جستجوی مقالات مرتبط با کلیدواژه « AquaCrop model » در نشریات گروه « آب و خاک »

Sensitivity analysis of crop models helps researchers to have the necessary information about the reaction of crop models to changes in input parameters before calibration and application. This makes it possible to introduce the input data to the model with more awareness and increase the accuracy of the model in the calibration stage. Due to using the AquaCrop model in recent years, in this study, the sensitivity of normalized water productivity (wp*), crop transpiration, initial canopy cover (CCo), canopy growth coefficient (CGC), canopy decline coefficient (CDC) and harvest index (HI) was analyzed using a new method. For this purpose, corn yield data collected from Research Institute of Seed and Plant Breeding during 2008-2010 were used. Data consisted of four irrigation levels (W1, W2, W3 and W4 indicate supply of 120, 100, 80 and 60% of water requirement, respectively) and four nitrogen fertilizer amount (N1, N2, N3 and N4 indicate supply of 100, 80, 60 and 0% of fertilizer requirements, respectively). The results showed that the AquaCrop was most sensitive to changes in harvest index (0.65≤Spi≤1.3) and normalized water productivity (0.55≤Spi≤1.2). The lowest sensitivity (0.02≤Spi≤0.07) was observed to changes in crop canopy decrease coefficient (CDC). Sensitivity coefficients were positive for all parameters except CDC. Therefore, by increasing the CDC value, the AquaCrop suffers from underestimated error. The sensitivity coefficients for treatments N1 to N4 were equal to 0.32, 0.41, 0.46 and 0.51, respectively. These results for irrigation treatments W1 to W4 were equal to 0.36, 0.39, 0.44 and 0.5, respectively. So, with increasing water stress and fertilizer, the sensitivity of the AquaCrop model to all parameters increased. The highest sensitivity was observed in W4N4 treatment.

Potato is one of the most important crops, which is very sensitive to the amount of irrigation water. This leads to a lot of research to determine its yield and water productivity in different irrigation conditions. Doing the researches waste time and cost. For this reason, researchers have suggested that crop growth models such as AquaCrop be sensitively analyzed and evaluated to simulate the crop under different conditions. Due to this issue, the present study was conducted using three irrigation treatments including T1: 100% supply; T2: 75% supply and T3: 50% supply of potato water requirement. To analyze the sensitivity step, Beven method was used. To evaluate AquaCrop, statistics criteria root mean square error (RMSE), normalized root mean square error (NRMSE), mean bias error (MBE), efficiency factor (EF) and agreement index (d) were used. The results showed that AquaCrop model was very sensitive to changes in water productivity parameters. The sensitivity of this model to changes in evapotranspiration coefficient and crop growth coefficient was moderate and to changes in initial crop canopy and crop decay coefficient was low. According to MBE values, AquaCrop model suffered a low estimation error in determining yield and water productivity. The error of this crop model was acceptable for determining yield (RMSE=1.5) and water productivity (RMSE=0.23). Its accuracy was also excellent for simulating yield (NRMSE=0.06) and water productivity (NRMSE=0.06). Based on the values of EF and d, AquaCrop was more efficient than water productivity in determining yield. Therefore, it is suggested that this crop model be used to simulate potatoes under similar conditions.

Gradual decline in water resources require suitable management of irrigation water and applying of deficit irrigation.Field tests to analysis of different states of deficit irrigation are constrained due to restriction of the experiment reliability to the physical conditions of the test place and limited number of the testing scenario in field and the high cost of testing.To overcome these limitations, crop models can be used as a powerful tool to simulate field tests.In this research, AquaCrop model was evaluated in deficit irrigation of winter wheat in Zanajn Region.The study data was collected from filed test values which was done in Research Farm of University of Zanjan at2010-2011.The filed test was carried out to assess reaction of winter wheat to deficit irrigation treatments.The test was done as split plot design on the base of complete randomized block.Irrigation treatments were done as supplying of100%,80%and60%of crop water requirement.The first replication data was used for model calibrating.By entering harvest index data of the second and third replications data of irrigation treatment to model.their yield and water use efficiency were simulated.On the result, the highest difference between simulated and recorded data of yield and water use efficiency were as6.9%and7.0%(respectively)at the third replication of 80%crop water requirement treatment.The least difference were as1.0%and0.5%(respectively)at the second replication of60%and80%crop water requirement treatment(respectively).R2of simulated and real data was calculated0.96for yield and0.99for water use efficiency.High value ofR2data was demonstrated suitable performance of AquaCrop model in simulation and forecasting of yield and water use efficiency of winter wheat in deficit irrigation condition.

