Irrigation
S.F. Mousavizadeh; H. Ansari; A. R. Faridhoseini
Abstract
Introduction: In the last decade, satellite-based methods, including remote sensing and microwave methods, have been used in many studies to detect soil surface moisture regionally. Thermal remote sensing method is quite effective for checking moisture for bare soil but shows poor correlation for vegetated ...
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Introduction: In the last decade, satellite-based methods, including remote sensing and microwave methods, have been used in many studies to detect soil surface moisture regionally. Thermal remote sensing method is quite effective for checking moisture for bare soil but shows poor correlation for vegetated surfaces. In addition, there is a widespread use of this method in the presence of temperature differences during the day. Satellite imagery enables the ability to measure humidity according to the environmental conditions at the surface. Thus, compared to field measurements, remote sensing techniques are promising because they are capable of spatial measurements at a relatively low cost. Water supply is one of the main causes of evapotranspiration, which can affect it. Soil moisture can be considered as the most direct and important variable describing drought and is the main parameter describing water circulation and energy exchange between the surface and the atmosphere. Scale reduction methods for soil moisture can be divided into three main groups including satellite-based method, GIS data and model-based methods. The same methods have been used extensively in monitoring soil moisture for different spectral patterns at different wavelengths, from visible to microwave remote sensing data. Spectral reflectance decreases with increasing soil moisture in the visible and near-infrared (NIR) range. Therefore, these methods can be used to estimate soil moisture using satellite data for water budgeting and other meteorological and agricultural applications.Materials and Methods: In this study, using the information provided by Zaki (2013), the measured humidity by the sensor was compared with the humidity obtained from the satellite. The soil moisture were measured in 16 points from an area of 13 hectares from Neyshabour plain of Khorasan Razavi province. The novelty of this study is to provide a simple method for using Landsat 7 satellite imagery to estimate the surface moisture of areas of the Earth to eliminate field sampling and optimal use for agriculture. One of the advantages of this method is the reduction of information obtained from the field as input values for crop modeling that can be used to estimate crop yield, so the moisture measured during the winter wheat crop period from November 2012 to March 2013 was used.Results and Discussion: The placement of band numbers 3 and 4 opposite each other to calculate M, the line equation was fitted. Since satellite imagery is not performed daily by satellite, six images were extracted during the growing season. On November 12, which is actually 12 days after planting, the plant is entering the germination stage and the soil is mostly bare. Because the satellite does not receive enough reflected green light, the accuracy of the image in measuring soil moisture decreases, but after the plant grows, the green light is reflected and the amount of digital digit of band 4 is affected, as a result, the amount of moisture in the plant leaves and stem is involved in measuring soil moisture, which is consistent with the results obtained by Petropoulos et al.Conclusion: In general, the results of this study showed that the simple and efficient Red-NIR spatial geometry model has a great ability to estimate soil surface moisture in favorable weather conditions and this method can be used for plant modeling as input data.
S. Jamali; H. Ansari; S.M. Zeynodin
Abstract
Introduction: Since the agriculture is the main water consumer, it is necessary to increase water use efficiency. As a management practice, deficit irrigation strategy is applied to cope with water shortages, especially during drought periods. A greenhouse experiment was conducted to investigate the ...
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Introduction: Since the agriculture is the main water consumer, it is necessary to increase water use efficiency. As a management practice, deficit irrigation strategy is applied to cope with water shortages, especially during drought periods. A greenhouse experiment was conducted to investigate the impact of water and salt stress on Quinoa plants (Chenopodium quinoa Willd.), Aly et al (2) showed that quinoa plants can tolerate water stress (50% FC) when irrigated with moderately saline water (T1 and T2, respectively). The results of some studies showed that Amaranth was the most responsive plant to water. Quinoa showed the best performance in the treatment with the upper-middle water level among the other evaluated species. Millet showed thermal sensitivity for cultivation in the winter, making grain production unfeasible; however, it showed exceptional ability to produce biomass even in the treatment with higher water deficit. Water stress can affect plants by reducing the plant height, relative growth rate, cell growth, photosynthetic rate, and the respiration activation. Cultivated plants have several mechanisms of adaptation to water deficit, but the responses are complex and adaptation is attributed to the ability of plants to control water losses by transpiration, which depends on the stomatal sensitivity and greater capacity of water absorption by the root system, among other factors. In PRD method, half of the root zone is watered and the other half is kept dry intermittently. The objective of this research was to study yield and yield components of Quinoa (Chenopodium quinoa Willd.) Titicaca cultivar, using PRD irrigation method in three growing bed, under greenhouse conditions.
Materials and Methods: This research was conducted to study the effects of water stress on yield and its components of Quinoa under the different growing beds in the experimental research greenhouse of Ferdowsi University of Mashhad during 2018. Titicaca cultivar of Quinoa was planted and experimental design was factorial, based on complete randomized design and three replications, included two irrigation managements (FI, full irrigation and PRD, partial root-zone drying method) and three levels of growing bed (S1, silty clay, S2 clay loam and, S3 sandy loam). Research station is located in north-east Iran at 36° 16' N latitude and 59° 36' E longitude and its height from sea level is 985 meters. The seeds of Quinoa were planted at a depth of 1.5 centimeters in the soil of each pot and were irrigated with tap water. Plants were harvested after 4 months and plant height, branches number, panicle number, thousand kernel weights, grain yield, biomass; steam, leaf, and panicle dry weight panicles were measured. Physical and chemical properties of irrigation water and soil were determined before the beginning of the experiment. The obtained data analyzed using the statistical software of SAS (Ver. 9.4) and the means were compared using LSD test at 5 % percent levels.
Results and Discussion: Results showed that the highest plant height (84.4 cm) was in FI treatment and the shortest plant height (82.5 cm) was in PRD treatment. The highest and the lowest 1000 kernel weights and grain yield were measured in FI (4.0 and 19.7 g per plant) and PRD (3.6 and 17.7 g per plant) treatments, respectively. With a 50 % reduction of water in PRD compared to FI treatment, 1000 kernel weights were decreased by 9.1%. Grain yield was decreased by 10.2% (changing from FI to PRD). The highest and the least grain yield (20.2 and 18.4 g per plant) were obtained in S1 and S2,3 soils, respectively. Silty clay soil with 1000 kernel yield of 4.12 g had higher than clay loam and sandy loam soil, which produced 3.78 g and 3.78 g, respectively.
