A geostatistical approach for modelling and combining spatial data with different support
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| Publicat a: | Advances in Animal Biosciences vol. 8, no. 2 (Jul 2017), p. 594-599 |
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| Autor principal: | |
| Altres autors: | , , |
| Publicat: |
Cambridge University Press
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| Matèries: | |
| Accés en línia: | Citation/Abstract Full Text - PDF |
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| 022 | |a 2040-4700 | ||
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| 024 | 7 | |a 10.1017/S2040470017000048 |2 doi | |
| 035 | |a 1904073466 | ||
| 045 | 2 | |b d20170701 |b d20170731 | |
| 084 | |a 177732 |2 nlm | ||
| 100 | 1 | |a Castrignanò, A | |
| 245 | 1 | |a A geostatistical approach for modelling and combining spatial data with different support | |
| 260 | |b Cambridge University Press |c Jul 2017 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The paper proposes a geostatistical framework to solve the issues of heterogeneous support for spatial estimation. Apparent soil electrical conductivity (ECa) was measured in a field cropped with San Marzano tomato using a multiple frequency electromagnetic profiler with 6 operating frequencies. Mixed support kriging was used to estimate ECa taking into account the change of support. The method includes punctual kriging with the error being the dispersion variance associated with each frequency. The individual ECa maps were weighted by the dispersion variance to obtain a map which was used for field partition in management zones. | |
| 651 | 4 | |a Italy | |
| 653 | |a Environmental | ||
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| 653 | |a Data analysis | ||
| 653 | |a Geostatistics | ||
| 653 | |a Diamonds | ||
| 653 | |a Methods | ||
| 653 | |a Datasets | ||
| 653 | |a Mining | ||
| 653 | |a Spatial data | ||
| 653 | |a Studies | ||
| 653 | |a Sensors | ||
| 653 | |a Mathematical analysis | ||
| 653 | |a Alluvial deposits | ||
| 653 | |a Rivers | ||
| 653 | |a Electrical resistivity | ||
| 653 | |a Computer vision | ||
| 653 | |a Vertical orientation | ||
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| 653 | |a Metallurgy | ||
| 653 | |a Agronomy | ||
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| 653 | |a Information processing | ||
| 653 | |a Geographic information systems | ||
| 653 | |a Soil conductivity | ||
| 653 | |a Statistics | ||
| 653 | |a Matrices (mathematics) | ||
| 653 | |a Functions (mathematics) | ||
| 653 | |a Agricultural management | ||
| 653 | |a Temporal distribution | ||
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| 653 | |a Alluvial plains | ||
| 653 | |a Probability theory | ||
| 653 | |a Remote sensors | ||
| 653 | |a Mines | ||
| 653 | |a Electrical conductivity | ||
| 653 | |a Soils | ||
| 653 | |a Tomatoes | ||
| 700 | 1 | |a Quarto, R | |
| 700 | 1 | |a Venezia, A | |
| 700 | 1 | |a Buttafuoco, G | |
| 773 | 0 | |t Advances in Animal Biosciences |g vol. 8, no. 2 (Jul 2017), p. 594-599 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/1904073466/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/1904073466/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |