جستجوی مقالات مرتبط با کلیدواژه « مدل گیاهی » در نشریات گروه « آب و خاک »

Crop growth simulation models are extensively used for various agricultural studies, including optimal crop selection, irrigation management, and assessing climate change impacts. Among these models, the DSSAT (Decision Support System for Agrotechnology Transfer) is particularly prominent for its ability to simulate growth, yield, and other dynamics for 34 different crops. The DSSAT model integrates various components such as soil, weather, crop management, and genetic factors to provide comprehensive insights into crop performance (Jones et al., 2003). Accurate parameter calibration in this model is crucial for reliable simulations. However, the inherent variability and uncertainty in parameter values pose significant challenges. Uncertainty can arise from various sources, including measurement errors, spatial and temporal variability, and model structure. Addressing these uncertainties is essential to enhance the reliability and accuracy of the model predictions. The Generalized Likelihood Uncertainty Estimation (GLUE) algorithm offers a robust framework for quantifying and incorporating parameter uncertainty into model simulations (Beven & Binley, 1992).In this study, we focus on the application of the GLUE algorithm to the DSSAT model for cotton, aiming to improve the model's predictive accuracy by accounting for parameter uncertainty. We utilize observational data from different irrigation treatments to calibrate the model and evaluate the posterior probability distributions of the parameters.

The study used data from a 2009 experiment conducted at the Birjand University research farm. The DSSAT v4.5 model was employed, requiring inputs such as weather, soil properties, and crop management data. Four irrigation treatments (50%, 75%, 100%, and 125% of crop water requirement) were tested to evaluate the GLUE algorithm’s performance in estimating model parameters.

The results demonstrated that the GLUE algorithm effectively estimated the probability distributions of the DSSAT model parameters for cotton. The algorithm’s performance was compared with previous models lacking uncertainty assessments, showing significant improvements in simulation accuracy (Qasemi et al., 2019). The findings highlighted the importance of considering parameter uncertainty for better predictive accuracy and model reliability.

The GLUE algorithm, through Monte Carlo simulations, provides a robust method for assessing and incorporating parameter uncertainty in crop growth models like DSSAT. This approach enhances the model's reliability in predicting crop performance under varying conditions, which is crucial for agricultural planning and management.

Optimal use and management of water resources is very important. Also, choosing the appropriate irrigation method plays a vital role in saving water consumption in the agricultural sector. Therefore, in the current research, the effect of the irrigation method on the simulation of sugar beet yield was investigated using the AquaCrop model. In this study, the effect of two methods of drip irrigation (tape) and surface irrigation (furrow) and three levels of irrigation water (50%, 75%, and 100% of the plant's water requirement) on the yield of sugar beet plants was investigated in Sarayan-Ayask region, Iran. A factorial experiment was conducted based on a completely randomized design with 4 replications in 2021-22. The results showed that irrigation method had a significant effect on leaf diameter, leaf length, leaf weight, tuber diameter, and tuber length and weight of sugar beet tuber. Also, according to the results, drip irrigation had greater effect than furrow irrigation on the studied traits. Then, the grain and biomass yield was simulated using the AquaCrop model and the simulated values were calibrated and verified using observational data. Calibration was done using two replications of 100% and 50% stress levels, and validation was done using the replication of 75% stress level. The NRMSE, RMSE, RD and R2 coefficients of the model calibration values showed that the simulated and validated values were close to each other and these values were more precise in drip irrigation than in furrow irrigation. Validation values of two irrigation methods also showed the ability of model in simulating grain yield and biomass.

Climate change means any specific change in the long-term average weather state that occurs for a given location or for the entire globe (Goudarzi and Koupaei, 2020). Climate change is one of the most critical factors threatening food security, and it is expected to make food and nutrition security more challenging in the future (Carpena et al., 2019). It will affect the agricultural sector by changing the irrigation water requirements, crop yield, and water productivity (Boonwichai et al., 2018; Liu et al., 2019). Rice is the third most important crop in the world, following wheat and maize (Kapela et al., 2020). The occurrence of water shortages and droughts have raised concerns about the sustainability of rice production, including the main rice cultivation production region of Mazandaran in Iran (Yosefian 2018). Most studies have reported that rice production will decrease in the future due to a projected increase in temperature and a projected decrease in precipitation) Basak et al. 2010; Boonwichai et al. 2018; Nasir et al. 2020; Nicolas et al. 2020). Although many farmers, particularly in Iran, feel that permanent flooding conditions for rice farming are inevitable, climate change forces the use of water-saving technologies to ensure the long-term viability of irrigated rice production in paddy fields (Yosefian 2018; Mirfenderski 2022). Accordingly, it is necessary to find new methods for rice cultivation that reduce water use and make optimal use of the available water for irrigation while maintaining yield under climate change. The goal of this study was to examine the water requirement, water productivity, and risk of rice yield for different irrigation levels under various climate change scenarios using a crop simulation model.

