<?xml version="1.0" encoding="UTF-8"?>
<?xml-stylesheet type="text/xsl" href="https://m2.mtmt.hu/xsl/gui3.xsl" ?>
<myciteResult>
  <serverUrl>https://m2.mtmt.hu/</serverUrl>
  <labelLang>hun</labelLang>
  <responseDate>2026-10-24 10:49</responseDate>
  <content>
    <publication>
      <otype>JournalArticle</otype>
      <mtid>35780003</mtid>
      <status>APPROVED</status>
      <published>true</published>
      <unhandledTickets>0</unhandledTickets>
      <deleted>false</deleted>
      <lastRefresh>2026-09-03T12:57:55.502+0000</lastRefresh>
      <lastModified>2026-09-03T12:56:40.163+0000</lastModified>
      <created>2025-02-22T21:03:29.188+0000</created>
      <creator>
        <snippet>true</snippet>
        <mtid>10060085</mtid>
        <familyName>Bojtor</familyName>
        <givenName>Csaba</givenName>
        <link>/api/author/10060085</link>
        <otype>Author</otype>
        <label>Bojtor Csaba (Növénytermesztés)</label>
        <published>true</published>
        <oldId>10060085</oldId>
      </creator>
      <lastDuplumOK>2026-09-03T12:56:40.417+0000</lastDuplumOK>
      <lastDuplumSearch>2026-09-03T12:56:40.417+0000</lastDuplumSearch>
      <core>true</core>
      <publicationPending>false</publicationPending>
      <type>
        <snippet>true</snippet>
        <mtid>24</mtid>
        <code>24</code>
        <link>/api/publicationtype/24</link>
        <otype>PublicationType</otype>
        <label>Folyóiratcikk</label>
        <listPosition>1</listPosition>
        <published>true</published>
        <oldId>24</oldId>
        <otypeName>JournalArticle</otypeName>
      </type>
      <subType>
        <snippet>true</snippet>
        <mtid>1134514</mtid>
        <nameEng>Survey paper</nameEng>
        <docType>
          <snippet>true</snippet>
          <mtid>24</mtid>
          <code>24</code>
          <link>/api/publicationtype/24</link>
          <otype>PublicationType</otype>
          <label>Folyóiratcikk</label>
          <listPosition>1</listPosition>
          <published>true</published>
          <oldId>24</oldId>
          <otypeName>JournalArticle</otypeName>
        </docType>
        <link>/api/subtype/1134514</link>
        <name>Összefoglaló cikk</name>
        <otype>SubType</otype>
        <label>Összefoglaló cikk (Folyóiratcikk)</label>
        <listPosition>102</listPosition>
        <published>true</published>
        <oldId>1134514</oldId>
      </subType>
      <category>
        <snippet>true</snippet>
        <mtid>1</mtid>
        <link>/api/category/1</link>
        <otype>Category</otype>
        <label>Tudományos</label>
        <published>true</published>
        <oldId>1</oldId>
      </category>
      <firstAuthor>Bakó, Károly</firstAuthor>
      <title>Advancements in Leaf Area Index Estimation for Maize Using Modeling and Remote Sensing Techniques: A Review</title>
      <journal>
        <snippet>true</snippet>
        <sciIndexed>true</sciIndexed>
        <link>/api/journal/10059153</link>
        <reviewType>REVIEWED</reviewType>
        <label>AGRONOMY (BASEL) 2073-4395</label>
        <published>true</published>
        <hungarian>false</hungarian>
        <oldId>10059153</oldId>
        <noIF>false</noIF>
        <mtid>10059153</mtid>
        <scopusIndexed>true</scopusIndexed>
        <eIssn>2073-4395</eIssn>
        <otype>Journal</otype>
        <lang>FOREIGN</lang>
      </journal>
      <volume>15</volume>
      <issue>3</issue>
      <internalId>519</internalId>
      <firstPageOrInternalIdForSort>519</firstPageOrInternalIdForSort>
      <pageLength>22</pageLength>
      <publishedYear>2025</publishedYear>
      <abstractText>Maize is an important crop used as food, feed, and industrial raw material. Therefore, it is critical to maximize maize yield on available land by using optimal inputs and adapting to challenges posed by climate change. The Leaf Area Index (LAI) is a key parameter that provides significant assistance in forecasting maize yields. This study focuses on modeling the Leaf Area Index for maize. Specifically, it compiles and systematizes the main findings of papers published over the past approximately 10–15 years. Our results are organized and presented based on the five most commonly used models: CERES-Maize, AquaCrop, WOFOST, APSIM, and RZWQM2. The limitations of these models’ applicability are also discussed. We present the limitations of these models and compare their minimum climate input requirements. Additionally, we evaluate the performance of the models across different climate zones, explore how the integration of remote sensing data sources can enhance model estimation accuracy, and examine the potential for spatial scalability in maize LAI modeling.</abstractText>
      <fundings>
        <funding>
          <otype>Funding</otype>
          <mtid>2052066</mtid>
          <link>/api/funding/2052066</link>
          <label>(GINOP-2.2.1-15-2016-00021) Támogató: NFÜ</label>
          <published>false</published>
          <snippet>true</snippet>
        </funding>
        <funding>
          <otype>Funding</otype>
          <mtid>2052067</mtid>
          <link>/api/funding/2052067</link>
          <label>(GINOP-2.2.1-15-2017-00076) Támogató: NFÜ</label>
          <published>false</published>
          <snippet>true</snippet>
        </funding>
        <funding>
          <otype>Funding</otype>
          <mtid>2052068</mtid>
          <link>/api/funding/2052068</link>
          <label>(2018-1.2.1-NKP-2018-00002) Támogató: NKFIH</label>
          <published>false</published>
          <snippet>true</snippet>
        </funding>
      </fundings>
      <digital>true</digital>
      <printed/>
      <sourceYear>2025</sourceYear>
      <foreignEdition>true</foreignEdition>
      <foreignLanguage>true</foreignLanguage>
      <fullPublication>true</fullPublication>
      <conferencePublication>false</conferencePublication>
      <nationalOrigin>true</nationalOrigin>
      <missingAuthor>false</missingAuthor>
      <oaType>GOLD</oaType>
      <oaCheckDate>2026-09-03</oaCheckDate>
      <oaFree>false</oaFree>
      <oaLink>http://www.mdpi.com/journal/agronomy</oaLink>
      <citationCount>2</citationCount>
      <citationCountUnpublished>0</citationCountUnpublished>
      <citationCountWoOther>2</citationCountWoOther>
      <independentCitCountWoOther>2</independentCitCountWoOther>
      <nationalOriginCitationCount>0</nationalOriginCitationCount>
      <foreignEditionCitationCount>2</foreignEditionCitationCount>
      <doiCitationCount>2</doiCitationCount>
      <wosCitationCount>2</wosCitationCount>
      <scopusCitationCount>0</scopusCitationCount>
      <wosScopusCitationCount>2</wosScopusCitationCount>
      <wosScopusCitationCountWoOther>2</wosScopusCitationCountWoOther>
      <wosScopusIndependentCitationCount>2</wosScopusIndependentCitationCount>
      <wosScopusIndependentCitationCountWoOther>2</wosScopusIndependentCitationCountWoOther>
      <independentCitationCount>2</independentCitationCount>
      <selfCitationCount>0</selfCitationCount>
      <unhandledCitationCount>0</unhandledCitationCount>
      <citingPubCount>2</citingPubCount>
      <independentCitingPubCount>2</independentCitingPubCount>
      <citingPubCountWoOther>2</citingPubCountWoOther>
      <independentCitingPubCountWoOther>2</independentCitingPubCountWoOther>
      <unhandledCitingPubCount>0</unhandledCitingPubCount>
      <citedPubCount>3</citedPubCount>
      <citedCount>3</citedCount>
      <pubStats>
        <types>
          <type>Folyóiratcikk</type>
          <typeEng>Journal Article</typeEng>
          <code>24</code>
          <count>2</count>
        </types>
        <types>
          <type>Könyvrészlet</type>
          <typeEng>Chapter in Book</typeEng>
          <code>25</code>
          <count>0</count>
        </types>
        <types>
          <type>Könyv</type>
          <typeEng>Book</typeEng>
          <code>23</code>
          <count>0</count>
        </types>
        <types>
          <type>Egyéb konferenciaközlemény</type>
          <typeEng>Conference paper</typeEng>
          <code>31</code>
          <count>0</count>
        </types>
        <types>
          <type>Egyéb konferenciakötet</type>
          <typeEng>Conference proceedings</typeEng>
          <code>32</code>
          <count>0</count>
        </types>
        <types>
          <type>Oltalmi formák</type>
          <typeEng>Protection forms</typeEng>
          <code>26</code>
          <count>0</count>
        </types>
        <types>
          <type>Disszertáció</type>
          <typeEng>Thesis</typeEng>
          <code>28</code>
          <count>0</count>
        </types>
        <types>
          <type>Egyéb</type>
          <typeEng>Miscellaneous</typeEng>
          <code>29</code>
          <count>0</count>
        </types>
        <types>
          <type>Alkotás</type>
          <typeEng>Achievement</typeEng>
          <code>22</code>
          <count>0</count>
        </types>
        <types>
          <type>Kutatási adat</type>
          <typeEng>Research data</typeEng>
          <code>33</code>
          <count>0</count>
        </types>
        <citationTypes>
          <type>Folyóiratcikk</type>
          <typeEng>Journal Article</typeEng>
          <code>24</code>
          <countUnknown>0</countUnknown>
          <countIndependent>0</countIndependent>
          <countSelfCitation>0</countSelfCitation>
        </citationTypes>
        <citationTypes>
          <type>Könyvrészlet</type>
          <typeEng>Chapter in Book</typeEng>
          <code>25</code>
          <countUnknown>0</countUnknown>
          <countIndependent>0</countIndependent>
          <countSelfCitation>0</countSelfCitation>
        </citationTypes>
        <citationTypes>
          <type>Könyv</type>
          <typeEng>Book</typeEng>
          <code>23</code>
          <countUnknown>0</countUnknown>
          <countIndependent>0</countIndependent>
          <countSelfCitation>0</countSelfCitation>
        </citationTypes>
        <citationTypes>
          <type>Egyéb konferenciaközlemény</type>
          <typeEng>Conference paper</typeEng>
          <code>31</code>
          <countUnknown>0</countUnknown>
          <countIndependent>0</countIndependent>
          <countSelfCitation>0</countSelfCitation>
        </citationTypes>
        <citationTypes>
          <type>Egyéb konferenciakötet</type>
          <typeEng>Conference proceedings</typeEng>
          <code>32</code>
          <countUnknown>0</countUnknown>
          <countIndependent>0</countIndependent>
          <countSelfCitation>0</countSelfCitation>
        </citationTypes>
        <citationTypes>
          <type>Oltalmi formák</type>
          <typeEng>Protection forms</typeEng>
          <code>26</code>
          <countUnknown>0</countUnknown>
          <countIndependent>0</countIndependent>
          <countSelfCitation>0</countSelfCitation>
        </citationTypes>
        <citationTypes>
          <type>Disszertáció</type>
          <typeEng>Thesis</typeEng>
          <code>28</code>
          <countUnknown>0</countUnknown>
          <countIndependent>0</countIndependent>
          <countSelfCitation>0</countSelfCitation>
        </citationTypes>
        <citationTypes>
          <type>Egyéb</type>
          <typeEng>Miscellaneous</typeEng>
          <code>29</code>
          <countUnknown>0</countUnknown>
          <countIndependent>0</countIndependent>
          <countSelfCitation>0</countSelfCitation>
        </citationTypes>
        <citationTypes>
          <type>Alkotás</type>
          <typeEng>Achievement</typeEng>
          <code>22</code>
          <countUnknown>0</countUnknown>
          <countIndependent>0</countIndependent>
          <countSelfCitation>0</countSelfCitation>
        </citationTypes>
        <citationTypes>
          <type>Kutatási adat</type>
          <typeEng>Research data</typeEng>
          <code>33</code>
          <countUnknown>0</countUnknown>
          <countIndependent>0</countIndependent>
          <countSelfCitation>0</countSelfCitation>
        </citationTypes>
        <years>
          <year>2025</year>
          <publicationCount>0</publicationCount>
          <citationCount>1</citationCount>
          <independentCitationCount>1</independentCitationCount>
          <citingPubCount>1</citingPubCount>
          <independentCitingPubCount>1</independentCitingPubCount>
          <oaStats/>
          <oaStats2/>
        </years>
        <years>
          <year>2026</year>
          <publicationCount>0</publicationCount>
          <citationCount>1</citationCount>
          <independentCitationCount>1</independentCitationCount>
          <citingPubCount>1</citingPubCount>
          <independentCitingPubCount>1</independentCitingPubCount>
          <oaStats/>
          <oaStats2/>
        </years>
      </pubStats>
