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Linking Arable Crop Occurrence with Site Conditions by the Use of Highly Resolved Spatial Data

Author

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  • Susanne Stein

    (Centre of Biodiversity and Sustainable Land Use, University of Goettingen, Büsgenweg 1, 37077 Göttingen, Germany
    Current Affiliation: Leibniz Centre for Agricultural Landscape Research (ZALF), Research Platform Data, Eberswalder Straße 84, 15374 Müncheberg, Germany.)

  • Horst-Henning Steinmann

    (Centre of Biodiversity and Sustainable Land Use, University of Goettingen, Büsgenweg 1, 37077 Göttingen, Germany)

  • Johannes Isselstein

    (Centre of Biodiversity and Sustainable Land Use, University of Goettingen, Büsgenweg 1, 37077 Göttingen, Germany)

Abstract
Agricultural land use is influenced in different ways by local factors such as soil conditions, water supply, and socioeconomic structure. We investigated at regional and field scale how strong the relationship of arable crop patterns and specific local site conditions is. At field scale, a logistic regression analysis for the main crops and selected site variables detected, for each of the analyzed crops, its own specific character of crop–site relationship. Some crops have diverging site relations such as maize and wheat, while other crops show similar probabilities under comparable site conditions, e.g., oilseed rape and winter barley. At the regional scale, the spatial comparison of clustered variables and clustered crop pattern showed a slightly stronger relationship of crop combination and specific combinations of site variables compared to the view of the single crop–site relationship.

Suggested Citation

  • Susanne Stein & Horst-Henning Steinmann & Johannes Isselstein, 2019. "Linking Arable Crop Occurrence with Site Conditions by the Use of Highly Resolved Spatial Data," Land, MDPI, vol. 8(4), pages 1-14, April.
    • Handle: RePEc:gam:jlands:v:8:y:2019:i:4:p:65-:d:224076
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    References listed on IDEAS

    as
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    Cited by:

    1. Simon Willcock & Javier Martinez-Lopez & Norman Dandy & James M. Bullock, 2021. "High Spatial-Temporal Resolution Data across Large Scales Are Needed to Transform Our Understanding of Ecosystem Services," Land, MDPI, vol. 10(7), pages 1-6, July.

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