Irrigation plays an important role in increasing crop yield especially in arid and semi-arid regions, where optimizing irrigation depth and leaching is crucial. Using simulation models such as AquaCrop help to analyze different irrigation scenarios and also help farmers to optimize water resources management in such conditions. In this study, calibrated and validated AquaCrop model was used for two wheat varieties in Birjand region and one wheat variety in Mashhad region. A Matlab program has been developed to link to the AquaCrop in order to achieve the optimized values of irrigation and leaching in the water constraint conditions. The optimization results showed that net profit for the best irrigation and leaching management at all salinity levels and different wheat varieties, except for salinity levels of 8.6 and 10 dS m-1 in the Mashhad variety and level of 9.6 dS m-1 in the Roshan variety, was more than the current management researches of Shahidi and Haghverdi. While the aim is to achieve maximum profit and minimal drainage water, the model by changing the type of irrigation management and reduce leaching, especially in the last two irrigations only, reduced the amount of water consumed and the amount of drainage water to zero. The results also showed the reduction of gross irrigation water in the salinity levels more than tolerance threshold of wheat was less than the other salinity levels and the difference was completely negligible. Because in high salinity levels, yield reduction in deficit irrigation was more in comparison with low salinity levels, therefore deficit irrigation in salinity levels of more than the wheat tolerance threshold is not economic. Generally, the optimization results showed that in the area of Birjand and Mashhad, using the best irrigation and leaching management at different salinity levels can increase the benefit of wheat cultivation.

Water is one of the most important physical factors to provide the food security of the world’s growing population. The limitation of Iran’s water resources, as well as the increasing competition of different sectors for the use of water make the optimal management of water resources necessary. Different strategies are used in irrigation networks for water resources management. One of these strategies is optimal allocation of water and land. In this research, an optimization model for water and land allocation with the aim of maximizing economic benefit is presented based on genetic algorithm and using the AquaCrop plug-in model. For this purpose, #C coding was done in Visual Studio. In order to measure the model performance, the lands covered by one of the Moghan irrigation network channels were investigated. In this model, the agricultural year was divided into 36 periods of ten days. The irrigation water depth in each period and the cultivated area were considered as decision variables. The results show that the highest increase in percentage of economic benefit is related to the first-cultivation maize, alfalfa and wheat by 9, 7.3 and 7 percent respectively. Although the lowest increase in economic benefit is related to the second-cultivation seed maize and soybeans. The optimal allocated water volume was decreased by 14.7 percent, meanwhile the economic benefit was increased by 5.7 percent. Therefore, the optimal water allocation in this region encourages saving water consumption more than increasing economic benefit.

Saving, optimizing water consumption, and improving productivity and efficiency indicators in agricultural sector are important and necessary. Miandoab region is of particular importance as a model of reducing consumption in order to conserve real water for the restoration of Lake Urmia. This research was conducted to evaluate water consumption reduction scenarios at the farm level using AquaCrop plant model in Miandoab region. For this purpose, the information on farms related to the sustainable agriculture project of Iran's Wetlands Protection Plan in Miandoab region was used. The data from the first year (the crop year 2016-2017) and the second year (the crop year 2017-2018) were used for model calibration and validation, respectively. The evaluation of statistical indicators shows the accuracy and high ability of the model in simulating grain yield. After the calibration and validation of the model, the irrigation planning scenarios were analyzed and evaluated in the form of reducing irrigation depth by 10, 20, and 30% for the conditions of the second year. The results of field measurements in 2017-2018 showed that the average irrigation depth and the applied irrigation (Irrigation + effective precipitation) for wheat were 405.44 and 580.44 mm respectively and mean grain yield was 8340 kg/ha, then irrigation water productivity and applied irrigation productivity were 2.27 and 1.52 kg/ , respectively. Also, the results of the study of deficit irrigation scenarios showed that the S3 scenario (30% irrigation water reduction) will reduce only 10% crop yield and water productivity will increase from 1.53 to 1.72 kg/ . The values of water productivity indices were calculated using balance components and performance simulated by the model for each farm. In general, the results showed that it is possible to increase the grain yield of wheat and water productivity using AquaCrop model for proper and accurate irrigation planning and crop management improvement, in order to save and conserve water on a farm scale.

