Anna V. McBeath

Monitoring microbial functional and structural diversity for management of disease-suppressive soils

The activities of the soil microbiota are essential to the long-term sustainability of agricultural systems, through their influences on biological, chemical and physical processes, which drive essential ecosystem services. Moreover, changes in microbial communities are considered as precursors to changes in the health and viability of the soil environment, due to their responses to changes in agricultural management practices. Therefore, measurements of soil microbial activity and diversity are ideal indicators for monitoring soil status and the efficacy of soil management. However, due to the vast taxonomic diversity of soil microorganisms, analysis of microbial diversity is often problematic, time consuming and costly. Two banana experiments were established in far north Queensland, Australia, under different nutrient and groundcover management practices, to understand the dynamic changes in soil microbial communities and their effectiveness to sustain ecosystem services, such as organic matter decomposition and disease suppression. Using a functional approach, soil biological communities were analysed using trophic analysis of soil nematode communities, community level physiological profiles and enzyme activity assays. A reduction in nitrogen fertiliser inputs was related to a greater number of fungivorous nematodes which was also linked to greater suppression of Fusarium oxysporum f. sp. cubense using a novel soil-baiting bioassay. Furthermore, community-level physiological profiles enabled differentiation of soils over time demonstrating a shift in soil microbial functional diversity. The results highlight how soil management can drive differences in soil microbial activity and diversity to improve soil functions in banana production.

Estimating organic carbon content of soil in Papua New Guinea using infrared spectroscopy

Quantification of soil organic carbon (SOC) content is important for sustainable agricultural management and accurate carbon accounting. Infrared (IR) absorbance can be used to estimate SOC content, but the relationship differs between regions due to matrix effects. We developed an IR-based model specific for SOC in Papua New Guinean soils. A total of 437 samples from 0.0–0.3m depth were analysed for SOC using Dumas combustion. IR absorption spectra were collected from the same samples, and a predictive regression model was developed using the 6000–1030cm–1 spectral range. Using a validation set, predicted SOC values resulting from the IR-based model compared well with values from Dumas combustion (R2=0.905; ratio of performance-to-deviation=5.64). Constraining wavelengths to positively correlated regions of the spectra was also explored and showed improved model performance (R2=0.932). Overall, IR analysis provides a robust method for estimating SOC content for a range of Papua New Guinean soils.