School of Agriculture, Food and Ecosystem Sciences - Theses
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ItemAssessing site quality of South Australian radiata pine plantations using airborne LiDAR data( 2011)Site quality information underpins many aspects of radiata pine plantation management in South Australia. Site quality assessment is essentially a problem of assessing the spatial variation of standing volume in unthinned stands at reference age nine or ten. Data collected during three experimental trials and two operational surveys were used to research a new site quality assessment system relying on airborne LiDAR data. A two-stage method was adopted characterised by the calibration of predictive models relating forest and LiDAR variables, and the subsequent application of these models to predict the forest variable across an area of interest. The four parts of the study examined the properties and behaviour of LiDAR prediction models, the sampling design and timing of field data collection, the spatial resolution of model application and the special case of partially thinned plantations. Single-variable, linear models, fitted to high altitude/low density LiDAR data captured across 20 sites scattered over an area of 10,000 km2 , had RMSE of 10-11% and 3-4% for stand volume and predominant height respectively. Evidence of site effects in the models was inconclusive. Models fitted to four LiDAR and field datasets acquired in 2002, 2006, 2007 and 2009 had consistent structure but model parameters were sensitive to the differences in operational LiDAR campaign parameters, indicating that prediction models should be re-calibrated each survey. Fifty field plots were found to be adequate to fit a regionally applicable volume prediction model. Sample selection methods only influenced model precision when sample sizes were small (less than twenty plots). The correlation between forest and LiDAR variables remained strong when field and LiDAR data were collected several years apart, but model parameter values changed rapidly as a result of tree growth. A time lag between LiDAR and field data collection can be tolerated but field measurements, once commenced, should be concentrated in time. The LiDAR predictor variable for volume was found to be insensitive to changes in the reference plot area indicating that volume prediction models may be applied in partitions (spatial units) with areas different than those of the calibration field plots. Comparison of alternative configurations of size, shape and arrangement of partitions, coupled with one of four spatial interpolation techniques, demonstrated significant differences in precision of predicted volume surfaces, with key factors being the dimensions of the interpolation neighbourhood and LiDAR data density. Several configurations closely approximated or, in the case of low density data, exceeded the precision of the prediction models. A method for assessment of partially thinned plantations appeared effective but requires further validation. The results of this study will be used to guide the second operational LiDAR-based site quality survey in South Australia, scheduled for early 2012.
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ItemThe integration of whole crop cereal silage into pasture-based dairy systems( 2011)Failure to meet the year-round nutritional requirements of genetically improved dairy cows has led to a need to evaluate complementary forage such as whole crop cereal in enhancing home-grown forage yield, utilization and production response from dairy cows. This thesis demonstrated that cereal crop triticale and wheat can be an alternative feed resource in pasture based production systems. The thesis identified that most cereal crops can offer as early grazing option, is robust in DM yield and flexible in utilization beside clear trade-off benefits from pasture in terms of harvestable nutrients. This was evident from the DM yield and nutrient composition of crops in first study at booting (GS34) where triticale (cv. Crackerjack) had potential of 4.29 metric tons DM and while wheat (cv. Wedgetail) had 3.66 metric tons DM. CP concentration was 206 g per kg DM and 245 g per kg DM with 10.5 MJ ME per kg DM and 10.7 MJ ME per kg DM for triticale and wheat at GS34 respectively. Delaying harvest for silage (GS84) yielded 15.81 metric tons DM by triticale and 13.18 metric tons DM by wheat. Meanwhile WSC concentration at GS84 for triticale crop had reached to 208 ± 25 g per kg DM and wheat was 175 ± 32 g per kg DM. Triticale had potential to harvest 129 GJ ME per hectare and wheat had 109 GJ ME per hectare at GS84. Whole crop cereals produced quality silage in storage with pH 3.09 to 4.40 with moderate energy values of 8.92 ± 0.07 MJ ME per kg DM for triticale silage and 9.40 ± 0.01 MJ ME per kg DM for wheat silage. Fermentation process of whole crop triticale and wheat with silage additives produced good quality silage while ensiling method by direct-cut technique was most appropriate for whole crop. Application of homo-fermentative SilAll additive was most efficient to produce LA (70.7 to 113.8 g/kg DM) while hetero-fermentative LaSil additives were effective to yield maximum AA (28.2 to 40.8 g/kg DM). The ME value of direct-cut silage of triticale and wheat was still better with 10.7 MJ per kg DM and 10.4 MJ per kg DM compared to 9.4 MJ per kg DM and 10 MJ per kg DM from wilted silage respectively. However, direct-cut silage had higher proteolysis effect with 9.3 to 12.3 g per kg total N compared to 7.3 to 9.6 g per kg total N from wilted silage. Whole crop triticale and wheat were comparable in degradability quality offering options in dry-land farming. The extent of triticale silage degradability at GS34 was 135 to 323 ml per g DM for direct-cut method silage while wheat silage was 204 to 257 ml per g DM. The extent of wilted triticale silage got reduced to 86.5 to 116.6 ml per gram DM while it was 105 to 116 ml per g DM for wilted wheat silage. However the rate of degradation of direct-cut silage was longer with 14 to 18 hours in both forage crops. On contrary, maximum time taken for wilted silage degradation was within 6 to 