Sampling Design and Spatial Modeling of Available Phosphorus in a Complex Agricultural Area in Southern Brazil
Abstract In this study, we have compared three sampling designs and two modeling methods applied in the spatial prediction of available P in the soil. The study was conducted in a 160 ha where three sampling methods were tested — simple regular grid (RG) with a fixed distance between points, spatial...
Ausführliche Beschreibung
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| Autor*in: |
Soligo, Matheus Flesch [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) under exclusive licence to Sociedad Chilena de la Ciencia del Suelo 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Journal of soil science and plant nutrition - [Cham] : Springer International Publishing, 2010, 23(2023), 4 vom: 15. Nov., Seite 6125-6138 |
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| Übergeordnetes Werk: |
volume:23 ; year:2023 ; number:4 ; day:15 ; month:11 ; pages:6125-6138 |
| Links: |
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| DOI / URN: |
10.1007/s42729-023-01470-6 |
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SPR054096421 |
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| 520 | |a Abstract In this study, we have compared three sampling designs and two modeling methods applied in the spatial prediction of available P in the soil. The study was conducted in a 160 ha where three sampling methods were tested — simple regular grid (RG) with a fixed distance between points, spatial coverage sampling (SCS) containing points over short distances, and simulated annealing sampling considering the marginal distribution of environmental covariates (DIST) — as a basis for prediction of the available phosphorus content in the soil, at a depth of 0–10 cm. Thus, each calibration set contains 160 samples, which were used to calibrate two predictive models: kriging with external drift (KED), considered a mixed model because it encompasses the geostatistical and deterministic approaches; and ordinary kriging (OK). The results were validated with an external and independent set containing 50 points. The best prediction result was found by combining the DIST sampling with the KED model, which has a lower mean absolute error (MAE) = 14.62 mg $ dm^{−3} $, mean error (ME) = −3.12 mg $ dm^{−3} $, and root mean squared error (RMSE) = 23.44 mg $ dm^{−3} $ and higher Nash-Sutcliffe efficiency (NSE) = 0.13. Sampling designs that consider environmental covariables contribute to the increase in the quality of the predicted available phosphorus maps. The most accurate map was generated from the DIST sampling combined with KED modeling. | ||
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| 700 | 1 | |a Dalmolin, Ricardo Simão Diniz |4 aut | |
| 700 | 1 | |a Nalin, Renan Storno |4 aut | |
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10.1007/s42729-023-01470-6 doi (DE-627)SPR054096421 (SPR)s42729-023-01470-6-e DE-627 ger DE-627 rakwb eng Soligo, Matheus Flesch verfasserin aut Sampling Design and Spatial Modeling of Available Phosphorus in a Complex Agricultural Area in Southern Brazil 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Sociedad Chilena de la Ciencia del Suelo 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In this study, we have compared three sampling designs and two modeling methods applied in the spatial prediction of available P in the soil. The study was conducted in a 160 ha where three sampling methods were tested — simple regular grid (RG) with a fixed distance between points, spatial coverage sampling (SCS) containing points over short distances, and simulated annealing sampling considering the marginal distribution of environmental covariates (DIST) — as a basis for prediction of the available phosphorus content in the soil, at a depth of 0–10 cm. Thus, each calibration set contains 160 samples, which were used to calibrate two predictive models: kriging with external drift (KED), considered a mixed model because it encompasses the geostatistical and deterministic approaches; and ordinary kriging (OK). The results were validated with an external and independent set containing 50 points. The best prediction result was found by combining the DIST sampling with the KED model, which has a lower mean absolute error (MAE) = 14.62 mg $ dm^{−3} $, mean error (ME) = −3.12 mg $ dm^{−3} $, and root mean squared error (RMSE) = 23.44 mg $ dm^{−3} $ and higher Nash-Sutcliffe efficiency (NSE) = 0.13. Sampling designs that consider environmental covariables contribute to the increase in the quality of the predicted available phosphorus maps. The most accurate map was generated from the DIST sampling combined with KED modeling. Digital soil mapping (dpeaa)DE-He213 Geostatistic (dpeaa)DE-He213 Sampling methods (dpeaa)DE-He213 Pedometric (dpeaa)DE-He213 Precision agriculture (dpeaa)DE-He213 Pedron, Fabrício de Araújo (orcid)0000-0002-5756-0688 aut Moura-Bueno, Jean Michel aut Horst, Taciara Zborowski aut Dalmolin, Ricardo Simão Diniz aut Nalin, Renan Storno aut Enthalten in Journal of soil science and plant nutrition [Cham] : Springer International Publishing, 2010 23(2023), 4 vom: 15. Nov., Seite 6125-6138 (DE-627)661265102 (DE-600)2611093-3 0718-9516 nnns volume:23 year:2023 number:4 day:15 month:11 pages:6125-6138 https://dx.doi.org/10.1007/s42729-023-01470-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_101 GBV_ILN_105 GBV_ILN_110 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 23 2023 4 15 11 6125-6138 |
