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An Economic Analysis of Small-Scale Cattle Fattening in Kushtia
District, Bangladesh
Mst. Rumana Eaismin1, Kazi Julfikar Ali2 and Md. Abdul Khalek3
1Lecturer, Department of Economics, Pabna Islamia Degree College, Pabna, Bangladesh
2Associate Professor, Department of Economics, University of Rajshahi, Bangladesh
3Professor, Department of Statistics, University of Rajshahi, Bangladesh
DOI: https://dx.doi.org/10.47772/IJRISS.2025.910000204
Received: 07 October 2025; Accepted: 14 October 2025; Published: 07 November 2025
ABSTRACT
The study analyzed the economics of cattle fattening in Kushtia district, Bangladesh, using a Cobb–Douglas type
profit function. Primary data were collected from 107 small-scale cattle fatteners through face-to-face structured
interviews, covering socioeconomic characteristics, cost–benefit details, and marketing practices. The results
indicate that cattle fattening was a profitable and flexible source of income, with an average profit of BDT
8,268.50 per farm in a season. Salt cost, fixed cost, and medicine cost were found to be significant at the 1%
level, while purchase cost was significant at the 5% level and negatively associated with profit, as expected.
Farm size showed a positive and significant association with profit at the 1% level, suggesting that larger farms
tend to achieve higher profitability. Based on the findings, the policy suggestions are that the government should
implement subsidies or price-control measures for veterinary medicines to reduce production costs and enhance
profitability. In addition, the government might help farmers expand their farms by providing access to credit,
encouraging cooperative farming, and supporting land use, making cattle fattening more sustainable and
profitable in the long run in Bangladesh.
INTRODUCTION
The fattening of cattle is a crucial and essential aspect of the agricultural and agribusiness framework in
Bangladesh (Ahmed, et al. 2010). The fattening of cattle is a thriving enterprise that offers job and cash to
disadvantaged rural populations. Agriculture, particularly the rearing of cattle, poultry, and aquaculture, is
essential to the economy of Bangladesh. The cultivation of livestock is an essential sub-sector of agriculture,
significantly impacting human health and the national economy. The technique of cattle fattening directly
enhances local meat output and indirectly diminishes the necessity for illicit livestock imports. The cattle sector
contributes 1.85% to the national GDP (DLS, 2023). Data from DLS in 2004 indicates a substantial disparit y
between the required daily animal protein intake of 120 grams per person and the actual availability of just 12.51
grams. Total meat output is 1.279 million tons, as reported by DLS in 2011. According to the Department of
Livestock Services (DLS) in 2011, the cow population in Bangladesh is 2.3122 million. Nevertheless, cattle
fattening for beef production has become an essential source of revenue for small-scale farmers in Bangladesh.
Cattle ranching is integral to the rural economy of Bangladesh, functioning as a significant complement to
agriculture (Hashem et al. 1999). A small-scale commercial cattle fattening initiative has been launched in
specific locations of Bangladesh. Small-scale cattle fattening is essential for revenue generation among
subsistence farmers in Kushtia District. Microcredit initiatives are frequently employed for the purpose of
livestock fattening. The advancement of this nation is intricately linked to the development of rural areas. The
density of cattle per unit area is elevated relative to industrialized nations; nonetheless, their productivity is
significantly low due to reasons like suboptimal genetic composition, insufficient feed availability, and a
deficiency in scientific understanding of housing and management practices. Their growth performance is
significantly inadequate due to the aforementioned cause. Beef cow production techniques are hardly
implemented in Bangladesh. Numerous folks residing in poor or extreme poverty participate in livestock
fattening around four to five months before Eid-Ul-Azha. Seasonal demand for beef cattle arises during Eid-Ul-
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
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Azha. Muslims customarily engage in Kurbani, the ritual slaughter of livestock, during the sacred holiday of
Eid-Ul-Azha. Cattle are slaughtered during the festivity. Sujan et al. (2001) estimate that almost 10 million
animals are slaughtered during this event. This study is to examine the economic factors associated with cattle
fattening in the Kushtia district.
The paper is split into five sections. Section 2 provides a comprehensive assessment of the relevant literature.
Section 3 delineates the technique pertinent to this investigation. Section 4 contains the empirical results and
discussion. Section 5 presents conclusions and policy implications.
