Tag Archives: Kenya

Tim Daw on ecosystem services tradeoffs

  • In the video below Tim Daw, from the University of East Anglia’s School of International Development and the Stockholm Resilience Centre, explains his project Participatory Modelling of Wellbeing Tradeoffs in Coastal Kenya. The project, in which I’m also participating, has examined tradeoffs between social wellbeing and ecological conservation in small scale fisheries in Kenya using a combination surveys, models, scenarios, and participatory workshops.

For more information on the project is available on the Stockholm Resilience Centre’s website. The project is funded by the UK’s Ecosystem Services and Poverty Alleviation programme. and there is more information on the ESPA website.

For more on poverty and ecosystem service tradeoffs see:

  • Bennett, E.M., Peterson, G.D. & Gordon, L.J. (2009) Understanding relationships among multiple ecosystem services. Ecology letters, 12, 1394–404. DOI: 10.1111/j.1461-0248.2009.01387.x
  • Daw, T., Brown, K., Rosendo, S. & Pomeroy, R. 2011. Applying the ecosystem services concept to poverty alleviation: the need to disaggregate human well-being. Environmental Conservation, 38, 370–379. DOI: 10.1017/S0376892911000506
  • Raudsepp-Hearne, C., Peterson, G.D. & Bennett, E.M. 2010. Ecosystem service bundles for analyzing tradeoffs in diverse landscapes. Proceedings of the National Academy of Sciences of the United States of America, 107, 5242–7. doi: 10.1073/pnas.0907284107

Livelihood landscapes – disentangling occupational diversity for natural resource management

A special contribution from Josh Cinner, from the ARC Centre of Excellence for Coral Reef Studies (see previous posts on his work here and here) and Örjan Bodin from the Stockholm Resilience Centre on their recent paper, Livelihood diversification in tropical coastal communities: a network-based approach to analyzing ‘livelihood landscapes’, which appeared in the August 11, 2010 issue of PLoS ONE, and is available free online.  They write:

In many developing countries, an individual household will often engage in a range of economic sectors, such as fishing, farming, and tourism. These diverse ‘livelihood portfolios’ are thought to help to spread risk and make households more resilient to shocks in a particular sector. Whether and how local people engage in multiple occupations has important implications for how people use and manage natural resources and is of particular relevance to people involved in managing natural resources. But for scientists, donors, and policy makers, unraveling the complexity of livelihoods in developing countries has been extremely challenging.

In our recent paper in PLoS ONE, we developed a novel method for exploring complex household livelihood portfolios.  We used a network-based approach to examine how the role of natural resource-based occupations changes along spectra of socioeconomic development and population density in 27 communities across 5 western Indian Ocean countries (see Fig. 1).

Figure 1. Kenyan livelihood landscape maps at various scales of social organization: a) Shela, Kenya; b) an aggregation of peri-urban sites in Kenya; c) an aggregation of rural sites in Kenya; d) all sites in Kenya.

In Figure 1 the links between occupations are indicated by arrows. The size of a node indicates the relative involvement in that occupational sector (larger node means more people are involved). The direction of the arrows indicates the priority of ranking. Thus an arrow into an occupation indicates that the occupation was ranked lower than the occupation the arrow came from. The thickness of the arrows corresponds to the proportion of households being engaged in the, by themselves, higher ranked occupation that are also engaged in the lower ranked occupation. The proportion of the node that is shaded represents the proportion of people that ranked that occupation as a primary occupation.

We found:

  • an increase in household-level specialization with development for most (but not all) occupational sectors, including fishing and farming, but that at the community-level, economies remained diversified.
  • We also found that households in less developed communities often share a common occupation, whereas that patterns is less pronounced in more developed communities. This may have important implications for how people both perceive and solve conflicts over natural resources.

Finally, our network-based approach to exploring livelihood portfolios can be utilized for many more types of analyses conducted at varying scales, ranging from small villages to states and regions.

