Recent emergence of the wheat Lr34 multi-pathogen resistance: insights from haplotype analysis in wheat, rice, sorghum and Aegilops tauschii

Spontaneous sequence changes and the selection of beneficial mutations are driving forces of gene diversification and key factors of evolution. In highly dynamic co-evolutionary processes such as plant-pathogen interactions, the plant's ability to rapidly adapt to newly emerging pathogens is paramount. The hexaploid wheat gene Lr34, which encodes an ATP-binding cassette (ABC) transporter, confers durable field resistance against four fungal diseases. Despite its extensive use in breeding and agriculture, no increase in virulence towards Lr34 has been described over the last century. The wheat genepool contains two predominant Lr34 alleles of which only one confers disease resistance. The two alleles, located on chromosome 7DS, differ by only two exon-polymorphisms. Putatively functional homoeologs and orthologs of Lr34 are found on the B-genome of wheat and in rice and sorghum, but not in maize, barley and Brachypodium. In this study we present a detailed haplotype analysis of homoeologous and orthologous Lr34 genes in genetically and geographically diverse selections of wheat, rice and sorghum accessions. We found that the resistant Lr34 haplotype is unique to the wheat D-genome and is not found in the B-genome of wheat or in rice and sorghum. Furthermore, we only found the susceptible Lr34 allele in a set of 252 Ae. tauschii genotypes, the progenitor of the wheat D-genome. These data provide compelling evidence that the Lr34 multi-pathogen resistance is the result of recent gene diversification occurring after the formation of hexaploid wheat about 8,000 years ago.

Highly heritable resistance to root-lesion nematode (Pratylenchus thornei) in Australian chickpea germplasm observed using an optimised glasshouse method and multi-environment trial analysis

Pratylenchus thornei is a root-lesion nematode (RLN) of economic significance in the grain growing regions of Australia. Chickpea (Cicer arietinum) is a significant legume crop grown throughout these regions, but previous testing found most cultivars were susceptible to P. thornei. Therefore, improved resistance to P. thornei is an important objective of the Australian chickpea breeding program. A glasshouse method was developed to assess resistance of chickpea lines to P. thornei, which requires relatively low labour and resource input, and hence is suited to routine adoption within a breeding program. Using this method, good differentiation of chickpea cultivars for P. thornei resistance was measured after 12 weeks. Nematode multiplication was higher for all genotypes than the unplanted control, but of the 47 cultivars and breeding lines tested, 17 exhibited partial resistance, allowing less than two fold multiplication. The relative differences in resistance identified using this method were highly heritable (0.69) and were validated against P. thornei data from seven field trials using a multi-environment trial analysis. Genetic correlations for cultivar resistance between the glasshouse and six of the field trials were high (>0.73). These results demonstrate that resistance to P. thornei in chickpea is highly heritable and can be effectively selected in a limited set of environments. The improved resistance found in a number of the newer chickpea cultivars tested shows that some advances have been made in the P. thornei resistance of Australian chickpea cultivars, and that further targeted breeding and selection should provide incremental improvements.

Statistical methods for analysis of multi-harvest data from perennial pasture variety selection trials

Variety selection in perennial pasture crops involves identifying best varieties from data collected from multiple harvest times in field trials. For accurate selection, the statistical methods for analysing such data need to account for the spatial and temporal correlation typically present. This paper provides an approach for analysing multi-harvest data from variety selection trials in which there may be a large number of harvest times. Methods are presented for modelling the variety by harvest effects while accounting for the spatial and temporal correlation between observations. These methods provide an improvement in model fit compared to separate analyses for each harvest, and provide insight into variety by harvest interactions. The approach is illustrated using two traits from a lucerne variety selection trial. The proposed method provides variety predictions allowing for the natural sources of variation and correlation in multi-harvest data.

