Agriculture and Food Systems - Theses

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    Functional genomics to discover biologically relevant regulatory variation
    Prowse-Wilkins, Claire Patricia ( 2022)
    Genomic prediction aims to predict the breeding value of animals based on their genotypes. It does this by looking for associations between genotypes and the phenotype in a training population. Animals which share these genotypes are then predicted to share the same phenotype. However, the association between a genotype and phenotype could be based on linkage disequilibrium (LD) between the variant identified and the causal mutation which is directly affecting the phenotype. Therefore, in populations where LD is different to the training population, such as other breeds or in subsequent generations, the association between genotype and phenotype would not occur, and genomic predictions are less accurate. It would therefore be advantageous to use the genetic variant which is directly affecting the phenotype (the causal variant) in our predictions. Identifying causal variants in cattle is difficult as there are millions of variants in the bovine genome. Work in humans shows that most variants affecting complex traits lie in non-coding functional regions, where it is thought they alter regulation of gene expression. However, functional regions are not well annotated in non-model organisms. This project annotated functional regions directly in dairy cows using a laboratory technique called ChIP-seq (Chromatin Immunoprecipitation followed by sequencing) to assay for histone modifications and a transcription factor, which are thought to mark functional regions. This was done in two ways, (1) across multiple tissues in three lactating dairy cows and (2) across multiple individuals in a single tissue (the mammary gland). Using this dataset, I annotated putative functional regions in dairy cows across a wide variety of tissues and individuals, and by doing so substantially increased the functional annotation of the bovine genome. As seen in other species, causal variants for gene expression and other complex traits were enriched in these functional regions, particularly at regions correlating with gene expression and thus identified as putative regulatory regions. Additionally, genetic sources of functional variation across 100 animals were identified by associating variation in peak height, across and within individuals, with genetic variation. These are also potential causal variants, which overlapped with eQTL and were found in putative transcription factor binding sites. The results of this study confirm that functional regions are involved in regulating gene expression, and that functional data can help to identify causal variants for complex traits. This work also provides an invaluable dataset of putative functional regions in the bovine genome. Future work will investigate whether the potential causal variants identified here improve genomic predictions.
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    Application of extrusion technology for hempseed (Cannabis sativa L.) processing by-products
    Leonard, William ( 2022)
    The seed of low-tetrahydrocannabinol (THC) hemp (Cannabis sativa L.) is an emerging food source with its recent legalization for cultivation and consumption in Australia from 2017. Industrial processing of hempseed generates oil cake and hull as by-products. These by-products are rich in protein, fiber and amide-containing phenolic compounds. Processing has the potential to further improve their nutritional value, functionality and applicability in the food industry. Extrusion is versatile food processing technology that combines multiple mixing, cooking and compressing processes, and has been utilised to prepare wide range of food products with high nutritional values as well as textural appeals. Considering the potential of extrusion, this thesis comprehensively investigated the effects of extrusion technology on the three major nutrients (protein, polyphenol, fiber) in hempseed by-products, specifically covering the: (i) proteome, structure and functionality of hempseed oil cake protein (Chapter 3), (ii) identification, quantification and biological activities of cake (Chapter 4) and hull (Chapter 5) phenolic fractions, (iii) absorption and cellular antioxidative effect of hempseed hull phenolic amides (Chapter 6), and (iv) the physical, functional and microbiota-modulating properties of hull fiber (Chapter 7). A full factorial design was used to complete the extrusion runs in the chapters of this thesis, allowing relative contribution of each individual parameter (i.e. moisture, screw speed, temperature), and their two- and three-way interactions to be determined. Extrusion, at selected combinations of parameters, improved the free-form and total amino acid content, protein solubility, water and oil binding capacity, emulsion capacity and in vitro digestibility of hempseed oil cake protein fractions (Chapter 3). The proteome-functionality correlational relationship was explored for the first time to identify protein groups or peptides with superior-inferior functionality. In addition to protein, hempseed oil cake contains a high concentration of unique amide-containing phenolics (phenylpropionamides), known as lignanamides and hydroxycinnamic acid amides. The proportion of free phenolics, flavonoids, phenylpropionamides, in vitro alpha-glucosidase and acetylcholinesterase inhibitory activities were enhanced in cake extruded at relatively lower moisture (30 %) and higher screw speed (300 rpm) (Chapter 4). At least 26 phenylpropionamides, such as N-trans-caffeoyltyramine and cannabisin A/B, were identified using LC/MS-MS, with the possibility of novel compounds and biological activities that are yet to be characterised. The hull of hempseed is richer in phenolic amides and fiber compared to the oil cake. Extrusion, at all parameters, increased the total phenolic, free in vitro antioxidant (DPPH/ABTS) activities, and total phenylpropionamides (Chapter 5). A novel absorption trial of phenylpropionamides and follow-up cellular antioxidant assay were performed in Chapter 6 using the intestinal Caco-2 cell culture. Significantly improved absorption of major phenylpropionamides, such as cannabisin B, was observed after extrusion and extraction of free phenolics fraction. The free fraction of extruded hempseed hull showed promising cellular antioxidative properties, protection of Caco-2 cells challenged by t-BOOH, and altered the transcriptome of the cells as indicated from RNA-Seq findings. In addition, extrusion improved the properties of hempseed hull fiber in terms of its solubility and content of total dietary fiber, water and oil binding capacity, viscosity in oil dispersions, inhibition of starch retrogradation (Chapter 7). 16 rRNA gene sequencing and metaproteomics further demonstrated the microbiota-modulating properties of hempseed hull fiber, with the increase in Megasphaera elsdenii and Lactobacillus spp. population, and their expressed protein of ferredoxin and enolases, respectively. Overall, this research could facilitate industrial utilization of hempseed by-products by extrusion.
