A cost-benefit analysis of smallholder dairy cattle enterprises in different agro-ecological zones in Kenya highlands

L M Mburu, K W Gitu* and J W Wakhungu**

Ministry of Livestock and Fisheries Development, Department of Livestock Production, PO Box 47010-00100, Nairobi- Kenya
leonardmburu@yahoo.com
*University of Nairobi, Department of Agricultural Economics, PO Box 29053-00625, Nairobi- Kenya
**University of Nairobi, Department of Animal Production, PO Box 29053-00625, Nairobi- Kenya
 

Abstract

The study was carried out in smallholder dairy farms in three agro-ecological zones in Kenya highlands between December 2004 and March 2005. The survey showed that dairy enterprise was the most important income generating farming activity in 96% of households in Kenya highlands. Revenue in a dairy enterprise accrues from sale of milk and animals, and milk consumed by households and calves. Though some farms had negative gross margins, on average revenues significantly exceeded costs and the dairy enterprise returned a profit. In the lower highlands the costs of production were 19.1 Kenya shillings (KES) /kg and unit profit was 2.3 KES/ kg; in the upper midlands the costs of production were 16.90 KES/ kg and unit profit was 6.3 KES /kg, and in the lower midlands the costs of production were 18.1 KES / kg and unit profit was 3.45 KES /kg. Nevertheless, a one-way analysis of variance showed that there were no significant differences (P > 0.05) between the arithmetic means of the costs of production for the three agro-ecological zones. However, the arithmetic means of unit profit from milk were significant (P < 0.05) between lower highlands and upper midlands.

These estimates are important for policy makers and development planners when making decisions related to costs and benefits of smallholder dairy enterprises. Farmers in upper midlands were making much more profit from milk than those in lower highlands due to higher milk prices offered by itinerant traders. These showed that cooperatives were not competitive in milk pricing and lower highlands farmers should utilize the other available milk marketing channels. Hence, policies to improve the operational and pricing efficiencies of dairy cooperatives would have a self-accelerating effect on productivity.

Key words: Costs of production, dairy cooperatives, unit profit

Introduction

A number of studies in the 1990s estimated the production costs and profitability of the smallholder Kenya highlands milk production. For example, Sellen et al (1990) estimated returns to smallholder dairy farming in Nyeri district at 3.1 KES per litre. In an update from the same district, the estimated profits were 2.8 KES per litre in 1992 (Staal 1995). Longitudinal surveys conducted between October 1997 and December 1998 for Kiambu district showed estimated returns of 4.1 KES per litre, and between November 1998 and March 2000 for Nakuru and Nyandarua districts showed estimated returns of 3.6 KES per litre and 4.8 KES per litre respectively. In the same study simulated estimates of cost of production and revenues, April 2002, in Kiambu, Nakuru and Nyandarua districts showed negative overall profits. A study carried out in 1998 using partial budget analysis reported returns of 3.4 KES /kg in the same area (Staal et al 2003).

Any factor that could lower or increase expenses is a source of risk to the economic performance of the dairy business (Bailey 2001). Some of these risks are: milk prices, purchased feed prices, hired labour, crop /forage production among others. Dairy production in Kenya highlands is faced by a multitude of perceived and often experienced risks, which contribute to high costs of production and low average productivity (Omore et al 1997; Kaguongo et al 1997).

Measuring the cost of production is important if a farmer wants to know whether or not he is making profit. While one can tell the milk price right away, it is often difficult to measure milk production costs and profits (Bailey 2001). The cost of milk production and its profitability is also affected by factors that determine farm-gate milk prices across the rural areas of Kenya (Muriuki et al 2003). The choice of production and marketing strategies by farmers therefore, contribute to high costs of production and low average productivity. As a result there has been continued interest from the public and from policy makers in the profitability and competitiveness of Kenya dairy production. Therefore, an understanding of the costs and benefits of smallholder dairy farmers is an important pre-requisite for policy formulations aimed at improving productivity levels.
 

Materials and methods

Description of study site

The study was carried out in Kiambu district, Central province located in Kenya highlands from December 2004 to March 2005. The district occupies 1323.9 km² with a population density of 562 persons per km² with 189,706 households (CBS 2001). The Kenya highlands comprise areas with altitude 1200-2550 meters, annual mean temperatures of 13.4⁰C to 21.9⁰C. The rainfall is bimodal varying from 600-1200 mm per year depending on location and altitude. Fertile soils here have good potential for biomass production and intensively cultivated and food cropped 1.4-1.7 times per year (Jaetzold and Schmidt 1983). Dairy cattle's farming in the district includes the intensive (zero grazing), semi-intensive and extensive grazing production systems.

