Livestock Research for Rural Development 27 (5) 2015 Guide for preparation of papers LRRD Newsletter

Citation of this paper

Goat and goat meat markets in selected districts of Malawi: value chain, structure and efficiency

Assa M Maganga1, 2, Fanny C Chigwa3 and Lawrence D Mapemba1

1 Lilongwe University of Agriculture and Natural Resources, Department of Agriculture and Applied Economics, Lilongwe, Malawi
assa.maganga@gmail.com
2 Everest Intelligence Consult, Lilongwe, Malawi
3 Lilongwe University of Agriculture and Natural Resources, Department of Animal Science, Lilongwe, Malawi

Abstract

The study examined the nature of goat marketing by determining the marketing margins, structure and efficiency in selected districts of Malawi. Simple random sampling technique was used to select goat producers giving a total of 425 producer respondents and 60 private traders who were used for the study. A structured questionnaire was used to collect relevant information. Data collected were analyzed using simple cost- return models, diagrams and multiple Regression analysis.

 

The result of the analysis showed that the business was profitable. The marketing channel showed the routes the product takes from production to consumption. The significant variables that positively influenced the marketing efficiency were market experience, household size, education level, food security status, livestock extension and market information while distance to road network negatively affected efficiency. This implies that these predictor variables should be considered in policy issues.

Keywords: Gini-coefficient, marketing costs, middlemen, price transmission


Introduction

Goat production ranks first among ruminants in terms of numbers and utilization. This is mainly due to their wide adaptation and capable of being tamed by households with low levels of land resources. In terms of numbers, goat has consistently been above those of cattle, pigs and sheep in Malawi (Banda and Sichinga 2001; Banda 2008). Goats are regarded as a renewable food source and are readily slaughtered and sold (Devendra 1985). Itty et al (1997) reported that goats are a living capital reserve and disaster insurance that can be easily liquidated to provide cash when required. In periods of recurring droughts, festivals and hunger months, selling of goat is one of the major ways of earning cash for family survival. On the demand side, goat meat demand is increasing in urban and rural areas, with over 30 goats sold in like Nsundwe and Nanjiri. Therefore goat production and marketing are becoming increasingly important in Malawi.

 

Despite the importance, goat production has not been commercialized, it remains a smallholder entity. Marketing chain has constraints that include continued low supply of the right quantity and quality goats as demanded by the markets, lack of uniform carcasses of goats, unclear market link that lead to producer exploitation,  and lack of post-slaughter processes. The National Livestock Policy is silent on goat-specific marketing policy, and this leads to quality of goats and goat meat supply, goat meat consumption patterns and profitability of the goat enterprise unregulated.

 

Marketing of live goat and goat meat in Malawi is traditional and is mostly handled by private traders. A number of middlemen are involved in the processing of goat and goat meat marketing in the country. In India, different types of intermediaries exist in the chain of marketing of live goats and goat meat (Miah et al 2005; Bhasin and Devendra 1988). Experience has shown that livestock product problems are caused by a combination of production and marketing deficiencies and until both are adequately and properly treated and integrated, the problem may be persistent (Udensi 2003) and goat markets will be inefficient.

 

An efficient marketing system is one capable of moving commodities from places of production to areas they are needed in a manner that is beneficial to the producers, marketing intermediaries and consumers (Mehta et al 2002). Price instability arising from inputs costs and availability, government policies, weather and market forces equally affect market efficiency (Mehta et al 2002). The term marketing efficiency is often used in evaluating the performance of the marketing process. According to Barker et al (1985), it reflects the consensus that output of the marketing process should be produced efficiently. Efficiency may be defined as increasing the output-input ratio in terms of monetary values.

 

Considering the paucity of information on small goat markets in Malawi, this study provides a useful information that enable smallholder farmers to consider goat marketing as a lucrative venture to diversify their income. This study attempted to examine the goat and goat meat intermediaries and quantify price build up along the chain; determine the structure of the goat market; analyze the costs, return market efficiency and its determinants in goat marketing at producer level.


