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首页> 外文期刊>Regional Environmental Change >Using data mining techniques to model primary productivity from international long-term ecological research (ILTER) agricultural experiments in Austria
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Using data mining techniques to model primary productivity from international long-term ecological research (ILTER) agricultural experiments in Austria

机译：使用数据挖掘技术对奥地利国际长期生态研究（ILTER）农业实验的初级生产力进行建模

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摘要

Primary productivity is in the foundation of farming communities. Therefore, much effort is invested in understanding the factors that influence the primary productivity potential of different soils. The International Long-Term Ecological Research (ILTER) is a network that enables valuable comparisons of data in understanding environmental change. In this study, we investigate three ILTER cropland sites and one long-term field experiment (LTE) outside of the ILTER network. The focus is on the influence of different management practices (tillage, crop residue incorporation, and compost amendments) on primary productivity. Data mining analyses of the experimental data were carried out in order to investigate trends in the productivity data. We generated predictive models that identify the influential factors that govern primary productivity. The data mining models achieved very high predictive performance (r0.80) for each of the sites. Preceding crop and crop of the current year were crucial for primary productivity in the tillage LTE and compost LTE, respectively. For both crop residue incorporation LTEs, plant-available Mg affected productivity the most, followed by properties such as soil pH, SOM, and the crop residue management. The results obtained by data mining are in line with previous studies and enhance our knowledge about the driving forces of primary productivity in arable systems. Hence, the models are considered very suitable and reliable for predicting the primary productivity at these ILTER sites in the future. They may also encourage researcher-farmer-advisor-stakeholder interaction, and thus create enabling environment for cooperation for further research around these ILTER sites.
机译：初级生产力是农业社区的基础。因此，在了解影响不同土壤初级生产力潜力的因素上投入了大量精力。国际长期生态研究（ILTER）是一个网络，可以对理解环境变化的数据进行有价值的比较。在这项研究中，我们调查了三个ILTER农田和一个ILTER网络之外的长期野外试验（LTE）。重点是不同管理实践（耕作，农作物残渣掺入和堆肥改良）对初级生产力的影响。为了研究生产率数据的趋势，对实验数据进行了数据挖掘分析。我们生成了预测模型，这些模型确定了控制初级生产力的影响因素。数据挖掘模型对每个站点都实现了非常高的预测性能（r> 0.80）。耕作LTE和堆肥LTE分别对本年度的前茬和后茬作物至关重要。对于两种掺入LTE的农作物残渣，植物可用的镁对生产力的影响最大，其次是土壤pH值，SOM和农作物残渣管理等特性。通过数据挖掘获得的结果与以前的研究相一致，并增强了我们对耕作系统中初级生产力驱动力的认识。因此，该模型被认为非常适合预测未来这些ILTER站点的主要生产力。他们还可以鼓励研究人员-农民-顾问-利益相关者之间的互动，从而为在这些ILTER地点周围开展进一步研究创造有利的合作环境。

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  • 来源
    《Regional Environmental Change》 |2019年第2期|325-337|共13页
  • 作者

    Trajanov Aneta; Spiegel Heide; Debeljak Marko; Sanden Taru;

  • 作者单位

    Jozef Stefan Inst, Dept Knowledge Technol, Jamova Cesta 39, Ljubljana 1000, Slovenia|Jozef Stefan Int, Postgrad Sch, Jamova Cesta 39, Ljubljana 1000, Slovenia;

    Austrian Agcy Hlth & Food Safety AGES, Dept Soil Hlth & Plant Nutr, Inst Sustainable Plant Prod, Spargelfeldstr 191, A-1220 Vienna, Austria;

    Jozef Stefan Inst, Dept Knowledge Technol, Jamova Cesta 39, Ljubljana 1000, Slovenia|Jozef Stefan Int, Postgrad Sch, Jamova Cesta 39, Ljubljana 1000, Slovenia;

    Austrian Agcy Hlth & Food Safety AGES, Dept Soil Hlth & Plant Nutr, Inst Sustainable Plant Prod, Spargelfeldstr 191, A-1220 Vienna, Austria;

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  • 正文语种 eng
  • 中图分类
  • 关键词

    Soil functions; Crop yield; Plant-available Mg; Tillage; Compost amendments; Crop residue incorporation;

    机译：土壤功能;作物产量;植物有效镁;耕作;堆肥改良;作物残渣掺入;

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