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	<title>Theileriosis &#8211; BIOENGINEER.ORG</title>
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		<title>AI Framework Aims to Rescue Zimbabwe&#8217;s Cattle Farmers From Climate Ruin</title>
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		<pubDate>Tue, 06 Oct 2026 16:00:35 +0000</pubDate>
				<category><![CDATA[Agriculture]]></category>
		<category><![CDATA[Artificial Intelligence]]></category>
		<category><![CDATA[biometric identification]]></category>
		<category><![CDATA[cattle insurance]]></category>
		<category><![CDATA[climate resilience]]></category>
		<category><![CDATA[communal farming]]></category>
		<category><![CDATA[index-based insurance]]></category>
		<category><![CDATA[logistic regression]]></category>
		<category><![CDATA[Natural Language Processing]]></category>
		<category><![CDATA[NDVI]]></category>
		<category><![CDATA[Smallholder farmers]]></category>
		<category><![CDATA[Theileriosis]]></category>
		<category><![CDATA[Zimbabwe]]></category>
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					<description><![CDATA[A survey of 219 communal cattle farming households in Lupane District, Zimbabwe, identifies affordability, awareness, and institutional trust as the key barriers to insurance adoption and proposes a tri-modular AI framework combining biometric identification, satellite drought triggers, and voice-first local-language interfaces to overcome them.]]></description>
		
		
		
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