9 hours for both forage crops. The total fermentation acid production from silage digestion was 11.8 to 14.46 mmol per L from both crop species ensiled by either method. Feeding WCS along with other forages demonstrated to support mid to late lactation cows without limitation to intake and production response. WCS intake of 3.71 to 4.02 kg DM was significant (p<0.001) with canola hay compared to 3.26 to 3.86 kg DM to pasture hay. WCS wastage accounted 9 to 23 percent on pasture hay while on canola hay wasted 6 to 12 percent only. WCS intake rate (g/minute) was higher (37.74 g/minute) to canola hay while it was only 34.96 g per minute on pasture hay. Yet the bite size of WCS was within 1.1 g per bite for both. The total VFI were similar with 18.95 to 20.62 kg DM for pasture and 19.39 to 20.61 kg DM for canola hay. Mean milk yield of 16.1 ± 2.85 kg per day from canola hay group cows was significant (p<0.01) from 13.9 ± 2.58 kg produced by pasture hay group cows. However the total milk solids produced from pasture group (1265 g/day) was not significant to canola group cows (1345 g/day). The ME and MP supply from the total VFI far exceeded nutrient requirement indicating good complementarity of ration with WCS. Mean LW gain was 1.16 to 1.54 kg per day for pasture while canola hay gained only 670 g per day with full allowance of wheat grain. BCS in pasture hay gained 0.08 to 0.48 points while it was 0.06 to 0.28 points gain for canola hay.
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ItemBiosorption of heavy metal cations from water solution using microwave-modified barks of Eucalyptus globulus Labill and Pinus radiata D.Don( 2011)The utilization of bark from Eucalyptus globulus Labill and Pinus radiata D.Don as adsorbents for Cu(II) and Zn(II) in solution was investigated in this study. Analysis of the chemical composition of the two bark species enabled a determination of their potential end use application for heavy metal removal. The higher lignin (49.4%) and extractive (20.5%) content of pine bark as well as the higher hemicellulose (35.7%) content of eucalypt facilitated the use of these barks as suitable adsorbents for heavy metal cations. The release of water soluble extractives (phenolic compounds), however, may impede the use of the barks as metal chelating agents because the leachates may under some circumstances be detrimental to aquatic ecosystems. In order to fix the extractives within the bark and prevent them from leaching, the eucalypt and pine barks were ground, air dried and then heated for 60 seconds using three different microwave power levels to obtain three determined bark temperatures (100, 150 and 200°C). UV spectroscopy showed that the absorbance value of untreated bark at 280 nm was not significantly different from that of the corresponding bark treated with microwave. This was observed for both bark species. This indicates that microwave heating for 1 minute using the three microwave energies examined did not result in any fixation of phenolic compounds within the bark. FTIR spectroscopy, however, indicated the occurrence of chemical bond transformations in various functional groups in bark hemicelluloses and lignin as a result of microwaving. Eucalypt and pine barks were evaluated in a Cu(II) and Zn(II) adsorption study with the following initial treatments: (1) Microwaving to various pre-determined temperatures (100, 150 and 200°C), (2) extracting with water (to remove leachable compounds) and (3) combined processing, incorporating both microwaving and extraction with water. Pressure steamed pine bark, ground pine wood and cotton were examined for comparison. All the samples were analyzed using UV spectroscopy to measure the absorbance value at 280 nm in order to elucidate the leachability of phenolic compounds from the absorbents. Batches of each sample were then soaked in heavy metals solutions (10, 20, 40 mg/L of a single metal element - Cu(II) and Zn(II)). After an hour of shaking, the adsorbent was filtered from the suspension and the solute was analyzed for residual copper using Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP-AES) to determine the efficiency of each material in binding the metal cations from the solution. The results showed that pressure steamed pine bark and water extracted eucalypt bark exhibited the highest adsorption capacity for Cu(II) and Zn(II) at lower concentrations (10, 20 and 40 mg/L). As observed for eucalypt and pine bark, microwave treated bark generally showed lower uptake capacity compared to the corresponding untreated bark due to their lower reactivity. Microwave treated bark also removed lower amounts of Cu(II) and Zn(II) than those exposed to the combined treatment. Cotton adsorbed the lowest amounts of Cu(II) and Zn(II). This performance was also observed for the bark when in contact with a mixed Cu(II)-Zn(II) solution. At higher Cu(II) concentration (3000 mg/L), there was a change in the performance of the absorbents examined. Untreated eucalypt bark exhibited the highest Cu(II) adsorption (11.1 mg/g), followed by pressure steamed pine bark (10.4 mg/g) and water extracted eucalypt bark (9.2 mg/g). Microwave treated eucalypt bark chelated more Cu(II) compared to the bark subjected to the combined treatment of microwave and water extraction, but this was not observed for microwave treated pine bark. Adsorption equilibrium parameters were generated using the graphical interpretation of the equations of the Freundlich and Langmuir models. On the basis of these models, Cu(II) and Zn(II) adsorption by both bark species were monolayer. The Freundlich isotherm model provided a better fit to the experimental data in describing both Cu(II) and Zn(II) adsorption by both eucalypt and pine barks. The type of metal species did not significantly affect the metal adsorption in the single cation solutions, but in a binary-metal solution, Cu(II) was adsorbed more than Zn(II). Initial concentration of the metal cations in solution influenced adsorption ability of the absorbent examined, in either single or multi metal solutions. Increasing metal concentration in solution increased the uptake capacity and decreased the uptake efficiency