Source: VIRGINIA POLYTECHNIC INSTITUTE submitted to NRP
DEVELOPMENT OF ADVANCED PHYSIOLOGICAL AND MOLECULAR MARKERS FOR STRESS TOLERANCE IN PEANUT
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
COMPLETE
Funding Source
AFRI COMPETITIVE GRANT
Reporting Frequency
Annual
Accession No.
1011983
Grant No.
2017-67013-26193
Cumulative Award Amt.
$470,000.00
Proposal No.
2016-10548
Multistate No.
(N/A)
Project Start Date
Mar 1, 2017
Project End Date
Feb 28, 2021
Grant Year
2017
Program Code
[A1152]- Physiology of Agricultural Plants
Recipient Organization
VIRGINIA POLYTECHNIC INSTITUTE
(N/A)
BLACKSBURG,VA 24061
Performing Department
Tidewater AREC
Non Technical Summary
Current peanut breeding programs have been successful with the development of high-yielding cultivars with biotic and abiotic stress resistances, but the current breeding approach is too slow and for some traits, such as drought tolerance, inefficient. Numerous physiological traits have been associated with peanut stress tolerance; and breeding for these targeted physiological traits was demonstrated superior to breeding for yield alone, but traditional methods are slow, uneconomical and inefficient for large breeding populations. Identification of more effective markers for the physiological traits is imperative to further mitigate stress and develop stress tolerant cultivars. Our hypothesis is that an integrated approach of plant physiological analysis and marker-assisted selection is a highly-efficient approach to improve biotic and abiotic stress tolerance in peanut. We propose new high throughput technologies including a field-scale infra-red thermography and unmanned aerial systems for canopy temperature and hyperspectral-derived vegetation indices, and LiDAR on carriage for canopy architecture estimations along with the development of DNA-based single nucleotide polymorphism marker for selection of phenotypic responses to environmental stress. The uniqueness in the diversity of environments this proposal covers, representing all major peanut growing areas in the U.S., warrants the discovery and refining of suitable physiological and molecular approaches for the entire U.S. peanut production and breeding.
Animal Health Component
50%
Research Effort Categories
Basic
10%
Applied
50%
Developmental
40%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
2031830102050%
2012499104050%
Knowledge Area
203 - Plant Biological Efficiency and Abiotic Stresses Affecting Plants; 201 - Plant Genome, Genetics, and Genetic Mechanisms;

Subject Of Investigation
1830 - Peanut; 2499 - Plant research, general;

Field Of Science
1040 - Molecular biology; 1020 - Physiology;
Goals / Objectives
Our long-term goal is to identify more effective physiological and molecular markers to mitigate stress and develop more stress tolerant peanut cultivars. To accomplish this we set a line of objectives, namely:1. Advance high-throughput modern technologies to estimate plant responses to abiotic stresses includingfield-scale infrared thermography, LiDAR technology and hyperspectral-derive vegetation indices from unmanned aerial systems (UAS).2. Develop machine-learning and big data management approaches for the stress-related traits collected with the modern technologies.3. Test the modern technologies on the U.S. peanut mini-core collection to determine the efficacy of the modern technologies and theis physological significance when compared with yield and other detailed physiological traits.4. Develop DNA-based single nucleotide polymorphism (SNP) markers for selection of phenotypic responses to abiotic stress.
Project Methods
Physiological techniques will include relatively large-plots field testing and controlled conditions, either in the field under rainout shelters or a green house in pots and small plots. As plant material, we will use the U.S. mini-core colection (COC) with regional checks. For the large-plot testing, we will use all 112 genotypes of the COC and checks to bring the total number to 120 in years 1 and 2. From these,24 contrasting genotypes will be identified and used in controlled condition experiments in years 2 and 3. In controlled conditions, water stress will be imposed from 40 days after planting (DAP)or beginning flowering to 105 DAP. During this time, measurements will be taken regularly with traditional devices but also with the SmartField Sentinel IR thermography, LiDAR, and UAS systems; and will include canopy temperature, nornalized difference vegetation index (NDVI) and RGB-derived vegetation indices. The LiDAR system will be used for canopy architecture estimations. Other measurements will include leaf wilting, visually, biomass, flower abundance, paraheliotropism, chlorophyll stability, CO2 assimilation, plant height, and yield and quality after harvest. Available software, e.g., Pix4D, ArcGIS, Imaje J, along with software in development for the modern techniques will be used for data collection.The molecular techniques include SNP analisys using the Qiagen DNAeasy kit, and bioinformatics using the newest versions of peanut A-genome and B-genome diploid sequences. Linkage disequilibrium andalysis and high-resolution association mapping using SPAGeDi will also be performed.