      <ratingsForSort>Q1</ratingsForSort>
      <mtaRatingsForSort>B nemzetközi</mtaRatingsForSort>
      <hasCitationDuplums>false</hasCitationDuplums>
      <importDuplum>false</importDuplum>
      <importOverwritten>false</importOverwritten>
      <importSkipped>false</importSkipped>
      <userChangeableUntil>2025-05-23T20:03:30.065+0000</userChangeableUntil>
      <directInstitutesForSort>Precíziós Növénytermesztési Kutatás-fejlesztési... (DE / MÉK)</directInstitutesForSort>
      <ownerAuthorCount>5</ownerAuthorCount>
      <ownerInstituteCount>19</ownerInstituteCount>
      <directInstituteCount>1</directInstituteCount>
      <authorCount>5</authorCount>
      <contributorCount>0</contributorCount>
      <hasQualityFactor>true</hasQualityFactor>
      <languages>
        <language>
          <otype>Language</otype>
          <mtid>10002</mtid>
          <link>/api/language/10002</link>
          <label>Angol</label>
          <name>Angol</name>
          <nameEng>English</nameEng>
          <published>true</published>
          <oldId>2</oldId>
          <snippet>true</snippet>
        </language>
      </languages>
      <authorships>
        <authorship>
          <otype>PersonAuthorship</otype>
          <mtid>123471221</mtid>
          <link>/api/authorship/123471221</link>
          <label>Bakó, Károly [Bakó, Károly István (Agrometeorológiai...), szerző] Precíziós Növénytermesztési Kutatás-fejlesztési... (DE / MÉK)</label>
          <listPosition>1</listPosition>
          <share>0.2</share>
          <first>true</first>
          <last>false</last>
          <corresponding>false</corresponding>
          <author>
            <otype>Author</otype>
            <mtid>10062028</mtid>
            <link>/api/author/10062028</link>
            <label>Bakó Károly István (Agrometeorológiai és Agroökológiai Monitoring Közp.)</label>
            <familyName>Bakó</familyName>
            <givenName>Károly István</givenName>
            <published>true</published>
            <oldId>10062028</oldId>
            <snippet>true</snippet>
          </author>
          <familyName>Bakó</familyName>
          <givenName>Károly</givenName>
          <authorTyped>true</authorTyped>
          <editorTyped>false</editorTyped>
          <otherTyped>false</otherTyped>
          <type>
            <otype>AuthorshipType</otype>
            <mtid>1</mtid>
            <link>/api/authorshiptype/1</link>
            <label>Szerző</label>
            <code>0</code>
            <published>true</published>
            <oldId>0</oldId>
            <snippet>true</snippet>
          </type>
          <published>false</published>
          <snippet>true</snippet>
        </authorship>
        <authorship>
          <otype>PersonAuthorship</otype>
          <mtid>123471222</mtid>
          <link>/api/authorship/123471222</link>
          <label>Rácz, Csaba [Rácz, Csaba (Agrometeorológia), szerző] Precíziós Növénytermesztési Kutatás-fejlesztési... (DE / MÉK)</label>
          <listPosition>2</listPosition>
          <share>0.2</share>
          <first>false</first>
          <last>false</last>
          <corresponding>false</corresponding>
          <author>
            <otype>Author</otype>
            <mtid>10048826</mtid>
            <link>/api/author/10048826</link>
            <label>Rácz Csaba (Agrometeorológia)</label>
            <familyName>Rácz</familyName>
            <givenName>Csaba</givenName>
            <published>true</published>
            <oldId>10048826</oldId>
            <snippet>true</snippet>
          </author>
          <familyName>Rácz</familyName>
          <givenName>Csaba</givenName>
          <authorTyped>true</authorTyped>
          <editorTyped>false</editorTyped>
          <otherTyped>false</otherTyped>
          <type>
            <otype>AuthorshipType</otype>
            <mtid>1</mtid>
            <link>/api/authorshiptype/1</link>
            <label>Szerző</label>
            <code>0</code>
            <published>true</published>
            <oldId>0</oldId>
            <snippet>true</snippet>
          </type>
          <published>false</published>
          <snippet>true</snippet>
        </authorship>
        <authorship>
          <otype>PersonAuthorship</otype>
          <mtid>123471223</mtid>
          <link>/api/authorship/123471223</link>
          <label>Dövényi-Nagy, Tamás [Dövényi-Nagy, Tamás (Agárinformatika), szerző] Precíziós Növénytermesztési Kutatás-fejlesztési... (DE / MÉK)</label>
          <listPosition>3</listPosition>
          <share>0.2</share>
          <first>false</first>
          <last>false</last>
          <corresponding>false</corresponding>
          <author>
            <otype>Author</otype>
            <mtid>10023812</mtid>
            <link>/api/author/10023812</link>
            <label>Dövényi-Nagy Tamás (Agárinformatika)</label>
            <familyName>Dövényi-Nagy</familyName>
            <givenName>Tamás</givenName>
            <published>true</published>
            <oldId>10023812</oldId>
            <snippet>true</snippet>
          </author>
          <familyName>Dövényi-Nagy</familyName>
          <givenName>Tamás</givenName>
          <authorTyped>true</authorTyped>
          <editorTyped>false</editorTyped>
          <otherTyped>false</otherTyped>
          <type>
            <otype>AuthorshipType</otype>
            <mtid>1</mtid>
            <link>/api/authorshiptype/1</link>
            <label>Szerző</label>
            <code>0</code>
            <published>true</published>
            <oldId>0</oldId>
            <snippet>true</snippet>
          </type>
          <published>false</published>
          <snippet>true</snippet>
        </authorship>
        <authorship>
          <otype>PersonAuthorship</otype>
          <mtid>123471224</mtid>
          <link>/api/authorship/123471224</link>
          <label>Molnár, Krisztina [Molnár, Krisztina (növénytermesztés), szerző] Precíziós Növénytermesztési Kutatás-fejlesztési... (DE / MÉK)</label>
          <listPosition>4</listPosition>
          <share>0.2</share>
          <first>false</first>
          <last>false</last>
          <corresponding>false</corresponding>
          <author>
            <otype>Author</otype>
            <mtid>10048599</mtid>
            <link>/api/author/10048599</link>
            <label>Molnár Krisztina (növénytermesztés)</label>
            <familyName>Molnár</familyName>
            <givenName>Krisztina</givenName>
            <published>true</published>
            <oldId>10048599</oldId>
            <snippet>true</snippet>
          </author>
          <familyName>Molnár</familyName>
          <givenName>Krisztina</givenName>
          <authorTyped>true</authorTyped>
          <editorTyped>false</editorTyped>
          <otherTyped>false</otherTyped>
          <type>
            <otype>AuthorshipType</otype>
            <mtid>1</mtid>
            <link>/api/authorshiptype/1</link>
            <label>Szerző</label>
            <code>0</code>
            <published>true</published>
            <oldId>0</oldId>
            <snippet>true</snippet>
          </type>
          <published>false</published>
          <snippet>true</snippet>
        </authorship>
        <authorship>
          <otype>PersonAuthorship</otype>
          <mtid>123471225</mtid>
          <link>/api/authorship/123471225</link>
          <label>Dobos, Attila [Dobos, Attila Csaba (Növénytermesztés), szerző] Precíziós Növénytermesztési Kutatás-fejlesztési... (DE / MÉK)</label>
          <listPosition>5</listPosition>
          <share>0.2</share>
          <first>false</first>
          <last>true</last>
          <corresponding>false</corresponding>
          <author>
            <otype>Author</otype>
            <mtid>10020301</mtid>
            <link>/api/author/10020301</link>
            <label>Dobos Attila Csaba (Növénytermesztés)</label>
            <familyName>Dobos</familyName>
            <givenName>Attila Csaba</givenName>
            <published>true</published>
            <oldId>10020301</oldId>
            <snippet>true</snippet>
          </author>
          <familyName>Dobos</familyName>
          <givenName>Attila</givenName>
          <authorTyped>true</authorTyped>
          <editorTyped>false</editorTyped>
          <otherTyped>false</otherTyped>
          <type>
            <otype>AuthorshipType</otype>
            <mtid>1</mtid>
            <link>/api/authorshiptype/1</link>
            <label>Szerző</label>
            <code>0</code>
            <published>true</published>
            <oldId>0</oldId>
            <snippet>true</snippet>
          </type>
          <published>false</published>
          <snippet>true</snippet>
        </authorship>
      </authorships>
      <identifiers>
        <identifier>
          <otype>PublicationIdentifier</otype>
          <mtid>28455681</mtid>
          <link>/api/publicationidentifier/28455681</link>
          <label>DOI: 10.3390/agronomy15030519</label>
          <source>
            <otype>PlainSource</otype>
            <mtid>6</mtid>
            <link>/api/publicationsource/6</link>
            <label>DOI</label>
            <type>
              <otype>PublicationSourceType</otype>
              <mtid>10001</mtid>
              <link>/api/publicationsourcetype/10001</link>
              <label>DOI</label>
              <mayHaveOa>true</mayHaveOa>
              <published>true</published>
              <snippet>true</snippet>
            </type>
            <name>DOI</name>
            <nameEng>DOI</nameEng>
            <linkPattern>https://doi.org/@@@</linkPattern>
            <publiclyVisible>true</publiclyVisible>
            <published>true</published>
            <oldId>6</oldId>
            <snippet>true</snippet>
          </source>
          <validState>IDENTICAL</validState>
          <idValue>10.3390/agronomy15030519</idValue>
          <realUrl>https://doi.org/10.3390/agronomy15030519</realUrl>
          <published>false</published>
          <snippet>true</snippet>
        </identifier>
        <identifier>
          <otype>PublicationIdentifier</otype>
          <mtid>29221335</mtid>
          <link>/api/publicationidentifier/29221335</link>
          <label>WoS: 001453506600001</label>
          <source>
            <otype>PlainSource</otype>
            <mtid>1</mtid>
            <link>/api/publicationsource/1</link>
            <label>WoS</label>
            <type>
              <otype>PublicationSourceType</otype>
              <mtid>10003</mtid>
              <link>/api/publicationsourcetype/10003</link>
              <label>Indexelő adatbázis</label>
              <mayHaveOa>false</mayHaveOa>
              <published>true</published>
              <snippet>true</snippet>
            </type>
            <name>WoS</name>
            <nameEng>WoS</nameEng>
            <linkPattern>https://www.webofscience.com/wos/woscc/full-record/@@@</linkPattern>
            <publiclyVisible>true</publiclyVisible>
            <published>true</published>
            <oldId>1</oldId>
            <snippet>true</snippet>
          </source>
          <validState>IDENTICAL</validState>
          <idValue>001453506600001</idValue>
          <realUrl>https://www.webofscience.com/wos/woscc/full-record/001453506600001</realUrl>
          <published>true</published>
          <snippet>true</snippet>
        </identifier>
        <identifier>
          <otype>PublicationIdentifier</otype>
          <mtid>29252889</mtid>
          <link>/api/publicationidentifier/29252889</link>
          <label>DEA: 389178</label>
          <source>
            <otype>PlainSource</otype>
            <mtid>38</mtid>
            <link>/api/publicationsource/38</link>
            <label>DEA</label>
            <type>
              <otype>PublicationSourceType</otype>
              <mtid>10007</mtid>
              <link>/api/publicationsourcetype/10007</link>
              <label>Repozitórium</label>
              <mayHaveOa>true</mayHaveOa>
              <published>true</published>
              <snippet>true</snippet>
            </type>
            <name>DEA</name>
            <linkPattern>http://hdl.handle.net/2437/@@@</linkPattern>
            <publiclyVisible>true</publiclyVisible>
            <published>true</published>
            <oldId>38</oldId>
            <snippet>true</snippet>
          </source>
          <validState>IDENTICAL</validState>
          <idValue>389178</idValue>
          <realUrl>http://hdl.handle.net/2437/389178</realUrl>
          <published>false</published>
          <snippet>true</snippet>
        </identifier>
        <identifier>
          <otype>PublicationIdentifier</otype>
          <mtid>32670215</mtid>
          <link>/api/publicationidentifier/32670215</link>
          <label>Scopus: 105001019445</label>
          <source>
            <otype>PlainSource</otype>
            <mtid>3</mtid>
