Comparison of soil physical properties and soil-vegetation indices to predict rice productivity in Malang Regency of East Java

Putra, Aditya Nugraha; Sudarto, S; Ridwan, Ananda Ginanthian Alpheratz; Aditama, Aftomi Firman; Janahtin, Sifaâ€™ul

doi:10.15243/jdmlm.2021.084.2891

Akses terbuka Jurnal SINTA 1

Comparison of soil physical properties and soil-vegetation indices to predict rice productivity in Malang Regency of East Java

Journal of Degraded and Mining Lands Management · Vol. 8 No. 4 (2021) · hlm. 2891–2901

DOI: 10.15243/jdmlm.2021.084.2891
ISSN: 2502-2458, 2339-076X
Terbit: 2021-07-01
Penerbit: Brawijaya University
Bahasa: eng
Lisensi: creativecommons.org/licenses/by-nc/4.0

Abstrak

Rice has become the leading food commodity in Indonesia, with total production reached +54.60 million tons in 2019. However, the production tended to decrease by around 8% from 2018 to 2019, while the rice consumption increased by +1.53 tons. This study aims to develop a rice production estimation model using the soil-vegetation index transformation (MSAVI and SAVI) and soil physical properties, which has the advantage of being faster, cheaper, and more accurate than conventional methods. The soil physical properties were taken based on soil mapping units and analyzed with soil physical parameters. The results showed strong relationships between rice productivity - soil physical characteristics and rice productivity â€“ MSAVI and EVI with r values of 0.97, 0.83, and 0.74, respectively. The soil physical properties have a better coefficient of determination and accuracy than soil-vegetation index. The prediction model of rice production by soil physical properties is formulated inward Î³ = -8.96+0.01 (Top Soil Sand) + 0.01 (Top Soil Silt) + 6.28 (Bulk Density) - 14.07 (Penetration) - 0.13 (Sub Soil Permeability). There is no difference in the productivity value between model and laboratory analysis result. These results indicate that the rice yield prediction model can be used for estimation purposes.

Kata kunci

Jaringan kolaborasi para penulis

28 penulis · 111 tautan kepenulisan bersama

Arahkan kursor atau ketuk sebuah nama untuk menyorot rekan penulisnya. Klik dua kali untuk membuka halaman penulis.

University of Brawijaya
Afiliasi tidak diketahui

Organisasi berbeda
Organisasi sama
Afiliasi tidak diketahui
Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel

Tampilkan sebagai tabel

Rekan penulis	Organisasi	Artikel bersama
Istika Nita	University of Brawijaya	3
Mochtar Lutfi Rayes	University of Brawijaya	3
Christanti Agustina	University of Brawijaya	2
Syahrul Kurniawan	University of Brawijaya	2
Sri Rahayu Utami	University of Brawijaya	2
Didik Suprayogo	University of Brawijaya	2
Syamsu Ridzal Indra Hadi	Afiliasi tidak diketahui	1
Soemarno Soemarno	University of Brawijaya	1
Novandi Rizky Prasetya	University of Brawijaya	1
Sudarto Sudarto	University of Brawijaya	1
Reni Ustiatik	University of Brawijaya	1
Erza Aulia Adara	University of Brawijaya	1
Mochammad Munir	University of Brawijaya	1
C Andriyanto	University of Brawijaya	1
Iva Dewi Lestariningsih	University of Brawijaya	1
W Widianto	University of Brawijaya	1
Anita Dwy Fitria	University of Brawijaya	1
Erekso Hadiwijoyo	University of Brawijaya	1
Rika Ratna Sari	University of Brawijaya	1
Rizki Maulana Ishaq	University of Brawijaya	1
Danny Dwi Saputra	University of Brawijaya	1
Maruf Hadi	Afiliasi tidak diketahui	1
Alia Fibrianingtyas	University of Brawijaya	1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://jdmlm.ub.ac.id/index.php/jdmlm/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.

Metadata asli (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">Comparison of soil physical properties and soil-vegetation indices to predict rice productivity in Malang Regency of East Java</dc:title>
	<dc:creator>Putra, Aditya Nugraha</dc:creator>
	<dc:creator>Sudarto, S</dc:creator>
	<dc:creator>Ridwan, Ananda Ginanthian Alpheratz</dc:creator>
	<dc:creator>Aditama, Aftomi Firman</dc:creator>
	<dc:creator>Janahtin, Sifaâ€™ul</dc:creator>
	<dc:subject xml:lang="en-US">remote sensing</dc:subject>
	<dc:subject xml:lang="en-US">. rice productivity</dc:subject>
	<dc:subject xml:lang="en-US">soil index</dc:subject>
	<dc:subject xml:lang="en-US">soil physical properties</dc:subject>
	<dc:subject xml:lang="en-US">vegetation index</dc:subject>
	<dc:description xml:lang="en-US">Rice has become the leading food commodity in Indonesia, with total production reached +54.60 million tons in 2019. However, the production tended to decrease by around 8% from 2018 to 2019, while the rice consumption increased by +1.53 tons. This study aims to develop a rice production estimation model using the soil-vegetation index transformation (MSAVI and SAVI) and soil physical properties, which has the advantage of being faster, cheaper, and more accurate than conventional methods. The soil physical properties were taken based on soil mapping units and analyzed with soil physical parameters. The results showed strong relationships between rice productivity - soil physical characteristics and rice productivity â€“ MSAVI and EVI with r values of 0.97, 0.83, and 0.74, respectively. The soil physical properties have a better coefficient of determination and accuracy than soil-vegetation index. The prediction model of rice production by soil physical properties is formulated inward Î³ = -8.96+0.01 (Top Soil Sand) + 0.01 (Top Soil Silt) + 6.28 (Bulk Density) - 14.07 (Penetration) - 0.13 (Sub Soil Permeability). There is no difference in the productivity value between model and laboratory analysis result. These results indicate that the rice yield prediction model can be used for estimation purposes.</dc:description>
	<dc:publisher xml:lang="en-US">Brawijaya University</dc:publisher>
	<dc:date>2021-07-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://jdmlm.ub.ac.id/index.php/jdmlm/article/view/792</dc:identifier>
	<dc:identifier>10.15243/jdmlm.2021.084.2891</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Degraded and Mining Lands Management; Vol. 8 No. 4 (2021); 2891-2901</dc:source>
	<dc:source>2502-2458</dc:source>
	<dc:source>2339-076X</dc:source>
	<dc:source>10.15243/jdmlm.2021.084</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jdmlm.ub.ac.id/index.php/jdmlm/article/view/792/pdf</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 Journal of Degraded and Mining Lands Management</dc:rights>
	<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-nc/4.0</dc:rights>
</oai_dc:dc>