Abstract
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.
Similar content being viewed by others
Notes
Rapport annuel sur les caractéristiques économiques pour chaque gouvernorat 1990–2012
Annuaire statistique de la Tunisie 2012, page 121, page 302, http://www.ins.nat.tn/indexfr.php
Annuaire statistique du secteur agricole en Tunisie from 1980 to 2013. (available at the library of Ministère de l'Agriculture.
Bulletin statistique de la température et des précipitations (1979-2013).
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.
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.
References
Ajewole O, Ogunlade I, Adewumi M (2010) Empirical analysis of agriculture production and climate change: a case study of Nigeria. J Sustain Dev Africa 12(6):275–283
Anselin L (2008) Spatial effects in econometric practice in environmental and resource economics. Am J Agric Econ 83(3):705–10
Ben Zaied Y, Ben Cheikh N (2015) Long-run versus short-run analysis of climate change impacts on agricultural Crops. Environ Model Assess 20(3):259–271
Bsaies A, Mokadem L (2009) L’impact du changement climatique sur l’agriculture et la croissance économique : Cas de la Tunisie In : Energie, changement climatique et développement durable : Le cas des pays Maghrébins, Centre de publication universitaire
Debarsy N, Ertur C (2010) Testing for spatial autocorrelation in a fixed effects panel data model. Reg Sci Urban Econ 40(6):453–470
Deschênes O, Greenstone M (2012) The economic impacts of climate change: evidence from agricultural output and random fluctuations in weather: reply. Am Econ Rev 102(7):3761–3773
Deschênes O, Greenstone M (2007) The economic impacts of climate change: evidence from agricultural output and random fluctuations in weather. Am Econ Rev 97(1):354–385
Elhorst J (2003) Specification and estimation of spatial panel data models. Int Reg Sci Rev 26(3):244–68
Fisher A, Haneman W, Roberts M, Schlenker W (2012) The economic impacts of climate change: evidence from agricultural output and random fluctuations in weather: comment. Am Econ Rev 102(7):3749–60
Giannakopoulos S, Moriondo M, Lesager P, Tin T (2005) Climate change impacts in the Mediterranean resulting from a 2 °C global temperature rise. A report for a WWF
IPCC (2013) Intergovernmental panel on climate change, climate change 2013: the Physical science basis, fifth assessment report (AR5)
Kabubo M, Karanja K (2007) The economic impact change on Kenyan crop agriculture: a Ricardian approach, world bank policy research working paper No.4334
Lee J, Nadolnya D, Hartarska V (2012) Impact of climate change on agricultural in Asian countries: Evidence from panel study”. Southern Agricultural Economics Association Annual Meeting, Birmingham, February 4–7, 2012
Mendelsohn RN, Shaw D (1994) The Impact of global warming on agriculture: a Ricardian analysis. Am Econ Rev 84(4):753–771
Muchena P (1994) Implications of climate change for maize yields in Zimbabwe, In: Implications of Climate Change for International Agriculture: Crop Modeling Study. Nature 367:133–138
Nefzi A, Bouzidi F (2009) Evolution de l’impact économique du changement climatique sur l’agriculture au Maghreb, Cinquième collègue international Hammamet Tunisie : Énergie, changement climatique et développement durable
Péridy N, Brunetto M, Ghoneim M. A (2012) Le coût économique du changement climatique dans les pays MENA: une évaluation quantitative micro-spatiale et une revue des politiques d’adaptation. FEMISE Report, FEM 34–03.
Temesgen T (2007) Measuring the economic impact of climate change on Ethiopian agriculture: a Ricardian approach, World Bank Policy Research Paper No. 4342
Thapa S, Joshi G (2010) A Ricardian analysis of climate change impact on Nepalese agriculture, MPRA (paper no29785)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
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). https://doi.org/10.1007/s10584-015-1458-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10584-015-1458-3