considerations. This methodology is limited in that it cannot provide any information at the household or holder level, but it can produce high quality data on livestock totals for a given area (see CSA 2004 for details on estimation techniques). As both Afar and Somali have a largely pastoralist population base and similar climates, we would expect the density of animals to be broadly similar in the two areas.

Table 5: Livestock Density (n/km<sup>2</sup> )

||Afar Region<br>RGCS<br>|s (Zones 1 &<br>RGCS<br>|3)||Somal|i Regio|n (Aerial|Survey 20|04)||
|---|---|---|---|---|---|---|---|---|---|---|
||(unadjusted)|(adjusted)|ERSS|Afder|Degehabur|Fik|Gode|Korahe|Shinile|Warder|
|Camels|2.5|3.8|6.4|2.6|3.6|0.8|2.8|4.2|2.6|8.4|
|Cattle|4.2|5.1|29.7|3.0|1.4|0.5|4.1|0.7|5.3|0.8|
|Goats|15.5|22.3|56.9|13.2|19.6|4.2|24.3|19.5|21.7|31.6|

Table 5 compares estimates of livestock per square kilometer from three different sources. The first three columns give estimates from the RGCS (using both the unadjusted and adjusted weights) and the ERSS survey. The last seven columns give estimates for the seven zones in Somali in 2004. We see that the RGCS estimates are within the range of those from the aerial surveillance, while the ERSS estimates are substantially higher. While it should be stressed that these estimates are not directly comparable, as they are for different areas in different time periods, we would expect the ranges to be similar for the reasons stated above. This increases our confidence in the accuracy of the RGCS estimates over those produced by the ERSS, at least with regard to livestock totals.

# **5. Discussion & Conclusion**

This pilot project of the RGCS technique to collect livestock data in the Afar region of Ethiopia demonstrated that the implementation of such a design is feasible; however, questions remain as to whether it is the best available method. The project showed that sufficient GIS information is available, often through the public domain, to create strata for the probability of finding livestock, and to select points within those strata. With maps and relatively inexpensive GPS devices, teams can navigate to points and identify eligible respondents within these clusters. These respondents can then be interviewed regarding their households socioeconomic conditions and livestock holdings, creating the linkages necessary to perform poverty analysis on these populations. In addition, using standard statistical methods, it is possible to calculate weights that take into account the varying probabilities of selection and sufficiently address overlap probabilities. Also, information generated as part of the GPS field implementation, such as the Viewshed results, can be used to estimate the area observed by individual interview teams, and account for undercoverage, if necessary. And finally, the methodology was able to do what it was designed to do -- capture households without permanent dwellings who would have been missed by a traditional dwelling-based sample design. The location and interviewing of these persons is a major benefit to the RGCS technique over the traditional household based approach to survey sampling.

A number of questions remain as to whether this method should be considered the best practice for collecting this type of information. The RGCS has demonstrated some advantages over the traditional household-based survey methodology, such as eliminating the need to conduct a clusterlisting exercise and allowing data collection to be completed in a single step. The methodology was also successfully implemented in a low capacity environment and avoided overly technical issues, such as those one would have with an adaptive sampling method. However, problems were still noted in the 14