Analysis of value addition in the processing of

cassava tubers to “garri” among cottage level

processors in southwestern Nigeria

Kehinde A.L. and K.O. Aboaba

Invited poster presented at the 5th

International Conference of the African Association

of Agricultural Economists, September 23-26, 2016, Addis Ababa, Ethiopia

Copyright 2016 by [authors]. All rights reserved. Readers may make verbatim copies of

this document for non-commercial purposes by any means, provided that this copyright

notice appears on all such copies.

1

Analysis of value addition in the processing of cassava tubers to “garri” among cottage

level processors in southwestern nigeria

*Kehinde A.L. and K.O. Aboaba

Department of Agricultural Economics and Extension

Osun State University, Nigeria

Email: kehindeal@uniosun.edu.ng

234-8037860000

*Corresponding author

Abstract

The agricultural economy in Nigeria is still largely rudimentary and its wealth creation potentials

have been hampered by inability of cottage level processors to add value to their produce among

others. The study investigated value addition to processed cassava tubers among cottage level

processors in Ogun state, Southwestern, Nigeria. Owode Local Government Area was

purposively selected and interview schedule was administered on one hundred and twenty five

(125) randomly selected processors. Data was analyzed using the descriptive statistics and the

multiple regression analysis. The study revealed that the most important constraints faced by

cassava processors are high perishability of cassava tubers, high transportation cost, and

inadequate capital among others. Also, the multiple linear regression analysis revealed that cost

of labour, cost on machine, and quantity of raw materials processed positively and statistically

influenced value added to cassava tuber at 1% significant level. It is recommended that processor

should organize themselves into cooperatives or associations that will help them mobilize funds

to take advantage of improved welfare that will result from increasing their scale of operation.

Keywords: Value addition, cottage industry, cassava processors.

Introduction

Agricultural economy in Nigeria is still largely characterized by production and direct sale of

agricultural outputs in its raw form with very little capacity for transformation of produce from

its raw form to other value added products. This is as a result of inadequate capacity of primary

producers to add value to their produce due to socio economic, economic, environmental and

technological constraints. This has manifested in the form of low production efficiency and

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limitation in the diversity of goods produced. This perhaps has been responsible for poor wealth

creation by farmers resulting in low farm and household incomes.

The rationale for value addition is predicated on the need to increase rural incomes, employment

and investment opportunities. Focusing on value addition by small scale operator is important.

This will permit investment on additional processing facilities so that marketable surpluses can

be pushed to processors and farmers can reduce post harvest losses thereby increasing farm

income. Value addition can help farmers to claim part of the unexploited profit going unclaimed

in the manufacture of food, fibre and industrial or other product from agricultural produce.

Value addition is important for the agricultural sector for Nigeria to be able to actualize the

economic agenda of different governments towards increasing agricultural Gross Domestic

Product and diversification of economic activities away from the oil sector. Suffice it to mention,

the NBS (2015) reported agriculture as a strategic component of the Nigerian economy

contributing between 19.65% and 26.63% to real GDP in 2014. Crop production constituted an

important activity in the agriculture sector and the main driver of growth in the agricultural

sector contributing between 85.39% and 90.13% to growth in this sector between quarter one and

three in 2014.

Cassava is a versatile crop and can be processed into a wide range of products such as garri,

starch, flour, tapioca, beverages and cassava chips for animal feed. According to Ofuya and

Akpoti, (1988); Ogiehor, (2002) cited in Adeola and Raji (2012:152) garri is the most popular

form in which cassava is consumed by several millions of people in Africa, especially in the

West Africa sub region. Garri is processed by peeling the cassava root, washing, grating,

followed by solid state fermentation, pulverizing and roasting (Oyewole and Sanni, 1995 cited in

Adeola and Raji 2012:152). Garri is a granulated, white or yellowish product, the colour depends

on production methods. It is a dehydrated staple food with a high swelling capability and can

absorb up to four times its volume in water.

Cassava is also gaining prominence as an important crop for the emerging biofuel industry and,

as opined by Ziska et al. (2009), it is a potential carbohydrate source for ethanol production. A

well planned strategy for the development and utilization of cassava and cassava products can

provide incentives for farmers, crop vendors and food processors to increase their incomes. It

can also provide food security for households producing and consuming cassava and cassava

products, Plucknett et al. (1998).

