Use of precision agriculture technology to investigate spatial variability in nitrogen yields in cut grassland
Identifieur interne : 000938 ( Istex/Corpus ); précédent : 000937; suivant : 000939Use of precision agriculture technology to investigate spatial variability in nitrogen yields in cut grassland
Auteurs : John S. Bailey ; Ke Wang ; Crawford Jordan ; Alex HigginsSource :
- Chemosphere [ 0045-6535 ] ; 2000.
Abstract
Spatial variability in N uptake and utilisation by swards within uniformly managed field units could be responsible for a significant proportion of the NH3, N2O, NO−3 and NOx (NO and NO2) ‘pollutants’ generated by agriculture and released to the environment. An investigation was commenced, therefore, to quantify, map and explain the spatial variability in sward N yield in a ‘large’ silage field and to assess the potential for managing this variability using some of the latest precision agriculture technology. Sward dry matter (DM) and N yields were predicted from the results of plant tissue analyses using mathematical models. Sward N yields were found to vary greatly across the field seemingly because of differences in net soil N mineralisation, but the pattern of variability appeared to remain constant with time. Conventional soil analysis of a range of soil chemical and physical properties, however, failed to explain this variability. It was concluded that the N-yield distribution map might be used in place of soil analysis as the basis for varying the rates of N application to different parts of the field with the twin objectives of maximising fertiliser use efficiency and minimising N emissions to air and water.
Url:
DOI: 10.1016/S0045-6535(00)00118-1
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<front><div type="abstract" xml:lang="en">Spatial variability in N uptake and utilisation by swards within uniformly managed field units could be responsible for a significant proportion of the NH3, N2O, NO−3 and NOx (NO and NO2) ‘pollutants’ generated by agriculture and released to the environment. An investigation was commenced, therefore, to quantify, map and explain the spatial variability in sward N yield in a ‘large’ silage field and to assess the potential for managing this variability using some of the latest precision agriculture technology. Sward dry matter (DM) and N yields were predicted from the results of plant tissue analyses using mathematical models. Sward N yields were found to vary greatly across the field seemingly because of differences in net soil N mineralisation, but the pattern of variability appeared to remain constant with time. Conventional soil analysis of a range of soil chemical and physical properties, however, failed to explain this variability. It was concluded that the N-yield distribution map might be used in place of soil analysis as the basis for varying the rates of N application to different parts of the field with the twin objectives of maximising fertiliser use efficiency and minimising N emissions to air and water.</div>
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<notesStmt><note type="content">Fig. 1: Smoothed contour map showing the variation in altitude across a large silage field.</note>
<note type="content">Fig. 2: Map of silage field showing: (a) the (original) 124 potential sampling points; (b) the locations of the 11 mowed strips at cut 1; (c) the locations of the 7 mowed strips at cut 2.</note>
<note type="content">Fig. 3: Model validation using independent data: actual DM yields for a range of silage fields in County Down (Bailey, 1999) vs model predicted yields: (a) for Model 1; (b) for Model 2.</note>
<note type="content">Fig. 4: Smoothed contour maps showing the spatial variability in predicted sward DM yield: (a) at first cut; (b) at second cut; (c) totalled over both cuts.</note>
<note type="content">Fig. 5: Smoothed contour maps showing the spatial variability in predicted sward N yield: (a) at first cut; (b) at second cut; (c) totalled over both cuts.</note>
<note type="content">Fig. 6: Relationship between total predicted DM yields and total predicted N yields. The dashed boundary line delimits the DM yield response to N (in the crop) when this nutrient alone was limiting to growth, and the perpendicular dashed line shows the predicted N yield at 95% of maximum DM yield.</note>
