Assessment of German nitrous oxide emissions using empirical modelling approaches

Abstract Direct nitrous oxide ($ N_{2} $O) emissions from agricultural soils contribute considerably to anthropogenic GHG emissions. Albeit a key source of emissions in many countries, direct $ N_{2} $O emissions are still calculated and reported to the United Nations Convention on Climate Change using default emission factors defined in the IPCC guidelines (IPCC 1996, 2006). It is known that processes controlling production and transport of $ N_{2} $O are highly sensitive to environmental conditions defined by weather, soil and management. The accuracy of $ N_{2} $O emission budgets and the efficiency of mitigation can be improved if those dependencies are considered with regionalized emission factors. In this study an empirical method originating from soft computing techniques based on measured data is developed and applied to quantify direct $ N_{2} $O emissions from agricultural soils at field and national level in Germany between 1990 and 2005. The method is used to derive maps of emission factor distribution of direct $ N_{2} $O emissions of agricultural land in Germany. Model results are compared with alternative empirical approaches from literature. Results from developing empirical models show that grassland and cropland have to be differentiated according to the key controls driving $ N_{2} $O emissions. $ N_{2} $O emissions of German croplands are highly influenced by climatic conditions and soil properties. The variability of $ N_{2} $O fluxes on grasslands is mainly driven by the fertilizer N applied. The model comparison using measured European $ N_{2} $O emissions exhibits profound discrepancies between the models used on a regional scale. The nationwide budgets derived span a narrow range of −8 to 28% relative to direct $ N_{2} $O emissions quantified by the German national inventory report. The emission factor of German agriculture estimated by the developed model is 0.91% of fertilizer N applied. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Dechow, Rene [verfasserIn] Freibauer, Annette [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2011

Schlagwörter:	Nitrous oxide Soil Cropland Grassland Model Fuzzy logic

Übergeordnetes Werk:	Enthalten in: Nutrient cycling in agroecosystems - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1980, 91(2011), 3 vom: 17. Sept., Seite 235-254
Übergeordnetes Werk:	volume:91 ; year:2011 ; number:3 ; day:17 ; month:09 ; pages:235-254

Links:	Volltext

DOI / URN:	10.1007/s10705-011-9458-9

Katalog-ID:	SPR012579807

Internformat


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520			\|a Abstract Direct nitrous oxide ($ N_{2} $O) emissions from agricultural soils contribute considerably to anthropogenic GHG emissions. Albeit a key source of emissions in many countries, direct $ N_{2} $O emissions are still calculated and reported to the United Nations Convention on Climate Change using default emission factors defined in the IPCC guidelines (IPCC 1996, 2006). It is known that processes controlling production and transport of $ N_{2} $O are highly sensitive to environmental conditions defined by weather, soil and management. The accuracy of $ N_{2} $O emission budgets and the efficiency of mitigation can be improved if those dependencies are considered with regionalized emission factors. In this study an empirical method originating from soft computing techniques based on measured data is developed and applied to quantify direct $ N_{2} $O emissions from agricultural soils at field and national level in Germany between 1990 and 2005. The method is used to derive maps of emission factor distribution of direct $ N_{2} $O emissions of agricultural land in Germany. Model results are compared with alternative empirical approaches from literature. Results from developing empirical models show that grassland and cropland have to be differentiated according to the key controls driving $ N_{2} $O emissions. $ N_{2} $O emissions of German croplands are highly influenced by climatic conditions and soil properties. The variability of $ N_{2} $O fluxes on grasslands is mainly driven by the fertilizer N applied. The model comparison using measured European $ N_{2} $O emissions exhibits profound discrepancies between the models used on a regional scale. The nationwide budgets derived span a narrow range of −8 to 28% relative to direct $ N_{2} $O emissions quantified by the German national inventory report. The emission factor of German agriculture estimated by the developed model is 0.91% of fertilizer N applied.