of adsorbents. In relation to bark species, eucalypt bark had a higher adsorption capacity than pine bark. Adsorbent particle size did not appear to affect the adsorption process, while adsorbent dosage did. An hour of reaction time was sufficient to reach adsorption equilibrium between the adsorbents and the metal solution. Pressure steam treatment on bark may have resulted in the insolubilisation of leachable compounds of the material, potentially rendering the bark to be more reactive with metal cations. Another possible reason for the improved capture of cations is the absence of leachable extractives. A proportion of leachable extractives had been removed during pressure steaming. The removal of soluble extractives may increase the number of adsorption sites available to heavy metals. Alternatively, steaming may result in the swelling of the bark material, leaving more accessible adsorption sites, thereby improving the adsorption ability of the bark. Water extraction treatment of bark is found to be beneficial for metal adsorption. In addition to the generation of an effective adsorbent for metal cations, water soluble extracts can be isolated for a range of purposes, including tannin adsorbents for heavy metals, proteins and oils, natural additives and a chemical source. This study has shown that eucalypt and pine bark have potential for binding heavy metals. The plentiful availability of eucalypt and pine bark in Australia allows their viable utilization for enhancing water quality in a variety of applications.
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ItemThe long term impact of thinning on water yield( 2011)The city of Melbourne, Australia, relies on forested catchments for most of its water supply. Up to 80% of the total catchment yield originates from mountain ash (Eucalyptus regnans) forests. Forest thinning has been considered as a management option to optimise the water yield of young mountain ash forests, but its long term impact has not been well understood. This study investigated the long term impact of different thinning treatments on vegetation structure and water yield in mountain ash forests so they can be predicted accurately in the future. The study was conducted at the North Maroondah experimental catchments. A range of thinning treatments (patch-cutting, uniform thinning, understorey removal and strip-thinning) was applied to those catchments in the late 1970s. Most of the thinning treatments resulted in a statistically significant monthly increase in water yield that persisted for more than a decade or until the suspension of streamflow measurement in 1997. The largest cumulative water yield increase in this period was 1833 mm at Crotty Creek. A decrease in water yield was observed in these catchments when the measurement resumed in 2007/2008. The exception to this was Ettercon 2 (understorey removal), where non-significant increase in monthly water yield appeared to persist when the measurement resumed. Vegetation structure attributes were obtained using Light Detection and Ranging (LiDAR) technology to assess the impact of thinning. The persistence of the water yield increase could be attributed to poor regeneration of mountain ash in the cleared areas after the treatment. The canopy height profiles showed that patch-cutting and strip-thinning have permanently altered the vegetation structure of the catchments, while the impact of uniform thinning on vegetation structure could no longer be identified. The spatial distribution of leaf area index (LAI) at catchment level was obtained from Quickbird multi-spectral imagery and LiDAR data. The remote sensing parameters were initially calibrated against the in situ LAI measurements obtained by cover and hemispherical photography at Crotty Creek. However, the calibration models were poor because the photography techniques overestimated LAI in the regrowth stands. The LAI distribution was projected based on LiDAR gap fractions. The remotely sensed overstorey LAI distribution showed that the mean LAI estimate of Ettercon 2 (1.7) was lower than the mean LAI estimate of the control catchment (2.2). This implied that Ettercon 2 had lower ET than the control catchment, which might explain the persistence of the modest water yield increase. However, the overstorey LAI distributions of the other thinned catchments did not fully explain the post-thinning decrease in water yield as their mean catchment LAI estimates were lower than the mean LAI estimate of the control catchment. The mean overstorey daily transpirations were 0.7 mm day-1 and 1.5 mm day-1 for the regrowth vegetation stands (mainly of Acacia spp.) and the retained mountain ash stands at Crotty Creek respectively. In addition to the difference in mean overstorey LAI estimates, the regrowth Acacia stands have lower sap flux density and stomatal conductance than the retained mountain ash stands. The transpiration of the regrowth Acacia stands is not likely to replace the transpiration of the removed mountain ash stands even if they have similar LAI. Thus, it was hypothesised that the post-thinning decrease in water yield was due to the increased transpiration of the retained mountain ash stands as well as the transpiration of the regrowth vegetation stands. The recent drought that affected south eastern Australia might have also amplified the magnitude of the water yield decrease. The transpiration measurements indicated that stand transpiration was controlled by evaporative demand rather than water availability. The transpiration was maintained at a high rate during the period of low rainfall at the expense of streamflow. This study has confirmed that the post-thinning changes in vegetation structure could account for the observed changes in water yield in mountain ash catchments. It has also shown that LAI spatial distribution can be robustly obtained from remote sensing data, particularly from LiDAR data. However, vegetation composition and canopy structure need to be incorporated along with LAI distribution in a process-based hydrological model to model the post-thinning ET of the complex forest structure and predict the long term changes in water yield.
