final reportp
Project code: SHP.005 Prepared by: Ian T. Riley and A. C. McKay South Australian Research Institute Date published: June 2007 ISBN: 9781741917246
PUBLISHED BY Meat & Livestock Australia Limited Locked Bag 991 NORTH SYDNEY NSW 2059
Molecular Tools to Study Soil Biological Constraints to Pasture Productivity
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Pasture soil biology molecular assays
Abstract
Research on biological constraints in pastures caused by high levels of root diseases and/or lack of beneficial organisms such as mycorrhizae, has been constrained by the available techniques. This project has developed and explored the use of DNA assays to assess levels of a variety of plant pathogens and beneficial organisms in a single soil or plant sample.
Assays were also developed for key plant species to use as a novel method to assess the amount of plant roots in soil samples. Early results indicate this method can detect responses in root systems before changes in root dry weight can be detected.
There are obvious advantages of using one technology platform to assess plant pathogens, beneficial organisms and plant DNA levels in each soil sample. This is expected to provide a powerful research tool to study the impact of different grazing systems and other stresses on pasture performance and survival of specific plant species in single or mixed swards.
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Executive Summary
Objective This project has developed a series quantitative DNA assays to demonstrate potential benefits of using one technology platform to measure levels of different soilborne organisms concurrently in a soil sample; organisms that could be interacting to affect pasture production.
The initial targets included a cross section of plant pathogens including fungi, oomycetes and nematodes and selected beneficial organisms. Assays were also developed for key plant species to measure amount of roots in the same soil sample. The beneficial organisms targeted included arbuscular mycorrhizal fungi (AMF) which are very difficult to study using current techniques, and Trichoderma spp, which are often associated with disease suppression.
The project is based on the high throughput DNA extraction system developed by SARDI/CSIRO to process 500 g soil samples. DNA extracted from the soil represents all living cells in the sample including soil microbes and plants. Quantitative assays can be used to determine the densities of the target organisms in the original soil sample. This system was originally developed to monitor levels of soilborne pathogens of broadacre crops.
Previous techniques have constrained research on biological constraints in pastures. These typically involved washing plant roots from soil samples, separating them into different species and scoring for disease. Many of the pasture diseases are caused by pathogen complexes and a variety of techniques were required to extract each organism. The nature of these techniques often meant they could not be used on the same sample. The DNA assays overcome these constraints, although a test is required for each target species.
Plant assays The plant DNA assays are expected to revolutionise studies of plant root systems, especially in mixed swards. They have potential to provide a better measure of root responses to different management strategies and environmental stresses.
Assays have been developed for Trifolium subterraneum, Phalaris aquatica, and Lolium/Festuca spp. These assays are very sensitive, and in spiking experiments can easily detect one mg dry root and one seed per 500 g soil. Results from pot trials using ryegrass as the target species show changes in soil plant DNA levels are more responsive than changes in root dry weight. In pot experiments, soil DNA levels declined more quickly than root dry weight when the plants were subjected to treatments such as defoliation and herbicide application. These results support previous findings that DNA degrades quickly in dying cells.
The plant assays were also used in combination with other assays to detect treatment differences in field trials (SHP018). Work has also commenced on using the assays to monitor root distribution in the soil profile. Samples from 12 soil pits across southern Australia have been used to check the DNA extraction system can cope with different subsoils. So far significant extraction problems have only been encountered with subsoils at 2 sites.
Oomycetes and fungal plant pathogens Work on plant pathogens has focussed on the oomycetes, Phytophthora clandestina and Pythium Clade F (which includes P. irregulare). Assays for Rhizoctonia solani AG2.1 and AG2.2 developed by a HAL funded project were also evaluated for use in pastures. These assays, along with those previously developed for broadacre crops, were used to assess pathogen levels
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in bioassay trials and pasture management trials across southern Australia by SHP.017 and SHP.018.
The assays were also used to determine which pathogens where present in diseased rots of sub clover and lucerne seedlings. The results show the DNA assays can detect pathogens that are difficult to isolate using standard plating techniques.
Plant parasitic nematodes Assays were developed for Pratylenchus penetrans, a cluster of root knot species Meloidogyne javanica/arenaria/incognita and Heterodera trifolii (white clover only). These complement those developed by previous projects. Also new assays for Meloidogyne fallax and M. hapla are expected to be funded by HAL. These nematodes also attack sub clover, lucerne and white clover, but their incidence is not known.
The nematode assays were included in the suite used to assess the pasture trials in the higher rainfall areas, but detected surprising few nematodes (SHP.017 and SHP.018). More work is required to determine if there is are other species operating in these areas.
Beneficial organisms Arbuscular Mycorrhizal Fungi (AMF). Research on this important group of fungi is constrained by lack of suitable techniques. AMF consist of a diverse range of genera, which cannot be cultured or distinguished in roots and are difficult to assess in soil. The potential of the DNA assays to overcome these constraints is creating a lot of interest amongst scientists.
A single test to detect all AMFs could not be developed; instead we had to develop a series of 5 tests. The specificity of each test has been confirmed by sequencing the PCR products from selected field soils. We still have to develop standards and expect this to be achieved within the next year or so, through collaboration with a SAGIT funded project supervised by Prof Sally Smith (University of Adelaide).
Despite the lack of standards, the assays were used to demonstrate treatment responses in the pasture trials assessed by SHP.018.
Trichoderma spp. Trichoderma is an important genus that is often associated with disease suppression in soil. Two assays are under development to cover the known species. These assays are still under development and have not yet been used to assess soil samples. We expect these will be ready for use within the next 12 months.
Beneficial nematodes. There is a broad range of nematodes in soil that perform different roles. They broadly fall into four groups, these that feed on bacteria, fungi, plants and other nematodes. The structure of the nematode population in soil can be used as an indicator of environmental impact. Considerable work was done in collaboration with SHP.002 to develop tests for each trophic group, but unfortunately the DNA sequence information did not allow a manageable number of tests to be developed. This work was deferred in favour of developing the tests discussed above.
Where to next In the next 12 months, the range of plant and pathogen tests should be expanded and greater emphasis placed on evaluating and demonstrating the benefits of the technology to encourage scientists to use it to facilitate development of profitable and robust grazing systems.
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A long term R&D program also needs to be developed to ensure the technology is customised to address the current issues confronting the grazing industry such as development of more water efficient and drought tolerant pastures and pasture management systems.
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Contents Page 1 Background ...... 8 2 Project Objectives ...... 8 3 Methodology ...... 8 3.1 Test selection...... 8 3.2 Test development ...... 9 3.3 Test calibration ...... 10 3.4 Glasshouse and field applications...... 11 3.4.1 Response of root rDNA in soil...... 11 3.4.2 Quantification of root rDNA in mixed plant populations...... 11 3.4.3 Quantification of plant DNA in subsoils...... 12 3.4.4 Pasture soils across southern Australia...... 12 3.4.5 Southern and northern pasture soil (SHP.002 and 004) ...... 12 3.4.6 Pasture experiments (SHP.017 and 018) ...... 12 4 Results and Discussion...... 13 4.1 Plant tests ...... 13 4.1.1 Lolium/Festuca spp. test ...... 13 4.1.2 Trifolium subterraneum ...... 17 4.1.3 Phalaris aquatica ...... 20 4.2 Oomycete tests...... 22 4.2.1 Phytophthora clandestina...... 22 4.2.2 Pythium Clade F ...... 24 4.3 Fungal tests ...... 26 4.3.1 Rhizoctonia solani ...... 26 4.3.2 Arbuscular mycorrhizal fungi...... 27 4.4 Nematode tests ...... 31 4.4.1 Clover cyst nematode Heterodera trifolii...... 31 4.4.2 Root Knot Nematode, Meloidogyne spp...... 33 4.4.2.1 Meloidogyne javanica/incognita/arenaria ...... 33 4.4.2.2 Other Meloidogyne spp...... 35 4.4.3 Pratylenchus penetrans...... 35 4.4.4 Beneficial Nematodes...... 37 4.5 Glasshouse and field applications...... 38 4.5.1 DNA assays to monitor treatment responses in plant roots ...... 38 4.5.2 Quantification of root rDNA in mixed plant populations...... 40
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4.5.3 Quantification of plant DNA in subsoils...... 43 4.5.4 Pasture soils across southern Australia...... 43 4.5.5 Southern and northern pasture soil (SHP.002 and 004) ...... 46 4.5.6 Pasture experiments (SHP.017 and 018) ...... 46 5 Success in Achieving Objectives ...... 47 6 Impact on Meat and Livestock Industry – now & in five years time ...... 47 7 Conclusions and Recommendations ...... 47 8 Bibliography...... 49 9 Appendices...... 49 9.1 Collaborators and contributors ...... 49 9.2 Draft Press Release ...... 50 9.3 Pythium group and clade comparison...... 51 9.4 Arbuscular mycorrhizal fungi assayed in pasture soil...... 52 9.5 DNA concentrations of organisms in pasture soil...... 55
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1 Background Soil provides plants with water, nutrients and anchorage, functions that are only made possible by the community of organisms that live therein. Plants provide photosynthates through live and dead roots and surface residues that feed the soil community. Understanding the impact of different management strategies on soil organisms is important to achieve and maintain optimal pasture production.
The development by SARDI/CSIRO of an efficient technique to extract DNA from biologically meaningful volumes of soil, 500 g, means that it is now possible to use one technology platform to assess levels of a broad range of microorganisms in field samples. In the past, this required a plethora of extraction, isolation and bioassay methods. This meant investigations had to be narrow and conducted by experienced specialists.
In this project new molecular tests were developed to quantify key pasture plants and associated pathogens (fungi, nematodes and oomycetes) and beneficial organisms (mycorrhizae) in pasture soil. These add to existing tests developed by previous projects for use in cropping systems that are also relevant to pastures associated rotational crops.
The new tests were selected to demonstrate the potential application to pasture research, and cover the different types of organisms that need to be assessed as well as being relevant to one or more pasture systems in winter rainfall areas of southern Australia. A comprehensive list of tests for each pasture system will be developed by future projects.
The DNA tests for plants were developed as a novel approach to measure root systems of specific species in mixed swards, and are a logical adjunct for the assays for plant pathogens and beneficial organisms. Use of DNA assays to quantify of roots is expected to provide a valuable new tool to study plant root responses in different soil types, environments, management systems, and interactions with pathogens and beneficial organisms.
2 Project Objectives By 30 June 2007: Develop and calibrate a comprehensive range (8 to 10) of molecular assays for soil and root based pathogenic and beneficial organisms important to Australian improved pastures systems. These assays must be able to assist researchers develop pasture management strategies which increase pasture productivity based on enhanced soil biological health under improved pasture systems.
3 Methodology 3.1 Test selection
Tests were developed to represent the different types of organisms that are important in pastures to demonstrate the potential of the DNA technology as a research tool, and are not intended to provide a comprehensive coverage for any one pasture system.
Specific tests were selected in consultation with researchers with experience in pasture agronomy and pathology.
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Some tests relevant to pastures were developed with support from other sources, these are marked * in the list below.
Plants Lolium/Festuca spp., Trifolium subterraneum and Phalaris aquatica Fungal pathogens Rhizoctonia solani (AG2.1*, AG2.2* and AG4*) Beneficial fungi Five groups of Arbuscular mycorrhizal fungi groups and two groups of Trichoderma spp. Oomycete pathogens Phytophthora clandestina and Pythium Clade F (after Lévesque and deCock, 2004) Nematode parasites Heterodera trifolii, Meloidogyne javanica/incognita/arenaria and Pratylenchus penetrans
In addition to the new tests, many tests already developed for field crops are relevant to pastures and were also used by this project. These tests were as follows.
