Forest Plantation Pest and Disease Forecast Model

DOI

10.2991/icedutech-17.2018.37How to use a DOI?

Keywords

forecasting model; forest plantation; nursery; ARIMA; Acacia mangium; Falcataria moluccana;

Abstract

This research aims to propose a forecast model of pest and disease plantation. The data sample collected by Laboratory of forest health and protection - Faculty of Forestry Universitas Gadjah Mada in the periods of times, so we have time series data of Powdery mildew disease growth which has observed from Acacia mangium nursery. This model combined with the expert system model, and the identification and calculation of damage size and the intensity of damage model. The expert system used for identification of pest and disease on plantation, model of identification used for collecting the data and calculting of a size and the intensity of plantation damage, and forecasting model will be used for seeing a disease growing without treatment. In this manuscript, the forecast model is using Auto-Regressive (AR), Moving Average (MA), and ARIMA method to see how the data sample are suitable and the model is working. We are using order (2,1,0) for AR, MA (0,1,2), and combined method ARIMA (2,1,1) which has better RSS value (0.6219). The model may be used by policymakers to take action if there any disease or pest in the nursery or plantation.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Andri Pranolo
AU  - Siti Muslimah Widyastuti
AU  - Azhari Azhari
PY  - 2017/11
DA  - 2017/11
TI  - Forest Plantation Pest and Disease Forecast Model
BT  - Proceedings of the 2017 International Conference on Education and Technology (2017 ICEduTech)
PB  - Atlantis Press
SP  - 188
EP  - 192
SN  - 1951-6851
UR  - https://doi.org/10.2991/icedutech-17.2018.37
DO  - 10.2991/icedutech-17.2018.37
ID  - Pranolo2017/11
ER  -