Research Article
Clustering and Modelling Beef Cattle Population in Probolinggo District with Spatial Autoregressive Method (SAR)
@INPROCEEDINGS{10.4108/eai.23-10-2019.2293022, author={Henny Pramoedyo and Vita Dewi Islami}, title={Clustering and Modelling Beef Cattle Population in Probolinggo District with Spatial Autoregressive Method (SAR)}, proceedings={Proceedings of the 13th International Interdisciplinary Studies Seminar, IISS 2019, 30-31 October 2019, Malang, Indonesia}, publisher={EAI}, proceedings_a={IISS}, year={2020}, month={3}, keywords={spatial clustering spatial autoregressive (sar)}, doi={10.4108/eai.23-10-2019.2293022} }- Henny Pramoedyo
Vita Dewi Islami
Year: 2020
Clustering and Modelling Beef Cattle Population in Probolinggo District with Spatial Autoregressive Method (SAR)
IISS
EAI
DOI: 10.4108/eai.23-10-2019.2293022
Abstract
Probolinggo is an area in East Java Province that has a large potential of livestock resources for the development of beef cattle breeding business. National demand for meat increases in line with the rate of economic growth which is getting better, the rate of population growth, and the increase of awareness in the importance of consuming nutrients from livestock. For this reason, the government puts a target in achieving the program of accelerating beef self-sufficiency (P2SDS) in Indonesia. The purpose of this research is to clustering beef cattle population in Probolinggo Regency and to obtain a model of beef cattle population distribution using SAR models. Beef cattle population (y), area of corn plant (x) are used as research variables. According to Spatial Clustering of beef cattle population conducted by Morans' I and Geary's, it finds out that Tongas and Wonomerto sub districts are categorized as the highest beef cattle population which are located around areas which has high number of beef cattle population. On the other hand, Maroon and Pajarakan Sub-districts have low beef cattle populations and are located around areas having low beef cattle as well. This research using the analysis of Spatial Autoregressive (SAR) shows that there is spatial dependence among districts. All independent variables are significant at the 5% level with the value of R^2 as many as 41,9%.