Fungal pathogens Bipolaris sorokinana (Common root rot), Gaeumannomyces graminis var. tritici and var avenae (Take-all), Fusarium culmorum/graminearum, Fusarium pseudograminearum (two types), Mycospaerella pinodes/Phoma medicaginis var. pinodes (black spot complex), Phoma sp (associated with Black Spot) and Rhizoctonia solani (AG8) Nematode parasites Ditylenchus dipsaci, Pratylenchus neglectus and P. thornei
During the project beneficial soil nematodes were considered as potentially useful targets for test development. Structural analysis of nematode populations is a well-developed tool for examining soil health, but requires high-level skills in nematode extraction and identification. Sequencing and phylogenic analysis were undertaken. Analysis of the sequence data revealed that developing assays for each of the important trophic groups was going to be difficult, so it was decided to devote the resources to development of the other assays.
3.2 Test development
Development of a new test proceeds though a series of stages; a) collection of DNA sequences of target and related organisms, b) phylogenic analysis, c) collection of organisms for test validation, d) selection of a diagnostic region, e) design of primers and probes, f) evaluation of the specificity and sensitivity of the assay again the target using pure DNA, g) then in soil, and finally h) validation in the field. This procedure may not be linear, some steps might need to be repeated to achieve the robust and sensitive test desired.
For this work, a proprietary DNA assay system, TaqMan® (Applied Biosystems) has been adopted, because it has many advantages including linearity of a wider range of target DNA concentrations.
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DNA from the soil is replicated using target-specific primers and a fluorescently labelled probe. Through each DNA replication cycle the fluorescent reporter is released and can be measured using laser technology. The diagram below illustrates this process.
The quantity of the target DNA is estimated by the number of PCR cycles taken for the fluorescence to reach a certain threshold and comparing this to standards of known concentration. The value is then converted to picograms of DNA per gram of soil (pg DNA/g soil) or to units such as nematode eggs per gram of soil, where such relationships have been determined.
Field validation of new tests is usually undertaken in collaboration with other researchers, preferably those experienced with the target organism. This helps to ensure the tests work as required and in the interpretation of the results and development of risk categories, where appropriate.
3.3 Test calibration
A series of spiking experiments was conducted using field soil to test the sensitivity of the new assays for root knot nematode, clover cyst nematode, Pratylenchus penetrans, Pythium Clade F and Phytophthora clandestina.
To evaluate the nematode assays, dilution series were made up for each species and confirmed by counting, prior to spiking into 200 g samples of field soil. Each level was replicated three times. The P. penetrans nematodes used were from a population grown on carrots, and contained various stages of development from second stage juveniles to adults. The root knot nematode assay was evaluated using Meloidogyne javanica eggs and some second stage juveniles extracted from tomato roots. The clover cyst nematode assay was evaluated using eggs extracted from pot cultures of white clover.
The Pythium and Phytophthora assays were evaluated by spiking 200 g samples of field soil with different amounts of freeze-dried cultures that were grown in liquid medium. Actively growing cultures were used to simulate the pathogens in soil samples collected during the growing season.
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3.4 Glasshouse and field applications
Advanced tests were evaluated using glasshouse and/or field experiments. The process was to further validate the specificity and reliability of the tests and to explore use of the test as a research tools.
This work included evaluation of the root DNA assays in glasshouse experiments to monitor changes in root DNA levels in response to application of treatments to foliage and show potential value of the assays to monitor individual species in mixed swards.
Soil samples from a diverse range of pastures across the southern Australia were collected and assayed to check for amplification of non-target DNA. This activity also demonstrated the value of using the assays to conduct surveys.
The assays were also applied to soil from allied but independently funded MLA projects, SHP.004, 017 and 018.
3.4.1 Response of root rDNA in soil The changes in soil DNA levels associated with plants that were defoliated (regrowth removed daily) or sprayed with herbicide (glyphosate at 7.2 g/L) was examined. Lolium perenne cv. Victorian was grown in 500 g Winulta soil in 600 mL square plastic containers in a controlled environment room (16/20OC 12 h diurnal cycle) for 4 weeks before treatments were applied. At intervals after treatment, the entire soil was removed from the container and either frozen (-20OC) and freeze dried for DNA assay or washed for recovery of roots for dry weight determination. Shoot dry weight was also determined at defoliation or harvest according to the treatment.
Two experiments were conducted, the first with the treated plants remaining in the control environment room and sampled at 0,1,4,7 and 10 days. For the second experiment, the plants were moved to a glasshouse for 6 days to acclimatise before treatments were applied and samples were taken every second day over a 16-day period. Four replicate pots for each treatment and untreated controls were harvested on each occasion, 2 for DNA assay and 2 for dry weight determination.
3.4.2 Quantification of root rDNA in mixed plant populations Two experiments were conducted to evaluate quantification of roots by PCR in soil in which the target species were grown alone, in combination with each other or with a non-target species.
In the first of these experiments, single plants of Lolium multiflorum, L. perenne, or L. rigidum were grown in 600 mL pots of Winulta soil in combination with 0, 1, 3, 9 or 27 Phalaris aquatica cv. Landmaster plants in the controlled environment room. Seeds were germinated in a misting cabinet and seedlings transplanted at the required density after 4 days. Four replicates, of each combination of Lolium species and Phalaris density were established and randomised, 2 for assessment by DNA assay and 2 for dry weight determination, giving a total of 60 pots. After 7 weeks, the shoots from the pots for DNA assay were cut at the soil surface, separated by species, dried and weigh. The soil was transferred intact to a plastic bag, frozen, lyophilised and then assayed for Lolium DNA. The plants for root dry weight determination were washed intact to allow separation by species. Washing and separation of the entangled roots, although conducted as carefully as possible, resulted in some loss of fine roots and possible miss classification of a small portion of roots.
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The second experiment consisted of varying densities and combinations of L. perenne cv. Victorian and T. subterraneum cv. Gosse under the same cultural conditions as above. Giving a total density of 16 plants per pot, the two species were grown at ratios of 16:0, 12:4, 8:8, 4:12, 0:16 Lolium to Trifolium. Also, pots of 2, 4 and 8 plants of each species alone, for comparison with that density in the mixed pots were included in the design. Eight replates of each plant density and combination were established and randomised, 4 for assessment by DNA assay and 4 for dry weight determination, giving a total of 238 pots. The plants were grown for 5 weeks and assessed as described above.
3.4.3 Quantification of plant DNA in subsoils Soil samples from different layers in the profile were collected initially from 8 sites in South Australia to test the ability to quantify plant DNA at in subsoils were soil chemistry or other properties might affect the efficiency of the extraction methods. Trifolium subterraneum seed was added to three replicate samples (14 seeds per 200 g soil) and assayed by DNA extraction and quantitative PCR. A further 6 sites have been tested since the original evaluation.
3.4.4 Pasture soils across southern Australia In winter of 2005 soil samples (about 2 kg) were collected in mostly permanent pastures and other sites in NSW, SA, Vic, and WA. About 75 samples were collected as listed in Appendix 2. The samples kept cool and air-dried or oven dried at 40OC for DNA extraction from 500 g subsamples.
Existing and new assays were applied to the extracted DNA to demonstrate that the primer and probe sets did not amplify non-target DNA and to show the incidence and densities of the pathogen across the regions sampled.
3.4.5 Southern and northern pasture soil (SHP.002 and 004) DNA has been extracted and stored from soil samples collected from the core sites from the early projects SHP.002 and SHP.004 undertaken by Greg Lodge and Graham Stirling.
Existing tests for root pathogens were applied to these samples. However, to make the best use of the stored DNA, it will be tested later in the program when a more comprehensive range of assays has been developed.
3.4.6 Pasture experiments (SHP.017 and 018) A field bioassay for pastures diseases was developed and established at sites in NSW, SA, WA in 2006 under the allied project SHP.017. The project was used to apply existing and new DNA assays to pasture in conjunction with germination, establishment and visual root health scores.
Likewise the assays were applied in 2006 under SHP.018 to assess selected treatments in EverGraze sites at Albany, WA and Wagga Wagga, NSW, the long-term lime trial (Managing Acid Soils Through Effective Rotations, MASTER, NSW DPI) site at Wagga and a P X grazing trial at Hall, ACT (CSIRO Plant Industry).
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4 Results and Discussion 4.1 Plant tests
4.1.1 Lolium/Festuca spp. test Initially one grass and legume species were selected for test development. Lolium was chosen as the grass assay because of its widespread importance across southern Australia in annual and perennial pastures.
Figure 1 shows the molecular phylogeny of Lolium and related species based on ribosomal ITS region sequences. It was not possible to separate the common Lolium spp., namely, L. multiflorum, L. perenne and L. rigidum. This is not surprising because these species readily hybridise in the field. The diagnostic region selected for development of the assay detects and quantifies all of the common ryegrass species and tall fescue, so is useful where these species don’t need to be distinguished. For the purpose of this report and for simplicity, it is referred to as the Lolium test.
The Lolium test is sufficiently sensitive to detect 1 mg freeze-dried roots/500 g soil (Figure 2), and one seed per 500 g soil.
Pot experiments have also been conducted to determine the persistence of DNA in dead roots. The results show the DNA degrades quickly (Figure 3); this means most of the plant DNA detected during the growing season in soil samples will be from living roots or viable seed.
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Lolium ITS sequence groups Lolium rigidum Lolium rigidum Schedonorus pratensis Micropyropsis tuberosa Lolium multiflorum Lolium temulentum Lolium subulatum Lolium canariense Lolium rigidum Lolium perenne Lolium remotum Lolium multiflorum Lolium persicum Lolium subulatum Schedonorus arundinaceus Lolium temulentum Schedonorus pratensis Festuca font-queri Festuca mairei Festuca arundinacea
Festuca atlantigena Festuca glaucescens Festuca letourneuxiana
Festuca durandoi
Vulpia ciliata Vulpia muralis Vulpia myuros Vulpia ciliata
Vulpia unilateralis
Vulpia bromoides Vulpia alopecuros
Vulpia fasciculata
Vulpia fasciculata
Vulpia brevis
Vulpia geniculata
0.1 Vulpia fontquerana
Figure 1. Molecular phylogeny based on ribosomal ITS region sequences of Lolium and related genera including Vulpia and Festuca. The position of the diagnostic region selected for development of the quantitative PCR test is indicated by the blue circle. Of the grasses found in the Australian pastures the test includes Lolium spp and some Festuca spp.
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AB
34 500 33 Seed Seed 32 400 31
30
29 300 28 y = 0.141x 2 27 200 R = 0.996 DNA (ng/g) DNA
PCR cycles PCR 26
25 y = -3.47x + 35.3 100 24 R2 = 0.988 23
22 0 1.0 1.5 2.0 2.5 3.0 3.5 200 400 600 800 1000 1200 1400 1600 34 500 33 Roots Roots 32 400 31
30
29 300 28 y = 0.155x 2 27 200 R = 0.991 DNA (ng/g) DNA
PCR cycles PCR 26
25 100 24 y = -3.34x + 33.5 y = 0.139x 2 2 23 R = 0.989 R = 0.993 22 0 1.0 1.5 2.0 2.5 3.0 3.5 200 400 600 800 1000 1200 1400 1600
Plant material (log(mg/kg)) Plant material (mg/kg)
Figure 2. Real-time PCR cycles (A) and DNA estimates (B) for Lolium perenne seed and L. perenne (solid line) and L. rigidum (dotted line) lyophilised roots added to soil at a range of concentrations up to 1600 mg/kg.
