Direct and indirect effects of climate on agriculture: an application of a spatial panel data analysis to Tunisia


North African countries (NACs) are particularly concerned with climate change because of their geographical position (close to deserts) and their economic dependence on agriculture. We aim to provide additional insight into the impact of climate on agriculture for NACs, through the example of Tunisia. We first use disaggregated data, both at the geographical level (for 24 regions in Tunisia) and at the product level (cereals, olives, citrus fruit, tomatoes, potatoes and palm trees). Second, through spatial panel data analysis, we explore both the time and spatial dimensions of the data. This makes it possible to consider spatial interactions in agricultural production and the role of climate in these spatial spillover effects. Finally, the model not only includes direct climate variables, such as temperature and precipitation, but also indirect climate-related variables such as the stock of water in dams and groundwater. Results show that Tunisian agriculture is strongly dependent on the direct effects of temperature and precipitation for all the products considered at the regional level. The presence of dams and groundwater generally has a positive effect on agricultural production for irrigated crops with interesting spillover effects with neighboring regions. However, this impact is still considerably lessened in the case of detrimental climate conditions (indirect effect). These results raise the question of the sustainability of the growth in agricultural production in Tunisia in the case of significant climate change.

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  1. 1.

    Rapport annuel sur les caractéristiques économiques pour chaque gouvernorat 1990–2012

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  3. 3.

    Annuaire statistique de la Tunisie 2012, page 121, page 302,

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  5. 5.

    Annuaire statistique du secteur agricole en Tunisie from 1980 to 2013. (available at the library of Ministère de l'Agriculture.

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    Bulletin statistique de la température et des précipitations (1979-2013).

  7. 7.

    As a sensitivity analysis, other types of spatial weight matrix have been used, namely the second-order contiguity matrix and the inverse distance matrix. Results are similar to those found with the nearest neighbor matrix.

  8. 8.

    Given that the sign and the magnitude of the other variables are unchanged and due to space limitation, parameter estimates corresponding to the P*T effect only are presented in Table 5.


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Correspondence to Nicolas Peridy.

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Zouabi, O., Peridy, N. Direct and indirect effects of climate on agriculture: an application of a spatial panel data analysis to Tunisia. Climatic Change 133, 301–320 (2015).

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  • Citrus Fruit
  • Neighboring Region
  • Spatial Interaction
  • Palm Tree
  • Irrigate Crop