Aim The search for possible factors influencing the spatial variation of grass quality is an important step towards understanding the distribution of herbivores, as well as a step towards identifying crucial areas for conservation and restoration. A number of studies have shown that grass quality at a regional scale is influenced by climatic variables. At a local scale, site factors and their interaction are considered important. In this study, we aimed at examining environmental correlates of grass quality at a local scale. The study also sought to establish if biotic factors interact significantly with abiotic factors in influencing a variation in grass quality.Location The study area is located in the Kruger National Park of South Africa. The study area stretches from west (22°49¢ S and 31°01¢ E) to east, (22°44¢ S and 31°22¢ E) covering an area of about 25 · 6 km in the far northern region of the Kruger National Park.Methods We collected environmental data such as soil texture, percentage grass cover and biomass as well as grass samples for chemical analysis from specific locations in the study area. In addition, a digital elevation model (DEM) with a resolution of 5 m was used to derive elevation, slope and aspect using a geographic information system (GIS), which were related to grass quality. We used correlation analysis and anova to relate environmental variables to grass quality. Multivariate analysis techniques were used to simultaneously analyse and explore the complex interactions between variables. Results and conclusionsOur results indicate that there is a significant relationship between grass quality parameters and site-specific factors such as slope, altitude, percentage grass cover, aspect and soil texture. Relatively, percentage grass cover and soil texture were more critical in explaining a variation in grass quality. Plant characteristics such as species type interact significantly with slope, altitude and geology in influencing nutrient distribution. The results of this study may give a better insight on foliar nutrient distribution patterns at a landscape scale in savanna rangelands. Furthermore, the results of this study may help in the selection of ancillary information, which could be used in conjunction with other data such as remotely sensed data to map grass quality -an important step towards understanding the distribution and feeding patterns of wildlife. However, we acknowledge that this study is based on one seasonal snapshot, therefore some slightly different findings may be obtained during other times of the year. Nevertheless, the study has revealed that under the conditions experienced during the study period, nutrient distribution varies with varying biotic and abiotic factors.

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The use of a single geographical unit to both collect and disseminate census data is common in many countries across the world, especially in developing countries. In South Africa this approach poses some challenges, as the design of small geographical units called enumeration areas to facilitate data collection differs considerably from the design of units that aid data analysis and interpretation. We aimed to create optimised census output areas using the Automated Zone-design Tool (AZTool) program, using the 2001 census enumeration areas as building blocks at various spatial levels, for both rural and urban settings in two South African provinces. The results were consistent and stable. The primary criterion of the confidentiality limit of 500 people was respected at all geographical levels or regions, in both urban and rural settings, for newly created optimised output areas. For the second criterion, lower intra-area correlation values at lower geographical levels for both rural and urban areas showed that higher geographical levels produced more homogeneous output areas than did lower geographical levels or regions. Our obtained intra-area correlation of 0.62 for the two provinces combined indicated that the selected homogeneity variables were good indicators of social homogeneity for creating optimised output areas in South Africa. We conclude that the AZTool software can be used to effectively and objectively create optimised output areas for South African data. Further research on the comparison of the newly created output areas with existing output areas in South Africa should be explored.