Location: Plant Physiology and Genetics Research2020 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, we developed a multi-modal sensing system for high throughput phenotyping (HTP) to enhance genetic improvement and management in soybean, camelina, and cotton. This new HTP system was equipped with low powered light weight sensors and a microcontroller to deliver a plug-n-play module with quick and easy mounting on any indoor or outdoor platform. The HTP system was adopted for plant monitoring in growth chambers in Maricopa, Arizona. Sub-objective 1A focuses on field-based evaluation of cotton using traditional and high-throughput phenotyping methods for the analysis. The first cotton trial evaluated (Sub-objective 1A.1) was a population developed by ARS researchers in College Station, Texas and Florence, South Carolina in 2016 and evaluated for yield and fiber quality traits in multi-year, multi-location trials. The 2019 field year was the last year for evaluation. The yield and fiber quality data from all three locations is being processed, we anticipate the release of breeding germplasm adapted to multiple locations. The second cotton trial evaluated (Sub-objective 1A.2) is comprised of selections from the Regional Breeders Testing Network (RBTN) population(s) and the North Carolina cotton association mapping population. These selections were made from trials in 2016 – 2019 for variation in leaf chlorophyll content, photosynthetic efficiency, light interception, and canopy architecture, all components of radiation use efficiency (RUE). High-throughput phenotyping methods are being developed to measure leaf chlorophyll content and is being addressed in another related project. Initial results in the cotton RUE trial indicate cotton has high leaf chlorophyll content and high light interception but is inefficient at photosynthesis. In support of Sub-objectives 1B and 1C, a seed characterization and counting system using computer vision technology is being developed to improve the speed and accuracy of characterization of small seeded (camelina) to large seeded (soybean) crops. To support Sub-objective 1B, 250 spring camelina accessions, plus ten commercial varieties, were planted in an alpha-lattice design, with three replications, under field conditions of Maricopa, Arizona. The genotypes were planted under well-irrigated and reduced-irrigation trials. The HTP data were collected on a weekly basis throughout the growing season. In addition to HTP measurements, traditional morphological and physiological traits including flowering time and plant height were collected. At physiological maturity, plots were harvested for seed yield and seed weight determination. Seed samples were collected for oil content, fatty acids, and glucosinolate compositions using near-infrared spectroscopy (NIRS). Data are initially processed and analyzed. Results revealed the high phenotypic variations for studied traits among genotypes and irrigation treatments. In support of Sub-objective 1C, 200 soybean genotypes belong to maturity group four (MG IV) were planted at Maricopa, Arizona in replicated trials, well-irrigated and reduced-irrigation. The experiments also included commercial varieties and standard drought tolerance genotypes as checks. Canopy wilting (CW) trait, a very common trait for drought tolerance evaluation in soybean, was recorded for genotypes planted at both trials. Data showed significant phenotypic variations for CW in stressed trial, where genotypes ranged from 20.9 units (drought tolerance) to 40.4 units (drought susceptible). There were no stress signs in unstressed trial where CW readings showed insignificant variations. Comprehensive statistical analyses over irrigation treatments, locations and years will be conducted. Sub-objective 1D focuses on understanding the phenotypic variations in traits related to rubber and resin production in guayule, a potential new crop for arid-lands and low input regions of Southwestern United States. A USDA guayule collection, including improved breeding germplasm and wild accessions collected from North Mexican and Southern Texas deserts, were grown in replicated field trials at Maricopa, Arizona, under two irrigation regimes (well-irrigation vs. reduced-irrigation). Morphological, resin and rubber related traits were measured. Results showed wide phenotypic variation in the studied traits among genotypes and irrigation treatments. Results indicated genotypes’ responses and stability under different irrigation regimes, in which water-stressed condition increased resin and rubber accumulation while well-watered condition increased dry weight biomass. Significant correlations between biomass-related traits and resin and rubber yields might suggest the possibility of selection for multiple traits at one time. The data indicated significant correlations between ploidy level and resin and rubber content. Sub-objective 2A focuses on identifying molecular markers associated with abiotic stress tolerance in cotton using a high-throughput phenotyping approach. In 2017, germplasm from the Regional Breeders Testing Network (RBTN) population was identified with reduced canopy temperatures under drought and with increased leaf wax, both traits are associated with drought tolerance. These lines were used to develop a reciprocal 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 in 2018. Five bolls from a single plant for each line were harvested. This year the F4:F5 seed was planted and the lines will be evaluated for maturity and growth type to facilitate next year’s experimental design in which lines will be evaluated for drought tolerance. To develop genomic tools and information for underpinning genome-wide association studies (GWAS) supporting objective 2B, the spring camelina panel was genotyped using genome-by-sequencing technology (GBS), resulting in identification of 6,192 high-quality nucleotide polymorphism (SNP) markers distributed throughout camelina’ genome. GWAS analyses revealed significant SNP markers on different chromosomes that were associated with plant height, seed weight and seed yield under water-limited and well-watered conditions. Initial results indicated that some genes might be actively triggered under either of the two conditions. Data from more environments, years, and locations will be combined for more comprehensive GWAS analyses.
