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

Energy management (Renewable Energies)

Die agrarische Produktion beruht auf den Einsatz des begrenzten Faktors technischer Energie, woraus sich die geforderte Energie - und Kraftstoffeffizienz ableitet. Der technische Fortschritt und dem wachsenden Bewusstsein Energie zu sparen sind Triebfedern die Energieeffizienz in der agrarischen Produktion zu verbessern. Der Einsatz von alternativen Energieträgern wie Pflanzenkraftstoffe und Biogas aus der kaskadischen Nutzung stellt einen bedeutenden Beitrag um die Abhängigkeit von fossiler Energie im Landwirtschaftssektor zu mindern.Agricultural production is based on the use of the limited factor technical energy, which leads to the necessity of energy- and fuel efficiency. The technical progress and the increased awareness of saving energy are important promoters to improve the energy efficiency in agricultural production. The application of alternative energy carriers like plant-based fuels (biofuel) and biogas from multipurpose utilisation is a significant contribution to mitigate the dependence from fossil energy in agriculture sector

Effect of Steam Explosion Pretreatment on the Specific Methane Yield of Miscanthus x giganteus

A highly promising energy crop for biogas production can be Miscanthus x giganteus. It has multiple advantages, which include low soil requirements and the existence of genotypes adapted to dry conditions in comparison to other energy crops. Miscanthus cannot be used in the biogas plant without a pretreatment due to the recalcitrant nature of lignocelluloses. One of the most efficient pretreatment methods for lignocellulosic biomass is steam explosion. This includes heating the biomass at high temperature values, followed by mechanical disruption of the biomass fibres by a rapid pressure drop. The objective of this study is to analyse the effect of the steam explosion pretreatment on the specific biogas and methane production of miscanthus. In addition methane hectare yields are calculated and compared to those of maize. Steam explosion pretreatment was carried out in a laboratory scale facility in Ĺs, Norway. The miscanthus was mixed with water and heated up to the desired temperature. After a defined pretreatment time the pressure in the reaction vessel was reduced rapidly, which caused the liquid water to vaporize immediately. The material was cooled down in a flushing tank and was then stored at 5°C until further analytical procedures. Pretreatment temperatures were 190°C and 210°C; holding times were 5, 10 and 15 minutes. Determination of the specific methane yield was done in triplicate using batch tests according to VDI 4630. The material was inoculated with the liquid fermentation residue of a biogas plant. The produced gas was collected in eudiometers and then analysed for the CH4 and CO2 content

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

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

Energy management (Renewable Energy)

Agrarische Roh- und Reststoffe können zur Bereitstellung von Biotreibstoffen für mobile An-wendungen (z.B.: Biodiesel, Biomethan, Bioethanol, …), von Strom und Wärme (z.B.: über Biogasproduktion mit anschließender Verstromung) sowie von Wärme (über Verbrennung) eingesetzt werden. Alternativ kann der Anbau von Feldfrüchten unter Photovoltaikpaneelen erfolgen, welcher eine Mehrfachnutzung des Bodens ermöglicht und zur Entschärfung der Flächenkonkurrenz beiträgt. Die Strategien einer stofflichen und energetischen Nutzung von Ackerfrüchten als Substitut fossiler Rohstoffe stehen teilweise in Konkurrenz zur Lebensmit-telproduktion. In eine nachhaltige Nutzungsstrategie müssen die Biomassepotentiale in Pro-duktionssystemen mit dem Ziel der Erfüllung der 4F (Food, Feed, Fiber and Fuels) genutzt werden.Agricultural raw and waste materials can be used to produce biofuels for transport (e.g. bio-diesel, biomethane, bioethanol, etc.), as well as electricity and heat (e.g. from biogas produc-tion with subsequent combustion in a gas engine, heat from incineration). Alternatively, culti-vating crops under photovoltaic panels allows shared usage of soil and eases land use com-petition. Substituting fossil resources with the material and energetic utilization of crops can-not be achieved without competing with food production. A sustainable utilization strategy must implement the biomass potentials with the goal of fulfilling the 4Fs (food, feed, fiber and fuels)

