Measuring Agricultural Crop Production Efficiency due to Climates and Hydrology in Bangladesh: An Application of Stochastic Frontier Model

The main objective of this study is to develop a Cobb-Douglas Stochastic Frontier Production model to measure the different types of agricultural crop production’s efficiency in Bangladesh due to climates and hydrology. Climatic and hydrological information is divided into two season named as dry season which covers the months October, November, December, January and February, and summer season which covers the months March, April, May, June, July, August  and September considering the climatic condition of Bangladesh. From the study, it is obtained that mean efficiency of the rice and cereal production are 0.9203 and 0.97385 respectively. There is a little opportunity to increase production to achieve maximum production by increasing technology. At the same time jute, potato, cereal and species get the maximum frontier production with mean efficiency approximately equal to one and it also implies there is no need any technological advancement and inefficiency occurs due to stochastic noise. Keywords: Agricultural production, Climates, Hydrology, Efficiency, Stochastic Frontier Mode

Climatic Effects on Major Pulse Crops Production in Bangladesh: An Application of Box-Jenkins ARIMAX Model

The objective of this study is to measure the climatic effects on different types of pulse crops production in Bangladesh using Box-Jenkins Auto-Regressive Integrated Moving Average with external regressor, that is, ARIMAX model. The ARIMAX model is used in this study to measure climatic effects as a measuring tool of cause-effect relation between response and predictor variables because of time sequence dataset. From this study, it is found that the best selected Box-Jenkins ARIMAX model for measuring the climatic effects on pulse crops production are ARIMAX(1,1,3), ARIMAX(2,1,0), ARIMAX(1,1,2) and ARIMAX(2,1,1) for Mug, Gram, Khesari and Masur productions respectively. Keywords: Climatic effects, Pulse Crops, ARIMAX Model, Bangladesh

Temperature and Rainfall Effects on Spice Crops Production and Forecasting the Production in Bangladesh: An Application of Box-Jenkins ARIMAX Model

The objective of this study is to develop the best Box-Jenkins Auto-Regressive Integrated Moving Average with External Regressor, that is, ARIMAX model for measuring the temperature and rainfall effects on major spice crops productions in the Bangladesh and forecasting the production using the same model. Due to time sequence dataset, ARIMAX model is considered as a measuring tool of cause-effect relation among the spice crops and climatic variables (temperature and rainfall) under study. From the study, it is found that ARIMAX(2,1,2), ARIMAX(2,0,1) and ARIMAX(2,1,1) are the best model for Chili, Garlic and Ginger crop respectively. From the comparison between original series and forecasted series, it shows that these fitted model are well representative of the practical situations and both series shows the same manner indicating good forecasting. Keywords: Temperature, Rainfall, Species Production, Forecasting, ARIMAX Model

Measuring Climatic and Hydrological Effects on Cereal crop Production in Bangladesh using Multiple Regression and Measuring Efficiency using Stochastic Frontier Model

The objective of this study is to develop a Multiple Regression model to measure the climatic and hydrological effects on cereal crop productions in Bangladesh and Stochastic Frontier model for measuring the production efficiency due to climate and hydrology. The month October, November, December, January and February are taken as “dry season” and  March, April, May, June, July, August, September as  a “summer season” considering the weather and climatic conditions of Bangladesh. From Multiple Regression model, it is found that the Multiple R-squared for maize, barley and wheat production model are 0.9447, 0.8995 and 0.7674 respectively, which are implied to a good model to measure the climatic and hydrological effects on cereal production; and Global test implies that these models are valid linear model. Again, from Stochastic Frontier model, it is found that there is a huge opportunity to increase barley and maize production; and wheat achieves maximum production due to climates and Hydrology in the Bangladesh. Keywords: Cereal Production, Multiple Regression Model, Efficiency, Stochastic Frontier Model

Measuring Agricultural Production Efficiency due to Climates and Hydrology in Bangladesh: An Application of Stochastic Frontier Model

