Livestock Research for Rural Development 24 (4) 2012 Guide for preparation of papers LRRD Newsletter

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Econometric analysis of the socio-economic factors affecting the profitability of smallholder dairy farming in Zambia

C Mumba, K L Samui, G S Pandey and G Tembo*

University of Zambia, School of Veterinary Medicine, Department of Disease Control, P.O. Box, 32379, Lusaka, Zambia.   and
* University of Zambia, School of Agricultural Sciences, Department of Agriculture Economics, P.O. Box, 32379, Lusaka, Zambia


The specific objectives of this cross sectional study included identifying the socio-economic characteristics of the smallholder dairy farmers; determining the effect of these socioeconomic factors on profitability of smallholder dairy farming; and making recommendations based on the findings. A total of 157 farmers were purposively selected from the study area and questionnaires were used to collect data. Data were analysed using descriptive statistics and regression analysis using Statistical Package for Social Scientists (SPSS version 16). The study was carried out over a period of one year (August 2009 to July 2010 inclusive). 

The findings of the multiple regression analysis indicated that level of education, dairy cow herd size and distance to the market significantly affected the profitability of smallholder dairy farming in Zambia. An increase in level of education and dairy cow herd size, with a unit decrease in distance to the market, led to an increase in profitability of smallholder dairy enterprise. Recommended policy actions should be directed towards construction of milk collection centers (markets) near the farmers in order to reduce the distance to the market; establishment of breeding centers for dairy animals; and knowledge transfer through provision of extension services to educate the farmers on dairy management. 

Keywords: Dairy enterprise, regression analysis, socio-economic variables


Zambia has three main types of dairy producers, namely: traditional dairy farmers; smallholder dairy farmers; and commercial dairy farmers (Phiri 1995; Neven at al 2006). Traditional dairy producers hold the largest number of cattle, but given that their cattle consist mostly of local breeds (zebu), they represent only an estimated 25 percent of marketed raw milk in Zambia (Kaluba 1992). Most of the milk produced by traditional dairy farmers is either consumed by the household or sold in informal rural markets and consumed as raw milk. Some traditional dairy producers sell their milk to milk collection centers (MCCs). 

Smallholder dairy farmers originate either from the ranks of the traditional dairy farmers or represent new entrants into the sub-sector. They own an average of 4 dairy cows and most of them are organized in cooperative societies around milk collection centers from where processors collect the raw milk (Mumba et al 2011). These smallholder dairy farmers use mostly mixed-breed cows. Unlike traditional dairy producers, smallholder dairy farmers sell the bulk of their output to processors in the formal market or consumers in the informal market with more than half of the milk produced in Zambia being produced by them (Mumba et al 2011). Their capacities in smallholder dairying are strengthened through the provision of resource persons, materials and financial support mainly by Non-Governmental Organizations (NGOs) in collaboration with the Government of Zambia (Mumba et al 2011). 

Commercial dairy farmers are capital-intensive and have larger herds, an average of about 80 purebred dairy cows. This set-up gives them greater control over production and hence they are able to concentrate on production during the dry season when prices are at peak. They sell in both informal and formal markets and supply about 80 percent of the milk into the formal dairy channels (Neven at al 2006). 

There is a lack of research based information on the socio-economic factors affecting the profitability of smallholder dairy sector which produces more than half of the milk produced in Zambia. This study therefore sought to narrow the existing knowledge gap to this important agricultural subsector that has massively socio-economically contributed to the rural development as indicated in the study by Mumba et al (2011). 


Study sites, design and sampling technique

Multi-stage and purposive sampling techniques were adopted for the selection of respondents who contributed to this study. A total of 157 smallholder dairy farmers were selected from six of the now 10 provinces of Zambia namely Southern, Eastern, Western, Lusaka, Copperbelt and Central provinces, based on the presence of smallholder dairy activities. The questionnaire sought for information on socio-economic characteristics such as age; gender; marital status; level of education; dairy herd size; distance to the market, and monthly profits realised from smallholder dairy enterprise over a period of one year (August 2009 to July 2010 inclusive).

