A systems thinking approach to address the complexity of agribusiness for sustainable development in Africa: a case study in Ghana

Abstract

African countries have comparative advantages in the production and export of primary commodities; however, they face many sustainability challenges in the agricultural sector. Since the democratisation of many African countries—notably Ghana—a number of interventions, costing billions of dollars, have been implemented to overcome the challenges facing the agricultural industry, but with little success. The agricultural industry is characterised by complex challenges such as famine, food insecurity, poor soil and quality standards, political instability, inappropriate agricultural practices, and the depletion of natural resources. These challenges have worsened the plight of African farmers. The increasingly complex nature of the agricultural industry in Africa has led to an urgent need for the use of a systemic rather than traditional approach to solve agricultural problems. Capacity building using a systems thinking approach and the concept of an Evolutionary Learning Laboratory during a series of stakeholder workshops in Ghana, has had a remarkable effect on the ability of the agricultural industry to evolve, improve and increase its efficacy. Causal Loop and Bayesian Belief Network (BBN) modelling were used to develop systems models to determine the components and interactions between the policy and the social, environmental and economic dimensions of the industry. Insights were made into potential system behaviours and leverage points for the systemic interventions required for sustainable agricultural development. The results reveal that the behaviour over time of agricultural productivity is declining, although new agricultural lands are being exploited, leading to environmental degradation. System archetypes as diagnostic tools have contributed to understanding the cause of a fix ‘now’, which gives rise to a much bigger problem to fix ‘later’. The results illustrate how the structure, conduct and performance elements of the agricultural industry interact together to influence the survival and growth of the sector. The study identifies that stakeholders adopt several strategies to survive and compete, leading to overexploitation of the ecosystem. Results from the BBN models indicate that the implementation of systemically determined interventions, policies and strategies could result in the chance of raising ‘agricultural productivity’ as high as 92.2% from 57.5%, and it might be plausible to reduce poverty levels from 44.9% to 10.0%. This would also lead to a significant increase in farmers’ yields and profits. These BBNs are used for scenario testing to determine the potential outcomes of different systemic interventions by observing what happens to the system as a whole when a particular intervention/strategy or combination of interventions/strategies is implemented—that is, before any time or money is invested in implementation. This approach provides clarity on dealing with complex sustainability challenges and should gradually replace the reductionist approach (e.g., short-term quick fixes and treating the symptoms) in dealing with challenges and developing policies. The systems models will help governments to anticipate the long-term consequences of their decisions and actions, as well as help to avoid significant unintended consequences of policies and strategies such as ‘silo mentality’ and ‘organisational myopia’.