A discussion on the significance associated with Pearson’s correlation in precision agriculture studies

Abstract Pearson’s correlation is a commonly used descriptive statistic in many published precision agriculture studies, not only in the Precision Agriculture Journal, but also in other journals that publish in this domain. Very few of these articles take into consideration auto-correlation in data...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Taylor, J. A. [verfasserIn] Bates, T. R. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2013

Schlagwörter:	Auto-correlation Pruning weight NDVI

Übergeordnetes Werk:	Enthalten in: Precision agriculture - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1999, 14(2013), 5 vom: 23. Apr., Seite 558-564
Übergeordnetes Werk:	volume:14 ; year:2013 ; number:5 ; day:23 ; month:04 ; pages:558-564

Links:	Volltext

DOI / URN:	10.1007/s11119-013-9314-9

Katalog-ID:	SPR016885570

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520			\|a Abstract Pearson’s correlation is a commonly used descriptive statistic in many published precision agriculture studies, not only in the Precision Agriculture Journal, but also in other journals that publish in this domain. Very few of these articles take into consideration auto-correlation in data when performing correlation analysis, despite a statistical solution being available. A brief discussion on the need to consider auto-correlation and the effective sample size when using Pearson’s correlation in precision agriculture research is presented. The discussion is supported by an example using spatial data on vine size and canopy vigour in a juice-grape vineyard. The example data demonstrated that the p-value of the correlation between vine size and canopy vigour increased when auto-correlation was accounted for, potentially to a non-significant level depending on the desired α-level. The example data also demonstrated that the method by which data are processed (interpolated) to achieve co-located data will also affect the amount of auto-correlation and the effective sample size. The results showed that for the same variables, with different approaches to data co-location, a lower r-value may have a lower p-value and potentially hold more statistical significance.
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allfields	10.1007/s11119-013-9314-9 doi (DE-627)SPR016885570 (SPR)s11119-013-9314-9-e DE-627 ger DE-627 rakwb eng 630 640 ASE 48.00 bkl 48.03 bkl Taylor, J. A. verfasserin aut A discussion on the significance associated with Pearson’s correlation in precision agriculture studies 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Pearson’s correlation is a commonly used descriptive statistic in many published precision agriculture studies, not only in the Precision Agriculture Journal, but also in other journals that publish in this domain. Very few of these articles take into consideration auto-correlation in data when performing correlation analysis, despite a statistical solution being available. A brief discussion on the need to consider auto-correlation and the effective sample size when using Pearson’s correlation in precision agriculture research is presented. The discussion is supported by an example using spatial data on vine size and canopy vigour in a juice-grape vineyard. The example data demonstrated that the p-value of the correlation between vine size and canopy vigour increased when auto-correlation was accounted for, potentially to a non-significant level depending on the desired α-level. The example data also demonstrated that the method by which data are processed (interpolated) to achieve co-located data will also affect the amount of auto-correlation and the effective sample size. The results showed that for the same variables, with different approaches to data co-location, a lower r-value may have a lower p-value and potentially hold more statistical significance. Auto-correlation (dpeaa)DE-He213 Pruning weight (dpeaa)DE-He213 NDVI (dpeaa)DE-He213 Bates, T. R. verfasserin aut Enthalten in Precision agriculture Dordrecht [u.a.] : Springer Science + Business Media B.V, 1999 14(2013), 5 vom: 23. Apr., Seite 558-564 (DE-627)320569551 (DE-600)2016333-2 1573-1618 nnns volume:14 year:2013 number:5 day:23 month:04 pages:558-564 https://dx.doi.org/10.1007/s11119-013-9314-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.00 ASE 48.03 ASE AR 14 2013 5 23 04 558-564
