Livestock Research for Rural Development 27 (10) 2015 Guide for preparation of papers LRRD Newsletter

Citation of this paper

Effects of season and location on cattle milk produced and producer milk prices in selected villages of Tanga and Morogoro Regions, Tanzania

Fred J Wassena, Walter E Mangesho1, Aluna Chawala2, Germana H Laswai3, Julius M N Bwire1, Abiliza E Kimambo3, Ben Lukuyu4, Gregory Sikumba4 and Brigitte L Maass5,6

International Center for Tropical Agriculture (CIAT), c/o PO Box 3004, SUA, Morogoro, Tanzania
1 Tanzania Livestock Research Institute (TALIRI), PO Box 5016, Tanga, Tanzania
2 TALIRI, PO Box 202, Mpwapwa, Tanzania
3 Sokoine University of Agriculture (SUA), PO Box 3004, Morogoro, Tanzania.
4 International Livestock Research Institute (ILRI), PO Box 30709-00100, Nairobi, Kenya
5 CIAT, PO Box 823-00621, Nairobi, Kenya
6 Current address: Georg-August University of Göttingen, Grisebachstr. 6, D-37077 Göttingen, Germany


The study was conducted in 4 villages of Kilosa and Mvomero districts in Morogoro Region and 4 villages of Handeni and Lushoto districts in Tanga Region. The aim was to compare differences in cattle milk produced and milk producer prices based on location and seasons in the two regions. The study applied the Feed Assessment Tool (FEAST) for gathering data through focus group discussions and individual interviews. Data on rainfall pattern throughout the year, milk produced per household per day, and milk prices received in Tanzanian Shilling (TSh) per litre were gathered.

The daily amount of milk produced per household was greater in Handeni (SE 2.33) and Kilosa (SE 2.12) districts than in Mvomero (SE 3.09) and Lushoto (SE 2.24) districts, with an overall range of 0.25-53 litres/household. There were also differences in milk producer prices, in Mvomero (SD 148), Lushoto (SD 62.26), Kilosa (SD 116.12) and Handeni (SD 123) with an overall range of 200-1000 TSh/litre of milk. Seasonality of rainfall had effects on both milk produced and milk prices. On the other hand, local feeding systems influenced milk produced per household, while marketing channels affected milk prices. More research on the use of innovation approaches to address issues of prices and seasonal milk supply, as well as training and use of improved forage technology were possible options recommended to achieve a year-round similar level of milk production.

Key words: dairy value chain, FEAST, seasonality


In Tanzania, like many other African countries, milk is produced mainly by small-scale farmers dispersed across the countryside, who are relatively isolated from the urban consumers. Their surplus production is small and often highly seasonal, in part owing to the low intensity of dairy production systems and their sensitivity to local weather conditions (Mdoe and Wiggins 1996). Seasonality is reflected by large variations of milk production and supply among the months of a year. Total annual milk production is currently estimated at 1.853 billion liters in Tanzania, out of which 0.597 billion litres are from high breed cows and 1.256 billion litres from indigenous cows (NBS 2012). To date, per capita milk consumption in the country is estimated at 43 litres, which is relatively low due to availability and increasing costs as compared to the per capita milk consumption of 200 litres recommended by FAO (Njombe et al 2011; Swai and Karimuribo 2011). According to Schooman and Swai (2011), privatization of the dairy sub-sector resulted in decontrolled prices for milk and milk products mainly in large cities and towns. It is within the urban and peri-urban areas that much of the marketed milk is consumed due to greater demand by the growing population (Kurwijila 2002). Milk in rural areas is mostly consumed within the producing households, produced in surplus and less marketed during the rainy season compared to very low supply during dry seasons (ILRI 2005; Schooman and Swai 2011). The only common marketing channels exist through direct sales to consumers/neighbours and through small-scale milk vendors. This study explored seasonal variations in milk production and milk prices received among smallholder farmers, suggesting pilot options that may improve productivity and prices of milk. The general objective was to assess the influence of location and season on cattle milk production and producer prices in Morogoro and Tanga regions. The following research questions were addressed: What are the effects of season on milk production and milk prices? What is the effect of location on milk production and milk prices? What are the possible options for stabilizing milk production and producer milk prices?


