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Food Security

, Volume 6, Issue 4, pp 541–554 | Cite as

Synergies and tradeoffs between cash crop production and food security: a case study in rural Ghana

  • Tal Lee Anderman
  • Roseline Remans
  • Stephen A. Wood
  • Kyle DeRosa
  • Ruth S. DeFries
Case Study

Abstract

Despite dramatic improvements in global crop yields over the past half-century, chronic food insecurity persists in many parts of the world. Farming crops for sale (cash cropping) has been recommended as a way to increase income that can, in turn, improve food security for smallholder farmers. Despite long-term efforts by development agencies and government to promote cash cropping, there is limited evidence documenting a relationship between these crops and the food security of households cultivating them. We used a mixed methods approach to build a case study to assess these relationships by collecting quantitative and qualitative data from cacao and oil palm farmers in the Ashanti region of Ghana. Three dimensions of food security were considered: food availability, measured by the months in a year households reported inadequate food; food access, indicated by the coping strategies they employed to secure sufficient food; and food utilization, gauged by the diversity of household diets and anthropometric measurements of child nutritional status. We found significant negative relationships between each of these pillars of food security and a household’s intensity of cash crop production, measured by both quantity and area. A qualitative assessment indicated community perception of these tradeoffs and identified potential mechanisms, including increasing food prices and competing activities for land use, as underlying causes. The adverse relationship between cash crop production and household food security observed in this paper calls for caution; results suggest that positive relationships cannot be assumed, and that further empirical evidence is needed to better understand these tradeoffs.

Keywords

Food security Nutrition Agriculture Cash crop Cacao Oil palm Ghana 

Notes

Acknowledgments

The authors would like to thank Christina Feng for her contribution to the qualitative study, and Cheryl Palm, Hope Michelson, Stephanie Pfirman, Hervé Bisseleua, Dallas Abbot, Timothy Michael Harris, and Satyajit Bose for their helpful comments.

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

12571_2014_360_Fig6_ESM.jpg (98 kb)
Figure S1

Overview of the MVP agricultural initiatives taking place in the Bonsaaso cluster at the time quantitative surveys were administered for this study (Phase 1), as well as some in the process of being rolled out (Phase 2) (JPEG 98 kb)

12571_2014_360_Fig7_ESM.gif (147 kb)
Table S1

Eleven food insecurity and coping strategy questions used to derive a Food Coping Score for households in the Millennium Village of Bonsaaso in Ghana. The methodology followed Coates et al. (2007) (GIF 147 kb)

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High resolution image (TIFF 183 kb)
12571_2014_360_Fig8_ESM.gif (69 kb)
Table S2

Twenty-one asset indicators providing a metric of wealth for the study. Reported ownership of each indicator is used to derive an asset index through a principal component analysis for households in the Millennium Village of Bonsaaso in Ghana. The methodology followed Michelson et al. (2013) and Filmer and Pritchett (2001) (TIFF 80 kb) (GIF 69 kb)

12571_2014_360_MOESM2_ESM.tif (81 kb)
High resolution image (TIFF 80 kb)
12571_2014_360_Fig9_ESM.gif (60 kb)
Table S3

Variance inflation factors of the linear regressions for predictor indicators of cash crop agricultural intensity (quantity “Quantity” and the percent of a farmer’s total land for cash cropping “% Area”) and response variables for the three dimensions of food security (food availability, access, and utilization). Reports mean variance inflation factor per regression model (GIF 60 kb)

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High resolution image (TIFF 71 kb)
12571_2014_360_Fig10_ESM.gif (199 kb)
Table S4

Multiple regressions between cash crop agriculture (quantity and the percent of a farmer’s total land for cash crop production) and the frequency of consuming each of thirteen food groups included in the diet diversity score. Indicators for household demographics and wealth were included as controls. Each column is a separate regression model. Column headings indicate response variables, while row labels delineate predictor variables. Each response variable was run once with a predictor variable for quantity and percent area of household-level cash cropping, respectively. Models were run with and without outliers (defined as quantities of oil palm fruit and/or cacao beans outside 1.5 × interquartile range) and were found to be robust; outliers were subsequently removed. Continuous predictor variables were standardized by their mean and two standard deviations. Binary predictor variables were centered. Response variables were left unstandardized. This standardization procedure was used to ensure that variables were expressed in common units so that correlation coefficients within each model could be compared. Standard error reported in parentheses (GIF 199 kb)

12571_2014_360_MOESM4_ESM.tif (278 kb)
High resolution image (TIFF 277 kb)

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Copyright information

© Springer Science+Business Media Dordrecht and International Society for Plant Pathology 2014

Authors and Affiliations

  • Tal Lee Anderman
    • 1
  • Roseline Remans
    • 1
    • 2
  • Stephen A. Wood
    • 1
    • 3
  • Kyle DeRosa
    • 1
  • Ruth S. DeFries
    • 3
  1. 1.Agriculture and Food Security Center, The Earth InstituteColumbia UniversityNew YorkUSA
  2. 2.Center for Globalization and Sustainable Development, The Earth InstituteColumbia UniversityNew YorkUSA
  3. 3.Department of Ecology, Evolution, and Environmental BiologyColumbia UniversityNew YorkUSA

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