Growth & Yield Projection System - Forest Management

The Alberta government is responsible for managing provincial Crown forest lands within the Green Area. As part of that responsibility, forest managers assess the growth rate of forests.

Environment and Sustainable Resource Development (ESRD) has developed a whole-stand growth prediction model named Growth and Yield Projection System (GYPSY). GYPSY is used to forecast the growth and yield of stands of trees on a per hectare basis.

The Government of Alberta acknowledges the substantial support for GYPSY development provided by the Forest Resource Improvement Association of Alberta (FRIAA) and by the ten Forest Management Agreement (FMA) holders who supported the provision of FRIAA funding.

System Versions

The first version of GYPSY was released in 2001. The forecasting capability of this version was limited to pure lodgepole pine stands.

The 2009 version has been updated to include modeling capability for multiple species in pure and mixed species stands. It also enables forest managers to predict future growth based on reforestation survey results.

Software Download

For licences, manuals and software implementations of GYPSY2009, see: GYPSY Tree Species Groups

GYPSY can forecast the growth of four tree species groups:



Aspen Group

balsam poplar (Populus balsamifera)
trembling aspen (Populus tremuloides)

Black spruce group

black spruce (Picea mariana)

Pine group

jack pine (Pinus banksiana)
lodgepole pine (Pinus contorta v. latifolia)
tamarack/larch (Larix laricina)

White spruce group

alpine fir (Abies lasiocarpa)
balsam fir (Abies balsamea)
Douglas fir (Pseudotsuga menziesii)
Engelmann spruce (Picea englemannii)
white spruce (Picea glauca)

GYPSY Sub-models

GYPSY is composed of a number of sub-models:


Prediction Area

Top height

Predicts the average height of the 100 largest diameter at breast height (DBH) trees per hectare

Percent stocking

Predicts the level of stocking in a stand. Provides a direct linkage between regeneration survey standards and yield forecasts

Non-spatial density

Predicts stand density changes over time without using percent stocking as an input

Non-spatial basal area increment

Predicts the annual basal area increment without using percent stocking as an input

Spatial density

Predicts stand density changes over time using percent stocking as an input

Spatial basal area increment

Predicts the annual basal area increment using percent stocking as an input

Gross total and merchantable volume

Predicts the species-specific gross total volume of a stand at 0/0 or any other user-defined utilization standard

Merchantable density

Predicts the species-specific density of a stand at any user-defined utilization standard

Where stocking information is available, the model can use either the spatial or the non-spatial sub-models for forecasting.

Sub-model Information

The sub-models were initially compiled into a fully functional growth-and-yield model using Statistical Analysis System (SAS®). A more user-accessible Microsoft® Office Excel version of the GYPSY Yield Table Generator Tool is now available. This model underwent a thorough independent validation process, with particular focus on early stand conditions. Copyright and Licence Agreement

All proprietary rights, including copyright in the GYPSY software, its documentation and data, are owned by and shall remain the property of the Government of Alberta (the "Crown"). All rights reserved.

The Crown makes no warranty or representations of any kind with respect to the GYPSY software, its use, suitability or application for any purpose. The Crown, its officials, Ministers, employees and contractors shall have no liability or responsibility for any loss or damage whatsoever arising from or as a result of the use of this software. Contacts

To join the GYPSY User Group, or for answers to technical questions regarding GYPSY and its associated tools, contact:
For more information about the content of this document, contact
This information published to the web on February 25, 2016.