B.S. Elbersen

Lay discourses of the rural and stated and revealed preferences for rural living. Some evidence of the existence of a rural idyll in the Netherlands

Abstract Dutch rural areas have changed into a post-modern countryside and have become marketable commodities. The demand for rural space and rural amenities has increased, with concomitant tensions on the rural housing market, tensions which are enhanced by the restrictive spatial policy in Dutch rural areas. The demand for rural residential environments appears to be large. This paper reports the results of our research into the preferences of urban households for living in a rural residential environment. These preferences will be linked with images and representations of the countryside. It is assumed that individual images of the countryside (whether idyllic or not) affect residential preferences and these preferences have, in turn, their effect on migration behaviour. Empirical evidence suggests that perceptions, preferences and behaviour pertaining to rural residential environments are indeed interrelated. The Dutch countryside commands a very positive image and the demand for residential environments with rural characteristics is considerable. Consequently, a rural idyll can be identified in The Netherlands.

Modelling the spatial distribution of livestock in Europe

Livestock remains the world’s largest user of land and is strongly related to grassland and feed-crop production. Assessments of environmental impacts of livestock farming require detailed knowledge of the presence of livestock, farming practices, and environmental conditions. The present Europe-wide livestock distribution information is generally restricted to a spatial resolution of NUTS 2 (province level). This paper presents a modelling approach to determine the spatial distribution of livestock at the landscape level. Location factors for livestock occurrence were explored and applied to consistent and harmonized EU-wide regional statistics to produce a detailed spatial distribution of livestock numbers. Both an expert-based and an empirical approach were applied in order to disaggregate the data to grid level. The resulting livestock maps were validated. Results differ between the two downscaling approaches but also between livestock types and countries. While both the expert-based and empirical approach are equally suited to modelling herbivores, in general, the spatial distribution of monogastrics can be better modelled by applying the empirical approach.

How effective are the sustainability criteria accompanying the European Union 2020 biofuel targets

The expansion of biofuel production can lead to an array of negative environmental impacts. Therefore, the European Union (EU) has recently imposed sustainability criteria on biofuel production in the Renewable Energy Directive (RED). In this article, we analyse the effectiveness of the sustainability criteria for climate change mitigation and biodiversity conservation. We first use a global agriculture and forestry model to investigate environmental effects of the EU member states National Renewable Energy Action Plans (NREAPs) without sustainability criteria. We conclude that these targets would drive losses of 2.2 Mha of highly biodiverse areas and generate 95 Mt CO 2 eq of additional greenhouse gas (GHG) emissions. However, in a second step, we demonstrate that the EU biofuel demand could be satisfied ‘sustainably’ according to RED despite its negative environmental effects. This is because the majority of global crop production is produced ‘sustainably’ in the sense of RED and can provide more than 10 times the total European biofuel demand in 2020 if reallocated from sectors without sustainability criteria. This finding points to a potential policy failure of applying sustainability regulation to a single sector in a single region. To be effective this policy needs to be more complete in targeting a wider scope of agricultural commodities and more comprehensive in its membership of countries.

Environmental and agricultural modeling: Integrated approaches for policy impact assessment

