Site-specific recommendations of cattle manure nitrogen and urea for silage maize
Abstract A novel decision support system (DSS) was developed to define site-specific cattle manure rates with the aim of increasing the stable pool of soil organic matter. A site-specific N balance model calculated urea rates for maize at side-dress. Inputs to the DSS were derived from geophysical m...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Corti, Martina [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Nutrient cycling in agroecosystems - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1980, 127(2023), 1 vom: 28. Juli, Seite 155-169 |
|---|---|
| Übergeordnetes Werk: |
volume:127 ; year:2023 ; number:1 ; day:28 ; month:07 ; pages:155-169 |
| Links: |
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| DOI / URN: |
10.1007/s10705-023-10302-z |
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| Katalog-ID: |
SPR053219864 |
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| 520 | |a Abstract A novel decision support system (DSS) was developed to define site-specific cattle manure rates with the aim of increasing the stable pool of soil organic matter. A site-specific N balance model calculated urea rates for maize at side-dress. Inputs to the DSS were derived from geophysical methods (soil texture), near infrared spectroscopy (NIR; soil C) and multispectral images of the crop and yield maps (maize N requirement). The DSS was compared to farmer fixed N rate (170 kg manure N $ ha^{−1} $ and 120–170 kg urea N $ ha^{−1} $) in a two-year field trial in Northern Italy. A replicated plot experiment was also established to study the effects of year, soil, and fertilizer rate on maize yield. Geophysical and NIR methods accurately mapped soil properties (errors < 20% for > 75% of the field compared to geostatistical mapping based on soil sampling and analysis). Site-specific application of manure and urea had high spatial accuracy (average error < 29 kg N $ ha^{−1} $ for > 81% of the field). The great impact of year and soil properties on silage yield and N uptake suggested adjusting spatially the in-season target yield to prevent over-fertilization. Compared to the fixed N management, the DSS showed no reduction in yield and N uptake, and some potential environmental benefits, especially for the low-yielding zone of the field (−80 kg applied N $ ha^{−1} $). Future evaluation of other soil types and environments would provide the necessary information to upgrade the DSS at a farm scale. | ||
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| 700 | 1 | |a Abu El Khair, Davide |4 aut | |
| 700 | 1 | |a Cabassi, Giovanni |4 aut | |
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10.1007/s10705-023-10302-z doi (DE-627)SPR053219864 (SPR)s10705-023-10302-z-e DE-627 ger DE-627 rakwb eng Corti, Martina verfasserin aut Site-specific recommendations of cattle manure nitrogen and urea for silage maize 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract A novel decision support system (DSS) was developed to define site-specific cattle manure rates with the aim of increasing the stable pool of soil organic matter. A site-specific N balance model calculated urea rates for maize at side-dress. Inputs to the DSS were derived from geophysical methods (soil texture), near infrared spectroscopy (NIR; soil C) and multispectral images of the crop and yield maps (maize N requirement). The DSS was compared to farmer fixed N rate (170 kg manure N $ ha^{−1} $ and 120–170 kg urea N $ ha^{−1} $) in a two-year field trial in Northern Italy. A replicated plot experiment was also established to study the effects of year, soil, and fertilizer rate on maize yield. Geophysical and NIR methods accurately mapped soil properties (errors < 20% for > 75% of the field compared to geostatistical mapping based on soil sampling and analysis). Site-specific application of manure and urea had high spatial accuracy (average error < 29 kg N $ ha^{−1} $ for > 81% of the field). The great impact of year and soil properties on silage yield and N uptake suggested adjusting spatially the in-season target yield to prevent over-fertilization. Compared to the fixed N management, the DSS showed no reduction in yield and N uptake, and some potential environmental benefits, especially for the low-yielding zone of the field (−80 kg applied N $ ha^{−1} $). Future evaluation of other soil types and environments would provide the necessary information to upgrade the DSS at a farm scale. Decision support system (dpeaa)DE-He213 Soil electrical resistivity (dpeaa)DE-He213 Near-infrared spectroscopy (dpeaa)DE-He213 Vegetation index (dpeaa)DE-He213 Yield map (dpeaa)DE-He213 Precision agriculture (dpeaa)DE-He213 Cavalli, Daniele aut Pricca, Nicolò aut Ferrè, Chiara aut Comolli, Roberto aut Marino Gallina, Pietro aut Abu El Khair, Davide aut Cabassi, Giovanni aut Enthalten in Nutrient cycling in agroecosystems Dordrecht [u.a.] : Springer Science + Business Media B.V, 1980 127(2023), 1 vom: 28. Juli, Seite 155-169 (DE-627)270932712 (DE-600)1478384-8 1573-0867 nnns volume:127 year:2023 number:1 day:28 month:07 pages:155-169 https://dx.doi.org/10.1007/s10705-023-10302-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 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 AR 127 2023 1 28 07 155-169 |