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912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_32
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_101
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_120
912			\|a GBV_ILN_138
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_152
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_171
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_250
912			\|a GBV_ILN_281
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_636
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2006
912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2031
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2037
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2039
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2057
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2070
912			\|a GBV_ILN_2086
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2093
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2107
912			\|a GBV_ILN_2108
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2116
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2119
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2144
912			\|a GBV_ILN_2147
912			\|a GBV_ILN_2148
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2188
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2360
912			\|a GBV_ILN_2446
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2472
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_2548
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4046
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4246
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4328
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4336
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
912			\|a GBV_ILN_4700
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Indexfelder

author_variant	r d rd a f af
matchkey_str	article:15730867:2011----::sesetfemniruoiemsinuigmiiam
hierarchy_sort_str	2011
bklnumber	48.52 48.16
publishDate	2011
allfields	10.1007/s10705-011-9458-9 doi (DE-627)SPR012579807 (SPR)s10705-011-9458-9-e DE-627 ger DE-627 rakwb eng 570 ASE 48.52 bkl 48.16 bkl Dechow, Rene verfasserin aut Assessment of German nitrous oxide emissions using empirical modelling approaches 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Direct nitrous oxide ($ N_{2} $O) emissions from agricultural soils contribute considerably to anthropogenic GHG emissions. Albeit a key source of emissions in many countries, direct $ N_{2} $O emissions are still calculated and reported to the United Nations Convention on Climate Change using default emission factors defined in the IPCC guidelines (IPCC 1996, 2006). It is known that processes controlling production and transport of $ N_{2} $O are highly sensitive to environmental conditions defined by weather, soil and management. The accuracy of $ N_{2} $O emission budgets and the efficiency of mitigation can be improved if those dependencies are considered with regionalized emission factors. In this study an empirical method originating from soft computing techniques based on measured data is developed and applied to quantify direct $ N_{2} $O emissions from agricultural soils at field and national level in Germany between 1990 and 2005. The method is used to derive maps of emission factor distribution of direct $ N_{2} $O emissions of agricultural land in Germany. Model results are compared with alternative empirical approaches from literature. Results from developing empirical models show that grassland and cropland have to be differentiated according to the key controls driving $ N_{2} $O emissions. $ N_{2} $O emissions of German croplands are highly influenced by climatic conditions and soil properties. The variability of $ N_{2} $O fluxes on grasslands is mainly driven by the fertilizer N applied. The model comparison using measured European $ N_{2} $O emissions exhibits profound discrepancies between the models used on a regional scale. The nationwide budgets derived span a narrow range of −8 to 28% relative to direct $ N_{2} $O emissions quantified by the German national inventory report. The emission factor of German agriculture estimated by the developed model is 0.91% of fertilizer N applied. Nitrous oxide (dpeaa)DE-He213 Soil (dpeaa)DE-He213 Cropland (dpeaa)DE-He213 Grassland (dpeaa)DE-He213 Model (dpeaa)DE-He213 Fuzzy logic (dpeaa)DE-He213 Freibauer, Annette verfasserin aut Enthalten in Nutrient cycling in agroecosystems Dordrecht [u.a.] : Springer Science + Business Media B.V, 1980 91(2011), 3 vom: 17. Sept., Seite 235-254 (DE-627)270932712 (DE-600)1478384-8 1573-0867 nnns volume:91 year:2011 number:3 day:17 month:09 pages:235-254 https://dx.doi.org/10.1007/s10705-011-9458-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.52 ASE 48.16 ASE AR 91 2011 3 17 09 235-254
