Simulation of furrow irrigation practices (SOFIP): a field-scale modelling of water management and crop yield for furrow irrigation
Abstract Because of the spatial and temporal variabilities of the advance infiltration process, furrow irrigation investigations should not be limited to a single furrow irrigation event when using a modelling approach. The paper deals with the development and application of simulation of furrow irr...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Mailhol, Jean Claude [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2005 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag 2005 |
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| Übergeordnetes Werk: |
Enthalten in: Irrigation science - Berlin : Springer, 1978, 24(2005), 1 vom: 17. Aug., Seite 37-48 |
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| Übergeordnetes Werk: |
volume:24 ; year:2005 ; number:1 ; day:17 ; month:08 ; pages:37-48 |
| Links: |
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| DOI / URN: |
10.1007/s00271-005-0006-8 |
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| Katalog-ID: |
SPR003537099 |
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| 100 | 1 | |a Mailhol, Jean Claude |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Simulation of furrow irrigation practices (SOFIP): a field-scale modelling of water management and crop yield for furrow irrigation |
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| 520 | |a Abstract Because of the spatial and temporal variabilities of the advance infiltration process, furrow irrigation investigations should not be limited to a single furrow irrigation event when using a modelling approach. The paper deals with the development and application of simulation of furrow irrigation practices (SOFIP), a model used to analyse furrow irrigation practices that take into account spatial and temporal variabilities of the advance infiltration process. SOFIP can be used to compare alternative furrow irrigation management strategies and find options that mitigate local deep-percolation risks while ensuring a crop yield level that is acceptable to the farmer. The model is comprised of three distinct modelling elements. The first element is RAIEOPT, a hydraulic model that predicts the advance infiltration process. Infiltration prediction in RAIEOPT depends on a soil moisture deficit parameter. PILOTE, a crop model, which is designed to simulate soil water balance and predict yield values, updates the soil moisture parameter. This parameter is an input of a parameter generator (PG), the third model component, which in turn provides RAIEOPT with the data required to simulate irrigation at the scale of an N-furrow set. The study of sources of variability and their impact on irrigation advance, based on field observations, allowed us to build a robust PG. Model applications show that irrigation practices must account for inter-furrow advance variability when optimising furrow irrigation systems. The impact of advance variability on deep percolation and crop yield losses depends on both climatic conditions and irrigation practices. | ||
| 650 | 4 | |a Irrigation Event |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Advance Time |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Irrigation Season |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Irrigation Practice |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Furrow Irrigation |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Ruelle, Pierre |4 aut | |
| 700 | 1 | |a Popova, Zornitsa |4 aut | |
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10.1007/s00271-005-0006-8 doi (DE-627)SPR003537099 (SPR)s00271-005-0006-8-e DE-627 ger DE-627 rakwb eng Mailhol, Jean Claude verfasserin aut Simulation of furrow irrigation practices (SOFIP): a field-scale modelling of water management and crop yield for furrow irrigation 2005 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2005 Abstract Because of the spatial and temporal variabilities of the advance infiltration process, furrow irrigation investigations should not be limited to a single furrow irrigation event when using a modelling approach. The paper deals with the development and application of simulation of furrow irrigation practices (SOFIP), a model used to analyse furrow irrigation practices that take into account spatial and temporal variabilities of the advance infiltration process. SOFIP can be used to compare alternative furrow irrigation management strategies and find options that mitigate local deep-percolation risks while ensuring a crop yield level that is acceptable to the farmer. The model is comprised of three distinct modelling elements. The first element is RAIEOPT, a hydraulic model that predicts the advance infiltration process. Infiltration prediction in RAIEOPT depends on a soil moisture deficit parameter. PILOTE, a crop model, which is designed to simulate soil water balance and predict yield values, updates the soil moisture parameter. This parameter is an input of a parameter generator (PG), the third model component, which in turn provides RAIEOPT with the data required to simulate irrigation at the scale of an N-furrow