Dairy production is a major contributor towards national economies and household food security and incomes in sub-Saharan Africa (SSA). Milk production in the region is estimated at 1.27 million metric tonnes year-1. However, this level of milk production is inadequate for the existing human population who would require 103 million metric tonnes year-1. In Uganda, milk production only meets approximately 20% of the population's nutritional requirements. As such, methods need to be sought to increase milk production in the region. 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. First and foremost, for exploiting the full potential of the dairy cattle population in the region, among the critical elements often overlooked in research and development processes is the recognition of systematic parametric variations within the 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. To enhance the process of targeting research and development in the Ugandan dairy sector, dairy production systems in the country were categorised on basis of level of intensification of production. Data were collected from 300 households in Mbarara, Masaka and Jinja districts in Uganda. The major variables derived from the data for the categorisation process were those related with milk production, expenditure, income, land area and cattle herds. The data was subjected to a cluster analysis which although produced 16 groups only five had prominent membership (above 5% of the farms). The five major clusters were selected as representative of the dairy production systems. A ranking system was used to develop an intensification continuum for the 5 systems. Herding-on own and communal land (cluster 9) was the least intensive, this was followed by Herding-mainly on own land (cluster 12) and Fenced (cluster 8) respectively. Semi-Zero Grazing (cluster 15) and Zero Grazing (cluster 13) were the most intensive dairy production systems with the latter being at the highest end of the continuum.

Pig production has increasingly become an important activity, especially among smallholder farmers in Uganda in the past three decades as evidenced by a dramatic rise in pig population from 0.19 to 3.2 million. This is linked to the rise in demand for pork due to changes in preferences. Per capita consumption of pork has been estimated at 3.4 kg/person/year representing a ten-fold increase in the last 30 years. Pigs are important assets for the poor smallholders in Uganda generating income for meeting planned and emergency household financial needs. Despite its importance, the smallholder pig systems are faced with a number of productivity and market related constraints ranging from diseases, poor nutrition and poorly organized markets. Strong growth opportunities to improve smallholder pig systems exist if the constraints are minimized. However the constraints and opportunities vary among smallholder producers as they are not a homogenous group and are affected by various factors. This paper applies a cluster analysis to characterize smallholder pig production systems into typologies in three districts in Uganda by utilizing village level data from 35 villages. The paper further explores the constraints and opportunities for the different typologies to engage with output and input market systems. The paper concludes that different interventions are necessary to improve market linkages with the smallholder pig production systems due to their varying differences in terms of farmers’ cooperative involvement, institutional linkages and intensification related indicators.

This study was undertaken to assess the potential for sweet potato silage making business by estimating sweet potato vines and root wastage and assessing the economic feasibility of investing in sweet potato silage. Information was collected through key informant interviews, secondary data review, sweet potato root peeling and weighing, focus group discussions with pig and sweet potato producers covering a sample of 180 farmers. Semi-structured interviews with 240 respondents (120 sweet potato farmers, 60 pig farmers and 60 sweet potato traders) were also conducted. The results showed that sweet potato production is seasonal with substantial wastage of sweet potato components existing across the various nodes of the sweet potato value chain. The study concludes that there is an opportunity for investment in sweet potato silage business that has the potential to reduce wastage of sweet potato and bridge the feed scarcity gap faced by pig farmers.

A study to develop body weight prediction equations based on various body measurements was conducted in rural Kamuli district, Uganda. Body weight (kg) and body measurement data (cm) were collected from 411 pigs between 15 and 127 kg from both local and exotic (mainly crossbreds) pigs. Five body measurements; body length, heart girth, height, body width and flank-to-flank were taken from each pig. Prediction models were developed by regressing weight on pig body measurements. The models were developed for pigs categorized as < 40kg, ≥ 40 kg and an overall single prediction model. Mean weights of < 40 kg and ≥ 40 kg were 27 ± 6.5 kg and 63 ± 19.6 kg, respectively. Body length and heart girth were used to predict (R2 = 0.89) weight for the < 40 kg pigs with the prediction equation; Weight = -41.814 + 0.296 (body length) + 0.654 (heart girth). Four body measurements; body length, heart girth, height and body width were strongly predictive (R2 = 0.92) of live body weight for the ≥ 40 kg pigs with the prediction equation; Weight = -108.198 + 0.228 (body length) + 1.094 (heart girth) + 0.267 (height) + 0.922 (body width). The flank-to-flank measurement did not affect model prediction (p > 0.05) and quadratic terms also did not improve accuracy and were not included in any prediction models. These results suggest that live weight could be accurately estimated using two or more pig body measurements. It was concluded that this weight estimation tool would empower Ugandan small scale pig farmers by providing them with an accurate estimate for the animal’s live weight and giving them better bargaining power when selling their pigs.