            <link>/api/publicationsource/3</link>
            <label>Scopus</label>
            <type>
              <otype>PublicationSourceType</otype>
              <mtid>10003</mtid>
              <link>/api/publicationsourcetype/10003</link>
              <label>Indexelő adatbázis</label>
              <mayHaveOa>false</mayHaveOa>
              <published>true</published>
              <snippet>true</snippet>
            </type>
            <name>Scopus</name>
            <nameEng>Scopus</nameEng>
            <linkPattern>http://www.scopus.com/record/display.url?origin=inward&amp;eid=2-s2.0-@@@</linkPattern>
            <publiclyVisible>true</publiclyVisible>
            <published>true</published>
            <oldId>3</oldId>
            <snippet>true</snippet>
          </source>
          <validState>IDENTICAL</validState>
          <idValue>105001019445</idValue>
          <realUrl>http://www.scopus.com/record/display.url?origin=inward&amp;eid=2-s2.0-105001019445</realUrl>
          <published>false</published>
          <snippet>true</snippet>
        </identifier>
        <identifier>
          <otype>PublicationIdentifier</otype>
          <mtid>30076766</mtid>
          <link>/api/publicationidentifier/30076766</link>
          <label>Egyéb URL: https://www.mdpi.com/2073-4395/15/3/519</label>
          <source>
            <otype>PlainSource</otype>
            <mtid>40</mtid>
            <link>/api/publicationsource/40</link>
            <label>Egyéb URL</label>
            <type>
              <otype>PublicationSourceType</otype>
              <mtid>10006</mtid>
              <link>/api/publicationsourcetype/10006</link>
              <label>Link</label>
              <mayHaveOa>true</mayHaveOa>
              <published>true</published>
              <snippet>true</snippet>
            </type>
            <name>Egyéb URL</name>
            <nameEng>Other URL</nameEng>
            <linkPattern>@@@</linkPattern>
            <publiclyVisible>true</publiclyVisible>
            <published>true</published>
            <oldId>40</oldId>
            <snippet>true</snippet>
          </source>
          <oaType>NONE</oaType>
          <validState>IDENTICAL</validState>
          <idValue>https://www.mdpi.com/2073-4395/15/3/519</idValue>
          <realUrl>https://www.mdpi.com/2073-4395/15/3/519</realUrl>
          <published>false</published>
          <snippet>true</snippet>
        </identifier>
      </identifiers>
      <subjects>
        <classification>
          <otype>Classification</otype>
          <mtid>12648</mtid>
          <link>/api/classification/12648</link>
          <label>Növénytermesztés és növényvédelem</label>
          <published>true</published>
          <snippet>true</snippet>
        </classification>
      </subjects>
      <ratings>
        <rating>
          <otype>MtaRating</otype>
          <mtid>11507891</mtid>
          <link>/api/mtarating/11507891</link>
          <label>IX. Szociológiai Tudományos Bizottság:B nemzetközi AGRONOMY (BASEL) 2073-4395</label>
          <ratingType>
            <otype>RatingType</otype>
            <mtid>10007</mtid>
            <link>/api/ratingtype/10007</link>
            <label>IX. Szociológiai Tudományos Bizottság</label>
            <code>SZTB</code>
            <institute>
              <otype>Institute</otype>
              <mtid>14102</mtid>
              <link>/api/institute/14102</link>
              <label>Szociológiai Tudományos Bizottság IXGJO SZTB [1901-]</label>
              <childrenCount>0</childrenCount>
              <allowInstForum>false</allowInstForum>
              <allowOnlineRegistration>true</allowOnlineRegistration>
              <specialAffiliation>false</specialAffiliation>
              <name>Szociológiai Tudományos Bizottság</name>
              <nameEng>Committee on Sociology</nameEng>
              <published>true</published>
              <oldId>14102</oldId>
              <snippet>true</snippet>
            </institute>
            <published>true</published>
            <snippet>true</snippet>
          </ratingType>
          <val>B nemzetközi</val>
          <published>false</published>
          <snippet>true</snippet>
        </rating>
        <rating>
          <otype>SjrRating</otype>
          <mtid>11553330</mtid>
          <link>/api/sjrrating/11553330</link>
          <label>sjr:Q1 (2025) Scopus - Agronomy and Crop Science AGRONOMY (BASEL) 2073-4395</label>
          <listPos>74</listPos>
          <rankValue>0.24</rankValue>
          <type>journal</type>
          <ratingType>
            <otype>RatingType</otype>
            <mtid>10002</mtid>
            <link>/api/ratingtype/10002</link>
            <label>sjr</label>
            <code>sjr</code>
            <published>true</published>
            <snippet>true</snippet>
          </ratingType>
          <subject>
            <otype>ClassificationExternal</otype>
            <mtid>1102</mtid>
            <link>/api/classificationexternal/1102</link>
            <label>Scopus - Agronomy and Crop Science</label>
            <published>true</published>
            <oldId>1102</oldId>
            <snippet>true</snippet>
          </subject>
          <ranking>Q1</ranking>
          <calculation>DIRECT</calculation>
          <published>true</published>
          <snippet>true</snippet>
        </rating>
      </ratings>
      <references>
        <reference>
          <otype>Reference</otype>
          <mtid>63957823</mtid>
          <link>/api/reference/63957823</link>
          <label>1. Erenstein 2022: Global maize production, consumption and trade: Trends and R&amp;D implications., Food Secur., 14, p. 1295, DOI: 10.1007/s12571-022-01288-7</label>
          <listPosition>1</listPosition>
          <doi>10.1007/s12571-022-01288-7</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957824</mtid>
          <link>/api/reference/63957824</link>
          <label>2. Ritchie, H., Rodés-Guirao, L., Mathieu, E., Gerber, M., Ortiz-Ospina, E., Hasell, J., and Roser, M. (2023, December 20). Population Growth; ourworldindata.org. Available online: https://ourworldindata.org/population-growth.</label>
          <listPosition>2</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957825</mtid>
          <link>/api/reference/63957825</link>
          <label>3. Poole 2021: Viewpoint: Agri-nutrition research: Revisiting the contribution of maize and wheat to human nutrition and health., Food Policy, 100, p. 101976, DOI: 10.1016/j.foodpol.2020.101976</label>
          <listPosition>3</listPosition>
          <doi>10.1016/j.foodpol.2020.101976</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957826</mtid>
          <link>/api/reference/63957826</link>
          <label>4. FAO (2022). FAOStat, FAO. Available online: http://www.fao.org/faostat.</label>
          <listPosition>4</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957827</mtid>
          <link>/api/reference/63957827</link>
          <label>5. Ranum 2014: Global maize production, utilization, and consumption., Ann. N. Y. Acad. Sci., 1312, p. 105, DOI: 10.1111/nyas.12396</label>
          <listPosition>5</listPosition>
          <doi>10.1111/nyas.12396</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957828</mtid>
          <link>/api/reference/63957828</link>
          <label>6. Shiferaw 2011: Crops that feed the world 6. Past successes and future challenges to the role played by maize in global food security., Food Secur., 3, p. 307, DOI: 10.1007/s12571-011-0140-5</label>
          <listPosition>6</listPosition>
          <doi>10.1007/s12571-011-0140-5</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957829</mtid>
          <link>/api/reference/63957829</link>
          <label>7. Bassu 2014: How do various maize crop models vary in their responses to climate change factors?., Glob. Change Biol., 20, p. 2301, DOI: 10.1111/gcb.12520</label>
          <listPosition>7</listPosition>
          <doi>10.1111/gcb.12520</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957830</mtid>
          <link>/api/reference/63957830</link>
          <label>8. Chen 2018: Impacts of climate change and climate extremes on major crops productivity in China at a global warming of 1.5 and 2.0 °C., Earth Syst. Dyn., 9, p. 543, DOI: 10.5194/esd-9-543-2018</label>
          <listPosition>8</listPosition>
          <doi>10.5194/esd-9-543-2018</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957831</mtid>
          <link>/api/reference/63957831</link>
          <label>9. Farooq, A., Farooq, N., Akbar, H., Hassan, Z.U., and Gheewala, S.H. (2023). A Critical Review of Climate Change Impact at a Global Scale on Cereal Crop Production. Agronomy, 13., DOI: 10.3390/agronomy13010162</label>
          <listPosition>9</listPosition>
          <doi>10.3390/agronomy13010162</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957832</mtid>
          <link>/api/reference/63957832</link>
          <label>10. Grote, U., Fasse, A., Nguyen, T.T., and Erenstein, O. (2021). Food Security and the Dynamics of Wheat and Maize Value Chains in Africa and Asia. Front. Sustain. Food Syst., 4., DOI: 10.3389/fsufs.2020.617009</label>
          <listPosition>10</listPosition>
          <doi>10.3389/fsufs.2020.617009</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957833</mtid>
          <link>/api/reference/63957833</link>
          <label>11. Muntean, L., Ona, A., Berindean, I., Racz, I., and Muntean, S. (2022). Maize Breeding: From Domestication to Genomic Tools. Agronomy, 12., DOI: 10.3390/agronomy12102365</label>
          <listPosition>11</listPosition>
          <doi>10.3390/agronomy12102365</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957834</mtid>
          <link>/api/reference/63957834</link>
          <label>12. Seidel 2018: Towards improved calibration of crop models—Where are we now and where should we go?., Eur. J. Agron., 94, p. 25, DOI: 10.1016/j.eja.2018.01.006</label>
          <listPosition>12</listPosition>
          <doi>10.1016/j.eja.2018.01.006</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957835</mtid>
          <link>/api/reference/63957835</link>
          <label>13. Confalonieri 2016: Uncertainty in crop model predictions: What is the role of users?., Environ. Model. Softw., 81, p. 165, DOI: 10.1016/j.envsoft.2016.04.009</label>
          <listPosition>13</listPosition>
          <doi>10.1016/j.envsoft.2016.04.009</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957836</mtid>
          <link>/api/reference/63957836</link>
          <label>14. Kimball 2019: Simulation of maize evapotranspiration: An inter-comparison among 29 maize models., Agric. For. Meteorol., 271, p. 264, DOI: 10.1016/j.agrformet.2019.02.037</label>
          <listPosition>14</listPosition>
          <doi>10.1016/j.agrformet.2019.02.037</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957837</mtid>
          <link>/api/reference/63957837</link>
          <label>15. Zhu 2011: The next generation models for crops and agro-ecosystems., Sci. China Inf. Sci., 54, p. 589, DOI: 10.1007/s11432-011-4197-8</label>
          <listPosition>15</listPosition>
          <doi>10.1007/s11432-011-4197-8</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957838</mtid>
          <link>/api/reference/63957838</link>
          <label>16. Birch 1999: Dry matter accumulation and distribution in five cultivars of maize (Zea mays): Relationships and procedures for use in crop modelling., Aust. J. Agric. Res., 50, p. 513, DOI: 10.1071/A98113</label>
          <listPosition>16</listPosition>
          <doi>10.1071/A98113</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957839</mtid>
          <link>/api/reference/63957839</link>
          <label>17. Welles 1990: Some indirect methods of estimating canopy structure., Remote Sens. Rev., 5, p. 31, DOI: 10.1080/02757259009532120</label>
          <listPosition>17</listPosition>
          <doi>10.1080/02757259009532120</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957840</mtid>
          <link>/api/reference/63957840</link>
          <label>18. Hall, D.O., Scurlock, J.M.O., Bolhàr-Nordenkampf, H.R., Leegood, R.C., and Long, S.P.L. (1993). Growth Analysis. Photosynthesis and Production in a Changing Environment: A Field and Laboratory Manual, Springer Netherlands., DOI: 10.1007/978-94-011-1566-7</label>
          <listPosition>18</listPosition>
          <doi>10.1007/978-94-011-1566-7</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957841</mtid>
          <link>/api/reference/63957841</link>
          <label>19. Omer, G., Mutanga, O., Abdel-Rahman, E.M., and Adam, E. (2016). Empirical Prediction of Leaf Area Index (LAI) of Endangered Tree Species in Intact and Fragmented Indigenous Forests Ecosystems Using WorldView-2 Data and Two Robust Machine Learning Algorithms. Remote Sens., 8., DOI: 10.3390/rs8040324</label>