Isolation and expression analysis of multiple isoforms of putative farnesoic acid O-methyltransferase in several crustacean species

Farnesoic acid O-methyltransferase (FaMeT) is the enzyme responsible for the conversion of farnesoic acid (FA) to methyl farnesoate (MF) in the final step of MF synthesis. Multiple isoforms of putative FaMeT were isolated from six crustacean species belonging to the families Portunidae, Penaeidae, Scyllaridae and Parastacidae. The portunid crabs Portunus pelagicus and Scylla serrata code for three forms: short, intermediate and long. Two isoforms (short and long) were isolated from the penaeid prawns Penaeus monodon and Fenneropenaeus merguiensis. Two isoforms were also identified in the scyllarid Thenus orientalis and parastacid Cherax quadricarinatus. Putative FaMeT sequences were also amplified from the genomic DNA of P. pelagicus and compared to the putative FaMeT transcripts expressed. Each putative FaMeT cDNA isoform was represented in the genomic DNA, indicative of a multi-gene family. Various tissues from P. pelagicus were individually screened for putative FaMeT expression using PCR and fragment analysis. Each tissue type expressed all three isoforms of putative FaMeT irrespective of sex or moult stage. Protein domain analysis revealed the presence of a deduced casein kinase II phosphorylation site present only in the long isoform of putative FaMeT.

Strategic Analysis: A key factor in developing horticultural supply chains in transitional Asian economies

Globalisation is set to have a major impact on world horticultural production and distribution of fruit and vegetables throughout the world. In contrast to developing countries such as China, production and consumption of fresh fruit and vegetables in most developed countries is relatively static. For developed countries, we are starting to see consolidation in the number of farms producing fruit and vegetables with falling or static prices and real farm incomes. Global supply chains are now dominated by a few large multi-national retailers supplied by preferred trans-national distribution companies. The major competitive advantages that are emerging are consistency of supply of high quality product over an extended season and the control of genetic resources and their marketing. To capture these new competitive advantages, new strategic analyses and planning processes must be implemented. In the past, strategic analyses and planning has been undertaken on an ad hoc basis without accurate global intelligence. In the future, working ‘on the supply chain’ will become equally, if not more important, than working ‘in the supply chain’. A revised approach to strategic planning, which encompasses and adjusts for the changes caused by globalisation, is urgently needed. A new 6-step strategic analyses process is described.

The accuracy of varietal selection using factor analytic models for multi-environment plant breeding trials

Modeling of cultivar x trial effects for multienvironment trials (METs) within a mixed model framework is now common practice in many plant breeding programs. The factor analytic (FA) model is a parsimonious form used to approximate the fully unstructured form of the genetic variance-covariance matrix in the model for MET data. In this study, we demonstrate that the FA model is generally the model of best fit across a range of data sets taken from early generation trials in a breeding program. In addition, we demonstrate the superiority of the FA model in achieving the most common aim of METs, namely the selection of superior genotypes. Selection is achieved using best linear unbiased predictions (BLUPs) of cultivar effects at each environment, considered either individually or as a weighted average across environments. In practice, empirical BLUPs (E-BLUPs) of cultivar effects must be used instead of BLUPs since variance parameters in the model must be estimated rather than assumed known. While the optimal properties of minimum mean squared error of prediction (MSEP) and maximum correlation between true and predicted effects possessed by BLUPs do not hold for E-BLUPs, a simulation study shows that E-BLUPs perform well in terms of MSEP.

SSR analysis of cultivated groundnut (Arachis hypogaea L.) germplasm resistant to rust and late leaf spot diseases

Cultivated groundnut (Arachis hypogaea L.) is an agronomically and economically important oilseed crop grown extensively throughout the semi-arid tropics of Asia, Africa and Latin America. Rust (Puccinia arachidis) and late leaf spot (LLS, Phaseoisariopsis personata) are among the major diseases causing significant yield loss in groundnut. The development of varieties with high levels of resistance has been constrained by adaptation of disease isolates to resistance sources and incomplete resistance in resistant sources. Despite the wide range of morphological diversity observed in the cultivated groundnut gene pool, molecular marker analyses have thus far been unable to detect a parallel level of genetic diversity. However, the recent development of simple sequence repeat (SSR) markers presents new opportunities for molecular diversity analysis of cultivate groundnut. The current study was conducted to identify diverse disease resistant germplasm for the development of mapping populations and for their introduction into breeding programs. Twenty-three SSRs were screened across 22 groundnut genotypes with differing levels of resistance to rust and LLS. Overall, 135 alleles across 23 loci were observed in the 22 genotypes screened. Twelve of the 23 SSRs (52%) showed a high level of polymorphism, with PIC values ≥0.5. This is the first report detecting such high levels of genetic polymorphism in cultivated groundnut. Multi-dimensional scaling and cluster analyses revealed three well-separated groups of genotypes. Locus by locus AMOVA and Kruskal-Wallis one-way ANOVA identified candidate SSR loci that may be valuable for mapping rust and LLS resistance. The molecular diversity analysis presented here provides valuable information for groundnut breeders designing strategies for incorporating and pyramiding rust and late leaf spot resistances and for molecular biologists wishing to create recombinant inbred line populations to map these traits.

