Sampling in Precision Agriculture View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2010-06-16

AUTHORS

R. Kerry , M. A. Oliver , Z. L. Frogbrook

ABSTRACT

This chapter considers the importance of spatial scale in sampling and investigates various methods by which the variogram can be used to determine an appropriate sampling scheme or interval for grid sampling. When no prior information is available on the scale of variation, and the variable of interest is unlikely to be strongly correlated to available ancillary data, a nested survey and analysis provides a first approximation to the variogram and the approximate spatial scale. If the variable of interest appears related to ancillary data such as aerial photographs or elevation, variograms of these data can provide an indication of the likely scale of variation in the soil or crop. Existing variograms of soil or crop properties can be used to determine how many cores of soil or samples from plants should be taken to form a composite (bulked) sample to reduce the local noise. Such variograms can also be used with the kriging equations to determine a grid sampling interval with a specific tolerable error, or an interval of less than half the variogram range can be used to ensure a spatially dependent sample. Finally, if the scale of variation is large in relation to the field size, a variogram estimated by residual maximum likelihood (REML) or standardized variograms from ancillary data can be used to krige data from a small, but spatially dependent sample. Each of the methods investigated is illustrated with a case study. More... »

PAGES

35-63

Book

TITLE

Geostatistical Applications for Precision Agriculture

ISBN

978-90-481-9132-1
978-90-481-9133-8

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-90-481-9133-8_2

DOI

http://dx.doi.org/10.1007/978-90-481-9133-8_2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1028390023


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