IntroductionWheat (Triticum aestivum L.) has become very important as a valuable strategic product with high energy level. The importance of investigating environmental stresses and their role in predicting and evaluating the growth and crops yield is essential. A wide range of plant response to stress is extended to morphological, physiological and biochemical responses. Considering the rapid advancement in computer model development, plant growth models have emerged as a valuable tool to predict changes in production yield. These growth simulation models effectively incorporate the intricate influences of various factors, such as climate, soil characteristics, and management practices on crop yield. By doing so, they offer a cost-effective and time-efficient alternative to traditional field research methods. Material and MethodsThis research was conducted in the research farm of Varamin province, which has a silty loam soil texture. The latitude and longitude of the region are 35º 32ʹ N and 51º 64ʹ E, respectively. Its height above sea level is 21 meters. According to Demarten classification, Varamin has a temperate humid climate. The long-term mean temperature of Varamin is 11.18 ° C and the total long-term rainfall is 780 mm. In this study, in order to simulate irrigated wheat cv. Mehregan growth under drought stress, an experimental based on completely randomized blocks (CRBD) including: non-stress as control (NS), water stress at booting stage (WSB), water stress at flowering stage (WSF), water stress at milking stage (WSM) and water stress at doughing stage (WSD) with three replications during growth season 2019-2020 was carried out in Varamin, Iran. Crop growth simulation was done using SSM-wheat model. This model simulates growth and yield on a daily basis as a function of weather conditions, soil characteristics and crop management (cultivar, planting date, plant density, irrigation regime). Results and DiscussionBased on the results, the simulation of the phenological stages of irrigated wheat cv. Mehregan under water stress condition using SSM-wheat model showed that there was no difference between observed and simulated values. Summary, the values of day to termination of seed growth (TSG) were observed under non- stress, stress in the booting stage, flowering, milking and doughing of the grains, 222, 219, 219, 221, 221 days, respectively andsimulation values with 224, 221, 220, 221, respectively. However, with their simulation values, there were slight differences with 224, 221, 220, 221, respectively. Acceptable values of RMSE (11.7 g.m-2) and CV (3.5) indexes showed the high ability of the SSM model in simulating the grain yield of irrigated wheat cv. Mehregan under water stress conditions. Grain yield values were observed in non-stress conditions of 5783, water stress in booting, flowering, milking and doughing of the grain stages in 5423, 5160, 5006 and 5100 kg. h-1, respectively. While the simulated values were 5630, 5220, 4920, 4680 and 4880 kg. h-1, respectively. Based on the findings, observed and simulated values of leaf area index (LAI) were observed under water stress condition in the booting, flowering, milking and doughing of the grain stages (4.3 and 4.47), (4.33) and 4.46), (4.4 and 4.57) and (4.4 and 4.58) cm-2, respectively. Evaluation of the 1000-grain weight of irrigated wheat cv. Mehregan under the water stress showed that the SSM model was highly accurate. RMSE (4.6 g.m-2) and CV (1.8) values indicate the ability of the SSM model to simulate the 1000-grain weight of irrigated wheat cv. Mehregan. Also, the simulated values of the harvest index were 34.7 % in non-stress conditions, which decreased by 6 % compared to the observed value. Harvest index values were observed under water stress conditions in the in the booting, flowering, milking and doughing of the grain stages in 30.2, 29.3, 29.9 and 29.5 %, respectively. Compared to its observed values, it was reduced by 3, 3.5, 5, and 5.5 %, respectively. ConclusionBased on the findings, the slight difference between the observed and simulated values demonstrates the SSM model's capability to accurately capture water stress impacts on the phenological stages, grain yield, and yield components of irrigated wheat cv. Mehregan during critical growth stages, including booting, flowering, milking, and doughing. The results indicate that the SSM model is effective in simulating wheat growth under water stress conditions, showcasing its potential as a valuable tool for modeling irrigated wheat growth. The model's ability to account for water stress and its effects on various growth parameters makes it a reliable and efficient tool for predicting crop performance in water-limited environments.

Evaluation of plant models in agriculture has been done by many researchers. The purpose of this work is to determine the appropriate plant model for planning and predicting the response of crops in different regions. This action is made it possible to study the effect of various factors on the performance and efficiency of plant water consumption by spending less time and money. Since the most important agricultural product in Iran is wheat, so proper management of wheat fields has an important role in food security and sustainable agriculture in the country. The main source of food for the people in Iran is wheat and its products, and any action to increase the yield of wheat is necessary due to limited water and soil resources. Evapotranspiration is a complex and non-linear process and depends on various climatic factors such as temperature, humidity, wind speed, radiation, type and stage of plant growth. Therefore, in the present study, by using daily meteorological data of Urmia, Rasht, Qazvin, Mashhad and Yazd stations, the average daily evapotranspiration values based on the results of the FAO-Penman-Monteith method are modeled and the accuracy of the two methods temperature method (Hargreaves-Samani and Blaney-Criddle) and three radiation methods (Priestley-Taylor, Turc and Makkink) were compared with FAO-56 for wheat.

The present study was conducted to evaluate the accuracy and efficiency of the AquaCrop model in simulation of evapotranspiration and biomass, using different methods for estimation reference evapotranspiration in five stations (Urmia, Qazvin, Rasht, Yazd and Mashhad). Four different climates (arid, semi-arid, humid and semi-humid) were considered in Iran for wheat production. The equations used to estimate the reference evapotranspiration in this study are: Hargreaves-Samani (H.S), Blaney-Criddle (B.C), Priestley-Taylor (P.T), Turc (T) and Makkink (Mak). Then, the results were compared with the data of the mentioned stations for wheat by error statistical criteria including: explanation coefficient (R2), normal root mean square error (NRMSE) and Nash-Sutcliffe index (N.S).

