Energy Use and Energy Efficiency in Selected Arable Farms in Central and South Eastern Europe

The main objective of the project “Mechanization and Energy use in selected arable farms in Central and South Eastern Europe (CASEE)” was to analyse energy characteristics of arable farming in Slovak Republic, Romania, Serbia and Austria, to compare results and identify possibilities of its improvements. The large scale farms are: the university farm of the Slovak University of Agriculture (SK) with 1.112 ha arable land, a cooperative farm in Risnovice (SK) with an arable land of 1.266 ha, a family farm in Apahida-Transylvania (RO) with 400 ha, a farm in Viisoara-Transylvania (RO) with 600 ha, a family farm in Sremska Mitrovica (SRB) with an arable land of 115 ha, a family farm near Novi Sad (SRB) with an arable land of 450 ha and a family farm in Ansfelden/Linz (A) with 368 ha. The farms were visited by the interviewer once or more times and the relevant data, used machinery, quantity of inputs, e.g. fuel, pesticides, fertilizer, seed and yields of harvested crops, were recorded, for the production season 2012. After collection of the basic data all energy inputs and outputs, energy content of crops, were calculated in accordance with data and procedure defined by CIGR (International Commission of Agricultural and Biosystems Engineering), Handbook Volume V – Energy and Biomass Engineering (1999). Energy input and net energy gain, expressed in MJ/ha, were used to calculate energy characteristics of crops’ production: energy productivity - kg/MJ, energy efficiency index, energy ratio, energy intensity - MJ/kg, fuel intensity - L/kg. The intensity of all used farm inputs (fuel, seeds, fertilizer and pesticide) in crop production systems influences the energy efficiency. The fuel consumption for winter wheat production of the analysed farms ranges between 54 and 91 l/ha. The mean energy ratio (energy-output/energy-input) for winter wheat is 5.6 with ranges between 4.8 and 7.1. Besides the fuel consumption the energy-input via the nitrogen-fertilizer is the main energy consumer in cropping systems. It is clearly identified that the highest possible energy savings are possible by reduction of fertilizers, first of all nitrogen

Energy Use and Energy Efficiency in Selected Arable Farms in Central and South Eastern Europe

The main objective of the project “Mechanization and Energy use in selected arable farms in Central and South Eastern Europe (CASEE)” was to analyse energy characteristics of arable farming in Slovak Republic, Romania, Serbia and Austria, to compare results and identify possibilities of its improvements. The large scale farms are: the university farm of the Slovak University of Agriculture (SK) with 1.112 ha arable land, a cooperative farm in Risnovice (SK) with an arable land of 1.266 ha, a family farm in Apahida-Transylvania (RO) with 400 ha, a farm in Viisoara-Transylvania (RO) with 600 ha, a family farm in Sremska Mitrovica (SRB) with an arable land of 115 ha, a family farm near Novi Sad (SRB) with an arable land of 450 ha and a family farm in Ansfelden/Linz (A) with 368 ha. The farms were visited by the interviewer once or more times and the relevant data, used machinery, quantity of inputs, e.g. fuel, pesticides, fertilizer, seed and yields of harvested crops, were recorded, for the production season 2012. After collection of the basic data all energy inputs and outputs, energy content of crops, were calculated in accordance with data and procedure defined by CIGR (International Commission of Agricultural and Biosystems Engineering), Handbook Volume V – Energy and Biomass Engineering (1999). Energy input and net energy gain, expressed in MJ/ha, were used to calculate energy characteristics of crops’ production: energy productivity - kg/MJ, energy efficiency index, energy ratio, energy intensity - MJ/kg, fuel intensity - L/kg. The intensity of all used farm inputs (fuel, seeds, fertilizer and pesticide) in crop production systems influences the energy efficiency. The fuel consumption for winter wheat production of the analysed farms ranges between 54 and 91 l/ha. The mean energy ratio (energy-output/energy-input) for winter wheat is 5.6 with ranges between 4.8 and 7.1. Besides the fuel consumption the energy-input via the nitrogen-fertilizer is the main energy consumer in cropping systems. It is clearly identified that the highest possible energy savings are possible by reduction of fertilizers, first of all nitrogen

