AFFILIATION : Centre for Environmental and Agricultural Informatics - UK
THEME : Agrifood systems models to support sustainable production within its landscape and ecology.
SUMMARY Over the coming decades, global demand for food is expected to grow by 70%. Most of the increase in food demand and production will be developing countries, such as countries in Africa. The growing demand for food cannot be met by simply using more land, as this would have serious impacts on the environment and the climate. Agriculture is already one of the economic sectors with the largest environmental impact, for instance, 25%–33% of greenhouse gases, accounts for over 70% of freshwater use, drives deforestation and pollutes natural aquatic and terrestrial ecosystems with agrochemicals. To manage agricultural landscapes more sustainably, it is important that stakeholders (farmers, advisors and policy makers) understand the impacts of their decisions on both productivity and the environment. Models and data can be used to facilitate this (for example they can help determine how to manage nutrients or minimise the impact of pests). Understanding how agri-landscapes can better support ecosystems service (including food production, regulating climate, crop pests and crop diseases, and supporting nutrient cycling) is a complex but ultimately tractable challenge. However, in reality, there are often socio-economic constraints that stand in the way of up-take of management advice. Here we describe how develop novel methods can be used to integrate models with remote sensing data (UAVs and EO) and data from other sources (e.g. legacy data) to address this. To manage agricultural landscapes more effectively, it is important that stakeholders (farmers, advisors and policy makers) understand the impacts of their decisions on productivity and the environment. Often decisions made on an individual basis can aggregate non-linearly, resulting in unintended consequences. Combining landscape level modelling efforts with agent-based models should allow us to simulate the dynamic decision-making process of individual farmers and how this impacts ES delivery at landscape scale. BIOGRAPHY

Dr. Corstanje Ronald is a professor of Data Sciences at Cranfield and Head of the Centre for Environmental and Agricultural Informatics. He specialises in the application of spatio-temporal models to understand the nature and behaviour of natural systems and processes. He is interested in the application of (spatial) modelling tools to understand the structure and function of environmental systems and processes. Environmental systems are complex, and this expresses itself as complex but determinable spatiotemporal patterns. The application of these approaches has led to significant advances in our understanding on the spatial dynamics of ecosystem services, in particular in Urban areas. Another key area in which these techniques have proven invaluable is in the area of resilience as applied to soil functions, allowing significant insights into the nature and functioning of resilience. Both these areas have significant societal significance, in helping inform how to infer resilience in the natural and man-made systems on which we depend, but also in how to design and plan the built environment to retain the benefits from the natural capital inherent in our greenspaces.