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    Factors influencing consumer response to wet and dry-aged mutton
    Hastie, Melindee ( 2021)
    Mutton is the term used to describe the meat from older ovines (older than 2 years); this meat is considered to have inferior eating quality when compared to lamb and as a result returns to producers are reduced for mutton animals. The sheepmeat industry seeks to improve returns for mutton, and the application of dry ageing to mutton is proposed as a post mortem intervention that will add value. As dry aged mutton is a novel product, the consumer response to this product is largely unknown. To assess the viability and feasibility of dry aged mutton products, this thesis investigated the retail opportunity for dry aged mutton from the perspective of the consumer, producer and foodservice. The thesis also developed understanding of the influence of ageing method and meat quality factors on consumer eating quality assessment and liking, as well as the extrinsic consumer related factors affecting consumer response to dry aged mutton. These investigations leveraged both qualitative and quantitative consumer methodologies, with the overarching aim of developing insights that would address knowledge gaps in the developing dry aged mutton supply chain. A cross cultural study comparing responses to sheepmeat from Asians (export market consumers) and Australians (domestic market consumers) found these groups assess meat quality differently; Australians rely on labels, are sceptical of provenance stories and prefer Australian product. Asian consumers make quality assessments based on visual attributes and find provenance stories compelling, but they were not particularly compelled by the “Australian made” label. These findings indicate different marketing strategies are required for the two markets. For both consumer groups the recommended channel for dry aged mutton products would be foodservice or retail butchers as these outlets take away the uncertainty associated with purchasing and preparing an unfamiliar product. The development of dry aged mutton dishes in collaboration with chefs followed by consumer assessment of the dishes for liking, premiumness and fit to foodservice outlet demonstrated that these products were well liked by consumers, could be considered premium, and that dry aged mutton was a versatile product that suited a wide range of foodservice outlets ranging from casual to fine dining. An ageing trial lasting 8 weeks and comparing wet and dry aged mutton leg and loin found that yield was reduced in the dry aged treatment as opposed to the wet aged, and that these losses increased with ageing period. There was opportunity to mitigate yield losses by selecting carcases that were heavier, had increased levels of subcutaneous fat, and by fabrication of retail cuts from dry aged primals. Consumers (n=540) assessed the eating quality (tenderness, juiciness, liking of flavour and overall liking) of the longissimus thoracis et lumborum (LTL) and semimembranosus (SM) derived from the ageing trial according to MSA sensory protocols (total sample n = 3240). No differences in consumer ratings were found due to ageing method but extending the ageing period beyond 14 days improved eating quality for both muscles. Consumers most often graded LTL as “better than every day quality” and they were willing to pay (WTP) 36.60 AUD/Kg for this grade, SM was most often graded as “good everyday day quality” and WTP was 26.90 AUD/Kg. These prices were similar to current Australian retail pricing for lamb, and if mutton can achieve these prices, it would be a significant price uplift. The ageing trial result of “no effect of ageing method on eating quality” was unexpected. A check all that apply (CATA) investigation into the consumer characterised flavour profile of wet and dry aged mutton confirmed that consumers can differentiate ageing treatments; dry aged mutton was associated with increased caramel and roasted flavours while the wet aged mutton was associated with increased sheepy and metallic flavours. Further subsequent investigations into the consumer related factors affecting the eating quality assessments of the MSA ageing trial indicated that consumers could be segmented according to their liking for mutton. Agglomerative hierarchical cluster analysis revealed 3 consumer clusters; Cluster 1 (n = 219) appreciated mutton and rated it highly, Cluster 2 (n = 235) found mutton acceptable and Cluster 3 (n =79) did not like mutton. Preference maps were prepared for the three clusters; all three clusters were found to prefer LTL over SM, but Cluster 1 demonstrated a preference for dry aged mutton over wet aged, Cluster 2 had no ageing method preference and Cluster 3 preferred wet aged mutton over dry aged mutton. Characterisation of these clusters indicate that consumer familiarity with mutton and ethnicity has a role to play in the observed preference patterns. The thesis concludes that dry aged mutton has value as a premium niche product targeted at those consumers who appreciate mutton, and that there is an opportunity for mutton in local and export markets (not dry aged) that warrants further industry exploration, especially for the LTL which has good eating quality.
