A Bayesian approach toward the use of qualitative information to inform on-farm decision making: The example of soil compaction
As the development of predictive tools to aid decision making in agriculture increases, it must be acknowledged that the ability to incorporate management decisions as input data is limited. The data is either not recorded, or highly inadequate in terms of the volume, variety or relevance. Some circ...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Roberton, S.D. [verfasserIn] Lobsey, C.R. [verfasserIn] Bennett, J.McL. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Geoderma - Amsterdam [u.a.] : Elsevier Science, 1967, 382 |
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| Übergeordnetes Werk: |
volume:382 |
| DOI / URN: |
10.1016/j.geoderma.2020.114705 |
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| Katalog-ID: |
ELV004967909 |
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| 245 | 1 | 0 | |a A Bayesian approach toward the use of qualitative information to inform on-farm decision making: The example of soil compaction |
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| 520 | |a As the development of predictive tools to aid decision making in agriculture increases, it must be acknowledged that the ability to incorporate management decisions as input data is limited. The data is either not recorded, or highly inadequate in terms of the volume, variety or relevance. Some circumstances are further hampered by the lack of benchmark data for comparison, and soil compaction is an example of this. The premise of this work was to take a probability based approach to decision making, utilising both qualitative and quantitative data to provide a probability distribution of risk against decisions made, in the context of grains production systems as an example of an agricultural enterprise. A Bayesian Belief Network (BBN) was constructed for soil compaction risk, as an exemplar. The BBN conditional probability tables for nodes were populated via a combination of biophysical model output (namely SoilFlex for soil stress distribution, and the APSIM package for soil-water and crop parameters) and expert opinion. Input nodes were parameterised with measured soil data, and the risk of soil compaction, given the soil stress at the wheel of a particular vehicle, was provided as the output. Potential effect on yield was subsequently calculated on the basis of percent change in soil bulk density, which was determined using literature based information (expert opinion). The tool broadly estimated yield impacts due to various agricultural traffic scenarios, providing means to highlight the financial consequences of failing to adopt controlled traffic farming management for a particular agricultural enterprise. Of significance, the BBN approach was determined useful for data limiting environments where empirical models struggle, thus providing a pragmatic and novel approach to on farm decision making incorporating management nuance. | ||
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10.1016/j.geoderma.2020.114705 doi (DE-627)ELV004967909 (ELSEVIER)S0016-7061(19)32946-5 DE-627 ger DE-627 rda eng 550 910 DE-600 38.60 bkl Roberton, S.D. verfasserin aut A Bayesian approach toward the use of qualitative information to inform on-farm decision making: The example of soil compaction 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As the development of predictive tools to aid decision making in agriculture increases, it must be acknowledged that the ability to incorporate management decisions as input data is limited. The data is either not recorded, or highly inadequate in terms of the volume, variety or relevance. Some circumstances are further hampered by the lack of benchmark data for comparison, and soil compaction is an example of this. The premise of this work was to take a probability based approach to decision making, utilising both qualitative and quantitative data to provide a probability distribution of risk against decisions made, in the context of grains production systems as an example of an agricultural enterprise. A Bayesian Belief Network (BBN) was constructed for soil compaction risk, as an exemplar. The BBN conditional probability tables for nodes were populated via a combination of biophysical model output (namely SoilFlex for soil stress distribution, and the APSIM package for soil-water and crop parameters) and expert opinion. Input nodes were parameterised with measured soil data, and the risk of soil compaction, given the soil stress at the wheel of a particular vehicle, was provided as the output. Potential effect on yield was subsequently calculated on the basis of percent change in soil bulk density, which was determined using literature based information (expert opinion). The tool broadly estimated yield impacts due to various agricultural traffic scenarios, providing means to highlight the financial consequences of failing to adopt controlled traffic farming management for a particular agricultural enterprise. Of significance, the BBN approach was determined useful for data limiting environments where empirical models struggle, thus providing a pragmatic and novel approach to on farm decision making incorporating management nuance. Precision agriculture Soil constraints Pedometrics Constraint economics Lobsey, C.R. verfasserin aut Bennett, J.McL. verfasserin aut Enthalten in Geoderma Amsterdam [u.a.] : Elsevier Science, 1967 382 Online-Ressource (DE-627)320414493 (DE-600)2001729-7 (DE-576)099603853 1872-6259 nnns volume:382 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.60 Bodenkunde: Allgemeines Geowissenschaften AR 382 |
| spelling |
