scholarly journals Growth and trend analysis of area, production and yield of rice: A scenario of rice security in Bangladesh

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261128
Author(s):  
Md. Abdullah Al Mamun ◽  
Sheikh Arafat Islam Nihad ◽  
Md. Abdur Rouf Sarkar ◽  
Md. Abdullah Aziz ◽  
Md. Abdul Qayum ◽  
...  
Keyword(s):  
Time Series Data ◽  
Regional Growth ◽  
Rice Production ◽  
Series Data ◽  
Production Environments ◽  
Different Seasons ◽  
Exponential Growth Model ◽  
New Varieties ◽  
Watson Test ◽  
Secondary Time

Bangladesh positioned as third rice producing country in the world. In Bangladesh, regional growth and trend in rice production determinants, disparities and similarities of rice production environments are highly desirable. In this study, the secondary time series data of area, production, and yield of rice from 1969–70 to 2019–20 were used to investigate the growth and trend by periodic, regional, seasonal and total basis. Quality checking, trend fitting, and classification analysis were performed by the Durbin-Watson test, Exponential growth model, Cochrane-Orcutt iteration method and clustering method. The production contribution to the national rice production of Boro rice is increasing at 0.97% per year, where Aus and Aman season production contribution significantly decreased by 0.48% and 0.49% per year. Among the regions, Mymensingh, Rangpur, Bogura, Jashore, Rajshahi, and Chattogram contributed the most i.e., 13.9%, 9.8%, 8.6%, 8.6%, 8.2%, and 8.0%, respectively. Nationally, the area of Aus and Aman had a decreasing trend with a -3.63% and -0.16% per year, respectively. But, in the recent period (Period III) increasing trend was observed in the most regions. The Boro cultivation area is increasing with a rate of 3.57% per year during 1984–85 to 2019–20. High yielding variety adoption rate has increased over the period and in recent years it has found 72% for Aus, 73.5% for Aman, and 98.4% for Boro season. As a result, the yield of the Aus, Aman, and Boro seasons has been found increasing growth for most of the regions. We have identified different cluster regions in different seasons, indicating high dissimilarities among the rice production regions in Bangladesh. The region-wise actionable plan should be taken to rapidly adopt new varieties, management technologies and extension activities in lower contributor regions to improve productivity. Cluster-wise, policy strategies should be implemented for top and less contributor regions to ensure rice security of Bangladesh.

Forecasting Rice Production in West Bengal State in India

2013 ◽  
Vol 4 (4) ◽  
pp. 68-91 ◽  
Author(s):  
Arindam Chaudhuri
Keyword(s):  
Time Series ◽  
Multiple Regression ◽  
Computational Intelligence ◽  
Time Series Data ◽  
Rice Production ◽  
Productivity Index ◽  
Series Data ◽  
Support Vector ◽  
Production Time ◽  
Inference System

Forecasting rice production is a challenging problem in agricultural statistics. The inherent difficulty lies in demand and supply affected by many uncertain factors viz. economic policies, agricultural factors, credit measures, foreign trade etc. which interact in a complex manner. Since last few decades, Statistical techniques are used for developing predictive models to estimate required parameters. Determination of nature of rice production time series data is difficult, expensive, time consuming and involves tedious tests. In this paper, we use Interval Type Fuzzy Auto Regressive Integrated Moving Average (ITnARIMA), Adaptive Neuro Fuzzy Inference System (ANFIS) and Modified Regularized Least Squares Fuzzy Support Vector Regression (MRLSFSVR) for prediction of Productivity Index percent (PI %) of rice production time series data and compare it with traditional Statistical tool of Multiple Regression. The accuracies of ITnARIMA and ANFIS techniques are evaluated as relatively similar. It is found that ANFIS exhibits high performance than ITnARIMA, MRLSFSVR and Multiple Regression for predicting PI %. The performance comparison shows that Computational Intelligence paradigm is a promising tool for minimizing uncertainties in rice production data. Further Computational Intelligence techniques also minimize potential inconsistency of correlations.

scholarly journals A Soft Computing Model to Predict the Rice Production in India

2019 ◽  
Vol 8 (6) ◽  
pp. 796-800
Keyword(s):  
Predictive Model ◽  
Soft Computing ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Rice Production ◽  
Series Data ◽  
Average Error ◽  
Optimum Result ◽  
Future Data ◽  
Artificial Neural Network Ann

