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A decision support method for designing vegetation layers with minimised irrigation need

  • S.I. : Agriculture Analytics, BigData and Sustainable Development
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Abstract

Selecting a vegetation layer design goes along with determining its future irrigation need. Therefore, it is essential to take a design decision that is minimising the cumulated construction and irrigation costs in a given depreciation period. This contribution showcases a decision support approach using long term weathering time series and soil water balances with example data for turf soccer fields in six German regions. The approach relies on minimising both material and irrigation costs by modifying soil layer design parameters; here the layer thickness and therefore its water retention capacity. E.g. suggested layer thicknesses between 200 and 250 mm for Stuttgart lead over 10–40 year depreciation periods to estimated substrate and water cost savings between 90 and 194% in comparison to a standard substrate layer thickness of 80 mm. For practical applications, the presented theoretical approach needs to be adapted with the usable soil water storage capacity and relationships describing evapotranspiration for given substrate-turfgrass combinations.

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Notes

  1. Textile structures do feature capillary properties that may complement those of vegetation layers: e.g. adsorption volumina of 100–1000 mass% or capillary rise levels up to 50 cm and more with comparatively high hydraulic conductivity values are common material properties for nonwovens designed as fluid absorbers (Maschler et al. 2016). Hence, these materials do form interesting aggregates for soil substrates, as their hydraulic conductivity is much higher than in soil substrates (Kaya et al. 2017).

  2. p. 92 gives further specific construction and irrigation recommendations.

  3. O’Brien and Oatis (2018), USGA (2017).

  4. Schlesiger et al. (2017).

  5. For optimal substrate mixtures, see e.g. Yin et al (2012).

  6. See e.g. Zotarelli et al (2010).

  7. See https://www.dwd.de/DE/leistungen/cdcftp/cdcftp.html; accessed on 09 March 2018.

  8. See ftp://ftp-cdc.dwd.de/pub/CDC/derived_germany/soil/daily/recent/derived_germany_soil_daily_recent_stations_list.txt; accessed on 09 March 2018.

  9. See ftp://ftp-cdc.dwd.de/pub/CDC/derived_germany/soil/daily/recent/DESCRIPTION_derivgermany_soil_daily_recent_en.pdf; accessed on 09 March 2018.

  10. The time series is for Bernburg/Saale, CDC station index 445, 84 m above mean sea level, latitude 51.82°, longitude 11.71° in Saxony-Anhalt, from 31/03/2017 to 30/09/2017.

  11. Equations (5) and (6) in Table 2.

  12. The authors forego listing the usage of time in the stock-flow models for reasons of simplicity.

  13. Typically ≥ 60 mm/h according to DIN 18035-4:2012-02, p. 10, Table 3.

  14. The main modelling entities do form stocks (state variables, boxed) that are connected with flows (double-lined arrows with valve symbols), representing their rates of change. Auxiliary variables (start and end points single-lined input/information arrows) are used to calculate dependent values, whereas constants and lookup tables provide fixed scalar and vectorial data (starting point for input arrows). Sources and drains do represent state variables with an arbitrary, infinite value (represented as cloud symbols). Stock-flow models can be mapped directly on nonlinear differential equation systems. See e.g. Sterman (2001, 2002a, b).

  15. The \( \hbox{max} \left( {a_{1} ; \ldots ;\, a_{i} ; \ldots ;\, a_{n} } \right) \) function returns the biggest of its arguments \( a_{i} \). Here, its role in (4) forms restricting \( x_{i} \) to \( x_{i} \in {\mathbb{R}}_{0}^{ + } \).

  16. Unit conversion: \( {\text{mm}} \cdot \frac{\text{t}}{{{\text{m}}^{ 3} }} \cdot \frac{{1\,{\text{m}}_{{{\text{H}}_{2} {\text{O}}}}^{3} }}{\text{t}} = \frac{\text{m}}{1000} \cdot \frac{\text{t}}{{{\text{m}}^{ 3} }} \cdot \frac{{1000\, {\text{l}}}}{\text{t}} = \frac{\text{l}}{{{\text{m}}^{2} }} \).

  17. There are quite better software environment choices for implementing the simulation and the cost model, as input and output data, the models and its documentation should be maintained, separately—especially in Decision Support Systems. Here, Microsoft Excel® 2010 was chosen to keep the barriers to entry on a simple level for planners and designers.

  18. The CDC weather station identifier is 4931, Stuttgart-Echterdingen, latitude 48.69°, longitude 9.22°. The values of VPGB and RSK between 01/04/2017 and 30/09/2017 were used.

  19. All time series cover 25 a with in total 9497 values from 01/01/1992 to 31/12/2017.

  20. The results of each task are listed in their corresponding results column.

  21. The standard soil layer height of 80 mm corresponds to a soil water storage capacity of 13.4 l/m²; the standard soil layer height of 120 mm corresponds to a soil water storage capacity of 20.2 l/m².

  22. Power (2001, p. 432).

  23. Ibidem, p. 436.

  24. Ibidem, p. 435.

  25. Ibidem, p. 433.

  26. See e.g. Zotarelli et al. (2010) or Cong et al. (2014).

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Authors and Affiliations

  1. Deutsche Institute für Textil- und Faserforschung (DITF) Denkendorf, Körschtalstr. 26, 73770, Denkendorf, Germany

    Tobias Maschler, Thomas Stegmaier & Meike Tilebein

  2. Institute of Agricultural Engineering, University of Hohenheim, Garbenstraße 9, 70599, Stuttgart, Germany

    Bastian Stürmer-Stephan, Jörg Morhard & Hans W. Griepentrog

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  1. Tobias Maschler
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Correspondence to Tobias Maschler.

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Maschler, T., Stürmer-Stephan, B., Morhard, J. et al. A decision support method for designing vegetation layers with minimised irrigation need. Ann Oper Res 314, 577–600 (2022). https://doi.org/10.1007/s10479-019-03401-0

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