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IMPACTS OF MAJOR CROP PRODUCTION ON THE AGRICULTURAL SECTOR IN BANGLADESH: A TIME SERIES ANALYSIS

Article History Received: 16 December 2021 Revised: 8 February 2022 Accepted: 22 February 2022 Published: 14 March 2022


INTRODUCTION
Rice, wheat, potato, jute, and sugarcane are considered the main agricultural crops of Bangladesh, and these crops have a considerable impact on the agricultural sector. Bangladesh is an agriculture-based developing nation and the most densely populated country in the world. The total population of Bangladesh is about 161.4 million (Word Bank, 2018). The economic development of Bangladesh is mainly dependent on the agricultural sector. The agricultural sector contributes the lion's share of the national income, forms the basis of foreign currency earnings, is a major source of food supplies, and generates employment opportunities in rural areas. Murshid and Yunus sector creates employment opportunities for men and women in rural areas and ensures Bangladesh's food security.
The country's agricultural economy is entirely dependent on the production of these crops. Rehman et al. (2015) investigated the economic impacts of main field harvests, namely wheat, sugarcane, rice, maize, and cotton in Pakistan from 1950 to 2015. Annual time series data were used in the study. Different econometric methods, such as the augmented Dickey-Fuller (ADF) test, ordinary least squares (OLS) method, and Johansen cointegration test, were employed to analyze the data. The results of the research demonstrated that wheat, rice, maize, and cotton had a positive influence on the agricultural gross domestic product (AGDP) of Pakistan. Here, the impacts of maize and cotton output were significant, but the impacts of rice and wheat output were not significant. Moreover, sugarcane had a negative, though not significant, influence on agricultural GDP.
They suggested that the Pakistani government should implement new funding schemes for the agricultural sector. Kyomugisha, Sebatta, and Mugisha, (2018) conducted a study to identify the factors limiting farmers' market entrance, the break-even point for potato farmers, and the market efficiency of potato market chains in Uganda.
They selected two exoteric potato-producing areas in Uganda, Kabale and Mbale, and randomly selected potato farmers and traders from the two districts. The OLS method was used to identify the factors that affect potato smallholder farmers' market entrance. Break-even analysis was conducted to calculate the potato growers' breakeven point for employing post-harvest price added actions. Finally, to identify the market efficiency, a value-added approach was employed. Their study revealed that having contracted with purchasers, land area owned, number of forked hoes owned and diversity of produce have a positive and significant impact on farmers' market access. They also indicated that adding value to potatoes on the farm generates comparatively more revenue for growers. Market chains where growers sell to local rural dealers were shown to be more effective than other selling options. Jahan (2019) conducted a study to discover the environmental, economic, and social factors of jute and its contribution to the sustainable development of Bangladesh. She also illustrated the environmental and economic benefits of jute. Data was collected through interviews, and qualitative methods were chosen to analyze the data.
Her study revealed that jute contributes to the environment in many ways. In addition, novel jute products create many economic opportunities in markets. Jute has a bright future in the geotextile, automobile, infrastructure, and packaging sectors. It has also created employment opportunities in Bangladesh. Approximately 25 million people are directly or indirectly linked to the jute industry. Malaiarasan, Paramasivam, Thomas Felix, and Balaji (2020) studied the interdependency between sugar supply and demand in India from 1970-1971 to 2013-2014. A simultaneous equation model and time series data were used in their research. To calculate the elasticity of demand and supply equations for sugar, a three-stage least square regression model was employed. Their results demonstrated that the price of sugar has a positive impact on sugar supply while sugar price negatively affects sugar demand. The recovery rate and quantity of cane crushed are positively related to sugar production in India.
Changes in the area harvested in the current year, yield, and FRP determine the future area under sugarcane cultivation. Rainfall and technological progress have a positive relationship with sugar production, and there is a direct link between population rate and sugar consumption. Rehman and Jingdong (2017) investigated the relationship between some main crops in China, namely rice, wheat, cotton, corn, sugarcane, and tubers, and agricultural gross domestic product (AGDP) in the period 1980-2015. Annual time-series data and the ordinary least squares (OLS) technique were used to analyze the relationship. The results of the study illustrated that cotton, wheat, sugarcane, corn, and tubers have a positive and significant effect on AGDP. On the other hand, rice production has an insignificant and negative impact on AGDP. Rahman, Kazal, Begum, and Alam (2017) analyzed the posterior dynamic of the jute sector in Bangladesh. They collected national time-series data from 1974 to 2013.
In addition, they selected 289 farmers from two jute cultivation zones in Bangladesh to supply farm survey data. To determine jute's future potentiality they considered its growth performance, universal competitiveness, profitability, and production proficiency. The results of the study demonstrated that the jute sector has experienced significant growth in area, production, productivity, prices, and exports. From 2004 to 2013 productivity stagnated, although from 1973 to 2003 jute grew at a rate of 1.3 each year. Furthermore, they opined that customary jute cultivation was universally competitive, but the monetary profitability of white jute was comparatively higher. Land, labor, and irrigation all play an important role in jute production, and marginal farmers were comparatively inefficient. In none of the studies in the above literature review did the researchers include the agriculture sector as a dependent variable. A comprehensive and econometric analysis-based study of the impacts of major crop production on the agricultural sector (value-added) has never before been carried out in the Bangladeshi context. Thus, this research paper will fill the research gap in the exploratory literature.

