Abstract
Efficient farm management can be aided by the identification of zones in the landscape. These zones can be informed from different measured variables by ensuring a sense of spatial coherence. Forming spatially coherent zones is an established method in the literature, but has been found to perform poorly when data are sparse. In this paper, we describe the different types of data sparsity and investigate how this impacts the performance of established methods. We introduce a set of methodological advances that address these shortcomings to provide a method for forming spatially coherent zones under data sparsity.
Highlights
It is a well-recognised aim of many on-farm management strategies to divide fields into zones to ensure efficient and effective management where each zone may be treated differently
There will be k possible variograms, one for each class membership, we find in practice that, with the exception of the nugget, very little difference can be seen in the variograms of the different class memberships
We describe the results from an extensive empirical study designed to investigate how the above methods address the issues created by different types of data sparsity
Summary
It is a well-recognised aim of many on-farm management strategies to divide fields into zones to ensure efficient and effective management where each zone may be treated differently Defining such zones has been a topic of research for at least 40 years (see e.g. Webster and Burrough [1]). Lark [9] first classified the data through fuzzy c-means clustering and smoothed the resulting clusters. This method was shown to outperform Oliver and Webster [7] and Bourgault et al [8] in Milne et al [2]. This allows one to see which points are well distinguished and which are “fuzzy”
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