Development of a Combined Crop and Climate Forecasting System for Seasonal to Decadal Predictions

In this paper we report the development of a bioeconomic modelling system, AgFIRM, designed to help close a relevance gap between climate science and policy in Australia. We do this by making a simple econometric farm income model responsive to seasonal forecasts of crop and pasture growth for the coming season. The key quantitative innovation was the use of multiple and M-quantile regression to calibrate the farm income model, using simulated crop and pasture growth from 2 agroecological models. The results of model testing demonstrated a capability to reliably forecast the direction of movement in Australian farm incomes in July at the beginning of the financial year (July–June). The structure of the model, and the seasonal climate forecasting system used, meant that its predictive accuracy was greatest across Australia’s cropping regions. In a second paper, Nelson et al. (2007, this issue), we have demonstrated how the bioeconomic modelling system developed here could be used to enhance the value of climate science to Australian drought policy.

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Development and Operational Status of Wind Power Forecasting System

IEEJ Transactions on Power and Energy ◽

10.1541/ieejpes.133.366 ◽

2013 ◽

Vol 133 (4) ◽

pp. 366-372 ◽

Cited By ~ 4

Author(s):

Isao Aoki ◽

Ryoichi Tanikawa ◽

Nobuyuki Hayasaki ◽

Mitsuhiro Matsumoto ◽

Shigero Enomoto

Keyword(s):

Wind Power ◽

Wind Power Forecasting ◽

Forecasting System ◽

Power Forecasting

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Implementasi Metode Winter untuk Forecasting Pertumbuhan Jumlah Penduduk: Studi Kasus Wilayah Provinsi NTB

Jurnal Pemikiran dan Penelitian Pendidikan Matematika (JP3M) ◽

10.36765/jp3m.v3i1.243 ◽

2020 ◽

Vol 3 (1) ◽

pp. 51-61

Author(s):

Syaharuddin ◽

Abdul Adhiim Rizky ◽

Lutfi Jauhari ◽

Siti Fatimah ◽

Wahyu Ningsih ◽

...

Keyword(s):

Time Series Data ◽

Optimal Parameter ◽

Series Data ◽

Percentage Error ◽

Average Error ◽

Ratio Data ◽

Female Data ◽

Forecasting System ◽

Conducting Research ◽

And Gender

This research aims to analyse the acceleration of population growth based on gender in West Nusa Tenggara Province (NTB) using the Forecasting system by constructing the winter's method in the shape of the Multiple Forecasting System (G-MFS) based on Matlab by calculating the period indicator for accuracy to find time series data in the year 2020-2029. At the simulation stage, researchers used the population and gender ratio data in NTB Province in 2009-2019. The method used in conducting research is to use the winter's method. The evaluation of Forecasting results is done by calculating the average error value using the Mean Absolute Percentage Error (MAPE) method. From this study obtained the most optimal parameter value on male data namely ʌ, β and γ sequential values of 0.9, 0.5 and 0.9 while in female data, the value of ʌ, β and γ respectively, 0.2, 0.1 and 0.5. Then with the value of the parameter obtained MAPE value in male data of 1.7785% and in female data of 0.89034%.

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