponding data processing workflow. The incorporation of the weather sensors enables the calculation of a basic crop water stress index and improved statistical models for the plant environment interaction. The improved models now enable scientists to better characterize cotton cultivars tolerant to heat or drought induced stress.
The addition of the weather sensors prompted a re-evaluation of the current FB-HTP deployment protocol to ensure optimization of trait data capture and sensor performance. The evaluation is ongoing for the duration of the cotton season and will be completed in November 2018. The evaluation parameters include: duration of sensor warm-up and shut-down, sensor calibration frequency, sensor to crop height, field travel pattern, field management, and collection meta-notes. The new sensors and deployment protocol are being tested in three cotton experiments under two field management strategies. The cotton trials are a continuation from last year and are described below. The data collected during the evaluation will be analyzed and compared against previous year’s data to determine if the deployment changes improve the efficiency and effectiveness of FB-HTP collections. These outcomes will be incorporated into a standardized deployment protocol for future collections.
The first cotton trial in which the FB-HTP deployment protocol is undergoing evaluation is comprised of a cotton population developed by ARS researchers in College Station, Texas, and Florence, South Carolina, which was received in 2016 and assessed for yield and fiber quality traits in multi-year, multi-location trials. The 2017 data were used to select 20 lines with superior performance. This year the selected lines were planted in replicated field trials in each of the three locations. In Maricopa, Arizona, the lines were assessed for heat tolerance using weekly FB-HTP techniques and complementary hand-held measurements including soil moisture, growth stage, flowering date, and pollen sterility. The field was managed by an ARS researcher in Maricopa, Arizona, with reduced-tillage and an overhead sprinkler irrigation system. After harvest, yield and fiber quality will be assessed. These lines will continue to be evaluated at Maricopa, Arizona, College Station, Texas, and Florence, South Carolina locations before final selection and release of germplasm adapted over multiple locations.
The second trial in which the FB-HTP protocol is undergoing evaluation is comprised of the Regional Breeders Testing Network (RBTN) population with 30 cotton lines. This year the lines were planted in two-row, 40-foot long plots with four replications per line. The lines were assessed for heat tolerance using weekly FB-HTP techniques and complementary hand-held measurements as above. The field was managed with traditional tillage for a cotton crop and sub-surface drip irrigation. After harvest, yield and fiber quality will be assessed. Under Sub-objective 2A, FB-HTP and hand-collected data will be analyzed, and lines selected to be included in genetic mapping populations.
Protocols for FB-HTP were established for camelina. Under Sub-objective 1B, a spring Camelina diversity panel was recently analyzed using a FB-HTP approach. The panel, which includes 250 accessions, plus check varieties, were planted in an alpha lattice design with three replications under well-watered and water-limited conditions in Maricopa, Arizona. The experiment was replicated by collaborators at the University of Nebraska, Lincoln, Nebraska. The water-limited treatment started after plant establishment but before the first flower stage and continued through maturity. Traditional measures, including flowering time, plant height and seed yield and oil content and composition, were collected and will be analyzed. FB-HTP data were collected throughout the growing season using FB-HTP methods that employed a Lee Agra tractor platform and included proximal data captured from electronic sensors that measured crop height, canopy multi-spectral reflectance and canopy temperature. The reflectance data will be used to construct vegetation indices. In collaboration with scientists from the Donald Danforth Plant Science Center, St. Louis, Missouri, the camelina panel was genotyped using Genotyping-by-Sequencing technology. Under Sub-objective 2B, Genome wide association studies (GWAS) will be used to associate genotypic and phenotypic data to identify molecular markers that are associated with and controlling the dynamic changes in plant growth under stress conditions, seed yield traits and stability and oil content and quality.
To screen and identify drought tolerant soybean germplasm under arid conditions, different soybean maturity groups were planted in Maricopa, Arizona, to optimize agricultural practices. Maturity groups 4 through 6, obtained from different sources, were planted to provide a wide range of responses to tested environments at three planting dates. The best maturity group that is suitable for southwestern conditions will be identified, and then diversity accessions from this targeted group will be used to screen for new drought tolerance traits and drought tolerant germplasm.
Sub-objective 1D, the USDA guayule collection was planted under arid conditions in Maricopa, Arizona, in a randomized complete block design with four replications. Germination percentages and seedling counts were recorded. For the duration of the experiment, data will be collected for traditional measurements including plant architecture, leaf classification and flowering time. At the end of the experiment, guayule plants from each plot will be harvested and fresh and dry biomass will be determined. Rubber and resin will be determined using standard protocols using an Ion chromatography system, Dionex ASE-200. FB-HTP data will be collected over the growth season on a weekly basis to calculate canopy temperature, plant height and different vegetation and chlorophyll content indexes to detect the differences among guayule germplasm in relation to growing conditions.
Accomplishments
