Legume cover crops can supply a significant amount of nitrogen (N) for cash crops, which is particularly important for organic farmers. Because N mineralization from cover crop residue depends on the amount of biomass, cover crop quality, as well as environmental conditions such as soil moisture and temperature, predicting the amount of N mineralized and the timing of release has been difficult. We have developed a Cover Crop Nitrogen Calculator based on the N subroutine of the CERES crop model and evaluated the use of the predicted N credits on yields of fall broccoli [Brassica oleracea L. (Italica group)] at a research farm and two working farms. Research farm trials consisted of a cowpea (Vigna unguiculata L. Walp.) cover crop and no cover crop treatments, each at four N rates (0N, 0.5N, 1N and 1.5N, with 1N the target N rate of 112 kg N ha−1 in 2013 and 168 kg N ha−1 in 2014 and 2015) in a randomized complete block design. On-farm trials consisted of a cowpea or sunn hemp (Crotolaria juncea L.) cover crop at four N rates (0N, 0.5N, 1N and 1.5N) and no cover crop treatment at the 1N rate in a completely randomized design. Cover crop biomass and quality (N%, carbohydrates%, cellulose% and lignin%) were measured and used with a 5-yr average soil moisture and soil temperature from a local weather station to predict an N credit. In the cover crop treatments, the N rate was modified by the predicted credit, while the no cover crop treatment received the full N fertilizer rate either as feathermeal (certified organic fields) or as urea (conventional field). At the research farm, broccoli yield increased up to the 0.5N rate, and there was no difference in yield between the no cover 0.5N rate and the cover crop 0.5N rate in 2013, 2014 and 2105. On-farm, we saw an N response in two site-years. In these site-years, there was no difference between the no cover 1N rate and the cover crop 1N rate. At the third site-year, no N response was seen. Overall, our results showed using the cover crop credit predicted by the Calculator did not reduce yields. The use of a decision support tool such as the Calculator may help farmers better manage N fertilizer when cover crops are used, and increase cover crop adoption.

Validation-study data were analysed to investigate retention interval (RI) and prompt effects on the accuracy of fourth-grade children’s reports of school-breakfast and school-lunch (in 24-h recalls), and the accuracy of school-breakfast reports by breakfast location (classroom; cafeteria). Randomly selected fourth-grade children at ten schools in four districts were observed eating school-provided breakfast and lunch, and were interviewed under one of eight conditions created by crossing two RIs (‘short’ – prior-24-hour recall obtained in the afternoon and ‘long’ – previous-day recall obtained in the morning) with four prompts (‘forward’ – distant to recent, ‘meal name’ – breakfast, etc., ‘open’ – no instructions, and ‘reverse’ – recent to distant). Each condition had sixty children (half were girls). Of 480 children, 355 and 409 reported meals satisfying criteria for reports of school-breakfast and school-lunch, respectively. For breakfast and lunch separately, a conventional measure – report rate – and reporting-error-sensitive measures – correspondence rate and inflation ratio – were calculated for energy per meal-reporting child. Correspondence rate and inflation ratio – but not report rate – showed better accuracy for school-breakfast and school-lunch reports with the short RI than with the long RI; this pattern was not found for some prompts for each sex. Correspondence rate and inflation ratio showed better school-breakfast report accuracy for the classroom than for cafeteria location for each prompt, but report rate showed the opposite. For each RI, correspondence rate and inflation ratio showed better accuracy for lunch than for breakfast, but report rate showed the opposite. When choosing RI and prompts for recalls, researchers and practitioners should select a short RI to maximise accuracy. Recommendations for prompt selections are less clear. As report rates distort validation-study accuracy conclusions, reporting-error-sensitive measures are recommended.

Within these pages you will find a ‘jovial crew’: rogues and vagabonds, the ‘mad’ and insane, gypsies, peddlers, poets, playwrights, pilgrims, rioters, convicts, constables, thieves, beggars, landed gentlemen, magistrates, and historians. When parliamentarians and projectors set out to proscribe mobility and legislate poverty in early modernity, a list of untrustworthy trades and professions not at all unlike this one frequently found its way into print and the statute book. The punishment for crimes of vagrancy could be severe, but thankfully ‘historians’ were not counted among the undeserving and mobile, nor would you find magistrates and landed gentlemen taken up, imprisoned, and whipped for a crime of movement. However, all three groups may well deserve some of John Locke's brand of draconian ‘improvement’; historians in particular have taken little account of the lived experiences of the mobile poor until relatively recently. Once we finally took a hard look at our inherited, literature-driven typologies of ‘rogues’ and ‘beggars’, they disappeared in ‘a storm of dust and lies.’ However, the literary, visualised vagabond still has much to tell us, and interdisciplinary approaches to vagrancy in the past have emerged as the strongest method yet of reconstructing the character, history, and cultural perception of the mobile poor. These are methods which the articles in this collection use to full effect.

This paper examines the relief of travellers in Warwickshire, England. By using an unusually rich set of Constables’ Accounts for the parish of Grandborough, it interrogates the relationship between charity, local justice, and both official and popular perceptions of migration. It argues that the large number of migrants who passed through rural parishes were categorised by the local constable according to cultural and discretionary criteria. This ‘typology’ of travellers determined the nature and extent of the relief they might receive and the actions that might be taken against them. Socially threatening migrants, such as poor pregnant women, the sick, and vagrants, also found themselves affected by this same ‘proscriptive calculation’, often to their detriment.

Extended abstract of a paper presented at Microscopy and Microanalysis 2004 in Savannah, Georgia, USA, August 1–5, 2004.