scholarly journals Forecasting Major Food Crops Production in Khyber Pakhtunkhwa,Pakistan

2017 ◽  
Vol 2 (1) ◽  
pp. 21
Author(s):  
Syed Asghar Ali Shah ◽  
Nagina Zeb ◽  
Alamgir Alamgir
Keyword(s):  
Time Series ◽  
Evaluation Criteria ◽  
Secondary Data ◽  
Random Model ◽  
Forecast Evaluation ◽  
Time Series Models ◽  
Food Crops ◽  
Khyber Pakhtunkhwa ◽  
Major Food ◽  
Arima Modeling

The present study was undertaken to investigate forecasting of major food crops production in Khyber Pakhtunkhwa. The study was based on secondary data covers a period of about 30 years i.e. starting from 1984-85 to 2013-14, whereas, ARIMA modeling has been employed to fit the best time series model for major food crops production i.e. wheat, maize, sugarcane and rice. It reveals through the results that for major food crops production, the time series models which were found to be most suitable are as ARIMA (0, 2, 1), ARIMA (1, 2, 3), ARIMA (0, 2, 1) and random model ARIMA (0, 1, 0) respectively based on forecast evaluation criteria. It was concluded from the results of analyzed data that time series models were found adequate for forecasting major food crops production in Khyber Pakhtunkhwa.    

scholarly journals Growth and Variability of Major Food Crops Production in Khyber Pakhtunkhwa, Pakistan

2017 ◽  
Vol 2 (1) ◽  
pp. 6
Author(s):  
Syed Asghar Ali Shah ◽  
Alamgir Khalil Khalil
Keyword(s):  
Growth Rate ◽  
Growth Model ◽  
Crop Production ◽  
Growth Models ◽  
Secondary Data ◽  
Food Crops ◽  
Food Crop ◽  
Khyber Pakhtunkhwa ◽  
Major Food ◽  
Food Crop Production

The present study was undertaken to investigate the growth and variability in major food crops production of Khyber Pakhtunkhwa. The study was based on secondary data, covers a period of about 30 years i.e. starting from 1984-85 to 2013-14, Whereas, the growth models has been employed to fit the best growth model and Cuddy Della Vella Index was applied to find variability in major food crops production i.e. wheat, maize, sugarcane and rice. Based on the results of analyzed data, it was found that in major food crops (wheat, maize, sugarcane, rice) Production, the growth models i.e.  Cubic growth model, power growth model, cubic growth model, cubic growth model respectively were found suitable, based on the R2 criteria and fitted trend line. After selecting best fitted model for each major food crop, the growth rate was calculated by using the selected fitted models which were found to be 10.97%, 8.00%, 45.31% and 1.19% respectively.  Moreover, the variability for each major food crop production was found to be 1.53%, 1.23%, 0.44% and 0.79% respectively. 

scholarly journals Impact of climate variables to major food crops’ yield in midhills of western development region, Nepal

2018 ◽  
Vol 17 ◽  
pp. 65-72
Author(s):  
M Aryal ◽  
PP Regmi ◽  
RB Thapa ◽  
KR Pande ◽  
KP Pant
Keyword(s):  
Crop Yield ◽  
Low Income ◽  
Secondary Data ◽  
Household Survey ◽  
Low Income Countries ◽  
Secondary Source ◽  
Food Crops ◽  
Climate Variables ◽  
Growing Seasons ◽  
Major Food

Climate change is threatening the agriculture sector especially on present and future food security in low income countries. Primary and secondary data collected through household survey and collected from different secondary source were used to assess the effects of climate variables on crop yield and the uniformity of effects across crops and growing seasons in Kaski district considering six major food crops as paddy, maize, wheat, millet, Barley and potato. A multivariate regression analysis, based on the first difference time series of crop yield and climate variables, is employed to estimate the empirical relationships between crop yield and climate variables. The results are discussed at district level empirically. It showed that climate variables significantly influence the crop yield, but not uniformly on all crops and in all growing seasons.

scholarly journals Forecasting Cycles in the Transportation Sector

2010 ◽  
Author(s):  
Vincent W. Yao ◽  
Brian W. Sloboda
Keyword(s):  
Time Series ◽  
Evaluation Criteria ◽  
Time Series Models ◽  
Leading Indicators ◽  
Modern Time ◽  
General State ◽  
Growth Cycles ◽  
Screening Techniques ◽  
Transportation Sector ◽  
Timely Fashion