Conclusion: In general, the effect of the PRD irrigation method on reducing water use in the greenhouse production of Quinoa was positive and recommendable. Silty clay soil with 1000 kernel yield of 4.12 g had higher than clay loam and sandy loam soil, which produced 3.78 g and 3.78 g, respectively.
mahdi selahvarzi; B. Ghahraman; H. Ansari; K. Davari
Abstract
Introduction: Evaporation takes place from vegetation cover, from bare soil, or water bodies. In the absence of a vegetation cover, soil surface is exposed to atmosphere which increases the rate of evaporation. Evaporation of soil moisture will not only lead to water losses but also increase the risk ...
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Introduction: Evaporation takes place from vegetation cover, from bare soil, or water bodies. In the absence of a vegetation cover, soil surface is exposed to atmosphere which increases the rate of evaporation. Evaporation of soil moisture will not only lead to water losses but also increase the risk of soil salinity. The risk is increased under low annual rainfall, saline irrigation water and deep water table. Soil and water salinity is common in arid and semiarid regions where using saline water is common under insufficient fresh water resources. Evaporation is one of the main components of water balance in each region and also one of the key factors for proper irrigation scheduling towards improving efficiency in the region. On the other hand evaporation has a significant role in global climate through the hydrological cycle and its proper estimation is important to predict crop yield soil salinity, water loss of irrigation canals, water structure and also on natural disasters such as drought phenomenon. There are three distinct phases for evaporation process. Step Rate – initial stage is when the soil reaches enough moisture to transfer water to evaporate at a rate proportional to the evaporative demand. During this stage, the evaporation rate by external weather conditions (solar radiation, wind, temperature, humidity, etc.) is limited and therefore can be controlled, in other words, the role of soil characteristics will occur. In this case the air phase - control (at this stage the stage profile – control). Next step is to reduce the rate of evaporation rates during this stage of succession is less than the potential rate (evaporation, atmospheric variability). At this point, evaporation rate (the rate at which the soil caused by the drying up) can deliver the level of moisture evaporation in the area is limited and controlled. So it can be a half step - called control. This may be longer than the first stage.. Apparently when the soil surface is dry to the extent that, it is effectively cut off from water, this phase starts. This stage is often called vapor diffusion process where the surface layer so as to be able to dry quickly can be important.
Materials and Methods: This study was conducted to test the texture of sandy clay and four salinity levels (0.7, 2, 4 and 8 dS m-1 (the study used a PVC pipe with a diameter of 110 mm and a height of about 1 m (for the 90 cm soil profile). Evaporation measurements and weight measurements were performed using a water balance. Also the water out of the soil columns were carefully measured. Weight was measured in soil columns has been done with a digital scale with an accuracy of 5 g. The calculation of evaporation ,obtained by subtracting the weight of the soil column twice in a row, low weight and water out of the soil column.
Results and Discussion: Evaporation decreased with increasing salinity of the soil, even in the first stage mentioned earlier by external meteorological conditions (eg, radiation, wind, temperature and humidity) controlled, observed. It should be recognized that the ability of the atmosphere to evaporate completely independent of the properties of the object that is no evaporation occurs. Moreover, if we assume that the object is completely independent of the properties of water surface evaporation exactly equals, salinity reduced the water vapor pressure resulting in reduced evaporates. The first stage of evaporation decreases by increasing salinity, evaporation would be justified.
najmeh khalili; Kamran Davary; Amin Alizadeh; Hossein Ansari; Hojat Rezaee Pazhand; Mohammad Kafi; Bijan Ghahraman
Abstract
Introduction:Many existing results on water and agriculture researches require long-term statistical climate data, while practically; the available collected data in synoptic stations are quite short. Therefore, the required daily climate data should be generated based on the limited available data. ...
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Introduction:Many existing results on water and agriculture researches require long-term statistical climate data, while practically; the available collected data in synoptic stations are quite short. Therefore, the required daily climate data should be generated based on the limited available data. For this purpose, weather generators can be used to enlarge the data length. Among the common weather generators, two models are more common: LARS-WG and ClimGen. Different studies have shown that these two models have different results in different regions and climates. Therefore, the output results of these two methods should be validated based on the climate and weather conditions of the study region.
Materials and Methods:The Sisab station is 35 KM away from Bojnord city in Northern Khorasan. This station was established in 1366 and afterwards, the meteorological data including precipitation data are regularly collected. Geographical coordination of this station is 37º 25׳ N and 57º 38׳ E, and the elevation is 1359 meter. The climate in this region is dry and cold under Emberge and semi-dry under Demarton Methods. In this research, LARG-WG model, version 5.5, and ClimGen model, version 4.4, were used to generate 500 data sample for precipitation and temperature time series. The performance of these two models, were evaluated using RMSE, MAE, and CD over the 30 years collected data and their corresponding generated data. Also, to compare the statistical similarity of the generated data with the collected data, t-student, F, and X2 tests were used. With these tests, the similarity of 16 statistical characteristics of the generated data and the collected data has been investigated in the level of confidence 95%.
Results and Discussion:This study showed that LARS-WG model can better generate precipitation data in terms of statistical error criteria. RMSE and MAE for the generated data by LAR-WG were less than ClimGen model while the CD value of LARS-WG was close to one. For the minimum and maximum temperature data there was no significant difference between the RMSE and CD values for the generated and collected data by these two methods, but the ClimGen was slightly more successful in generating temperature data. The X2 test results over seasonal distributions for length of dry and wet series showed that LARS-WG was more accurate than ClimGen.The comparison of LARS-WG and ClimGen models showed that LARS-WG model has a better performance in generating daily rainfall data in terms of frequency distribution. For monthly precipitation, generated data with ClimGen model were acceptable in level of confidence 95%, but even for monthly precipitation data, the LARS-WG model was more accurate. In terms of variance of daily and monthly precipitation data, both models had a poor performance.In terms of generating minimum and maximum daily and monthly temperature data, ClimGen model showed a better performance compared to the LARS-WG model. Again, both models showed a poor performance in terms of variance of daily and monthly temperature data, though LAR-WG was slightly better than ClimGen. For lengths of hot and frost spells, ClimGen was a better choice compared to LARS-WG.
