Using global and regional crop models to inform adaptation
Lead Research Organisation:
University of Leeds
Department Name: School of Earth and Environment
Abstract
The global imperative is to enable adaptation to changes in the world's climates if we are to succeed in delivering global food security. One of the challenges of adaptation is being able to know in advance and with some degree of certainty what the effects of climate change will be on our ability to grow crops. Crop modelling is an established scientific method for predicting the impacts of climate on food production. Predictions are made for general or specific contexts on either global or regional scales. The uncertainty inherent in the information produced by predictive models is a contributing barrier to the use of this information in adaptation planning. This CASE studentship will explore this problem by looking at how scaling in impact modelling affects the meaningful use of impact assessments for adaption planning.Existing stakeholder networks will be used to ensure that the regional-scale assessments are useful. The project will also reduce the uncertainty in global scale assessments of crop yield, thus providing more robust evidence for policy makers.
Crop models will be used to make directly-comparable assessments of impacts at both regional and global scales. This studentship will explore how regional and global studies might be used synergistically to develop regional adaptation options and deliver global-scale assessment of impacts and adaptation. The underlying research questions for this aim are:
1. What is the value of the greater detail used in regional impacts studies for the skill of a given impacts model?
2. What are the potential synergies between global and regional-scale impacts models? The student will determine, for example, whether the value of regional models is primarily through model calibration techniques or improved data quality.
Crop models will be used to make directly-comparable assessments of impacts at both regional and global scales. This studentship will explore how regional and global studies might be used synergistically to develop regional adaptation options and deliver global-scale assessment of impacts and adaptation. The underlying research questions for this aim are:
1. What is the value of the greater detail used in regional impacts studies for the skill of a given impacts model?
2. What are the potential synergies between global and regional-scale impacts models? The student will determine, for example, whether the value of regional models is primarily through model calibration techniques or improved data quality.
| Description | Plants experience heat stress when exposed to high temperatures. High temperature events have caused shocks to food production at local, regional and global scales, and the frequency and magnitude of these shocks is expected to increase with global heating. Plants regulate their temperature through evapotranspirative cooling, which is controlled by characteristics such as stomatal conductance and the availability of water. Water availability depends on how farmers manage their fields and how regional planners allocate water resources. How farmers and regional planners adapt to climate change is intricately linked to water resources. This project demonstrates that evapotranspirative cooling is an important heat avoidance mechanism across spatial scales. At the plant scale, the results of this project show that enhanced evapotranspirative cooling can confer heat tolerance and that the mechanisms through which heat tolerant plants cool more may be common to bean and wheat plants. At the field scale, evapotranspirative cooling is shown to be a dominant process in modelling the trade-off between saving water and resilience to heat stress in one of the world's most important rice farming systems. This project uses a state of the art rice model to demonstrate that inclusion of transpirational cooling is a crucial source of uncertainty in modelling farmer adaptation to the combination of water scarcity and global heating. At the regional scale, evapotranspirative cooling from widespread irrigation is shown to reduce the frequency and maximum duration of damaging heatwaves. Despite being an important means of adaptation to high temperatures, modelling of evapotranspirative cooling remains one of the largest sources of uncertainty in multi-scale assessments of heat stress in the future. In this project, an empirical model of evapotranspirative cooling was developed using machine learning techniques trained on big data sets of plant thermal regulation. If the high model performance achieved in this project can be replicated across plant types, then it may be possible to significantly reduce uncertainty in crop model based assessments of multi-scale interactions between water management adaptations and heat stress in future climates. |
| Exploitation Route | Crop breeders could use the results of this project to help them to select and breed for heat tolerant crops. Crop modellers could use the results of this project to improve simulation of evapotranspirative cooling in their models. |
| Sectors | Agriculture, Food and Drink,Environment |