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Modeling Soil Thermal Regimes During a Solarization Treatment in Closed Greenhouse by Means of Symbolic Regression via Genetic Programming

  • A. D’EmilioEmail author
Conference paper
  • 27 Downloads
Part of the Lecture Notes in Civil Engineering book series (LNCE, volume 67)

Abstract

Modeling soil thermal regimes during a solarization treatment in closed greenhouse is useful to estimate the required duration of the treatment in relation to the climatic conditions, as well as the efficacy of the technique. Several studies have been carried out, based on two main strategies: modeling the physical processes of the soil-mulch-greenhouse system or applying numerical procedures based on neural networks (NNs). However, the application and reliability of physical models require accurate knowledge of the thermo-physical properties of each component of the system, while NNs do not give any symbolic function which can be easily used. Symbolic regression via genetic programming represents an alternative method for finding a function that best fit a given set of data. In this paper, a such model is proposed, which use air temperature and global solar radiation flux outside the greenhouse, depth into the soil, existence of mulch and time of day as input variables and provides soil temperatures at different depths as output. The results allowed to obtain an easy to use symbolic function that is able to estimate soil temperature with an accuracy comparable to that one attained with other simulation models.

Keywords

Soil solarization Symbolic regression Greenhouse Soil temperature 

Notes

Acknowledgements

The activity presented in the paper is part of the research grant “University Research – Research Plan 2016/2018” by University of Catania.

References

  1. Augusto, D. A., & Barbosa, H. J. (2000). Symbolic regression via genetic programming. In Sixth Brazilian Symposium on Neural Networks (Vol. 1, pp. 173–178). IEEE.Google Scholar
  2. Cascone, G., D’Emilio, A., & Mazzarella, R. (2012). Polyamide-based film as greenhouse covering in soil solarization. Acta Horticulture, 927, 659–666.CrossRefGoogle Scholar
  3. Castello, I., D’Emilio, A., Raviv, M., & Vitale, A. (2017). Soil Solarization as a sustainable solution to control tomato pseudomonads infections in greenhouses. Agronomy for Sustainable Development, 37(6), 59.Google Scholar
  4. Cenis, J. L. (1989). Temperature evaluation in solarized soils by Fourier analysis. Phytopathology, 79, 506–510.CrossRefGoogle Scholar
  5. D’Emilio, A., Mazzarella, R., Porto, S. M. C., & Cascone, G. (2012). Neural networks for predicting greenhouse thermal regimes during soil solarization. Transactions of the ASABE, 55(3), 1093–1103.CrossRefGoogle Scholar
  6. D’Emilio, A. (2014). Predictive model of soil temperature and moisture during solarization in closed greenhouse. Transactions of the ASABE, 57(6), 1817–1830.Google Scholar
  7. D’Emilio, A. (2017a). Soil temperature in greenhouse soil solarization using TIF and VIF as mulching films. Transactions of the ASABE, 60(4), 1349–1355.CrossRefGoogle Scholar
  8. D’Emilio, A. (2017b). Parametric analysis of soil temperature in a solarization treatment in closed greenhouse. Acta Horticulture, 1170, 243–250.CrossRefGoogle Scholar
  9. Katan, J. (1981). Solar heating (solarization) of soil for control of soilborne pests. Annual Review of Phytopathology, 19(1), 211–236.CrossRefGoogle Scholar
  10. Koza, J. R. (1994). Genetic programming as a means for programming computers by natural selection. Statistics and Computing, 4, 87.CrossRefGoogle Scholar
  11. Mahrer, Y., Avissar, R., Naot, O., & Katan, J. (1987). Intensified soil solarization with closed greenhouses: Numerical and experimental studies. Agricultural and Forest Meteorology, 41(3–4), 325–334.CrossRefGoogle Scholar
  12. Miceli, A., Moncada, A., Camerata Scovazzo, G., & D’Anna, F. (2008). Influence of greenhouse volume/area ratio on soil solarization efficiency. Acta Horticulture, 801, 211–218.CrossRefGoogle Scholar
  13. Morra, L., Carrieri, R., Fornasier, F., Mormile, P., Rippa, M., Baiano, S., et al. (2018). Solarization working like a “solar hot panel” after compost addition sanitizes soil in thirty days and preserves soil fertility. Applied Soil Ecology, 126, 65–74.CrossRefGoogle Scholar
  14. Öz, H., Coskan, A., & Atilgan, A. (2017). Determination of effects of various plastic covers and biofumigation on soil temperature and soil nitrogen form in greenhouse solarization: New solarization cover material. Journal of Polymers and the Environment, 25(2), 370–377.Google Scholar
  15. Öz, H. (2018). A new approach to soil solarization: Addition of biochar to the effect of soil temperature and quality and yield parameters of lettuce (Lactuca Sativa L. Duna). Scientia Horticulturae, 228, 153–161.CrossRefGoogle Scholar
  16. Shi, C.-H., Hu, J.-R., Wei, Q.-W., Yang, Y.-T., Cheng, J.-X., Han, H.-L., et al. (2018). Control of Bradysia odoriphaga (Diptera: Sciaridae) by soil solarization. Crop Protection, 114, 76–82.CrossRefGoogle Scholar
  17. Tiba, C., & Ghini, R. (2006). Numerical procedure for estimating temperature in solarized soils. Pesquisa Agropecuária Brasileira, 41(3), 533–537.CrossRefGoogle Scholar
  18. Van Wijk, W., & De Vries, D. (1963). Periodic temperature variations in a homogeneous soil. In W. R. Van Wijk (Ed.), Physics of plant environment (pp. 102–143). Amsterdam: North-Holland Publishing.Google Scholar
  19. Vitale, A., Castello, I., Cascone, G., D’Emilio, A., Mazzarella, R., & Polizzi, G. (2011). Reduction of corky root infections on greenhouse tomato crops by soil solarization in South Italy. Plant Disease, 95(2), 195–201.CrossRefGoogle Scholar
  20. Vitale, A., Castello, I., D’Emilio, A., Mazzarella, R., Perrone, G., Epifani, F., et al. (2013). Short-term effects of soil solarization in suppressing Calonectria microsclerotia. Plant and Soil, 368(1–2), 603–617.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Agriculture, Food and Environment (Di3A)Università degli Studi di CataniaCataniaItaly

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