Data and code from: Assessing varied maize germplasm lines for resistance to fall armyworm and corn earworm with agronomic quality consideration

Authors: Woolfolk, Sandra; Jeffers, Daniel; Read, Quentin; Hawkins, Leigh; Babiker, Ebrahiem

Submitted to: Ag Data Commons
Publication Type: Database / Dataset
Publication Acceptance Date: 11/20/2025
Publication Date: 12/4/2025
Citation: Woolfolk, S.W., Jeffers, D., Read, Q.D., Hawkins, L.K., Babiker, E.M. 2025. Data and code from: Assessing varied maize germplasm lines for resistance to fall armyworm and corn earworm with agronomic quality consideration. Ag Data Commons. https://doi.org/10.15482/USDA.ADC/29882447.
DOI: https://doi.org/10.15482/USDA.ADC/29882447

Interpretive Summary: Because of the huge negative impact fall armyworm (FAW; Spodoptera frugiperda) and corn earworm (CEW; Helicoverpa zea) have on maize production, it is critically important to discover and describe genetic sources of resistance to FAW and CEW in maize. In the paper associated with this dataset, we present the results of a two-year trial where dozens of maize genotypes were screened for FAW and CEW resistance. We observed FAW and CEW damage and agronomic traits of all the genotypes. The dataset includes all the raw data and statistical software code in R needed to reproduce all the statistical models and their predictions that are presented in the paper. The results highlight variation in FAW and CEW resistance among genotypes, and their relationship with valuable agronomic traits, that may be useful for maize breeders and growers.

Technical Abstract: Fall armyworm (FAW; Spodoptera frugiperda) and corn earworm (CEW; Helicoverpa zea) cause massive damage to maize and other grain crops worldwide. It is critically important to discover and describe genetic sources of resistance to FAW and CEW in maize, and how they are related to agronomic performance of maize lines. In the paper associated with this dataset, we present the results of a two-year trial where 78 maize genotypes were screened for FAW and CEW resistance. We observed FAW and CEW damage and four agronomic traits across all the genotypes. The dataset includes all the raw data and statistical software code in R needed to reproduce all the statistical models and their predictions that are presented in the paper. Bayesian generalized linear mixed models were fit to each response variable; multinomial models with cumulative logit links were fit to response variables on a discrete categorical scale including FAW damage and agronomic trait scores, and a hurdle Gamma model with log link was fit to the CEW ear damage variable, a continuous response including zeros. Posterior estimates of mean scores were generated for each genotype and pairwise comparisons made. A custom R function is included to generate multiple comparison letter summaries from Bayesian posterior draws. The results presented here highlight variation in FAW and CEW resistance among maize genotypes that may be useful for maize breeders and growers.