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.
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References
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.
Cascone, G., D’Emilio, A., & Mazzarella, R. (2012). Polyamide-based film as greenhouse covering in soil solarization. Acta Horticulture, 927, 659–666.
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.
Cenis, J. L. (1989). Temperature evaluation in solarized soils by Fourier analysis. Phytopathology, 79, 506–510.
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.
D’Emilio, A. (2014). Predictive model of soil temperature and moisture during solarization in closed greenhouse. Transactions of the ASABE, 57(6), 1817–1830.
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.
D’Emilio, A. (2017b). Parametric analysis of soil temperature in a solarization treatment in closed greenhouse. Acta Horticulture, 1170, 243–250.
Katan, J. (1981). Solar heating (solarization) of soil for control of soilborne pests. Annual Review of Phytopathology, 19(1), 211–236.
Koza, J. R. (1994). Genetic programming as a means for programming computers by natural selection. Statistics and Computing, 4, 87.
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.
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.
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.
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.
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.
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.
Tiba, C., & Ghini, R. (2006). Numerical procedure for estimating temperature in solarized soils. Pesquisa Agropecuária Brasileira, 41(3), 533–537.
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.
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.
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.
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The activity presented in the paper is part of the research grant “University Research – Research Plan 2016/2018” by University of Catania.
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D’Emilio, A. (2020). Modeling Soil Thermal Regimes During a Solarization Treatment in Closed Greenhouse by Means of Symbolic Regression via Genetic Programming. In: Coppola, A., Di Renzo, G., Altieri, G., D'Antonio, P. (eds) Innovative Biosystems Engineering for Sustainable Agriculture, Forestry and Food Production. MID-TERM AIIA 2019. Lecture Notes in Civil Engineering, vol 67. Springer, Cham. https://doi.org/10.1007/978-3-030-39299-4_32
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