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ItemAcceleration of the chemical modification of wood using microwave heating and catalyst( 2011)Wood is a versatile material with many superior properties. However, dimensional instability and biodegradability are the most undesirable properties of wood. There are different methods of improving wood properties and chemical modification is one potential approach. Interest in the chemical modification of wood arises from the need to substitute conventional biocides currently used to protect timber from decay and insect attack while maintaining a substrate that is dimensionally stable. Lower moisture content, preferably 5-6%, is a prerequisite for most of the chemical modification reactions. Economically it is not feasibly to dry the timber to such low moisture content by conventional drying. However, microwave heating was evaluated and found to be effective in reducing the moisture content of radiata pine from 13% to 6% in a microwave cycle of 2 minutes without any drying degrade. The energy consumed to achieve this was 55 kWh/m3. Acetylation of wood is considered to be one of the most promising ways of enhancing wood properties. However, conventional heating leads to a long reaction time. Reduction in reaction time is an advantage both in terms of economy of the process and properties of the end product. The use of potassium acetate as a catalyst, methanol and water as solvent carriers for this catalyst and microwave heating were investigated to accelerate the acetylation reaction. The properties of the microwave assisted acetylated wood were compared with those of the conventional heated acetylated wood. Solvents imparted no significant effect on the rate and degree of reaction. Potassium acetate significantly accelerated the reaction. The rate of acetylation was 7.5 times faster in catalysed condition after 30 minutes of reaction. In the catalyzed reaction, an average weight gain of 20.6% was achieved after 30 minutes reaction time, while for the uncatalyzed reaction it was less than 3%. Microwave heating proved to be effective in the acetylation of wood in the presence of potassium acetate. However, a lower catalyst loading is desirable since higher loadings led to charring of samples, particularly where acetylation uses a limited supply of reagent. A weight gain of 21.6% was achieved in 10 minutes of microwave heating using a catalyst compared to 13.4% where no catalyst was employed. The presence of a catalyst provided more uniform acetylation across the sample thickness compared to the uncatalyzed reaction. The catalyst and mode of heating did not influence the adsorption behaviour of the acetylated sample once the catalyst was leached. Un-leached acetylated samples showed significantly higher EMC values, particularly at higher relative humidities, due to the hygroscopic nature of the catalyst. The mode of heating and catalyst also had no influence on the swelling coefficient, anti-shrink efficiency, modulus of elasticity, modulus of rupture, resilience and decay resistance of the samples. Improvement in the mechanical properties was anticipated with the reduction in reaction time. Although the use of a catalyst and microwave heating reduced the reaction time significantly, no improvement in the strength properties was achieved. Wood modification with furfuryl alcohol results in a wood polymer composite. Curing by conventional heating is a time consuming process. The dielectric properties of furfuryl alcohol indicate that it can be heated under a microwave field. Microwave curing of furfuryl alcohol caused complete polymerization but the resin yield was 7% lower compared to conventional curing. Oven curing of impregnated samples caused significantly higher weight percent gains compared to microwave curing. A microwaving time of 10 minutes was optimal in terms of weight percent gain and uniformity of colour. Microwave cured samples showed similar anti-shrink efficiency and decay resistance to oven cured samples even at lower weight gains.
