Combining satellite data with a simulation model to describe spatial variability in pasture growth at a farm scale

Abstract

Practical application of simulation modelling as a decision aid for grazing system management usually involves an assumption of uniformity of model inputs over a farm paddock or property. In reality, paddocks and farms display high spatial variability in model inputs. There is considerable interest in assessing the significance of this spatial variablity for anmal production and enterprise profitability. This study seeks to demonstrate the use of spatial data with the GRAZPLAN pasture model to provide estimates of annual net primary production from pastures at a farm scale on the Northern Tablelands of New South Wales, Australia. The GRAZPLAN pasture model was validated against data from 2 separate field experiments for a typical improved pasture based on Phalaris aquatica from 1968 to 1972. A spatial coverage, classifying paddocks into 9 pasture types based on a botanical survey, was used to define the pasture parameter sets used in simulations. A Landsat TM satellite image classified to give 3 pasture growth status classes was used to define within-paddock levels of a fertility index used in the simulation model. Simulations over 1975–94 were conducted for all combinations of pasture types and fertility scalar values using climate data for the CSIRO Pastoral Research Laboratory near Armidale. Simulation output was written to a lookup table and imported into a PC-based geographic information system. The spatial data layers were combined to form a display template representing spatial variation in pasture type, pasture condition and fertility. The spatial template was reclassified using the lookup tables to create maps of annual net primary production from pastures. Spatial variability in simulated annual net primary production was greater for the paddocks with diverse mixtures of sown and native species than for the more uniform highly improved or pure native pastures. The difference in response to rainfall of simulated net primary production was greater between different pastures types than between different levels of the fertility index. The resulting maps provide a demonstration of the way in which satellite imagery and other data can be interfaced with a decision support system to provide information for use in precision management of grazing systems. Implementation of such methods as a management tool will depend on development of quantitative spatial data layers which provide accurate and repeatable initial conditions and parameter values for simulation models.