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Toowoomba, Australia

Smith I.,University of Queensland | Syktus J.,University of Queensland | McAlpine C.,University of Queensland | Wong K.,Science Delivery
Climatic Change

We present a synthesis of CMIP5 model results for projected rainfall changes for a single region (south-east Queensland, Australia) and note that, as was evident in CMIP3 results, the multi-model mean projected changes for the late 21st century are not statistically significant for any season nor annually. Taking account of the number of statistically significant changes to mean rainfall, we find some evidence favouring a decrease in both spring and annual rainfall, but this is not compelling. In almost all cases the most frequent result is for no significant change. However, if we consider the number of results where there is a statistically significant change in the distributions of rainfall amounts, there appears to be slightly more information available for risk assessment studies. These numbers suggest an increase in the frequency of both wet and dry events during summer and spring, and a shift towards more frequent dry events during winter. There is no evidence for any significant changes to the distributions for either autumn or annually. The findings suggest that, in one respect, multi-model rainfall projections may contain more information than is evident from syntheses which focus on changes to the means and that, for some regions where changes in the frequency of wet and dry seasons/years have known impacts, the model projections may be more valuable than previously thought. © 2013 Springer Science+Business Media Dordrecht. Source

Liu X.,Griffith University | Chen C.,Griffith University | Wang W.,Science Delivery | Hughes J.M.,Griffith University | And 3 more authors.
Microbial Ecology

Soil biogeochemical cycles are largely mediated by microorganisms, while fire significantly modifies biogeochemical cycles mainly via altering microbial community and substrate availability. Majority of studies on fire effects have focused on the surface soil; therefore, our understanding of the vertical distribution of microbial communities and the impacts of fire on nitrogen (N) dynamics in the soil profile is limited. Here, we examined the changes of soil denitrification capacity (DNC) and denitrifying communities with depth under different burning regimes, and their interaction with environmental gradients along the soil profile. Results showed that soil depth had a more pronounced impact than the burning treatment on the bacterial community size. The abundance of 16S rRNA and denitrification genes (narG, nirK, and nirS) declined exponentially with soil depth. Surprisingly, the nosZ-harboring denitrifiers were enriched in the deeper soil layers, which was likely to indicate that the nosZ-harboring denitrifiers could better adapt to the stress conditions (i.e., oxygen deficiency, nutrient limitation, etc.) than other denitrifiers. Soil nutrients, including dissolved organic carbon (DOC), total soluble N (TSN), ammonium (NH4 +), and nitrate (NO3 −), declined significantly with soil depth, which probably contributed to the vertical distribution of denitrifying communities. Soil DNC decreased significantly with soil depth, which was negligible in the depths below 20 cm. These findings have provided new insights into niche separation of the N-cycling functional guilds along the soil profile, under a varied fire disturbance regime. © 2015, Springer Science+Business Media New York. Source

Bell M.J.,University of Queensland | Moody P.W.,Science Delivery | Anderson G.C.,Western Australian Department of Agriculture and Food | Strong W.,Australian Department of Primary Industries and Fisheries
Crop and Pasture Science