Progress 03/01/17 to 02/28/21

Outputs
Target Audience: Undergraduate and graduate students: Training opportunities were provided in Oklahoma, Texas, and Virginia on the design and development of automatic control systems, data acquisition, data processing and analysis algorithms, and field experiments. Visiting scientists: Training was provided in all three states on automatic, high-throughput data collection and processing system for peanut; share methodologies, experimental results, and challenges through technical conferences and meetings. Peanut producers and industry: In all three states, results on the developed technologies were presented at field days in three successive years during the award (see details below). Changes/Problems:In TX, due to the pandemic in 2020, there was limited ability to conduct field experiments, in a similar manner as in 2018 and 2019. Instead, the focus was on data analysis from previous years. In OK, due to unexpected issues with the drone system, peanut crop and weather conditions, and malfunctions of cameras, the 2019 data collection was not of good quality. In addition, due to the pandemic in 2020, there was limited ability to conduct experiments similarly with 2017 and 2018. Hence the algorithms for analyzing data collected with the drone-based platform were not able to be tested and optimized. In VA, the focus in 2020 was not on additional field experiments, but on data analysis, graduation of the PhD student Sarkar, and thesis and manuscripts preparation. These were accomplished with no problems. What opportunities for training and professional development has the project provided? In all three states, the project provided training opportunities to multiple undergraduate and graduate students on the design and development of automatic control systems, data acquisition, data processing and analysis algorithms, and a drone system. The students also gained knowledge on field experimental design and implementation through field tests. With the multidisciplinary team for this project, the students obtained the experience to work with researchers, scientists, and students from different disciplines and institutions. In addition, the students had the opportunities to present the research in regional, national, and international meetings and conferences. In TX, Technician Jennifer Chagoya and Research Associate Hanh Pham obtained experience in taking field measurements associated with response to water deficit. Jennifer performed statistical analysis of 2017 data. In VA, postdocs Joseph Oakes and Alexandre-Brice Cazenave participated at data collection and, along with PhD student Sayantan Sarkar, developed prediction models for plant morphological and physiological characteristics associated with peanut yield under water deficit including plant height and lateral growth, leaf area index, biomass, and wilting. In TX, several postdocs developed prediction models for peanut crop growth from the collected UAV images. How have the results been disseminated to communities of interest?The results were disseminated to researchers, scientists, and producers through journal publications, presentations in meetings and conferences, and field day demonstrations. Examples include: Peanut Field Tour at the USDA-ARS-CSRL Research Farm, August 8, 2019 Peanut Experiments at the USDA-ARS-CSRL Farm, August 7, 2018 Peanut Experiments at the USDA-ARS-CSRL Farm, September 15, 2017 VA-NC Pre-harvest Field Tour, Farmer field, Williamston, NC, Sep 2019 VA Peanut Field Tour, Tidewater AREC, Aug 14, 2018 & Jul 29, 2019 What do you plan to do during the next reporting period to accomplish the goals? Nothing Reported