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Figure 3. Quantitative PCR assays used to monitor degradation of DNA in 200 mg freeze-dried Lolium perenne roots added to 500 g moist field soil
The concentration of DNA in roots for various Lolium spp. has also been determined. DNA concentrations are less in L multiflorum compared to L. rigidum and L. perenne (Figure 4). L multiflorum is a large plant type and may have more structural compounds per unit weight of root thus reducing the concentration of DNA.
pg DNA / g soil
Figure 4. Variation in DNA detected/g soil in 500 g soil samples spiked with 200 mg freeze dried roots.
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The effect of plant age on the concentration of DNA was likewise determined and found to decline with increasing age (Figure 5). If DNA concentrations are higher in young roots the plant DNA assays should be very responsive to growth or loss of new roots. (The fine roots are prone to being lost during washing).
500
400
300
200 g/g plant tissue) g/g plant LSD 5%
100 DNA ( DNA
0 6 8 10 12 Plant age (weeks)
Figure 5. Real-time PCR estimates of the DNA in lyophilised roots (solid symbol) and shoots (open symbol) from plants grown for 6, 9 and 12 weeks of Lolium perenne (solid line) and Trifolium subterraneum (dotted line) added to soil.
4.1.2 Trifolium subterraneum Sub clover, T. subterraneum was selected as the representative pasture legume because of it’s importance in southern Australia.
Figure 6 shows the molecular phylogeny of Trifolium and some related species based on ribosomal ITS region sequences. A good diagnostic site was identified to develop a test to distinguish T. subterraneum from other species. The test detected all three T. subterraneum subspecies, brachycalycinum, subterraneum and yanninicum (e.g. cvs Rosedale, Seaton Park and Gosse, respectively).
The Trifolium test is about 5 PCR cycles more sensitive than the Lolium test. It can detect 10 seeds or 10 mg dry root / kg of soil at 30 PCR cycles (Figure 7) compared to 35 for Lolium seed (Figure 2).
DNA in freeze-dried Trifolium roots also decayed rapidly in moist soil (Figure 8), indicating that DNA detected by the test will be largely from live roots and viable seed.
The mean DNA concentrations in roots of the T. subterraneum cultivars, Grosse, Rosedale and Seaton Park were 115, 130 and 139 μg/g, respectively, but these were not statistically significant different. This material was all the same age, but as with Lolium the concentration of DNA in T. subterraneum declined with age (Figure 5).
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Medicago platycarpa Medicago ruthenica Medicago popovii
Trifolium pallescens
Vicia aintabensis
Trifolium incarnatum Trifolium incarnatum var. molineri
Trifolium israeliticum Trifolium lupinaster Trifolium subterraneum Grosse Trifolium batmanicum Trifolium subterraneum Trifolium subterraneum Seaton Park Trifolium decorum Trifolium subterraneum Rosedale
Trifolium hirtum
Trifolium mattirolianum Trifolium burchellianum Trifolium semipilosum Trifolium ambiguum Trifolium michelianum Trifolium hybridum Trifolium glomeratum Trifolium riograndense Trifolium nigrescens Trifolium retusum Trifolium longipes Trifolium occidentale Trifolium stoloniferum Trifolium amabile AF053144 Trifolium repens -white clover Trifolium bejariense Trifolium isthmocarpum
Trifolium buckwestiorum Trifolium gracilentum
Trifolium haydenii
Trifolium ciliolatum Trifolium albopurpureum
Figure 6. Molecular phylogeny based on ribosomal ITS region sequences of Trifolium and related genera. The position of the diagnostic region selected for development of the quantitative PCR test is indicated by the blue circle. The region selected provides a T. subterraneum specific test including the three subspecies.
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AB 28 500
27 Seed Seed
26 400
25
24 300
23
22 200 DNA (ng/g) DNA
PCR cycles PCR 21 20 y = -3.59x + 30.3 100 y = 0.250x 19 R2 = 0.995 R2 = 0.991
18 0 1.0 1.5 2.0 2.5 3.0 3.5 200 400 600 800 1000 1200 1400 1600 28 500
27 Roots Roots
26 400
25
24 300
23
22 200 DNA (ng/g) DNA
PCR cycles 21 20 y = -3.47x + 30.2 100 y = 0.207x 2 2 19 R = 0.996 R = 0.993
18 0 1.0 1.5 2.0 2.5 3.0 3.5 200 400 600 800 1000 1200 1400 1600
Plant material (log(mg/kg)) Plant material (mg/kg)
Figure 7. Real-time PCR cycles (A) and DNA estimates (B) for Trifolium subterraneum seed and lyophilised roots added to soil at a range of concentrations up to 1600 mg/kg.
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Figure 8. Quantitative PCR assays used to monitor degradation of DNA in 200 mg freeze-dried Trifolium subterraneum roots added to 500 g moist field soil
4.1.3 Phalaris aquatica Phalaris aquatica is the most persistent and productive temperate perennial pasture grass used the tablelands and slopes of NSW, and given that this project had strong collaborative linkages with work in this area, it was included as an additional plant target during the course of the project. Also, P. aquatica is the most widely-sown perennial grass for the inland high-rainfall, and adjacent cropping, areas in southern Australia, so its inclusion as a target compliments the Lolium test.
The analysis of the genetic relationships for Phalaris and related species allowed the design of primers and probe for a specific P. aquatica test (Figure 9).
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Phalaris minor 138336 Position of the Phalaris aquatica test based on Internal Transcribed
Phalaris minor SARDI 020806 Spacer region sequences Phalaris canariensis 18679 Phalaris paradoxa 1977 Phalaris coerulescens 1970/71
Phalaris coerulescens 12587
Phalaris coerulescens 1146 Phalaris aquatica x P arundinacea x P aquatica AT cultivar Phalaris aquatica cv Sirolan Phalaris aquatica cv Australian Phalaris truncata Phalaris aquatica cv Landmaster SARDI Phalaris aquatica cv Sirocco also called P elongata Phalaris aquatica cv Holdfast Phalaris aquatica cv Sirosa SARDI Lolium perenne
Phalaris arundinacea cv Venture
Phalaris arundinacea cv Palaton
Lachnagrostis uda Lachnagrostis ammobia Agrostis magellanica Lachnagrostis leptostachys Agrostis pallescens Agrostis muscosa Agrostis personata Agrostis petriei Agrostis imbecilla Agrostis muelleriana Deyeuxia aucklandica Deyeuxia avenoides
Phleum phleoides
Dichelachne lautumia
Deschampsia sukatschewii subsp. borealis Deschampsia alpina Calamagrostis epigejos Deschampsia cespitosa Deschampsia cespitosa
Figure 9. Molecular phylogeny based on ribosomal ITS region sequences of Phalaris and related taxa.
As the Phalaris test was added late in the project, full calibration and sensitivity testing with dried roots and seed is pending. P. aquatica seed was added to soil as part of an experiment to determine the ability to detect plant DNA in subsoil, and the sensitivity is similar to that of the Lolium assay. The assay easily detected 10 phalaris seeds per 200 g soil and by extrapolation should quantify P. aquatica DNA to down the equivalent of 1 seed per 500g of soil (Figure 10).
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AB 30 300
29 Seed Seed
28
27 200 26
25
24
DNA (ng/g) DNA 100 PCR cycles PCR 23 22 y = -3.402x + 32.718 y = 0.164x 21 R2 = 0.997 R2 = 0.984
20 0 1.0 1.5 2.0 2.5 3.0 3.5 200 400 600 800 1000 1200 1400 1600 30 700
29 Roots Roots 600 28
27 500
26 400 25 300 24 DNA (ng/g) DNA PCR cycles 23 200 22 y = -3.5552x + 32.394 y = 0.324x 2 100 2 21 R = 0.987 R = 0.952
20 0 1.0 1.5 2.0 2.5 3.0 3.5 200 400 600 800 1000 1200 1400 1600
Plant material (log(mg/kg)) Plant material (mg/kg)
Figure 10. Real-time PCR cycles (A) and DNA estimates (B) for Phalaris aquatica seed and lyophilised roots added to soil at a range of concentrations up to 1600 mg/kg.
4.2 Oomycete tests
4.2.1 Phytophthora clandestina P. clandestina is a serious pathogen of Trifolium subterraneum, so was selected as one of two oomycete tests developed in the first phase of the work.
The phylogenic basis for the test is shown in Figure 11. Tests on DNA of the closely related species P. infestans and P. iranica confirmed the assay is specific to P. clandestina.
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ipomoeae phaseoli nicotianae mirabilis sojae infestans tentaculata clandestina andina iranica palmivora idaei cactorum iranica clandestina pseudotsugae arecae cactorum hedraiandra
megakarya
citrophthora
capsici drechsleri cryptogea citricola erythroseptica medicaginis trifolii
fragariae var. rubi megasperma fragariae var. fragariae Planned lateralis cambivora
syringae cinnamomi
vignae porri primulae
Figure 11. Molecular phylogeny based on ribosomal ITS region sequences of Phytophthora spp. A test for P. clandestina is species specific and is indicated in the upper circle. A proposed second test for P. medicaginis/trifolii/megasperma is indicated by the lower circle.
The Phytophthora clandestina assays can detect less than 1 μg of fungal DNA/g soil (Figure 12). Based on the pure DNA standards the assay has more than adequate efficiency and sensitivity for a workable test for soil.
The current methods for estimating Phytophthora populations by plating and baiting methods often express the density as oospores per unit weight/volume of soil. However, these are only the survival stage and such units are not overly applicable for the full life cycle. Attempts were made to produce large quantities of oospores for calibrating the test but this proved technically difficult.
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4000 3500 y = 64.15x - 10.60 3000 R2 = 0.99 2500 2000 1500
pg g/ DNA soil 1000 500 0 0 102030405060 µg freeze dried hyphae / g soil
Figure 12. Detection of Phytophthora clandestina in field soil spiked with freeze-dried hyphae by DNA assay
4.2.2 Pythium Clade F A test for Pythium irregulare was selected because it was an important oomycete, which causes of damping off diseases in pastures.
Specific tests were initially developed for P. irregulare but these proved to be too specific and did not detect many of the isolates from cropping and pasture soils in southern Australia (Appendix 1). Pythium taxonomy is difficult, and it appears too many species have been classified as P. irregulare.
A new assay was designed to detect Pythium Clade F (Lévesque and deCock 2004). Clade F includes; P. irregulare (groups 1, 2, 3 and 4), P. cylindrosporum, P. debaryanum, P. intermedium, P. kunmingense, P. macrosporum, P. mamillatum, P. paroecandrum, P. regulare, P. spinosum, P. sylvaticum, P. viniferum, and P. violae (Figure 13).
Some delays in test development were caused by incorrect identification and contamination in cultures obtained from different collections. All cross-reactions with species outside Clade F investigated were due to incorrect identification.
The Pythium Clade F assays can detect less than 1 μg of fungal DNA/g soil (Figure 14). Based on the pure DNA standards the assay has more than adequate efficiency and sensitivity for a workable test for soil.
The four Pythium isolates from Paskeville SA, not detected by the Clade F assay (Appendix 1), possibly belong to Clade I. A test for Clade I would be desirable to quantify P. ultimum, which is known to cause problems in southeastern Australia (Figure 13). Pythium is proving to be more complex than expected and tests for other clades may be required.
