Abstract
Due to its nature, agricultural land use depends on local site characteristics such as production potential, costs and external effects. To assess the relevance of the modifying areal unit problem (MAUP), we investigated as to how a change in the data resolution regarding both soil and land use data influences the results obtained for different land use indicators. For the assessment we use the example of the greenhouse gas (GHG) emissions from agriculturally used organic soils (mainly fens and bogs). Although less than 5 % of the German agricultural area in use is located on organic soils, the drainage of these areas to enable their agricultural utilization causes roughly 37 % of the GHG emissions of the German agricultural sector. The abandonment of the cultivation and rewetting of organic soils would be an effective policy to reduce national GHG emissions. To assess the abatement costs, it is essential to know which commodities, and at what quantities, are actually produced on this land. Furthermore, in order to limit windfall profits, information on the differences of the profitability among farms are needed. However, high-resolution data regarding land use and soil characteristics are often not available, and their generation is costly or the access is strictly limited because of legal constraints. Therefore, in this paper, we analyse how indicators for land use on organic soils respond to changes in the spatial aggregation of the data. In Germany, organic soils are predominantly used for forage cropping. Marked differences between the various regions of Germany are apparent with respect to the dynamics and the intensity of land use. Data resolution mainly impairs the derived extent of agriculturally used peatland and the observed intensity gradient, while its impact on the average value for the investigated set of land-use indicators is generally minor.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles and news from researchers in related subjects, suggested using machine learning.References
Bach M, Breuer L, Frede HG, Huisman JA, Otte A, Waldhardt R (2006) Accuracy and congruency of three different digital land-use maps. Landscape and Urban Planning 78:289–299
BGR (Bundesanstalt für Geowissenschaften und Rohstoffe) (2003) GUEK 200 (Geologische Uebersichtskarte der Bundesrepublik Deutschland 1:200 000). Hannover, Germany
BGR (Bundesanstalt für Geowissenschaften und Rohstoffe) (2010) BUEK 1000 (Bodenuebersichtskarte von Deutschland 1:1 000 000 Hannover, Germany
BKG (Bundesamt für Kartographie und Geodäsie) (2008) Basis-DLM (Digitales Basis-Landschaftsmodell) 1:25 000. Frankfurt, Germany
Boden Ad-hoc AG (2005) Bodenkundliche Kartieranleitung, 5th edn. Hannover, Germany
Dierschke H, Briemle G (2002) Kulturgrasland: Wiesen. Weiden und verwandte Staudenfluren, Stuttgart, Germany
Drösler M, Freibauer A, Adelmann W, Augustin J, Bergman L, Beyer C, Chojnicki B, Förster C, Giebels M, Görlitz S, Höper H, Kantelhardt J, Liebersbach H, Hahn-Schöfl M, Minke M, Petschow U, Pfadenhauer J, Schaller L, Schägner P, Sommer M, Thuille M, Wehrhan M (2011) Klimaschutz durch Moorschutz in der Praxis. Arbeitsberichte aus dem vTI-Institut für Agrarrelevante Klimaforschung (04/2011). http://www.vti.bund.de/fileadmin/dam_uploads/Institute/AK/PDFs/Klimaschutz_Moorschutz_Praxis_BMBF_vTI-Bericht_20110408.pdf. Braunschweig, Berlin, Freising, Jena, Müncheberg, Wien
Eggelsmann R, Barthels R (1975) Oxidativer Torfverzehr im Niedermoor in Abhängigkeit von Entwässerung, Nutzung und Düngung. Mitteilung der Deutschen Bodenkundlichen Gesellschaft 22:215–221
EUROSTAT (2009) Statistical disclosure control. http://epp.eurostat.ec.europa.eu/portal/page/portal/research_methodology/methodology/statistical_disclosure_control. Accessed date 25 April 2009
FAO (2006) World reference base for soil resources. World soil resources report 103 ftp://ftp.fao.org/agl/agll/docs/wsrr103e.pdf. Rome
FDZ (Research Data Centres of the Federal Statistical Office and the Statistical Offices of the Länder) (2010) AFID-panel agriculture (Farm structure Survey (FSS) 1999, 2003 and 2007
Giri C, Zhiliang Zhu Z, Reed B (2005) Comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets. Remote Sensing of Environment 94:123–132
Haenel HD (ed) (2010) Calculation of emissions from German agriculture—national emission inventory report (NIR) 2010 for 2008. Landbauforschung Völkenrode (334). Braunschweig, Germany
Havlik P, Schneider UA, Schmid E, Bottcher H, Fritz S, Skalsky R, Aoki K, De Cara S, Kindermann G, Kraxner F, Leduc S, McCallum I, Mosnier A, Sauer T, Obersteiner M (2011) Global land-use implications of first and second generation biofuel targets. Energy Policy 39:5690–5702
