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  • The dataset contains model output from an agricultural land use model at kilometre scale resolution over Great Britain (GB) for four different climate and policy scenarios. Specifically, arable area is modelled for with or without a climate tipping point (standard (medium emissions scenario SRES-A1B) climate change vs Atlantic Meridional Overturning Circulation (AMOC) collapse) and with or without widespread irrigation use for farmers from 2000 to 2089. Full details about this dataset can be found at https://doi.org/10.5285/e1c1dbcf-2f37-429b-af19-a730f98600f6

  • These data comprise apparent densities, species and sex and of mosquitos collected in irrigated and non-irrigated areas in Bura, Tana River County Kenya, between September 2013 and November 2014. Sampling was repeated four times over the period to cover the wet season, dry season, irrigation season and fallow periods. Mosquitoes were trapped using carbon dioxide-baited (CDC) light traps. Mosquitoes harvested from each of these traps were immobilized using 99.5% triethyleamine (Sigma-Aldrich, St. Louis, Missouri) and transferred to distinct bar-coded centrifuge tubes or cryogenic vials. The samples were transported in liquid nitrogen to the entomology section of Arbovirus/Viral haemorrhagic fever (VHF) laboratory at the Kenya Medical Research Institute (KEMRI) where they were sorted by species, sex, village, collection date and counted. The study was implemented to assess the impact of land use change (specifically the conversion of pastoral rangeland into crop land) on the suitability of the habitats to mosquito development and colonization. It also aimed to identify relative abundance of mosquitoes associated with Rift Valley fever virus transmission. The data were collected and analysed by experienced researchers from the International Centre of Insect Physiology and Ecology (Kenya), the International Livestock Research Institute (Kenya) and the Kenya Medical Research Institute. This dataset is part of a wider research project, the Dynamic Drivers of Disease in Africa Consortium (DDDAC). The research was funded by NERC project no NE-J001570-1 with support from the Ecosystem Services for Poverty Alleviation Programme (ESPA). Additional funding was provided by the Consultative Group on International Agricultural Research (CGIAR) Program Agriculture for Nutrition and Health. Full details about this dataset can be found at https://doi.org/10.5285/813f99c4-d07a-42dc-993a-1c35df9f028e

  • The data comprises of two datasets. The first consists of text files of anonymised transcripts from focus group discussions (FGDs) on livelihood activities, ecosystem services and the prevalent human and animal health problems in irrigated and non-irrigated areas in northeastern Kenya. The second comprises of scores from proportional piling exercises which showed the distribution of wealth categories and livestock species kept. The study was conducted between August and October, 2013 and the data were collected as open-ended meeting notes and audio clips captured using digital recorders. Written/thumb print consent was always obtained from each individual in the group. All the discussions were also recorded, with the participant's permission. Thirteen FGDs were held in the irrigated areas in Bura and Hola, Tana River County involving farmers who grew a variety of crops for subsistence and commercial purposes. The others were held in Ijara and Sangailu, Garissa County inhabited by transhumance pastoralists. Each group comprised of 10 to 12 people and the discussions were guided by a check list. The transcribed documents were formatted in Microsoft Word (2013) and saved as text files in preparation for analysis. The aim of the study was to collate perceptions of land use change and their effects on ecosystem services. The data were collected by enumerators trained by experienced researchers from the University of Nairobi and the International Livestock Research Institute (Kenya). This dataset is part of a wider research project, the Dynamic Drivers of Disease in Africa Consortium (DDDAC). The research was funded by NERC project NE-J001570-1 with support from the Ecosystem Services for Poverty Alleviation Programme (ESPA). Additional funding was provided by the CGIAR Research Program Agriculture for Nutrition and Health. Full details about this dataset can be found at https://doi.org/10.5285/4f569d73-30c5-4b12-bca7-8901fb567594