Approaches to Improve Yield in Sorghum. William L. Rooney Regents Professor Borlaug-Monsanto Chair in Crop Improvement Sorghum Breeding and Genetics

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1 Approaches to Improve Yield in Sorghum William L. Rooney Regents Professor Borlaug-Monsanto Chair in Crop Improvement Sorghum Breeding and Genetics

2 Productivity Trends in U.S. agronomic crops Crop Annual Gain Corn 2.31 % Cotton 2.13 % Soybean 1.83 % Wheat 1.17 % Sorghum 0.56 %

3 Genetic Gain in Grain Yield B.K. Pfeiffer Source Variance Percent Environment Genotype G x E Rep(Env.) Residual TOTAL t/ha annually Gizza and Gambin, 2015, Field Crops Research t/ha annually

4 Genetic Gain in Grain Yield Study B.K. Pfeiffer yield gain experiment yield gain Texas farm avg.008 t/ha.013 t/ha 61.75% Contribution of breeding to total yield gains

5 Why have yield gains lagged? Research Investments Private Public Production Environments Reduced area under Irrigated Acres Reduced production in higher rainfall areas Increased marginal ground Breeding Strategies Sterilization to produce new seed parents Defensive Trait Priority Phenotypic Expectations Plant Height Lodging

6 Source: USDA-NASS 6

7 Why have yield gains lagged? Research Investments Private Public Production Environments Reduced area under Irrigated Acres Reduced production in higher rainfall areas Increased marginal ground Breeding Strategies Sterilization to produce new seed parents Defensive Trait Priority Phenotypic Expectations Plant Height Lodging

8 Increasing Rates of Gain How do we improve the rates of gain? Modifications to the breeding approach to improve efficiency Doubled Haploids, Fertility modifications Genomic Selection Remote Sensing/Phenotyping Modifications to the plant ideotype Integration and Utilization of Traits Implications for Gene Flow to Wild Relatives

9 Summer Year 5 Summer Year 0 Winter Year 0 Summer Year 1 Summer Year 2 Summer Year 3 Winter Year 3 Increase and Testcross P1 x P2 F1 F2 F3 F4 F5 Early Evaluation Hybrids Doubled Haploid Technology Frequency of Induction Logistics of Production (temporal sterility) Genomic Selection Time of Application Suitable Training Modules Remote Phenotyping Methods (equipment, sensors) Processing (image analysis, conversion) Application in Breeding (traits, generation) Summer Year 6 Summer Year 7-8 Intermediate Hybrids Advanced Hybrids Traits Transgenic, Gene Editing Breeding Gene Flow to S. halepense?

10 Doubled Haploid Technology Revolutionized Corn Improvement HIP produce 15-20% haploid plants Benefits Faster to Inbred Lines No Selfing to Inbred Increased Numbers P1 X X P2 Phenotyping Testcrossing Testcross Hybrids Facilitates Genomic Selection F1 HIL Genomic Selection Minimal Genetic Recombination Drawbacks Minimal Genetic Recombination Haploids colchicine Doubled Haploids Inbred Lines

11 Doubled Haploid Technology in Sorghum With USCP funding, Pioneer identified and made available HIP inducer lines (~2% induction frequency). Others are screening for DH induction Procedure is logistically similar to corn Induction possible, but improvements are essential for deployment Increase in induction frequency Phenotypic identification of haploid seed Deployable and temporal Sterility Systems P1 F1 X X Haploids colchicine Doubled Haploids P2 HIL

12 TFMSA as a male gametocide (G Hodnett) Trifluorosulfonamide Produces male sterility with no obvious negative effects Ideal time of application is ~10-12 days prior to anthesis effective in multiple genotypes Hodnett et al., Can J. Pl. Sci, accepted Red=sterile Blue=fertile

13 Efficacy of TFMSA Field Studies (N. Boerman) Genotype 0 mg 5 mg 30 mg Self Pollinations (%) BTxARG a 0 b 0.3 b BTx a 1.6 b 3.0 b RTx a 1.3 b 2.2 b Two locations Sprayer Applications Three genotypes Application Breeding Crosses DH Deployment Hybrid Testing New Seed Parent Prior to Sterilization Reduce Sterilization

14 Remote Sensing/Phenotyping Website: Goal is to reduce manual phenotyping to increase Numbers evaluated Relative Accuracy Multidisciplinary project focused on UAS at Texas A&M University computer scientists Engineers Plant breeding: wheat, cotton, sorghum corn, roses, perennial grasses, turfgrasses Sorghum plant height, anthracnose, panicle number, size and shape and grain yield

15 Breeding Cross Summer Year 0 Winter Year 0 Self-pollinated P1 x P2 F1 UAV Estimations of Height to Sorghum Breeding Summer Year 1 College Station, Corpus Summer Year 5 Summer Year 6 Summer Year 2 CS, CC and LB (select) Summer Year 3 CS, CC and LB select rotated Summer Year 4 Grown in CS, CC and LB Testcross in CS Summer Year 7 F2 F3 F4 F5 Coded R-lines Early Evaluation Hybrids Intermediate Hybrids Advanced Hybrids Release Assess the relative value of UAVheights in a breeding program Early Generation Hybrids Advance Hybrids Methods of Assessment Repeatability and Variance partition Pearson correlations r Ranks and Minimum/Maximums Pugh et al., 2018; Plant Phenome