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P. vexans Pythium clade F & I ITS based test Clade K Pythium irregulare P. macrosporum P. spinosum P. viniferum P. kunmingense P. intermedium P. debaryanum P. irregulare Clade F P. violae P. mamillatum P. sylvaticum P. paroecandrum P. irregulare P. cylindrosporum P. regulare Clade G P. okanoganense P. nagaii Clade G P. paddicum
P. iwagamai
P. violae P. heterothallicum
Clade B2 Clade I P. violae P. dissotocum
P. splendens
P. ultimum var. sporangiiferum P. inflatum Clade H P. ultimum Clade B1 P. ultimum var. ultimum
Clade D P. prolatum
P. acanthicum P. anandrum P. dimorphum P. periplocum P. helicandrum Levesque & Cock 2004 P. undulatum
Figure 13. Molecular phylogeny based on ribosomal ITS region sequences of Pythium (Lévesque and de Cock 2004). A test for Clade F captures the species in the upper circle. A proposed second test for Clade I is indicated by the lower circle.
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20000 18000 y = 338.29x + 12.03 16000 R2 = 1.00 14000 12000 10000 8000
pg DNA / g soil / pg DNA 6000 4000 2000 0 0 102030405060 µg freeze dried hyphae / g soil
Figure 14. Detection of Pythium irregulare in field soil spiked with freeze-dried hyphae by DNA assay specific to Pythium Clade F.
4.3 Fungal tests
4.3.1 Rhizoctonia solani R. solani anastomosis groups (AG) 2.1 and 2.2 can cause damping off diseases in pasture legumes and brassica spp. Assays for both are being evaluated for use in pastures by this project. These tests are being developed by a project funded by Horticulture Australia Ltd along with tests for AG3, AG4 and AG5 as research and potential management tools for soilborne diseases of potatoes.
The test for AG 4 could also prove useful in pasture research, since several recent cases have found it to be associated with poor performing sub clover pastures.
Figure 15 shows the molecular phylogeny of the R. solani AGs. AGs often exhibit distinct pathology. The anastomosis groups are genetically isolated, and rDNA ITS sequence differences allow development of specific assays.
R. solani exists as vegetative hyphae and survives in pieces of organic matter in the soil. The DNA assays provide the first practical method of quantifying this fungus in soil.
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AG-11 AG-Bi AG-Bi Rhizoctonia solani AG (anastomosis groups) based on internal transcribed spacer (ITS) region.
AG-11
AG-4 HG-I
AG-5 AG-4 HG-II AG-8
AG-7
AG2-2
AG-3
AG-9 AG2-2
AG2-1 AG2-2
AG2-2 AG2-2 AG2-1 AG-10 AG2-1 AG2-1 AG2-1
Figure 15. Molecular phylogeny based on ribosomal ITS region sequences of Rhizoctonia solani anastomosis groups (AGs). A test for AG 2.1 and 2.2 were further developed under this project and are complemented by the existing test for AG 8 and the new tests for AG 3, 4 and 5 (developed in a parallel project), as these can all occur in pastures.
4.3.2 Arbuscular mycorrhizal fungi Arbuscular mycorrhizal fungi (AMF) are important in that they establish a mostly symbiotic relationship with plants. They colonise the roots and have hyphal networks that extend through the soil. In exchange for photosynthates, AMF provide the host plant with P and water that would otherwise be beyond the reach of the root system. Although of significance, the inability to grow AMF in pure culture constrains research on their role in pastures. Therefore, AMF were selected as the first beneficial soil fungi for which tests were developed. The selection of AMF involved establishing a strong collaboration with Prof. Sally Smith, Soil and Land Systems, School of Earth & Environmental Sciences, University of Adelaide, Waite Campus.
AMF species fall into six clades, designated a to f (Figure 16). Since pure AMF DNA is difficult to obtain, the assays were developed using DNA extracted from a set of 75 pasture soils collected from around Australia (Appendix 2). To confirm specificity of each assay the PCR products from selected samples were cloned and sequenced. This revealed that AMF f was not specific. AMF f is not considered important in Australian soils, so the lack of specificity explains the high incidence detected in the field samples in Appendix 2. Since AMF f are not considered important in Australia it was decided further work on this test was not warranted.
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Figure 16. Molecular phylogeny based on ribosomal ITS region sequences of arbuscular mycorrhizal fungi showing six clades. Primers and probes for quantitative PCR for five of the clades have been developed as part of this project.
The AMF results in Appendix 2 are the Ct values. The Ct value is number of PCR cycles required to amplify the target DNA to a detectable, threshold level. Normally the Ct value for each sample is converted to a DNA level via a calibration curve fitted through a series of standards. Due to difficulty in obtaining AMF DNA we have not yet been able to develop standards. In theory the DNA level doubles with each PCR cycle, so the lower the Ct value the higher the AMF DNA level in the soil sample.
While the AMF a, b, c, d and e assays appear to be working we still need to be cautious interpreting the results. For example, the Qld samples in Appendix 2 came from an organic amendment trial. The low mycorrhizae levels were expected in the bare fallow treatments but not in the soybean plots. Fallow is known to be associated with decline of AMF levels, but soybeans are mycorrhizal.
Work on validation and calibration of AMF tests a, b, c, d and e is continuing in collaboration with Australian Research Council (ARC)-Linkage project, with SAGIT as linkage partner: LP0669161 - Novel technologies to resolve interactions between arbuscular mycorrhizal (AM) fungi, phosphate fertilisers and root disease in wheat production (Prof. Sally Smith, The University of Adelaide).
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Trichoderma
Trichoderma is an important fungal genus associated with suppression of plant fungal diseases, including Pythium, Rhizoctonia and take-all (Gaeumannomyces graminis). Particular isolates have been developed as commercial products and work to increasing the range of Trichoderma- based biopesticides continues around the world. So, Trichoderma was selected as the second group of beneficial fungi for the development of tests.
Figure 16 shows the molecular phylogeny for Trichoderma spp., which fall into two distinct groups, except for a small number of outliers.
The taxonomy of Trichoderma is complex and misclassification of isolates is suspected to be common. A large amount of sequence information is available in the public domain, and this has been used to design the new assays. The species often associated with disease suppression fall broadly into two groups and tests have been designed to detect each (Figure 16).
The test B primers and probe are working well, however we are having difficulty with finding a diagnostic site that will work for Test A. Test A includes a lot of species associated with disease suppression so we need to get this test to work. Evaluation of the DNA assays to correlate Trichoderma populations to disease suppression will be performed in collaboration with a SAGIT funded project, DNA monitoring tools for suppressive microflora (Kathy Ophel Keller).
Developing more specific assays for Trichoderma will be a challenge. Most of the public domain sequence information is in the ITS regions, and is surprisingly devoid of good diagnostic sites to design TaqMan® assays. The amount of sequence information in other regions is limited, if tests need to be developed in these then a lot more sequencing will be required and other techniques may need to be evaluated.
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Hypocrea catoptron
Test A
SARDI isolate TC SARDI isolate TB SARDI isolate TH SARDI isolate TA Hypocrea albocornea Hypocrea lixii Trichoderma tomentosum Trichoderma harzianum Hypocrea cf. nigricans Hypocrea virens Trichoderma sp. Hypocrea vinosa Trichoderma aureoviride Trichoderma hamatum Trichoderma velutinum Trichoderma spirale Hypocrea ceracea Trichoderma inhamatum Hypocrea virescentiflava Hypocrea straminea Hypocrea cuneispora Trichoderma longipile Hypocrea tawa Trichoderma aggressivum f. europaeum
Trichoderma aggressivum
Hypocrea semiorbis
Trichoderma rogersonii
Trichoderma koningiopsis Hypocrea koningii SARDI isolate TD Trichoderma brevicompactum Trichoderma intricatum
Trichoderma harzianum Hypocrea sp. SARDI isolate TG SARDI isolate TF Trichoderma atroviride
Trichoderma parceramosum
Trichoderma asperellum Trichoderma longibrachiatum Trichoderma harzianum Trichoderma hamatum Trichoderma harzianum
Test B Test A outliers
Trichoderma viride
Figure 16. Molecular phylogeny based on ribosomal ITS region sequences of Trichoderma spp. These relationships point to the development of two high level tests (A and B), with the Test A being either designed to include several outliers or restricted to a more tightly clustered group.
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4.4 Nematode tests
4.4.1 Clover cyst nematode Heterodera trifolii Cyst nematodes are highly specialised parasites of plants with narrow host range and usually quite damaging. The only cyst nematode common in Australian pastures is Heterodera trifolii, which attacks white clover (Trifolium repens), an important pasture legume in southern, high rainfall areas with long growing season. Given the recognised importance of this nematode in New Zealand and limited studies in Australia, H. trifolii was selected for test development.
A molecular phylogeny of cyst nematodes is presented in Figure 17. A test for the “avenae” group which includes cereal cyst nematode has be developed and delivery commercially for many years. Based on the available diagnostic regions, a test for clover cyst nematode (Heterodera trifolii) would need to include H. ciceri (a cyst nematode of chickpea not found in Australia).
The standard unit of measure for cyst nematode is eggs/g soil. The eggs are produced by the female are retained with in the body, with the body wall forming a cysts when the nematode dies. Each cyst contains about 200 to 300 eggs. The test developed has been calibrated against eggs extracted from cysts and added to soil (Figure 18). The assay is very sensitive, detecting one egg/10 g soil.
The assay should be very useful to monitor decline of clover cyst nematode in rotations with white clover.
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Cereal Cyst Nematode Heterodera filipjevi Heterodera sacchari Heterodera australis Heterodera avenae Heterodera cynodontis Heterodera aucklandicaHeterodera arenaria Heterodera sorghi Heterodera pratensis Heterodera mani Heterodera iri Heterodera ustinovi Heterodera bifenestra Heterodera mediterranea Heterodera latipons Heterodera sp. Heterodera medicaginis Heterodera schachtii Heterodera cajani Heterodera glycines H. ciceri Heterodera goettingiana Heterodera trifolii Heterodera urticae Clover Cyst Nematode Heterodera carotae Heterodera cruciferae
Heterodera hordecalis Heterodera riparia Heterodera ripae Heterodera vallicola Heterodera oryzicola Heterodera humuli
Heterodera fici Heterodera litoralis Heterodera elachista Heterodera turcomanica Heterodera salixophila
Heterodera cyperi
Heterodera mothi
Globodera sp.
Globodera artemisiae Globodera achilleae Globodera tabacum tabacum Heterodera zeae Globodera rostochiensis Globodera pallida Globodera millefolii Globodera tabacum solanacium Globodera tabacum virginiae
Figure 16. Molecular phylogeny based on ribosomal ITS region sequences of cyst nematodes, Heterodera and Globodera.
38 37 y = -3.36x + 33.37 36 R2 = 0.98 35 34 33 Ct values 32 31 30 -1.00 -0.75 -0.50 -0.25 0.00 0.25 0.50 0.75 1.00 Log(eggs)/g soil
Figure 18. Detection of Heterodera trifolii eggs in spiked field soil by Taqman assay.
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4.4.2 Root Knot Nematode, Meloidogyne spp. 4.4.2.1 Meloidogyne javanica/incognita/arenaria Nematodes in the genus Meloidogyne are collectively known as root knot nematodes (RKN) and are some of the most damaging nematodes of agricultural crops. The phylogeny of the main Meloidogyne species including all of those present in Australia is summarised in Figure 19.
A test was designed to detect the most damaging group, which includes M. javanica, M. incognita, M. arenaria (Figure 19). A collection of each of these species, which are all present in Australia, was assembled to check the specificity and sensitivity of the assay and develop standards.
