Cocowood processing manual: from coconut wood to quality flooring.

This manual describes best practice for producing high-value flooring products from coconut ‘wood’—or cocowood. It meets international standards for flooring products and accounts for the recognised, specific, local conditions of the Pacific Islands. The information is intended for operators skilled in timber processing, who need to work with the unusual properties of cocowood, and specifies where cocowood processes differ from standard practice for timber. For other processes, refer to the relevant standards set by the importing country. These technical guidelines are based on the research outcomes of the ACIAR project, 'Improving value and marketability of coconut wood'. The manual is divided into three chapters. Each chapter adds to different aspects of primary and secondary processing. Chapter 1 provides an overview of the best practice steps for harvesting and processing cocowood. There is also a glossary of terms specifically associated with processing cocowood and a section on managing processing risks. Chapter 2 covers cocowood’s unique properties and how they relate to critical processing techniques. This is followed by sections that set out the processing methods in more detail, explaining why these practices are essential when working with cocowood. Chapter 3 provides more information, including contacts, current timber standards and some useful publications.

Improved estimation of size-transition matrices using tag-recapture data

We derive a new method for determining size-transition matrices (STMs) that eliminates probabilities of negative growth and accounts for individual variability. STMs are an important part of size-structured models, which are used in the stock assessment of aquatic species. The elements of STMs represent the probability of growth from one size class to another, given a time step. The growth increment over this time step can be modelled with a variety of methods, but when a population construct is assumed for the underlying growth model, the resulting STM may contain entries that predict negative growth. To solve this problem, we use a maximum likelihood method that incorporates individual variability in the asymptotic length, relative age at tagging, and measurement error to obtain von Bertalanffy growth model parameter estimates. The statistical moments for the future length given an individual’s previous length measurement and time at liberty are then derived. We moment match the true conditional distributions with skewed-normal distributions and use these to accurately estimate the elements of the STMs. The method is investigated with simulated tag–recapture data and tag–recapture data gathered from the Australian eastern king prawn (Melicertus plebejus).