Forecasting milk production in Bangladesh toward achieving self-sufficiency

M M Uddin, A Akter, A B M Khaleduzzaman¹ and M N Sultana

Research group: Dairy Nutrition, Economics, Environment and Marketing, Department of Animal Nutrition, Bangladesh Agricultural University, Mymensingh
mohammad.uddin@bau.edu.bd
¹ Department of Livestock Services (DLS), Ministry of Fisheries and Livestock, Dhaka, Bangladesh

Abstract

Bangladesh dairy has been emerging as a transformation from livelihood-oriented dairy toward enterprise-driven dairy while it can still be seen as a key source of income and livelihoods for millions of people. There is an increasing emphasis by the government for achieving self-sufficiency. However, there have been challenges as the lack in authentic milk production data availability which counteract in estimating the self-sufficiency. There is a growing demand for milk along with the increasing consumer’s preferences on high-quality milk. The objective of this study was to forecast Bangladesh milk production to analyze the possible timeframe for achieving the self-sufficiency. This study applies Autoregressive Integrated Moving Average (ARIMA) model and compares with the classical Trend Analysis Model (TAM) with three scenarios i) Exponential growth, ii) Compounding Average Growth Rate (CAGR), iii) Simple Average Growth Rate(SAGR), built in MS Excel using time series milk data (2006 to 2019) obtained from the Department of Livestock Services (DLS), Integrated Dairy Research Network (IDRN) Sector database and International Farm Comparison Network (IFCN) Sector database.

The results revealed the superiority of ARIMA model over the trend analysis. The maximum milk production of Bangladesh in 2025 and 2030 will be 10.24 and 13.65 million tons, respectively (according to IDRN data) while that will be 16.32 and 17.13 million ton, respectively according to DLS. Considering the only population as demand factor (deterministic approach), taking the results from the ARIMA model, Bangladesh will be self-sufficient in 2029 according to DLS data and according to IDRN data, Bangladesh will achieve 71% self-sufficient in 2029. The forecasting was calculated only with normal year with some mild shock in the milk production. However, the impact of severe shock-like Coronavirus in 2020 has not been possible to integrate due to anecdotal in data, which implies that including such shock in future this forecasting might lead to the different pace of the self-sufficiency achievement. The results of ARIMA model using DLS data was substantially different than IDRN data which might need to take into account to identify why those data were different and might revise the data backwards to increase the degree of precision for estimating the future milk production data

From this study, it is evident that under normal condition, Bangladesh could reach to the self-sufficiency in 2029 but the year like 2020 (with pandemic coronavirus) need to be observed in the future and might need strategic decision to continue the current growth rate. The precise documentation of the milk production data would lead to the right path toward self-sufficiency. The future study might focus on the stochastic demand and supply factor for self-sufficiency analysis.

Keywords: ARIMA, coronavirus, IDRN, IFCN, milk production, self-sufficiency, and Trend analysis

Introduction

Dairying in Bangladesh has been transforming from traditional subsistence to more market-oriented and enterprise-driven approach in the dairy production system (Uddin et al., 2020) which would open the opportunity for dairy farmers to exploit the rising demand on milk and milk products at national as well as global dairy markets. Bangladesh has milk production of 9.92 million tons in 2018 (DLS, 2019). In contrast, the Integrated Dairy Research Network (IDRN) estimated for 2018 as 7.98 million tons positing 23rd in the global milk production while the topmost milk producing in the world is India with estimated 201.22 million ton of milk production (IFCN, 2019). The second milk producing country in the world is the United States of America with the estimated milk production of 95.31 million ton. The third and fourth in milk production were Pakistan and Germany corresponding to 48.36 and 33.18 million ton, respectively.

The milk availability per capita is reported as 165.07 ml/day/capita against the minimal requirement of 250 ml/day/capita (DLS, 2019). The self-sufficiency according to DLS is 64% while the same is 53% according to IDRN. Bangladesh needs a substantial increase in milk production to be self-sufficient while India and Pakistan are self-sufficient with an average consumption of 430 and 670 ml/day/capita, respectively.