In this study the possibility of using fuzzy inference system efficiency, creating a bridge between meteorological, plant parameters, and Daily Yield, and comparing the accuracy of Daily Yield using these systems were investigated. After analyzing the different models and different combinations of daily meteorological data, seven models for estimating daily Yield were presented. For these models, the calculated Yield from AQUACROP model was considered as a base and 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 Sabbagh et al. (R2/t) criteria. an experiment was carried out during the 2014-2015 growing season in the Agricultural Research and Education Center of Khorasane Razavi province using a randomized complete block design with a split plot arrangement and four replications. This experiment was including of three irrigation levels treatments as the main plot and three method of planting treatments (transplanting 20-days, transplanting 30-days and direct seeded) as subplots. From the available data,75 percent was 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 AQUACROP model showed that fuzzy systems were very well able to estimate the daily Yield.Fuzzy model so that the highest correlation with the 9 input variables (r=0.98) had in mind and evaluate other parameters, the model with2 parameters, match very well with the AQUACROP model had stage training. In the test phase,training phase was very similar results and the model with the second phase of harvest index and canopy cover 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 estimate the daily yield .

Today, climate change is one of the issues that has always been the focus of the world. The use of general circulation models (GCM) is one of the most reliable methods for simulating climate variables in future periods. One of the influential factors in plant growth and yield is temperature. Therefore, in this study, the future temperature trend in Abhar Plain under the influence of climate change during future periods was investigated to study the effects of climate change on tomato yield.

The current study tried to simulate the tomato yield using the AquaCrop plant growth simulation model. We used synoptic stations of Khorramdareh which was located at a very short distance from Abhar (6 km) in the central part of the region and it had a similar climate to Abhar. The minimum, average and maximum temperatures measured at Khorramdareh Synoptic Station were used in the period of 1991-2010. LARS-WG software and A2 climate scenario and Hadcm3 model were also used for climate simulation. Then, using plant yield simulation by the AquaCrop model, plant yield was simulated and estimated in future periods and at different cultivation times. In this study, the observation period of 1991-2010, near future (2011-2030), middle future (2046-2065), and far future (2080-2099) have been considered.

The highest yield in tomato crop cultivation at present is related to 5 June (15 Khordad in the Solar year) cultivation with 55.57 t ha-1. Since the conventional planting time in the Abhar region is 26 May (5 Khordad in the Solar year), with 10 days of transferring the conventional cultivation time to 5 June (15 Khordad in the Solar year), the yield will increase by 0.51 t ha-1. Tomato yield will also increase over the next horizon, which may be due to the plant's C3 photosynthetic system and premature fruiting and flowering.

The LARS-WG model in predicting minimum, average, and maximum temperature, it shows an increasing trend in the future. The values of maximum and minimum temperature in the middle future will be higher than in the near future and the far future will be higher than the middle future compared to the observed period. The highest yield in tomato crop cultivation at present is related to 5 June (15 Khordad in the Solar year), cultivation with 55.57 57 t ha-1. Since the conventional planting time of the study region is 26 May (5 Khordad in the Solar year), with ten days of transferring the conventional cultivation time to 5 June (15 Khordad in the solar year), the yield will increase by 0.51 57 t ha-1. Tomato yield will also increase over the next horizon, which may be due to the plant's C3 photosynthetic system and more early ripening and flowering.