set. The study of sources of variability and their impact on irrigation advance, based on field observations, allowed us to build a robust PG. Model applications show that irrigation practices must account for inter-furrow advance variability when optimising furrow irrigation systems. The impact of advance variability on deep percolation and crop yield losses depends on both climatic conditions and irrigation practices. Irrigation Event (dpeaa)DE-He213 Advance Time (dpeaa)DE-He213 Irrigation Season (dpeaa)DE-He213 Irrigation Practice (dpeaa)DE-He213 Furrow Irrigation (dpeaa)DE-He213 Ruelle, Pierre aut Popova, Zornitsa aut Enthalten in Irrigation science Berlin : Springer, 1978 24(2005), 1 vom: 17. Aug., Seite 37-48 (DE-627)271175362 (DE-600)1478936-X 1432-1319 nnns volume:24 year:2005 number:1 day:17 month:08 pages:37-48 https://dx.doi.org/10.1007/s00271-005-0006-8 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_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_267 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_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 24 2005 1 17 08 37-48 |
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10.1007/s00271-005-0006-8 doi (DE-627)SPR003537099 (SPR)s00271-005-0006-8-e DE-627 ger DE-627 rakwb eng Mailhol, Jean Claude verfasserin aut Simulation of furrow irrigation practices (SOFIP): a field-scale modelling of water management and crop yield for furrow irrigation 2005 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2005 Abstract Because of the spatial and temporal variabilities of the advance infiltration process, furrow irrigation investigations should not be limited to a single furrow irrigation event when using a modelling approach. The paper deals with the development and application of simulation of furrow irrigation practices (SOFIP), a model used to analyse furrow irrigation practices that take into account spatial and temporal variabilities of the advance infiltration process. SOFIP can be used to compare alternative furrow irrigation management strategies and find options that mitigate local deep-percolation risks while ensuring a crop yield level that is acceptable to the farmer. The model is comprised of three distinct modelling elements. The first element is RAIEOPT, a hydraulic model that predicts the advance infiltration process. Infiltration prediction in RAIEOPT depends on a soil moisture deficit parameter. PILOTE, a crop model, which is designed to simulate soil water balance and predict yield values, updates the soil moisture parameter. This parameter is an input of a parameter generator (PG), the third model component, which in turn provides RAIEOPT with the data required to simulate irrigation at the scale of an N-furrow set. The study of sources of variability and their impact on irrigation advance, based on field observations, allowed us to build a robust PG. Model applications show that irrigation practices must account for inter-furrow advance variability when optimising furrow irrigation systems. The impact of advance variability on deep percolation and crop yield losses depends on both climatic conditions and irrigation practices. Irrigation Event (dpeaa)DE-He213 Advance Time (dpeaa)DE-He213 Irrigation Season (dpeaa)DE-He213 Irrigation Practice (dpeaa)DE-He213 Furrow Irrigation (dpeaa)DE-He213 Ruelle, Pierre aut Popova, Zornitsa aut Enthalten in Irrigation science Berlin : Springer, 1978 24(2005), 1 vom: 17. Aug., Seite 37-48 (DE-627)271175362 (DE-600)1478936-X 1432-1319 nnns volume:24 year:2005 number:1 day:17 month:08 pages:37-48 https://dx.doi.org/10.1007/s00271-005-0006-8 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_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_267 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_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 24 2005 1 17 08 37-48 |
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10.1007/s00271-005-0006-8 doi (DE-627)SPR003537099 (SPR)s00271-005-0006-8-e DE-627 ger DE-627 rakwb eng Mailhol, Jean Claude verfasserin aut Simulation of furrow irrigation practices (SOFIP): a field-scale modelling of water management and crop yield for furrow irrigation 2005 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2005 Abstract Because of the spatial and temporal variabilities of the advance infiltration process, furrow irrigation investigations should not be limited to a single furrow irrigation event when using a modelling approach. The paper deals with the development and application of simulation of furrow irrigation practices (SOFIP), a model used to analyse furrow irrigation practices that take into account spatial and temporal variabilities of the advance infiltration process. SOFIP can be used to compare alternative furrow irrigation management strategies and find options that mitigate local deep-percolation risks while ensuring a crop yield level that is acceptable to the farmer. The model is comprised of three distinct modelling elements. The first element is RAIEOPT, a hydraulic model that predicts the advance infiltration process. Infiltration prediction in RAIEOPT depends on a soil moisture deficit parameter. PILOTE, a crop model, which is designed to simulate