





































American Journal of Agricultural Science, Engineering and Technology

Impact of Some Climatic Variables on the Yields of
Boro Rice in Bangladesh

Md. Idris Ali1†, Provash Kumar Karmokar1, Mahendran Shitan2 and A. B. M. Rabiul Alam Beg 3

Abstract
Bangladesh is primarily an Agriculture based country and its economy largely depends on the

agriculture. Weather and climate are key determinants of the productivity of crops grown in 

an agrarian country like Bangladesh. Boro rice constitutes a large share in the domestic food 

grain of the country. Sometimes its production affected by some climatic factors. Therefore, 

the objective of this research was to determine the likely climatic factors for Boro rice 

production in Bangladesh. In this study we employed traditional OLS method and recent 

Bootstrap   technique to identify the influential climatic factors on Boro rice production. Our 

study revealed that the considered variables rainfalls (RAIN), maximum temperature (MAX), 

minimum temperature (MIN) and wind speed (WIND) have significant effect on Boro rice 

production both by OLS and Bootstrap method. Bootstrap method exhibits lower standard 

errors in comparison to the OLS method indicating that this estimate could be useful in Boro 

rice production of Bangladesh. The messages from this study could be useful for the policy 

makers of the country.

1Department of Statistics, University of Rajshahi, Rajshahi-6205, Bangladesh
Email: sprovash@yahoo.com

2Laboratory of Computational Statistics and Operations Research
Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia

3School of Business, James Cook University, Australia

† Research Student

81AJASET, ISSN: 2158-8104 (Online), 2164-0920 (Print), Vol. 2, Issue. 1

Keywords: Multiple Regression, Bootstrap Technique, Climatic Factors, Boro Rice 
Production, Ordinary Least Squares (OLS)



American Journal of Agricultural Science, Engineering and Technology

Introduction
Bangladesh is primarily an Agriculture based country and its economy largely depends on
agriculture. Poverty is still a problem for the country. In South Asia it is still a problem for
the rural people. About 70% of South Asia’s population lives in rural areas, and it accounts
for about 75% of the poor. Most of the rural poor depend on agriculture for their livelihood.
Agriculture employs about 60% of the labor force in South Asia and contributes 22% of
regional Gross Domestic Product (World Bank).  In Bangladesh agriculture sector accounts
for about 20% of the country’s Gross Domestic Product (GDP). About 90% of the
Bangladesh people depend on rice for their calories intake. Rice is a major source of
livelihood in terms of providing food, income and employment in Bangladesh. Rice occupies
the maximum share of agricultural GDP and it employs about 60% of the total labour force
(Alam et al. 2008; WDI, 2010).
Because of the increasing demand for food and jobs for many inhabitants, it became

necessary for households to embark on agriculture as a means of filling the food demand and
supply gap and providing income for other household requirements. In addition, the practice
of agriculture has continued to increase in recent years with the structural adjustment of the
Bangladeshi economy. The rise in food price, un-employment and inflation brought by the
structural adjustment (World Bank, 1990) and the decline in the average real income of both
rural and urban households have completed many in to farming areas.  Among all other cereal
crops, Boro rice has the largest share in the domestic food grain production of the country
(WEP, 2011).  Since 1999-2000, Boro contributes more than half of the total rice production
in Bangladesh. Currently Boro occupies about 41% of total rice area and contributes 56% of
total rice production in Bangladesh (Deb et al. 2009).
Weather and climate are key determinants of the productivity of crops grown in various
regions of the world. Moreover the forecasts of crop production for the coming season
require accurate seasonal weather forecasting (Challinor, et al. 2003). Climate change is
anticipated to have for reaching effects on the sustainable development of developing
countries including their ability to attain the United Nations Millennium Development Goals
by 2015 (UN 2007). Agriculture is extremely vulnerable to climate change (IPCC, 1990),
changes in temperature. Mendelshon (1994) has examined the impacts of the climate change
on agriculture for all countries in the world between 1960 and 2000. According to him,
temperature and precipitation has the effect from a loss of 0.05% to a gain of 0.9% of global
agricultural GDP.
Some studies on various aspects of rice production and its influential factors (Basorun and
Fasakin, 2012; Karmokar et al., 2012; Karmokar and Shitan, 2011; Nargis and Lee, 2013)
have been found in the literature.  Most of the cases they used the econometric time series
analyses to conclude their research.
Karmokar and shitan (2011) studied some influential factors like, annual rainfall, uses of
chemical fertilizer, area of cultivation and lag harvest prices on rice production of Bangladesh
on basis of Food and Agriculture Organisation of the United Nations online database
(FAOSTAT) for the years 1975 to 2003 to identify which factors play a crucial role on the
production of rice in Bangladesh.
As a very simple and widely used technique they considered OLS for their study. But
Sometime OLS estimates may be affected by the occurrence of outliers, non-normality,
multicollinearity or other causes (Ho and Naugher, 2000).  In reliable aspect bias may create
problem for the estimators.  Bootstrap technique may give the good and minimum biased
estimators. As such it would be interested to estimate the Bootstrap estimators of parameters
along with the OLS estimators which could be comparatively less affected by any biased or
non-normality of errors.