The Agri-Environmental Zonation (AEnZ) is a biophysical typology based on a recently available detailed database on organic carbon content of the topsoil of Europe, the Environmental Stratification (EnS) and an Agri-mask. The AEnZ is used within the integrated assessment framework of SEAMLESS. The basis for this typology is the Environmental Stratification of Europe (EnS) building mainly on climate and altitude characteristics. The 84 environmental strata were aggregated into 13 environmental zones (EnZs). The environmental zones were then combined with organic carbon topsoil data (OCTOP) to cover the wide range of agri-environmental diversity of Europe. The OCTOP content was selected as soil variable as it explained most of the variation in soils in Europe. The EnZs/OCTOP land units were combined with an Agri-mask representing major obstacles for farming resulting in the final AEnZ typology. The Agri-mask, which is based on CORINE Land Cover, soil, altitude and slope data, divides Europe into three zones with different agricultural potential (suited, unsuited and marginally suited). The AEnZ consists of 238 land types of which 82 classes are referred as suitable for agriculture (75.8% of EU27+). For the SEAMLESS framework two of the three dimensions of the Agri-Environmental Zonation have been used to build the spatial framework to link information on farming and biophysics. The spatial building block of SEAMLESS is thus the Seamzones, that is an overlay of the 13 environmental zones, the seven OCTOP classes and 270 administrative (NUTS2) regions. In total this results in 3,513 Seamzones that are used to structure the biophysical data as well as the data on farming across the EU27+.The Agri-Environmental Zonation (AEnZ) is a biophysical typology based on a recently available detailed database on organic carbon content of the topsoil of Europe, the Environmental Stratification (EnS) and an Agri-mask. The AEnZ is used within the integrated assessment framework of SEAMLESS. The basis for this typology is the Environmental Stratification of Europe (EnS) building mainly on climate and altitude characteristics. The 84 environmental strata were aggregated into 13 environmental zones (EnZs). The environmental zones were then combined with organic carbon topsoil data (OCTOP) to cover the wide range of agri-environmental diversity of Europe. The OCTOP content was selected as soil variable as it explained most of the variation in soils in Europe. The EnZs/OCTOP land units were combined with an Agri-mask representing major obstacles for farming resulting in the final AEnZ typology. The Agri-mask, which is based on CORINE Land Cover, soil, altitude and slope data, divides Europe into three zones with different agricultural potential (suited, unsuited and marginally suited). The AEnZ consists of 238 land types of which 82 classes are referred as suitable for agriculture (75.8% of EU27+).

A Biophysical Typology in Agri-environmental Modelling

The Agri-Environmental Zonation (AEnZ) is a biophysical typology based on a recently available detailed database on organic carbon content of the topsoil of Europe, the Environmental Stratification (EnS) and an Agri-mask. The AEnZ is used within the integrated assessment framework of SEAMLESS. The basis for this typology is the Environmental Stratification of Europe (EnS) building mainly on climate and altitude characteristics. The 84 environmental strata were aggregated into 13 environmental zones (EnZs). The environmental zones were then combined with organic carbon topsoil data (OCTOP) to cover the wide range of agri-environmental diversity of Europe. The OCTOP content was selected as soil variable as it explained most of the variation in soils in Europe. The EnZs/OCTOP land units were combined with an Agri-mask representing major obstacles for farming resulting in the final AEnZ typology. The Agri-mask, which is based on CORINE Land Cover, soil, altitude and slope data, divides Europe into three zones with different agricultural potential (suited, unsuited and marginally suited). The AEnZ consists of 238 land types of which 82 classes are referred as suitable for agriculture (75.8% of EU27+). For the SEAMLESS framework two of the three dimensions of the Agri-Environmental Zonation have been used to build the spatial framework to link information on farming and biophysics. The spatial building block of SEAMLESS is thus the Seamzones, that is an overlay of the 13 environmental zones, the seven OCTOP classes and 270 administrative (NUTS2) regions. In total this results in 3,513 Seamzones that are used to structure the biophysical data as well as the data on farming across the EU27+.The Agri-Environmental Zonation (AEnZ) is a biophysical typology based on a recently available detailed database on organic carbon content of the topsoil of Europe, the Environmental Stratification (EnS) and an Agri-mask. The AEnZ is used within the integrated assessment framework of SEAMLESS. The basis for this typology is the Environmental Stratification of Europe (EnS) building mainly on climate and altitude characteristics. The 84 environmental strata were aggregated into 13 environmental zones (EnZs). The environmental zones were then combined with organic carbon topsoil data (OCTOP) to cover the wide range of agri-environmental diversity of Europe. The OCTOP content was selected as soil variable as it explained most of the variation in soils in Europe. The EnZs/OCTOP land units were combined with an Agri-mask representing major obstacles for farming resulting in the final AEnZ typology. The Agri-mask, which is based on CORINE Land Cover, soil, altitude and slope data, divides Europe into three zones with different agricultural potential (suited, unsuited and marginally suited). The AEnZ consists of 238 land types of which 82 classes are referred as suitable for agriculture (75.8% of EU27+).

