From the tea estates of Malawi… to those of North-East India

EXCHANGE VISIT #1: JENNY GOES TO JORHAT

By Jenny Willbourn
MSc Applied GIS and Remote Sensing
2013-2014 University of Southampton

In 2008 I wanted an adventure, I ended up in the middle of nowhere, forty minutes walk from a road. I was on my gap year in Mulanje, southern Malawi where I lived on a tea plantation and taught at the local school for 8 months. Little did I know at the time that tea would shape my time in academia. Three years later I jumped at an opportunity to go back to Malawi and secured my place as Lujeri Tea Estates summer GIS consultant  – a job that ultimately lead me to the University of Southampton to study GIS and RS.

Figure 1 – A view of Mulanje tea, tropical forest and Mt Mulanje.

A view of Mulanje tea, tropical forest and Mt Mulanje.

I extended my stay in Mulanje to collect data for my dissertation which questioned to what extent people’s personal accounts can be combined with climatic change data and to what extent the datasets are looking at the same thing. I concluded that the local Mulanje people, whose lives were intricately tied to the wellbeing of the tea estates and to success of local subsistence agricultural, were certain that climate change was occurring and negatively affecting their livelihoods. However, the climatic data, in general, did not support the views of the local people and hence many of the increasing struggles they were facing were as a result of socio-economic changes, not exclusively climate.

Two years later I was invited to participate in UKIERI exchange programme for a project on climate-smarting tea in Assam, India. This was rather serendipitous as no one was aware of my tea background. I again jumped at the opportunity and assisted in the collection of GPS ground control points across several tea plantations in Assam.

Figure 2 – Assam tea and shade trees. The vines on the shade trees produce black pepper corns.

Assam tea and shade trees. The vines on the shade trees produce black pepper corns.

It was my first trip to India and needless to say it was fascinating – not only from a socio-cultural point of view, but being able to compare the tea management systems between Mulanje and Assam. The tea grown in Mulanje has a smaller leaf and plucking table (the height the bush is pruned to), it also tended to be very well maintained and very closely planted. Importantly much of it is irrigated. In contrast, the tea in Assam is slightly larger, higher and not all the tea estates have the resources to maintain the bushes so well. There is no irrigation. It is important to note, however, that the scale of production in Assam is vast compared to Mulanje. The other visible difference is that the majority of tea in Assam is grown in shade under trees to reduce the ambient temperature, but not in Mulanje. Both regions rely on low-skilled low-paid workers for plucking the tea but both are now looking at mechanisation, although in very different ways. If (or when) mechanisation takes off this will significantly alter the socio-economic relationships between tea plantations and the local people.

The climate setting is also vastly different. Tea, actually a tree not a bush, originated from the Assam region where it is now grown on the huge flat flood plain of the Brahmaputra river, whereas tea was introduced to Mulanje in the colonial period and is able to grow due to Mount Mulanje, a 3000m granite inselberg, that increases the rainfall and lowers the temperature of the local area. It is currently unclear how tea will respond to future climatic changes and how this will affect the people who rely on it.

Figure 3 – An individual tea bush. The top bright green leaves, normally two and a bud, are plucked to produce tea.

An individual tea bush. The top bright green leaves, normally two and a bud, are plucked to produce tea.

During my time in India I stayed in the Tea Research Institute of India and having also met with the Tea Research Association in Malawi in 2012, I was quite surprised to learn that there is not greater collaboration between the various tea research institutions across the world. Sadly, some of these institutions do not have the funding and resources they need, which highlights the importance of cross-country/cross-institutional research such as the UKIERI project.

I also needed to tie in my time in Assam with my MSc research project. Aware of the sensitivity of tea to climate and keen to keep my project quantitative I was drawn towards to the emerging concept of physical water productivity – the amount of yield produced per unit of water consumed. In the context of a changing climate, this concept allows us to assess:

  • Where tea is grown with the least water consumption;
  • What the management practises in high water productivity areas are;
  • What locations have scope for improvements;
  • What locations are most at risk in the future.

This is particularly important as water resources are likely to be under increasing strain as the global temperature rises, precipitation patterns and quantities change, and the world population continues to expand therefore increasing demand.

Based on the work of Zwart et al, 2010 I utilised and adapted the WATPRO model which negates the need for a vast number of agro-climatic variables normally needed for water productivity modelling and replaces them with inputs available from satellite remote sensing data. The aim was to create an average water productivity baseline from which changes can be measured in the future in Mulanje and Assam.

Figure 4 – Mulanje tea estates estimated water productivity baseline 2002-2012.

Mulanje tea estates estimated water productivity (WP) baseline 2002-2012.

I am currently in the stage of processing the results and it is clear that the model has produced sensible results for Mulanje, although it has most likely a slight underestimation. It is also clear that the management of tea in Assam (the use of shade trees in particular) has significantly reduced the viability of the model in Assam. Although there is not yet full validation data for model work such as this, such models are likely to play an increasingly important role in agri-management especially in locations where money, time, knowledge and facilities limit what is known about the tea crop. In the long-term this increases the potential for precision agricultural to ultimately produce higher yields with less inputs.

I’m not sure where my next adventure will take me, but the betting is definitely on somewhere that grows tea!

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