The value of the explanation coefficient (R2) of simulation ET and biomass in the Blaney-Criddle method is close to one, which shows a good correlation between the data. The NRMSE and Nash-Sutcliffe values for both parameters and the five stations are in the range of 0-20 and close to one, respectively, which indicates the AquaCrop model's ability to simulate ET and biomass. On the other hand, the value of R2 in the Hargreaves-Samani method for biomass close to one, NRMSE in the range of 0-10 and Nash-Sutcliffe index is more than 0.5, which indicates a good simulation. The NRMSE index in the evaluation of ET and biomass wheat is excellent for the Blaney-Criddle method and about Hargreaves-Samani for ET is poor and for the biomass is excellent.The Turc method with NRMSE in the range of 0-30, explanation coefficient close to or equal to one and a Nash-Sutcliffe index of one or close to one can be used to simulate ET and biomass at all five stations. Also, for biomass simulation, Priestley-Taylor and Makkink methods have acceptable statistical values in all five stations.Based on the value of explanation coefficient (R2) of estimation ET and biomass wheat for radiation methods, the correlation between the data in all three radiation methods is high. Percentage of NRMSE index of Makkink method for wheat in ET evaluation in Qazvin station is poor category and in Urmia and Rasht is good and in Mashhad and Yazd is moderate and about biomass in all five stations (Qazvin, Rasht, Mashhad, Urmia and Yazd) is excellent category, the error percentage of Priestley-Taylor method for wheat in ET evaluation in Yazd station is good and the rest of the stations is poor, about biomass is excellent in all five stations (Qazvin, Rasht, Mashhad, Urmia and Yazd). The error rate of Turc method for wheat in ET evaluation in Urmia, Rasht and Mashhad stations is good and in Qazvin and Yazd is poor and about biomass is excellent in all five stations (Qazvin, Rasht, Mashhad, Urmia and Yazd).

According to the results obtained using Blaney-Criddle method with R2 value close to one, NRMSE in the range of 0-20% (excellent to good) and Nash-Sutcliffe index close to one and Turc method with R2 value close to one, NRMSE in the range of 0-10% (excellent) and Nash-Sutcliffe index close to one was showed a good accuracy of AquaCrop model in simulation of evapotranspiration and biomass with these methods of estimation of evapotranspiration compared to other methods.

Sensitivity analysis is the most important step before calibrating crop models. It helps researchers to have enough information about the effectiveness of each parameter, and changes them during calibration stage. This issue is more important due to the increasing use of AquaCrop model for crop simulation. Therefore, in the study, the sensitivity of AquaCrop to change some crop growth parameters; normalized water productivity (WP *), maximum transpiration coefficient (KCTrx), initial canopy cover (CC0), canopy growth coefficient (CGC), canopy decline coefficient (CDC) and harvest index (HI) were assessed using Beven (1979) method. For this purpose, the data collected in a research farm in Ahvaz during 2014 were used. The studied treatments include irrigation method (D: sprinkler irrigation using saline water, F: sprinkler irrigation using both saline and fresh water and S: furrow irrigation using saline water) with five irrigation water qualities (S1: 2.5, S2: 2.3, S3 : 3.9, S4: 4.6 and S5: 1.5 dS m-1). The results showed that the highest sensitivity was to changes in normalized water productivity (0.95≤Spi≤1.04) and maximum transpiration coefficient (0.95≤Spi≤0.67). After that, the sensitivity of harvest index (0.51≤Spi≤0.56) was in the middle category. The range of yield changes in different values of normalized water productivity, maximum transpiration coefficient, harvest index and canopy decline coefficient were 1.3-3.3, 0.8-1.6, 0.6-1.16 and 0.32-0.64 ton ha-1, respectively. Sensitivity coefficients were positive for all parameters except CDC. Therefore, by increasing the CDC, AquaCrop suffers from underestimation error and by increasing the value of other parameters; the model suffers from overestimation error. Therefore, in situations where the difference between observed and simulated yield is large, it is better to consider the parameters of normalized water productivity and maximum transpiration coefficient. Otherwise, the parameters of harvest index and canopy decline coefficient should be considered.

It is so vital to note the cultivation of rice because of its high irrigation water consumption. This crop is the most important food source in the Iran, hence, it is necessary to know how much irrigation water must be used to grow different rice cultivars under various cultivation types. On the other hand, applying farm tests to achieve the goal need high costs and waste much time. Regarding that, it is necessary to use crop modeling. In this study, AquaCrop was used to simulate yield, biomass and water use efficiency of rice. The research was conducted at Khuzistan Agricultrual Research Station. In this study, three types of cultivation (D1: transplanting, D2: current directs seeding consorted seeding, and D3: dry bed seeding) and rice cultivars (V1: Red-Anbori, V2: Champa, V3: Danial) were considered to simulate abovementioned parameters. MBE, RMSE and NRMSE values for seed yield were 0.25 ton.ha-1, 1.0 ton.ha-1 and 0.1, respectively. Those values for biomass were 0.3 ton.ha-1, 1.15 ton.ha-1 and 0.05, respectively, and for water use efficiency were 0.07 kg.m-3, 0.24 kg.m-3 and 0.03, respectively. EF values for seed yield, biomass and water use efficiency were 0.87, 0.56 and 0.65, respectively, and d values for all parameters were equal to 0.99. Regarding the results, AquaCrop had good accuracy for simulation of rice yield, biomass and water use efficiency.

