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Agricultural production and farm management are inextricable, since managerial aspects for safe and of high-quality food products have led to the development of successful production plans but multifaceted controversies as well. These controversies arise from the focus of policymakers, especially in the EU, to the environmental aspects of agricultural production, creating conflicting objectives for farmers. Energy from biomass derivatives could play a significant role in the dispute for economic and environmental sustainability in agriculture, along with the formulation of agro-energy districts. In this context, an MCDM model was developed integrating LCA data for the assessment of economic, environmental and energy sustainability regarding thirteen major crops in the Region of Central Macedonia in Greece. The model's objectives consist of maximization of farmers' gross income, minimization of emissions coming from farming practices and maximization of energy potentially coming from biomass. Furthermore, three different scenario-based directions allocate different weights to the respective objectives, creating different managerial strategies. The optimal production plan was the scenario in which the weights were allocated by goal programming. The optimal plan proposes the cultivation expansion of energy crops, tree crops, alfalfa and hard wheat to a higher degree. Moreover, a significant reduction to the cultivated areas of tobacco, rice, barley and soft wheat could lead to a potentially viable production plan.

This study evaluates and forecasts the competitiveness of the Greek olive oil sector in the world market, using the indicator of relative trade advantage (RTA) by estimating the Box-Jenkins auto-regressive integrated moving average (ARIMA) model. Results based on data over the last fifty years indicate that ARIMA (0,1,2) is the best model to forecast the competitiveness of the olive oil sector in the world agricultural market. According to the short-term forecast, the commercial advantage of Greek olive oil will improve up to 2022, validating the design of holistic value creation strategies to produce competitive advantage under variant products and market conditions.

This paper aims to make a comparison between Greek and Bulgarian rural areas, in terms of social sustainability indicators. Social sustainability is related to social capital, social inclusion, social exclusion and social cohesion in rural economies, terms that can be measured by relevant social indicators. The paper focuses on the effects of farm household behaviour on social sustainability in regard to changes in employment, gender, migration and social capital. To this end two case study areas were selected; Macedonia and Thrace in Greece and South east Planning Region in Bulgaria. The data were collected by a survey carried out in the context of the European FP7 project entitled CAP-IRE (Assessing the multiple Impacts of the Common Agricultural Policy on Rural Economies). The survey included eleven case study areas in nine case study areas of the European Union. The paper also compares the results from the Greek and Bulgarian case study areas with the average results of the eleven European case study areas.

Social sustainability is “one of the three legs of the sustainability stool” (the other two are environmental and economic. There is limited literature that focuses on social sustainability to the extent that a comprehensive study of this concept is still missing. As a result, there have been very few attempts to define social sustainability as an independent dimension of sustainable development. Social sustainability is related to social capital, social inclusion, social exclusion and social cohesion in rural economies, terms that are measured by relevant social sustainability indicators. This paper aims to measure these indicators and to compare them. In order to measure these indicators, a household survey carried out in the context of the research project CAP-IRE. This survey included eleven case study areas in nine case study areas of the European Union. From the results of this survey we measured a set of economic and social sustainability indicators. We have selected the main social sustainability indicators on which we based on to proceed to the ranking of the eleven European rural areas.

Social sustainability is “one of the three legs of the sustainability stool” (the other two are environmental and economic. There is limited literature that focuses on social sustainability to the extent that a comprehensive study of this concept is still missing. As a result, there have been very few attempts to define social sustainability as an independent dimension of sustainable development. Social sustainability is related to social capital, social inclusion, social exclusion and social cohesion in rural economies, terms that are measured by relevant social sustainability indicators. This paper aims to measure these indicators and to compare them. In order to measure these indicators, a household survey carried out in the context of the research project CAP-IRE. This survey included eleven case study areas in nine case study areas of the European Union. From the results of this survey we measured a set of economic and social sustainability indicators. We have selected the main social sustainability indicators on which we based on to proceed to the ranking of the eleven European rural areas.

This paper aims to make a comparison between Greek and Bulgarian rural areas, in terms of social sustainability indicators. Social sustainability is related to social capital, social inclusion, social exclusion and social cohesion in rural economies, terms that can be measured by relevant social indicators. The paper focuses on the effects of farm household behaviour on social sustainability in regard to changes in employment, gender, migration and social capital. To this end two case study areas were selected; Macedonia and Thrace in Greece and South east Planning Region in Bulgaria. The data were collected by a survey carried out in the context of the European FP7 project entitled CAP-IRE (Assessing the multiple Impacts of the Common Agricultural Policy on Rural Economies). The survey included eleven case study areas in nine case study areas of the European Union. The paper also compares the results from the Greek and Bulgarian case study areas with the average results of the eleven European case study areas.

Deliverable n.D2.13‐23 du projet CAP-IRE Assessing the multiple impacts of the Common Agricultural Policies (CAP) on Rural Economics (Fp7 SSH- 216672); Survey description

This paper presents the results of a research concerning the type, category and fields of applications of Decision Support Systems (DSS) and their contribution to decision-making in agriculture, mainly in the fields of planning and management of farms, farm regions and agricultural resources. More specifically, the paper includes a taxonomy survey that has been based on an analysis of all published works on applications of DSS from 1987 to 2001, as well as a categorized presentation of these applications. The relevant classification of DSS is made by treating subject (theoretical DSS or applicative), the source of publication (proceedings, scientific journal or dissertation), the type of DSS (Decision Support System, Expert System or Knowledge Based DSS), the operational research model that each one DSS uses, the operational field (Diagnostic Forecasting, Advisory, Control, Educational Informational, Operational), the category of decisions (Strategic or Tactic planning decision) and the year of application. The basic concepts and characteristics of DSSs along with the important role that they play in the decision-making process in agriculture, are also described at the beginning of this paper.