10.1016/j.geoderma.2020.114705 doi (DE-627)ELV004967909 (ELSEVIER)S0016-7061(19)32946-5 DE-627 ger DE-627 rda eng 550 910 DE-600 38.60 bkl Roberton, S.D. verfasserin aut A Bayesian approach toward the use of qualitative information to inform on-farm decision making: The example of soil compaction 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As the development of predictive tools to aid decision making in agriculture increases, it must be acknowledged that the ability to incorporate management decisions as input data is limited. The data is either not recorded, or highly inadequate in terms of the volume, variety or relevance. Some circumstances are further hampered by the lack of benchmark data for comparison, and soil compaction is an example of this. The premise of this work was to take a probability based approach to decision making, utilising both qualitative and quantitative data to provide a probability distribution of risk against decisions made, in the context of grains production systems as an example of an agricultural enterprise. A Bayesian Belief Network (BBN) was constructed for soil compaction risk, as an exemplar. The BBN conditional probability tables for nodes were populated via a combination of biophysical model output (namely SoilFlex for soil stress distribution, and the APSIM package for soil-water and crop parameters) and expert opinion. Input nodes were parameterised with measured soil data, and the risk of soil compaction, given the soil stress at the wheel of a particular vehicle, was provided as the output. Potential effect on yield was subsequently calculated on the basis of percent change in soil bulk density, which was determined using literature based information (expert opinion). The tool broadly estimated yield impacts due to various agricultural traffic scenarios, providing means to highlight the financial consequences of failing to adopt controlled traffic farming management for a particular agricultural enterprise. Of significance, the BBN approach was determined useful for data limiting environments where empirical models struggle, thus providing a pragmatic and novel approach to on farm decision making incorporating management nuance. Precision agriculture Soil constraints Pedometrics Constraint economics Lobsey, C.R. verfasserin aut Bennett, J.McL. verfasserin aut Enthalten in Geoderma Amsterdam [u.a.] : Elsevier Science, 1967 382 Online-Ressource (DE-627)320414493 (DE-600)2001729-7 (DE-576)099603853 1872-6259 nnns volume:382 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.60 Bodenkunde: Allgemeines Geowissenschaften AR 382 |
| allfields_unstemmed |
10.1016/j.geoderma.2020.114705 doi (DE-627)ELV004967909 (ELSEVIER)S0016-7061(19)32946-5 DE-627 ger DE-627 rda eng 550 910 DE-600 38.60 bkl Roberton, S.D. verfasserin aut A Bayesian approach toward the use of qualitative information to inform on-farm decision making: The example of soil compaction 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As the development of predictive tools to aid decision making in agriculture increases, it must be acknowledged that the ability to incorporate management decisions as input data is limited. The data is either not recorded, or highly inadequate in terms of the volume, variety or relevance. Some circumstances are further hampered by the lack of benchmark data for comparison, and soil compaction is an example of this. The premise of this work was to take a probability based approach to decision making, utilising both qualitative and quantitative data to provide a probability distribution of risk against decisions made, in the context of grains production systems as an example of an agricultural enterprise. A Bayesian Belief Network (BBN) was constructed for soil compaction risk, as an exemplar. The BBN conditional probability tables for nodes were populated via a combination of biophysical model output (namely SoilFlex for soil stress distribution, and the APSIM package for soil-water and crop parameters) and expert opinion. Input nodes were parameterised with measured soil data, and the risk of soil compaction, given the soil stress at the wheel of a particular vehicle, was provided as the output. Potential effect on yield was subsequently calculated on the basis of percent change in soil bulk density, which was determined using literature based information (expert opinion). The tool broadly estimated yield impacts due to various agricultural traffic scenarios, providing means to highlight the financial consequences of failing to adopt controlled traffic farming management for a particular agricultural enterprise. Of significance, the BBN approach was determined useful for data limiting environments where empirical models struggle, thus providing a pragmatic and novel approach to on farm decision making incorporating management nuance. Precision agriculture Soil constraints Pedometrics Constraint economics Lobsey, C.R. verfasserin aut Bennett, J.McL. verfasserin aut Enthalten in Geoderma Amsterdam [u.a.] : Elsevier Science, 1967 382 Online-Ressource (DE-627)320414493 (DE-600)2001729-7 (DE-576)099603853 1872-6259 nnns volume:382 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.60 Bodenkunde: Allgemeines Geowissenschaften AR 382 |
