Simulating the effects of prescribed burning on fuel loading and timber production (EcoFL) in managed northern Wisconsin forests

We developed a fuel loading prediction model (EcoFL), which quantified fuel loads at ecosystem level using a classical ecosystem productivity concept and aboveground biomass measurements. EcoFL predicted fuel loading with high precision (r2 = 0.94), but the linear relationship between measured and predicted was slightly off from the 1:1 line (slope = 0.86–1.17). The study showed a possibility of developing landscape level fuel loading model. EcoFL indicated that carbon allocation had to be parameterized accurately to predict fuel loading precisely. Prescribed burning has been widely used to reduce fuel and promote ecosystem functions throughout the United States, but the long-term ecological impacts of prescribed burning are widely unknown. We evaluated the effect of alternative prescribed burning rotations (ROT), magnitude in productivity reduction by prescribed burning (MAG), and duration of productivity reduction (DUR) on fuel loading (Mg ha−1) and timber production (Mg ha−1) in managed northern hardwood, red pine (Pinus resinosa), and jack pine (P. banksiana) forests in northern Wisconsin. The results showed that DUR would be the most critical information when deciding ROT in order to preserve timber production and to maintain low fuel loading. The results implied that forest productivity could reduce timber production drastically, if ROT was shorter than DUR and ecosystem productivity was affected cumulatively by prescribed burning. Under a 5-year ROT, the harvested biomass was reduced up to 17.9, 30.4, and 25.5%, respectively, of controls in hardwood, red pine, and jack pine forests. For accurate long-term prediction of prescribed burning, substantial field data are still needed to identify productivity recovery pattern, carbon allocation after burning, and long-term effect of burning on productivity. EcoFL has the potential to be used for evaluating the effect of various disturbances (e.g., thinning, harvesting) on fuel loading, and the output of EcoFL can be used as an input data for fire spread models. The results of this study will be fundamental for forest managers to balance the timber production and fuel reduction using prescribed burning.