About This Project

1.6 GtC is emitted every year from deforestation. Understanding best practices for afforestation is essential. We will test the Miyawaki Method and our Eden method against traditional methods. These methods prescribe denser plantings, but Eden uses a generative optimization model to place all individuals based on their growth preferences and the specie's ability to sequester carbon. We will implement these methods and monitor for above and below ground carbon and plant health.

Ask the Scientists

Motivating Factor

Carbon dioxide is a fascinating Greenhouse Gas because it is one of the main contributors to climate change, but it is also the backbone of life. Approximately 11.2 Gigatons of Carbon (GtC) are emitted globally every year with 1.6 GtC of this linked to deforestation (Friedlingstein et al. 2020). Since 2015, 10 Mha of forest have been lost annually to deforestation (UN, 2023). The counterpoint to this dynamic is the fact that photosynthesis is one of the primary methods in which CO2 is and can be incorporated back into the Earth. This is acknowledged through an increased interest in afforestation efforts with many countries and cities committing to ambitious reforestation goals (Depauw et al. 2024; McPhearson et al. 2016; Delang and Yuan 2015; UN 2011). This makes it essential to understand best practices for afforestation.

Specific Bottleneck

Experiments and silviculturists use alternating trees from a small pool of native plants in a widely spaced grid, not mimicing natural regeneration. The Miyawaki method is a popular high-density afforestation approach relying on planting native species close, mimicking the early stages of forest succession. This does not consider specific relationships and needs of the plants or target the maximization of ecosystem services. The Miyawaki method lacks a more nuanced consideration for site characteristics and physical structure of the resulting forests. Planting too many species that reach the upper canopy can lead to a forest unable to support a substantial under story or ground cover. Throughout afforestation there has been minimal adaptation of available computational tools. Remote sensing and drones have led to an increase in our ability to monitor reforested areas, but this technology does not extend to aiding the actual design of the ecosystems.

Actionable Goals

We propose that rather than just planting a randomized mix of native species at a high density, we would like to build on the Miyawaki method to use ecological data—such as growth rate, leaf area index (LAI), potential to increase soil carbon—to design plant communities that most effectively sequester carbon. The goal is to ensure a forest structure that not only enhances overall resilience and biodiversity but also optimizes for growth. By merging the proven density-based benefits of the Miyawaki method with a data-driven design process, our system aims to create forests that excel at increasing biomass while supporting robust ecosystem services.