Melola Discovery

← Melola Discovery

Jurnal SINTA 1

Relationship model of land biophysical properties and their influence on Arabica coffee production in Bandung Regency, Indonesia by using the PCR method

SAINS TANAH - Journal of Soil Science and Agroclimatology · Vol. 22 No. 2 (2025) · hlm. 211–222

DOI
10.20961/stjssa.v22i2.93206
ISSN
2356-1424, 1412-3606
Terbit
2025-09-19
Penerbit
Universitas Sebelas Maret
Bahasa
en

Abstrak

Coffee is an essential agricultural commodity that significantly contributes to Indonesia's foreign exchange revenue. Arabica coffee (Coffea arabica L.), generally grown in highland areas at elevations between 1,000 to 2,000 m above sea level, exhibits persistently low productivity within the country. A primary factor influencing this low yield is the biophysical condition of the land. This study aims to examine the impact of land biophysical characteristics on Arabica coffee productivity and to investigate the interrelationships among those biophysical factors. This study was conducted in Bandung Regency, West Java Province, Indonesia. This study utilized 60 data sets encompassing 22 land biophysical parameters and one plant parameter, specifically coffee productivity. The data collection utilized a survey approach. Land biophysical data were collected through field observations and laboratory analyses, whereas coffee productivity data were obtained through farmer interviews. The principal component regression (PCR) method, incorporating principal component analysis (PCA) and multiple regression, was employed for statistical analysis. The findings indicated 7 principal components (PC) with a data representation level of 75.8%. PC1 comprises primary components consisting of sand content, clay content, exchangeable Mg, Ca, and Na, with a representation level of 18.6%. PC2 consists of exchangeable K and potential K2O, while PC3 consists of total N, organic C, altitude, and slope. These components are the most significant factors influencing Arabica coffee production. The application of K and N fertilizers along with organic materials is expected to increase Arabica coffee production.

Kata kunci

Jaringan kolaborasi para penulis

32 penulis · 112 tautan kepenulisan bersama · 51 lintas organisasi

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

  • National Research and Innovation Agency
  • IPB University
  • Satya Wacana Christian University
  • Padjadjaran University
  • University of Göttingen
  • Indonesian Agency for Agricultural Research and Development
  • The University of Sydney
  • Organisasi lain
  • 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 penulisOrganisasiArtikel bersama
Rachmat Abdul GaniNational Research and Innovation Agency2
Sukarman SukarmanNational Research and Innovation Agency2
Rufaidah Qonita MuslimNational Research and Innovation Agency2
Hanna Arini ParhusipSatya Wacana Christian University1
Yosia Adi SusetyoAfiliasi tidak diketahui1
Suryasatriya TrihandaruAfiliasi tidak diketahui1
Yosia Adi SusetyoSatya Wacana Christian University1
Suryasatriya TrihandaruSatya Wacana Christian University1
Santun R.P. SitorusIPB University1
Atang SutandiIPB University1
Widiatmaka WidiatmakaIPB University1
Sudarsono SudarsonoIPB University1
Iskandar IskandarIPB University1
Budi MulyantoIPB University1
Erna SuryaniIndonesian Agency for Agricultural Research and Development1
Pronika KricellaAfiliasi tidak diketahui1
Setiyo PurwantoAfiliasi tidak diketahui1
Sofyan RitungAfiliasi tidak diketahui1
Yiyi SulaemanNational Research and Innovation Agency1
Budiman MinasnyThe University of Sydney1
Destika CahyanaNational Research and Innovation Agency1
Bambang H. TrisasongkoIPB University1
Budi MulyantoIPB University1
Haikal Caesa PrayudiIPB University1
Heppy Suci WulanningtyasTokyo University of Agriculture and Technology1

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) https://jurnal.uns.ac.id/tanah/oai, pertama di-harvest 10 Nov 2026, terakhir terlihat 10 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">Relationship model of land biophysical properties and their influence on Arabica coffee production in Bandung Regency, Indonesia by using the PCR method</dc:title>
	<dc:creator xml:lang="en">Pratamaningsih, Mira Media</dc:creator>
	<dc:creator xml:lang="en">Hati, Diah Puspita</dc:creator>
	<dc:creator xml:lang="en">Susanto, Bambang</dc:creator>
	<dc:creator xml:lang="en">Solihin, Muhammad Amir</dc:creator>
	<dc:creator xml:lang="en">Yatno, Edi</dc:creator>
	<dc:creator xml:lang="en">Susanti, Winda Ika</dc:creator>
	<dc:creator xml:lang="en">Hikmat, Muhammad</dc:creator>
	<dc:subject xml:lang="en">Arabica coffee</dc:subject>
	<dc:subject xml:lang="en">Land biophysical</dc:subject>
	<dc:subject xml:lang="en">Principal component</dc:subject>
	<dc:subject xml:lang="en">Principal Component Regression</dc:subject>
	<dc:description xml:lang="en">Coffee is an essential agricultural commodity that significantly contributes to Indonesia's foreign exchange revenue. Arabica coffee (Coffea arabica L.), generally grown in highland areas at elevations between 1,000 to 2,000 m above sea level, exhibits persistently low productivity within the country. A primary factor influencing this low yield is the biophysical condition of the land. This study aims to examine the impact of land biophysical characteristics on Arabica coffee productivity and to investigate the interrelationships among those biophysical factors. This study was conducted in Bandung Regency, West Java Province, Indonesia. This study utilized 60 data sets encompassing 22 land biophysical parameters and one plant parameter, specifically coffee productivity. The data collection utilized a survey approach. Land biophysical data were collected through field observations and laboratory analyses, whereas coffee productivity data were obtained through farmer interviews. The principal component regression (PCR) method, incorporating principal component analysis (PCA) and multiple regression, was employed for statistical analysis. The findings indicated 7 principal components (PC) with a data representation level of 75.8%.  PC1 comprises primary components consisting of sand content, clay content, exchangeable Mg, Ca, and Na, with a representation level of 18.6%. PC2 consists of exchangeable K and potential K2O, while PC3 consists of total N, organic C, altitude, and slope. These components are the most significant factors influencing Arabica coffee production. The application of K and N fertilizers along with organic materials is expected to increase Arabica coffee production.</dc:description>
	<dc:publisher xml:lang="en">Universitas Sebelas Maret</dc:publisher>
	<dc:date>2025-09-19</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://jurnal.uns.ac.id/tanah/article/view/93206</dc:identifier>
	<dc:identifier>10.20961/stjssa.v22i2.93206</dc:identifier>
	<dc:source xml:lang="en">SAINS TANAH - Journal of Soil Science and Agroclimatology; Vol. 22 No. 2 (2025): December; 211-222</dc:source>
	<dc:source>2356-1424</dc:source>
	<dc:source>1412-3606</dc:source>
	<dc:source>10.20961/stjssa.v22i2</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://jurnal.uns.ac.id/tanah/article/view/93206/51552</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2025 Mira Media Pratamaningsih, Diah Puspita Hati, Bambang Susanto, Muhammad Amir Solihin, Edi Yatno, Muhammad Hikmat</dc:rights>
</oai_dc:dc>