spelling	10.1007/s11119-013-9314-9 doi (DE-627)SPR016885570 (SPR)s11119-013-9314-9-e DE-627 ger DE-627 rakwb eng 630 640 ASE 48.00 bkl 48.03 bkl Taylor, J. A. verfasserin aut A discussion on the significance associated with Pearson’s correlation in precision agriculture studies 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Pearson’s correlation is a commonly used descriptive statistic in many published precision agriculture studies, not only in the Precision Agriculture Journal, but also in other journals that publish in this domain. Very few of these articles take into consideration auto-correlation in data when performing correlation analysis, despite a statistical solution being available. A brief discussion on the need to consider auto-correlation and the effective sample size when using Pearson’s correlation in precision agriculture research is presented. The discussion is supported by an example using spatial data on vine size and canopy vigour in a juice-grape vineyard. The example data demonstrated that the p-value of the correlation between vine size and canopy vigour increased when auto-correlation was accounted for, potentially to a non-significant level depending on the desired α-level. The example data also demonstrated that the method by which data are processed (interpolated) to achieve co-located data will also affect the amount of auto-correlation and the effective sample size. The results showed that for the same variables, with different approaches to data co-location, a lower r-value may have a lower p-value and potentially hold more statistical significance. Auto-correlation (dpeaa)DE-He213 Pruning weight (dpeaa)DE-He213 NDVI (dpeaa)DE-He213 Bates, T. R. verfasserin aut Enthalten in Precision agriculture Dordrecht [u.a.] : Springer Science + Business Media B.V, 1999 14(2013), 5 vom: 23. Apr., Seite 558-564 (DE-627)320569551 (DE-600)2016333-2 1573-1618 nnns volume:14 year:2013 number:5 day:23 month:04 pages:558-564 https://dx.doi.org/10.1007/s11119-013-9314-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.00 ASE 48.03 ASE AR 14 2013 5 23 04 558-564
allfields_unstemmed	10.1007/s11119-013-9314-9 doi (DE-627)SPR016885570 (SPR)s11119-013-9314-9-e DE-627 ger DE-627 rakwb eng 630 640 ASE 48.00 bkl 48.03 bkl Taylor, J. A. verfasserin aut A discussion on the significance associated with Pearson’s correlation in precision agriculture studies 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Pearson’s correlation is a commonly used descriptive statistic in many published precision agriculture studies, not only in the Precision Agriculture Journal, but also in other journals that publish in this domain. Very few of these articles take into consideration auto-correlation in data when performing correlation analysis, despite a statistical solution being available. A brief discussion on the need to consider auto-correlation and the effective sample size when using Pearson’s correlation in precision agriculture research is presented. The discussion is supported by an example using spatial data on vine size and canopy vigour in a juice-grape vineyard. The example data demonstrated that the p-value of the correlation between vine size and canopy vigour increased when auto-correlation was accounted for, potentially to a non-significant level depending on the desired α-level. The example data also demonstrated that the method by which data are processed (interpolated) to achieve co-located data will also affect the amount of auto-correlation and the effective sample size. The results showed that for the same variables, with different approaches to data co-location, a lower r-value may have a lower p-value and potentially hold more statistical significance. Auto-correlation (dpeaa)DE-He213 Pruning weight (dpeaa)DE-He213 NDVI (dpeaa)DE-He213 Bates, T. R. verfasserin aut Enthalten in Precision agriculture Dordrecht [u.a.] : Springer Science + Business Media B.V, 1999 14(2013), 5 vom: 23. Apr., Seite 558-564 (DE-627)320569551 (DE-600)2016333-2 1573-1618 nnns volume:14 year:2013 number:5 day:23 month:04 pages:558-564 https://dx.doi.org/10.1007/s11119-013-9314-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.00 ASE 48.03 ASE AR 14 2013 5 23 04 558-564
allfieldsGer	10.1007/s11119-013-9314-9 doi (DE-627)SPR016885570 (SPR)s11119-013-9314-9-e DE-627 ger DE-627 rakwb eng 630 640 ASE 48.00 bkl 48.03 bkl Taylor, J. A. verfasserin aut A discussion on the significance associated with Pearson’s correlation in precision agriculture studies 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Pearson’s correlation is a commonly used descriptive statistic in many published precision agriculture studies, not only in the Precision Agriculture Journal, but also in other journals that publish in this domain. Very few of these articles take into consideration auto-correlation in data when performing correlation analysis, despite a statistical solution being available. A brief discussion on the need to consider auto-correlation and the effective sample size when using Pearson’s correlation in precision agriculture research is presented. The discussion is supported by an example using spatial data on vine size and canopy vigour in a