Bayesian analysis of within-field variability of corn yield using a spatial hierarchical model
Abstract Understanding relationships of soil and field topography to crop yield within a field is critical in site-specific management systems. Challenges for efficiently assessing these relationships include spatially correlated yield data and interrelated soil and topographic properties. The objec...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Jiang, Pingping [verfasserIn] He, Zhuoqiong [verfasserIn] Kitchen, Newell R. [verfasserIn] Sudduth, Kenneth A. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2008 |
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| Schlagwörter: |
Crop yield spatial variability |
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| Übergeordnetes Werk: |
Enthalten in: Precision agriculture - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1999, 10(2008), 2 vom: 09. Juli, Seite 111-127 |
|---|---|
| Übergeordnetes Werk: |
volume:10 ; year:2008 ; number:2 ; day:09 ; month:07 ; pages:111-127 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11119-008-9070-4 |
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| Katalog-ID: |
SPR016879309 |
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| 520 | |a Abstract Understanding relationships of soil and field topography to crop yield within a field is critical in site-specific management systems. Challenges for efficiently assessing these relationships include spatially correlated yield data and interrelated soil and topographic properties. The objective of this analysis was to apply a spatial Bayesian hierarchical model to examine the effects of soil, topographic and climate variables on corn yield. The model included a mean structure of spatial and temporal co-variates and an explicit random spatial effect. The spatial co-variates included elevation, slope and apparent soil electrical conductivity, temporal co-variates included mean maximum daily temperature, mean daily temperature range and cumulative precipitation in July and August. A conditional auto-regressive (CAR) model was used to model the spatial association in yield. Mapped corn yield data from 1997, 1999, 2001 and 2003 for a 36-ha Missouri claypan soil field were used in the analysis. The model building and computation were performed using a free Bayesian modeling software package, WinBUGS. The relationships of co-variates to corn yield generally agreed with the literature. The CAR model successfully captured the spatial association in yield. Model standard deviation decreased about 50% with spatial effect accounted for. Further, the approach was able to assess the effects of temporal climate co-variates on corn yield with a small number of site-years. The spatial Bayesian model appeared to be a useful tool to gain insights into yield spatial and temporal variability related to soil, topography and growing season weather conditions. | ||
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| 700 | 1 | |a He, Zhuoqiong |e verfasserin |4 aut | |
| 700 | 1 | |a Kitchen, Newell R. |e verfasserin |4 aut | |
| 700 | 1 | |a Sudduth, Kenneth A. |e verfasserin |4 aut | |
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10.1007/s11119-008-9070-4 doi (DE-627)SPR016879309 (SPR)s11119-008-9070-4-e DE-627 ger DE-627 rakwb eng 630 640 ASE 48.00 bkl 48.03 bkl Jiang, Pingping verfasserin aut Bayesian analysis of within-field variability of corn yield using a spatial hierarchical model 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Understanding relationships of soil and field topography to crop yield within a field is critical in site-specific management systems. Challenges for efficiently assessing these relationships include spatially correlated yield data and interrelated soil and topographic properties. The objective of this analysis was to apply a spatial Bayesian hierarchical model to examine the effects of soil, topographic and climate variables on corn yield. The model included a mean structure of spatial and temporal co-variates and an explicit random spatial effect. The spatial co-variates included elevation, slope and apparent soil electrical conductivity, temporal co-variates included mean maximum daily temperature, mean daily temperature range and cumulative precipitation in July and August. A conditional auto-regressive (CAR) model was used to model the spatial association in yield. Mapped corn yield data from 1997, 1999, 2001 and 2003 for a 36-ha Missouri claypan soil field were used in the analysis. The model building and computation were performed using a free Bayesian modeling software package, WinBUGS. The relationships of co-variates to corn yield generally agreed with the literature. The CAR model successfully captured the spatial association in yield. Model standard deviation decreased about 50% with spatial effect accounted for. Further, the approach was able to assess the effects of temporal climate co-variates on corn yield with a small number of site-years. The spatial Bayesian model appeared to be a useful tool to gain insights into yield spatial and temporal variability related to soil, topography and growing season weather conditions. Crop yield spatial variability (dpeaa)DE-He213 Bayesian statistics (dpeaa)DE-He213 Conditional auto-regressive model (dpeaa)DE-He213 WinBUGS (dpeaa)DE-He213 He, Zhuoqiong verfasserin aut Kitchen, Newell R. verfasserin aut Sudduth, Kenneth A. verfasserin aut Enthalten in Precision agriculture Dordrecht [u.a.] : Springer Science + Business Media B.V, 1999 10(2008), 2 vom: 09. Juli, Seite 111-127 (DE-627)320569551 (DE-600)2016333-2 1573-1618 nnns volume:10 year:2008 number:2 day:09 month:07 pages:111-127 https://dx.doi.org/10.1007/s11119-008-9070-4 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_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 10 2008 2 09 07 111-127 |
