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This article in JEQ

  1. Vol. 40 No. 3, p. 842-852
    Received: Mar 8, 2010

    * Corresponding author(s): jbrandle@unl.edu
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A Spatial Model Approach for Assessing Windbreak Growth and Carbon Stocks

  1. Qingjiang Houa,
  2. Linda J. Youngb,
  3. James R. Brandle *c and
  4. Michele M. Schoenebergerd
  1. a Univ. of Kansas Medical Center, Dep. of Biostatistics, Mail-Stop 1026, 3901 Rainbow Blvd., Kansas City, KS 66160
    b Dep. of Statistics, Univ. of Florida, 408 McCarty Hall C, P.O. Box 110339, Gainesville, FL 32611-0339
    c School of Natural Resources, Univ. of Nebraska, Lincoln, NE 68583-0814
    d USDA FS/NRCS National Agroforestry Center, Southern Research Station, East Campus-UNL, Lincoln, NE 68583-0822. Assigned to Associate Editor Scott Chang


Agroforestry, the deliberate integration of trees into agricultural operations, sequesters carbon (C) while providing valuable services on agricultural lands. However, methods to quantify present and projected C stocks in these open-grown woody systems are limited. As an initial step to address C accounting in agroforestry systems, a spatial Markov random field model for predicting the natural logarithm (log) of the mean aboveground volume of green ash (Fraxinus pennsylvanica Marsh.) within a shelterbelt, referred to as the log of aboveground volume, was developed using data from an earlier study and web-available soil and climate information. Windbreak characteristics, site, and climate variables were used to model the large-scale trend of the log of aboveground volume. The residuals from this initial model were correlated among sites up to 24 km from a point of interest. Therefore, a spatial dependence parameter was used to incorporate information from sites within 24 km into the prediction of the log of the aboveground volume. Age is an important windbreak characteristic in the model. Thus, the log of aboveground volume can be predicted for a given windbreak age and for values of other explanatory variables associated with a site of interest. Such predictions can be exponentiated to obtain predictions of aboveground volume for windbreaks without repeated inventory. With the capability of quantifying uncertainty, the model has the potential for large regional planning efforts and C stock assessments for many deciduous tree species used in windbreaks and riparian buffers once it is calibrated.

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