Parameterisation of LCI/LCIA models of agricultural systems emissions under future pressures

Agricultural production currently faces two important challenges that need to be overcome in the next decades. Firstly, the expected increase of the global human population will put more pressure on productive ecosystems to accommodate the growing need for food. Secondly, climate change as a consequence of anthropogenic emissions is forecasted to increase the pressure on natural and semi-natural systems’ productivity through various mechanisms resulting in e.g. an increased frequency of severe weather events or loss of nutrients in soil. An aspect that both the increasing food demand and environmental pressures have in common is the urge to enhance agricultural/food production efficiency, i.e. to produce more (while maintaining an acceptable quality) despite the difficulties raised by climate-driven pressures. Land-based food production is expected to compete with feed/non-food crops, forestry and protected areas for biodiversity, as well as land for bioenergy. Increasing agricultural yield may then be the best option. Accessible ways to rapidly ensure it consists of additional application of fertilisers and pesticides and an increase of their efficiency, while dealing with scarcity of phosphorus. Life Cycle Assessment (LCA) has been dealing with the environmental impacts from emissions and resources consumption from human activities including agriculture. Several approaches for inventory (LCI) and impact assessment (LCIA) modelling of agricultural activities have been published recently. To enhance the agriculture yield by adding nutrients and chemicals, humans will potentially increase the magnitude of the resulting emission flows to the ecosphere. The linearity of the emissions’ fate and impact modelling suggests the assertion that the more nutrients or chemicals we apply in these systems, the greater the emissions and hence the impacts will be. This consequence will be illustrated by case studies describing how the impacts from fertilizer and pesticide use increase for such agricultural intensification and under future climatic circumstances. Models’ sensitivity to the varying parameterisation from e.g. temperature raise or surface runoff (increased rainfall, drought), and the variation range of such inputs will also be addressed. LCA methodologies can provide useful information on the possible and predictable effects and damage to ecosystems and anticipate management and safety practices to minimise ecological, social, and economic impacts.