Effect of manure application on crop yield and soil chemical properties in a long-term field trial of semi-arid Kenya

The sustainability of cereal/legume intercropping was assessed by monitoring trends in grain yield, soil organic C (SOC) and soil extractable P (Olsen method) measured over 13 years at a long-term field trial on a P-deficient soil in semi-arid Kenya. Goat manure was applied annually for 13 years at 0, 5 and 10 t ha(-1) and trends in grain yield were not identifiable because of season-to-season variations. SOC and Olsen P increased for the first seven years of manure application and then remained constant. The residual effect of manure applied for four years only lasted another seven to eight years when assessed by yield, SOC and Olsen P. Mineral fertilizers provided the same annual rates of N and P as in 5 t ha(-1) manure and initially ,gave the same yield as manure, declining after nine years to about 80%. Therefore, manure applications could be made intermittently and nutrient requirements topped-up with fertilizers. Grain yields for sorghum with continuous manure were described well by correlations with rainfall and manure input only, if data were excluded for seasons with over 500 mm rainfall. A comprehensive simulation model should correctly describe crop losses caused by excess water.

A new method for the determination of Holocene palaeohydrology

This paper describes a new method for the assessment of palaeohydrology through the Holocene. A palaeoclimate model was linked with a hydrological model, using a weather generator to correct bias in the rainfall estimates, to simulate the changes in the flood frequency and the groundwater response through the late Pleistocene and Holocene for the Wadi Faynan in southern Jordan, a site considered internationally important due to its rich archaeological heritage spanning the Pleistocene and Holocene. This is the first study to describe the hydrological functioning of the Wadi Faynan, a meso-scale (241 km2) semi-arid catchment, setting this description within the framework of contemporary archaeological investigations. Historic meteorological records were collated and supplemented with new hydrological and water quality data. The modelled outcomes indicate that environmental changes, such as deforestation, had a major impact on the local water cycle and this amplified the effect of the prevailing climate on the flow regime. The results also show that increased rainfall alone does not necessarily imply better conditions for farming and highlight the importance of groundwater. The discussion focuses on the utility of the method and the importance of the local hydrology to the sustained settlement of the Wadi Faynan through pre-history and history.

Boundary value problems for the elliptic sine-Gordon equation in a semi-strip

We study boundary value problems posed in a semistrip for the elliptic sine-Gordon equation, which is the paradigm of an elliptic integrable PDE in two variables. We use the method introduced by one of the authors, which provides a substantial generalization of the inverse scattering transform and can be used for the analysis of boundary as opposed to initial-value problems. We first express the solution in terms of a 2 by 2 matrix Riemann-Hilbert problem whose \jump matrix" depends on both the Dirichlet and the Neumann boundary values. For a well posed problem one of these boundary values is an unknown function. This unknown function is characterised in terms of the so-called global relation, but in general this characterisation is nonlinear. We then concentrate on the case that the prescribed boundary conditions are zero along the unbounded sides of a semistrip and constant along the bounded side. This corresponds to a case of the so-called linearisable boundary conditions, however a major difficulty for this problem is the existence of non-integrable singularities of the function q_y at the two corners of the semistrip; these singularities are generated by the discontinuities of the boundary condition at these corners. Motivated by the recent solution of the analogous problem for the modified Helmholtz equation, we introduce an appropriate regularisation which overcomes this difficulty. Furthermore, by mapping the basic Riemann-Hilbert problem to an equivalent modified Riemann-Hilbert problem, we show that the solution can be expressed in terms of a 2 by 2 matrix Riemann-Hilbert problem whose jump matrix depends explicitly on the width of the semistrip L, on the constant value d of the solution along the bounded side, and on the residues at the given poles of a certain spectral function denoted by h. The determination of the function h remains open.

Constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation

Many modern statistical applications involve inference for complex stochastic models, where it is easy to simulate from the models, but impossible to calculate likelihoods. Approximate Bayesian computation (ABC) is a method of inference for such models. It replaces calculation of the likelihood by a step which involves simulating artificial data for different parameter values, and comparing summary statistics of the simulated data with summary statistics of the observed data. Here we show how to construct appropriate summary statistics for ABC in a semi-automatic manner. We aim for summary statistics which will enable inference about certain parameters of interest to be as accurate as possible. Theoretical results show that optimal summary statistics are the posterior means of the parameters. Although these cannot be calculated analytically, we use an extra stage of simulation to estimate how the posterior means vary as a function of the data; and we then use these estimates of our summary statistics within ABC. Empirical results show that our approach is a robust method for choosing summary statistics that can result in substantially more accurate ABC analyses than the ad hoc choices of summary statistics that have been proposed in the literature. We also demonstrate advantages over two alternative methods of simulation-based inference.

