Research Article | Open Access

Volume 2025 |Article ID 100066 | https://doi.org/10.1016/j.plaphe.2025.100066

Near-infrared spectroscopy as a high-throughput phenotyping method for fusiform rust resistance in loblolly pine

Simone Lim-Hing ,^1,5 Anna O. Conrad,² Cristian � R. Montes,³ Kamal J.K. Gandhi,¹ Kitt G. Payn,⁴ Trevor D. Walker,⁵ and Caterina Villari¹

¹D.B. Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA, 30602, USA
²USDA Forest Service, Northern Research Station, Delaware, OH, 43015, USA
³Rayonier Inc., Wildlight, FL, 32097, USA
⁴Terviva, Fort Pierce, FL, 34951, USA
⁵Department of Forestry and Environmental Resources, College of Natural Resources, North Carolina State University, Raleigh, NC, 27695, USA

Abstract

Fusiform rust, caused by the pathogen Cronartium quercuum (Berk.) Miyabe ex Shirai f. sp. fusiforme, is the most important disease of loblolly pine (Pinus taeda L.) in the U.S., causing millions of dollars in damage each year. Using resistant genotypes has proven a successful strategy to limit the disease, but resistance selection still relies on visual inspection for symptoms, which can lead to misclassification due to human error and the presence of ‘escaped susceptibles’ (i.e., susceptible individuals with no visible symptoms due to either an extended asymptomatic phase of the disease or the lack of adequate disease pressure to become infected). Here, we propose the use of near-infrared (NIR) spectroscopy and chemometrics to improve the accuracy of how phenotypes are rated. We collected and analyzed phloem and needle spectra from 34 non-related families replicated across eight stands in three states in the southeastern region of the U.S. using a portable, handheld NIR spectrometer. We also used a benchtop Fourier-transformed mid-infrared (FT-IR) spectrometer to analyze phloem phenolic extracts of the same samples, as this phenotyping approach has proved successful in other pathosystems. Our results show a moderate association between the phloem spectra and resistance, and models built with NIR spectra were able to classify extremes (i.e., very resistant or very susceptible) with up to 69 % testing accuracy. This study provides a framework for using NIR spectroscopy for phenotyping loblolly pine resistance against pathogens and advocates for using alternative technologies in forestry.