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10.1007/s10705-023-10302-z doi (DE-627)SPR053219864 (SPR)s10705-023-10302-z-e DE-627 ger DE-627 rakwb eng Corti, Martina verfasserin aut Site-specific recommendations of cattle manure nitrogen and urea for silage maize 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract A novel decision support system (DSS) was developed to define site-specific cattle manure rates with the aim of increasing the stable pool of soil organic matter. A site-specific N balance model calculated urea rates for maize at side-dress. Inputs to the DSS were derived from geophysical methods (soil texture), near infrared spectroscopy (NIR; soil C) and multispectral images of the crop and yield maps (maize N requirement). The DSS was compared to farmer fixed N rate (170 kg manure N $ ha^{−1} $ and 120–170 kg urea N $ ha^{−1} $) in a two-year field trial in Northern Italy. A replicated plot experiment was also established to study the effects of year, soil, and fertilizer rate on maize yield. Geophysical and NIR methods accurately mapped soil properties (errors < 20% for > 75% of the field compared to geostatistical mapping based on soil sampling and analysis). Site-specific application of manure and urea had high spatial accuracy (average error < 29 kg N $ ha^{−1} $ for > 81% of the field). The great impact of year and soil properties on silage yield and N uptake suggested adjusting spatially the in-season target yield to prevent over-fertilization. Compared to the fixed N management, the DSS showed no reduction in yield and N uptake, and some potential environmental benefits, especially for the low-yielding zone of the field (−80 kg applied N $ ha^{−1} $). Future evaluation of other soil types and environments would provide the necessary information to upgrade the DSS at a farm scale. Decision support system (dpeaa)DE-He213 Soil electrical resistivity (dpeaa)DE-He213 Near-infrared spectroscopy (dpeaa)DE-He213 Vegetation index (dpeaa)DE-He213 Yield map (dpeaa)DE-He213 Precision agriculture (dpeaa)DE-He213 Cavalli, Daniele aut Pricca, Nicolò aut Ferrè, Chiara aut Comolli, Roberto aut Marino Gallina, Pietro aut Abu El Khair, Davide aut Cabassi, Giovanni aut Enthalten in Nutrient cycling in agroecosystems Dordrecht [u.a.] : Springer Science + Business Media B.V, 1980 127(2023), 1 vom: 28. Juli, Seite 155-169 (DE-627)270932712 (DE-600)1478384-8 1573-0867 nnns volume:127 year:2023 number:1 day:28 month:07 pages:155-169 https://dx.doi.org/10.1007/s10705-023-10302-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 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 AR 127 2023 1 28 07 155-169 |
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10.1007/s10705-023-10302-z doi (DE-627)SPR053219864 (SPR)s10705-023-10302-z-e DE-627 ger DE-627 rakwb eng Corti, Martina verfasserin aut Site-specific recommendations of cattle manure nitrogen and urea for silage maize 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract A novel decision support system (DSS) was developed to define site-specific cattle manure rates with the aim of increasing the stable pool of soil organic matter. A site-specific N balance model calculated urea rates for maize at side-dress. Inputs to the DSS were derived from geophysical methods (soil texture), near infrared spectroscopy (NIR; soil C) and multispectral images of the crop and yield maps (maize N requirement). The DSS was compared to farmer fixed N rate (170 kg manure N $ ha^{−1} $ and 120–170 kg urea N $ ha^{−1} $) in a two-year field trial in Northern Italy. A replicated plot experiment was also established to study the effects of year, soil, and fertilizer rate on maize yield. Geophysical and NIR methods accurately mapped soil properties (errors < 20% for > 75% of the field compared to geostatistical mapping based on soil sampling and analysis). Site-specific application of manure and urea had high spatial accuracy (average error < 29 kg N $ ha^{−1} $ for > 81% of the field). The great impact of year and soil properties on silage yield and N uptake suggested adjusting spatially the in-season target yield to prevent over-fertilization. Compared to the fixed N management, the DSS showed no reduction in yield and N uptake, and some potential environmental benefits, especially for the low-yielding zone of the field (−80 kg applied N $ ha^{−1} $). Future evaluation of other soil types and environments would provide the necessary information to upgrade the DSS at