LETARETURE REVIEW
Several studies have been conducted to evaluate the profitability of cattle fattening in Bangladesh, including
those by Kamal et al. (2019), Sarma and Raha (2015), and Sarma and Raha(2014). Kamal et al. (2019) examined
the cattle fattening method in a particular location of the nation. It was shown that farmers engage in both dairy
and beef cattle fattening. A majority of farmers, around 86%, choose to rear uncastrated males for fattening
rather than steers. It was shown that farmers reared beef cattle for fattening without any scientific understanding.
Pamukova and Momchilov (2017) performed a study of the income and production expenses of a dairy sheep
farm in northeastern Bulgaria. Revenues and manufacturing expenses were examined by statistical and
comparative methodologies. It was shown that farmers achieved greater money from milk sales. The cost of feed
was identified as the most exorbitant. Mekuria (2016) discovered that livestock and meat products are
undergoing substantial increase within the global agricultural and food sector. The observed rise may be ascribed
to the escalating demand for meat resulting from expanding worldwide incomes, with innovations in production,
processing, and transportation that have enhanced efficiency. Moreover, decreasing actual feed prices have
contributed to this expansion. Ahmed and Egwuma (2015) emphasized the considerable influence of cattle on
human welfare and the prevailing research. Sarma and Raha (2015) evaluated the tactics of beef cattle
development enterprises in selected regions of Bangladesh. They assessed the existing characteristics of the
farmer about beef cattle production. A total of 180 cattle fatteners were recruited from these regions. The
Quantitative Strategic Programming Matrix (QSPM) and SWOT matrix approach were employed to evaluate
the feasibility of a beef cattle development firm. The analysis indicated that the prospects and strengths of beef
cattle agriculture surpassed the risks and limitations. Sarma and Raha (2014) performed a comprehensive
analysis of the economics of beef cattle fattening in the northern char region of Bangladesh. A random sample
of 150 cattle fatteners was selected from two districts with the largest proportion of fatteners in the char region.
Information on socioeconomic aspects and beef fattening was gathered from residents of Pabna and Sirajgonj
districts. Demircan et al. (2007) conducted an economic analysis of 100 beef cattle ranches in Turkey. The beef
cattle ranches were categorized into three classes. Production expenses per animal were determined to range
from 1.647 to 1.658 US$. Despite the profitability of beef cattle raising in agribusiness, numerous farmers
continue to adhere to the conventional beef fattening technique. This is mostly undertaken to satisfy the demand
for livestock during the Muslim celebration of Eid-ul-Azha. A considerable body of research has been
undertaken on this problem; however, regression analysis has not been employed to explain the data. In addition,
there is a deficiency of research pertaining to this topic field. A substantial vacuum exists in the literature about
the availability of information on the socioeconomic factors of cattle fattening.
METHODOLOGY OF THE STUDY
The study area, sampling technique and data
Kushtia district, located in the southwestern part of Bangladesh, comprises six sub-districts (upazilas), namely
Kushtia Sadar. Kumarkhali, Khoksa, Daulatpur, Bheramara, and Mirpur. For this study, Kushtia Sadar (sub-
district) was selected as the research area due to its high concentration of cattle fattening activities, making it
representative of small-scale cattle production in the region. The field survey was conducted in three villages—
Abdalpur, Gopalpur, and Sahapur—within the Sadar sub-district, where small-scale cattle fattening is a
prominent livelihood activity in this study area. The study targeted all beef cattle fatteners actively engaged in
cattle fattening operations, and the sample frame was developed from records obtained from the sub-district
livestock office. A total population of 150 cattle fatteners was identified, from which a random sample of 107
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respondents was drawn, following the random sampling procedure established by Arkin and Cotton (1963). The
sampling approach ensured that every eligible cattle fattener had an equal chance of being selected, minimizing
selection bias and improving the representativeness of the sample. Data collection was carried out through face-
to-face interviews using a structured interview guide. The questionnaire captured information on socioeconomic
characteristics, farm practices, input costs, production outputs, and income from cattle fattening. The survey was
administered between June and August 2023, during which the researchers directly observed farm practices to
verify reported data and ensure accuracy. In addition to primary data, secondary data sources were utilized to
enrich the analysis and provide contextual understanding. These included published articles, books, peer-
reviewed journals, Bangladesh Economic Review, Bangladesh Agriculture Census, Directorate of Livestock
Services and Statistical Yearbook of Bangladesh. The combination of primary and secondary data allowed for a
robust examination of the economic dynamics of cattle fattening, including cost structures, profitability, and
factors affecting farm performance. This methodological approach ensures a comprehensive and reliable
assessment of the economics of small-scale cattle fattening in Kushtia Sadar, providing insights that are relevant
for both local development planning and broader policy formulation in Bangladesh’s livestock sector.