          <listPosition>19</listPosition>
          <doi>10.3390/rs8040324</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957842</mtid>
          <link>/api/reference/63957842</link>
          <label>20. Raghavan 2020: Realistic and simplified models of plant and leaf area indices for a seasonally dry tropical forest., Int. J. Appl. Earth Obs. Geoinf., 85, p. 101992</label>
          <listPosition>20</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957843</mtid>
          <link>/api/reference/63957843</link>
          <label>21. Ni 2001: Synthesis and analysis of biomass and net primary productivity in Chinese forests., Ann. For. Sci., 58, p. 351, DOI: 10.1051/forest:2001131</label>
          <listPosition>21</listPosition>
          <doi>10.1051/forest:2001131</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957844</mtid>
          <link>/api/reference/63957844</link>
          <label>22. Gower 1999: Direct and Indirect Estimation of Leaf Area Index, fAPAR, and Net Primary Production of Terrestrial Ecosystems., Remote Sens. Environ., 70, p. 29, DOI: 10.1016/S0034-4257(99)00056-5</label>
          <listPosition>22</listPosition>
          <doi>10.1016/S0034-4257(99)00056-5</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957845</mtid>
          <link>/api/reference/63957845</link>
          <label>23. Mosandl 2000: Comparison of direct and indirect estimation of leaf area index in mature Norway spruce stands of eastern Germany., Can. J. For. Res., 30, p. 440, DOI: 10.1139/x99-227</label>
          <listPosition>23</listPosition>
          <doi>10.1139/x99-227</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957846</mtid>
          <link>/api/reference/63957846</link>
          <label>24. Fassnacht 1994: A comparison of optical and direct methods for estimating foliage surface area index in forests., Agric. For. Meteorol., 71, p. 183, DOI: 10.1016/0168-1923(94)90107-4</label>
          <listPosition>24</listPosition>
          <doi>10.1016/0168-1923(94)90107-4</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957847</mtid>
          <link>/api/reference/63957847</link>
          <label>25. Šesták, Z., Čatský, J., and Jarvis, P.G. (1971). Assessment of Leaf Area and Other Assimilating Plant Surfaces. Plant Photosynthetic Production, Manual of Methods, Dr. W. Junk.</label>
          <listPosition>25</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957848</mtid>
          <link>/api/reference/63957848</link>
          <label>26. Musa 2016: Leaf Area Determination for Maize (Zea mays L.), Okra (Abelmoschus esculentus L.) and Cowpea (Vigna unguiculata L.) Crops Using Linear Measurements., J. Biol. Agric. Healthc., 6, p. 103</label>
          <listPosition>26</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957849</mtid>
          <link>/api/reference/63957849</link>
          <label>27. Chason 1991: A comparison of direct and indirect methods for estimating forest canopy leaf area., Agric. For. Meteorol., 57, p. 107, DOI: 10.1016/0168-1923(91)90081-Z</label>
          <listPosition>27</listPosition>
          <doi>10.1016/0168-1923(91)90081-Z</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957850</mtid>
          <link>/api/reference/63957850</link>
          <label>28. Darwin, B., Dharmaraj, P., Prince, S., Popescu, D.E., and Hemanth, D.J. (2021). Recognition of Bloom/Yield in Crop Images Using Deep Learning Models for Smart Agriculture: A Review. Agronomy, 11., DOI: 10.3390/agronomy11040646</label>
          <listPosition>28</listPosition>
          <doi>10.3390/agronomy11040646</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957851</mtid>
          <link>/api/reference/63957851</link>
          <label>29. Jones, C.A., Kiniry, J.R., and Dyke, P.T. (1986). CERES-Maize: A Simulation Model of Maize Growth and Development, Texas A&amp;M University Press.</label>
          <listPosition>29</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957852</mtid>
          <link>/api/reference/63957852</link>
          <label>30. Hodges 1987: Using the CERES-Maize model to estimate production for the U.S. Cornbelt., Agric. For. Meteorol., 40, p. 293, DOI: 10.1016/0168-1923(87)90043-8</label>
          <listPosition>30</listPosition>
          <doi>10.1016/0168-1923(87)90043-8</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957853</mtid>
          <link>/api/reference/63957853</link>
          <label>31. Rasse 2000: Simulating Inbred-Maize Yields with CERES-IM., Agron. J., 92, p. 672, DOI: 10.2134/agronj2000.924672x</label>
          <listPosition>31</listPosition>
          <doi>10.2134/agronj2000.924672x</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957854</mtid>
          <link>/api/reference/63957854</link>
          <label>32. Tekle 2021: Seasonal Analysis of Maize Production Using DSSAT-CERES Model in Central Rift Valley of Ethiopia., J. Climatol. Weather Forecast., 9, p. 1</label>
          <listPosition>32</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957855</mtid>
          <link>/api/reference/63957855</link>
          <label>33. Zelenák, A., Szabó, A., Nagy, J., and Nyéki, A. (2022). Using the CERES-Maize Model to Simulate Crop Yield in a Long-Term Field Experiment in Hungary. Agronomy, 12., DOI: 10.3390/agronomy12040785</label>
          <listPosition>33</listPosition>
          <doi>10.3390/agronomy12040785</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957856</mtid>
          <link>/api/reference/63957856</link>
          <label>34. Liu 2014: Simulating maize (Zea mays L.) growth and yield, soil nitrogen concentration, and soil water content for a long-term cropping experiment in Ontario, Canada., Can. J. Soil Sci., 94, p. 435, DOI: 10.4141/cjss2013-096</label>
          <listPosition>34</listPosition>
          <doi>10.4141/cjss2013-096</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957857</mtid>
          <link>/api/reference/63957857</link>
          <label>35. Tovihoudji, P.G., Akponikpè, P.B.I., Agbossou, E.K., and Bielders, C.L. (2019). Using the DSSAT Model to Support Decision Making Regarding Fertilizer Microdosing for Maize Production in the Sub-humid Region of Benin. Front. Environ. Sci., 7., DOI: 10.3389/fenvs.2019.00013</label>
          <listPosition>35</listPosition>
          <doi>10.3389/fenvs.2019.00013</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957858</mtid>
          <link>/api/reference/63957858</link>
          <label>36. Corbeels 2016: Performance and sensitivity of the DSSAT crop growth model in simulating maize yield under conservation agriculture., Eur. J. Agron., 76, p. 41, DOI: 10.1016/j.eja.2016.02.001</label>
          <listPosition>36</listPosition>
          <doi>10.1016/j.eja.2016.02.001</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957859</mtid>
          <link>/api/reference/63957859</link>
          <label>37. Raes 2009: AquaCrop—The FAO Crop Model to Simulate Yield Response to Water: II. Main Algorithms and Software Description., Agron. J., 101, p. 438, DOI: 10.2134/agronj2008.0140s</label>
          <listPosition>37</listPosition>
          <doi>10.2134/agronj2008.0140s</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957860</mtid>
          <link>/api/reference/63957860</link>
          <label>38. Ran 2018: Parameterization of the AquaCrop model for full and deficit irrigated maize for seed production in arid Northwest China., Agric. Water Manag., 203, p. 438, DOI: 10.1016/j.agwat.2018.01.030</label>
          <listPosition>38</listPosition>
          <doi>10.1016/j.agwat.2018.01.030</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957861</mtid>
          <link>/api/reference/63957861</link>
          <label>39. DeCoursey, D.G., and Rojas, K.W. (1989, January 19–20). A Root Zone Water Quality Mode. (RZWQM). Proceedings of the Towards a Sustainable Agriculture for the Great Plains: A Symposium, Fort Collins, CO, USA.</label>
          <listPosition>39</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957862</mtid>
          <link>/api/reference/63957862</link>
          <label>40. Hanson 1998: RZWQM: Simulating the effects of management on water quality and crop production., Agric. Syst., 57, p. 161, DOI: 10.1016/S0308-521X(98)00002-X</label>
          <listPosition>40</listPosition>
          <doi>10.1016/S0308-521X(98)00002-X</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957863</mtid>
          <link>/api/reference/63957863</link>
          <label>41. McCown 1996: APSIM: A Novel Software System for Model Development, Model Testing and Simulation in Agricultural Systems Research., Agric. Syst., 50, p. 255, DOI: 10.1016/0308-521X(94)00055-V</label>
          <listPosition>41</listPosition>
          <doi>10.1016/0308-521X(94)00055-V</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957864</mtid>
          <link>/api/reference/63957864</link>
          <label>42. Wolf 1989: WOFOST: A simulation model of crop production., Soil Use Manag., 5, p. 16, DOI: 10.1111/j.1475-2743.1989.tb00755.x</label>
          <listPosition>42</listPosition>
          <doi>10.1111/j.1475-2743.1989.tb00755.x</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957865</mtid>
          <link>/api/reference/63957865</link>
          <label>43. Boogaard 2019: 25 years of the WOFOST cropping systems model., Agric. Syst., 168, p. 154, DOI: 10.1016/j.agsy.2018.06.018</label>
          <listPosition>43</listPosition>
          <doi>10.1016/j.agsy.2018.06.018</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957866</mtid>
          <link>/api/reference/63957866</link>
          <label>44. Gilardelli 2018: Sensitivity of WOFOST-based modelling solutions to crop parameters under climate change., Ecol. Model., 368, p. 1, DOI: 10.1016/j.ecolmodel.2017.11.003</label>
          <listPosition>44</listPosition>
          <doi>10.1016/j.ecolmodel.2017.11.003</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957867</mtid>
          <link>/api/reference/63957867</link>
          <label>45. Ferreira 2023: Drought patterns: Their spatiotemporal variability and impacts on maize production in Limpopo province, South Africa., Int. J. Biometeorol., 67, p. 133, DOI: 10.1007/s00484-022-02392-1</label>
          <listPosition>45</listPosition>
          <doi>10.1007/s00484-022-02392-1</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957868</mtid>
          <link>/api/reference/63957868</link>
          <label>46. Simunek, J., Sejna, M., and van Genuchten, M.T. (1998). The HYDRUS-1D Software Package for Simulating the One-Dimensional Movement of Water, Heat, and Multiple Solutes in Variably-Saturated Media, Version 2.0. International Ground Water Modeling Center; Colorado School of Mines. IGWMC-TPS-70.</label>
          <listPosition>46</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957869</mtid>
          <link>/api/reference/63957869</link>
          <label>47. Zhu 2018: Water Stress in Maize Production in the Drylands of the Loess Plateau., Vadose Zone J., 17, p. 180117, DOI: 10.2136/vzj2018.06.0117</label>
          <listPosition>47</listPosition>
          <doi>10.2136/vzj2018.06.0117</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957870</mtid>
          <link>/api/reference/63957870</link>
          <label>48. Brisson 1998: STICS: A Generic Model for the Simulation of Crops and Their Water and Nitrogen Balances. I. Theory and Parameterization Applied to Wheat and Corn., Agronomie, 18, p. 311, DOI: 10.1051/agro:19980501</label>
          <listPosition>48</listPosition>
          <doi>10.1051/agro:19980501</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957871</mtid>
          <link>/api/reference/63957871</link>
          <label>49. Pattey 2011: Evaluation of the STICS Crop Growth Model with Maize Cultivar Parameters Calibrated for Eastern Canada., Agron. Sustain. Dev., 31, p. 557, DOI: 10.1007/s13593-011-0014-4</label>
          <listPosition>49</listPosition>
          <doi>10.1007/s13593-011-0014-4</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957872</mtid>
          <link>/api/reference/63957872</link>
          <label>50. Basso 2006: Simulation of Tillage Systems Impact on Soil Biophysical Properties Using the SALUS Model., Ital. J. Agron., 1, p. 677, DOI: 10.4081/ija.2006.677</label>
          <listPosition>50</listPosition>
          <doi>10.4081/ija.2006.677</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957873</mtid>
          <link>/api/reference/63957873</link>
          <label>51. Osborne 2015: JULES-crop: A parametrisation of crops in the Joint UK Land Environment Simulator., Geosci. Model Dev., 8, p. 1139, DOI: 10.5194/gmd-8-1139-2015</label>
          <listPosition>51</listPosition>
          <doi>10.5194/gmd-8-1139-2015</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957874</mtid>
          <link>/api/reference/63957874</link>
