School of Agriculture, Food and Ecosystem Sciences - Theses
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ItemEffects of adding nutrients on soil chemistry and tree growth in native Eucalyptus forests of south-eastern Australia( 2007)The decreasing area available for timber extraction in south-eastern Australia, due largely to social pressure to reserve greater areas of forest, has led to the consideration of fertiliser-application to increase wood output from the remaining available forest. Potentially deleterious effects of fertilising on water quality must be assessed before implementation on a wide scale. This is in accordance with relevant forest management policies. This study examined the effects of applying fertilisers containing nitrogen and phosphorus, on soil and soil-water chemistry in two pole-sized stands of mixed Eucalyptus spp in the Wombat Forest, in the Midlands Forest Management Area, Victoria, Australia. The findings are synthesised and discussed in relation to management of regenerating mixed-eucalypt forests in south-eastern Australia. Fertiliser treatments were none (R); 400 kg N ha-1 as ammonium-sulphate (N); or 400 kg ha-1 plus 202 kg P ha-1 as triple superphosphate coated with 10% sulphur (NP). It was calculated that incidental additions of S were 1371 kg ha -1 (N treatments), and 1696 kg ha-1 (NP treatments). It was expected that P would be principally adsorbed on soil surfaces; N immobilised in the soil organic pool and that metallic cations would enter the soil solution to varying degrees. Fertiliser-addition increased both plot-basal-area (BA) growth and the rate of stand self-thinning. In 3.8 years, BA in reference (R) plots at two sites increased by 7.3% and 23.4%. Where N alone was added, BA increased by 14.2% and 27.1%, while in NP plots BA increased by 17.1% and 42.7% respectively. Mortality was 9% in untreated plots compared to 14% in NP plots. Estimated increases in biomass growth equated to additional above-ground nutrient accumulation of 0.4 to 1.5 kg ha-1 of P, and 5.5 to 20.8 kg ha-1 of N. This represented only 0.2 to 0.7% of added P, and 1.4 to 5.2% of added N. Soil solution was extracted from 10 and 50 cm with porous-ceramic-cup tension-lysimeters (-0.6 kPa). Concentrations of P and N were low both before and after adding fertiliser. Across all treatments the maximum median PO43- concentration in soil-water at 50 cm was 0.12 ppm (mean 0.28 ppm). Typically PO43- concentrations were not higher than 0.03 ppm. The 400 kg ha-1 of added N was rapidly immobilised in the soil organic pool. The greatest mean NH4' concentration from a single sampling occasion was 1.1 ppm. The mean NO3 concentration at 50 cm was never higher than 0.26 ppm. After adding N in fertiliser the proportion of NO3- relative to NH4* in soil-water increased and was correlated with decreasing soil-water pH. Less than 1% of added P and N was recovered from soil solution at 50 cm. The largest pool of added P recovered was PO43- adsorbed to soil between 0 and 20 cm, due to the soil adsorption capacity being well in excess of the applied 202 kg P ha-1. Phosphate desorption using sequential extractions with a mild acid extractant (0.3M NH4F, 0.1M HCI) recovered between 25% and 116% of added P. Differences were attributed to both the amount of P added and the effect of time since treatment at different sites. Soil disturbance during sampler installation was found to be more likely to raise soil-water P concentrations at 50 cm than would adding up to 202 kg P ha-1. Among the ions in solution. SO42- and CI' were the dominant anions while Cat+ dominated the cation chemistry. In untreated forest 5042- in soil-water ranged from 7.7 to 16.0 ppm at 10 cm and 7.9 to 12.2 ppm at 50 cm. In fertilised plots up to 100.5 ppm SO42 was measured in soil-water at 50 cm depth. In the N treatment at 50 cm, SO42- in soil-water accounted for 9.4 % of applied S. compared to 14.0 % in NP. In untreated forest, soil-water Cl- and SO42- accounted for over 98% of the total soil-water anions, in roughly equal proportions at 10 cm, and CI- slightly higher at 50 cm. Following fertiliser-application soil-water pH at 10 cm fell from 6.3 in R to as low as 4.81 (N) and 4.45 (NP). At 50 cm pH never dropped below 6 and there were no visible departures from reference concentrations. Relative activities of K+ and Mg2+ in solution increased with decreasing pH, indicating increased leaching potential. Sulphate in soil-water increased total anion charge further in NP than in N. Total charge (cmolc L-1) for cations followed anions. A slight deficit in anion charge was likely due to the unquantified contribution of organic anions. These results confirm that despite the quantity of fertilisers added in this trial being double likely operational quantities, the forest and associated soils had the capacity to retain these nutrients through a variety of processes. The study validates the environmental sustainability of proposed intensive management practices including fertiliser-application in this forest type. It also emphasises the importance of understanding fundamental forest nutrient cycling processes when aiming to carry out intensive forest management practices in an environmentally sensitive manner.