<note type="content">Fig. 7: Smoothed contour map of herbage DRIS N indices at cut 1.</note>
<note type="content">Fig. 8: Relationships between: (a) actual N yield and soil C/N ratio on the 11 mowed strips at cut 1; (b) predicted total N yield and soil C/N ratio at the 104 field sampling points.</note>
<note type="content">Fig. 9: Relationships between: (a) actual N yield at cut 1 and soil pH on the 11 mowed strips; (b) predicted total N yield and soil pH at the 104 sampling points.</note>
<note type="content">Fig. 10: Smoothed contour maps of: (a) soil pH; (b) soil organic-C content; (c) soil available P.</note>
<note type="content">Fig. 11: Relationship between actual N yield on the 11 mowed strips at cut 1 and the depth of topsoil (A horizon) under each strip.</note>
<note type="content">Table 1: Variogram properties for N yield data at cut 1, at cut 2 and at both cuts combined</note>
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<ce:abstract><ce:section-title>Abstract</ce:section-title>
<ce:abstract-sec><ce:simple-para>Spatial variability in N uptake and utilisation by swards within uniformly managed field units could be responsible for a significant proportion of the NH<ce:inf>3</ce:inf>
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) ‘pollutants’ generated by agriculture and released to the environment. An investigation was commenced, therefore, to quantify, map and explain the spatial variability in sward N yield in a ‘large’ silage field and to assess the potential for managing this variability using some of the latest precision agriculture technology. Sward dry matter (DM) and N yields were predicted from the results of plant tissue analyses using mathematical models. Sward N yields were found to vary greatly across the field seemingly because of differences in net soil N mineralisation, but the pattern of variability appeared to remain constant with time. Conventional soil analysis of a range of soil chemical and physical properties, however, failed to explain this variability. It was concluded that the N-yield distribution map might be used in place of soil analysis as the basis for varying the rates of N application to different parts of the field with the twin objectives of maximising fertiliser use efficiency and minimising N emissions to air and water.</ce:simple-para>
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<ce:keyword><ce:text>Nitrogen yield</ce:text>
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<ce:keyword><ce:text>Precision agriculture</ce:text>
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<name type="personal"><namePart type="given">Crawford</namePart>
<namePart type="family">Jordan</namePart>
<affiliation>Department of Agriculture and Rural Development for Northern Ireland, Agricultural and Environmental Science Division, Newforge Lane, Belfast BT9 5PX, UK</affiliation>
<role><roleTerm type="text">author</roleTerm>
</role>
</name>
<name type="personal"><namePart type="given">Alex</namePart>
<namePart type="family">Higgins</namePart>
<affiliation>Department of Agriculture and Rural Development for Northern Ireland, Agricultural and Environmental Science Division, Newforge Lane, Belfast BT9 5PX, UK</affiliation>
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<originInfo><publisher>ELSEVIER</publisher>
<dateIssued encoding="w3cdtf">2000</dateIssued>
<copyrightDate encoding="w3cdtf">2000</copyrightDate>
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<language><languageTerm type="code" authority="iso639-2b">eng</languageTerm>
<languageTerm type="code" authority="rfc3066">en</languageTerm>
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<abstract lang="en">Spatial variability in N uptake and utilisation by swards within uniformly managed field units could be responsible for a significant proportion of the NH3, N2O, NO−3 and NOx (NO and NO2) ‘pollutants’ generated by agriculture and released to the environment. An investigation was commenced, therefore, to quantify, map and explain the spatial variability in sward N yield in a ‘large’ silage field and to assess the potential for managing this variability using some of the latest precision agriculture technology. Sward dry matter (DM) and N yields were predicted from the results of plant tissue analyses using mathematical models. Sward N yields were found to vary greatly across the field seemingly because of differences in net soil N mineralisation, but the pattern of variability appeared to remain constant with time. Conventional soil analysis of a range of soil chemical and physical properties, however, failed to explain this variability. It was concluded that the N-yield distribution map might be used in place of soil analysis as the basis for varying the rates of N application to different parts of the field with the twin objectives of maximising fertiliser use efficiency and minimising N emissions to air and water.</abstract>