Reinforcement Learning with Ensemble Model Predictive Safety Certification

Reinforcement learning algorithms need exploration to learn. However, unsupervised exploration prevents the deployment of such algorithms on safety-critical tasks and limits real-world deployment. In this paper, we propose a new algorithm called Ensemble Model Predictive Safety Certification that combines model-based deep reinforcement learning with tube-based model predictive control to correct the actions taken by a learning agent, keeping safety constraint violations at a minimum through planning. Our approach aims to reduce the amount of prior knowledge about the actual system by requiring only offline data generated by a safe controller. Our results show that we can achieve significantly fewer constraint violations than comparable reinforcement learning methods.Comment: Published in: Proc. of the 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2024

Effects of working depth and wheel slip on fuel consumption of selected tillage implements

Rising fossil fuel prices are leading to an increasing awareness of energy efficiency in plant production.  Tillage in particular can consume large amounts of fuel.  For four tillage implements (reversible mouldboard plough, short disc harrow, universal-cultivator, subsoiler), this study quantifies the effect of different working depths on fuel consumption, wheel slip, field capacity and specific energy consumption.  A four-wheel drive tractor (92 kW) was equipped with a data-acquisition system for engine speed, vehicle speed, wheel speed and fuel consumption.  Fuel consumption was measured in the fuel system with an integrated high-precision flow-meter.  The results show that the area-specific fuel consumption increased linearly with working depth for both the mouldboard plough and the short disc harrow, but disproportionately for the subsoiler.  Wheel slip was found to increase fuel consumption and decrease field capacity performance at all depths.  The influence of the engine speed was shown in a separate experiment with a universal-cultivator.  Increasing the engine speed from 1,513 r min-1 to 2,042 r min-1 results in an increase of 80% for the fuel consumption rate (L/h) and 35% for the area-specific fuel consumption (L/ha).  Future measurement of drawbar pull will allow a more detailed analysis of the energy efficiency losses at the engine, the transmission, and at the wheel/soil interface.   Keywords: fuel consumption, wheel slip, mouldboard plough, subsoiler, universal-cultivator, short disc harro

Environmental hot spot analysis in agricultural lifecycle assessments – three case studies

Present-day agricultural technology is facing the challenge of limiting the environmental impacts of agricultural production – such as greenhouse gas emissions and demand for additional land – while meeting growing demands for agricultural products. Using the well-established method of life-cycle assessment (LCA), potential environmental impacts of agricultural production chains can be quantified and analyzed. This study presents three case studies of how the method can pinpoint environmental hot spots at different levels of agricultural production systems. The first case study centers on the tractor as the key source of transportation and traction in modern agriculture. A common Austrian tractor model was investigated over its life-cycle, using primary data from a manufacturer and measured load profiles for field work. In all but one of the impact categories studied, potential impacts were dominated by the operation phase of the tractor’s life-cycle (mainly due to diesel fuel consumption), with 84.4-99.6% of total impacts. The production phase (raw materials and final assembly) caused between 0.4% and 12.1% of impacts, while disposal of the tractor was below 1.9% in all impact categories. The second case study shifts the focus to an entire production chain for a common biogas feedstock, maize silage. System boundaries incorporate the effect of auxiliary materials such as fertilizer and pesticides manufacturing and application. The operation of machinery in the silage production chain was found to be critical to its environmental impact. For the climate change indicator GWP100 (global warming potential, 100-year reference period), emissions from tractor operation accounted for 15 g CO2-eq per kg silage (64% of total GWP100), followed by field emissions during fertilizer (biogas digestate) application with 6 g CO2-eq per kg silage (24% of total GWP100). At a larger system scale that includes a silage-fed biogas plant with electricity generated by a biogas engine, silage cultivation operations are no longer the largest contributor; the most important contributor (49.8%) is methane slip from the exhaust of the biogas engine. In the third case study, the biogas plant model is energy system in an Alpine municipality of Western Austria is expanded to include a hypothetical system that uses mainly hay from currently unused alpine grassland in a local biogas plant. Here, the relative environmental impacts depend strongly on the fossil fuels that are assumed to be displaced by the local biogas plant; methane slip emissions from the exhaust dominate the impact of the hypothetical local biogas scenario. Taken together, the case studies demonstrate the potential and limitations of LCA as a technique to support decisions of agricultural stakeholders at a variety of scales. Choosing the proper system scale is key to a successful application of this method