Search

This article describes the development of three major forms of peat processing by the manufacturing industry in The Netherlands since the last quarter of the 19th century. At a time when peat as a fuel was gradually being replaced by coal, the first form was the peat moss litter industry. Peat moss litter was made from white peat that was ground and sieved in factories which were located mainly in bog areas in the south-east of the province of Drenthe. It served as excellent bedding for horses and cattle. The second form of industrial peat processing was the manufacture, from 1921 onwards, of active carbon made from black peat. The Purit (Norit) factory, now part of the Cabot Corporation, is still the only active carbon factory using peat as a raw material. The third form of peat processing was the production of garden peat and potting soil. This is still a widespread activity in peat areas all over the world. The peat moss litter industry thrived from the 1880s until shortly after the First World War. The arrival of the horse-drawn tram in all of the major cities of Europe created a great demand for animal bedding to be used in the vast stables of the tramway companies. Peat moss litter was cleaner, healthier and easier to handle than straw. There was similar demand from the armies, which used millions of horses during the First World War. Owing to the development of motorised vehicles, the peat market collapsed after the war and this plunged the industry into a prolonged crisis which was not overcome until peat was found to be a suitable growing medium for horticulture in the 1950s. Living and working conditions in peatlands were harsh, earnings irregular and labourers’ rights limited. The peat manufacturing industry was the first to introduce collective labour agreements, medical benefits and pension plans. Nonetheless massive unemployment, poverty and the necessity to migrate to other parts of the country were clear signs that the era of peat was over for all but a handful of labourers.

Abstract: Statistical inference from truncated height data is often based on distributional assumptions. In this paper we analyze a data set of over 23,000 conscript height observations, covering nearly all conscripts in Drenthe, a province of the Netherlands, over the period 1826–1860. The data do not satisfy the normality assumption. We demonstrate that the ML estimators of the mean proposed for normally distributed data do not yield satisfactory results. We propose a new estimator that exploits the relationship between the conditional mean of the observations above the minimum height requirement and the conditional mean and proportion of conscripts below the minimum height requirement. [Copyright &y& Elsevier]