soil water balance and predict yield values, updates the soil moisture parameter. This parameter is an input of a parameter generator (PG), the third model component, which in turn provides RAIEOPT with the data required to simulate irrigation at the scale of an N-furrow set. The study of sources of variability and their impact on irrigation advance, based on field observations, allowed us to build a robust PG. Model applications show that irrigation practices must account for inter-furrow advance variability when optimising furrow irrigation systems. The impact of advance variability on deep percolation and crop yield losses depends on both climatic conditions and irrigation practices. Irrigation Event (dpeaa)DE-He213 Advance Time (dpeaa)DE-He213 Irrigation Season (dpeaa)DE-He213 Irrigation Practice (dpeaa)DE-He213 Furrow Irrigation (dpeaa)DE-He213 Ruelle, Pierre aut Popova, Zornitsa aut Enthalten in Irrigation science Berlin : Springer, 1978 24(2005), 1 vom: 17. Aug., Seite 37-48 (DE-627)271175362 (DE-600)1478936-X 1432-1319 nnns volume:24 year:2005 number:1 day:17 month:08 pages:37-48 https://dx.doi.org/10.1007/s00271-005-0006-8 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_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_267 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_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 24 2005 1 17 08 37-48 |
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10.1007/s00271-005-0006-8 doi (DE-627)SPR003537099 (SPR)s00271-005-0006-8-e DE-627 ger DE-627 rakwb eng Mailhol, Jean Claude verfasserin aut Simulation of furrow irrigation practices (SOFIP): a field-scale modelling of water management and crop yield for furrow irrigation 2005 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2005 Abstract Because of the spatial and temporal variabilities of the advance infiltration process, furrow irrigation investigations should not be limited to a single furrow irrigation event when using a modelling approach. The paper deals with the development and application of simulation of furrow irrigation practices (SOFIP), a model used to analyse furrow irrigation practices that take into account spatial and temporal variabilities of the advance infiltration process. SOFIP can be used to compare alternative furrow irrigation management strategies and find options that mitigate local deep-percolation risks while ensuring a crop yield level that is acceptable to the farmer. The model is comprised of three distinct modelling elements. The first element is RAIEOPT, a hydraulic model that predicts the advance infiltration process. Infiltration prediction in RAIEOPT depends on a soil moisture deficit parameter. PILOTE, a crop model, which is designed to simulate soil water balance and predict yield values, updates the soil moisture parameter. This parameter is an input of a parameter generator (PG), the third model component, which in turn provides RAIEOPT with the data required to simulate irrigation at the scale of an N-furrow set. The study of sources of variability and their impact on irrigation advance, based on field observations, allowed us to build a robust PG. Model applications show that irrigation practices must account for inter-furrow advance variability when optimising furrow irrigation systems. The impact of advance variability on deep percolation and crop yield losses depends on both climatic conditions and irrigation practices. Irrigation Event (dpeaa)DE-He213 Advance Time (dpeaa)DE-He213 Irrigation Season (dpeaa)DE-He213 Irrigation Practice (dpeaa)DE-He213 Furrow Irrigation (dpeaa)DE-He213 Ruelle, Pierre aut Popova, Zornitsa aut Enthalten in Irrigation science Berlin : Springer, 1978 24(2005), 1 vom: 17. Aug., Seite 37-48 (DE-627)271175362 (DE-600)1478936-X 1432-1319 nnns volume:24 year:2005 number:1 day:17 month:08 pages:37-48 https://dx.doi.org/10.1007/s00271-005-0006-8 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_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_267 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_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 24 2005 1 17 08 37-48 |
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10.1007/s00271-005-0006-8 doi (DE-627)SPR003537099 (SPR)s00271-005-0006-8-e DE-627 ger DE-627 rakwb eng Mailhol, Jean Claude verfasserin aut Simulation of furrow irrigation practices (SOFIP): a field-scale modelling of water management and crop yield for furrow irrigation 2005 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2005 Abstract Because of the spatial and temporal variabilities of the advance infiltration process, furrow irrigation investigations should not be limited to a single furrow irrigation event when using a modelling approach. The paper deals with the development and application of simulation of furrow irrigation practices (SOFIP), a model used to analyse furrow irrigation practices that take into account spatial and temporal variabilities of the advance infiltration process. SOFIP can be used to compare alternative furrow irrigation management strategies and find options that mitigate local deep-percolation risks while ensuring a crop yield level that is acceptable to the farmer. The model is comprised