Microbial dynamics in maize-growing soil under different tillage and residue management

Non-Peer ReviewedMicroorganisms are involved in the fertility-related processes of agricultural fields. The long-term impact of tillage and residue management on soil microorganisms was studied over the growing season, in a sandy loam to loamy sand soil of southwestern Quebec. Tillage and residue treatments had been first imposed in fall 1991, on a maize (Zea mays L.) monoculture. Treatments consisted of no till, reduced tillage, and conventional tillage with crop residues either removed from (-R) or retained on (+R) experimental plots, laid out in a randomized complete block design. Soil microbial biomass carbon (SMB-C), soil microbial nitrogen (SMB-N) and phospholipid fatty acid (PLFA) concentrations were measured four times over the 2001 growing season i.e., in May 7 (preplanting), June 25, July 16, and September 29 (prior to corn harvest). The effect of time was larger than those of tillage or residue treatments. While SMB-C showed little seasonal change (160 μg C g-1 soil), SMB-N was responsive to post emergence mineral nitrogen fertilization, and PLFA analysis showed an increase in fungi and total PLFA throughout the season. The effect of residue was more pronounced than that of tillage, with increased SMB-C and SMB-N (61% and 96%) in +R plots compared to –R plots. This study illustrated that measuring soil quality based on soil microbial components must take into account seasonal changes in soil physical and chemical conditions

Towards good practice guidelines for the contour method of residual stress measurement

Accurate measurement of residual stress in metallic components using the contour method relies on the achievement of a good quality cut, on the appropriate measurement of the deformed cut surface and on the robust analysis of the measured data. There is currently no published standard or code of practice for the contour method. As a first step towards such a standard, this study draws on research investigations addressing the three main steps in the method: how best to cut the specimens; how to measure the deformation contour of the cut surface; and how to analyse the data. Good practice guidance is provided throughout the text accompanied by more detailed observations and advice tabulated in Appendi

Creating a green supply chain

Greener Management International3571-8

The machined surface Statistics and characterization

SIGLELD:D49704/84 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Use of circle-segments as a data visualization technique for feature selection in pattern classification

One of the issues associated with pattern classification using data based machine learning systems is the &ldquo;curse of dimensionality&rdquo;. In this paper, the circle-segments method is proposed as a feature selection method to identify important input features before the entire data set is provided for learning with machine learning systems. Specifically, four machine learning systems are deployed for classification, viz. Multilayer Perceptron (MLP), Support Vector Machine (SVM), Fuzzy ARTMAP (FAM), and k-Nearest Neighbour (kNN). The integration between the circle-segments method and the machine learning systems has been applied to two case studies comprising one benchmark and one real data sets. Overall, the results after feature selection using the circle segments method demonstrate improvements in performance even with more than 50% of the input features eliminated from the original data sets.<br /