          <label>52. Yin, X., and van Laar, H.H. (Crop Systems Dynamics: An Ecophysiological Simulation Model for Genotype-By-Environment Interactions, 2005). Crop Systems Dynamics: An Ecophysiological Simulation Model for Genotype-By-Environment Interactions.</label>
          <listPosition>52</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957875</mtid>
          <link>/api/reference/63957875</link>
          <label>53. Cabelguenne 1996: EPIC—PHASE Real Time: A Model for Tactical Irrigation Management., Comptes Rendus e L’academie D’agriculture Fr., 82, p. 39</label>
          <listPosition>53</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957876</mtid>
          <link>/api/reference/63957876</link>
          <label>54. Stockle 1994: CropSyst, a cropping systems simulation model: Water/nitrogen budgets and crop yield., Agric. Syst., 46, p. 335, DOI: 10.1016/0308-521X(94)90006-2</label>
          <listPosition>54</listPosition>
          <doi>10.1016/0308-521X(94)90006-2</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957877</mtid>
          <link>/api/reference/63957877</link>
          <label>55. Kucharik 2000: Testing the performance of a dynamic global ecosystem model: Water balance, carbon balance, and vegetation structure., Glob. Biogeochem. Cycles, 14, p. 795, DOI: 10.1029/1999GB001138</label>
          <listPosition>55</listPosition>
          <doi>10.1029/1999GB001138</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957878</mtid>
          <link>/api/reference/63957878</link>
          <label>56. Samuhel 2007: Parameterization of crop simulation model “Ceres-Maize” in Nitra-Dolna Malanta., J. Environ. Eng. Landsc. Manag., 15, p. 25, DOI: 10.3846/16486897.2007.9636904</label>
          <listPosition>56</listPosition>
          <doi>10.3846/16486897.2007.9636904</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957879</mtid>
          <link>/api/reference/63957879</link>
          <label>57. Bert 2007: Sensitivity of CERES-Maize simulated yields to uncertainty in soil properties and daily solar radiation., Agric. Syst., 94, p. 141, DOI: 10.1016/j.agsy.2006.08.003</label>
          <listPosition>57</listPosition>
          <doi>10.1016/j.agsy.2006.08.003</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957880</mtid>
          <link>/api/reference/63957880</link>
          <label>58. Wolf, J., Mandryk, M., Kanellopoulos, A., van Oort, P.A.J., Schaap, B.F., Reidsma, P., and van Ittersum, M.K. (2010). Methodologies for Analyzing Future Farming Systems in Flevoland as Applied Within the Agri Adapt Project, Wageningen UR. Available online: https://edepot.wur.nl/291008.</label>
          <listPosition>58</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957881</mtid>
          <link>/api/reference/63957881</link>
          <label>59. Hadiya 2018: Use of WOFOST Model in Agriculture—A Review., Agric. Rev., 39, p. 234</label>
          <listPosition>59</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957882</mtid>
          <link>/api/reference/63957882</link>
          <label>60. Levitan, N., and Gross, B. (2018). Utilizing Collocated Crop Growth Model Simulations to Train Agronomic Satellite Retrieval Algorithms. Remote Sens., 10., DOI: 10.3390/rs10121968</label>
          <listPosition>60</listPosition>
          <doi>10.3390/rs10121968</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957883</mtid>
          <link>/api/reference/63957883</link>
          <label>61. Ma 2012: Root Zone Water Quality Model (RZWQM2): Model Use, Calibration, and Validation., Trans. ASABE, 55, p. 1425, DOI: 10.13031/2013.42252</label>
          <listPosition>61</listPosition>
          <doi>10.13031/2013.42252</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957884</mtid>
          <link>/api/reference/63957884</link>
          <label>62. DeJonge 2012: Improving evapotranspiration simulations in the CERES-Maize model under limited irrigation., Agric. Water Manag., 115, p. 92, DOI: 10.1016/j.agwat.2012.08.013</label>
          <listPosition>62</listPosition>
          <doi>10.1016/j.agwat.2012.08.013</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957885</mtid>
          <link>/api/reference/63957885</link>
          <label>63. Amiri 2022: Assessment of CERES-Maize model in simulating maize growth, yield and soil water content under rainfed, limited and full irrigation., Agric. Water Manag., 259, p. 107271, DOI: 10.1016/j.agwat.2021.107271</label>
          <listPosition>63</listPosition>
          <doi>10.1016/j.agwat.2021.107271</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957886</mtid>
          <link>/api/reference/63957886</link>
          <label>64. Katerji 1998: A modified version of CERES-Maize model for predicting crop response to salinity stress., Ecol. Model., 111, p. 107, DOI: 10.1016/S0304-3800(98)00084-2</label>
          <listPosition>64</listPosition>
          <doi>10.1016/S0304-3800(98)00084-2</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957887</mtid>
          <link>/api/reference/63957887</link>
          <label>65. Attia 2021: Sensitivity of the DSSAT model in simulating maize yield and soil carbon dynamics in arid Mediterranean climate: Effect of soil, genotype and crop management., Field Crop. Res., 260, p. 107981, DOI: 10.1016/j.fcr.2020.107981</label>
          <listPosition>65</listPosition>
          <doi>10.1016/j.fcr.2020.107981</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957888</mtid>
          <link>/api/reference/63957888</link>
          <label>66. Mubeen 2016: Application of CSM-CERES-Maize model in optimizing irrigated conditions., Outlook Agric., 45, p. 173, DOI: 10.1177/0030727016664464</label>
          <listPosition>66</listPosition>
          <doi>10.1177/0030727016664464</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957889</mtid>
          <link>/api/reference/63957889</link>
          <label>67. Adnan, A.A., Jibrin, J.M., Kamara, A.Y., Abdulrahman, B.L., Shaibu, A.S., and Garba, I.I. (2017). CERES–Maize Model for Determining the Optimum Planting Dates of Early Maturing Maize Varieties in Northern Nigeria. Front. Plant Sci., 8., DOI: 10.3389/fpls.2017.01118</label>
          <listPosition>67</listPosition>
          <doi>10.3389/fpls.2017.01118</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957890</mtid>
          <link>/api/reference/63957890</link>
          <label>68. Kipkulei 2022: Assessment of Maize Yield Response to Agricultural Management Strategies Using the DSSAT–CERES-Maize Model in Trans Nzoia County in Kenya., Int. J. Plant Prod., 16, p. 557, DOI: 10.1007/s42106-022-00220-5</label>
          <listPosition>68</listPosition>
          <doi>10.1007/s42106-022-00220-5</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957891</mtid>
          <link>/api/reference/63957891</link>
          <label>69. Adnan 2020: CERES-Maize model for simulating genotype-by-environment interaction of maize and its stability in the dry and wet savannas of Nigeria., Field Crop. Res., 253, p. 107826, DOI: 10.1016/j.fcr.2020.107826</label>
          <listPosition>69</listPosition>
          <doi>10.1016/j.fcr.2020.107826</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957892</mtid>
          <link>/api/reference/63957892</link>
          <label>70. Saddique, Q., Cai, H., Ishaque, W., Chen, H., Chau, H.W., Chattha, M.U., Hassan, M.U., Khan, M.I., and He, J. (2019). Optimizing the Sowing Date and Irrigation Strategy to Improve Maize Yield by Using CERES (Crop Estimation through Resource and Environment Synthesis)-Maize Model. Agronomy, 9., DOI: 10.3390/agronomy9020109</label>
          <listPosition>70</listPosition>
          <doi>10.3390/agronomy9020109</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957893</mtid>
          <link>/api/reference/63957893</link>
          <label>71. Attia 2021: Evaluating deficit irrigation scheduling strategies to improve yield and water productivity of maize in arid environment using simulation., Agric. Water Manag., 249, p. 106812, DOI: 10.1016/j.agwat.2021.106812</label>
          <listPosition>71</listPosition>
          <doi>10.1016/j.agwat.2021.106812</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957894</mtid>
          <link>/api/reference/63957894</link>
          <label>72. Mastrorilli 2003: Using the CERES-Maize model in a semi-arid Mediterranean environment. Validation of three revised versions., Eur. J. Agron., 19, p. 125, DOI: 10.1016/S1161-0301(02)00024-2</label>
          <listPosition>72</listPosition>
          <doi>10.1016/S1161-0301(02)00024-2</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957895</mtid>
          <link>/api/reference/63957895</link>
          <label>73. Cavero 2014: DSSAT Nitrogen Cycle Simulation of Cover Crop–Maize Rotations under Irrigated Mediterranean Conditions., Agron. J., 106, p. 1283, DOI: 10.2134/agronj13.0560</label>
          <listPosition>73</listPosition>
          <doi>10.2134/agronj13.0560</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957896</mtid>
          <link>/api/reference/63957896</link>
          <label>74. Katerji 2000: Using the CERES-Maize model in a semi-arid Mediterranean environment. Evaluation of model performance., Eur. J. Agron., 13, p. 309, DOI: 10.1016/S1161-0301(00)00063-0</label>
          <listPosition>74</listPosition>
          <doi>10.1016/S1161-0301(00)00063-0</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957897</mtid>
          <link>/api/reference/63957897</link>
          <label>75. Shivani 2021: Determination of Sowing Window for Kharif Maize in Punjab, India Using Sensitized, Calibrated and Validated CERES-Maize Model., Maydica, 66, p. 12</label>
          <listPosition>75</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957898</mtid>
          <link>/api/reference/63957898</link>
          <label>76. Rugira, P., Ma, J., Zheng, L., Wu, C., and Liu, E. (2021). Application of DSSAT CERES-Maize to Identify the Optimum Irrigation Management and Sowing Dates on Improving Maize Yield in Northern China. Agronomy, 11., DOI: 10.3390/agronomy11040674</label>
          <listPosition>76</listPosition>
          <doi>10.3390/agronomy11040674</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957899</mtid>
          <link>/api/reference/63957899</link>
          <label>77. Chen 2014: An empirical model for changes in the leaf area of maize., Can. J. Plant Sci., 94, p. 749, DOI: 10.4141/cjps2013-2221</label>
          <listPosition>77</listPosition>
          <doi>10.4141/cjps2013-2221</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957900</mtid>
          <link>/api/reference/63957900</link>
          <label>78. Ren, H., Li, Z., Cheng, Y., Zhang, J., Liu, P., Li, R., Yang, Q., Dong, S., Zhang, J., and Zhao, B. (2020). Narrowing Yield Gaps and Enhancing Nitrogen Utilization for Summer Maize (Zea mays L.) by Combining the Effects of Varying Nitrogen Fertilizer Input and Planting Density in DSSAT Simulations. Front. Plant Sci., 11., DOI: 10.3389/fpls.2020.560466</label>
          <listPosition>78</listPosition>
          <doi>10.3389/fpls.2020.560466</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957901</mtid>
          <link>/api/reference/63957901</link>
          <label>79. Zhang 2022: Simulation of soil water balance and crop productivity of long-term continuous maize cropping under high planting density in rainfed agroecosystems., Agric. For. Meteorol., 312, p. 108740, DOI: 10.1016/j.agrformet.2021.108740</label>
          <listPosition>79</listPosition>
          <doi>10.1016/j.agrformet.2021.108740</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957902</mtid>
          <link>/api/reference/63957902</link>
          <label>80. 2019: AquaCrop Model Evaluation in Maize under Different Water Availabilities in the Western of Uruguay., Int. J. Plant Anim. Environ. Sci., 9, p. 103</label>
          <listPosition>80</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957903</mtid>
          <link>/api/reference/63957903</link>
          <label>81. Jin 2020: Estimation of Maize Yield by Assimilating Biomass and Canopy Cover Derived from Hyperspectral Data into the AquaCrop Model., Agric. Water Manag., 227, p. 105846, DOI: 10.1016/j.agwat.2019.105846</label>
          <listPosition>81</listPosition>
          <doi>10.1016/j.agwat.2019.105846</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957904</mtid>
          <link>/api/reference/63957904</link>
          <label>82. Paredes 2014: Assessing the Performance of the FAO AquaCrop Model to Estimate Maize Yields and Water Use under Full and Deficit Irrigation with Focus on Model Parameterization., Agric. Water Manag., 144, p. 81, DOI: 10.1016/j.agwat.2014.06.002</label>
          <listPosition>82</listPosition>
          <doi>10.1016/j.agwat.2014.06.002</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957905</mtid>
          <link>/api/reference/63957905</link>