a farm scale. Decision support system (dpeaa)DE-He213 Soil electrical resistivity (dpeaa)DE-He213 Near-infrared spectroscopy (dpeaa)DE-He213 Vegetation index (dpeaa)DE-He213 Yield map (dpeaa)DE-He213 Precision agriculture (dpeaa)DE-He213 Cavalli, Daniele aut Pricca, Nicolò aut Ferrè, Chiara aut Comolli, Roberto aut Marino Gallina, Pietro aut Abu El Khair, Davide aut Cabassi, Giovanni aut Enthalten in Nutrient cycling in agroecosystems Dordrecht [u.a.] : Springer Science + Business Media B.V, 1980 127(2023), 1 vom: 28. Juli, Seite 155-169 (DE-627)270932712 (DE-600)1478384-8 1573-0867 nnns volume:127 year:2023 number:1 day:28 month:07 pages:155-169 https://dx.doi.org/10.1007/s10705-023-10302-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 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 AR 127 2023 1 28 07 155-169 |
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10.1007/s10705-023-10302-z doi (DE-627)SPR053219864 (SPR)s10705-023-10302-z-e DE-627 ger DE-627 rakwb eng Corti, Martina verfasserin aut Site-specific recommendations of cattle manure nitrogen and urea for silage maize 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract A novel decision support system (DSS) was developed to define site-specific cattle manure rates with the aim of increasing the stable pool of soil organic matter. A site-specific N balance model calculated urea rates for maize at side-dress. Inputs to the DSS were derived from geophysical methods (soil texture), near infrared spectroscopy (NIR; soil C) and multispectral images of the crop and yield maps (maize N requirement). The DSS was compared to farmer fixed N rate (170 kg manure N $ ha^{−1} $ and 120–170 kg urea N $ ha^{−1} $) in a two-year field trial in Northern Italy. A replicated plot experiment was also established to study the effects of year, soil, and fertilizer rate on maize yield. Geophysical and NIR methods accurately mapped soil properties (errors < 20% for > 75% of the field compared to geostatistical mapping based on soil sampling and analysis). Site-specific application of manure and urea had high spatial accuracy (average error < 29 kg N $ ha^{−1} $ for > 81% of the field). The great impact of year and soil properties on silage yield and N uptake suggested adjusting spatially the in-season target yield to prevent over-fertilization. Compared to the fixed N management, the DSS showed no reduction in yield and N uptake, and some potential environmental benefits, especially for the low-yielding zone of the field (−80 kg applied N $ ha^{−1} $). Future evaluation of other soil types and environments would provide the necessary information to upgrade the DSS at a farm scale. Decision support system (dpeaa)DE-He213 Soil electrical resistivity (dpeaa)DE-He213 Near-infrared spectroscopy (dpeaa)DE-He213 Vegetation index (dpeaa)DE-He213 Yield map (dpeaa)DE-He213 Precision agriculture (dpeaa)DE-He213 Cavalli, Daniele aut Pricca, Nicolò aut Ferrè, Chiara aut Comolli, Roberto aut Marino Gallina, Pietro aut Abu El Khair, Davide aut Cabassi, Giovanni aut Enthalten in Nutrient cycling in agroecosystems Dordrecht [u.a.] : Springer Science + Business Media B.V, 1980 127(2023), 1 vom: 28. Juli, Seite 155-169 (DE-627)270932712 (DE-600)1478384-8 1573-0867 nnns volume:127 year:2023 number:1 day:28 month:07 pages:155-169 https://dx.doi.org/10.1007/s10705-023-10302-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 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 AR 127 2023 1 28 07 155-169 |
| allfieldsSound |
10.1007/s10705-023-10302-z doi (DE-627)SPR053219864 (SPR)s10705-023-10302-z-e DE-627 ger DE-627 rakwb eng Corti, Martina verfasserin aut Site-specific recommendations of cattle manure nitrogen and urea for silage maize 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract A novel decision support system (DSS) was developed to define site-specific cattle manure rates with the aim of increasing the stable pool of soil organic matter. A site-specific N balance model calculated urea rates for maize at side-dress. Inputs to the DSS were derived from geophysical methods (soil texture), near infrared spectroscopy (NIR; soil C) and multispectral images of the crop and yield maps (maize N requirement). The DSS was compared to farmer fixed N rate (170 kg manure N $ ha^{−1} $ and 120–170 kg urea N $ ha^{−1} $) in a two-year field trial in Northern Italy. A replicated plot experiment was also established to study the effects of year, soil, and fertilizer rate on maize yield. Geophysical and NIR methods accurately mapped soil properties (errors < 20% for > 75% of the field compared to geostatistical mapping based on soil sampling and analysis). Site-specific application of manure and urea had high spatial accuracy (average error < 29 kg N $ ha^{−1} $ for > 81% of the field). The great impact of year and soil properties on silage yield and N uptake suggested adjusting spatially the in-season