Roderick Glass © ALL RIGHTS RESERVED.
Farm performance is a question that has been posed many perspectives from actors both inside and outside the farming business. In this research we will seek to understand farm performance from multiple perspectives but most importantly from the perspective of the farmer given the complexity of their business. Business performance occurs at multiple levels, systems leadership theory describes businesses operating at seven levels of complexity (Burke et al., 2012, Macdonald et al., 2006, Jaques, 1986). In this research we will seek to understand the ability of systems leadership theory to assist farmers to understand the level at which they work and understand why actors in the chain behave to achieve their goals.
Farmer decision making regarding natural systems is difficult due to the complexity and uncertainty of the natural system. Farmer performance can be seen as a complex interaction of disciplines both on and on farm, and systems theory can be seen as a providing connectivity for the long and short term planning along with the economic, social and environmental elements of the research (Smith, 2011). Systems theory underpins this research with Smith (2011) drawing the conclusion that for participants to integrate economic, social and environmental dimensions, critical systems theory is required.
Farmer managers must deal with change both strategically and tactically, with the ability to be flexible to adjust our resources to the changing on farm and off farm conditions. Farming is a complex business and we must seek to understand why some farmers appear to not make the ‘right’ decisions; what factors are affecting these farmers not taking certain courses of action. The right decisions are there for them to make. What are the actions of this individual that affects their level of systems thinking in order for a farm to be productive and successful? The success of that organisation is dependent upon the levels seamlessly working together (Jaques, 1986, Grobler, 2005).
The high level of complexity in decision making excludes prescriptive quantitative measures of performance being extrapolated with consistent success across farm businesses and within the horticultural value chain. A starting point is to consider the effects on the value chain as a whole by first understanding the qualitative factors relating business decisions and personal goals need to be considered to determine appropriate quantitative measures of performance and the effect these goals have upon the levels of work. Benchmarks are guides to performance but need to be adapted to match individual farm and value chain characteristics and circumstances.
The deconstruction of profitability distinguishes between profit change and profitability change. In understanding the Total Productivity Factor we must understand the output-input oriented technical efficiency (adoption), scale efficiency, product mix efficiency and scale efficiency. These indexes been further deconstructed to allow farm data to be collected and analysed (O'Donnell, 2010, O'Donnell and Fallah-Fini, 2011, Islam et al., 2014). The adoption of innovation is important for producers to improve profitability through productivity enhancements, understanding the barriers to adoption and the drivers of adoption may assist the uptake of innovation on farm.
Set of concepts that attempts to explain complex phenomenon not explainable by traditional (mechanistic) theories. It integrates ideas derived from chaos theory, cognitive psychology, computer science, evolutionary biology, general systems theory, fuzzy logic, information theory, and other related fields to deal with the natural and artificial systems as they are, and not by simplifying them (breaking them down into their constituent parts). It recognizes that complex behavior emerges from a few simple rules, and that all complex systems are networks of many interdependent parts which interact according to those rules.