Zoning in on disease

Figure 1. Satellite data zones (via Silverfox)

Maps of production zones are being used for soil sampling aimed at improving information on the spatial distribution of soilborne pathogens within paddocks, before seeding.

Field trials are also being established to determine if specific soilborne pathogens cause similar yield losses in different zones.

The outcomes of this work are expected to include better methods to assess yield-loss risk from soilborne pathogens. These will include better sampling strategies, estimates of potential yield loss and ways to target control strategies.

This project builds on the sampling research of GRDC-funded project DAS 311, and uses DNA assays developed by SARDI and CSIRO and marketed as PreDicta B by Bayer CropScience.

By John Heap and Alan McKay

Soilborne pathogens, including those that cause Take-all and Rhizoctonia bare patch, often occur in patches of varying sizes. This can make it difficult to estimate the risk of yield loss across a paddock or in particular zones.

Many of the factors that drive variability in crop performance, such as soil type, pH, water-holding capacity, and compaction layers, also affect soilborne pathogen levels, so we expect zone maps of both to be similar.

A variety of paddock data can now be collected, including yield maps, satellite images, EM maps and elevation maps. To produce useful zone maps, multiple data layers are generally combined using statistical programs.

Each combination of data layers may produce different zones, so field trials are needed to determine which combinations are the most useful. Studies to determine the most useful data layers to generate zone maps are being undertaken with the Australian Centre for Precision Agriculture at University of Sydney and the company Silverfox Solutions in WA.

The outputs of this project will include recommendations on how to zone paddocks to target soilborne pathogens and estimates of potential yield losses in different zones.

In the research so far, zone maps are providing interesting information on the distribution of soilborne crop pathogens within paddocks. Figures 1 and 2 are zone maps of the same paddock produced with different data layers.

Figure 1 was generated by Silverfox using five years of satellite data of crop biomass during the growing season, and figure 2 was generated using two yield maps, an EM map and an elevation map.

Figure 1. Satellite data zones (via Silverfox) Figure 2. Zones based on two yield maps, EM and elevation map.
Figure 2. Zones based on two yield maps, EM and elevation map.
Figure 2. Zones based on two yield maps, EM and elevation map.

Each zone was sampled and tested for a range of soilborne pathogens using PreDicta B. What we are looking for are zones that segregate soilborne pathogen levels, which relate to crop responses. For example, the common root rot (CRR) pathogen, Bipolaris sorokiniana is correlated with the unstable zones (low, medium & high) in the Silverfox maps.

Table 2 shows that the Green zone in figure 2 had significantly less Bipolaris than the Red and Blue zones.

Table 1. Common root rot pathogen levels in figure 1 zones

Zones CRR Area
  Low Stable 105 11%
  Low Unstable 376 14%
  Medium Stable 77 33%
  Medium Unstable 696 11%
  High Stable 170 23%
  High Unstable 321 8%
 Low Stable 10511% Low Unstable 37614% Medium Stable 7733% Medium Unstable69611% High Stable17023% High Unstable 3218%

Table 2. Common root rot pathogen levels in figure 2 zones

Technical support provided by this project to other SIP09 projects is helping to identify some of the biological constraints to yield in their focus paddocks.

Targeted sampling in the ACPA focus paddock at Albury revealed a high level of the Take-all fungus in one zone (Figure 3).

Figure 3. ACPA focus paddock at Albury, 2004

Figure 3. ACPA focus paddock at Albury, 2004

Table 3. Pathogen levels detected in management zones in ACPA focus paddock at Albury.

If a susceptible crop is grown in this zone and seasonal conditions favour the fungus, yield losses could be high. Alternatively, if this is known to be a stable high-yield zone, the grower could consider using a fungicide seed dressing at sowing for this part of the paddock.

To manage the risk of yield losses caused by soilborne pathogens, information is required on the levels of specific pathogens in each management zone, estimates of the potential yield losses they could cause, and a list of management options.

Work is in progress to measure the yield losses caused by soilborne diseases in different production zones. This will run over a number of years to cover different seasons. Early results from the SPAA focus paddock at Crystal Brook, 2003, indicate medium levels (for bread wheats) of crown rot inoculum at seeding, were correlated with a 13 percent yield reduction in the blue and green zones.

More detailed studies are under way using small fumigated plots to provide better data on yield losses. The yield responses for similar pathogen levels in different zones will be used to develop simple economic models to help allocate inputs to each zone.

We expect the economics of controlling root diseases will be different between zones. When we understand which data layers produce the best zone maps to target soilborne pathogens, the information will be made available to consultants and grain growers to help make better crop selection decisions.

Those using variable rate systems will be able to target higher inputs to the more reliable areas of the paddock, and target treatments such as seed dressings to reduce the risk of yield loss where appropriate. These outcomes will thus contribute to more flexible and profitable cropping strategies.

For more information:
Dr John Heap, 08 83039444, heap.john@saugov.sa.gov.au
Dr Alan McKay, 08 83039375, mckay.alan@saugov.sa.gov.au

GRDC research code: DAS 00035

Region South