Conclusion:In this research, the performances of LARS-WG and ClimGen models were compared in terms of their capability of generating daily and monthly precipitation and temperature data for Sisab Station in Northern Khorasan. The results showed that for this station, LARS-WG model can better simulate precipitation data while ClimGen is a better choice for simulating temperature data. This research also showed that both models were not very successful in the sense of variances of the generated data compared to the other statistical characteristics such as the mean values, though the variance for monthly data was more acceptable than daily data.
N. Khalili; K. Davary; A. Alizadeh; M. Kafi; H. Ansari
Abstract
Modeling of crop growth plays an important role in evaluation of drought impacts on rainfed yield, choosing an optimum sowing date, and managerial decision-makings. Aquacrop model is a new crop model that developed by Food and Agriculture Organization (FAO), that is a model for simulation of crop yield ...
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Modeling of crop growth plays an important role in evaluation of drought impacts on rainfed yield, choosing an optimum sowing date, and managerial decision-makings. Aquacrop model is a new crop model that developed by Food and Agriculture Organization (FAO), that is a model for simulation of crop yield based on “yield response to water“ with meteorological, crop, soli and management practices data as inputs. This model has to be calibrated and validated for each crop species and each location. In this paper, the Aquacrop has been calibrated and evaluated for rainfed wheat in Sisab station (Northern Khorasan). For this purpose, daily meteorological data and historical yield data from two cropping season (2007-2008 and 2008-2009) in the Sisab station have been used to calibrate this model. Next, meteorological data and historical yield data of five cropping season (2002-2003 to 2006-2007) are used to validate the model. The result shows that the Aqucrop can accurately predict crop yield as R2, RMSE, NRMSE, ME, and D-Index are achieved 0.86, 0.062, 5.235, 0.917 and 0.877, respectively.
javad baghani; A. Alizadeh; H. Ansari; M. Azizi
Abstract
Introduction: Production and growth of plants in many parts of the world due to degradation and water scarcity have been limited and particularly, in recent decades, agriculture is faced with stress. In the most parts of Iran, especially in the Khorasan Razavi province, drought is a fact and water is ...
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Introduction: Production and growth of plants in many parts of the world due to degradation and water scarcity have been limited and particularly, in recent decades, agriculture is faced with stress. In the most parts of Iran, especially in the Khorasan Razavi province, drought is a fact and water is very important. Due to melon cultivation in this province, and the conditions of quality and quantity of water resources and water used to produce the melon product in this province, any research done on the use of saline and brackish waters is statistically significant.
Materials and Methods: To study the effects of different water salinity and water management on some of the agronomic traits of late summer melon with drip irrigation, an experiment with 7 treatments and 3 repetitions was conducted in a randomized complete block design, in Torogh station, Mashhad. The irrigation treatments were: 1- fresh water from planting to harvesting, 2- water (3 dS/m) from planting to harvesting, 3- water (6 dS/m) from planting to harvesting, 4- water (6 dS/m) from 20 days after plantation to harvesting, 5-water (6 dS/m) from 40 days after plantation to harvesting, 6-water (3 dS/m) from 20 days after plantation to harvesting, 7-water (6 dS/m) from 40 days after plantation to harvesting.
Row spacing and plant spacing were 3 m and 60 cm, respectively and the pipe type had 6 liters per hour per unit of meters in the drip irrigation system.
Finally, the amount of salinity water, number of male and female flowers, number of seed germination, dry leaves' weight, leaf area, chlorophyll (with SPAD) etc. were measured and all data were analyzed by using MSTAT-C software and all averages of data, were compared by using the Duncan test.
Results and Discussion The results of analysis of data showed the following:
Number of seeds germination: Salinity in water irrigation had no significant effects on the number of seed germination. However, there was the most number of seed germinations in the fresh water treatments. However, with increased water salinity, the time of seed germination reduced. The maximum delay in germination of seeds was in the treatment that was irrigated with fresh water from the beginning of cultivation.
Number of flowers: First, the male flowers appeared and after 5 to 7 days, the appearance of female flowers began. The effect of irrigation treatments on female flower appearance was significant. With increased water salinity, the number of male flowers decreased. There was the lowest male flower in the treatment that was irrigated with saline water from the beginning, but there was no significant difference among the other treatments.
Root, steam and leaves: The effect of saline irrigation water on dried leaves’ weight and dry root weight was significant at 1% and 5% levels, respectively. Fresh treatment and salinity treatment have the least and the most root dries weight, respectively (irrigated from the beginning with fresh or saline water). Two treatments that were irrigated with fresh and brackish water from thebeginning of cultivation have the highest leaf growth. The same trend was true for steams.
In general, in all treatments, after applying different quality water to the end of the growing season, the trend of plant growth was similar to the others.
Chlorophyll: One of the most common measurements made by plant scientists is the determination of Chlorophyll concentration. The SPAD index was used for comparison of chlorophylls. With an increase of the salt in irrigation water, the SPAD index was also increased.
The maximum and minimum SPAD was in the treatments that were irrigated with saline water (treatment A) and fresh water (treatment C) from the beginning of cultivation, respectively.
Yield: With increasing the salinity of water, the total yield decreased. Salinity in irrigation water had a significant effect (at the 5% level) on total yield. The mean yield of brackish and salinity irrigation water treatments were 17.5% and 26% less than the fresh water irrigation treatment, respectively.These differences were significant. However, there was no significant difference between the yield of cases using brackish or salt water.
Conclusion: The results showed the following:
Salinity in irrigation water had no significant effect on the number of seed germinations. However, there was the most number of seed germinations in the fresh water treatments, but by raising the salinity of water, the time of seed germination was reduced.
With increasing the salinity of water, the number of male flowers decreased. There was the lowest male flower in the treatment that were irrigated with salt water from the beginning, but there was no significant difference between the other treatments.
The effect of salinity water on leaf dry weight and dry root was significant at 1% and 5% levels, respectively. Fresh and salinity treatments have the least and the most root dry weight, respectively (irrigated from the beginning with fresh or salt water). Two treatments that were irrigated with fresh and brackish water from the beginning of cultivation have the highest leaf growth.
The same trend was true for steams.
Two treatments that were irrigated with fresh and brackish water from the beginning of cultivation have the highest leaves areas. And they had significant difference with other irrigation treatments.
With an increase in the salt in irrigation water, the SPAD index also increased.