India, which has the most rice tillage area in the world, is one of the massive cultivators of this white crop. Besides, rice is the main staple food of many Indians. The main purpose of this study is to develop a predictive model on Indian rice production. In this, we have used different types of soft computing models like Fuzzy Logic, Statistical Equations, Artificial Neural Network (ANN) and Genetic Algorithm (GA) and developed a hybrid model to get the optimum result. The vital aspect of this predictive model is the accuracy of the future data prediction on the basis of past time series data. The Prediction performance has been assessed by using error finding equations like Mean Squared Error (MSE), Root Mean Square Error (RMSE) and Average Error.

scholarly journals Forecasting Rice Production of Bangladesh Using ARIMA and Artificial Neural Network Models

2020 ◽  
Vol 68 (2) ◽  
pp. 143-147
Author(s):  
Abira Sultana ◽  
Murshida Khanam
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Time Series Data ◽  
Arima Model ◽  
Rice Production ◽  
Series Data ◽  
Ann Model ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Estimated Error

Forecasting behavior of Econometric and Machine Learning models has recently attracted much attention in the research sector. In this study an attempt has been made to compare the forecasting behavior of Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) using univariate time series data of annual rice production (1972 to 2013) of Bangladesh. Here, suitable ARIMA has been chosen from several selected ARIMA models with the help of AIC and BIC values. A simple ANN model using backpropagation algorithm with appropriate number of nodes or neurons in a single hidden layer, adjustable threshold value and learning rate, has been constructed. Based on the RMSE, MAE and MAPE values, the results showed that the estimated error of ANN is much higher than the estimated error of chosen ARIMA. So, according to this study, it can be said that the ARIMA model is better than ANN model for forecasting the rice production in Bangladesh. Dhaka Univ. J. Sci. 68(2): 143-147, 2020 (July)

scholarly journals Climate Change Impact On Rice Production in Pakistan: An ARDL-Bounds Testing Approach to Cointegration

2018 ◽  
Author(s):  
Abbas Ali Chandio ◽  
Yuansheng Jiang ◽  
Habibullah Magsi
Keyword(s):  
Climate Change ◽  
Time Series Data ◽  
Rice Production ◽  
Series Data ◽  
Bounds Testing ◽  
Long Run ◽  
Short Run ◽  
Bounds Testing Approach ◽  
The Impact ◽  
Ardl Bounds Testing Approach

This research paper aims to examine the relationship between CO2, temperature, area, fertilizers and rice production in Pakistan. This study used Augmented Dickey Fuller (ADF) and Phillips Perron (PP) unit root tests to check the order of integration of each variable. The cointegration analysis with ARDL bounds testing approach is used to examine the impact of climate change on rice production in Pakistan over time series data from the period 1968 to 2014. The parameter stability test of the model is also checked at the end. The results of estimation show that the important variables of the study are cointegrated demonstrating the presence of long-run association among them. Furthermore, climate change factors, e.g. CO2 and temperature have a long-run and short-run positive effect on the production of rice in Pakistan. This present work is original and it is first time empirically tested the impact of climate change on rice production in Pakistan. The annual time series data of 47 years enhances the validity of the empirical findings. The most fruitful finding of this research is that rice production in Pakistan is positively influenced by emission of carbon dioxide (CO2) at 5 percent significance level in both long-run and short-run.

scholarly journals Optimalisasi Ekspor Produk Ekonomi Kreatif Indonesia Menuju Peningkatan Dayasaing

2020 ◽  
Vol 2 (3) ◽  
pp. 273-284
Author(s):  
Sugiyono Madelan
Keyword(s):  
Time Series ◽  
Research Method ◽  
Time Series Data ◽  
Education Level ◽  
Series Data ◽  
Creative Economy ◽  
Labor Costs ◽  
Fashion Products ◽  
Export Competitiveness ◽  
Secondary Time

Indonesia’s creative economy product exports have not been optimal. The purpose of this study is to optimize the goals of creative economic development in Indonesia. This research was conducted using secondary time series data for the period 2010-2017. The research method uses linear programming and goal programming. The results showed that exports of creative economy products responded to an increase in export selling prices based on the demand behavior of the exports of creative economy products. The factor of export competitiveness of Indonesia’s creative economy products lies in the use of cheaper labor costs. Exports of creative economy products do not automatically increase, if the education level of the workforce increases, but rather comes from an increase in creativity. Fashion products are efficient products compared to producing exports of craft products and culinary products. Finally, the development of the creative economy is more optimal for the purpose of increasing exports of creative economy products than for the purpose of increasing employment, namely by producing fashion products.

