Application of fuzzy models to assess susceptibility to droughts from a socio-economic perspective

Acosta, Lilibeth A.; Kumar, K. S. Kavi; Klein, Richard J.T.; Campe, Sabine

doi:10.1007/s10113-008-0058-4

Cited by 33 publications

(13 citation statements)

References 15 publications

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“…This study Analysed a quantitative Vulnerability score by using an equation which combined three contributing indices of exposure, sensitivity and adaptive capacity each normalized to 0-1 Scale (Adger and Vincent, 2005;Allison et al 2009): Vulnerability= exposure + sensitivity -adaptive capacity For measuring exposure using fuzzy methodology. In assessing inclination to drought the researcher was finding out a) Which indicators were used to quantify and measure this inclination b) How these indicators were interpreted c) How the relationship between different factors for this inclination was described and quantitatively modelled d) How a single numerical index would be computed for comparing this inclination between different villages and social groups This then lead to a four step methodology according to (Acosta-Michlik et al 2008 and Ta¨nzler and Carius (2008) as follows: i) Selection of input variables: Here the researcher used a questionnaire to get data to derive membership functions for different categories of the indicator 'Consequences when drought occurs'. The researcher asked the participants to appraise eight classes of possible consequences of drought.…”

Section: Data Analysis Methodsmentioning

confidence: 99%

“…Exposure the researcher assessed defenselessness to drought. That included getting the indicators to quantify and measure proneness according to (Acosta-Michlik et al 2008;Kro¨mker et al 2008) and Ta¨nzler and Carius (2008). From the questionnaire the researcher got data to derive membership functions for different categories of the indicator of Negative consequences of drought.…”

Section: Data Collection Proceduresmentioning

confidence: 99%

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Vulnerability of Smallholder Farmers to Climate Related Shocks in Kinakomba Ward, Tana River County, Kenya

2020

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According to IPCC, (2007) Vulnerability is the degree to which a system is susceptible to, and unable to cope with, adverse effects of climate change, including climate variability and extremes. It is a function of exposure, sensitivity, and adaptive capacity (Marshall et al. 2010; Wongbusarakum and Loper 2011, Mucke 2012). The study sought to determine the vulnerability to climate related shocks among smallholder farmers in Kinakomba Ward. The main purpose of the study was to assess the extent to which, exposure, sensitivity and adaptive capacity contributed to the vulnerability of the smallholder farmers on their livelihoods. The study was carried out in Kinakomba ward in Tana River County, Kenya. A descriptive survey research design was used. Stratified random sampling was employed to select 390 out of a population of 3,908 households. Data was collected using questionnaires and Focus Group Discussions. Descriptive statistics, metric of sensitivity, composite index was used to analyse sensitivity. Two methods were used to analyse exposure. Firstly the fuzzy logic in assessing susceptibility to drought involving a selection of input variables, Fuzzification, inference modelling and defuzzification and secondly DrinC Model software. The researcher used two methods to analyse adaptive capacity namely the interview with 390 farming households to gather data on farming and household characteristics and natural resources availability and secondly a panel of 15 Key Informants provided ratings of indicators of adaptive capacity using analytic hierarchy process (AHP). To understand the interaction of these variables this study Analysed a quantitative Vulnerability score by using an equation which combined three contributing indices of exposure, sensitivity and adaptive capacity each normalized to 0-1 Scale (Adger and Vincent, 2005;Allison et al. 2009), the vulnerability score was obtained by adding exposure to sensitivity and subtracting the adaptive capacity. The key results from the study showed that exposure (p=0.000066) and sensitivity (P=0.00038) had a significant effect on livelihoods. These factors were also found to have a negative influence on livelihoods in the area. Further statistical findings showed that as adaptive capacity increased vulnerability decreased while when sensitivity increased vulnerability increased at the same time. The study concluded that these two dimensions of vulnerability could be modified by policy and development. The study also concluded that exposure contributed more than sensitivity and adaptive capacity to the cumulative vulnerability. The study recommended that the County and National governments and stakeholders to employ measures to adapt to climate change and variability. This study also recommended that the Government in partnership with other stakeholders develop a comprehensive disaster risk management framework to address the drought hazards and undertake mitigation and adaptation measures by equipping the smallholder farmers with knowledge on how to cope with the cyclic...