Predicting yield is increasingly important to optimize irrigation under limited available water to enhance sustainable production. Calibrated crop simulation models therefore increasingly are being used an alternative means for rapid assessment of water-limited crop yield over a wide range of environmental and management conditions. AquaCrop is a multi-crop model that simulates the water-limited yield of herbaceous crop types under different biophysical and management conditions. It requires a relatively small number of explicit and mostly-intuitive parameters to be defined compared to other crop models, and has been validated and applied successfully for multiple crop types across a wide range of environmental and agronomic setting. This study was conducted as a two-year field experiment with the aim of the simulation of water productivity, above ground biomass and fresh and dry yield of tomato using AquaCrop model under different irrigation regimes applied at two growth stages in Mashhad climate conditions. A two-year field experiment was conducted during 2016-2017 growing seasons in the experimental field of Ferdowsi University of Mashhad located in Khorasan Razavi province, North East of Iran. The water-driven AquaCrop model developed by FAO was calibrated and validated to simulate water productivity, above-ground biomass and yield of tomato crop under varying irrigation regimes. AquaCrop was calibrated and validated for tomato under full (100% of water requirements) and deficit (75 and 50% of water requirements) irrigation regimes at vegetative (100V, 75V, and 50V) and reproductive stages (100R, 75R, and 50R). Model performance was evaluated in terms of the normalized root mean squared error (NRSME), the Nash–Sutcliffe model efficiency coefficient (EF), Willmott’s index of agreement (d) and coefficient of determination (R2). The drip irrigation method was used for irrigation. The tomato water requirement was calculated using CROPWAT 8.0 software. The irrigation water was supplied based on total gross irrigation and obtained irrigation schedule of CROPWAT. The 2016 and 2017 measured data sets were used for calibration and validation of AquaCrop model, respectively. Calibration results showed good agreement between simulated and observed data for water productivity in all treatments with high R2 value (0.93), good ME (0.23), low estimation errors (RMSE=0.09 kgm3) and high d value (0.85). The goodness of fit results showed that measured WP values were closer to simulated WP values for the validation season (2017) than for the calibration season (2016). During calibration, (2016), the model simulated the biomass with good accuracy. The simulated above ground biomass values were close to the observed values during calibration (2016) for all treatments with R2 ranging from 0.92 to 0.99, NRMSE in range of 7.4 to 23%, d varying from 0.94 to 1, and ME ranging from 0.71 to 0.98. Validation results indicated good performance of model in simulating above ground biomass for most of the treatments (0.92 < R2 < 0.98, 6.5% < NRMSE < 21.3%, 0.76 < ME < 0.99). During validation (2017 growing season), overall, the trend of biomass growth (or accumulation) was captured well by model. However, the range of biomass of simulation errors was high, especially in treatments with higher stress. Accurate simulation of the response of yield to water is important for agricultural production, especially in an arid region where agriculture depends closely heavily on irrigation. During validation, the model predicted dry and fresh yield satisfactorily (NRMSE = 15.64% and 11.80% for dry and fresh yield, respectively). In general, the AquaCrop model was able to simulate the observed water productivity, above ground biomass and yield of tomato satisfactorily in both calibration and validation stage. However, the model performance was more accurate in non- and/or moderate stress conditions than in sever water-stress environments. In conclusion, the AquaCrop model could be calibrated to simulate growth and yield of tomato under temperate condition, reasonably well, and become a very useful tool to support decision on when and how much irrigate. This study provides the first estimate of the soil and plant parameter values of AquaCrop for simulation of tomato growth in Iran. Model parameterization is site specific, and thus the applicability of key calibrated parameters must be tested under different climate, soil, variety, irrigation methods, and field management.