Methodological construct

The study was conducted in 2014 across 7 districts in Malawi including Nsanje, Chikwawa, Mulanje, Lilongwe, Dowa, Mzimba and Rumphi. A sample of 425 goat farmers was randomly selected from the districts. Structured questionnaire and focus group discussions were used to collect data on goat marketing channels, production, economic productivity, and constraints to goat marketing. The study was conducted along with a survey of 60 private traders in goat marketing to understand price transmission along the value chain. The quantitative data collection was substantiated with focus groups discussion for triangulation.

 

The first objective on goat and goat meat intermediaries was augmented by drawing the players along the chain and analysing price transmission along the chain. The second objective on market structure was analysed using Gini coefficient and Lorenz curve (Brown 1994). The Gini Coefficient technique that gives a more precise measure of the market structure was employed to measure the level of buyers and sellers concentration in the market in order to determine the degree of competition or monopoly in the market. The Gini coefficient is given by:

 

[1]        GC = 1 – XY

 

Where;

            X    = Percentage of sellers in the class of ith class marketer

            Y    = Percentage of sellers in the class of ith class marketer

            GC = Gini coefficient

 

The last objective on the costs, return and efficiency of the marketers were analyzed as follows:

 

[2]        Gross margin = Total Return – Variable cost

           

[3]        Net return (profit) = Total Return – Total cost

 

The

 

[2]                   

 

 

 

 

Determinants of marketing efficiency were analyzed using a multiple linear regression model in STATA 13, implicitly stated as follows:

 

Where:

             Y = Market efficiency of goat producers given as ratio of total returns to total cost

            X1 = Marketing experience in goat

            X2 = Household size

            X3 = Education level

            X4 = Distance to road network

            X5 = Gender

            X6 = Household food security status

            X7 = Livestock extension

            X8 = Market information


Results and Discussion

Key stakeholders

 

Malawi’s goat value chain comprises a number of key stakeholders such as breeding, veterinary, feed manufacturers and input suppliers that form the primary layer of stakeholders. They supply inputs to goat farmers who in turn sell either live or slaughtered animals to middlemen or local butchers, respectively. The processors normally perform a number of functions including packing, grading, wholesaling and in some cases direct retailing. However, many processors supply processed meat products to final distributors such as supermarkets, restaurants while butchers supply to institutions such as schools.

 

Price Transmission along the Chain

 

Goat production by smallholder farmers who were visited for this study were mostly operating on implicit production costs. Mostly goats are reared on a free range and grazing system making it difficult to account for labour costs of production. Goats are either left to graze freely or tethered. The gross margins at farm level are presented in the table below.

Figure 1: Goat Marketing Channels/ Value Chain

Despite the low input technologies used by farmers which help to cut on costs, the prices they offer are very low compared to what is offered at retail shops in trading centers. This could however be attributed to value addition that come in along the channels from the farm to the final consumer. The goats are usually sold when they are grown and matured around 18 to 30 kilograms. The price of live goat ranged from MK15,000 to MK25,000 with an average of MK19,000. This yielded a gross margin of MK15,400 per live weight (Table 1). Goats were reported to be resistant to diseases compared to other livestock like pigs. Thus, this reduced the cost of veterinary services. Mostly, goats were just kept as medium of financial reservoirs which could be converted into cash during the times of food insecurity or other household shocks. However, during these times farmers tend to be desperate and accept lowest price offers by middlemen, which is contrary to what happen immediately after crop harvest in April and May. On the other end, no single farmer reported to be selling goat milk or milk products. This is more to do with the culture of Malawians for whom goat milk is not traditionally a common food and socially acceptable. It could be due to lack of awareness. However, other goat products reported to be sold include meat, skin and manure.

