Comparative Analysis of Energy Efficiency in Wheat Production in Different Climate Conditions of Europe

This paper presents results concerning energy efficiency of wheat production considered in the context of specific energy input variation in different climatic conditions of Europe as well as case studies on implementation of selected energy saving measures in practice. The source data collected from the six european union (EU) countries represent five agricultural regions of continental Europe and three climates: continental, temperate and Mediterranean. The life cycle assessment (LCA) methodology was applied to analyze the data excluding of pre-farm gate activities. The total primary energy consumption was decomposed into main energy input streams and it was regressed to yield. In order to compare energy efficiency of wheat production across the geographical areas, the data envelopment analysis (DEA) was applied. It was shown that the highest wheat yield (6.7 t/ha to 8.7 t/ha) at the lowest specific energy input (2.08 GJ/t to 2.56 GJ/t) is unique for temperate climate conditions. The yield in continental and Mediterranean climatic conditions is on average lower by 1.3 t/ha and 2.7 t/ha and energy efficiency lower by 14% and 38%, respectively. The case studies have shown that the energy saving activities in wheat production may be universal for the climatic zones or specific for a given geographical location. It was stated that trade-offs between energy, economic, and environmental effects, which are associated with implementation of a given energy saving measure or a set of measures to a great extent depend on the current energy efficiency status of the farm and opportunity for investment, which varies substantially across Europe

Case studies and comparative analysis of energy efficiency in wheat production in different climatic conditions of Europe

The paper presents results concerning energy efficiency of wheat production considered in the context of specific energy input variation in different climatic conditions of Europe as well as case studies on implementation of selected energy saving measures in practice. It was shown that the highest wheat yield (6.7-8.7 t·ha-1) at the lowest specific energy input (2.08-2.56 GJ·t-1) is unique for temperate climate conditions. The yield in continental and Mediter-ranean climatic conditions is on average lower by 1.3 t·ha-1 and 2.7 t·ha-1 and energy effi-ciency lower by 14% and 38%, respectively. The case studies have shown that the energy saving activities in wheat production may be universal for the climatic zones or specific for a given geographical location. It was stated that trade-offs between energy, economic and en-vironmental effects, which are associated with implementation of a given energy saving measure or a set of measures to a great extent depend on the current energy efficiency sta-tus of the farm and opportunity for investment, which varies substantially across Europe

Energy efficiency (EE) and cost-effective means to increase EE and to mitigate the climate change of pork and broiler meat production in five European countries

Production of pork and broiler meat in the European Union (EU) has increased by 7.8 and 16.1%, respectively, in the period of 2001 – 2011. At that time pork and broiler meat produced, amounted together to over four times the cattle meat. Meat is an important protein source in human diet, but on the other hand, livestock uses globally 30% of ice-free terrestrial land and produces 18% of global greenhouse gas (GHG) emissions. This exceeds the global emissions of the transport sector. Furthermore, energy ratio (output/input) for meat production is less than 1.0 in general and it is much lower than that of plant production. This paper presents cost-effectiveness of EE measures in pork and broiler meat production and it is based on the results of the Agriculture and Energy Efficiency Project (www.AGREE.aua.gr). The structure of the energy input appeared to be very similar in pork and broiler meat production. Feed was found to be the major indirect energy input. Its contribution to the total energy demand varied from 51% to 82% in pork production and from 55% to 94% in broiler meat production. The percentage of feed was the lowest in the Northern European countries and the highest in the south. This difference was mainly attributable to the demand for heating of animal houses during the winter period. Differences could also be found in the absolute energy input of feed. It indicated that there may be possibilities to improve feeding strategies or feed conversation rate of animals. In pork production, the energy input of feed was 12.5 GJ t-1 (live weight) in average and 8.6 GJ t-1 (live weight) in broiler production. The difference between pork and broiler meat is a consequence of the higher feed conversation rate of broilers in contrast to pigs. The category “Other energy use” was the second highest energy input and it consisted of energy input for ventilation, illumination, feeding, and heating of animal houses. In pork production, the input of this category was 4.7 GJ t-1 (live weight) in average (25% from the total energy input) and 2.4 GJ t-1 (live weight) in broiler meat production (22% from the total energy input). The specific energy input in pork production was the lowest in The Netherlands ( 14.5 GJ t-1) and that of broiler meat production in Germany (9.8 GJ t-1). Case studies analysed in five participating countries demonstrated EE measures capable to reduce costs, to increase EE, and to cut GHG emissions at the same time. Proposed EE measures were related to ventilation, heating, feeding, animal bedding, energy generation from manure, and feed production. As an example, an airtight grain storage met all three goals at the same time. Investment costs were lower than those for a grain dryer, no energy was needed for drying, and no GHG emissions were generated because no gas or oil was needed for drying. All suggested EE measures were not as successful. They might appear negative for costs but positive for EE and GHG reduction, resulting in a trade-off situation. An approach like this helps to rank potential EE measures in terms of their cost-effectiveness and capability to cut GHG emissions

