Spatio-temporal modeling of high-throughput multispectral aerial images improves agronomic trait genomic prediction in hybrid maize

Authors: MORALES, NICHOLAS - Cornell University; ANCHE, MAHLET - Cornell University; KACZMAR, NICHOLAS - Cornell University; Lepak, Nicholas; NI, PENGZUN - Cornell University; ROMAY, M CINTA - Cornell University; SANTANTONIO, NICHOLAS - Cornell University; Buckler, Edward - Ed; GORE, MICHAEL - Cornell University; MUELLER, LUKAS - Boyce Thompson Institute; ROBBINS, KELLY - Cornell University

Submitted to: Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/18/2024
Publication Date: 3/12/2024
Citation: Morales, N., Anche, M.T., Kaczmar, N.S., Lepak, N.K., Ni, P., Romay, M., Santantonio, N., Buckler Iv, E.S., Gore, M.A., Mueller, L.A., Robbins, K.R. 2024. Spatio-temporal modeling of high-throughput multispectral aerial images improves agronomic trait genomic prediction in hybrid maize. Genetics. 227(1):iyae037. https://doi.org/10.1093/genetics/iyae037.
DOI: https://doi.org/10.1093/genetics/iyae037

Interpretive Summary: This study aimed to improve the understanding of how different factors influence crop growth in agricultural field experiments. The researchers used advanced techniques, including high-resolution aerial imagery captured by drones, to measure various aspects of the crops. They focused on a parameter called the normalized difference vegetation index (NDVI), which indicates plant health and growth. To analyze the data, the researchers developed a two-stage approach. In the first stage, they separated the effects of environmental factors from the genetic influences on crop growth over the entire growing season. This allowed them to understand how the environment impacted the crops separately from the inherent genetic traits. In the second stage, they combined the information about environmental effects with genetic data to create more accurate predictions of crop performance. They tested their approach using both computer-generated simulation data and real-world maize field experiments conducted in different years. The results showed that their two-stage approach significantly improved the accuracy of their predictions, helping them better understand the complex interactions between genetics, environment, and crop growth in the field. Overall, this study showed that considering spatial and environmental factors can enhance the accuracy of agricultural experiments and lead to a deeper understanding of crop performance in different conditions.

Technical Abstract: Design randomizations and spatial corrections have increased understanding of genotypic, spatial, and residual effects in field experiments, but precisely measuring spatial heterogeneity in the field remains a challenge. To this end, our study evaluated approaches to improve spatial modeling using high-throughput phenotypes (HTP) via unoccupied aerial vehicle (UAV) imagery. The normalized difference vegetation index was measured by a multispectral MicaSense camera and processed using ImageBreed. Contrasting to baseline agronomic trait spatial correction and a baseline multitrait model, a two-stage approach was proposed. Using longitudinal normalized difference vegetation index data, plot level permanent environment effects estimated spatial patterns in the field throughout the growing season. Normalized difference vegetation index permanent environment were separated from additive genetic effects using 2D spline, separable autoregressive models, or random regression models. The Permanent environment were leveraged within agronomic trait genomic best linear unbiased prediction either modeling an empirical covariance for random effects, or by modeling fixed effects as an average of permanent environment across time or split among three growth phases. Modeling approaches were tested using simulation data and Genomes-to-Fields hybrid maize (Zea mays L.) field experiments in 2015, 2017, 2019, and 2020 for grain yield, grain moisture, and ear height. The two-stage approach improved heritability, model fit, and genotypic effect estimation compared to baseline models. Electrical conductance and elevation from a 2019 soil survey significantly improved model fit, while 2D spline permanent environment were most strongly correlated with the soil parameters. Simulation of field effects demonstrated improved specificity for random regression models. In summary, the use of longitudinal normalized difference vegetation index measurements increased experimental accuracy and understanding of field spatio-temporal heterogeneity.