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 democratization of many African countries, notably Ghana, there have been a number of interventions costing billions of dollars to overcome the challenges facing the agricultural industry but with little success. The agricultural industry is a complex system. Casual loop and Bayesian belief network 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 into potential system behaviors and leverage points for systemic interventions required for sustainable agricultural development were identified. The systems models will help governments to anticipate the long‐term consequences of their decisions and actions, as well as help to avoid any unintended consequences of policies and strategies such as ‘silo mentality’ and ‘organizational myopia’. Copyright © 2014 John Wiley & Sons, Ltd.

The African agricultural system is characterized by complex challenges such as famine, food insecurity, poor soil and quality standards, political instability, and inappropriate agricultural practices. The behavior over time graph revealed that as the African population increases, people explore new agricultural land that is in direct conflict with the conservation of forested areas, hence degrading the environment. These challenges in addition to the depletion of natural resources have worsened the plights of African farmers. The increasingly complex nature of the agricultural industry in Africa has necessitated an urgent need for the use of a systemic rather than a traditional approach in solving problems in agriculture. System archetypes were applied as diagnostic tools to anticipate potential problems and problem symptoms. Eleven system archetypes serve as the means for gaining insights into the underlying system structures from which the archetypal behaviors emerge. As part of a suite of tools, they are extremely valuable in developing broad understandings about agriculture and their environments and contribute to more effectively understanding the cause of a fix 'now' that gives rise to a much bigger problem to fix 'later'. The study revealed that opportunity and risk matrix as a policy tool does not solve the problems, but systemic approach would lead to the provision of the right management strategies. This approach facilitates adaptation and mitigation strategies towards the sustainable development for the agriculture in Africa.

The continuous growth in population and consumption, the intensity of competition for land, water and energy and the overexploitation of the ecosystem have all affected Africa's ability to sustain its food security and natural resources. In recent years, many promising agricultural development initiatives were unable to provide sustainable solutions to agricultural challenges in most parts of Africa, including Ghana, as a result of policy failures. The agricultural sector is a complex system and requires a holistic approach to deal with the root causes of challenges. This research therefore uses systems thinking tools, including causal loop diagrams and Bayesian belief network modelling, to develop new structural systems models whereby stakeholders can determine the components and interactions between the structure, conduct and performance (SCP) of the agricultural sector in Ghana, by using the first five steps of the Evolutionary Learning Laboratory. The results illustrate how the SCP elements interact together to influence the survival and growth of the agricultural sector. The study identifies that stakeholders adopt several strategies to survive and compete, which lead to overexploitation of the ecosystem. The results from the Bayesian belief network models indicate that the implementation of systemically determined interventions, policies and strategies could significantly improve the probability of business survival and growth from 58.8 to 73%. Also, the chances of improving the SCP could be increased from 39, 28.3 and 36.4 to 80.1, 55.9 and 62.4%, respectively, and these may vary based on the conditional probability tables. This paper contributes to the systemic approach to SCP, in that improvements to production and allocative efficiency may usher in a greater potential for improving food security, supporting the ecosystem and further strengthening agricultural sustainability. Copyright © 2016 John Wiley & Sons, Ltd.

Constraints and challenges in the agricultural industry of Ghana limit its productivity. Policy constraints could be a major issue when it comes to agricultural sustainability. Whether policymaking is based on sound principles that take into account the intended and unintended consequences led to exploring the use of a fresh approach towards determining effective interventions (policies) through a systems approach. Capacity building using a systems thinking approach that focuses on the four levels of thinking and using the concepts of an Evolutionary Learning Laboratory during a series of stakeholder workshops in Ghana has shown a remarkable impact on the ability of the agricultural industry to evolve, improve and raise its efficacy. Results from Bayesian belief network (BBN) models indicated that the implementation of systemically determined interventions, policies and strategies could result in chances of 'agricultural productivity' being 'good' as high as 92.2% from 57.5%, while the chances of reducing poverty levels from 44.9% to 10.0% are plausible. These would also lead to a significant increase in the yield and profit of the farmers. These BBNs are used for scenario testing to determine the potential outcomes of different systemic interventions by observing what would happen to the system as a whole when a particular intervention/strategy or combination of interventions/strategies are implemented: that is, before any time or money is invested in actual implementation. This approach provides more 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.