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American Journal of Agricultural Science, Engineering and Technology

Methods and Materials
A brief discussion of data sources ad selected variables are presented in this section.

Data Source and Selected Variables
This study based on the Bangladesh Meteorological Department (BMD) data. The BMD was
implemented through a collaborative effort of the Bangladesh Agricultural Research Council
of the Ministry of Agriculture, Government of the People’s Republic of Bangladesh. A
sample of 40 years (1971 to 2010) has been collected from the meteorological stations in
Bangladesh. The yield data used in this study have been collected by Bangladesh Agricultural
Research Council.
The predictor variables namely, Rainfall in centimeter (RAIN), Maximum temperature in

degree Celsius (MAX), Minimum temperature in degree Celsius (MIN)  and  Wind Speed in
meter per second (WIND) and the response variable per hectare Boro Rice production in
kilogram (BRICE) have been used in this study.

Multiple Regression
Regression analysis is one of the most widely used statistical tools which provide simple

methods for establishing a functional relationship among a set of variables. It has extensive

use in a variety of areas including agricultural science. In this study we are interested to fit a

multiple linear regression model to identify the variables influencing the BRICE of

Bangladesh. The under usual assumptions the multiple linear regression model is as follows

,      (1)

where BRICE is the response variable, RAIN is the Rainfall, MAX is the Maximum

temperature, MIN is the Minimum temperature and WIND is the Wind Speed. is the

intercept term, , , . . . , are the unknown regression coefficients, and is the error

term with a distribution.

Regression Diagnostics
Sometimes the regression results may mislead the outcomes due to some causes. Among

them multicollinearity is phenomenon of data set which occurs due to the dependency or any

relationship among the predictors variables. In such case the diagnostic, Variance Inflation

Factor (VIF) may indicate either the data set is infected by milticollinearity problem or not?

The VIF for independent variables is

, ,

where k is the number of predictor variables and is the square of the multiple correlation

coefficient of the jth variable with the remaining (k -1) variables where,

1) if , there is no evidence of multicollinearity problem

2) if , there is a moderate multicollinearity problem  and

3) if , there is a seriously multicollinearity problem of variables (Chatterjee & Hadi,

2006).

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When multicollinearity is present in a set of predictor (explanatory)

variables, the ordinary least squares estimation of the individual regression coefficients tend

to be unstable and can lead to erroneous inferences. There are some alternative estimation

methods that provide a more informative analysis of the data than the OLS method when

multicollinearity is present. These are

(i) Dropping of bad variable from analysis

(ii) Estimation of linear function of regression coefficient

(iii) Method of estimation by adjusting dependent variable

(iv) Method of reparametarization of the model

(v) Principal component regression

(vi) Ridge regression.

Bootstrapping Method
There are mainly two technique of bootstrap, they are

(i) Fixed X-resampling ( Bootstrap-1)

(ii) Random X-resampling (Bootstrap-2)

Fixed X-resampling (Bootstrap-1): when the model matrix X is fixed we can generate

bootstrap replication in the following way:

Step-1: Based on an observed sample we have to consider a regression model

.

Step-2: Fit the model by the OLS denote the estimated by and compute

residuals .

Step-3: Resample from to obtain ;

Step-4: Enumerate the response values from the equation

;

Step-4: Estimate the regression parameter by the OLS from the model

To obtain then the estimated regression coefficient by using fixed X-

resampling bootstrap technique is

The bootstrap bias and variance

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American Journal of Agricultural Science, Engineering and Technology

The bootstrap bias equals,

(for further discussion see Efron and Tibshirani, 1993). Following

Liu and stine (Liu, 1988; Stine 1990) the bootstrap variance from the distribution are

calculated as

The bootstrap confidence interval by normal approach is obtained as

, where is the critical value

of t with the probability of the right for n-p degrees of freedom and is the standard

error of .

Results Discussion
We have considered BRICE, RAIN, MAX, MIN and WIND in this research. To have an idea

about these variables the Summary statistics have been enlisted in the Table 1.