Optimal use of available water resources and their management is very important according to modern science. This has led to the importance of using plant models to study the effects of low water on agricultural yields. In order to investigate the effect of moisture stress on yield and growth traits in three cultivars of horizon, clear and narin and weeding and validation of vegetative model AQUACROP for these cultivars were performed in 96-97 crop year on a research farm located in Birjand. The research was conducted in the form of a factorial randomized complete block design with 6 treatments and three replications. Irrigation treatments included 100 (control treatment), 75 (medium stress) and 50 (severe stress) percentage of wheat water requirement and 3 other treatments related to two new cultivars of Ofogh and Narin and one old and native cultivar Roshan. The results of the mean comparison of the mean mean of the simple effect of moisture stress, the simple effect of cultivars and the interaction effect of stress on different cultivars were significant in different traits. After analyzing and comparing the average plant traits of the AQUACROP plant model for stresses of 100 and 75%, the water requirement was measured in two parameters of grain yield and biomass. After the validation stage, the model validation model was performed with 50% water stress. The results of the model validation showed that the model calculations showed a close and acceptable simulation value compared to the measured value of grain performance according to NRMSE and R2 evaluation indicators. Also, to ensure the accuracy of the model, using the measured performance data, the bright figure in the 2009-2010 crop year was validated by changing the re-meteorological data. The NRMSE index value of the simulated and measured grain mass for the mean moisture stresses of 100 and 75% in the horizontal and horizontal digits of Rosin, Narin and Narin cultivars was 0.99, 0.88 and 1.51%, respectively. These values were observed in the model validation in 50% stress to 1.49, 3.14 and 0.88%, respectively. According to the values calculated in the statistical indicators, it can be concluded that the model can provide us with an acceptable efficiency for measuring different wheat cultivars, which is very important in the region due to the high and increasing number of different wheat cultivars.

Because of evaluating and measurement of different irrigation management and identifying soil salinity consumes more time with high cost, so applying models can overcome these problems and could be used in different irrigation scenarios. Variation Crop models are an appropriate tool for investigating irrigation management and their effect on plant production. The purpose of this study was to evaluate the efficiency of two models including AquaCrop and Saltmed for simulation of yield and soil salinity variation. The accuracy of the results of the simulation models depends on the accuracy of the required data of the model and if the measurement and determination of the input data is accurate, then the applicability model will be in different conditions after calibration and validation. For applying Aquacrop and Saltmed models, having crop growth stages, root effective depth, sowing and havest date, harvest index, crop coefficients in different growth stages, duration of wheat phonological growth stages, initial soil salinity in different layers and some irrigation data including depth and time of water application are necessary. In this regard, the two models including AquaCrop and Saltmed are among the functional models that have high ability and efficiency to simulate plant yield and soil salinity performance have been investigated.

This research was conducted in the Ramseh region of Hamidieh, Khuzestan, in latitude and longitude 47 degrees 41 minutes and 33 degrees and 4 minutes, respectively. In this regard, three pilots of 10 hectares were selected and in each of these parts three pilot trials with an area of 2000 square meters were considered for evaluation and measurement. In the first year, using traditional irrigation management of farmers and using the initial salinity levels, soil physical and chemical characteristics compared to the simulation using two models including Aquacrop and Saltmed model. Saltmed and Aquacrop Models can simulate crop yield, Biomass and soil moisture variation in saline and non saline condition. In this investigation Aquacrop version No. 4(2012) and Saltmed No. 3-04-02(2015) has been used for crop production and soil salinity simulation. During irrigation season reference evapotranspiration has been determined using climatic data of Ahvaz Synoptic station. Data gathering including minimum and maximum temperature, minimum and maximum relative humidity, precipitation, sunshine hours and wind speed. Also, during the growing season, the volume of irrigation water, irrigation hours, water use efficiency and root depth were determined. At the end of the growing season, direct measurement of selected farms in three replications was carried out to determine the wheat grain yield and biomass along with soil moisture changes in soil profile. According to the measured data, two models of Saltmed and Aquacrop were calibrated for the first year of cultivation. Results of calibration of the first year were evaluated for verifying the results of the two models in the second year. The standard error (SE), Root mean square error (RMSE), Normalized root mean square error (NRMSE), correlation coefficient (R2), mean bias error (MBE), and model efficiency (EF) were used to determine the accuracy of the models.

The results of the first year showed that the normalized error of root mean square index for grain yield and biomass values in the AquaCrop model were 4 and 5%, respectively, and these values for the Saltmed model, were 8 and 9%, respectively. This statistical index for simulation of soil salinity with Saltmed model was about 18% and in AquaCrop model was 53%. Therefore, the Saltmed model is more consistent with the measured data of soil salinity. Also, the results of the second year that were used for validation showed that, the normalized error of root mean square index for yield and biomass in the AquaCrop model was 4% and 4% respectively, and these corresponding values for Saltmed model were 22 and 14% respectively. Also, simulated of soil salinity results in the second year with Saltmed model showed that this model with normalized error of mean square error(26%) was more accurate for soil salinity simulation than AquaCrop model with normalized error of mean square error equal to 47%; therefore, the AquaCrop model has a better performance than Saltmed model for yield simulation. Also the performance of Saltmed model has high efficiency and accuracy in simulating soil salinity than AquaCrop model.So based on the goal of projects and precision of simulation data, these individual models can be applied.

