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The Impact of Technological Changes on Crop Yields in Sub-Saharan Africa, 1967–2004

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An African Green Revolution

Abstract

Many specialists in African agriculture doubt whether a Green Revolution similar to the one achieved in Asia is possible in Sub-Saharan Africa (SSA). The major reasons why SSA has failed to realize a Green Revolution are considered to be its unfavorable, dry and diverse climate. The purpose of this study is to assess the impacts of climate, as well as population pressure on the agricultural crop yields in SSA from the late 1960s to the early 2000s. Using a country-level panel data set, we found evidence that technology advancements in SSA have mitigated the adverse effects of climatic factors on wheat, rice, and maize yields.

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Notes

  1. 1.

    To date, most of the empirical studies on how agro-climate factors affect agricultural productivity have focused on the United States (Adams et al. 1995; Mendelsohn et al. 1994) and other ­developed countries (Olesen and Bindi 2002; Bruce et al. 1996; Reilly et al. 1996).

  2. 2.

    See the case studies of Côte d’Ivoire by Diagne (2006) and Sakurai (2006), Cameroon by Goufo (2008), Mozambique by Kajisa and Payongayong (2011), and Uganda by Kijima et al. (2006).

  3. 3.

    The reason why we compare the yields in SSA with those in South Asia instead of with the whole of Asia is that South Asia is the only region in Asia where sorghum and millet, the two major crops in SSA, are grown (see Fig. 4.1 in Chap. 4 in this volume).

  4. 4.

    Tsusaka and Otsuka also found for India that wheat crop has been becoming more heat-tolerant over time (Table 4.5 in Chap. 4). Therefore, the potential of wheat expansion in SSA may not be totally excluded.

  5. 5.

    It is desirable to use the output-input price ratio to account for the practical impact on the output. However, we cannot do so because the input prices such as fertilizer prices are unavailable.

  6. 6.

    The 14 countries with no meteorological stations, which are hence excluded from the regression analyses, are Burundi, Central African Republic, Comoros, Djibouti, Gambia, Ghana, Guinea, Guinea-Bissau, Lesotho, Liberia, Rwanda, Sao Tome and Principe, Somalia, and Uganda.

  7. 7.

    As for the deflator, it is ideal to use the producer price indices because producer prices and consumer prices may exhibit different behaviors. We cannot do so, however, because of the data unavailability.

  8. 8.

    The instruments we attempted to use in the first step probit estimations are the longer-term temperature and rainfall, which are likely to affect the crop choice decisions. For maize, the probit regression does not succeed because maize is grown in all the countries included in the regression. For the other crops, the coefficients on the instrumental variables are estimated to be highly significant. However, in the second step outcome estimations, the sample selection bias associated with this first step estimation is identified to be insignificant in all cases.

  9. 9.

    The negative effect of the real output price is unexpected. A possible reason is that the wheat price is lower (higher) in major (minor) wheat growing and exporting (importing) regions, where the wheat yield is higher (lower).

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Correspondence to Keijiro Otsuka .

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Tsusaka, T., Otsuka, K. (2013). The Impact of Technological Changes on Crop Yields in Sub-Saharan Africa, 1967–2004. In: Otsuka, K., Larson, D. (eds) An African Green Revolution. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5760-8_5

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