Table 1. Summary statistics of study variables

Variable Mean Maximum minimum Variance Skewness Kurtosis
BRICE 4443.750 5960.000 3290.000 416285.577 0.810 3.080

MAX 30.408 31.320 29.410 0.151 0.222 3.582

MIN 21.106 21.650 20.580 0.073 0.085 2.269

RAIN 49.993 80.250 23.360 172.922 0.237 2.626

WIND 1.252 1.680 0.870 0.049 −0.166 1.985

It is noticed from the results of Table 1 that the average of BRICE is 4443.750, variance is

416285.577 maximum is 5960.000, minimum is 3290.000, skewness is 0.810 and kurtosis is

3.080. For maximum temperature the average is 30.408, variance is 0.151, maximum is

31.320, minimum is 29.410, skewness is 0.222 and kurtosis is 3.582. The average value of

minimum temperature is 21.106, variance is 0.073, maximum is 21.650, minimum is 20.580,

skewness is 0.085 and kurtosis is 2.269. The average of RAIN is 49.993, variance is 172.922,

maximum is 80.250, minimum is 23.360, skewness is 0.237, kurtosis is 2.626. For WIND,

average is 1.252, variance is 0.049, maximum is 1.680, minimum is 0.870, skewness is

−0.166 and kurtosis is 1.985. Therefore, all the variables selected in this study are non-

normally distributed.

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American Journal of Agricultural Science, Engineering and Technology

To have an idea about the production trend of Boro rice of Bangladesh five

year production percentages of Boro rice among all variety of rice have been computed. A

time series plot of these percentages is shown in the figure1.

Fig 1. Five year production percentages of Boro rice

It is seen from the above figure that the five year production percentages have somewhat an

increasing trend. Therefore year-wise production of Boro rice is increasing in Bangladesh.

To investigate the relationship among a set of variables, regression is one of the most

commonly used statistical techniques. In linear regression, the relationship between two

variables can be judged by fitting a linear equation. The multiple linear regression attempts to

model the relationship between two or more explanatory variables and a response variable by

fitting a linear equation to observed data. The ordinary least squares (OLS) method is a way

of estimating the parameters of a regression model. We fitted a multiple regression model as

given in equation 1 by OLS methods and the estimators, t-statistics, p-values and VIF are

presented in Table 2.

Table 2. OLS estimators with VIF of multiple regressions

Predictors Coefficient t-value Standard  Error p-value VIF

RAIN 6.124 2.251 2.720 0.031 1.351
MAX 668.679 2.817 237.386 0.008 1.949
MIN –585.254 –1.798 325.504 0.081 1.779

WIND –2029.464 –5.975 339.683 0.000 1.301

In Table 2, the estimated value for RAIN is 6.124 and its t-value is 2.252 with p-value 0.031,

the estimated value for MAX is 668.679and its t-value is 2.817 with p-value 0.008, the

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American Journal of Agricultural Science, Engineering and Technology

estimated value for MIN is –585.254 and its t-value is –1.798 with p-value

0.081, the estimated value for WIND is –2029.464 and its t-value is –5.975 with p-value

0.000.

Although MIN is significant at 10% level all other variables are significant at 5% level. The

VIF values for all the variables are less than 5 indicating that there is no such multicolinearity

in the data set.  The R2 value for this model is 0.634 and the Adj R2 value is 0.592. Therefore,

the predictor variables can explain about 63.4% of total variation by R2 and about 59.2% of

total variation by Adj. R2.

Bootstrap is a re-sampling technique used in statistical analyses has been considered in this

research. The Bootstrap results using same variables as used in the multiple regression model

are given in the Table 3.

Table 3. Bootstrap results for study variables
Variable Regression

Coefficient
Bias Standard

Error
p-value 95% Confidence Interval

Lower Upper
BRICE 6.680 0.570 2.250 0.031 0.880 12.470
MAX 668.270 –0.400 200.130 0.007 152.720 1183.810
MIN –586.640 –1.390 264.120 0.072 –1267.030 93.750
WIND –2030.720 –1.260 327.870 0.000 –2875.330 –86.110

From bootstrapping result in the above table it is seen that the regression coefficients are

approximately same as it was found in the multiple regression model but the standard errors

of all the estimators are less than that of multiple regression standard errors. This may be due

to bias correction of Bootstrap method.   Hence, we may recommend these estimators as an

alternative of OLS estimators.

Conclusion
In this research, the objective was to determine the likely climatic factors for Boro rice

production in Bangladesh. Our study shows that the considered variables RAIN, MAX, MIN

and WIND have significant effect on Boro rice production of Bangladesh both by OLS and

Bootstrap method. The predictor variables can explain about 63.4% of total variation by R2

and about 59.2% of total variation by Adj. R2 for OLS estimators. Due to bias correction,

Bootstrap method exhibits lower standard errors in comparison to the OLS method indicating

that this estimate could be useful in agricultural production like Boro rice production of

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American Journal of Agricultural Science, Engineering and Technology

Bangladesh. The results and message could be useful and significant for the

policy makers in the agriculture sector of the country.

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