One of the experts' suggestions for restoring Lake Urmia is to convert irrigated lands in to rainfed farming in the Urmia lake basin. Rainfed wheat farming might be considered as one of the effective alternatives to the irrigated areas. So, evaluating the soil available moisture as the most important limiting factor of the rainfed wheat farming is necessary. Since the field experiments are costly and time-consuming, application of the crop yield simulating models, e.g. AquaCrop which can estimate the crop yield based on soil water content with high accuracy, would be desirable. In this study, after calibration and validation of the mentioned model for simulating rainfed wheat yield in Tabriz plain during a period of 35-years (1360- 1395 HJ), the meteorological variables of the studied region were predicted for a 5-years ahead prediction interval using Thomas- Fering method. The rainfed wheat yield was then estimated using Aquacrop model for the period between 1396 and 1400 (HJ). Finally, management scenario for increasing the potential of rainfed wheat was proposed. The results of this study confirmed the ability of the conjunctive crop model and time series technique for simulating crop yield and meteorological variables in the studied region. Results showed that despite no significant change will be occurred in rainfall amount, ascending trend of the air-temperatures will cause an increase of about 13% in rainfed wheat yield during the next five years. Also, due to precipitation insufficiency for rainfed wheat farming, predicted supplemental irrigation is recommended equal to 40 mm, to increase the potential yield production.

The objective of this study was the evaluation of the Ceres-Rice and ORYZA2000 models under different water-managements and planting densities of Rice, in order to determine difference between potential and actual yield and invest its reasons. An experiment was conducted in a randomized complete block design (RCBD) with three replications in Kooshal-Lahijan located in north of Iran during cropping seasons of 2014 and 2015. There were irrigation treatments in this research including: I1 = Full irrigation, I2 = Saturation, I3 = Irrigation with 8 days alternative before anthesis, I4 = Irrigation with 8 days alternative after anthesis, I5= Irrigation with 8 days alternative whole growth season) and there were density including, D1=15×15, D2=20×20, D3=25×25cm. Evaluation simulated and measured yield and biomass by adjusted coefficient of correlation and by absolute and normalized root mean square errors (RMSE). ORYZA2000 model was more accurate in simulation of grain yield (RMSE= 533 and RMSEn= 14%). Observed and predicted of I3D2 showed good agreement in both calibration and evaluation steps. The average amount of water-based components of irrigation, evapotranspiration, transpiration and total evapotranspiration and deep penetration (WPI, WPI + R, WPET, WPT and WPETQ) simulated by the ORYZA2000 and Ceres-Rice models, were 1.09, 0.90, 1.00, 1.59 and 0.79 kg/m3 respectively.

This study was conducted in Qazvin plain (with latitude 50˚ 8’ and longitude 36˚ 8’ and elevation 1240 m) during 2010-2012 to evaluate AquaCrop model for simulation of canola yield. The results showed that this model is very sensitive to transpiration crop coefficient and low to medium sensitive to other input parameters. Normalized root mean square errors (NRMSE) were 0.10, 0.04, 0.11 and 0.04 for yield, biomass, water use efficiency and harvest index, respectively. The values of agreement index (d) for the all parameters were greater than 0.98. Although, AquaCrop model tended to overestimate outputs; however, the simulation results could be acceptable.