A cross-sectional survey was carried out to characterize the pig production systems in four districts along the Kenya-Uganda border. Information was collected by administering structured questionnaires to 645 households in 32 randomly selected clusters. The study showed that the majority of the farmers owned very small pig herds (2.4±0.1) which were mainly tethered. Their main objective for pig keeping was income generation. Decisions on pig purchases were predominantly made by either women or by women and men in collaboration while day to day care of the animals was performed by women. Disease especially African swine fever (ASF) was considered to be the biggest risk to pig investment, while feeding was the biggest production constraint. Studies that look into cheaper locally available feed options should be undertaken to enable the farmers solve the feed constraint. Sensitization of farmers to adopt biosecurity measures would reduce disease risk especially ASF risk.

Limited access to quality feeds and reliable feed supply are amongst the priority constraints of smallholder pig production in Uganda. Among the feeds given to pigs, sweetpotato (SP), banana and other root tubers residues are common. However, information on farmers’ perceptions and practices on the proper use of these residues for pig feeding is limited. Therefore, this study aimed at assessing those aspects, as well as to identify opportunities for better use of these residues in the pig-SP systems. A qualitative survey was undertaken in Masaka and Kamuli, two districts of Uganda with high pig population and SP production. Focus Group Discussions (FGD) and Key Informant Interviews (KII) were undertaken with 80 small scale pig and SP producers and 24 key informants. Results from this study revealed that the majority of pig farmers in those districts use SP and other RTB crop residues as animal feed. During the rainy season, farmers scored high the utilization of SP crop residues, with the latter being the leading contributor to the pig diet especially in the rural area. SP crop residues are usually fed to pigs fresh without processing. Among the residues, fresh raw vines represent the largest part fed to pig (70%), as compared to roots and peels. The way these residues are offered vary, for example in peri-urban areas with easier access to commercial feeds, farmers feed the crop residues mixed with concentrates; whereas in rural areas with limited access to commercial feeds, crop residues tend to be given without supplementation. However, the full potential of SP and other RTB crop residues for pig feeding is not yet fully exploited as farmers accept that a large amount is wasted (37% in Masaka and 40% in Kamuli). In Masaka, the proportion of SP crop residues utilized at household level mostly for pig feeding was 40%, while in Kamuli was 52%. This study demonstrated that there is potential for better use of SP and other RTB crop residues as pig feed in the smallholder pig farming systems in Uganda, but the major constraint as pointed out by farmers is the poor access to technologies for preserving these resources. Therefore, there is a need for further exploration of strategies for conserving SP and other RTB crop residues during the harvesting period for use in pig feeding during times of feed scarcity.

The Ankole pastoral production system in South Western Uganda is based on grazing without supplementary feeding. A stochastic simulation model was developed to determine the dynamics of pastures grazed by Ankole cattle and their Holstein Friesian crosses and the carrying capacity (CC) of the livestock grazing system. The model used the concept of rain use efficiency which relates pasture production to rainfall. A cross sectional study was carried out on 16 selected farms and data on number of animals, sex, age group and size of available grazing land was collected. The similarity between the results of the simulation rainfall runs and field data are considered to be satisfactory. The overall annual forage production is 3905 ± 72kg/ha. The lowest CC (5.65 ± 0.75) occurs in long dry season (June to August) while the highest CC (1.41 ± 0.06 ha/TLU) occurs in short rain season (September to November). Annual carrying capacity ranges between 1.88 and 2.08 ha/TLU with an overall mean of 1.95 ± 0.04 ha/TLU. Sixty three (63%) percent of the surveyed farms have stocking rates that are higher than the CC throughout the year while the rest are overstocked in the dry seasons of the year. The results indicate that CC is dynamic and its variability is more pronounced within the year than between years. In response to seasonal CC, the major point of intervention in regard to reduction of actual stocking rates could be done in May shortly before the start of the long dry season. For Ankole pastoral system to be sustainable, the stocking rate should not go below 1.41 ha/TLU.