Does Adaptation to Climate Change Provide Food Security? A Micro-Perspective from Ethiopia
By Salvatore Di Falco, M. Veronesi, Mahmud Yesuf. 2011
1,408 citations recorded by the source corpus.
Subfields: Ecology, Evolution, Behavior and Systematics, General Agricultural and Biological Sciences, Soil Science
View Paper: https://www.semanticscholar.org/paper/d7b2cd766d287c33aed257923a86d73d3de2335e
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Ecology, Evolution, Behavior and Systematics
- Salvatore Di Falco
- M. Kassie
- H. Teklewold
- Anna Josephson
- J. Michler
- Ashley Gorst
- B. Groom
- A. Dehlavi
In General Agricultural and Biological Sciences
In Soil Science
Citations in the corpus
- Farmer’s Adaptation to Extreme Weather Events through Farm Management and Its Impacts on the Mean and Risk of Rice Yield in China (2015, neutral): “Exceptions include studies by Yesuf et al. (2008) and Di Falco, Veronesi, and Yesuf (2011).”
- Adoption of agroforestry and the impact on household food security among farmers in Malawi (2016, methodology): “Other studies (Di Falco et al. 2011a, Di Falco et al. 2011b) also incorporated proxies of climate change in the form of long-term trends in temperature and rainfall.”
- What determines gender inequality in household food security in Kenya? Application of exogenous switching treatment regression (2014, methodology): “Following Carter and Milon (2005) and Di Falco et al. (2011) and the impact evaluation literature, we compute the average food security for both MHHs and FHHs by comparing the expected values of the outcomes of MHHs and FHHs in actual and counterfactual scenarios.”
- Managing Environmental Risk in Presence of Climate Change: The Role of Adaptation in the Nile Basin of Ethiopia (2014, methodology): “Following Di Falco et al. (2011), we use as selection instruments the variables related to the information sources (e.g., government extension, farmer-to-farmer extension, information from the radio or the neighbourhood and, if received information in particular on climate), which enter in z but…”
- Tackling fall armyworm (Spodoptera frugiperda) outbreak in Africa: an analysis of farmers’ control actions (2019, positive): “Our findings also support numerous studies (e.g., Di Falco et al. 2011; Kabunga et al. 2012; Tambo and Abdoulaye 2012) that showed that information is pivotal to the adoption of agricultural technologies as well as adaptation to stresses among farm households.”
- To Specialize or Diversify: Agricultural Diversity and Poverty Dynamics in Ethiopia (2017, positive): “…is an improvement on instruments used in other recent studies of agricultural households and adaptation strategies (such as crop diversification) in Ethiopia (Asrat, Berhane, Getachew, Hoddinott, Nisrane, & Taffesse, 2011; Di Falco et al. , 2011; Di Falco & Veronesi, 2013; Krishnan & Patnam, 2013).”
- Estimating the Impact of Climate Change on Agriculture in Low-Income Countries: Household Level Evidence from the Nile Basin, Ethiopia (2011, background): “…key climatic variables (precipitation and temperature) in cross-sectional data is one bottleneck to conducting a micro level study on climate change (Di Falco et al. 2011).3 This is particularly true in developing countries, where one meteorological station is set to cover a wide geographic area.”
- Limitations of Contract Farming as a Pro-poor Strategy: The Case of Maize Outgrower Schemes in Upper West Ghana (2018, methodology): “We also test the validity of these instruments using a simple falsification test following Di Falco, Veronesi, and Yesuf (2011) and Shiferaw and colleagues (2014).”
- Sustainability Standards, Gender, and Nutrition among Smallholder Farmers in Uganda (2016, methodology): “To test the validity of the instrument further, we used an approach suggested by Di Falco, Veronesi, and Yesuf (2011) and regressed the nutrition outcome variables on altitude and other relevant controls for the sub-sample of non-certified farms.”
- Poverty Reduction Effects of Agricultural Technology Adoption: A Micro-evidence from Rural Tanzania (2012, methodology): “An efficient method to estimate endogenous switching regression models is by full information maximum likelihood (FIML) estimation (Lee and Trost, 1978; Lokshin and Sajaia, 2004; Di Falco et al., 2011).”