Bruce Hawke

USE OF FATTY ACIDS FOR IDENTIFICATION OF AM FUNGI AND ESTIMATION OF THE BIOMASS OF AM SPORES IN SOIL

Abstract Fatty acid methyl ester (FAME) analysis performed on the spores of four arbuscular mycorrhizal (AM) fungi (Glomus coronatum, Glomus mosseae, Gigaspora margarita and Scutellospora calospora) showed 16:1ω5c to be the dominant fatty acid present. In addition, spores of Gi. margarita contained large quantities of 18:1ω9c and three 20-C fatty acids (20:1ω9c, 20:2ω6c and 22:1ω9c) that were not present in the spores of the other two species. Addition of a known number of spores of each AM species to soil demonstrated that the spore fatty acids could be readily detected and quantified against the background of soil fatty acids. Addition of different combinations and quantities of spores to soil gave the expected ratios of the marker fatty acids in the soil FAME profiles. The results confirm the use of 16:1ω5c as a marker fatty acid for AM fungi in controlled environments and suggest that 18:1ω9c, 20:1ω9c, 20:2ω6c and 22:1ω9c could be used as possible markers for the detection of Gi. margarita.

Predicting contents of carbon and its component fractions in Australian soils from diffuse reflectance mid-infrared spectra

Quantifying the content and composition of soil carbon in the laboratory is time-consuming, requires specialised equipment and is therefore expensive. Rapid, simple and low-cost accurate methods of analysis are required to support current interests in carbon accounting. This study was completed to develop national and state-based models capable of predicting soil carbon content and composition by coupling diffuse reflectance mid-infrared (MIR) spectra with partial least-squares regression (PLSR) analyses. Total, organic and inorganic carbon contents were determined and MIR spectra acquired for 20 495 soil samples collected from 4526 locations from soil depths to 1 m within Australia’s agricultural regions. However, all subsequent MIR/PLSR models were developed using soils only collected from the 0–10, 10–20 and 20–30 cm depth layers. The extent of grinding applied to air-dried soil samples was found to be an important determinant of the variability in acquired MIR spectra. After standardisation of the grinding time, national MIR/PLSR models were developed using an independent test-set validation approach to predict the square-root transformed contents of total, organic and inorganic carbon and total nitrogen. Laboratory fractionation of soil organic carbon into particulate, humus and resistant forms was completed on 312 soil samples. Reliable national MIR/PLSR models were developed using cross-validation to predict the contents of these soil organic carbon fractions; however, further work is required to enhance the representation of soils with significant contents of inorganic carbon. Regional MIR/PLSR models developed for total, organic and inorganic carbon and total nitrogen contents were found to produce more reliable and accurate predictions than the national models. The MIR/PLSR approach offers a more rapid and more cost effective method, relative to traditional laboratory methods, to derive estimates of the content and composition of soil carbon and total nitrogen content provided that the soils are well represented by the calibration samples used to build the predictive models.

Quantifying the allocation of soil organic carbon to biologically significant fractions

Soil organic carbon (OC) exists as a diverse mixture of organic materials with different susceptibilities to biological decomposition. Computer simulation models constructed to predict the dynamics of soil OC have dealt with this diversity using a series of conceptual pools differentiated from one another by the magnitude of their respective decomposition rate constants. Research has now shown that the conceptual pools can be replaced by measureable fractions of soil OC separated on the basis of physical and chemical properties. In this study, an automated protocol for allocating soil OC to coarse (>50 µm) and fine (≤50 µm) fractions was assessed. Automating the size fractionation process was shown to reduce operator dependence and variability between replicate analyses. Solid-state 13C nuclear magnetic resonance spectroscopy was used to quantify the content of biologically resistant poly-aryl carbon in the coarse and fine size fractions. Cross-polarisation analyses were completed for coarse and fine fractions of 312 soils, and direct polarisation analyses were completed for 38 representative fractions. Direct polarisation analyses indicated that the resistant poly-aryl carbon was under-represented in the cross-polarisation analyses, on average, by a factor of ~2. Combining this under-representation with a spectral analysis process allowed the proportion of coarse- and fine-fraction OC existing as resistant poly-aryl C to be defined. The content of resistant OC was calculated as the sum of that found in the coarse and fine fractions. Contents of particulate and humus OC were calculated after subtracting the resistant OC from the coarse and fine fractions, respectively. Across the 312 soils analysed, substantial variations in the contents of humus, particulate, and resistant carbon were noted, with respective average values of 9.4, 4.0, and 4.5 g fraction C/kg soil obtained. When expressed as a proportion of the OC present in each soil, the humus, particulate, and resistant OC accounted for 56, 19, and 26%, respectively. The nuclear magnetic resonance analyses also indicated that the use of a 50-µm sieve to differentiate particulate (>50 µm) from humus (≤50 µm) forms of OC provided an effective separation based on extents of decomposition. The procedures developed in this study provided a means to differentiate three biologically significant forms of soil OC based on size, extent of decomposition, and chemical composition (poly-aryl content).

