×B6. Integrated modelling to support sustainable agricultural systems

As increases in the intensity and frequency of extreme climatic events place continued stress on production systems, the ability to understand the risks to sustainable agricultural production is of central importance. An integrated modelling framework can increase our understanding of the impacts of climate change and land use intensification on agricultural production to support sustainable agricultural systems that deliver production and climate solutions, and provide adaptation pathways. We recognise there are large uncertainties in climate change impact assessment on agriculture when multiple models and different land management strategies are considered. Using integrated modelling (e.g., biophysical and/or statistical models) to accurately assess agricultural production and the ecosystems that support production (e.g., carbon, water and nutrient processes and dynamics, GHG emissions) over different spatiotemporal scales with different land management practices under climate change scenarios is the focus of this session.

Specifically, we invite submissions focusing on:

1. assessing the impacts of climate change (e.g., climate warming and increased CO2 fertilization) and variability (e.g., drought and heat stress) on agricultural productivity
2. evaluating land-use change options and effective land management practices including agronomic solutions (e.g., reduced/zero tillage, crop rotations, retaining residues, green manure cropping, organic amendments, biochar, pasture phase, agroforestry, intercropping, genotypes, and sowing time) to mitigate the adverse impacts of climate change on yield, SOC stock, GHG emissions, economic and social capital, and resource condition
3. Identifying dominant sources of uncertainty in climate change impact assessment on agriculture and providing potential pathways to reduce total uncertainty in the whole modelling chain.

Key topics: Sustainable agriculture, Climate change, Land management practices, Impact uncertainty