|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|409865||679101||2015||10 صفحه PDF||سفارش دهید||دانلود رایگان|
Profitability and other economic aspects of farming in Finland are analyzed using clustering of the self-organizing map. The analysis of profitability bookkeeping data reveals several interesting relationships between the monitored financial variables. Economic profiles of farms are presented based on the clustering, and the findings are confirmed with statistical tests. A weight optimization system is proposed for upscaling financial figures of the sample of profitability bookkeeping farms to the whole country level. The system output is analyzed, and it is confirmed that the most important large and medium-sized enterprises are represented well by the sample. Furthermore, it seems that the utilized arable area is the key factor in guiding the weight optimization process. These findings may turn out to be useful in developing the sampling of bookkeeping farms in the future.
Journal: Neurocomputing - Volume 147, 5 January 2015, Pages 197–206