Höper H (2007) Freisetzung von Treibhausgasen aus deutschen Mooren. Telma 37:85–116
IPCC (2006) Good practice guidance for land use, land use change and forestry. http://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_03_Ch3_Representation.pdf. Geneva. Switzerland
Kantelhardt J, Hoffmann H (2001) Economic evaluation of ecological management instructions for agriculture—the example of the Donauried. Berichte über Landwirtschaft 79:415–436
Keil, M, Kiefl R, Strunz G, Mehl H, Mohaupt-Jahr B (2004) Examples and experiences of the update interpretation process for CLC2000 in Germany. In: Proceedings CORINE land cover workshop, 20–21 January 2004, Berlin. UBA-Texte 04/04: 52-61
Lösel G (2005) Informationsgüte kleinmassstäbiger Bodenkarten—Probleme und Entwicklungsmöglichkeiten. PhD-Thesis. Universität Hannover. Germany
Oleszczuk R, Regina K, Szajdak L, Höper H, Maryganova V (2008) Impacts of agricultural utilization of peat soils on the greenhouse gas balance. In Strack M (ed) Peatlands and climate change, Jyväskylä, Finland, pp 70–97. http://www.peatsociety.org/sites/default/files/files/PeatlandsandClimateChangeBookIPS2008.pdf. Accessed Date 02 Oct 2008
Openshaw S, Taylor PJ (1979) A million or so correlation coefficients: Three experiments on the modifiable areal unit problem. In Wrigley N (ed) Statistical applications in the spatial sciences. London, pp 127–144
Poeplau C, Don A, Vesterdal L, Leifeld J, Van Wesemael B, Schumacher J, Gensior A (2011) Temporal dynamics of soil organic carbon after land-use change in the temperate zone—carbon response functions as a model approach. Global Change Biology 17:2415–2427
Röder N, Grützmacher F (2012) Emissionen aus landwirtschaftlich genutzten Mooren–Vermeidungskosten und Anpassungsbedarf. Natur und Landschaft 87:56–61
Sbresny J (1997) Fehlerquellen in raumbezogenen Informationssystemen. Geologisches Jahrbuch. Series F (33). Hannover, Germany
Schaller L, Kantelhardt, J (2009) Prospects for climate friendly peatland management—results of a socioeconomic case study in Germany. Paper presented at the 83rd annual conference of the Agricultural Economics Society, March 30—April 1, 2009, Dublin, p 23. http://purl.umn.edu/51074. Accessed Date 16 March 2009
Schmidt C, Rounsevell M, La Jeunesse I (2006) The limitations of spatial land use data in environmental analysis. Environmental Science & Policy 9:174–188
Schothorst CJ (1977) Subsidence of low moor peat soils in the Western Netherlands. Geoderma 17:265–291
Searchinger T, Heimlich R, Houghton RA, Dong F, Elobeid A, Fabiosa J, Tokgoz S, Hayes D, Yu TH (2008) Use of U.S. croplands for biofuels increases greenhouse gases through emissions from land-use change. Science 319:1238–1240
UBA (Umweltbundesamt) (2010) National inventory report for the German Greenhouse Gas Inventory 1990–2008 http://www.umweltdaten.de/publikationen/fpdf-l/3958.pdf. Accessed Date 14 June 2010
Zitzmann A (2003) Die Geologische Übersichtskarte 1:200 000—von der Karte bis zur Sachdatenbank. Z dt geol Ges 154:121–139
Acknowledgments
Thanks to Thomas Schmidt and Heike Nitsch for commenting on an earlier draft of this paper and for the useful comments of the two anonymous reviewers. The research was funded by the Johann Heinrich von Thünen Institute of Rural Studies.
Author information
Authors and Affiliations
Corresponding author
Additional information
Glossary
Arable forage crops: Summarizes all arable crops grown to feed grazing livestock (e.g., green maize, alfalfa, temporary grassland, clover) normally produced on farm and not sold via a market; concentrates (feed grains) are not part of the arable forage crops.
Arable land: Area cultivated with annual crops and temporary grassland fields in ley farming systems. Temporary grassland in ley farming systems is only included in the arable area if the area is ploughed at least once in a five-year interval.
Cash crop: All crops grown to be sold primarily at commodity markets (crops for human consumption or industrial use).
Grazing Livestock: Cattle, sheep, goats and horses.
Main forage area: Summarizes all crops (including grassland) grown to feed grazing livestock.
Suckler cow: A cow which rears its own calf and is later used for beef production; normally grazing during the vegetation period and supplemented only with low levels of concentrates.
Rights and permissions
About this article
Cite this article
Roeder, N., Osterburg, B. The Impact of Map and Data Resolution on the Determination of the Agricultural Utilisation of Organic Soils in Germany. Environmental Management 49, 1150–1162 (2012). https://doi.org/10.1007/s00267-012-9849-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00267-012-9849-y