Comparative Economic Analysis of Rice Processing Methods in Benue State, Nigeria

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International Journal of Environment, Agriculture and Biotechnology (IJEAB) Vol-2, Issue-6, Nov-Dec- 2017 ISSN: 2456-1878 Page | 2776
Comparative Economic Analysis of Rice
Processing Methods in Benue State, Nigeria
Tondo, D.T.
Department of Agribusiness, Michael Okpara University of Agriculture Umudike, Abia State.
Abstract The study examined the comparative economic
analysis of rice processing methods in Benue state, Nigeria.
Random sampling technique was used to select 63
respondents made up of modern and traditional rice
processing methods. The study also identified the major
inputs used in rice processing, estimate the cost and returns
in processing rice; identify the major factors militating
against the modern rice processing methods .primary data
were collected using structured questionnaire. The data
were analyzed using descriptive statistics, gross margin and
cobb-Douglas production model. The result of the analysis
showed that the modern rice processors were dominated
(74.6%) by male while the traditional were dominated
(75.4%) by female. The gross margin for the modern rice
processing methods was N16,770.00 per 100kg of rice
higher tha n the traditiona l with N4,143.00 per 100kg of
rice. The milling capacity of the modern was 200 kg/min. as
against 50 kg/min. in the traditional methods. The study
identified lack of awareness, low capital, poor
infrastructure, and lack of ski lled technical workers as
factors militating on the adoption of the modern methods.
The study recommended that the government should
subsidized the cost of modern technology equipment’s for
the processors. The processors should form cooperative
groups to help train their members to acquire technical
skills and also to access inputs and other resources that will
boost their business.
Keywords Comparative, Economic, Analysis, Rice,
Processing, Methods.
Rice supplies 7% 0f total per capital calorie consumption in
Nigeria (1RR1,2015), and occupies about 1.88 million
hectares o f arable land, making it rank second most
important cereal in the world after wheat in terms of
processing (CBN,2014). The domestics consumption of
rice rose from 5kg /person/week in 2012 to about
10kg/person/week in 2013 (Okafor and Chima,
2014).Currently, annual per capital consumption of milled
rice is 25kg/person/month (Musa,2014).The relative ease of
its preservation a nd cooking has influenced the processing
trend in its consumption.
The quality of rice has become an important issue among
Nigerian consumers who clearly show strong concern for
imported rice, because of its quality in terms of cleanliness
(WARDA,2015).This has brought about competitio n of
imported rice and locally processed rice. The low quality of
locally processed rice reflects low level of improved
processing technology. T his can reduced the efforts in
achieving progress of raising output to meet the consumers
The difficulty of processors in Nigeria to adopt and develop
modern technology is due to inadequate resources. Poverty
has become a significant factor in increasing processing of
rice in Nigeria (Jerry ,2016).One of the major problem of
rice processing in Nigeria is to develop appropriate
technology. I f the cost a nd returns of p rocessing locally
produced rice is known, it will be easy to address the
problem of quality in locally processed rice.
Rice processing in Nigeria contributes to food security,
employment, poverty reduction and national development.
Rice processing is increasingly creating employment for
new processors while the old processors have diversified
into processing tree crops like cocoa and rubber in which
their prices ar e unpredictable over years now. Income and
employment generation in rice processing has been
substantial (Msendoo,2016).
1.1 Objective
The specific objectives were to:
i. identify major inputs used in processing rice in the
study area,
ii. determine the cost a nd returns in modern and
traditional rice processing method,
iii. identify the factors militating against the adoption of
the modern method of rice processing in the study
International Journal of Environment, Agriculture and Biotechnology (IJEAB) Vol-2, Issue-6, Nov-Dec- 2017 ISSN: 2456-1878 Page | 2777
2.1 Study Area
Benue state is located between latitudes 6° 11´ and 11° 20´
N and longitudes 5° 25´ and 7° 15´ E of equator. It covers
an area of 6,250 km². The mean rainfall ranges between 750
and 1000mm.The average annual number of rainy days
ranges from 190 to 230days.The rains start from April and
end in October with the highest point in July. T he dry
season is from November to March making it co nducive for
agro-processing. The minimum average temperature is
about 27°c while the maxi mum average temperature is
37°c. The mean relative humidity ranges between 60% from
January to February and 80% from June to September. The
State falls within the guinea savannah vegetation zone. T he
vegetation supports the production of grains and root crops.
The predominant crops are rice, sorghum, millet, yam,
maize, groundnut and soya -beans. Benue Agricultural a nd
Rural Development Authority (BNARDA, 2015).This also
justified the selection of the study area.
2.2 Data collection
For the objective of the study to be achieved, data was
collected through primary and secondary sources. This was
done through structured questionnaires and internet. The
data was collected based on the intensity of the rice
processors in the study area.
2.3 Sampling techniques
Benue state is divided into three agro-processing zones (A,
B and C) consisting of 7 (Ukum, Logo, Kwande, Katsina-
Ala, Vande-ikya, Ushongu, Konshisha) Local Government
Areas in zone A, and 6 (Makurdi, Gboko, Guma, Gwer,
Gwer-west, Buruku) local Government Areas in zone B
while zone C has 7(Otukpo, Ohimini, Adhoc, Okpokwo,
Ogbadigbo, Oju, Obi) local Government Areas. In each of
the zone, 3 local Government Areas were purposi vely
selected based on their intensity in rice processing, making
a total of 9 Local Government Areas,7(2 modern and 5
traditional) rice processors were randomly selected making
a total of 63 rice processors in the study area.
