Agriculture is the main and, in some cases, the only, source of income and employment in rural areas. The change in the conditions under which agriculture is practiced has various effects on the agricultural economy but also on the social structure of rural areas. Climate change has multiple effects on agricultural production, necessitating the reorganization of agricultural production in some cases. These effects of climate change will also impact the economic and social aspects of farms in rural areas. This paper attempts to identify these effects by measuring the socioeconomic impacts of climate change in the region of Central Macedonia in Greece. For this reason, a multicriteria model was developed to simulate these impacts by estimating a set of seven social and economic indicators. The model was implemented to the average farm which was estimated from the main cultivations of the region. A scenario analysis was also used in combination with the multicriteria model. The multicriteria model suggests modifications are needed in the average farm crop plan of the region as a result of the climate change impact. The scenarios results show that climate change will negatively affect all the social and economic indicators and will continue to affect them over the years. These results can be used by policymakers to understand the economic and social impacts of climate change in the region to plan their future policies.
Rural Development Plan (RDP) measures support farmers in improving the sustainability of their agricultural holdings. The implementation of these policies has economic, social, and environmental impacts, which are monitored either ex-ante, ongoing, or ex-post, as required from the European Commission impact assessment guidelines. In this frame, this paper aims to assess the impacts of RDP measures on the sustainability of agricultural holdings. For this reason, a positive mathematical programming (PMP) model was developed and implemented in combination with a set of economic, social, and environmental indicators. The model was used to assess the ex-post impacts of the measure titled ‘Modernization of agricultural holdings’ of the Greek RDP 2007–2013. This research was conducted on a sample of 219 agricultural holdings in a region of northern Greece. The impacts were measured through the changes of the crop plan in the agricultural land. The results show that the measure has positive economic impacts, negative social impacts, and negative impacts on most of the environmental indicators. The results also underline the significant role of the impact assessment process in supporting policymakers in understanding the impacts of their policies.
Glasshouse farming is one of the most intensive types of production of agricultural products. Via this process, consumers have the ability to consume mainly off-season vegetables and farmers are able to reduce operational risks, due to their ability to control micro-climate conditions. This type of farming is quite competitive worldwide, this being the main reason for formulating and implementing assessment models measuring operational performance. The methodology used in this study is Data Envelopment Analysis (DEA), which has wide acceptance in agriculture, among other sectors of the economy. The production protocols of four different vegetables—cucumber, eggplant, pepper, and tomato—were evaluated. Acreage (m2), crop protection costs (€), fertilizers (€), labor (Hr/year), energy (€), and other costs (€) were used as inputs. The turnover of every production unit (€) was used as the output. Ninety-eight agricultural holdings participated in this survey. The dataset was obtained by face-to-face interviews. The main findings verify the existence of significant relative deficiencies (including a mean efficiency score of 0.87) as regards inputs usage, as well as considerably different efficiency scores among the different cultivations. The most efficient of these was the eggplant production protocol and the least efficient was that used for the tomato. The implementation of DEA verified its utility, providing incentives for continuing to use this methodology for improving land management decision making.
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