Co-integration analysis on all-variety Boro rice yields

The Boro rice is one of the most important rice in Bangladesh. It has three varieties namely: high yielding, local and Pajam. The all-variety Boro rice yield of Rajshahi and Rangpur districts have been selected for the period from 1985 to 2007 to measure the relationship between them. Our study shows a cointegrating relationship exist between the yields. The speed of adjustment coefficient of the error correction model (ECM) for Rajshahi is 0.311. This indicates that there is about 31.1% of any deviation from the long-run path is corrected within a year by the yield of Rangpur. This ECM is adequate from the statistical view point

Computation of extreme-value parameters and inference by approximation covariance technique.

Ordinary least squares (OLS) and Linear (LIN) estimators are commonly used in estimating the parameters of location-scale family of distributions. Various works have been done to compare the efficiency between these two estimators for the two-parameter exponential distribution and the two-parameter Weibull distribution. Motivated by these works, it would be of interest to evaluate the performance of the LIN method for the extreme-value distribution. We found that the performance of LIN estimator is better than that of OLS estimator in the sense that it had smaller standard errors and better efficiency

Investigating impact of outliers in both independent and dependent variables on agricultural production data.

The production of high yielding variety (HYV) Boro rice depends on both climatic variables and some other non-climatic variables. Outliers may occur commonly in agriculture data. Regression outliers either in independent variables or in dependent variables pose a serious threat to traditional least squares analysis. The impact of some climatic and non-climatic variables like temperature, rainfall, net solar radiation, humidity and wind speed, lag-price and fertilizer on HYV Boro rice production have been investigated using regression diagnostics and robust regression techniques. In this study, we considered the annual HYV Boro rice production data from 1980 to 2000 for Mymensingh and Dinajpur districts in Bangladesh. We found that there were outliers in both the independent and dependent variables. The outlying observations that were found in the independent variables were corrected by the median of the respective variable series, the outliers in the dependent variables have been corrected by the robust least-trimmed squares (LTS) predicted observations of the HYV Boro production of the selected districts. Hence, the re-weighted least squares (RLS) estimation techniques have been used to judge the impact of outliers. The regression diagnostics for the selected districts were computed by both the OLS and RLS methods. Our study reveals that proper correction of outliers is very important for the regression models and there was improvement in the R-squared values for both the districts

Cointegration analysis for rice production in the states of Perlis and Johor, Malaysia

Rice is ranked the third most important crop in Malaysia after rubber and palm oil in terms of production. Unlike the industrial crops, although its contribution to Malaysia’s economy is minimal, it plays a pivotal role in the country’s food security as rice is consumed by almost everyone in Malaysia. Rice production is influenced by factors such as geographical location, temperature, rainfall, soil fertility, farming practices, etc. and hence the productivity of rice may differ in different state. In this study, our particular interest is to investigate the interrelationship between the rice production of Perlis and Johor. Data collected from Department of Agriculture, Government of Malaysia are tested for unit roots by Augmented Dickey-Fuller (ADF) unit root test while Engle-Granger (EG) procedure is used in the cointegration analysis. Our study shows that cointegrating relationship exists among the rice production in both states. The speed of adjustment coefficient of the error correction model (ECM) of Perlis is 0.611 indicating that approximately 61.1% of any deviation from the long-run path is corrected within a year by the production of rice in Johor

Prediction of hexaconazole concentration in top most layer of oil palm plantation soil using Exploratory Data Analysis (EDA)

Ganoderma boninense is a fungus that can affect oil palm trees and cause a serious disease called the basal stem root (BSR). This disease causes the death of more than 80% of oil palm trees midway through their economic life and hexaconazole is one of the particular fungicides that can control this fungus. Hexaconazole can be applied by the soil drenching method and it will be of interest to know the concentration of the residue in the soil after treatment with respect to time. Hence, a field study was conducted in order to determine the actual concentration of hexaconazole in soil. In the present paper, a new approach that can be used to predict the concentration of pesticides in the soil is proposed. The statistical analysis revealed that the Exploratory Data Analysis (EDA) techniques would be appropriate in this study. The EDA techniques were used to fit a robust resistant model and predict the concentration of the residue in the topmost layer of the soil

Panel cointegration analysis and generalized Causality of HYV Boro production of six different districts of Bangladesh.

Boro rice of High Yielding Variety (HYV) is cultivated all over the country and has an important role in supplying the food requirements for the inhabitants of Bangladesh. Various researchers have investigated the relationship among some variables by cointegration/multicointegration technique using Dickey Fuller (DF) and Augmented Dickey Fuller (ADF) Unit Root Test within the Error Correction Model (ECM) framework. In view of Im, Pesaran and Shin, panel unit root test is used in the present study, to test the stationarity of the data set of the six districts of Bangladesh. The six districts are Rajshahi, Rangpur, Jessore, Barisal, Mymensingh and Dinajpur. The variables for each of these districts are the yields of Boro rice. We denote these variables as Yraj, Yran, Yjes, Ybar, Ymym and Ydin, respectively for the six districts. Further, Generalized Granger casualty has been judged within the ECM framework. Our study revealed that a cointegrating relationship exist between Yraj and Yran. Further, we also found Generalized Granger Causality from HYV Boro rice production of Rangpur to HYV Boro rice production of Rajshahi within the ECM framework

Multicointegration analysis on the high yielding Boro rice of six selected districts in Bangladesh

Rice is the principal agricultural crop in Bangladesh and Boro rice is a special variety of rice. Production of Boro rice differs from district to district due to various factors like environment, geographical location, climatic changes, etc. In this article, our particular interest is whether or not cointegration/multicointegration exists amongst six major rice producing districts in Bangladesh. The six districts are Rajshahi, Dinajpur, Tangail, Kushtia, Mymensingh and Barisal. Multicointegration of rice productions in these districts is analyzed within the error correction framework. Our analysis indicated that multicointegtation exist amongst Boro rice production of Mymensingh, Boro rice production of Barisal and the estimated Boro rice production of Kushtia. As such, the error correction technique was required and the results indicated that the estimated speed of adjustment coefficient of the 'error correction model' for Barisal indicates that approximately 66% of any deviation from the long-run path is corrected within a year. Further, the Granger causality from Mymensingh and estimated Boro production of Kushtia to Barisal exist within this estimated 'error correction model'. The results of this study would aid in policy decision making

Prediction of Hexaconazole Concentration in the Top Most Layer of Oil Palm Plantation Soil Using Exploratory Data Analysis (EDA).

Ganoderma boninense is a fungus that can affect oil palm trees and cause a serious disease called the basal stem root (BSR). This disease causes the death of more than 80% of oil palm trees midway through their economic life and hexaconazole is one of the particular fungicides that can control this fungus. Hexaconazole can be applied by the soil drenching method and it will be of interest to know the concentration of the residue in the soil after treatment with respect to time. Hence, a field study was conducted in order to determine the actual concentration of hexaconazole in soil. In the present paper, a new approach that can be used to predict the concentration of pesticides in the soil is proposed. The statistical analysis revealed that the Exploratory Data Analysis (EDA) techniques would be appropriate in this study. The EDA techniques were used to fit a robust resistant model and predict the concentration of the residue in the topmost layer of the soil

Predicted values and intervals for the recommended dosage

<p>Predicted values and intervals for the recommended dosage</p