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<p>In agroforestry systems, trees can facilitate crop growth but also enhance tree-crop competition, depending on factors like tree size and species, row and field orientation, crop species, and environmental conditions. In temperate Europe, light reduction from trees is a significant constraint on crop growth in agroforestry systems, with studies showing mixed effects on crop yield under varying shade. Whereas field experiments provide useful insights, they are often limited in scope, space and time, and cannot fully disentangle interacting factors for all theoretical combinations of, e.g., shade and soil moisture availability. Mathematical modeling offers a flexible approach to study agroforestry systems under a broader range of conditions, thereby allowing for higher spatiotemporal resolution. There is a need for tools that can simulate light conditions more accurately in agroforestry systems, accounting for the complex structure of real-world trees. Here we introduce an intuitive 3D modeling tool designed to simulate light availability at ground level in agroforestry systems, utilizing detailed quantitative structure models obtained from terrestrial LiDAR (Light detection and ranging) scans of real trees. The tool consists of an online application, accessible at <a href="https://agroforestry.ilvo.be/agroforlight/">https://agroforestry.ilvo.be/agroforlight/</a>, and the R package <i>agroforlight</i>. We showcase its potential in a case study on how tree size and row orientation impact light availability for inter-crops in alley cropping systems. By combining architectural detail with high-speed light computation, the model effectively simulates realistic agroforestry systems at high spatio-temporal resolution. Our model successfully replicates previous experimental findings and trends, enables definition of new hypotheses and provides valuable quantitative insights for precision agriculture practices to enhance agroforestry performance.</p>

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Agroforlight: 3D light distribution modelling in agroforestry systems using high-resolution tree LiDAR scans

  • 期刊论文

  • 发表日期：

    2025年5月23日

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  • Tom De Swaef,
  • Willem Coudron,
  • Toon Baeyens,
  • Kim Calders,
  • Peter Lootens,
  • Paul Pardon,
  • Bert Reubens,
  • Sanne Van Den Berge,
  • Pieter De Frenne,
  • Kris Verheyen

摘要

In agroforestry systems, trees can facilitate crop growth but also enhance tree-crop competition, depending on factors like tree size and species, row and field orientation, crop species, and environmental conditions. In temperate Europe, light reduction from trees is a significant constraint on crop growth in agroforestry systems, with studies showing mixed effects on crop yield under varying shade. Whereas field experiments provide useful insights, they are often limited in scope, space and time, and cannot fully disentangle interacting factors for all theoretical combinations of, e.g., shade and soil moisture availability. Mathematical modeling offers a flexible approach to study agroforestry systems under a broader range of conditions, thereby allowing for higher spatiotemporal resolution. There is a need for tools that can simulate light conditions more accurately in agroforestry systems, accounting for the complex structure of real-world trees. Here we introduce an intuitive 3D modeling tool designed to simulate light availability at ground level in agroforestry systems, utilizing detailed quantitative structure models obtained from terrestrial LiDAR (Light detection and ranging) scans of real trees. The tool consists of an online application, accessible at https://agroforestry.ilvo.be/agroforlight/, and the R package agroforlight. We showcase its potential in a case study on how tree size and row orientation impact light availability for inter-crops in alley cropping systems. By combining architectural detail with high-speed light computation, the model effectively simulates realistic agroforestry systems at high spatio-temporal resolution. Our model successfully replicates previous experimental findings and trends, enables definition of new hypotheses and provides valuable quantitative insights for precision agriculture practices to enhance agroforestry performance.