The standard unit of measure for root knot nematode is eggs per g soil. Root knot nematode causes serious production losses on a broad range of crops around the world. In pastures, susceptible plants include legumes e.g. sub clover and lucerne. Meloidogyne javanica/incognita/arenaria prefer warm soils, some other Meloidogyne spp. are more prevalent in cooler districts.
The root knot assay is efficient and sensitive (Figure 20). It detected 0.4 eggs/g of soil, the lowest level evaluated, and is probably capable of detecting at least 0.1 egg/g soil. The DNA level detected per root knot nematode egg is 1.5 times greater than for the average P. penetrans nematode in the above assay (see below).
The root knot nematode assay needs to be more sensitive than for other nematodes because root knot nematodes are more damaging so have lower economic thresholds. Fortunately, the eggs are produced in clusters, which assists with detection. The current assay should be sensitive enough for research and possibly as a management tool.
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Root knot nematode M. naasi (Meloi dogyne spp) ITS M. trifoliophila
M. hapla M. f allax Meloidogy ne artiellia M. chitwoodi
Meloidogy ne minor
M. incognita M. jav anica M. arenaria
Meloidogy ne pany uensis
Figure 19. Relationship between root knot nematode species based on ITS rDNA sequences. The group in red are the most damaging but cannot be readily distinguished by a suitable diagnostic difference in the ITS region.
3000 2500 y = 132.59x + 23.81 R2 = 0.98 2000 1500 1000
pg g DNA / soil 500 0 0 5 10 15 20 25 Eggs+J2s/g soil
Figure 20. Detection of Root Knot Nematode (Meloidogyne javanica/incognita/arenaria) eggs in spiked field soil by Taqman assay.
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4.4.2.2 Other Meloidogyne spp Meloidogyne fallax and M. hapla are root knot nematodes that occur in cool areas. They have been identified as a priority for future development by HAL because of their importance in potatoes. Their host ranges also include important pasture legumes such as lucerne and white clover, so development of these tests will benefit pasture research.
4.4.3 Pratylenchus penetrans Root lesion nematodes, Pratylenchus spp., are root-invading nematodes that cause damage to plants when population densities are high and plants are under stress. Stress conditions include drought, poor fertility and potentially some grazing regimes. Pratylenchus neglectus and P. thornei have been identified as problematic in crop/pasture rotations and DNA tests have already been developed to monitor their population density.
Worldwide, P. penetrans is an important species and was targeted for test development by this project. It has been detected in Australia, but little is known about its distribution. The P. penetrans assay can detect less than one nematode per gram of soil (Figure 22). The sensitivity of this assay is similar to those for P. neglectus and P. thornei and should detect levels below the economic threshold for production losses in pastures.
The slope of the regression line presented in Figure 21 represents the average DNA level detected per nematode and is lower than for P. neglectus and P. thornei. This is probably due to the population of P. penetrans used containing a significant proportion of second stage juveniles. We generally report nematode results standardised to a particular stage of nematode development. Another experiment will be conducted using hand picked adults to recalibrate the assay over a smaller range. This is time-consuming work and we are still trying to develop the most efficient technique to conduct these studies.
Further species maybe required when more is learnt about the distribution of specific species within Australia. Pratylenchus species are generally distinct in the ITS region, and there appear to be enough potential diagnostic sites to develop a series of specific assays (Figure 21).
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Pratylenchus penetrans
Pratylenchus thornei
Pratylenchus brachyurus
Pratylenchus vulnus
Pratylenchus coffeae Pratylenchus crenatus
Pratylenchus teres Pratylenchus neglectus Pratylenchus zeae
Figure 21. Molecular phylogeny based on ribosomal ITS region sequences of root lesion nematodes, Pratylenchus. Tests for P. neglectus and P. thornei have be developed and delivered commercially for many years. A test for P. penetrans was developed under this project and tests for P. zeae and P. teres are under developed by an allied project.
2500
2000 y = 85.051x R2 = 0.9835
1500
1000 DNA (pg/gDNA . soil)
500
0 0 5 10 15 20 25 P. penetrans (no./g soil)
Figure 22. Detection of Pratylenchus penetrans juveniles in spiked field soil.
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4.4.4 Beneficial Nematodes There is a wide range of nematodes in soil that perform different roles. They broadly fall into four groups, these that feed on bacteria, fungi, plants and other nematodes. The structure of the nematode population in soil can be used as an indicator of environmental impact. Considerable work was done in collaboration with SHP.002 to develop tests for each trophic group, but unfortunately the DNA sequence information did not allow a manageable number of tests to be developed. This work was deferred in favour of developing the tests discussed above.
The nematode phylogeny (Figure 23) highlights the difficulty of developing tests for trophic groups. Plant, fungal and bacterial feeders cannot be readily separated. There is more chance of developing tests for mononchids and dorylaimids. These nematodes have long generation times, slow reproduction rates and are good indicators of stable, healthy soil ecosystems. However, they are difficult to culture, and this will present some significant technical challenges when developing tests.
Group Mononchids Dorylaimids Plant/fungi Bacteria/other
?
?
? ?Probes
Figure 23. Phylogenic relationship between soil dwelling nematodes based on rDNA sequences. The species have been divided into mononchids and dorylaimids (long generation species), stylet-bearing nematodes that feed on plants and fungi and others (mostly bacterial and substrate feeders). The two nematode Orders, Secernentea (dotted line) and Adenophorea (continuous line) are indicated.
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4.5 Glasshouse and field applications
4.5.1 DNA assays to monitor treatment responses in plant roots The decline in target DNA following treatment of L. perenne plants by defoliation or herbicide application is shown in Figure 24. In the first experiment, conducted in a growth room, there was not significant change in root dry weight in the 10 days following treatment (Figure 24A). During this time the dry weight of the untreated plants increased slightly (y = 0.310 + 0.292x, P=0.01).
The soil DNA concentration in the pots of treated plants declined significantly. For the defoliation treatment y = 321 – 26.0x, P = 0.02 and herbicide treatment y = 452 – 36.2x, P = 0.002. These regressions were not significantly different from each other, but were significantly different from the untreated (P<0.001), which did not change significantly over the period.
In the second experiment, conducted in the glasshouse, there was a significant change in root dry weight during the 18 days of the experiment (Figure 24B). The root mass of the untreated plants increased significantly (y = 0.955 + 0.0191x, P = 0.016) while the root mass of the treated plants declined (defoliation, y = 0.796 + 0.0154x, P = 0.049; herbicide, y = 0.791 + 0.0175x, P = 0.026).
The DNA assay was even more sensitive in detecting the treatment responses. Soil DNA concentrations in the pots of the treated plants significantly declined. For the defoliation treatment, y = 242 + 15.3x, P = 0.004 and for the herbicide treatment, y = 446 + 28.5x, P < 0.001. There was no significant linear trend for the untreated plants. However, the soil DNA concentrations in the untreated pots were increasing until around week 8, and then either reached a plateau or declined thereafter. The week 8 results were significantly higher than the others and may be do to chance variation in plant sizes. Figure 25 shows that the top growth of untreated pots also reached a plateau at around week 8. These results could indicate the root systems had saturated the pots around this time, thus constraining both top and root growth.
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A B 1.8 1.8
1.6 1.6
1.4 1.4
1.2 1.2
1.0 1.0
0.8 0.8
0.6 0.6
0.4 0.4
Root dry weightRoot dry (g/pot) 0.2 0.2
0.0 0.0 2 4 6 8 10 2 4 6 8 10 12 14 16 18
400 400
300 300 g/pot)
200 200 DNA ( DNA
100 100 Root
0 0 2 4 6 8 10 2 4 6 8 10 12 14 16 18
Time (days) Time (days)
Figure 24. Change in Lolium perenne root biomass and rDNA content following complete defoliation (dashed line), treatment with glyphosate (dotted line) and untreated (solid line) for plants grown in (A) a controlled environment room and (B) a glasshouse. Bars = LSD 5%.
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1.6
t 1.2
0.8 Shoot dry weight (g/po weight dry Shoot 0.4
Cut Herbicide None
0 0 2 4 6 8 10 12 14 16 18 Time (days)
Figure 25. Changes in top growth of the untreated and herbicide treated pots during the course of the experiment.
4.5.2 Quantification of root rDNA in mixed plant populations Growing the three Lolium spp. in a range of Phalaris densities did not produce any statistically significant effect on Lolium root density measured by dry weight or DNA assay due to high variability between individual Lolium plants.
The mean dry weight of roots and Lolium DNA content per pot did vary between species; L. multiflorum, L. perenne and L. rigidum produced 119, 60 and 58 mg root/pot (LSD 5% = 48) and 64, 24 and 45 mg Lolium rDNA/pot (LSD 5% = 24), respectively. Although the root weight of the single Lolium plants was highly variable, the ratio of roots to shoot weight was more stable. Figure 26 shows the relationship between shoot weight and root dry weight and soil DNA levels. Highly significant regressions (P<0.01) were found using both measures and both accounted for a similar proportion of the variation (c. 70%). Regression analysis grouped by Lolium sp. did not show any significant differences in root/shoot relationship.
The responses in root mass and root DNA for L. perenne and T. subterraneum when grown at different densities, alone or together, are shown in Figure 27. Both root mass and DNA content increased with increasing plant density (Figure 27A). As the density increased, the rate of
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increase for T. subterraneum declined in comparison to that for L. perenne. Similarly for the DNA estimation, the rate of increase of T. subterraneum was lower, although the absolute amount was initially greater. Regression of target DNA content against root mass confirmed the response of the two species differed (the slopes were significantly different, P=0.05). When the plants were grown at varying ratios of L. perenne to T. subterraneum to give a combined density of 16 plants per pot (Figure 27B), the response for both species using both measures was the same. Regression of target DNA content against root mass did not differ significantly between species. This experiment provided more data than can be presented within the scope of this report and these will be presented in detail elsewhere.
A B 0.3 120
100
0.2 80 g/pot)
60 DNA ( DNA
0.1 40
Y = 016x + 0.019 Root y = 63.4x +14.4 20 Root dry weight (g/pot) Root dry R2 = 0.68 R2 = 0.73
0.0 0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6
Shoot dry weight (g/pot) Shoot dry weight (g/pot)
Figure 26. Relationship between shoot dry weight and roots estimated by dry weight and quantitative DNA assay.
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A B 1.2 1.2
1.0 1.0
0.8 0.8
0.6 0.6
0.4 0.4
0.2 0.2 Root dry weight Root dry (g/pot) Lolium Trifolium Lolium Trifolium
0.0 0.0 2 4 6 8 10 12 14 16 2 4 6 8 10 12 14 16 240 240
200 200
160 160 g/pot)
120 120 DNA ( DNA 80 80 Root 40 40 Lolium Trifolium Lolium Trifolium
0 0 2 4 6 8 10 12 14 16 2 4 6 8 10 12 14 16 Plants Plants
240 240
y = 134x + 83.6 y = 233x + 1.2 200 2 200 R = 0.923 R2 = 0.979
160 160 g/pot)
120 120 DNA ( DNA
80 80 Root
40 y = 207x - 27.9 40 y = 153x + 35.0 R2 = 0.977 R2 = 0.950
0 0 0.0 0.20.4 0.6 0.81.0 1.2 0.0 0.2 0.40.6 0.8 1.01.2 Root dry weight (g/pot) Root dry weight (g/pot)
Figure 27. Root dry weight and specific root rDNA for Lolium perenne (solid line) Trifolium subterraneum (dashed line) grown (A) separately at varying densities from 2 to 16 plants per pot and (B) in mixtures with a combined 16 plants per pot.