Digital Transformation in Smart Farm and Forest Operations Needs Human-Centered AI: Challenges and Future Directions

The main impetus for the global efforts toward the current digital transformation in almost all areas of our daily lives is due to the great successes of artificial intelligence (AI), and in particular, the workhorse of AI, statistical machine learning (ML). The intelligent analysis, modeling, and management of agricultural and forest ecosystems, and of the use and protection of soils, already play important roles in securing our planet for future generations and will become irreplaceable in the future. Technical solutions must encompass the entire agricultural and forestry value chain. The process of digital transformation is supported by cyber-physical systems enabled by advances in ML, the availability of big data and increasing computing power. For certain tasks, algorithms today achieve performances that exceed human levels. The challenge is to use multimodal information fusion, i.e., to integrate data from different sources (sensor data, images, *omics), and explain to an expert why a certain result was achieved. However, ML models often react to even small changes, and disturbances can have dramatic effects on their results. Therefore, the use of AI in areas that matter to human life (agriculture, forestry, climate, health, etc.) has led to an increased need for trustworthy AI with two main components: explainability and robustness. One step toward making AI more robust is to leverage expert knowledge. For example, a farmer/forester in the loop can often bring in experience and conceptual understanding to the AI pipeline—no AI can do this. Consequently, human-centered AI (HCAI) is a combination of “artificial intelligence” and “natural intelligence” to empower, amplify, and augment human performance, rather than replace people. To achieve practical success of HCAI in agriculture and forestry, this article identifies three important frontier research areas: (1) intelligent information fusion; (2) robotics and embodied intelligence; and (3) augmentation, explanation, and verification for trusted decision support. This goal will also require an agile, human-centered design approach for three generations (G). G1: Enabling easily realizable applications through immediate deployment of existing technology. G2: Medium-term modification of existing technology. G3: Advanced adaptation and evolution beyond state-of-the-art

150 years BOKU Vienna – Agricultural technology in the spotlight

Seit Gründung der Universität für Bodenkultur im Jahr 1872, haben 9 Professoren die Geschicke des Instituts für Landtechnik gelenkt. Aus der 1906/1907 gegründeten Prüfstation entwickelte sich ein Maschinen- und Prüfwesen, das mit der Gründung der Bundesversuchs- und Prüfungsanstalt für Landwirtschaftliche Maschinen 1946 zu Ende ging. 1953 wurde das Institut für Landmaschinen- und Arbeitstechnik geschaffen und zum „Institut für Landtechnik und Energiewirtschaft" ausgebaut (1967). 1992 erfolgte die Erweiterung des Themenfelds um die Fachgebiete Energie- und Umwelttechnik. 2004 wurde die BOKU neu strukturiert und das „Institut für Landtechnik“ unter dem Dach des Departments für „Nachhaltige Agrarsysteme“ firmiert. Ab 2011 wurden neue Schwerpunkte "Systemwissenschaften" und der Bereich „smart farming technologies“ gelegt. Mit der 2019 erfolgten Gründung des „Digitalisierungs- und Innovationslabors in den Agrarwissenschaften“ an der BOKU konnte ein erster Schritt gesetzt werden.Since the founding of the University of Natural Resources and Applied Life Sciences in 1872, 9 professors have guided the fortunes of the Institute of Agricultural Engineering. The testing station, founded in 1906/1907, developed into a machine and testing department, which came to an end with the founding of the Federal Testing and Research Institute for Agricultural Machines in 1946. In 1953, the Institute for Agricultural Machinery and Work Technology was created and expanded into the "Institute for Agricultural Technology and Energy Management" (1967). In 1992, the subject area was expanded to include energy and environmental technology. BOKU was restructured and the "Institute of Agricultural Engineering" became part of the Department of "Sustainable Agricultural Systems" in 2004. From 2011 onwards, new focal points "systems science" and the field of "smart farming technologies" were established. The establishment of the "Digitalization and Innovation Laboratory in Agricultural Sciences" at BOKU in 2019 was a first step