Data collected were analysed using descriptive statistics and multiple regression analysis using Statistical Package for Social Scientist (SPSS) software. Data on monthly profits was also entered in SPSS and average annual profit was calculated. 

Econometric specification and estimation of the regression empirical model

The empirical literature on dairy economics reflects the investigation into the relationship between socio-economic variables and profitability by means of multiple regression methods (Olubiyo et al 2009). Studies conducted by Nchinda and Mendi (2008), Otieno et al (2009), and Chagunda et al (2006) have demonstrated the impact of age, gender, marital status, education level, household size and distance on relative profitability of smallholder dairy enterprise by use of multiple regression models. This formed the basis of inclusion of the socio-economic explanatory variables in this study. Average annual profit was used as a dependent variable (Y) while seven socio-economic characteristics of the respondents namely age, sex, marital status, education level, household size and distance to milk collection centers were used as explanatory variables (X). The implicit model of the regression was as indicated in the equation 1 below: 

Y = β1age + β2sex + β3mar_sta + β4no_home + β5educ_lev + β6herd_siz + β7 dist_mcc + e……………..Equation 1


Y = Annual profit (ZMK)

X1 = Age of the farmer (years)

X2 = Gender of farmer

X3 = Marital status (single, married and widowed)

X4 = Household size (number of persons)

X5 = Educational level (no formal education, primary, secondary, tertiary)

X6 = Herd size (number of dairy cows)

X7 = Distance travelled to deliver milk to MCCs (km)

e = Error Term; where the error terms are assumed to be independent and normally distributed with mean zero and constant variance.  

The null (default) hypothesis was that each independent variable (age, gender, marital status, household size, level of education, dairy cow herd size and distance to MCCs was having absolutely no effect (has a coefficient of 0) and we were looking for a reason to reject this theory. The F-ratio was used to test the joint hypothesis to show whether the included variables exerted any significant influence on the dependent variable, the value of average annual profit. It tested the null hypothesis that all the estimated coefficients are zero. The hypotheses are explicitly represented as follows: 

Ho: β1 to β7 = 0…………………………Equation 2

Against the alternative hypothesis that at least one of the coefficients are not zero

Before running a multiple regression analysis, the following preliminary tests were carried out on the data;

Results and Discussion

Socio-economic characteristics of the respondents

The findings in Table 1 showed that 14.6% of the respondents were aged between 31-40 years, 45.9% between 41-50 years, 23.6% between 51-60 years and 15.9% between 61-70 years, respectively. The mean age of the respondents was 48.8 years. This seems to suggest that smallholder dairy farming is mainly practiced by people in the old age as these are the ones targeted by donor funded projects on the assumption that they have had experience of cattle rearing from their traditional cattle breeds. There is need for these smallholder dairy donor funded projects to also engage the youths so that there is continuity upon the demise of their parents in the older age group. The majority of the respondents were male (63.1%), while 36.9% were female. Marital status of the respondents was distributed as follows; 82.8% were married, 12.1% were widowed and 5.1% were single, respectively. It was also observed that 9.6% of the respondents had household sizes of 1-5 persons, 42.7% had 6-10 persons, 22.3% had 11-15 persons, 10.2% had 16-20 persons and 15.2% had 21-25 persons, respectively. The average household size was 10 persons. This is in agreement with the Central Statistical Office census report that indicates that the average number of persons per home in rural areas is ten (CSO 2010). The majority of the respondents (56.7%) had primary education, 29.9% had secondary education, 5.1% had tertiary education and 8.3% had no formal education. Distance MCCs where milk is marketed seemed to be an important factor in the viability of the smallholder dairy enterprise. About 17.2% of the respondents lived at distances less than 1 km from the respective milk collection centers, 43.9% at 1-5km, 25.5% at 5-10 km, 12.1 at 10-20 km and 1.3% at over 20 km i.e. the longer the distance from the MCC the lower the number of smallholder dairy farmers delivering the milk. The average annual profit was ZMK 5,508,620 which is currently equivalent to US$ 1,101.72 at a rate of USD1=ZMK5, 000. This translates to an average monthly income of ZMK 450,000 (US$ 92) from smallholder dairy farming only. This amount is equal to the current minimum wage for general workers in Zambia.