The mean yield of brackish and salinity water irrigation treatments were 17.5% and 26% less than that of fresh water irrigation treatment, respectively.These differences were significant. But there was no significant difference between the yield of brackish and salt water.
Farzaneh Nazarieh; H. Ansari
Abstract
Introduction: Rainfall is affected by changes in the global sea level change, especially changes in sea surface temperature SST Sea Surface Temperature and sea level pressure SLP Sea level Pressure. Climate anomalies being related to each other at large distance is called teleconnection. As physical ...
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Introduction: Rainfall is affected by changes in the global sea level change, especially changes in sea surface temperature SST Sea Surface Temperature and sea level pressure SLP Sea level Pressure. Climate anomalies being related to each other at large distance is called teleconnection. As physical relationships between rainfall and teleconnection patterns are not defined clearly, we used intelligent models for forecasting rainfall. The intelligent models used in this study included Fuzzy Inference Systems, neural network and Neuro-fuzzy. In this study, first the teleconnection indices that could affect rainfall in the study area were identified. Then intelligent models were trained for rainfall forecasting. Finally, the most capable model for forecasting rainfall was presented. The study area for this research is the Khorasan Razavi Province. In order to present a model for rainfall forecasting, rainfall data of seven synoptic stations including Mashhad, Golmakan, Nishapur, Sabzevar, Kashmar, Torbate and Sharks since 1991 to 2010 were used.
Materials and Methods: Based on previous studies about Teleconnection Patterns in the study area, effective Teleconnection indexes were identified. After calculating the correlation between the identified teleconnection indices and rainfall in one, two and three months ahead for all stations, fourteen teleconnection indices were chosen as inputs for intelligent models. These indices include, SLP Adriatic , SLP northern Red Sea, SLP Mediterranean Sea, SLP Aral sea, SST Sea surface temperature Labrador sea, SST Oman Sea, SST Caspian Sea, SST Persian Gulf, North Pacific pattern, SST Tropical Pacific in NINO12 and NINO3 regions, North Pacific Oscillation, Trans-Nino Index, Multivariable Enso Index. Inputs of the intelligent models include fourteen teleconnection indices, latitude and altitude of each station and their outputs are the prediction of rainfall for one, two and three months ahead. For calibration of the models, eighty percent of the data belonged to six stations. Mashhad, Golmakan, Sabzevar, Kashmar, Torbate and Sarakhs were used. Verification of the model was carried out in two parts. The first part of verification was done with twenty percent of the remaining data which belonged to the mentioned six stations. The second part of verification was done with data from the Nishapur station. Nishapur geographically is located between other stations and did not participate in the calibration. So, it provides a ondition for assessing models in location except for the calibration stations. To assess and compare the accuracy of the models, the following statistical criteria have been used: correlation coefficient (R), normal root mean square error (NRMSE), mean bias error (MBE), Jacovides criteria (t), and ratio (R2/t). To evaluate models in different rainfall depths, rainfall data based on standard precipitation index (SPI) was divided into seven classes, and the accuracy of each class was calculated separately.
Results and Discussion: By comparing the models' ability to predict rainfall according to the R2 /t criteria it can be concluded that the ranking of the models is Neuro-fuzzy model, Fuzzy Inference Systems, and Neural network, respectively. R2 /to criteria for prediction of rainfall one, two, and three month earlier in the Neuro-fuzzy model are 0.91, 0.4, 0.36, in Fuzzy Inference Systems are 0.76, 0.38, 0.31 and in the neural network model are 0.43,0.27, 0.2. The statistical criteria of Neuro-fuzzy model (R, MBE, NRMSE, t, R2/ t) for rainfall forecasting one month earlier are 0.8, -0.55,0.43, 0.7 , 0.91; two months earlier are 0.79, -1.32, 0.48, 1.56, 0.4; and three months earlier are 0.73,-1.37, 0.54, 1.47, 0.36 . Calculation of MBE criteria for Neuro-fuzzy models in all classes of SPI indicated that this model has a lower estimate in extremely wet and very wet classes. This is because of lack of data belonging to these classes for model training.
Conclusion: The results of this research showed that teleconnection indices are suitable inputs for intelligent models for rainfall prediction. Computing the best structure of fuzzy, neural network and Neuro-fuzzy models showed that Neuro-fuzzy can predict rainfall the most accurately. But, the results of these models in very wet and extremely wet condition are not reliable .So, these models should be used with more caution in these conditions.
M. Mohammadi; B. Ghahraman; K. Davary; H. Ansari; A. Shahidi
Abstract
Introduction: FAO AquaCrop model (Raes et al., 2009a; Steduto et al., 2009) is a user-friendly and practitioner oriented type of model, because it maintains an optimal balance between accuracy, robustness, and simplicity; and it requires a relatively small number of model input parameters. The FAO AquaCrop ...
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Introduction: FAO AquaCrop model (Raes et al., 2009a; Steduto et al., 2009) is a user-friendly and practitioner oriented type of model, because it maintains an optimal balance between accuracy, robustness, and simplicity; and it requires a relatively small number of model input parameters. The FAO AquaCrop model predicts crop productivity, water requirement, and water use efficiency under water-limiting and saline water conditions. This model has been tested and validated for different crops such as maize, sunflower and wheat (T. aestivum L.) under diverse environments. In most of arid and semi-arid regions water shortage is associated with reduction in water quality (i.e. increasing salinity). Plants in these regions in terms of water quality and quantity may be affected by simultaneous salinity and water stress. Therefore, in this study, the AquaCrop model was evaluated under simultaneous salinity and water stress. In this study, AquaCrop Model (v4.0) was used. This version was developed in 2012 to quantify the effects of salinity. Therefore, the objectives of this study were: i) evaluation of AquaCrop model (v4.0) to simulate wheat yield and water use efficiency under simultaneous salinity and water stress conditions in an arid region of Birjand, Iran and ii) Using different treatments for nested calibration and validation of AquaCrop model.
