Spatial variation in soil inorganic nitrogen across an arid urban ecosystem

We explored variations in inorganic soil nitrogen (N) concentrations across metropolitan Phoenix, Arizona, and the surrounding desert using a probability-based synoptic survey. Data were examined using spatial statistics on the entire region, as well as for the desert and urban sites separately. Concentrations of both NO₃-N and NH₄-N were markedly higher and more heterogeneous amongst urban compared to desert soils. Regional variation in soil NO₃-N concentration was best explained by latitude, land use history, population density, along with percent cover of impervious surfaces and lawn, whereas soil NH₄-N concentrations were related to only latitude and population density. Within the urban area, patterns in both soil NO₃-N and NH₄-N were best predicted by elevation, population density and type of irrigation in the surrounding neighborhood. Spatial autocorrelation of soil NO₃-N concentrations explained 49% of variation among desert sites but was absent between urban sites. We suggest that inorganic soil N concentrations are controlled by a number of ‘local’ or ‘neighborhood’ human-related drivers in the city, rather than factors related to an urban-rural gradient.     

"Within the Pale." British Intellectual Society and the Image of the Russian Jew, 1890–1907

Abstract  "Within the Pale": This paper examines British responses to the East European Jew in the late nineteenth and early twentieth centuries. Unlike other analyses, which have concentrated on the impact of Jewish immigration to the UK and the so-called aliens' debate, the author interprets the narrative constructed by British commentators who visited the Tsarist Empire (sometimes to specifically examine its "Jewish Question") and who spent long periods there (either as independent observers, or as newspaper/journal correspondents). It discusses how Russian attitudes, images and allusions about Jews were transmitted to British discourse and the extent to which Anglo-Russian boundaries of Judeophobia were intertwined. In its assessment it considers British perceptions of anti-Tsarist persecution, life in the Pale, the Jewish-peasant relationship and Anglo-Jewish responses.     

Neither ‘family’ nor ‘corporate’ farming: Australian tomato growers as farm family entrepreneurs

For the past two decades there has been much debate about the future of family farming. The basic question on which this debate has turned is whether current pressures on family farm systems should be understood as symptomatic of a terminal condition, in which farmers are replaced progressively by corporate ownership; or whether family farms will persist as a social formation, albeit increasingly subsumed by off-farm interests. Using evidence from the Australian processing tomato sector, this article documents the changing social and economic formation of ‘family farming’. We argue that in this industry, the appropriate way to describe farmers is through the deployment of that a new category of farming; farm family entrepreneurs. This phrase is coined to describe the situation where family units remain at the social and economic heart of farm ownership and operation, but in the context where they relate to their land-based assets through legal and financial structures characteristic of the wider economy. As this article explores, this formation seems to represent an accommodating modus operandi for farm units within neo-liberal agricultural governance. Nevertheless, however, this duality of family-based structures and capitalist entrepreneurialism inevitably provokes a series of tensions, whose resolution requires a variety of organizational strategies to be put in place.     

A semi-parametric approach to robust parameter design

Parameter design or robust parameter design (RPD) is an engineering methodology intended as a cost-effective approach for improving the quality of products and processes. The goal of parameter design is to choose the levels of the control variables that optimize a defined quality characteristic. An essential component of RPD involves the assumption of well estimated models for the process mean and variance. Traditionally, the modeling of the mean and variance has been done parametrically. It is often the case, particularly when modeling the variance, that nonparametric techniques are more appropriate due to the nature of the curvature in the underlying function. Most response surface experiments involve sparse data. In sparse data situations with unusual curvature in the underlying function, nonparametric techniques often result in estimates with problematic variation whereas their parametric counterparts may result in estimates with problematic bias. We propose the use of semi-parametric modeling within the robust design setting, combining parametric and nonparametric functions to improve the quality of both mean and variance model estimation. The proposed method will be illustrated with an example and simulations.     

Strategic planning in smaller rapid growth companies

The research reported in this article is based on a study of the strategic planning practices of the 500 fastest growing privately held smaller companies in the United States ranked according to percentage of sales increases from 1978 to 1982.^p1 Based on the information provided, a clear picture emerges regarding the actual role of strategic planning in rapid growth companies.     

Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances

We study the properties of the quasi-maximum likelihood estimator (QMLE) and related test statistics in dynamic models that jointly parameterize conditional means and conditional covariances, when a normal log-likelihood os maximized but the assumption of normality is violated. Because the score of the normal log-likelihood has the martingale difference property when the forst two conditional moments are correctly specified, the QMLE is generally Consistent and has a limiting normal destribution. We provide easily computable formulas for asymptotic standard errors that are valid under nonnormality. Further, we show how robust LM tests for the adequacy of the jointly parameterized mean and variance can be computed from simple auxiliary regressions. An appealing feature of these robyst inference procedures is that only first derivatives of the conditional mean and variance functions are needed. A monte Carlo study indicates that the asymptotic results carry over to finite samples. Estimation of several AR and AR-GARCH time series models reveals that in most sotuations the robust test statistics compare favorably to the two standard (nonrobust) formulations of the Wald and IM tests. Also, for the GARCH models and the sample sizes analyzed here, the bias in the QMLE appears to be relatively small. An empirical application to stock return volatility illustrates the potential imprtance of computing robust statistics in practice.