Integrating spatially referenced social and biophysical data to explore landholder responses to dryland salinity in Australia

Researchers attempting to integrate socio-economic data in watershed planning often draw on nationally collected census data. However, there are critical limitations to the usefulness of this type of data for decision makers operating at the watershed scale. In this paper we demonstrate the relevance of spatially referenced socio-economic data collected using mail surveys to random selections of rural landholders. The issue explored was dryland salinity management in two large watersheds in the Murray-Darling Basin of south-eastern Australia. Contrary to the assumptions underlying public policy in Australia, but consistent with the literature on farmer knowledge, comparisons of expert maps and landholder identified salinity sites suggested that landholders in these watersheds had excellent knowledge of the current extent of salinity on their property. Our research also suggested that salinity education was a sound investment by governments. At the same time, the expert maps failed to predict half of the saline affected sites identified by landholders. Accurately mapping the extent of salinity would seem a first step in addressing this nationally significant land degradation issue.     

Compartmentalization of metals within the diverse colloidal matrices comprising activated sludge microbial flocs

Activated sludge floc from a wastewater treatment system was characterized, with regard to principal structural, chemical, and microbiological components and properties, in relation to contaminant-colloid associations and settling. Multiscale analytical microscopies, in conjunction with multimethod sample preparations, were used correlatively to characterize diverse colloidal matrices within microbial floc. Transmission electron microscopy, in conjunction with energy dispersive spectroscopy (EDS), revealed specific associations of contaminant heavy metals with individual bacterial cells and with extracellular polymeric substances (EPS). Floc structure was mapped from the level of gross morphology down to the nano-scale, and flocs were described with respect to settling properties, size, shape, density, porosity, bound water content, and EPS chemical composition; gross surface properties were also measured for correlation with principal floc features. Compartmentalization results based on 171 EDS analyses and representative high-resolution images showed that nano-scale agglomerations of (i) silver (100%) and (ii) zinc (91%) were confined almost entirely to EPS matrices while (iii) Pb (100%) was confined to intracellular granules and (iv) aluminum was partitioned between EPS matrices (41%) and intracellular matrices (59%). The results suggest that engineered changes in microbial physiology and/or in macromolecular EPS composition may influence metal removal efficiencies.     

Introduction-global to local: Ecological Land Classification

Ecological Land Classification (ELC) is a scientific endeavour which attempts to organize, stratify and evaluate ecosystems (and complexes of ecosystems) for the purposes of land resource management. Since ecosystems themselves are not easily defined in practical terms, ELC is likewise not a trivial concept. Nonetheless, ELC is a prerequisite for ecosystem management and the conservation of biological diversity simply because ecosystems must be described, characterized and spatially-located before they can be managed. Regarding the current status and future direction of ELC, mainly in relation to forest management: 1) approaches to ELC construction and utilization have shifted considerably over the past 2 decades; 2) there appears to be a current consensus regarding basic approaches to ELC; 3) spatial scale is a critical variable that must be addressed by ELCs; 4) ELCs must strive to more directly address management objectives; 5) natural ecosystem functions need to be better integrated within ELC frameworks; and, 6) the need for quality, georeferenced ELC-related data will continue to grow.     

CONTEMPORANEOUS AUTOREGRESSIVE-MOVING AVERAGE (CARMA) MODELING IN WATER RESOURCES1

ABSTRACT: The Contemporaneous Autoregressive-Moving Average (CARMA) model is a simple and efficient model that can be used to fit many multivariate hydrological time series. For certain types of multistation river flow systems, the CARMA model is naturally obtained when the physical restrictions of the system or the characteristics of the data are taken in consideration during the formulation of the model. It is shown how the CARMA model can optimally be used to handle multiple time series where the number of observations in each series may be different. Adequate model building techniques, as well as computational and statistical efficient algorithms to estimate the parameters of the model, are given. The methodologies and applications of the CARMA model are illustrated with three examples. It is also shown how the full multivariate ARMA model may lead to losses in efficient of the estimators when the CARMA model is adequate.     

Effects of crop diversification and low pesticide inputs on bird populations on arable land

This 6-year experimental study measured the response of bird populations and abundance to combinations of mixed cropping and low pesticide regimes associated with a commercial crop rotation. The results show a rapid and sustained population increase among a wide range of bird species, in contrast to local regional trends for the same species. Seventy percent of the increase occurred within the first 3 years of the experiment, with species of high conservation concern, and those monitored as environmental indicators on lowland farmland in the UK, increasing on average, by 30% and 20% respectively (reaching respective peaks of 44% and 33% after 4 years). For some individual species, the increase was higher still, i.e., 300% (1–4 pairs) for grey partridge (Perdix perdix) and 46% (13–19 pairs) for skylarks (Alauda arvensis) in peak years. The results demonstrate that bird species typical of lowland arable farmland in the UK are responsive to suitable farm-scale changes in habitat and food provision (roughly, manipulation within less than 1-km²). They show that the carrying capacity of modern, commercially viable, arable farmland can be increased significantly for birds, in this case, mainly by using crops mosaics to create habitats alongside the appropriate use of herbicides on non-cropped habitats.     

Addressing the dynamics of agri-food systems: an emerging agenda for social science research

The ‘modernist’ project that has come to dominate food and agricultural policy has failed to provide sustainable outcomes for many poor people in developing countries. Conventional agricultural science is not able to explain let alone address these concerns because it is based on a static equilibrium-centred view that provides little insight into the dynamic character of agri-food systems. This paper analyses how prevailing narratives of technological change and economic growth have come to dominate key food and agriculture policy debates. It seeks to counter these orthodox notions by emphasising that agri-food systems are embedded in complex ecological, economic and social processes, and showing how their interactions are dynamic and vulnerable to short-term shocks and long-term stresses like climate change. The paper makes the case for a deeper understanding of diverse ‘rural worlds’ and their potential pathways to sustainability through agriculture. Moreover, it argues for a normative focus on poverty reduction and concern for the distributional consequences of dynamic changes in agri-food systems, rather than aggregates and averages. Finally, it sets out an interdisciplinary research agenda on agri-food systems that focuses on dynamic system interactions in complex, risk-prone environments and explores how pathways can become more resilient and robust in an era of growing risk and uncertainty.