Mapping variability of pasture sward height, dry matter availability and disappearance during grazing

This study investigated whether geostatistical methods can be applied to severely drought-affected pastures to assess spatial variability in sward height (SH) and dry matter yield (DMY) and change in SH and DM in response to grazing. Geo-referenced SH data were collected using a rapid, non-destructive method (rapid pasture meter) and analysed by geostatistical methodology. Eight severely drought-affected paddocks (similar to 1.25 ha) were grazed individually by two groups of 20 Angus heifers in two 28-day phases (P1 and P2) between 2 July and 29 August 2019. Pasture DMY was estimated from calibration equations developed for P1 and P2. Ordinary kriging was used to generate estimated surface forming maps with which to visualise the spatial variability. The degree of spatial dependence (dSD) was strongest for SH during P2 post-grazing (11%) and for DMY during P2 pre-grazing (6%). For change in SH, the dSD was 50% for P1 and 0% for P2. Disappearance of DMY dSD was 56% for P1 and 47% for P2. The range of spatial dependence (distance until variability stabilised) for both SH and DMY was lowest for P1 post-grazing (11 m), indicating that intensive sampling is required. The ranges of spatial dependence for the change in both SH and DMY were similar for P1 and P2. These results confirm that intensity of grazing by cattle is not random. Incorporation of this methodology into rapid, non-destructive pasture data collection devices would assist producers and their advisers in improving grazing management decisions. Further analysis with data from non-drought affected pastures is required to determine the robustness of this method.

Automated summary

This study investigated the application of geostatistical methods to assess spatial variability in sward height and dry matter yield in severely drought-affected pastures. The results showed that the intensity of grazing by cattle is not random, demonstrating the potential for improving grazing management decisions with the incorporation of this methodology into rapid pasture data collection devices.