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Using active canopy sensors and chlorophyll meters to estimate grapevine nitrogen status and productivity
Effects of nitrogen (Ν) and water supply on grapevine leaf transmittance and canopy reflectance were studied over 2 years (2009–2010) in two vineyards planted with cvs. Cabernet Sauvignon and Xinomavro ( Vitis vinifera L.) located in northern Greece. Three N (0, 60 and 120 kg ha −1 ) and two irrigat...
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Published in: | Precision agriculture 2015-02, Vol.16 (1), p.77-98 |
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description | Effects of nitrogen (Ν) and water supply on grapevine leaf transmittance and canopy reflectance were studied over 2 years (2009–2010) in two vineyards planted with cvs. Cabernet Sauvignon and Xinomavro (
Vitis vinifera
L.) located in northern Greece. Three N (0, 60 and 120 kg ha
−1
) and two irrigation (irrigated at 70 % of crop evapotranspiration and non-irrigated) treatments were managed in triplicate in randomized blocks. Measurements with two transmittance-based chlorophyll meters (CM: SPAD-502 and CCM-200) and two active canopy sensors (Crop Circle ACS-210; amber band and Crop Circle ACS-430; red and red edge bands) were conducted on four and two growth stages, respectively. Fertilization increased leaf N and chlorophyll concentrations, as well as CM readings and vegetation indices. The CCM and SPAD values did not vary across growth stages and better estimated leaf N and chlorophyll concentrations in Cabernet Sauvignon than in Xinomavro. The SPAD readings provided better predictions of leaf N concentration. The vegetation indices of the ACS-430 sensor correlated with leaf N and chlorophyll concentrations and dormant wood in Cabernet Sauvignon; the red edge-based indices were generally more efficient than the red-based indices. Consistent relationships were observed for all ACS-430 indices with yield and for red edge-based indices with total phenols early in the season for both vineyards. The correlation between ACS-210 sensor indices and vine properties was low. These results showed that the two technologies examined could provide information on grapevine performance early in the season but have limitations that may restrict their use in vineyards because the acquired relationships were not consistent across vineyards and instruments. |
doi_str_mv | 10.1007/s11119-014-9363-8 |
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Vitis vinifera
L.) located in northern Greece. Three N (0, 60 and 120 kg ha
−1
) and two irrigation (irrigated at 70 % of crop evapotranspiration and non-irrigated) treatments were managed in triplicate in randomized blocks. Measurements with two transmittance-based chlorophyll meters (CM: SPAD-502 and CCM-200) and two active canopy sensors (Crop Circle ACS-210; amber band and Crop Circle ACS-430; red and red edge bands) were conducted on four and two growth stages, respectively. Fertilization increased leaf N and chlorophyll concentrations, as well as CM readings and vegetation indices. The CCM and SPAD values did not vary across growth stages and better estimated leaf N and chlorophyll concentrations in Cabernet Sauvignon than in Xinomavro. The SPAD readings provided better predictions of leaf N concentration. The vegetation indices of the ACS-430 sensor correlated with leaf N and chlorophyll concentrations and dormant wood in Cabernet Sauvignon; the red edge-based indices were generally more efficient than the red-based indices. Consistent relationships were observed for all ACS-430 indices with yield and for red edge-based indices with total phenols early in the season for both vineyards. The correlation between ACS-210 sensor indices and vine properties was low. These results showed that the two technologies examined could provide information on grapevine performance early in the season but have limitations that may restrict their use in vineyards because the acquired relationships were not consistent across vineyards and instruments.