This study investigated factors that influence value addition in the processing of cassava tubers

to garri one of the staples in Nigeria and some other African countries. Specifically, this study

will helps to provide information on the amount of value added to cassava during processing, the

cost incurred at each stage of processing, identify and quantify coefficients of factors that

influence value addition and also identified socioeconomic, economic and technological

constraints to value addition in the processing of Cassava tubers to garri.

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Conceptual and Theoretical framework

Value addition simply implies the process of increasing the economic worth or value of a

commodity by transforming it to another commodity termed as a value added commodity.

Coltrain, (2000) added that the process should contribute to changing the current place, time and

form characteristics of the commodity to characteristics more preferred in the market place. The

value of the changed commodity is thus referred to as its added value. Other definitions of value

added include that of the Bureau of Economic Analysis (BEA) that defines value added as the

difference in the value of goods and services produced and the cost of inputs used to produce

them. It also describes it as the industry’s gross receipts and other incomes, commodities taxes

and inventory changes minus expenditure for goods and services purchased from other firms.

Theoretically, The Agricultural Marketing Resource Centre (AMRC) defines agricultural value

added in terms of factors that motivates value addition. It stated that value addition must involve

changes in the physical state or form of an agricultural product; changes in the production

process that enhances the value of the final product; marketing a product based on his special

physical characteristics through physical segregation. It opined that the two important ways of

improving (influencing) value added to include;

i. Improving production efficiency there by widening the gap between the gross value

of output and the cost of intermediate input, and

ii. Changing the form, function, quantity or other product or process characteristics that

increase the margin and cost of intermediate inputs

Therefore, the concept of improving value addition is intrinsically embedded in the concept of

improving technical, allocative and scale efficiencies. Thus, factors that increase the

aforementioned will optimize/maximize value addition to agricultural products. Studies have

shown that when firms are inefficient in the production of their core product, increasing value

addition can be addressed by correction of factors causing inefficiency.

Studies have shown that most of the discussion on value addition in agriculture focus on change

in form of agricultural product, changes in production process or change in marketing strategies.

The justification/assumption for focusing on value addition is that there are unexplioted profits

going unclaimed in the manufacture of food, fibre and industrial or other products from raw

agricultural output/produce. (Lambert et al., 2006)

Evidence exist that there are economies of scale in food manufacturing (Morrison and Siegel

1998). In many cases it does cost large firms less money per unit to produce a product than small

firms. Because of economies of scales in food manufacturing farms are large and there are

consequent few buyers of raw agricultural produce. Arising from this, buyers are able to dictate

the price of raw agricultural produce. The reasoning/argument therefore is that if farmers can

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invest in additional processing activities, they could bypass the monopoly power of large

agribusiness firms and retain more of the value of the raw agricultural product by selling directly

into the wholesale or retail markets.

In terms of theoretical analysis, Dodamani and Kunnal (2007: 521-524) in a study of value

addition to organically produced naturally-coloured cotton under contract farming in

Uppinabetageri village of the Dharwad district, Karnataka state India used the simple descriptive

statistics, means and percentages were used to generate estimates of value addition at the

different stages in the processing of Kapas (Coloured cotton) into cotton garments (shirts). The

study found that an additional value of Rs 5,875 was generated through processing kapas into

cotton garments (shirts). Its break-up at different levels of processing has been recorded as

follows: ginning, Rs 327 (5.57%); spinning, Rs 781 (13.30%); weaving, Rs 1626 (27.68%); and

garments manufacturing, Rs 3140 (53.45%).

Wanyama (2013:1) utilized the Propensity Score Method (PSM) to determine the gendered effect

of peanut value addition on household income among 310 randomly selected peanut farmers in

Rongo and Ndhiwa districts of Kenya. From the results, farmers were found to undertake only

one form of value addition, shelling. Although they appreciated the higher profitability

associated with other forms of value addition like processing, inadequate capital to purchase

processing equipment was a major constraint. The PSM results suggest that value addition raises

household per capita income by Kshs.88 per day. Male headed households recorded higher levels

of income compared to female headed households. This study suggests that potential exists in

using value addition opportunity in peanut processing to raise farmers’ household incomes.