Estimating crop water requirement, crop yield and their temporal and spatial variability using crop simulation models are essential for analysis of food security, assessing impact of current and future climates on crop yield and yield gap analysis, however it requires long-term historical daily weather data to obtain robust predictions. Depending on the degree of weather variability among years, at least 10–20 years of daily weather data are necessary for reliable estimates of crop yield and its inter-annual variability. In many regions where crops are grown, daily weather data of sufficient quality and duration are not available. In this way, gridded weather databases with complete terrestrial coverage are available which require comprehensive validation before any application. These weather databases typically derived from global circulation computer models, interpolated weather station data or remotely sensed surface data from satellites. The aims of this study were to evaluate differences between grided AgMERRA weather data and ground observed data and quantify the impact of such differences on simulated water requirement and yield of rainfed wheat at 9 different locations in Khorasan Razavi province.
AgMERRA dataset (NASA’s Modern-Era Retrospective analysis for Research and Applications) was selected as the girded weather data source for use in this study because it is publically accessible. We evaluated AgMERRA weather data against observed weather data (OWD) from 9 meteorological stations (Torbat Jam, Torbat Heydarieh, Sabzevar, Sarakhs, Ghoochan, Kashmar, Gonabad, Mashhad, and Neyshabour) in Khorasan Razavi province. For each weather variable (solar radiation, maximum temperature, minimum temperature, precipitation, and wind speed), the degree of correlation and agreement between OWD and AgMERRA data for the grid cell in which weather stations were located were evaluated. The intercept (b), slope (m), and coefficient of determination (r2) of the linear regression were calculated to determine the strength and bias of the relationship, while the root mean square error (RMSE) and normalized root mean square error (NRMSE) were computed to measure the degree of agreement between data sources. Crop water requirement or actual crop evapotranspiration (ETc) under standard condition was computed using CROPWAT 8.0. The CSM-CERES-Wheat (Cropping System Model-Crop Environment Resource Synthesis-Wheat) model, included in the Decision Support System for Agrotechnology Transfer (DSSAT v4.6) software package was used to calculate rainfed wheat yield. For each location in this study, rainfed wheat grain yield and water requirement were simulated using ground-observed and AgMERRA weather data and outputs were compared with each other.
The results of this study showed that AgMERRA daily maximum and minimum temperature and solar radiation showed strong correlation and good agreement with data from ground weather stations. AgMERRA daily precipitation had low correlation and good agreement (mean r2= 0.34, RMSE= 2.25 mm and NRMSE= 4.94% across the 9 locations) with OWD daily values, but correlation with 15-day precipitation totals were much better (mean r2 >0.7 across the 9 locations). There was reasonable agreement between a number of observed dry and wet days with AgMERRA compared to OWD. Results indicated that coefficient of variation of simulated water requirement and yield using AgMERRA weather data was remarkably similar to the degree of variation observed in simulated water requirement and yield using OWD at all locations (distribution of CVs in simulated water requirement and yield using AgMERRA weather data were within ±5% of the CV calculated for simulated water requirement and yield using observed weather data) except Torbat Jam, Torbat Heydarieh and Gonabad for water requirement and Mashhad, Kashmar and Ghoochan for yield. There was good agreement between long-term average yield simulated with AgMERRA weather data and long-term average yield simulated using observed weather data. For example, the distribution of simulated yields using AgMERRA data was within 10% of the simulated yields using observed data at all locations. Using AgMERRA weather data resulted in simulated crop water requirement that were not in close agreement with crop water requirement simulated with ground station data at two location including Gonabad and Torbat Heydarieh.
These results supported the use of uncorrected AgMERRA daily maximum and minimum temperature and solar radiation in areas that their weather stations only have a few years of daily weather records available or areas without weather station. Considering the advantage of continuous coverage and availability, use of AgMERRA dataset appears to be a promising option for simulation of long-term average yield and water requirement, as well as for assessing impact of climate change on crop production and also estimating the magnitude of existing gaps between yield potential and current average farm yield in Khorasan Razavi province. But they are not very reliable for accurate simulation of water requirement and yield in a specific year and estimate their inter-annual variation.

This research was conducted to evaluate AquaCrop model to simulate sugar beet yield and water use efficiency (WUE). In this research, data from two sugar beet cultivars were used at Shahrekord Agricultural Research Center. Treatments were consisted of water deficit (in five levels: E0: 100%, E1: 85%, E2: 70%, E3: 55%, and E4: 30%) in different growth stages (T1: initial, T2: T2: mid-season, and T3: late season). AquaCrop had a low sensitivity to change in PWP moisture and minimum temperature values and high sensitivity to change in crop coefficient for transpiration values. The yield results of RMSE and NRMSE were 0.57 and 0.11 ton.ha-1, respectively. The yield results for two statistics criteria (EF and d) were 0.62 and 0.99, respectively. Evaluation of AquaCrop revealed that this model had good accuracy for simulation of sugar beet yield and WUE.

AquaCrop model was developed to simulate crop response to water consumption and irrigation management. The model is easy to use, works with limited input, and has acceptable accuracy. In this study, the data of an alfalfa field (as a perennial fodder plant) in the Iranian city of Ardestan was used to calibarate and validate the performance of AquaCrop model to simulate the crop productivity in relation to water supply and irrigation management.
The data of Fajr-e Esfahan Company farms of Ardestan County were used for calibration and validation of the AquaCrop model, simulating the alfalfa performance in different harvests and over different years. The farms are 1004 m above sea level and located in 33°2' to 33°30' North and 55°20' to 55°22' East. The farm under investigation included ten plots of alfalfa field, with an area of 280 hectares. The data of two plots were used for calibration and, two others used for validation.
Considering that alfalfa is a perennial plant, the data regarding the first harvest was defined as sowing, and transplanting was used to refer to the next harvests. Considering the physiological changes of plants over a year and during different harvests, the numerical value of different parameters, including primary vegetation, maximum vegetation, the depth of primary root development, the maximum depth of primary root development, crop coefficient, germination date, flowering, vegetation senescence, and physiological maturity, were defined for the model. The CRM, NRMSE, R2, and EF indices were used for verification of the calibration results. The CRM index determines the overestimation or underestimation of the model. The EF index is variable between 1 and 0, where 1 indicates optimal performance of the model. If all estimated and measured values were equal, the value of CRM and NRMSE would be zero, and EF would be one.
After calibration, validation was performed to examine the performance of the model. Hence, the actual performance rate for different harvests and the results of simulations were compared. Lower NRMSE value is indicative of high accuracy of the model in estimation of the performance. The value of CRM was mostly positive, showing the underestimation of the model in most of the simulations. The maximum performance happened during the first harvest year. The annual harvest decreased with an average rate of 1.2, compared to former years. The evaporation and transpiration rate was calculated by the model and the results were compared with potential evapotranspiration (FAO Penman-Monteith) and National Document of Irrigation (NET WAT). The reference crop evapotranspiration (ET0) had the highest value, and was calculated through FAO Penman-Monteith equation. The numerical value of potential crop evapotranspiration (ETc), which is the result of multiplication of crop coefficient by reference crop evapotranspiration (ET0), was greater than the results of the model, i.e. the estimated actual evapotranspiration. The discrepancy between them is the result of stress coefficient (ET0×Kc×Ks), which the model takes into account in estimation of actual plant water requirement. Evapotranspiration refers to two factors, namely the water lost by transpiration from plants and by evaporation from the soil. The plant transpiration and green cover are considered to be the generating part; AquaCrop is able to examine and improve transpiration efficiency through managerial statements. The values of transpiration from plants and evaporation from the soil for alfalfa were differentiated from the values estimated by the model. The productivity of evaporation, transpiration, and evapotranspiration were calculated by the model. The difference in the productivity values of the plots during different years was the result of difference in chemical composition, harvest index, and transpiration rate.
The AquaCrop model performed well in simulation of crop performance compared to actual annual, and even monthly, performance, and its results were very close to the actual performance. The model is sensitive to temperature changes, and it is suggested to use the Growing Degree Days (GDD) instead of Calendar Days section. . The Version 5 of AquaCrop model can, in addition to moisture stress, include salinity stress in calculations; this is evident in the variation of actual evaporation and transpiration values estimated by the model. In this study, the annual evaporation and transpiration rate was predicted by the model. The higher rate of evaporation can lead to a 27 to 44 percent decrease in the efficiency of evapotranspiration (Y ET-1), compared to transpiration efficiency (Y T-1).