Chapter 1 – Biomass Supply Assessments in Europe: Research Context and Methodologies

Abstract Since early 2000, several biomass assessment studies were delivered at European and global level mostly driven by the increasing demand for the development of bioenergy and biofuels, and the need to secure sustainable, continuous supply for the emerging plants. Ongoing research and development and industrial development plus increased drivers to use renewable raw materials in industrial sectors beyond energy have seen the focus of the biomass markets widen to include value chains for bio-based chemicals, pharmaceuticals, and other materials. Consequently, research is now exploring increasingly varied configurations of value chains with the aims of understanding which types and quantifying how much biomass can be extracted sustainably, generate financial returns, and help the industry achieving high-quality products for consumers. This chapter sets the scene for research on biomass supply assessments in Europe and reviews 40 studies delivered during the last 14 years. It analyzes context, key components in terms of terminology, framework conditions and assumptions, models used, and evidence provided so far for policy, research, and industry. It further discusses the main challenges, identifies gaps, and provides recommendations.

A biophysical typology for a spatially-explicit agri-environmental modelling framework

The Agri-Environmental Zonation (AEnZ) is a biophysical typology based on a recently available detailed database on organic carbon content of the topsoil of Europe, the Environmental Stratification (EnS) and an Agri-mask. The AEnZ is used within the integrated assessment framework of SEAMLESS. The basis for this typology is the Environmental Stratification of Europe (EnS) building mainly on climate and altitude characteristics. The 84 environmental strata were aggregated into 13 environmental zones (EnZs). The environmental zones were then combined with organic carbon topsoil data (OCTOP) to cover the wide range of agri-environmental diversity of Europe. The OCTOP content was selected as soil variable as it explained most of the variation in soils in Europe. The EnZs/OCTOP land units were combined with an Agri-mask representing major obstacles for farming resulting in the final AEnZ typology. The Agri-mask, which is based on CORINE Land Cover, soil, altitude and slope data, divides Europe into three zones with different agricultural potential (suited, unsuited and marginally suited). The AEnZ consists of 238 land types of which 82 classes are referred as suitable for agriculture (75.8% of EU27+). For the SEAMLESS framework two of the three dimensions of the Agri-Environmental Zonation have been used to build the spatial framework to link information on farming and biophysics. The spatial building block of SEAMLESS is thus the Seamzones, that is an overlay of the 13 environmental zones, the seven OCTOP classes and 270 administrative (NUTS2) regions. In total this results in 3,513 Seamzones that are used to structure the biophysical data as well as the data on farming across the EU27+.The Agri-Environmental Zonation (AEnZ) is a biophysical typology based on a recently available detailed database on organic carbon content of the topsoil of Europe, the Environmental Stratification (EnS) and an Agri-mask. The AEnZ is used within the integrated assessment framework of SEAMLESS. The basis for this typology is the Environmental Stratification of Europe (EnS) building mainly on climate and altitude characteristics. The 84 environmental strata were aggregated into 13 environmental zones (EnZs). The environmental zones were then combined with organic carbon topsoil data (OCTOP) to cover the wide range of agri-environmental diversity of Europe. The OCTOP content was selected as soil variable as it explained most of the variation in soils in Europe. The EnZs/OCTOP land units were combined with an Agri-mask representing major obstacles for farming resulting in the final AEnZ typology. The Agri-mask, which is based on CORINE Land Cover, soil, altitude and slope data, divides Europe into three zones with different agricultural potential (suited, unsuited and marginally suited). The AEnZ consists of 238 land types of which 82 classes are referred as suitable for agriculture (75.8% of EU27+).

