Weed Mapping with Co-Kriging Using Soil Properties

Weed Mapping with Co-Kriging Using Soil Properties Our aim is to build reliable weed maps to control weeds in patches. Weed sampling is time consuming but there are some shortcuts. If an intensively sampled variable (e.g. soil property) can be used to improve estimation of a sparsely sampled variable (e.g. weed distribution), one can reduce weed sampling. The geostatistical estimation method co-kriging uses two or more sampled variables, which are correlated, to improve the estimation of one of the variables at locations where it was not sampled. We did an experiment on a 2.1ha winter wheat field to compare co-kriging using soil properties, with kriging based only on one variable. The results showed that co-kriging Lamium spp. from 96 0.25m2 sample plots ha−1 with silt content improved the prediction variance by 11 % compared to kriging. With 51 or 18 sample plots ha−1 the prediction variance was improved by 21 and 15 %. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Weed Mapping with Co-Kriging Using Soil Properties

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Publisher
Kluwer Academic Publishers
Copyright
Copyright © 1999 by Kluwer Academic Publishers
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
D.O.I.
10.1023/A:1009921718225
Publisher site
See Article on Publisher Site

Abstract

Our aim is to build reliable weed maps to control weeds in patches. Weed sampling is time consuming but there are some shortcuts. If an intensively sampled variable (e.g. soil property) can be used to improve estimation of a sparsely sampled variable (e.g. weed distribution), one can reduce weed sampling. The geostatistical estimation method co-kriging uses two or more sampled variables, which are correlated, to improve the estimation of one of the variables at locations where it was not sampled. We did an experiment on a 2.1ha winter wheat field to compare co-kriging using soil properties, with kriging based only on one variable. The results showed that co-kriging Lamium spp. from 96 0.25m2 sample plots ha−1 with silt content improved the prediction variance by 11 % compared to kriging. With 51 or 18 sample plots ha−1 the prediction variance was improved by 21 and 15 %.

Journal

Precision AgricultureSpringer Journals

Published: Oct 6, 2004

References

  • Soil properties affecting the distribution of 37 weed species in Danish fields
    Andreasen, C.; Streibig, J. C.; Haas, H.

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