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Economic analysis of smallholder dairy cattle farms: Case study of Nandi and Makueni Counties in Kenya
Abstract
Kenya has the highest annual per capita milk consumption on the African continent (110 litres), and is projected to increase to 130 litres by 2030. This has supported the development of the smallholder dairy production system which supplies 80% of the milk. Dairy farming enhances nutrition and generates income for more than 1.8 million smallholder farmers in Kenya. This study aimed at analysing the economic performance of dairy cattle farms in Nandi and Makueni counties and compare their performances as farms practicing mixed farming and as dairy farming alone, using gross margins. Further, this study sought to determine exogenous variables influencing dairy farms’ economic performance. The study used a mixed research design (quantitative and focus group discussions) with key informants in the dairy sector in the 2 study counties. Purposive sampling was used to select the farms and the county based dairy data gathering and monitoring harmonized profit and loss tool developed by the Kenya Dairy Board (KDB), Kenya Dairy Processors Association (KDPA) and State Department of Livestock was used to collect data. The 2 counties differ in the level of smallholder dairy development. Nandi County was classified as a highly dairy county while Makueni County as a potentially dairy county hence their selection for inclusion in this study. Gross margins were determined for all farms by total cash income less total cash costs, while multivariable regression using Akaike Information Criterion was used to determine exogenous variables influencing gross margin levels. The findings revealed that dairy enterprises alone have positive albeit minimal gross margins while the typical smallholder mixed farming (dairy and other enterprises) result in losses in both study counties. On average, a farm in Nandi and Makueni counties made a profit of Kenya shilling (Ksh.) 2,848.30 and 880.80 per year respectively. Although the differences were not statistically significant (p>0.5) due to high variances, incomes in Ksh. from milk (Nandi: 21,470.3, Makueni: 51,555.3) and manure (Nandi: 7,609.2, Makueni: 605.9); and costs of feed (Nandi: 23,337.0, Makueni: 37,806.4), labour (Nandi: 10,792.9, Makueni: 13,943.4), mineral salts (Nandi: 20.9, Makueni: 33.9), artificial insemination (Nandi: 770.9, Makueni: 996.0), veterinary services(Nandi: 1,541.8, Makueni: 1,991.9), transport (Nandi1:130.0, Makueni:2,062.2) and water (Nandi: 187.6, Makueni: 363.5) were significantly different between Nandi and Makueni counties (p=0.00). Final models with exogenous variables had low prediction of gross margins, R2<0.30. Due to the high costs of dairy farm inputs accounting for 94% and 99% in Nandi and Makueni counties respectively, and the involvement in several farm enterprises at the same time, farmers in both counties risk making losses or getting very minimal profits. Policies focused on making farm inputs especially feed and water affordable and accessible to smallholder dairy cattle farmers are highly recommended.