 

Table 1: Farm Level Analysis per live animal

Goat (live weight)

Per Head

Per Ton

 

MWK

USD

MKW

USD

Gross revenue (output *price)

19,000.00

47.50

760000.00

1900.00

Variable costs

3600.00

9.01

144000.00

360.00

       Hired labour

1420

3.55

56800.00

142.00

       Animal feed

1146

2.87

45840.00

114.60

       Medicines and vaccines

714

1.79

28560.00

71.40

       Marketing and other costs

320

0.80

12800.00

32.00

Fixed costs

700

1.75

28000.00

70.00

Total Costs

4,300.00

10.76

172,000.00

430.00

Gross margin (revenue-variable costs)

15,400.00

38.50

616000.00

1540.00

Source: Study data (2014)

 

Goats are marketed through a number of channels. These consists of individuals buying goat from farmers for different reasons which include slaughter, as an investment or for social functions such as funerals, customary celebrations, weddings and religious celebrations. Usually sales for such purposes are conducted right at the farmer’s kraal and this has the advantage that there are no transportation costs incurred for the farmer. Sometimes, the farmers slaughter the animals themselves and sell the meat to fellow villagers. This market channel was reported to be very unreliable due to its unorganized and thus unpredictable availability of buyers. Besides, such a marketing channel forces small scale farmers to sell their goats to people that have low purchasing power, at relatively low prices for their livestock and mostly in-kind exchange.

 

Apart from the above channel, there are middlemen who go into the rural areas to purchase goats and transport them to urban centers or trading centers. The first level of intermediaries is the middlemen who purchase the live goats from farmers and sell them to processors. These middlemen go into the rural areas and pay for transportation costs of goats. In some cases, the areas are hard to rich, thus, taking it as a reason to bargain the prices at farm gate so low and price it high to wherever they sell it. It was found that the price offered by middlemen in their outlet markets was 42% high than the farm gate price on average. The same goat purchased from farmers at MK19,000 was later sold at about MK26,980 on average. It was learnt that when they are buying from farmers pricing is not based on live weight. This has been a disadvantage to farmers as this has resulted in understating the true value of the live goat, hence low profits for farmers. Whereas, when middlemen are selling to their counterparts along the chain, weight of the goat is taken into consideration when pricing goats. Castrated goats normally have high quality meat and thus were sold more expensive as compared to un-castrated ones.

 

The middlemen sold their live goats to processors or butchers, who in turn slaughter the goats for value addition. The processors, butchers sell their product on a per kg basis. The average price charged by these intermediaries was MK1,600 per Kg accounting for about a margin of 18% relative to the middlemen. The live weight of the goat is usually higher than the marketable portions when slaughtered. Apart from the carcass, it was reported that butchers were also selling the head and legs of the slaughtered goat to beer clubs where demand is usually high while some were purchased by households for consumption as well. The processors sell to retail shops while butchers sell either to retail shops or directly to final consumers. Prices of goat purchased in retail shops were well cut and packaged. This addition in value was reflected in the high prices (MK2, 200) observed in these retail shops.

 

Market structure

 

Goat market structure was analysed for those traders that transacted with the goat producers first using a Lorenz curve. The Lorenz curve shows the cumulative percentage of market share for traded goats versus cumulative percentage of traders. The curve shows that the largest 10% of total primary traders controlled about 22% of total market trade and the largest 20% of traders accounted for about 41% of total market trade.

Figure 2: Degree of inequality in market shares of private traders

Similarly half of traders controlled about 73%. This implies that marketing of goats is dominated by moderately many primary traders and is relatively equitably shared. The Gini Coefficient technique that gives a more precise measure of the market structure was employed to measure the level of buyers and sellers concentration. Empirical result for computed values of the Gini coefficients for retailers was 1-0.65 = 0.35. The study result of Gini coefficient of 0.35 shows a moderately concentrated market of private traders which is a reflection of some level of inefficiency of goat market structure.

 

Market Efficiency

 

The average live goat costs and returns for deriving marketing efficiency of the goat producers are shown in Table 2. The result shows that goat business is profitable in Malawi. The business records an average live goat return of MK14,700. This is a proof of the profitability and viability of the business in the studied areas. Furthermore, the business was economically efficient in terms of input - output ratios. Marketing efficiency of 4.42 shows that for every MK1 invested or incurred as a cost in the business, a return of MK4.42 was realized. Therefore, the marketers maintained return to capital invested in carrying out the marketing activities. Though the efficiency score seem to be on a high side, this could also be attributed to family labour and free range grazing system which is usually not costed by the producers. Much lower efficiency score of 1.46 for goats was reported by Maikasuwa and Jabo (2014) in Sokoto State of Nigeria.

