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Trend of Betel Leaf Production in Bangladesh: Prospects and Challenges

Eshrat Jahan Mahfuza
Department of Agribusiness and Marketing, Bangladesh Agricultural University, Mymensingh-2202, Bangladesh

Sheikh Mohammad Sayem
Department of Agricultural and Applied Statistics, Bangladesh Agricultural University, Mymensingh-2202, Bangladesh

Md Fuad Hassan
Department of Agricultural and Applied Statistics, Bangladesh Agricultural University, Mymensingh-2202, Bangladesh

Abstract/ Executive Summary:

Betel leaf is one of the important cash crops that gaining popularity in recent time in Bangladesh. This study was conducted to identify the present and future production pattern of betel leaf in Bangladesh as well as its prospects and challenges. Secondary data of 29 years from 1991 to 2019 have been used to specify the production pattern and forecasting of betel leaf at national level by using deterministic, stochastic and dynamic time series model. While the primary data were collected from three sub-districts of the Rajshahi district namely Bagmara, Durgapur and Mohanpur to identify the prospects and challenges through SWOT analysis. This study found significant relationship among betel leaf production, price and annual average temperature of Bangladesh. A total of six growth models of different types were used for forecasting production, cultivation area, and price of betel leaf. Based on the model selection criteria cubic model, quadratic model, and growth model were chosen for initial forecasting of betel leaves production, cultivation area, and price respectively. Among the stochastic model auto regressive integrated moving average ARIMA (0, 1, 0) was fitted well to forecast the production. Finally, a dynamic regression model of ARIMA (1, 1, 0) with dynamic regressors has been used to forecast the production of betel leaf from 2020 to 2023. Forecast values range minimum 202770.41 tons to 296217.14 tons with 95% confidence interval. All the best-fitted models revealed that the production of betel leaf has an upward trend in the future. At the end SWOT analysis was implemented to identify the strengths, weakness, opportunities and threats involved in betel leaf farming which will be helpful for making future national level policy options. Diseases control at the betel leaf garden and access to credit for initial investments were identified as the major challenges for this enterprise.

Keywords: Betel leaf, Time series, ARIMA model, SWOT analysis,

Location:

Start Date: 00-00-1991

End Date: 00-00-2019

Program Area: Variety and Species

Commodity: Betel leaf

Objectives:

To identify the present and future production pattern of betel leaf in Bangladesh and investigate the prospects and challenges of betel leaf cultivation.

Methodology:

The study was conducted using both primary and secondary data. Secondary data on betel leaf production, cultivated area, and price in Bangladesh from 1991 to 2019 were collected from Bangladesh Bureau of Statistics (BBS) to measure the production trend. In addition, monthly temperature and rainfall data have been collected from Bangladesh Meteorological Department (BMD) to incorporate in the production models. Primary data were collected from the Rajshahi district as a large number of farmers are directly engaged in betel leaf production in this district. Betel leaf cultivation is increasing day by day in this district as it requires less cultivation and irrigation cost compared to many other crops (BSS, 2019). In the year 2017-18, the total betel leaf cultivation area in the Rajshahi district was 4780 acres and producing 28197 tons of betel leaf (BBS, 2018). In the study are, face to face surveys including three key informant interview (KII) and three focus group discussion (FGD) was carried out among agricultural extension officer (AEO), sub assistant agricultural officers (SAAO), and farmers to have a better understanding of the existing risk, and potentiality of betel leaf business. Time series data on betel leaf production was analyzed by deterministic, stochastic, and mixed-method model approach. The deterministic type model analyzes multiple time series data having trend patterns and provides an idea of what will happen in the future based on historical data. A stochastic approach of time series model is a function that relates the value of time series to previous value of that time series and its uncertain errors. The initial step in stochastic time series analysis is to check whether the series exhibits constant mean and variance over time. Most of the time series data tend to stationary after the first differences. Stationarity can also be checked by correlogram which is a graph of autocorrelation function (ACF) or partial autocorrelation function (PACF) at various lags. For a non-stationary series, the autocorrelation coefficient starts at a high value and declines slowly towards zero as the lag increases. The autoregressive integrated moving average (ARIMA) is one of the popular stochastic approaches of forecasting time series data. ARIMA (p, d, q) is the general model for forecasting purposes where p is the order of auto- regression, d is the order of integration, q is the order of moving average. The method of least squares or the method of maximum likelihood estimation can be used to estimate the parameters of the models. The Box- Jenkins methodology can be applied to fit the best autoregressive integrated moving average model for time series forecasting. ARIMA model is considered as better model for forecasting prices over the deterministic model; however, exceptions are also available (Hassan et al., 2013). In the recent days the dynamic regression model is getting popularity as the model performs better to detect short run forecast (Hassan and Kornher, 2019). Bangladesh Bureau of Statistics (BBS) is also started to forecast food grain and potato prices through this model (BBS, 2017). A dynamic regression is a mixed-model approach that allows lagged values of the explanatory variables to be included in the model space. This model is used to predict what will happen to the forecast variable if the explanatory variable changes, while the values of the explanatory variable are extrapolated for the forecast period. A SWOT analysis was employed for evaluating the strengths, weaknesses, opportunities, and threats involved in betel leaf cultivation based on the primary survey, FGD, and KII. The acronym SWOT stands for ‘strengths’, ‘weakness’, ‘opportunities’ and ‘threats’. SWOT analysis is a simple but powerful tool for sorting up an enterprise or organization’s resource capabilities and deficiencies, its market opportunities, and the external threats to its future (Chermack and Kasshanna, 2007). It has four components: ‘strengths’, ‘weaknesses’, ‘opportunities’, ‘threats’. The SWOT analysis was chosen to identify both the internal and external factors that are favorable or unfavorable towards achieving the goals of the betel leaf enterprise. Strengths are categorized as internal helpful factors and weaknesses as internal hindering factors of betel leaf. While opportunities are external helpful factors and threats the external harmful factors.

Source of Information: Res. Agric., Livest. Fish.7(2): 209-223, August 2020

Article Link (Online Journal): DOI: https://doi.org/10.3329/ralf.v7i2.48861

Funding Source:

Budget:

Total Budget (Tk.) :

Achievement/Findings:

Betel leaf production has an upward trend. The entire best-fitted model predicts that the quantity demand for betel leaf will increase from 2020 to 2023. The area of cultivation and production of betel leaf is also rising. Methodologically, the dynamic regression model gives a better result for prediction than the deterministic and ARIMA models. This study also revealed the opportunities of betel leaf cultivation in the selected study area as a large group of people directly earned their livelihood through it. Factors like hired labor, crop insurance, and access to credit were the most important factors that may significantly influence the betel leaf business. This enterprise becomes risky for investment due to the undetectable solution of rotten leaf diseases. Pathologists may research for controlling diseases of betel leaf. The capital shortage was one of the major threats faced by the betel leaf growers. Without institutional credit support, it is difficult for the farmers to devote a large area to the betel leaf production. It is, therefore, necessary that credit on easy terms and conditions may be provided to the farmers for betel leaf production. The policymakers should pay immediate attention to this matter. It is also imperative to carry out more research on developing high yielding varieties of betel leaf and to develop appropriate production technologies for maximizing the yield as well as profit.

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