To increase the availability of milk at the consumers level might depend on the supply of adequate quality of milk with affordable price which can be done ensuring the responsible actions by the topmost processors, dairy farmers and other marketing agencies. Concerning the processing profile in Bangladesh, only 9% of the total milk production is delivered to the processors and the remaining 91% is traded as informally (IFCN, 2019). The major market players in the country are Milk vita, Pran Dairy Ltd, BRAC Dairy and Food (Arong) and Akij Dairy Ltd (Farm fresh) corresponding to only 5% share to the total milk production in the country.

Along the supply chain of milk production to consumption, milk price is one of the major factors for dairy sector competitiveness (Roland et al., 2016) because of its huge influence on the future development of the sector. Creating updated knowledge on the current situation and trend back to the past on milk production is the utmost importance in making the reliable forecasting of the future growth and dynamics of the dairy sector development. In order to predict the future, the past and the present have to be carefully analyzed since the future is the further development of the present (Roland et al., 2016).

Forecasting for the future (e.g. making outlook) is important to make a strategic plan by any government in one hand, is complex and need a strong database and statistical knowledge on the other hand. Since the data is scarce in Bangladesh, utilization of alternative data source is justified to show how the database is supporting the country’s dairy sector development. As Bangladesh is currently deficient in liquid milk production and a net importing country for the powder milk, the government has been implementing a megaproject called Livestock and Dairy Development Project (LDDP) in cooperation with World Bank. Among several objectives of this project, one of the major objectives is to increase milk production to a level that leads to becoming self-sufficient in the near future. Given such background and data scarcity in mind, this study aimed at forecasting the milk production based on the two data sources in order to estimate the possible timeframe for achieving self-sufficiency in Bangladesh.

Methodology

The nature of this study inquires a multiple forecasting methodology as forecasting of milk production is a complex procedure and forecasting works under several conditional assumptions. Even with such limitation, the straightforward forecasting of milk production using different methodology would provide an output which can be compared with the previous period (that has already occurred) and simultaneously can be extended for prediction for the desired year. The predicted forecasting will provide a proxy to the real situation but still is an important tool for policy application and strategic decision process for enhancing dairy development. This study has applied multiple forecasting tools for making a valid conclusion on the forecasted milk production along with the number of years required to be self-sufficient in Bangladesh.

Data

For modelling any forecasting model, sources of data, data consistency and long-term time-series data are extremely important. In this study, we use two sources of data sets: i) milk production and dairy cattle population reported by Department of Livestock Services (DLS) as annual data; ii) milk production database developed by Integrated Dairy Research Network (IDRN) with the collaboration of International Farm Comparison Network (IFCN), Germany (Hemme et al 2014 and www.idrn-dairy.org). The DLS data is available from 2006 to 2018 and for IDRN data, it is from 1996 to 2019 (also available monthly data from 2006). For making standardization dataset, we take the data from 2006 to 2019 (estimated for DLS data for 2019).

Forecasting tools

There are several methods available for forecasting (Gooijer and Hyndman 2006) which might vary from the sector to sector and local need and context. In our study, four different forecasting tools were applied: Autoregressive Integrated Moving Average (ARIMA); Exponential Growth Rate; Compounded Average Growth Rate (CAGR), and Simple Average Growth Rate (SAGR).

ARIMA model

ARIMA model is also known as Box-Jenkins method (Box and Jenkins 1970) who developed a coherent, versatile three-stage iterative cycle for time series identification, estimation, and verification which is also known as the Box-Jenkins approach. This method has an enormous impact on the theory and practice of modern time series analysis and forecasting (Gooijer and Hyndman 2006). This helps to analyze both probabilistic and stochastic properties of single time series data. Unlike regression model, ARIMA model allows being explained by the movement of a variable by its past or lagged values. It produces predictions based on the synthesis of historical data. ARIMA can be run for single and multiple variables, however, for this study, we run this as a single variable which is called “Univariate ARIMA”. ARIMA model is most widely used elsewhere, however, this would be the first time (so far author’s knowledge goes) for the case of milk production forecasting in Bangladesh. The exception is the study done by Hossain and Hassan 2013 who forecasted milk production using multiple regression techniques (linear, compound, exponential, quadratic, and cubic). The ARIMA model consists of:

• AR: variables regressed on own lagged or prior values,
• I: indicates that the data values that have been replaced with the differences between their values and the previous values,
• MA: regression error representing the linear combination of error terms or repeated values.