Energy efficiency in agriculture

One of the EU headline target indicators for Europe is “20% increase in energy efficiency” by 2020. It is anticipated that in the following decades energy use will increase significantly and will have a widespread impact on the economy, including the agricultural sector. Energy use reduction can be achieved by reduced energy input. Improved energy efficiency, however, is only achieved, if energy input per unit yield is reduced. Therefore, improved energy efficiency can be realized with either increased or decreased energy inputs depending on the input-output relationship. In agricultural production the need for energy as an input can determine the profitability of farming which, in turn, impacts heavily upon the farmers’ investment in improved farming systems. This paper presents some of the results obtained in the WP2 of the KBBE.2011.4-04 project “Energy Efficiency in Agriculture - AGREE” supported by the 7th Framework Program. It gives an overview into energy use and energy efficiency of agriculture in various agro-climatic zones of Europe

Energy efficiency in agriculture. Showcase and alternatives for wheat production in Portugal.

This chapter presents some results obtained in the KBBE.2011.4-04 project “Energy Efficiency in Agriculture - AGREE” supported by the 7th Framework Program. It gives an overview into energy use and energy efficiency in wheat production in various agro-climatic zones of Europe. Among cereals, wheat is the crop with the largest cultivated area in Europe. In 2008, the percentage share of the area occupied by common and durum wheat in the countries analysed in the AGREE project ranged from 2.4% in Portugal to 18.9% in Germany (Gołaszewski et al., 2012). The different production systems in different climates vary substantially in their energy use and energy saving potential. A showcase of conventional wheat production in Portugal, where in 2012 it was cultivated in 54,761 ha (INE, 2013), is presented and some production alternatives are analysed. The main objective was to analyse the effect in the economic results, energy consumption and environmental impacts of three wheat production systems alternatives: 1. no tillage cropping systems, 2. reduction of phosphorous application and 3. the use of supplemental irrigation

Priorities for energy efficiency measures in agriculture.

This report provides a compilation of energy efficiency measures in agriculture, their opportunities and constraints to implement energy efficient agricultural systems across Europe as a result of the AGREE (Agriculture & Energy Efficiency) Coordination and Support Action funded by the 7th research framework of the EU (www.agree.aua.gr). The report dwells on earlier reports of the consortium, which listed potential energy efficiency measures (Project Deliverable 2.3: Energy Saving Measures in Agriculture – Overview on the Basis of National Reports) and identified trade-offs and win-win situations of various energy efficiency measures in agriculture (Project Deliverable 3.1: Economic and environmental analysis of energy efficiency measures in agriculture). It shows research gaps in crop production, greenhouse production, animal husbandry and system approaches, which can be regarded as priorities for energy efficiency measures in agriculture. The report is na important input for the strategic research agenda, which is one of the main outputs of the AGREE project

Economic and environmental analysis of energy efficiency measures in agriculture. Case Studies and trade offs.

