Energetic Aspects of Turfgrass Mowing: Comparison of Different Rotary Mowing Systems

Turfgrass mowing is one of the most important operations concerning turfgrass maintenance. Over time, different mowing machines have been developed, such as reel mowers, rotary mowers, and flail mowers. Rotary mowers have become the most widespread mowers for their great versatility and easy maintenance. Modern rotary mowers can be equipped with battery-powered electric motors and precise settings, such as blade rpm. The aim of this trial was to evaluate the differences in power consumption of a gasoline-powered rotary mower and a battery-powered rotary mower. Each mower worked on two different turfgrass species (bermudagrass and tall fescue) fertilized with two different nitrogen rates (100 and 200 kg ha−1). The battery-powered mower was set at its lowest and highest blade rpm value, while the gasoline-powered mower was set at full throttle. From the data acquired, it was possible to see that the gasoline-powered mower had a much higher primary energy requirement, independent of the turf species. Moreover, comparing the electricity consumption of the battery-powered mower over time, it was possible to see that the power consumption varied according to the growth rate of both turf species. These results show that there is a partial waste of energy when using a gasoline-powered mower compared to a battery-powered mower.

Forecasting Primary Energy Requirements of Territories by Autoregressive Integrated Moving Average and Backpropagation Neural Network Models

Forecasting energy data, especially the primary energy requirement, is the key part of policy-making. For those territories of different developing types, seeking a knowledge-based and dependable forecasting model is an essential prerequisite for the prosperous development of policy-making. In this paper, both autoregressive integrated moving average and backpropagation neural network models which have been proved to be very efficient in forecasting are applied to the forecasts of the primary energy consumption of three different developing types of territories. It is shown that the average relative errors between the actual data and simulated value are from 4.5% to 5.9% by the autoregressive integrated moving average and from 0.04% to 0.47% by the backpropagation neural network. Specially, this research shows that the backpropagation neural network model presents a better prediction of primary energy requirement when considering gross domestic product, population, and the particular values as predictors. Furthermore, we indicate that the single-input backpropagation neural network model can still work when the particular values have contributed most to the energy consumption.