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Regionality in Norwegian farmland abandonment: Inferences from production data
Institution:1. State Key Laboratory of Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China;2. Institute of Arid Agroecology, School of Life Sciences, Lanzhou University, Lanzhou 730000, China;3. The Institute of Grassland Science, College of Animal Science & Technology, China Agricultural University, Beijing 100094, China;1. Institute of Physicochemical and Biological Problems in Soil Science, RAS, Institutskaya st., 2, Pushchino, Moscow region 142290, Russia;2. Department of Soil Science of Temperate Ecosystems, Georg-August University of Göttingen, Büsgenweg 2, 37077 Göttingen, Germany;3. Institute of Environmental Sciences, Kazan Federal University, Kremlevskaya st., 18, Kazan 420000, Russia
Abstract:Economic subsidies continue to be in focus as potentially important drivers of agricultural change. Their exact functioning as drivers in very complex systems are not all that well analysed or documented however, and their effect e.g. in terms of environmental output are currently being questioned. In the work reported here, we focus on how the regionality of the farming system may influence the potential effect of agricultural subsidies, also in terms of farmland abandonment. We do this through using multiple linear regressions (MLR) and geographically weighted regression (GWR) on the Norwegian database on agricultural production data, combined with farm location data. Our findings demonstrate how the outcome of certain support systems may differ between regions, and how a region may dominate national statistic. We conclude that as subsidies continue to be a key tool in achieving agricultural policy aims, we need a better understanding of how the subsidy systems work. To understand the impact of a change in subsidy it is necessary to consider the local context in which it operates, e.g. demographics, bio-physical resources and feasibility of land rental. Spatial data and techniques such as spatial MLR and GWR are increasingly accessible to policy makers and should be used to provide insight into the local impacts of current policy. However this understanding must also emphasize farmer motivation and decision making and these investigations must be regionally based.
Keywords:Multiple Linear Regression  Geographically Weighted Regression  Subsidy  Production  Systems  Abandonment
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