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ItemPredicting the grain protein concentration of wheat from non-destructive measurements of the crop at anthesis( 2005)Grain protein concentration is an important specification for wheat, which determines the quality grade and price received by growers. It is difficult to achieve target grain protein concentration in semi-arid southern Australia, because of the low and variable rainfall. Growers may benefit from being able to predict grain protein concentration before harvest, especially where there is a threshold or `window' requirement for a particular grade. Grain outside specifications could be forward sold into other grades while prices were good. Spatial predictions of grain protein concentration would allow the pattern of harvest to be managed to optimise profit. This thesis proposed a method for predicting grain protein concentration from non-destructive measurements of the crop (spikes, spikelets) at or after anthesis. The theoretical propositions underlying the method were then evaluated using data from nitrogen fertiliser experiments, data from the literature, and a simulation exercise. The proposed method was to estimate grain number from spike or spikelet number. Variance in grain number, together with the diminishing returns response of grain number to nitrogen, would then be used. to estimate maximum grain number. Maximum grain number would be linked to a unique `critical' grain protein concentration, from which grain protein concentration at other grain numbers could be estimated. Spike and spikelet number were counted throughout grain-filling in nitrogen fertiliser experiments to determine the importance of time of counting. The time of counting was important for absolute, but not relative spike and spikelet numbers: Spike and spikelet number varied, throughout grain-filling, but interactions with nitrogen treatments were rare. Inclusion of spikelets in counting was based on glume length, which interacted with time of counting. Spike death was frequently observed and occurred in proportion to post-anthesis growth, at 0.187(±0.018) spikes/g. The rate with respect to grain yield was similar, at 0.190(±0.038) spikes/g. An analysis of mass/number relationships between grain, spike and spikelet number, and crop and spike biomass at anthesis, showed that grain number was better related to spike biomass, and that spike and particularly spikelet number, were better related to crop biomass. Spikelet number changed at .a rate of between 6.6 and 9.3 spikelets/g biomass across 'a range of experiments; spike number changed at a rate between 0.14 and 0.62 spikes/g. The interrelationships showed grain number should be related to spikelets/spike, and proportion of crop biomass in the spike. The relationships, however, only existed in some experiments and were not universal. An alternative suggested by the analysis was use of spike number as a direct proxy for grain number (ie. assuming constant grains per spike). Spike number was tested as a proxy for grain number initially by analysing the components of variance of grain number across nitrogen, rotation and plant density experiments. Spike number was the main component of variance in grain number (59.8- 71.0% of log(variance)) in nitrogen experiments, with no significant covariance between spike number and grains per spike. Grains per spike and covariance were much greater components of variance in plant density experiments, and grains per spike and spike number were equal sources of variance in rotation experiments, with small positive covariance. Spike number would be an unbiased, but not perfect proxy for grain number when nitrogen was the main factor varying, but not for factors related to rotation or plant density. Spike number and crop biomass at anthesis were compared as estimators of grain number in nitrogen experiments, in an analysis of the nature of the responses to nitrogen fertiliser. Grain number as an estimator of grain yield was included in the analysis to understand the likely effect of using grain number rather than yield as a predictor of grain protein concentration. Crop biomass at anthesis, spike number and grain number all reached maxima at similar nitrogen fertiliser rates, but crop biomass at anthesis was a more precise estimator for the maximum rate required for grain number (RMSE of nitrogen for maximum, 2.4 kg N/ha vs. 26.4 kg N/ha). Grain number had a maximum consistently higher (+32.6±8.0 kg N/ha) than the maximum for yield. Once nitrogen fertiliser rates were corrected for the different maxima, grain number and yield had identical relative response rates to nitrogen. The response rates of crop biomass at anthesis and spike number were both related to the response rate of grain number by a power relationship with exponent 0.6. The lack of methods for anticipating phase differences caused by late nitrogen application and pre-anthesis water deficit will prevent exploitation of these relationships in all environments. The estimation of maximum spike number from its variance was simulated across the width of an air-seeder, using consistent variations in nitrogen fertiliser rate between tynes to drive variance in spike number. Nitrogen fertiliser was normally distributed. It was possible to extrapolate the variance/spike number relationship to estimate the maximum only where the slope of the relationship was negative. Slopes close to zero caused errors. of fitting, where the `signal' from the