scholarly journals Confirmed and Unreported COVID-19-Like Illness Death Counts: An Assessment of Reporting Discrepancy

2020 ◽  
Author(s):  
Mazbahul G Ahamad ◽  
Fahian Tanin ◽  
Byomkes Talukder
Keyword(s):  
Seasonal Influenza ◽  
Time Series Data ◽  
Statistical Tests ◽  
Data Driven ◽  
Series Data ◽  
Response Strategies ◽  
Pandemic Response ◽  
Reporting Discrepancy ◽  
Death Count ◽  
Secondary Time

Objective: To assess the reporting discrepancy between officially confirmed COVID-19 death counts and unreported COVID-19-like illness (CLI) death counts. Study Design: The study is based on secondary time-series data. Methods: We used publicly available data to explore the differences between confirmed COVID-19 death counts and deaths with probable COVID-19 symptoms in Bangladesh between March 8, 2020, and July 18, 2020. Both tabular analysis and statistical tests were performed. Results: During the week ending May 9, 2020, the unreported CLI death count was higher than the confirmed COVID-19 death count; however, it was lower in the following weeks. On average, unreported CLI death counts were almost equal to the confirmed COVID-19 death counts during the study period. However, the reporting authority neither considers CLI deaths nor adjusts for potential seasonal influenza-like illness or other related deaths, which might produce incomplete and unreliable COVID-19 data and respective mortality rates. Conclusions: Deaths with probable COVID-19 symptoms needs to be included in provisional death counts in order to estimate an accurate COVID-19 mortality rate and to offer data-driven pandemic response strategies. An urgent initiative is needed to prepare a comprehensive guideline for reporting COVID-19 deaths.

scholarly journals Multiple Regression Model Fitted for Rice Production Forecasting in Nepal: A Case of Time Series Data

2018 ◽  
Vol 2 ◽  
pp. 89-98
Author(s):  
Chuda Prasad Dhakal
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Multiple Regression ◽  
Rural Population ◽  
Time Series Data ◽  
Multiple Regression Model ◽  
Rice Production ◽  
Series Data ◽  
Agricultural Labor ◽  
Production Forecasting

Background: Fitting a multiple regression model is always challenging and the level of difficulty varies according to the purpose for which it is fitted. Two major difficulties that arise while fitting a multiple regression model for forecasting are selecting 'potential predictors' from numerous possible variables to influence on the forecast variable and investigating the most appropriate model with a subset of the potential predictors.Objective: Purpose of this paper is to demonstrate a procedure adopted while fitting multiple regression model (with an attempt to optimize) for rice production forecasting in Nepal.Materials and Methods: This study has used fifty years (1961-2010) of time series data. A list of twenty-one predictors thought to impact on rice production was scanned based upon past literature, expert's hunches, availability of the data and the researcher's insight which left eleven possible predictors. Further, these possible predictors were subjected to family of automated stepwise methods which left five ‘potential predictors’ namely harvested area, rural population, farm harvest price, male agricultural labor force and, female agricultural labor force. Afterwards, best subset regression was performed in Minitab Version 16 which finally left three 'appropriate predictors' that best fit the model namely harvested area, rural population and farm harvest price.Results: The model fit was significant with p < .001. Also, all the three predictors were found highly significant with p < 0.001. The model was parsimonious which explained 93% variation in rice production with 54% overlapping predictive work done. Forecast error was less than 5%.Conclusion: Multiple regression model can be used in rice production forecasting in the country for the enhanced ease and efficiency.Nepalese Journal of Statistics, Vol. 2, 89-98

scholarly journals Co-Movement between Tourist Arrivals of Inbound Tourism Markets in South Korea: Applying the Dynamic Copula Method Using Secondary Time Series Data

2021 ◽  
Vol 13 (3) ◽  
pp. 1283
Author(s):  
Ki-Hong Choi ◽  
Insin Kim
Keyword(s):  
Time Series ◽  
South Korea ◽  
Time Series Data ◽  
Tail Dependence ◽  
Tourism Industry ◽  
Series Data ◽  
Dynamic Copula ◽  
Source Countries ◽  
Secondary Time ◽  
Dependence Structures