This study intends to utilize the Ordinary Least Square regression analysis to understand the

cause and effect relationship between value addition and factors that influence it among cassava

processors in the study area.

Methodology

Study Area

The study area is Obafemi Owode Local Government Area in Ogun State, Nigeria. It’s located at

6°57′N 3°30′E/ 6.950°N 3.500°E. It has an area of 1,410 km² and a human population of 228,851

(NPC, 2006). Obafemi Owode Local Government Area is endowed with vast fertile land suitable

for the cultivation of rice, kolanut, sugarcane, maize, cassava, tomatoes and a wide variety of

vegetables. The major food crops produced in the area are: cassava, rice, cocoyam, plantain,

maize and vegetable, while palm produced and cocoa form the major cash crops. Data used in

this study were obtained from primary sources with the aid of structured interview schedule. This

was designed to collect information on socio-economic characteristics of cassava processors,

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cost incurred at each stage of cassava processing, technology available to processors and

constraints faced by processors in the study area.

Sampling Technique

A combination of purposive and simple random sampling techniques was used to select

representative samples. Obafemi Owode Local Government Area (LGA) was purposively

selected out of the 20 local governments in the state due to the prevalence of cassava growers

and processors in the area. Five villages were purposively selected from the LGA, the selected

villages were Bara, Adewolu, Abule tuntun, Owode and Adebiopon. Twenty-five (25)

respondents were randomly selected each from Bara from the five villages, making a total

sample size of 125 respondents. However, during the data clean up, the records for 103

respondents were fit for analysis.

Analytical Techniques and Model Specification

Data in this study were analyzed with both descriptive and inferential statistics. The descriptive

statistics employed include mean, minimum, maximum, frequency counts and percentages while

the inferential statistics used were the correlation analysis and the ordinary least square

regression technique. The ordinary least square regression technique was used to determine the

variables that influences total value addition in the processing of cassava tuber to garri in the

study area. The empirical model that was used in the study is specified as follows:

Y = f (X1 , X2 , X3 , X4 , X5 , X6 ,X7,X8,X9)

The explicit equation is;

Y=b0+ b1X1+ b2X2+ b3X3+ b4X4+ b5X5+ b6X6+ b7X7+ b8X8+b9X9+ µ

Where Y is the total value addition in Naira; X1 is the total cost on labour in Naira; X2 is the total

cost on machine in Naira; X3 is the asset of the processors in Naira; X4 is the age of the

processors in Years; X5 is the sex of the processors; X6 is membership of cooperative society; X7

is the experience in cassava processing in years; X8 is the level of education; X9 is the quantity of

raw cassava processed in Kilogram; µ is the error term or disturbance term and b0, b1, b2, b3, b4, b5,

b6, b7, b8, b9 are parameters to be estimated. The equation was fitted using the linear, semi-log,

double-log and exponential models. The model with best fit, and which satisfied statistical as

well as econometric criteria was chosen as the lead equation.

Results and Discussion

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Socioeconomic Characteristics of the Respondents

The selected socio-economic characteristics of interest in this study include age, sex, religion,

marital status, secondary occupation, level of formal education, membership of cooperative

society, acquisition of processing machine. The result (Table 1) shows that cottage level cassava

processing is dominated by the female sex. Majority of processor are of the processors are

between 41 – 60 years. Most (44.66%) cassava processor have no formal education which may

constitute resistance to quick economical and technological transformation of the industry.

Majority (70.87%) are non-members of cooperative societies and this may impede progress

associated with group action such as opportunity of group bargain and capital mobilization.

Majority ( of the processors acquired their processing equipment through personal purchase due

to low set-up cost for cottage processing. Perhaps better cooperative action can result in the

purchase of more sophisticated and expensive equipment.