spelling	10.1007/s10705-011-9458-9 doi (DE-627)SPR012579807 (SPR)s10705-011-9458-9-e DE-627 ger DE-627 rakwb eng 570 ASE 48.52 bkl 48.16 bkl Dechow, Rene verfasserin aut Assessment of German nitrous oxide emissions using empirical modelling approaches 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Direct nitrous oxide ($ N_{2} $O) emissions from agricultural soils contribute considerably to anthropogenic GHG emissions. Albeit a key source of emissions in many countries, direct $ N_{2} $O emissions are still calculated and reported to the United Nations Convention on Climate Change using default emission factors defined in the IPCC guidelines (IPCC 1996, 2006). It is known that processes controlling production and transport of $ N_{2} $O are highly sensitive to environmental conditions defined by weather, soil and management. The accuracy of $ N_{2} $O emission budgets and the efficiency of mitigation can be improved if those dependencies are considered with regionalized emission factors. In this study an empirical method originating from soft computing techniques based on measured data is developed and applied to quantify direct $ N_{2} $O emissions from agricultural soils at field and national level in Germany between 1990 and 2005. The method is used to derive maps of emission factor distribution of direct $ N_{2} $O emissions of agricultural land in Germany. Model results are compared with alternative empirical approaches from literature. Results from developing empirical models show that grassland and cropland have to be differentiated according to the key controls driving $ N_{2} $O emissions. $ N_{2} $O emissions of German croplands are highly influenced by climatic conditions and soil properties. The variability of $ N_{2} $O fluxes on grasslands is mainly driven by the fertilizer N applied. The model comparison using measured European $ N_{2} $O emissions exhibits profound discrepancies between the models used on a regional scale. The nationwide budgets derived span a narrow range of −8 to 28% relative to direct $ N_{2} $O emissions quantified by the German national inventory report. The emission factor of German agriculture estimated by the developed model is 0.91% of fertilizer N applied. Nitrous oxide (dpeaa)DE-He213 Soil (dpeaa)DE-He213 Cropland (dpeaa)DE-He213 Grassland (dpeaa)DE-He213 Model (dpeaa)DE-He213 Fuzzy logic (dpeaa)DE-He213 Freibauer, Annette verfasserin aut Enthalten in Nutrient cycling in agroecosystems Dordrecht [u.a.] : Springer Science + Business Media B.V, 1980 91(2011), 3 vom: 17. Sept., Seite 235-254 (DE-627)270932712 (DE-600)1478384-8 1573-0867 nnns volume:91 year:2011 number:3 day:17 month:09 pages:235-254 https://dx.doi.org/10.1007/s10705-011-9458-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.52 ASE 48.16 ASE AR 91 2011 3 17 09 235-254
allfields_unstemmed	10.1007/s10705-011-9458-9 doi (DE-627)SPR012579807 (SPR)s10705-011-9458-9-e DE-627 ger DE-627 rakwb eng 570 ASE 48.52 bkl 48.16 bkl Dechow, Rene verfasserin aut Assessment of German nitrous oxide emissions using empirical modelling approaches 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Direct nitrous oxide ($ N_{2} $O) emissions from agricultural soils contribute considerably to anthropogenic GHG emissions. Albeit a key source of emissions in many countries, direct $ N_{2} $O emissions are still calculated and reported to the United Nations Convention on Climate Change using default emission factors defined in the IPCC guidelines (IPCC 1996, 2006). It is known that processes controlling production and transport of $ N_{2} $O are highly sensitive to environmental conditions defined by weather, soil and management. The accuracy of $ N_{2} $O emission budgets and the efficiency of mitigation can be improved if those dependencies are considered with regionalized emission factors. In this study an empirical method originating from soft computing techniques based on measured data is developed and applied to quantify direct $ N_{2} $O emissions from agricultural soils at field and national level in Germany between 1990 and 2005. The method is used to derive maps of emission factor distribution of direct $ N_{2} $O emissions of agricultural land in Germany. Model results are compared with alternative empirical approaches from literature. Results from developing empirical models show that grassland and cropland have to be differentiated according to the key controls driving $ N_{2} $O emissions. $ N_{2} $O emissions of German croplands are highly influenced by climatic conditions and soil properties. The variability of $ N_{2} $O fluxes on grasslands is mainly driven by the fertilizer N applied. The model comparison using measured European $ N_{2} $O emissions exhibits profound discrepancies between the models used on a regional scale. The nationwide budgets derived span a narrow range of −8 to 28% relative to direct $ N_{2} $O emissions quantified by the German national inventory report. The emission factor of German agriculture estimated by the developed model is 0.91% of fertilizer N applied. Nitrous oxide (dpeaa)DE-He213 Soil (dpeaa)DE-He213 Cropland (dpeaa)DE-He213 Grassland (dpeaa)DE-He213 Model (dpeaa)DE-He213 Fuzzy logic (dpeaa)DE-He213 Freibauer, Annette verfasserin aut Enthalten in Nutrient cycling in agroecosystems Dordrecht [u.a.] : Springer Science + Business Media B.V, 1980 91(2011), 3 vom: 17. Sept., Seite 235-254 (DE-627)270932712 (DE-600)1478384-8 1573-0867 nnns volume:91 year:2011 number:3 day:17 month:09 pages:235-254 https://dx.doi.org/10.1007/s10705-011-9458-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.52 ASE 48.16 ASE AR 91 2011 3 17 09 235-254
