Assessment of Energy, Dynamic and Economic Balance of Chipping Operation in Poplar Medium Rotation Coppice (MRC) Plantations: Fid-Bau Portal

Assessment of Energy, Dynamic and Economic Balance of Chipping Operation in Poplar Medium Rotation Coppice (MRC) Plantations

Walter Stefanoni / Roberto Fanigliulo / Daniele Pochi / Laura Fornaciari / Renato Grilli / Stefano Benigni / Francesco Latterini / Giulio Sperandio

Abstract

Exploiting renewable energy sources is one of the main strategies defined by the EU to overcome dependence on foreign markets for energy supply. Wood fuel sourced from the agroforestry sector can contribute significantly to achieving the goal, though its economic and environmental sustainability is intimately dependent on proper harvesting and chipping operations. In the present article, both economic and environmental aspects of Medium Rotation Coppice (MRC) were investigated regarding chipping. A small-scale chipper and tractor were equipped with real-time sensors to monitor time, t (s); fuel consumption, F (cm³); PTO torque, M (daNm); PTO speed, s (min⁻¹); and stem diameter, D (mm) during the comminution of 61 poplar plants (gathered in 5 classes according to trunk diameter) grown in MRC system. More than 29,000 records were taken and analyzed. Predictive models for working time, working productivity, CO₂ emission, energy consumption, fuel consumption and costs were also produced. Higher diametric classes exhibited lower fuel consumption, less CO₂ emission and less energy demand during chipping. Time and operating costs were statistically different among classes, with minimum values of 0.22 (SD ± 0.02) h·Mg⁻¹ and 12.07 (SD ± 0.93) €·Mg⁻¹ in class 5 and maximum values of 0.64 (SD ± 0.09) h·Mg⁻¹ and 35.34 (SD ± 4.88) €·Mg⁻¹ in class 1, respectively. Fuel consumption ranges from 3.04 (SD ± 0.88) L·Mg⁻¹ in class 5 to 7.32 (SD ± 1.46) L·Mg⁻¹ in class 1. The lowest CO₂ emission of 8.03 (SD ± 2.32) kg·Mg⁻¹ was found class 5. However, the total cost of coppice production did not exceed large-scale MRC production due to the lower purchase price of the machinery involved. Eventually, predictive models showed high reliability as estimating tools for important variables, such as working time, working productivity, CO₂ emissions, energy consumption, fuel consumption and costs.