Empirical model
Profitability of small-scale beef cattle fattening
The economic theory of farm emphasizes the significance of profit in advancing agricultural enhancement, since
it predominantly concentrates on financial elements. The information shown provides a more accurate
representation of sales income relative to gross revenue. The concepts of profit entails determining the difference
between total revenue and total cost (Prasetyo et al. 2012; Sarma and Ahmed 2011; Beattie and Taylor 1994;
Jones 2000; Teegerstrom and Tronstad 2000). The total return represents the aggregate benefits derived from
investing in an asset, whereas the total cost includes both fixed and variable expenses. Production levels do not
influence fixed costs, which stay unchanged. Variable costs vary with output levels. In this study, profit was
calculated as the difference between gross return and total gross costs paid in cash by farmers (Cherchye et al.
2010). To perform the economic analysis an activity budget was made to assess the profitability of small scale
beef cattle fattening. In doing so, the gross return, net return, and per cattle profit were calculated. The gross
return of cattle fattening was derived from the sells revenue of cattle. The per farm profit is considered to be net
return divided by total number of farms. Thus, Total variable cost (TVC), Gross return (GR) = Q×P [Where, Q
= cattle and P = Price of per cattle], Gross margin(CM) = (GR-TVC), Net return(NR) = (GR–GC), Per-farm
profit = (Net profit ÷ N) [Where, N = Total number of farm]. The formula for profit is delineated in Eq (1).
Determinants of revenue of small scale beef cattle fattening
The Cobb-Douglas type profit function is used to identify the determinants of revenue of small scale beef cattle
fattening in the study area. The regression model is presented as follows:
iiiiiiiiii XXXXXXXX 88776655443322110 lnlnlnlnlnlnlnlnln where, iln
profit of ith cattle fattening farm.
1ln X fixed cost
2ln X purchase cost
3ln X feed cost
4ln X medicine cost
5ln X salt cost
6ln X years of education
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7ln X farm size
i error term
0 is the intercept term and the coefficients 72... are called the partial regression coefficients. The error term
( ) is assumed to be normally distributed with mean zero and variance 2
u . The coefficient of the variables is
estimated by the ordinary least squares (OLS) method. Diagnostic tests namely; multicollinearity, is checked
using tolerance and VIF, and autocorrelation is detected by the Durbin-Watson d test. According to Greene
(2012) and Gujarati and Porter (2009), a Variance Inflation Factor (VIF) greater than 10 and a tolerance value
less than 0.1 are indicative of serious multicollinearity among explanatory variables. The Durbin-Watson (DW)
statistic value around 2 indicates that there is no first-order autocorrelation in the residuals of the regression
model (Gujarati & Porter, 2009; Greene, 2012).
RESULTS AND DISCUSSION
Results of profitability of small scale beef cattle fattening
Table1shows that the gross margin of the small-scale cattle fattening is BDT 50,49,400 per season. Again, the
net return or profit is BDT 16,58,390 per season.
Table 1: Profitability of small-scale beef cattle fattening
Item Total cost and return
(BDT)
Per farm profit (BDT)
A.Gross return 50,49,400
8,268.50 B.Total variable cost 30,07,010
C.Gross margin(A-B) 20,42,390
D.Total fixed cost 3,84,000
E.Total gross cost(B+D) 33,91,010
F. Net return or profit(A-E) 16,58,390
It is also found that the per-farm profit of the small-scale cattle fattening is BDT 8,268.50 per season. Since the
average net profit is positive, the small-scale cattle fattening farm is profitable in the study area.
Results of the correlation matrix among the study variables
A correlation matrix that shows the strength and direction of relationships between pairs of variables. A value
of 0 indicates no linear correlation, while values range from -1 (perfect negative correlation) to +1 (perfect
positive correlation). Negative values show that one variable tends to rise when the other falls, whereas positive
values suggest that variables tend to rise or fall together. The correlation results (Table 2) indicate that Y is
positively and significantly associated with X7 (r = 0.524, p < 0.01) and negatively and significantly
associated with X1 (r = −0.344, p < 0.01) , X2(r = −0.243, p < 0.05) , and X4(r = −0.241, p < 0.05 . No
significant relationships are found between Y and X3, X5, or X6. Among the independent variables, X1 and X2
are strongly correlated, while X1 , X2 , and X4 show several negative interrelationships. A strong positive
relationship is also observed between X4 and X5.
