Location: Corn Insects and Crop Genetics Research
2020 Annual Report
Accomplishments
1. Process-based crop growth model calibration. Predicting how different crop cultivars will respond to varying environmental, weather, and management conditions remains a significant challenge in agriculture. Variable and unpredictable cultivar response to weather and management creates a significant challenge for producers to consistently choose the best cultivars. ARS researchers at Ames, Iowa, have a collaboration with the Genomes to Fields Initiative (G2F), to contribute to publicly available data sets containing integrated genotype, phenotype, climatic, and soil data from diverse environments and genotypes. Diverse hybrids are grown at a large number of environments to collect agronomic performance data in order to develop modeling approaches to better predict cultivar response to diverse environmental conditions. The results of this collaboration have resulted in publicly available data sets from trials conducted in 2014 through 2018 which contain results of a total of 16 independent trials conducted in Ames, Iowa. These data are a novel resource used by researchers nationally and internationally to improve our ability to predict how crop cultivars respond to diverse environments.
Review Publications
Duangpapeng, P., Lertrat, K., Lomthiasong, K., Aguilar, F., Scott, M.P., Suriharn, B. 2020. Quantitative inheritance of total anthocyanin content in the tassel of small-ear waxy corn (Zea mays var. ceratina). SABRAO J. of Breeding and Genetics. 52(1):30-44.
Abel, C.A., Coates, B.S., Millard, M.J., Williams, W.P., Scott, M.P. 2020. Evaluation of XL370A-derived maize germplasm for resistance to leaf feeding by fall armyworm. Southwestern Entomologist. 45(1):69-74. https://doi.org/10.3958/059.045.0107.
Abel, C.A., Coates, B.S., Scott, M.P. 2019. Evaluation of maize germplasm from Saint Croix for resistance to leaf feeding by fall armyworm. Southwestern Entomologist. 44(1):99-103. https://doi.org/10.3958/059.044.0111.
Ramstein, G.P., Larsson, S.J., Cook, J.P., Edwards, J.W., Ersoz, E.S., Flint Garcia, S.A., Gardner, C.A., Holland, J.B., Lorenz, A.J., Mcmullen, M.D., Millard, M.J., Rocheford, T.R., Tuinstra, M.R., Bradbury, P., Buckler IV, E.S., Romay, M.C. 2020. Dominance effects and functional enrichments improve prediction of agronomic traits in hybrid maize. Genetics. 215:215-230. https://doi.org/10.1534/genetics.120.303025.
Mcfarland, B.A., Alkhalifah, N., Bohn, M., Bubert, J., Buckler IV, E.S., Ciampitti, I., Edwards, J.W., Ertl, D., Gage, J.L., Falcon, C.M., Flint Garcia, S.A., Gore, M., Graham, C., Hirsch, C., Holland, J.B., Hood, E., Hooker, D., Jarquin, D., Kaeppler, S., Knoll, J.E., Kruger, G., Lauter, N.C., Lee, E.C., Lima, D.C., Lorenz, A., Lynch, J.P., Mckay, J., Miller, N.D., Moose, S.P., Murray, S.C., Nelson, R., Poudyal, C., Rocheford, T., Rodriguez, O., Romay, M., Schnable, J.C., Schnable, P.S., Scully, B.T., Sekhon, R., Silverstein, K., Singh, M., Smith, M., Spalding, E.P., Springer, N., Thelen, K., Thomison, P., Tuinstra, M., Wallace, J., Walls, R., Wills, D., Wisser, R.J., Xu, W., Yeh, C., De Leon, N. Maize genomes to fields (G2F): 2014 –2017 field seasons: genotype, phenotype, climatic, soil and inbred ear image datasets. BMC Research Notes. 13,71 (2020). https://doi.org/10.1186/s13104-020-4922-8.
Falcon, C.M., Kaeppler, S.M., Spalding, E.P., Miller, N.D., Haase, N., Alkhalifah, N., Bohn, M., Buckler IV, E.S., Campbell, D.A., Ciampitti, I., Coffey, L., Edwards, J.W., Ertl, D., Flint Garcia, S.A., Gore, M.A., Graham, C., Hirsch, C.N., Holland, J.B., Jarquin, D., Knoll, J.E., Lauter, N.C., Lawrence-Dill, C.J., Lee, E.C., Lorenz, A., Lynch, J.P., Murray, S.C., Nelson, R., Romay, M., Rocheford, T., Schnable, P., Scully, B.T., Smith, M.C., Springer, N., Tuinstra, M., Walton, R., Weldekidan, T., Wisser, R.J., Xu, W., De Leon, N. Relative utility of agronomic, phenological, and morphological traits for assessing genotype-by-environment interaction in maize inbreds. Crop Science. 2020; 60:62-81. https://doi.org/10.1002/csc2.20035
