Russell, Graham

Wilson, Ron

Matthews, Keith

Rivington, Michael

2011-09-07T12:37:35Z

2011-09-07T12:37:35Z

2011-06-28

This Thesis explored a range of approaches to study the uncertainty and impacts associated with climate change at the farm scale in Scotland. The research objective was to use a process of uncertainty evaluation and simulation modelling to provide evidence of how primary production components of agriculture in Scotland may change under a future climate. The work used a generic Integrated Modelling Framework to structure the following sequence of investigations: Evaluate a Regional Climate Model‟s hindcast estimates (1960-1990) against observed weather data; Develop bias correction „downscaling factors‟ to be applied to the Regional Climate Model‟s future estimates; Evaluate the impacts of weather data sources (observed and modelled) on estimates made by a cropping systems model (CropSyst); Estimate values for a range of agro-meteorological metrics using observed and estimated downscaled future weather data; Simulate spring barley and winter wheat growth using CropSyst with observed and modelled weather data; Develop CropSyst in order to represent grass growth, evaluate estimates against a set of a priori criteria and determine suitability for use in a whole farm model. Conduct counter-factual assessments of the impacts of climate change and potential adaptation options using a whole farm model (LADSS). The study aimed to use tools on a spectrum of land use modelling complexity: agro-meteorological metrics (simple), CropSyst (intermediate), and the whole-farm integrated model (complex). Such an approach had a path dependency, in that to use the livestock system model component within the whole farm model, CropSyst had to make estimates of an acceptable quality for grass production. CropSyst however failed to meet the a priori evaluation criteria. This, coupled with technical and time constraints in running LADSS, led to the decision not to run the whole farm model. The findings were organised within the concepts of resilience and adaptive capacity. Results gained showed that the HadRM3 Regional Climate Model was capable of making both good and poor estimates of weather variables in the UK, and that downscaling improved the match between hindcast and observed weather data significantly. A sensitivity analysis involving introducing uncertainty from weather data sources within CropSyst showed that care was needed in interpreting estimates of future crop production. The agro-meteorological metrics indicated that whilst growing season length increases, the date of end of field capacity does not. The projected changes in crop production will likely be more positive if crop responses to elevated CO2 are considered. However, there will be additional constraints on crop growth due to increases in duration and magnitude of periods of growth limiting soil water deficits. Without adaptation to crop varieties with slower phenological development, yield decreases are seen in spring barley and winter wheat. The thesis concludes, whilst recognising the caveats and limitations of the methods used and the multiple range of external influencing issues, that the biophysical impacts at the farm scale in Scotland are within the boundaries of resilience, given that achievable adaptation options exist and are undertaken. The dynamics of farm scale management will need to adjust to cope with higher levels of water stress, but opportunities will also arise for greater flexibility in land use mixes. Crop yield can increase due to more favourable growing conditions and cultivar adaptations. These conclusions, when placed within the context of climate change impacts and adaptive cycles at a global scale, indicate that agriculture in Scotland has the potential to cope with the impacts but that substantial changes are required in farming practices.

http://hdl.handle.net/1842/5274

en

The University of Edinburgh

The University of Edinburgh. College of Science and Engineering

Rivington, M., Bellocchi, G., Matthews, K.B. and Buchan, K. (2004). An integrated modelling approach to conduct multi-factorial analyses on the impacts of climate change on whole-farm systems. The International Environmental Modelling and Software Society Conference: Complexity and Integrated Resources Management, University of Osnabruck, Germany, 14-17 June 2004.

Rivington, M., Bellocchi, G., Matthews, K.B. and Buchan, K. (2005). Evaluation of three model estimates of solar radiation at 24 UK stations. Agricultural and Forest Meteorology 132, 228-243.

Rivington, M. and Koo, J. (2011). Report on the Meta-Analysis of Crop Modelling for Climate Change and Food Security Survey. Consultative Group on International Agricultural Research / Earth Systems Science Partnership Climate Change, Agriculture and Food Security Challenge Program. http://www.macaulay.ac.uk/climatechange/CC_CCAFS.php http://ccafs.cgiar.org/content/publications http://labs.harvestchoice.org/2011/02/meta-analysis-of-crop-modeling-for-climate-change-and-food-security/

Rivington, M., Matthews, K.B., Bellocchi, G. and Buchan, K. (2006b). Evaluating uncertainty introduced to process-based simulation model estimates by alternative sources of meteorological data. Agricultural Systems 88, 451-471.

Rivington, M., Matthews, K.B., Bellocchi, G., Buchan, K., Stöckle, C.O. and Donatelli, M. (2007). An integrated assessment approach to conduct analyses of climate change impacts on whole-farm systems. Environmental Modelling Software 22, 202-210.

Rivington, M., Matthews, K.B. and Buchan, K. (2002). A comparison of methods for providing solar radiation data to crop models and decision support systems. In: Proceedings of the International Environmental Modelling and Software Society, Lugano, Switzerland, 24-27 June Vol 3, 193-198.

Rivington, M., Matthews, K.B., and Buchan, K., (2003). Quantifying the uncertainty in spatially-explicit land-use model predictions arising from the use of substituted climate data. In: Proc. of MODSIM 2003 Int. Congress on Modelling and Simulation: Integrative modelling of biophysical, social and economic systems for resource management solutions. 14-17 July 2003 Townsville, Australia. Vol. 4, 1528-1533.

Rivington, M., Matthews, K.B., Buchan, K. and Miller, D. (2006a). An integrated assessment approach to investigate options for mitigation and adaptation to climate change at the farm-scale. Proceedings of NJF Seminar 380: Adaptation of crops and cropping systems to climate change. Nordic Association of Agricultural Scientists, Odeense, Denmark, 7-8 November 2005.

Rivington, M., Matthews, K.B., Buchan, K., Miller, D.G. (2009a). The need for better science-stakeholder relations in policy and practice: agro-meteorological metrics as tools for communication and strategic planning. International Association of Research Universities Climate Change Congress: Global Risks, Challenges and Decisions, Copenhagen 10-12th March 2009. Session 37, P37.32.

Rivington, M., Matthews, K.B., Buchan, K., Miller, D., Bellocchi, G. and Russell, G. (2008a). Agro-meteorological metrics for communicating climate change impacts to land managers. Aspects of Applied Biology 88, 85-91.

Rivington, M., Matthews, K.B., Buchan, K., Miller, D. and Russell, G. (2009b). Investigating climate change impacts and adaptation options using integrated assessment methods. Aspects of Applied Biology 93, 85-91.

Rivington, M., Miller, D., Matthews, K.B., Russell, G., Bellocchi, G. and Buchan, K. (2008b). Evaluating Regional Climate Model estimates against site-specific observed data in the UK. Climatic Change, 88, 157-185.

Rivington, M., Miller, D., Matthews, K.B., Russell, G., Bellocchi, G. and Buchan, K. (2008c) Downscaling Regional Climate Model estimates of daily precipitation, temperature and solar radiation data. Climate Research 35, 181-202.

Climate Change

uncertainty

Scotland

modelling

agriculture

resilience

Climate Change

uncertainty

Scotland

modelling

agriculture

resilience

Global Change Research Institute

Climate change uncertainty evaluation, impacts modelling and resilience of farm scale dynamics in Scotland

Thesis or Dissertation

Doctoral

PhD Doctor of Philosophy