R. Moazenzadeh
Abstract
Introduction: In two last decades, greenhouse cultivation of different plants has developed among Iranian farmers, approximately 45 percent of national greenhouse cultures consisting of cucumber, tomato and pepper. As huge amounts of agricultural water in Iran are extracted from groundwater resources ...
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Introduction: In two last decades, greenhouse cultivation of different plants has developed among Iranian farmers, approximately 45 percent of national greenhouse cultures consisting of cucumber, tomato and pepper. As huge amounts of agricultural water in Iran are extracted from groundwater resources and a large number of Iranian plains are in critical conditions, and because irrigation is the major consumer of water (95 percent), it must be performed in a scientific manner. One approach to this is to obtain the knowledge of the consumptive use of major crops which is named evapotranspiration (ETc).
Materials and Methods: This research was carried out in a north-south greenhouse belonging to Plant Protection Research Institute, located on northern Tehran, Iran, for estimating greenhouse cucumber evapotranspiration. Trickle irrigation method was used, and meteorological data such as temperature, humidity and solar radiation were measured daily. Physical and chemical measurements were conducted and electric conductivity (EC) and pH values of 3.42 dsm-1 and 7.19, respectively, were recorded. Soil texture and bulk density were measured as to be sandy loam and 1.4 gr cm-3, respectively. In order to measure the actual evapotranspiration, cucumber seeds were also cultured in six similar microlysimeters and irrigation of each microlysimeter was based on FC moisture. If any drained water was available, it was measured. Finally, with measured meteorological characteristics in greenhouse which are suggested to have an effect on ET and were measurable, the best multiple linear regression and artificial neural network were established. The average data from three microlysimeters were used for calibration and that from three other microlysimeters were used for validation set.
Results and Discussion: In the former case, when we used one multiple linear regression with measurable meteorological variables inside the greenhouse to predict cucumber ET for the entire growth period, high and considerable amounts of error occurred, as the difference between measured and predicted values of ET is approximately 2.86 mm day-1 which is noticeable. Overestimation of the cucumber ET in the first and last stages which will result in decreasing water use efficiency and underestimation in blooming and yielding fruit stages, when cucumber is more susceptible to water stress, are the other disadvantages of using one equation for the entire growth period to describe and predict cucumber ET. In contrast, when we divided growth period into four steps, the MLR method’s performance in prediction of ET was improved and the difference mentioned above between measured and predicted values of ET (2.86 mm day-1) decreased to about 1.32 mm day-1. The results showed that measured and predicted values of ET ranged from (0.08 to 4.75) and (0.13 to 4.25) when the whole growth period is considered as one step, respectively. These mentioned values were obtained (0.08 to 1.5) and (0.13 to 1.75); (0.71 to 2.64) and (1.31 to 4.25); (2.18 to 4.75) and (1.69 to 4.13); (1.32 to 2.61) and (2.66 to 3.74) for each of growth period stages, respectively. Also the value of total ET for the entire growth period is measured 273.45 mm and predicted 275.7 and 275.59 mm, when the whole growth period is considered as one step or divided into four stages, respectively. Although dividing the growth period improved ET prediction, the results in the first and especially the third stage are still discussable. Therefore, as with MLR method, the capability of ANN technique was investigated in prediction of cucumber ET. Comparison of measured and predicted values of ET confirms that ANN has better performance than MLR, even when growth period is divided.
Conclusion: Determining cucumber evapotranspiration in the greenhouse was the main objective of this study. For this purpose we used Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) techniques. In MLR, first we used one equation for the entire growth period. The results showed that this single equation is not able to simulate actual ET of cucumber. To overcome this problem, we divided the growth period into four stages and derived a separate equation for each stage. The results showed that this procedure improves prediction of cucumber ET, especially in the second and last stages of growth period. Statistical indices such as RMSE, Ens, PBIAS and PSR, t-statistical results, measured versus predicted ET values, and predicted values of ET in the growth period indicate that ANN technique is not only reliable, but also easier than the MLR technique.
F. Fahalian; R. Moazenzadeh; M.R. Nori Emamzadeie
Abstract
A precise estimation of water consumption throughout a crop's growth season and of the amount of water consumed in each growth stage may play an important role in water resources management, integrated water and soil management, and proper irrigation scheduling. In a greenhouse, this faces with the conditions ...