Lignocellulosic Biomass Quality: Matching Characteristics With Biomass Conversion Requirements

Lignocellulosic biomass consists mostly of lignin, cellulose, and hemicellulose, and to a more or lesser extent other plant components such as sugars, starch, acids, fats, and oils, and last but not least water (moisture) and ash. Quality is essentially a set of biomass characteristics that determine the value of that biomass type for a certain conversion system. Fundamental biomass quality characteristics determine if biomass can be matched to a conversion system. We distinguish “fundamental” biomass characteristics from thermal conversion to be chlorine and ash content and ash melting temperature. For biological conversion, carbohydrate and lignin are the most relevant, while for anaerobic digestion biogas yield and digestate applicability are the most important. Other quality characteristics can more easily be adjusted such as particle size, moisture content, and bulk density. It is important to understand what determines the quality composition of biomass so that quality can be optimized. Plant type, plant part, soil type, time of harvest can determine that quality.Lignocellulosic biomass consists mostly of lignin, cellulose, and hemicellulose, and to a more or lesser extent other plant components such as sugars, starch, acids, fats, and oils, and last but not least water (moisture) and ash. Quality is essentially a set of biomass characteristics that determine the value of that biomass type for a certain conversion system. Fundamental biomass quality characteristics determine if biomass can be matched to a conversion system. We distinguish “fundamental” biomass characteristics from thermal conversion to be chlorine and ash content and ash melting temperature. For biological conversion, carbohydrate and lignin are the most relevant, while for anaerobic digestion biogas yield and digestate applicability are the most important. Other quality characteristics can more easily be adjusted such as particle size, moisture content, and bulk density. It is important to understand what determines the quality composition of biomass so that quality can be optimized. Plant type, plant part, soil type, time of harvest can determine that quality.

Chapter 9 – Assessing the Potentials for Nonfood Crops

Given the ambitious EU targets to further decarbonize the economy, it can be expected that demand for lignocellulosic biomass will continue to grow. Provisioning of part of this biomass by dedicated biomass crops becomes an option. This chapter presents yields and cost levels that can be reached in Europe with different perennial crops in different climatic, soil, and management situations. The AquaCrop model developed by FAO was used and fed with phenological parameters per crop and detailed weather data to simulate the crop growth in all European NUTS3 regions. Yield levels were simulated for a maximum and a water limited yield situation and further converted to match with low, medium, and high input management systems. Low input systems are suitable for the lower quality soils often characterized as “marginal” because of their low suitability to be used for annual (rotational) cropping. In addition, suitability maps specific per crop were prepared according to important limiting factors such as killing frost, length of growing season, and slope. The cost productions were assessed with an activity-based costing (ABC) model, developed to assess the roadside Net Present Value (NPV) cost per ton of biomass. The yield, crop suitability, and cost simulation results were then combined to identify the best performing crop–management mix per region.

A framework with an integrated computer support tool to assess regional biomass delivery chains

Abstract In this paper, we first provide a brief overview of other decision support tools for bioenergy and assess to which extent the integrated tool central in this paper is different and novel. Next, a description is given of the tool, the different models used and the functionalities. The working of the tool is then illustrated with three case studies based in the northern part of The Netherlands. The computerised tool is meant to support the communication process between stakeholders to come to the implementation of regional biomass delivery chains. It helps to create a quick and common understanding of optimal biomass use in a region. Although the tool has been applied only to bioenergy chains, other biochemical and biomaterial chains are also suitable to be incorporated. The three case studies presented include a conventional sugar beet bioethanol production chain, an advanced Miscanthus bioethanol conversion chain and a straw-based electricity chain. The main conclusions are that optimal biomass use for non-food purposes from a sustainability and resource-efficient perspective depend on many different factors specific to the conversion chains. For example, the green house gas (GHG) emission and mitigation potential of a sugar beet-based bioethanol chain requires careful organisation particularly on the primary biomass production and transport, while in a straw-based electricity chain, the largest efficiency gains can be reached in the conversion part. Land use change (LUC) to sugar beet generally causes more negative environmental impacts than LUC to Miscanthus. This applies to both GHG efficiency, soil organic carbon content and emissions of nitrogen to surface waters. At the same time, it becomes clear that the different scenario assumptions can be very influential, particularly on the final economic performance of a chain. Overall, it is clear from the cases that the users understand much better under which circumstances and through which mechanisms the designed chains can become profitable and can become more environmentally sustainable.