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10.1016/j.geoderma.2020.114705 doi (DE-627)ELV004967909 (ELSEVIER)S0016-7061(19)32946-5 DE-627 ger DE-627 rda eng 550 910 DE-600 38.60 bkl Roberton, S.D. verfasserin aut A Bayesian approach toward the use of qualitative information to inform on-farm decision making: The example of soil compaction 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As the development of predictive tools to aid decision making in agriculture increases, it must be acknowledged that the ability to incorporate management decisions as input data is limited. The data is either not recorded, or highly inadequate in terms of the volume, variety or relevance. Some circumstances are further hampered by the lack of benchmark data for comparison, and soil compaction is an example of this. The premise of this work was to take a probability based approach to decision making, utilising both qualitative and quantitative data to provide a probability distribution of risk against decisions made, in the context of grains production systems as an example of an agricultural enterprise. A Bayesian Belief Network (BBN) was constructed for soil compaction risk, as an exemplar. The BBN conditional probability tables for nodes were populated via a combination of biophysical model output (namely SoilFlex for soil stress distribution, and the APSIM package for soil-water and crop parameters) and expert opinion. Input nodes were parameterised with measured soil data, and the risk of soil compaction, given the soil stress at the wheel of a particular vehicle, was provided as the output. Potential effect on yield was subsequently calculated on the basis of percent change in soil bulk density, which was determined using literature based information (expert opinion). The tool broadly estimated yield impacts due to various agricultural traffic scenarios, providing means to highlight the financial consequences of failing to adopt controlled traffic farming management for a particular agricultural enterprise. Of significance, the BBN approach was determined useful for data limiting environments where empirical models struggle, thus providing a pragmatic and novel approach to on farm decision making incorporating management nuance. Precision agriculture Soil constraints Pedometrics Constraint economics Lobsey, C.R. verfasserin aut Bennett, J.McL. verfasserin aut Enthalten in Geoderma Amsterdam [u.a.] : Elsevier Science, 1967 382 Online-Ressource (DE-627)320414493 (DE-600)2001729-7 (DE-576)099603853 1872-6259 nnns volume:382 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.60 Bodenkunde: Allgemeines Geowissenschaften AR 382 |
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10.1016/j.geoderma.2020.114705 doi (DE-627)ELV004967909 (ELSEVIER)S0016-7061(19)32946-5 DE-627 ger DE-627 rda eng 550 910 DE-600 38.60 bkl Roberton, S.D. verfasserin aut A Bayesian approach toward the use of qualitative information to inform on-farm decision making: The example of soil compaction 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As the development of predictive tools to aid decision making in agriculture increases, it must be acknowledged that the ability to incorporate management decisions as input data is limited. The data is either not recorded, or highly inadequate in terms of the volume, variety or relevance. Some circumstances are further hampered by the lack of benchmark data for comparison, and soil compaction is an example of this. The premise of this work was to take a probability based approach to decision making, utilising both qualitative and quantitative data to provide a probability distribution of risk against decisions made, in the context of grains production systems as an example of an agricultural enterprise. A Bayesian Belief Network (BBN) was constructed for soil compaction risk, as an exemplar. The BBN conditional probability tables for nodes were populated via a combination of biophysical model output (namely SoilFlex for soil stress distribution, and the APSIM package for soil-water and crop parameters) and expert opinion. Input nodes were parameterised with measured soil data, and the risk of soil compaction, given the soil stress at the wheel of a particular vehicle, was provided as the output. Potential effect on yield was subsequently calculated on the basis of percent change in soil bulk density, which was determined using literature based information (expert opinion). The tool broadly estimated yield impacts due to various agricultural traffic scenarios, providing means to highlight the financial consequences of failing to adopt controlled traffic farming management for a particular agricultural enterprise. Of significance, the BBN approach was determined useful for data limiting environments where empirical models struggle, thus providing a pragmatic and novel approach to on farm decision making incorporating management nuance. Precision agriculture Soil constraints Pedometrics Constraint economics Lobsey, C.R. verfasserin aut Bennett, J.McL. verfasserin aut Enthalten in Geoderma Amsterdam [u.a.] : Elsevier Science, 1967 382 Online-Ressource (DE-627)320414493 (DE-600)2001729-7 (DE-576)099603853 1872-6259 nnns volume:382 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.60 Bodenkunde: Allgemeines Geowissenschaften AR 382 |
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Roberton, S.D. ddc 550 bkl 38.60 misc Precision agriculture misc Soil constraints misc Pedometrics misc Constraint economics A Bayesian approach toward the use of qualitative information to inform on-farm decision making: The example of soil compaction |
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550 910 DE-600 38.60 bkl A Bayesian approach toward the use of qualitative information to inform on-farm decision making: The example of soil compaction Precision agriculture Soil constraints Pedometrics Constraint economics |
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a bayesian approach toward the use of qualitative information to inform on-farm decision making: the example of soil compaction |
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A Bayesian approach toward the use of qualitative information to inform on-farm decision making: The example of soil compaction |
