Melola Discovery

← Melola Discovery

Open access Journal SINTA 1

Land Suitability Analysis for Agricultural Land Use using Hyperscale DEM Data

AGRIVITA, Journal of Agricultural Science · Vol. 44 No. 2 (2022) · pp. 187–198

DOI
10.17503/agrivita.v44i2.2985
ISSN
2477-8516, 0126-0537
Published
2022-03-01
Publisher
Faculty of Agriculture University of Brawijaya in collaboration with PERAGI
Language
eng
License
creativecommons.org/licenses/by-nc/4.0

Abstract

Cropland suitability analysis is a crucial process for achieving the optimum utilization of land use. It is frequently used today and continues to develop, especially in the methods. Digital soil mapping is a new technique that can generate spatial predictions of soil attributes obtained from digital soil covariates, reducing the cost, time, and land-use suitability evaluation accuracy. This study aims to determine the land suitability for agriculture commodities based on a hyperscale soil texture predictive model and compare it with the conventional land-use suitability evaluation based on determining soil texture. The target agricultural commodities in this study are leafy vegetables, carrots, apples, and coffee. This study finds that the Hyperscale Soil Texture estimation model can be used as a parameter for land suitability assessment for agricultural commodities. The prediction obtained by this model is not significantly different from that obtained by the traditional approach. This research discovers that the two procedures produced similar outcomes. The hyperscale approach can be an alternative method for land suitability estimation and reduce the time and cost compared to traditional techniques.

Keywords

Collaboration network of the authors

32 authors · 106 co-authorship links

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • University of Brawijaya
  • National Research and Innovation Agency
  • Riau University
  • University of Pembangunan Nasional Veteran Jawa Timur
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Novi ArfaritaAffiliation unknown7
Muhammad HaidarAffiliation unknown5
Masita Dwi Mandini ManessaAffiliation unknown5
Sigit SutiknoAffiliation unknown3
Cahyo PrayogoAffiliation unknown3
Rinaldi RinaldiAffiliation unknown2
Muhamad YusaAffiliation unknown2
Ahmad MuhammadAffiliation unknown2
Henny PramoedyoUniversity of Brawijaya1
Atiek IrianyUniversity of Brawijaya1
Wigbertus NgabuUniversity of Brawijaya1
Budi PrasetyaAffiliation unknown1
Djuhari DjuhariAffiliation unknown1
Anton MuhibuddinAffiliation unknown1
Mohammad Jasa AfroniAffiliation unknown1
S SugiartoAffiliation unknown1
Hendra SaputraAffiliation unknown1
Khusnul Setia WardaniNational Research and Innovation Agency1
ZULKIFLI ZULKIFLIAffiliation unknown1
Soewignjo Agus NugrohoAffiliation unknown1
Syawal SatibiAffiliation unknown1
Agus Ika PutraAffiliation unknown1
Nur HidayatiAffiliation unknown1
Nurul QomarRiau University1
Sugiarto SugiartoUniversity of Pembangunan Nasional Veteran Jawa Timur1
Masyhuri MasyhuriAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://agrivita.ub.ac.id/index.php/agrivita/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Land Suitability Analysis for Agricultural Land Use using Hyperscale DEM Data</dc:title>
	<dc:creator>Riza, Sativandi</dc:creator>
	<dc:creator>Sekine, Masahiko</dc:creator>
	<dc:creator>Kanno, Ariyo</dc:creator>
	<dc:creator>Yamamoto, Koichi</dc:creator>
	<dc:creator>Imai, Tsuyoshi</dc:creator>
	<dc:creator>Higuchi, Takaya</dc:creator>
	<dc:subject xml:lang="en-US">soil-plant sciences</dc:subject>
	<dc:subject xml:lang="en-US">Digital Soil Mapping; Hyperscale Modeling; Land Suitability; Land-Use; Soil Texture</dc:subject>
	<dc:description xml:lang="en-US">Cropland suitability analysis is a crucial process for achieving the optimum utilization of land use. It is frequently used today and continues to develop, especially in the methods. Digital soil mapping is a new technique that can generate spatial predictions of soil attributes obtained from digital soil covariates, reducing the cost, time, and land-use suitability evaluation accuracy. This study aims to determine the land suitability for agriculture commodities based on a hyperscale soil texture predictive model and compare it with the conventional land-use suitability evaluation based on determining soil texture. The target agricultural commodities in this study are leafy vegetables, carrots, apples, and coffee. This study finds that the Hyperscale Soil Texture estimation model can be used as a parameter for land suitability assessment for agricultural commodities. The prediction obtained by this model is not significantly different from that obtained by the traditional approach. This research discovers that the two procedures produced similar outcomes. The hyperscale approach can be an alternative method for land suitability estimation and reduce the time and cost compared to traditional techniques.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Agriculture University of Brawijaya in collaboration with PERAGI</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2022-03-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://agrivita.ub.ac.id/index.php/agrivita/article/view/2985</dc:identifier>
	<dc:identifier>10.17503/agrivita.v44i2.2985</dc:identifier>
	<dc:source xml:lang="en-US">AGRIVITA Journal of Agricultural Science; Vol 44, No 2 (2022); 187-198</dc:source>
	<dc:source>2477-8516</dc:source>
	<dc:source>0126-0537</dc:source>
	<dc:source>10.17503/agrivita.v44i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://agrivita.ub.ac.id/index.php/agrivita/article/view/2985/1479</dc:relation>
	<dc:relation>https://agrivita.ub.ac.id/index.php/agrivita/article/downloadSuppFile/2985/2504</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 The Author(s)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-nc/4.0</dc:rights>
</oai_dc:dc>