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Table 2: Correlation matrix among the study variables
Y X1 X2 X3 X4 X5 X6 X7
Y 1
X1 -0.344 ** 1
X2 -0.243 * 0.619 ** 1
X3 -0.090
-0.365 ** -0.417 ** 1
X4 -0.241 * -0.318 ** -0.361 ** 0.343 ** 1
X5 0.006
-0.027
-0.157
0.170
0.398 ** 1
X6 0.140
0.130
0.032
-0.199 * -0.033
0.119 1
X7 0.524 ** -0.257 ** -0.247 * -0.015
-0.044
-0.072 -0.099 1
** indicates significant at 1% level and * indicates significant at 5% level
The analysis indicates that fixed cost has a significant negative relationship with profit at the 1% level. Likewise,
purchase cost shows a negative correlation with profit at the 5% level. Furthermore, medicine cost is also
negatively associated with profit, significant at the 5% level.
Results of factor affecting determinants for small-scale beef cattle fattening
An ANOVA test was performed before regression analysis to assess the overall significance of the model. This
test is crucial as it partitions the variance in the dependent variable into explained and unexplained components
and provides the F-test, which determines whether the predictors jointly contribute to explaining variation in the
dependent variable. In a regression analysis, the ANOVA (Analysis of Variance) Table 3 is used to test whether
the regression model as a whole is statistically significant.
Table 3: ANOVA table for regression analysis
Source of variation (SV) SS df MS F p-value
Regression 4.116 7 0.588 11.752 0.000
Residual 4.953 99 0.050
Total 9.069 106
The ANOVA results indicate that the overall regression model is statistically significant, F(7, 106) = 11.752, p
< 0.001. This suggests that the set of independent variables jointly explains a significant proportion of variance
in the dependent variable. Specifically, the model accounted for a substantial proportion of the variability,
suggesting that it provides a good fit to the data. Thus, we reject the null hypothesis that all regression coefficients
are equal to zero, confirming the overall usefulness of the model in predicting the outcome variable.
Regression results for beef cattle fattening
The Cobb-Douglas type profit function is employed to assess the impact of these factors on the revenue generated
by small-scale cattle fattening in the study. Table 4 presents the parameters, tolerance and Variance Inflation
Factor (VIF), R2, R̅2, and Durbin-Watson d statistic of the variables in the regression model. The coefficient of
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fixed cost (lnX1) is significant at 1% level and the coefficient of fixed cost of cattle fattener is -0.335. The results
indicate that 1% increase of this input, keeping other factor constant, would result in a decrease of net return by
0.335%. The tolerance and VIF of fixed cost of cattle fattener ( 1ln X ) are 0.531 and 1.884 respectively, so there
is no multicollinearity problem in case of cattle fattener fixed cost variable.
The coefficient of purchased cost of cattle 2ln X is significant at 1% level and the coefficient of purchased
cost of cattle fattener is -1.293.The results indicate that 1% increase of this input, keeping other factor constant,
would result in a decrease of net return by 1.293%. The toralance and VIF of fixed cost of cattle fattener ( 2ln X
) is 0.493 and 2.027 respectively, so there is no multicollinearity problem in case of the cattle purchased cost
variable.
The coefficient of feed cost of cattle is insignificant and coefficient of feed cost of cattle -0.177.The results
indicate that 1% increase of this input, keeping other factor constant, would result in an decrease of net return
by 0.177%.The Variance Inflation Factor (VIF) for feed cost in the cattle fattening model is 1.366, indicating
that this variable does not exhibit multicollinearity.
The coefficient of medicine cost of cattle 3ln X is -1.434.The results indicate that 1% increase of this input,
keeping other factor constant, would result in an decrease of net return by 75% in Table 6.2.The tolerance and
VIF of the variable of cattle fattening 3ln X is 0.659 and 1.517 respectively , so there is no multicollinearity
problem in case of cattle fattening medicine cost variable ( 4ln X ) which is desirable.