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    Using data mining to improve the prediction of key nitrogen loss from terrestrial ecosystems
    Pan, Baobao ( 2021)
    Nitrogen (N) losses through nitrification, denitrification and nitrate leaching in terrestrial ecosystems reduce fertiliser N use efficiency (NUE), affect environmental quality and human health. In the past 50 years, many individual studies both on-site and in laboratories have been carried out to investigate N loss from different pathways. However, field measurements of N dynamics are generally time-consuming, expensive and some of the processes are quite difficult to measure. Process-based models have been developed to better understand the complex, multivariate and unpredictable N cycling by integrating soil, environmental and management factors. However, the performances of these models are restricted by data availability, inconsistent responses of N loss to key drivers, difficulty of parameter derivation and limited capacity of larger scale simulation. To address the limitations of process-based models, improve the prediction and simulation of N losses, increase NUE and environmental quality, comprehensive databases of N loss from different pathways were compiled by data mining, advanced machine learning models were developed to reflect the linkage between N loss pathways and soil, environmental and climatic conditions on global scale. The research reported in this thesis quantified the nitrification rate (Rnit) and the fraction of nitrous oxide (N2O) from nitrification (fN2O_nit), denitrification and associated N2O, dinitrogen (N2) production, the contribution of N2O from autotrophic nitrification, heterotrophic nitrification and denitrification and nitrate (NO3-) leaching, investigated the key drivers of N loss from each pathway, performed global performed accurate global prediction of nitrification rate and the fraction of N2O from nitrification, NO3-N leaching with fewer input variables using data mining, machine learning models and 15N tracing experiment. Findings include: Data mining and machine learning were integrated to predict R_nit and fN2O_nit. According to the compiled global database on Rnit and fN2O_nit, the average potential Rnit in the topsoil was 1.4 kg N ha-1 d-1, and fN2O_nit was from 0.004 to 9.19% (average 0.46%). The machine-learning based stochastic gradient boosting (SGB) model outperformed three widely used process-based models in estimating R_nit and N2O emission from nitrification by using the same input variables. SGB technique was then applied for global prediction of Rnit and fN2O_nit with only a few input variables (R2 = 0.76 and 0.55, respectively). The potential Rnit was driven by long-term mean annual temperature, soil C/N ratio and soil pH, whereas fN2O_nit by mean annual precipitation, soil clay content, soil pH, soil total N. The global fN2O_nit varied by over 200 times (0.006-1.2%), it should be adjusted according to edaphic and environmental conditions when used in process-based models or global climate models in projecting N2O emissions. A global assessment of soil denitrification rate, N2O/(N2O+N2), and their driving factors and mitigation strategies was conducted based on 225 studies (3367 observations). N loss through denitrification varied greatly across land uses and climatic regions with an average of 0.25 kg N ha-1d-1. The average emission factor of denitrification (EFD) was 4.8%. The wide range of N2O/(N2O+N2) (mean: 0.33) demonstrated that the adoption of a fixed ratio in some process-based models for estimating N2 emissions from denitrification is not suitable. N2 loss accounted for 67% of total denitrification. N loss as N2, although harmless to the environment, deserves more attention from the perspective of improving NUE. Soil denitrification rate was significantly related to soil WFPS, NO3- content and soil temperature and soil oxygen (O2) content. N2 emissions were significantly correlated with latitude, WFPS, soil mineral N and soil oxygen content. Soil oxygen content, NO3- content, organic C, C/N ratio and WFPS were the key drivers of N2O/(N2O+N2) ratio. The meta-analysis showed that optimizing N application rates, using ammonium-based fertilizers compared to nitrate-based fertilizers, biochar amendment and application of nitrification inhibitors could effectively reduce soil denitrification rate and associated N2 emissions by 34-219% and 15-226%, respectively. These findings highlight that N loss via soil denitrification and N2 emissions cannot be neglected, and that mitigation strategies should be adopted to reduce N loss and improve N use efficiency. Our study provides a solid foundation to large-scale estimations of denitrification and the refinement of relevant parameters used in the submodels of denitrification in process-based models. The contribution of N2O production pathways and its driving factors in forest soils were investigated by global data analysis and an incubation experiment with 15N tracing technique in both Australia and worldwide. Based on 13 forest soils sampled within Australia, forest soils in temperate areas had the highest N2O emission rate (19.5 ug N kg-1 soil d-1), followed by subtropical and arid soils (3.84 and 3.80 ug N kg-1 soil d-1, respectively). Heterotrophic nitrification dominated N2O production in Australian forest soils; its contribution followed the order of arid (78%) > subtropical (69%) > temperate (59%). N2O from heterotrophic nitrification was negatively related to MAT and the contribution of heterotrophic nitrification to N2O production was negatively related to soil TN and TC. These results partially agreed with the global literature data synthesis, which showed that in addition to heterotrophic nitrification (42%), denitrification (43.5%) was also a key pathway of N2O production in global forest