Quantifying life cycle inventories of agricultural field operations by considering different operational parameters

Abstract

The ability to estimate fuel and lubricant consumption as well as depreciated weight of agricultural machinery used for field operations is very useful for energy and environmental analyses. In this study, life cycle inventory data of agricultural field operations were established by considering different parameters of such operations. Agricultural operations considered in this study include tillage, cultivation, planting, harvesting and post-harvest operations. For these operations, the fuel and lubricant consumption as well as depreciated weight of tractors, combine harvesters and agricultural implements was estimated by considering different operational parameters such as tractor power, field condition, depth of operation, soil condition, tractor type, operational capacity of machine, width of operation and speed. Technical standards were used to estimate different types of power required for most agricultural operations (drawbar power, rotary power and motion power). The standards were then used to evaluate the variability of the fuel and lubricant consumption as well as depreciated weight of the implements by varying the aforementioned parameters. The results were compared to those that can be calculated with other approaches for life cycle inventory analysis of agricultural operations. Such comparison indicates that by using different parameters, representing the diverse local conditions of different field operations, a great variability of the results is obtained. For instance, diesel fuel consumption of tillage operations ranges from 12.6 to 76.0 L ha−1, with an average of 34.15 L ha−1 and standard deviation of 11.7 L ha−1. Such representativeness of the different conditions of each field operation cannot be modelled with other tools or via the use of standard LCI datasheets. The final result of this study is a novel approach for the life cycle inventory analysis of agricultural operations, in terms of fuel and lubricant consumption and of depreciated weight of the machines, which are estimated by simply selecting the operational parameters which best represent the effect of local conditions.