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Updated: Jun 2, 2026
04:35
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
A spatial model approach for assessing windbreak growth and carbon stocks
Qingjiang Hou1, Linda J Young, James R Brandle
1University of Kansas Medical Center, Department of Biostatistic, Kansas City , KS 66160, USA.
Journal of Environmental Quality
|May 7, 2011
Summary
Quantifying carbon (C) in agroforestry is challenging. This study developed a spatial model to predict tree volume in windbreaks, aiding C stock assessments for better land management.
Area of Science:
- Agroforestry and Ecosystem Services
- Forestry and Carbon Sequestration
- Spatial Modeling and Quantitative Ecology
Background:
- Agroforestry integrates trees into farming, offering carbon sequestration and ecosystem services.
- Accurate quantification of carbon (C) stocks in open-grown trees within agroforestry systems remains a challenge.
- Existing methods for C accounting in these systems are limited, hindering effective land management and climate change mitigation efforts.
Purpose of the Study:
- To develop a spatial model for predicting aboveground tree volume in agroforestry systems, specifically windbreaks.
- To address the need for improved methods in quantifying present and projected carbon (C) stocks in these open-grown woody systems.
- To provide a foundation for enhanced C accounting in agroforestry through accurate volume predictions.
Main Methods:
- Developed a spatial Markov random field model to predict the natural logarithm (log) of aboveground volume.
- Utilized existing data, web-available soil and climate information, and windbreak characteristics (including age) as predictors.
- Incorporated spatial dependence among sites within a 24 km radius to improve prediction accuracy.
Main Results:
- Successfully modeled the large-scale trend of log of aboveground volume using windbreak, site, and climate variables.
- Demonstrated that residuals are spatially correlated, necessitating the use of spatial dependence parameters for accurate predictions.
- Age was identified as a significant factor influencing tree volume within windbreaks.
Conclusions:
- The developed spatial model can predict aboveground volume for green ash (Fraxinus pennsylvanica) in windbreaks.
- Predictions can be obtained for specific windbreak ages and site conditions without the need for repeated inventories.
- The model's capability to quantify uncertainty holds potential for regional C stock assessments and planning for various deciduous tree species.
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