Description of the study area

The study was conducted within the MilkIT project (‘Enhancing Dairy-based Livelihoods in India and the United Republic of Tanzania through Feed Innovation and Value Chain Development Approaches’) in Kilosa and Mvomero districts of Morogoro region and Handeni and Lushoto districts of Tanga region. Selection of the districts was based on their production and marketing channels; either production and marketing take place in rural areas (‘rural-to-rural’ in Kilosa and Handeni districts) or production in rural but marketing to urban areas (‘rural-to-urban’ in Mvomero and Lushoto districts). Two villages were purposely selected from each district.

Sampling frame

Farmers were selected who represented each hamlet within a village, with reasonable representation of men and women. To assure representation of every hamlet, two focus group discussions (FGDs) were conducted in each village involving 20-25 persons per FGD, with a total of 305 respondents in all 8 villages; 6 to 7 farmers were selected from three wealth groups (based on land size or number of cattle) out of the FGD group for subsequent individual interviews. The interviews were done using the structured questionnaire of the Feed Assessment Tool (FEAST version 5.3; Duncan et al 2012). A total of 104 interviews were held from February to March 2013, involving 58 men and 46 women.

Data collection and analysis

The parameters assessed through individual interviews were average milk produced per day/household throughout a year and average price received for milk per litre. Data on rainfall pattern was assessed through FGDs on a 0-5 scale based on rainfall intensity per month and converted to percentage. Collected data were compiled in the FEAST Beta data sheet and analyzed using MS Excel and SAS (2002). Pairwise comparisons between means of all variables were computed by Duncan’s Multiple Range Test (DMRT) at p ≤ 0.05.

Results and discussion

Annual rainfall distribution

Respondents rated rainfall the highest from March to May in all districts, with the peak in April, and lowest from July to October except for Mvomero and Lushoto, where relatively high rainfall was also experienced in October and November, respectively (Figure 1).

Figure 1. Average rainfall (in %) throughout the year in Kilosa, Handeni, Mvomero and Lushoto districts of Tanzania
as estimated in 4 assessments per district by focus groups discussions with farmers; n=305
Average household milk production in four districts (location effect)

On average, there was more milk produced per household per day (p ≤ 0.05) in Handeni and Kilosa than in Mvomero and Lushoto (Table 1). The difference is marked between the feeding systems practiced in the areas. Zero-grazed improved dairy cows usually produced more milk per cow (up to ten times) compared to the traditional zebu cattle, which are mainly kept under extensive systems (Kurwijila et al 2012; NBS 2007/2008). This considerable difference between feeding systems explains the very different levels of milk produced (White et al 2002). On the other hand, the districts with a dominant practice of extensive/pastoral systems (Handeni, Kilosa and partly Mvomero) have substantially greater average milk produced per household compared to the district with semi-intensive/zero grazing systems (Lushoto) due to the large number of cows milked in the former. The increased amount of milk produced or supplied in Tanzania is mostly reflected by number of cattle, rather than improved productivity (Kurwijila 2002).

Table 1. Average daily milk production per household in four districts of Tanzania across a year as estimated by farmers; n=105


Milk produced (litres/household/day)









0.25 – 48





0.50 – 53





1.00 – 12





0.50 – 27

* Least square means with different superscripts differ (p ≤ 0.05 ) according to Duncan’s Multiple Range Test
N# refers to total number of values given by all interviewees and months in a year for which estimates were made

Average household milk production in different months of the year (seasonal effect)

The seasonal trends of milk production per household in the different districts mostly reflect the rainfall pattern, except for Lushoto (Figure 2). Smallholder dairy systems are highly sensitive to local weather, thus, affecting milk production (Mdoe and Wiggins 1996). During the rainy season, milk produced was greater due to availability of pasture (in terms of biomass) for grazing and water for the animals in extensive production (Handeni, Kilosa and Mvomero) as well as for animals that rely on collected fodder in zero-grazed areas (Lushoto). Currently, the milk yield level in Lushoto is below 15 litres per cow per day expected from improved cows (Msanga and Kavana 2002).