Impacts
What was accomplished under these goals? Field responses to water deficit of 104 peanut accessions, the U.S. peanut mini-core germplasm collection, were collected in Oklahoma, Texas, and Virginia in 2017. Ground-based measures were taken at multiple times during the growing season, and included plant height, lateral growth, duration from planting to beginning flowering, flower number per plant, SPAD chlorophyll content,paraheliotropism, wilting, and yield. Statistically-significant differences were found for all traits measured. Comparing results among locations (TX, OK, VA), highly-significant correlations were found for plant height and lateral growth, flower production, SPAD chlorophyll content, and pod yield. Further, proximal and aerial remote sensing measurements were taken includingcanopy temperature (CT), normalized difference vegetation index (NDVI), and red-green-blue (RGB) reflectance of peanut plants. The RGB reflectance was used to calculate vegetation indiceswere Blue Green Pigment Index (BGI, Zarco-Tejada et al, 2005), Red-Gree Ration (RGR, Gamon and Surfus, 1999), Normalized Plant Pigment Ration (NPPR, Sarkar, 2021), Normalized Green Red Difference Index (NGRDI, Tucker, 1979), Plant Pigment Ration (PPR, Metternicht, 2003), and Normalized Pigment Chlorophyll Index (NPCI, Penuelas et al., 1994). In addition, the proximal and aerial RGB images from a digital camera were used to extract color space indices intensity, saturation hue angle, lightness, a*, b*, u*, and v* from the CIE-Lab and CIE-Luv; and the green vegetation indices green area (GA) (percentage of pixels in 60-120° hue angle) and greener area (GGA) (percentage of pixels in 60-120° hue angle) were computed. These indices, were analyzed for association with the morphological and physiological characteristics of peanut under water stress, and used to develop prediction models for the plant characteristics. Models with over 85% accuracy were derived for plant height, lateral growth, leaf area index, biomass, wilting and yield. The models used simple and multiple linear regression, and ordinal and binary logistic regression. Artificial Neural Network regression was used to estimate peanut leaf area index and lateral growth. Plant height was constructed from 3-D reconstruction from the RGB imagines in Arc-GIS software based on digital surface models as described by Sarkar et al. (2020) and Sarkar (2021). In the two succeeding years, 2019 and 2020, a subset of 28 accessions with contrasting responses across all three states were planted in open field in TX and OK and under rain exclusion shelters in VA for detailed physiological ground measurements, and proximally and aerially images were further used as described above to refine and validate the high-throughput models. In addition, post-harvest data were expanded to include 100-seed weight, harvest index. In TX, a ground penetrating radar was used to collect root mass data. Refinement of parameters was performed, and a significant correlations between GPR and pod weight was observed (Dobreva et al. 2021, submitted). In TX, DNA was extracted from 125 accessions of the minicore collection plus checks, and was sent for analysis on the Axiom Array II peanut SNP chip. Approx. 8,200 polymorphic SNPS were used in the GWAS analysis. Using field response traits and yield from TX, OK, and VA, 99 unique significant marker-trait associations for four of the traits were obtained at p<0.0001. Nine SNPs were common to two or three traits. MTAs were found for some additional traits also. In OK, a ground-based, high-throughput peanut data collection system, equipped with a 3D range camera, RGB cameras, and thermal camera, was developed and tested under field conditions. At the end of the project, a smaller, compact system with the same capabilities was constructed. The compact system is easier to transport to field sites. In all three states, sets of data and image analysis algorithms using machine learning methods was developed to extract feature information from the collected data, including peanut canopy structure, color, and flower counting, etc. In all three states, a drone platform with a thermal camera, an RGB camera, and an NDVI camera was designed, developed, and tested under field conditions. A total of 26 journal articles, conference presentations, and other publications (19 reported here and 7 in previous reports) were produced during the four years (3 funded and one extended with no additional funds) of this project.

