Modelling the impacts of climate variability and change on tropical crop yields

The provision of food for an increasing global population presents huge challenges to governments worldwide. Much of the world's population rely for their food supply on annual crops grown in the tropics under rainfed conditions. The productivity of crops in these regions is highly vulnerable to inter-annual and sub-seasonal climate variability. Moreover, productivity forecasts to aid planning of food supplies in these countries are very difficult. Climate change adds further uncertainty, with changes in mean climate and variability expected to have non-linear impacts on crop yields. Forecasting how risks to food production in the tropics may change over the coming decades thus presents a major research challenge. Understanding the impact of climate variability and change on crop yields is fundamental to the success of such research. It is also an essential step towards the development of key adaptive strategies to cope with climate change.

A key issue in modelling crop production over large areas is dealing with the disparity in spatial scale between the crop model and the climate prediction model. 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, but 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 aims to combine the benefits of more empirical modelling methods with low input data requirements and validity over large areas, with the benefits of a process-based approach (the potential to capture variability due to different sub-seasonal weather patterns and any previously unobserved weather conditions). This means that the model is more likely to produce valid results under climate change than the pragmatic empirical models currently used in forecasts of seasonal productivity.

GLAM has been used with a variety of input data to simulate crop yields over India. Observed gridded data on a 2.5 by 2.5 degree grid for the period 1966 to 1989 were used as the first test of the model. The optimal crop-specific model parameters were within observed reported ranges and were also stable over space and time, implying that crop growth and development were simulated realistically. The model accurately reproduced yields over large areas where there was a climate signal in the observed yields. The upscaled all-India yields matched very well the yields recorded in national yields statistics. This simulation provides confidence that the GLAM system is able to capture the sensitivity of crop productivity to climate over a long time series.

Accurate productivity forecasts will rely not only on crop simulation, but on the quality of the input weather data. Climate model output is unlikely to be as accurate as observations. Reanalysis data is output from General Circulation Models (GCMs) which have had observations assimilated into the analysis. Hence reanalysis data are an ideal testbed for a combined forecasting systems such as this. A study using GLAM with reanalysis data has shown that, where there is a climate signal, GCM output can result in accurate simulation of the relationship between weather and yield, as well as accurate simulation of yield itself. Whilst the issue of GCM skill in representing the mean climate and its variability remains, it is encouraging to note that gridded model output can be used with some success. This is a particularly pertinent point when one considers that rainfall is the least reliable reanalysis output, whilst often being the most important weather variable for the simulation of crops and vegetation.

In this presentation, the rationale behind our modelling approach is presented, together with a brief description of the GLAM crop model, and key results from the two studies described above.