Materials and Methods: This study was carried out as split plot design (factorial form) in Birjand, east of Iran, in order to evaluate the AquaCrop model.Treatments consisted of three levels of irrigation water salinity (S1, S2, S3 corresponding to 1.4, 4.5, 9.6 dS m-1) as main plot, two wheat varieties (Ghods and Roshan), and four levels of irrigation water amount (I1, I2, I3, I4 corresponding to 125, 100, 75, 50% water requirement) as sub plot. First, AquaCrop model was run with the corresponding data of S1 treatments (for all I1, I2, I3, and I4) and the results (wheat grain yield, average of soil water content, and ECe) were considered as the “basic outputs”. After that and in the next runs of the model, in each step, one of the inputs was changed while the other inputs were kept constant. The interval of variation of the inputs was chosen from -25 to +25% of its median value. After changing the values of input parameters, the model outputs were compared with the “basic outputs” using the sensitivity coefficient (Sc) of McCuen, (1973). Since there are four irrigation treatments for each salinity treatment, the model was calibrated using two irrigation treatments for each salinity treatment and validated using the other two irrigation treatments. In fact, six different cases of calibration and validation for each salinity treatment were [(I3 and I4), (I2 and I4), (I1 and I4), (I2 and I3), (I1 and I3), and (I1 and I2) for calibration and (I1 and I2), (I1 and I3), (I2 and I3), (I1 and I4), (I2 and I4), and (I3 and I4) for validation, respectively]. The model was calibrated by changing the coefficients of water stress (i.e. stomata conductance threshold (p-upper) stomata stress coefficient curve shape, senescence stress coefficient (p-upper), and senescence stress coefficient curve shape) for six different cases. Therefore, the average relative error of the measured and simulated grain yield was minimized for each case of calibration. After calibrating the model for each salinity treatment, the model was simultaneously calibrated using six different cases for three salinity treatments as a whole.
Results and Discussion: Results showed that the sensitivity of the model to crop coefficient for transpiration (KcTr), normalized water productivity (WP*), reference harvest index (HIo), θFC, θsat, and maximum temperature was moderate. The average value of NRMSE, CRM, d, and R2 for soil water content were 11.76, 0.055, 0.79, and 0.61, respectively and for soil salinity were 24.4, 0.195, 0.72, and 0.57, respectively. The model accuracy for simulation of soil water content was more than for simulation of soil salinity. In general, the model accuracy for simulation yield and WP was better than simulation of biomass. The d (index of agreement) values were very close to one for both varieties, which means that simulated reduction in grain yield and biomass was similar to those of measured ones. In most cases the R2 values were about one, confirming a good correlation between simulated and measured values. The NRMSE values in most cases were lower than 10% which seems to be good. The CRM values were close to zero (under- and over-estimation were negligible). Based on higher WP under deficit irrigation treatments (e.g. I3) compared to full irrigation treatments (e.g. I1 and I2), it seems logical to adopt I3 treatment, especially in Birjand as a water-short region, assigning the remaining 25% to another piece of land. By such strategy, WP would be optimized at the regional scale.
Conclusion: The AquaCrop was separately and simultaneously nested calibrated and validated for all salinity treatments. The model accuracy under simultaneous case was slightly lower than that for separate case. According to the results, if the model is well calibrated for minimum and maximum irrigation treatments (full irrigation and maximum deficit irrigation), it could simulate grain yield for any other irrigation treatment in between these two limits. Adopting this approach may reduce the cost of field studies for calibrating the model, since only two irrigation treatments should be conducted in the field. AquaCrop model can be a valuable tool for modelling winter wheat grain yield, WP and biomass. The simplicity of AquaCrop, as it is less data dependent, made it to be user-friendly. Nevertheless, the performance of the model has to be evaluated, validated and fine-tuned under a wider range of conditions and crops.
Keywords: Biomass, Plant modeling, Sensitivity analysis
N. Validi; Alinaghi Ziaei; B. Ghahraman; H. Ansari
Abstract
For optimal management of a catchment, the time and space downscaling of hydrological properties is essential. To achieve accurate energy and water budget equations in every time or space resolution, spatial and temporal downscaled information of water budget's components are used. The fractal geometry ...
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For optimal management of a catchment, the time and space downscaling of hydrological properties is essential. To achieve accurate energy and water budget equations in every time or space resolution, spatial and temporal downscaled information of water budget's components are used. The fractal geometry is a branch of mathematics which has been utilized in discrete and periodic fields to generate data with different scales from observed data. In this research, the fractal interpolation functions were used for temporal downscaling of daily temperature data. The fractal dimension was used to express the rate of irregularities or fluctuations in the quatity. The fractal dimension of Mashhad daily temperature datasets for the period of 1992- 2007 was calculated. The mean of the fractal dimension was obtained 1.54. Moreover, using the fractal interpolation functions and the midday temperature dataset with 15 days resolution, hourly temperature dataset has been estimated and compared with observed dataset. It was shown that despite the considerable time interval between two consecutive measurements (as 15 days), the temperature time series with 3 hours resolution were obtained. The determination coefficient and the root mean square error of the model are 0.77 and 7, respectively.
M. Khorami; A. Alizadeh; H. Ansari
Abstract
Increased use of drip irrigation systems in the country and farmer's tendency to use more efficient irrigation systems, has caused need to know about parameters and factors that affect irrigation efficiency. This Study was done to examine how water moves in the soil and soil moisturere distribution at ...
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Increased use of drip irrigation systems in the country and farmer's tendency to use more efficient irrigation systems, has caused need to know about parameters and factors that affect irrigation efficiency. This Study was done to examine how water moves in the soil and soil moisturere distribution at Weather Station of Ferdowsi University of Mashhad. Inthisstudy, Hydrus 2D/3D Model performed by using data from laboratory and field analysis. Thes imulation results of soil moistureina 48 hour period were compared with the results offield measurements. The results showed that the model is very capable in simulating moisture contentin thesoil. Statisticalerroranalysiswas described to estimate model parameters using Maximumerror (ME), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Based on the results of RMSE parameter in volume tricsoil moisture, forallintervals and all discharges RMSE was less than 10 percent that it shows that model hashigh ability in simulation. Maximum Error was up to 5% of and Mean Absolute Error was up to 2.05 % of volumetric moisture content.
S. Kermanshahi; K. Davari; majid hashemi nia; A. Farid Hosseini; H. Ansari
Abstract
The requiring of reducing agricultural water demand as the world’s largest consumer of water, for having sustainable water resources is not concealed to anyone. With measurements such as increasing irrigation efficiency, changing in cropping pattern, reducing the cultivation area, etc, this goal can ...
