Zhanyou Xu
Research Geneticist
Office Phone: (612) 625-3709

Personal Background

Publications

Disciplinary Areas

Alfalfa and soybean genetics and breeding
Genomics selection
Bioinformatics
Machine learning 

Current Research Focus

Alfalfa germplasm screening and development for stress resistance/tolerance and yield
Alfalfa root architecture genetics and breeding for specific environments
Alfalfa genomic selection and machine learning for alfalfa digestible biomass yield 
Image recognition and deep learning for alfalfa live phenotyping

• Publications

  • (Clicking on the reprint icon will take you to the publication reprint.)
  • Quantifying winter survival of alfalfa (Medicago sativa L.)-(Peer Reviewed Journal)
    
    
  • Improved forage quality in alfalfa (Medicago sativa L.) via selection for increased stem fiber digestibility -(Peer Reviewed Journal)
    Xu, Z., Heuschele, D.J., Lamb, J., Jung, H.G., Samac, D.A. 2023. Improved forage quality in alfalfa (Medicago sativa L.) via selection for increased stem fiber digestibility. Agronomy. 13(3). Article 770. https://doi.org/10.3390/agronomy13030770.
    
  • Alfalfa gene editing-(Abstract Only)
    Curtin, S.J., Poudel, K., Scheaffer, C.C., Xu, Z., Samac, D.A. 2023. Alfalfa gene editing. Plant and Animal Genome Conference. January 13-18, 2023. San Diego, California.
    
  • What have we learned from 20 years of breeding for alfalfa root system architecture?-(Abstract Only)
    Xu, Z., Bucciarelli, B., Cao, Y., York, L., Gamble, J.D., Lamb, J.F., Samac, D.A. 2022. What have we learned from 20 years of breeding for alfalfa root system architecture?. Meeting Abstract. November 14-17, 2022. San Diego, California.
    
  • Apply spatial statistics analysis to ordinal data for soybean iron deficiency chlorosis -(Peer Reviewed Journal)
    Xu, Z., Cannon, S.B., Beavis, W.D. 2022. Apply spatial statistics analysis to ordinal data for soybean iron deficiency chlorosis. Agronomy. 12(9). Article 2095. https://doi.org/10.3390/agronomy12092095.
    
  • Good statistical practices in agronomy using categorical data analysis, with alfalfa examples having poisson and binomial underlying distributions -(Peer Reviewed Journal)
    Mowers, R.P., Bucciarelli, B., Cao, Y., Samac, D.A., Xu, Z. 2022. Good statistical practices in agronomy using categorical data analysis, with alfalfa examples having poisson and binomial underlying distributions. Crops. 2(2):154-171. https://doi.org/10.3390/crops2020012.
    
  • Objective phenotyping root system architecture using image augmentation and machine learning in alfalfa (Medicago sativa L.) -(Peer Reviewed Journal)
    Xu, Z., York, L.M., Seethepalli, A., Bucciarelli, B., Cheng, H., Samac, D.A. 2022. Objective phenotyping root system architecture using image augmentation and machine learning in alfalfa (Medicago sativa L.). Plant Phenomics. 2022. Article 9879610. https://doi.org/10.34133/2022/9879610.
    
  • Overwintering and yield responses of two late-summer seeded alfalfa cultivars to phosphate supply -(Peer Reviewed Journal)
    Wang, Y., Yu, L., Zhang, J., Xu, Z., Samac, D.A. 2022. Overwintering and yield responses of two late-summer seeded alfalfa cultivars to phosphate supply. Agronomy. 12(2). Article 327. https://doi.org/10.3390/agronomy12020327.
    
  • Phenotyping seedlings for selection of root system architecture in alfalfa (Medicago sativa L.) -(Peer Reviewed Journal)
    Bucciarelli, B., Xu, Z., Ao, S., Cao, Y., Monteros, M., Topp, C., Samac, D.A. 2021. Phenotyping seedlings for selection of root system architecture in alfalfa (Medicago sativa L.). Plant Methods. 7. Article 125. https://doi.org/10.1186/s13007-021-00825-3.
    
  • Divergent selection in wild rice for resistance and susceptibility to foliar diseases to decrease reliance on foliar fungicides-(Abstract Only)
    
    
  • Predictions from algorithmic modeling result in better decisions than from data modeling for soybean iron deficiency chlorosis -(Peer Reviewed Journal)
    Xu, Z., Kurek, A., Cannon, S.B., Beavis, W.D. 2021. Predictions from algorithmic modeling result in better decisions than from data modeling for soybean iron deficiency chlorosis. PLoS ONE. 16(7). Article e0240948. https://doi.org/10.1371/journal.pone.0240948.
    
  • Real-time live phenotyping-A path to next generation breeding and farming-(Trade Journal)
    Xu, Z., Jeo, R.T., Samac, D.A., Ramos-Giraldo, P., Reberg-Horton, C., Mirsky, S.B. 2021. Real-time live phenotyping-A path to next generation breeding and farming. Forage Focus. (March 2021):14-15.