ARIMA (p,d,q) takes the times series data where p denotes the number of autoregression need to be conducted (AR), d is the number of differentiation need to be done for the time series data in case the data is not found to be stationary (I) and q the number of moving average terms (q). The detailed mathematical notion of ARIMA model as explained by Deshmukh and Paramasivam 2016 is stated below:

Auto-Regressive Process of order (p) is,

Yt = μ + Ø₁ Y_t-1 + Ø₂ Y_t-2 + ….. + Ø_pY_t-p + ε_t ;

And the general form of ARIMA model of order (p, d, q) is

Yt = μ + Ø₁ Y_t-1 + Ø₂ Y_t-2 + ….. + Ø_pY_t-p + ε_t + μ + θ₁ ε_t-1 + θ₂ ε_t-2 + …… + θ_q ε_t-q + ε_t;

Where,

Y_t is milk production, ε_t are independently and normally distributed with zero mean and constant variance for t = 1, 2, …., n; and Ø_p and θ_q are also estimated

The data were tested using graphical representation and Augmented Dickey-Fuller test for both IDRN-BAU and DLS data. The graphical representation shows that milk production data in the time series is upward trending with substantial fluctuation. This implies that time series of milk production data cannot have the constant mean and variance leading to the decision on the lack of stationary data. To confirm this Augmented Dickey-Fuller test was performed. For time-series data to be stationary, the Z(t) should have a large negative number, the p-value should be significant at least on 5% level. In our data set (both IDRN-BAU and DLS) that were tested, neither of these conditions were met. Therefore, the null hypothesis i.e. time-series data of milk production is non-stationary, cannot be rejected. And since the time series milk production is non-stationary, further analysis cannot be performed on. A solution on this to be still used by ARIMA model is to estimate the first differencing of the time series and the newly created variable for milk production needs to be again tested by graphical and Augmented Dickey-Fuller test. This was done for both data sets and after doing the 1^st Order differences, the null hypothesis was rejected, indicating that both data fulfil the stationary property which can then proceed for running ARIMA model.

Exponential growth

This method originated with the work of Brown (1959) which is, thereafter, used in statistical framework  by Box and Jenkins (1970) and Abraham and Ledolter (1986)

Exponential Growth Rate (EGR)

This is used for a specific case where the growth occurs instantaneous rate of change (that is derivative) of quantity concerning time is proportional to the quantity itself. In this case, the time is considered as an exponent (considering the constant for other types of growth, such as quadratic). In contrast, if the quantity decreases over time, this is called exponential decay. In our study, we consider only the exponential growth which is estimated as below:

Y=b x m^x

Where,

X is the number of years in the calculation for predicted milk production (independent variable),
Y is the final milk production value (dependent variable),
m is the constant base for the x value,
b is a constant which is the value of Y when x = 0.

For the case of the multiple of ranges of x values, the estimation becomes different which is not used in this study as our datasets did not permit to do that.

Compounded Average Growth Rate (CAGR)

This estimate compounding growth over multiple years. The limitation of the SAGR where the growth rate is assumed to be constant over two subsequent years, which is not in reality, CAGR is used to solve this problem. CAGR takes into account geometric progression ratio that provides a constant rate of growth over the period. However, this does not include the effect of the volatility of periodic milk production. This is, however, useful to compare the growth rates from various sets of data, which is the case for this study. The CAGR is calculated as below following the model of Anson et al (2010):

End value = milk production in the last year to be estimated,
Start value = Milk production in the base year to be estimated,
No. of year count = Total number of years from the base year to the last estimated year.

Simple average Growth Rate

Simple Average Growth Rate (SAGR): this shows simply the growth from the previous years and uses the equal growth rate for the forecasted years (Hayes 2019). Using the procedure, changes over time in the base from which the growth is estimated include the increase in milk production from year-to-year growth. This is simple and easy to estimate using excel and less time consuming, especially where there is limited data, this is the valid procedure to make the outlook for production. The mathematical notion for this

End value = Milk production in the last year to be estimated,
Start value = Milk production in the base year to be estimated,
No. of year count = Total number of years form the base year to the last estimated year.