In recent years human activities induced increases in atmospheric carbon dioxide (CO2). Increases in [CO2] caused global warming and Climate change. Climate change is anticipated to cause negative and adverse impacts on agricultural systems throughout the world. Higher temperatures are expected to lead to a host of problems. On the other hand, increasing of [CO2] anticipated causing positive impacts on crop yield. Considering the socio-economic importance of agriculture for food security, it is essential to undertake assessments of how future climate change could affect crop yields, so as to provide necessary information to implement appropriate adaptation strategies. In this perspective, the aim of this study was to assess potential climate change impacts and on production for one of the most important varieties of wheat (chamran) in Khouzestan plain and provide directions for possible adaptation strategies.
For this study, The Ahvaz region located in the Khuzestan province of Iran was selected.
Ahvaz has a desert climate with long, very hot summers and mild, short winters. At first, thirteen GCM models and two greenhouse gases emission (GHG) scenarios (A2 and B1) was selected for determination of climate change scenarios. ∆P and ∆T parameters at monthly scale were calculated for each GCM model under each GHG emissions scenario by following equation: Where ∆P, ∆T are long term (thirty years) precipitation and temperature differences between baseline and future period, respectively. average future GCM temperature (2015-2044) for each month, , average baseline period GCM temperature (1971-2000) for each month, , average future GCM precipitation for each month, , average baseline period GCM temperature (1971-2000) for each month and i is index of month. Using calculated ∆Ps for each month via AOGCM models and Beta distribution, Cumulative probability distribution function (CDF) determined for generated ∆Ps. ∆P was derived for risk level 0.10 from CDF. Using the measured precipitation for the 30 years baseline period (1971-2000) and LARS-WG model, daily precipitation time series under risk level 0.10 were generated for future periods (2015-2045 and 2070-2100). Mentioned process in above was performed for temperature. Afterwards, wheat growth was simulated during future and baseline periods using DSSAT, CERES-Wheat model. DSSAT, CERES4.5 is a model based on the crop growth module in which crop growth and development are controlled by phenological development processes. The DSSAT model contains the soil water, soil dynamic, soil temperature, soil nitrogen and carbon, individual plant growth module and crop management module (including planting, harvesting, irrigation, fertilizer and residue modules). This model is not only used to simulate the crop yield, but also to explore the effects of climate change on agricultural productivity and irrigated water. For model validation, field data from different years of observations were used in this study. Experimental data for the simulation were collected at the experimental farm of the Khuzestan Agriculture and Natural Resources Research Center (KANRC), located at Ahwaz in south western Iran.
Results showed that wheat growth season was shortened under climate change, especially during 2070-2100 periods. Daily evapotranspiration increased and cumulative evapotranspiration decreased due to increasing daily temperatures and shortening of growth season, respectively. Comparing the wheat yield under climate change with base period based on the considered risk value (0.10) showed that wheat yield in 2015-2045 and 2070-2100 was decreased about 4 and 15 percent, respectively. Four adaptation strategies were assessed (shifting in the planting date, changing the amount of nitrogenous fertilizer, irrigation regime and breeding strategies) in response to climate change. Results indicated that Nov, 21 and Dec, 11 are the best planting dates for 2015-2045 and 2070-2100, respectively. The late season varieties with heat-tolerant characteristic had higher yield in comparison with short and normal season varieties. It indicated that breeding strategy was an appropriate adaptation under climate change. It was also found that the amount of nitrogen application will be reduced by 20 percent in future periods. The increase and decease of one irrigation application (40mm) to irrigation regime of base period resulted in maximum yield for 2015-2045 and 2070-2100, respectively. But, reduction of two irrigation application (80mm) resulted in maximum water productivity (WPI).
In the present study, four adaptation strategies of wheat (shifting in the planting date, changing the amount of nitrogenous fertilizer, irrigation regime and breeding strategies) under climate change in Ahvaz region were investigated. Result showed that Nov, 21 and Dec, 11 were the best planting dates for 2015-2045 and 2070-2100, respectively. The late season varieties with heat-tolerant characteristic had higher yield in comparison with short and normal season varieties. It indicated that breeding strategy was an appropriate adaptation strategy under climate change. It was also found that the amount of nitrogen application will be reduced by 20 percent in future periods. The increase and decease of one irrigation application (40mm) to irrigation regime of base period resulted in maximum yield for 2015-2045 and 2070-2100, respectively.