Arid and semi-arid regions always face the challenges of food security, ecosystem sustainability and, consequently, increased water productivity. Crop water productivity can be used as a suitable indicator to identify areas prone to cultivate a particular crop in these areas. In return, identifying and selecting suitable cultivation areas will help increase productivity. Selecting suitable areas for cultivation is not enough to achieve high crop water productivity. Using appropriate management scenarios to achieve this goal is essential. Deficit irrigation is one of the management strategies to increase water productivity. Investigating the effect of various management scenarios on water productivity in the field studies will be time-consuming and costly. The computer models could be used as tools to simulate the effects of management scenarios on crop yield and water productivity. So far, various crop growth simulation models have been developed and used to investigate the impacts of management scenarios on crop water productivity. In large-scale decision and policy-making, the growing conditions of crops in different farms are not the same. Most of crop growth simulation models are designed for point and field scales, and for large-scale use, the use of auxiliary tools such as GIS is essential. Agricultural activities are the most important means of livelihood for a large number of people in Kermanshah province. According to the Jihad Agricultural Organization, this province is a leading province in terms of the area under cultivation of crops. Soybean is a raw material for the production of many food products and an important source of vegetable oils and proteins which could be a good choice for the farmers of this province. This study was performed to investigate temporal and spatial variations of soybean water productivity, under different irrigation scenarios, in Gavshan irrigation and drainage network –Razavar river- Kermanshah and Kordestan provinces, as an important indicator to identify areas suitable for the cultivation of this crop. For this purpose, a GIS-based software (for presentation and analyzing spatial data), was used along with a tool called Reference Weather from the Crop Growth Monitoring System (for weather data interpolation) and AquaCrop software plug-in version (for simulating scenarios). The study area was divided into 37 regular grids of 5 by 5 km. Based on the available soil reports, 24 homogeneous soil units were identified in the study area. Then to simulate a model, 94 homogeneous units were delineated by overlaying of grid weathers and soil units. For each grid of weather, daily reference crop evapotranspiration was calculated using the Penman-Monteith equation based on daily weather data (1988-2015). The files required to run the AquaCrop model were prepared based on the available information for each homogeneous unit. Then, soybean growth was simulated under 3 irrigation scenarios (60, 80 and 100% potential irrigation requirement) for all homogeneous units, and for 28 years (1988-2015), using a calibrated model. For these simulations, 7896 projects were prepared and implemented with the AquaCrop plug-in version. The results showed that grain yield, seasonal evapotranspiration and consequently soybean water productivity are affected by irrigation scenarios. The mean grain yield, seasonal evapotranspiration, and water productivity under 60% irrigation scenario decreased to 56.64, 30.76, and 37.78% relative to the full irrigation scenario, respectively. The results also indicated that these parameters had temporal and spatial variations. These changes increased with increasing water stress intensity (except for temporal changes in seasonal evapotranspiration) in irrigation scenarios. The reason for temporal changes in the studied parameters can be due to annual changes in weather parameters (temperature, precipitation, sundial, etc.). But, spatial changes are due to the simultaneous effect of spatial changes in climatic and soil conditions (water holding capacity, topography, hydraulic conductivity, etc.) and their interactions on each other. With increasing water stress intensity, the annual fluctuations of seasonal evapotranspiration decreased. The reason for this is the dependence of seasonal evapotranspiration on the growth period and air temperature, which does the opposite. In general, the average water productivity of soybean in Mian-Darband plain was higher than in Bile-var plain. According to simulated water productivity, B1 and B4 irrigation zones in the Bile-var plain, and D4 and D9 irrigation zones in Mian-Darband plain were more suitable to soybean cultivation from the point of view of water productivity, which is the key factor in arid and semi-arid climate of Iran.

The modeling approach for the simulation of the growth and development of tomatoes in Iran has been overlooked. Calibrated crop simulation models, therefore, are increasingly being used as an alternative means for the rapid assessment of water-limited crop yield over a wide range of environmental and management conditions. AquaCrop is a multi-crop model that simulates the water-limited yield of herbaceous crop types under different biophysical and management conditions. It requires a relatively small number of explicit and mostly intuitive parameters to be defined compared to other crop models and has been validated and applied successfully for multiple crop types across a wide range of environmental and agronomic settings. This study was conducted as a two-year field experiment with the aim of the simulation of soil water content, evapotranspiration, and green canopy cover of tomato using AquaCrop model under different irrigation regimes at two growth stages in Mashhad climate conditions.

A field experiment was conducted over two consecutive seasons (2016-2017) in the experimental field of Ferdowsi University of Mashhad, located in Khorasan Razavi province, North East of Iran. The experiment was laid out in a split-plot design with different irrigation regimes at the vegetative and at the reproductive stage as the main and subplot factors, replicated thrice. In total, 27 plots of 4.5×3 m (13.5 m2) were used at a planting density of 2.7 plants per m2. Seedlings were planted in a zigzag pattern into twin rows, with a distance of 1.5 m between them, so there were four twin rows of three meters in each plot. The distance between tomato plants within each twin-row was 0.5 meters. A buffer zone spacing of 3 and 1.5 m was provided between the main plots and subplots, respectively. The following experimental factors were studied: three irrigation regimes (100= 100% of water requirement, 75= 75% of water requirement, 50= 50% of water requirement) and two crop growth stages (V= vegetative stage and R= Reproductive stage). The drip irrigation method was used for irrigation. The tomato water requirement was calculated using CROPWAT 8.0 software. The irrigation water was supplied based on total gross irrigation and obtained irrigation schedule of CROPWAT. Model accuracy was evaluated using statistical measures, e.g., R2, normalized root means square error (NRMSE), model efficiency (E.F.), and d-Willmott. The 2016 and 2017 measured soil and canopy data sets were used for calibration and validation of the AquaCrop model, respectively.