The data were forecasted from 2020 to 2030 although there might be a caution to forecast for so long time by ARIMA. But in this study, this was done purposively in order to estimate the required time to be self-sufficient.

All of the models (ARIMA and different types of Trend Analysis model) were applied for both IDRN-BAU datasets and DLS data set in order to predict which datasets have consistency property. The data were analyzed using the STATA software version 12.0 and Microsoft Excel 2016.

Results and discussion

Milk production and its development (2006 – 2019)

Milk production is the main output variable in dairy farms which drives the profitability for a particular dairy farm. To meet the demand of the growing population, the Bangladesh government has been investing significant resources to increase milk production. In order to boost future production, understanding the behaviour of historic milk production is necessary. To keep this in purview, the milk production (million ton) and its development (% growth change) from 2006 to 2019 have been depicted in Figure 1a and 1b, respectively.

Figure 1a. Milk production (mton/year)	Figure 1b. Milk production growth (%)

The milk production in Bangladesh as revealed from both datasets has been increasing at a rate faster during the period from 2006 to 2019 but with a strong fluctuation in milk production. However, except at the beginning of the development (e.g. 2006 and 2007) when it was found almost a similar amount of milk production. The milk production in 2006 and 2007, was close to each other and slight variation was observed. Interestingly to note that milk production has been reported by DLS was lower than IDRN-BAU until 2011. From 2012 onward, the milk production data reported by DLS was significantly higher than IDRN-BAU. The reasons were not explained and there is not found a methodological explanation why such an increase was reported. A difference of milk production at the year 2006 was found that DLS has lower milk production (-0.23 million ton) which was turned into a difference of the DLS from IDRN-BAU was 2.33 million ton in 2019.

Looking into the annual growth rate change (expressed as %), the average growth rate for IDRN-BAU and DLS is found to be 8.8 and 11.5%, respectively (Figure 1b). The development of milk production for the DLS data fluctuated with the highest growth rate of 47% in 2012 while the lowest was -14% in 2008. In contrast, the IDRN-BAU milk production growth was the highest in 2008 which was 20% and the lowest was found as -5% in 2012. The contradictory results for 2008 and 2012 were due to the fact that IDRN-BAU data incorporated the natural calamity and other shocks that was happened over the time which might not include in the DLS data or there could be any other reason which is not published by DLS. The IDRN-BAU data sets considered the Tsunami (Locally called “Sidor”) in the south-west region in 2007, the world economic recession in 2009 and the melamine crisis in 2008, the prevalence of Anthrax in the main dairy region in 2011. The data published by DLS lacks this explanation of how do they address those shocks, especially milk production in 2012 has increased by 47%.

Since Bangladesh dairy sector is progressing faster, the accurate estimation of milk production and related other factors that drives the milk production variation like to play a vital role in helping to make the right intervention. Due to the shortage of the database, the lack of appropriate methodological tools for the DLS data, and the lack of institutional endorsement of the IDRN (since IDRN-Data is still considered as research-oriented database), it is highly relevant for the DLS (as a regulatory body of the government) to rethink on the possible validation process to revise the historic data and to take a holistic approach to estimate the future outlook data for milk production. Milk production is the key aspect of understanding the country’s dairy sector development. The DLS and IDRN-BAU can work together while the DLS has institutional mandate along with countrywide network while the IDRN-BAU is strong on methodology, modelling, data generation and data validation expertise tools. Synchronization of the working of both might solve the inconsistency in the dataset in the near future.

Forecasting milk production in Bangladesh (2020 – 2030)

Based on the historic development of milk production (2006 – 2019) which was found to be inconsistent, the forecasting of milk production was done for both datasets in order to compare the milk production growth potential in the coming year along with the self-sufficiency achievement for Bangladesh.

The data from two sources (IDRN and DLS) were checked for its stationarity (e.g. having equal mean and variance over the year). The data were diagnosed as non-stationary as explained in the methodology section. The problem of non-stationarity was solved by performing 1st differencing order of the historic data which was then plotted with the help of AC and PAC graphical representation and depicted in Figure 2a and 2b.

Figure 2a. Graphical representation of correlogram (ac)	Figure 2b. Graphical representation of partial correlogram (pac)

Figure 2a and 2b shows for which array the ARIMA should take into account and this was helpful to construct and select the ARIMA model which is depicted in Table 1 where it is found that ARIMA model should run from 1 to 12.

