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Long-term dynamics and interrelationships of soil Collembola and microorganisms in an arable landscape following land use change

Long-term dynamics and interrelationships of soil Collembola and microorganisms in an arable landscape following land use change

Geoderma 105(3-4): 201-221

An arable landscape (150 ha) comprising a broad variety of soil types had been cultivated conventionally for many years. A small section had been intensively managed (hops), while another had been used as grassland. For 2 years at the beginning of our study, all arable land was cultivated with cereals only. After that, management was changed to integrated or organic farming, grassland or fallow land. Within a group of sites representing the variety of soil types and management systems, the development of soil microorganisms and Collembola was monitored every spring during an 8-year period. The microbial biomass compared to initial conditions under uniform management increased, particularly in land that had been set aside. General Collembola abundance slightly decreased, except for areas that had been converted to integrated farming. Protaphorura armata and Lepidocyrtus cyaneus decreased over the entire area, whereas the Isotoma viridis group was positively affected by conversion to integrated farming. Although recorded only once per year, the main results were comparable to data recorded in this area at higher temporal or spatial resolution. Nevertheless, site conditions may influence the reactions of soil organisms to land use change, and should be taken into account for evaluation. Biota resident in the upper 0-10 cm of the soil reflected current management practice, whereas those at greater soil depth reflected instead management history and soil properties. The microbial biomass and abundance of selected Collembola taxa were modeled using artificial intelligence methods (regression trees). Land management type was the most important factor determining soil biota performance. The variation of Collembola abundance depended additionally on microbial biomass. When supported by a sufficiently large data set, regression trees are powerful tools for explaining complex non-linear relationships. Finally, suggestions for the sampling design in future long-term studies at the landscape scale are given.

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Accession: 003836031

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DOI: 10.1016/s0016-7061(01)00104-5

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