target yield to prevent over-fertilization. Compared to the fixed N management, the DSS showed no reduction in yield and N uptake, and some potential environmental benefits, especially for the low-yielding zone of the field (−80 kg applied N $ ha^{−1} $). Future evaluation of other soil types and environments would provide the necessary information to upgrade the DSS at a farm scale. Decision support system (dpeaa)DE-He213 Soil electrical resistivity (dpeaa)DE-He213 Near-infrared spectroscopy (dpeaa)DE-He213 Vegetation index (dpeaa)DE-He213 Yield map (dpeaa)DE-He213 Precision agriculture (dpeaa)DE-He213 Cavalli, Daniele aut Pricca, Nicolò aut Ferrè, Chiara aut Comolli, Roberto aut Marino Gallina, Pietro aut Abu El Khair, Davide aut Cabassi, Giovanni aut Enthalten in Nutrient cycling in agroecosystems Dordrecht [u.a.] : Springer Science + Business Media B.V, 1980 127(2023), 1 vom: 28. Juli, Seite 155-169 (DE-627)270932712 (DE-600)1478384-8 1573-0867 nnns volume:127 year:2023 number:1 day:28 month:07 pages:155-169 https://dx.doi.org/10.1007/s10705-023-10302-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 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 AR 127 2023 1 28 07 155-169 |
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Enthalten in Nutrient cycling in agroecosystems 127(2023), 1 vom: 28. Juli, Seite 155-169 volume:127 year:2023 number:1 day:28 month:07 pages:155-169 |
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Corti, Martina @@aut@@ Cavalli, Daniele @@aut@@ Pricca, Nicolò @@aut@@ Ferrè, Chiara @@aut@@ Comolli, Roberto @@aut@@ Marino Gallina, Pietro @@aut@@ Abu El Khair, Davide @@aut@@ Cabassi, Giovanni @@aut@@ |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract A novel decision support system (DSS) was developed to define site-specific cattle manure rates with the aim of increasing the stable pool of soil organic matter. A site-specific N balance model calculated urea rates for maize at side-dress. Inputs to the DSS were derived from geophysical methods (soil texture), near infrared spectroscopy (NIR; soil C) and multispectral images of the crop and yield maps (maize N requirement). The DSS was compared to farmer fixed N rate (170 kg manure N $ ha^{−1} $ and 120–170 kg urea N $ ha^{−1} $) in a two-year field trial in Northern Italy. A replicated plot experiment was also established to study the effects of year, soil, and fertilizer rate on maize yield. Geophysical and NIR methods accurately mapped soil properties (errors < 20% for > 75% of the field compared to geostatistical mapping based on soil sampling and analysis). Site-specific application of manure and urea had high spatial accuracy (average error < 29 kg N $ ha^{−1} $ for > 81% of the field). The great impact of year and soil properties on silage yield and N uptake suggested adjusting spatially the in-season target yield to prevent over-fertilization. Compared to the fixed N management, the DSS showed no reduction in yield and N uptake, and some potential environmental benefits, especially for the low-yielding zone of the field (−80 kg applied N $ ha^{−1} $). 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Corti, Martina |
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Corti, Martina misc Decision support system misc Soil electrical resistivity misc Near-infrared spectroscopy misc Vegetation index misc Yield map misc Precision agriculture Site-specific recommendations of cattle manure nitrogen and urea for silage maize |
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Site-specific recommendations of cattle manure nitrogen and urea for silage maize Decision support system (dpeaa)DE-He213 Soil electrical resistivity (dpeaa)DE-He213 Near-infrared spectroscopy (dpeaa)DE-He213 Vegetation index (dpeaa)DE-He213 Yield map (dpeaa)DE-He213 Precision agriculture (dpeaa)DE-He213 |
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Corti, Martina Cavalli, Daniele Pricca, Nicolò Ferrè, Chiara Comolli, Roberto Marino Gallina, Pietro Abu El Khair, Davide Cabassi, Giovanni |
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site-specific recommendations of cattle manure nitrogen and urea for silage maize |
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Site-specific recommendations of cattle manure nitrogen and urea for silage maize |
| abstract |