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ItemThe physiological and metabolic responses to heat in ruminants( 2011)Climatic stress, whether cold or heat, is the greatest stressor faced by animals. Heat stress is a very real problem in Australian climates, to which ruminants are particularly susceptible, costing producers millions of dollars in production losses annually. An individual animal’s ability to cope with, and thrive in, extreme temperatures can often be the difference between a high and low producing animal. However, selection for more tolerant animals often leads to a decline in efficiency. As production systems have increasingly become more intensive as well as efficient, so too has the desire to continue to improve productivity and profitability. Furthermore, society dictates that animals be treated ethically and humanely, which in turn has altered the manner in which production systems operate. Therefore, advances in our knowledge of the stress responses of animals, as well as the methods they employ to cope with such stressors, will generate new practices that will improve not only production but also animal welfare. The characterisation of physiological and metabolic responses to heat is imperative for the development of amelioration methods and the improvement of production in heat-stressed ruminants. The research presented in this thesis firstly examined the physiological and metabolic responses to heat stress in various ruminant species to map system wide adaptations to heat. Cellular responses to heat were also examined, with particular focus on the expression of heat shock protein (HSP) genes, a set of cellular proteins with multifaceted roles including protecting and re-folding damaged proteins. The experiments presented demonstrate the novel finding that HSP genes are present in white blood cells of stressed and unstressed sheep and cattle. The experiments presented in this thesis also present the original finding that HSP genes are expressed in the mRNA of skeletal muscle and adipose tissue of both stressed and un-stressed ruminants and this expression is influenced by the type/location of the adipose tissue. This thesis also explores some metabolic responses to heat stress in sheep by examining plasma metabolite and hormone responses to glucose, insulin, ACTH and epinephrine challenges. Both mild and more severe heat stress regimes were shown to influence plasma prolactin concentrations, while insulin responsiveness and sensitivity were affected by heat exposure which supports the notion that insulin may play a role in the adaptive responses to heat. In addition, methods to ameliorate heat stress in ruminants were examined, focusing on the use of the organic osmolyte betaine as a dietary supplement. Betaine was shown to have a pronounced dose-dependent response in sheep, as lower doses were shown to ameliorate some of the physiological responses to heat such as respiration rate, while higher doses are shown to increase respiration rate, rectal and skin temperatures during both thermoneutral and heat stress conditions. A lower dose of betaine successfully ameliorated the increased skin and core temperatures, heart rates and respiration rates associated with heat; while also altering metabolism. This novel finding of a significant dose response to dietary betaine supplementation perhaps indicates that excess dietary betaine may increase metabolic heat loads in sheep, likely due to the increased need to metabolise excess betaine. Dietary betaine is also shown to somewhat influence fat depths and carcass characteristics in feedlot cattle in a dose dependent manner. Thus, it is concluded that dietary betaine may be utilized as a dietary supplement to ameliorate the effects of heat stress in ruminants although the dose rate must be carefully considered to ensure that metabolic head loads are not exasperated by the supplementation.
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ItemSharing expertise: creating collaborative knowledge networks to strengthen RD&E outcomes( 2011)The complexity of current and future agricultural research problems requires that Research, Development and Extension (RD&E) stakeholders design and develop effective, collaborative, transdisciplinary ways of working together. This means not only that potentially conflicting priorities need to be aligned but also that new social arrangements be assembled and fostered. Professional interactions need to be negotiated across disciplinary boundaries and extended to include people and practices who do not customarily work together. The significance of such social challenges, in addition to the technical tasks, cannot be underestimated. This thesis explores how collaboration as practice evolved in a dairy industry case study Project 3030, including attributes of project design, leadership and participation of stakeholders. Research questions investigate how knowledge was co-constructed and shared between participants and groups within Project 3030’s transdisciplinary RD&E knowledge network as they worked together to address the project goal of increasing home grown forage productivity by 30% and profitability by 30%. This social research project is foremost an ethnographic case study and is supported with social network analysis to make relational connections (connectivity) of the knowledge network visible and tangible for discussion and reflection at a network-wide scale. Key findings that emerged from this study are firstly, that in the context a planned transdisciplinary RD&E project, knowledge co-construction and sharing required an understanding of collaboration as a relational process and could not be assumed to be unproblematic. In practice it is likely to be complex and ‘eventful’ as it draws on the social capital of both individuals and groups. Secondly, collaboration involving knowledge co-construction is highly dependent on the relational trust and reciprocity of strong social capital but equally dependent on the brokerage capability of bridging social capital to facilitate knowledge sharing. A conceptual model was proposed for assessing systemic collaboration capability in a transdisciplinary knowledge network. This model of collaboration ‘fitness’ offers an organising framework based on three interlinking parts representing three scales of the knowledge network: individuals, relationships and the network as a whole. For the knowledge network to function effectively each part needs to be ‘fit’ for the challenges of extended collaboration. For individuals this means not only contributing technical competence but also social competence and engagement in joint activities that support the overall purpose of the network. Relational ‘fitness’ refers to achieving a network specific balance of bonding, bridging and linking social capital across the knowledge network. Network-wide ‘fitness’ refers to the way that the mix of authority, organisations, disciplines and network culture(s), temporal and geographic ranges are appropriately coordinated, aligned and empowered. The model shows each part connected by a central hub representing the dedicated coordination and facilitation needed to enable partnership and collaboration across the diverse scales, functions, activities, positions and roles assembled for the purpose of generating complementary forage innovation. Large RD&E projects represent a significant investment in people and resources. Taking care of the investment in people begins with prioritising the establishment of trust based working relationships within and between groups. Furthermore each member of the project from board room to paddock needs to clearly understand the project vision and the accountabilities each has towards this vision. They also need to identify as members of a collaborating team co-constructing and sharing unique knowledge. The proposed collaboration ‘fitness’ model requires further empirical testing and development. Nevertheless it highlights the need for collaboration processes to be incorporated in the design of RD&E projects. The complementary use of social network analysis and ethnographic methodologies generated useful relational insights regarding the significance and implications of social capital for knowledge co-construction and sharing.
