Ground Cover Supplement : GC Supplement - Precision Agriculture
GROUND COVER PRECISION AGRICULTURE 10 FROM THE SOIL UP would be at about 35 kilometres an hour, about half the speed at which it is possible to gather EMI data. The equipment used had a depth range of between 25 and 200 centimetres, so layers between the surface and 25cm down could not be located. However, equipment could be modified to measure soil horizons at shallower and greater depths. It is not suitable for very deep strata. Following the collection of information in the field, a system was devised to identify the duplex horizons and interpolate that information across the entire length of the paddock. From this, contour maps of the subsurface horizon were generated (Figure 1b, below). Dr Adam O'Neill, from DownUnder GeoSolutions, assisted with this process. A comparison of GPR results with physically collected soil samples from across all sites found that GPR can accurately measure the depth to the clay horizon of duplex soils to within 10cm. The trials found that it was not always possible to obtain the same level of accuracy using EMI. Figure 1 illustrates how the GPR data on depth to clay is more closely correlated with yield compared to the bulk soil conductivity data gathered with an EM38. The low-yielding areas related to the shallow duplex soils. GPR will work well in most soil conditions, but it cannot work on saline soils with bulk soil conductivity greater than about 300mS/m (milliSiemens per metre) or where standing water is present. This is because the conductivity of saline soils and water is high and the electromagnetic signals from the GPR are adsorbed and not reflected. EMI is also ineffective in these situations. While further studies are required, this is a very exciting breakthrough in the delivery of more rapid and spatially detailed soil information. The research team led by Professor Bob Gilkes has also investigated a range of other tools to provide rapid soil tests in the field. □ GRDC Research Code UWA00089 More information: Professor Bob Gilkes, professor of soil science, UWA, 08 6488 2509, firstname.lastname@example.org www.grdc.com.au/UWA00089 Deeper clay -- better crop yield Wheat yield 2004 (t/ha) Depth to clay (m) Soil bulk conductivity (mS/m) Shallower clay -- reduced crop yield No relationship to EMI-measured soil bulk conductivity. 78 64 49 35 21 6.2 5.2 4.3 3.3 2.3 Figure 1 The relationship between yield (a) and the depth to the clay horizon of a duplex soil gathered by ground penetrating radar (b), compared to the relationship between yield and bulk density based on electromagnetic induction (c)(a) (b) (c) 1.3 1.2 1 0.86 0.7 0.55 0.4 VARIABLE RATES FOR CONSTRAINED SUBSOILS Precision agriculture data and tools offer more cost-effective management of crops grown on hostile soils By Yash Dang SUBSOIL CONSTRAINTS SUCH as salinity, sodicity, acidity and phytotoxic concentrations of chloride are known to reduce crop yield by reducing the root zone. However, subsoil constraints are rarely uniform across a paddock, making a blanket approach to remediation unnecessary and expensive. Working with seven farmers in Biloela, Goondiwindi and Roma in Queensland and Moree, Narrabri and Bellata in New South Wales, we looked at using spatial data sets to help achieve site-specific management of subsoil constraints. The paddocks selected ranged from 40 to 260 hectares and all had a history of yield maps and were managed by growers committed to gathering yield data in the future. Yield maps are the best way to measure the variability of subsoil constraints, but in Australia not all growers have yield mapping facilities. Therefore, in this project we attempted to use the normalised difference vegetation index (NDVI), obtained from Landsat images, as a surrogate to predict grain yield and areas suspected of subsoil constraints. For a 2840ha farm in Goondiwindi yield data was available for 40 of the 60 wheat crops grown in 17 paddocks between 2000 and 2009. The data was cleaned, mapped and augmented with NDVI satellite images to predict yield where harvest data was missing.
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