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Climate change impacts and adaptation scope for agriculture indicated by agro-meteorological metrics

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  • Rivington, M.
  • Matthews, K.B.
  • Buchan, K.
  • Miller, D.G.
  • Bellocchi, G.
  • Russell, G.

Abstract

Agro-meteorological metrics are indicators of weather determined environmental conditions on which agricultural management decisions are made. Metrics derived from an estimated future climate provide an opportunity to characterise the impacts of climate change on a wide range of agricultural systems, land use practices and ecosystem services. Such indications are vital for determining how changes in the biophysical environment can lead to land management and policy adaptations to achieve multiple objectives of financial viability, food security, biodiversity conservation and environmental sustainability. They provide valuable links between probable management adaptation responses and capacity for mitigating greenhouse gas emissions. However, there are large uncertainties associated with projected future climates, including the climate models’ spatial scale of representation and those at which agro-meteorological metrics are applied. This paper describes the estimation of agro-meteorological metrics derived from observed weather and downscaled Regional Climate Model projection data for 12 sites in Scotland. Results show that projected changes to seasonal rainfall distribution, growing season length, soil moisture deficits and accessibility will be substantially different from the present climate. Fundamentally, the metrics indicate a substantial shift in land management requirements and potential need for substantial changes in agricultural systems and land use that will have implications across a wide range of research disciplines.

Suggested Citation

  • Rivington, M. & Matthews, K.B. & Buchan, K. & Miller, D.G. & Bellocchi, G. & Russell, G., 2013. "Climate change impacts and adaptation scope for agriculture indicated by agro-meteorological metrics," Agricultural Systems, Elsevier, vol. 114(C), pages 15-31.
    • Handle: RePEc:eee:agisys:v:114:y:2013:i:c:p:15-31
    DOI: 10.1016/j.agsy.2012.08.003
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    References listed on IDEAS

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    1. James M. Murphy & David M. H. Sexton & David N. Barnett & Gareth S. Jones & Mark J. Webb & Matthew Collins & David A. Stainforth, 2004. "Quantification of modelling uncertainties in a large ensemble of climate change simulations," Nature, Nature, vol. 430(7001), pages 768-772, August.
    2. Rivington, M. & Matthews, K.B. & Bellocchi, G. & Buchan, K., 2006. "Evaluating uncertainty introduced to process-based simulation model estimates by alternative sources of meteorological data," Agricultural Systems, Elsevier, vol. 88(2-3), pages 451-471, June.
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    Cited by:

    1. Morgese, S. & Casale, F. & Movedi, E. & Confalonieri, R. & Bocchiola, D., 2024. "Modelling the effects of potential climate change on the dynamics of multi-species mountain pastures: A case study in Gran Paradiso National Park, Italy," Agricultural Systems, Elsevier, vol. 217(C).
    2. Islam, AFM Tariqul & Islam, AKM Saiful & Islam, GM Tarekul & Bala, Sujit Kumar & Salehin, Mashfiqus & Choudhury, Apurba Kanti & Dey, Nepal C. & Hossain, Akbar, 2022. "Adaptation strategies to increase water productivity of wheat under changing climate," Agricultural Water Management, Elsevier, vol. 264(C).
    3. Marion Sautier & Roger Martin-Clouaire & Robert Faivre & Michel Duru, 2013. "Assessing climatic exposure of grassland-based livestock systems with seasonal-scale indicators," Climatic Change, Springer, vol. 120(1), pages 341-355, September.
    4. Yakubu Abdul-Salam & Melf-Hinrich Ehlers & Jelte Harnmeijer, 2017. "Anaerobic Digestion of Feedstock Grown on Marginal Land: Break-Even Electricity Prices," Energies, MDPI, vol. 10(9), pages 1-21, September.
    5. A. Harding & M. Rivington & M. Mineter & S. Tett, 2015. "“Agro-meteorological indices and climate model uncertainty over the UK”," Climatic Change, Springer, vol. 128(1), pages 113-126, January.

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