Diploid and tetraploid progenitors of wheat are valuable sources of resistance to the root lesion nematode Pratylenchus thornei

The root lesion nematode Pratylenchus thornei is widely distributed in Australian wheat (Triticum aestivum) producing regions and can reduce yield by more than 50%, costing the industry AU$50 M/year. Genetic resistance is the most effective form of management but no commercial cultivars are resistant (R) and the best parental lines are only moderately R. The wild relatives of wheat have evolved in P. thornei-infested soil for millennia and may have superior levels of resistance that can be transferred to commercial wheats. To evaluate this hypothesis, a collection of 251 accessions of wheat and related species was tested for resistance to P. thornei under controlled conditions in glasshouse pot experiments over two consecutive years. Diploid accessions were more R than tetraploid accessions which proved more R than hexaploid accessions. Of the diploid accessions, 11 (52%) Aegilops speltoides (S-[B]-genome), 10 (43%) Triticum monococcum (A (m) -genome) and 5 (24%) Triticum urartu (A (u) -genome) accessions were R. One tetraploid accession (Triticum dicoccoides) was R. This establishes for the first time that P. thornei resistance is located on the A-genome and confirms resistance on the B-genome. Since previous research has shown that the moderate levels of P. thornei resistance in hexaploid wheat are dose-dependent, additive and located on the B and D-genomes, it would seem efficient to target A-genome resistance for introduction to hexaploid lines through direct crossing, using durum wheat as a bridging species and/or through the development of amphiploids. This would allow resistances from each genome to be combined to generate a higher level of resistance than is currently available in hexaploid wheat.

Identifying major contributing sources to odour annoyance using a non-specific gas sensor array

Identification of major contributors to odour annoyance in areas with multiple emission sources is necessary to address and resolve odour disputes. In an effort to develop an appropriate tool for this task, odour samples were collected on-site at a piggery and an abattoir (the major odour sources in the area) and at surrounding off-site areas, then analysed using a commercial non-specific chemical sensor array to develop an odour fingerprint database. The developed odour fingerprint database was analysed using two pattern recognition algorithms including a partial least squares-discriminant analysis (PLS-DA) and a Kohonen self-organising map (KSOM). The KSOM model could identify odour samples sourced from the piggery shed 15, piggery pond 8, piggery pond 9, abattoir, motel and others with mean percentage values of 77.5, 65.0, 90.2, 75.7, 44.8 and 64.6%, respectively.