Evaluating livelihood vulnerability of farming communities to winter storms in Iowa
Driven by unusually warm air in the Arctic, severe winter weather moves southward to mid-latitude areas, indicating the complexity in the ways that climate change may affect local weather extremes. The vulnerability of farming communities to climate risks and differential response capabilities have...
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| Format: | Text |
| Language: | English |
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UNI ScholarWorks
2019
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| Online Access: | https://scholarworks.uni.edu/etd/1000 https://scholarworks.uni.edu/context/etd/article/2000/viewcontent/yiyi_zhang_thesis.pdf |
| Summary: | Driven by unusually warm air in the Arctic, severe winter weather moves southward to mid-latitude areas, indicating the complexity in the ways that climate change may affect local weather extremes. The vulnerability of farming communities to climate risks and differential response capabilities have drawn much research attention. Winter storms are recognized as one of the common catastrophic events leading to agricultural damage and loss. However, research is notably lacking in understanding the consequences extreme winter weather could bring in farmer livelihood. This study is concerned with the vulnerability patterns of farming communities shaped under varying climate and socio-physical conditions. Focusing on Iowa as a case study, this research determined indicators capable of differentiating households with unequal vulnerability to winter storms based on semi-structured interviews. Spatial analysis was incorporated to quantify spatial information (i.e. winter temperature variation, natural shelter, energy capacity and facility density) subject to data aggregation. Factor analysis was used to investigate the relationships between adaptive capacity indicators. It extracted three underlying factors that could determine adaptive capacity, namely, farming economic status, environmental institutional capital and innovative capital. The exposure, sensitivity, adaptive capacity and overall vulnerability were calculated for each county in Iowa. The output maps demonstrated high vulnerability in Southeast Iowa due to low farming economic status and innovative capital, and high vulnerability in Northwest Iowa due to high exposure and low environmental institutional capital. The limitations in normalization and index development were also addressed and discussed. To understand complex farmer decisions that lead to different outcomes in storm losses, a conceptual agent-based model was constructed in an attempt to examine geographically and temporally, the multiple reasons that drive the decisions and key pathways in the ... |
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