soils. Globally, soil pH, moisture content, total N content, total C content and MAT contributed to heterotrophic nitrification and denitrification to N2O production. A machine learning model (NLNO3 model) was developed based on a global literature-based database of NO3-N leaching from field experiments (1818 observations) to predict NO3-N leaching from agroecosystems. The NLNO3 model can reliably predict NO3-N leaching using a few easily accessible input variables (R2=0.75). According to the model estimation of NO3-N leaching, the global spatiotemporal pattern and hotspots were identified. The total NO3-N leaching in agroecosystems increased from 23.2 Tg N yr-1 in 1961 to 32.8 Tg N yr-1 in 2000 and 39.7 Tg N yr-1 in 2010. Hotspots of NO3-N leaching in agroecosystems expanded from Europe in 1961 to China, South Asia and Brazil in 2000 and 2010. The high spatiotemporal heterogeneity of NO3-N leaching was mainly driven by soil properties (soil TN, soil pH, soil texture), aridity index and farming practices (N fertilization and irrigation). Results of the present research demonstrate that the capacities of data mining in better understanding the complex N cycling in terrestrial ecosystems and informing potential mitigation strategies to reduce N loss. Data mining coupled with advanced machine learning methods could not only address the limitations of process-based models and improve model simulation performances, but also provide an alternative approach in predicting N dynamics at a larger scale.
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    Genomic and Phenomic Indicators of Persistence in Perennial Ryegrass Cultivar Evaluation
    JAYASINGHE, ATTALA GAMARALLAGE CHINTHAKA SANDARUWAN ( 2021)
    Poor persistence of perennial ryegrass creates many challenges for livestock producers to use their arable lands more productively and intensively within sustainable limits. Therefore, the persistence of perennial ryegrass cultivars has become one of the primary objectives in pasture breeding. The expression of persistence in a perennial ryegrass sward may depend on a combination of the sowing environment (management and climate), host grass genetics, presence of endophyte and interactions between these factors. Hence, pasture persistence has proven to be challenging to define and measure during pasture cultivar evaluation. The most commonly used techniques to evaluate persistence of perennial ryegrass in pasture breeding are laborious, time-consuming, and may require special training. Long-term grazing along with environmental stresses could change phenomic and genomic variation within a sward as an adaptive mechanism to maintain survival and productivity. In this selection process, less fit genotypes can be eliminated from a population, whereas well-adapted genotypes may become dominant in a sward. Understanding this adaptive process may be important for handling genetic resources, cultivar improvement, and pasture management. With the rapid technology development in recent years, high-throughput phenotyping and advanced molecular techniques to evaluate plant traits have increased in agriculture research. High throughput phenotyping platforms detect reflectance from plants and reliably generate phenomic data representing plant development, architecture, growth or biomass productivity of single plants or populations. However, the implementation of high throughput approaches in perennial ryegrass breeding has received limited attention compared to crop species. Therefore, this thesis aimed to develop and implement tools to predict the persistence of perennial ryegrass cultivars suited for growing in dairy environments of Australia. Fractional plant ground cover may provide information to understand the relationship of the plant resistance to abiotic and biotic stresses. Therefore, the first objective of the thesis was to develop a phenomic tool to assess pasture ground cover in field plots. In this experiment, data acquisition was conducted over three years after sowing using aerial-based and ground-based image sensors. Object-based and pixel-based image analysis techniques were implemented to extract phenomic features from images. The experimental results indicated a strong positive relationship between ground truth data and sensor-based estimates (p<0.001). Therefore, subsequent image analysis and image sensors may have a potential to accurately and efficiently estimate pasture persistence in large-scale breeding programs. Pasture senescence may indicate changes in plant growth, development and resistance to abiotic and biotic stresses. Therefore, the thesis further investigated the feasibility of existing HTP (High-Throughput Phenotyping) approaches for pasture senescence estimation. Aerial-based multispectral images, ground-based RGB (Red, Green and Blue) images, and ground-based hyperspectral data were acquired from a three-year-old perennial ryegrass trial. Software packages and machine learning scripts were used to develop a phenomic pipeline for high-throughput data acquisitions and phenomic feature extraction. Based on the results of this study, it was confirmed that high-resolution images and machine learning scripts offer a precision tool to assess perennial ryegrass senescence in pasture breeding programs. The study was further extended to compare phenomic and genomic variation of surviving populations following multiple years of grazing by dairy cattle with that present within the original populations. Phenomic and genomic data were collected from a field experiment in three subsequent growing periods (2018-2020) using advanced phenotyping and pioneering molecular tools. The results of this experiment indicated that morphological characters of the original cultivar can be influenced and changed due to environmental stress, such as grazing and heat stress. However, the