Remote Sensing for Precision Nitrogen Management
This book focuses on the fundamental and applied research of the non-destructive estimation and diagnosis of crop leaf and plant nitrogen status and in-season nitrogen management strategies based on leaf sensors, proximal canopy sensors, unmanned aerial vehicle remote sensing, manned aerial remote s...
Saved in:
| HerausgeberIn: | |
|---|---|
| Sonstige: | |
| Year of Publication: | 2022 |
| Language: | English |
| Physical Description: | 1 electronic resource (602 p.) |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
993573565704498 |
|---|---|
| ctrlnum |
(CKB)5470000001631591 (oapen)https://directory.doabooks.org/handle/20.500.12854/94502 (EXLCZ)995470000001631591 |
| collection |
bib_alma |
| record_format |
marc |
| spelling |
Miao, Yuxin edt Remote Sensing for Precision Nitrogen Management Basel MDPI - Multidisciplinary Digital Publishing Institute 2022 1 electronic resource (602 p.) text txt rdacontent computer c rdamedia online resource cr rdacarrier This book focuses on the fundamental and applied research of the non-destructive estimation and diagnosis of crop leaf and plant nitrogen status and in-season nitrogen management strategies based on leaf sensors, proximal canopy sensors, unmanned aerial vehicle remote sensing, manned aerial remote sensing and satellite remote sensing technologies. Statistical and machine learning methods are used to predict plant-nitrogen-related parameters with sensor data or sensor data together with soil, landscape, weather and/or management information. Different sensing technologies or different modelling approaches are compared and evaluated. Strategies are developed to use crop sensing data for in-season nitrogen recommendations to improve nitrogen use efficiency and protect the environment. English Technology: general issues bicssc History of engineering & technology bicssc Environmental science, engineering & technology bicssc UAS multiple sensors vegetation index leaf nitrogen accumulation plant nitrogen accumulation pasture quality airborne hyperspectral imaging random forest regression sun-induced chlorophyll fluorescence (SIF) SIF yield indices upward downward leaf nitrogen concentration (LNC) wheat (Triticum aestivum L.) laser-induced fluorescence leaf nitrogen concentration back-propagation neural network principal component analysis fluorescence characteristics canopy nitrogen density radiative transfer model hyperspectral winter wheat flooded rice pig slurry aerial remote sensing vegetation indices N recommendation approach Mediterranean conditions nitrogen vertical distribution plant geometry remote sensing maize UAV multispectral imagery LNC non-parametric regression red-edge NDRE dynamic change model sigmoid curve grain yield prediction leaf chlorophyll content red-edge reflectance spectral index precision N fertilization chlorophyll meter NDVI NNI canopy reflectance sensing N mineralization farmyard manures Triticum aestivum discrete wavelet transform partial least squares hyper-spectra rice nitrogen management reflectance index multiple variable linear regression Lasso model Multiplex®3 sensor nitrogen balance index nitrogen nutrition index nitrogen status diagnosis precision nitrogen management terrestrial laser scanning spectrometer plant height biomass nitrogen concentration precision agriculture unmanned aerial vehicle (UAV) digital camera leaf chlorophyll concentration portable chlorophyll meter crop PROSPECT-D sensitivity analysis UAV multispectral imagery spectral vegetation indices machine learning plant nutrition canopy spectrum non-destructive nitrogen status diagnosis drone multispectral camera SPAD smartphone photography fixed-wing UAV remote sensing random forest canopy reflectance crop N status Capsicum annuum proximal optical sensors Dualex sensor leaf position proximal sensing cross-validation feature selection hyperparameter tuning image processing image segmentation nitrogen fertilizer recommendation supervised regression RapidSCAN sensor nitrogen recommendation algorithm in-season nitrogen management nitrogen use efficiency yield potential yield responsiveness standard normal variate (SNV) continuous wavelet transform (CWT) wavelet features optimization competitive adaptive reweighted sampling (CARS) partial least square (PLS) grapevine hyperparameter optimization multispectral imaging precision viticulture RGB multispectral coverage adjusted spectral index vegetation coverage random frog algorithm active canopy sensing integrated sensing system discrete NIR spectral band data soil total nitrogen concentration moisture absorption correction index particle size correction index coupled elimination 3-0365-5709-1 Khosla, Raj edt Mulla, David J. edt Miao, Yuxin oth Khosla, Raj oth Mulla, David J. oth |
| language |
English |
| format |
eBook |
| author2 |
Khosla, Raj Mulla, David J. Miao, Yuxin Khosla, Raj Mulla, David J. |
| author_facet |
Khosla, Raj Mulla, David J. Miao, Yuxin Khosla, Raj Mulla, David J. |
| author2_variant |
y m ym r k rk d j m dj djm |
| author2_role |
HerausgeberIn HerausgeberIn Sonstige Sonstige Sonstige |
| title |
Remote Sensing for Precision Nitrogen Management |
| spellingShingle |
Remote Sensing for Precision Nitrogen Management |
| title_full |
Remote Sensing for Precision Nitrogen Management |
| title_fullStr |
Remote Sensing for Precision Nitrogen Management |
| title_full_unstemmed |
Remote Sensing for Precision Nitrogen Management |
| title_auth |
Remote Sensing for Precision Nitrogen Management |
| title_new |
Remote Sensing for Precision Nitrogen Management |
| title_sort |
remote sensing for precision nitrogen management |
| publisher |
MDPI - Multidisciplinary Digital Publishing Institute |
| publishDate |
2022 |
| physical |
1 electronic resource (602 p.) |
| isbn |
3-0365-5710-5 3-0365-5709-1 |
| illustrated |
Not Illustrated |
| work_keys_str_mv |
AT miaoyuxin remotesensingforprecisionnitrogenmanagement AT khoslaraj remotesensingforprecisionnitrogenmanagement AT mulladavidj remotesensingforprecisionnitrogenmanagement |
| status_str |
n |
| ids_txt_mv |
(CKB)5470000001631591 (oapen)https://directory.doabooks.org/handle/20.500.12854/94502 (EXLCZ)995470000001631591 |
| carrierType_str_mv |
cr |
| is_hierarchy_title |
Remote Sensing for Precision Nitrogen Management |
| author2_original_writing_str_mv |
noLinkedField noLinkedField noLinkedField noLinkedField noLinkedField |
| _version_ |
