Article 1 Assessing Vulnerability of Maasai Pastoralist in 2 Kenya to Climate Change and Variability 3 Ayodotun Bobadoye *, William Ogara, Gilbert Ouma and Joshua Onono 4 1 Institute for climate change and adaptation, University of Nairobi, Nairobi, Kenya; wogara@gmail.com 5 (W.O.); gouma@uonbi.ac.ke (G.O.); jshonono@gmail.com (J.O.) 6 * Correspondence: bobadoyed@gmail.com; 7 Abstract: Human adaptive responses to climate change occur at the local level, where climatic 8 variability is experienced. Therefore analyzing vulnerability at the local level is important in 9 planning effective adaptation options in a semi‐arid environment. This study was conducted to 10 assess vulnerability of Maasai pastoralist communities in Kajiado County, Kenya to climate change 11 by generating vulnerability index for the communities. Data was collected using questionnaires that 12 were administered to 305 households in the five different administrative wards 13 (Oloosirkon/Sholinke, Kitengela, Kapetui North, Kenyawa‐Poka and Ilmaroro) in Kajiado East. 14 Vulnerability was measured as the net effect of adaptive capacity, sensitivity and exposure to 15 climate change. Principal Component Analysis (PCA) was used to assign weights to the 16 vulnerability indicators used for the study and also to calculate the household vulnerability index. 17 A vulnerability map was produced using the GIS software package ArcGIS 10.2. Results showed 18 that gender of household head, age of household head, educational level, access to extension agents, 19 herd size, livestock diversity and access to credit facility influenced vulnerability of the Maasai 20 pastoralists to climate change in Kajiado East. The result showed that the most vulnerable 21 communities with the highest negative vulnerability index value are Ilpolosat (‐2.31), Oloosirikon 22 (‐2.22), Lenihani (‐2.05), Konza (‐1.81) and Oloshaiki (‐1.53). The communities with the highest 23 positive vulnerability index values were Kekayaya (4.02), Kepiro (3.47), Omoyi (2.81), Esilanke 24 (2.23), Kisaju (2.16) and Olmerui (2.15). We conclude that provision of basic amenities such as good 25 roads and electricity; access to extension agents, access to credit facilities and herd mobility will 26 reduce vulnerability of Maasai pastoralists in Kajiado east to climate change and variability 27 Keywords: vulnerability index, Maasai pastoralists, principal component analysis, climate change 28 29 1. Introduction 30 Many studies have been conducted on vulnerability to climate change and its extremes and 31 different researchers have defined vulnerability according to their own perception. Adger (1999) 32 defined vulnerability to climate change as “the extent to which a natural or social system is 33 susceptible to sustaining the damage from climate change”. IPCC (2014) defines vulnerability to 34 climate change as “the degree of system susceptibility and its inability to cope with adverse effect of 35 climate change and variability. Therefore vulnerability is a function of character, magnitude and rate 36 of climate change and variability to which a system is exposed to. This also includes its sensitivity 37 and adaptive capacity to climate change and variability”. 38 The concept and definition of vulnerability that has been used by different studies revolves 39 around the explanation of lack of adaptive capacity in both social and natural system. Climate 40 change vulnerability has been studied by different scholars as a composite of adaptive capacity, 41 sensitivity and exposure to hazard (Adger and Kelly 1999; Paavola 2008; Yuga et al., 2010; Deressa, 42 2010; Acheampong et al., 2014). Adaptive capacity can be defined as the ability to withstand or adjust 43 to the changing context; it is the ability to implement adaptation measures that help avert potential 44 impacts of climate change and variability (Opiyo, 2014; Acheampong et al., 2014). Sensitivity can be 45 defined as the ability of a system to be affected by climate change and its extremes; it describes 46 Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 18 December 2017 doi:10.20944/preprints201712.0127.v1 © 2017 by the author(s). Distributed under a Creative Commons CC BY license.