Publications

  • Type: Conference Papers and Presentations Status: Published Year Published: 2017 Citation: Balota, M. 2017. Using UAVs for phenotyping yield and abiotic stress. The 49th Am. Peanut Res. Edu. Soc. annual meeting, July 11-13, Albuquerque, NM.
  • Type: Theses/Dissertations Status: Published Year Published: 2021 Citation: Sarkar, S. 2021. Development of high-throughput phenotyping methods and evaluation of morphological and physiological characteristics of peanut in a sub-humid environment. PhD Thesis. Virginia Polytechnic Institute and State University.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2017 Citation: Balota, M., Oakes, J., Sadeghpour, A., Sinclair, T., & Isleib, T. 2017. Ground and aerial remote sensing for peanut drought tolerance phenotyping. ASA-CSSA-SSSA annual meeting, Oct 22-25, 2017, Tampa, FL.
  • Type: Other Status: Published Year Published: 2020 Citation: Burow, M. D., R. Oteng-Frimpong, I. Faye, and Charles E. Simpson. (Jun 2020) Breeding and Enhancement of Resistance to Leaf Spot, Tolerance to Water Deficit, and Improved Oil Composition in Groundnut. Annual Meeting of the Peanut Innovation Laboratory (virtual).
  • Type: Other Status: Published Year Published: 2019 Citation: Burow, M. R. Oteng-Frimpong, I. Faye, and C. Simpson. (Jul. 2019). Breeding and Enhancement of Resistance to Leaf Spot, Tolerance to Water Deficit, and Improved Oil Composition in Groundnut. Joint meeting of the Ghana Groundnut Workshop and the Peanut Innovation Lab Groundnut Projects, Tamale, Ghana.
  • Type: Journal Articles Status: Submitted Year Published: 2020 Citation: Dobreva, I. D., H. G. Ruiz, I. Barrios Perez, T. Adams, M. D. Burow, and D. B. Hays. (submitted) Thresholding analysis and feature extraction from 3D ground penetrating radar data for noninvasive assessment of peanut yield.
  • Type: Book Chapters Status: Published Year Published: 2019 Citation: Gangurde, S. S., R. Kumar, A. K. Pandey, M. Burow, H. E. Laza, S. N. Nayak, B. Guo, B. Liao, R. S. Bhat, N. Madhuri, S. Hemalatha, H. K. Sudini, P. Janila, P. Latha, H. Khan, B. N. Motagi, T. Radhakrishnan, N. Puppala, R. K. Varshney, and M. K. Pandey. 2019. Climate-smart groundnuts for achieving high productivity and improved quality: current status, challenges, and opportunities. in Genomic Design of Climate-Smart Oilseed Crops. Springer. Cham, Switzerland. Pages 133-172.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2017 Citation: Prieto, C., M.A. Contreras, J. Ma, R.S. Bennett, K.D. Chamberlin, and Wang, N. 2017. Preliminary work in measuring peanut canopy architecture with LiDAR. In: Advances in Arachis through Genomics and Biotechnology Conference, 14-17 March, 2017. Cordoba, Argentina.
  • Type: Journal Articles Status: Published Year Published: 2018 Citation: Qiu, Q., Z. Fan, Z. Meng, Q. Zhang, Y. Cong, B. Li, N. Wang, and C. Zhao, 2018. Extended Ackerman Steering Principle for the coordinated movement control of a four-wheel drive agricultural mobile robot. Computers and Electronics in Agriculture. 152(2018): 40-50.
  • Type: Journal Articles Status: Under Review Year Published: 2021 Citation: Sarkar, S., Balota, M., Ramsey, F., and Cazenave, A.-B. 2021. Peanut wilting estimation from RGB color indices and logistic models. Frontiers in Plant Sci. (in review).
  • Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Sarkar, S., Cazenave, A. B., and Balota, M. 2020. High-throughput techniques to estimate leaf wilting in peanut. The 52nd Am. Peanut Res. Ed. Soc. July 14-15, 2020 (virtual).
  • Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Sarkar, S., Cazenave, A. B., and Balota, M. 2020. High-throughput estimation of peanut leaf wilting using RGB indices. ASA-CSSA-SSSA annual meeting, Nov 8-11, (virtual).
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: Sarkar, S., Cazenave, A. B., Oakes, J., McCall, D., Wade, T., Lynn, A., and Balota, M. 2020. High-throughput measurement of peanut canopy height using Digital Surface Models (DSMs). The Plant Phenome J. 3(1): e20003. doi:10.1002/ppj2.20003.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2017 Citation: Wang, N. and R. Bennett, 2017. Sensor Evaluations for In-Field Crop Monitoring. International Conference on Intelligent Agriculture (ICIA2017), August 12-15, 2017, Changchun, Jilin, China.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2018 Citation: Wang, N. and R. Bennett, 2018. Modern Technologies on Crop Sensing for Phenotyping Applications. ASABE Oklahoma Section Meeting, February 23, 2018, Stillwater, Oklahoma.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2018 Citation: Wang, N., H. Yuan, R. Bennett, K. Chamberlin, B. Luo. 2018. High-throughput phenotyping of peanut canopy architecture by ground LiDAR sensing technology and image analysis technology. ASABE Paper No. 1700855. The 2018 ASABE Annual Meeting, July 29-August 1, 2018, Detroit, Michigan, USA.
  • Type: Journal Articles Status: Published Year Published: 2018 Citation: Yuan, H., N. Wang, R. Bennett, D.Burditt, A. Cannon, K.Chamberlin, 2018. Development of a Ground-Based Peanut Canopy Phenotyping System. The Special Issue of the 6th IFAC Conference on Bio-Robotics BIOROBOTICS 2018. IFAC-PaperOnLine. 51(17):162-165.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2017 Citation: Yuan, H., Wang, N. and R. Bennett, Kelly Chamberlin, Bin Luo. 2017. High-throughput phenotyping of peanut canopy architecture by ground LiDAR sensing technology and image analysis technology. ASABE Paper No. 1801626. The 2018 ASABE Annual Meeting, July 29-August 1, 2018, Detroit, Michigan, USA.
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Balota, M., Oakes, J. 2017. UAV remote sensing for phenotyping drought tolerance in peanuts. Proc. Ann. Soc. Photographic Instrumentation Engineers (SPIE) Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II, 102180C (May 16, 2017) Vol 10218 (doi: 10.1117/12.2262496).