| spelling |
10.1007/s11119-008-9070-4 doi (DE-627)SPR016879309 (SPR)s11119-008-9070-4-e DE-627 ger DE-627 rakwb eng 630 640 ASE 48.00 bkl 48.03 bkl Jiang, Pingping verfasserin aut Bayesian analysis of within-field variability of corn yield using a spatial hierarchical model 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Understanding relationships of soil and field topography to crop yield within a field is critical in site-specific management systems. Challenges for efficiently assessing these relationships include spatially correlated yield data and interrelated soil and topographic properties. The objective of this analysis was to apply a spatial Bayesian hierarchical model to examine the effects of soil, topographic and climate variables on corn yield. The model included a mean structure of spatial and temporal co-variates and an explicit random spatial effect. The spatial co-variates included elevation, slope and apparent soil electrical conductivity, temporal co-variates included mean maximum daily temperature, mean daily temperature range and cumulative precipitation in July and August. A conditional auto-regressive (CAR) model was used to model the spatial association in yield. Mapped corn yield data from 1997, 1999, 2001 and 2003 for a 36-ha Missouri claypan soil field were used in the analysis. The model building and computation were performed using a free Bayesian modeling software package, WinBUGS. The relationships of co-variates to corn yield generally agreed with the literature. The CAR model successfully captured the spatial association in yield. Model standard deviation decreased about 50% with spatial effect accounted for. Further, the approach was able to assess the effects of temporal climate co-variates on corn yield with a small number of site-years. The spatial Bayesian model appeared to be a useful tool to gain insights into yield spatial and temporal variability related to soil, topography and growing season weather conditions. Crop yield spatial variability (dpeaa)DE-He213 Bayesian statistics (dpeaa)DE-He213 Conditional auto-regressive model (dpeaa)DE-He213 WinBUGS (dpeaa)DE-He213 He, Zhuoqiong verfasserin aut Kitchen, Newell R. verfasserin aut Sudduth, Kenneth A. verfasserin aut Enthalten in Precision agriculture Dordrecht [u.a.] : Springer Science + Business Media B.V, 1999 10(2008), 2 vom: 09. Juli, Seite 111-127 (DE-627)320569551 (DE-600)2016333-2 1573-1618 nnns volume:10 year:2008 number:2 day:09 month:07 pages:111-127 https://dx.doi.org/10.1007/s11119-008-9070-4 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_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 10 2008 2 09 07 111-127 |
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10.1007/s11119-008-9070-4 doi (DE-627)SPR016879309 (SPR)s11119-008-9070-4-e DE-627 ger DE-627 rakwb eng 630 640 ASE 48.00 bkl 48.03 bkl Jiang, Pingping verfasserin aut Bayesian analysis of within-field variability of corn yield using a spatial hierarchical model 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Understanding relationships of soil and field topography to crop yield within a field is critical in site-specific management systems. Challenges for efficiently assessing these relationships include spatially correlated yield data and interrelated soil and topographic properties. The objective of this analysis was to apply a spatial Bayesian hierarchical model to examine the effects of soil, topographic and climate variables on corn yield. The model included a mean structure of spatial and temporal co-variates and an explicit random spatial effect. The spatial co-variates included elevation, slope and apparent soil electrical conductivity, temporal co-variates included mean maximum daily temperature, mean daily temperature range and cumulative precipitation in July and August. A conditional auto-regressive (CAR) model was used to model the spatial association in yield. Mapped corn yield data from 1997, 1999, 2001 and 2003 for a 36-ha Missouri claypan soil field were used in the analysis. The model building and computation were performed using a free Bayesian modeling software package, WinBUGS. The relationships of co-variates to corn yield generally agreed with the literature. The CAR model successfully captured the spatial association in yield. Model standard deviation decreased about 50% with spatial effect accounted for. Further, the approach was able to assess the effects of temporal climate co-variates on corn yield with a