Double trouble: investigating the interaction of soilborne pathogens in wheat, using three-dimensional response surfaces

Double trouble: investigating the interaction of soilborne pathogens in wheat, using three-dimensional response surfaces

Plant diseases caused by soilborne pathogens can be devastating for cereal crop growth, resulting in adverse impacts on crop productivity. Typically, experimentation quantifies the impact of one pathogen on crop response variables such as grain yield and grain quality. However, in practice, growers are faced with disease complexes, where multiple soilborne pathogens can be present in the farming system.
Fusarium pseudograminearum, which causes crown rot, Bipolaris sorokiniana, which causes common root rot, and Pratylenchus thornei, a root-lesion nematode, are all major yield-limiting soilborne pathogens of wheat in the northern Australian grain growing region. These pathogens also commonly co-occur in this region. For example, of the 35% of paddocks with medium-high levels of P. thornei, 44% of those paddocks also had F. pseudograminearum at medium-high levels. To investigate the potential interaction between selected pairs of these soilborne pathogens, a robust and novel statistical method was implemented. This required the development of a bespoke experimental design approach, before applying a state-of-the-art analysis technique using tensor cubic smoothing splines in a linear mixed model framework.
This statistical methodology was applied to two experiments conducted in south-east Queensland, in which two wheat varieties were tested. The first experiment explored responses to established continuums of F. pseudograminearum and P. thornei, while the second explored responses to established continuums of F. pseudograminearum and B. sorokiniana. As a result, threedimensional response surfaces were generated, formed using two explanatory pathogen continuum variables, along with a response variable of interest such as yield. These response surfaces provided insight into how these pathogens simultaneously affected the response variables. Additionally, the method provided insight into pathogen dynamics, including changes in population densities of the co-occurring pathogens and disease severity. Altogether, the application of the techniques described enabled a more robust exploration of soilborne disease interactions than previously conducted

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