Data collection

The study used conceptual framework for dairy systems analysis of production-to-consumption approach developed by International Livestock Research Institute (Rey et al 1993). Primary data were collected through personal interviews by trained enumerators using a survey questionnaire covering measures from resources to parameters reflecting farm functioning from respondents with at least one dairy cow at the time of survey. All information collected referred to the situation of the day before the survey. The livestock inventory and production data was collected using "participatory herd history" method for calendar years 2002 - 2004. The data from questionnaires was entered into Statistical Program for Social Scientists (SPSS) from SPSS Inc.

Sampling procedure

Purposive multi stage using Probability Proportion to Size sampling design was used. Three agro-ecological zones: Upper midlands, Lower highlands and Lower midlands were chosen purposively. Locations with a higher population size (CBS 2001) had a proportionately higher sample size in the survey. In order to capture as much local variations as possible, the sample in each zone was spread across the 27 sub-locations (smallest administrative unit in Kenya) among farms selected as randomly as possible. In some, if the farmer could not be reached or did not wish to participate in the study, another one in the locality was substituted.

The sample size was obtained from estimating the number of observations potentially needed to distinguish between the three agro-ecological zones by a difference of 30% in some of the important farm/household variables. Assuming a desired confidence interval of 95%, and using a coefficient of variation of 68 %, which was the observed co-efficient of variation of households in Kiambu dairy herd from previous studies (Kaguongo et al 1997); a minimum sample size of 40 in each agro-ecological zone was calculated (Poate and Daplyn 1993).

The calculation of sample size in each stratification class, to estimate a difference, was based on the equation:

Where: n = minimum sample size, z = 1.96 for 95% confidence interval, c = Coefficient of Variation, d = Level of difference (Poate and Daplyn 1993).

The chosen sample required then 14 observations in each location. After maintaining a minimum of 10 observations in each location, the total sample size obtained was 134 households (or 0.07 % of the households in Kiambu district).

Calculation of gross margins

Gross output in dairy production constitutes of those products which become routinely available through the production process. Such products can be marketed through a diversified and well established marketing system e.g. milk and breeding stock. However, in Kenya highlands, the livestock production systems deal with products which do not have a clear defined market value e.g. calves reared at different intensity levels to be used for breeding later on, and to a certain extent heifers (Staal et al 2003) and hence not included in analysis. The value of purchased animals and manure were also not included.

Due to lack of reliable data and ease of computation, forage- crop residues gathered on-farm and off-farm were not included in the analysis, although associated costs (labour and transport costs) were included. Fixed costs were ignored since were unrelated to higher levels of milk production and do not affect the optimal combination of the variable inputs. Quantities of inputs used and outputs obtained whether sold and/or consumed by household were calculated as an arithmetic mean of sampled households. In this analysis, no attempts were made to quantify the non-marketed benefits to the smallholder dairy enterprise.

The receipts of milk produced were calculated using the farm profit (KES /kg) formula characterized as follows:

Farm profit= [Milk margin (KES /kg) X Milk volume (kg)] - Other variable costs

Where: Milk margin = [Milk price (KES /kg) - Feed cost (KES /kg)]

The milk margin was important as it represented Kenya shillings left over to pay for other costs and realize a profit. The primary objective of any farmer was to protect the milk margin on a portion of the milk he or she sold (Bailey 2001). Therefore, milk price and amount of milk produced were just part of economic equation that determined dairy enterprise profitability. The production costs not only include the costs of transport, labour and marketing, plus reasonable profit, but also costs of risks posed to buyers and sellers of non-delivery and non payment. These were the risk management strategies that affected the unit profit margin.

Land under dairy enterprise was valued at the full reported rental rate. This was reflected in the analysis as the cost of own produced forage. Family labour was valued at 80% of the reported lowest monthly wage of permanent labour. It was a reflection of the assumption that the opportunity cost of family labour was below the wage rate simply because off-farm employment was not always readily available to family farm members.

The value of milk used by household and calves is included under costs but also under revenues since it is a product of the farm. Revenues included the total value of milk produced in the farm i.e. sales of milk and the value of milk consumed on the farm and calves, and sales of cattle, whether culled cows, males or heifers. Unit profits are mean revenues less mean costs.

