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  <front>    <journal-meta>
      <journal-title>African Journal of Environmental Science and Technology </journal-title>
      <issn pub-type="epub">1996-0786</issn>      <publisher>
        <publisher-name>Academic Journals</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.5897/AJEST2024.3294</article-id>
      <title-group>
        <article-title><![CDATA[Farmers’ vulnerability to climate variability and extreme climate events in selected agro-ecological zones in Kitui County, Kenya]]></article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author" xlink:type="simple">
        		        	<name name-style="western">
	            <surname>Charles</surname>
            <given-names>K. Ndungu</given-names>
	          </name>	
        		        	<name name-style="western">
	            <surname>Evelyn</surname>
            <given-names>J. Mutunga</given-names>
	          </name>	
        		        	<name name-style="western">
	            <surname>Moses</surname>
            <given-names>Mwangi</given-names>
	          </name>	
        		        	<name name-style="western">
	            <surname>Patrick</surname>
            <given-names>C. Kariuki</given-names>
	          </name>	
        	        </contrib>
      </contrib-group>
      <author-notes>
		<corresp id="cor1">* E-mail: <email xlink:type="simple">evelynjane44@gmail.com</email></corresp>
      </author-notes>
      <pub-date pub-type="collection">
        <year>2025</year>
      </pub-date>
      <pub-date pub-type="epub">
      	<day>28</day>
        <month>02</month>
        <year>2025</year>
      </pub-date>
      <history>
      			<date date-type="received">
			<day>19</day>
			<month>07</month>
			<year>2024</year>
		</date>
						<date date-type="accepted">
			<day>22</day>
			<month>01</month>
			<year>2025</year>
		</date>
			  </history>
      <volume>19</volume>
      <issue>2</issue>
	  	  <fpage>70</fpage>
	  <lpage>88</lpage>
      <permissions>
		<license xlink:type="simple">
			<license-p>
			This is an open-access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
			</license-p>
		</license>
	  </permissions>
	  <self-uri xlink:href="http://politicalwaffle.uk/journal/AJEST/article-abstract/471E58F73020">
		This article is available from http://politicalwaffle.uk/journal/AJEST/article-abstract/471E58F73020	  </self-uri>
	  <self-uri xlink:href="http://politicalwaffle.uk/journal/AJEST/article-full-text-pdf/471E58F73020">
		The full text article is available as a PDF file from http://politicalwaffle.uk/journal/AJEST/article-full-text-pdf/471E58F73020	  </self-uri>
	  
      <abstract><![CDATA[A field survey was conducted in Kitui County, Kenya, to assess farmersrsquo; vulnerability to climate variability and extreme climate events in selected agroecological zones. Primary data was collected through the administration of questionnaires to 341 respondents. Rainfall data for the study was obtained from the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) rainfall dataset while temperature data was obtained from the Climatic Research Unit gridded Time Series (CRU TS) dataset. The study used the indicator approach to calculate the overall household vulnerability index. Principal Component Analysis (PCA) was used to allocate weights to indicators of exposure, sensitivity, and adaptive capacity. Vulnerability results indicated that the arid zone had the highest vulnerability index (17.29) followed by the transitional (1.63) and semi-arid (1.49) zones while the semi-humid zone had the least (-2.65). Additionally, results from one-way Analysis of Variance (ANOVA) indicated a statistically significant difference in the vulnerability index and its componentsrsquo; indices across the four agroecological zones. The study established that adaptive capacity was the most important component in reducing householdsrsquo; vulnerability to climate variability and extreme climate events and can thus be influenced by policy to enhance householdsrsquo; resilience to climatic shocks. Policy measures and climate change adaptation programs should therefore aim at enhancing the adaptive capacity of households. 

	 

	Key words: Vulnerability index, principal component analysis, adaptive capacity, arid and semi-arid lands (ASALs), households.]]></abstract>
    </article-meta>
  </front>
      <body/>
    <back>
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