Biomass bales infield aggregation logistics energy for tractors and automatic bale pickers — A simulation study.

Infield bale aggregation is essential for bale removal and preparing the field for subsequent crops, which can be more efficiently performed using the modern automatic bale picker (ABP) that supports multiple bales/trip (BPT) than commonly used tractors. But the energy involved in the bale aggregation logistics using ABP has not been thoroughly evaluated. Therefore, the energy involved in the bale aggregation, in terms of fuel consumption, was studied for different logistic scenarios using a tractor (control) and ABP through a user-developed simulation program in R. Different variables such as field areas (8 to 259 ha), biomass yields (3–40 Mg ha − 1), four outlet locations, and five equipment speeds (6.6–10.5 km h − 1) using realistic equipment turning paths were used in the simulation. The Nebraska Tractor Test general method and fuel efficiency method were considered for the fuel consumption calculations. Fuel consumption for the ABP (8–259 ha) with 8 BPT on an average decreased by 72 % and 53 % compared to a tractor with 1 and 2 BPT, respectively, based on logistics distance and equipment operation. Field area, biomass yield, and BPT were the most influential variables affecting logistics distance; while, field area, biomass yield, BPT, and equipment speed affecting the operation time and fuel quantity. Convenient prediction models (multi-variate nonlinear) for logistics distance, operation time, and fuel quantity, using the influential field variables, produced very good fits ( R 2 ≥ 0.98). Overall, an ABP with a capacity of 8 BPT, split which can also handle 11 BPT, is recommended considering the logistics energy. • Bale aggregation with tractor and automatic bale picker (ABP) were simulated using R. • Field area, biomass yield, and bales/trip (BPT) were influential variables for modeling. • ABP with 8 BPT greatly decreased the logistics distance and fuel compared to tractor. • Multi-variate models predicted logistics distances, operation time, and fuel quantity. [ABSTRACT FROM AUTHOR]