An economic evaluation of site-specific input application $ R_{x} $ maps: evaluation framework and case study
Abstract Commercial consultants frequently sell site-specific crop input management recommendation maps (Rxs) to their farmer-clients. This study proposes a method to empirically evaluate the efficacy of commercial Rxs. The method takes three steps: (1) it uses precision agriculture technology to co...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Gardner, Grant [verfasserIn] Mieno, Taro [verfasserIn] Bullock, David S. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
Site-specific input management On-farm precision experimentation |
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| Anmerkung: |
© This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Precision agriculture - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1999, 22(2021), 4 vom: 03. März, Seite 1304-1316 |
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| Übergeordnetes Werk: |
volume:22 ; year:2021 ; number:4 ; day:03 ; month:03 ; pages:1304-1316 |
| Links: |
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| DOI / URN: |
10.1007/s11119-021-09785-z |
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| Katalog-ID: |
SPR044421508 |
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| 520 | |a Abstract Commercial consultants frequently sell site-specific crop input management recommendation maps (Rxs) to their farmer-clients. This study proposes a method to empirically evaluate the efficacy of commercial Rxs. The method takes three steps: (1) it uses precision agriculture technology to conduct randomized on-farm precision experiments; (2) it estimates yield response functions for the Rx’s management zones using the data; and (3) it conducts economic analysis to test the hypothesis that implementing the Rx is an economically optimal strategy. The method is illustrated using data from a 2018 on-farm nitrogen and seed rate precision experiment on a 31-ha Ohio field, for which nitrogen and seed Rxs were created by the farmer’s professional consultant. The study demonstrates the promise of improving input management through on-farm precision experimentation and data analysis. Future research must conduct trials over multiple years to account for weather. A call is made for the development of public and private research infrastructure to lower the costs on-farm precision experimentation and data analysis. | ||
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| 650 | 4 | |a Shape-constrained generalized additive modeling |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Economically optimal input rates |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Mieno, Taro |e verfasserin |4 aut | |
| 700 | 1 | |a Bullock, David S. |e verfasserin |4 aut | |
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10.1007/s11119-021-09785-z doi (DE-627)SPR044421508 (SPR)s11119-021-09785-z-e DE-627 ger DE-627 rakwb eng 630 640 ASE 48.00 bkl 48.03 bkl Gardner, Grant verfasserin aut An economic evaluation of site-specific input application $ R_{x} $ maps: evaluation framework and case study 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2021 Abstract Commercial consultants frequently sell site-specific crop input management recommendation maps (Rxs) to their farmer-clients. This study proposes a method to empirically evaluate the efficacy of commercial Rxs. The method takes three steps: (1) it uses precision agriculture technology to conduct randomized on-farm precision experiments; (2) it estimates yield response functions for the Rx’s management zones using the data; and (3) it conducts economic analysis to test the hypothesis that implementing the Rx is an economically optimal strategy. The method is illustrated using data from a 2018 on-farm nitrogen and seed rate precision experiment on a 31-ha Ohio field, for which nitrogen and seed Rxs were created by the farmer’s professional consultant. The study demonstrates the promise of improving input management through on-farm precision experimentation and data analysis. Future research must conduct trials over multiple years to account for weather. A call is made for the development of public and private research infrastructure to lower the costs on-farm precision experimentation and data analysis. Site-specific input management (dpeaa)DE-He213 Nitrogen rate (dpeaa)DE-He213 Seed rate (dpeaa)DE-He213 On-farm precision experimentation (dpeaa)DE-He213 Shape-constrained generalized additive modeling (dpeaa)DE-He213 Economically optimal input rates (dpeaa)DE-He213 Mieno, Taro verfasserin aut Bullock, David S. verfasserin aut Enthalten in Precision agriculture Dordrecht [u.a.] : Springer Science + Business Media B.V, 1999 22(2021), 4 vom: 03. März, Seite 1304-1316 (DE-627)320569551 (DE-600)2016333-2 1573-1618 nnns volume:22 year:2021 number:4 day:03 month:03 pages:1304-1316 https://dx.doi.org/10.1007/s11119-021-09785-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_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_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_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_4393 GBV_ILN_4700 48.00 ASE 48.03 ASE AR 22 2021 4 03 03 1304-1316 |