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4.5.3 Quantification of plant DNA in subsoils To use the DNA assays to map root distribution in the soil profile, we need to confirm that the extraction system would work for a broad range of subsoils. Soil samples were collected from the different layers form soil pits across southern Australia. The samples were spiked with T. subterraneum seed before DNA extraction. The DNA assays results for the different depths are presented in Figure 27. Some sub-soils were problematic and adjustments were made to the DNA extraction system to achieve acceptable results. The variation in recovery was mostly within on standard deviation of the mean.
These results indicate that the DNA extraction system works well enough to study of root distribution and function down the profile.
Figure 28. Quantification of Trifolium subterraneum DNA in soil from a range of depths at eight sites in South Australia
4.5.4 Pasture soils across southern Australia To facilitate test development a collection of soil samples was assembled to cover a diverse range of pastures and soil types from across southern Australia. This collection was not designed to be a representative survey of soilborne pathogens, so the results presented below should not be used to make conclusive recommendations or interpretations at this stage.
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Detections (n=74) Target DNA (pg/g soil) Test (no.) (%) (min) (max) (median) (mean)
Pythium 72 97 3 561 53 88 Phytophthora 11 15 1 60 2 9 Rhizoctonia solani 35 47 1 1067 45 150 AG 2.1 AG 2.2 43 58 2 1761 181 309 AG 4 3 4 859 20,327 9,274 10,153 AG 8 4 5 4 97 7 29 H. trifolii 9 12 1 1282 23 261 P. penetrans 0 - - - - - AMF a 71 96 4 4,694 336 696 AMF b 53 72 3 2,970 146 387 AMF c 27 36 4 449 25 59 AMF d 45 61 6 5,635 72 322 AMF e 21 28 4 23 7 10
In this collection, Pythium Clade F was the most common pathogen detected. It is considered by some to be a relative minor pathogen in comparison to Phytophthora, although its apparently ubiquitous nature suggests its’ overall impact could be significant.
Some Rhizoctonia solani anastomosis groups were common and occurred at densities that are likely to be problematic for intolerant pasture species.
The absence of Pratylenchus penetrans in this study and to low occurrence of P. neglectus and P. thornei indicates that more work is required to check if other species are active in these regions. It is unlikely that plant-feeding nematodes do not have a role in pasture soil ecosystems. Pratylenchus, not identified to species, have been recorded in pastures in earlier work, so tests for P. crenatus, M. fallax and others might prove more useful.
AMFs are considered to have an important role in nutrient acquisition in plants. The tools developed in this project allow the study of the abundance and diversity of AMFs that has not be possible before. So this is seminal work that will need considerable further research to provide new insights into AMF significance and ecology in pasture soils and roots.
There was clearly a high degree of variability in target DNA concentrations measured (Figure 29). Such variability is an indicator that the significance of particular organisms varies greatly between sites and this variability is likely to be driven by management, floristic, climatic and soil factors. An understanding of this variability and its potential of positive or negative impact on pasture productivity will be facilitated as these DNA assays are applied within existing or future pasture research.
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Pythium Phytophthora AG 2.1 AG 2.2 AG 4 AG 8 H. trifolii AMF a AMF b AMF c AMF d AMF e
01234
Target DNA log10(pg DNA/g soil)
Figure 29. The log concentrations (non-zero only) of pathogen DNA from pasture soil samples collected across southern Australia. The box represents the inner half of the sample values and with median indicated by the line dividing the box. The width of the box is proportional to the square root of the number of positive samples and the lines represent the full range of the data (minimum to maximum values).
The data for individual samples is tabulated in Appendix 3.
Hieratical cluster analysis identified particular samples that stood out as distinct. Some examples follow.
Sample 3, an irrigated site at Flaxley SA, was distinct because of the relatively high levels of Rhizoctonia, a high density of H. trifolii and high densities of three AMF groups.
Sample 67, Taralga NSW, was distinct because it had a high density of Rhizoctonia AG4, which was only found at one other site. It also had the highest concentration of AMF a.
Sample 46, a white clover/perennial ryegrass pasture at Boyanup WA, had the highest density of Heterodera trifolii. This nematode is relatively specific to white clover and only detected at 9 sites, because most samples were from sites without white clover.
Sample 8, a lucerne/tall fescue pasture at Willalooka SA, gave the only significant detection of Rhizoctonia AG8.
Samples 55-62 were from a sugarcane field in Bundaberg, so are somewhat outside the main focus of the collection, but are notable for their low densities of AMFs and Pythium detected.
DNA from this soil collection has provided an opportunity to verify the specificity of the DNA probes and has been a valuable resource for this, its primary purpose.
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These data also show the diversity and abundance of target organisms across a wide geographic range and help in the determination of the relative value of the tests and the need for additional tests to be developed. This is exemplified in Figure 30, which show a comparison of Pythium Clade F and P. clandestina densities across the samples.
3 Pythium F P. clandestina 2
1
Pathogen DNA -Pathogen +1) soil log(pg/g 0 Pasture soil samples (NSW, Qld, SA, Vic, WA)
Figure 30. Concentrations of Pythium Clade F and Phytophthora clandestina for soil samples collected across southern Australian (ordered by Pythium DNA concentration)
As indicated above Pythium Clade F was the most commonly detected pathogen, whereas the other oomycete, P. clandestina, was only found at 11 sites. The current tests underestimate both the Pythium and Phytophthora species present. Additional tests are required to get a comprehensive assessment of the risk posed by these genera.
The data from individual samples highlight the variability and differences between sites. It is clear that studies to determine the importance of the organisms for pasture productivity and the impact of management on soil biology will need to focus on particular pasture systems.
More work is required to the risk posed by the different levels to pasture production.
4.5.5 Southern and northern pasture soil (SHP.002 and 004) Results will be presented in the final report
4.5.6 Pasture experiments (SHP.017 and 018) Final reports for both these projects have been lodged with MLA. The work demonstrated the merits and progress toward field application of the test developed under SHP.005.
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5 Success in Achieving Objectives The project has over achieved on its objective to develop a range of tests to quantify plant DNA in soil, different groups of plant pathogens including fungi oomycetes and nematodes, made strong progress to develop tests for beneficials, and demonstrated their potential value as research tools. Test development does not always progress smoothly, and sometimes this triggered decisions to reprioritise assays for development.
The application in glasshouse and field experiments and the engagement with pasture scientist undertaken as part of the project and in allied projects has confirmed the value of this approach and has stimulated strong interest.
6 Impact on Meat and Livestock Industry – now & in five years time This project has developed enabling technology consistent with the MLA Strategic Science charter to assist researchers to better study soil biology under pastures and accelerate the rate and potential of knowledge development which will support development of management strategies to favour soil biological health.
The demonstrated advantages that combining DNA assays provide as research tools to understand biological constraints in pastures and overcome the limitations of previous techniques, should facilitate rapid progress in identifying and alleviating biological constraints to pasture production.
While much more work needs to be done to achieve the benefits of the technology, sufficient progress has already been made for the technology to start to be used to assess field trials. The strong interest in use of the DNA assays to quantify plant roots will help to ensure uptake of the technology. There are clear potential benefits for scientists who want to study root distribution of key pasture species under different management systems, and for plant breeders who want to assess root architecture of different lines. Combining assays for plants, pathogens and beneficial organisms such as AMF should help scientists develop strategies to improve water use efficiency in pastures and adapt to changing conditions.
Five years from now we expect there will be a comprehensive range of assays developed and a significant number of scientists using the technology to assess field trails to develop better grazing systems, more water efficient pasture plants, and better pasture establishment strategies in high rainfall and drought prone regions.
7 Conclusions and Recommendations Although the progress has been pleasing there is still more work to be done before the technology can be routinely used in addressing biological constraints in pasture. A long term commitment to R&D is required achieve the benefits. However this is difficult in the short term due to the impact of the 2006 drought on funds available for R&D.
The following short term program should be funded to enable the program to continue and allow time to develop a longer term program.
Work program for the next 12 months should include:
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1. Continue development of assays for AMF and Trichoderma.
2. Priorities for new tests include: A lucerne test, this is a high priority to support a collaborating CRNM project. Work will commence on developing assays for: o Grass weeds; barley grass, vulpia and brome grass. o Pathogenic Oomycetes; Aphanomyces euteiches, Pythium Clade I, Phytophthora megasperma/trifolii /medicaginis), o Pathogenic fungi; Fusarium acuminatum/avenaceum, Phoma medicaginis var medicaginis). o Parasitic nematodes; Pratylenchus crenatus.
2. Field bioassays and mini-swards
To build on the techniques and data generated under SHP.017 a more bioassays sites should be conducted at the same sites as those established in 2006 to assess variation between seasons. The data collected in 2007 should contrast the drought conditions experienced in 2006.
A new mini-sward method being developed by Richard Simpson, (CSIRO Plant Industries) also need to be evaluated to examine the relationships between soil biology and pasture productivity.
3. EverGraze and MASTERS engagement
Continue to monitor the EverGraze sites in Wagga Wagga (Iain Hume) and Albany (Paul Sanford) to complement the results generated in 2006 by SHP.018. The aim is to look for changes in soil biology associated with the treatments become more established.
The MASTERS site (Guangdi Li, NSW DPI) should be sampled as last year to correlate plant DNA with pasture composition assessments. An additional Spring sampling should also be included to examine the impact of liming on root distribution down the soil profile. The lime treatments have moved through the profile and have caused changes in soil chemical and physical properties. These samples will be important for the field validation of the new plant tests and help demonstrate the potential of the assays to monitor root growth into hostile subsoils.
4. Lucerne establishment
The extended project to establish stronger linkages with an initiative of the National Action Plan for Dryland Salinity through the Centre of Natural Resource Management (CRNM) to address soilborne disease issues associated with lucerne establishment in pasture ley farming systems in the Murray Mallee of SA. Previous results indicate a significant soilborne disease problem caused by a complex of pathogens.
5. Delivery / training
Run two further training workshop to building on those delivered in 2007 under SHP.018 to cover the wider range of target organisms and to further encourage the adoption by the technology by other pasture research teams.
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8 Bibliography Lévesque, C. A. and A. W. A. M. de Cock (2004). Molecular phylogeny and taxonomy of the genus Pythium. Mycological Research, 108: 1363–1383.
9 Appendices 9.1 Collaborators and contributors
Dr Bob Hannam, RJ Hannam & Co., advice, SHP.018 collaborator Dr Richard Simpson, CSIRO, Canberra, ACT, advice and provision of soil samples, SHP.017 and SHP.018 collaborator Dr Martin Barbetti, UWA, literature Review, technical input Dr K Sivasithamparam, UWA, literature Review, technical input Dr Kathy Ophel Keller, SARDI, guidance and advice Di Hartley, CSIRO Entomology, design of DNA primers and probes Dr Sue Wiebkin and Dr Herdina, SARDI, for developing DNA assays Dr Mingpei You, Dept Agriculture, WA, provision of cultures Dr Graeme Stirling, Biological Crop Protection Pty Ltd, Moggill, Qld, advice and provision of soil samples Dr Greg Lodge, NSW DPI, Tamworth, advice and provision of soil samples Andrew Craig, SARDI, Struan, provision of soil samples Cam Nicolson and Rohan Wardle, Southern Farming Systems Ltd, provision of soil samples Neil James, DPI Victoria, provision of soil samples Bill Russell and Martin Staines, Dept Agriculture, WA, provision of soil samples Various farmers and consultants in South Australia and Victoria, provision of soil samples Dr Suzanne McKay – SARDI, SHP.018 collaborator Dr Steven Wakelin – CSIRO Land and Water, SHP.018 collaborator Dr Guangdi Li – NSW DPI, MASTER Site Manager, SHP.018 collaborator Dr Michael Friend – CSU, EverGraze Site Leader, Wagga Wagga, SHP.018 collaborator Dr Paul Sanford – DPI WA, EverGraze site Leader, Albany, SHP.018 collaborator Ina Dumitrescu, Danuta Szot, Irena Dadej, Russell Burns and Aidan Thomson, sample processing
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9.2 Draft Press Release
New research tools for pasture soil biology
Dr Alan McKay and his team from the South Australian Research and Development Institute (SARDI) in Adelaide have developed a new array of DNA assays as research tools for scientists to study biological constraints in pasture trials.