</description><identifier>ISSN: 1385-2256</identifier><identifier>EISSN: 1573-1618</identifier><identifier>DOI: 10.1007/s11119-014-9363-8</identifier><language>eng</language><publisher>Boston: Springer US</publisher><subject>Agriculture ; Atmospheric Sciences ; Biomedical and Life Sciences ; Canopies ; Chemistry and Earth Sciences ; Chlorophyll ; Computer Science ; Crop science ; Crops ; Evapotranspiration ; Irrigation ; Laboratories ; Leaves ; Life Sciences ; Microclimate ; Nitrogen ; Phenols ; Physics ; Precision farming ; Productivity ; Remote sensing ; Remote Sensing/Photogrammetry ; Seasons ; Sensors ; Soil Science & Conservation ; Statistics for Engineering ; Studies ; Transmittance ; Variables ; Vegetation ; Vineyards ; Vitaceae ; Vitis vinifera ; Water supply ; Wineries & vineyards</subject><ispartof>Precision agriculture, 2015-02, Vol.16 (1), p.77-98</ispartof><rights>Springer Science+Business Media New York 2014</rights><rights>Springer Science+Business Media New York 2015</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c349t-7fe3a954b109e93694b724877035c9868d2a090673939b5d474dc71c3b9094d53</citedby><cites>FETCH-LOGICAL-c349t-7fe3a954b109e93694b724877035c9868d2a090673939b5d474dc71c3b9094d53</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/1645537067/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/1645537067?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,11688,27924,27925,36060,36061,44363,74895</link.rule.ids></links><search><creatorcontrib>Taskos, D. G.</creatorcontrib><creatorcontrib>Koundouras, S.</creatorcontrib><creatorcontrib>Stamatiadis, S.</creatorcontrib><creatorcontrib>Zioziou, E.</creatorcontrib><creatorcontrib>Nikolaou, N.</creatorcontrib><creatorcontrib>Karakioulakis, K.</creatorcontrib><creatorcontrib>Theodorou, N.</creatorcontrib><title>Using active canopy sensors and chlorophyll meters to estimate grapevine nitrogen status and productivity</title><title>Precision agriculture</title><addtitle>Precision Agric</addtitle><description>Effects of nitrogen (Ν) and water supply on grapevine leaf transmittance and canopy reflectance were studied over 2 years (2009–2010) in two vineyards planted with cvs. Cabernet Sauvignon and Xinomavro (
Vitis vinifera
L.) located in northern Greece. Three N (0, 60 and 120 kg ha
−1
) and two irrigation (irrigated at 70 % of crop evapotranspiration and non-irrigated) treatments were managed in triplicate in randomized blocks. Measurements with two transmittance-based chlorophyll meters (CM: SPAD-502 and CCM-200) and two active canopy sensors (Crop Circle ACS-210; amber band and Crop Circle ACS-430; red and red edge bands) were conducted on four and two growth stages, respectively. Fertilization increased leaf N and chlorophyll concentrations, as well as CM readings and vegetation indices. The CCM and SPAD values did not vary across growth stages and better estimated leaf N and chlorophyll concentrations in Cabernet Sauvignon than in Xinomavro. The SPAD readings provided better predictions of leaf N concentration. The vegetation indices of the ACS-430 sensor correlated with leaf N and chlorophyll concentrations and dormant wood in Cabernet Sauvignon; the red edge-based indices were generally more efficient than the red-based indices. Consistent relationships were observed for all ACS-430 indices with yield and for red edge-based indices with total phenols early in the season for both vineyards. The correlation between ACS-210 sensor indices and vine properties was low. These results showed that the two technologies examined could provide information on grapevine performance early in the season but have limitations that may restrict their use in vineyards because the acquired relationships were not consistent across vineyards and instruments.</description><subject>Agriculture</subject><subject>Atmospheric Sciences</subject><subject>Biomedical and Life Sciences</subject><subject>Canopies</subject><subject>Chemistry and Earth Sciences</subject><subject>Chlorophyll</subject><subject>Computer Science</subject><subject>Crop science</subject><subject>Crops</subject><subject>Evapotranspiration</subject><subject>Irrigation</subject><subject>Laboratories</subject><subject>Leaves</subject><subject>Life Sciences</subject><subject>Microclimate</subject><subject>Nitrogen</subject><subject>Phenols</subject><subject>Physics</subject><subject>Precision farming</subject><subject>Productivity</subject><subject>Remote sensing</subject><subject>Remote Sensing/Photogrammetry</subject><subject>Seasons</subject><subject>Sensors</subject><subject>Soil Science & Conservation</subject><subject>Statistics for Engineering</subject><subject>Studies</subject><subject>Transmittance</subject><subject>Variables</subject><subject>Vegetation</subject><subject>Vineyards</subject><subject>Vitaceae</subject><subject>Vitis vinifera</subject><subject>Water supply</subject><subject>Wineries & vineyards</subject><issn>1385-2256</issn><issn>1573-1618</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>M0C</sourceid><recordid>eNp1kM1qxCAUhaW00Om0D9Cd0E03aTVqjMsy9A8GuumsxRgn45DRVM1A3r4O6aIU6kIvcr5z7z0A3GL0gBHijxHnIwqEaSFIRYr6DCww46TAFa7Pc01qVpQlqy7BVYx7hDJFywWwm2hdB5VO9migVs4PE4zGRR8iVK6Fetf74Ifd1PfwYJLJ38lDE5M9qGRgF9RgjtYZ6GwKvjMOxqTSOMND8O14srZpugYXW9VHc_PzLsHm5flz9VasP17fV0_rQhMqUsG3hijBaIORMHkVQRte0ppzRJgWdVW3pUICVZwIIhrWUk5bzbEmjUCCtowswf3sm5t_jXlQebBRm75XzvgxSlwxxmm-SZbe_ZHu_Rhcni6rKGOEn_osAZ5VOvgYg9nKIeTlwyQxkqfw5Ry-zOHLU_iyzkw5MzFrXWfCL-d_oW8-HYd4</recordid><startdate>20150201</startdate><enddate>20150201</enddate><creator>Taskos, D. 