In Indonesia, it has been detected that the share of Gross Value Added in the agricultural sector to the total Gross Domestic Product tends to decline. One of the causes of this decline is the decreasing number of farmers involved in the agricultural sector. This study aims to analyze the determinants of productivity in Indonesia's agricultural sector. This study uses secondary data sourced from the Central Bureau of Statistics and Bank Indonesia. The research reference used is 2023. The unit of analysis used for processing is 34 provinces in Indonesia. Data analysis using ridge regression. In modelling the variables that affect the productivity of the agricultural sector, there are three variables that have a significant association. These variables include the adoption of technology in agriculture, the categorization of the plantation sector, and the use of urban farming. The R square obtained was 0.8065, which means that 80.65% of the dependent variable can be explained jointly by the independent variables. In labor modelling, the variables that have significant associations are the number of farmers who own land and the Human Development Index. The R square obtained was 0.9451, which means that 94.51% of the dependent variable can be explained jointly by the independent variables. Complementing these findings, Cluster analysis revealed three distinct regional groups. Provinces such as Central Java and East Java fall into cluster with characterized by advanced agricultural performance, while several eastern and outer-island regions fall into clusters that indicating lower competitiveness and greater needs for technology and financial inclusion.
| Published in | International Journal of Agricultural Economics (Volume 10, Issue 6) |
| DOI | 10.11648/j.ijae.20251006.15 |
| Page(s) | 387-401 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2025. Published by Science Publishing Group |
Technology Adoption, Agricultural Labor, Productivity, Agricultural Sector
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APA Style
Tamba, I. M. (2025). Determinant of Productivity in the Indonesian Agricultural Sector. International Journal of Agricultural Economics, 10(6), 387-401. https://doi.org/10.11648/j.ijae.20251006.15
ACS Style
Tamba, I. M. Determinant of Productivity in the Indonesian Agricultural Sector. Int. J. Agric. Econ. 2025, 10(6), 387-401. doi: 10.11648/j.ijae.20251006.15
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Download@article{10.11648/j.ijae.20251006.15,
author = {I Made Tamba},
title = {Determinant of Productivity in the Indonesian Agricultural Sector},
journal = {International Journal of Agricultural Economics},
volume = {10},
number = {6},
pages = {387-401},
doi = {10.11648/j.ijae.20251006.15},
url = {https://doi.org/10.11648/j.ijae.20251006.15},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijae.20251006.15},
abstract = {In Indonesia, it has been detected that the share of Gross Value Added in the agricultural sector to the total Gross Domestic Product tends to decline. One of the causes of this decline is the decreasing number of farmers involved in the agricultural sector. This study aims to analyze the determinants of productivity in Indonesia's agricultural sector. This study uses secondary data sourced from the Central Bureau of Statistics and Bank Indonesia. The research reference used is 2023. The unit of analysis used for processing is 34 provinces in Indonesia. Data analysis using ridge regression. In modelling the variables that affect the productivity of the agricultural sector, there are three variables that have a significant association. These variables include the adoption of technology in agriculture, the categorization of the plantation sector, and the use of urban farming. The R square obtained was 0.8065, which means that 80.65% of the dependent variable can be explained jointly by the independent variables. In labor modelling, the variables that have significant associations are the number of farmers who own land and the Human Development Index. The R square obtained was 0.9451, which means that 94.51% of the dependent variable can be explained jointly by the independent variables. Complementing these findings, Cluster analysis revealed three distinct regional groups. Provinces such as Central Java and East Java fall into cluster with characterized by advanced agricultural performance, while several eastern and outer-island regions fall into clusters that indicating lower competitiveness and greater needs for technology and financial inclusion.},
year = {2025}
}
TY - JOUR T1 - Determinant of Productivity in the Indonesian Agricultural Sector AU - I Made Tamba Y1 - 2025/12/31 PY - 2025 N1 - https://doi.org/10.11648/j.ijae.20251006.15 DO - 10.11648/j.ijae.20251006.15 T2 - International Journal of Agricultural Economics JF - International Journal of Agricultural Economics JO - International Journal of Agricultural Economics SP - 387 EP - 401 PB - Science Publishing Group SN - 2575-3843 UR - https://doi.org/10.11648/j.ijae.20251006.15 AB - In Indonesia, it has been detected that the share of Gross Value Added in the agricultural sector to the total Gross Domestic Product tends to decline. One of the causes of this decline is the decreasing number of farmers involved in the agricultural sector. This study aims to analyze the determinants of productivity in Indonesia's agricultural sector. This study uses secondary data sourced from the Central Bureau of Statistics and Bank Indonesia. The research reference used is 2023. The unit of analysis used for processing is 34 provinces in Indonesia. Data analysis using ridge regression. In modelling the variables that affect the productivity of the agricultural sector, there are three variables that have a significant association. These variables include the adoption of technology in agriculture, the categorization of the plantation sector, and the use of urban farming. The R square obtained was 0.8065, which means that 80.65% of the dependent variable can be explained jointly by the independent variables. In labor modelling, the variables that have significant associations are the number of farmers who own land and the Human Development Index. The R square obtained was 0.9451, which means that 94.51% of the dependent variable can be explained jointly by the independent variables. Complementing these findings, Cluster analysis revealed three distinct regional groups. Provinces such as Central Java and East Java fall into cluster with characterized by advanced agricultural performance, while several eastern and outer-island regions fall into clusters that indicating lower competitiveness and greater needs for technology and financial inclusion. VL - 10 IS - 6 ER -
Agribusiness Department, Universitas Mahasaraswati Denpasar, Denpasar, Indonesia
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