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A precise estimation of water consumption throughout a crop's growth season and of the amount of water consumed in each growth stage may play an important role in water resources management, integrated water and soil management, and proper irrigation scheduling. In a greenhouse, this faces with the conditions specific to this environment. This study was conducted to propose a model to make an appropriate and accurate prediction of evapotranspiration (ET) for greenhouse cucumber. Two same microlysimeters with 28 cm diameter and 30 cm height were deployed simultaneously in the greenhouse for the cucumber culture. Amount of ET was measured daily by the weighing method in both mycrolysimeters. The data from the first microlysimeter were used to derive, and those from the second to validate the proposed models. The developed models were evaluated by root mean square error (RMSE), drawing measured versus predicted ET values, and t-statistics. The proposed model was initially developed in the form of a single regression equation with independent variables such as vapor pressure curve slope and relative humidity for the whole growth season; further however, a separate equation was developed for each of the four growth stages, as the initial model did not perform well (RMSE= 46.61%). The results showed that the proposed models made appropriate predictions of greenhouse cucumber ET. Average amount of cucumber ET were obtained with proposed models 0.398, 1.79, 3.428 and 2.061 mm for four growth stages. RMSE values also were obtained 15.78, 11.48, 9.11 and 7.08 percentage for four growth stages. Correlation coefficient from measured and predicted values of cucumber ET varied from 0.4 (using single equation) to 0.95 (using variable equations for different growth stages). All of the proposed models were significant (p
R. Moazenzadeh; B. Ghahraman; K. Davary; A.A. Khoshnood Yazdi
Abstract
Soil moisture retention curve (SMRC) is an important soil property which expresses reaction between matric potential and moisture of soil. Direct measurement of soil matric potential and moisture is labour- and time-consuming. In order to prevail this problem, indirect methods are used for SMRC prediction. ...
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Soil moisture retention curve (SMRC) is an important soil property which expresses reaction between matric potential and moisture of soil. Direct measurement of soil matric potential and moisture is labour- and time-consuming. In order to prevail this problem, indirect methods are used for SMRC prediction. Pedotransfer functions (PTFs) are one of these indirect methods. This study was carried out to evaluate three internal pedotransfer functions, first and second models of Ghorbani and Homaee (1381) and Sepaskhah and Bondar (2002) derived in Iran, to predict SMRC in some Iranian soils. Also we tried to develop new different PTFs with better performance using the available information. Therefore 42 soil samples with spatial distribution from northern region of Iran, Amol, Babol and Karaj were selected and divided in Loam (20 samples) and Clay Loam (22 samples) texture classes. In evaluation of all existing PTFs, all 42 soil samples, and in developing new PTFs, 36 soil samples were used. The remaining six samples (three samples in each texture class) were used for validation of the new developed PTFs. In evaluation of the existing PTFs, results showed that the first and second models of Ghorbani and Homaee had alike and appropriate prediction of moisture in whole range of matric potential, whereas Sepaskhah and Bondar did not show an appropriate prediction. By the way, none of these PTFS had noticeable preference in specific texture classes in comparison with the others. New developed PTFS were highly significant (p
R. Moazenzadeh; B. Ghahraman; F. Fathalian; A.A. Khoshnood Yazdi
Abstract
Abstract
Pedotransfer functions (PTFS) are useful means of prediction many properties of the soil, and especially the hydraulic characteristics of this porous media. The main advantages of this functions, as compare to conventional methods used to directly estimate soil hydraulic properties, is that ...
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Abstract
Pedotransfer functions (PTFS) are useful means of prediction many properties of the soil, and especially the hydraulic characteristics of this porous media. The main advantages of this functions, as compare to conventional methods used to directly estimate soil hydraulic properties, is that they are not time-cost consuming. Different approaches such as classic linear and non linear regressions, artificial neural networks and regressions tree are being employed to develop the PTFS. Rosetta is a software package to predict soil hydraulic properties making use of artificial neural networks- based PTFS. In the present study, the impacts of the type and count of input variables to this software, on the prediction of the moisture retention curve and saturated hydraulic conductivity were evaluated in some soils from northern region of Iran, classed as of Loam and Clay Loam textures (USDA). Our results indicated that addition of bulk density as input variable decreased the performance of moisture retention curve prediction in both textural classes. Addition of bulk density showed on RMSE, ME, GMER and GSDER a positive and negative effect in Loam and Clay Loam textures, respectively. Addition of one or two moisture retention point(s) (the moisture content at matric potential of -33 and -1500 kpa) significantly decreased the RMSE at the medium range of matric potential (i.e. -33 to -500 kpa) and especially at -33 kpa. All of the studied PTFS tended to underestimate both saturated hydraulic conductivity and moisture content at different matric potential.
Key words: Pedotransfer Functions, Hydraulic properties, Moisture retention curve, Saturated hydraulic conductivity, Rosetta, Iran
