Preliminary modeling of light availability in a diverse agroforestry system using a spatially explicit forest simulator

Stanek, Erik; Wolz, Kevin; Lovell, Sarah Taylor

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https://hdl.handle.net/2142/95955 Copy

Description

Title

Preliminary modeling of light availability in a diverse agroforestry system using a spatially explicit forest simulator

Author(s)

• Stanek, Erik
• Wolz, Kevin
• Lovell, Sarah Taylor

Issue Date

2016

Keyword(s)

• Agroforestry
• light
• modeling
• woody crops
• polyculture

Abstract

Researchers theorize there is a particular spacing within and between rows that maximizes light capture given size, shape, and opacity of woody species in diverse agroforestry systems (DAS). Studies of these mixed perennial cropping systems have failed to analyze this optimum spacing quantitatively. This study attempts to address this issue through the following aims: (1) determine optimal layouts for light capture, (2) calculate percentage of light received by species at different layout densities, and (3) better understand differences in light availability at plant and plot scales. This study modeled four University of Illinois DAS research treatments ranging from one to three species within a tree row. The spatially explicit forest simulator, SORTIE-ND, was used to analyze the light availability, referred to as global light index (GLI), at treatment maturity on a 1-m2 basis across the field site. Results reveal that GLI is lowest when species spacing is decreased and canopy levels do not overlap. On a plot scale, treatments containing tree rows with multiple canopy levels of distinctly separate heights allowed for maximum GLI while tree rows with only a single species had the lowest. On a plant scale, the tallest trees received near full light as long as canopies did not overlap. Understory shrubs received little to no light when density and number of tree canopies increased. Adjusting the density and number of canopy levels in DAS has significant effects on GLI, but should be further studied using additional treatments to identify quantitative optimum.

Publisher

College of Agricultural, Consumer and Environmental Sciences, University of Illinois at Urbana-Champaign

Type of Resource

text

Permalink

http://hdl.handle.net/2142/95955

Copyright and License Information

Copyright 2016 Erik Stanek, Kevin Wolz, and Sarah Taylor Lovell

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