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The requiring of reducing agricultural water demand as the world’s largest consumer of water, for having sustainable water resources is not concealed to anyone. With measurements such as increasing irrigation efficiency, changing in cropping pattern, reducing the cultivation area, etc, this goal can be achieved. In this study, the status of water resources and irrigation demands within the Neyshabour Plane was evaluated by using Water Evaluation and Planning model (WEAP). To assess the effect of these strategies in WEAP model, scenarios with different topics for cropping pattern, reducing cultivation area, and combined scenarios were developed and then the simulations were performed for 20 years in future. The results suggested that above measurements reduced the mean annual water demand of agriculture by 9, 10 and 18 percents respectively and subsequently reduced the average of annual groundwater deficit by 13, 8 and 18 percents. On the other hand these measurements had a significant role in reducing the agricultural water demand, and therefore, in reducing the extraction from different water resources.
S. Khazaei; H. Ansari; B. Ghahraman; A.N. Ziaee
Abstract
With increasing population and scarcity of fresh water,one of possible solutions is, using marginal waters (saline and sodic water). Using marginal waters should be taken into consideration and special studies. Since most processes related to soil and water, take place in unsaturated field condition, ...
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With increasing population and scarcity of fresh water,one of possible solutions is, using marginal waters (saline and sodic water). Using marginal waters should be taken into consideration and special studies. Since most processes related to soil and water, take place in unsaturated field condition, The purpose of this research is evaluation of saline and sodic water effect on diffusivity and unsaturated hydraulic conductivity.for this purpose, two soil types include loamy and sandy, two levels of SAR, 5 and 20, two levels of EC, 4 and 12 ds/m and distilled water were used. NaCl, CaCl2 and MgCl2 salts at Ca:Mg=2:1 were used to prepare treatments. Diffusivity was measured by one step out flow method at the suction of 15 bar. Unsaturated hydraulic conductivity calculated by using the diffusivity and the slope of the soil moisture charactristic curve. At both soils with increasing SAR and decreasing EC, diffusivity and unsaturated hydraulic conductivity decreased and this reduction was more at low moistures. Sandy soil was affected less than loamy soil. In comparison of treatments that cause the least and the most dispersion, diffusivity and hydraulic conductivity for loamy soil, decreased 100% and for sandy soil at low moistures, diffusivity and hydraulic conductivity decreased about 91% and 99%, respectively.
R. Mansouri; K. Esmaili; A.N. Ziaei; Hossein Ansari; S. R. Khodashenas
Abstract
In arid and semi-arid regions, collection of surface and subsurface waters in small seasonal rivers is very crucial, particularly in dry seasons. The cost of construction and maintenance of classical water intakes makes them inappropriate for these rivers. In this study a rather new method to divert ...
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In arid and semi-arid regions, collection of surface and subsurface waters in small seasonal rivers is very crucial, particularly in dry seasons. The cost of construction and maintenance of classical water intakes makes them inappropriate for these rivers. In this study a rather new method to divert surface and subsurface water is experimentally evaluated. In this kind of intakes, a couple of trenches are excavated and the drain pipes are installed in them and then filled with very porous materials. Indeed the system acts as a river drainage network. This method not only reduces the construction and maintenance costs but also minimize the disturbance of river topology and morphology. Therefore this intake is also suitable for rivers with high sedimentary loads. In a few small rivers in Khorasan Razavi province, Islamic republic of Iran, such systems have been installed but their design and applicability have not been evaluated. In this research, experimental model of the intake to collect flow was built for flow diversion and flow rate deviation examined. Results showed a direct relationship between flow diversion with water level and with increasing distance between the drainages, the drainage flow increases. Drainage flow in the porous medium is initially decreased and then increased and drainage flow is the lowest in the middle drainage. In the review drainage arrange, the drainage of two deep with shorter porous medium is more suitable. Finally, regression mathematical model for the structural design of the intake subsurface with porous medium and drainage system were presented.
H. Moradi; H. Ansari; majid hashemi nia; A. Alizadeh; A. Vahidian Kamyad; S.M.J. Mosavi
Abstract
Evapotranspiration is one of the major components of hydrologic cycle and estimation of irrigation needs. In recent years the use of intelligent systems for estimating hydrological phenomena has increased significantly.In this study the possibility of using fuzzy inference system efficiency, creating ...
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Evapotranspiration is one of the major components of hydrologic cycle and estimation of irrigation needs. In recent years the use of intelligent systems for estimating hydrological phenomena has increased significantly.In this study the possibility of using fuzzy inference system efficiency, creating a bridge between meteorological parameters and evapotranspiration, and comparing the accuracy of reference evapotranspiration using these systems were investigated. After analyzing the different models and different combinations of daily meteorological data, five models for estimating daily reference evapotranspiration were presented. For these models, the calculated evapotranspirationfrom Penman-Monteith-FAO equation was considered as a baseand the efficiency of other models was evluated using statistical methods such as root mean squared error, error of the mean deviation, coefficient of determination,Jacovides(t) and Sabbaghet al. (R2/t) criteria. The used data were collected from Mashhad’s meteorological synoptic station for a period of 50-years (from 1339 to 1389).From the available data, 75 percentwas used for training the model and the rest of 25 percent was utilized for the testing purposes. The results derived from the fuzzy models with different input parameters as compared with Penman-Monteith-FAO and Hargreaves-Samani methods showed that fuzzy systems were very well able to estimate the daily reference evapotranspiration.Fuzzy model so that the highest correlation with the four input variables (r=0.99) had in mind and evaluate other parameters, the model with two parameters, temperature and relative humidity (RMSE=0.96, MBE =0.18, R2=0.95, t=22, = and R2 / t=0.04) match very well with the model Penman - Monteith - FAO had stage training. In the test phase, training phase was very similar results and the model with the second phase of temperature and relative humidity will get the best match. According to the results of this study it can be concluded that fuzzy model approach is an appropriate method to estimatethe daily reference evapotranspiration. In addition, the fuzzy models do not require complex calculations which are required forcombination methods.
Z. Shirmohammadi; H. Ansari; A. Alizadeh
Abstract
Abstract
Potential evapotranspiration is one of basic parameter in hydrologic cycle that should be estimate in irrigation design and scheduling, watershed hydrology studies. Many applications in diverse disciplines require estimates of evapotranspiration (ET) at hourly or smaller time steps. The ...