magnitude of these changes may depend on the growing season and cultivar. Plant survival percentage and productivity of a perennial ryegrass population are associated with infected endophyte strains and directional selection due to long-term environmental effects. This study also showed that persistence of perennial ryegrass was accounted for in the three-way interactions among host grass, endophyte and environmental factors. Based on the observations of the experiment, the thesis identified agronomically elite host grass-endophyte interaction (commercial cultivars) for the Southwest region of Victoria. Advanced phenotyping and pioneering molecular tools used in this thesis may offer a potential to develop novel cultivars with prolonged pasture persistence more quickly than ever before. The research findings of this thesis (indicators of persistence) may support seed companies and the wider industries to suggest a novel definition of persistence, which includes farmers expectations such as increased productivity and later heading date.
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    Understanding the Determinants of Environmental Resistomes in Urban and Natural Ecosystems
    Yan, Zhenzhen ( 2021)
    The spread of antibiotic resistance genes (ARGs) is a great public health threat faced by humanity in the 21st century. However, our understanding of the abiotic and biotic factors determining the distribution of ARGs in the environment is lacking. This knowledge gap hampers our ability to design efficient management strategies to mitigate the dissemination of environmental ARGs. Soil and plant phyllosphere are the two largest environmental habitats of microorganisms and the most important reservoirs of environmental ARGs. In urban ecosystems, soil and plants in green spaces are major interfaces between environmental microbiome and human microbiome, which are closely related to the welling being of urban residents. In this study, we investigated resistome profiles and bacterial community assemblies in soil and plant phyllosphere samples collected from 40 urban parks across Melbourne, Australia. A total of 218 and 217 unique ARGs, which confer resistance to all major classes of antibiotics commonly used in humans and animals, were identified in soil and plant phyllosphere samples, respectively suggesting that urban green spaces were important reservoirs of ARGs. The results of correlation analyses indicated that the abundance of ARGs was significantly (P < 0.001) positively correlated with industrial factors (represented by the numbers of industries in a region). Structural equation model revealed that industrial distribution was one of the most important factors that contributed to the variations in resistome profiles in urban green spaces after accounting for multiple drivers. We further explored the main factors influencing the assemblies of bacterial communities in urban green spaces. Industrial development was also identified as a key factor in shaping bacterial communities as variation partitioning analysis results revealed that industrial factors could explain 20% and 28% of the variations in soil and grass phyllosphere bacterial communities, respectively. In natural ecosystems, ARGs are considered as an important proxy of ecosystem multifunctionality. To explore the biogeographic patterns of ARGs in natural ecosystems, we constructed ARG abundance atlas for plant phyllosphere and soil from an over 4,000 km transect across eastern and northern Australia. The results revealed that plant phyllosphere and soil resistomes exhibited opposite biogeographic patterns. The results of Spearman’s correlation analysis and structural equation model revealed that ARG abundance in plant phyllosphere was mainly related to the community assemblies of co-occurring bacterial, fungal, and protistan communities. However, ARG abundance in soil was mainly correlated with mean annual temperature, mean annual precipitation and soil nutrition content. These results indicated the distinct roles of biotic and abiotic factors in shaping resistomes in plant phyllosphere and soil and could potentially explain the observed contrasting biogeographic patterns of ARG abundance in these two different environmental habitats. Given the profound roles of soil fauna on the evolution and spreading of ARGs, we also profiled the resistomes in termite mounds, which is a unique habitat for diverse microorganisms. By collecting termite mound and soil samples from an over 1,500 km transect in northern Australia, we found that both the abundance and diversity of ARGs in termite mounds were significantly (P < 0.001) lower than that in surrounding bulk soils. Further analyses revealed that the differences between termite mound and bulk soil resistomes might result from the substantial increases in pH and nutrient availability due to termite mound formation. These results provided valuable implications for utilizing soil faunal activity for the regulating of the dissemination of environmental ARGs. Taken together, this thesis provided important evidence that both urban and natural environments are critical reservoirs of ARGs. In urban environments, changes in ARGs were more impacted by anthropogenic factors, particularly industrial development. In natural environments, climatic and edaphic factors were the main predictors of resistome profile. We also found that the resistome profiles in soil and plant phyllosphere were distinct from each other suggesting the important roles of habitat properties in influencing resistome profiles. These findings advance our understanding of the biogeographical distribution patterns of ARGs in the environmental settings and provide important scientific data for refining the management strategies to combat the spreading of environmental ARGs, a potential major threat to the public health.
