Environmental heterogeneity and commodity sharing in smallholder agroecosystems.

Smallholder farmers undertake a number of strategies to cope with climate shocks in a community. The sharing of resources across households constitutes one coping mechanism when environmental shocks differentially impact households. This paper investigates commodity sharing dynamics among households in eight communities in an environmentally heterogeneous highland-lowland area in central Kenya. We use survey data and meteorological data to test whether commodity sharing, measured at the household level by net inflow of commodities, varies across a regional precipitation gradient, and we reveal how sharing fluctuates with rainfall over the course of a year. We find both precipitation and income to be significant predictors of households' net value of shared commodities. Specifically, farmers who live in drier areas with less income are more likely to receive more commodities than they give. We also find that the length of time a household has been established in the area is significantly related to commodity sharing. Further, commodity sharing follows the pattern of harvest and food storage over the course of the year, with households giving the most commodities at times when food storage levels are higher, that is, post-harvest. The study sheds light on the relationship between commodity sharing as a coping mechanism and environmental heterogeneity in a region prone to seasonal food insecurity.

Tsetse Fly Control in Kenya's Spatially and Temporally Dynamic Control Reservoirs: A Cost Analysis.

Human African trypanosomiasis (HAT) and animal African trypanosomiasis (AAT) are significant health concerns throughout much of sub-Saharan Africa. Funding for tsetse fly control operations has decreased since the 1970s, which has in turn limited the success of campaigns to control the disease vector. To maximize the effectiveness of the limited financial resources available for tsetse control, this study develops and analyzes spatially and temporally dynamic tsetse distribution maps of Glossina subgenus Morsitans populations in Kenya from January 2002 to December 2010, produced using the Tsetse Ecological Distribution Model. These species distribution maps reveal seasonal variations in fly distributions. Such variations allow for the identification of "control reservoirs" where fly distributions are spatially constrained by fluctuations in suitable habitat and tsetse population characteristics. Following identification of the control reservoirs, a tsetse management operation is simulated in the control reservoirs using capital and labor control inputs from previous studies. Finally, a cost analysis, following specific economic guidelines from existing tsetse control analyses, is conducted to calculate the total cost of a nationwide control campaign of the reservoirs compared to the cost of a nationwide campaign conducted at the maximum spatial extent of the fly distributions from January 2002 to December 2010. The total cost of tsetse management within the reservoirs sums to $14,212,647, while the nationwide campaign at the maximum spatial extent amounts to $33,721,516. This savings of $19,508,869 represents the importance of identifying seasonally dynamic control reservoirs when conducting a tsetse management campaign, and, in the process, offers an economical means of fly control and disease management for future program planning.

Climate Change and Risk Projection: Dynamic Spatial Models of Tsetse and African Trypanosomiasis in Kenya.

African trypanosomiasis, otherwise known as sleeping sickness in humans and nagana in animals, is a parasitic protist passed cyclically by the tsetse fly. Despite more than a century of control and eradication efforts, the fly remains widely distributed across Africa and coextensive with other prevalent diseases. Control and planning are hampered by spatially and temporally variant vector distributions, ecologically irrelevant boundaries, and neglect. Tsetse are particularly well suited to move into previously disease-free areas under climate change scenarios, placing unprepared populations at risk. Here we present the modeling framework ATcast, which combines a dynamically downscaled regional climate model with a temporally and spatially dynamic species distribution model to predict tsetse populations over space and time. These modeled results are integrated with Kenyan population data to predict, for the period 2050 to 2059, exposure potential to tsetse and, by association, sleeping sickness and nagana across Kenya.

Population vulnerability and disability in Kenya's tsetse fly habitats.

BACKGROUND: Human African Trypanosomiasis (HAT), also referred to as sleeping sickness, and African Animal Trypanosomaisis (AAT), known as nagana, are highly prevalent parasitic vector-borne diseases in sub-Saharan Africa. Humans acquire trypanosomiasis following the bite of a tsetse fly infected with the protozoa Trypanosoma brucei (T.b.) spp. -i.e., T.b. gambiense in West and Central Africa and T.b. rhodesiense in East and Southern Africa. Over the last decade HAT diagnostic capacity to estimate HAT prevalence has improved in active case-finding areas but enhanced passive surveillance programs are still lacking in much of rural sub-Saharan Africa. METHODOLOGY/PRINCIPAL FINDINGS: This retrospective-cross-sectional study examined the use of national census data (1999) to estimate population vulnerability and disability in Kenya's 7 tsetse belts to assess the potential of HAT-acquired infection in those areas. A multilevel study design estimated the likelihood of disability in individuals, nested within households, nested within tsetse fly habitats of varying levels of poverty. Residents and recent migrants of working age were studied. Tsetse fly's impact on disability was conceptualised via two exposure pathways: directly from the bite of a pathogenic tsetse fly resulting in HAT infection or indirectly, as the potential for AAT takes land out of agricultural production and diseased livestock leads to livestock morbidity and mortality, contributing to nutritional deficiencies and poverty. Tsetse belts that were significantly associated with increased disability prevalence were identified and the direct and indirect exposure pathways were evaluated. CONCLUSIONS/SIGNIFICANCE: Incorporating reports on disability from the national census is a promising surveillance tool that may enhance future HAT surveillance programs in sub-Saharan Africa. The combined burdens of HAT and AAT and the opportunity costs of agricultural production in AAT areas are likely contributors to disability within tsetse-infested areas. Future research will assess changes in the spatial relationships between high tsetse infestation and human disability following the release of the Kenya 2009 census at the local level.