Predicting pasture and sheep production in the Victorian Mallee with the decision support tool, GrassGro
S. M. RobertsonA Primary Industries Research Victoria, Department of Primary Industries, Private Bag 1, Walpeup, Vic. 3507, Australia.
B Current address: ‘Nindethana’, Turners Lane, Old Junee, NSW 2652, Australia.
C Email: susanrob@dragnet.com.au
Australian Journal of Experimental Agriculture 46(8) 1005-1014 https://doi.org/10.1071/EA04034
Submitted: 7 March 2004 Accepted: 17 June 2004 Published: 5 July 2006
Abstract
The GrassGro decision support tool was designed to quantify sheep and pasture production in response to management and climate variability in temperate Australia, and has been tested in temperate but not low-rainfall Australian conditions. Data from field experiments and from on-farm monitoring was used to test GrassGro predictions of annual and perennial pasture production, and sheep production at 4 locations throughout the Victorian Mallee, which is a low-rainfall area (275–375 mm annually). Predictions of long-term pasture production were then made. Predictions of the herbage biomass of annual pastures closely matched observed data for both a sandy loam (1991–2002 data) and a whole paddock (combining sandy loam and loam and sand) (2001–2002 data) soil type, at several locations across the Victorian Mallee. Linear regression between observed and simulated (April to September) data produced coefficients, significance and root mean square error of r2 = 0.81, P<0.001, 217 kg DM/ha, respectively, for sandy loam soil types and r2 = 0.94, P<0.001, 72 kg DM/ha, respectively, for whole paddock soil types. A series of simulations for individual years from 1970 to 2002 quantified the large impact of climate variability and demonstrated that seedbank and location, but not soil fertility, had a large influence on annual pasture production. However, GrassGro underestimated the production of the perennial pasture, lucerne (r2 = 0.2). GrassGro was also unable to adequately predict sheep production because it failed to take into account the sparse, clumpy structure of the low biomass pastures typical of this region. Methods to improve GrassGro were identified and included: (i) the need to adjust sheep intake from low biomass, sparse pastures, (ii) the ability to predict summer growing and autumn growing plant species, (iii) the ability to graze crop stubbles and (iv) refinements to the coefficients of equations used to model lucerne growth.
Additional keywords: climate variability, farming system, medic, modelling, pasture growth.
Acknowledgments
The assistance of the GrassGro team at CSIRO Canberra, particularly Libby Salmon, who provided valuable technical advice, is acknowledged. Garry O’Leary made valuable comments on drafts of this paper.
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