Managing Complexity in Modern Farming

Modern farming in Australia is no longer simple. Farms are large, multi-enterprise businesses underpinned by expensive capital investments, changing production technologies, volatile markets and pervasive regulation. The complexity of modern broadacre farming leads to the question: what is the nature of the relationship between farm business complexity and farm profitability? This study uses bioeconomic farm modelling and employs eight measures of complexity to examine the profitability and complexity of a wide range of broadacre farming systems in Australia. Rank order correlations between farm profitability and each measure of complexity show inconsistent relationships, although the most profitable farming systems are found to be reasonably complex on several criteria. Among the set of highly profitable systems are found some characterised by less complexity. Using the farmer’s annual hours worked as a measure of complexity that affects current farm management, the trade-off between profit and this measure of complexity is found not to be large. A case is outlined where the farmer’s annual hours worked could be reduced by 9 percent for a 3 percent reduction in farm profit. If farmers’ workloads are proving problematic now and in the future, then agricultural R&D, service delivery and policy development will need to focus much more on being highly attractive to time-poor farm managers.complexity, farm modelling, management, profitability, Farm Management,

Managing complexity of control software through concurrency

In this thesis, we are concerned with the development of concurrent software for embedded systems. The emphasis is on the development of control software. Embedded systems are concurrent systems whereby hardware and software communicate with the concurrent world. Concurrency is essential, which cannot be ignored. It requires a proper handling to avoid pathological problems (e.g. deadlock and livelock) and performance penalties (e.g. starvation and priority conflicts). Multithreading, as such, leads to sources of complexity in concurrent software. This complexity is considered frightening, because it complicates the software designs and the resulting code. Moreover, this paradigm complicates the understanding of the behaviour of concurrent software. A paradigm with a precise understanding of concurrency is essential. In this thesis, a methodology is proposed that comprises a paradigm of fundamental aspects of concurrency

Managing complexity in a distributed digital library

As the capabilities of distributed digital libraries increase, managing organizational and software complexity becomes a key issue. How can collections and indexes be updated without impacting queries currently in progress? How can the system handle several user-interface clients for the same collections? Computer science professors and lectors from the University of Waikato have developed a software structure that successfully manages this complexity in the New Zealand Digital Library. This digital library has been a success in managing organizational and software complexity. The researchers' primary goal has been to minimize the effort required to keep the system operational and yet continue to expand its offerings

Learning participation as systems practice

We describe an evolving praxeology for Systems Practice for managing complexity built on 30 years of developing supported open learning opportunities in the area of Systems within the curriculum of The Open University (UK). We ground this description in two specific examples of how notions of participation are incorporated conceptually and practically into a learners programme of study by considering: (i) the postgraduate course 'Environmental Decision Making. A Systems Approach' (T860) and (ii) the undergraduate course 'Managing complexity. A systems approach' (T306)

Approaches to Managing Complexity in Project Production

Since the seminal contribution by Shewhart, the dominating approach to production is to minimize all variation in order to get the productive activities into control. Thus, the goal is to avoid all such complexity and uncertainty which could disturb this tight control. This approach is applied in lean production,which is considered to be the superior production template of today.It has to be noted that usually our concepts, for example “waste”, are based on this understanding of production. However, there are production situations with inherent complexity and unpredictability not least in project production. The primary goal of the paper is to chart and analyze the different approaches available for coping with these situations. Four different strategies are identified and discussed: reducing complexity, codifying procedures, learning to improvise and buffering. A secondary goal of the paper is to discuss whether and how the conceptual framework in production management should be further developed for taking these different approaches to project complexity into account

Managing Complexity in Socio-Economic Systems

This contribution summarizes some typical features of complex systems such as non-linear interactions, chaotic dynamics, the 's'sbutterfly effect'', phase transitions, self-organized criticality, cascading effects, and power laws. These imply sometimes quite unexpected, counter-intuitive, or even paradoxical behaviors of socio-economic systems. A typical example is the faster-is-slower effect. Due to their tendency of self-organization, complex systems are often hard to control. Instead of trying to control their behavior, it would often be better to pursue the approach of guided self-organization, i.e. to use the driving forces of the system rather than to fight against them. This is illustrated by the example of hierarchical systems, which need to fulfill certain principles in order to be efficient and robust in an ever-changing environment. We also discuss the important role of fluctuations and heterogeneity for the adaptability, flexibility and robustness of complex systems. The presentation is enriched by a number of examples ranging from decision behavior up to production systems and disaster spreadin