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Section: Data Analysis Methodsmentioning

confidence: 99%

Section: Data Collection Proceduresmentioning

confidence: 99%

Vulnerability of Smallholder Farmers to Climate Related Shocks in Kinakomba Ward, Tana River County, Kenya

2020

FSQM

View full text Add to dashboard Cite

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“…This was achieved by asking the respondents to appraise eight classes of possible consequences of drought which led to a four step methodology according to (Acosta-Michlik et al 2008;Kro¨mker et al 2008) and Ta¨nzler and Carius, (2008) as follows: i) Selection of input variables, ii) Fuzzification, iii) Inference modelling and iv) Defuzzification (Bothe 1998, Aliev et al 2000 2.7.1 Selection of input variables. The researcher using the data collected on the indicators analyzed the variables according to how different respondents had responded.…”

Section: Measuring Exposure By Fuzzification Methodsmentioning

confidence: 99%

Climate Change Shocks Exposure Index to Drought on the Livelihoods of the Smallholder Farmers in Kinakomba Ward, Tana River County, Kenya

2020

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Being susceptible to climate change means being unable to cope with the adverse effects of climate change especially droughts and a likelihood of experiencing harm due to its occurrence. The study sought to evaluate the effects of exposure to Climate related shocks on the livelihoods of the smallholder farmers with the intent of formulating appropriate policies to enable them cope with its impacts. A descriptive survey research design was used. Stratified random sampling was employed to select 390 households. Two methods were used to analyse exposure. Firstly the fuzzy logic in assessing susceptibility to drought involving a selection of input variables, Fuzzification, inference modelling and defuzzification and secondly DrinC Model software. The results revealed that the final value of the negative consequences of drought was 0.35.The study also established a single index as 0.45 for exposure for the entire study period of 35 years for Kinakomba Ward .The study showed that exposure was statistically significant at (0.000066). The study further revealed that the periods between occurrence of extreme droughts were reducing and at the same time that droughts were moving from being severe to being extreme within shorter periods of time leaving smallholder farmers who depend on rain fed agriculture with high exposures and risks as well as experiencing longer hunger periods with severe implications on their food and nutritional security for the vast populations in the study area. The Study concluded that the exposure to drought of the smallholder farmers in Kinakomba Ward is significantly related to their farming livelihood systems. This study recommends that the County Government in partnership with the National Government and other stakeholders develop a comprehensive disaster risk management framework to address the drought hazards and undertake mitigation and adaptation measures by equipping the smallholder farmers with knowledge on how to cope with the cyclic and vicious droughts' impacts that have led to serious irreversible harm to humans and livestock in the area.

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“…Examples include modelling human vulnerability to droughts (e.g. Vörösmarty et al , 2000; Acosta‐Michlik et al , 2008), impact of climate warming (Liverman, 1992) and extreme climate events (Patt et al , 2010) on H–E systems in arid zones, and the economic consequences of warming on food security (Nordhaus and Boyer, 2000). Stahel (2005) reasons that the economic and ecological value of ecosystem goods and services (Figure 2 and related text) must be assessed within a particular spatiotemporal context, which is recognized as an important aspect of a desertification monitoring programme (see Sommer et al , 2011; Verstraete et al , 2011).…”

Section: System Models Of Drylandsmentioning

confidence: 99%

Scientific concepts for an integrated analysis of desertification

et al. 2011

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The Global Drylands Observing System proposed in this issue should reduce the huge uncertainty about the extent of desertification and the rate at which it is changing, and provide valuable information to scientists, planners and policy-makers. However, it needs careful design if information outputs are to be scientifically credible and salient to the needs of people living in dry areas. Its design would benefit from a robust, integrated scientific framework like the Dryland Development Paradigm to guide/inform the development of an integrated global monitoring and assessment programme (both directly and indirectly via the use of modelling). Various types of dryland system models (e.g. environmental, socioeconomic, land-use cover change, and agent-based) could provide insights into how to combine the plethora of monitoring information gathered on key socioeconomic and biophysical indicators to develop integrated assessment models. This paper shows how insights from models can help in selecting and integrating indicators, interpreting synthetic trends, incorporating cross-scalar processes, representing spatio-temporal variation, and evaluating uncertainty. Planners could use this integrated global monitoring and assessment programme to help implement effective policies to address the global problem of desertification.

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Application of fuzzy models to assess susceptibility to droughts from a socio-economic perspective

Cited by 33 publications

References 15 publications

Vulnerability of Smallholder Farmers to Climate Related Shocks in Kinakomba Ward, Tana River County, Kenya

Vulnerability of Smallholder Farmers to Climate Related Shocks in Kinakomba Ward, Tana River County, Kenya

Climate Change Shocks Exposure Index to Drought on the Livelihoods of the Smallholder Farmers in Kinakomba Ward, Tana River County, Kenya

Scientific concepts for an integrated analysis of desertification

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