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Drivers of?farm performance in?Czech crop farms

机译:司机?农场表演?捷克农业农场

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When analysing drivers affecting the farm performance, the presence of different technologies should be taken into account. We assume that the technology used by crop farms is not the same for all producers and therefore we use latent class model to identify technological classes at first. Class definition is based on multidimensional classification and determination of indices given by the values of individual components. The principal components analysis is applied to estimate significant and robust weights for the index components. FADN (Farm Accountancy Data Network) database, Czech crop farms data from 2005 to 2017 were used and three groups of technology classes of farms were identified with a determinant influence of the structure index and localisation. The other indices characterise sustainability, innovation, technology, diversification, and individual characteristics. Three distinct classes of crop farms were found, one major class and two minor classes. Family driven farms are usually smaller farms in terms of acreage. Highly sustainable crop farms are most likely located in lower altitudes and not in less-favoured areas. Innovative farms are also likely to be more productive. The results indicate that agricultural production farms with a more sustainable way of farming are most likely to be more productive.
机译:在分析影响农场性能的驱动因素时,应考虑不同技术的存在。我们假设农作物农场使用的技术对所有生产者不一样,因此我们使用潜在级模型起初识别技术课程。类定义基于多维分类和各个组件值给出的指标的确定。主要成分分析应用于估计索引组件的显着和强大的权重。 FADN(农场会计数据网络)数据库,使用2005年至2017年的捷克农业农场数据,并确定了三组技术课程,具有结构指数和本地化的决定性影响。其他索引表征可持续性,创新,技术,多样化和个人特征。发现了三个不同的作物农场,一个主要阶级和两个小课程。家庭驱动的农场通常在种植面积方面的较小农场。高度可持续的作物农场最可能位于较低的海拔,而不是在较低的地区。创新的农场也可能更加富有成效。结果表明,具有更可持续的农业方式的农业生产农场最有可能更加富有成效。

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