The objective of this study is to develop a Cobb-Douglas Translog Stochastic Frontier Production Function or model to measure the different types of agricultural production’s efficiency in Bangladesh due to climates and hydrology. Climatic and hydrological information is divided into two season named as dry season which covers the months October, November, December, January and February, and summer season which covers the months March, April, May, June, July, August  and September considering the climatic condition of Bangladesh. From the analysis, it is obtained that mean efficiency of the rice and cereal production are 0.9203 and 0.97385 respectively. There is a little opportunity to increase production to achieve maximum production by increasing technology. At the same time jute, potato, cereal and species get the maximum frontier production with mean efficiency approximately equal to one and it also implies there is no need any technological advancement and inefficiency occurs due to stochastic noise. Keywords: Agricultural production, Climate, Hydrology, Efficiency, Stochastic Frontier Mode

Rice Production Forecasting in Bangladesh: An Application Of Box-Jenkins ARIMA Model

The study was undertaken to fit the best Auto-Regressive Integrated Moving Average (ARIMA) model that could be used to forecast the rice productions of Bangladesh such as in Aus, Boro, Aman season covering the whole country. This data for the present study is available in the Bangladesh Agricultural Ministry’s websites www.moa.gov.bd. The best selected ARIMA model for Aus productions is ARIMA (2,1,2), for Aman it is ARIMA (2,1,2) and, for Boro it is ARIMA (1,1,3). In this study, it was tried to make a comparison between the original series and forecasted series which also shows the same manner indicating fitted model are statistically well behaved to forecast rice productions in Bangladesh. It is found from the analysis that ARIMA model gives good forecasting for short term analysis. KEYWORDS: Rice production, ARIMA, Forecasting, Bangladesh

Climatic Effects on Rice Crop Productions in Bangladesh Using Multiple Regression Model and Measuring Production Efficiency Due to Climates Using Stochastic Frontier Model

The main objective of the study is to develop the best Multiple Regression Model for measuring the significance of climatic effects on rice crop production in Bangladesh and measuring the production efficiency due to climates by using Cobb-Douglas Stochastic Frontier Model. To perform this study, amount of land used corresponding to a year’s production is an important variable which is sometimes used as a regressor variable and sometimes as weights to fit a Weighted Least Square Regression model. From the study, it is found that the Multiple R-squared values of Aus, Aman and Boro crop production are 0.9694, 0.9481 and 0.9544 respectively which are implied that these models can explain the most of the variability by the regressor variables, that is, these model are very good model. From the model validation test, it is obvious that these models are valid linear models. From the Stochastic Frontier model, Mean Efficiency of Aus, Aman and Boro production model are 0.8966353, 0.9159081 and 0.8540012 respectively. These results are indicated that there are huge opportunity to increase production by increasing technology. Keywords: Climatic Effects, Rice Crop, Multiple Regression Model and Stochastic Frontier Mode

Climatic Effects on Cotton and Tea Productions in Bangladesh and Measuring Efficiency using Multiple Regression and Stochastic Frontier Model Respectively

The main objective of this study is to develop Multiple Regression model to measure the Climatic effects on cash crop (Cotton and Tea) productions and to measure productions efficiency due to Climates using Stochastic Frontier model. From the analysis of the Multiple Regression model which gives the high R-square value implies to accept a good model. At the same time, all other assumptions and model validation checking test are very well satisfied which implies these fitted model are good model to measure the climatic effects in Bangladesh. Again, From the Stochastic Frontier model, there is a huge opportunity to increase Cotton production by increasing Technology to get maximum productions and Tea achieves maximum productions.Key words: Cash crop, Climate, Multiple Regression model and Stochastic Frontier mode

Climatic Effects on Major Oilseed Crops Production in Bangladesh: An Application of Multiple Regression Model

Climate has most vital effects on different agricultural crop production and sometimes climatic factors are main natural factors for crop production. In this study, it is tried to measure the climatic effects on different major oilseed crops production in Bangladesh. The main purpose of this study is to develop the Multiple Regression Model which is a well-established model to measure cause and effect relationship among the variables under study. To conduct this study, amount of land area corresponding year’s production under study is the most important variable which should not be avoided because without including this variable appropriate model couldn’t be fitted and from the study it is found that it has positively significant effects on each oilseed crop production. From the study, it is found that the value of R-square for Mustard, Linseed and Groundnut production are 0.9858, 0.9362 and 0.9039 respectively which are indicated that most of the variability can be explained by the climatic variables under study. From the global model validation test, it is clear that these models are valid linear regression model. Keywords: Climatic Effects, Oilseed crops, Multiple Regression Model and Bangladesh