| spelling |
10.1007/s11119-021-09785-z doi (DE-627)SPR044421508 (SPR)s11119-021-09785-z-e DE-627 ger DE-627 rakwb eng 630 640 ASE 48.00 bkl 48.03 bkl Gardner, Grant verfasserin aut An economic evaluation of site-specific input application $ R_{x} $ maps: evaluation framework and case study 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2021 Abstract Commercial consultants frequently sell site-specific crop input management recommendation maps (Rxs) to their farmer-clients. This study proposes a method to empirically evaluate the efficacy of commercial Rxs. The method takes three steps: (1) it uses precision agriculture technology to conduct randomized on-farm precision experiments; (2) it estimates yield response functions for the Rx’s management zones using the data; and (3) it conducts economic analysis to test the hypothesis that implementing the Rx is an economically optimal strategy. The method is illustrated using data from a 2018 on-farm nitrogen and seed rate precision experiment on a 31-ha Ohio field, for which nitrogen and seed Rxs were created by the farmer’s professional consultant. The study demonstrates the promise of improving input management through on-farm precision experimentation and data analysis. Future research must conduct trials over multiple years to account for weather. A call is made for the development of public and private research infrastructure to lower the costs on-farm precision experimentation and data analysis. Site-specific input management (dpeaa)DE-He213 Nitrogen rate (dpeaa)DE-He213 Seed rate (dpeaa)DE-He213 On-farm precision experimentation (dpeaa)DE-He213 Shape-constrained generalized additive modeling (dpeaa)DE-He213 Economically optimal input rates (dpeaa)DE-He213 Mieno, Taro verfasserin aut Bullock, David S. verfasserin aut Enthalten in Precision agriculture Dordrecht [u.a.] : Springer Science + Business Media B.V, 1999 22(2021), 4 vom: 03. März, Seite 1304-1316 (DE-627)320569551 (DE-600)2016333-2 1573-1618 nnns volume:22 year:2021 number:4 day:03 month:03 pages:1304-1316 https://dx.doi.org/10.1007/s11119-021-09785-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_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_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_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_4393 GBV_ILN_4700 48.00 ASE 48.03 ASE AR 22 2021 4 03 03 1304-1316 |
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10.1007/s11119-021-09785-z doi (DE-627)SPR044421508 (SPR)s11119-021-09785-z-e DE-627 ger DE-627 rakwb eng 630 640 ASE 48.00 bkl 48.03 bkl Gardner, Grant verfasserin aut An economic evaluation of site-specific input application $ R_{x} $ maps: evaluation framework and case study 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2021 Abstract Commercial consultants frequently sell site-specific crop input management recommendation maps (Rxs) to their farmer-clients. This study proposes a method to empirically evaluate the efficacy of commercial Rxs. The method takes three steps: (1) it uses precision agriculture technology to conduct randomized on-farm precision experiments; (2) it estimates yield response functions for the Rx’s management zones using the data; and (3) it conducts economic analysis to test the hypothesis that implementing the Rx is an economically optimal strategy. The method is illustrated using data from a 2018 on-farm nitrogen and seed rate precision experiment on a 31-ha Ohio field, for which nitrogen and seed Rxs were created by the farmer’s professional consultant. The study demonstrates the promise of improving input management through on-farm precision experimentation and data analysis. Future research must conduct trials over multiple years to account for weather. A call is made for the development of public and private research infrastructure to lower the costs on-farm precision experimentation and data analysis. Site-specific input management (dpeaa)DE-He213 Nitrogen rate (dpeaa)DE-He213 Seed rate (dpeaa)DE-He213 On-farm precision experimentation (dpeaa)DE-He213 Shape-constrained generalized additive modeling (dpeaa)DE-He213 Economically optimal input rates (dpeaa)DE-He213 Mieno, Taro verfasserin aut Bullock, David S. verfasserin aut Enthalten in Precision agriculture Dordrecht [u.a.] : Springer Science + Business Media B.V, 1999 22(2021), 4 vom: 03. März, Seite 1304-1316 (DE-627)320569551 (DE-600)2016333-2 1573-1618 nnns volume:22 year:2021 number:4 day:03 month:03 pages:1304-1316 https://dx.doi.org/10.1007/s11119-021-09785-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_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_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_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_4393 GBV_ILN_4700 48.00 ASE 48.03 ASE AR 22 2021 4 03 03 1304-1316 |