of three distinct modelling elements. The first element is RAIEOPT, a hydraulic model that predicts the advance infiltration process. Infiltration prediction in RAIEOPT depends on a soil moisture deficit parameter. PILOTE, a crop model, which is designed to simulate soil water balance and predict yield values, updates the soil moisture parameter. This parameter is an input of a parameter generator (PG), the third model component, which in turn provides RAIEOPT with the data required to simulate irrigation at the scale of an N-furrow set. The study of sources of variability and their impact on irrigation advance, based on field observations, allowed us to build a robust PG. Model applications show that irrigation practices must account for inter-furrow advance variability when optimising furrow irrigation systems. The impact of advance variability on deep percolation and crop yield losses depends on both climatic conditions and irrigation practices. Irrigation Event (dpeaa)DE-He213 Advance Time (dpeaa)DE-He213 Irrigation Season (dpeaa)DE-He213 Irrigation Practice (dpeaa)DE-He213 Furrow Irrigation (dpeaa)DE-He213 Ruelle, Pierre aut Popova, Zornitsa aut Enthalten in Irrigation science Berlin : Springer, 1978 24(2005), 1 vom: 17. Aug., Seite 37-48 (DE-627)271175362 (DE-600)1478936-X 1432-1319 nnns volume:24 year:2005 number:1 day:17 month:08 pages:37-48 https://dx.doi.org/10.1007/s00271-005-0006-8 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_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_267 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_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 24 2005 1 17 08 37-48 |
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Enthalten in Irrigation science 24(2005), 1 vom: 17. Aug., Seite 37-48 volume:24 year:2005 number:1 day:17 month:08 pages:37-48 |
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Enthalten in Irrigation science 24(2005), 1 vom: 17. Aug., Seite 37-48 volume:24 year:2005 number:1 day:17 month:08 pages:37-48 |
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Mailhol, Jean Claude @@aut@@ Ruelle, Pierre @@aut@@ Popova, Zornitsa @@aut@@ |
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The paper deals with the development and application of simulation of furrow irrigation practices (SOFIP), a model used to analyse furrow irrigation practices that take into account spatial and temporal variabilities of the advance infiltration process. SOFIP can be used to compare alternative furrow irrigation management strategies and find options that mitigate local deep-percolation risks while ensuring a crop yield level that is acceptable to the farmer. The model is comprised of three distinct modelling elements. The first element is RAIEOPT, a hydraulic model that predicts the advance infiltration process. Infiltration prediction in RAIEOPT depends on a soil moisture deficit parameter. PILOTE, a crop model, which is designed to simulate soil water balance and predict yield values, updates the soil moisture parameter. This parameter is an input of a parameter generator (PG), the third model component, which in turn provides RAIEOPT with the data required to simulate irrigation at the scale of an N-furrow set. The study of sources of variability and their impact on irrigation advance, based on field observations, allowed us to build a robust PG. Model applications show that irrigation practices must account for inter-furrow advance variability when optimising furrow irrigation systems. 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Mailhol, Jean Claude |
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Mailhol, Jean Claude misc Irrigation Event misc Advance Time misc Irrigation Season misc Irrigation Practice misc Furrow Irrigation Simulation of furrow irrigation practices (SOFIP): a field-scale modelling of water management and crop yield for furrow irrigation |
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Simulation of furrow irrigation practices (SOFIP): a field-scale modelling of water management and crop yield for furrow irrigation Irrigation Event (dpeaa)DE-He213 Advance Time (dpeaa)DE-He213 Irrigation Season (dpeaa)DE-He213 Irrigation Practice (dpeaa)DE-He213 Furrow Irrigation (dpeaa)DE-He213 |
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misc Irrigation Event misc Advance Time misc Irrigation Season misc Irrigation Practice misc Furrow Irrigation |
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simulation of furrow irrigation practices (sofip): a field-scale modelling of water management and crop yield for furrow irrigation |
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Simulation of furrow irrigation practices (SOFIP): a field-scale modelling of water management and crop yield for furrow irrigation |
| abstract |