16 LESS ELITE HYBRIDS RSC ELITE HYBRIDS ADFH Pearson Correlations Repeatablity N P95 ADFH-June ADFH-June ADFH-June ADFH-June Low to ADFH-July Moderate ADFH-July Overall Mean 0.40 May 13 June 16 July 8 Field P(90) P(95) High RSC-June RSC-June RSC-June RSC-June Generally RSC-July 1 High RSC-July Overall Mean 0.81 May 13 June 16 July 8 Field P(90) P(95) Pugh et al., 2018; Plant Phenome L. Malombo, S Popescu, A Pugh and D Horne

17 Early Hybrids Ground Truth Early Hybrids UAS Estimate Advanced Hybrid Ground Truth Advanced Hybrid UAS Estimates Relative Rankings for Plant Height Measured on July 8 th, 2016 Pugh et al., 2018; Plant Phenome

18 Grain Yield Estimates Panicle Points 3D Point Cloud Cylinder Fitting Disk Stacking RGB DSM Panicle Extraction Panicle Delineation

19 Biomass Yield Allows assessment of overall vigor of a genotype over time, and determine at which point temporal shifts or crossovers occur between genotypes.

20 Anthracnose Infection Early infection difficult to discern Correlation becomes better as the diseased plots increased R square values Combined UAV 06/16 vs Ground 06/14 UAV 06/29 vs Ground 07/03 UAV 07/25 vs Ground 07/27 UAV 08/10 vs Ground 08/

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22 Remote Sensing/Phenotyping Digitizes early evaluation opportunities effectively Required further developments in equipment, processing and assessment Likely to be effective in the same manner as genomic selection Deployment Testing in our Program

23 Trait Development Industry based necessity Herbicide Resistance (ALS) likely first to market Gene Editing offers ease to market Non-regulated Some transgenics may be nonregulated (US) Stewardship to manage trait Gene-flow to wild relative If pest, then development of tolerance How concerned should we be about gene flow?

24 Gene Flow Assessment in Sorghum (M. Bagavathian) S. halepense/s. bicolor (and reciprocal) Feral Sorghum often derived from F2 progeny ~25% male sterile Viability/Fitness of Hybrids Ploidy Balance Endosperm formation Fertility/Perenniality Chance of Occurrence? Frequency of Hybridization?

25 S. Bicolor x Johnsongrass Crossing 4X hybrid 6X hybrid Sorghum x Johnsongrass; Spring 2017 Florets Ploidy Level of Live Progeny Genotype total Expanding 2x 3x 4x 5x 6x ATx623 39,607 35, a 371a 2 3 BTx623ms3 29,433 26, a 44b 0 2 BTx623TFM 32,913 28, a 483a 3 2 Total 101,953 90, A 898A 5 7 ATx631 45,777 39, a 25b 0 0 BTx631ms3 11,443 10, a 6b 1 0 BTx631TFM 40,647 35, a 18b 1 0 Total 97,867 85, A 49B 2 0 absence of bicolor pollen, Fertilization is ~90% Viable progeny ~1% Most are 4x=40 Female fertile Male fertile (contingent) 2n gamete formation differs among seed parents presence of bicolor pollen, Rates will be lower Studies underway Hodnett, Ohadi, Bagavatthian and Rooney, in preparation

26 Yield The Ideal Sorghum Ideotype r 2 =.07 r 2 = Pfeiffer et al., in review

27 The Sorghum Ideotype U.S. producers have not accepted/been offered taller hybrids (1.4 meters is tall Breeders have long seen a positive association between height and yield. Marketing and Producers perceive that taller hybrids lodge more. Is it true?

28 Crop Testing Program Texas West Texas Pearson Correlations Central Texas South Texas L Harvey

29 Conclusions If developed, DH Technology will Speed the breeding process Facilitate Genomic Selection DH breeding needs Higher Induction Rates Improved Gametocides Remote Sensing/Phenotyping allows For greater number of evaluations Visualization of new traits Remote Sensing/Phenotyping needs Optimized equipment/sensors Improved Processing methods Brent Bean

30 Conclusions Trait integration will Enhance value producer seed company Provide traits not available in sorghum Trait Integration requires Diligence to control gene flow Maintain the value of trait in seed Rethinking the Plant Ideotype is needed to facilitate changes in productivity is region/production system dependent Requires buy-in from all levels Breeder Marketing Agronomy Producer

31 Contributors People: Support Staff: D Collins, V Horn, S Labar, L Hoffmann, K Schaefer, G Hodnett, B Young Collaborators: B Klein, T Klein, J Mullet, S Kresovich, C Magill, G Odvody, L Prom, S Murray,, D Stelly, B Bean, JA Thomasson, S Popescu, J Jang, J Landivar, M Bagavatthian Post-Docs and Graduate Students: G Carvalho, B Pfeiffer, A Pugh, D Horne, L Harvey, S Ohadi L Malambo, A Chang Funding: Public: USDA-DOE BRDI, USDA Feedstock Genomics, South Central SunGrant, National SunGrant, United Sorghum Checkoff Program, US AID INTSORMIL, US AID Feed the Future, Texas Cropping Systems, Bioenergy Initiative, ARPA-E TERRA, and ARPA-E ROOTS Private: Ceres, Inc., Chevron, Inc., Pioneer HiBred, Forage Genetics