Australian cropping systems are dominated by winter cereals; however, grain legumes, oilseeds and summer cereals play an important role as break crops. Inputs of phosphorus (P) fertiliser account for a significant proportion of farm expenditure on crop nutrition, so effective fertiliser-use guidelines are essential. A national database (BFDC National Database) of field experiments examining yield responses to P fertiliser application has been established. This paper reports the results of interrogating that database using a web application (BFDC Interrogator) to develop calibration relationships between soil P test (0-10cm depth; Colwell NaHCO3 extraction) and relative grain yield. Relationships have been developed for all available data for each crop species, as well as for subsets of those data derived by filtering processes based on experiment quality, presence of abiotic or biotic stressors, P fertiliser placement strategy and subsurface P status. The available dataset contains >730 entries but is dominated by data for lupin (Lupinus angustifolius; 62% of all P experiments) from the south-west of Western Australia. The number of treatment series able to be analysed for other crop species was quite small (<50-60 treatment series) and available data were sometimes from geographic regions or soil types no longer reflective of current production. There is a need for research to improve information on P fertiliser use for key species of grain legumes [faba bean (Vicia faba), lentil (Lens culinaris), chickpea (Cicer arietinum)], oilseeds [canola (Brassica napus), soybean (Glycine max)] and summer cereals [sorghum (Sorghum bicolor), maize (Zea mays)] in soils and farming systems reflecting current production. Interrogations highlighted the importance of quantifying subsurface P reserves to predict P fertiliser response, with consistently higher 0-10cm soil test values required to achieve 90% maximum yield (CV90) when subsurface P was low (<5mg P/kg). This was recorded for lupin, canola and wheat (Triticum aestivum). Crops grown on soils with subsurface P >5mg/kg consistently produced higher relative yields than expected on the basis of a 0-10cm soil test. The lupin dataset illustrated the impact of improving crop yield potentials (through more effective P-fertiliser placement) on critical soil test values. The higher yield potentials arising from placement of P-fertiliser bands deeper in the soil profile resulted in significantly higher CV90 values than for crops grown on the same sites but using less effective (shallower) P placement. This is consistent with deeper bands providing an increased and more accessible volume of profile P enrichment and supports the observation of the importance of crop P supply from soil layers deeper than 0-10cm. Soil P requirements for different species were benchmarked against values determined for wheat or barley (Hordeum vulgare) grown in the same regions and/or soil types as a way of extrapolating available data for less researched species. This approach suggested most species had CV90 values and ranges similar to winter cereals, with evidence of different soil P requirements in only peanut (Arachis hypogaea-much lower) and field pea (Pisum sativum-slightly higher). Unfortunately, sorghum data were so limited that benchmarking against wheat was inconclusive. © CSIRO 2013. Source

Moody P.W.,Science Delivery | Speirs S.D.,Graham Center for Agricultural Innovation | Scott B.J.,Charles Sturt University | Mason S.D.,University of Adelaide
Crop and Pasture Science

The phosphorus (P) status of 535 surface soils from all states of Australia was assessed using the following soil P tests: Colwell-P (0.5m NaHCO3), Olsen-P (0.5m NaHCO3), BSES-P (0.005m H2SO4), and Mehlich 3-P (0.2m CH3COOH+0.25m NH4NO3+0.015m NH4F+0.013m HNO3+0.001m EDTA). Results were correlated with soil P assays selected to estimate the following: soil solution P concentration (i.e. 0.01m CaCl2 extractable P; Colwell-P/P buffer index); rate of P supply to the soil solution (i.e. P released to FeO-impregnated filter paper); sorbed P (i.e. Colwell-P); mineral P (i.e. fertiliser reaction products and/or soil P minerals estimated as BSES-P minus Colwell-P); the diffusive supply of P (i.e. P diffusing through a thin gel film, DGT-P); and P buffer capacity (i.e. single-point P buffer index corrected for Colwell-P, PBICol). Across all soils, Colwell-P and BSES-P were highly correlated with FeO-P (r≤0.76 and 0.58, respectively). Colwell-P was moderately correlated with mineral P (r≤0.24), but not solution P. Olsen-P and Mehlich-P were both highly correlated with FeO-P (r≤0.80 and 0.78, respectively) but, in contrast to Colwell-P and BSES-P, also showed moderate correlations with soil solution P (r≤0.29 and 0.34, respectively) and diffusive P supply (r≤0.31 and 0.49, respectively). Correlation coefficients with mineral P were r≤0.29 for Olsen-P and r≤0.17 for Mehlich-P. Soils were categorised according to their pH, clay activity ratio, content of mineral P and CaCO3 content, and the relationships between the empirical soil P tests examined for each soil category. Olsen-P and Colwell-P were correlated across all soil categories (r range 0.66-0.90), and a widely applicable linear equation was obtained for converting one soil test to the other. However, the correlations between other soil tests varied markedly between soil categories and it was not possible to develop such widely applicable conversion equations. Multiple step-up linear regressions were used to identify the key soil properties affecting soil solution P, P buffer capacity, and diffusive P supply, respectively. For all soil categories, solution P concentration (measured by CaCl2-P) increased as rate of P supply (measured as FeO-P) increased and P buffer capacity decreased. As an assay of sorbed P, Colwell-P alone did not significantly (P>0.05) explain any of the variability in soil solution P, but when used in the index (Colwell-P/P buffer index), it was highly correlated (r≤0.74) with CaCl2-P. Soil P buffer capacity was dependent on different properties in different soil categories, with 45-65% of the variation in PBI accounted for by various combinations of Mehlich-Al, Mehlich-Fe, total organic C, clay content, clay activity ratio, and CaCO3 content, depending on soil category. The diffusive supply of P was primarily determined by rate of P supply (measured as FeO-P; r range 0.34-0.49), with significant (P<0.05) small improvements due to the inclusion of PBICol and/or clay content, depending on soil category. For these surface soil samples, key properties of pH, clay activity ratio, clay content, and P buffer capacity varied so widely within individual Australian Soil Orders that soil classification was not useful for inferring intrinsic surface soil P properties such as P buffer capacity or the relationships between soil P tests. © CSIRO 2013. Source

