Does sampling more than one increment core in growth-index-ratio-based stand table projection improve forest yield prediction precision?Senior forestry major Leah Leonard conducted research to investigate whether sampling more than one tree for previous diameter growth measurement significantly improves the precision of forest yield predictions from stand table projections. Obtaining future forest yield is not a straightforward process in comparison to obtaining current yields, which can be measured directly in the field. Stand Table Projection (STP) is one of the approaches of predicting future yields of forest stands. STP is simple to apply because it only requires previous diameter growth measurements from a sample of the trees in the stand, which can be measured from increment cores taken from the sample trees. Usually, one increment core is sampled per tree dbh class. One increment core may not be a good representation of a dbh class due to possibility of various measurement and growth ring formation issues. Thus, taking more than one increment core per tree dbh class may help minimize the effect of increment core measurement errors on yield predictions from STP. Individual tree measurements from 364 longleaf pine research plots in the Southeastern United States were used to investigate whether sampling more than one tree per tree size class, for previous diameter growth measurements, would significantly reduce future yield prediction errors from STP. Findings from the research indicated that sampling more than one tree per dbh class, for STP increment cores, may not result in lower STP prediction errors. In addition, it was observed that STP prediction errors for stands that were less than 50 years old were more than 2 times larger than the errors for stands that were older than 50 years.
News / Recognition
Undergraduate Research Symposium
Katherine Abell, a wildlife, fisheries, and aquaculture major, and Zachary Senneff, a forestry major, were among the winners of the 2014 MSU Undergraduate Research Symposium. Abell placed first in the community engagement and social sciences categories and Senneff placed second in the biological sciences and engineering category.