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10.1007/s11119-021-09785-z doi (DE-627)SPR044421508 (SPR)s11119-021-09785-z-e DE-627 ger DE-627 rakwb eng 630 640 ASE 48.00 bkl 48.03 bkl Gardner, Grant verfasserin aut An economic evaluation of site-specific input application $ R_{x} $ maps: evaluation framework and case study 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2021 Abstract Commercial consultants frequently sell site-specific crop input management recommendation maps (Rxs) to their farmer-clients. This study proposes a method to empirically evaluate the efficacy of commercial Rxs. The method takes three steps: (1) it uses precision agriculture technology to conduct randomized on-farm precision experiments; (2) it estimates yield response functions for the Rx’s management zones using the data; and (3) it conducts economic analysis to test the hypothesis that implementing the Rx is an economically optimal strategy. The method is illustrated using data from a 2018 on-farm nitrogen and seed rate precision experiment on a 31-ha Ohio field, for which nitrogen and seed Rxs were created by the farmer’s professional consultant. The study demonstrates the promise of improving input management through on-farm precision experimentation and data analysis. Future research must conduct trials over multiple years to account for weather. A call is made for the development of public and private research infrastructure to lower the costs on-farm precision experimentation and data analysis. Site-specific input management (dpeaa)DE-He213 Nitrogen rate (dpeaa)DE-He213 Seed rate (dpeaa)DE-He213 On-farm precision experimentation (dpeaa)DE-He213 Shape-constrained generalized additive modeling (dpeaa)DE-He213 Economically optimal input rates (dpeaa)DE-He213 Mieno, Taro verfasserin aut Bullock, David S. verfasserin aut Enthalten in Precision agriculture Dordrecht [u.a.] : Springer Science + Business Media B.V, 1999 22(2021), 4 vom: 03. März, Seite 1304-1316 (DE-627)320569551 (DE-600)2016333-2 1573-1618 nnns volume:22 year:2021 number:4 day:03 month:03 pages:1304-1316 https://dx.doi.org/10.1007/s11119-021-09785-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_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_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_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_4393 GBV_ILN_4700 48.00 ASE 48.03 ASE AR 22 2021 4 03 03 1304-1316 |
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10.1007/s11119-021-09785-z doi (DE-627)SPR044421508 (SPR)s11119-021-09785-z-e DE-627 ger DE-627 rakwb eng 630 640 ASE 48.00 bkl 48.03 bkl Gardner, Grant verfasserin aut An economic evaluation of site-specific input application $ R_{x} $ maps: evaluation framework and case study 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2021 Abstract Commercial consultants frequently sell site-specific crop input management recommendation maps (Rxs) to their farmer-clients. This study proposes a method to empirically evaluate the efficacy of commercial Rxs. The method takes three steps: (1) it uses precision agriculture technology to conduct randomized on-farm precision experiments; (2) it estimates yield response functions for the Rx’s management zones using the data; and (3) it conducts economic analysis to test the hypothesis that implementing the Rx is an economically optimal strategy. The method is illustrated using data from a 2018 on-farm nitrogen and seed rate precision experiment on a 31-ha Ohio field, for which nitrogen and seed Rxs were created by the farmer’s professional consultant. The study demonstrates the promise of improving input management through on-farm precision experimentation and data analysis. Future research must conduct trials over multiple years to account for weather. A call is made for the development of public and private research infrastructure to lower the costs on-farm precision experimentation and data analysis. Site-specific input management (dpeaa)DE-He213 Nitrogen rate (dpeaa)DE-He213 Seed rate (dpeaa)DE-He213 On-farm precision experimentation (dpeaa)DE-He213 Shape-constrained generalized additive modeling (dpeaa)DE-He213 Economically optimal input rates (dpeaa)DE-He213 Mieno, Taro verfasserin aut Bullock, David S. verfasserin aut Enthalten in Precision agriculture Dordrecht [u.a.] : Springer Science + Business Media B.V, 1999 22(2021), 4 vom: 03. März, Seite 1304-1316 (DE-627)320569551 (DE-600)2016333-2 1573-1618 nnns volume:22 year:2021 number:4 day:03 month:03 pages:1304-1316 https://dx.doi.org/10.1007/s11119-021-09785-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_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_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_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_4393 GBV_ILN_4700 48.00 ASE 48.03 ASE AR 22 2021 4 03 03 1304-1316 |