1796652572528869377 |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>06841nam-a2201957z--4500</leader><controlfield tag="001">993573565704498</controlfield><controlfield tag="005">20231214132941.0</controlfield><controlfield tag="006">m o d </controlfield><controlfield tag="007">cr|mn|---annan</controlfield><controlfield tag="008">202212s2022 xx |||||o ||| 0|eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">3-0365-5710-5</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(CKB)5470000001631591</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(oapen)https://directory.doabooks.org/handle/20.500.12854/94502</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(EXLCZ)995470000001631591</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Miao, Yuxin</subfield><subfield code="4">edt</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Remote Sensing for Precision Nitrogen Management</subfield></datafield><datafield tag="260" ind1=" " ind2=" "><subfield code="a">Basel</subfield><subfield code="b">MDPI - Multidisciplinary Digital Publishing Institute</subfield><subfield code="c">2022</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 electronic resource (602 p.)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">computer</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This book focuses on the fundamental and applied research of the non-destructive estimation and diagnosis of crop leaf and plant nitrogen status and in-season nitrogen management strategies based on leaf sensors, proximal canopy sensors, unmanned aerial vehicle remote sensing, manned aerial remote sensing and satellite remote sensing technologies. Statistical and machine learning methods are used to predict plant-nitrogen-related parameters with sensor data or sensor data together with soil, landscape, weather and/or management information. Different sensing technologies or different modelling approaches are compared and evaluated. Strategies are developed to use crop sensing data for in-season nitrogen recommendations to improve nitrogen use efficiency and protect the environment.</subfield></datafield><datafield tag="546" ind1=" " ind2=" "><subfield code="a">English</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Technology: general issues</subfield><subfield code="2">bicssc</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">History of engineering & technology</subfield><subfield code="2">bicssc</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Environmental science, engineering & technology</subfield><subfield code="2">bicssc</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">UAS</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">multiple sensors</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">vegetation index</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">leaf nitrogen accumulation</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">plant nitrogen accumulation</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">pasture quality</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">airborne hyperspectral imaging</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">random forest regression</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">sun-induced chlorophyll fluorescence (SIF)</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">SIF yield indices</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">upward</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">downward</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">leaf nitrogen concentration (LNC)</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">wheat (Triticum aestivum L.)</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">laser-induced fluorescence</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">leaf nitrogen concentration</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">back-propagation neural network</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">principal component analysis</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">fluorescence characteristics</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">canopy nitrogen density</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">radiative transfer model</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">hyperspectral</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">winter wheat</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">flooded rice</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">pig slurry</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">aerial remote sensing</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">vegetation indices</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">N recommendation approach</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Mediterranean conditions</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">nitrogen</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">vertical distribution</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">plant geometry</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">remote sensing</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">maize</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">UAV</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">multispectral imagery</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">LNC</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">non-parametric regression</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">red-edge</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">NDRE</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">dynamic change model</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">sigmoid curve</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">grain yield prediction</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">leaf chlorophyll content</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">red-edge reflectance</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">spectral index</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">precision N fertilization</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">chlorophyll meter</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">NDVI</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">NNI</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">canopy reflectance sensing</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">N mineralization</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">farmyard manures</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Triticum aestivum</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">discrete wavelet transform</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">partial least squares</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">hyper-spectra</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">rice</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">nitrogen management</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">reflectance index</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">multiple variable linear regression</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Lasso model</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Multiplex®3 