Accounting for data sparsity when forming spatially coherent zones

A - Papers appearing in refereed journals

Hassall, K. L., Whitmore, A. P. and Milne, A. E. 2019. Accounting for data sparsity when forming spatially coherent zones. Applied Mathematical Modelling. 72 (August), pp. 537-552.

AuthorsHassall, K. L., Whitmore, A. P. and Milne, A. E.
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.

KeywordsPrecision agriculture; Spatial coherence; Data sparsity; Cluster analysis; Crop yields
Year of Publication2019
JournalApplied Mathematical Modelling
Journal citation72 (August), pp. 537-552
Digital Object Identifier (DOI)doi:10.1016/j.apm.2019.03.030
Open accessPublished as ‘gold’ (paid) open access
FunderBiotechnology and Biological Sciences Research Council
Funder project or codeS2N - Soil to Nutrition - Work package 3 (WP3) - Sustainable intensification - optimisation at multiple scales
Management of Rotations, Soil Structure and Water
Publisher's version
Copyright license
CC BY
Accepted author manuscript
Copyright license
CC BY
Supplemental file
Copyright license
CC BY
Output statusPublished
Publication dates
Online27 Mar 2019
Publication process dates
Accepted20 Mar 2019
PublisherElsevier Science Inc
ISSN0307-904X

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