Effect of tillage and stubble management on chemical and microbiological properties and the development of suppression towards cereal root disease in soils from two sites in NSW, Australia

Different tillage and stubble management practices were compared at two sites in New South Wales, Australia, to determine their effect on soil chemical and microbiological properties and the development of suppression towards Gaeumannomyces graminis var. tritici (Ggt) and Rhizoctonia solani. At one site (Harden), the management practices were direct-drilling of crops with stubble retained (DD) and conventional sowing of crops with stubble burnt (CC) for 6 years prior to sampling. At the second site (Cowra), a stubble incorporated (SI) treatment (SI with a single cultivation prior to sowing) was compared with DD and CC treatments and the practices had been in place for 16 years. By comparing the difference in plant growth in γ-irradiated and natural (unsterilised) soil in the presence of added Ggt and Rhizoctonia inoculum, evidence of suppression towards Ggt was observed in soil from both the sites. The suppression was greater in the DD compared to the CC soils. This was associated with higher levels of organic C and total N in the DD compared to the CC soils at Cowra and with higher microbial biomass, CO2 respiration and populations of fungi (including cellulolytic fungi) in the DD compared to the CC soils at both the sites. There was less evidence of suppression towards Rhizoctonia but higher disease levels were obtained from the added Rhizoctonia inoculum in the CC soil compared to the DD soil at both the sites. The results showed that the DD practice augmented a build-up of organic C and microbial biomass in the surface soil and increased its suppressiveness towards two introduced fungal pathogens.

Influence of tillage and crop rotation on the epidemiology of Pythium infections of wheat in a red-brown earth of South Australia

Abstract Propagules of Pythium spp were concentrated in the top 10 cm and associated with soil aggregates >250 μm and 80% of isolates recovered from wheat seed and wheat roots.

Influence of barley straw and the lumbricid earthworm Aporrectodea trapezoides on Rhizobium meliloti L5-30R, Pseudomonas corrugata 2140R, microbial biomass and microbial activity in a red-brown earth soil

Abstract In greenhouse experiments, the ability of barley straw and the earthworm Aporrectodea trapezoides to influence the persistence of Pseudomonas corrugata 2140R and Rhizobium meliloti L5-30R, previously inoculated separately into soil, was examined. The addition of barley straw (0.62% w/w), significantly increased the numbers of both introduced bacteria ca. 1000- to 3000-fold after 29 d incubation and ca. 25-to 100-fold after 63 d incubation in soil. In the absence of barley straw, there was a significant positive linear relationship between the number of A. trapezoides (at densities equivalent to 0, 105, 315 or 525 m −2 ) and the numbers of both introduced bacteria after 29 d, but not after 63 d incubation. In contrast, in the presence of barley straw, there was a significant negative linear relationship between the number of A. trapezoides and the numbers of both introduced bacteria after 29 and 63 d incubation. By combining data from both sampling times, there was a significant linear relationship between the persistence of both introduced bacteria and changes in microbial biomass only in the presence of added barley straw. This would suggest that A. trapezoides had a selective effect upon the persistence of both introduced bacteria in the absence of barley straw, which was not manifest upon the whole microbial community.

Assessing soil carbon vulnerability in the Western USA by geospatial modeling of pyrogenic and particulate carbon stocks

To predict how land management practices and climate change will affect soil carbon cycling, improved understanding of factors controlling soil organic carbon fractions at large spatial scales is needed. We analyzed total soil organic (SOC), as well as pyrogenic (PyC), particulate (POC) and other soil organic carbon (OOC) fractions in surface layers from 650 stratified-sampling locations throughout Colorado, Kansas, New Mexico, and Wyoming. PyC varied from 0.29 to 18.0 mg C g-1 soil with a mean of 4.05 mg C g-1 soil. The mean PyC was 34.6% of the SOC and ranged from 11.8 to 96.6%. Both POC and PyC were highest in forests and canyon bottoms. In the best random forest regression model, normalized vegetation index (NDVI), mean annual precipitation (MAP), mean annual temperature (MAT) and elevation (ELEV) were ranked as the top four important variables determining Pic and POC variability. Random forests regression kriging (RFK) with environmental co-variables improved predictions over ordinary kriging by 20 and 7% for PyC and POC, respectively. Based on RFK, 8% of the study area was dominated (≥50% of SOC) by PyC and less than 1% was dominated by POC. Furthermore, based on spatial analysis of the ratio of POC to PyC, we estimated about 16% of the study area is medium to highly vulnerable to SOC mineralization in surface soil. These are the first results to characterize PyC and POC stocks geospatially using stratified sampling scheme at the scale of 1,000,000 km2, and the methods are scalable to other regions.

Stocks, composition and vulnerability to loss of soil organic carbon predicted using mid-infrared spectroscopy

Developing a routine and cost effective capability for measuring soil organic carbon (SOC) content and composition will allow identification of land management practices with a potential to maintain or enhance SOC stocks. Coupling SOC content data and mid-infrared (MIR) spectra through the application of partial least-squares regression (PLSR) analyses has been used to develop such a prediction capability. The objective of this study was to determine whether MIR/PLSR analyses provide accurate estimates of the content and composition of SOC that can be used to quantify SOC stocks and its potential vulnerability to loss. Soil was collected from a field trial incorporating a range of land use (pasture, arable cropping and bare fallow) and tillage (intensive, minimum and no tillage) treatments over a nine-year period. The SOC content was measured by dry combustion analysis. Particulate organic carbon was separated from other forms of carbon on the basis of particle size (SOC in the >50 µm fraction). Resistant organic carbon was quantified using solid-state 13C nuclear magnetic resonance. The MIR/PLSR algorithms were successfully developed to predict the natural logarithms of the contents of SOC and POC in the collected soils. With initial calibration, a single MIR analysis could be used in conjunction with PLSR algorithms to predict the content of SOC and its allocation to component fractions. The MIR/PLSR predicted SOC contents provided reliable estimates of the impact of agricultural management on the 0–25-cm SOC stocks, as well as an indication of the vulnerability of SOC to loss. Development of this capability will facilitate the rapid and cost effective collection of SOC content data for detecting the impact of agricultural management treatments on SOC stocks, composition and potential vulnerability to change.