The aim of this study was to determine whether a predictive geometric
soil-landscape model, potentially applicable to 400 000 ha of seasonally dry
greywacke steeplands in New Zealand, could be created for 29 soil properties,
using a very low soil sampling density. We postulated that in these deeply
dissected steeplands which have relatively uniform geology and slope form,
landscape geometry (through its effects on microclimate), rather than
vegetation, geology, or slope form will control the soil pattern. To create
and test the soil-landscape model we sampled the 26 000 ha Benmore Range,
South Canterbury, New Zealand, in a formally stratified way so that trends of
soil carbon, soil nutrients, and profile characteristics could be established
for predominant slopes, at various altitudes and aspects. We used a factorial
sampling system (3 land systems × 3 altitudes × 4 aspects ×
2 slope positions), giving 72 sampling sites in total, and a sampling density
of one site per 360 ha. Altitude and aspect had significant
(P < 0.05) effects on many topsoil characteristics,
particularly those likely to be related to soil moisture status, leaching, and
weathering (e.g. topsoil pH, carbon, nitrogen, and phosphate retention). For
most soil properties the effect of slope position was not significant
(P > 0.05).
The soil-landscape model was tested by comparing predicted and actual soil
properties at a further 22 sites. Soil properties that were
laboratory-determined were generally satisfactorily predicted by the model,
but properties based on several measurements (e.g. nutrient amounts in units
of kg/ha) were less satisfactorily predicted, presumably because they
incorporate more measurement error. Trends of soil properties that showed
strong altitude and aspect relationships were effectively illustrated using
360° ‘radar diagrams’. We conclude that for dry steeplands of
uniform geology, with simple and repeated landforms at the output scale being
used, a geometric soil-landscape model based on a very low sampling density
successfully predicts soil properties on dominant landscape units. The
methodology has application to national resource inventories.
Soils of Seabee Hook, Cape Hallett, northern Victoria Land, Antarctica