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10.1007/s42729-023-01470-6 doi (DE-627)SPR054096421 (SPR)s42729-023-01470-6-e DE-627 ger DE-627 rakwb eng Soligo, Matheus Flesch verfasserin aut Sampling Design and Spatial Modeling of Available Phosphorus in a Complex Agricultural Area in Southern Brazil 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Sociedad Chilena de la Ciencia del Suelo 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In this study, we have compared three sampling designs and two modeling methods applied in the spatial prediction of available P in the soil. The study was conducted in a 160 ha where three sampling methods were tested — simple regular grid (RG) with a fixed distance between points, spatial coverage sampling (SCS) containing points over short distances, and simulated annealing sampling considering the marginal distribution of environmental covariates (DIST) — as a basis for prediction of the available phosphorus content in the soil, at a depth of 0–10 cm. Thus, each calibration set contains 160 samples, which were used to calibrate two predictive models: kriging with external drift (KED), considered a mixed model because it encompasses the geostatistical and deterministic approaches; and ordinary kriging (OK). The results were validated with an external and independent set containing 50 points. The best prediction result was found by combining the DIST sampling with the KED model, which has a lower mean absolute error (MAE) = 14.62 mg $ dm^{−3} $, mean error (ME) = −3.12 mg $ dm^{−3} $, and root mean squared error (RMSE) = 23.44 mg $ dm^{−3} $ and higher Nash-Sutcliffe efficiency (NSE) = 0.13. Sampling designs that consider environmental covariables contribute to the increase in the quality of the predicted available phosphorus maps. The most accurate map was generated from the DIST sampling combined with KED modeling. Digital soil mapping (dpeaa)DE-He213 Geostatistic (dpeaa)DE-He213 Sampling methods (dpeaa)DE-He213 Pedometric (dpeaa)DE-He213 Precision agriculture (dpeaa)DE-He213 Pedron, Fabrício de Araújo (orcid)0000-0002-5756-0688 aut Moura-Bueno, Jean Michel aut Horst, Taciara Zborowski aut Dalmolin, Ricardo Simão Diniz aut Nalin, Renan Storno aut Enthalten in Journal of soil science and plant nutrition [Cham] : Springer International Publishing, 2010 23(2023), 4 vom: 15. Nov., Seite 6125-6138 (DE-627)661265102 (DE-600)2611093-3 0718-9516 nnns volume:23 year:2023 number:4 day:15 month:11 pages:6125-6138 https://dx.doi.org/10.1007/s42729-023-01470-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_101 GBV_ILN_105 GBV_ILN_110 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 23 2023 4 15 11 6125-6138 |
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10.1007/s42729-023-01470-6 doi (DE-627)SPR054096421 (SPR)s42729-023-01470-6-e DE-627 ger DE-627 rakwb eng Soligo, Matheus Flesch verfasserin aut Sampling Design and Spatial Modeling of Available Phosphorus in a Complex Agricultural Area in Southern Brazil 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Sociedad Chilena de la Ciencia del Suelo 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In this study, we have compared three sampling designs and two modeling methods applied in the spatial prediction of available P in the soil. The study was conducted in a 160 ha where three sampling methods were tested — simple regular grid (RG) with a fixed distance between points, spatial coverage sampling (SCS) containing points over short distances, and simulated annealing sampling considering the marginal distribution of environmental covariates (DIST) — as a basis for prediction of the available phosphorus content in the soil, at a depth of 0–10 cm. Thus, each calibration set contains 160 samples, which were used to calibrate two predictive models: kriging with external drift (KED), considered a mixed model because it encompasses the geostatistical and deterministic approaches; and ordinary kriging (OK). The results were validated with an external and independent set containing 50 points. The best prediction result was found by combining the DIST sampling with the KED model, which has a lower mean absolute error (MAE) = 14.62 mg $ dm^{−3} $, mean error (ME) = −3.12 mg $ dm^{−3} $, and root mean squared error (RMSE) = 23.44 mg $ dm^{−3} $ and higher Nash-Sutcliffe efficiency (NSE) = 0.13. Sampling designs that consider environmental covariables contribute to the increase in the