Table 1: Selected Socioeconomic characteristics of respondents

Variable Frequency Percentage (%)

Sex

Male 9 8.74

Female 94 91.26

Total 103 100.00

Age (Years)

> 40 7 6.80

41 – 60 56 54.36

61 – 80 40 38.84

Total 103 100.00

Level of formal

education

None 46 44.66

Primary 36 34.95

Secondary 21 20.93

Tertiary 0 0.00

Total 103 100.00

Secondary Occupation

7

None 12 11.65

Crop farming 25 24.70

Agricultural produce

processing

19 18.45

Artisan 12 11.65

Trading 35 33.98

Total 103 100.00

Membership of

cooperative society

Non member 73 70.87

Member 30 29.13

Total 103 100.00

Mode of acquiring

processing machine

Outright purchase 98 95.15

Inheritance 2 1.94

Lease 3 2.91

Total 103 100.00

Labour engaged

Own labour 1 0.97

Communual labour 1 0.97

Hired labour 101 98.06

Total 103 100.00

Source: Field survey, 2015.

Cost incurred at each stage of processing raw cassava tuber to garri

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The average weekly costs of purchasing raw cassava tuber; transportation; peeling; grating,

pressing, frying; sifting, sealing and stitching in the study area were ₦1,1677.18; ₦1,842.23;

₦1,631.85; ₦1,939.03; ₦371.26; ₦3,514.56; ₦829.61; and ₦290.68, respectively. Cost of raw

tuber was highest followed by frying, grating, transportation and peeling respectively. Cassava

processing to garri is largely carried out using manual labour thus activities such as frying,

grating and peeling which are highly labour demanding command high costs. Cassava processing

to garri can benefit from more automated activities if farmers have access to resources to acquire

labour saving equipment thereby increasing their scale of operation.

Table 2: Weekly cost incurred at each stage of processing cassava tuber to garri

Variables Mean Standard deviation Minimum Maximum

Cost of raw tuber 11677.18 5106.05 3000 30000

Cost of transportation 1842.23 925.34 0 5000

Cost of peeling 1631.85 552.23 840 3000

Cost of grating 1939.03 553.32 360 6000

Cost of pressing 371.26 274.27 0 1400

Cost of frying 3514.56 917.44 500 6000

Cost of sifting 829.61 489.24 0 2000

Cost of sealing& grating 290.68 488.96 0 2800

Source: Field survey, 2015.

Constraints Affecting Value Added to Cassava to Produce Garri.

Table 3 shows that the most significant constraints face by cassava processors are high

perishability of raw cassava tuber, high transportation cost of cassava tubers, inadequate capital,

long period of fermentation, poor electricity supply, high transport cost of transportation to point

of sale of garri, fluctuation in the price of garri. Concerted effort need to be put in place by

different stakeholders in the processing industry to minimize this constraint. This will go a long

way in increasing productivity.

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Table 3: Constraints faced by respondents

Constraints Most severe Very

severe

Severe Less severe Not at all Total

(n)

High perishability

of raw cassava

tuber

101

(98.06%)

0

(0%)

1

(0.97%)

1

(0.97%)

0

(0%)

103

High transportation

cost of inputs

65

(63.11%)

28

(27.18%)

6

(5.83%)

4

(3.88%)

0

(0%)

103

Inadequate capital 62

(60.19%)

24

(23.30%)

15

(14.56%)

2

(1.94%)

0

(0%)

103

Poor accessible

roads

13

(12.62%)

25

(24.27%)

23

(22.33%)

19

(18.45%)

23

(22.33%)

103

Problem of water

supply

21

(20.39%)

11

(10.68%)

18

(17.48%)

30

(29.13%)

23

(22.33%)

103

Long period of

fermentation

69

(66.99%)

16

(15.53%)

6

(5.83%)

7

(6.80%)

5

(4.85%)

103

Poor electricity 55

(53.40%)

12

(11.65%)

12

(11.65%)

11

(10.68%)

13

(12.62%)

103

Labour scarcity 11

(10.68%)

8

(7.77%)

12

(11.65%)

21

(20.39%)

51

(49.51%)

103

Poor access to

information

8

(7.77%)

24

(23.30%)

18

(17.48%)

23

(22.33%)

30

(29.13)

103

High transportation

cost to point of sale

of Garri

54

(52.43%)

39

(37.86%)

6

(5.83%)

4

(3.88%)

0

(0%)

103

Fluctuation in price

of Garri

95

(92.23%)

8

(7.77%)

0

(0%)

0

(0%)

0

(0%)

103

Source: Field survey, 2015.