juice-grape vineyard. The example data demonstrated that the p-value of the correlation between vine size and canopy vigour increased when auto-correlation was accounted for, potentially to a non-significant level depending on the desired α-level. The example data also demonstrated that the method by which data are processed (interpolated) to achieve co-located data will also affect the amount of auto-correlation and the effective sample size. The results showed that for the same variables, with different approaches to data co-location, a lower r-value may have a lower p-value and potentially hold more statistical significance. Auto-correlation (dpeaa)DE-He213 Pruning weight (dpeaa)DE-He213 NDVI (dpeaa)DE-He213 Bates, T. R. verfasserin aut Enthalten in Precision agriculture Dordrecht [u.a.] : Springer Science + Business Media B.V, 1999 14(2013), 5 vom: 23. Apr., Seite 558-564 (DE-627)320569551 (DE-600)2016333-2 1573-1618 nnns volume:14 year:2013 number:5 day:23 month:04 pages:558-564 https://dx.doi.org/10.1007/s11119-013-9314-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.00 ASE 48.03 ASE AR 14 2013 5 23 04 558-564
allfieldsSound	10.1007/s11119-013-9314-9 doi (DE-627)SPR016885570 (SPR)s11119-013-9314-9-e DE-627 ger DE-627 rakwb eng 630 640 ASE 48.00 bkl 48.03 bkl Taylor, J. A. verfasserin aut A discussion on the significance associated with Pearson’s correlation in precision agriculture studies 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Pearson’s correlation is a commonly used descriptive statistic in many published precision agriculture studies, not only in the Precision Agriculture Journal, but also in other journals that publish in this domain. Very few of these articles take into consideration auto-correlation in data when performing correlation analysis, despite a statistical solution being available. A brief discussion on the need to consider auto-correlation and the effective sample size when using Pearson’s correlation in precision agriculture research is presented. The discussion is supported by an example using spatial data on vine size and canopy vigour in a juice-grape vineyard. The example data demonstrated that the p-value of the correlation between vine size and canopy vigour increased when auto-correlation was accounted for, potentially to a non-significant level depending on the desired α-level. The example data also demonstrated that the method by which data are processed (interpolated) to achieve co-located data will also affect the amount of auto-correlation and the effective sample size. The results showed that for the same variables, with different approaches to data co-location, a lower r-value may have a lower p-value and potentially hold more statistical significance. Auto-correlation (dpeaa)DE-He213 Pruning weight (dpeaa)DE-He213 NDVI (dpeaa)DE-He213 Bates, T. R. verfasserin aut Enthalten in Precision agriculture Dordrecht [u.a.] : Springer Science + Business Media B.V, 1999 14(2013), 5 vom: 23. Apr., Seite 558-564 (DE-627)320569551 (DE-600)2016333-2 1573-1618 nnns volume:14 year:2013 number:5 day:23 month:04 pages:558-564 https://dx.doi.org/10.1007/s11119-013-9314-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.00 ASE 48.03 ASE AR 14 2013 5 23 04 558-564
language	English
source	Enthalten in Precision agriculture 14(2013), 5 vom: 23. Apr., Seite 558-564 volume:14 year:2013 number:5 day:23 month:04 pages:558-564
sourceStr	Enthalten in Precision agriculture 14(2013), 5 vom: 23. Apr., Seite 558-564 volume:14 year:2013 number:5 day:23 month:04 pages:558-564
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Auto-correlation Pruning weight NDVI
dewey-raw	630
isfreeaccess_bool	false
container_title	Precision agriculture
authorswithroles_txt_mv	Taylor, J. A. @@aut@@ Bates, T. R. @@aut@@
publishDateDaySort_date	2013-04-23T00:00:00Z
hierarchy_top_id	320569551
dewey-sort	3630
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A.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A discussion on the significance associated with Pearson’s correlation in precision agriculture studies</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2013</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Pearson’s correlation is a commonly used descriptive statistic in many published precision agriculture studies, not only in the Precision Agriculture Journal, but also in other journals that publish in this domain. 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author	Taylor, J. A.
spellingShingle	Taylor, J. A. ddc 630 bkl 48.00 bkl 48.03 misc Auto-correlation misc Pruning weight misc NDVI A discussion on the significance associated with Pearson’s correlation in precision agriculture studies
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title	A discussion on the significance associated with Pearson’s correlation in precision agriculture studies