Progress 03/01/19 to 02/29/20

Outputs
Target Audience:This project is aimed at the development of more rapid and effective physiological and molecular markers for improvement of drougth tolerance in peanut. Thus, the primary audience is the peanut breeing programs in US and worldwide. But ultimately, the benefits of the work will include the growers, shellers, and processors of peanut. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?Texas Tech University/USDA-ARS PhD student Joy Sung has the opportunity to conduct GWAS analysis on field response data. William Dodge completed MS work on the development of photogrammetric analysis of crop growth using UAS. Mr. Dodge was awarded an NSF Graduate Fellowship to continue his Ph.D. studies on the development of remote sensing platforms for large-scale phenotyping and crop growth modeling. Oklahoma State University/USDA-ARS Two undergraduate students were involved in the hardware design and field testing. An additional seven undergraduate students participated in the field tests. One graduate student participated in processing data for 2019 season. Four professionals were involved in field testing, and one was highly involved in software development. Virginia Tech PhD student Sayantan Sarkar has led the efforts for this work in Virginia. He will complete his program in Dec 2020. Sarkar has published one peer-reviewed journal article, and prepared a second manuscript for submission. Both articles will be included in his Thesis, along with two others, one each the other objectives of his research proposal. How have the results been disseminated to communities of interest?Texas Tech University/USDA-ARS Data on the Lubbock, Texas trial and the development of the UAV analytics platform for growth analysis were presented to the public on August 6 at the annual field day. Oklahoma State University/USDA-ARS One journal publication One conference presentation Two journal publications are under preparation. Virginia Tech One journal article in print One journal article prepared (under internal review) Three conference presentations, one with growers in the audience, one with a combination of researchers and growers, and one with researchers. What do you plan to do during the next reporting period to accomplish the goals?Texas Tech University/USDA-ARS We will complete analysis of yield data, and correlate field responses of yield data with field measures. We will also perform GWAs analysis to identify DNA markers associated with plant response and yield. A manuscript will be submitted for review describing the development of the UAS analytics platform and its utility as well as the growth data for the genotype response to water deficit impact on growth and canopy development. Oklahoma State University/USDA-ARS Test the single-row peanut phenotyping platform Finalize the user interface software for data processing. Virginia Tech Sayantan Sarkar will continue to analyze the data, prepare journal articles and conference presentations, and a PhD thesis to defend in Dec 2020.

