Ground Cover Supplement : GC Supplement - Precision Agriculture 2004
information is that because it is available after crop harvest it can only improve management of future cereal crops. Remote sensing promises a similar opportunity to identify areas of nutritional or other crop stress, with the advantage of enabling management decisions before harvest. Satellite and aerial spectral sensors that capture in-season spectral information (reflected off crop canopies) could allow interventions to crop management, provided the spectral information used is a surrogate measure of crop yield, grain protein, or of a crop stress responsible for creating variations within these attributes. In a second project completed in 2003, imagery derived from satellites and other aerial platforms, including aircraft and balloons (see pictures, previous page), showed promise in identifying areas in cereal crops of high and/or low grain protein. This capability would be invaluable for better grain segregation at harvest. An equally valuable use for this information would be improved forecasting of grain production by protein classification to secure an early marketing advantage (Fig. 1). Part of our contribution to the GRDC's Strategic Initiative on Precision Agriculture (SIP09) will be to pursue applications of remotely sensed spectral data that enable improved land and crop management decisions. Spatial (or pixel size) and spectral resolutions (the number of spectral bands) of remotely sensed imagery are being improved as technology adapts to agricultural requirements. Images captured from satellite, aircraft or balloons can be taken at repeated intervals, allowing for timely data acquisition and near- real-time remedial action. Spectral data from satellite images have been used in Australia and elsewhere to forecast production, locate and identify foliar diseases, aid in irrigation scheduling and to enhance fertiliser management. If forecasts were available by protein classification, such advice could provide valuable marketing advantage for the producer and/or grain marketer. As well as providing opportunity to segregate grain by protein class at harvest, remotely sensed imagery during crop growth will provide early detection of areas of under-performance within the paddock. Identifying the underlying cause for under-performance by systematic ''ground-truthing'' could lead to intervention, avoidance and/or corrective strategies. With Dr Apan (University of Southern Queensland), Dr Stuart Phinn (University of Queensland), and David Lester (Incitec/Pivot), we have linked canopy reflectance with yield-deficient supplies of sulfur, phosphorus and N in wheat. Identification, avoidance and/or management of any soil dysfunction, including soil-borne pests (such as nematodes) or diseases that may affect sustainability of northern crops should improve production. Tools such as this could greatly facilitate the collection of information useful for crop auditing or monitoring critical growth stages. An example of such an application for remotely sensed imagery in this region is to identify areas where subsoil constraints (sodicity, salinity, unfavourable pH or compaction) may restrict water use by dryland field crops. GRDC RESEARCH CODE DAQ 00067 For more information: Dr Rob Kelly, 07 46881524, email@example.com 2 By DON YULE, STEW CANNON and TIM NEALE Controlled Traffic Farming (CTF) is a farming system that incorporates farm design and paddock layout, based on soil profiles and soil attributes. It is the farming system of the future, and Information Rich Agriculture (IRA) will be its driver. Systems and technologies are emerging that will provide growers with a wealth of information and data that will shape farm, paddock and machinery design and management, as well as decision making, marketing, knowledge growth, and grain products. Spatial information can now detail performance across every part of the farm, and it is whole-farm performance that counts, not just the best paddock or zone. The availability of global positioning systems (GPS), remote sensing, automatic monitoring and geographic information systems (GIS) to analyse the data, has also made this performance monitoring affordable. Aerial imagery provides inform- ation on soils, landscapes and the effects of past land management. Multi-spectral satellite or aerial imagery produces up-to-date digital data at a wide range of scales and qualities (pixel sizes) when we want it (limited mainly by cloud cover). Different spectra have been related to crop growth, nutrient content, disease and pest damage. Topography can be measured to centimetre accuracy, sub-soil properties can be measured with electro-magnetic sensors, ground-penetrating radar or geophysical sensors. Grain yield, water content and protein monitors are also available. Satellite images range in resolution from less than one square metre to hundreds of square metres. Soil measurements are typically a spot- measurement every few hundred square metres and grain monitors average continuously every 500 square metres or so. The scale of measurement defines the uses of the data. Spatial information has highlighted variability across farms, but in many ways it is simply science now being able to measure what growers have long known. Scientists are interested in the causes of spatial variability that previously have been in the too-hard basket. This variability can be