Abstract Because of the spatial and temporal variabilities of the advance infiltration process, furrow irrigation investigations should not be limited to a single furrow irrigation event when using a modelling approach. The paper deals with the development and application of simulation of furrow irrigation practices (SOFIP), a model used to analyse furrow irrigation practices that take into account spatial and temporal variabilities of the advance infiltration process. SOFIP can be used to compare alternative furrow irrigation management strategies and find options that mitigate local deep-percolation risks while ensuring a crop yield level that is acceptable to the farmer. The model is comprised of three distinct modelling elements. The first element is RAIEOPT, a hydraulic model that predicts the advance infiltration process. Infiltration prediction in RAIEOPT depends on a soil moisture deficit parameter. PILOTE, a crop model, which is designed to simulate soil water balance and predict yield values, updates the soil moisture parameter. This parameter is an input of a parameter generator (PG), the third model component, which in turn provides RAIEOPT with the data required to simulate irrigation at the scale of an N-furrow set. The study of sources of variability and their impact on irrigation advance, based on field observations, allowed us to build a robust PG. Model applications show that irrigation practices must account for inter-furrow advance variability when optimising furrow irrigation systems. The impact of advance variability on deep percolation and crop yield losses depends on both climatic conditions and irrigation practices. © Springer-Verlag 2005 |
| abstractGer |
Abstract Because of the spatial and temporal variabilities of the advance infiltration process, furrow irrigation investigations should not be limited to a single furrow irrigation event when using a modelling approach. The paper deals with the development and application of simulation of furrow irrigation practices (SOFIP), a model used to analyse furrow irrigation practices that take into account spatial and temporal variabilities of the advance infiltration process. SOFIP can be used to compare alternative furrow irrigation management strategies and find options that mitigate local deep-percolation risks while ensuring a crop yield level that is acceptable to the farmer. The model is comprised of three distinct modelling elements. The first element is RAIEOPT, a hydraulic model that predicts the advance infiltration process. Infiltration prediction in RAIEOPT depends on a soil moisture deficit parameter. PILOTE, a crop model, which is designed to simulate soil water balance and predict yield values, updates the soil moisture parameter. This parameter is an input of a parameter generator (PG), the third model component, which in turn provides RAIEOPT with the data required to simulate irrigation at the scale of an N-furrow set. The study of sources of variability and their impact on irrigation advance, based on field observations, allowed us to build a robust PG. Model applications show that irrigation practices must account for inter-furrow advance variability when optimising furrow irrigation systems. The impact of advance variability on deep percolation and crop yield losses depends on both climatic conditions and irrigation practices. © Springer-Verlag 2005 |
| abstract_unstemmed |
Abstract Because of the spatial and temporal variabilities of the advance infiltration process, furrow irrigation investigations should not be limited to a single furrow irrigation event when using a modelling approach. The paper deals with the development and application of simulation of furrow irrigation practices (SOFIP), a model used to analyse furrow irrigation practices that take into account spatial and temporal variabilities of the advance infiltration process. SOFIP can be used to compare alternative furrow irrigation management strategies and find options that mitigate local deep-percolation risks while ensuring a crop yield level that is acceptable to the farmer. The model is comprised of three distinct modelling elements. The first element is RAIEOPT, a hydraulic model that predicts the advance infiltration process. Infiltration prediction in RAIEOPT depends on a soil moisture deficit parameter. PILOTE, a crop model, which is designed to simulate soil water balance and predict yield values, updates the soil moisture parameter. This parameter is an input of a parameter generator (PG), the third model component, which in turn provides RAIEOPT with the data required to simulate irrigation at the scale of an N-furrow set. The study of sources of variability and their impact on irrigation advance, based on field observations, allowed us to build a robust PG. Model applications show that irrigation practices must account for inter-furrow advance variability when optimising furrow irrigation systems. The impact of advance variability on deep percolation and crop yield losses depends on both climatic conditions and irrigation practices. © Springer-Verlag 2005 |
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Simulation of furrow irrigation practices (SOFIP): a field-scale modelling of water management and crop yield for furrow irrigation |
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https://dx.doi.org/10.1007/s00271-005-0006-8 |
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Ruelle, Pierre Popova, Zornitsa |
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10.1007/s00271-005-0006-8 |
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| score |
7.4012985 |