allfieldsGer	10.1007/s10705-011-9458-9 doi (DE-627)SPR012579807 (SPR)s10705-011-9458-9-e DE-627 ger DE-627 rakwb eng 570 ASE 48.52 bkl 48.16 bkl Dechow, Rene verfasserin aut Assessment of German nitrous oxide emissions using empirical modelling approaches 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Direct nitrous oxide ($ N_{2} $O) emissions from agricultural soils contribute considerably to anthropogenic GHG emissions. Albeit a key source of emissions in many countries, direct $ N_{2} $O emissions are still calculated and reported to the United Nations Convention on Climate Change using default emission factors defined in the IPCC guidelines (IPCC 1996, 2006). It is known that processes controlling production and transport of $ N_{2} $O are highly sensitive to environmental conditions defined by weather, soil and management. The accuracy of $ N_{2} $O emission budgets and the efficiency of mitigation can be improved if those dependencies are considered with regionalized emission factors. In this study an empirical method originating from soft computing techniques based on measured data is developed and applied to quantify direct $ N_{2} $O emissions from agricultural soils at field and national level in Germany between 1990 and 2005. The method is used to derive maps of emission factor distribution of direct $ N_{2} $O emissions of agricultural land in Germany. Model results are compared with alternative empirical approaches from literature. Results from developing empirical models show that grassland and cropland have to be differentiated according to the key controls driving $ N_{2} $O emissions. $ N_{2} $O emissions of German croplands are highly influenced by climatic conditions and soil properties. The variability of $ N_{2} $O fluxes on grasslands is mainly driven by the fertilizer N applied. The model comparison using measured European $ N_{2} $O emissions exhibits profound discrepancies between the models used on a regional scale. The nationwide budgets derived span a narrow range of −8 to 28% relative to direct $ N_{2} $O emissions quantified by the German national inventory report. The emission factor of German agriculture estimated by the developed model is 0.91% of fertilizer N applied. Nitrous oxide (dpeaa)DE-He213 Soil (dpeaa)DE-He213 Cropland (dpeaa)DE-He213 Grassland (dpeaa)DE-He213 Model (dpeaa)DE-He213 Fuzzy logic (dpeaa)DE-He213 Freibauer, Annette verfasserin aut Enthalten in Nutrient cycling in agroecosystems Dordrecht [u.a.] : Springer Science + Business Media B.V, 1980 91(2011), 3 vom: 17. Sept., Seite 235-254 (DE-627)270932712 (DE-600)1478384-8 1573-0867 nnns volume:91 year:2011 number:3 day:17 month:09 pages:235-254 https://dx.doi.org/10.1007/s10705-011-9458-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.52 ASE 48.16 ASE AR 91 2011 3 17 09 235-254
allfieldsSound	10.1007/s10705-011-9458-9 doi (DE-627)SPR012579807 (SPR)s10705-011-9458-9-e DE-627 ger DE-627 rakwb eng 570 ASE 48.52 bkl 48.16 bkl Dechow, Rene verfasserin aut Assessment of German nitrous oxide emissions using empirical modelling approaches 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Direct nitrous oxide ($ N_{2} $O) emissions from agricultural soils contribute considerably to anthropogenic GHG emissions. Albeit a key source of emissions in many countries, direct $ N_{2} $O emissions are still calculated and reported to the United Nations Convention on Climate Change using default emission factors defined in the IPCC guidelines (IPCC 1996, 2006). It is known that processes controlling production and transport of $ N_{2} $O are highly sensitive to environmental conditions defined by weather, soil and management. The accuracy of $ N_{2} $O emission budgets and the efficiency of mitigation can be improved if those dependencies are considered with regionalized emission factors. In this study an empirical method originating from soft computing techniques based on measured data is developed and applied to quantify direct $ N_{2} $O emissions from agricultural soils at field and national level in Germany between 1990 and 2005. The method is used to derive maps of emission factor distribution of direct $ N_{2} $O emissions of agricultural land in Germany. Model results are compared with alternative empirical approaches from literature. Results from developing empirical models show that grassland and cropland have to be differentiated according to the key controls driving $ N_{2} $O emissions. $ N_{2} $O emissions of German croplands are highly influenced by climatic conditions and soil properties. The variability of $ N_{2} $O fluxes on grasslands is mainly driven by the fertilizer N applied. The model comparison using measured European $ N_{2} $O emissions exhibits profound discrepancies between the models used on a regional scale. The nationwide budgets derived span a narrow range of −8 to 28% relative to direct $ N_{2} $O emissions quantified by the German national inventory report. The emission factor of German agriculture estimated by the developed model is 0.91% of fertilizer N applied. Nitrous oxide (dpeaa)DE-He213 Soil (dpeaa)DE-He213 Cropland (dpeaa)DE-He213 Grassland (dpeaa)DE-He213 Model (dpeaa)DE-He213 Fuzzy logic (dpeaa)DE-He213 Freibauer, Annette verfasserin aut Enthalten in Nutrient cycling in agroecosystems Dordrecht [u.a.] : Springer Science + Business Media B.V, 1980 91(2011), 3 vom: 17. Sept., Seite 235-254 (DE-627)270932712 (DE-600)1478384-8 1573-0867 nnns volume:91 year:2011 number:3 day:17 month:09 pages:235-254 https://dx.doi.org/10.1007/s10705-011-9458-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.52 ASE 48.16 ASE AR 91 2011 3 17 09 235-254