due to natural resources (soil type and properties, topography, drainage, rainfall), to soil and land degradation (erosion, waterlogging, salinity), and to farming operations (wheel-track effects, weed and pest control, harvester trails, machinery use, cropping history). Appropriate measurement and management will vary according to the cause. Our project is studying the measurement and management of variability. We have four groups -- Central Queensland, Darling Downs, Liverpool Plains and South- West Victoria. We are evaluating information sources through grower experience at paddock and farm scale. Initial results Aerial imagery supported by soil profile examination have been useful for identifying soil changes. We include growers in the field work to explain profile properties and implications for water movement and root growth, and management impacts. Aerial imagery is widely available but appears to be under- used by growers and advisers as a remote sensing tool. There is a need for more training in aerial imagery interpretation. At our sites, detailed topography has often related to soil changes and crop responses. Topography is crucial information for farm and paddock design, roads and drainage lines, controlled traffic layouts and managing waterlogging. It is also important in flat areas where subtle differences in elevation can cause waterlogging and erosion. EM38 surveys have generally supported the aerial imagery and topography data. Centimetre-accurate topography data is becoming much easier to obtain due to RTK GPS and two-centimetre auto-steer systems that also produce topography data. However, there seems to be a long way to go before most growers start routinely using the information available. For example, yield monitoring provides essential information on performance, but few growers have records for more than a few years and most have no records at all. Many harvesters have yield monitors, but few are linked to a GPS and data logger, and few are even turned on. In addition, industry-support people often lack the skills or interest to make sure the equipment is working properly and that records are processed and interpreted. It is up to growers to recognise how valuable this data is and demand the necessary support. GIS has value-added to each layer of information and allows in-depth analysis of factors and associations. For example, we have found that lower crop NIR (near infra-red) reflectance was associated with wheel tracks and harvester trails (Figure 1). The larger scale responses in imagery at about flowering time were often reflected in yield monitoring but sometimes high NIR led to poor yields (excessive early growth with a dry finish). Preliminary conclusions Imagery and photography provide information at the scale needed to understand responses to machinery impacts and some previous history, and allow accurate mapping of these impacts. We have measured individual row yields and shown that wheel tracks can reduce yields by 25 to 33 percent. Yet 90 percent of Australian graingrowers still use random traffic and cultivated farming systems. Their soils are repeatedly degraded by wheel traffic and consequent yield losses occur across all paddocks. All machinery needs to use the same wheel tracks, although far too many CTF growers don't include the harvester. In our experience, the harvester is the place to start with CTF machinery. IRA and precision agriculture technologies are wasted on farms until the basics are adopted. These include farm design, paddock layout, controlled traffic for all ma- chines, zero till, and best agronomy. Data logged GPS on machines provide the basis for automatic farm record in a GIS framework (Figure 2). This will be the scale of spatial distribution for farm records, not paddocks. However, our IRA work has shown a lack of necessary skills and experience among grains industry professionals. It is difficult to even obtain the data required because few contractors are providing these services. Even when the data is obtained there are few professionals able to present the data to growers and interpret it with them. This lack of expertise will be a major constraint to adoption of IRA technology until it is addressed. GRDC RESEARCH CODE CTF 00002 For more information: Dr Don Yule, 073871 0359, firstname.lastname@example.org Figure 1. Protein map (far left) derived from a barley field, near Dalby, in 1999. The Landsat-5 image (left), obtained in mid- September, displayed a similar pattern to grain protein harvested in early December (r2=0.71). From previous page Managing paddocks more effectively Information drives farming systems of the future Figure 1: Near infra red aerial imagery of canola crop (2m pixel) with harvester tracks from previous harvest (windrowed barley) shown as dots. Higher reflectance (more growth) indicated as lighter shades. Harvester tracks have reduced growth of the following crop. Map of fertiliser spreader distance (speed) from GPS logger. The map provides a detailed record of the operation -- the speed varied, one row (at the bottom of the yellow section) was completely missed, the upper section is very evenly spaced (guidance provided by raised beds) but the bottom section has variable widths (no beds). This information provides an accurate, automatic computer record of what was actually done, and highlights the need for accurate guidance.
GC Supplement - Managing Subsoils 2004