An indicator tool for assessing local vulnerability to climate change in the Mexican agricultural sector

Ahumada-Cervantes, Ramiro; Velázquez-Angulo, Gilberto; Rodríguez-Gallegos, Hugo B.; Flores-Tavizón, Edith; Félix-Gastélum, Rubén; Romero-González, Jaime; Granados, Alfredo

doi:10.1007/s11027-015-9670-z

Cited by 19 publications

(10 citation statements)

References 13 publications

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“…Also, Trans Nzoia was the most sensitive county, but had the second highest adaptive capacity. Vulnerability increases when sensitivity and exposure increases, but reduces as adaptive capacity increases and vice versa (Ahumada-Cervantes et al, 2015). The vulnerability in Trans Nzoia and Uasin Gishu was considerably reduced by significant adaptive capacity realized in the counties.…”

Section: Vulnerability Indicesmentioning

confidence: 95%

“…www.ijsrp.org temperatures are still expected to continue increasing and therefore there is urgent need to explore mechanisms for adapting to the continuing change in climate, specifically for developing countries which face the biggest brunt of the adverse impacts of climate change (Ahumada-Cervantes et al, 2015).…”

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confidence: 99%

“…AC c is the adaptive capacity of the County • S c is the sensitivity of the County • E c is the exposure of the county • Y E ,Y S and Y AC are standardized values of variables under exposure, sensitivity and adaptive capacity respectively.• P i is the weight of the indicators.The vulnerability index of the counties(VI c ) was computed by getting the sum of S c and E c and then subtracting AC c . using the method adopted from(Ahumada-Cervantes et al, 2015) given in equation 17.…”

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confidence: 99%

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Vulnerability of Maize Production to Climate Change in Maize Producing Counties of Rift Valley Kenya: The Indicator Approach

Masambaya¹,

Oludhe²,

Lukorito³

et al. 2018

IJSRP

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Efforts towards attainment of sustainable economic growth and food security in Kenya are under increasing threats due the adverse effects of climate change. This study therefore, sought to assess the vulnerability of maize production to climate change in major maize producing counties of Kenya's Rift Valley region, using the indicator approach. Climate data including monthly rainfall, maximum and minimum temperature was obtained from Kenya Meteorological Department. The Sen's Slope Estimator was used to determine the rate of change of rainfall and temperature (1981-2010), which constituted the exposure indicators. The Standardized Precipitation Index (SPI) was used to determine the frequency of extreme climate events that formed part of exposure variables. As for the socioeconomic and biophysical data sets, sensitivity indicators included demographic and ecological sensitivity variables while adaptive capacity variables encopmassed social capital, literacy rate, financial capital and physical capital. Sensitivity and adaptive capacity data was obtained from Kenya Bureau of Statistics, Ministry of agriculture and Fisheries and Tegemeo Institute of Agricultural Policy and Development. After normalization, Principal Component Analysis (PCA) was used to assign weights to the indicators. Subsequently, the normalized values of each variable were multiplied with their respective weights as obtained from PCA and later aggregated to obtain Exposure, sensitivity, adaptive capacity and vulnerability indices Trans Nzoia had the least exposure index (0.19) and second highest adaptive capacity(2.59) which made it record the lowest vulnerability index of-0.212. Narok recorded the highest vulnerability index of 1.51 with the peak exposure (1.03) and least adaptive capacity (-2.28). Nakuru had the second highest vulnerability index (0.35) while Uasin Gishu recorded the second least vulnerability index (-0.12). Generally, counties with considerable socioeconomic development recorded high adaptive capacity which reduced their vulnerability significantly. Hence, in order to cushion most vulnerable maize producing counties, climate change policies and strategies should prioritize adaptive capacity enhancement through socio economic development initiatives such as irrigation, rural infrastructural development and educational programs.

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Section: Vulnerability Indicesmentioning

confidence: 95%

mentioning

confidence: 99%

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confidence: 99%

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Vulnerability of Maize Production to Climate Change in Maize Producing Counties of Rift Valley Kenya: The Indicator Approach

Masambaya¹,

Oludhe²,

Lukorito³

et al. 2018

IJSRP

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“…9A). In most of these articles, a traffic light signal was used to express the level of the vulnerability index; green or light colors correspond to low vulnerability, and red or dark at the highest levels (Villers-Ruiz and Trejo-Vázquez, 1998;Ahumada-Cervantes et al, 2015). However, in 23% of articles a range was not specified, and the information was presented in graphical form , or the articles did not use an index (24%), and the results were presented as a described explanation (Browning-Aiken et al, 2007;.…”

Section: Variablementioning

confidence: 99%

Vulnerability assessment studies on climate change: a review of the research in Mexico

González

González²

2022

Atm.

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“…The standardized proxy variables are then compiled into each component indicator χ j for exposure E , sensitivity S , coping C , or lack of coping LC with the weighting factor w i for the total variable number k as used in previous studies 19,20,27–29,38 :…”

Section: Conventional Framework Of Vulnerability Indicatorsmentioning

confidence: 99%

Assessment of Aggregation Frameworks for Composite Indicators in Measuring Flood Vulnerability to Climate Change

Choi

2019

Sci Rep

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The IPCC Third Assessment Report presents a conceptual framework for vulnerability to climate change with the three attribute components of exposure, sensitivity, and coping. Since the vulnerability assessments have been conducted mainly by the composite indicators aggregated from the IPCC’s components, it is necessary to assess aggregation frameworks for constructing the composite indicators that have an influence on vulnerability assessment outcomes. This study therefore investigates the robustness of assessment outcomes for flood vulnerability to climate change through a comparative analysis of the six vulnerability indicators aggregated from the IPCC’s components by the conventional aggregation frameworks. The comparative analysis has been illustrated through both the possible combinations of reference values for vulnerability attribute components and a case study on the flood vulnerability assessment to climate change for coastal areas in the Republic of Korea. The study demonstrates that there can be large fluctuations and reversals in ranking orders across the six vulnerability outcomes by different aggregation frameworks. It concludes that for flood vulnerability assessment to climate change in coastal areas, the vulnerability indicator needs to be aggregated by a multiplicative utility function from all the three assessment components with positive elasticity to vulnerability.

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An indicator tool for assessing local vulnerability to climate change in the Mexican agricultural sector

Cited by 19 publications

References 13 publications

Vulnerability of Maize Production to Climate Change in Maize Producing Counties of Rift Valley Kenya: The Indicator Approach

Vulnerability of Maize Production to Climate Change in Maize Producing Counties of Rift Valley Kenya: The Indicator Approach

Vulnerability assessment studies on climate change: a review of the research in Mexico

Assessment of Aggregation Frameworks for Composite Indicators in Measuring Flood Vulnerability to Climate Change

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