Development of a multi-locus sequence typing scheme for avian isolates of Pasteurella multocida.

A total of 63 isolates of Pasteurella multocida from Australian poultry, all associated with fowl cholera outbreaks, and three international reference strains, representing the three subspecies within P. multocida were used to develop a multi-locus sequence typing scheme. Primers were designed for conserved regions of seven house-keeping enzymes - adk, est, gdh, mdh, pgi, pmi and zwf - and internal fragments of 570-784 bp were sequenced for all isolates and strains. The number of alleles at the different loci ranged from 11 to 20 and a total of 29 allelic profiles or sequence types were recognised amongst the 66 strains. There was a strong concordance between the MLST data and the existing multi-locus enzyme electrophoresis and ribotyping data. When used to study a sub-set of isolates with a known detailed epidemiological history, the MLST data matched the results given by restriction endonuclease analysis, pulsed-field gel electrophoresis, ribotyping and REP-PCR. The MLST scheme provides a high level of resolution and is an excellent tool for studying the population structure and epidemiology of P. multocida.

Identifying risk-efficient strategies using stochastic frontier analysis and simulation: An application to irrigated cropping in Australia

In irrigated cropping, as with any other industry, profit and risk are inter-dependent. An increase in profit would normally coincide with an increase in risk, and this means that risk can be traded for profit. It is desirable to manage a farm so that it achieves the maximum possible profit for the desired level of risk. This paper identifies risk-efficient cropping strategies that allocate land and water between crop enterprises for a case study of an irrigated farm in Southern Queensland, Australia. This is achieved by applying stochastic frontier analysis to the output of a simulation experiment. The simulation experiment involved changes to the levels of business risk by systematically varying the crop sowing rules in a bioeconomic model of the case study farm. This model utilises the multi-field capability of the process based Agricultural Production System Simulator (APSIM) and is parameterised using data collected from interviews with a collaborating farmer. We found sowing rules that increased the farm area sown to cotton caused the greatest increase in risk-efficiency. Increasing maize area also improved risk-efficiency but to a lesser extent than cotton. Sowing rules that increased the areas sown to wheat reduced the risk-efficiency of the farm business. Sowing rules were identified that had the potential to improve the expected farm profit by ca. $50,000 Annually, without significantly increasing risk. The concept of the shadow price of risk is discussed and an expression is derived from the estimated frontier equation that quantifies the trade-off between profit and risk.

Estimation and evaluation of multi-decadal fire severity patterns using Landsat sensors

Retrospective identification of fire severity can improve our understanding of fire behaviour and ecological responses. However, burnt area records for many ecosystems are non-existent or incomplete, and those that are documented rarely include fire severity data. Retrospective analysis using satellite remote sensing data captured over extended periods can provide better estimates of fire history. This study aimed to assess the relationship between the Landsat differenced normalised burn ratio (dNBR) and field measured geometrically structured composite burn index (GeoCBI) for retrospective analysis of fire severity over a 23 year period in sclerophyll woodland and heath ecosystems. Further, we assessed for reduced dNBR fire severity classification accuracies associated with vegetation regrowth at increasing time between ignition and image capture. This was achieved by assessing four Landsat images captured at increasing time since ignition of the most recent burnt area. We found significant linear GeoCBI–dNBR relationships (R2 = 0.81 and 0.71) for data collected across ecosystems and for Eucalyptus racemosa ecosystems, respectively. Non-significant and weak linear relationships were observed for heath and Melaleuca quinquenervia ecosystems, suggesting that GeoCBI–dNBR was not appropriate for fire severity classification in specific ecosystems. Therefore, retrospective fire severity was classified across ecosystems. Landsat images captured within ~ 30 days after fire events were minimally affected by post burn vegetation regrowth.