To maximise pasture production and demonstrate livestock industries are good custodians of soil resources and the environment, it is important that researchers can assess the detrimental and beneficial impacts on soil microbial communities.
The new methods involve extraction of DNA from all of the organisms in a single soil sample. The DNA is then tested using a range of assays to quantify specific organisms, community structure and frequency of key genes that drive important biochemical processes.
Dr McKay says “the information we can now get from DNA extracted from a single soil sample would have taken a huge effort and the skills of many specialists using the old methods. So this development can be seen as moving pasture soil research into a new era.”
The SARDI team has been funded under a Pasture Soil Biology Initiative lead by Meat and Livestock Australia in partnership with Australian Wool Innovation Ltd and Grains Research and Development Corporation.
The new assays cover pathogenic and beneficial soil microorganisms and plants, vastly extending the capacity to study role and impact of soil organisms and their interaction with pasture plants over those previously developed for field crops.
Dr McKay said that already the technology is attract interest from researchers, especially the ability to look in a fresh way at roots below the ground and in the field.
The project has engaged researchers in other agencies and programs such as EverGraze. Recently, at sessions in Wagga Wagga and Albany about 30 pasture scientists were trained in the background and potential use of the technique.
Dr McKay said the feedback from the training highlight strong interest in the capability the tests. In particular the use of DNA assays to study root development and distribution in soils created a lot of interest. Assessing root growth in mixed swards using existing techniques is very difficult.
Dr McKay said it now seems the use of DNA assays to monitor root growth could drive the use of the technology in pastures. Any serious study root growth will need to consider the biological factors affecting root health, and the DNA technology can deliver this information.
For further information contact Dr Alan McKay on 08 8303 9375.
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9.3 Pythium group and clade comparison
Results for screening Pythium irregulare Group 1 & 2 and Pythium Clade F specific assays against DNA of Pythium isolates from sites in SA, Vic and NSW, provided by Paul Harvey (CSIRO).
P. irregulare Pythium irregulare Pythium Grp 1 & 2 Clade F Grp 1 & 2 Clade F Prevous Prevous Sample Sitecrop fg DNA / PCR fg DNA / PCR Sample Site crop fg DNA / PCR fg DNA / PCR 82 Paskeville SA Wheat 0 3126 44 Clare SA Wheat 0 4542 202 Paskeville SA Wheat 0 4773 66 Clare SA Wheat 0 6405 80 Paskeville SA Wheat 0 2363 158 Clare SA Wheat 0 8278 163 Paskeville SA Wheat 1 23 167 Clare SA Wheat 0 6359 171 Paskeville SA Wheat 2 8 170 Clare SA Wheat 1 3485 173 Paskeville SA Wheat 0 4363 195 Clare SA Wheat 33 4289 56 Paskeville SA Wheat 0 2 23 Clare SA Wheat 1 3043 57 Paskeville SA Barley 0 4833 25 Clare SA Barley 0 10190 60 Paskeville SA Barley 0 4649 30 Clare SA Barley 0 6344 160 Paskeville SA Barley 9 6822 107 Clare SA Barley 6997 2587 161 Paskeville SA Barley 21269Clare SABarley 0 5862 174 Paskeville SA Barley 1 3494 71 Clare SA Barley 0 3339 201 Paskeville SA Barley 1 2874 98 Clare SA Barley 2 8376 203 Paskeville SA Peas 5 2055 193 Clare SA Barley 26 4733 157 Paskeville SA Peas 2 4117 31 Clare SA Barley 0 7284 159 Paskeville SA Peas 3 3954 32 Clare SA Peas 0 5259 169 Paskeville SA Peas 1 5 33 Clare SA Peas 0 2496 34 Paskeville SA Peas 4 3614 37 Clare SA Peas 0 4814 35 Paskeville SA Canola 0 5281 165 Clare SA Peas 0 4612 36 Paskeville SA Canola 1 6442 166 Clare SA Peas 0 5205 177 Paskeville SA Canola 1 9830 198 Clare SA Peas 0 4646 178 Paskeville SA Canola 0 3154 199 Clare SA Peas 6 2677 11 Paskeville SA Canola 30044 12185 39 Clare SA Peas 0 5785 129 Elmore Vic Wheat 8502 6058 43 Clare SA Canola 2821 1238 130 Elmore Vic Wheat 11040 8358 45 Clare SA Canola 0 8111 179 Elmore Vic Wheat 11120 3841 68 Clare SA Canola 2 3011 180 Elmore Vic Wheat 2 3169 48 Clare SA Canola 0 5416 183 Elmore Vic Wheat 0 3802 50 Clare SA Canola 0 4705 186 Elmore Vic Wheat 19 2538 52 Clare SA Canola 0 6198 141 Elmore Vic Wheat 5165 4742 53 Clare SA Canola 4 4104 143 Elmore Vic Lupins 15 2310 128 Clare SA Canola 7 4551 151 Elmore Vic Lupins 17976 5947 12 Marrina NSW Wheat 12 4900 153 Elmore Vic Lupins 7958 2561 16 Marrina NSW Wheat 32 3829 102 Elmore Vic Lupins 11585 8850 136 Marrina NSW Wheat 22 2569 181 Elmore Vic Lupins 20547 8377 1 Marrina NSW Wheat 17420 6415 185 Elmore Vic Lupins 1 4714 5 Marrina NSW Wheat 27140 9377 103 Elmore Vic Lupins 7 2113 6 Marrina NSW Wheat 6235 9086 119 Elmore Vic Canola 1 4584 7 Marrina NSW Wheat 7691 2821 120 Elmore Vic Canola 12950 4076 85 Marrina NSW Wheat 12616 4779 124 Elmore Vic Canola 18845 6499 89 Marrina NSW Canola 12889 5380 125 Elmore Vic Canola 15255 5384 113 Marrina NSW Canola 3 4016 187 Elmore Vic Canola 16298 5829 118 Marrina NSW Canola 17620 6951 190 Elmore Vic Canola 8489 4233 20 Marrina NSW Canola 8770 13635 194 Elmore Vic Canola 8699 3191 97 Marrina NSW Canola 0 5937 243 Elmore Vic Canola 36454 11545 188 Marrina NSW Canola 10791 3557 99 Marrina NSW Canola 2 7210 40 Marrina NSW Canola 0 6406
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9.4 Arbuscular mycorrhizal fungi assayed in pasture soil
Ct values at threshold 0.1, for arbuscular mycorrhizal fungi (AMF) group A to F assays for 75 pasture soils from across Australia. * indicates target DNA not detected by 40 PCR cycles.
Pasture Ct values for each AMF group test Sample Town State (see codes below) A B C D E F AA45751 Flaxley SA Lp Ts Dg 31.63 33.65 * 33.93 * 29.45 AA45752 Flaxley SA Lp Ts 31.68 36.95 * 33.02 * 30.23 AA45753 Flaxley SA Lp Tr 30.16 29.75 * 28.80 * 26.06 AA45754 Flaxley SA Lp Ts Cd Pc 30.66 33.24 38.32 31.92 * 28.55 AA45755 Kybybolite SA crop 35.50 * * * * 31.24 AA45756 Kybybolite SA Lr Ts 31.24 35.00 * 32.70 37.18 27.61 AA45757 Cadgee SA Lr Ts Hx 34.89 34.17 * 37.32 * 29.98 AA45758 Willalooka SA Ms Fa 34.97 34.42 * * * 29.01 AA45759 Kieth SA Px Hx 33.93 * * 32.68 * 32.71 AA45760 Kalangadoo SA Ts Hl 36.76 37.55 35.32 * * 26.94 AA45761 Woodside SA * 34.89 34.25 37.62 36.92 * 27.97 AA45762 Winchelsea Vic crop 31.58 36.87 * 36.88 * 24.62 AA45763 Winchelsea Vic Ms 32.34 33.12 * 36.74 * 26.75 AA45764 Winchelsea Vic Ms 33.80 34.24 * 34.58 * 27.67 AA45765 Winchelsea Vic Fa Ts Pa 31.89 32.23 * * * 28.10 AA45766 Inverleigh Vic Ms 31.26 30.12 * 38.57 * 26.23 AA45767 Inverleigh Vic Ms 30.62 30.75 * 36.78 * 28.97 AA45768 Derinallum Vic Pa Ts 30.69 36.79 * 33.76 36.93 27.20 AA45769 Derinallum Vic cultivated 32.61 * * * * 27.74 AA45770 Warncort Vic * 33.42 34.21 * 35.67 * 28.38 AA45771 Warncort Vic Lp Ts 32.25 37.98 * 35.00 * 26.20 AA45772 St Leonards Vic Pa Ts Lp 32.56 35.66 * * * 27.67 AA45773 Clunes Vic grasses 30.66 * * 35.45 * 25.34 AA45774 Clunes Vic Pa Ts 33.98 * * 35.05 * 25.80 AA45775 Smeaton Vic Lp 33.91 35.41 * 35.70 * 26.09 AA45776 Smeaton Vic Fa 33.67 * * * * 28.12 AA45777 Smeaton Vic natïve 33.49 * 36.89 * 37.05 26.98 AA45778 Smeaton Vic Dx Ta 31.69 * 36.66 35.54 36.88 25.28 AA45779 Creswick Vic Lp Ts Tr 33.49 33.49 * 33.24 * 27.47 AA45780 Creswick Vic Tm Ts Lp Tr 35.98 * * 33.83 38.59 26.96 AA45781 Bald Hills Vic Vx Ts Ac Lp 32.26 30.81 * 30.60 38.84 26.49 AA45782 Beaufort Vic Ms Ts 31.93 33.80 * * * 26.23 AA45783 Beaufort Vic Lp 30.55 35.12 * 35.19 * 27.25 AA45784 Beaufort Vic Pa Lp Vx Ts 30.83 36.51 * 32.79 * 25.85 AA45785 Beaufort Vic Ta 31.60 * 38.13 * * 24.63 AA45786 Beaufort Vic Ta Ts 31.42 * * 35.38 38.72 26.25 AA45787 Illabrook Vic natïve 33.12 * 36.87 35.69 38.05 25.43 AA45788 Illabrook Vic Ta 33.50 * 37.52 36.85 * 25.94 AA45789 Mt Compass SA Ec Ts 37.67 * * 35.44 * 28.27 AA45790 Vasse WA Lr Ts Tm Ac 33.31 32.40 36.59 * * 27.62 AA45791 Vasse WA Lr Ts Tm Ac 33.60 33.50 38.18 * * 28.97 AA45792 Vasse WA Lr Ts Tm Ac 34.47 31.76 35.90 * * 30.00 AA45793 Vasse WA Lr Ts Tm Ac 32.55 33.93 35.83 37.80 * 27.35 AA45794 Vasse WA Lr Ts Tm Ac 32.49 31.55 34.85 * * 27.22 AA45795 Vasse WA Lp Tr Ts 34.83 33.74 35.26 * * 29.50
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Pasture Ct values for each AMF group test Sample Town State (see codes below) A B C D E F AA45796 Boyanup WA Tr Lp 34.52 33.20 * * * 28.48 AA45797 Boyanup WA Lr Ts Lm 35.76 36.92 * * * 29.21 AA45798 Roelands WA Ts Lm 33.64 34.28 36.53 36.95 * 28.44 AA45799 Roelands WA Tr Lp Pc 30.57 29.86 * * * 27.57 AA45800 NSW * 29.68 38.10 * * * 27.61 AA45801 NSW * 30.90 32.60 * 31.74 * 26.15 AA45802 NSW native 33.00 37.39 * 33.94 * 28.33 AA45803 NSW * 32.30 34.45 * 36.79 * 28.06 AA45804 NSW * 36.18 * * * * 31.89 AA45805 Qld 30 months bare fallow * * * * * 32.81 AA45806 Qld 12 months bare fallow * * * * * 33.77 AA45807 Qld 1 month bare fallow 38.73 * * * * 32.11 6 months fallow + AA45808 Qld 37.84 * * 37.13 * 30.92 added OM Grass pasture last 30 AA45809 Qld 38.70 36.89 * 38.17 * 32.51 months AA45810 Qld 5 soybean crops * * * * * 33.89 AA45811 Qld 18 months bare fallow * * * * * 32.88 AA45812 Qld 6 months bare fallow * * * * * 32.62 AA45813 Curse NSW * 34.61 * 37.18 35.30 * 29.22 AA45814 Sawyers Gully NSW * 30.12 36.09 38.07 * 37.91 27.58 AA45815 Black Burn NSW * 34.22 * 34.37 * * 28.84 AA45816 Mines NSW * 33.85 37.03 * * * 29.07 AA45817 Croakers NSW * 29.07 32.09 * 32.23 37.79 26.40 AA45818 Wallarooz NSW * 34.56 * * * * 27.26 AA45819 Gilly Gooly NSW * 30.89 32.98 33.56 35.09 37.74 27.36 AA45820 Airstrip NSW * 32.89 34.17 34.76 32.67 * 28.63 AA45821 Kia Ora NSW * 36.45 * 32.57 * 38.54 29.80 AA45822 North Angera NSW * 31.00 32.68 * 32.68 * 25.71 AA45823 Shea (limed) NSW * 30.03 31.55 * 36.58 * 26.90 AA45824 Yass Plains NSW * 31.04 34.58 35.31 32.55 38.26 23.95
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Pasture abbreviations codes
Code Definition Ac Arctotheca calendula crop cropping paddock cultivated cultivated pasture Dg Dactylis glomerata Dx Danthonia sp. Fa Festuca arundinacea grasses mixed grass pasture Hl Holcus lanatus Hx Hordeum spp. Lm Lolium multiflorum Lp Lolium perenne Lr Lolium rigidum Ms Medicago sativa native native grasses Pa Phalaris aquatica Pc Pennisetum clandestinum Px Pucinella Ta Themeda australis Tm Trifolium michelianum Tr Trifolium repens Ts Trifolium subterraneum Vx Vulpia sp.