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ItemIntegrating traditional ecological knowledge and scientific knowledge to improve conservation planning of gibbons (Hylobatidae) in Lao PDR( 2011)Biodiversity conservation planning and management requires the use of knowledge from a wide range of sources. It also requires an understanding of the beliefs and attitudes towards target species among local people in order to integrate them effectively in planning and management activities. Gibbons (Hylobatidae) are an important species for conservation in Laos. Laos is also generally important for conservation of biodiversity and is part of the Indo-Burma biodiversity hotspot (Myers et al. 2000). For gibbons, Laos holds a significant proportion of the global biological diversity of the family. There is also more intact habitat in Laos than in neighboring range states making Laos a priority for gibbon conservation. This research was conducted to investigate the nature and status of traditional ecological knowledge (TEK) of gibbons in the Nam Kading National Protected Area in central Laos and demonstrate a methodology that would allow the combination of Traditional Ecological Knowledge (TEK) and scientific ecological knowledge (SEK) for improved conservation planning. The research used a broad-definition of TEK and used focus group interviews and comparative case study methodology to investigate TEK in two contrasting villages. Using factors identified from this study and subsequent analysis the research then investigated the utility of Bayesian Belief Networks (BBN) linked to a GIS to incorporate differing knowledge types to build a spatial model and predict gibbon occurrence across the Nam Kading landscape. Results indicated that there was a range of traditional knowledge of gibbons in the region, with more detailed knowledge of specific aspects of gibbon habitat preferences and behavior in the more remote village (Khontao) that had been less subject to outside influence. Knowledge was stronger among males, particularly hunters who had more direct interaction with gibbons. Knowledge in the more accessible village of Kengbit was more anecdotal and less informed on specific aspects of gibbon ecology. Specific knowledge of gibbons and traditional taboos appears to be degrading under new social pressures, but communal knowledge and understanding of gibbons appears more resilient. A comparison of SEK and TEK in a BBN modeling framework indicated that both forms of knowledge share many pieces of common information about the factors affecting gibbon occurrence across the landscape. Models developed using BBN to integrate the two forms of knowledge did not produce sufficiently accurate models of occurrence for conservation planning, however, the BBN approach does have significant benefits in incorporating different data sets, incorporating traditional knowledge, involving local people and promoting a shared understanding of the target conservation species between different stakeholders. It is recommended that this type of modeling be included in adaptive management framework for gibbon conservation in the Nam Kading landscape and more widely in conservation planning and management.
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ItemGreenhouse gas emissions from Australian beef feedlots( 2011)Emissions of the greenhouse gases, methane (CH4) and nitrous oxide (N2O) and the indirect greenhouse gas ammonia (NH3) play an increasing role in public concern about the environmental impact of concentrated animal feeding operations, including feedlots. However, there is a lack of emissions measurements under typical commercial conditions and there is high uncertainty in the estimation. The lack of accurate measurements and baseline emissions also makes it difficult to evaluate efficiency of current mangemange practices and identify the potential reductions under mitigation options. The objective of this study was to achieve increased understanding of greenhouse gas emissions from Australian beef feedlots, elucidating the biophysical factors controlling emissions from feedlot systems. Specifically, the study utilises measurements of greenhouse gas emissions undertaken at commercial feedlots in Australia using micrometeorological methods and integrates data collected from the feedlot operators into empirical models with the aim to identify and quantify the sources of variation in measured emissions between sites and seasons; test the validity the modelling approach used specifically for feedlots and quantify the link between animal behaviour and diurnal emissions patterns. This study comprised two detailed modelling exercises. The first utilising the results of published studies to validate a range of equations for predicting enteric methane emissions and for predicting emissions of methane, nitrous oxide and ammonia from manure. The second modelling exercise utilised the results of measurements undertaken in two commercial Australian feedlots to evaluate a range of models under commercial conditions. Finally, the diurnal variation in micrometeorological measurements of CH4 and NH3 were examined in the context of animal feeding behaviour in order to examine implications for measurement accuracy and examine correlations between fluxes and behaviour. This thesis indicates that the current Australian Inventory methodology for estimating greenhouse gas emissions from feedlots (enteric CH4, manure CH4, N2O and NH3) suffers from considerable inaccuracies. Although more accurate estimates of CH4 emissions appear to be associated with utilising an equation based on ration composition, particularly carbohydrate fractions the current approach over estimates emissions considerably. Inaccuracies in prediction of emissions of N2O and NH3 are related primarily to the use of single “emissions factors” which do not adequately reflect the changes in potential emissions associated with changing environmental conditions. This thesis also explored the contribution of CH4, N2O and NH3 using IPCC default factor of 1.25% deposited NH3 is lost as N2O to total feedlot emissions, represented as CO2-e. Initial estimates suggest that feedlot emissions were dominated by CH4, with minor contributions of direct and indirect N2O. However, based on the measurements nitrogenous greenhouse gases are predicted to contribute up to 52% of total CO2-e. These results indicate that mitigation options to reduce feedlot emissions need to be applied to both enteric CH4 and nitrogenous gas emission, particularly NH3.
