Title |
Drivers of household food availability in sub-Saharan Africa based on big data from small farms
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Published in |
Proceedings of the National Academy of Sciences of the United States of America, December 2015
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DOI | 10.1073/pnas.1518384112 |
Pubmed ID | |
Authors |
Romain Frelat, Santiago Lopez-Ridaura, Ken E. Giller, Mario Herrero, Sabine Douxchamps, Agnes Andersson Djurfeldt, Olaf Erenstein, Ben Henderson, Menale Kassie, Birthe K. Paul, Cyrille Rigolot, Randall S. Ritzema, Daniel Rodriguez, Piet J. A. van Asten, Mark T. van Wijk |
Abstract |
We calculated a simple indicator of food availability using data from 93 sites in 17 countries across contrasted agroecologies in sub-Saharan Africa (>13,000 farm households) and analyzed the drivers of variations in food availability. Crop production was the major source of energy, contributing 60% of food availability. The off-farm income contribution to food availability ranged from 12% for households without enough food available (18% of the total sample) to 27% for the 58% of households with sufficient food available. Using only three explanatory variables (household size, number of livestock, and land area), we were able to predict correctly the agricultural determined status of food availability for 72% of the households, but the relationships were strongly influenced by the degree of market access. Our analyses suggest that targeting poverty through improving market access and off-farm opportunities is a better strategy to increase food security than focusing on agricultural production and closing yield gaps. This calls for multisectoral policy harmonization, incentives, and diversification of employment sources rather than a singular focus on agricultural development. Recognizing and understanding diversity among smallholder farm households in sub-Saharan Africa is key for the design of policies that aim to improve food security. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Kenya | 4 | 18% |
Germany | 1 | 5% |
Colombia | 1 | 5% |
Belgium | 1 | 5% |
United States | 1 | 5% |
India | 1 | 5% |
Sweden | 1 | 5% |
Tunisia | 1 | 5% |
Australia | 1 | 5% |
Other | 0 | 0% |
Unknown | 10 | 45% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 20 | 91% |
Scientists | 2 | 9% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Colombia | 3 | <1% |
Netherlands | 3 | <1% |
France | 2 | <1% |
South Africa | 2 | <1% |
United States | 2 | <1% |
Sweden | 1 | <1% |
Belgium | 1 | <1% |
Argentina | 1 | <1% |
Japan | 1 | <1% |
Other | 1 | <1% |
Unknown | 769 | 98% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 158 | 20% |
Student > Ph. D. Student | 141 | 18% |
Student > Master | 116 | 15% |
Student > Bachelor | 47 | 6% |
Student > Doctoral Student | 45 | 6% |
Other | 114 | 15% |
Unknown | 165 | 21% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 210 | 27% |
Environmental Science | 84 | 11% |
Social Sciences | 63 | 8% |
Economics, Econometrics and Finance | 55 | 7% |
Engineering | 23 | 3% |
Other | 117 | 15% |
Unknown | 234 | 30% |