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Abstract:
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This research develops a methodology for determining farm scale land management units (LMUs) using soil sampling data, high resolution digital multi-spectral imagery (DMSI) and a digital elevation model (DEM). The LMUs are zones within a paddock suitable for precision agriculture which are managed according to their productive capabilities. Soil sampling and analysis are crucial in depicting landscape characteristics, but costly. Data based on DMSI and DEM is available cheaply and at high resolution. The design and implementation of a two-stage methodology using a spatially weighted multivariate classification, for delineating LMUs is described. Utilising data on physical and chemical soil properties collected at 250 sampling locations within a 1780ha farm in Western Australia, the methodology initially classifies sampling points into LMUs based on a spatially weighted similarity matrix. The second stage delineates higher resolution LMU boundaries using DMSI and topographic variables derived from a DEM on a 10m grid across the study area. The method groups sample points and pixels with respect to their characteristics and their spatial relationships, thus forming contiguous, homogenous LMUs that can be adopted in precision agricultural applications. The methodology combines readily available and relatively cheap high resolution data sets with soil properties sampled at low resolution. This minimises cost while still forming LMUs at high resolution. The allocation of pixels to LMUs based on their DMSI and topographic variables has been verified. Yield differences between the LMUs have also been analysed. The results indicate the potential of the approach for precision agriculture and the importance of continued research in this area.
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