IDEAS home Printed from https://ideas.repec.org/p/ags/eaae11/114358.html
   My bibliography  Save this paper

Adaptation Of Mediterranean Crops To Water Pressure In The Ebro Basin: A Water Efficiency Index

Author

Listed:
  • Fernandez-Haddad, Zaira
  • Quiroga, Sonia

Abstract

In this paper, we assess the output-oriented technical efficiency of agricultural production functions in order to compare, over time, economic and environmental production processes in the different regions of the Spanish Ebro basin, in a climate change context. The measurement of technical efficiency in agriculture can provide useful information about the competitiveness of farms and their potential to increase its productivity moreover can help in the crops adaptation to water pressure by improving the management of scarce resources. Here, we generate an agricultural water efficiency index to evaluate the adaptation of some Mediterranean crops to the water pressures in this area. We estimate frontier production functions and technical efficiency measures, using panel data models. This will allow us to observe changes in production due to individual specific effects and those that are time specific. To characterize our model, we use historical data, about crop yields, water requirements and climate as well as socio-economic and geographical aspects of the most representative crops in the provinces of the Ebro basin during 1976-2007. Then we generate a ranking of the most efficient crops across geographical areas, given their water use and other inputs, to evaluate policy scenarios with adjustments in water supply.

Suggested Citation

  • Fernandez-Haddad, Zaira & Quiroga, Sonia, 2011. "Adaptation Of Mediterranean Crops To Water Pressure In The Ebro Basin: A Water Efficiency Index," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 114358, European Association of Agricultural Economists.
    • Handle: RePEc:ags:eaae11:114358
    DOI: 10.22004/ag.econ.114358
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/114358/files/Fernandez-Haddad_Zaira_529.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.114358?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Ian T. Jolliffe, 1982. "A Note on the Use of Principal Components in Regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 31(3), pages 300-303, November.
    2. J. N. R. Jeffers, 1967. "Two Case Studies in the Application of Principal Component Analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 16(3), pages 225-236, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yu Yu & Nita Umashankar & Vithala R. Rao, 2016. "Choosing the right target: Relative preferences for resource similarity and complementarity in acquisition choice," Strategic Management Journal, Wiley Blackwell, vol. 37(8), pages 1808-1825, August.
    2. Jolliffe, Ian, 2022. "A 50-year personal journey through time with principal component analysis," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    3. Kohei Adachi & Nickolay T. Trendafilov, 2016. "Sparse principal component analysis subject to prespecified cardinality of loadings," Computational Statistics, Springer, vol. 31(4), pages 1403-1427, December.
    4. Dennis Shen & Peng Ding & Jasjeet Sekhon & Bin Yu, 2022. "Same Root Different Leaves: Time Series and Cross-Sectional Methods in Panel Data," Papers 2207.14481, arXiv.org, revised Oct 2022.
    5. Carlos Moreno-Miranda & Hipatia Palacios & Daniele Rama, 2019. "Small-holders perception of sustainability and chain coordination: evidence from Arriba PDO Cocoa in Western Ecuador," Bio-based and Applied Economics Journal, Italian Association of Agricultural and Applied Economics (AIEAA), vol. 8(3), December.
    6. Zaijun Li & Jianquan Cheng & Qiyan Wu, 2016. "Analyzing regional economic development patterns in a fast developing province of China through geographically weighted principal component analysis," Letters in Spatial and Resource Sciences, Springer, vol. 9(3), pages 233-245, October.
    7. Kawano, Shuichi & Fujisawa, Hironori & Takada, Toyoyuki & Shiroishi, Toshihiko, 2015. "Sparse principal component regression with adaptive loading," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 192-203.
    8. Heni Masruroh & Soemarno Soemarno & Syahrul Kurniawan & Amin Setyo Leksono, 2023. "A Spatial Model of Landslides with A Micro-Topography and Vegetation Approach for Sustainable Land Management in the Volcanic Area," Sustainability, MDPI, vol. 15(4), pages 1-26, February.
    9. Minjung Kyung & Ju-Hyun Park & Ji Yeh Choi, 2022. "Bayesian Mixture Model of Extended Redundancy Analysis," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 946-966, September.
    10. Hugh L. Christensen, 2015. "Algorithmic arbitrage of open-end funds using variational Bayes," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 2(04), pages 1-38, December.
    11. Jiaju Miao & Pawel Polak, 2023. "Online Ensemble of Models for Optimal Predictive Performance with Applications to Sector Rotation Strategy," Papers 2304.09947, arXiv.org.
    12. Mirza Pasic & Halima Hadziahmetovic & Ismira Ahmovic & Mugdim Pasic, 2023. "Principal Component Regression Modeling and Analysis of PM 10 and Meteorological Parameters in Sarajevo with and without Temperature Inversion," Sustainability, MDPI, vol. 15(14), pages 1-22, July.
    13. Cai, Yuezhou & Hanley, Aoife, 2012. "Building BRICS: 2-Stage DEA analysis of R&D efficiency," Kiel Working Papers 1788, Kiel Institute for the World Economy (IfW Kiel).
    14. Travaglini, Guido, 2010. "Supervised Principal Components and Factor Instrumental Variables. An Application to Violent CrimeTrends in the US, 1982-2005," MPRA Paper 22077, University Library of Munich, Germany.
    15. Jinhak Kim & Mohit Tawarmalani & Jean-Philippe P. Richard, 2019. "Convexification of Permutation-Invariant Sets," Purdue University Economics Working Papers 1315, Purdue University, Department of Economics.
    16. Elkin Castaño & Santiago Gallón, 2017. "A solution for multicollinearity in stochastic frontier production function models," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, issue 86, pages 9-23, Enero - J.
    17. Mansouri, Majdi & Hajji, Mansour & Trabelsi, Mohamed & Harkat, Mohamed Faouzi & Al-khazraji, Ayman & Livera, Andreas & Nounou, Hazem & Nounou, Mohamed, 2018. "An effective statistical fault detection technique for grid connected photovoltaic systems based on an improved generalized likelihood ratio test," Energy, Elsevier, vol. 159(C), pages 842-856.
    18. Ranjith Vijayakumar & Ji Yeh Choi & Eun Hwa Jung, 2022. "A Unified Neural Network Framework for Extended Redundancy Analysis," Psychometrika, Springer;The Psychometric Society, vol. 87(4), pages 1503-1528, December.
    19. Mishra, Aditya & Dey, Dipak K. & Chen, Yong & Chen, Kun, 2021. "Generalized co-sparse factor regression," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    20. Anish Agarwal & Keegan Harris & Justin Whitehouse & Zhiwei Steven Wu, 2023. "Adaptive Principal Component Regression with Applications to Panel Data," Papers 2307.01357, arXiv.org, revised Oct 2023.

    More about this item

    Keywords

    Crop Production/Industries; Resource /Energy Economics and Policy;

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:eaae11:114358. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/eaaeeea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.