Impacts
What was accomplished under these goals? ? Texas Tech University/USDA-ARS A subset of the minicore collection was planted, ground-based measures taken, and plots were harvested. Data were taken at multiple time points during the growing season, and included flowering, SPAD chlorophyll, canopy temperature, paraheliotropism, wilting, and NDVI. Statistically-significant difference were found among accessions for all traits measured. Post-harvest data included harvest index and root mass on two replications. Ground penetrating radar data were taken for comparison to root mass data. Yield data have yet to be taken for the majority of the replications. Other growth and development data were collected at the Lubbock site using UAS and data are currently being analyzed. DNA was extracted from the entire minicore collection and was sent for analysis on the Axiom Array II peanut SNP chip. We have received back genotyping data and are beginning analysis of the data. Oklahoma State University/USDA-ARS Twenty-eight accessions were planted on May 17, 2019 with 3 replications in a water-restricted field, and 2 replications in a normal-irrigation field. The following data were collected: 54 DAP (7/9/2019): flower counts, SPAD chlorophyll 61 DAP (7/16/2019): flower counts, SPAD chlorophyll, paraheliotropism, wilting, Kinect canopy architecture, canopy temperature 75 DAP (7/30/2019): flower counts, SPAD chlorophyll, paraheliotropism, wilting, Kinect canopy architecture, canopy temperature 89 DAP (8/13/2019): flower counts, SPAD chlorophyll, paraheliotropism, wilting, Kinect canopy architecture, canopy temperature Yield and grade In addition, the following changes have been made to the hardware and software: Hardware: Fine-tune the control of the designed high-throughput peanut phenotyping platform. Add a solar-charging unit to the platform for long-term operations. Design and construct a one-row platform for portable phenotyping operations Software: Optimize the procedures for the image processing and analysis. Develop a user-interface for data process and presentation Virginia Tech Twenty-eight accessions were planted on Apr 29, 2019, in two-row plots, 0.9 wide and 2.4 m long, in six replications. In mid-summer, three replications were covered with rain out shelters to create a soil water deficit and three were allowed to receive plentiful rain and irrigation to create a well-watered regime. Data are in process of being analyzed by graduate student Sayantan Sarkar. Observations, measurements, and the dates when they were taken: Emergence, 5/9; Plant height, 5, 6, 7, 8, and 10 weeks from planting (WFP); Lateral vine growth, 5, 6, 7, 8, and 10 weeks from planting (WFP); Normalized Difference Vegetative Index (NDVI), 5, 7, 8, 10, 13, 14, 17, and 19 WAP; Canopy temperature (CT), 7, 8, 10, 13, 14, 17, and 19 WAP; Flower count, 8 WP; Wilting, 9, 13, 15, 17 WAP; Chlorophyll fluorescence, 12, 15, 18 WAP; Photosynthesis, 9, 11, 15, 18 WAP; RGB pictures, 15, 17 WAP; Aerial imagery (RGB, NDVI, and CT), 5, 6, 7, 17, and 18 WAP. This is relatively intense phenotypic observations that will be compared with data from TX and OK for identification of drought tolerant peanut accessions across large agro-climatic environments. The same information will be used to validate the molecular markers developed in TX associated with phenotypes of the entire core-collection evaluated in 2017 and 2018 in all three states.

Publications

  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Yuan, H., R.S. Bennett, N. Wang, K.D. Chamberlin, 2019. Development of a Peanut Canopy Measurement System Using a Ground-Based LiDAR Sensor, Frontiers in Plant Science. 10:203.
  • Type: Journal Articles Status: Awaiting Publication Year Published: 2020 Citation: Sarkar, S., Cazenave, A.B., Oakes, J., McCall, D., Wade, T., Lynn, A., and Balota, M. 2020. High-throughput measurement of peanut canopy height using Digital Surface Models (DSMs). The Plant Phenome J. (in press) doi:10.1002/ppj2.20003.