| abstract |
As the development of predictive tools to aid decision making in agriculture increases, it must be acknowledged that the ability to incorporate management decisions as input data is limited. The data is either not recorded, or highly inadequate in terms of the volume, variety or relevance. Some circumstances are further hampered by the lack of benchmark data for comparison, and soil compaction is an example of this. The premise of this work was to take a probability based approach to decision making, utilising both qualitative and quantitative data to provide a probability distribution of risk against decisions made, in the context of grains production systems as an example of an agricultural enterprise. A Bayesian Belief Network (BBN) was constructed for soil compaction risk, as an exemplar. The BBN conditional probability tables for nodes were populated via a combination of biophysical model output (namely SoilFlex for soil stress distribution, and the APSIM package for soil-water and crop parameters) and expert opinion. Input nodes were parameterised with measured soil data, and the risk of soil compaction, given the soil stress at the wheel of a particular vehicle, was provided as the output. Potential effect on yield was subsequently calculated on the basis of percent change in soil bulk density, which was determined using literature based information (expert opinion). The tool broadly estimated yield impacts due to various agricultural traffic scenarios, providing means to highlight the financial consequences of failing to adopt controlled traffic farming management for a particular agricultural enterprise. Of significance, the BBN approach was determined useful for data limiting environments where empirical models struggle, thus providing a pragmatic and novel approach to on farm decision making incorporating management nuance. |
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As the development of predictive tools to aid decision making in agriculture increases, it must be acknowledged that the ability to incorporate management decisions as input data is limited. The data is either not recorded, or highly inadequate in terms of the volume, variety or relevance. Some circumstances are further hampered by the lack of benchmark data for comparison, and soil compaction is an example of this. The premise of this work was to take a probability based approach to decision making, utilising both qualitative and quantitative data to provide a probability distribution of risk against decisions made, in the context of grains production systems as an example of an agricultural enterprise. A Bayesian Belief Network (BBN) was constructed for soil compaction risk, as an exemplar. The BBN conditional probability tables for nodes were populated via a combination of biophysical model output (namely SoilFlex for soil stress distribution, and the APSIM package for soil-water and crop parameters) and expert opinion. Input nodes were parameterised with measured soil data, and the risk of soil compaction, given the soil stress at the wheel of a particular vehicle, was provided as the output. Potential effect on yield was subsequently calculated on the basis of percent change in soil bulk density, which was determined using literature based information (expert opinion). The tool broadly estimated yield impacts due to various agricultural traffic scenarios, providing means to highlight the financial consequences of failing to adopt controlled traffic farming management for a particular agricultural enterprise. Of significance, the BBN approach was determined useful for data limiting environments where empirical models struggle, thus providing a pragmatic and novel approach to on farm decision making incorporating management nuance. |
| abstract_unstemmed |
As the development of predictive tools to aid decision making in agriculture increases, it must be acknowledged that the ability to incorporate management decisions as input data is limited. The data is either not recorded, or highly inadequate in terms of the volume, variety or relevance. Some circumstances are further hampered by the lack of benchmark data for comparison, and soil compaction is an example of this. The premise of this work was to take a probability based approach to decision making, utilising both qualitative and quantitative data to provide a probability distribution of risk against decisions made, in the context of grains production systems as an example of an agricultural enterprise. A Bayesian Belief Network (BBN) was constructed for soil compaction risk, as an exemplar. The BBN conditional probability tables for nodes were populated via a combination of biophysical model output (namely SoilFlex for soil stress distribution, and the APSIM package for soil-water and crop parameters) and expert opinion. Input nodes were parameterised with measured soil data, and the risk of soil compaction, given the soil stress at the wheel of a particular vehicle, was provided as the output. Potential effect on yield was subsequently calculated on the basis of percent change in soil bulk density, which was determined using literature based information (expert opinion). The tool broadly estimated yield impacts due to various agricultural traffic scenarios, providing means to highlight the financial consequences of failing to adopt controlled traffic farming management for a particular agricultural enterprise. Of significance, the BBN approach was determined useful for data limiting environments where empirical models struggle, thus providing a pragmatic and novel approach to on farm decision making incorporating management nuance. |
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| score |
7.3999033 |