After computing the milk margins for the three survey sites, a one-way analysis of variance was used to find out whether the costs of production, profits and price were significantly different at (P < 0.05) 2-tailed test for the three regions. The milk margin was expressed in KES /kg of milk produced /day. The hypotheses to be tested were:

H₀: G1 = G2 = G3

H₁: G1≠ G2 ≠G3

Where

G1 = Costs of production/ profits/ price in lower highlands

G2 = Costs of production/ profits/ price in upper midlands

G3 = Costs of production/ profits/ price in Lower midlands
 

Results

Characteristics of surveyed farms

The characteristics of surveyed farms varied across the agro-ecological zones in Kenya highlands (Table 1).

In lower highlands and upper midlands the land area devoted to crops and fodder were equal. Lower midlands had lowest proportion of land area devoted to fodder production, number of parcels of farms, household head age and population density but highest total farm acreage. Lower highlands had the highest household head age; proportion land area devoted to fodder, cows in milk and milk output per cow per day but lowest distance to market and average price per kilogram. Upper midland had the highest milk price per kilogram, number of farms and population density but lowest in all others except household head age, distance to market and percentage area devoted to forage production which were moderate.

Estimated costs of production strategies

The computation of production costs and revenues (from sale of milk and animals, and milk consumed by household and calves) were based on the dairy enterprise only. The costs of production strategies per unit of milk produced varied across the agro-ecological zones (Table 2).

Lower highlands had the lowest costs of labour and purchased fodder but highest costs of water and concentrates and milling by-products per kilogram of milk produced. Upper midlands had highest costs of labour only and lowest in costs of water. Lower midlands had highest costs of purchased and homegrown fodder, and veterinary services per kilogram of milk produced. Cost of mineral salts included in cost of concentrates and milling by-products.

Gross margins

Dairy enterprise was the most important income generating farming activity in 96% of households in the area (Table 1). Though some farms had negative gross margins, on average revenues significantly exceeded costs and the dairy enterprise returned of profit. In lower highlands the profit was 2.3 KES / kg; in the upper midlands the profit was 6.3 KES / kg and in the lower midlands the profit was 3.45 KES / kg of milk produced (Table 3).

The profits were however affected by amount of milk consumed by household and calves, which was not sold. During the time of the survey, calves and households consumed 6% and 18.5% of total milk output respectively (Table 1).

The average price received, cost of production and profit from milk varied across sites (Table 3). The cost of milk production was 12.7% higher in lower highlands and 6.8% higher in lower midlands than in upper midlands. The cost of milk production was highest in lower highlands and lowest in upper midlands while sales price was highest in upper midlands and lowest in lower highlands.

Nevertheless, a one-way analysis of variance showed that there were no significant differences (P > 0.05) between the arithmetic means of the cost of production for the three agro-ecological zones. The arithmetic means of returns of milk in KES /kg was significant (P < 0.05) between lower highlands and upper midlands while that of price of milk (KES /kg) were significant (P < 0.05) between lower highlands and upper midlands and lower midlands (Table 4).   

Discussion

Sales price

Market milk prices are reflective of a number of supply, demand and policy factors (Muriuki et al 2003). Lower highlands had lowest sales price of 17.5 KES /kg and highest proportion of farmers who marketed their milk through the dairy cooperative channel which offered lower sales price (Table 3). The sales price was highest in upper midlands at 19.3 KES /kg due to highest proportion of farmers who marketed their milk through itinerant traders who offered higher sales price. Households in upper midlands were less likely to market their milk through the dairy cooperatives than those in other areas due to its close proximity to Nairobi city (Staal et al 1998). Lower midlands had a sales price of 17.9 KES /kg. In all agro-ecological zones the informal markets offered higher sales prices than dairy cooperatives. This is attributed to the increased competition from dairy cooperatives.

The per capita milk consumption was 165 in the lower highlands, 116 in the upper midlands, and 168 in the lower midlands. The per capita milk consumption was a reflection of proximity to capital city Nairobi and had an inverse relationship. Upper midlands zone which was nearest to the city had lowest per capita consumption while lower midlands which was furthest from the city had the highest per capita consumption (Table 1).

Cost of production

The costs of production were expected to be highest in the most intensive systems and lowest in most extensive systems reflecting the high amounts of concentrate feeds used. However, this premise did not hold, as they were no significant differences in the cost of production between the three agro-ecological zones (Table 4). The pattern of differences associated with greater intensification did not hold. The costs of production were highest in lower highlands at 19.1 KES /kg and lowest in upper midlands at 16.9 KES /kg.