Data Collection
The annual time-series data  of the variables is collected from various secondary sources.
Agricultural value-added (in current US dollars) (AGR) data is taken from World Bank and OECD national accounts data files. Rice production data (in 1,000 metric tons) is collected from the United States Department of Agriculture. In addition, the production data for jute, wheat, potato, and sugarcane (in 1,000 metric tons) comes from the Finance Division (2018), Department of Finance, Ministry of Finance.

Econometric Methods
An augmented Dickey-Fuller (ADF) test was utilized to examine the stationarity of dependent and independent variables. After investigating the stationarity of the variables, the long-run relationships between the agricultural sector and the independent variables (rice production, jute production, wheat production, potato production, and sugarcane production) were checked using the Johansen cointegration test. The ordinary least squares (OLS) method was employed to test the impacts of rice production, jute production, wheat production, potato production, and sugarcane production on the agriculture sector of Bangladesh in the period 1988-2017. Finally, some residual diagnostic tests, namely the Jarque-Bera test, Breusch-Pagan Godfrey test, Breusch-Godfrey serial correlation LM test, and stability test, were conducted to justify the model.

Multiple Regression Model
To explore the relationship between the agricultural sector (dependent variable) and the independent variables (rice, jute, wheat, potato, and sugarcane), the following model was established: (1) Equation 1 depicts the model of the study. Applying the natural logarithm (LN) of Equation 1 and incorporating five explanatory variables, Equation 1 was altered to: (2)

Unit Root Test
The augmented Dickey-Fuller (ADF) test was applied to check the stationarity of the dependent and independent variables. The results of the ADF test are reported in Table 1. Table 1 illustrates that at the level form all the variables are non-stationary, but after taking the first difference they become stationary. Therefore, the variables follow the integrated order one, which can be written as I (1).

Cointegration Test
Cointegration means that the series in question are interrelated and can therefore be grouped in rows. This means that even short-term shocks, which can affect movement in a given series, converge in the long run.
Cointegration can estimate both long-term and short-term models (Adeleye, 2018). Applying the Johansen cointegration test, it is found that there are two long-run relationships or two cointegrating equations among the variables. To run the Johansen cointegration test, the optimum lag length is considered, which was selected according to the Akaike (2011) information criterion (AIC) and the Schwarz (1978)