2.4 Data Analysis
Data collected for this study were analyzed using simple
descriptive statistics such as frequency tables, percentages
and average. Cost and returns of the processors were also
determined. Cobb-Douglas production function models was
used to determine the efficient use of resources by the
processors. The choice of the model was based on a similar
study previously conducted by Aondofanan (2016). The
ordinary least square (OLS) was used for estimating the
parameters in line with different independent variables.
The model is specified as follow.
Y=a.X1c1, X2c2 , X3c3, X4c4 , dc5 …(1)
Where: Y= output from capacity of processed rice
X1=cost of paddy rice/ 100kg of processing
X2 =cost of firewood/100kg of processing
X3= cost of labour /100kg of processing
X4= cost of water used/100kg of processing
C = capacity (100 kg bag)
D = dummy
Gross margin (GM) analysis was used to determine the
difference between the total revenue and total variable cost
for the processors.
Where: GM=Gross margin
TR= total revenue
TVC= Total variable cost
The Net income (NI) or profit is the difference between the
gross margin and total fixed cost of the rice processors.
… (3)
Where: NI= Net income
TFC= Total fixed cost
π = TC-TR
Where: π=profit
TC=Total cost
TR=Total revenue
3.1 The major inputs used in processing rice outside the
processing equipments are, Paddy rice, slab for drying,
firewood, water, lab our, tran sportation, drums, ra kes a nd
Table1, shows the estimates of regression in the modern
rice processing methods, the result of Cobb-Douglas
production function was fitted to find out the relationship
between the output of paddy and the independent variables,
as supported in a similar study previously conducted by
Msendoo (2015). Firewood, labour and paddy rice were
significant at 1% and 5% respectively. Jerry (2016 ) in his
study confirmed that the cost of paddy rice dominated the
processing cost with the processors spending more on
paddy. The quantity a nd quality of rice may have effect on
the cost and returns.
International Journal of Environment, Agriculture and Biotechnology (IJEAB) Vol-2, Issue-6, Nov-Dec- 2017 ISSN: 2456-1878 Page | 2778
The coefficient of t he cost of fire wood and labour showed
that there was 1% and 5% increase in expenditure from its
mean level to have a negative effect o n output or revenue,
while the cost of milling was insignificant. The increase in
the cost of firewood and labour will have a negative effect
on revenue. However, the cost of water may not affect
revenue negatively; but the quantity a nd quality of water
may affect the quality of rice which may in turn affect the
revenue. The coefficient of the multiple determinations R2
of the function was 0.568 , which shows that 58.7% of the
variation in output was explained in the independent
variables included in the model.
Table.1: Estimates of Regression of Modern Rice Processors
Variables Symbols Regression Coefficient Standard error T-value
intercept A 10.54 29.40 0.458
Qty of paddy kg X1 4∙30 0∙64 8∙245
Cost of firewood X2 5∙60* 0∙354 -2∙895*
Cost of water X3 6∙70 0∙425 -1∙683
Cost of labour X4 9∙404∗∗ 0∙498 2∙905**
Cost of milling X5 7.50 0.456 -1.954
*Significant at 5%, **Significant at 1%
Table 2 shows regression estimates for the tr aditional rice
processing method. The firewood used in the traditional rice
processing system was significant at 10%, while water,
labour and milling were insignificant. The co efficient
indicated increase in the cost of firewood by 1% indicating
25% decrease in revenue without equal increase i n the
quantity of paddy from its mean. Water, labour and milling
cost were insignificant indicating that water, milling and
labour had no significant influence on output or revenue in
the study. The cost of water was negligible because most of
the processors had their own sources of water supply. It was
the quality of water used that affect the quality of rice which
in turn affects the revenue.
The coefficient of multiple determination R2 of the function
was 0.600, indicating 60% of the variation in revenue or
output as explained in the three independent variable s
included in the model.
Table.2: Regression Estimates of Traditional Rice Processing System
Variables Symbols Regression coefficient Standard error T-value
Intercept A 20∙89 40∙08 0∙534
Qty of paddy kg X1 0∙0037 0∙008 2∙284
Cost of firewood X2 -0∙350⃰ 0∙038 -1∙834⃰
Cost of water X3 0.308 0∙041 1∙594
Cost of labour X4 0.218 0.018 1.684
Cost of milling X5 0.421 0.043 1.754
Significant at 10%
Gross margins can be used to evaluate various rice
processing situations by co mparing different processing
methods, estimating profit and loss, calculating costs in
processing rice and assist in making investment decisions
(Jerry 2016). The gross margin from the modern rice
processing method in the study area was ₦16,770 higher
than the traditional rice processing method by ₦4,143 per
100kg. This may be as a result of adopting the modern
technology in p rocessing rice. Despite the fact that the
modern rice processing activities add cost to pro cessors, the
products could be sold at a fixed price. The result s hows
that the modern rice processing method is more profitable
than the traditional rice processing method.