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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Genetic Improvement for Fruits & Vegetables Laboratory » People & Locations » Jeffrey Neyhart
Jeffrey Neyhart
Research Geneticist (Plants)


Publications (Clicking on the reprint icon Reprint Icon will take you to the publication reprint.)
Of buds and bits: a meta-QTL study identifies stable QTL for berry quality and yield traits in cranberry mapping populations(Vaccinium macrocarpon Ait.) - (Peer Reviewed Journal)
Maule, A., Loarca, J., Diaz-Garcia, L., Lopez-Moreno, H., Johnson-Cicalese, J., Vorsa, N., Iorizzo, M., Neyhart, J., Zalapa, J.E. 2024. Of buds and bits: a meta-QTL study identifies stable QTL for berry quality and yield traits in cranberry mapping populations(Vaccinium macrocarpon Ait.). Frontiers in Plant Science. 15:1-20. https://doi.org/10.3389/fpls.2024.1294570.
Local adaptation and broad performance are synergistic to productivity in modern barley - (Peer Reviewed Journal)
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.
Where the wild things are: Genetic associations of environmental adaptation in the oryza rufipogon species complex - (Peer Reviewed Journal)
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.
Adapting perennial grain and oilseed crops for climate resiliency - ()
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.
A workflow for segmenting soil and plant X-ray computed tomography images with deep learning in Google’s Colaboratory - (Peer Reviewed Journal)
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.
Genomic-environmental associations in wild cranberry (vaccinium macrocarpon ait.) - (Peer Reviewed Journal)
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.
Accurate predictions of barley phenotypes using genomewide markers and environmental covariates - (Peer Reviewed Journal)
A workflow for segmenting soil and plant X-ray CT images with deep learning in Google’s Colaboratory - ()
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.
Optimizing the choice of test locations for multi-trait genotypic evaluation - (Peer Reviewed Journal)
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.