small number of site-years. The spatial Bayesian model appeared to be a useful tool to gain insights into yield spatial and temporal variability related to soil, topography and growing season weather conditions. Crop yield spatial variability (dpeaa)DE-He213 Bayesian statistics (dpeaa)DE-He213 Conditional auto-regressive model (dpeaa)DE-He213 WinBUGS (dpeaa)DE-He213 He, Zhuoqiong verfasserin aut Kitchen, Newell R. verfasserin aut Sudduth, Kenneth A. verfasserin aut Enthalten in Precision agriculture Dordrecht [u.a.] : Springer Science + Business Media B.V, 1999 10(2008), 2 vom: 09. Juli, Seite 111-127 (DE-627)320569551 (DE-600)2016333-2 1573-1618 nnns volume:10 year:2008 number:2 day:09 month:07 pages:111-127 https://dx.doi.org/10.1007/s11119-008-9070-4 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_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 10 2008 2 09 07 111-127 |
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10.1007/s11119-008-9070-4 doi (DE-627)SPR016879309 (SPR)s11119-008-9070-4-e DE-627 ger DE-627 rakwb eng 630 640 ASE 48.00 bkl 48.03 bkl Jiang, Pingping verfasserin aut Bayesian analysis of within-field variability of corn yield using a spatial hierarchical model 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Understanding relationships of soil and field topography to crop yield within a field is critical in site-specific management systems. Challenges for efficiently assessing these relationships include spatially correlated yield data and interrelated soil and topographic properties. The objective of this analysis was to apply a spatial Bayesian hierarchical model to examine the effects of soil, topographic and climate variables on corn yield. The model included a mean structure of spatial and temporal co-variates and an explicit random spatial effect. The spatial co-variates included elevation, slope and apparent soil electrical conductivity, temporal co-variates included mean maximum daily temperature, mean daily temperature range and cumulative precipitation in July and August. A conditional auto-regressive (CAR) model was used to model the spatial association in yield. Mapped corn yield data from 1997, 1999, 2001 and 2003 for a 36-ha Missouri claypan soil field were used in the analysis. The model building and computation were performed using a free Bayesian modeling software package, WinBUGS. The relationships of co-variates to corn yield generally agreed with the literature. The CAR model successfully captured the spatial association in yield. Model standard deviation decreased about 50% with spatial effect accounted for. Further, the approach was able to assess the effects of temporal climate co-variates on corn yield with a small number of site-years. The spatial Bayesian model appeared to be a useful tool to gain insights into yield spatial and temporal variability related to soil, topography and growing season weather conditions. Crop yield spatial variability (dpeaa)DE-He213 Bayesian statistics (dpeaa)DE-He213 Conditional auto-regressive model (dpeaa)DE-He213 WinBUGS (dpeaa)DE-He213 He, Zhuoqiong verfasserin aut Kitchen, Newell R. verfasserin aut Sudduth, Kenneth A. verfasserin aut Enthalten in Precision agriculture Dordrecht [u.a.] : Springer Science + Business Media B.V, 1999 10(2008), 2 vom: 09. Juli, Seite 111-127 (DE-627)320569551 (DE-600)2016333-2 1573-1618 nnns volume:10 year:2008 number:2 day:09 month:07 pages:111-127 https://dx.doi.org/10.1007/s11119-008-9070-4 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_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 10 2008 2 09 07 111-127 |
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10.1007/s11119-008-9070-4 doi (DE-627)SPR016879309 (SPR)s11119-008-9070-4-e DE-627 ger DE-627 rakwb eng 630 640 ASE 48.00 bkl 48.03 bkl Jiang, Pingping verfasserin aut Bayesian analysis of within-field variability of corn yield using a spatial hierarchical model 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Understanding relationships of soil and field topography to crop yield within a field is critical in site-specific management systems. Challenges for efficiently assessing these relationships include spatially correlated yield data and interrelated soil and topographic properties. The objective of this analysis was to apply a spatial Bayesian hierarchical model to examine the effects of soil, topographic and climate variables on corn yield. The model included a mean structure of spatial and temporal co-variates and an explicit random spatial effect. The spatial co-variates included elevation, slope and apparent soil electrical conductivity, temporal co-variates included mean maximum daily temperature, mean daily temperature range and cumulative precipitation in July and August. A conditional auto-regressive (CAR) model was used to model the spatial association in yield. Mapped corn yield data from 1997, 1999, 2001 and 2003 for a 36-ha Missouri claypan soil field were used in the analysis. The model building and computation were performed using a free Bayesian modeling software package, WinBUGS. The relationships of co-variates to corn yield generally agreed with the literature. The CAR model