Published May 28, 2018 | Version v1
Journal article Open

Using data mining techniques to model primary productivity from international long-term ecological research (ILTER) agricultural experiments in Austria

  • 1. Jozef Stefan Institute
  • 2. Austrian Agency for Health & Food Safety – AGES

Description

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 (r > 0.80) 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 themost, 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 researcherfarmer-
advisor-stakeholder interaction, and thus create enabling environment for cooperation for further research around
these ILTER sites.

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2019-REEC-Trajanov_et_al.pdf

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Additional details

Funding

LANDMARK – LAND Management: Assessment, Research, Knowledge base 635201
European Commission