This report is the result of the collaboration of the partners of the AGREE work-package “Economic and environmental analysis”, which is based on case study analyses of the partners in seven countries of the EU. The case studies show economic and environmental trade-offs in the different regions in the EU, for which each partner is responsible. Nevertheless prior to the reporting of the case studies an intensive discussion on a common methodological approach has been accomplished and applied to the case studies. The case studies show a wide range of different perspectives of energy efficiency in agriculture, but they are all based on the common methodology presented in Chapter 3. In Chapter 4, the case studies are presented, with authors indicated at the beginning of each section. Each section of Chapter 4 ends with a synthesis analysis of the results from the different case studies. Chapter 5 summarizes and concludes the report by highlighting the major findings of the analyses. The report builds upon the “State of the Art in Energy Efficiency in Europe” published separately by the AGREE consortium (Gołaszewski et al. 2012), which shows the status quo of energy use and possible energy efficiency measures in agriculture across different production systems and regions in Europe. This report presents an economic and environmental analysis based on in-depth case studies which show the potential for, and constraints on, energy efficiency measures in agriculture with respect to the specific environments in Europe

A Generic Bio-Economic Farm Model for Environmental and Economic Assessment of Agricultural Systems

Bio-economic farm models are tools to evaluate ex-post or to assess ex-ante the impact of policy and technology change on agriculture, economics and environment. Recently, various BEFMs have been developed, often for one purpose or location, but hardly any of these models are re-used later for other purposes or locations. The Farm System Simulator (FSSIM) provides a generic framework enabling the application of BEFMs under various situations and for different purposes (generating supply response functions and detailed regional or farm type assessments). FSSIM is set up as a component-based framework with components representing farmer objectives, risk, calibration, policies, current activities, alternative activities and different types of activities (e.g., annual and perennial cropping and livestock). The generic nature of FSSIM is evaluated using five criteria by examining its applications. FSSIM has been applied for different climate zones and soil types (criterion 1) and to a range of different farm types (criterion 2) with different specializations, intensities and sizes. In most applications FSSIM has been used to assess the effects of policy changes and in two applications to assess the impact of technological innovations (criterion 3). In the various applications, different data sources, level of detail (e.g., criterion 4) and model configurations have been used. FSSIM has been linked to an economic and several biophysical models (criterion 5). The model is available for applications to other conditions and research issues, and it is open to be further tested and to be extended with new components, indicators or linkages to other models

Advanced Robotic Therapy Integrated Centers (ARTIC): an international collaboration facilitating the application of rehabilitation technologies

Background: The application of rehabilitation robots has grown during the last decade. While meta-analyses have shown beneficial effects of robotic interventions for some patient groups, the evidence is less in others. We established the Advanced Robotic Therapy Integrated Centers (ARTIC) network with the goal of advancing the science and clinical practice of rehabilitation robotics. The investigators hope to exploit variations in practice to learn about current clinical application and outcomes. The aim of this paper is to introduce the ARTIC network to the clinical and research community, present the initial data set and its characteristics and compare the outcome data collected so far with data from prior studies. Methods: ARTIC is a pragmatic observational study of clinical care. The database includes patients with various neurological and gait deficits who used the driven gait orthosis Lokomat® as part of their treatment. Patient characteristics, diagnosis-specific information, and indicators of impairment severity are collected. Core clinical assessments include the 10-Meter Walk Test and the Goal Attainment Scaling. Data from each Lokomat® training session are automatically collected. Results: At time of analysis, the database contained data collected from 595 patients (cerebral palsy: n = 208; stroke: n = 129; spinal cord injury: n = 93; traumatic brain injury: n = 39; and various other diagnoses: n = 126). At onset, average walking speeds were slow. The training intensity increased from the first to the final therapy session and most patients achieved their goals. Conclusions: The characteristics of the patients matched epidemiological data for the target populations. When patient characteristics differed from epidemiological data, this was mainly due to the selection criteria used to assess eligibility for Lokomat® training. While patients included in randomized controlled interventional trials have to fulfill many inclusion and exclusion criteria, the only selection criteria applying to patients in the ARTIC database are those required for use of the Lokomat®. We suggest that the ARTIC network offers an opportunity to investigate the clinical application and effectiveness of rehabilitation technologies for various diagnoses. Due to the standardization of assessments and the use of a common technology, this network could serve as a basis for researchers interested in specific interventional studies expanding beyond the Lokomat®