On average, the cost of milk production is 13 % higher in lower highlands than in upper midlands, reflecting particularly the costs of the greater quantities of concentrates feed used (Table 2). Lower highlands had the highest cost of production due high costs and amount of concentrates and milling by-products at 8.5 KES /kg and 4.7 kg/ cow/ day respectively (Tables 1 and 2). Lower midlands had the highest cost of concentrates and milling by-products at 17.6 KES /kg (Table 1). However, the cost of production was 18.1 KES /kg due to low amounts concentrates and milling by-products used of 2.9 kg /cow /day (Table 1).

Use of commercial dairy supplement is common, but for a variety of reasons and appear unrelated to the level of milk production. The amounts fed varied from 4.7 kg/cow /day in the lower highlands; 2.9 kg/cow /day in the upper midlands and 2.3 kg/cow /day in the lower midlands (Table 1). Utilization and cost of concentrates and milling by-products affected the cost of milk production.

In upper midlands, the low cost of production was attributed to low labour and water expenses where only 6.7% households employed labour on permanent basis. In upper midlands none of the farms bought water and costs incurred were that of labour for fetching it from nearby streams and rivers and shallow wells. In contrast all farms in lower highlands and 80% of households in lower highlands bought water for use by dairy enterprise from water projects scattered all over. Physical access to markets has a direct bearing on farmers' production costs and the price they receive. As transport costs increase with increasing distance, the price farmers must pay for their material input rises while the prices they receive for their marketed commodities falls, implying deteriorating terms of trade (Upton 1997). This means that the returns per unit of land declines and so does the incentive to produce for market.

Farm profit

Revenue in a dairy enterprise accrues from sale of milk and animals, and milk consumed by households and calves. There were significant differences between returns in lower highlands and upper midlands. The returns were lowest in lower highlands at 2.3 KES /kg and highest in upper midlands at 6.3 KES /kg. In the lower midlands the returns were 3.45 (Table 3). The high returns in upper midlands can be attributed to low cost of production, high milk prices offered by informal milk marketing channels and low labour and water expenses.

The low returns in lower highlands were due to low milk prices of 17.5 KES /kg offered by dairy cooperatives and high costs of production of 19.1 KES /kg (Table 3). A study carried out in 1998 using partial budget analysis reported returns of 3.4 KES / kg. In the same study simulated estimates of cost of production and revenues, April 2002, in Kiambu, Nakuru and Nyandarua districts showed negative overall profit (Staal et al 2003).

The value of manure used on crops as an immediate input, planted forage and functions of livestock as security against risks represented an additional revenue to the farm. Studies in Kenya highlands have estimated that the value of manure may be some 30% of the value of milk sold (Lekasi et al 1998).
 

Conclusions

• The survey showed that dairy enterprise was the most important income generating farming activity in 96% of households in Kenya highlands and probably the most single important farming activity in the region. Farmers in upper midlands were making much more profit from milk than those in lower highlands due to higher milk prices. These showed that cooperatives were not competitive in milk pricing and lower highlands farmers should utilize the other available milk marketing channels. In Kiambu district, the cooperatives experience shortage and surplus of milk in the dry (January- April) and wet (rest of the year) seasons respectively. Hence, policies to improve the operational and pricing efficiencies of dairy cooperatives would have a self-accelerating effect on productivity.

• The pattern of differences associated with greater intensification did not hold. A holistic approach to development is required in attempts to remove the genotype- environment interaction. Therefore, the breeding policies targeting smallholder dairy farmers should incorporate the farmers' multiple objectives in light of available resources. Risks can be reduced and production increased by exploiting diversity of such farming systems as well as introducing elements that can create additional opportunities.

• Dairy farmers should visit their local extension agents and have computed their break-even costs of production based on prevailing milk price per kilogram. Once calculated, policy makers and planners when making decisions related to design of appropriate policies and investment respectively to support smallholder dairy development can use these estimates. In addition alternative uses of manure could be investigated especially in the production of biogas as fuel for the household.
   

Acknowledgements

The authors are grateful to Deutscher Akademischer Austausch Dienst (DAAD) for their research funds to run this study. Thanks also due to all enumerators and farmers who participated in this study.
 

References

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Received 22 March 2007; Accepted 1 May 2007; Published 6 July 2007

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