Understanding factors influencing rose productivity in Central Java Province, Indonesia

Central Java Province is recognized as one of the main rose-producing regions in Indonesia, ranking second in production volume nationwide. However, this high production level has not been accompanied by a positive trend in productivity. This study aims to analyze the factors influencing rose productivity using the Cobb–Douglas production function with the Ordinary Least Squares (OLS) method for parameter estimation. The independent variables considered include plant population (X1), organic fertilizer (X2), chemical fertilizer (X3), labor (X4), and pesticides (X5), with rose productivity as the dependent variable. The results reveal that plant population, chemical fertilizer, labor, and pesticides positively and significantly affect productivity, while organic fertilizer shows no significant impact. The findings highlight the importance of optimizing production inputs particularly plant density, chemical fertilizers, labor allocation, and effective pest control to enhance rose productivity efficiently and sustainably.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-09-30
DOI
https://doi.org/10.5281/zenodo.17774200
Primary Topic
Greenhouse Technology and Climate Control
Type
article
Field-Weighted Citation Impact
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article

Understanding factors influencing rose productivity in Central Java Province, Indonesia

Zenodo (CERN European Organization for Nuclear Research)
Greenhouse Technology and Climate Control
article

Understanding factors influencing rose productivity in Central Java Province, Indonesia

article en

Abstract

Central Java Province is recognized as one of the main rose-producing regions in Indonesia, ranking second in production volume nationwide. However, this high production level has not been accompanied by a positive trend in productivity. This study aims to analyze the factors influencing rose productivity using the Cobb–Douglas production function with the Ordinary Least Squares (OLS) method for parameter estimation. The independent variables considered include plant population (X1), organic fertilizer (X2), chemical fertilizer (X3), labor (X4), and pesticides (X5), with rose productivity as the dependent variable. The results reveal that plant population, chemical fertilizer, labor, and pesticides positively and significantly affect productivity, while organic fertilizer shows no significant impact. The findings highlight the importance of optimizing production inputs particularly plant density, chemical fertilizers, labor allocation, and effective pest control to enhance rose productivity efficiently and sustainably.

Zenodo (CERN European Organization for Nuclear Research)
Diponegoro University (ID)
Openalex Percentile: Top 28%
Greenhouse Technology and Climate Control
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