This paper predicted fluctuations in the transportation sector using leading indicators. From 25 initial candidates, we selected seven leading indicators, using various screening techniques and modern time series models. A composite leading index was constructed and found to perform well in predicting transportation reference cycles. The leading index signals downturns in the transportation sector 10 months ahead and upturns six months ahead on average. The index predicted the latest recession in transportation with a lead of 20 months. The analysis also confirms the predictive contents of the composite leading index (CLI) in relation to transportation growth cycles. These evaluation criteria ensure accurate forecasts of the general state of the transportation sector in a timely fashion.

scholarly journals POTENSI TANAMAN PANGAN DAN PERKEBUNAN UNTUK PENGEMBANGAN WILAYAH KABUPATEN KEPAHIANG

2018 ◽  
Vol 11 (3) ◽  
pp. 22
Author(s):  
Siti Mutmaidah
Keyword(s):  
Time Series ◽  
Economic Development ◽  
Agricultural Sector ◽  
Secondary Data ◽  
Domestic Product ◽  
Food Crops ◽  
Food Crop ◽  
Location Quotient ◽  
Analysis Tools ◽  
Plantation Crops

This study aims to determine the regional leading sector of Kepahiang Regency as the information and considerations in planning economic development. Secondary data such as time series of the Gross Regional Domestic Product (GRDP) of Kepahiang and Bengkulu in the period 2011-2014 are applied. Klassen Typology and Location Quotient (LQ) are tools of analysis. The results of the analysis based on two analysis tools indicate that the leading sector with the criterias developed, base, and competitive is agricultural sector. The results showed that the agricultural sector can be used as a leading sector in Kepahiang Regency with criteria of the advanced sector and grow rapidly and is the base sector. Seberang Musi Sub-district has the most potential for cultivation of food crops and plantations with 13 commodities that become the base sector. For the specialization of food crop base sector is Kaba wetan Subdistrict with 5 commodities with base criteria and for plantation crops Merigi and Seberang Musi subdistricts with 9 commodities crops.

Time-Series Models in Marketing: Some Recent Developments

Marketing ZFP ◽  
2010 ◽  
Vol 32 (JRM 1) ◽  
pp. 24-29
Author(s):  
Marnik G. Dekimpe ◽  
Dominique M. Hanssens
Keyword(s):  
Time Series ◽  
Time Series Models ◽  
Recent Developments

A COMPARATIVE STUDY BETWEEN UNIVARIATE AND BIVARIATE TIME SERIES MODELS FOR CRUDE PALM OIL INDUSTRY IN PENINSULAR MALAYSIA

2020 ◽  
Vol 5 (1) ◽  
pp. 374
Author(s):  
Pauline Jin Wee Mah ◽  
Nur Nadhirah Nanyan
Keyword(s):  
Time Series ◽  
Palm Oil ◽  
Moving Average ◽  
Forecast Accuracy ◽  
Peninsular Malaysia ◽  
Time Series Models ◽  
Crude Palm Oil ◽  
Univariate Time Series ◽  
Import And Export ◽  
Bivariate Time Series

The main purpose of this study is to compare the performances of univariate and bivariate models on four time series variables of the crude palm oil industry in Peninsular Malaysia. The monthly data for the four variables, which are the crude palm oil production, price, import and export, were obtained from Malaysian Palm Oil Board (MPOB) and Malaysian Palm Oil Council (MPOC). In the first part of this study, univariate time series models, namely, the autoregressive integrated moving average (ARIMA), fractionally integrated autoregressive moving average (ARFIMA) and autoregressive autoregressive (ARAR) algorithm were used for modelling and forecasting purposes. Subsequently, the dependence between any two of the four variables were checked using the residuals’ sample cross correlation functions before modelling the bivariate time series. In order to model the bivariate time series and make prediction, the transfer function models were used. The forecast accuracy criteria used to evaluate the performances of the models were the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results of the univariate time series showed that the best model for predicting the production was ARIMA  while the ARAR algorithm were the best forecast models for predicting both the import and export of crude palm oil. However, ARIMA  appeared to be the best forecast model for price based on the MAE and MAPE values while ARFIMA  emerged the best model based on the RMSE value.  When considering bivariate time series models, the production was dependent on import while the export was dependent on either price or import. The results showed that the bivariate models had better performance compared to the univariate models for production and export of crude palm oil based on the forecast accuracy criteria used.

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