Show simple item record

dc.contributor.authorLiang, B
dc.contributor.authorQuine, TA
dc.contributor.authorLiu, H
dc.contributor.authorCressey, EL
dc.contributor.authorBateman, I
dc.date.accessioned2022-06-10T12:23:03Z
dc.date.issued2021-04-21
dc.date.updated2022-06-10T10:04:13Z
dc.description.abstractTo meet the sustainable development goals in rocky desertified regions like Guizhou Province in China, we should maximize the crop yield with minimal environmental costs. In this study, we first calculated the yield gap for 6 main crop species in Guizhou Province and evaluated the quantitative relationships between crop yield and influencing variables utilizing ensembled artificial neural networks. We also tested the influence of adjusting the quantity of local fertilization and irrigation on crop production in Guizhou Province. Results showed that the total yield of the selected crops had, on average, reached over 72.5% of the theoretical maximum yield. Increasing irrigation tended to be more consistently effective at increasing crop yield than additional fertilization. Conversely, appropriate reduction of fertilization may even benefit crop yield in some regions, simultaneously resulting in significantly higher fertilization efficiency with lower residuals in the environment. The total positive impact of continuous intensification of irrigation and fertilization on most crop species was limited. Therefore, local stakeholders are advised to consider other agricultural management measures to improve crop yield in this region.en_GB
dc.description.sponsorshipNational Natural Science Foundation of Chinaen_GB
dc.description.sponsorshipNatural Environment Research Councilen_GB
dc.format.extent1614-
dc.identifier.citationVol. 13, No. 9, article ARTN 1614en_GB
dc.identifier.doihttps://doi.org/10.3390/rs13091614
dc.identifier.grantnumber41571130044en_GB
dc.identifier.grantnumberNE/S009175/1en_GB
dc.identifier.urihttp://hdl.handle.net/10871/129901
dc.identifierORCID: 0000-0002-5143-5157 (Quine, Timothy A)
dc.identifierORCID: 0000-0002-2535-6420 (Cressey, Elizabeth L)
dc.identifierORCID: 0000-0002-2791-6137 (Bateman, Ian)
dc.identifierScopusID: 7005934781 (Bateman, Ian)
dc.identifierResearcherID: F-8011-2010 (Bateman, Ian)
dc.language.isoenen_GB
dc.publisherMDPIen_GB
dc.rights© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)en_GB
dc.subjectcrop yielden_GB
dc.subjectartificial neural networken_GB
dc.subjectagricultural managementen_GB
dc.subjectcritical zoneen_GB
dc.subjectDSTen_GB
dc.titleHow can we realize sustainable development goals in rocky desertified regions by enhancing crop yield with reduction of environmental risks?en_GB
dc.typeArticleen_GB
dc.date.available2022-06-10T12:23:03Z
dc.identifier.issn2315-4675
exeter.article-numberARTN 1614
dc.descriptionThis is the final version. Available from MDPI via the DOI in this record. en_GB
dc.descriptionData Availability Statement: The data utilized in the study is available from the authors, on reasonable request.en_GB
dc.identifier.eissn2072-4292
dc.identifier.journalRemote Sensingen_GB
dc.relation.ispartofRemote Sensing, 13(9)
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_GB
dcterms.dateAccepted2021-04-01
rioxxterms.versionVoRen_GB
rioxxterms.licenseref.startdate2021-04-21
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2022-06-10T12:19:51Z
refterms.versionFCDVoR
refterms.dateFOA2022-06-10T12:24:17Z
refterms.panelCen_GB
refterms.dateFirstOnline2021-04-21


Files in this item

This item appears in the following Collection(s)

Show simple item record

© 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
Except where otherwise noted, this item's licence is described as © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)