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9.5 DNA concentrations of organisms in pasture soil
Sample Locality State Pasture* Target DNA (pg/g soil) Pythium Phytophthora AG 2.1 AG 2.2 AG 4 AG 8 Pratylenchus penetrans Heterodera trifolii AMF a AMF b AMF c AMF d AMF e 1 Flaxley SA Lp Ts Dg 89 1 190 44 0 0 0 0 843 217 0 180 0 2 Flaxley SA Lp Ts 137 0 73 3 0 0 0 0 814 24 4 331 0 3 Flaxley SA Lp Tr 345 0 1067 223 20327 0 0 710 2265 2970 0 5635 0 4 Flaxley SA Lp Ts Cd Pc 51 0 23 43 0 0 0 0 1613 286 9 696 0 5 Kybybolite SA crop 23 0 0 0 0 7 0 0 63 0 0 0 0 6 Kybybolite SA Lr Ts 49 0 0 6 0 6 0 0 1094 88 0 410 5 7 Cadgee SA Lr Ts Hx 23 0 0 0 0 4 0 0 94 153 0 19 0 8 Willalooka SA Ms Fa 17 0 3 0 0 97 0 0 89 130 0 0 0 9 Keith SA Px Hx 0 0 0 0 0 0 0 0 181 0 0 415 0 10 Kalangadoo SA Ts Hl 561 0 0 10 0 0 0 0 27 16 71 0 7 11 Woodside SA - 178 1 28 0 0 0 0 0 95 145 15 24 21 12 Winchelsea Vic crop 42 0 28 0 0 0 0 0 874 25 0 25 0 13 Winchelsea Vic Ms 83 0 45 0 0 0 0 0 524 309 0 27 0 14 Winchelsea Vic Ms 208 0 130 0 0 0 0 0 197 146 0 116 0 15 Winchelsea Vic Fa Ts Pa 142 0 0 0 0 0 0 0 709 564 0 0 0 16 Inverleigh Vic Ms 153 0 565 24 0 0 0 0 1083 2327 0 8 0 17 Inverleigh Vic Ms 113 0 357 159 0 0 0 0 1661 1520 5 27 0 18 Derinallum Vic Pa Ts 170 0 70 0 0 0 0 0 1584 26 0 201 12 19 Derinallum Vic cultivated 189 0 359 1108 0 0 0 0 436 0 0 0 0 20 Warncort Vic - 45 0 4 4 0 0 0 0 253 150 0 56 0 21 Warncort Vic Lp Ts 30 0 84 0 0 0 0 0 554 12 6 88 0 22 St Leonards Vic Pa Ts Lp 196 60 0 824 0 0 0 0 452 56 0 0 4 23 Clunes Vic grasses 25 0 3 14 0 0 0 0 1617 0 0 65 4 24 Clunes Vic Pa Ts 269 0 18 0 0 0 0 0 174 0 0 85 7
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Sample Locality State Pasture* Target DNA (pg/g soil) Pythium Phytophthora AG 2.1 AG 2.2 AG 4 AG 8 Pratylenchus penetrans Heterodera trifolii AMF a AMF b AMF c AMF d AMF e 25 Smeaton Vic Lp 54 0 386 0 0 0 0 0 182 66 0 55 0 26 Smeaton Vic Fa 66 0 1 2 0 0 0 0 214 3 0 6 0 27 Smeaton Vic natïve 61 1 4 0 0 0 0 0 241 5 25 0 14 28 Smeaton Vic Dx Ta 8 0 0 0 0 0 0 0 810 0 29 61 4 29 Creswick Vic Lp Ts Tr 11 0 154 361 0 0 0 0 241 242 5 287 0 30 Creswick Vic Tm Ts Lp Tr 65 9 155 0 0 0 0 0 45 0 0 192 6 31 Bald Hills Vic Vx Ts Ac Lp 67 0 262 98 0 0 0 0 551 1464 0 1685 11 32 Beaufort Vic Ms Ts 155 0 604 0 0 0 0 0 687 197 0 0 16 33 Beaufort Vic Lp 38 0 131 260 0 0 0 0 1737 81 5 77 0 34 Beaufort Vic Pa Lp Vx Ts 78 0 229 459 0 0 0 0 1441 32 0 386 0 35 Beaufort Vic Ta 17 0 0 0 0 0 0 0 862 5 11 6 19 36 Beaufort Vic Ta Ts 21 0 0 0 0 0 0 0 973 4 0 68 7 37 Illabrook Vic natïve 50 0 0 0 0 0 0 0 310 0 25 55 0 38 Illabrook Vic Ta 5 0 0 56 0 0 0 0 240 0 16 25 8 39 Mt Compass SA Ec Ts 16 0 0 0 0 0 0 0 15 0 0 65 23 40 Vasse WA Lr Ts Tm Ac 143 0 0 1761 0 0 0 1 274 503 30 0 0 41 Vasse WA Lr Ts Tm Ac 170 0 0 13 0 0 0 0 224 239 10 0 0 42 Vasse WA Lr Ts Tm Ac 15 0 0 112 0 0 0 0 126 773 48 0 0 43 Vasse WA Lr Ts Tm Ac 80 0 12 742 0 0 0 0 455 180 50 13 0 44 Vasse WA Lr Ts Tm Ac 112 2 8 346 0 0 0 0 473 888 97 0 0 45 Vasse WA Lp Tr Ts 115 2 24 1030 0 0 0 0 98 205 74 0 0 46 Boyanup WA Tr Lp 82 0 38 181 0 0 0 1282 121 295 0 0 0 47 Boyanup WA Lr Ts Lm 42 0 31 9 0 0 0 1 53 24 0 0 0 48 Roelands WA Ts Lm 380 0 0 107 0 0 0 3 219 143 31 24 0 49 Roelands WA Tr Lp Pc 10 0 0 4 0 0 0 69 1718 2758 0 0 0 50 Manilla NSW - 194 0 0 279 0 0 0 0 3127 11 0 0 0
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Pasture soil biology molecular assays
Sample Locality State Pasture* Target DNA (pg/g soil) Pythium Phytophthora AG 2.1 AG 2.2 AG 4 AG 8 Pratylenchus penetrans Heterodera trifolii AMF a AMF b AMF c AMF d AMF e 51 Manilla NSW - 102 0 7 6 0 0 0 0 1375 440 0 784 0 52 Tamworth NSW native 87 0 5 254 0 0 0 0 336 18 0 179 0 53 Tamworth NSW - 264 0 15 111 0 0 0 0 537 127 0 26 0 54 Tamworth NSW - 0 0 0 0 0 0 0 0 40 5 0 0 0 55 Bundaberg Qld - 12 0 0 0 0 0 0 0 5 0 0 0 0 56 Bundaberg Qld - 3 0 0 0 0 0 0 0 4 0 0 0 0 57 Bundaberg Qld - 6 0 0 0 0 0 0 0 7 0 0 0 0 58 Bundaberg Qld - 9 0 0 0 0 0 0 0 13 0 0 21 0 59 Bundaberg Qld - 8 0 0 0 0 0 0 0 7 25 0 10 0 60 Bundaberg Qld - 11 0 0 0 0 0 0 0 0 0 0 0 0 61 Bundaberg Qld - 5 0 0 0 0 0 0 0 0 0 0 0 0 62 Bundaberg Qld - 8 0 0 0 0 0 0 0 0 0 0 0 0 63 Westbrook NSW - 67 1 0 313 859 0 0 23 114 0 20 72 0 64 Wee Jasper NSW - 39 14 0 350 0 0 0 0 2317 42 11 0 11 65 Yass NSW - 53 0 0 577 0 0 0 0 148 0 134 0 0 66 Hall ACT - 188 5 0 69 0 0 0 0 190 22 0 0 0 67 Taralga NSW - 63 5 125 1521 9274 0 0 0 4694 619 0 562 7 68 Hall ACT - 44 0 0 489 0 0 0 0 118 0 0 0 0 69 Braidwood NSW - 5 0 0 221 0 0 0 0 1388 340 231 83 8 70 Westbrook NSW - 52 0 0 573 0 0 0 0 363 154 103 419 5 71 Bookham NSW - 25 0 0 26 0 0 0 0 33 0 449 0 0 72 Braidwood NSW - 34 0 0 194 0 0 0 18 1287 416 0 415 0 73 Braidwood NSW - 49 0 0 292 0 0 0 238 2470 888 0 30 0 74 Yass NSW - 43 0 0 0 0 0 0 0 1252 117 71 455 7
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