<note type="content">Fig. 1: Smoothed contour map showing the variation in altitude across a large silage field.</note>
<note type="content">Fig. 2: Map of silage field showing: (a) the (original) 124 potential sampling points; (b) the locations of the 11 mowed strips at cut 1; (c) the locations of the 7 mowed strips at cut 2.</note>
<note type="content">Fig. 3: Model validation using independent data: actual DM yields for a range of silage fields in County Down (Bailey, 1999) vs model predicted yields: (a) for Model 1; (b) for Model 2.</note>
<note type="content">Fig. 4: Smoothed contour maps showing the spatial variability in predicted sward DM yield: (a) at first cut; (b) at second cut; (c) totalled over both cuts.</note>
<note type="content">Fig. 5: Smoothed contour maps showing the spatial variability in predicted sward N yield: (a) at first cut; (b) at second cut; (c) totalled over both cuts.</note>
<note type="content">Fig. 6: Relationship between total predicted DM yields and total predicted N yields. The dashed boundary line delimits the DM yield response to N (in the crop) when this nutrient alone was limiting to growth, and the perpendicular dashed line shows the predicted N yield at 95% of maximum DM yield.</note>
<note type="content">Fig. 7: Smoothed contour map of herbage DRIS N indices at cut 1.</note>
<note type="content">Fig. 8: Relationships between: (a) actual N yield and soil C/N ratio on the 11 mowed strips at cut 1; (b) predicted total N yield and soil C/N ratio at the 104 field sampling points.</note>
<note type="content">Fig. 9: Relationships between: (a) actual N yield at cut 1 and soil pH on the 11 mowed strips; (b) predicted total N yield and soil pH at the 104 sampling points.</note>
<note type="content">Fig. 10: Smoothed contour maps of: (a) soil pH; (b) soil organic-C content; (c) soil available P.</note>
<note type="content">Fig. 11: Relationship between actual N yield on the 11 mowed strips at cut 1 and the depth of topsoil (A horizon) under each strip.</note>
<note type="content">Table 1: Variogram properties for N yield data at cut 1, at cut 2 and at both cuts combined</note>
<subject><genre>Keywords</genre>
<topic>Grassland</topic>
<topic>Nitrogen yield</topic>
<topic>Precision agriculture</topic>
<topic>Spatial variability</topic>
<topic>Yield mapping</topic>
</subject>
<relatedItem type="host"><titleInfo><title>Chemosphere</title>
</titleInfo>
<titleInfo type="abbreviated"><title>CHEM</title>
</titleInfo>
<name type="conference"><namePart>Chemistry of protection of the Environment</namePart>
<namePart>Gaseous Emissions</namePart>
</name>
<name type="personal"><namePart>Peter Christie</namePart>
<role><roleTerm type="text">editor</roleTerm>
</role>
</name>
<genre type="journal">journal</genre>
<originInfo><dateIssued encoding="w3cdtf">20010115</dateIssued>
</originInfo>
<identifier type="ISSN">0045-6535</identifier>
<identifier type="PII">S0045-6535(00)X0142-7</identifier>
<part><date>20010115</date>
<detail type="issue"><title>Chemistry of protection of the Environment</title>
</detail>
<detail type="volume"><number>42</number>
<caption>vol.</caption>
</detail>
<detail type="issue"><number>2</number>
<caption>no.</caption>
</detail>
<extent unit="issue pages"><start>103</start>
<end>226</end>
</extent>
<extent unit="pages"><start>131</start>
<end>140</end>
</extent>
</part>
</relatedItem>
<identifier type="istex">605E82869A0F62A51A59C5E2CC83FEDD09D226B0</identifier>
<identifier type="DOI">10.1016/S0045-6535(00)00118-1</identifier>
<identifier type="PII">S0045-6535(00)00118-1</identifier>
<accessCondition type="use and reproduction" contentType="copyright">©2000 Elsevier Science Ltd</accessCondition>
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<recordOrigin>Elsevier Science Ltd, ©2000</recordOrigin>
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