relationship was indistinguishable from the `noise' in estimating variance. This coincided with low (below 0.8) relative spike numbers and led to over-estimation of low relative spike numbers. Low spike number because of sub- or supra-optimal nitrogen could be distinguished by the second derivative of the fitted function, which was positive for supra-optimal nitrogen. There was no unique `critical' grain protein concentration (for maximum yield or grain number) in southeastern Australia, but there was a consistent relationship between `critical' grain protein concentration and grain weight. The relationship in terms of grain nitrogen content was a linear function of grain weight. The parameters also varied with genotype, and signed relative grain number, calculated as GRS=1-G/GMax for supraoptimal nitrogen, and GRS=G/GMax-1 for sub-optimal nitrogen, where G is grain number. The best estimation of grain nitrogen across genotypes was: Grain N (mg N/grain) = 0.317 + 1.00 x GRS + (0.0115 -0.0181 x GRS) x W, where W is grain weight in mg/grain. The root mean squared error of grain protein concentration estimated from this function was 0.91%. Grain weight would need to be estimated to estimate grain protein concentration. Errors due to grain weight had more effect at higher GRS, and at lower grain weight. The conclusion was that grain protein concentration may be predicted using crop biomass or spike number as a proxy for grain number. Predictions would be best in the absence of pre-anthesis water deficit or nitrogen applied after Zadoks 32. The predictions would be best for relative grain number greater than 0.8 at sub-optimal nitrogen, and for any relative grain number at supra-optimal nitrogen. A confidence interval could still be provided for grain protein concentration at lower relative grain numbers with sub-optimal nitrogen. Predictions would be most accurate if grain weight was reliably above 35 mg/grain.
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ItemA molecular genetic study of seed dormancy in aegilops tauschii and expression of sprouting resistance in common hexploid wheat( 2004)The wild wheat relative Aegilops tauschii, has been identified as a useful source of preharvest sprouting (PHS) resistance for hexaploid bread wheat. Seed dormancy, a major contributor to PHS resistance, was shown to be partly expressed in hexaploid wheat derived from direct hybridisation between Triticum aestivum and Ae. tauschii. The enhanced seed dormancy possessed by the Ae. tauschii derived direct-cross wheat lines was manifested by embryo and seedcoat related mechanisms. The embryo related mechanism could not confer full expression of dormancy without the presence of seedcoat related factors, suggesting that the two mechanisms may be independently inherited. The presence of seedcoat related dormancy however, was not associated with the red seedcoat phenotype, which has traditionally been associated with PHS resistance in wheat. Red pigmentation of the seedcoat is likely to be "involved in the extreme dormancy possessed by Ae. tauschii but does not preclude partial expression within a white seedcoat background. The ability of Ae. tauschii derived wheat lines to enhance seed dormancy may have potential economic benefit to breeding for PHS resistance in white wheat varieties. Presently, white wheat varieties grown in the sprouting susceptible regions of Australia possess inadequate protection, costing the industry up to $100M annually. Inheritance of seed dormancy in Ae. tauschii was found to be controlled by one or two major genes which were influenced by minor genes and/or environmental factors. These results are consistent with the findings of several previous reports. Inheritance was shown to be dominant at the F3 grain generation, consistent with the generally dominant nature of dormancy possessed by red seeded genotypes. However, preliminary assessment of individual F2 seeds indicated recessive control of dormancy. Because genes possessed by the maternal tissues of the seedcoat do not segregate until the F3 seed generation, the F2 recessive model may be indicative of separate genetic control for the embryo related dormancy mechanism(s). Based on the above inheritance information, a bulked segregant analysis approach was initially undertaken for the development of linked molecular markers for seed dormancy. One microsatellite marker on chromosome 1D produced polymorphism between resistant and susceptible DNA bulks. A mapping approach was subsequently undertaken, revealing two significant QTL mapping to chromosome 1D. The putative QTL for seed dormancy will relate to the embryo component of dormancy, as the trait data employed related to the F2 seed generation, which was segregating for embryo related genes. The D genome of hexaploid wheat presently possesses the fewest QTL for PHS resistance of the three contributing genomes. Within the D genome, chromosome 1D was poorly represented in the literature. As such, 4e. tauschii represents a potential to bolster numbers of QTL for sprouting resistance in hexaploid wheat. Given the homology between the D genomes of Ae. tauschii and T aestivum, the microsatellite markers identified, flanking the putative QTL, will likely be transferable to hexaploid bread wheat. Seed dormancy is strongly influenced by conditions during growth. As such, unambiguous selection through use of molecular markers will expedite the introgression of this economically important trait into elite wheat cultivars.