          <label>83. Panek-Chwastyk, E., Ceren, N.O., Dąbrowska-Zielińska, K., and Gurdak, R. (2024). Advancing Crop Yield Predictions: AQUACROP Model Application in Poland’s JECAM Fields. Agronomy, 14., DOI: 10.3390/agronomy14040854</label>
          <listPosition>83</listPosition>
          <doi>10.3390/agronomy14040854</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957906</mtid>
          <link>/api/reference/63957906</link>
          <label>84. Shan, Y., Li, G., Su, L., Zhang, J., Wang, Q., Wu, J., Mu, W., and Sun, Y. (2022). Performance of AquaCrop Model for Maize Growth Simulation under Different Soil Conditioners in Shandong Coastal Area, China. Agronomy, 12., DOI: 10.3390/agronomy12071541</label>
          <listPosition>84</listPosition>
          <doi>10.3390/agronomy12071541</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957907</mtid>
          <link>/api/reference/63957907</link>
          <label>85. Greaves, G.E., and Wang, Y. (2016). Assessment of FAO AquaCrop Model for Simulating Maize Growth and Productivity under Deficit Irrigation in a Tropical Environment. Water, 8., DOI: 10.3390/w8120557</label>
          <listPosition>85</listPosition>
          <doi>10.3390/w8120557</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957908</mtid>
          <link>/api/reference/63957908</link>
          <label>86. Abedinpour 2012: Performance Evaluation of AquaCrop Model for Maize Crop in a Semi-arid Environment., Agric. Water Manag., 110, p. 55, DOI: 10.1016/j.agwat.2012.04.001</label>
          <listPosition>86</listPosition>
          <doi>10.1016/j.agwat.2012.04.001</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957909</mtid>
          <link>/api/reference/63957909</link>
          <label>87. Heng 2009: Validating the FAO AquaCrop Model for Irrigated and Water Deficient Field Maize., Agron. J., 101, p. 488, DOI: 10.2134/agronj2008.0029xs</label>
          <listPosition>87</listPosition>
          <doi>10.2134/agronj2008.0029xs</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957910</mtid>
          <link>/api/reference/63957910</link>
          <label>88. Raja 2018: Validating the AquaCrop Model for Maize under Different Sowing Dates., Water Policy, 20, p. 826, DOI: 10.2166/wp.2018.123</label>
          <listPosition>88</listPosition>
          <doi>10.2166/wp.2018.123</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957911</mtid>
          <link>/api/reference/63957911</link>
          <label>89. Eshete 2022: Simulating Maize Water Productivity at Deficit Irrigated Field in North West Ethiopia., Sustain. Water Resour. Manag., 8, p. 186, DOI: 10.1007/s40899-022-00771-5</label>
          <listPosition>89</listPosition>
          <doi>10.1007/s40899-022-00771-5</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957912</mtid>
          <link>/api/reference/63957912</link>
          <label>90. Herbst 2018: A Model Study on the Effect of Water and Cold Stress on Maize Development under Nemoral Climate., Agric. For. Meteorol., 263, p. 169, DOI: 10.1016/j.agrformet.2018.08.011</label>
          <listPosition>90</listPosition>
          <doi>10.1016/j.agrformet.2018.08.011</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957913</mtid>
          <link>/api/reference/63957913</link>
          <label>91. Saseendran 2014: Enhancing the Water Stress Factors for Simulation of Corn in RZWQM2., Agron. J., 106, p. 81, DOI: 10.2134/agronj2013.0300</label>
          <listPosition>91</listPosition>
          <doi>10.2134/agronj2013.0300</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957914</mtid>
          <link>/api/reference/63957914</link>
          <label>92. Ma 2012: Calibrating RZWQM2 Model for Maize Responses to Deficit Irrigation., Agric. Water Manag., 103, p. 140, DOI: 10.1016/j.agwat.2011.11.005</label>
          <listPosition>92</listPosition>
          <doi>10.1016/j.agwat.2011.11.005</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957915</mtid>
          <link>/api/reference/63957915</link>
          <label>93. Sima 2020: Direct Assimilation of Measured Soil Water Content in Root Zone Water Quality Model Calibration for Deficitirrigated Maize., Agron. J., 112, p. 844, DOI: 10.1002/agj2.20088</label>
          <listPosition>93</listPosition>
          <doi>10.1002/agj2.20088</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957916</mtid>
          <link>/api/reference/63957916</link>
          <label>94. Cheng, H., Yu, Q., Mohmed, A., Li, F., Qi, Z., Zhu, T., Cai, W., Chen, X., and Feng, S. (2022). RZWQM2 Simulated Drip Fertigation Management to Improve Water and Nitrogen Use Efficiency of Maize in a Solar Greenhouse. Agriculture, 12., DOI: 10.3390/agriculture12050672</label>
          <listPosition>94</listPosition>
          <doi>10.3390/agriculture12050672</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957917</mtid>
          <link>/api/reference/63957917</link>
          <label>95. Manschadi 2021: Full Parameterisation Matters for the Best Performance of Crop Models: Inter-comparison of a Simple and a Detailed Maize Model., Int. J. Plant Prod., 15, p. 61, DOI: 10.1007/s42106-020-00116-2</label>
          <listPosition>95</listPosition>
          <doi>10.1007/s42106-020-00116-2</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957918</mtid>
          <link>/api/reference/63957918</link>
          <label>96. Santos 2020: A Modelling Assessment of the Maize Crop Growth, Yield and Soil Water Dynamics in the Northeast of Brazil., Unesp.Br., 14, p. 897</label>
          <listPosition>96</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957919</mtid>
          <link>/api/reference/63957919</link>
          <label>97. Chen 2021: Simulation of Yield and Water Balance Using WHCNS and APSIM Combined with Geostatistics across a Heterogeneous Field., Agric. Water Manag., 258, p. 107174, DOI: 10.1016/j.agwat.2021.107174</label>
          <listPosition>97</listPosition>
          <doi>10.1016/j.agwat.2021.107174</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957920</mtid>
          <link>/api/reference/63957920</link>
          <label>98. Chisanga 2021: Evaluating APSIM-and-DSSAT-CERES-Maize Models under Rainfed Conditions Using Zambian Rainfed Maize Cultivars., Nitrogen, 2, p. 392, DOI: 10.3390/nitrogen2040027</label>
          <listPosition>98</listPosition>
          <doi>10.3390/nitrogen2040027</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957921</mtid>
          <link>/api/reference/63957921</link>
          <label>99. Archontoulis 2014: Evaluating APSIM Maize, Soil Water, Soil Nitrogen, Manure, and Soil Temperature Modules in the Midwestern United States., Agron. J., 106, p. 1025, DOI: 10.2134/agronj2013.0421</label>
          <listPosition>99</listPosition>
          <doi>10.2134/agronj2013.0421</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957922</mtid>
          <link>/api/reference/63957922</link>
          <label>100. Kivi 2022: Development of a Data-assimilation System to Forecast Agricultural Systems: A Case Study of Constraining Soil Water and Soil Nitrogen Dynamics in the APSIM Model., Sci. Total Environ., 820, p. 153192, DOI: 10.1016/j.scitotenv.2022.153192</label>
          <listPosition>100</listPosition>
          <doi>10.1016/j.scitotenv.2022.153192</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957923</mtid>
          <link>/api/reference/63957923</link>
          <label>101. Machwitz 2014: Enhanced Biomass Prediction by Assimilating Satellite Data into a Crop Growth Model., Environ. Model. Softw., 62, p. 437, DOI: 10.1016/j.envsoft.2014.08.010</label>
          <listPosition>101</listPosition>
          <doi>10.1016/j.envsoft.2014.08.010</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957924</mtid>
          <link>/api/reference/63957924</link>
          <label>102. Peng 2018: Improving Maize Growth Processes in the Community Land Model: Implementation and Evaluation., Agric. For. Meteorol., 250-251, p. 64, DOI: 10.1016/j.agrformet.2017.11.012</label>
          <listPosition>102</listPosition>
          <doi>10.1016/j.agrformet.2017.11.012</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957925</mtid>
          <link>/api/reference/63957925</link>
          <label>103. Eweys 2017: Integrating WOFOST and Noah LSM for Modeling Maize Production and Soil Moisture with Sensitivity Analysis, in the East of the Netherlands., Field Crop. Res., 210, p. 147, DOI: 10.1016/j.fcr.2017.06.004</label>
          <listPosition>103</listPosition>
          <doi>10.1016/j.fcr.2017.06.004</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957926</mtid>
          <link>/api/reference/63957926</link>
          <label>104. Li 2014: Assimilating Remote Sensing Information into a Coupled Hydrology-crop Growth Model to Estimate Regional Maize Yield in Arid Regions., Ecol. Model., 291, p. 15, DOI: 10.1016/j.ecolmodel.2014.07.013</label>
          <listPosition>104</listPosition>
          <doi>10.1016/j.ecolmodel.2014.07.013</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957927</mtid>
          <link>/api/reference/63957927</link>
          <label>105. Dzotsi 2015: Parameter and Uncertainty Estimation for Maize, Peanut and Cotton Using the SALUS Crop Model., Agric. Syst., 135, p. 31, DOI: 10.1016/j.agsy.2014.12.003</label>
          <listPosition>105</listPosition>
          <doi>10.1016/j.agsy.2014.12.003</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957928</mtid>
          <link>/api/reference/63957928</link>
          <label>106. Dzotsi 2013: Development, Uncertainty and Sensitivity Analysis of the Simple SALUS Crop Model in DSSAT., Ecol. Model., 260, p. 62, DOI: 10.1016/j.ecolmodel.2013.03.017</label>
          <listPosition>106</listPosition>
          <doi>10.1016/j.ecolmodel.2013.03.017</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957929</mtid>
          <link>/api/reference/63957929</link>
          <label>107. Williams 2017: Evaluation of JULES-Crop Performance against Site Observations of Irrigated Maize from Mead, Nebraska., Geosci. Model Dev., 10, p. 1291, DOI: 10.5194/gmd-10-1291-2017</label>
          <listPosition>107</listPosition>
          <doi>10.5194/gmd-10-1291-2017</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957930</mtid>
          <link>/api/reference/63957930</link>
          <label>108. Ingwersen 2018: Coupling the Land Surface Model Noah-MP with the Generic Crop Growth Model Gecros: Model Description, Calibration and Validation., Agric. For. Meteorol., 262, p. 322, DOI: 10.1016/j.agrformet.2018.06.023</label>
          <listPosition>108</listPosition>
          <doi>10.1016/j.agrformet.2018.06.023</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957931</mtid>
          <link>/api/reference/63957931</link>
          <label>109. Cavero 2000: Simulation of Maize Yield under Water Stress with the EPICphase and CROPWAT Models., Agron. J., 92, p. 679, DOI: 10.2134/agronj2000.924679x</label>
          <listPosition>109</listPosition>
          <doi>10.2134/agronj2000.924679x</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957932</mtid>
          <link>/api/reference/63957932</link>
          <label>110. Bellocchi 2002: Using the CropSyst Model in Continuous Rainfed Maize (Zea mais L.) under Alternative Management Options., Ital. J. Agron., 6, p. 43</label>
          <listPosition>110</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957933</mtid>
          <link>/api/reference/63957933</link>
          <label>111. Amuti, T., Luo, G., Yin, G., Hu, Q., and Walter-Shea, E.A. (2018). Validation of a Process-Based Agro-Ecosystem Model (Agro-IBIS) for Maize in Xinjiang, Northwest China. Agronomy, 8., DOI: 10.3390/agronomy8030029</label>
          <listPosition>111</listPosition>
          <doi>10.3390/agronomy8030029</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957934</mtid>
          <link>/api/reference/63957934</link>
          <label>112. Didan, K. (2025, February 12). MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V061. Distributed by NASA EOSDIS Land Processes DAAC, Available online: https://lpdaac.usgs.gov/products/mod13q1v061/.</label>
          <listPosition>112</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957935</mtid>
          <link>/api/reference/63957935</link>
          <label>113. Zylshal 2021: Correcting the Topographic Effect On Spot-6/7 Multispectral Imageries: A Comparison of Different Digital Elevation Models., Geogr. Tech., 16, p. 163</label>
          <listPosition>113</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957936</mtid>
          <link>/api/reference/63957936</link>
          <label>114. Liu 2023: UAV Multispectral Images for Accurate Estimation of the Maize LAI Considering the Effect of Soil Background., Int. J. Appl. Earth Obs. Geoinf., 121, p. 103383</label>
          <listPosition>114</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957937</mtid>