A nonparametric ensemble transform method for Bayesian inference

Many applications, such as intermittent data assimilation, lead to a recursive application of Bayesian inference within a Monte Carlo context. Popular data assimilation algorithms include sequential Monte Carlo methods and ensemble Kalman filters (EnKFs). These methods differ in the way Bayesian inference is implemented. Sequential Monte Carlo methods rely on importance sampling combined with a resampling step, while EnKFs utilize a linear transformation of Monte Carlo samples based on the classic Kalman filter. While EnKFs have proven to be quite robust even for small ensemble sizes, they are not consistent since their derivation relies on a linear regression ansatz. In this paper, we propose another transform method, which does not rely on any a priori assumptions on the underlying prior and posterior distributions. The new method is based on solving an optimal transportation problem for discrete random variables. © 2013, Society for Industrial and Applied Mathematics

The composite-tendency RAW filter in semi-implicit integrations

The time discretization in weather and climate models introduces truncation errors that limit the accuracy of the simulations. Recent work has yielded a method for reducing the amplitude errors in leapfrog integrations from first-order to fifth-order. This improvement is achieved by replacing the Robert--Asselin filter with the RAW filter and using a linear combination of the unfiltered and filtered states to compute the tendency term. The purpose of the present paper is to apply the composite-tendency RAW-filtered leapfrog scheme to semi-implicit integrations. A theoretical analysis shows that the stability and accuracy are unaffected by the introduction of the implicitly treated mode. The scheme is tested in semi-implicit numerical integrations in both a simple nonlinear stiff system and a medium-complexity atmospheric general circulation model, and yields substantial improvements in both cases. We conclude that the composite-tendency RAW-filtered leapfrog scheme is suitable for use in semi-implicit integrations.

Efficient animal-serum free 3D cultivation method for adult human neural crest-derived stem cell therapeutics

Due to their broad differentiation potential and their persistence into adulthood, human neural crest-derived stem cells (NCSCs) harbour great potential for autologous cellular therapies, which include the treatment of neurodegenerative diseases and replacement of complex tissues containing various cell types, as in the case of musculoskeletal injuries. The use of serum-free approaches often results in insufficient proliferation of stem cells and foetal calf serum implicates the use of xenogenic medium components. Thus, there is much need for alternative cultivation strategies. In this study we describe for the first time a novel, human blood plasma based semi-solid medium for cultivation of human NCSCs. We cultivated human neural crest-derived inferior turbinate stem cells (ITSCs) within a blood plasma matrix, where they revealed higher proliferation rates compared to a standard serum-free approach. Three-dimensionality of the matrix was investigated using helium ion microscopy. ITSCs grew within the matrix as revealed by laser scanning microscopy. Genetic stability and maintenance of stemness characteristics were assured in 3D cultivated ITSCs, as demonstrated by unchanged expression profile and the capability for self-renewal. ITSCs pre-cultivated in the 3D matrix differentiated efficiently into ectodermal and mesodermal cell types, particularly including osteogenic cell types. Furthermore, ITSCs cultivated as described here could be easily infected with lentiviruses directly in substrate for potential tracing or gene therapeutic approaches. Taken together, the use of human blood plasma as an additive for a completely defined medium points towards a personalisable and autologous cultivation of human neural crest-derived stem cells under clinical grade conditions.

The composite-tendency Robert-Asselin-Williams (RAW) filter in semi-implicit integrations

Timediscretization in weatherandclimate modelsintroduces truncation errors that limit the accuracy of the simulations. Recent work has yielded a method for reducing the amplitude errors in leap-frog integrations from first-order to fifth-order.This improvement is achieved by replacing the Robert–Asselin filter with the Robert–Asselin–Williams (RAW) filter and using a linear combination of unfiltered and filtered states to compute the tendency term. The purpose of the present article is to apply the composite-tendency RAW-filtered leapfrog scheme to semi-implicit integrations. A theoretical analysis shows that the stability and accuracy are unaffected by the introduction of the implicitly treated mode. The scheme is tested in semi-implicit numerical integrations in both a simple nonlinear stiff system and a medium-complexity atmospheric general circulation model and yields substantial improvements in both cases. We conclude that the composite-tendency RAW-filtered leap-frog scheme is suitable for use in semi-implicit integrations.