Estimating Impact of Brown Spot Needle Blight on Loblolly Pine Using 3-PG Growth Model
| Authors: | Tyler J. Dreaden, Rabiu Olatinwo, John J. Riggins, Carlos Gonzalez-Benecke |
| Year: | 2026 |
| Type: | Scientific Journal |
| Station: | Southern Research Station |
| DOI: | https://doi.org/10.1111/efp.70059 |
| Source: | Forest Pathology |
Abstract
Lecanosticta acicola is a fungal pathogen that causes brown spot needle blight (BSNB). It primarily impacts the genus Pinus and has been introduced to much of the Northern Hemisphere. Brown spot needle blight causes a reduction of leaf area and photosynthetic capacity that can lead to reduced growth and mortality in extreme cases. In the southeastern USA, the disease has historically primarily impacted grass stage longleaf pine (Pinus palustris). Around 2015, large areas of plantation loblolly pine (Pinus taeda) were found to be infected with L. acicola, which is outside of historic norms for the pathogen. The aims of this research were to get a first estimate of the impact of the disease on loblolly pine plantations and outline methods for managers to model growth with their specific stand characteristics. We used the 3-PG model (Physiological Processes Predicting Growth) to estimate the impact of defoliation caused by the disease. We found the season of defoliation did not have an impact on stem volume and leaf area index recovered to undefoliated levels approximately 5 years following the last defoliation event. Increasing defoliation levels reduced stem volume growth as expected with 1 to 5 consecutive years of defoliation delaying stand development by 0.1 to 1 year. We find that defoliation delays stand development and that this delay is sustained going forward. Lower productivity sites had a larger reduction in stand volume highlighting the need for a measure of stand productivity in estimating the impact of BSNB defoliation and the need to include stand productivity in future models. This framework is primitive but provides managers with the ability to input their stand characteristics and get more specific estimates for growth loss due to BSNB defoliation and provides the ability to model different disease scenarios to better manage losses due to this emerging disease phenomenon.