          <link>/api/reference/63957937</link>
          <label>115. Lewis 2022: Location, Biophysical and Agronomic Parameters for Croplands in Northern Ghana., Earth Syst. Sci. Data, 14, p. 5387, DOI: 10.5194/essd-14-5387-2022</label>
          <listPosition>115</listPosition>
          <doi>10.5194/essd-14-5387-2022</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957938</mtid>
          <link>/api/reference/63957938</link>
          <label>116. Haboudane 2004: Hyperspectral Vegetation Indices and Novel Algorithms for Predicting Green LAI of Crop Canopies: Modeling and Validation in the Context of Precision Agriculture., Remote Sens. Environ., 90, p. 337, DOI: 10.1016/j.rse.2003.12.013</label>
          <listPosition>116</listPosition>
          <doi>10.1016/j.rse.2003.12.013</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957939</mtid>
          <link>/api/reference/63957939</link>
          <label>117. Kayad 2022: Radiative Transfer Model Inversion Using Highresolution Hyperspectral Airborne Imagery—Retrieving Maize LAI to Access Biomass and Grain Yield., Field Crop. Res., 282, p. 108449, DOI: 10.1016/j.fcr.2022.108449</label>
          <listPosition>117</listPosition>
          <doi>10.1016/j.fcr.2022.108449</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957940</mtid>
          <link>/api/reference/63957940</link>
          <label>118. Battude 2016: Estimating Maize Biomass and Yield over Large Areas Using High Spatial and Temporal Resolution Sentinel-2 like Remote Sensing Data., Remote Sens. Environ., 184, p. 668, DOI: 10.1016/j.rse.2016.07.030</label>
          <listPosition>118</listPosition>
          <doi>10.1016/j.rse.2016.07.030</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957941</mtid>
          <link>/api/reference/63957941</link>
          <label>119. Kira, O., Nguy-Robertson, A.L., Arkebauer, T.J., Linker, R., and Gitelson, A.A. (2017). Toward Generic Models for Green LAI Estimation in Maize and Soybean: Satellite Observations. Remote Sens., 9., DOI: 10.3390/rs9040318</label>
          <listPosition>119</listPosition>
          <doi>10.3390/rs9040318</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957942</mtid>
          <link>/api/reference/63957942</link>
          <label>120. Egea 2022: Leaf Area Index Estimations by Deep Learning Models Using RGB Images and Data Fusion in Maize., Precis. Agric., 23, p. 1949, DOI: 10.1007/s11119-022-09940-0</label>
          <listPosition>120</listPosition>
          <doi>10.1007/s11119-022-09940-0</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957943</mtid>
          <link>/api/reference/63957943</link>
          <label>121. Liu 2021: Estimating Leaf Area Index Using Unmanned Aerial Vehicle Data: Shallow vs. Deep Machine Learning Algorithms., Plant Physiol., 187, p. 1551, DOI: 10.1093/plphys/kiab322</label>
          <listPosition>121</listPosition>
          <doi>10.1093/plphys/kiab322</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957944</mtid>
          <link>/api/reference/63957944</link>
          <label>122. Justice 1998: The Moderate Resolution Imaging Spectroradiometer (MODIS): Land remote sensing for global change research., IEEE Trans. Geosci. Remote Sens., 36, p. 1228, DOI: 10.1109/36.701075</label>
          <listPosition>122</listPosition>
          <doi>10.1109/36.701075</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957945</mtid>
          <link>/api/reference/63957945</link>
          <label>123. Kang 2019: Field-level Crop Yield Mapping with Landsat Using a Hierarchical Data Assimilation Approach., Remote Sens. Environ., 228, p. 144, DOI: 10.1016/j.rse.2019.04.005</label>
          <listPosition>123</listPosition>
          <doi>10.1016/j.rse.2019.04.005</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957946</mtid>
          <link>/api/reference/63957946</link>
          <label>124. Vintrou 2014: A Comparative Study on Satellite- and Model-Based Crop Phenology in West Africa., Remote Sens., 6, p. 1367, DOI: 10.3390/rs6021367</label>
          <listPosition>124</listPosition>
          <doi>10.3390/rs6021367</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957947</mtid>
          <link>/api/reference/63957947</link>
          <label>125. Sakamoto 2013: MODIS-based Corn Grain Yield Estimation Model Incorporating Crop Phenology Information., Remote Sens. Environ., 131, p. 215, DOI: 10.1016/j.rse.2012.12.017</label>
          <listPosition>125</listPosition>
          <doi>10.1016/j.rse.2012.12.017</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957948</mtid>
          <link>/api/reference/63957948</link>
          <label>126. Ban, H.-Y., Ahn, J.-B., and Lee, B.-W. (2019). Assimilating MODIS Data-Derived Minimum Input Data Set and Water Stress Factors into CERES-Maize Model Improves Regional Corn Yield Predictions. PLoS ONE, 14., DOI: 10.1371/journal.pone.0211874</label>
          <listPosition>126</listPosition>
          <doi>10.1371/journal.pone.0211874</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957949</mtid>
          <link>/api/reference/63957949</link>
          <label>127. Jin 2016: Improvement of Spatially and Temporally Continuous Crop Leaf Area Index by Integration of CERES-Maize Model and MODIS Data., Eur. J. Agron., 78, p. 1, DOI: 10.1016/j.eja.2016.04.007</label>
          <listPosition>127</listPosition>
          <doi>10.1016/j.eja.2016.04.007</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957950</mtid>
          <link>/api/reference/63957950</link>
          <label>128. Hamze 2023: Detection of Irrigation Dates and Amounts on Maize Plots from the Integration of Sentinel-2 Derived Leaf Area Index Values in the Optirrig Crop Model., Agric. Water Manag., 283, p. 108315, DOI: 10.1016/j.agwat.2023.108315</label>
          <listPosition>128</listPosition>
          <doi>10.1016/j.agwat.2023.108315</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957951</mtid>
          <link>/api/reference/63957951</link>
          <label>129. Houborg 2009: Utility of an Image-based Canopy Reflectance Modeling Tool for Remote Estimation of LAI and Leaf Chlorophyll Content at the Field Scale., Remote Sens. Environ., 113, p. 259, DOI: 10.1016/j.rse.2008.09.014</label>
          <listPosition>129</listPosition>
          <doi>10.1016/j.rse.2008.09.014</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957952</mtid>
          <link>/api/reference/63957952</link>
          <label>130. Anderson 2004: Upscaling Ground Observations of Vegetation Water Content, Canopy Height, and Leaf Area Index during SMEX02 Using Aircraft and Landsat Imagery., Remote Sens. Environ., 92, p. 447, DOI: 10.1016/j.rse.2004.03.019</label>
          <listPosition>130</listPosition>
          <doi>10.1016/j.rse.2004.03.019</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957953</mtid>
          <link>/api/reference/63957953</link>
          <label>131. Lovell 2003: Using Airborne and Ground-based Ranging Lidar to Measure Canopy Structure in Australian Forests., Can. J. Remote Sens., 29, p. 607, DOI: 10.5589/m03-026</label>
          <listPosition>131</listPosition>
          <doi>10.5589/m03-026</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957954</mtid>
          <link>/api/reference/63957954</link>
          <label>132. Taheriazad 2019: Calculation of Leaf Area Index in a Canadian Boreal Forest Using Adaptive Voxelization and Terrestrial LiDAR., Int. J. Appl. Earth Obs. Geoinf., 83, p. 101923</label>
          <listPosition>132</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957955</mtid>
          <link>/api/reference/63957955</link>
          <label>133. Hu 2019: Pixel Size of Aerial Imagery Constrains the Applications of Unmanned Aerial Vehicle in Crop Breeding., ISPRS J. Photogramm. Remote Sens., 154, p. 1, DOI: 10.1016/j.isprsjprs.2019.05.008</label>
          <listPosition>133</listPosition>
          <doi>10.1016/j.isprsjprs.2019.05.008</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957956</mtid>
          <link>/api/reference/63957956</link>
          <label>134. Cheng, Q., Xu, H., Fei, S., Li, Z., and Chen, Z. (2022). Estimation of Maize LAI Using Ensemble Learning and UAV Multispectral Imagery under Different Water and Fertilizer Treatments. Agriculture, 12., DOI: 10.3390/agriculture12081267</label>
          <listPosition>134</listPosition>
          <doi>10.3390/agriculture12081267</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957957</mtid>
          <link>/api/reference/63957957</link>
          <label>135. Qiao 2022: Estimating Maize LAI by Exploring Deep Features of Vegetation Index Map from UAV Multispectral Images., Field Crop. Res., 289, p. 108739, DOI: 10.1016/j.fcr.2022.108739</label>
          <listPosition>135</listPosition>
          <doi>10.1016/j.fcr.2022.108739</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957958</mtid>
          <link>/api/reference/63957958</link>
          <label>136. Ma, H., Li, X., Ji, J., Cui, H., Shi, Y., Li, N., and Yang, C. (2023). Monitoring Indicators for Comprehensive Growth of Summer Maize Based on UAV Remote Sensing. Agronomy, 13., DOI: 10.3390/agronomy13122888</label>
          <listPosition>136</listPosition>
          <doi>10.3390/agronomy13122888</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957959</mtid>
          <link>/api/reference/63957959</link>
          <label>137. de Magalhães, L.P., and Rossi, F. (2024). Use of Indices in RGB and Random Forest Regression to Measure the Leaf Area Index in Maize. Agronomy, 14., DOI: 10.3390/agronomy14040750</label>
          <listPosition>137</listPosition>
          <doi>10.3390/agronomy14040750</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957960</mtid>
          <link>/api/reference/63957960</link>
          <label>138. Cheng, Z., Meng, J., Shang, J., Liu, J., Huang, J., Qiao, Y., Qian, B., Jing, Q., Dong, T., and Yu, L. (2020). Generating Time-Series LAI Estimates of Maize Using Combined Methods Based on Multispectral UAV Observations and WOFOST Model. Sensors, 20., DOI: 10.3390/s20216006</label>
          <listPosition>138</listPosition>
          <doi>10.3390/s20216006</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957961</mtid>
          <link>/api/reference/63957961</link>
          <label>139. Peng, X., Han, W., Ao, J., and Wang, Y. (2021). Assimilation of LAI Derived from UAV Multispectral Data into the SAFY Model to Estimate Maize Yield. Remote Sens., 13., DOI: 10.3390/rs13061094</label>
          <listPosition>139</listPosition>
          <doi>10.3390/rs13061094</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957962</mtid>
          <link>/api/reference/63957962</link>
          <label>140. Du, L., Yang, H., Song, X., Wei, N., Yu, C., Wang, W., and Zhao, Y. (2022). Estimating Leaf Area Index of Maize Using UAV-based Digital Imagery and Machine Learning Methods. Sci. Rep., 12., DOI: 10.1038/s41598-022-20299-0</label>
          <listPosition>140</listPosition>
          <doi>10.1038/s41598-022-20299-0</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957963</mtid>
          <link>/api/reference/63957963</link>
          <label>141. Wang, X., Ren, J., and Wu, P. (2024). Analysis of Growth Variation in Maize Leaf Area Index Based on Time-Series Multispectral Images and Random Forest Models. Agronomy, 14., DOI: 10.3390/agronomy14112688</label>
          <listPosition>141</listPosition>
          <doi>10.3390/agronomy14112688</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957964</mtid>
          <link>/api/reference/63957964</link>
          <label>142. Qiao 2022: UAV-based Chlorophyll Content Estimation by Evaluating Vegetation Index Responses under Different Crop Coverages., Comput. Electron. Agric., 196, p. 106775, DOI: 10.1016/j.compag.2022.106775</label>
          <listPosition>142</listPosition>
          <doi>10.1016/j.compag.2022.106775</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>63957965</mtid>
          <link>/api/reference/63957965</link>
          <label>143. Szabó, A., Mousavi, S.M.N., Bojtor, C., Ragán, P., Nagy, J., Vad, A., and Illés, Á. (2022). Analysis of Nutrient-Specific Response of Maize Hybrids in Relation to Leaf Area Index (LAI) and Remote Sensing. Plants, 11., DOI: 10.3390/plants11091197</label>
          <listPosition>143</listPosition>
          <doi>10.3390/plants11091197</doi>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
      </references>
      <link>/api/publication/35780003</link>
