Grains Research and Development

Date: 01.08.2004

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.

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 information 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.

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).

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.

This information provides an accurate, automatic computer record of what was actually done, and highlights the need for accurate guidance.

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).

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 machines, 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.

Map of fertiliser spreader distance (speed) from GPS logger.Figure 2. Map of fertiliser spreader distance (speed) from GPS logger. The map provides a detailed record of the operation - the speed varied, on 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.

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.

For more information:
Dr Don Yule, 073871 0359, yules@bigpond.com

GRDC Research code: CTF 00002

Region North, South, West