Incorporating Climate and Weather Information into Growth and Yield Models: Experiences from Modeling Loblolly Pine Plantations Ralph L. Amateis Department of Forest Resources and Environmental Conservation, Virginia Tech

Precipitation and Growth Precipitation impacts growth (The Effect of Rainfall and Temperature on the Annual Radial Growth of Pine in the Southern United States – T.S. Coile) Corroborated with methods of dendrochronology for periods from 1950s through drought of 1980 Radial growth seems to be impacted more than apical growth

Precipitation and Growth Models Most models do not explicitly account for level of precipitation. –Precipitation varies widely spatially and temporally –Availability of data –Difficult to estimate at any site for any given year Models assume that annual variation in growth: –Averages out over the rotation –Is confounded with other site factors; but models often don’t account for these either (e.g. soils, temp) But what about…

Cumulative rainfall

Approach Determine the correlation between annual precipitation and growth across the loblolly growing region Determine whether annual precipitation is a significant predictor of growth in the presence of other stand and site factors Develop a model that could be used to assess growth loss for periods of drought

Baseline Growth Models Growth Data

Climate Data ( climate.org) climate.org

Results G BA but not G HD was found to be affected by annual precipitation For G BA, R 2 increased from 0.50 to 0.53 by adding PPT; both PRESS and MSE were reduced (MSE by 7%) Attempts to add other regressors including lagged climate variables and interactions were not successful

Applications Can be integrated directly into growth and yield models to estimate BA for the year; let the model predict volume accordingly Can use to adjust model output directly by a percentage Hard to estimate annual PPT for any given year

A Simple Extension Allow the geographic coordinates to be surrogates for a host of climatic and edaphic factors Conspicuously absent is site index Model (w & w/o Geo Coords) Reduction in MSE (%) BA Prediction20 BA Projection5 N Projection3 Dbh Distribution (Weibull) 5 – 20 Total Yield4 Product Class Proportions 3 (AIC) Height-diameter<1

Test on Development Data Development plots loaded into software and projections made to each measurement age for all plots. Overall, using geo coordinates results in mean residual of -0.7 tons. Not using geo results in residual of 3.0 tons (87 tons mean across all measurements).

Interpretation For the same stand and site conditions there are differences in growth and yield for loblolly pine across the South Locale affects radial development more than apical development Plantations at northern latitudes and western longitudes have greater dbh for given stand and site conditions Geographic coordinates seem to capture at least some of the variability associated with many of the abiotic factors affecting growth across the region

Additional Investigations What effect does projection length have on model predictions with and without geo coordinates (are specific location effects blurred over time)? Are geo coordinates more helpful in certain parts of the region (perhaps certain physiographic regions)? Which model outputs (total, merchantable yields, dbh distributions, etc.) can geo coordinates be expected to improve most?

Questions?