Modelling nutrient concentration to determine the environmental factors influencing grass quality

Abstract

This paper uses the spatial and the least squares (Analysis of Covariance-ANCOVA) regression methods to evaluate the important environmental factors in estimating quality grass for grazing (based on the nitrogen (N) content in grass). The environmental variables such as those based on climate (temperature and precipitation), land-use, geology, slope, aspect and altitude were specifically evaluated in these models. Spatial regression accounted for higher variability (61%) when compared to the 41% variability explained by the ANCOVA model. The models indicate that some environmental variables are useful in assessing N variability. This provides an opportunity for the design of an intergraded system to incorporate both the environmental and remote sensing variables in the estimation and mapping of nitrogen content in grazing grass across the Kruger National Park (KNP) and the surrounding areas.