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    Understanding the contribution of comammox bacteria to nitrification in terrestrial ecosystems
    Li, Chaoyu ( 2021)
    The discovery of complete ammonia oxidizers, comammox Nitrospira, represents a breakthrough in the history of nitrification research. Nitrospira inopinata, which was obtained from the aquatic ecosystem, is the only pure comammox bacterial isolate reported so far. Ammonia oxidation kinetics of N. inopinata revealed that they prefer an oligotrophic lifestyle, but such evidence in terrestrial ecosystems is lacking. The niche differentiation between comammox Nitrospira and canonical ammonia oxidizers, and the relative contribution of comammox Nitrospira to soils are largely unknown. Here, the responses of comammox Nitrospira to nitrogen (N) fertilizers and nitrification inhibitors (NIs) in agricultural and forest soils, and metabolic activity and community composition of comammox Nitrospira involved in soil nitrification process, and the biogeographical distribution and ecological preference of comammox Nitrospira in large-scale soil surveys were studied in order to improve our understanding of the potential role of comammox Nitrospira in terrestrial ecosystems. The microcosm incubations treated with different N fertilizers (ammonium sulfate and urea) in agricultural and forest soils demonstrated that nitrogen applications significantly increased the abundance of comammox Nitrospira clade A over time. Besides, six NIs, including acetylene (C2H2), 2-chloro-6-(trichloromethyl) pyridine (nitrapyrin), 3,4-dimethylpyrazole phosphate (DMPP), allylthiourea (ATU), dicyandiamide (DCD) and 1-octyne (C8H14) were applied in two agricultural soils, including arable and pasture soils. The results revealed that the amendment of all the six NIs significantly decreased soil nitrate concentrations, but to varying degrees throughout the incubations. Except for C8H14, the other five NIs effectively inhibited the growth of comammox Nitrospira clade A in the pasture soil. 13CO2-DNA-stable isotope probing (SIP) revealed that comammox Nitrospira clade A incorporated 13CO2 into their genomes in fertilized soils during the microcosm incubations, indicating that comammox Nitrospira clade A may play an active role in the soils with high nutrient inputs. Phylogenetic analyses revealed that the majority of 13C-labelled comammox Nitrospira clade A belonged to a distinct cluster separated from most sequences obtained from aquatic and engineering systems. The community structure and evolutionary adaptation of soil comammox bacteria may have a strong influence on their niche specialization. These comammox Nitrospira species in the terrestrial ecosystems may be less oligotrophic than their aquatic counterparts. The large-scale field investigations further explored the biogeographic distributions of comammox Nitrospira and ecological niches of individual comammox Nitrospira phylotypes in a range of Australian soils. A total of 295 soil samples were collected from the regions over 4000 km in eastern Australia. Among all the tested edaphic and climatic parameters, mean annual precipitation (MAP) was the strongest driver affecting the abundance, richness and community structure of comammox Nitrospira clade A. Four phylogenetic clusters were identified in these soil habitats, and two of them were the dominant clusters. MAP was consistently the best predictor correlated with the relative abundances of the dominant clusters. Notably, these two dominant clusters had contrasting correlations with the environmental factors, including MAP, mean annual temperature, soil nitrate and total nitrogen. Taken together, a comprehensive understanding of the ecological characteristics of comammox Nitrospira in terrestrial ecosystems was conducted at various levels from laboratory microcosm incubations in representative agricultural and forest soils to large-scale field investigations in a wide range of Australian soils. This research provides novel evidence that the growth and activity of comammox Nitrospira clade A can be stimulated by some soil management practices, and the distribution of comammox Nitrospira clade A might be not strictly oligotrophic and instead have a broader ecological niche breadth than previously considered. These findings have profoundly expanded our knowledge of the environmental niches of comammox Nitrospira and their relative contribution to nitrification in soils.