      <label>Bakó Károly et al. Advancements in Leaf Area Index Estimation for Maize Using Modeling and Remote Sensing Techniques: A Review. (2025) AGRONOMY (BASEL) 2073-4395 15 3</label><template>&lt;div class=&quot;JournalArticle Publication short-list&quot;&gt; &lt;div class=&quot;authors&quot;&gt; &lt;span class=&quot;author-name&quot; mtid=&quot;10062028&quot;&gt; &lt;a href=&quot;/gui2/?type=authors&amp;mode=browse&amp;sel=10062028&quot; target=&quot;_blank&quot;&gt;Bakó, Károly&lt;/a&gt; &lt;/span&gt; &lt;span class=&quot;author-type&quot;&gt; &lt;/span&gt; ; &lt;span class=&quot;author-name&quot; mtid=&quot;10048826&quot;&gt; &lt;a href=&quot;/gui2/?type=authors&amp;mode=browse&amp;sel=10048826&quot; target=&quot;_blank&quot;&gt;Rácz, Csaba&lt;/a&gt; &lt;/span&gt; &lt;span class=&quot;author-type&quot;&gt; &lt;/span&gt; ; &lt;span class=&quot;author-name&quot; mtid=&quot;10023812&quot;&gt; &lt;a href=&quot;/gui2/?type=authors&amp;mode=browse&amp;sel=10023812&quot; target=&quot;_blank&quot;&gt;Dövényi-Nagy, Tamás&lt;/a&gt; &lt;/span&gt; &lt;span class=&quot;author-type&quot;&gt; &lt;/span&gt; ; &lt;span class=&quot;author-name&quot; mtid=&quot;10048599&quot;&gt; &lt;a href=&quot;/gui2/?type=authors&amp;mode=browse&amp;sel=10048599&quot; target=&quot;_blank&quot;&gt;Molnár, Krisztina&lt;/a&gt; &lt;/span&gt; &lt;span class=&quot;author-type&quot;&gt; &lt;/span&gt; ; &lt;span class=&quot;author-name&quot; mtid=&quot;10020301&quot;&gt; &lt;a href=&quot;/gui2/?type=authors&amp;mode=browse&amp;sel=10020301&quot; target=&quot;_blank&quot;&gt;Dobos, Attila&lt;/a&gt; &lt;/span&gt; &lt;span class=&quot;author-type&quot;&gt; &lt;/span&gt; &lt;/div &gt;&lt;div class=&quot;title&quot;&gt;&lt;a href=&quot;/gui2/?mode=browse&amp;params=publication;35780003&quot; mtid=&quot;35780003&quot; target=&quot;_blank&quot;&gt;Advancements in Leaf Area Index Estimation for Maize Using Modeling and Remote Sensing Techniques: A Review&lt;/a&gt;&lt;/div&gt; &lt;div class=&quot;pub-info&quot;&gt; &lt;span class=&quot;journal-title&quot;&gt;AGRONOMY (BASEL)&lt;/span&gt; &lt;span class=&quot;journal-volume&quot;&gt;15&lt;/span&gt; : &lt;span class=&quot;journal-issue&quot;&gt;3&lt;/span&gt; &lt;span class=&quot;page&quot;&gt; Paper: 519 , 22 p. &lt;/span&gt; &lt;span class=&quot;year&quot;&gt;(2025)&lt;/span&gt; &lt;/div&gt; &lt;div class=&quot;pub-end&quot;&gt;&lt;div class=&quot;identifier-list&quot;&gt; &lt;span class=&quot;identifiers&quot;&gt; &lt;span class=&quot;id identifier oa_none&quot; title=&quot;none&quot;&gt; &lt;a style=&quot;color:blue&quot; title=&quot;10.3390/agronomy15030519&quot; target=&quot;_blank&quot; href=&quot;https://doi.org/10.3390/agronomy15030519&quot;&gt; DOI &lt;/a&gt; &lt;/span&gt; &lt;span class=&quot;id identifier oa_none&quot; title=&quot;none&quot;&gt; &lt;a style=&quot;color:blue&quot; title=&quot;001453506600001&quot; target=&quot;_blank&quot; href=&quot;https://www.webofscience.com/wos/woscc/full-record/001453506600001&quot;&gt; WoS &lt;/a&gt; &lt;/span&gt; &lt;span class=&quot;id identifier oa_none&quot; title=&quot;none&quot;&gt; &lt;a style=&quot;color:blue&quot; title=&quot;389178&quot; target=&quot;_blank&quot; href=&quot;http://hdl.handle.net/2437/389178&quot;&gt; DEA &lt;/a&gt; &lt;/span&gt; &lt;span class=&quot;id identifier oa_none&quot; title=&quot;none&quot;&gt; &lt;a style=&quot;color:blue&quot; title=&quot;105001019445&quot; target=&quot;_blank&quot; href=&quot;http://www.scopus.com/record/display.url?origin=inward&amp;eid=2-s2.0-105001019445&quot;&gt; Scopus &lt;/a&gt; &lt;/span&gt; &lt;span class=&quot;id identifier oa_NONE&quot; title=&quot; Nincs &quot;&gt; &lt;a style=&quot;color:blue&quot; title=&quot;https://www.mdpi.com/2073-4395/15/3/519&quot; target=&quot;_blank&quot; href=&quot;https://www.mdpi.com/2073-4395/15/3/519&quot;&gt; Egyéb URL &lt;/a&gt; &lt;/span&gt; &lt;/span&gt; &lt;/div&gt; &lt;div class=&quot;short-pub-prop-list&quot;&gt; &lt;span class=&quot;short-pub-mtid&quot;&gt; Közlemény:35780003 &lt;/span&gt; &lt;span class=&quot;status-holder&quot;&gt;&lt;span class=&quot;status-data status-APPROVED&quot;&gt; Nyilvános &lt;/span&gt;&lt;/span&gt; &lt;span class=&quot;pub-core&quot;&gt;Forrás Idéző &lt;/span&gt; &lt;span class=&quot;pub-type&quot;&gt;Folyóiratcikk (Összefoglaló cikk ) &lt;/span&gt; &lt;!-- &amp;&amp; !record.category.scientific --&gt; &lt;span class=&quot;pub-category&quot;&gt;Tudományos&lt;/span&gt; &lt;div class=&quot;publication-citation&quot; style=&quot;margin-left: 0.5cm;&quot;&gt; &lt;span title=&quot;Nyilvános idézőközlemények összesen, említések nélkül&quot; class=&quot;citingPub-count&quot;&gt;Nyilvános idéző összesen: 2&lt;/span&gt; | Független: 2 | Függő: 0 | Nem jelölt: 0 | WoS jelölt: 2 | WoS/Scopus jelölt:&amp;nbsp;2 | DOI jelölt:&amp;nbsp;2 &lt;/div&gt; &lt;/div&gt; &lt;/div&gt; &lt;/div&gt;</template><template2>&lt;div class=&quot;JournalArticle Publication long-list&quot;&gt; &lt;div class=&quot;authors&quot;&gt; &lt;img title=&quot;Forrásközlemény&quot; style=&quot;float: left&quot; src=&quot;/frontend/resources/grid/publication-core-icon.png&quot;&gt; &lt;img title=&quot;Idézőközlemény&quot; style=&quot;float: left&quot; src=&quot;/frontend/resources/grid/publication-citation-icon.png&quot;&gt; &lt;div class=&quot;autype autype0&quot;&gt; &lt;span class=&quot;author-name&quot; mtid=&quot;10062028&quot;&gt;&lt;a href=&quot;/gui2/?type=authors&amp;mode=browse&amp;sel=10062028&quot; target=&quot;_blank&quot;&gt;Bakó Károly (&lt;span class=&quot;authorship-author-name&quot;&gt;Bakó Károly István&lt;/span&gt; &lt;span class=&quot;authorAux-mtmt&quot;&gt; Agrometeorológiai és Agroökológiai Monitoring K...&lt;/span&gt;) &lt;/a&gt; &lt;/span&gt; &lt;span class=&quot;author-affil&quot;&gt;&lt;span title=&quot;Debreceni Egyetem&quot;&gt;DE&lt;/span&gt;/&lt;span title=&quot;Mezőgazdaság-, Élelmiszertudományi és Környezetgazdálkodási Kar&quot;&gt;MÉK&lt;/span&gt;/Precíziós Növénytermesztési Kutatás-fejlesztési Szolgáltató Központ&lt;/span&gt; ;&amp;nbsp;&amp;nbsp;&amp;nbsp; &lt;span class=&quot;author-name&quot; mtid=&quot;10048826&quot;&gt;&lt;a href=&quot;/gui2/?type=authors&amp;mode=browse&amp;sel=10048826&quot; target=&quot;_blank&quot;&gt;Rácz Csaba (&lt;span class=&quot;authorship-author-name&quot;&gt;Rácz Csaba&lt;/span&gt; &lt;span class=&quot;authorAux-mtmt&quot;&gt; Agrometeorológia&lt;/span&gt;) &lt;/a&gt; &lt;/span&gt; &lt;span class=&quot;author-affil&quot;&gt;&lt;span title=&quot;Debreceni Egyetem&quot;&gt;DE&lt;/span&gt;/&lt;span title=&quot;Mezőgazdaság-, Élelmiszertudományi és Környezetgazdálkodási Kar&quot;&gt;MÉK&lt;/span&gt;/Precíziós Növénytermesztési Kutatás-fejlesztési Szolgáltató Központ&lt;/span&gt; ;&amp;nbsp;&amp;nbsp;&amp;nbsp; &lt;span class=&quot;author-name&quot; mtid=&quot;10023812&quot;&gt;&lt;a href=&quot;/gui2/?type=authors&amp;mode=browse&amp;sel=10023812&quot; target=&quot;_blank&quot;&gt;Dövényi-Nagy Tamás (&lt;span class=&quot;authorship-author-name&quot;&gt;Dövényi-Nagy Tamás&lt;/span&gt; &lt;span class=&quot;authorAux-mtmt&quot;&gt; Agárinformatika&lt;/span&gt;) &lt;/a&gt; &lt;/span&gt; &lt;span class=&quot;author-affil&quot;&gt;&lt;span title=&quot;Debreceni Egyetem&quot;&gt;DE&lt;/span&gt;/&lt;span title=&quot;Mezőgazdaság-, Élelmiszertudományi és Környezetgazdálkodási Kar&quot;&gt;MÉK&lt;/span&gt;/Precíziós Növénytermesztési Kutatás-fejlesztési Szolgáltató Központ&lt;/span&gt; ;&amp;nbsp;&amp;nbsp;&amp;nbsp; &lt;span class=&quot;author-name&quot; mtid=&quot;10048599&quot;&gt;&lt;a href=&quot;/gui2/?type=authors&amp;mode=browse&amp;sel=10048599&quot; target=&quot;_blank&quot;&gt;Molnár Krisztina (&lt;span class=&quot;authorship-author-name&quot;&gt;Molnár Krisztina&lt;/span&gt; &lt;span class=&quot;authorAux-mtmt&quot;&gt; növénytermesztés&lt;/span&gt;) &lt;/a&gt; &lt;/span&gt; &lt;span class=&quot;author-affil&quot;&gt;&lt;span title=&quot;Debreceni Egyetem&quot;&gt;DE&lt;/span&gt;/&lt;span title=&quot;Mezőgazdaság-, Élelmiszertudományi és Környezetgazdálkodási Kar&quot;&gt;MÉK&lt;/span&gt;/Precíziós Növénytermesztési Kutatás-fejlesztési Szolgáltató Központ&lt;/span&gt; ;&amp;nbsp;&amp;nbsp;&amp;nbsp; &lt;span class=&quot;author-name&quot; mtid=&quot;10020301&quot;&gt;&lt;a href=&quot;/gui2/?type=authors&amp;mode=browse&amp;sel=10020301&quot; target=&quot;_blank&quot;&gt;Dobos Attila (&lt;span class=&quot;authorship-author-name&quot;&gt;Dobos Attila Csaba&lt;/span&gt; &lt;span class=&quot;authorAux-mtmt&quot;&gt; Növénytermesztés&lt;/span&gt;) &lt;/a&gt; &lt;/span&gt; &lt;span class=&quot;author-affil&quot;&gt;&lt;span title=&quot;Debreceni Egyetem&quot;&gt;DE&lt;/span&gt;/&lt;span title=&quot;Mezőgazdaság-, Élelmiszertudományi és Környezetgazdálkodási Kar&quot;&gt;MÉK&lt;/span&gt;/Precíziós Növénytermesztési Kutatás-fejlesztési Szolgáltató Központ&lt;/span&gt; &lt;/div&gt; &lt;/div&gt; &lt;div class=&quot;title&quot;&gt;&lt;a href=&quot;/gui2/?mode=browse&amp;params=publication;35780003&quot; target=&quot;_blank&quot;&gt;Advancements in Leaf Area Index Estimation for Maize Using Modeling and Remote Sensing Techniques: A Review&lt;/a&gt;&lt;/div&gt; &lt;div&gt; &lt;span class=&quot;journal-title&quot;&gt;AGRONOMY (BASEL)&lt;/span&gt; &lt;span class=&quot;journal-issn&quot;&gt;( &lt;a target=&quot;_blank&quot; href=&quot;https://portal.issn.org/resource/ISSN/2073-4395&quot;&gt;2073-4395&lt;/a&gt;)&lt;/span&gt;: &lt;span class=&quot;journal-volume&quot;&gt;15&lt;/span&gt; &lt;span class=&quot;journal-issue&quot;&gt;3&lt;/span&gt; &lt;span class=&quot;page&quot;&gt; Paper 519. 22 p. &lt;/span&gt; &lt;span class=&quot;year&quot;&gt;(2025)&lt;/span&gt; &lt;/div&gt; &lt;div class=&quot;pub-footer&quot;&gt;  &lt;span class=&quot;language&quot; xmlns=&quot;http://www.w3.org/1999/html&quot;&gt;Nyelv: Angol | &lt;/span&gt; &lt;span class=&quot;identifiers&quot;&gt; &lt;span class=&quot;id identifier oa_none&quot; title=&quot;none&quot;&gt; &lt;a style=&quot;color:blue&quot; title=&quot;10.3390/agronomy15030519&quot; target=&quot;_blank&quot; href=&quot;https://doi.org/10.3390/agronomy15030519&quot;&gt; DOI &lt;/a&gt; &lt;/span&gt; &lt;span class=&quot;id identifier oa_none&quot; title=&quot;none&quot;&gt; &lt;a style=&quot;color:blue&quot; title=&quot;001453506600001&quot; target=&quot;_blank&quot; href=&quot;https://www.webofscience.com/wos/woscc/full-record/001453506600001&quot;&gt; WoS &lt;/a&gt; &lt;/span&gt; &lt;span class=&quot;id identifier oa_none&quot; title=&quot;none&quot;&gt; &lt;a style=&quot;color:blue&quot; title=&quot;389178&quot; target=&quot;_blank&quot; href=&quot;http://hdl.handle.net/2437/389178&quot;&gt; DEA &lt;/a&gt; &lt;/span&gt; &lt;span class=&quot;id identifier oa_none&quot; title=&quot;none&quot;&gt; &lt;a style=&quot;color:blue&quot; title=&quot;105001019445&quot; target=&quot;_blank&quot; href=&quot;http://www.scopus.com/record/display.url?origin=inward&amp;eid=2-s2.0-105001019445&quot;&gt; Scopus &lt;/a&gt; &lt;/span&gt; &lt;span class=&quot;id identifier oa_NONE&quot; title=&quot; Nincs &quot;&gt; &lt;a style=&quot;color:blue&quot; title=&quot;https://www.mdpi.com/2073-4395/15/3/519&quot; target=&quot;_blank&quot; href=&quot;https://www.mdpi.com/2073-4395/15/3/519&quot;&gt; Egyéb URL &lt;/a&gt; &lt;/span&gt; &lt;/span&gt; &lt;OnlyViewableByAuthor&gt;&lt;div class=&quot;ratings&quot;&gt; &lt;div class=&quot;journal-subject&quot;&gt;Folyóirat szakterülete: Scopus - Agronomy and Crop Science&amp;nbsp;&amp;nbsp;&amp;nbsp;SJR indikátor:&amp;nbsp;Q1&lt;/div&gt; &lt;div class=&quot;journal-subject&quot;&gt;Szociológiai Tudományos Bizottság IXGJO SZTB [1901-]&amp;nbsp;&amp;nbsp;&amp;nbsp;B nemzetközi&lt;/div&gt; &lt;/div&gt;&lt;/OnlyViewableByAuthor&gt; &lt;div class=&quot;publication-citation&quot; style=&quot;margin-left: 0.5cm;&quot;&gt; &lt;span title=&quot;Nyilvános idézőközlemények összesen, említések nélkül&quot; class=&quot;citingPub-count&quot;&gt;Nyilvános idéző összesen: 2&lt;/span&gt; | Független: 2 | Függő: 0 | Nem jelölt: 0 | WoS jelölt: 2 | WoS/Scopus jelölt:&amp;nbsp;2 | DOI jelölt:&amp;nbsp;2 &lt;/div&gt;   &lt;div class=&quot;publication-citation&quot;&gt; &lt;a target=&quot;_blank&quot; href=&quot;/api/publication?cond=citations.related;eq;35780003&amp;sort=publishedYear,desc&amp;sort=title&quot;&gt; Idézett közlemények száma: 3 &lt;/a&gt; &lt;/div&gt; &lt;div class=&quot;mtid&quot;&gt;&lt;span class=&quot;long-pub-mtid&quot;&gt;Közlemény: 35780003&lt;/span&gt; | &lt;span class=&quot;status-data status-APPROVED&quot;&gt; Nyilvános &lt;/span&gt; Forrás Idéző | &lt;span class=&quot;type-subtype&quot;&gt;Folyóiratcikk ( Összefoglaló cikk ) &lt;/span&gt; | &lt;span class=&quot;pub-category&quot;&gt;Tudományos&lt;/span&gt; | &lt;span class=&quot;publication-sourceOfData&quot;&gt;kézi felvitel&lt;/span&gt; &lt;/div&gt; &lt;div class=&quot;funder&quot;&gt; (GINOP-2.2.1-15-2016-00021) Támogató: NFÜ, (GINOP-2.2.1-15-2017-00076) Támogató: NFÜ, A magyar fogyasztók rövid és hosszú távú aflatoxin-terhelésének meghatározása a tejtermékláncban ...(2018-1.2.1-NKP-2018-00002) Támogató: NKFIH &lt;/div&gt; &lt;div class=&quot;lastModified&quot;&gt;Utolsó módosítás: 2026.09.03. 14:56 Szendrei László (DE/BTK admin5) &lt;/div&gt; &lt;/div&gt;&lt;/div&gt;</template2>
    </publication>
  </content>
</myciteResult>
