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Using the levy point quadrant to assess botanical composition of dairy pastures in the Adelaide hills

E.D. Carter and MI Cochrane

The University of Adelaide, Waite Agricultural Research Institute, Glen Osmond SA 5064
South Australian Department of Agriculture, Box 1671, G.P.O. Adelaide SA 5001

Current shortages of research funds provide increasing urgency to streamline research techniques. No longer can we afford the luxury of detailed and expensive hand-separation of herbage samples except where chemical composition is required. We have successfully used the Levy Point Quadrat (LPQ) and visual scoring for estimating botanical composition of grazed pastures for many years and data from both these methods is highly correlated with the more tedious, and more expensive, hand-separation data. Some data from our research on dairy pastures are presented here.

Methods

As part of a survey of 10 sites in the Adelaide Hills dairy pastures (1) herbage botanical composition was measured in situ by the LPQ method (2) in July 1984, July 1985 and September 1986. Ten random sites on each of the 10 pastures were examined, and vegetative hits on clovers, grasses and weeds recorded, along with bare ground percentage. Point data were converted to Percentage Overlapping Cover which is directly related to botanical composition. Pasture availability samples were cut to ground level immediately after each area was examined by LPQ. These samples were hand-separated into clover, grass and weed components, oven dried at 100°C for 12 h, weighed and dry matter percentages and yields for each botanical component calculated.

Linear regression analyses relating percentage botanical composition derived from hand-separa- tions and oven drying (Y) and from the in situ LPQ method have been calculated for the clover, grass and weed components, along with the total of these three components.

Results and discussion

Botanical composition (clover, grass and weeds) derived from hand separation is highly correlated with the data from the LPQ method for all years and all components. Table I summarizes the pooled data for the three years 1984-86.

Table 1. Regression equations showing relationships between botanical composition data for clover, grass, weeds and total plants derived from hand separation (Y) and the LPQ (x).

Clearly the data from the LPQ method gives an accurate estimate of botanical composition which is important in terms of stability and productivity of the pasture. Furthermore, the LPQ gives an objective assessment of percentage bareground which is one important indicator of decline in any grazed pasture situation. The speed and accuracy of the LPQ method is greatest when the pasture is shortest. We regard a pasture height of 10cm as being an upper limit for fast and accurate pointing with the LPQ; windy conditions reduce the accuracy of LPQ data in taller pastures.

Acknowledgements

This project was part-funded by the Dairy Research and Development Corporation. Mr S. Challis provided assistance in the field.

References

Caner, E.D. and Cochrane, M.J. 1985. Proc. 3rd Aust. Agron. Conf. Hobart. p. 217.

Levy, B. and Madden, E.A. 1933. N.Z. J. Agric. 46, 267-279.

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