For a water-driven model, such as AquaCrop, it is important to evaluate its effectiveness in simulating soil water content. During calibration (2016), the model simulated the soil water content with good accuracy. The simulated soil water content values were close to the observed values during calibration (2016) for all treatments with R2 ranging from 0.90 to 0.97, NRMSE in range of 8.47 to 17.96%, d varying from 0.76 to 0.99, and M.E. ranging from 0.87 to 0.96. Validation results indicated the good performance of the model in simulating soil water content for most of the treatments (0.79<R2<0.99, 10.04%<NRMSE<18.65%, 0.77<ME<0.92). Appropriate parameterization of canopy cover curve is critical for the model to provide accurate estimates of soil evaporation, crop transpiration, biomass, and yield. In general, the calibration results showed good agreement between simulated and observed data for canopy cover development in all treatments with high R2 values (>0.87), good E.F. (>0.61), low estimation errors (RMSE, ranging from only 4.5 to 9.2) and high d values (>0.92).

The AquaCrop model (version 6.1) was calibrated and validated for modeling soil water content, evapotranspiration, and green canopy cover for tomatoes under drought stress conditions. In general, soil water content, evapotranspiration, and green canopy cover of tomato were simulated by AquaCrop model with acceptable accuracy in both calibration and validation stages. However, the model performance was more accurate in no and/or moderate stress conditions than in severe water stress environments. In conclusion, the AquaCrop model could be calibrated to simulate the growth and soil water content of tomatoes under temperate conditions reasonably well and become a very useful tool to support the decision on when and how much irrigate. For R2, values > 0.90 were considered very well, while values between 0.70 and 0.90 were considered good. Values between 0.50 and 0.70 were considered moderately well, while values less than 0.50 were considered poor. Root mean square error ranges from 0 to positive infinity and expresses in the units of the studied variable. An RMSE approaching 0 indicates good model performance.

The poor performance of irrigation districts led to the unreliable distribution of surface water among farmers. Therefore, it is necessary to improve the water distribution in the irrigation district. For this purpose, there are two practical solutions, improve operational method and adjustment of estimation of agricultural water demand, each of which has been researched separately. In this study, for the first time, the effect of improving the adjustment of estimation of water demand was investigated by developing and combining AquaCrop model and ICSS hydrodynamic model for Roodasht irrigation district under a water shortage scenario in the status quo. For this purpose, the daily agricultural water demand was estimated for each of the secondary irrigated units based on the cultivation pattern by using the AquaCrop model. After calculating the average daily flow rate to each off-take (the output of the water distribution simulator model in the main canal) the adequacy index was calculated for two conditions, 1) with considering the amount of requirements specified by the network administrator  (available condition) and 2) adjustment of water demand. The results showed that the performance evaluation indicator improved from 3% to 16% for all off-takes by adjustment of agricultural water demand. This improvement has been achieved by reducing the amount of water right in the upstream and middle of the main canal and increasing the amount of water right for downstream. Also, the equity of water distribution has improved among water users by adjustment of agricultural water demand.