quality of the predicted available phosphorus maps. The most accurate map was generated from the DIST sampling combined with KED modeling. Digital soil mapping (dpeaa)DE-He213 Geostatistic (dpeaa)DE-He213 Sampling methods (dpeaa)DE-He213 Pedometric (dpeaa)DE-He213 Precision agriculture (dpeaa)DE-He213 Pedron, Fabrício de Araújo (orcid)0000-0002-5756-0688 aut Moura-Bueno, Jean Michel aut Horst, Taciara Zborowski aut Dalmolin, Ricardo Simão Diniz aut Nalin, Renan Storno aut Enthalten in Journal of soil science and plant nutrition [Cham] : Springer International Publishing, 2010 23(2023), 4 vom: 15. Nov., Seite 6125-6138 (DE-627)661265102 (DE-600)2611093-3 0718-9516 nnns volume:23 year:2023 number:4 day:15 month:11 pages:6125-6138 https://dx.doi.org/10.1007/s42729-023-01470-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_101 GBV_ILN_105 GBV_ILN_110 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 23 2023 4 15 11 6125-6138 |
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10.1007/s42729-023-01470-6 doi (DE-627)SPR054096421 (SPR)s42729-023-01470-6-e DE-627 ger DE-627 rakwb eng Soligo, Matheus Flesch verfasserin aut Sampling Design and Spatial Modeling of Available Phosphorus in a Complex Agricultural Area in Southern Brazil 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Sociedad Chilena de la Ciencia del Suelo 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In this study, we have compared three sampling designs and two modeling methods applied in the spatial prediction of available P in the soil. The study was conducted in a 160 ha where three sampling methods were tested — simple regular grid (RG) with a fixed distance between points, spatial coverage sampling (SCS) containing points over short distances, and simulated annealing sampling considering the marginal distribution of environmental covariates (DIST) — as a basis for prediction of the available phosphorus content in the soil, at a depth of 0–10 cm. Thus, each calibration set contains 160 samples, which were used to calibrate two predictive models: kriging with external drift (KED), considered a mixed model because it encompasses the geostatistical and deterministic approaches; and ordinary kriging (OK). The results were validated with an external and independent set containing 50 points. The best prediction result was found by combining the DIST sampling with the KED model, which has a lower mean absolute error (MAE) = 14.62 mg $ dm^{−3} $, mean error (ME) = −3.12 mg $ dm^{−3} $, and root mean squared error (RMSE) = 23.44 mg $ dm^{−3} $ and higher Nash-Sutcliffe efficiency (NSE) = 0.13. Sampling designs that consider environmental covariables contribute to the increase in the quality of the predicted available phosphorus maps. The most accurate map was generated from the DIST sampling combined with KED modeling. Digital soil mapping (dpeaa)DE-He213 Geostatistic (dpeaa)DE-He213 Sampling methods (dpeaa)DE-He213 Pedometric (dpeaa)DE-He213 Precision agriculture (dpeaa)DE-He213 Pedron, Fabrício de Araújo (orcid)0000-0002-5756-0688 aut Moura-Bueno, Jean Michel aut Horst, Taciara Zborowski aut Dalmolin, Ricardo Simão Diniz aut Nalin, Renan Storno aut Enthalten in Journal of soil science and plant nutrition [Cham] : Springer International Publishing, 2010 23(2023), 4 vom: 15. Nov., Seite 6125-6138 (DE-627)661265102 (DE-600)2611093-3 0718-9516 nnns volume:23 year:2023 number:4 day:15 month:11 pages:6125-6138 https://dx.doi.org/10.1007/s42729-023-01470-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_101 GBV_ILN_105 GBV_ILN_110 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 23 2023 4 15 11 6125-6138 |
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10.1007/s42729-023-01470-6 doi (DE-627)SPR054096421 (SPR)s42729-023-01470-6-e DE-627 ger DE-627 rakwb eng Soligo, Matheus Flesch verfasserin aut Sampling Design and Spatial Modeling of Available Phosphorus in a Complex Agricultural Area in Southern Brazil 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Sociedad Chilena de la Ciencia del Suelo 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In this study, we have compared three sampling designs and two modeling methods applied in the spatial prediction of available P in the soil. The study was conducted in a 160 ha where three sampling methods were tested — simple regular grid (RG) with a fixed distance between points, spatial coverage sampling (SCS) containing points over short distances, and simulated annealing sampling considering the marginal distribution of environmental covariates (DIST) — as a basis for prediction of the available phosphorus content in the soil, at a depth of 0–10 cm. Thus, each calibration set contains 160 samples, which were used to calibrate two predictive models: kriging with external drift (KED), considered a mixed model because it encompasses the geostatistical and deterministic approaches; and ordinary kriging (OK). The results were validated with an external and independent set containing 50 