Poverty mapping based on livelihood assets: A meso-level application in the Indo-Gangetic Plains, India

Erenstein, Olaf; Hellin, Jon; Chandna, Parvesh

doi:10.1016/j.apgeog.2009.05.001

Cited by 89 publications

(66 citation statements)

References 26 publications

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“…Nonetheless, there is no standard protocol for determining indicators of resilience. Here, we propose that using the sustainable livelihoods approach provides one innovative method for determining indicators of resilience and has been used in a handful of studies (Elasha et al 2005, Erenstein et al 2010, Thulstrup 2015. As stated by Thulstrup (2015), sustainable livelihoods are a key component of resilience and vice versa.…”

Section: Sustainable Livelihoods Approach To Measuring Livelihood Resmentioning

confidence: 99%

“…Human capital encompasses the skills, knowledge, education, health, and labor availability of the household (Tacoli 1999). Financial capital refers to savings, credit, remittances, and financial assets (Campbell et al 2001, Erenstein et al 2010. Lastly, natural capital includes access to environmental services and resources (Campbell et al 2001, Erenstein et al 2010.…”

Section: Sustainable Livelihoods Approach To Measuring Livelihood Resmentioning

confidence: 99%

“…Financial capital refers to savings, credit, remittances, and financial assets (Campbell et al 2001, Erenstein et al 2010. Lastly, natural capital includes access to environmental services and resources (Campbell et al 2001, Erenstein et al 2010. Here, we organize indicators of resilience by the five livelihood capitals, ensuring that a wide diversity of indicators are included in livelihood resilience (Table 1).…”

Section: Sustainable Livelihoods Approach To Measuring Livelihood Resmentioning

confidence: 99%

“…These interviews were in-depth, unstructured, and while they were centered around a clear plan of six to eight topics, they also allowed people to open up and express themselves in their own terms (Bernard 2011). The purpose of these interviews was to capture what Bernard (2011) (Campbell et al 2001, Erenstein et al 2010, Thulstrup 2015. In previous work, Erenstein et al (2010) used the five capital assets to organize indicators of resilience and compare asset scores between different types of households, whereas Campbell et al (2001) organized indicators of resilience based on the five livelihood capital assets and then discussed the benefits of creating simple composite indices by aggregating the scores of individual indicators.…”

Section: Data Collectionmentioning

confidence: 99%

“…The purpose of these interviews was to capture what Bernard (2011) (Campbell et al 2001, Erenstein et al 2010, Thulstrup 2015. In previous work, Erenstein et al (2010) used the five capital assets to organize indicators of resilience and compare asset scores between different types of households, whereas Campbell et al (2001) organized indicators of resilience based on the five livelihood capital assets and then discussed the benefits of creating simple composite indices by aggregating the scores of individual indicators. Using those works as a guide, we gave the variety of answers for each indicator in the survey a score on a scale from 0 (worst, less desirable) to 1 (best, more desirable).…”

Section: Data Collectionmentioning

confidence: 99%

See 4 more Smart Citations

The role of agroforestry in building livelihood resilience to floods and drought in semiarid Kenya

Quandt¹,

Neufeldt²,

McCabe³

2017

E&S

105

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ABSTRACT. Climate change may create serious problems for farmers by increasing precipitation variability and drought and flood events. Understanding how to build livelihood resilience to these effects is a pressing need. Agroforestry is one potential solution. Although many people intuitively link agroforestry with livelihood resilience to floods and drought, little comprehensive empirical evidence exists. Here, we strive to answer the call for more empirical evidence by drawing on field work in Isiolo County, Kenya to ask the research question: How does agroforestry help smallholder farmers build livelihood resilience to floods and drought? We used a mixed-methods approach that included 20 qualitative case study households and 338 quantitative household surveys. Our major findings are: (1) practicing agroforestry can influence perceptions of floods and drought, (2) specific environmental and livelihood benefits of agroforestry contribute to livelihood resilience, and (3) agroforestry directly and indirectly builds livelihood resilience to floods and drought. Identifying potential climate change adaptation strategies is crucial for smallholder farmers. Our research indicates that agroforestry is one promising option.