Progress 03/01/18 to 02/28/19

Outputs
Target Audience:This project is aimed at the development of more rapid and effective physiological and molecular markers to mitigate stress and select for drought tolerant peanut cultivars. Therefore, the primary oudience is the peanut breedeing programs in the USA and worldwide. This incudes breeders, graduate students working in abiotic stress breeding and physiology, as well as molecular researchers and bioinformatitions. Ultimately, the beneficiaries of the end products derived from this research, improved peanut varieties for yield under soil moisture stress, will be peanut growers in the contry and beyond. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?During 2018, several graduate students have been trained in Texas, Oklahoma, and Virginia. The students have been trained in plant physiology, molecular biology, and also remote sensing. Some students were funded from this project but other were funded through different venues while working on this project. For example, Saynatan Sarkar, a PhD student with Virginia Tech received funding from the Virginia Crop Improvement Association while performing his graduate research work on this project. How have the results been disseminated to communities of interest?On Aug 15, 2018, a Peanut Field Tour has been organized at TAREC in Suffolk, VA. Graduate student Sayantan Sarkar presented to an approximately 150 audience his results on remote sensing for high throughput phenotyping as part of this project. Over 85% people in the audience were peanut growers from all peanut growing counties in Virginia. What do you plan to do during the next reporting period to accomplish the goals?We will repeat a second year of detailed field evaluations as described above. We will continue to develop remote sensing tools for sensing drought and low yield production under soil moisture defficiency. We will also continue to developDNA-based single nucleotide markers for drought tolerance selection in peanut, along with intensifying presentations of these descoverisers to professional and grower meetings; will also hope to have the first articles from this work publish in refereed journal papers.

Impacts
What was accomplished under these goals? TTU-AREC, Texas Twenty eight accessions were planted on May 17, 2018 as 6 replications at the USDA-ARS in Lubbock; data were taken on 4 replications. The other two replications were used for determination of harvest index and comparison to ground-penetrating radar and camera measurements of flowering. Field notes were taken as follows: 63 DAP (7/19) - flower number (FLWR), chlorophyll content estimate (SCMR), canopy temperature (TEMP), paraheliotropism (CLOS) 75 DAP (7/31) - FLWR, SCMR, TEMP, CLOS, wilting (WILT) 90 DAP (8/15) - FLWR, SCMR, TEMP, canopy temperature depression (CTD), CLOS, WILT, Normalized Difference Vegetation Index (NDVI)(AM), NDVI(PM), in the morning (AM) and afternoon (PM) 104 DAP (8/29) - FLWR, SCMR, TEMP, CTD, CLOS, NDVI(AM), NDVI(PM) ANOVA indicated significant differences for all field measures except TEMP and CTD. Significant intermittent cloudiness on several of the measurement dates may have been responsible for the lack of statistically-significant difference for canopy temperature. Significant correlations were also noted among traits measured. It appeared that differences in means generally reflected the criteria used in 2017 to select the accessions (correlation across years has not been yet run). Yield and grade data are yet to be determined. OK-ARS Twenty eight accessions were planted in a randomized block design with four blocks. Plots were planted on May 18; blocks 1 & 2 were dug on 9/28, and 3 & 4 were dug on 10/12. Water was restricted to 25% ET from late June to late August. Field measurements were taken as follows: SCMR, FLWR, CLOS, and WILT data were collected 3-4 times during the season on 7/3, 7/17, 7/25, 7/31, and 8/31. After harvest, yield, 100-seed weight, and percent shell-out data were collected. ANOVA showed significant differences for all field measurements among accessions. Significant correlations were noted between SCRM and CLOS, the greener the leaves, the more they closed to protect from excessive radiation by the physiological mechanism called paraheliotropism. Virginia Twenty eight accessions were planted in 6 replications, 3 under well-water regime on May 10 and 3 under rain exclusion shelters on May 15. Varieties were arranged in a RCBD. The well-watered plots received 25 inches of precipitation from May through September uniformly distributed monthly and weekly. Because of this, disease pressure was high and the well-watered plots were dug on Sep 9. Under the rainout shelters, water was stopped on June 18 through August 27. Plants were smaller but healthier. They were dug on Sep 18. Field measurements were taken as follows: 1. Plant height (PH), width (PW), NDVI, CTD, WILT, SCMR, and specific leaf area (SLA) on 6/14, 6/18/, 6/21, 6/25, 7/06, 7/20, 7/27, 8/03, and 8/27. 2. Aerial remote sensing of NDVI, TEMP, and red-blue-green (RGB) were performed on 6/11, 6/25, 7/02, 8/27, and 9/10. 3. After harvest, yield, 100-seed weight, and percent shell-out were collected. For water stressed plots alone, ANOVA shewed significant differences for all traits except ground NDVI and CTD. Significant correlations were noted among yield and 100-seed weight and WILT with more wilted plants having less yield and 100-seed weight. Accessions with greener leaves yielded significantly better and had more weight for 100 seeds sample than less green leaves with reduced chlorophylls. Interestingly, WILT was significantly more for taller than for shorter plants. Graduate student Sayantan Sarkar identified the steps and demonstrated the suitability of the Digital Surface Models to estimate peanut plant height. He presented this information at several conferences, won the first prize at the GIS poster graduate student competition, and prepared a manuscript to be submitted for publication. Summary of results across three states: There were numerous consistencies for the morphological and physiological traits of the 28 accessions among the three states under reduced soil water availability. For example, in all three states, it was clear that smaller leaves appeared to be greener, probably from chlorophyll concentration on a smaller leaf area, than larger leaves. Small leaves were less prone to wilting in TX and OK. In VA, the relationship between SCMR and WILT was not significant. However, in VA, SLA was directly related with plant wilting, showing the same thing, that smaller leaves wilt at a lesser extent than larger leaves. In VA, wilting had a significant negative effect on yield and the 100-seed weight, and greener leaves produced better yields under reduced soil moisture than less green leaves. This is in agreement with earlier literature and demonstrates that our selected accessions for detailed physiological, genetic, and remote sensing investigation will provide us with means for the advancement of physiological and molecular marker selection for drought tolerance in peanut. ?

