Climate and Atmospheric Science (ICAS) PhD Projects
A combined crop-climate modelling system for seasonal to multi-decadal prediction of yield in India
Supervisors: Dr Andy Challinor and Dr Xavier Rodo (IC3)
Introduction
Climate-related variability in agricultural production affects billions of lives worldwide, both directly and through intermediate drivers such as irrigation water availability and incidence of pests or diseases. Many of the processes associated with these intermediate drivers are understood. However, despite continued calls by the IPCC, climate models have not yet been used to assess the predictability implicit in these relationships. This project combines a unique collection of datasets and crop modelling capability, in order to simulate, for the first time, both the direct and intermediate drivers of regional-scale crop yield, whilst taking full account of associated uncertainty. The understanding and modelling capability gained during the project will underpin assessments of the adaptive capacity of cropping systems to climate variability and change. By focussing on the seasonal-to-multi-decadal timescales, the project will ensure relevance to adaptation efforts and policy.
Objectives
- Develop a model that has the potential to be used both as part of an operational forecasting system and as part of climate change assessments, such as those of the IPCC.
- Assess the threat posed by climate change to wheat production in India over the coming decades
- Determine the efficacy of a range of adaptation options, resulting in a predictive tool that can be used to reduce vulnerability to climate variability and change.
Research questions
- What are the relationships, at the regional scale, between crop yield, climate and intermediate drivers such as water availability and biotic stresses?
- What are the implications of these relationships for the predictability of yield?
- How can a change of crop type to another existing variety, the use of seasonal forecasts, and changes in crop management be used to maximize crop yield in the face of climate change?
Figure 1. Observed (solid line) and simulated (dashed line) groundnut yields for all-India (i.e. total production divided by total growing area), using the GLAM crop model driven by observed weather data on a 2.5° grid (Challinor et al 2004). Observed yields have been linearly detrended to 1966 levels.
Figure 2. The number of years of wheat yield data (left) and the mean monsoon-season sorghum yield (right, kg/ha) in the GIS dataset of the International Crop Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India. The data is on a much higher resolution than more commonly-available datasets such as that of FAOSTAT. The project will test the hypotheses that, on large-scales in India:
Collaboration
The student will be part of a growing team of interdisciplinary scientists across the campus who are examining climate change impacts and adaptation. The studentship will be a CASE award with the Catalan Institute for Climate Sciences (IC3), a new research institute funded by the Catalan Government and established early this year in conjunction with the University of Barcelona. The center will conduct cutting-edge, wide-scoped research in the field of fundamental climate dynamics, ranging from climate diagnosis to the improvement of climate predictions at subseasonal, seasonal to interannual and longer timescales. IC3 will employ around 30 people in late 2009 with a target of 100-120 scientists in 8-10 years time. Francisco Doblas-Reyes of the European Centre for Medium Range Weather Forecasts will also provide input to the project.
Reference
Challinor, A. J., T. R. Wheeler, J. M. Slingo, P. Q. Craufurd and D. I. F. Grimes (2004). Design and optimisation of a large-area process-based model for annual crops. Agricultural and Forest Meteorology, 124, (1-2) 99-120.
Entry requirements/necessary background for students:
The prospective student should have skills in numerical modelling (e.g. FORTRAN) and a background in crop or climate science, or a related discipline such as physics.
Please state clearly the potential for the project to yield 4* outputs:
The project enables quantitative examination of the non-climatic determinants of crop yield, thus helping us to define limits on the predictability of food production in a changing climate. It will also provide a mechanistic look at water use, through examination of the relationships between irrigation, climate and crop yield. These are topics that require significant time investment – i.e. they cannot currently be turned into papers by myself or any of my team.