During the dry season, the decline in milk production per household, especially in the extensive system, is likely due to shortage of water and pasture as shown by low rainfall and decline in quantity and quality of available feeds, which are among the major constraints on increased milk production in Tanzania (Mdoe and Wiggins 1996; Mtengeti et al 2008; Njombe et al 2011). The uniform distribution in the amount of milk produced from January to October in Lushoto, with only a slight decline in November and December, could be attributed to the feeding system where farmers who practiced zero-grazing search for green chop in the flood plains and on river banks and also supplement some concentrate to their animals. Access to supplementary feeds like concentrates is unlikely in the extensive areas, where the animals rely solely on grazing natural pastures.

Rural-to-rural marketing channel Rural-to-urban marketing channel
Figure 2. Average daily milk production in litre per household throughout the year in (a) Kilosa, (b) Mvomero,
(c) Handeni and (d) Lushoto districts of Tanzania as estimated by farmers; n=105
Average milk producer prices (location effect)

Milk prices were different (p ≤ 0.05) among the four districts (Table 2). The higher milk prices in Mvomero could be due to the closeness to urban markets. For example, milk from Wami Sokoine village is sold to Morogoro urban area due to the short distance between the village and Morogoro Municipality. Similarly, Manyinga village, also in Mvomero, is situated in the urban surroundings of Turiani, where people working in Mtibwa Sugar Company are settled and, probably, purchase milk directly from the farmers. Villages that are closer to urban and peri-urban market channels get higher prices and more reliable markets for their milk, due to greater demand driven by urban population growth (Kurwijila 2002; Muriuki and Thorpe 2002).

On the other hand, proximity to milk collection centres for dairy processing factories can have implications on milk price due to a monopoly-like price control (Cadilhon et al 2014). Farmers who sell their milk to processing factories like Tanga Fresh (Handeni, Lushoto, Kilosa and Mvomero) or Tan Dairy (Mvomero and Kilosa) claim to receive lower prices as compared to those selling their milk directly to individuals or a kiosk. The latter have more windows for price negotiation and are, thus, likely to receive better prices than the former who have limited negotiation power. Producer organization could complement the good practices of value chain development through their linkages and shareholding in the processing facilities and, hence, the ability to affect price control (NIRAS 2010). The Tanga Dairy Platform, a multi-stakeholder forum, has already played a role in helping to increase the milk price for producers who deliver to Tanga Fresh Ltd. milk factory (Cadilhon et al 2014).

Table 2. Average producer milk price (in TSh/litre) with standard errors (SE) in four districts of Tanzania as estimated by farmers; n=105

Price of milk in TSh per litre #









200 – 700





200 – 1000





400 – 1000





300 – 600

* Least square means with different superscripts differ (p ≤ 0.05 ) according to Duncan’s Multiple Range Test
# At the time of research TSh100 ≈ US$0.06
N§ refers to total number of values given by all interviewees and months in a year for which estimates were made

Average milk producer prices in different months of the year (seasonal effect)

Higher milk prices in some months (Figure 3) correspond to depressed milk production at the onset of the dry season that decreases feed and water availability to the animals, thus, forcing large numbers of cattle to migrate to suitable grazing areas. This situation is more severe in extensive systems. Lower prices (Figure 3) during March to May reflect the season when rainfall is at its peak (Figure 1) and plenty of milk is produced (Figure 2). According to Kurwijila et al (2012), seasonality of rainfall (and access to water) is extreme and reflected in producers’ management of their animals in dairy systems, resulting in severe seasonality in milk volume produced. Obviously, this affects milk price stability as well.. Further, processing of milk into durable products, such as cheese, is realized little in Tanzania due to low demand. Information is unavailable on the supply and demand of milk and the capacity of dairy processing industries to accommodate the greater milk available during wet seasons in Tanzania. Strategies need to be developed on how to meet increased consumer demand during dry seasons.

Rural-to-rural marketing channel Rural-to-urban marketing channel
Figure 3. Average milk prices (in TSh/litre) with standard deviations (SD) received throughout the year in (a) Kilosa,
(b) Mvomero, (c) Handeni and (d) Lushoto districts of Tanzania as estimated by farmers; n=105

Conclusions and recommendations


Financial support by the International Fund for Agricultural Development (IFAD) to the MilkIT project is gratefully acknowledged. We thank all survey respondents for the unreserved collaboration and openness in sharing views and experiences. We also appreciate the participation of facilitators from SUA, TALIRI and MilkIT district and village extension staffs.


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Received 15 April 2015; Accepted 23 August 2015; Published 1 October 2015

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