In recent years, there has been high attention towards soilless cultivation or hydroponic for growing plants in greenhouses. In the current study we addressed normalized water productivity of lettuce (Lactuca sativa) between soil and hydroponic cultivations and also the assessment of AquaCrop model in simulation of yield and water use efficiency of lettuce cultivation methods. The Normalized Water Productivity is used for simulating plant growth in AquaCrop model. The experiments on two mentioned cultivations were conducted during two cultivation periods. The first cultivation’s data were used for calibrating the productivity parameter of normalized water and the second’s cultivation data were used for validation. The obtained results suggested that the productivity parameter of normalized water was different for soil and hydroponic cultivations, it was 14.3 and 15.8 g m-2, respectively for the two cultivation types. AquaCrop model was performed using these amounts for second cultivation period and the value of biomass was simulated during growth period and then was compared to the measured values. The results indicated that AquaCrop model simulated the performance of lettuce with a suitable accuracy during growth period using the calibrated values of normalized water productivity. Water use efficiency (WUE) was also measured and simulated in both cultivation periods for hydroponic and soil cultivations. The results indicated that the water use efficiency (WUE) in hydroponic is higher than WUE in soil cultivation. Also, the amount of canopy cover was measured for both culture mediums in both cultivation periods and was simulated by the model and the results showed that the model simulates the amount of canopy cover well for both culture mediums. Also AquaCrop model has higher accuracy in the simulation of performance and WUE for lettuce. According to the obtained results, it was concluded that the culture medium influenced the performance of lettuce plant; and performance and WUE in hydroponic cultivation is higher than soil cultivation. Furthermore, AquaCrop model has relatively high accuracy in the simulation of performance, WUE, and also simulation of canopy cover for lettuce plant.

Crop model parameters are influenced by different management and environmental conditions. Sensitivity analysis is recognized as an effective approach for identifying the most influential parameters in the modelling process and output uncertainty assessment. In present study, the sensitivity of AquaCrop model parameters for basil was evaluated under different nitrogen fertilizer stresses. For this purpose, an experiment was conducted in the research greenhouse of the College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran. Crop growth parameters used for sensitivity analysis include normalized water productivity (Wp < sup>*), initial canopy cover (CC0), maximum transpiration coefficient (), canopy growth coefficient (CGC) and canopy decline coefficient (CDC) which analyzed by Beven (1979) approach. The results showed that the highest sensitivity of the AquaCrop model was due to the change in the Wp < sup>* parameter. Therefore, it is necessary to calibrate this parameter under different environmental conditions and for diverse crop species to increase the accuracy and performance of the model. Also, comparison of the sensitivity coefficients obtained for each of the growth parameters showed that by increasing nitrogen fertilizer stress, the model sensitivity also increased. But the growth rate was not the same among the selected parameters. In other words, the impressibility of parameters was different from nitrogen deficiency.

The agricultural sector is particularly important in the economic growth and development of different societies. The application of Nitrogen fertilizer is one of the most influential factors in agricultural productivity enhancement. Management and optimum fertilizer consumption based on field or greenhouse experiments are time and cost consuming. Therefore, the application of models that simulate the effects of different fertility stresses on crop production are useful tools in fertilizer planning and optimization. In the AquaCrop model, the crop biomass is simulated using a semi-quantitative method. The purpose of this study was to simulate basil response to different fertilizer treatments and evaluate the semi-quantitative method used in the AquaCrop model. For this purpose, a study was carried out in a research greenhouse of the College of Agriculture and Natural Resources, University of Tehran, during two growth periods. Five levels of nitrogen fertilizer application (Urea fertilizer) with three replications was investigated to find out the effect of fertility stress on basil yield. Initially, the semi-quantitative method was calibrated. The calibration results showed that the normalized water productivity coefficient decreased by 41%. Then, the AquaCrop model was validated using statistical measures to estimate biomass and canopy cover. The variation range for R2, MBE and RRMSE indices for crop biomass were 0.95-0.98, 1.72-21.56 g m-2, 17/42-19.07% and for canopy cover were 0.66-0.78, 6.44-12.86% and 19.66-21.83%, respectively. According to the results, the AquaCrop model and the semi-quantitative method can be used as a suitable tool to simulate crop growth under soil fertility stress conditions.

This study conducted in order to assess accurate of Aquacrop Model in yield grain and Biomass simulation under rice(Hasham cultivar) deficit and saline irrigation management conditions in Rasht Rice Research Institute of Iran, during two year from 2009-2011. This experiment was conducted in form of factorial randomized complete block design with 3 replications. Treatments consisted of 4 levels of salinity as follows: S1, tap water with salinity less than 1 dS m, S2, S3, S4, respectively salinity of 2, 4, 6 dS m and five irrigation methods include I0: Irrigation continuous, I1, I2, I3, I4, respectively, in water saturation (AWD), irrigation at field capacity (FC), irrigation water content of 100, 90 and 80% of field capacity. AquaCrop model was evaluated for rice under deficit and salinity stress. Measured data of first and second year were used to calibrate and validate respectively. the root mean square error (RMSE), normalized root mean square error (RMSEn), the percentage relative error and test (T-test) was used in order to comparing simulation results with measured data. Grain yield was simulated with lower the threshold 0.85 and 9% to 0.43 and 33% in the model simulation results show that Model has acceptable performance in salinities less than the threshold salinity.

