IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v9y2020i6p197-d371651.html
   My bibliography  Save this article

Characteristics of Soil Parameters of Agricultural Land Use Types, Their Location and Development Forecast

Author

Listed:
  • Jozef Vilček

    (National Agricultural and Food Centre, Soil Science and Conservation Research Institute, 08001 Prešov, Slovakia
    Department of Geography and Applied Geoinformatics, University of Prešov, 08001 Prešov, Slovakia)

  • Štefan Koco

    (National Agricultural and Food Centre, Soil Science and Conservation Research Institute, 08001 Prešov, Slovakia
    Department of Geography and Applied Geoinformatics, University of Prešov, 08001 Prešov, Slovakia)

  • Eva Litavcová

    (Department of Mathematical Methods and Managerial Informatics, University of Prešov, 08001 Prešov, Slovakia)

  • Stanislav Torma

    (National Agricultural and Food Centre, Soil Science and Conservation Research Institute, 08001 Prešov, Slovakia)

Abstract

In this paper we point out the basic soil parameters characterizing current arable land, permanent grassland, vineyards, and orchards in Slovakia. While the area of permanent land use types is more or less stable, there is a noticeable decrease in the area of arable land. In Slovakia, arable land is located mainly on the plain. The value of its production potential is 67 points (the highest quality soil has 100 points). Permanent grassland is found at higher altitudes on slopes, with a higher gravel content, and the value of their production potential is 35 points. Vineyards are predominantly located in the warm regions of southern Slovakia on the middle slopes. These soils are generally loamy, without significant gravel content, and the value of their production potential is 59 points. Most orchards are located on the plains. The soils are predominantly loamy and deep, without significant gravel content, and the value of their production potential is 63 points. Characteristics of agricultural land use types were determined using vector databases of soil parameters obtained from Soil Science and Conservation Research Institute information systems and a current vector layer for identification of agriculturally used soils, the Land Parcel Identification System, using geographic information systems. Moreover, our analysis tries to determine what developments can be expected in the use of four agricultural land use types. The modeling assumptions concern the future performance of these variables using exponential smoothing and Box–Jenkins methodology.