1. Identification of candidate genes controlling soybean canopy greenness. Nitrogen (N) plays a key role in plants because it is a major component of chlorophyll and dark and light reactions of photosynthesis. Genotypic variation in canopy greenness provides insights into the variation of N and chlorophyll concentration, photosynthesis rates, and N fixation in legumes. Researchers from ARS in Maricopa, Arizona, Colombia, Missouri, and Stoneville, Mississippi, and researchers from universities of Arkansas and Missouri were the first to map soybean canopy greenness using unmanned aerial imaging and dark green color index (DGCI) measurements. The study identified genomic regions associated with the intensity of greenness of the soybean’ canopy, and genotypes with extreme DGCI values within the USDA soybean collection. Those genomic regions could be important resources for pyramiding favorable genes for improved N and chlorophyll concentrations photosynthesis rates, and N2 fixation ability in soybean breeding programs.
2. Identification of candidate genes controlling fatty acids profiles in rapeseed seeds. Finding environmentally responsible solutions to produce hydrotreated renewable fuels is an alternative path for carbon-based energy production. To meet market and user demands, current bioenergy feedstocks, such as rapeseed oil, must be optimized with respect to end-product composition and quality before these biofuels can be utilized for large-scale energy production. ARS scientists at Maricopa, Arizona, Peoria, Illinois, Morris, Minnesota, Sidney, Montana, Mandan, North Dakota, Temple, Texas, Ames, Iowa, Akron, Colorado, Pendleton, Oregon, and scientists from Idaho State University, University of Arizona and Cornell University, identified candidate genes controlling rapeseed fatty acid synthesis. These candidate genes could serve as precise targets for genomics-assisted breeding to directly alter seed oil composition and quality to meet market criteria. The outcomes from this research provide information on how genomics can be leveraged to enhance the speed and effectiveness of rapeseed cultivar development for biofuel production.
3. Phenotyping of USDA guayule germplasm collection. Guayule, a plant native to semi-arid regions of Northern Mexico and Southern Texas deserts, is a potential domestic source of natural rubber. To date there is no research on phenotyping the global USDA guayule collection, including improved germplasm and wild accessions collected from natural habitats, and possibly using this collection in guayule genetic improvement programs. ARS researchers from Maricopa, Arizona, explored the phenotypic diversity in traits related to rubber and resin production. The results summarized USDA guayule germplasm response and stability grown under different irrigation regimes. Water-stress increased resin and rubber accumulation while well-watered conditions increased dry weight biomass. This study lays the foundation for guayule breeding efforts to select parental candidates suitable for breeding programs to grow under different agricultural systems, to extend its growing areas into different geographical zones and to meet different end-user demands and goals.
4. High-throughput quantification of resin and rubber in guayule. Natural rubber (NR) is a critical industrial natural resource. However, the current production of NR, mainly harvested from Hevea rubber trees, is faced with many obstacles, including the shortage of supply due to increased demands, and the risks of fatal diseases in rubber-producing regions. Guayule is a domestic source for NR in the U.S. semi-arid and arid regions. ARS researchers at Maricopa, Arizona, have successfully adapted reliable high-throughput prediction models for the determination of resin and rubber in guayule using near-Infrared spectroscopy. The established models might be useful to form a simple, low-cost and efficient pipeline to maximize the rubber/resin phenotyping efficiency in guayule. The established models will enable guayule breeders and researchers to efficiently screen large populations of genotypes at fairly short time compared to the wet chemistry protocols currently being used.
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