• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.6
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• pp.111-121
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• 2019

Evapotranspiration (ET) of vegetation is one of the major components of the hydrologic cycle, and its accurate estimation is important for hydrologic water balance, irrigation management, crop yield simulation, and water resources planning and management. For agricultural crops, ET is often calculated in terms of a short or tall crop reference, such as well-watered, clipped grass (reference crop evapotranspiration, $ET_o$). The Penman-Monteith equation recommended by FAO (FAO 56-PM) has been accepted by researchers and practitioners, as the sole $ET_o$ method. However, its accuracy is contingent on high quality measurements of four meteorological variables, and its use has been limited by incomplete and/or inaccurate input data. Therefore, this study evaluated the applicability of Backpropagation Neural Network (BPNN) model for estimating $ET_o$ from less meteorological data than required by the FAO 56-PM. A total of six meteorological inputs, minimum temperature, average temperature, maximum temperature, relative humidity, wind speed and solar radiation, were divided into a series of input groups (a combination of one, two, three, four, five and six variables) and each combination of different meteorological dataset was evaluated for its level of accuracy in estimating $ET_o$. The overall findings of this study indicated that $ET_o$ could be reasonably estimated using less than all six meteorological data using BPNN. In addition, it was shown that the proper choice of neural network architecture could not only minimize the computational error, but also maximize the relationship between dependent and independent variables. The findings of this study would be of use in instances where data availability and/or accuracy are limited.

• Horticultural Science & Technology
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• v.28 no.4
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• pp.574-579
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• 2010

Waste nutrient solution (WNS) that was the drained nutrient solution of Horticultural Research Institute of Japan for culture tomato in perlite hydroponics showed $1.9-2.4dS{\cdot}m^{-1}$ of EC and 5.7-7.1 pH from April to July. Although ${NH_4}^+-N$ concentration of WNS decreased remarkably, the other nutrients did not change significantly, as compared with supplied solution. There were no significant differences in plant height, stem diameter, and the other growth characteristics of tomato plants grown by 2 fertigation nutrient solutions; BHF (Bountiful Harvest Fertilizer, 10% of N, 13% of $PO_4$, 13% of K, 0.05% of B, 0.05% of Zn, and 0.0023% of Cu that made in Korea) and Megasol (11% of N, 8% of $PO_4$, 34% of K, 0.032% of Mn, 0.002% of B, 0.048% of Fe, 0.0122% of Zn, and 0.0023% of Cu that made in Belgium.); however, the chlorophyll content of tomato leaf was highest in WNS. The fresh and dry weight of tomato plants were higher in 3 fertigation treatments than irrigation of tap water, while there were no significant differences in fresh and dry weight among the 3 fertigation treatments. The mineral content of tomato leaf also did not show any differences among the 3 fertigation treatments and any regular tendency in all minerals. Total yield, fruit weight and fruit numbers of tomato were higher in WNS, followed by Megasol, BHF and control, although there were not any difference among the 3 fertigation nutrient solution treatments. BER(blossom-end rot)of tomato fruits decreased in fertigation treatments, especially, fruits grown in WNS and BHF showed lower BER. However, the transpiration rate of leaf was higher in control, followed by BHF, WNS and Megasol, The fruit size and soluble solids content was higher in 3 fertigation nutrient treatments than control. These results suggest that WNS can be used for fertigation solution in tomato because yield and quality of tomato fruit grown in WNS fertigation treatment were similar to those in 2 fertigation nutrient solutions treatments(BHF, Megasol).