Challenges in estimating soil water

[Introduction]: Most of Australia’s dryland cropping is characterised by unreliable rainfall with frequent long gaps between falls. Stored soil water is therefore essential to support crop growth during the growing season while water stored during fallows has varying importance, depending on soil type and rainfall patterns in relation to cropping periods. For example, a winter crop at Walpeup in Victoria derives 10% of its water supply from soil water at planting while a winter crop at Emerald will access 80% of its water supply from stored soil water (Thomas et al 2007). Even when dependence on stored water is small, extra water can make a valuable difference to crop yield and profitability, especially in typical dry-finish seasons (Kirkegaard et al 2014). An understanding of available water before a crop is planted can influence management decisions (area planted, fertilizer rates). Estimating plant available water (PAW) also requires an estimate of a soils ability to store water, its plant available water capacity (PAWC). This paper presents some observations of soil water from a 17-year study comparing water balances (runoff, evaporation and deep drainage) for a set of small contour bay catchments near Roma in southern Queensland. Our aim is to demonstrate some of the challenges associated with field measurement of both PAWC and PAW. This analysis is an extension of a detailed description of the development of SoilWaterApp (Freebairn et al. 2018)

A reversible infinite HMM using normalised random measures

We present a nonparametric prior over reversible Markov chains. We use completely random measures, specifically gamma processes, to construct a countably infinite graph with weighted edges. By enforcing symmetry to make the edges undirected we define a prior over random walks on graphs that results in a reversible Markov chain. The resulting prior over infinite transition matrices is closely related to the hierarchical Dirichlet process but enforces reversibility. A reinforcement scheme has recently been proposed with similar properties, but the de Finetti measure is not well characterised. We take the alternative approach of explicitly constructing the mixing measure, which allows more straightforward and efficient inference at the cost of no longer having a closed form predictive distribution. We use our process to construct a reversible infinite HMM which we apply to two real datasets, one from epigenomics and one ion channel recording.Comment: 9 pages, 6 figure

An Empirical Study of Stochastic Variational Algorithms for the Beta Bernoulli Process

Stochastic variational inference (SVI) is emerging as the most promising candidate for scaling inference in Bayesian probabilistic models to large datasets. However, the performance of these methods has been assessed primarily in the context of Bayesian topic models, particularly latent Dirichlet allocation (LDA). Deriving several new algorithms, and using synthetic, image and genomic datasets, we investigate whether the understanding gleaned from LDA applies in the setting of sparse latent factor models, specifically beta process factor analysis (BPFA). We demonstrate that the big picture is consistent: using Gibbs sampling within SVI to maintain certain posterior dependencies is extremely effective. However, we find that different posterior dependencies are important in BPFA relative to LDA. Particularly, approximations able to model intra-local variable dependence perform best.Comment: ICML, 12 pages. Volume 37: Proceedings of The 32nd International Conference on Machine Learning, 201

Active Learning with Statistical Models

For many types of machine learning algorithms, one can compute the statistically `optimal' way to select training data. In this paper, we review how optimal data selection techniques have been used with feedforward neural networks. We then show how the same principles may be used to select data for two alternative, statistically-based learning architectures: mixtures of Gaussians and locally weighted regression. While the techniques for neural networks are computationally expensive and approximate, the techniques for mixtures of Gaussians and locally weighted regression are both efficient and accurate. Empirically, we observe that the optimality criterion sharply decreases the number of training examples the learner needs in order to achieve good performance.Comment: See http://www.jair.org/ for any accompanying file

Rare Radiative Bc→Ds1(2460)γB_{c}\rightarrow D_{s1}(2460)\gamma Transition in QCD

We investigate the radiative $B_{c} \to D_{s1} \gamma$ transition in the framework of QCD sum rules. In particular, we calculate the transition form factors responsible for this decay in both weak annihilation and electromagnetic penguin channels using the quark condensate, mixed and two-gluon condensate diagrams as well as propagation of the soft quark in the electromagnetic field as non-perturbative corrections. These form factors are then used to estimate the branching ratios of the channels under consideration. The total branching ratio of the $B_{c} \to D_{s1} \gamma$ transition is obtained to be in order of $10^{-5}$, and the dominant contribution comes from the weak annihilation channel.Comment: 24 Pages and 3 Figure