This study was conducted to evaluate AquaCrop for the simulation of potato yield and water use efficiency (WUE) under different water stress values at five levels (E0, E1, E2, E3 and E4, indicating 100, 85, 70, 50 and 30 percent of crop water needed, respectively) in three times during growth cycles (T1, T2, and T3, indicating 50, 100, and 150 days after sowing, respectively). The results showed that AquaCrop had overestimated and underestimated error for the simulation of yield and WUE, respectively. Based on RMSE and NRMSE values, the errors for yield and WUE were acceptable. The maximum and minimum error were also 0.3 (E1T3) and 3.15 (E1T2), respectively. The results obtained for WUE showed that the maximum and minimum were 0.53 (E3T2) and 0.03 (E4T2), respectively. The average differences between simulated and observed results (ADSO) of WUE for E1, E2, E3 and E4 were 0.24, 0.25, 0.19, and 0.44 ton.ha-1, respectively; the ADSO of yield for T1, T2, and T3 was 0.19, 0.36, and 0.22 ton.ha-1, respectively. Therefore, AquaCrop showed a high error for WUE when water stress was increased and crop was in its initial crop growth.

This study was carried out using AquaCrop plant growth model to simulate potato crop yield in irrigation levels of 100, 80 and 65 percent of plant water requirement under climate change conditions in Shahrekord region. The data of two years of 2013 and 2014 were used to calibrate and validate the AquaCrop plant growth model. In order to investigate the effect of climate change on product yield, the output data of the HadCM3 general circulation model under two scenarios A2 and B1 for the periods 2011- 2030 and 2046-2065 are downscaled using LARS-WG model and used as inputs of the growth model. Based on the results, the simulation of potato yields with the AquaCrop plant growth model was carried out with high precision so that the average difference between observed and simulated values in calibration and validation stage was 0.56 and 0.68 ton/ha, respectively. Also, the mean absolute error (MAE) for simulating minimum temperature, maximum temperature, sunshine hours and precipitation with LARS-WG model was 0.05, 0.03, 0.01 and 0.16, respectively which reflects the good performance of the model in producing climate data for the future periods. Simulation results showed that the potato yield for the periods 2011-2030 and 2046-2065 under the A2 scenarios was 15.8 and 24.5 percent, respectively, and under B1 scenario, 11.8 and 22.4 percent increase compared to the base period. Also, for I80 and I65 treatments, the yield value decreases in both the future periods and the two scenarios campared to the base period.

This study was carried out using AquaCrop plant growth model to simulate potato crop yield in irrigation levels of 100, 80 and 65 percent of plant water requirement under climate change conditions in Shahrekord region. The data of two years of 2013 and 2014 were used to calibrate and validate the AquaCrop plant growth model. In order to investigate the effect of climate change on product yield, the output data of the HadCM3 general circulation model under two scenarios A2 and B1 for the periods 2011- 2030 and 2046-2065 are downscaled using LARS-WG model and used as inputs of the growth model. Based on the results, the simulation of potato yields with the AquaCrop plant growth model was carried out with high precision so that the average difference between observed and simulated values in calibration and validation stage was 0.56 and 0.68 ton/ha, respectively. Also, the mean absolute error (MAE) for simulating minimum temperature, maximum temperature, sunshine hours and precipitation with LARS-WG model was 0.05, 0.03, 0.01 and 0.16, respectively which reflects the good performance of the model in producing climate data for the future periods. Simulation results showed that the potato yield for the periods 2011-2030 and 2046-2065 under the A2 scenarios was 15.8 and 24.5 percent, respectively, and under B1 scenario, 11.8 and 22.4 percent increase compared to the base period. Also, for I80 and I65 treatments, the yield value decreases in both the future periods and the two scenarios campared to the base period.