Towards functional trait-based indices of climate change vulnerability in agricultural systems
In order to feed the Earth’s growing population, increases of ~70% in global food production are required by 2050. Approximately 90% of these increases are expected to come from increased yields in existing agricultural systems. At the same time, climate change models predict major shifts in precipitation and temperature regimes in many agricultural regions, which are expected to negatively affect crop growth and yield at varying intensities. Therefore critical uncertainties exist on whether or not major agricultural regions will be able to meet growing food demands, or continue to support agriculture-based livelihoods.
In response there is considerable interest in spatially predicting and quantifying climate change vulnerability in agricultural regions. To date these analyses, such as those of the Intergovernmental Panel on Climate Change, largely predict regional vulnerabilities as a function of either i) changes in abiotic conditions (e.g. climate, soil degradation), or ii) socio-economic conditions (e.g. reliance on and/or adaptability of agricultural economies). While instructive, these approaches have generally not integrated key aspects of plant functional biology into vulnerability predictions.
We used a meta-analytical approach focused on field studies, to compile and derive a spatially explicit, functional trait-based index of crop vulnerability to climate change for maize (Zea mays). We then examined if this trait-based vulnerability index, which is based on leaf- and root traits, corresponds with published climate- or socio-economic-based indices of agricultural vulnerability.
Spatially-explicit coverage of maize functional traits measured under field conditions was patchy, particularly for root traits. For regions where data was available, our analysis detected considerable spatial variation in a trait-based index of agricultural vulnerability to climate change. Trait-based indices of climate change vulnerability were broadly concordant with indices based on climate models and soil degradation, but less strongly with socio-economic indices.
Our results suggest that knowledge of functional traits contributes additional information on crop-specific vulnerability predictions, particularly in relation to changing abiotic conditions. However in finding few data on key root functional traits, our analysis suggests limited information that might mechanistically predict maize responses to changes in precipitation and water availability. Published socio-economic predictions of vulnerability were not closely correlated with trait-based estimates, though further research on how crop functional traits vary in relation to management conditions might refine this discordance. Overall, our study suggests intraspecific variation in crop functional biology across environmental gradients has important applied consequences for understanding and predicting agricultural responses to climate change.