For most of these shocks and risks, food insecurity has been perceived mainly in terms of food crop availability and accessibility. The role of livestock, which touches upon the livelihoods of approximately60 percent of the people in Southern Africa, is not fully appreciated. This neglect of the role livestock plays is partly due to lack of solid empirical evidence on the actual contribution livestock make to livelihoods and the survival strategies that are employed during times of shocks. Understanding the role of livestock in more specific terms could facilitate development of informed policy and other intervention strategies aimed at further strengthening the livestock-based livelihood systems. In Zambia, this knowledge is especially critical in livestock-based livelihood systems of Southern Province, where droughts and seasonal hunger are frequent and, often,rural people have to turn to their livestock as a coping strategy. Of the country’s 10 provinces, Southern Province also stands out as one with the largest and most diversified smallholder livestock sub-sector.
Unfortunately,there is a glaring dearth of empirical evidence on the underlying livelihood systems and the position of livestock in the hierarchy of livelihood activities. Elsewhere, studies have shown that livestock could impact nutritional status, income and rural poverty.The study presented in this paper represents one of the first comprehensive mixed-methods treatments of the role of livestock as a source of livelihood and income, and in risk management against shocks. This study will assist policy makers and stakeholders in mainstreaming livestock intervention strategies in Zambia by understanding the livelihood systems and defining the role that livestock plays among livestock-owning smallholder households. In the remainder of the paper, we first present the methods and procedures used in Section two, followed by results and discussions in Section three. Summary and conclusion are presented in Section four. Within each wealth stratum, focus groups were convened,ebb and flow tray each comprising a random sample of 8 – 10 individuals.In addition to being a powerful tool for collecting data that is enriched by purposeful use of interaction, Focus Group Discussions also offer considerable advantages in terms of cost per informant.The number of participants per FGD was determined in conformity with recommendations of some sections of the literature.
Similarly, the number of FGDs per district was arrived at taking recommendations in the literature into account. Reference,for example, suggests an optimal number of FGDs of 4 – 12 while contends that data generated after about 10 FGDs are likely to be largely redundant.During the focus group discussions a number of standard participatory tools were used to identify the issues and livelihood activities, and to determine the relative importance of the identified alternatives. These included tools such as participatory scoring and pair-wise ranking. Relative importance and seasonality of livelihood and income-generating activities were derived by using analytical calendars with scoring. Analytical calendars are matrices with each row representing a competing activity and each column representing the month. The activities in the analytical calendar are arrived at by the communities through a participatory process. Once this is done, two types of scoring are then undertaken. Vertical scoring, usually by distributing a fixed number of grains, is done to determine the relative importance of each competing activity, overall and without taking seasonality into account. The more the grains assigned to the activity the more important the community considers the activity to be. The second type of scoring involves, for each activity, distributing the grains across the 12months of the year.
During seasonality analysis, the horizontal scores are weighted by the relative importance of the activity/row as determined by the vertical scores. That is, the weight for each row entry is equal to the vertical score presented as a percentage of the total number of grains . Unlike the un weighted seasonality scores, the weighted seasonality scores can be compared not only within activity but also across the activities. During the community mapping and after all the households in the community had been identified, a very short questionnaire or listing form was administered to each of the identified households. A total of 309 households were listed and interviewed across all the communities in the three districts. While this information was important in its own right, the resultant list with wealth strata was also used as a sampling frame from which households were randomly selected for in-depth interviews. Using a semi-structured questionnaire, each in-depth household interview deciphered basic socio-economic characteristics and livelihood activities of each household.Care was taken to ensure representation of each of the wealth strata in these household case studies.