sensor</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">nitrogen balance index</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">nitrogen nutrition index</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">nitrogen status diagnosis</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">precision nitrogen management</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">terrestrial laser scanning</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">spectrometer</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">plant height</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">biomass</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">nitrogen concentration</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">precision agriculture</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">unmanned aerial vehicle (UAV)</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">digital camera</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">leaf chlorophyll concentration</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">portable chlorophyll meter</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">crop</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">PROSPECT-D</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">sensitivity analysis</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">UAV multispectral imagery</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">spectral vegetation indices</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">machine learning</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">plant nutrition</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">canopy spectrum</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">non-destructive nitrogen status diagnosis</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">drone</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">multispectral camera</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">SPAD</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">smartphone photography</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">fixed-wing UAV remote sensing</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">random forest</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">canopy reflectance</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">crop N status</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Capsicum annuum</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">proximal optical sensors</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Dualex sensor</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">leaf position</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">proximal sensing</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">cross-validation</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">feature selection</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">hyperparameter tuning</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">image processing</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">image segmentation</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">nitrogen fertilizer recommendation</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">supervised regression</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">RapidSCAN sensor</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">nitrogen recommendation algorithm</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">in-season nitrogen management</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">nitrogen use efficiency</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">yield potential</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">yield responsiveness</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">standard normal variate (SNV)</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">continuous wavelet transform (CWT)</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">wavelet features optimization</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">competitive adaptive reweighted sampling (CARS)</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">partial least square (PLS)</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">grapevine</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">hyperparameter optimization</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">multispectral imaging</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">precision viticulture</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">RGB</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">multispectral</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">coverage adjusted spectral index</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">vegetation coverage</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">random frog algorithm</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">active canopy sensing</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">integrated sensing system</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">discrete NIR spectral band data</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">soil total nitrogen concentration</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">moisture absorption correction index</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">particle size correction index</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">coupled elimination</subfield></datafield><datafield tag="776" ind1=" " ind2=" "><subfield code="z">3-0365-5709-1</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Khosla, Raj</subfield><subfield code="4">edt</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mulla, David J.</subfield><subfield code="4">edt</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Miao, Yuxin</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Khosla, Raj</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mulla, David J.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="906" ind1=" " ind2=" "><subfield code="a">BOOK</subfield></datafield><datafield tag="ADM" ind1=" " ind2=" "><subfield code="b">2023-12-15 05:37:30 Europe/Vienna</subfield><subfield code="f">system</subfield><subfield code="c">marc21</subfield><subfield code="a">2023-01-09 04:44:33 Europe/Vienna</subfield><subfield code="g">false</subfield></datafield><datafield tag="AVE" ind1=" " ind2=" "><subfield code="i">DOAB Directory of Open Access Books</subfield><subfield code="P">DOAB Directory of Open Access Books</subfield><subfield code="x">https://eu02.alma.exlibrisgroup.com/view/uresolver/43ACC_OEAW/openurl?u.ignore_date_coverage=true&portfolio_pid=5341789280004498&Force_direct=true</subfield><subfield code="Z">5341789280004498</subfield><subfield code="b">Available</subfield><subfield code="8">5341789280004498</subfield></datafield></record></collection> |