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Abstract
Potential evapotranspiration is one of basic parameter in hydrologic cycle that should be estimate in irrigation design and scheduling, watershed hydrology studies. Many applications in diverse disciplines require estimates of evapotranspiration (ET) at hourly or smaller time steps. The primary objectives of this study were to compare the American Society of Civil Engineers (ASCE) and FAO-56 Penman–Monteith equations for hourly ET0 (ET0,hourly,ASCE and ET0, hourly,FAO) estimations for semiarid climate conditions and to compare the 24 h sum of ASCE (ET0,24 h,ASCE) and FAO-56 hourly ET0 (ET0,24 h,FAO) with the daily ET0 (ET0,d,FAO) computed from the daily FAO-56 equation, which is identical to ASCE daily ET0 equation. 278-days, i.e., 2008–2009 continuous hourly and daily weather data from the automated internet weather station where placed in private Farm in fariman khorasan razavi province were used. It was evident that during the day, ET0,hourly,ASCE was higher than ET0, hourly ,FAO due to a lower surface resistance parameter value, while at night ET0, hourly,ASCE was lower than ET0, hourly,FAO due to a higher surface resistance parameter value. The ET0, hourly,FAO was about 18% less than ET0, hourly,ASCE and ET0,24 h,FAO was about 14% lower than ET0,24 h,ASCE. The difference between ET0, hourly,ASCE and ET0, hourly,FAO during the day and night was highly dependent on wind speed. For the entire year, ET0,24 h,FAO was 2.6% higher than ET0,d,FAO while ET0,24 h,ASCE was 17% higher than ET0,d,FAO. These results demonstrated that for applications that require hourly time steps or daily ET0 for the entire year, the use of ET0, hourly ,FAO and ET0,24 h,FAO, respectively, will yield more consistent outcomes.
Keywords: Reference evapotranspiration, Surface resistance, ASCE penman-monteith, FAO-56Penman-Monteith, Summation of hourly, Fariman
H. Ansari; H. Moradi
Abstract
Abstract
Evapotranspiration as one of the most important components of the hydrologic cycle, plays a key role in water resources management, crop yield simulation and irrigation scheduling. Therefore, presenting a low cost and precision model is very essential for calculations of hourly ETo. Although, ...
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Abstract
Evapotranspiration as one of the most important components of the hydrologic cycle, plays a key role in water resources management, crop yield simulation and irrigation scheduling. Therefore, presenting a low cost and precision model is very essential for calculations of hourly ETo. Although, there are empirical formulas, their performances are not all satisfactory due to the complicated nature of the hourly evapotranspiration process, the data availability, and high cost and error for gathering data. This paper develops hourly ETo estimation model based on fuzzy inference system (FIS) technique. We follow the idea of using the least input parameters, so the net radiation (Rn) selected, as the only input parameter. The used data has been picked on UC-Andrade station for training model, that have the most variation on Rn and climatically conditions, and another thirteen stations, that selected randomly, among 114 automated stations in US California. There is not proper hourly data in Iran. FIS model estimates hourly ETo as crisp number using of 230 rules with 48 level, centeroid defuzzification method and inference Mamdani method. FIS results compared with Penman-Monteith-FA056 and CIMIS-Penman combined model. It has been found that FIS technique has high accuracy and good performance (for the train data set, R2 = 0.97, RMSE= 0.07, MBE=-0.004 and R2/t (t: Jacovides criteria)=0.21). Comparing FIS with CIMIS and FAO56 results shows that FIS has better correlation with CIMIS than FAO56 for test data set, with R2 = 0.94, RMSE= 0.0693, MBE=-0.0384 and R2/t=0.018. Among FIS, CIMIS and FAO56, FIS model is economical, because of the parsimony principal; in conclusion, it raises model accuracy.
Keywords: Fuzzy model, Hourly Evapotranspiration, CIMIS-Penman, Penman-Montieth-FAO56
A. Faalian; H. Ansari; S.A.A. Sadredini
Abstract
Abstract
A model based on Fuzzy Logic has been developed to simulate the distribution pattern of a single sprinkler. A Nelson R3000 sprayer with rotator pad and water application angle of 360o which is one of the most common types of center pivot and linear move sprinklers was selected. Several field ...
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Abstract
A model based on Fuzzy Logic has been developed to simulate the distribution pattern of a single sprinkler. A Nelson R3000 sprayer with rotator pad and water application angle of 360o which is one of the most common types of center pivot and linear move sprinklers was selected. Several field experiments according to the ISO-8026 and ASAE-S398.1 standards were performed to assess the water distribution pattern in no-wind and windy conditions at the Research Center of Agricultural Faculty, University of Tabriz-Iran. Results of thirty reliable experiments were used to educate & validate the model. Minimum and maximum wind speeds recorded under the field conditions were 0.57 and 7.41 ms-1, respectively. In order to comparative analyze between simulated values and observations several statistical criteria like R2, CD, EF, CRM, MAE and RRMSE were used. For the training dataset the average values of R2 and RRMSE were achieved 0.98 and 0.257, respectively. Also the values of parameters R2 and RRMSE for the validation dataset were calculated 0.96 and 0.34 respectively. As well as the other above mentioned statistical parameters for both training dataset and validation dataset were found satisfactory. To make the results practical a model was developed as a MATLAB m-file, using Fuzzy Logic that takes wind velocity and direction as the inputs and could simulate the distribution pattern of single sprinkler and have ability to display graphical and Excel file of results. With the statistical comparisons between simulated water distributions patterns with observed ones it was finally concluded that Fuzzy model had excellent ability to simulate the water distribution pattern.
Keywords: Water Distribution Pattern, Single Sprinkler, Fuzzy Logic, Simulation, Center Pivot
H. Dehghan; A. Alizadeh; A.Gh Haghayeghi; H. Ansari
Abstract
Abstract
Using mathematical models for irrigation management have great impacts to increase irrigation efficiency and product amount, in fields. In this study, simulation results by SWAP model for moisture, compared with soil profiles moisture values, measured in the field. Moisture data, measured at ...