Abstract A novel decision support system (DSS) was developed to define site-specific cattle manure rates with the aim of increasing the stable pool of soil organic matter. A site-specific N balance model calculated urea rates for maize at side-dress. Inputs to the DSS were derived from geophysical methods (soil texture), near infrared spectroscopy (NIR; soil C) and multispectral images of the crop and yield maps (maize N requirement). The DSS was compared to farmer fixed N rate (170 kg manure N $ ha^{−1} $ and 120–170 kg urea N $ ha^{−1} $) in a two-year field trial in Northern Italy. A replicated plot experiment was also established to study the effects of year, soil, and fertilizer rate on maize yield. Geophysical and NIR methods accurately mapped soil properties (errors < 20% for > 75% of the field compared to geostatistical mapping based on soil sampling and analysis). Site-specific application of manure and urea had high spatial accuracy (average error < 29 kg N $ ha^{−1} $ for > 81% of the field). The great impact of year and soil properties on silage yield and N uptake suggested adjusting spatially the in-season target yield to prevent over-fertilization. Compared to the fixed N management, the DSS showed no reduction in yield and N uptake, and some potential environmental benefits, especially for the low-yielding zone of the field (−80 kg applied N $ ha^{−1} $). Future evaluation of other soil types and environments would provide the necessary information to upgrade the DSS at a farm scale. © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract A novel decision support system (DSS) was developed to define site-specific cattle manure rates with the aim of increasing the stable pool of soil organic matter. A site-specific N balance model calculated urea rates for maize at side-dress. Inputs to the DSS were derived from geophysical methods (soil texture), near infrared spectroscopy (NIR; soil C) and multispectral images of the crop and yield maps (maize N requirement). The DSS was compared to farmer fixed N rate (170 kg manure N $ ha^{−1} $ and 120–170 kg urea N $ ha^{−1} $) in a two-year field trial in Northern Italy. A replicated plot experiment was also established to study the effects of year, soil, and fertilizer rate on maize yield. Geophysical and NIR methods accurately mapped soil properties (errors < 20% for > 75% of the field compared to geostatistical mapping based on soil sampling and analysis). Site-specific application of manure and urea had high spatial accuracy (average error < 29 kg N $ ha^{−1} $ for > 81% of the field). The great impact of year and soil properties on silage yield and N uptake suggested adjusting spatially the in-season target yield to prevent over-fertilization. Compared to the fixed N management, the DSS showed no reduction in yield and N uptake, and some potential environmental benefits, especially for the low-yielding zone of the field (−80 kg applied N $ ha^{−1} $). Future evaluation of other soil types and environments would provide the necessary information to upgrade the DSS at a farm scale. © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract A novel decision support system (DSS) was developed to define site-specific cattle manure rates with the aim of increasing the stable pool of soil organic matter. A site-specific N balance model calculated urea rates for maize at side-dress. Inputs to the DSS were derived from geophysical methods (soil texture), near infrared spectroscopy (NIR; soil C) and multispectral images of the crop and yield maps (maize N requirement). The DSS was compared to farmer fixed N rate (170 kg manure N $ ha^{−1} $ and 120–170 kg urea N $ ha^{−1} $) in a two-year field trial in Northern Italy. A replicated plot experiment was also established to study the effects of year, soil, and fertilizer rate on maize yield. Geophysical and NIR methods accurately mapped soil properties (errors < 20% for > 75% of the field compared to geostatistical mapping based on soil sampling and analysis). Site-specific application of manure and urea had high spatial accuracy (average error < 29 kg N $ ha^{−1} $ for > 81% of the field). The great impact of year and soil properties on silage yield and N uptake suggested adjusting spatially the in-season target yield to prevent over-fertilization. Compared to the fixed N management, the DSS showed no reduction in yield and N uptake, and some potential environmental benefits, especially for the low-yielding zone of the field (−80 kg applied N $ ha^{−1} $). Future evaluation of other soil types and environments would provide the necessary information to upgrade the DSS at a farm scale. © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Site-specific recommendations of cattle manure nitrogen and urea for silage maize |
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https://dx.doi.org/10.1007/s10705-023-10302-z |
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Cavalli, Daniele Pricca, Nicolò Ferrè, Chiara Comolli, Roberto Marino Gallina, Pietro Abu El Khair, Davide Cabassi, Giovanni |
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Cavalli, Daniele Pricca, Nicolò Ferrè, Chiara Comolli, Roberto Marino Gallina, Pietro Abu El Khair, Davide Cabassi, Giovanni |
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10.1007/s10705-023-10302-z |
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2024-07-03T17:56:13.953Z |
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| score |
7.3994665 |