JEFFREY NEYHART
Research Geneticist (Plants)
Publications
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Local adaptation and broad performance are synergistic to productivity in modern barley
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(Peer Reviewed Journal)
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Ewing, P.M., Kantar, M.B., Killian, E., Neyhart, J.L., Sherman, J., Williams, J., Lachowiec, J., Eberly, J. 2024. Local adaptation and broad performance are synergistic to productivity in modern barley. Crop Science. 64(1):192-199.
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Where the wild things are: Genetic associations of environmental adaptation in the oryza rufipogon species complex
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(Peer Reviewed Journal)
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Wang, D.R., Kantar, M.B., Murugaiyan, V., Neyhart, J.L. 2023. Where the wild things are: Genetic associations of environmental adaptation in the oryza rufipogon species complex. Genes, Genomes, Genetics. https://doi.org/10.1093/g3journal/jkad128.
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Adapting perennial grain and oilseed crops for climate resiliency
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Jungers, J., Runck, B.C., Ewing, P.M., Maaz, T., Carlson, C.H., Neyhart, J.L., Fumira, N., Bajgain, P., Subedei, S., Sharma, V., Senay, S., Hunter, M.C., Cureton, C., Gutknecht, J.L., Kantar, M.B. 2023. Adapting perennial grain and oilseed crops for climate resiliency. Crop Science. 63(4):1701–1721. https://doi.org/10.1002/csc2.20972.
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A workflow for segmenting soil and plant X-ray computed tomography images with deep learning in Google’s Colaboratory
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(Peer Reviewed Journal)
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Rippner, D.A., Raja, P., Earles, J.M., Momayyezi, M., Buchko, A., Duong, F., Forrestel, E., Parkinson, D., Shackel, K., Neyhart, J.L., McElrone, A.J. 2022. A workflow for segmenting soil and plant X-ray computed tomography images with deep learning in Google’s Colaboratory. Frontiers in Plant Science. 13. Article 893140. https://doi.org/10.3389/fpls.2022.893140.
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Genomic-environmental associations in wild cranberry (vaccinium macrocarpon ait.)
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(Peer Reviewed Journal)
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Neyhart, J.L., Kantar, M.B., Zalapa, J.E., Vorsa, N. 2022. Genomic-environmental associations in wild cranberry (vaccinium macrocarpon ait.). G3, Genes/Genomes/Genetics. https://doi.org/10.1093/g3journal/jkac203.
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Accurate predictions of barley phenotypes using genomewide markers and environmental covariates
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(Peer Reviewed Journal)
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A workflow for segmenting soil and plant X-ray CT images with deep learning in Google’s Colaboratory
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Rippner, D.A., Raja, P., Earles, J., Buchko, A., Momayyezi, M., Duong, F., Parkinson, D., Gupta, L., Forrestel, E., McElrone, A.J. 2022. A workflow for segmenting soil and plant X-ray CT images with deep learning in Google’s Colaboratory. ArXiv. https://doi.org/10.48550/arXiv.2203.09674.
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Optimizing the choice of test locations for multi-trait genotypic evaluation
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(Peer Reviewed Journal)
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Neyhart, J.L., Gutiérrez, L., Smith, K.P. 2021. Optimizing the choice of test locations for multi-trait genotypic evaluation. Crop Science. 62(1):192-202. https://doi.org/10.1002/csc2.20657.
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