Dynamic Active Earth Pressure Against Retaining Walls

Equations of equilibrium expressed along the stress characteristics are transformed onto the Zero Extension Line (ZEL) directions. The new dynamic equilibrium equations are then applied to simple ZEL field (composed of Rankine, Goursat, and Coulomb zones) behind retaining walls. Integration of differential equilibrium equations along the assumed field boundary, thus provide the final equations for the active static (Kast) and dynamic (Kady) earth pressure coefficients, which are functions of friction and dilation angles of the soil and friction angle of the wall surface. Numerical evaluation of Kast, and Kady indicates that these coefficients are not sensitive to the wall roughness for practical values of angle of friction of backfill material between 35° and 45°. In this range, the coefficients can be approximated by: Kast=tan2(π/4 -φ/2) and Kady =tan(π/4 - ν/2)

Climate change adaptation-mitigation tradeoffs in the southern Australian livestock industry: GHG emissions

The trade-offs between farm system production and profitability, adaptation to climate change and mitigation of greenhouse gas (GHG) emissions are associated with complex interactions. The GHG mitigation consequences of effective adaptations should be taken into account when including them in mitigation policies. In this paper, we present the results of 2 modelling studies of climate change adaptation x mitigation interactions in southern Australian broadacre livestock production: (a) case studies of adapting to climate change by increasing soil fertility at 2 locations that examine the effects on farm-level GHG balances, and (b) an examination how systematic combinations of adaptations (grassland management and animal genetic improvement) might affect future methane (CH4) emissions across the whole of southern Australia (33.25 Mha). We used the AusFarm model to simulate the effects of climate change under the SRES A2 scenario in 2030. Merino ewe grazing systems were modelled at 2 locations (Lake Grace, WA and Wellington, NSW) under historical climate and climates projected for 2030. The effects of adapting to climate change by increasing soil fertility by adding phosphorus (P) on system productivity, profitability, N2O emissions, enteric CH4 emissions, and changes in soil carbon stocks were estimated. The negative impacts of climate change on productivity were reduced by achieving higher soil fertility, so increasing profitability at 2030. CH4 emissions declined under 2030 climate owing to lower sustainable stocking rates, but the reduction was smaller when soil fertility was increased. Soil C stocks were predicted to decrease under 2030 climate due to a decrease in net primary productivity of the pasture. Increasing soil fertility was predicted to cause little change in soil carbon stocks, because its main effect was to increase NPP consumed by livestock instead of NPP left to be incorporated into the soil. An increase in N2O emissions under 2030 climate can be related to changes in rainfall regime. Increased soil fertility by P could slightly reduce this increase. Higher soil P fertility decreased N2O emissions compared with no adaptation by 7% at Lake Grace and 25% at Wellington. CH4 is the second most important anthropogenic GHG. Ruminants (2.4 Gt CO2-eq yr-1) are the largest source of CH4 emissions. By modelling 5 livestock enterprises at 25 representative locations, we estimated an areaaverage ruminant CH4 emission rate of 70 kg ha-1 yr-1 during the historical period, which is consistent with previous estimates. By decreasing optimal sustainable stocking rates (OSSR), climate change impacts were projected to decrease ruminant CH4 emissions to 55, 51, and 42 kg ha-1 yr-1 in 2030, 2050, and 2070, respectively. Ruminant CH4 emissions under the most profitable systemic adaptation were estimated to vary among sites, depending mainly on OSSR. If the most profitable adaptations were fully adopted, average ruminant CH4 emissions were estimated to increase to 84 kg ha-1 yr-1 in 2030, 83 kg ha-1 yr-1 in 2050, and 75 kg ha-1 yr-1 in 2070. Across regions and averaging among enterprises, a linear relationship was found between CH4 emissions (kg ha-1) and profit (A$ha-1). A linear relationship was predicted between CH4 emission and meat production. In 2050, the most profitable combination of adaptations will result in CH4 emission changes that range between factors of -0.82 and +1.08 relative to the reference period. In addition, CH4 emissions will reach an intensity of 0.26 kg ha-1 yr-1 (6.5 CO2-eq kg ha-1 yr-1) for each A$1 of profit and 0.99 kg ha-1 yr-1 (24.9 CO2-eq kg ha-1 yr-1) for 1 kg of meat production. Across regions and averaging among enterprises, changes in the CH4 emissions for the most profitable combinations had a logarithmic relationship with changes in profitability (e.g. for 2050: ΔCH4= 0.207ln (Δprofit)-0.326, R2=0.63). Ruminant CH4 emissions will depend on animal numbers (i.e. stocking rates) that, in turn, will be controlled by adaptation intensity. Greater intensification and ruminant CH4 emission are likely to occur, because increasing demand of meat has been projected for the future and there is capacity for higher and profitable production to respond this demand. Future food market projections have shown such a great demand even under price effects