Project Highlights:

  • Understanding climate change impacts on agricultural ecosystems in sub Saharan Africa
  • Predicting climate impacts on food security in sub Saharan Africa
  • Data assimilation as a cutting edge technology for data exploitation

Climate change is expected to impact African water and food security, sustainable development, and economic sustainability (ISS, 2010). Already, climate change is affecting people, their livelihoods and cultures on the African continent (IPCC, 2001). Subsistence agriculture, which is frequently rain fed in large parts of Africa, will be potentially very severely impacted by climate change.  Alexandratos et al. (2012) predict that anticipated climate variability in sub-Sahara Africa would adversely affect agriculture, e.g. by reducing cereal crop yields between 20%-30% by the end of 2050. This would pose significant food security, employment and economic challenge to poorer nations in sub-Sahara Africa (UNDP, 2007). Furthermore, the Inter-governmental Panel on Climate Change (IPCC, 2007) explicitly stated in its fourth assessment report that sub-Sahara Africa’s vulnerability is as a results of its high dependence on agriculture. According to the international labour organization (ILO, 2011), over 60% of all employment in sub- Saharan Africa depend on agriculture, which has become extremely vulnerable to climate change. Furthermore sub-Sahara Africa’s vulnerability has been exacerbated by extreme poverty and inequality (Brooks, 2003 and Boko et al., 2007). There is therefore an urgent need to explore climate change impacts in more detail to facilitate the development of effective adaptations. Past work in the research group has considered precipitation patterns as well as land cover change in West Africa based on remotely sensed data (Figure 1). We now want to extend the work to modelling approaches. Recent improvements in remote sensing data sets have resulted in a range of different data at 1km resolution (e.g. soil, vegetation, surface temperature). This offers the opportunity to simulate land surface processes at a scale that becomes relevant for agriculture. 

Research questions:

  • Does the assimilation of medium resolution data (1km) remote sensing data into a land surface model provide useful information in West Africa?
  • What is the impact of climate variability and change in West Africa on subsistence agriculture?
  • What is the impact of future climate change in West Africa on subsistence agriculture?
Spatial distribution slopes of the residuals of regressions of NDVI against time (a) from the RESTREND analysis using soil moisture and (b) areas with significant negative trends (95% confidence) (From Ibrahim et al., 2015)


The methods will be centred around simulations with and data assimilation into the JULES, the joint UK land environment simulator, version 4.6, which includes a crop growth model. The work will explore using a range of different data at 1km resolution (e.g. soil, vegetation, surface temperature). First past impacts of climate variability will be explored. Using data from the past decades and data assimilation the model will be calibrated for West Africa. This then allows to explore the impact of future climate change using simulated climate for the next decades.    Results will be written up for publication in scientific journals.

Training and Skills

CENTA students are required to complete 45 days training throughout their PhD including a 10 day placement. In the first year, students will be trained as a single cohort on environmental science, research methods and core skills. Throughout the PhD, training will progress from core skills sets to master classes specific to CENTA research themes.  Further training will be provided in programming, computer skills and data analysis. For data processing training will be given on a data processing chain, in particular training on the use of linux systems, compilers and the ferret and R software packages.


Year 1 Training in site maintenance, software usage and development, attending workshops on the eddy covariance method. Initial data analysis and evaluation of GHG fluxes. Initial writing and upgrade.

Year 2: Continued station maintenance, workshops, data analysis and evaluation of management and climate impacts on GHG fluxes, conference attendance, preparation of manuscript for journal submission. Continued development of thesis chapters.

Year 3: Continued station maintenance, data analysis, manuscript submission and revision, conference attendance, thesis preparation.

Partners and collaboration (including CASE)

Dr Jörg Kaduk has 25 years experience in carbon cycle and ecosystem modelling with particular focus on ecophysiology and has supervised 7PhD students to completion. Dr Darren Ghent has 10 years experience in interpretation and application of remote sensing data and data assimilation and has supervised Master students.

Further Details

The student will join a vibrant and diverse research students community of the Centre for Landscape and Climate Research (CLCR) and the department of Geography working on different aspects of global change.

Please contact:

Jörg Kaduk

Department of Geography

University of Leicester

University Road

Leicester, LE1 7RH

Jk61@le.ac.uk, 0116 252 3848