Andrew P. Smith1, Cameron J. P Gourley1, Alice R. Melland1, Ken I. Peverill2, Ivor Awty1, Jim M. Scott3, and Paul Strickland1. (1) Primary Industries Research Victoria, Hazeldean Rd RMB 2460 Ellinbank, Victoria, Australia, 3821, Ellinbank, (2) KIP Consultancy Services Pty Ltd, 4 Collier Court, Wheelers Hill, Australia, (3) University of New England, Relational Database Unit, Armidale, Australia
The application of fertiliser in Australian grazing systems has traditionally been an inexact and inefficient process. The adoption, application and interpretation of soil testing to improve fertiliser recommendations will rely on more refined and regionally specific calibrations, based on the best available information. This paper reports on the collation of current and historical experimental data relating to pasture production - fertiliser response relationships (nitrogen, phosphorus, potassium and sulphur) for various pasture types, climatic zones and soils across all grazing regions. A national team of scientists and fertiliser agronomists has contributed to the collation of a comprehensive set of response data from field studies. These data sets have been integrated using a relational database to derive appropriate response relationships for the grazing industries. More than 350 experimental data sets have been collated consisting of circa 2600 sites and >3800 experimental trial years. The number of sites is made up of around 479 N, 662 P, 692 K and 810 S trials, with a total of 615 N, 1313 P, 933 K and 974 S experimental trial years. Less than a third of the experiments had enough statistically rigour to enable response curves to be generated. A few studies involved a number of sites each with a comprehensive range of nutrient rates, in various regions with different soil types and climatic zones. The majority of studies however, provided simpler data sets that could not be used to establish nutrient response curves. In some single site studies, soil test levels were strongly related to pasture response to fertiliser applications (variance accounted for (VAF) > 0.9), but when applied to a range of soils and environments, they invariably lack precision (VAF ranging from 0.0 - 0.5). The addition of other variables such as soil type and climatic zones only marginally improved these generalised relationships.
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