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G.</creatorcontrib><creatorcontrib>Koundouras, S.</creatorcontrib><creatorcontrib>Stamatiadis, S.</creatorcontrib><creatorcontrib>Zioziou, E.</creatorcontrib><creatorcontrib>Nikolaou, N.</creatorcontrib><creatorcontrib>Karakioulakis, K.</creatorcontrib><creatorcontrib>Theodorou, N.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Environment Abstracts</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>Agricultural Science Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Global</collection><collection>Agriculture Science Database</collection><collection>ProQuest Science Journals</collection><collection>Environmental Science Database</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>Environment Abstracts</collection><collection>Aqualine</collection><collection>Water Resources Abstracts</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><jtitle>Precision agriculture</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Taskos, D. G.</au><au>Koundouras, S.</au><au>Stamatiadis, S.</au><au>Zioziou, E.</au><au>Nikolaou, N.</au><au>Karakioulakis, K.</au><au>Theodorou, N.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Using active canopy sensors and chlorophyll meters to estimate grapevine nitrogen status and productivity</atitle><jtitle>Precision agriculture</jtitle><stitle>Precision Agric</stitle><date>2015-02-01</date><risdate>2015</risdate><volume>16</volume><issue>1</issue><spage>77</spage><epage>98</epage><pages>77-98</pages><issn>1385-2256</issn><eissn>1573-1618</eissn><abstract>Effects of nitrogen (Ν) and water supply on grapevine leaf transmittance and canopy reflectance were studied over 2 years (2009–2010) in two vineyards planted with cvs. Cabernet Sauvignon and Xinomavro (
Vitis vinifera
L.) located in northern Greece. Three N (0, 60 and 120 kg ha
−1
) and two irrigation (irrigated at 70 % of crop evapotranspiration and non-irrigated) treatments were managed in triplicate in randomized blocks. Measurements with two transmittance-based chlorophyll meters (CM: SPAD-502 and CCM-200) and two active canopy sensors (Crop Circle ACS-210; amber band and Crop Circle ACS-430; red and red edge bands) were conducted on four and two growth stages, respectively. Fertilization increased leaf N and chlorophyll concentrations, as well as CM readings and vegetation indices. The CCM and SPAD values did not vary across growth stages and better estimated leaf N and chlorophyll concentrations in Cabernet Sauvignon than in Xinomavro. The SPAD readings provided better predictions of leaf N concentration. The vegetation indices of the ACS-430 sensor correlated with leaf N and chlorophyll concentrations and dormant wood in Cabernet Sauvignon; the red edge-based indices were generally more efficient than the red-based indices. Consistent relationships were observed for all ACS-430 indices with yield and for red edge-based indices with total phenols early in the season for both vineyards. The correlation between ACS-210 sensor indices and vine properties was low. These results showed that the two technologies examined could provide information on grapevine performance early in the season but have limitations that may restrict their use in vineyards because the acquired relationships were not consistent across vineyards and instruments.</abstract><cop>Boston</cop><pub>Springer US</pub><doi>10.1007/s11119-014-9363-8</doi><tpages>22</tpages></addata></record> |
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subjects | Agriculture Atmospheric Sciences Biomedical and Life Sciences Canopies Chemistry and Earth Sciences Chlorophyll Computer Science Crop science Crops Evapotranspiration Irrigation Laboratories Leaves Life Sciences Microclimate Nitrogen Phenols Physics Precision farming Productivity Remote sensing Remote Sensing/Photogrammetry Seasons Sensors Soil Science & Conservation Statistics for Engineering Studies Transmittance Variables Vegetation Vineyards Vitaceae Vitis vinifera Water supply Wineries & vineyards |
title | Using active canopy sensors and chlorophyll meters to estimate grapevine nitrogen status and productivity |
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