Suggested Citation

  • Jozef Vilček & Štefan Koco & Eva Litavcová & Stanislav Torma, 2020. "Characteristics of Soil Parameters of Agricultural Land Use Types, Their Location and Development Forecast," Land, MDPI, vol. 9(6), pages 1-17, June.
  • Handle: RePEc:gam:jlands:v:9:y:2020:i:6:p:197-:d:371651
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/9/6/197/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2073-445X/9/6/197/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Peter R. Winters, 1960. "Forecasting Sales by Exponentially Weighted Moving Averages," Management Science, INFORMS, vol. 6(3), pages 324-342, April.
    2. Clive W.J. Granger, 2004. "Time Series Analysis, Cointegration, and Applications," American Economic Review, American Economic Association, vol. 94(3), pages 421-425, June.
    3. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    4. Robert Engle, 2004. "Risk and Volatility: Econometric Models and Financial Practice," American Economic Review, American Economic Association, vol. 94(3), pages 405-420, June.
    5. Jozef Vilček & Štefan Koco, 2018. "Integrated index of agricultural soil quality in Slovakia," Journal of Maps, Taylor & Francis Journals, vol. 14(2), pages 68-76, November.
    6. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    7. Michael Brady & Elena Irwin, 2011. "Accounting for Spatial Effects in Economic Models of Land Use: Recent Developments and Challenges Ahead," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 48(3), pages 487-509, March.
    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. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    2. Ataurima Arellano, Miguel & Rodríguez, Gabriel, 2020. "Empirical modeling of high-income and emerging stock and Forex market return volatility using Markov-switching GARCH models," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    3. Shehu Usman Rano, Aliyu, 2010. "Does inflation has an Impact on Stock Returns and Volatility? Evidence from Nigeria and Ghana," MPRA Paper 30091, University Library of Munich, Germany, revised 19 Mar 2011.
    4. He, Xue-Zhong & Li, Kai & Santi, Caterina & Shi, Lei, 2022. "Social interaction, volatility clustering, and momentum," Journal of Economic Behavior & Organization, Elsevier, vol. 203(C), pages 125-149.
    5. Arthur M. Berd & Robert F. Engle & Artem Voronov, 2010. "The Underlying Dynamics of Credit Correlations," Papers 1001.0786, arXiv.org.
    6. Buczyński Mateusz & Chlebus Marcin, 2018. "Comparison of Semi-Parametric and Benchmark Value-At-Risk Models in Several Time Periods with Different Volatility Levels," Financial Internet Quarterly (formerly e-Finanse), Sciendo, vol. 14(2), pages 67-82, June.
    7. J Keith Ord & Ralph D Snyder & Anne B Koehler & Rob J Hyndman & Mark Leeds, 2005. "Time Series Forecasting: The Case for the Single Source of Error State Space," Monash Econometrics and Business Statistics Working Papers 7/05, Monash University, Department of Econometrics and Business Statistics.
    8. Tim Bollerslev, 2008. "Glossary to ARCH (GARCH)," CREATES Research Papers 2008-49, Department of Economics and Business Economics, Aarhus University.
    9. Dahiru A. Balaa & Taro Takimotob, 2017. "Stock markets volatility spillovers during financial crises: A DCC-MGARCH with skewed-t density approach," Borsa Istanbul Review, Research and Business Development Department, Borsa Istanbul, vol. 17(1), pages 25-48, March.
    10. Wen Cheong, Chin & Hassan Shaari Mohd Nor, Abu & Isa, Zaidi, 2007. "Asymmetry and long-memory volatility: Some empirical evidence using GARCH," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 373(C), pages 651-664.
    11. Feng, Yuanhua & Härdle, Wolfgang Karl, 2020. "A data-driven P-spline smoother and the P-Spline-GARCH models," IRTG 1792 Discussion Papers 2020-016, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    12. Carol Alexander & Emese Lazar & Silvia Stanescu, 2011. "Analytic Approximations to GARCH Aggregated Returns Distributions with Applications to VaR and ETL," ICMA Centre Discussion Papers in Finance icma-dp2011-08, Henley Business School, University of Reading.
    13. Schindler, Felix, 2009. "Volatilitätseffekte am US-amerikanischen Häusermarkt," ZEW Discussion Papers 09-048, ZEW - Leibniz Centre for European Economic Research.
    14. Aliyu, Shehu Usman Rano, 2020. "What have we learnt from modelling stock returns in Nigeria: Higgledy-piggledy?," MPRA Paper 110382, University Library of Munich, Germany, revised 06 Jun 2021.
    15. Oberndorfer, Ulrich & Ulbricht, Dirk, 2007. "Lost in Transmission? Stock Market Impacts of the 2006 European Gas Crisis," ZEW Discussion Papers 07-030, ZEW - Leibniz Centre for European Economic Research.
    16. Ardia, David & Bluteau, Keven & Boudt, Kris & Catania, Leopoldo, 2018. "Forecasting risk with Markov-switching GARCH models:A large-scale performance study," International Journal of Forecasting, Elsevier, vol. 34(4), pages 733-747.
    17. Degiannakis, Stavros & Floros, Christos & Dent, Pamela, 2013. "Forecasting value-at-risk and expected shortfall using fractionally integrated models of conditional volatility: International evidence," International Review of Financial Analysis, Elsevier, vol. 27(C), pages 21-33.
    18. Park, Beum-Jo, 2011. "Asymmetric herding as a source of asymmetric return volatility," Journal of Banking & Finance, Elsevier, vol. 35(10), pages 2657-2665, October.
    19. Gürtler, Marc & Rauh, Ronald, 2012. "Challenging traditional risk models by a non-stationary approach with nonparametric heteroscedasticity," Working Papers IF41V1, Technische Universität Braunschweig, Institute of Finance.
    20. Audrone Virbickaite & M. Concepción Ausín & Pedro Galeano, 2015. "Bayesian Inference Methods For Univariate And Multivariate Garch Models: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 29(1), pages 76-96, February.

    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:gam:jlands:v:9:y:2020:i:6:p:197-:d:371651. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.