Table 4: Empirical results for beef cattle fattening
Variable Coefficient Std. error t-value p-value Tolerance VIF
Constant 32.337 6.882 4.699 0.000
Fixed cost ( 1ln X ) -0.335 0.109 -3.070 0.003 0.531 1.884
Purchase cost ( 2ln X ) -1.293 0.634 -2.039 0.044 0.493 2.027
Feed cost ( 3ln X ) -0.177 0.118 -1.493 0.139 0.732 1.366
Medicine cost ( 4ln X ) -1.434 0.305 -4.700 0.000 0.659 1.517
Salt cost( 5ln X ) 0.401 0.240 1.671 0.098 0.776 1.289
Years of schooling ( 6ln X ) 0.075 0.032 2.335 0.022 0.892 1.121
Farm size ( 7ln X ) 0.294 0.076 3.849 0.000 0.862 1.161
R2 0.454 DW = 1.953
Adjusted
2R 0.415
The coefficient of salt cost 5ln X of cattle fattening is also exactly significant at p< 0.10 and the coefficient of
salt cost of cattle ( 5ln X ) is 0.401.The results indicate that 1% increase of this input, keeping other factor
constant, would result in an increase of net return by 0.401%. Now salt is a most potential variable which is not
used in previous days fattening program. The VIF of the salt cost variable for cattle fattening ( 5ln X ) is 1.289
which is less than 5%, so there is no multicollinearity problem in case of cattle fattening salt cost variable.
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The coefficient of years of schooling 6ln X of cattle fattening is also significant at p<0.05 and the coefficient
of years of schooling for cattle fattener is 0.075.The results indicate that 1% increase of this input, keeping other
factor constant, would result in an increase of net return by 0.075%. The tolerance and VIF for the years of
schooling variable in the cattle fattening profit model are 0.892 and 1.121, respectively, indicating that there is
no multicollinearity problem, which is desirable.
The coefficient of farm size 7ln X for cattle fattening is also significant at p<0.01 and the coefficient of farm
size of cattle is 0.294.The results indicate that 1% increase of this variable keeping other factor constant, would
result in an increase of net return by 0.294%. The tolerance and VIF of the variable ( 7ln X ) of cattle fattening
are 0.862 and 1.161 respectively, so there is no multicollinearity problem in case of farm size variable ( 7ln X )
which is desirable. The coefficient of determination R2 for cattle fattening is 0.454, which indicates that 45.4 %
of the dependent variable of small-scale cattle fattening is explained by the independent variables included in
the model. The selected revenue function has a sufficient degree of freedom for testing statistically significant
and stable with respect to the sign of their regression. The Durbin–Watson statistic (DW = 1.953) is close to the
benchmark value of 2, indicating the absence of autocorrelation in the residuals. This suggests that the error
terms are independently and randomly distributed, thereby satisfying a key assumption of the regression model.
CONCLUSIONS AND POLICY IMPLICATIONS
This study provides an economic analysis of small-scale cattle fattening in Bangladesh using a Cobb–Douglas
type profit function, based on data from 107 small-scale cow fatteners. The correlation matrix indicates that
fixed cost has a significant negative relationship with profit at the 1% level, while purchase cost and medicine
cost are also negatively associated with profit, both significant at the 5% level. The findings reveal that higher
fixed costs, purchase costs, and medicine costs are significantly associated with reduced profitability,
underscoring the importance of effective cost management in small-scale livestock operations. The ANOVA
results show that the set of independent variables jointly explains a significant proportion of variance in the
dependent variable. The regression results demonstrate that the years of schooling and profit is positively and
statistically significant at 5% level. Evidence indicates that persons with higher educational attainment are more
likely to generate more income from cattle fattening operations. The similar findings are found in the study of
Sarma and Ahmed (2011). The medicine costs and fattening income adversely affect the profitability of small-
scale cattle fattening in this study area. Based on the findings, several policy implications emerge. First, the
government should implement subsidies or price-control measures for veterinary medicines to reduce production
costs and improve farm profitability. Second, policies that promote farm expansion through access to credit,
cooperative farming models, and land-use support could enhance the sustainability and long-term viability of
cattle fattening farms. By addressing cost constraints and supporting farm growth, these interventions can help
farmers adopt improved management practices, increase efficiency, and achieve higher and more stable incomes
from cattle fattening operations.
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