This paper presents the results of a research concerning the type, category and fields of applications of Decision Support Systems (DSS) and their contribution to decision-making in agriculture, mainly in the fields of planning and management of farms, farm regions and agricultural resources. More specifically, the paper includes a taxonomy survey that has been based on an analysis of all published works on applications of DSS from 1987 to 2001, as well as a categorized presentation of these applications. The relevant classification of DSS is made by treating subject (theoretical DSS or applicative), the source of publication (proceedings, scientific journal or dissertation), the type of DSS (Decision Support System, Expert System or Knowledge Based DSS), the operational research model that each one DSS uses, the operational field (Diagnostic–Forecasting, Advisory, Control, Educational–Informational, Operational), the category of decisions (Strategic or Tactic planning decision) and the year of application. The basic concepts and characteristics of DSSs along with the important role that they play in the decision-making process in agriculture, are also described at the beginning of this paper.

In accordance with the European Water Framework Directive (WFD), the management of water resources should be delegated to appropriately established local bodies, referred to as River Basin Districts (Bogaert 2012). Basin Districts are responsible for the full cost of water usage in order to ensure the good hydrological status of the territory under their jurisdiction. Such costs should then be transmitted to end users through appropriate (higher) tariffs. The existence of economic tools, that enable local water authorities to allocate costs among users and to control water uses in accordance with the WFD principles change considerably depending on several factors. Lack of metering is the main constraint. This condition hinders the ability to monitor volumes used and to promote volumetric pricing as a way to allocate costs and to ensure efficient water use (Viaggi et al. 2010; Johansson et al. 2002; Smith and Tsur 1997; Bowen and Young 1986). This issue is a challenge especially for the agricultural sector since the most common delivery system for irrigation water worldwide is open canals (Bogaert 2012). Here, the presence of a heterogeneous population of farmers poses implicitly the two typical problems of asymmetric information: a) adverse selection, due to the non-observability of a farm’s type and its technology; b) moral hazard, due to the non-observability of a farm’s actions, such as the actual use of irrigation water (Viaggi et al. 2011).

Nutrient pollution is one of the main causes of impairment of water quality. Recent studies estimated that nitrogen pollution-related damage in EU27 ranges between 70 and 320 billion Euro each year, equivalent to 150–750 euro/capita (Brink et al. 2011).Agriculture is the main source of nitrogen loading (EEA 2012) and is the sector with the largest remaining emission reduction potential (Sutton et al. 2011). A nutrient defi ciency can reduce soil fertility and crop yields, but a nutrient surplus, in excess of crop and forage needs can lead to nutrient release in the environment and, potentially, to water contamination. Surpluses of nitrogen and phosphate are forecast to grow in the next decade, while those of potash are likely to remain more or less stable (FAO 2008). This trend is expected in spite of the fact that the price of nitrogenbased fertilizers has almost doubled from year 2000 to 2010, when the yearly increase was on average equal to 10% in the US (USDA 2012). The value of main agricultural inputs, such as fertilizers and fuel, represent more than one third of the value of agricultural income in Italy (Fanfani and Gutierrez 2011). The European Union efforts to reduce nutrient over enrichment of waters were put into action since the early ’90s with the enactment of the Nitrates Directive (1991), which aims to protect water quality across Europe by preventing nitrates from agricultural sources polluting ground and surface waters and by promoting the use of good farming practices. The Common Agricultural Policy also contributes to the mitigation of pollution of waters by nitrates, mainly through Rural Development measures. The intervention strategy is based on direct support to farmers who will voluntary apply agri-environmental measures in order to reduce nitrates pollution, such as organic farming, low input farming, cover crops, effi cient management of livestock waste and extensive farming. The EU demands an evaluation of Rural Development Plans (RDPs) from the national authorities of the European member states. The EU Commission has provided a set of indicators and evaluation criteria to be adopted for the RDPs assessment (CMEF 2006). Within the designed monitoring framework, the gross nutrient balance (baseline indicator 20), provides an estimate of the potential surplus of nitrogen and phosphorous on agricultural land (kg/ha). Nutrient balance indicators, as an environmental driving force, link to the state (or concentration) of nutrients in water bodies. This study aims to evaluate the role of RDPs implementation in preventing water quality deterioration due to nutrient runoff and infi ltration. The analysis focuses on the impact of Agri-Environmental Schemes on the reduction of nitrogen surplus through the implementation of specifi c submeasures such as organic farming, set aside, etc. In order to provide a quantitative evaluation, we applied a spatial regression model. The model aims to explain the variation of nitrogen surplus between two monitoring years: 2000 and 2010. In order to build a suitable dependent variable, we carried out a calculation of the GrossNitrogen Balance (a proxy for nitrogen surplus) at the municipality scale in Emilia-Romagna region, for both monitoring years. The study area is characterized by the presence of an intensive agricultural and livestock farming system. Gross Nitrogen Balance was calculated following the OECD and EUROSTAT method. The chapter is structured as follows: the next section describes background and study area, then methodology, results and discussion are presented.