Table 2: Market Efficiency among goat producers

Parameters

Monetary values

Unit variable costs

MK3,600

Unit fixed costs

MK700

Unit Total cost

MK4,300

Unit returns from sales

MK19,000

Net returns

MK14,700

Market efficiency

4.42

The market efficiency scores for the studied goat producers could not make much policy sense without and understanding of the factors that determine the level of efficiency for each producer. The regression analysis result which presents the determinants of market efficiency is shown in Table 3 below.

 

Linear regression was the function form that proved to be an appropriate and could help to explain predictors of market efficiency among goat producers, based on the number of significant variables, the value of R2 and F-statistic. The R2 was 0.78 meaning that about 78% of the variation in the marketing efficiency was explained by the independent variables while the remaining 22% was accounted for by the error term. The joint significance test of the model (F-value of 426) showed that the all the variables included in the estimation adequately explained producers marketing efficiency.

 

Level of formal education was a positively significant variable that influenced the marketing efficiency of the producers. This implies that education improves efficiency. This conforms to the finding of Mkpado and Onuoha (2012) who reported that increasing years of formal education increases level of efficiency. Those who are more educated are able to calculate the expected returns and try as much as possible to cut on costs of production and marketing, in turn increasing the marketing efficiency scores.

Table 3: Determinants of goat marketing efficiency among goat producers

Variable

Definition of variable

Coef.

t-value

p-value

Market efficiency

Dependent variable

-

-

 

Marketing experience

Number of years selling goats

0.209*** (0.0339)

6.2

0.000

Household size

Number of persons in the household

0.0981*** (0.00625)

15.7

0.000

Education level

Years of schooling

0.270*** (0.0278)

9.7

0.000

Distance

Distance to established road network

-0.358*** (0.0394)

-9.1

0.000

Gender

Gender of producer: 1=Male, 0=Female

0.0197 (0.0387)

0.5

0.617

Food security status

1= Food secure; 0= Food insecure

0.0816** (0.0401)

2.0

0.046

Livestock extension

1=Access to extension; 0=otherwise

0.0125*** (0.00188)

6.6

0.000

Market information

1=Access to information; 0= otherwise

0.0797** (0.0378)

2.1

0.036

Constant

Regression intercept

0.0958*** (0.00856)

11.2

0.000

F-Value

 

184***

 

0.000

R-Squared

 

0.78

 

 

Observations (n)

 

425

 

 

Standard errors in parentheses

*** p<0.01, ** p<0.05

Household size was found to be positively related to marketing efficiency. With more household members, the household is well endowed with free labour that does not enter the gross margin formula as costs. Unlike those households with inadequate household members, they have to hire labour which adds to production costs and in turn undermining marketing efficiency. Marketing experience had a positive significant effect on the efficiency. Thus, with repeated transactions in the goat markets, producers are able to know where high costs are born and then find prospective ways on how to cut them unlike the new timers in the market. The current result goes against Obasi and Amaechi (2013) who established that market experience had a negative significant effect on marketing efficiency arguing that in developing countries; some marketers do not maximize their experience to improve efficiency. Gender aspect seems to be neutral in determining marketing efficiency. Food security status of the household in year of the survey was positively related to market efficiency. Those households which were food insecure reported to have low levels of efficiency. This can be attributed to the fact that they sold their goats out of desperation at any price offer. The middlemen try to bargain for the price to the lowest possible when food insecurity is prevalent. Livestock extension and access to livestock market information were found to be positive predictors of marketing efficiency. In terms of livestock extension, the strength of relationship was weak given the fact that agricultural extension services are more streamlined toward crop production than livestock production. Lastly, distance to an established road network increased the marketing costs and consequently undermined the efficiency of goat markets. Similar result was reported by Mutukumira et al (1996) and Mumba et al (2012).


Conclusions and Recommendations


Acknowledgements

The authors are grateful to the Goat project, financed by the Regional Universities Forum for Capacity Building in Agriculture (RUFORUM). Thanks are also due to all farmers in study districts, who shared their experiences and participated actively in this study. The usual disclaimer applies: all views expressed in this study are those of the authors based on the study data.


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Received 25 December 2014; Accepted 10 April 2015; Published 1 May 2015

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