Spatial requirements of animals: Allometry and beyond

Space allowance is a major factor influencing animal welfare. For livestock, at least, it plays a critical role in profitability, yet there is little information on the amount of space that animals require. The amount of space an animal occupies as a consequence of its shape and size can be estimated using allometry; linear dimensions (L) can be expressed as L = kW1/3 and surface area (S) as S = kW2/3, where k = a constant and W = the weight of the animal. Such equations have been used to determine the amount of space needed by standing (area [m2] = 0.019W0.66) and lying (area [m2] = 0.027W0.67) animals. Limited studies on the lying down and standing up behaviors of pigs and cattle suggest that the amount of space required can be estimated by area (m2) = 0.047W0.66. Linear space required per animal for behaviors such as feeding or drinking from a trough can be estimated from 0.064W0.33, but in groups this requirement will be affected by social interactions among group members and the amount of competition for the resource. Determining the amount of space for groups of animals is complex, as the amount of useable space can vary with group size and by how group members share space in time. Some studies have been conducted on the way in which groups of domestic fowl use space, but overall, we know very little about the ways in which livestock time-share space, synchronicity in the performance of behaviors, and the effects of spatial restrictions on behavior and welfare.

Development and testing of allometric equations for estimating above-ground biomass of mixed-species environmental plantings

To quantify the impact that planting indigenous trees and shrubs in mixed communities (environmental plantings) have on net sequestration of carbon and other environmental or commercial benefits, precise and non-biased estimates of biomass are required. Because these plantings consist of several species, estimation of their biomass through allometric relationships is a challenging task. We explored methods to accurately estimate biomass through harvesting 3139 trees and shrubs from 22 plantings, and collating similar datasets from earlier studies, in non-arid (>300mm rainfallyear-1) regions of southern and eastern Australia. Site-and-species specific allometric equations were developed, as were three types of generalised, multi-site, allometric equations based on categories of species and growth-habits: (i) species-specific, (ii) genus and growth-habit, and (iii) universal growth-habit irrespective of genus. Biomass was measured at plot level at eight contrasting sites to test the accuracy of prediction of tonnes dry matter of above-ground biomass per hectare using different classes of allometric equations. A finer-scale analysis tested performance of these at an individual-tree level across a wider range of sites. Although the percentage error in prediction could be high at a given site (up to 45%), it was relatively low (<11%) when generalised allometry-predictions of biomass was used to make regional- or estate-level estimates across a range of sites. Precision, and thus accuracy, increased slightly with the level of specificity of allometry. Inclusion of site-specific factors in generic equations increased efficiency of prediction of above-ground biomass by as much as 8%. Site-and-species-specific equations are the most accurate for site-based predictions. Generic allometric equations developed here, particularly the generic species-specific equations, can be confidently applied to provide regional- or estate-level estimates of above-ground biomass and carbon. © 2013 Elsevier B.V.

Infrared spectroscopy as a rapid tool to detect methylglyoxal and antibacterial activity in Australian honeys

Methylglyoxal (2-oxopropanal) is a compound known to contribute to the non-peroxide antimicrobial activity of honeys. The feasibility of using infrared spectroscopy as a predictive tool for honey antibacterial activity and methylglyoxal content was assessed. A linear relationship was found between methylglyoxal content (279–1755 mg/kg) in Leptospermum polygalifolium honeys and bacterial inhibition for Escherichiacoli (R2 = 0.80) and Staphylococcusaureus (R2 = 0.64). A good prediction of methylglyoxal (R2 0.75) content in honey was achieved using spectroscopic data from the mid infrared (MIR) range in combination with partial least squares regression. These results indicate that robust predictive equations could be developed using MIR for commercial application where the prediction of bacterial inhibition is needed to ‘value’ honeys with methylglyoxal contents in excess of 200 mg/kg.