Improving smallholder performance remains a seemingly intractable central issue for beef farming development in Indonesia. Studying a complex system such as beef farming requires a systems thinking approach. In the body of systems thinking, System Dynamics (SD) is considered to be a powerful methodology for taming the complexity of a system. However, SD has been criticized as being insensitive to the multiple interests and power structures likely to occur in a smallholder system. This thesis reports on the possibility of combining Soft Systems Methodology (SSM) and Critical Systems Heuristics (CSH) to overcome that limitation.
The objective of this research was to devise an approach that optimises the participation of farmers and other stakeholders in: (1) understanding the overall systems well enough to identify the problematic situations - the situations which participants considered as uncomfortable; and (2) formulate the most feasible strategies for mitigating the problematic situation.
A series of interviews and workshops involving two farmer groups and other stakeholders in the smallholder beef farming system was undertaken in two separate field studies in Central Java, Indonesia. The five steps of SD’s methodology were adopted with an enhancement at the problem structuring process where the CATWOE analysis of SSM and the 12 questions of CSH complement the SD.
As a result, a four dimensional representation of the problematic situation of smallholder beef farming was generated. The dimensions involve; motivation, control, knowledge, and legitimacy. From this a Causal Loop Diagram (CLD) was assembled. This CLD has a total of nine loops, four reinforcing and five balancing, and seven archetypes: two limit to growth archetypes, three shifting the burden, and one each of archetypes of success to successful and fixes that fail, which together defined the systems’ behaviour. A total of seven leverages were able to be identified: increase forage availability, control of the trading of cattle, improve farming productivity to generate income, improve breeder cow performance, strengthen waste management skills, balance the breeding and fattening ratio on farm, and focus on increasing the cattle population.
After refinement, and in consultation with respondent farmers, this CLD was translated into a quantitative dynamic model to allow simulation of these leverage points. The result suggests the following strategies: forage availability is not an issue as the current cattle population is less than the carrying capacity, provide education about herd replacement strategies to maintain the desired sales rate at a sustainable level, improve the feed, reduce the risk of overpriced purchasing and under-priced selling, provide education about farm planning and budgeting, educate farmers on animal assessment i.e. to select quality breeding cows, manure composting, and buying cattle using non-grant schemes.
Although increasing the complexity of the methodology, the inclusion of CSH and SSM in the research protocol provided depth and richness to the findings through the ability of the models to embrace the opinions of the farmers who are often reluctant to express their opinion. Thus, for the stakeholders, the described models provide a better understanding of the system than can be provided by SD alone and thereby provides the potential for facilitating development of more effective interventions. Further, the study produces a rigorous dynamic model which can be used to simulate intervention strategies. Three key statements were produced as recommendations for the development of smallholder beef farming in Rural Java. This includes: the importance of improving the local breeding cows’ reproductive performance; the necessity to consider farmers’ opinions in policy making; and the need to re-think the design of government programs to support smallholders.