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Section: Sustainable Livelihoods Approach To Measuring Livelihood Resmentioning

confidence: 99%

Section: Sustainable Livelihoods Approach To Measuring Livelihood Resmentioning

confidence: 99%

Section: Sustainable Livelihoods Approach To Measuring Livelihood Resmentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

See 3 more Smart Citations

The role of agroforestry in building livelihood resilience to floods and drought in semiarid Kenya

Quandt¹,

Neufeldt²,

McCabe³

2017

E&S

105

View full text Add to dashboard Cite

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How Livelihood Assets Contribute to Sustainable Development of Smallholder Farmers

Li¹,

Shuai

et al. 2020

J of Intl Development

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There is a knowledge gap and practical demand to understand the relationship between capitals for developing sustainable livelihoods to realize poverty reduction and sustainable development, in particular for the smallholder farmers. This paper intends to make sights into an important livelihood challenge faced by smallholder farmers, which also aims to find out strategy (ies) in accelerating smallholder farmers' poverty reduction and achieving sustainable development. More specifically, we used a hybrid approach by adopting both qualitative and quantitative methods along with the concept of sustainable livelihood approach to reveal six capitals of farmers' household capacity and their potential relationships. Based on an analysis of Grounded Theory for smallholder farmers, we proposed a newly built framework for smallholder farmers' sustainable livelihoods by taking the Psychological capital (Psycap) into the sustainable livelihoods' assets. With a total sample of 796 households representing 26 counties in Three Gorges Reservoir Region (TGRR) in China, we adopted the Structural Equation Modelling using partial least squares (PLS‐SEM) to explore the key factors and key paths affecting smallholder farmers' sustainable livelihoods. Results indicate that Psycap can effectively promote the performance of Human capital (Humcap) and Physical capital (Phycap), with both significant direct and significant indirect effects on sustainable livelihoods; Phycap and Humcap have significant impacts on the new sustainable livelihood index. Finally, this paper proposed corresponding policy recommendations for poverty alleviation and sustainable livelihoods of smallholder farmers. © 2020 John Wiley & Sons, Ltd.

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A review of machine learning and satellite imagery for poverty prediction: Implications for development research and applications

Hall

Dompae

Wahab

et al. 2023

J of Intl Development

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The field of artificial intelligence is seeing the increased application of satellite imagery to analyse poverty in its various manifestations. This nascent but rapidly growing intersection of scholarship holds the potential to help us better understand poverty by leveraging big data and recent advances in machine vision. In this study, we statistically analyse the literature in the expanding field of welfare and poverty predictions from the combination of machine learning and satellite imagery. Here, we apply an integrative review method to extract key data on factors related to the predictive power of welfare. We found that the most important factors correlated to the predictive power of welfare are the number of pre‐processing steps employed, the number of datasets used, the type of welfare indicator targeted and the choice of AI model. Studies that used stock measure indicators (assets) as targets achieved better performance—17 percentage points higher—in predicting welfare than those that targeted flow measures (income and consumption) ones. Additionally, we found that the combination of machine learning and deep learning significantly increases predictive power—by as much as 15 percentage points—compared to using either alone. Surprisingly, we found that the spatial resolution of the satellite imagery used is important but not critical to the performance as the relationship is positive but not statistically significant. These findings have important implications for future research in this domain and for anyone aspiring to use the methodology.

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Poverty mapping based on livelihood assets: A meso-level application in the Indo-Gangetic Plains, India

Cited by 89 publications

References 26 publications

The role of agroforestry in building livelihood resilience to floods and drought in semiarid Kenya

The role of agroforestry in building livelihood resilience to floods and drought in semiarid Kenya

How Livelihood Assets Contribute to Sustainable Development of Smallholder Farmers

A review of machine learning and satellite imagery for poverty prediction: Implications for development research and applications

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