points. The best prediction result was found by combining the DIST sampling with the KED model, which has a lower mean absolute error (MAE) = 14.62 mg $ dm^{−3} $, mean error (ME) = −3.12 mg $ dm^{−3} $, and root mean squared error (RMSE) = 23.44 mg $ dm^{−3} $ and higher Nash-Sutcliffe efficiency (NSE) = 0.13. Sampling designs that consider environmental covariables contribute to the increase in the quality of the predicted available phosphorus maps. The most accurate map was generated from the DIST sampling combined with KED modeling. Digital soil mapping (dpeaa)DE-He213 Geostatistic (dpeaa)DE-He213 Sampling methods (dpeaa)DE-He213 Pedometric (dpeaa)DE-He213 Precision agriculture (dpeaa)DE-He213 Pedron, Fabrício de Araújo (orcid)0000-0002-5756-0688 aut Moura-Bueno, Jean Michel aut Horst, Taciara Zborowski aut Dalmolin, Ricardo Simão Diniz aut Nalin, Renan Storno aut Enthalten in Journal of soil science and plant nutrition [Cham] : Springer International Publishing, 2010 23(2023), 4 vom: 15. Nov., Seite 6125-6138 (DE-627)661265102 (DE-600)2611093-3 0718-9516 nnns volume:23 year:2023 number:4 day:15 month:11 pages:6125-6138 https://dx.doi.org/10.1007/s42729-023-01470-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_101 GBV_ILN_105 GBV_ILN_110 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 23 2023 4 15 11 6125-6138 |
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Soligo, Matheus Flesch @@aut@@ Pedron, Fabrício de Araújo @@aut@@ Moura-Bueno, Jean Michel @@aut@@ Horst, Taciara Zborowski @@aut@@ Dalmolin, Ricardo Simão Diniz @@aut@@ Nalin, Renan Storno @@aut@@ |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In this study, we have compared three sampling designs and two modeling methods applied in the spatial prediction of available P in the soil. The study was conducted in a 160 ha where three sampling methods were tested — simple regular grid (RG) with a fixed distance between points, spatial coverage sampling (SCS) containing points over short distances, and simulated annealing sampling considering the marginal distribution of environmental covariates (DIST) — as a basis for prediction of the available phosphorus content in the soil, at a depth of 0–10 cm. Thus, each calibration set contains 160 samples, which were used to calibrate two predictive models: kriging with external drift (KED), considered a mixed model because it encompasses the geostatistical and deterministic approaches; and ordinary kriging (OK). The results were validated with an external and independent set containing 50 points. The best prediction result was found by combining the DIST sampling with the KED model, which has a lower mean absolute error (MAE) = 14.62 mg $ dm^{−3} $, mean error (ME) = −3.12 mg $ dm^{−3} $, and root mean squared error (RMSE) = 23.44 mg $ dm^{−3} $ and higher Nash-Sutcliffe efficiency (NSE) = 0.13. Sampling designs that consider environmental covariables contribute to the increase in the quality of the predicted available phosphorus maps. 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Soligo, Matheus Flesch |
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Soligo, Matheus Flesch misc Digital soil mapping misc Geostatistic misc Sampling methods misc Pedometric misc Precision agriculture Sampling Design and Spatial Modeling of Available Phosphorus in a Complex Agricultural Area in Southern Brazil |
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Sampling Design and Spatial Modeling of Available Phosphorus in a Complex Agricultural Area in Southern Brazil Digital soil mapping (dpeaa)DE-He213 Geostatistic (dpeaa)DE-He213 Sampling methods (dpeaa)DE-He213 Pedometric (dpeaa)DE-He213 Precision agriculture (dpeaa)DE-He213 |
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Sampling Design and Spatial Modeling of Available Phosphorus in a Complex Agricultural Area in Southern Brazil |
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Sampling Design and Spatial Modeling of Available Phosphorus in a Complex Agricultural Area in Southern Brazil |
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Soligo, Matheus Flesch Pedron, Fabrício de Araújo Moura-Bueno, Jean Michel Horst, Taciara Zborowski Dalmolin, Ricardo Simão Diniz Nalin, Renan Storno |
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sampling design and spatial modeling of available phosphorus in a complex agricultural area in southern brazil |
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Sampling Design and Spatial Modeling of Available Phosphorus in a Complex Agricultural Area in Southern Brazil |
| abstract |
