Proceedings of the 2nd International Conference on Neural Networks and Machine Learning 2023 (ICNNML 2023)

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Stochastic Frontier Approach on Technical Efficiency of Rice Farming in Jember

Authors
Intan Kartika Setyawati1, *, Ahmad Zainuddin1, Illia Seldon Magfiroh1, Rena Yunita Rahman1, Luh Putu Suciati1
1University of Jember, Jember, Indonesia
*Corresponding author. Email: intan.faperta@unej.ac.id
Corresponding Author
Intan Kartika Setyawati
Available Online 29 June 2024.
DOI
10.2991/978-94-6463-445-7_19How to use a DOI?
Keywords
Rice harvest decline; technical efficiency analysis; stochastic frontier model
Abstract

In 2022, the rice harvest area will reach around 118.49 thousand hectares with production of 607.37 thousand tons of GKG. The production of rice, if converted, would total 350.71 thousand tons in 2022. The rice harvest area in Jember Regency is expected to be approximately 118.49 hectares in 2022; this is a 4.47 percent decline, or 5.54 thousand hectares, from the 124.03 thousand hectares in 2021. The average yield of rice per hectare might fluctuate due to a number of factors, such as issues with the fertility of the soil, fertilizer use, seeds, agricultural practices, pests, weather, and so forth. The purpose of this research is to analyze the technical efficiency of rice farming in Jember. Since there were no sampling frames available in the research area, the approach employed to sample farmer families was purposive. The data analysis method uses a stochastic frontier production function model. According to the research findings, Jember's rice farming has an average technical efficiency rating of 70%. It may be claimed that Jember's rice growing is highly technically proficient.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2nd International Conference on Neural Networks and Machine Learning 2023 (ICNNML 2023)
Series
Advances in Intelligent Systems Research
Publication Date
29 June 2024
ISBN
978-94-6463-445-7
ISSN
1951-6851
DOI
10.2991/978-94-6463-445-7_19How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Intan Kartika Setyawati
AU  - Ahmad Zainuddin
AU  - Illia Seldon Magfiroh
AU  - Rena Yunita Rahman
AU  - Luh Putu Suciati
PY  - 2024
DA  - 2024/06/29
TI  - Stochastic Frontier Approach on Technical Efficiency of Rice Farming in Jember
BT  - Proceedings of the 2nd International Conference on Neural Networks and Machine Learning 2023 (ICNNML 2023)
PB  - Atlantis Press
SP  - 184
EP  - 189
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-445-7_19
DO  - 10.2991/978-94-6463-445-7_19
ID  - Setyawati2024
ER  -