Application of New Statistical Method Shows Promise in Mitigating Climate Change Effects on Critical Pine Plantations in Southern US

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Confronting evidence that the global climate is changing rapidly relative to historical trends, researchers at North Carolina State University have developed a new statistical model that, when applied to the loblolly pine tree populations in the southeastern United States, will benefit forest landowners and the forest industry in future decades. The research, titled “Optimal Seed Deployment Under Climate Change Using Spatial Models: Application to Loblolly Pine in the Southeastern US” appears in the Journal of The American Statistical Association.

Confronting evidence that the global climate is changing rapidly relative to historical trends, researchers at North Carolina State University have developed a new statistical model that, when applied to the loblolly pine tree populations in the southeastern United States, will benefit forest landowners and the forest industry in future decades. The research, titled “Optimal Seed Deployment Under Climate Change Using Spatial Models: Application to Loblolly Pine in the Southeastern US” appears in the Journal of The American Statistical Association.

“In the past, statistical approaches that were used to help guide forest management decisions like strategic seedling planting had limitations,” note the authors. “Our proposed model, which is based on future climate change scenarios, produces more accurate predictions than previous methods. As a result, it can be used as a quantitative tool for designing forest management strategies that mitigate the negative impacts of climate change.” The findings are the result of the Cooperative Tree Improvement Program, a joint effort between NC State’s Department of Forestry and Natural Resources and Department of Statistics, in which NC State and its members carried out breeding of loblolly pine families and established a large number of field trials in approximately 25 locations across the southern U.S. in the early 1990s.

Read more at American Statistical Association

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