language	English
source	Enthalten in Nutrient cycling in agroecosystems 91(2011), 3 vom: 17. Sept., Seite 235-254 volume:91 year:2011 number:3 day:17 month:09 pages:235-254
sourceStr	Enthalten in Nutrient cycling in agroecosystems 91(2011), 3 vom: 17. Sept., Seite 235-254 volume:91 year:2011 number:3 day:17 month:09 pages:235-254
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Nitrous oxide Soil Cropland Grassland Model Fuzzy logic
dewey-raw	570
isfreeaccess_bool	false
container_title	Nutrient cycling in agroecosystems
authorswithroles_txt_mv	Dechow, Rene @@aut@@ Freibauer, Annette @@aut@@
publishDateDaySort_date	2011-09-17T00:00:00Z
hierarchy_top_id	270932712
dewey-sort	3570
id	SPR012579807
language_de	englisch
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Model results are compared with alternative empirical approaches from literature. Results from developing empirical models show that grassland and cropland have to be differentiated according to the key controls driving $ N_{2} $O emissions. $ N_{2} $O emissions of German croplands are highly influenced by climatic conditions and soil properties. The variability of $ N_{2} $O fluxes on grasslands is mainly driven by the fertilizer N applied. The model comparison using measured European $ N_{2} $O emissions exhibits profound discrepancies between the models used on a regional scale. The nationwide budgets derived span a narrow range of −8 to 28% relative to direct $ N_{2} $O emissions quantified by the German national inventory report. 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author	Dechow, Rene
spellingShingle	Dechow, Rene ddc 570 bkl 48.52 bkl 48.16 misc Nitrous oxide misc Soil misc Cropland misc Grassland misc Model misc Fuzzy logic Assessment of German nitrous oxide emissions using empirical modelling approaches
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topic_title	570 ASE 48.52 bkl 48.16 bkl Assessment of German nitrous oxide emissions using empirical modelling approaches Nitrous oxide (dpeaa)DE-He213 Soil (dpeaa)DE-He213 Cropland (dpeaa)DE-He213 Grassland (dpeaa)DE-He213 Model (dpeaa)DE-He213 Fuzzy logic (dpeaa)DE-He213
topic	ddc 570 bkl 48.52 bkl 48.16 misc Nitrous oxide misc Soil misc Cropland misc Grassland misc Model misc Fuzzy logic
topic_unstemmed	ddc 570 bkl 48.52 bkl 48.16 misc Nitrous oxide misc Soil misc Cropland misc Grassland misc Model misc Fuzzy logic
topic_browse	ddc 570 bkl 48.52 bkl 48.16 misc Nitrous oxide misc Soil misc Cropland misc Grassland misc Model misc Fuzzy logic
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hierarchy_parent_title	Nutrient cycling in agroecosystems
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title	Assessment of German nitrous oxide emissions using empirical modelling approaches
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title_full	Assessment of German nitrous oxide emissions using empirical modelling approaches
author_sort	Dechow, Rene
journal	Nutrient cycling in agroecosystems
journalStr	Nutrient cycling in agroecosystems
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title_sort	assessment of german nitrous oxide emissions using empirical modelling approaches
title_auth	Assessment of German nitrous oxide emissions using empirical modelling approaches
abstract	Abstract Direct nitrous oxide ($ N_{2} $O) emissions from agricultural soils contribute considerably to anthropogenic GHG emissions. Albeit a key source of emissions in many countries, direct $ N_{2} $O emissions are still calculated and reported to the United Nations Convention on Climate Change using default emission factors defined in the IPCC guidelines (IPCC 1996, 2006). It is known that processes controlling production and transport of $ N_{2} $O are highly sensitive to environmental conditions defined by weather, soil and management. The accuracy of $ N_{2} $O emission budgets and the efficiency of mitigation can be improved if those dependencies are considered with regionalized emission factors. In this study an empirical method originating from soft computing techniques based on measured data is developed and applied to quantify direct $ N_{2} $O emissions from agricultural soils at field and national level in Germany between 1990 and 2005. The method is used to derive maps of emission factor distribution of direct $ N_{2} $O emissions of agricultural land in Germany. Model results are compared with alternative empirical approaches from literature. Results from developing empirical models show that grassland and cropland have to be differentiated according to the key controls driving $ N_{2} $O emissions. $ N_{2} $O emissions of German croplands are highly influenced by climatic conditions and soil properties. The variability of $ N_{2} $O fluxes on grasslands is mainly driven by the fertilizer N applied. The model comparison using measured European $ N_{2} $O emissions exhibits profound discrepancies between the models used on a regional scale. The nationwide budgets derived span a narrow range of −8 to 28% relative to direct $ N_{2} $O emissions quantified by the German national inventory report. The emission factor of German agriculture estimated by the developed model is 0.91% of fertilizer N applied.