Abstract In this study, we have compared three sampling designs and two modeling methods applied in the spatial prediction of available P in the soil. The study was conducted in a 160 ha where three sampling methods were tested — simple regular grid (RG) with a fixed distance between points, spatial coverage sampling (SCS) containing points over short distances, and simulated annealing sampling considering the marginal distribution of environmental covariates (DIST) — as a basis for prediction of the available phosphorus content in the soil, at a depth of 0–10 cm. Thus, each calibration set contains 160 samples, which were used to calibrate two predictive models: kriging with external drift (KED), considered a mixed model because it encompasses the geostatistical and deterministic approaches; and ordinary kriging (OK). The results were validated with an external and independent set containing 50 points. The best prediction result was found by combining the DIST sampling with the KED model, which has a lower mean absolute error (MAE) = 14.62 mg $ dm^{−3} $, mean error (ME) = −3.12 mg $ dm^{−3} $, and root mean squared error (RMSE) = 23.44 mg $ dm^{−3} $ and higher Nash-Sutcliffe efficiency (NSE) = 0.13. Sampling designs that consider environmental covariables contribute to the increase in the quality of the predicted available phosphorus maps. The most accurate map was generated from the DIST sampling combined with KED modeling. © The Author(s) under exclusive licence to Sociedad Chilena de la Ciencia del Suelo 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract In this study, we have compared three sampling designs and two modeling methods applied in the spatial prediction of available P in the soil. The study was conducted in a 160 ha where three sampling methods were tested — simple regular grid (RG) with a fixed distance between points, spatial coverage sampling (SCS) containing points over short distances, and simulated annealing sampling considering the marginal distribution of environmental covariates (DIST) — as a basis for prediction of the available phosphorus content in the soil, at a depth of 0–10 cm. Thus, each calibration set contains 160 samples, which were used to calibrate two predictive models: kriging with external drift (KED), considered a mixed model because it encompasses the geostatistical and deterministic approaches; and ordinary kriging (OK). The results were validated with an external and independent set containing 50 points. The best prediction result was found by combining the DIST sampling with the KED model, which has a lower mean absolute error (MAE) = 14.62 mg $ dm^{−3} $, mean error (ME) = −3.12 mg $ dm^{−3} $, and root mean squared error (RMSE) = 23.44 mg $ dm^{−3} $ and higher Nash-Sutcliffe efficiency (NSE) = 0.13. Sampling designs that consider environmental covariables contribute to the increase in the quality of the predicted available phosphorus maps. The most accurate map was generated from the DIST sampling combined with KED modeling. © The Author(s) under exclusive licence to Sociedad Chilena de la Ciencia del Suelo 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract In this study, we have compared three sampling designs and two modeling methods applied in the spatial prediction of available P in the soil. The study was conducted in a 160 ha where three sampling methods were tested — simple regular grid (RG) with a fixed distance between points, spatial coverage sampling (SCS) containing points over short distances, and simulated annealing sampling considering the marginal distribution of environmental covariates (DIST) — as a basis for prediction of the available phosphorus content in the soil, at a depth of 0–10 cm. Thus, each calibration set contains 160 samples, which were used to calibrate two predictive models: kriging with external drift (KED), considered a mixed model because it encompasses the geostatistical and deterministic approaches; and ordinary kriging (OK). The results were validated with an external and independent set containing 50 points. The best prediction result was found by combining the DIST sampling with the KED model, which has a lower mean absolute error (MAE) = 14.62 mg $ dm^{−3} $, mean error (ME) = −3.12 mg $ dm^{−3} $, and root mean squared error (RMSE) = 23.44 mg $ dm^{−3} $ and higher Nash-Sutcliffe efficiency (NSE) = 0.13. Sampling designs that consider environmental covariables contribute to the increase in the quality of the predicted available phosphorus maps. The most accurate map was generated from the DIST sampling combined with KED modeling. © The Author(s) under exclusive licence to Sociedad Chilena de la Ciencia del Suelo 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| title_short |
Sampling Design and Spatial Modeling of Available Phosphorus in a Complex Agricultural Area in Southern Brazil |
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https://dx.doi.org/10.1007/s42729-023-01470-6 |
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Pedron, Fabrício de Araújo Moura-Bueno, Jean Michel Horst, Taciara Zborowski Dalmolin, Ricardo Simão Diniz Nalin, Renan Storno |
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Pedron, Fabrício de Araújo Moura-Bueno, Jean Michel Horst, Taciara Zborowski Dalmolin, Ricardo Simão Diniz Nalin, Renan Storno |
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|
| score |
7.4000216 |