Table 2 shows the values of Log-Likelihood, P-value and AIC and BIC values which were used for the selection of the best fitting ARIMA model to run the forecasting. Taking the values from table 2, ARIMA (1,1,1) was selected for forecasting the milk production both for DLS and IDRN data.

The ARIMA model results for forecasting from 2020 to 2030 and Trend analysis results are depicted in Table 3. The table shows that ARIMA model and CAGR results seem plausible while other two Exponential and SAGR is ended up with extremely high values for the DLS and even it is significantly lower compared to the DLS but substantially higher in the context of the real situation of Bangladesh dairy sector.

The ARIMA model shows that the maximum milk production in 2030 can be achieved is 13.6 million ton (as per the IDRN-BAU data) and 18.1 million ton (as per the DLS data) which is significantly different from each other.

Also, it was found significant differences in milk production using CAGR. Among the datasets, DLS data has a very high variation (too extreme in upper and lower bound) which influences the forecasting results. Due to such high variation, it is again argued that it might difficult to make realistic inferences based on the data forecasting. However, the advantages of this forecasting are that it identified the inconsistency as well as it guides how shall the future data collection be.

Forecasting demand and self-sufficiency in Bangladesh (2020 – 2030)

The demand for milk in Bangladesh is calculated by DLS using the deterministic approach, e.g. taking the World Health Organization (WHO) recommended average daily intake per capita of 250 g per day over the year. The demand is also changed with the changes of per capita income, rate of urbanization and changes in the consumers' preference which were not taken into account in this demand calculation. The forecasted milk production from table 3 is used to estimate the self-sufficiency until 2030 which are shown in Figure 3.

Figure 3. Forecasted milk demand and self-sufficiency (2020 – 2030)

Figure 3 shows that milk demand will increase to 17.22 million ton in 2030 which could be achieved according to DLS data in 2029 while the IDRN-BAU data shows 80% self-sufficiency in 2030. The diverse of the self-sufficiency forecasting results might be because both databases have a different dimension of development path with different peak and lean growth over the year which has impacted the forecasting results. The rate of increase in self-sufficiency is, somewhat, similar pace with both databases. The IDRN-BAU data shows an increase of 22% (initial 56% in 2020 and 80% in 2030) while that for DLS dataset shows 34% (initial 72% in 2020 and 106% in 2030). However, this does not take into account the other dynamic drivers in the demand calculation as well as the demand of the WHO might be changed in future. This enquires that the future self-sufficiency will, highly, depend on the strategic decision, how the government would define the future nutritional security and dietary recommendation for the overall population.

Policy implications

The data of dairy sector is relatively scarce in Bangladesh compared with other agricultural commodities even substantial efforts being made by the Department of Livestock Services (DLS) under the Ministry of Fisheries and Livestock (MoFL). Concerning dairy farms and farming system, the DLS update three key data on milk production, total cattle and total buffalo number while it estimates the number of variables such as demand of milk, milk availability and milk surplus/deficiency.

At the same pace, the Integrated Dairy Research Network (IDRN) which is a proposed network under the Department of Animal Nutrition of Bangladesh Agricultural University has been constantly working to update the latest data by utilizing the methods and models developed by the International Farm Comparison Network (IFCN), Germany (Hemme 2000) which has been further refined and extended to address the dynamics of the global changes in a dairy farm and dairy sector. The IDRN has Dairy Sector Database consisting of 29 variables and dairy farm database consisting of 647 variables.

The milk production data reported by IDRN and DLS has been diagnosed in order to establish a logical back-calculation procedure. This calculation will help the validation of the data on which the decision can be made more precisely.

Herd structure data is estimated based on the coefficients (different proportion of the cattle found in the farm level using the IDRN farm survey data).

The herd structure used in this calculation is depicted in Table 4. Using this data, the milk production from both IDRN and DLS data is diagnosed. The detailed diagnosis of the milk production is depicted in Table 5.

