The role of modeling and systems thinking in contemporary agriculture

The images and perceived roles of agriculture in our societies have changed over the last few decades. Today agriculture is regarded as an integral part of interconnected value chains that sit at the heart of our economies, providing invaluable services to society. In response, most governments around the world are now actively developing policies to support and grow their bio-economies. This increases the expectations that society and governments have in terms of agricultures services and performance: agriculture is not only expected to generate food for our growing populations and income for farmers, it must be part of value chains that provide raw materials that can be incorporated or converted into feed, fiber, fuel, pharmaceuticals, and other industrial products. Farmers are expected to be responsible custodians of our landscapes and their farming practices must be economically, environmentally, and socially sustainable and aligned with the broader and changing values of our societies. Often these three objectives conflict and consequently societal expectations are not met. In a world that is increasingly data rich, practicing agriculture in a way that lives up to these expectations requires tools that can help to foresee the consequences of complex interactions. Hence, this chapter explores the role of modeling and systems thinking to manage this complexity by explicitly considering three attributes of complex, adaptive systems, whereby (i) order emerges rather than being predetermined; (ii) the systems future can only be assessed probabilistically rather than deterministically predicted; and (iii) the history of the system is largely irreversible. The chapter reflects on the contemporary use of models against these three systems characteristics and concludes that scientifically based and tested algorithms (i.e., models) are already a ubiquitous and indispensable management tool for modern farming. Countless apps are already in use for short-term, tactical decision making, while more complex modeling approaches are vital for strategic scenario planning and risk assessments for farmers, policymakers, and scientists.