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ItemLiDAR estimation of aboveground tree biomass in native sclerophyll forest( 2011)Accurate estimation of aboveground tree biomass is a fundamental aspect of studies on carbon stocks of forest ecosystems. Destructive sampling is the most accurate method of estimation of biomass. However, because of its destructive nature and being both labor intensive and time consuming, destructive sampling cannot be applied for large areas. Alternatively, allometric equations developed for a particular species of trees or a general allometric equation for a specific type of forest can be used to estimate aboveground biomass for larger areas. This requires massive fieldwork, which itself is problematic, and it is not always possible to carry out field inventory in forests which are remote and inaccessible. Advanced remote sensing technology is now in the process of being established as the best and most practical alternative of the field-based methods of biomass estimation for large areas and is being used in the study of forests at the regional and national levels of a growing number of countries. Light Detection And Ranging (LiDAR) is a relatively new, active, remote sensing technology, which is capable of providing three-dimensional structural information of forests and, therefore, can be used to estimate various structural and biophysical parameters of forest stands more accurately than by other optical and RADAR-based remote sensing technologies. The development of hardware and software for the LiDAR system has rapidly advanced during the last decade and has matured to a degree that it is now possible to analyze LiDAR points, which are from individual tree crowns. As a result, LiDAR has now been used as an operational tool in European and North American forestry. In Australia, the use of LiDAR is still in an initial, research phase and there are only a few studies that have investigated its applicability in the broadleaf evergreen forests that dominate the forested lands of Australia. The main focus of this study was the LiDAR-estimation of aboveground tree biomass in two different types of Eucalyptus-dominated sclerophyll forests of the Central Highlands of Victoria, Australia. The applicability of LiDAR remote sensing in predicting stem density, canopy height indices (mean dominant height, Lorey's mean height and quadratic mean canopy height) and basal area was also explored in this research. Furthermore, the scaling-up of LiDAR estimates of biomass across the landscape and biomass mapping for large areas were also demonstrated. Using LiDAR data for the respective sampling plots, the mean dominant height for the Central Highlands Ash Regrowth (CHAR) forest was estimated with an R2 of 87.1 % and an RMSE of 3.9 m (9.5 %) and for the Black Range Mixed Species (BRMS) forest with an R2 of 92.1 % and an RMSE of 1.9 m (6 %). The R2 of the model predicting Lorey's mean height for the CHAR forest had 84.6 % with an RMSE of 4.03 m (11.1 %) and for the BRMS forest, it was 94.6 % with an RMSE of 1.7 m (5.9 %). Similarly, the quadratic mean canopy height was estimated with an R2 of 48.4 % and an RMSE of 4.9 m (17.9 %) for the CHAR forest and with an R2 of 92.7 % and RMSE of 1.9 m (7.4 %) for the BRMS forest. New methods to estimate the number of trees and the basal area from LiDAR data were developed in this study. When these methods were used, the number of trees was predicted with a mean prediction error of - 64.1 trees/ha (- 7.6 %) with a predicted value of 776.5 trees/ha for the calibration plots and a mean prediction error of 105.3 trees/ha (14.4 %) with a predicted value of 838.2 trees/ha for the validation plots in the CHAR forest. The mean prediction error for the basal area in the CHAR forest was 9.7 m2/ha (16.4 %) with a predicted value of 68.9 m2/ha for the calibration plots and 0.2 m2/ha (0.32 %) with a predicted value of 66.4 m2/ha for the validation plots. In the BRMS forest, the mean prediction error for the number of trees was 80 trees/ha (8.6 %) with a predicted value of 1010.5 trees/ha for the calibration plots and 5 trees/ha (0.9 %) with a predicted value of 584.4 trees/ha for the validation plots. The mean prediction error for the basal area in the BRMS forest was 8.3 m2/ha (13.9 %) with a predicted value of 68.4 m2/ha for the calibration plots and 6.13 m2/ha (9.3 %) with a predicted value of 71.9 m2/ha for the validation plots. LiDAR metrics such as the mean height, quadratic mean height, 90th percentile height and standard deviation of heights of LiDAR points were used as predictors of biomass. Three additional metrics, which have not been used in previous studies, were also derived and used in the regression analysis. These metrics were: scale parameter of the 2-parameter exponential distribution, largest extreme value distribution and smallest extreme value distribution of elevation (above sea level) data of the LiDAR points. Six prediction models for the CHAR forest and seven prediction models for the BRMS forest were developed. All the models predicted the biomass quite accurately. The models for the CHAR forest had R2 values that ranged from 58 % to 64 % and the R2 values of the models for the BRMS forest ranged from 58 % to 79.8 %. The results of the validation of the models in this study showed that the range of the average prediction bias of the models for the CHAR forest ranged from - 13.6 tons/ha to 6.1 tons/ha, and