Roving bandits, piracy, and fishing

Piracy has been in the news a lot over the past few years.  Less noticed is the impact of’ roving bandit fishing fleets from the rich world that outfish local fisherman.  The associated press reports on a perverse consquence of Somalian piracy Kenya fishermen see upside to pirates: more fish

A report on pirates this year by the S. Rajaratnam School of International Studies in Singapore said the value of illegal catches from Somalia’s maritime jurisdiction is estimated at between $90 million and $300 million a year, and that foreign fishing vessels hail from all around the world.

The report’s author, Clive Schofield, a research fellow with the Australian Centre for Ocean Resources and Security at the University of Wollongong, called it ironic that nations contributing warships to anti-piracy efforts are in some cases directly linked to the foreign fishing vessels “stealing Somalia’s offshore resources.”

“This situation has led some pirates to justify their actions on basis of illegal foreign fishing activities — styling themselves ‘coastguards’ and characterizing ransom demands as ‘fines,’” the report said. “Without condoning acts of violence at sea, it is clear that the Somalis who hijack shipping off their coast are in fact not the only ‘pirates’ operating in these waters,” it said.

Piracy has not had a huge effect on Kenya’s overall fishing industry, which is not very well developed on the coast, according to the permanent secretary for Kenya’s Ministry of Fisheries Development, Micheni Japhet Ntiba. Kenya has brought in between 5,000 and 7,000 metric tons of fish off its Indian Ocean coast each of the last several years, he said, less than a tenth of Kenya’s yearly catch from Lake Victoria, on Kenya’s western edge.

Piracy “is a negative thing for Kenya fisherman. It’s a negative thing for the Kenyan economy. It’s a negative thing for the western Indian Ocean economy,” Ntiba said. “What I think is important for us is to invest in security so the government and the private sector can invest in the deep sea ocean resources.”

Still, Kenya’s sports fisherman say the pirates appear to have had a hugely positive effect on their industry. Angus Paul, whose family owns the Kingfisher sports fishing company, said that over the past season clients on his catch-and-release sports fishing outings averaged 12 or 13 sail fish a day. That compares with two or three in previous years.

Somali pirates, Paul said, are a group of terrorists, “but as long as they can keep the big commercial boats out, not fishing the waters, then it benefits a lot of other smaller people.”

Kenyan elephants send text messages to warn of crop raiding

Kenya’s elephants send text messages to rangers

The text message from the elephant flashed across Richard Lesowapir’s screen: Kimani was heading for neighboring farms.

The huge bull elephant had a long history of raiding villagers’ crops during the harvest, sometimes wiping out six months of income at a time. But this time a mobile phone card inserted in his collar sent rangers a text message. Lesowapir, an armed guard and a driver arrived in a jeep bristling with spotlights to frighten Kimani back into the Ol Pejeta conservancy.

Kenya is the first country to try elephant texting as a way to protect both a growing human population and the wild animals that now have less room to roam. …

The race to save Kimani began two years ago. The Kenya Wildlife Service had already reluctantly shot five elephants from the conservancy who refused to stop crop-raiding, and Kimani was the last of the regular raiders. The Save the Elephants group wanted to see if he could break the habit.

So they placed a mobile phone SIM card in Kimani’s collar, then set up a virtual “geofence” using a global positioning system that mirrored the conservatory’s boundaries. Whenever Kimani approaches the virtual fence, his collar texts rangers.

They have intercepted Kimani 15 times since the project began. Once almost a nightly raider, he last went near a farmer’s field four months ago.

It’s a huge relief to the small farmers who rely on their crops for food and cash for school fees. Basila Mwasu, a 31-year-old mother of two, lives a stone’s throw from the conservancy fence. She and her neighbors used to drum through the night on pots and pans in front of flaming bonfires to try to frighten the elephants away.

…the experiment with Kimani has been a success, and last month another geofence was set up in another part of the country for an elephant known as Mountain Bull. Moses Litoroh, the coordinator of Kenya Wildlife Service’s elephant program, hopes the project might help resolve some of the 1,300 complaints the Service receives every year over crop raiding.

More details are on Save the Elephants founded by Elephant researcher Iain Douglas-Hamilton, and this 2005 article from the BBC, and Youtube.