30/07/2022
The dairy sector is a major pillar in the socio-economic standing of sub-Saharan Africa (SSA); functioning both food security and income generation roles, particularly at small household level. In general, dairy cattle remain the key player among the livestock groups in the sector, accounting for 80% in the milk industry (De Leeuw et al 1999). Recent statistics show an average of 0.17 animal units (AU of 400 kg live weight) household-1in the region (Winrock International 1992), with an estimated milk yield TLU-1(TLU of 250 kg liveweight) of 70 kg year-1(Staal et al 1997). The overall production for the region is estimated at 1.27 million tonnes annually, against the annual demand of 103 million tonnes, basing on the FAO requirement of 200 litres person-1year-1and the estimated population of SSA of 519 million (Sere and Steinfeld 1996). Moreover, statistics present very low milk yield dairy cow-1year-1of 340 kg in SSA compared to 5100 in developed countries (De Leeuw et al 1999). These statistics are evidently chilling in light of the rapidly growing human population in most parts of the region.
In Uganda, the dairy sector contributes 40-50% of the livestock GDP (DDA 2001/2002), which in turn contributes 17-19% of the agricultural GDP. Dairy plays a crucial role in the nutrition of most households with per capita milk consumption of about 40 litres (DDA 2001/2002). Recent statistics for the country present a desperate scenario, of annual milk yield of 900 thousand tonnes, against a requirement of 4.8 million tonnes (based on FAO annual per capita requirement and current national population). Research efforts have made strides in identifying the causes of the production-demand gap in the SSA region and a spectrum of interventions to bolster the productivity. Unfortunately, these efforts have by far yielded insignificant results.
Among the critical elements often overlooked in research and development processes is the recognition of systematic parametric variations within a sector, which if considered could provide entry-points for targeting intervention efforts. One such high potential entry-point is the recognition of the existence of a dairy intensification "vector" across a country or region, along which exist sections with sequentially marked nuclei of fairly uniform socio-economic and biophysical dairy sub-systems features. In this case, intensification is defined as an increase in agricultural production per unit of inputs (which may be labour, land, time, fertiliser, seed, feed or cash). If the dairy intensification "vector" is properly mapped out as groups or "categories", the product would provide a guide for targeting interventions with fair precision. To achieve this definitely requires systematic and detailed understanding of the structure of this perceived vector, including all instrumental socio-economic and biophysical phenomena, particularly those related to resources and managerial capacity of the dairy systems.
Categorisation of dairy production systems in Uganda has been done variously. Okwenye (1994) classifies dairy production systems into three groups, namely pastoral, small-scale crop and livestock farms and specialised dairy farms. This classification is based on number of stock, feeding and grazing management and breeds reared. The Ministry of Agriculture, Animal Industry and Fisheries (MAAIF) jointly with the International Livestock Research Institute (ILRI) (MAAIF/ILRI 1996) indicated that cattle production systems in Uganda form a continuum with semi-nomadic pastoralism at one end and zero grazing on the other. The study, which used non-detailed and overly qualitative information, categorised dairying in the country into intensive, semi-intensive and extensive systems. A critical consideration in the process was the level of capital investment and dairy cattle management (MAAIF/ILRI 1996), but excluded the inherent pressure exerted on the available resources such as livestock herd populations relative to available land, and its extended effect on plant nutrient stocks and their sustainable supply. Kasirye (2003) later categorised dairy production in the country based on size of holding, as communal grazing, free range grazing, fenced grazing and zero grazing. On the other hand, Fonteh et al (1998) conducted a fairly more detailed characterisation within smallholder dairy systems in Uganda and ended up with three categories, namely, urban, peri-urban and rural. Furthermore, each of the categories was further sub-divided into 4-10 sub-categories based on grazing and feeding management, major limiting resource, sources of cash income and wealth assessment.
Admittedly, the diversity of dairy categories generated by previous efforts reflects not only on the diversity of the foundation criteria used, but also on the objectives of each research effort. Hence, intervention efforts must take cognisance of the original aims and criteria for each categorisation process, as well as their (categories) strengths and limitations in representing the presumed categories. The more obvious inference is that the range of categories generated based on non-uniform criteria is a good recipe for category overlaps; a factor that renders intervention targeting fairly erratic and less objective. As such, efforts are required to harmonise the categorisation process and attain more robust categories particularly based on intensification. This is only achievable through a systematic and detailed process involving largely quantitative data.