The influence of climate change on the occurrence of crop pests and diseases
Supervisors: Andy Challinor a.j.challinor(at)leeds.ac.uk | Andy Jarvis a.jarvis(at)cgiar.org
Introduction
Climate-related variability in agricultural production affects billions of lives worldwide. Models exist that simulate the response of crops to climate variability and change by using direct climate model output (i.e. at the regional scale, ~100km). Climate is known to be a strong determinant of the yield of a number of crops, 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 established capacity in crop-climate modelling with a recently-developed set of tools that simulate the occurrence of crop disease and/or pests. The project will be amongst the first to develop and evaluate process-based modelling suites that assess both the direct and intermediate drivers of regional-scale crop yield, whilst taking full account of associated uncertainty. The project will also examine the efficacy of climate analogue techniques in translating regional-scale climate predictions into farmer-relevant information on likely future biotic and abiotic stresses. 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. The project is linked to the CGIAR programme on Climate Change, Agriculture and Food Security (CCAFS), giving the student will access to a broad range of expertise, data and models.
Objectives
- Use data to identify those specific regions, crops and biotic stresses that are most appropriate for model use and development
- Develop a modelling system capable of assessing both biotic and crop abiotic stresses.
- 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 and biotic stresses?
- What are the implications of these relationships for the predictability of yield at the regional scale?
- How do regional-scale processes relate to the conditions experienced by crops in the field?
Methods
The project will use and modify the GLAM crop model (figure 1), which was designed for use with large-scale gridded meteorological data, such as that produced by weather and climate models. GLAM has been used with climate model output for a range of crops in tropical and extra-tropical environments. A number of pest and disease models are available, for example the potato moth model of CIP, the CGIAR centre that leads on potato research. Once an integrated forecast system has been developed, it will be used to assess the efficacy of adaptation options.
- 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.
Collaboration
The student will be part of a growing team of interdisciplinary scientists in ICAS, led by Prof. Challinor. CASE support is provided by CIAT (the International Centre for Tropical Agriculture), which lead the CCAFS programme.
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.