Speirs S.D.,Graham Center for Agricultural Innovation | Scott B.J.,Graham Center for Agricultural Innovation | Moody P.W.,Science Delivery | Mason S.D.,University of Adelaide
Crop and Pasture Science

The performance of a wide range of soil phosphorus (P) testing methods that included established (Colwell-P, Olsen-P, BSES-P, and CaCl2-P) and more recently introduced methods (DGT-P and Mehlich 3-P) was evaluated on 164 archived soil samples corresponding to P fertiliser response experiments with wheat (Triticum aestivum) conducted in south-eastern Australia between 1968 and 2008. Soil test calibration relationships were developed for relative grain yield v. soil test using (i) all soils, (ii) Calcarosols, and (iii) all 'soils other than Calcarosols'. Colwell-P and DGT-P calibration relationships were also derived for Calcarosols and Vertosols containing measureable CaCO3. The effect of soil P buffer capacity (measured as the single-point P buffer index corrected for Colwell-P, PBICol) on critical Colwell-P values was assessed by segregating field sites based on their PBICol class: very very low (15-35), very low (36-70), low (71-140), and moderate (141-280). All soil P tests, except Mehlich 3-P, showed moderate correlations with relative grain yield (R-value ≥0.43, P<0.001) and DGT-P exhibited the largest R-value (0.55). Where soil test calibrations were derived for Calcarosols, Colwell-P had the smallest R-value (0.36), whereas DGT-P had an R-value of 0.66. For 'soils other than Calcarosols', R-values >0.45 decreased in the order: DGT-P (r≤0.55), Colwell-P (r≤0.49), CaCl2-P (r≤0.48), and BSES-P (r≤0.46). These results support the potential of DGT-P as a predictive soil P test, but indicate that Mehlich 3-P has little predictive use in these soils. Colwell-P had tighter critical confidence intervals than any other soil test for all calibrations except for soils classified as Calcarosols. Critical Colwell-P values, and confidence intervals, for the very very low, very low, and low P buffer capacity categories were within the range of other published data that indicate critical Colwell-P value increases as PBICol increases. Colwell-P is the current benchmark soil P test used in Australia and for the field trials in this study. With the exception of Calcarosols, no alternative soil P testing method was shown to provide a statistically superior prediction of response by wheat. Although having slightly lower R-values (i.e. <0.1 difference) for some calibration relationships, Colwell-P yielded tighter confidence intervals than did any of the other soil tests. The apparent advantage of DGT-P over Colwell-P on soils classified as Calcarosols was not due to the effects of calcium carbonate content of the analysed surface soils. © CSIRO 2013. Source

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