the range of the prediction bias of the models for the BRMS forest was from - 30.6 tons/ha to 8.1 tons/ha. In this study, I also developed a new multistage processing technique of LiDAR data to isolate individual trees. In this technique, LiDAR data for the sampling plots were first split vertically into four separate data sets representing reflections from the canopy layers of the dominant, co-dominant, intermediate and understory trees. Each data set was then processed using the LiDAR software, Toolbox for Lidar Data Filtering and Forest Studies (TiFFS) to isolate and obtain information on individual trees. By using this new multistage processing technique, about 52 % of the trees were isolated correctly. Individual tree information extracted from LiDAR data was then used to estimate the aboveground biomass of the trees. The aboveground biomass of individual trees isolated from LiDAR data was estimated quite accurately using the LiDAR-derived DBH from the LiDAR-estimated height of the trees. First, the LiDAR-derived DBH was used to estimate the aboveground biomass of individual trees from the general allometric equation for the native sclerophyll forest. A linear regression equation (R2 of 66.4 %) was then developed to estimate the individual tree biomass with the LiDAR-derived biomass as the predictor. To validate the linear model, the aboveground biomass of 76 new trees was estimated, and the average prediction bias obtained was - 78.1 kg, 9.6 % lower than the average biomass estimated from the field-measured DBH and the general allometric equation. Finally, based on the predictive model developed in this study, the aboveground tree biomass was estimated for 100,000 plots of 20 m × 20 m in size stretching across the landscape for an area of 4,000 hectares of the Central Highlands Ash Regrowth (CHAR) forest. The GPS coordinates and biomass data for these 100,000 plots were then used to map the biomass, which was of high (20 m) resolution and provided accurate information on the spatial distribution of the aboveground biomass across the landscape. The very simple and quite accurate methods of estimating the stem density and the basal area from LiDAR data developed in this study could be very useful in various aspects of forest management in Australia. Similarly, new LiDAR metrics (scale parameters of distribution of LiDAR elevation) developed and used in estimating the aboveground tree biomass could be very useful in predicting biomass of hilly forest areas. The multistage processing technique of LiDAR data developed in this study was very effective in detecting a greater number of intermediate and understory trees in forests that have a multistory structure. The scaling-up demonstrations of this study showed that LiDAR can be used to estimate the aboveground biomass across the landscape and for biomass mapping of eucalypt forests. The results of this study can have a great impact on the application of LiDAR as an operational tool for sustainable forest management and in estimating forest biomass and monitoring its change over time for climate change mitigation and adaptation research in Australia. This is the first thesis focused on application of LiDAR remote sensing in estimating various structural as well as biophysical parameters in the evergreen temperate forests having a multistory and heterogeneous structure dominated by Eucalyptus species in the moist and dry temperate region of the Central Highlands of Victoria, southeastern Australia. The thesis not only explored the applicability of LiDAR in estimating various canopy height indices, stem density, basal area and aboveground tree biomass but also demonstrated that LiDAR estimates of the plot-level biomass can be scaled-up to the landscape level quite easily and can then be used to produce very high resolution biomass maps for large areas. Three new LiDAR metrics derived in this study from the analysis of distribution of elevation of LiDAR points can play a very important role in accurately estimating the aboveground tree biomass of hilly forest areas. The accuracy of LiDAR-generated DEM and DSM and the other LiDAR height metrics for forests having a hilly terrain could be low compared to those variables for forests spread over a plain terrain. This might introduce some error in the predicted biomass from LiDAR metrics for hilly-terrain forests, which are related to the DEM and DSM generated. On the other hand, distributional parameters derived from the elevation data of LiDAR points do not depend on the DEM and DSM and, therefore, could provide more accurate prediction of biomass for forests in mountainous regions. This study has for the first time developed indirect methods of estimating stem density and basal area using LiDAR data. This is very important because direct methods (developing regression models with various LiDAR metrics as the predictors) of estimating these attributes usually do not produce good results even for pine forests for which LiDAR estimates are more accurate compared to the estimates for non-uniform and mixed species broad-leaved forests. The multistage processing technique of LiDAR data developed in this study could have great impact on the application of LiDAR as an operational tool in forestry because the process is able to detect more intermediate and understory trees in multistory mixed species forests, which have a great influence in the overall functioning of forest ecosystems.