Publications

  • Type: Conference Papers and Presentations Status: Published Year Published: 2018 Citation: Sarkar, S., Balota, M., Oakes, J., Burrow, M., & Bennett, R. (2018). Use of Aerial Imagery and Digital Elevation Models for Deriving Plant Height of Peanuts. In 2018 ASA and CSSA Meeting. Baltimore, MD.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2018 Citation: Sarkar, S., Balota, M., & Oakes, J. (2018). Deriving Peanut Plant Height from Aerial Imagery and Digital Elevation Models. American Peanut Research and Education Society (APRES), July 10-12, Williamsburg, VA.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2018 Citation: Sarkar, S., Oakes, J., Sadeghpour, A., & Balota, M. (2018). High-throughput phenotyping of biomass and biomass sorghum using remote sensing and computer vision techniques. Poster session presented at the meeting of 2018 GIS and Remote Sensing Symposium, Virginia Tech (Sarkar won the first price in the graduate student competition).
  • Type: Conference Papers and Presentations Status: Published Year Published: 2018 Citation: Burow, M., Balota, M., Bennett, R., Wang, N., Payton, P., Mahan, J., . . . Sung, C. (n.d.). Evaluation of the U.S. minicore collection under water deficit in three states. American Peanut Research and Education Society (APRES), July 10-12, Williamsburg, VA. American Peanut Research and Education Society (APRES), July 10-12, Williamsburg, VA.


Progress 03/01/17 to 02/28/18

Outputs
Target Audience:Researchers, agronomists, geneticists, breeders, students. Changes/Problems:SmartField (the company that makes our thermographic camera system for the Texas team) is likely going out of business (or they may be in the process of being purchased by another company). Regardless, there is no confidence that we are going to be able to get the data analysis support from them and we would like to propose that we go back to using point measurements with FLIR. This will be discussed and decided in a February meeting. What opportunities for training and professional development has the project provided? Nothing Reported How have the results been disseminated to communities of interest? Nothing Reported What do you plan to do during the next reporting period to accomplish the goals?Soon, we will have a conference call to make plans for the summer 2018. We will decide which 20% contrasting genotypes from the over 100 in the mini-core will be used for detailed physiological measurements in 2018 and 2019.

Impacts
What was accomplished under these goals? The new technologies, field-scale infrared thermography, LiDAR and UAS, were used during the summer to collect remotely plantimages that will later be used to estimate the physiological and agronomic plant charactestics directly measured. Development of machine-learning and big data management has started in Oklahoma by hireing of a post-doc with computer science background. DNA from seeds collected from over 100 mini-core collection genotypes was extracted and it will be sent for SNP chip analysis.

Publications

  • Type: Other Status: Published Year Published: 2018 Citation: Balota, M., Bennett, R., Burow, M., Payton, P., and Wang, N. 2018. NIFA PI Progress Report for the project "Development of Advanced Physiological and Molecular Markers for Stress Tolerance in Peanut".