The relationship between agriculture and climate change is complex.  Agricultural productivity is likely to be significantly impacted by changes in climate and weather, and these impacts must be well understood in order to develop effective adaptation measures.  At the same time, agriculture is itself a significant contributor to anthropogenic greenhouse gas (GHG) emissions, and well-managed agricultural systems can thus play a mitigating role.  Agro-ecosystem models are increasingly being used to assess the range of possible climate change impacts on agriculture and identify options for adaptation and mitigation.  A particularly pressing question is to what extent productivity losses from more-frequent drought and extreme heat may be offset by the increased productivity of crops grown under elevated atmospheric [CO2] (eCO2).

DayCent is a dynamic ecosystem model derived from Century, which was originally developed to simulate stocks of soil carbon over periods from decades to centuries.  DayCent has since been used in a wide range of applications, with a particular emphasis on soil carbon and agricultural trace gas emissions. 

This work had two inter-related parts.  First, the crop parameters that modify crop growth and transpiration in response to eCO2 were adjusted to reflect results from recent Free Air CO2 Enrichment (FACE) experiments.  Second, a daily canopy temperature routine was developed from field data and used to detect heat stress during reproductive and grain-filling phases.  Specifically, we found that observations of canopy temperature depression (Tdep) could be predicted by regressing Tdep on vapor pressure deficit and modeled crop transpiration.  A heat stress reduction factor was then applied to the harvest index for cumulative post-anthesis exposure to canopy temperatures above a critical threshold, based on empirical relationships developed by Schlenker and Roberts (2009). 

Taken together, these updates allow DayCent to reproduce changes in growth, transpiration, canopy temperature and heat response of crops under FACE conditions.  Simulating these complex linkages in a semi-mechanistic way may be important for accurately projecting crop performance under unprecedented future climate conditions.