Climate variability and agriculture in Italy: a stochastic frontier analysis at the regional level

Abstract

In the next future, climate change effects will represent a challenge for Europe and the Mediterranean area. These will have to cope with a rapid increase in climate variability. Although many economic sectors may be affected, agriculture is the most susceptible as climate heavily affects crop production trends, yield variability and the availability of areas suitable for cultivation. Using the stochastic frontier approach, the aim of this work is to analyse the impacts of climate variability on Italian regional technical efficiency in the agricultural sector for a period spanning from 2000 to 2009. Considering that technical inefficiency could be influenced by two main annual meteorological variables—the deviation of rainfalls and minimum temperature from the 1971–2000 mean value—and by seasonal rainfalls and minimum temperature moving average values, we find that annual, as well as spring and autumn rainfalls, have significant and beneficial effects on efficiency and hence on regional crop yields. The minimum temperature is efficiency-increasing in summer and winter while is detrimental in autumn.

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Notes

  1. 1.

    In 2013, the European Commission launched the "EU strategy on adaptation to climate change" whose aim consists in pursuing three main objectives: (1) promoting the adoption of comprehensive adaptation strategies by EU Members, (2) supporting better-informed decision-making processes by reducing knowledge gaps about adaptation and finally (3) fostering adaptation in vulnerable key sectors such as agriculture (European Commission 2013).

  2. 2.

    Following the World Meteorological Organization (WMO)’s definition, we consider “climate variability” as deviations of climatic mean values of a given period of time (e.g. a month, season or year) with respect to long-term mean values for the same period. These deviations are usually defined as anomalies.

  3. 3.

    Using a set of data that meets classification and methodology criteria internationally shared guarantees spatial and temporal comparability of official statistics.

  4. 4.

    This data is referred to the crop year.

  5. 5.

    “Air temperatures between 45 and 55 °C that occur for at least 30 min directly damage crop leaves in most environments; even lower temperatures (35–40 °C) can be damaging if they persist longer” (Iglesias et al. 2009, p. 12).

  6. 6.

    The NUTS classification (Nomenclature of territorial units for statistics) is a hierarchical system for dividing up the economic territory of the EU. NUTS2 divides an EU country into regions for the application of regional policies.

  7. 7.

    Since the unavailability of datasets at farmer level as the FADN, we were able to collect a regional dataset from Istat using openly published and unpublished data as for the climatic variables.

  8. 8.

    The IPCC (2007) points out that minimum temperature is increasing more rapidly than maximum temperature.

  9. 9.

    The CLINO period, defined by the World Meteorological Organization, corresponds to the normal value of a benchmark period to which one should refer to compare the actual values of temperature and rainfalls (WMO 2012).

  10. 10.

    According to the official territorial classification of Istat, Italian regions are grouped in 5 macro-areas: North-west (Piedmont, Aosta Valley, Liguria, Lombardy), North-east (Trentino-Alto Adige, Veneto, Friuli Venezia Giulia, Emilia Romagna), Centre (Tuscany, Umbria, Marche, Lazio), South (Abruzzo, Basilicata, Calabria, Campania, Molise, Apulia), Islands (Sardinia, Sicily).

  11. 11.

    For a more detail description of the variables and data sources see Table 5 in “Appendix”.

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Acknowledgements

The authors acknowledge the support of the Italian Ministry of Education, University and Research through the PRIN-MIUR 2010–2011 Italian National Research Project ‘Climate changes in the Mediterranean area: scenarios, mitigation policies and technological innovation’. The authors also thank the anonymous referees for useful insights and comments. Usual disclaimers apply.

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Appendix

Appendix

See Tables 5, 6 and 7.

Table 5 Overview of the model variables
Table 6 Specification of inputs, inefficiency factors and heteroscedasticity factors among the models implemented
Table 7 Descriptive statistics by macro-areas

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Auci, S., Vignani, D. Climate variability and agriculture in Italy: a stochastic frontier analysis at the regional level. Econ Polit 37, 381–409 (2020). https://doi.org/10.1007/s40888-020-00172-x

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Keywords

  • Climate change
  • Climate variability
  • Agricultural sector
  • Italian regions technical efficiency
  • Stochastic frontier approach

JEL Classification

  • Q10
  • Q54
  • Q18