successfully captured the spatial association in yield. Model standard deviation decreased about 50% with spatial effect accounted for. Further, the approach was able to assess the effects of temporal climate co-variates on corn yield with a small number of site-years. The spatial Bayesian model appeared to be a useful tool to gain insights into yield spatial and temporal variability related to soil, topography and growing season weather conditions. Crop yield spatial variability (dpeaa)DE-He213 Bayesian statistics (dpeaa)DE-He213 Conditional auto-regressive model (dpeaa)DE-He213 WinBUGS (dpeaa)DE-He213 He, Zhuoqiong verfasserin aut Kitchen, Newell R. verfasserin aut Sudduth, Kenneth A. verfasserin aut Enthalten in Precision agriculture Dordrecht [u.a.] : Springer Science + Business Media B.V, 1999 10(2008), 2 vom: 09. Juli, Seite 111-127 (DE-627)320569551 (DE-600)2016333-2 1573-1618 nnns volume:10 year:2008 number:2 day:09 month:07 pages:111-127 https://dx.doi.org/10.1007/s11119-008-9070-4 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_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 10 2008 2 09 07 111-127 |
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Enthalten in Precision agriculture 10(2008), 2 vom: 09. Juli, Seite 111-127 volume:10 year:2008 number:2 day:09 month:07 pages:111-127 |
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Enthalten in Precision agriculture 10(2008), 2 vom: 09. Juli, Seite 111-127 volume:10 year:2008 number:2 day:09 month:07 pages:111-127 |
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Crop yield spatial variability Bayesian statistics Conditional auto-regressive model WinBUGS |
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Jiang, Pingping @@aut@@ He, Zhuoqiong @@aut@@ Kitchen, Newell R. @@aut@@ Sudduth, Kenneth A. @@aut@@ |
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Challenges for efficiently assessing these relationships include spatially correlated yield data and interrelated soil and topographic properties. The objective of this analysis was to apply a spatial Bayesian hierarchical model to examine the effects of soil, topographic and climate variables on corn yield. The model included a mean structure of spatial and temporal co-variates and an explicit random spatial effect. The spatial co-variates included elevation, slope and apparent soil electrical conductivity, temporal co-variates included mean maximum daily temperature, mean daily temperature range and cumulative precipitation in July and August. A conditional auto-regressive (CAR) model was used to model the spatial association in yield. Mapped corn yield data from 1997, 1999, 2001 and 2003 for a 36-ha Missouri claypan soil field were used in the analysis. The model building and computation were performed using a free Bayesian modeling software package, WinBUGS. The relationships of co-variates to corn yield generally agreed with the literature. The CAR model successfully captured the spatial association in yield. Model standard deviation decreased about 50% with spatial effect accounted for. Further, the approach was able to assess the effects of temporal climate co-variates on corn yield with a small number of site-years. 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Jiang, Pingping |
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Jiang, Pingping ddc 630 bkl 48.00 bkl 48.03 misc Crop yield spatial variability misc Bayesian statistics misc Conditional auto-regressive model misc WinBUGS Bayesian analysis of within-field variability of corn yield using a spatial hierarchical model |
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630 640 ASE 48.00 bkl 48.03 bkl Bayesian analysis of within-field variability of corn yield using a spatial hierarchical model Crop yield spatial variability (dpeaa)DE-He213 Bayesian statistics (dpeaa)DE-He213 Conditional auto-regressive model (dpeaa)DE-He213 WinBUGS (dpeaa)DE-He213 |
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ddc 630 bkl 48.00 bkl 48.03 misc Crop yield spatial variability misc Bayesian statistics misc Conditional auto-regressive model misc WinBUGS |
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Bayesian analysis of within-field variability of corn yield using a spatial hierarchical model |
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Jiang, Pingping He, Zhuoqiong Kitchen, Newell R. Sudduth, Kenneth A. |
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bayesian analysis of within-field variability of corn yield using a spatial hierarchical model |
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Bayesian analysis of within-field variability of corn yield using a spatial hierarchical model |
| abstract |
