This study was conducted to analyze the profitability of rice farming in Bangladesh. In doing so, it utilized the multistage sampling technique to collect the cross-sectional data from seven rice producing districts in Bangladesh during 2016. A total of 140 samples were directly interviewed using a structured questionnaire for achieving the purpose. Apart from the descriptive analysis of the socioeconomic variables of the selected respondents, the benefit-cost and functional profitability analysis of rice were also performed. The log-linear form of Cobb-Douglas production function was chosen to determine the effects of various inputs on the profitability of rice. The finding of cost-benefit analysis reveals that rice farming is a profitable activity in Bangladesh as the estimated cost of production was lower than the return in the selected study areas. However, the profitability differs among different farmers’ group and large farmers are more profitable in rice cultivation than small and medium farmers. In addition, the functional analysis identifies three inputs such as the cost of power tiller, fertilizer and hired labor as the significant determinants of profitability for all farmers in the study regions. Moreover, these factors also differ across the farmer's groups except the cost of fertilizer. Therefore, it is recommended in this study that the concerned authority of the government should ensure adequate and timely fertilizer use at a subsidized price which would be affordable by the farmers. Besides, a fair pricing policy should be set so that fluctuation in the price level can be controlled. Effective extension service may also help the farmers using a better combination of input that will generate higher productivity and return, thereby, will contribute to being food secure and self-sufficient in rice cultivation. J. Bangladesh Agril. Univ. 17(1): 86–91, March 2019

The study examined the consumer preference for processed milk in Mymensingh town. The study was mainly based on primary data in which 40 consumers were purposively selected from Mymensingh town. In the study, preference of consumers for processed milk i.e. powder milk, condensed milk and pasteurized milk were investigated. Consumers' preference for processed milk was ascertained through a 4-point numerical rating scale. The consumers highly preferred powder milk and the computed preference index of powder milk was 80. The computed preference index of pasteurized milk was 71. The computed preference index of raw milk was 54. The lowest preference of consumers was for condensed milk and the computed preference index of condensed milk was 36. The study revealed that Milk Vita has ranked first (90) followed by Diploma (85), Dano (81), Arong (68) and Red Cow (61) were the major brands preferred by the consumers. On the other hand, Danish (36), Nido (34), Starship (34), Marks (27) and Farmland (26) were less preferred for consumption of processed milk. The relationship between the factors that influencing consumer's preferences and their preferences of processed milk was also explored. Spearman rank correlation coefficient test was used to explore relationship between the variables. The monthly income of the family, price level, taste level, fat content, nutritional value and attitudes towards processed milk of the consumers were significantly related with their preferences of processed milk while the other factors (age, family size, education level) were not significantly related.

In this study, we attempt to anticipate annual rice production in Bangladesh (1961–2020) using both the Autoregressive Integrated Moving Average (ARIMA) and the eXtreme Gradient Boosting (XGBoost) methods and compare their respective performances. On the basis of the lowest Corrected Akaike Information Criteria (AICc) values, a significant ARIMA (0, 1, 1) model with drift was chosen based on the findings. The drift parameter value shows that the production of rice positively trends upward. Thus, the ARIMA (0, 1, 1) model with drift was found to be significant. On the other hand, the XGBoost model for time series data was developed by changing the tunning parameters frequently with the greatest result. The four prominent error measures, such as mean absolute error (MAE), mean percentage error (MPE), root mean square error (RMSE), and mean absolute percentage error (MAPE), were used to assess the predictive performance of each model. We found that the error measures of the XGBoost model in the test set were comparatively lower than those of the ARIMA model. Comparatively, the MAPE value of the test set of the XGBoost model (5.38%) was lower than that of the ARIMA model (7.23%), indicating that XGBoost performs better than ARIMA at predicting the annual rice production in Bangladesh. Hence, the XGBoost model performs better than the ARIMA model in predicting the annual rice production in Bangladesh. Therefore, based on the better performance, the study forecasted the annual rice production for the next 10 years using the XGBoost model. According to our predictions, the annual rice production in Bangladesh will vary from 57,850,318 tons in 2021 to 82,256,944 tons in 2030. The forecast indicated that the amount of rice produced annually in Bangladesh will increase in the years to come.

In Bangladesh, onion is the widely used spices both for preparing food and curing diseases as it has medicinal values. As the demand for onion is increasing day by day, it is necessary to make actual projections of onion for undertaking some policies based on it. Therefore, the study investigates the future changes in the area, yield and production of onion in Bangladesh by using the most popular Box-Jenkins methodology. The auto regressive integrated moving average model has been used to understand the pattern of change over a period of 57 years (1961 to 2017) as well as to forecast the changes in the upcoming years. Some information criteria (such as AIC, AICc and BIC) was considered for selecting the best-fitted models of each variable. The forecasted results showed an upward trend for all the variables considered in this study. It implies that the area of onion will increase from 193932.6 hectares in 2018 to 265770.9 hectare in 2027. Again, the amount of onion production will increase from 2073.61 M tons to 3574.06 M tons and for onion yield, it will rise from 10343.17 Kg/ha to 12988.02 kg/ha from 2018 to 2027. These predictions may help the government balancing the demand with the supply and also regulating the price of onion in the domestic markets of Bangladesh.

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