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ItemChoosing sheep for lifetime profitability( 2004)This project investigated the selection of wool sheep for lifetime profitability by measuring the lifetime productive phenotypes of breeding ewes, their lambs and the rams to which the ewes were mated, on a commercial farm in East Gippsland. The measurements were recorded from 1992 to 2002. The seasons varied during this time, including a severe drought from 1996 to 1998. The results clearly demonstrate that the environment, its resources and demands, limit the full phenotypic expression of the genotype of the sheep. This expression varies over the lifetime of the animal. The results identify the sheep that were most suited to their environment. Phenotype interaction: It was found that enhancement of any single profitable phenotypic character resulted in changes to all other profitable phenotypic characters, usually in a negative direction. These phenotypic interactions frequently show curvilinearity and nonlinear relationships, demonstrating that to select on a linear model is frequently not appropriate for profit indicators or biological reality. Measurement of ewe body weight and the weight gain ratio of lambs A method is described for measuring the yearly and lifetime body weights of sheep from which the changing wool weights and weight due to pregnancy were removed. Birth weight, wool weight and weight gain of the suckling lamb were then expressed as a percentage of the body weight of the ewe. This resulted in a clearer understanding of how the ewe allocates metabolic resources. It also demonstrated that too high a bodyweight was itself an excessive user of resources. When the average daily weight gain of the lamb from birth to weaning was expressed as a percentage of the weight of the mother the results provided an early prediction of lifetime profitability of the lamb and indirectly of the mother. This percentage had a strong positive relation to the birth weight, weaning weight, greasy fleece weight of the lamb and to the survival rate of the progeny and the test ewes during the drought. However prediction of the fibre diameter required an independent measurement. Measuring the value of a sheep's wool: A method is described for assessing the value of wool. This eliminates the influence of monetary inflation and helps the farmer make a more accurate judgement of the wool value in the selection of his sheep. Auction prices from all districts of Victoria based on the 1987-1997 auction prices of wool were converted into a Price fibre-diameter ratio. This ratio was used to determine a commercial wool value (Ewe wool score) for each test ewe. For each of the test ewe's male and female progeny, the same ratio was used to obtain a wool score (up to two years of age). These progeny values of all her lambs were added to provide a Progeny wool score for each test ewe. A Combined wool score combined both the ewe's own woolscore and the woolscore of her progeny. The top ten test ewes were identified for each category then compared to the subjective assessments of a sheep classer, the farm manager and the wool classer. Sheep that had high Combined wool scores and were therefore the most profitable over two generations had different phenotypes from those with high individual wool scores. It should be noted that while wethers might be chosen for wool score only, ewes should be chosen for wool score and the ability to produce profitable progeny. This thesis has highlighted the fact that selection for lifetime profitability will differ for ewes and for wethers. Using the Statistica 4.1 (1994) for McIntosh program stepwise multiple regressions were carried out on the test ewes for Ewe wool score, Progeny wool score and Combined wool score. The factors with significant influence (p-level < .05) on each of the three wool scores were identified. For the Ewe wool score, the factors in order of importance are, average fibre diameter (negative), greasy fleece weight, average visual assessment of the fleece and lambs alive December 1996 (negative). Those four factors "explain" 42% of the sums of squares in the Ewe wool score. For the Progeny wool score, the factors in order of importance are, lambs alive in December 1996, which was the end of the recording of the test ewes, and the average fibre diameter (negative). These two factors "explain" 64% of sums of squares in the Progeny wool score. In the Combined wool score, the factors in order of importance are, lambs alive in December 1996, average fibre diameter 1992-6 (negative) and average greasy fleece weight 1992-6 (negative). These three factors "explain" 60% of the sums of squares in the Combined wool score. The negative partial regression for fibre diameter is explained by the position of the average fibre diameter on the Price-fibre diameter curve (finer fibres bring higher prices). The negative partial regression of the Combined wool score on greasy fleece weight suggests that there is competition between resources required for producing wool and for successful reproduction. Heritability estimates: Heritability estimates were calculated from intra-sire regressions of progeny on dams. This was done for body weight, greasy fleece weight, fibre diameter and the visual assessment of the fleece at specific ages over the years for which paired data for the test ewes and their progeny were available. Such estimates were available for hoggets and 2,3,4 and five-year olds of both the dams and progeny, with a varying numbers of pairs at different ages. The results varied between ages and between the sexes of the progeny. There were more data available (pairs of dams and progeny) from the middle age-years. 