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This study proposes a method to empirically evaluate the efficacy of commercial Rxs. The method takes three steps: (1) it uses precision agriculture technology to conduct randomized on-farm precision experiments; (2) it estimates yield response functions for the Rx’s management zones using the data; and (3) it conducts economic analysis to test the hypothesis that implementing the Rx is an economically optimal strategy. The method is illustrated using data from a 2018 on-farm nitrogen and seed rate precision experiment on a 31-ha Ohio field, for which nitrogen and seed Rxs were created by the farmer’s professional consultant. The study demonstrates the promise of improving input management through on-farm precision experimentation and data analysis. Future research must conduct trials over multiple years to account for weather. 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economic evaluation of site-specific input application $ r_{x} $ maps: evaluation framework and case study |
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An economic evaluation of site-specific input application $ R_{x} $ maps: evaluation framework and case study |
| abstract |
Abstract Commercial consultants frequently sell site-specific crop input management recommendation maps (Rxs) to their farmer-clients. This study proposes a method to empirically evaluate the efficacy of commercial Rxs. The method takes three steps: (1) it uses precision agriculture technology to conduct randomized on-farm precision experiments; (2) it estimates yield response functions for the Rx’s management zones using the data; and (3) it conducts economic analysis to test the hypothesis that implementing the Rx is an economically optimal strategy. The method is illustrated using data from a 2018 on-farm nitrogen and seed rate precision experiment on a 31-ha Ohio field, for which nitrogen and seed Rxs were created by the farmer’s professional consultant. The study demonstrates the promise of improving input management through on-farm precision experimentation and data analysis. Future research must conduct trials over multiple years to account for weather. A call is made for the development of public and private research infrastructure to lower the costs on-farm precision experimentation and data analysis. © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2021 |
| abstractGer |
Abstract Commercial consultants frequently sell site-specific crop input management recommendation maps (Rxs) to their farmer-clients. This study proposes a method to empirically evaluate the efficacy of commercial Rxs. The method takes three steps: (1) it uses precision agriculture technology to conduct randomized on-farm precision experiments; (2) it estimates yield response functions for the Rx’s management zones using the data; and (3) it conducts economic analysis to test the hypothesis that implementing the Rx is an economically optimal strategy. The method is illustrated using data from a 2018 on-farm nitrogen and seed rate precision experiment on a 31-ha Ohio field, for which nitrogen and seed Rxs were created by the farmer’s professional consultant. The study demonstrates the promise of improving input management through on-farm precision experimentation and data analysis. Future research must conduct trials over multiple years to account for weather. A call is made for the development of public and private research infrastructure to lower the costs on-farm precision experimentation and data analysis. © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2021 |
| abstract_unstemmed |
Abstract Commercial consultants frequently sell site-specific crop input management recommendation maps (Rxs) to their farmer-clients. This study proposes a method to empirically evaluate the efficacy of commercial Rxs. The method takes three steps: (1) it uses precision agriculture technology to conduct randomized on-farm precision experiments; (2) it estimates yield response functions for the Rx’s management zones using the data; and (3) it conducts economic analysis to test the hypothesis that implementing the Rx is an economically optimal strategy. The method is illustrated using data from a 2018 on-farm nitrogen and seed rate precision experiment on a 31-ha Ohio field, for which nitrogen and seed Rxs were created by the farmer’s professional consultant. The study demonstrates the promise of improving input management through on-farm precision experimentation and data analysis. Future research must conduct trials over multiple years to account for weather. A call is made for the development of public and private research infrastructure to lower the costs on-farm precision experimentation and data analysis. © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2021 |
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| title_short |
An economic evaluation of site-specific input application $ R_{x} $ maps: evaluation framework and case study |
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https://dx.doi.org/10.1007/s11119-021-09785-z |
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Mieno, Taro Bullock, David S. |
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| doi_str |
10.1007/s11119-021-09785-z |
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2024-07-04T00:35:44.348Z |
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|
| score |
7.4012003 |