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Assessing trade-offs and synergies in climate-smart agriculture across timescales

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  • Full or part time
    Prof Andy Challinor
    Dr S Whitfield
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

Project Description

Agriculture is at the interface between sustainability and development. On one hand, roughly half of the ice-free area suitable for farming is already in use by either crops or livestock; global freshwater withdrawals for agriculture are ca. 70 % of global freshwater withdrawals; and agricultural activities are responsible for roughly 25 % of global greenhouse gas (GHG) emissions –thus contributing significantly to climate change (Foley et al. 2011). On the other hand, future projections of increasing population, in conjunction with increasing demand for meat and dairy and calorie-intensive products mean that either global demand needs to alter trajectory, or global agricultural production has to increase substantially during the 21st century (Bajželj et al. 2014). Whilst some increase in food production may occur through agricultural expansion, this option is limited by available land, so that significant increases will have to come from increasing productivity (Foley et al. 2011).

The Paris Agreement specifies that countries should enhance the implementation of the UNFCCC by “holding the increase in the global average temperature to well below 2 ºC above pre-industrial levels and to pursue efforts to limit the temperature increase to 1.5 ºC above pre-industrial levels” (UN, 2015). This implies that the challenge of producing more food needs to be met together with a substantial reduction in current GHG emissions levels. At the same time, agriculture’s reliance on favourable weather makes it vulnerable to climate variability and change, with consequences on human health through food quality and availability (Challinor et al. 2014; Rippke et al. 2016).

Climate-smart agriculture (CSA) has been proposed as an approach for agricultural development under climate change. CSA is defined as “agriculture that sustainably increases productivity, enhances resilience (adaptation), reduces or removes GHGs where possible (mitigation), and enhances the achievement of national food security and development goals”

The PhD project
The aim of this project is to use models and data to generate evidence for CSA, focusing on quantifying trade-offs and synergies between the three CSA pillars (resilience, mitigation, and productivity) for select CSA practices. Work will focus on case studies of cropping strategies that have been much heralded for their CSA potential and where existing datasets would allow for process-based modelling of emissions, productivity and resilience. The project will involve the innovative development and combination of models in order to evaluate the multiple objectives of CSA strategies in an integrated way. This modelling innovation will have wide application in the evaluation of CSA systems globally.

The project will focus on two CSA practices, intermittent irrigation or Alternate Wetting and Drying (AWD) and Conservation Agriculture as they are, together with agroforestry, two of the most widely promoted practices under the Climate-Smart Agriculture context (Campbell et al. 2016).

Funding Notes

Funding for this programme is linked with The 3 years funding award will pay UK/EU/International tuition fees and UK research level stipend (£14,296 for 2016/17).


Arslan, A., McCarthy, N., Lipper, L., Asfaw, S., Cattaneo, A., Kokwe, M., 2015. Climate Smart Agriculture? Assessing the Adaptation Implications in Zambia. J. Agric. Econ. 66, 753–780.

Bajželj, B. et al. 2014. The importance of food demand management for climate mitigation. Nature Climate Change 4, 924–929.

Campbell, B.M., Vermeulen, S.J., Aggarwal, P.K., Corner-Dolloff, C., Girvetz, E., Loboguerrero, A.M., Ramirez-Villegas, J., Rosenstock, T., Sebastian, L., Thornton, P., Wollenberg, E., 2016. Reducing risks to food security from climate change. Glob. Food Sec. 1–10.

Challinor, A.J., Watson, J., Lobell, D.B., Howden, S.M., Smith, D.R., Chhetri, N., 2014. A meta-analysis of crop yield under climate change and adaptation. Nat. Clim. Chang. 4, 287–291.

Chirinda, N., Kracher, D. Lægdsmand, M., Porter, J.R., Olesen, J.E., Petersen, B. M., Doltra, J., Kiese, R., Butterbach-Bahl, K. (2011). Simulating soil N2O and CO2 emissions from arable cropping systems using FASSET and MOBILE-DNDC. Plant and Soil 343, 139-160.

Corbeels, M., de Graaff, J., Ndah, T.H., et al. 2014. Understanding the impact and adoption of conservation agriculture in Africa: A multi-scale analysis. Agric. Ecosyst. Environ. 187, 155–170.

Foley, J.A., Ramankutty, N., Brauman, K.A., et al. 2011. Solutions for a cultivated planet. Nature 478, 337–342.

Li, C., Frolking, S., Frolking, T.A. 1992. A model of nitrous oxide evolution from soil driven by rainfall events, 1, Model structure and sensitivity, J. Geophys. Res., 97, 9759-9776,

Lipper, L., Thornton, P., Campbell, B.M., et al. 2014. Climate-smart agriculture for food security. Nat. Clim. Chang. 4, 1068–1072.

Rippke, U., Ramirez-Villegas, J., Jarvis, A., Vermeulen, S.J., Parker, L., Mer, F., Diekkrüger, B., Challinor, A.J., Howden, M., 2016. Timescales of transformational climate change adaptation in sub-Saharan African agriculture. Nat. Clim. Chang. 6, 605–609.

Ramirez-Villegas, J., Challinor, A.J., 2016. Towards a genotypic adaptation strategy for Indian groundnut cultivation using an ensemble of crop simulations. Clim. Change 1–16. doi: 10.1007/s10584-016-1717-y

United Nations. 2015. Paris Agreement. United Nations Treaty Collection. 2016-04-22. Available at:

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