Factors influencing the dissolution of phosphate rock in a range of high P-fixing soils from Cameroon

A laboratory incubation experiment was conducted to evaluate the soil factors that influence the dissolution of two phosphate rocks (PRs) of different reactivity (Gafsa, GPR, reactive PR; and Togo-Hahotoe, HPR, low reactivity PR) in seven agricultural soils from Cameroon having variable phosphorus (P)- sorption capacities, organic carbon (C) contents, and exchangeable acidities. Ground PR was mixed with the soils at a rate of 500 mg P kg 21 soil and incubated at 30 degrees C for 85 days. Dissolution of the PRs was determined at various intervals using the Delta NaOH-P method ( the difference of the amount of P extracted by 0.5 M NaOH between the PR-treated soils and the control). Between 4 and 27% of HPR and 33 and 50% of GPR were dissolved in the soils. Calcium (Ca) saturation of cation exchange sites and proton supply strongly affected PR dissolution in these soils. Acid soils with pH-(H2O), < 5 (NKL, ODJ, NSM, MTF) dissolved more phosphate rock than those with pH-(H2O) > 5 (DSC, FGT, BAF). However, the lack of a sufficient Ca sink in the former constrained the dissolution of both PRs. The dissolution of GPR in the slightly acidic soils was limited by increase in Ca saturation and that of HPR was constrained by limited supply in protons. Generally, the dissolution of GPR was higher than that of HPR for each soil. The kinetics of dissolution of PR in the soils was best described by the power function equation P At B. More efficient use of PR in these soils can be achieved by raising the soil cation exchange capacity, thereby increasing the Ca sink size. This could be done by amending such soils with organic materials.

Verifying and fixing password authentication protocol

Password Authentication Protocol (PAP) is widely used in the Wireless Fidelity Point-to-Point Protocol to authenticate an identity and password for a peer. This paper uses a new knowledge-based framework to verify the PAP protocol and a fixed version. Flaws are found in both the original and the fixed versions. A new enhanced protocol is provided and the security of it is proved The whole process is implemented in a mechanical reasoning platform, Isabelle. It only takes a few seconds to find flaws in the original and the fixed protocol and to verify that the enhanced version of the PAP protocol is secure.

Reduced space optimal interpolation of daily rain gauge precipitation in Switzerland

The coarse spacing of automatic rain gauges complicates near-real- time spatial analyses of precipitation. We test the possibility of improving such analyses by considering, in addition to the in situ measurements, the spatial covariance structure inferred from past observations with a denser network. To this end, a statistical reconstruction technique, reduced space optimal interpolation (RSOI), is applied over Switzerland, a region of complex topography. RSOI consists of two main parts. First, principal component analysis (PCA) is applied to obtain a reduced space representation of gridded high- resolution precipitation fields available for a multiyear calibration period in the past. Second, sparse real-time rain gauge observations are used to estimate the principal component scores and to reconstruct the precipitation field. In this way, climatological information at higher resolution than the near-real-time measurements is incorporated into the spatial analysis. PCA is found to efficiently reduce the dimensionality of the calibration fields, and RSOI is successful despite the difficulties associated with the statistical distribution of daily precipitation (skewness, dry days). Examples and a systematic evaluation show substantial added value over a simple interpolation technique that uses near-real-time observations only. The benefit is particularly strong for larger- scale precipitation and prominent topographic effects. Small-scale precipitation features are reconstructed at a skill comparable to that of the simple technique. Stratifying the reconstruction method by the types of weather type classifications yields little added skill. Apart from application in near real time, RSOI may also be valuable for enhancing instrumental precipitation analyses for the historic past when direct observations were sparse.

Fixing the currency: analysing the implications for property investment performance

This paper analyses the historic effects of exchange rate movements on returns, risk and diversification of office markets within the Euro zone in order to gain insights into the investment consequences of conversion to a fixed rate currency regime. The data used in the study represents annual office rental growth rates for 22 European cities from nine European Union countries between 1985 and 1996. Relative performance is reported in terms of domestic currency and in terms of deutsche marks. The evidence presented suggests that Euro zone property investors in ‘southern’ countries are now protected from short term jump risk associated with flexible peg currency arrangements and medium/long-term currency volatility. Historically exchange rate movements have produced decreases in returns and increases in volatility. For northern ‘bloc’ cities, the effects of fixing the exchange rate are minimal. For these cities, national exchange rate fluctuations against the deutsche mark have been minor and the resultant implications for property risk and return to non-domestic SCA investors have been negligible. Moreover, although previous research would suggest that the effect of currency volatility is to decrease market correlation, this cannot be observed within the Euro zone.

Combined use of satellite estimates and rain gauge observations to generate high-quality historical rainfall time series over Ethiopia

Climate data are used in a number of applications including climate risk management and adaptation to climate change. However, the availability of climate data, particularly throughout rural Africa, is very limited. Available weather stations are unevenly distributed and mainly located along main roads in cities and towns. This imposes severe limitations to the availability of climate information and services for the rural community where, arguably, these services are needed most. Weather station data also suffer from gaps in the time series. Satellite proxies, particularly satellite rainfall estimate, have been used as alternatives because of their availability even over remote parts of the world. However, satellite rainfall estimates also suffer from a number of critical shortcomings that include heterogeneous time series, short time period of observation, and poor accuracy particularly at higher temporal and spatial resolutions. An attempt is made here to alleviate these problems by combining station measurements with the complete spatial coverage of satellite rainfall estimates. Rain gauge observations are merged with a locally calibrated version of the TAMSAT satellite rainfall estimates to produce over 30-years (1983-todate) of rainfall estimates over Ethiopia at a spatial resolution of 10 km and a ten-daily time scale. This involves quality control of rain gauge data, generating locally calibrated version of the TAMSAT rainfall estimates, and combining these with rain gauge observations from national station network. The infrared-only satellite rainfall estimates produced using a relatively simple TAMSAT algorithm performed as good as or even better than other satellite rainfall products that use passive microwave inputs and more sophisticated algorithms. There is no substantial difference between the gridded-gauge and combined gauge-satellite products over the test area in Ethiopia having a dense station network; however, the combined product exhibits better quality over parts of the country where stations are sparsely distributed.

Rainfall estimation by rain gauge-radar combination: a concurrent multiplicative-additive approach

A procedure (concurrent multiplicative-additive objective analysis scheme [CMA-OAS]) is proposed for operational rainfall estimation using rain gauges and radar data. On the basis of a concurrent multiplicative-additive (CMA) decomposition of the spatially nonuniform radar bias, within-storm variability of rainfall and fractional coverage of rainfall are taken into account. Thus both spatially nonuniform radar bias, given that rainfall is detected, and bias in radar detection of rainfall are handled. The interpolation procedure of CMA-OAS is built on Barnes' objective analysis scheme (OAS), whose purpose is to estimate a filtered spatial field of the variable of interest through a successive correction of residuals resulting from a Gaussian kernel smoother applied on spatial samples. The CMA-OAS, first, poses an optimization problem at each gauge-radar support point to obtain both a local multiplicative-additive radar bias decomposition and a regionalization parameter. Second, local biases and regionalization parameters are integrated into an OAS to estimate the multisensor rainfall at the ground level. The procedure is suited to relatively sparse rain gauge networks. To show the procedure, six storms are analyzed at hourly steps over 10,663 km2. Results generally indicated an improved quality with respect to other methods evaluated: a standard mean-field bias adjustment, a spatially variable adjustment with multiplicative factors, and ordinary cokriging.