The impact of extreme temperatures on crop yield: observations and modelling

During critical phases in the development of a crop, such as near flowering, the transgression of high temperature thresholds can reduce the number of seeds or grains that subsequently contribute to crop yield. This impact is independent of seasonal mean temperature. A few days of high temperature are sufficient to have a potentially large impact on yield. Under future climates, such high temperature episodes may occur with increasing frequency, making the quantitative assessment of the impact on yield an important research topic. In this presentation, observations of this effect are briefly reviewed, and subsequent modelling work is described.

The potential importance of this effect in future climates suggests that it should be investigated using climate prediction models to provide input weather data. A key issue in the use of climate model data with crop models is dealing with the disparity in spatial scale between these models. Crop models are generally designed to operate at the field level, and they rely on detailed field-scale inputs, such as the soil, plant genotype and weather, to predict yield and other crop variables at that scale. In contrast, climate prediction models have a much coarser resolution, typically from tens (in regional models) to hundreds of kilometres. These disparities need to be resolved in order for a coupled crop / climate modelling system to produce realistic simulations of crop yield. A common approach is to adopt some form of downscaling of the climate. However, this assumes stationarity in the statistics of the climate (and weather variability) which may not be appropriate for a changing climate.

Our group in Reading has been developing a new approach to combined crop and climate forecasting. This modelling system couples crop simulation and numerical weather models on a common spatial scale based on observed weather / yield relationships. The development of a new crop model, the General Large Area Model for annual crops (GLAM), is a key part of this work, and it has enabled simulation of crop yield on a regional scale using output directly from climate models and gridded observed weather data.

GLAM is ideal for the study of the impacts of high temperature episodes on crop yields since it is process-based, has a relatively low input data requirement, and is able to run on spatial scales commensurate with the resolution of climate models. The results presented here, using GLAM with current and future climate simulations in India, show that the occurrence of high temperature episodes is likely to increase, and that this could have a serious impact on yield in some regions.