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

Mira Media Pratamaningsih, Diah Puspita Hati, Bambang Susanto, Muhammad Amir Solihin, Edi Yatno, Winda Ika Susanti, Muhammad Hikmat

Abstract

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.

Keywords

Arabica coffee; Land biophysical; Principal component; Principal Component Regression

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DOI: https://doi.org/10.20961/stjssa.v22i2.93206

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