Evapotranspiration Modeling by Linear, Nonlinear Regression and Artificial Neural Network in Greenhouse (Case study Reference Crop, Cucumber and Tomato)

Introduction: Greenhouse cultivation is a steadily developing agricultural sector throughout the world. In addition, it is known that water is a major issue almost all part of the world especially for countries which have insufficient water source. With this great expansion of greenhouse cultivation, the need of appropriate irrigation management has a great importance. Accurate determination of irrigation scheduling (irrigation timing and frequency) is one of the main factors in achieving high yields and avoiding loss of quality in greenhouse tomato and cucumber. To do this, it is fundamental to know the crop water requirements or real evapotranspiration. Accurate estimation on crop water requirement is needed to avoid the excess or deficit water application, with consequent impacts on nutrient availability for plants. This can be done by using appropriate method to determine the crop evapotranspiration (ETc). In greenhouse cultivation, crop transpiration is the most important energy dissipation mechanisms that influence ETc rate. There are a large number of literatures on methods to estimate ETc in greenhouses. ETc can be measured or estimated by direct or indirect methods. The most common direct method estimates ETc from measurements with weighing lysimeters. Thisalsoincludes the evaporation measuring equipment, class A pan, Piche atmometer and modified atmometer. Indirect method includes the measurement of net radiation, temperature, relative humidity, and air vapour pressure deficit. A large number of models have been developed from these measurements to estimate ETc. Due to the fast development of under greenhouse cultivation all around the world, the needs of information on how it affects ETc in greenhouses has to be known and summarized. The existing models for ETc calculation have to be studied to know whether it is reliable for greenhouse climate (hereafter, microclimate) or not. Regression and artificial neural network models are two important models to estimate ETc in greenhouse. The inputs of these models are net radiation, temperature, day after planting and air vapour pressure deficit (or relative humidity). Materials and Methods: In this study, daily ETc of reference crop, greenhouse tomato and cucumber crops were measured using lysimeter method in Urmia region. Several linear, nonlinear regressions and artificial neural networks were considered for ETc modelling in greenhouse. For this purpose, the effective meteorological parameters on ETc process includes: air temperature (T), air humidity (RH), air pressure (P), air vapour pressure deficit (VPD), day after planting (N) and greenhouse net radiation (SR) were considered and measured. According to the goodness of fit, different models of artificial neural networks and regression were compared and evaluated. Furthermore, based on partial derivatives of regression models, sensitivity analysis was conducted. The accuracy and performance of the employed models was judged by ten statistical indices namely root mean square error (RMSE), normalized root mean square error (NRMSE) and coefficient of determination (R2). Results and Discussion: Based on the results, the most accurate regression model to reference ETc prediction was obtained three variables exponential function of VPD, RH and SR with RMSE=0.378 mm day-1. The RMSE of optimal artificial neural network to reference ET prediction for train and test data sets were obtained 0.089 and 0.365 mm day-1, respectively. The performance of logarithmic and exponential functions to prediction of cucumber ETc were proper, with high dependent variables especially, and the most accurate regression model to cucumber ET prediction was obtained for exponential function of five variables: VPD, N, T, RH and SR with RMSE=0.353 mm day-1. In addition, for tomato ET prediction, the most accurate regression model was obtained for exponential function of four variables: VPD, N, RH and SR with RMSE= 0.329 mm day-1. The best performance of artificial neural network for ET prediction of cucumber and tomato were obtained with five inputs include: VPD, N, T, RH and SR. The RMSE values of test data sets for cucumber and tomato ET were obtained 0.24 and 0.26 mm day-1. Moreover, the sensitivity analysis results showed that VPD is the most sensitive parameter on ETc. Conclusion: The greenhouse industry has expanded across many parts of the word and the need of information on a reliable ETc method especially by indirect method is crucial. In this research, the artificial neural network models indicated good performance compared with linear and nonlinear regressions. The evaluated method could be used for scheduling irrigation of greenhouse tomato and cucumber

Effects of Partial Root-Zone Irrigation on the Water Use Efficiency and Root Water and Nitrate Uptake of Corn

Due to water shortages and the increasing need for food in recent years, the optimization of water consumption parameters, fertilizers, and food production are essential and a priority. The aim of this study is to investigate the effect of partial root-zone irrigation (PRI) methods on corn plant characteristics. The study also tried to measure the water use efficiency (WUE) of corn in pot cultivation and provide the best method of management in the fields of irrigation and fertigation. For this purpose, three irrigation methods, including alternate partial root-zone irrigation (APRI), fixed partial root-zone irrigation (FPRI), and conventional irrigation (CI) were studied in pots, and completely randomized blocks with eight replications were carried out. Each pot was evenly separated with plastic sheets into two sub-parts of equal volume, between which no water exchange occurred. The water content of the field capacity was calculated by the weighting method. The water requirement was provided daily, equal to 95% of the field capacity water content. Parameters including shoot and root dry weight, nitrate (N) uptake, the remaining nitrate in the soil, leaf area index, and WUE during the growing season were measured and compared. According to the results, the amount of saved water using the FPRI and APRI methods compared to the CI method were 28% and 32%, respectively. The highest and lowest WUE were observed as equal to 4.88 and 3.82 g/L using the APRI and CI methods, respectively, among which the CI method had the highest yield according to the amount of utilized water. Given the statistical examinations, there was no significant difference in the nitrate level of plants between CI and APRI, and the lowest uptake was observed in FPRI. Finally, considering indicators of yield production and WUE simultaneously, the APRI method was selected as the best method of management

Design and fabrication of nanocomposite-based polyurethane filter for‏ ‏improving municipal waste water quality and removing organic pollutants

Nanotechnology has been used in different industries for years. In this study, polyurethane filter, modified with nano-sized polypyrrole–ZnO was used for wastewater quality improvement. The effect of coating method and influential parameters on polymer morphology was studied by Fourier transform infrared spectroscopy and scanning electron microscopy. The results revealed that the uniformly synthesized polymers are seed like. The size of synthesized nanoparticles was observed to be about 50–120 nm. The effect of the number of iterative filtration and the height of the filter on improving the quality of the waste water was investigated using central composite design. After filtration, spectroscopy method, gas chromatography method, and some other devices such as biochemical oxygen demand meter and salt meter were used to evaluate the quality of the waste water. The results indicated that the filter efficiency in optimizing parameters such as total dissolved solids, biochemical oxygen demand, chemical oxygen demand, color, salinity, hardness, pH, and organic compounds removal is desirable. After data modeling, the optimal thickness of the filter was 3.8 cm and the most appropriate iteration for filtration was eight times obtained using a graphical method. Results showed that the designed filter had an excellent ability to improve wastewater quality and can be used in water and wastewater refining instruments