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Landscape function analysis: a system for monitoring rangeland function


David Tongway
Norman Hindley

Abstract

Landscape Function Analysis (LFA) is a monitoring procedure that uses quickly determined field indicators to assess the functional status of rangelands. As such, it complements existing procedures that assess condition. It comprises three modules — a conceptual framework, a field methodology and an interpretational framework — and is intended to generate chronosequences of data. The conceptual framework is based on the economy of vital resources and focuses on the processes that regulate the spatial movement and use of water, topsoil and organic matter in the landscape. The field methodology uses simple, visual indicators closely related to a range of physical, chemical and biological processes, taking only a few seconds per indicator to assess in the field after training. Observations of system dynamics are made in two spatially nested scales (‘hillslope' and ‘patch'). A patch is an area on a hillslope where scarce, vital resources tend to be accumulated. A software template generates a series of tables containing data at both scales. The interpretational framework is based on a sigmoidal response surface linking the lowest and highest functional examples of a given landscape type across a stress/disturbance gradient. It facilitates the identification of target values for rehabilitation and the propinquity of monitored sites to a critical threshold distinguishing ‘sustainable' from ‘unsustainable' management/climate combinations. The procedure enables critically vulnerable processes to be identified, so that rehabilitation procedures can be appropriately designed. LFA has been developed, tested and implemented in a range of climate types (200mm to 4 000mm rainfall per year) and land-uses (pastoralism, mining, nature conservation).



Keywords: indicators, interpretational framework, rapid assessment, trajectory


African Journal of Range & Forage Science 2004, 21(2): 109–113

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eISSN: 1727-9380
print ISSN: 1022-0119