abstractGer	Abstract Direct nitrous oxide ($ N_{2} $O) emissions from agricultural soils contribute considerably to anthropogenic GHG emissions. Albeit a key source of emissions in many countries, direct $ N_{2} $O emissions are still calculated and reported to the United Nations Convention on Climate Change using default emission factors defined in the IPCC guidelines (IPCC 1996, 2006). It is known that processes controlling production and transport of $ N_{2} $O are highly sensitive to environmental conditions defined by weather, soil and management. The accuracy of $ N_{2} $O emission budgets and the efficiency of mitigation can be improved if those dependencies are considered with regionalized emission factors. In this study an empirical method originating from soft computing techniques based on measured data is developed and applied to quantify direct $ N_{2} $O emissions from agricultural soils at field and national level in Germany between 1990 and 2005. The method is used to derive maps of emission factor distribution of direct $ N_{2} $O emissions of agricultural land in Germany. Model results are compared with alternative empirical approaches from literature. Results from developing empirical models show that grassland and cropland have to be differentiated according to the key controls driving $ N_{2} $O emissions. $ N_{2} $O emissions of German croplands are highly influenced by climatic conditions and soil properties. The variability of $ N_{2} $O fluxes on grasslands is mainly driven by the fertilizer N applied. The model comparison using measured European $ N_{2} $O emissions exhibits profound discrepancies between the models used on a regional scale. The nationwide budgets derived span a narrow range of −8 to 28% relative to direct $ N_{2} $O emissions quantified by the German national inventory report. The emission factor of German agriculture estimated by the developed model is 0.91% of fertilizer N applied.
abstract_unstemmed	Abstract Direct nitrous oxide ($ N_{2} $O) emissions from agricultural soils contribute considerably to anthropogenic GHG emissions. Albeit a key source of emissions in many countries, direct $ N_{2} $O emissions are still calculated and reported to the United Nations Convention on Climate Change using default emission factors defined in the IPCC guidelines (IPCC 1996, 2006). It is known that processes controlling production and transport of $ N_{2} $O are highly sensitive to environmental conditions defined by weather, soil and management. The accuracy of $ N_{2} $O emission budgets and the efficiency of mitigation can be improved if those dependencies are considered with regionalized emission factors. In this study an empirical method originating from soft computing techniques based on measured data is developed and applied to quantify direct $ N_{2} $O emissions from agricultural soils at field and national level in Germany between 1990 and 2005. The method is used to derive maps of emission factor distribution of direct $ N_{2} $O emissions of agricultural land in Germany. Model results are compared with alternative empirical approaches from literature. Results from developing empirical models show that grassland and cropland have to be differentiated according to the key controls driving $ N_{2} $O emissions. $ N_{2} $O emissions of German croplands are highly influenced by climatic conditions and soil properties. The variability of $ N_{2} $O fluxes on grasslands is mainly driven by the fertilizer N applied. The model comparison using measured European $ N_{2} $O emissions exhibits profound discrepancies between the models used on a regional scale. The nationwide budgets derived span a narrow range of −8 to 28% relative to direct $ N_{2} $O emissions quantified by the German national inventory report. The emission factor of German agriculture estimated by the developed model is 0.91% of fertilizer N applied.
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title_short	Assessment of German nitrous oxide emissions using empirical modelling approaches
url	https://dx.doi.org/10.1007/s10705-011-9458-9
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author2	Freibauer, Annette
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doi_str	10.1007/s10705-011-9458-9
up_date	2024-07-03T13:54:10.302Z
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Assessment of German nitrous oxide emissions using empirical modelling approaches

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