Table 5 clearly shows that milk production data in both sources has been overestimated as the real dairy farming situation might not reflect. As the country dairy farming has milk production mostly from local cows representing 67% of the total cows with an average production of only 2.80 while for remaining is cross bred 33% and produce 6.39 kg/day (Huque 2014). However, over the time and entrepreneur initiative, the ratio might have been changed which is not yet explored. In case of IDRN data, the coefficient that was taken using the farm survey data- but those data tended to be biased toward mostly from the major dairy region in the country which might be different if the data is generated from all 64 districts. On the other hand, the DLS data is estimated institutional office record and less used the statistical tool and survey tools which also might tending toward upper bound bias. If we estimate the dairy farm density to produce the total milk, it is found that for producing the DLS reported milk production, the density is 359 farms/km² and for that of IDRN milk production, it is 279 dairy farm/km². This density data seems to be overestimated as well and is divergent from reality. However, the mega project of Livestock and Dairy Development Project (LDDP) could lead to the refining of the current milk production data and also need to include more variables to make the estimate that reflects the reality.

Similar to milk production data, the estimation of demand also needs to be analyzed further. The DLS estimates the demand for utilizing the deterministic approach. Under the deterministic approach, the constant amount of milk demand is used in the calculation, while for the stochastic approach, the demand is taken as a function of stochastic variables such as an increase in income, population and urbanization and change in consumer preferences.

If we take the stochastic nature of the demand, the demand might change over time. Taking both deterministic and stochastic approach in demand calculation, the self-sufficiency estimation has been depicted in Table 6. The forecasted milk production and demand is taken from ARIMA model. Bangladesh will achieve self-sufficiency in next 10 years if the deterministic approach is applied but as the country has been undergoing tremendous economic progress and becoming a middle-income country in 2024 (World Bank, 2019), the demand might increase which will lead to supporting the stochastic approach. However, the data used for self-sufficiency by IDRN and DLS has to be taken into account as both data shows two different periods to achieve self-sufficiency. At the similar pace, the impact of Coronavirus (COVID-19) which has occurred only in the year 2020, could not be taken into account during forecasting modelling due to anecdotal in data which could lead the future decrease in milk production.

As policy implication perspectives, the analysis used in this study is not enough to judge the superiority of the data one over other, however, it could be speculated that both databases are overestimated. The government might focus on the revision of the historic development of milk production data for developing DLS database. At the same pace, IDRN as a research-based database network might work together with DLS to support the methods and models to estimate the milk production which is closer to reality. However, it is also noteworthy to mention that dairy data is very much inconsistent and proper care must be taken during any research or database update. This implies that any data related finding might be tested and validated with the national panel of experts before publishing where the Livestock and Dairy Development Project (LDDP) can take initiatives. The precise estimation of the milk production data is linked with self-sufficiency defining the country’s position as surplus or deficit which might be addressed by the national dairy development policy in Bangladesh.

Conclusion

• This study applied both Econometric forecasting tools like ARIMA (the most widely used globally) and compared with Trend Analysis built in MS Excel and found that ARIMA model and Compounded Annual Growth Rate (CAGR) can forecast better than other two models.
  
  
• The maximum milk production in 2030 will be 13.6 and 18.1 million ton as forecasted by the IDRN and DLS, respectively.
  
  
• Based on the DLS data using ARIMA model, Bangladesh would require another 10 years to achieve self-sufficiency if the demand stays constant of 250g/day/capita. Based on the IDRN data, 80% self-sufficiency will be achieved in 10 years. However, if the demand is changed over time in pace with economic growth of the country, the country has to either intervene to increase the productivity to catch demand pace.
  
  
• Both data sources might be overestimated for the milk production which can be tested by taking backward validation and use appropriate methods and models for forward estimation which in turn will help for increasing the precision of the data.

Acknowledgement

The authors sincerely acknowledge the Department of Livestock Services (DLS), the Department of Animal Nutrition, all of the expert members, database members and Team members of the IDRN-BAU research network. The authors also acknowledge the International Farm Comparison Network (IFCN), Germany for providing the models and database access. The Authors also acknowledge the Ministry of Education (BANBEIS) project (Project reference 490/2018) for financial support. The authors also sincerely acknowledge Dr. Khan Shahidul Huque, former Director General of Bangladesh Livestock Research Institute (BLRI) for his excellent feedback and comments.

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Received 3 April 2020; Accepted 7 April 2020; Published 1 May 2020

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