Table 1: Socioeconomic characteristics of respondents




Age (years)




























Marital status















Household size (number of persons)



















Level of education



No formal education















Distance travelled to MCCs (km)

Less than 1












Over 20 km






Average age= 48.8 years; average household size=10 persons

Multiple regression estimates of the socio-economic factors affecting profitability of smallholder dairy enterprise.

The F-value was 23.3, with a p-value 0.001, indicating that the model was statistically significant. The coefficient of determination (R2) was 0.523, meaning that approximately 52.3% of variability of the dependent variable (profitability) was accounted for by the explanatory variables in the model. Thus, the regression model was adequate. Gujarati (2004) states that, in determining model adequacy, we look at some broad features of the results, such as the R2 value and F-value, which were both statistically significant in this study.  

Table 2 summarizes the multiple regression estimates of factors affecting profitability of smallholder dairy enterprise. Age of the farmer had a positive coefficient but not statistically significant (0.28). This meant that this variable had no effect on the profitability of smallholder dairy farming, provided other variables held constant. All the farmers regardless of age had equal chances of making profit. However, a unit increase in age would lead to an increase in the profit of smallholder dairy enterprise by ZMK 32, 386.26 (US$ 6.5). Gender and marital status of the farmer had a positive relationship but not statistically significant (0.34) with profitability. Thus profitability does not depend on gender and marital status of the farmers, respectively. 

The coefficient of household size (27046.42) was positive though not statistically significant. This meant that a unit increase in the number of persons at home would increase the profit of smallholder dairy enterprise by ZMK 27,046.42 (US$ 6). Household size has been described as the most important determinant of labour investment for family farms because in addition to being a source of labour, it also influences the need for increased milk production for home consumption as well as for the market (Ngongoni et al 2006). The p-value for household size was 0.074 which was very close to the level of significant (0.05) hence a very weak rejection point. 

Level of education was statistically significant (0.01) and had a positive value. This meant that the higher the level of education, the more the profit. This has to do with understanding of smallholder dairy farming as a business. This assumption results in proper management of dairy animals, feeding and good milk hygiene, thereby improving milk yield and profits. A unit increase in level of education leads to an increase in profit by ZMK 2, 064, 319.73 (US$ 413). 

Dairy cow herd size (milking cows) was statistically significant (0.00). The coefficient of dairy herd was positive meaning that a unit increase in the herd size of milking cows resulted in the increase of profit of smallholder dairy enterprise by ZMK, 64, 898.07 (US$ 12), other variables held constant.  

The distance to milk collection centers was statistically significant (0.00). A unit decrease in distance to the MCC leads to an increase in profit by ZMK 710755.86 (US$ 142), other factors held constant. Mutukumira et al (1996) stated that long distance to MCCs is a hindrance to a viable dairy enterprise. The longer the distance to the MCCs the less the number of smallholder dairy farmers delivering milk, hence the less the profit.  


Table 2: Multiple regression estimates of factors affecting profitability of smallholder dairy enterprise.


Unstandardized Coefficients

Standardized Coefficients




Std. Error




















Marital status






Household size






Level of education






Dairy herd size






Distance to MCC






Dependent Variable: Annual profit      R2 = 52.3          F = 23.3      *Significant at P < 0.05

Conclusions and recommendations


The authors are grateful to Golden Valley Agricultural Trust, Zambia National Zakah and Welfare Trust and University of Zambia Staff Development Office for financial and logistical support towards this research.  


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Received 8 March 2012; Accepted 18 March 2012; Published 2 April 2012

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