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Irrigation water intensity and climate variability: an agricultural crops analysis of Italian regions

  • Research in Environmental Planning and Management
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Abstract

This paper examines the relationship between the requirement of water resources for irrigation and climate variability and analyses the ranking of the best and worst performance of irrigation water intensity for each Italian region. To measure water resources demanded by agriculture, the irrigation water intensity (IWI) indicator has been computed as the ratio between volumes of irrigation water and total crop harvested. By applying panel data methodologies to a regional dataset spanning from 2000 to 2009, we may address heterogeneity and omitted variable issues. By merging meteo-climatic with agricultural variables, we may confirm that water precipitations, maximum temperature, irrigation propensity indicator, and yields are the main and relevant determinants of the IWI indicator. Moreover, results confirm our expectations that regions belonging to South and Islands macro-areas seem the best performers in terms of irrigation water intensity. Also, our analysis reveals limited availability of information on water resource data and suggests strengthening the need for regularly collecting data and producing statistics to support in designing adequate tools for optimal policies.

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Data availability

The datasets generated and/or analysed during the current study are available in the Istat repository, http://dati.istat.it/ and https://www.istat.it/it/archivio/5679

Notes

  1. As underlined by EEA (2019) even if the increased concentration of atmospheric CO2 may affect crop yield, the effect of future CO2 fertilisation is still controversial (see, for example, Fitzgerald et al. 2016; Ciscar et al. 2018). In the Peseta III report, the simulation results show that yield declines are offset by the CO2 fertilisation effect for C3 crops, which are temperate or cool-season plants (Szewczyk et al. 2018).

  2. For a more detailed evaluation of the impact of the CAP on water in 2019, see https://ec.europa.eu/info/food-farming-fisheries/key-policies/common-agricultural-policy/cmef/sustainability/impact-cap-water_en

  3. See the Communication to the European Parliament and the Council COM/2007/0414 final https://eur-lex.europa.eu/legal-content/en/ALL/?uri=CELEX:52007DC0414

  4. For a more detailed discussion of the VI General Agricultural Census, see Istat (2012).

  5. The pooled OLS estimator might be applied only when the dataset relies on large-N and T. This because the error term that depends on the unobserved effect is not weakly dependent across time. However, in general, the pooled OLS estimator is not efficient.

  6. For a more detailed description of these econometric methods, see Baltagi (1995) and Wooldridge (2010).

  7. For a detailed demonstration of the statistical relationship among fixed-, random-, and correlated random-effects models, see Wooldridge (2010) and Schunck (2013).

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Acknowledgements

The authors would like to thank the Editor and gratefully acknowledge the constructive comments provided by the two anonymous referees. Also, the authors would like to thank seminar participants at the online International Conference on Economics, Energy and Environment (ICEE) conference, 25–27 June 2020, Cappadocia, Turkey, for useful comments and discussions.

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Authors and Affiliations

  1. Department of Political Science and International Relations, University of Palermo, Via Maqueda, 324, Palermo, Italy

    Sabrina Auci

  2. Istat, Italian National Institute of Statistics, Environmental and Territorial Statistics Directorate, Via C. Balbo, 16, Rome, Italy

    Donatella Vignani

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  1. Sabrina Auci
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All authors contributed to the study conception and design. SA developed the methodology and the formal analysis by applying statistical techniques to the study data. SA prepared the first original draft of the manuscript and contributed to the review and editing of the final version of the published work. DV investigated and collected the data of this study, by curating the research data for initial use and later re-use. DV contributed to the review and editing of the final version of the published work. All authors read and approved the final manuscript.

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Correspondence to Sabrina Auci.

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Table 6 Descriptive statistics by Italian macro-areas and Italy
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Auci, S., Vignani, D. Irrigation water intensity and climate variability: an agricultural crops analysis of Italian regions. Environ Sci Pollut Res 28, 63794–63814 (2021). https://doi.org/10.1007/s11356-020-12136-6

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