'When the male and female progeny were considered together, the corrected body weight in years two and four gave highly significant results of 0.45 and 0.44 respectively. Year three had significant results of 0.27. Years one and five were not significantly different from zero. Fibre diameter had highly significant results of 0.89 in year one and significant results of 0.28 in year two and 0.32 in year three. Years four and five were not significant. Greasy fleece weight had significant results of 0.70 in year one. Other years were less than 0.30 and were not significant. Fleece visual assessment had highly significant results of 0.35 in year three; the other years were not significant. One wool classer classified all the fleeces subjectively at shearing over an eight-year period giving a yearly visual score to each fleece. He was unaware of the identity of the fleeces. The results showed a high degree of consistency. The above results shows that visual scores can be heritable. Fibre diameter, greasy fleece weight and their interaction: Fibre diameter was examined for lifetime variation in individual sheep and groups of sheep selected on micron. Lifetime group measurement of fibre diameter was highly predictable. This allows a fanner to get a reasonable lifetime group fibre diameter result from one year of measurement. Lifetime measurements of fibre diameter for individuals were less predictable. Fibre diameter was also examined for the effect of resources and their availability, heritability, ageing, lambing and lactation, and the health of the sheep. The two-generation realized heritability of fibre diameter for the test ewes in 1995 and the one-year old progeny in 1993 to 1995 was 0.50. Greasy fleece weight was examined for lifetime variation, in individual and group measurements, for the effect of the availability of resources, the variations of ageing and the health of the sheep. Greasy fleece weight had lower heritability estimates at hogget age than did fibre diameter. Group measurements of greasy fleece weights had more lifetime variation than did fibre diameter. Therefore a single greasy fleece group measurement would not be as reliable an indicator for lifetime results as a single measure of fibre diameter. Using 1992-6 average values, the fleeces of the Tubbut flock were examined for the relationship of the fibre diameters to greasy fleece weights, from the finest to the broadest fibre diameters. This relationship was not linear. From 25-21 microns the decrease of the greasy fleece weight for each decrease of one micron was 5.7%, from 21-18 microns the result was 9.6 %, from 17-16 microns the result was 11.4%. The limitation of the environment: The data presented in this thesis clearly demonstrate, that with limited resources available from an environment, there is an overriding and fundamental response within animals to allocate those resources to maximize their survival and that of their progeny. Any artificial selection must be carried out with the knowledge, that over time, the animals will attempt to return to the allocation of resources that maintains the best chance of survival for themselves and their progeny. Within this thesis there are many examples where, if sheep had been artificially selected for one character this would have altered all or most of the other characters usually in a negative direction. It has been shown that high artificial selection tends to have that selection reduced in value over the animal's lifetime. Important principles: Results in this thesis highlight that in selecting for lifetime profitability breeders should note that 1) The environment, its resources and demands, limit the full expression of the genotype of the sheep. The effect varies over the lifetime of the animal. 2) In the selection of animals for particular traits, due regard must be given to the effects that the selection will have on the whole of the phenotype. 3) Increased profitability resulting from the selection of one trait may result in the overall loss of profitability from the decrease in other profitable traits. 4) Where research is carried out on one particular trait to either enhance or decrease that trait, the research needs to demonstrate the effect of that selection on the whole animal over its lifetime. 5) Sheep need to be selected for an increase in lifetime profitability in their own commercial environment. Taking note of these principles will ensure true progress is made in phenotypes and genotypes suitable for any particular environment. It will also produce greater profits for Australian farmers.