Structure repair of a compacted vertisol with wet-dry cycles and crops

We hypothesized that the four rotation crops: wheat (Triticum aestivum L.), sorghum [Sorghum bicolor (L.) Merr.], lablab [Lablab purpureus (L.) Sweet] and mung bean [ Vigna radiata (L.) R. Wilczek] differ in their ability to repair soil structure. The study was conducted on a Typic Haplustert, Queensland, Australia, locally termed a Black Earth and considered a prime cropping soil. Large (0.5-m depth by 0.3-m diam.) soil cores, collected from compacted wheel furrows in an irrigated cotton (Gossypium hirsutum L.) field, were subjected to three, six, or nine wet-dry cycles that simulated local flood irrigation practices. After each cycle, soil profiles were sampled for clod bulk density, image analysis of soil structure, and evapotranspiration. Generally, all crops improved soil structure over the initial field condition but lablab and mung bean gave improvements to greater depths and more rapidly than wheat and sorghum. Mung bean and lablab caused up to a threefold increase in clod porosity in the 0.1- to 0.4-m soil layer after only three wet-dry cycles, whereas sorghum required nine wet-dry cycles to increase clod porosity in only the 0.2- to 0.3-m layer, and wheat gave no improvement even after nine wet-dry cycles. Image analysis of soil structure showed that lablab and mung bean rapidly (by three wet-dry cycles) produced smaller peds with more interconnected pore space than wheat and sorghum. By nine wet-dry cycles, sorghum achieved deep cracking of the soil but the material between the cracks remained large and dense. Evapotranspiration was double under lablab and mung bean compared with wheat and sorghum. Our results indicate greater cycles of wetting and drying under lablab and mung bean than wheat and sorghum that have led to rapid repair of soil compaction.

Incorporating level set methods in Geographical Information Systems (GIS) for land-surface process modelling

Land-surface processes include a broad class of models that operate at a landscape scale. Current modelling approaches tend to be specialised towards one type of process, yet it is the interaction of processes that is increasing seen as important to obtain a more integrated approach to land management. This paper presents a technique and a tool that may be applied generically to landscape processes. The technique tracks moving interfaces across landscapes for processes such as water flow, biochemical diffusion, and plant dispersal. Its theoretical development applies a Lagrangian approach to motion over a Eulerian grid space by tracking quantities across a landscape as an evolving front. An algorithm for this technique, called level set method, is implemented in a geographical information system (GIS). It fits with a field data model in GIS and is implemented as operators in map algebra. The paper describes an implementation of the level set methods in a map algebra programming language, called MapScript, and gives example program scripts for applications in ecology and hydrology.

BM+-tree: A hyperplane-based index method for high-dimensional metric spaces

In this paper, we propose a novel high-dimensional index method, the BM+-tree, to support efficient processing of similarity search queries in high-dimensional spaces. The main idea of the proposed index is to improve data partitioning efficiency in a high-dimensional space by using a rotary binary hyperplane, which further partitions a subspace and can also take advantage of the twin node concept used in the M+-tree. Compared with the key dimension concept in the M+-tree, the binary hyperplane is more effective in data filtering. High space utilization is achieved by dynamically performing data reallocation between twin nodes. In addition, a post processing step is used after index building to ensure effective filtration. Experimental results using two types of real data sets illustrate a significantly improved filtering efficiency.

Applying a Systems Approach to the Analysis of the Benefits of Conversion to Organic Production

Although the current level of organic production in industrialised countries amounts to little more than 1-2 percent, it is recognised that one of the major issues shaping agricultural output over the next several decades will be the demand for organic produce (Dixon et al. 2001). In Australia, the issues of healthy food and environmental concern contribute to increasing demand and market volumes for organic produce. However, in Indonesia, using more economical inputs for organic production is a supply-side factor driving organic production. For individual growers and processors, conversion from conventional to organic agriculture is often a challenging step, entailing a thorough revision of established practices and heightened market insecurity. This paper examines the potential for a systems approach to the analysis of the conversion process, to yield insights for household and community decisions. A framework for applying farming systems research to investigate the benefits of organic production in both Australia and Indonesia is discussed. The framework incorporates scope for farmer participation, crucial to the understanding of farming systems; analysis of production; and relationships to resources, technologies, markets, services, policies and institutions in their local cultural context. A systems approach offers the potential to internalise the external effects that may be constraining decisions to convert to organic production, and for the design of decision-making tools to assist households and the community. Systems models can guide policy design and serve as a mechanism for predicting the impact of changes to the policy and market environments. The increasing emphasis of farming systems research on community and environment in recent years is in keeping with the proposed application to organic production, processing and marketing issues. The approach will also facilitate the analysis of critical aspects of the Australian production, marketing and policy environment, and the investigation of these same features in an Indonesian context.