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title_sort	discussion on the significance associated with pearson’s correlation in precision agriculture studies
title_auth	A discussion on the significance associated with Pearson’s correlation in precision agriculture studies
abstract	Abstract Pearson’s correlation is a commonly used descriptive statistic in many published precision agriculture studies, not only in the Precision Agriculture Journal, but also in other journals that publish in this domain. Very few of these articles take into consideration auto-correlation in data when performing correlation analysis, despite a statistical solution being available. A brief discussion on the need to consider auto-correlation and the effective sample size when using Pearson’s correlation in precision agriculture research is presented. The discussion is supported by an example using spatial data on vine size and canopy vigour in a juice-grape vineyard. The example data demonstrated that the p-value of the correlation between vine size and canopy vigour increased when auto-correlation was accounted for, potentially to a non-significant level depending on the desired α-level. The example data also demonstrated that the method by which data are processed (interpolated) to achieve co-located data will also affect the amount of auto-correlation and the effective sample size. The results showed that for the same variables, with different approaches to data co-location, a lower r-value may have a lower p-value and potentially hold more statistical significance.
abstractGer	Abstract Pearson’s correlation is a commonly used descriptive statistic in many published precision agriculture studies, not only in the Precision Agriculture Journal, but also in other journals that publish in this domain. Very few of these articles take into consideration auto-correlation in data when performing correlation analysis, despite a statistical solution being available. A brief discussion on the need to consider auto-correlation and the effective sample size when using Pearson’s correlation in precision agriculture research is presented. The discussion is supported by an example using spatial data on vine size and canopy vigour in a juice-grape vineyard. The example data demonstrated that the p-value of the correlation between vine size and canopy vigour increased when auto-correlation was accounted for, potentially to a non-significant level depending on the desired α-level. The example data also demonstrated that the method by which data are processed (interpolated) to achieve co-located data will also affect the amount of auto-correlation and the effective sample size. The results showed that for the same variables, with different approaches to data co-location, a lower r-value may have a lower p-value and potentially hold more statistical significance.
abstract_unstemmed	Abstract Pearson’s correlation is a commonly used descriptive statistic in many published precision agriculture studies, not only in the Precision Agriculture Journal, but also in other journals that publish in this domain. Very few of these articles take into consideration auto-correlation in data when performing correlation analysis, despite a statistical solution being available. A brief discussion on the need to consider auto-correlation and the effective sample size when using Pearson’s correlation in precision agriculture research is presented. The discussion is supported by an example using spatial data on vine size and canopy vigour in a juice-grape vineyard. The example data demonstrated that the p-value of the correlation between vine size and canopy vigour increased when auto-correlation was accounted for, potentially to a non-significant level depending on the desired α-level. The example data also demonstrated that the method by which data are processed (interpolated) to achieve co-located data will also affect the amount of auto-correlation and the effective sample size. The results showed that for the same variables, with different approaches to data co-location, a lower r-value may have a lower p-value and potentially hold more statistical significance.
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title_short	A discussion on the significance associated with Pearson’s correlation in precision agriculture studies
url	https://dx.doi.org/10.1007/s11119-013-9314-9
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author2	Bates, T. R.
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doi_str	10.1007/s11119-013-9314-9
up_date	2024-07-04T01:17:56.303Z
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A discussion on the significance associated with Pearson’s correlation in precision agriculture studies

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