Native Reforestation Growth Model

Stand-level carbon ($tCO_{2}e/ha$) is modeled with the Chapman-Richards (CR) growth equation, which is typical for this use case¹. This curve accounts for initial slow development as saplings establish a root system, followed by a period of rapid growth until canopy closure when competition among trees increases and growth is limited by light, water, and nutrients. At this point, biomass approaches the maximum mature biomass, which is determined by the climate and edaphic factors of the site.

A form of the CR curve was used that has three parameters: maximum biomass in tons dry matter per hectare ($MAX$), a dimensionless growth rate ($k$), and a dimensionless shape parameter ($m$):

$Biomass=MAX*[1-exp(-k*Age)]^{(11-m)}$

Where $Age$ is tree age in years and $Biomass$ is aboveground and belowground biomass.

Potential maximum biomass is a key aspect of the carbon curve because it characterizes the upper limit of the carbon projection. The potential biomass ($MAX$) is the greatest biomass density (t dm/ha converted to $tCO_2e/ha$) the site can support once the forest stand is fully developed. Although tree planting and interventions such as weeding or irrigation can speed up the restoration process and enable regeneration in places it would not occur otherwise², the local environment determines the mature forest biomass the project area can support. For stand-level modeling, it is assumed that the potential stand-level biomass is independent of the species mix, planting density, or other project interventions, given that a native forest establishes on the site. While these variables don’t affect the potential biomass, they do affect the growth rate. In other words, factors such as species mix don’t affect how much is sequestered but when.

This model estimates aboveground biomass accumulation and carbon sequestration over time for native reforestation projects. It provides default growth curves derived from peer-reviewed literature and regionally calibrated datasets available within TerraVista.

However, because forest growth is highly context-specific—driven by local climate, soil, species composition, and management practices—users can customize the growth data used in the model. Many project developers, investors, or research partners have access to field-collected or locally validated data that more accurately reflects conditions in their project area. Incorporating this data can significantly improve the precision of carbon forecasts and align project modeling with on-the-ground observations.

By allowing user-defined inputs, the growth model balances scientific rigor with flexibility, ensuring that results remain transparent, reproducible, and relevant to the specific ecological and methodological context of each project.