Abstract Understanding relationships of soil and field topography to crop yield within a field is critical in site-specific management systems. Challenges for efficiently assessing these relationships include spatially correlated yield data and interrelated soil and topographic properties. The objective of this analysis was to apply a spatial Bayesian hierarchical model to examine the effects of soil, topographic and climate variables on corn yield. The model included a mean structure of spatial and temporal co-variates and an explicit random spatial effect. The spatial co-variates included elevation, slope and apparent soil electrical conductivity, temporal co-variates included mean maximum daily temperature, mean daily temperature range and cumulative precipitation in July and August. A conditional auto-regressive (CAR) model was used to model the spatial association in yield. Mapped corn yield data from 1997, 1999, 2001 and 2003 for a 36-ha Missouri claypan soil field were used in the analysis. The model building and computation were performed using a free Bayesian modeling software package, WinBUGS. The relationships of co-variates to corn yield generally agreed with the literature. The CAR model successfully captured the spatial association in yield. Model standard deviation decreased about 50% with spatial effect accounted for. Further, the approach was able to assess the effects of temporal climate co-variates on corn yield with a small number of site-years. The spatial Bayesian model appeared to be a useful tool to gain insights into yield spatial and temporal variability related to soil, topography and growing season weather conditions. |
| abstractGer |
Abstract Understanding relationships of soil and field topography to crop yield within a field is critical in site-specific management systems. Challenges for efficiently assessing these relationships include spatially correlated yield data and interrelated soil and topographic properties. The objective of this analysis was to apply a spatial Bayesian hierarchical model to examine the effects of soil, topographic and climate variables on corn yield. The model included a mean structure of spatial and temporal co-variates and an explicit random spatial effect. The spatial co-variates included elevation, slope and apparent soil electrical conductivity, temporal co-variates included mean maximum daily temperature, mean daily temperature range and cumulative precipitation in July and August. A conditional auto-regressive (CAR) model was used to model the spatial association in yield. Mapped corn yield data from 1997, 1999, 2001 and 2003 for a 36-ha Missouri claypan soil field were used in the analysis. The model building and computation were performed using a free Bayesian modeling software package, WinBUGS. The relationships of co-variates to corn yield generally agreed with the literature. The CAR model successfully captured the spatial association in yield. Model standard deviation decreased about 50% with spatial effect accounted for. Further, the approach was able to assess the effects of temporal climate co-variates on corn yield with a small number of site-years. The spatial Bayesian model appeared to be a useful tool to gain insights into yield spatial and temporal variability related to soil, topography and growing season weather conditions. |
| abstract_unstemmed |
Abstract Understanding relationships of soil and field topography to crop yield within a field is critical in site-specific management systems. Challenges for efficiently assessing these relationships include spatially correlated yield data and interrelated soil and topographic properties. The objective of this analysis was to apply a spatial Bayesian hierarchical model to examine the effects of soil, topographic and climate variables on corn yield. The model included a mean structure of spatial and temporal co-variates and an explicit random spatial effect. The spatial co-variates included elevation, slope and apparent soil electrical conductivity, temporal co-variates included mean maximum daily temperature, mean daily temperature range and cumulative precipitation in July and August. A conditional auto-regressive (CAR) model was used to model the spatial association in yield. Mapped corn yield data from 1997, 1999, 2001 and 2003 for a 36-ha Missouri claypan soil field were used in the analysis. The model building and computation were performed using a free Bayesian modeling software package, WinBUGS. The relationships of co-variates to corn yield generally agreed with the literature. The CAR model successfully captured the spatial association in yield. Model standard deviation decreased about 50% with spatial effect accounted for. Further, the approach was able to assess the effects of temporal climate co-variates on corn yield with a small number of site-years. The spatial Bayesian model appeared to be a useful tool to gain insights into yield spatial and temporal variability related to soil, topography and growing season weather conditions. |
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| container_issue |
2 |
| title_short |
Bayesian analysis of within-field variability of corn yield using a spatial hierarchical model |
| url |
https://dx.doi.org/10.1007/s11119-008-9070-4 |
| remote_bool |
true |
| author2 |
He, Zhuoqiong Kitchen, Newell R. Sudduth, Kenneth A. |
| author2Str |
He, Zhuoqiong Kitchen, Newell R. Sudduth, Kenneth A. |
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| doi_str |
10.1007/s11119-008-9070-4 |
| up_date |
2024-07-04T01:16:05.933Z |
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|
| score |
7.400099 |