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Abstract
Using mathematical models for irrigation management have great impacts to increase irrigation efficiency and product amount, in fields. In this study, simulation results by SWAP model for moisture, compared with soil profiles moisture values, measured in the field. Moisture data, measured at three wheat farms in the Neyshabur plain, were used to predict moisture. Results show good agreement between simulated and measured moisture values. R2 coefficient values was 0.611 for Farob Roman farm, 0.648 for Haji Abad farm and 0.679 for Soleimani farm, respectively. Model absolute value was between 1.5 to 2.9 percent and root mean square error (RMSE) value was between 1.9 to 4 percent. According to these statistical indices, SWAP model has been able to simulate moisture, in soil profile in different depths and times, accurately. Therefore, SWAP can be used for irrigation management in Neyshabur plain, with relatively sufficient accuracy.
Keywords: Moisture simulation, Irrigation management, Soil hydraulic parameters, Neyshabur plain, SWAP
H. Ansari; K. Davary; S.H. Sanaei-Nejad
Abstract
Abstract
Drought is a natural creeping event that starts due to lower moisture compared to normal condition. This phenomenon impacts all aspects of human activities. However there is neither any detailed definition nor a general and proper index for drought monitoring. In this study, fuzzy logic has ...
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Abstract
Drought is a natural creeping event that starts due to lower moisture compared to normal condition. This phenomenon impacts all aspects of human activities. However there is neither any detailed definition nor a general and proper index for drought monitoring. In this study, fuzzy logic has been applied to deal with inherent uncertainties of the real world data. We presented a fuzzy model to evaluate and analysis the drought. Using the Fuzzy logic for drought monitoring of Mashhad synoptic station showed its higher capability and efficiency compared to Boolean logic. We combined two membership functions related to SPI (Standardized precipitation index) and SEI (a presumable standardized index for evapotranspiration), to provide a new index (SEPI: Standardized Evapotrans-Precipitation Index). The results showed that fuzzy model which employed 81 rules with minimum of 2 and maximum of 4 rules is the most accurate approach. The new index (SEPI) not only covers all advantages of SPI, but also can be calculated using different time scales of available data. Moreover, it considers temperature effects on drought occurrence and severity too. Monitored drought using SPI and SEPI indices demonstrated high correlation (more than 90%) between these two indices across all time scales. Drought monitored by SEPI for Mashhad synoptic station, at 1 to 3 monthly scales showed high drought frequency but low duration. Increasing time scales resulted in low frequency but higher duration. Employing SEPI also showed that high intensity and frequency of drought occurred in years 2000 and 2001 across all time scales. The longest drought duration, by 3 years across all time scales, occurred between 1995 to 1998.
Keywords: Fuzzy logic, Drought index, Standardized Precipitation index (SPI), Standardized Evapotransprecipitation Index (SEPI).
N. Khalili; K. Davari; H. Ansari; A. Alizadeh
Abstract
Abstract
Drought is one the most complicated and unknown natural disasters and rainfed agriculture is often the first sector to be affected by drought. In this research, we consider the drought monitoring from both meteorological and agricultural points of view. We have selected Standardized Precipitation ...
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Abstract
Drought is one the most complicated and unknown natural disasters and rainfed agriculture is often the first sector to be affected by drought. In this research, we consider the drought monitoring from both meteorological and agricultural points of view. We have selected Standardized Precipitation Index (SPI) among the meteorological indices, with a one month time scale for the synoptic station of Bojnurd. Although there are few exceptions in during (1996-2005) in 1996, 1998, 1999, and 2000, in which the severely and extremely dry category have been matched to the growth season of the rainfed, the results of SPI index from precipitation data of this station and the trend of drought variations from 1996 to 2005 show that in Bojnord synoptic station, the meteorological drought has not happened in the growth season of the rainfed wheat (23 Oct. To 17 June) or at least it has been near normal category. The periods from June 1998 to May 1999 and from June 2004 to June 2005 have been the driest and wettest periods, respectively. The meteorological indices such as SPI, either are only the function of precipitation, or consider a long term time scale. In the first case they do not give a comprehensive analysis on the drought phenomena and cannot give be used for the monitoring of the crop moisture situation and in the later case, they are not applicable for short term time scales such as daily or weekly monitoring. Therefore, to monitor the agricultural drought and influence the other factors such as the temperature along with precipitation, the crop moisture index (CMI) has been introduced for weekly monitoring. To achieve this goal, we have used the climatic data of Bojnord synoptic station over ten years from 1996 to 2005. The results from CMI index show that in the last week of grain filling, around the last week of May, extremely drought (-2.7>CMI>-3) has happened. Also, during the crop maturity, a exceptional drought has been monitored with CMI
H. Ansari
Abstract
Abstract
To calculate the index and optimal depths of crop water use, and maximizing the profit under deficit irrigation practices, this study was performed using a split plot design. In this experiment three cultivars of early maturing corns (301, 303 and 315) were set as the main treatments with ...
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Abstract
To calculate the index and optimal depths of crop water use, and maximizing the profit under deficit irrigation practices, this study was performed using a split plot design. In this experiment three cultivars of early maturing corns (301, 303 and 315) were set as the main treatments with 3 replications, while the irrigation levels were considered to be the minor treatments. A line source irrigation method (after Hanks) with 6 irrigation levels on both sides of the line was used. In this research, three sub-functions such as yield Y(w), cost C(w), and benefit B(w) were developed initially. Then, the optimal depths of irrigation were extracted from these functions. The results indicated that: a) although the complete irrigation had the maximum yield, however, the marginal net profit was not maximum because of the cost rise, b) with deficit irrigation under land restriction conditions and aiming to maximize the use of unit land, the optimized water depth for all cultivars was 3% less than the complete irrigation, and c) using deficit irrigation under water limitations and aiming to maximize the use of unit volume of water, the optimized irrigation depth would be 19% less than the complete irrigation practices. Also, the results showed that the net benefit was the same for the equivalent depth and the maximum water depth; therefore, it is logical to use the equivalent depth. Meanwhile, applying 17.5% deficit irrigation for cultivar 301, 20.4% for cultivar 303 and 19.1% for cultivar 315, the highest earning return (Rials per m3 of water) will be 1011.2, 1206.1 and 1543.6 Rials, respectively. It can be concluded that with the savings of water under deficit irrigation practices, the planting area would increase and ultimately the marginal net profit increases substantially. Also, the most net profit among the cultivars was obtained for cultivar 315.
Key words: Deficit irrigation, Optimal irrigation depth, Early maturing corn, Yield function, Benefit, Earning return