Tourism demand is severely affected by unpredicted events, which has prompted scholars to examine ways of predicting the effects of positive and negative shocks on tourism, to ensure a sustainable tourism industry. The purpose of this study was to investigate if non-linear dependence structures exist between tourist flows into South Korea from five major source countries, as South Korea has undergone fluctuations in tourist arrivals due to diverse circumstances and has complex relations with tourism source countries. Additionally, the study examines the structures of extreme tail dependence, which is indicated in the case of unexpected events, and identifies how co-movements vary over time through dynamic copula–GARCH (generalized autoregressive conditional heteroskedasticity) tests. The secondary time series data for the 2005–2019 period of tourist arrivals to Korea were derived from the Korea Tourism Knowledge and Information System for testing the copula models. The copula estimations indicate significant dependencies among all market pairs as well as the strongest dependence between China and Taiwan. Moreover, extreme tail dependence structures show co-movements for four pairs of tourism markets in only negative shocks, for five pairs in both positive and negative conditions, but no co-movement in the China–Taiwan pair. Finally, the dynamic dependence structures reveal that the China–Taiwan dependence is higher than the other time-varying dependence structures, implying that the two markets complement each other.

scholarly journals IMPACT OF IMPORT TARIFF POLICIES ON RICE DEMAND AND SUPPLY IN INDONESIA

2020 ◽  
Vol 7 (2) ◽  
pp. 59-67
Author(s):  
Akhmad Pide
Keyword(s):  
Time Series Data ◽  
Rice Production ◽  
Series Data ◽  
Purchase Price ◽  
Demand And Supply ◽  
Import Tariff ◽  
Simultaneous Equation System ◽  
Government Purchase ◽  
Rice Price ◽  
The Impact

Indonesia’s economic policies cannot be separated to its rice policies since rice is the staple food for Indonesian people. The paper aims to find out the impact of rice import tariff policies on rice demand and supply in Indonesia. The research uses time series data in 1981-2018. Data obtained are analyzed using econometric model with simultaneous equation system. Estimation results show that rice supply was positively and significantly influenced by grain rice price in farmer level, amount of rice production, and rice supply of the previous year. Meantime, domestic rice demand was negatively and significantly influenced by domestic rice price and positively and significantly influenced by domestic rice demand of the previous year. The result of policy simulation indicates that scenario of policy combination through an increase in rice import tariff and government purchase price brings a sizeable impact on the increase in domestic rice production and rice demand and supply. Therefore, Indonesia needs to conduct protection to farmers in the form of rice import tariff imposition as well as government purchase price.

scholarly journals DAYA SAING EKSPOR PALA INDONESIA DI PASAR INTERNASIONAL

2019 ◽  
Vol 33 (2) ◽  
pp. 179-188
Author(s):  
Asrol Asrol ◽  
Heriyanto Heriyanto
Keyword(s):  
Time Series Data ◽  
International Market ◽  
World Market ◽  
Series Data ◽  
Maturity Stage ◽  
Composition Effects ◽  
Average Value ◽  
The World ◽  
Constant Market Share ◽  
Secondary Time

Indonesia is one of the largest producing and exporting countries for nutmeg commodities in the world market. Indonesia as a nutmeg exporting country is a country that imports nutmeg products. Nutmeg is one of Indonesia's leading spice export commodities on the world market. Based on the description in general, this study aims to analyze the competitiveness of Indonesian nutmeg in the world market. Specifically, this study aims to analyze the export position of nutmeg and the competitiveness of Indonesian nutmeg in the international market. The power used in this study is secondary time series data from 2007-2016. To answer the research objectives, it was analyzed using the Trade Specialization Index (ISP), Revealed Comparative Advantage (RCA) and Constant Market Share (CMS). Based on the results of the study indicate that for the position of Indonesian nutmeg exports on the world market, the average value of Indonesian ISPs on the world market from 2007-2016 was 0.988. This value indicates that the position or stage of Indonesian nutmeg export is at the maturity stage with an indicator value (0.81-1.00). Furthermore, the competitiveness of the results of the average Indonesian nutmeg RCA value on the international market which is calculated from 2007-2016 reached 19,554 because the value of Indonesian nutmeg RCA is greater than one, so Indonesia has a strong competitiveness in the export of nutmeg in the world and tends to be a country exporter rather than importer. For the CMS value of Indonesian nutmegs in the last five years period is negative on the standard growth, composition effects, and market distribution effects but the positive value on the effect of competitiveness.

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