Result of regression analysis

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Of the entire model fitted to the data, the multiple linear regression model was found to have the

best fit. The result of the regression analysis (Table 4) shows the relationship between the total

value added and factors that influence it namely; total cost on labour, total cost on machine,

assets, age, sex, membership of cooperative society, numbers of years spent in processing

(experience), level of formal education and quantity of cassava processed by the respondents.

Table 4: Result of regression analysis between Value added and factors that influence value

addition in the processing of cassava to garri

Variables Coefficient Standard

Error

T Value P Value

Constant 494.3584 984.5446 0.50 0.617

X1 1.146127*** .1080013 10.61 0.000

X2 .9969249*** .158209 6.30 0.000

X3 -3.071199 2.236488 -1.37 0.173

X4 9.238559 16.9348 0.55 0.587

X5 -13.50918 484.1669 -0.03 0.978

X6 27.82481 280.1595 0.10 0.921

X7 -11.84317 13.92662 -0.85 0.397

X8 -14.42286 193.426 -0.07 0.941

X9 10.5281*** .3960907 26.58 0.000

R2 0.9737

Adjusted R 0.9711

Source: Field Survey, 2015.

*** Significant at 1% ; Probability > F = 0.0000

Breusch-pagan/Cook Weisberg test for heteroscedasticity Chi2= 0.04; Prob > Chi

2= 0.8403;

Significant level = 5%

The estimated regression equation is Y= 494.3584+1.146127X1+0.9969249X2 – 3.071199X3 +

9.238559X4 – 13.50918X5 + 27.82481X6 – 11.84317X7 – 14.42286X8+10.5281X9+ µ

The result shows that the coefficients of cost of labour (X1), cost of machine (X2) and cost of raw

materials (X9) are positively and statistically related to value added at 1% significance level. The

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coefficient on labour indicates that if labour cost is increased by ₦1 value added to cassava tuber

increases by ₦1.15. Also, the coefficient of total cost on machine shows that if the cost expended

on machines increases by ₦1 value added to cassava increases by approximately ₦1.

Furthermore, if the quantity of cassava processed is increased by 1kg, value added to cassava

tuber is increased by N10.52. The R2 indicates that 97.4% change in total value added is jointly

explained by the explanatory variables while the remaining 2.6% was explained by variables not

included in the model. Furthermore the heteroskedasticity test to check for presence of non –

constant variance of the error term indicated the absence of such at the 5% significance level and

a chi square of 0.04 thus confirming the appropriateness of the coefficients of the independent

variables in the estimated model. This result shows the pivotal roles of labour, equipment and

working capital in cassava processing at the cottage level. The positive relationship between cost

of labour and value added brings up two issues. Processors are open to opportunity to increase

value added to cassava by engaging available household labour so as to reduce labour cost or

hire more quality labour which will increase cost. However, the former option will place a limit

on achieving an increased scale of operation. The result shows the positive effect of investment

in machine, but indicates a constant relationship and suggests that labour will play a more

significant role. This is expected because the cassava tuber processing at the cottage level is a

labour intensive process. Finally, an opportunity for processor to increase their working capital

through loans from cooperative or any other organized credit granting intervention will help

processor to drastically increase the volume of cassava tubers processed. This has the potential of

increasing their scale of operation and ultimately income from their enterprise.

Conclusion

The study investigated the factors that influenced value addition to cassava tuber by cottage level

processors in Nigeria. The study came up with findings that can serve as useful inferences for

other cottage processing units in other African countries. The study showed that cost of labor,

cost on machines and quantity of raw cassava processed were major determinants of value added

to cassava tuber in its processing to garri. These suggest that a deliberate effort to focus on value

addition to cassava tubers can help increase incomes generated by processors, increase

employment and investment in machines. A way to achieve this can be through the use of

cooperative societies or group association to mobilize funds for purchase of better machines and

increasing working capital thereby creating a process for the transformation of present cottage

processing units into small scale units.

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