Use of the McHargian LUSA in agricultural research and decision-making in the age of non-stationarity and big earth observation data

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In the past fifty years, there have been two major changes that are of methodological and consequential importance to the McHargian land-use suitability analysis (LUSA): increasing evidence of nonstationarity of global and regional ecological conditions and increasing availability of high resolution spatial-temporal earth observation data. For fifty years, the McHargian LUSA has been an important analysis tool for designers and planners for both regional conservation planning and development. McHarg's LUSA is a decision support tool that reduces the dimensions of spatial-temporal data. This makes the technique relevant beyond decision support to spatial identification and prediction of areas of socio-ecological opportunity, risk, and priority. In this article, I use a set of recent studies relating to agricultural LUSA to reveal relationships between the traditional McHargian LUSA and related spatialtemporal research methods that are adapting to more data and non-stationary ecological conditions. Using a classification based on descriptive, predictive, and prescriptive research activities, I organize these related methods and illustrate how linkages between research activities can be used to assimilate more kinds of spatial “big data,” address non-stationarity in socio-ecological systems, and suggest ways to enhance decision-making and collaboration between planners and other sciences.