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            "abstractNote": "Organic agriculture refers to a farming system that enhance soil fertility through maximizing the efficient use of local resources, while foregoing the use of agrochemicals, the use of Genetic Modified Organisms (GMO), as well as that of many synthetic compounds used as food additives. Organic agriculture relies on a number of farming practices based on ecological cycles, and aims at minimizing the environmental impact of the food industry, preserving the long term sustainability of soil and reducing to a minimum the use of non renewable resources. This paper carries out a comparative review of the environmental performances of organic agriculture versus conventional farming, and also discusses the difficulties inherent in this comparison process. The paper first provides an historical background on organic agriculture and briefly reports on some key socioeconomic issues concerning organic farming. It then focuses on how agricultural practices affect soil characteristics: under organic management soil loss is greatly reduced and soil organic matter (SOM) content increases. Soil biochemical and ecological characteristics appear also improved. Furthermore, organically managed soils have a much higher water holding capacity than conventionally managed soils, resulting in much larger yields compared to conventional farming, under conditions of water scarcity. Because of its higher ability to store carbon in the soil, organic agriculture could represent a means to improve CO2 abatement if adopted on a large scale. Next, the impact on biodiversity is highlighted: organic farming systems generally harbor a larger floral and faunal biodiversity than conventional systems, although when properly managed also the latter can improve biodiversity. Importantly, the landscape surrounding farmed land also appears to have the potential to enhance biodiversity in agricultural areas. The paper then outlines energy use in different agricultural settings: organic agriculture has higher energy efficiency (input/output) but, on average, exhibits lower yields and hence reduced productivity. Nevertheless, overall, organic agriculture appears to perform better than conventional farming, and provides also other important environmental advantages, such as halting the use of harmful chemicals and their spread in the environment and along the trophic chain, and reducing water use. Looking at the future of organic farming, based on the findings presented in this review, there is clearly a need for more research and investment directed to exploring potential of organic farming for reducing the environmental impact of agricultural practices; however, the implications of reduced productivity for the socioeconomic system should also be considered and suitable agricultural policies should be developed.",
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            "title": "Environmental impacts of organic and conventional agricultural products - Are the differences captured by life cycle assessment?",
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                    "firstName": "Matthias S.",
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            "abstractNote": "Comprehensive assessment tools are needed that reliably describe environmental impacts of different agricultural systems in order to develop sustainable high yielding agricultural production systems with minimal impacts on the environment. Today, Life Cycle Assessment (LCA) is increasingly used to assess and compare the environmental sustainability of agricultural products from conventional and organic agriculture. However, LCA studies comparing agricultural products from conventional and organic farming systems report a wide variation in the resource efficiency of products from these systems. The studies show that impacts per area farmed land are usually less in organic systems, but related to the quantity produced impacts are often higher. We reviewed 34 comparative LCA studies of organic and conventional agricultural products to analyze whether this result is solely due to the usually lower yields in organic systems or also due to inaccurate modeling within LCA. Comparative LCAs on agricultural products from organic and conventional farming systems often do not adequately differentiate the specific characteristics of the respective farming system in the goal and scope definition and in the inventory analysis. Further, often only a limited number of impact categories are assessed within the impact assessment not allowing for a comprehensive environmental assessment. The most critical points we identified relate to the nitrogen (N) fluxes influencing acidification, eutrophication, and global warming potential, and biodiversity. Usually, N-emissions in LCA inventories of agricultural products are based on model calculations. Modeled N-emissions often do not correspond with the actual amount of N left in the system that may result in potential emissions. Reasons for this may be that N-models are not well adapted to the mode of action of organic fertilizers and that N-emission models often are built on assumptions from conventional agriculture leading to even greater deviances for organic systems between the amount of N calculated by emission models and the actual amount of N available for emissions. Improvements are needed regarding a more precise differentiation between farming systems and regarding the development of N emission models that better represent actual N-fluxes within different systems. We recommend adjusting N- and C-emissions during farmyard manure management and farmyard manure fertilization in plant production to the feed ration provided in the animal production of the respective farming system leading to different N- and C-compositions within the excrement. In the future, more representative background data on organic farming systems (e.g. N content of farmyard manure) should be generated and compiled so as to be available for use within LCA inventories. Finally, we recommend conducting consequential LCA if possible when using LCA for policy-making or strategic environmental planning to account for different functions of the analyzed farming systems. (C) 2014 Elsevier Ltd. All rights reserved.",
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