Abstract
Agricultural crop yield data usually are highly nonlinear and complex. Basic mathematical and statistical techniques are sometimes insufficient to describe the nature, trend, or cause of variations in yield. This article investigates fractal analysis of agricultural yield maps and describes a method of applying fractal analysis to multiple years of yield data. It also shows how patterns of yield variation can be described by fractal geometry. Crop yield was measured and mapped for an agricultural field for five consecutive years. In order to obtain a sufficiently dense set of points necessary for valid fractal analysis, a method was proposed to transfer the data points from to . Analysis of the resulting data set revealed multifractality of the yield variations. It was shown that multifractal measures such as the Rnyi spectrum can be used to quantify and compare global and local yield variations.
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