Impacts of smallholder agricultural adaptation on food security: evidence from Africa, Asia, and Central America

Abstract

Understanding the efficacy of smallholder adaptation to changing environments is crucial to policy design. Past efforts in understanding whether, and to what extent, adaptation improves household welfare have faced some key challenges including: 1) endogeneity of adaptation; 2) localized results that are difficult to generalize; and 3) understanding whether the efficacy of adaptation depends on the reasons for adaptation (e.g. market conditions vs climate change). In this study we estimate effects of smallholder agricultural adaptation on food security, while addressing each of these three challenges. First, we identify and test instrumental variables based on neighbor networks. Second, we use a dataset that contains information from 5159 households located across 15 countries in Africa, Asia, and Central America. Third, we investigate whether adaptation that is motivated by changes in market conditions influences the efficacy of adaptation differently than adaptation motivated by climate change. Across our global sample, an average household made almost 10 adaptive changes, which are responsible for approximately 47 days of food security yearly; an amount nearly 4 times larger than is indicated if endogeneity is not addressed. But these effects vary depending on what is motivating adaptation. Adaptation in response to climate change alone is not found to significantly affect food security. When climate adaptation is paired with adaptation in response to changing market conditions, the resulting impact is 96 food secure days. These results suggest the need for further work on the careful design of climate change interventions to complement adaptive activities.

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Notes

  1. 1.

    Smallholder farming adaptation is typically defined along the lines of actions undertaken by households in order to better cope with or adjust to some changing condition, stress, hazard, risk or opportunity (e.g. Smit and Wandel 2006). Note that this concept of adaptation is similar to technology adoption, but different in at least two ways. First, while adaptation refers to a suite of potential actions that household can undertake, technology adoption is focussed on a particular activity. Second, while technology adoption focuses on a new activity that a household may try, adaptation can include ceasing activities, or reverting to old approaches that were temporarily abandoned

  2. 2.

    We describe these difficulties briefly below, with a literature review supporting this statement in the next section.

  3. 3.

    We also consider two measures of adaptation that assign weights to different adaptive activities. Specifically, first we follow Shikuku et al. (2017) and estimate models where adaptation is measured using a food security-based index that assigns weights to activities based on their contributions to food security. Next, we used a principal component analysis and assign weights to different activities based on the first principal component.

  4. 4.

    We also note that matching approaches are often motivated by the fact that IVs are hardly available. Interestingly, PSM estimates would not benefit from having an IV available. Recent research shows that the inclusion of IVs in matching approaches actually maximizes inconsistency (Wooldridge 2016).

  5. 5.

    Lobell et al. (2008) identify South Asia, East Africa, and West Africa, three regions where households in our sample are located, as major food-insecure regions in the world.

  6. 6.

    The data are available online at Harvard Dataverse (https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/IUJQZV).

  7. 7.

    We discuss these variables in detail in the next section.

  8. 8.

    In the results that follow, we also do robustness checks for shorter and longer distances and show that results are not sensitive to the truncation point.

  9. 9.

    Source: United Nations Climate Change. Available online at https://unfccc.int/climate-action/momentum-for-change/ict-solutions/enabling-farmers-to-adapt-to-climate-change (Accessed on July 10, 2018).

  10. 10.

    The findings of all statistical tests are discussed in the results section.

  11. 11.

    Our measure for food security primarily captures food access and is expected to be correlated with caloric availability. However, the concept of food security is thought to have a number of dimensions that are difficult to capture with any one measure (FAO et al. 2018). Nevertheless, for our study, we are limited to the data collected as described above.

  12. 12.

    Full model estimates are available upon request.

  13. 13.

    The confidence intervals of these two estimates (i.e. 7.51 and 7.09) significantly overlap indicating that they are not statistically different from one another.

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Acknowledgements

“This work was implemented as part of the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), which is carried out with support from the CGIAR Trust Fund and through bilateral funding agreements. For details please visit https://ccafs.cgiar.org/donors. The views expressed in this document cannot be taken to reflect the official opinions of these organizations.”

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Correspondence to Peter Läderach.

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Lim, K., Wichmann, B., Luckert, M.K. et al. Impacts of smallholder agricultural adaptation on food security: evidence from Africa, Asia, and Central America. Food Sec. 12, 21–35 (2020). https://doi.org/10.1007/s12571-019-00993-0

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Keywords

  • Adaptation
  • Smallholder agriculture
  • Food security
  • Global dataset
  • Instrumental variables