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    Sorghum grain phenolic compounds, bioactive functions, and its application of its bran in food systems
    Xiong, Yun ( 2021)
    Sorghum is one of the world's most important but underutilised cereal crops. Sorghum grain, in addition to being a source of nutrition and energy, is replete with bioactive phytochemicals with many potential health benefits. In particular, sorghum grain has a distinct phenolic profile that is more abundant and diverse than other major cereal grains. Furthermore, the phenolic compounds in sorghum grain are concentrated in the bran, making the bran a potentially valuable functional ingredient or source of natural bioactive compounds for food and pharmaceutical applications. Therefore, understanding the phenolic profile and bioactive properties of sorghum grain/bran is important for its development and applications. The aim of this PhD project was to gain a comprehensive understanding of the phenolic profile of sorghum grain/bran, its bioactive functions, and its potential in food application. Five representative Australian sorghum grain varieties were selected for this project: white Liberty, red Mr-Buster, red Nuseed Cracka, brown IS131C, and black Shawaya Short Black 1 sorghum grains. In relation to sorghum phenolic profile, a total of 110 phenolic compounds (38 phenolic acids, 59 flavonoids and 13 other phenolic compounds) were identified and characterised in the five sorghum grains using HPLC-DAD-ESI-QTOF-MS/MS analysis; 56 of them were reported for the first time in sorghum. The distribution pattern of individual and subclass phenolic compounds among the sorghum grain varieties, location (bran or kernel fraction), and form (free or bound form) were directly visualised by the multiple factor analysis and heatmap. In general, the phenolic compounds were concentrated in the bran in terms of both diversity and quantity, with brown sorghum grain/bran having the most diverse and abundant phenolic compounds. In terms of bioactive properties, sorghum demonstrated significant in vitro inhibitory activity against alpha-glucosidase alpha-amylase digestive enzymes, and potent in vitro and cellular antioxidant activities. The enzyme inhibition and antioxidant activity of sorghum grains were related to their phenolic concentration and composition; the bioactivities varied between individual assays but were generally in the following order: brown and black > red > white coloured sorghum; bran > kernel; free > bound. Brown sorghum-bran-free and black sorghum-bran-free phenolic extracts were found to be the most effective in both enzyme inhibition and antioxidant activity studies. The active phenolic compounds that may be responsible for these bioactive properties were also identified. 3-Deoxyanthocyanidins were discovered to be a key group of active phenolic compounds that confer high antioxidant activity on sorghum. The structure-activity relationship study revealed that the B-ring catechol functional group was essential for 3-deoxyanthocyanidins to have high antioxidant activity. Regarding food application, the effects of brown and black sorghum bran incorporation on the physicochemical and microbiological properties of beef sausage were investigated. Unexpectedly, the incorporation of sorghum bran in sausages promoted discolouration, oxidation, and pH fluctuation. Nevertheless, sorghum bran demonstrated some antimicrobial activity. This thesis addresses a number of research gaps in sorghum grain research and expands our knowledge on sorghum grain phenolic profile, bioactive functions, and food applications, which could promote further research and development of sorghum in the food industry.
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    Finding the hidden smoke: Exploring the use of digital technologies for assessing grapevine smoke contamination and taint in grapes and wine
    Summerson, Vasiliki ( 2021)
    Grapevine smoke contamination and the subsequent development of smoke taint in wine has resulted in significant financial losses for winemakers throughout the world. Unfortunately, the incidence of grapevine smoke exposure is expected to rise as the number and intensity of wildfires increase due to the effects of climate change. Wines produced from smoke affected grapes are characterised by unpleasant smoky aromas, rendering them unpalatable and therefore unprofitable. Traditionally, chromatographic techniques such as gas chromatography-mass spectrometry (GC-MS) and high-performance liquid chromatography (HPLC) have been used for assessing the levels of smoke-derived volatile phenols and their glycoconjugates in grapes and wine. However, these methods are time consuming, expensive and require destructive sample preparation as well as the use of trained personnel. Furthermore, sensory evaluation of wine samples using human panels may be subject to bias due to individual variability of the participants, as well as being expensive and time consuming as large groups of participants must be recruited and trained. In addition to this, a number of methods have been identified for ameliorating smoke taint in wine such as the use of activated carbon and reverse osmosis. While effective at reducing levels of volatile phenols for smoke taint amelioration, they are unable to act on glycoconjugates, and therefore a gradual resurgence of smoky aromas may arise as these glycoconjugates are hydrolysed back into their free active forms over time. This research therefore investigated alternative methods for assessing the degree of grapevine smoke exposure and the level of smoke taint in wine using digital technologies coupled with machine learning (ML) modelling based on artificial