Regression Analysis
To analyze the effects of rice, jute, wheat, potato, and sugarcane production on the agriculture sector of Bangladesh, the ordinary least squares (OLS) method was utilized. The results of the multiple regression analysis are provided in Table 3. Here the calculated value of R-squared is 0.9740 or 97.40%. This indicates that roughly 97.4 % of the total variation in the agriculture sector can be illustrated by rice, jute, wheat, potato, and sugarcane.
Moreover, the computed value of F-statistics is 180.26, and the probability value is 0.0000, indicating the fitness of the multiple regression model.
The results of the multiple regression analysis illustrate that the coefficient of jute production is highly significant (at 1 or 5 percent significance level), and there is a solid positive (Rehman et al., 2015) relationship between jute production and the agriculture sector. This means that every 1 percent rise in jute production raised the agricultural value addition by 0.165 percent. The analysis also revealed a positive (Rehman et al., 2015) relationship between rice, wheat, and potato production and the agriculture sector. Although the impact of wheat production is significant at the 10 percent level of significance, the effects of rice and potato are not significant at the 5 or 10 percent significance level. Nonetheless, the results imply that every 1 percent increase in rice production, wheat production, and potato production leads, respectively, to a 0.392 percent, 0.1137 percent, and 0.0381 percent increase in agricultural value addition. Furthermore, the relationship between sugarcane production and the agriculture sector is negative and highly significant at the 1 or 5 percent significance level. This means that a 1 percent increase in sugarcane production leads to a 0.8305 percent decrease in agricultural value added in Bangladesh. Through the implementation of nuclear and biotechnology, the government of Bangladesh has provided farmers with salt-tolerant and short-term crop varieties. Nowadays, in the coastal (southern) areas of Bangladesh, most farmers grow salt-tolerant crops. In addition, high-quality seeds are another key input to increase crop yields. In Bangladesh, the government, non-governmental organizations, and different seed production organizations are providing high-quality hybrid crop seeds to growers. The 75 contract planting areas also provide certified crop seeds. The country only provides 15-20% additional production for high-quality seeds (Finance Division, 2018). The 9 horticultural development centers and 14 agricultural service centers of the Bangladesh Agricultural Development Corporation (BADC) produce and distribute seeds, seedlings, and other planting materials across the country. Yet, on the other hand, the agricultural sector is facing different kinds of problems.
The high prices of agricultural inputs, lack of subsidies and funding, dearth of irrigation facilities, poor agricultural marketing system, and underdeveloped infrastructure facilities are the main challenges. The study has shown that the impact of sugarcane production on the agriculture sector is negative (Rehman et al., 2015) and significant. This result is unexpected but might be due to unfavorable climatic conditions and support price fluctuations.

RESIDUAL DIAGNOSTIC TESTS
The Jarque and Bera (1980) test of the model acknowledges that the sample data matches a normal distribution.
Furthermore, the results of the heteroskedasticity test (Breusch, 1978;Breusch & Pagan, 1979) reveal that the estimated model is homoscedastic, and the serial correlation LM test (Breusch, 1978;Breusch & Pagan, 1979) confirms that there is no serial correlation in residuals. The outcomes of the diagnostic tests are provided in Table   4.

1. Stability Diagnostics Test
The result of the CUSUM square test (Brown, Durbin, & Evans, 1975) is provided below. Figure 1 shows that the blue line is between the two red lines, meaning that the econometric model is stable.

CONCLUSION
This study examined the impacts of rice, wheat, jute, potato, and sugarcane production on the agriculture sector in Bangladesh in the period 1988-2017. Annual time series data was utilized to conduct the study. The econometric outcomes of the Johansen cointegration test demonstrate that there is a long-run relationship between the agricultural sector (dependent variable) and the independent variables (rice, jute, wheat, potato, sugarcane). The results of the ordinary least squares (OLS) method revealed that both jute and wheat production have positive and significant impacts on the agriculture sector. Furthermore, although the impacts of rice and potato production are positive, these are not significant at the 5 or 10 percent significance level. In addition, the impact of sugarcane production on the agricultural sector is negative and significant. The results of the diagnostic tests reveal that the model is free from problems of heteroskedasticity and serial correlation and that the data matches a normal distribution. Finally, the CUSUM square test (Brown et al., 1975) demonstrates that the model is stable for the study period.
Funding: This study received no specific financial support.