neural networks (ANN), and whether the use of a cleaving enzyme prior to the addition of activated carbon could be effective at ameliorating smoke taint in wine. Near-infrared (NIR) spectroscopy was used to obtain a chemical fingerprint of grape berries, leaves, must and wine. These readings were then used as inputs to develop ANN models that showed high accuracy in the classification of berries and leaves according to the level of smoke exposure and degree of taint (97% – 98%), as well as predicting the levels of smoke-derived volatile phenols and their glycoconjugates in grapes, must and wine (R = 0.98 – 0.99). Additionally, models predicting consumer responses towards smoke tainted wines using NIR berry and wine spectral readings were created which displayed high accuracy in their predictive abilities (R = 0.97 – 0.98). The results demonstrated that NIR spectroscopy coupled with ML modelling can provide accurate, rapid and non-destructive tools for assessing grapevine smoke contamination and smoke taint in wine, in addition to predicting the sensory responses of consumers towards smoke tainted wines. Furthermore, the models developed can be used together to form an integrated smoke taint detection system that growers and winemakers can use in-field or in the winery to assess grapes and wine. A low-cost electronic nose (E-nose) was used to assess the aroma potential of smoke-tainted wines. Readings from the e-nose were used as inputs to develop ML models that showed high accuracy in predicting the levels of eight volatile aromatic compounds in wine (R = 0.99), the degree of smoke aroma intensity (R = 0.97). These two models may be used together with previously developed models that predict the levels of smoke-derived volatile phenols and their glycoconjugates and 12 wine descriptors to provide winemakers with a greater picture of the degree of smoke taint and the aroma profiles of smoke-tainted wines. In addition to this, the use of a cleaving enzyme (ZIMAROM, Enologica Vason) prior to treatment with activated carbon was found to be effective in ameliorating smoke taint and may help delay the resurgence of smoky aromas by hydrolysing glycoconjugates into their free volatile phenol forms which can then be removed by the addition of activated carbon. An ANN model displaying high accuracy (98%) was also developed using the readings from the e-nose to classify wine samples according to the type of smoke-taint amelioration treatment applied to assess their effectiveness. The model may offer winemakers a cost-effective, non-destructive, rapid, and accurate tool to assess the effectiveness of smoke taint amelioration treatment by activated carbon with/without the addition of a cleaving enzyme.
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    The effect of temperature on the timing of grapevine phenology
    Cameron, Wendy ( 2021)
    Climate change and associated temperature increase is having a profound impact on the timing of grapevine phenology. The expectation that this will continue necessitates grape growers to adapt to climate change. Part of adaption will be understanding what cultivars to plant in existing and new vineyard sites, specific knowledge of which is currently lacking. This thesis aimed to increase our knowledge of the phenological response of grape cultivars to temperature, with the longer-term goal of assisting future selection of cultivars. Using historical data from four commercial vineyards in climatically different regions in Victoria, Australia, and using advanced statistical modelling techniques, the study showed that the phenological stages maturity, veraison and flowering had advanced as related to springtime maximum temperature and there are significant differences between cultivars and their rate of advancement. Later ripening cultivars advanced maturity at a greater rate than earlier ripening cultivars at the same vineyard. Yet, the same cultivar at different vineyards, advanced maturity at a greater rate in the cooler vineyard. A significant delay in the timing of maturity was also demonstrated for one cultivar. These findings provide explanations for the observation of time compression of the harvest period and noted that as temperature further increases, vines will move beyond their optimal growth temperature range for growth and show the impact of heat stress with delayed rates of phenological advancement. Comparisons of the rates of advancement for a given phenological stage across different vineyard regions showed that the response of cultivar to temperature was nonlinear and differed according to the vineyard grouping and the temperature. The intervals between the four major phenological stages, budburst, flowering, veraison and maturity were also investigated. It was found that the budburst to flowering interval was more highly correlated to temperature than intervals between other phenological stages and had shortened at a significantly greater rate than the flowering to veraison and veraison to maturity intervals, as related to springtime maximum temperature. The relationship between temperature and budburst to flowering interval was best described by a curve, indicating that as the average daily maximum temperature during this interval increases, the decrease in interval length will slow and plateau. By extension, this also indicates that the observed advancement in maturity timing, as related to springtime maximum temperature, is expected to slow and plateau with future increases in temperature. These results will inform future management decisions as the wine industry responds to the challenges of a changing climate and show that cultivar diversity will be an important tool in these adaptation strategies.