Useful and extensive use of crop models depends on understanding their behavior in response to probable errors in input parameters. The aim of this study was to assess the sensitivity of the simulated sillage-corn yield by the most commonly used crop model، CERES – Maize، to physical soil parameters under two nitrogen fertilizer levels. In this study، by using one year field data of sillage corn in Varamin، the model-output sensitivity to field capacity، saturated water content، curve number، root growth factor، drainage coefficient and albedo was analyzed for two N-fertilizer levels (0 and 150 kg N/ha). To determine the response of model to changes of input parameters، model was run 80 times. After collecting data، the normalized sensitivity of each parameter was calculated using the program written in MATLAB environment. The results showed that the highest model sensitivity in the simulation of the yield in both treatments was shown to field capacity. But the response of the model to changes in input parameters was strongly influenced by the amount of fertilizer. Finally، by using the calculated relative sensitivity values، the relationship between probable error in estimating yield for both fertilizer treatments and error in estimation input data was presented. Useful and extensive use of crop models depends on understanding their behavior in response to probable errors in input parameters. The aim of this study was to assess the sensitivity of the simulated sillage-corn yield by the most commonly used crop model، CERES – Maize، to physical soil parameters under two nitrogen fertilizer levels. In this study، by using one year field data of sillage corn in Varamin، the model-output sensitivity to field capacity، saturated water content، curve number، root growth factor، drainage coefficient and albedo was analyzed for two N-fertilizer levels (0 and 150 kg N/ha). To determine the response of model to changes of input parameters، model was run 80 times. After collecting data، the normalized sensitivity of each parameter was calculated using the program written in MATLAB environment. The results showed that the highest model sensitivity in the simulation of the yield in both treatments was shown to field capacity. But the response of the model to changes in input parameters was strongly influenced by the amount of fertilizer. Finally، by using the calculated relative sensitivity values، the relationship between probable error in estimating yield for both fertilizer treatments and error in estimation input data was presented. Useful and extensive use of crop models depends on understanding their behavior in response to probable errors in input parameters. The aim of this study was to assess the sensitivity of the simulated sillage-corn yield by the most commonly used crop model، CERES – Maize، to physical soil parameters under two nitrogen fertilizer levels. In this study، by using one year field data of sillage corn in Varamin، the model-output sensitivity to field capacity، saturated water content، curve number، root growth factor، drainage coefficient and albedo was analyzed for two N-fertilizer levels (0 and 150 kg N/ha). To determine the response of model to changes of input parameters، model was run 80 times. After collecting data، the normalized sensitivity of each parameter was calculated using the program written in MATLAB environment. The results showed that the highest model sensitivity in the simulation of the yield in both treatments was shown to field capacity. But the response of the model to changes in input parameters was strongly influenced by the amount of fertilizer. Finally، by using the calculated relative sensitivity values، the relationship between probable error in estimating yield for both fertilizer treatments and error in estimation input data was presented. Useful and extensive use of crop models depends on understanding their behavior in response to probable errors in input parameters. The aim of this study was to assess the sensitivity of the simulated sillage-corn yield by the most commonly used crop model، CERES – Maize، to physical soil parameters under two nitrogen fertilizer levels. In this study، by using one year field data of sillage corn in Varamin، the model-output sensitivity to field capacity، saturated water content، curve number، root growth factor، drainage coefficient and albedo was analyzed for two N-fertilizer levels (0 and 150 kg N/ha). To determine the response of model to changes of input parameters، model was run 80 times. After collecting data، the normalized sensitivity of each parameter was calculated using the program written in MATLAB environment. The results showed that the highest model sensitivity in the simulation of the yield in both treatments was shown to field capacity. But the response of the model to changes in input parameters was strongly influenced by the amount of fertilizer. Finally، by using the calculated relative sensitivity values، the relationship between probable error in estimating yield for both fertilizer treatments and error in estimation input data was presented.

In recent years, simulatiom modelling of yield has been the focus of attention for many researchers. Because, while reducing adminestrative costs, it can easily provide simulation models of different situations. In this study, while a subroutine on simulation of canola was added to CRPSM model, effect of different water treatments on canola was also investigated. In this research, canola (Talaye) under 5 irrigation treatments (full irrigation treatment during the growing period, water stress treatment at the spring re-growth stage, the flowering stage and pod formation, the grain formation stage and dry land treatment) was sown in complete randomized block designs at the college of Agriculture, Shiraz University during 2007-2008, and then the model was calibrated based on available information (soil-location -plant-water). Review of statistical indicators between simulated and measured yield show high accuracy in the estimation of crop yield (R2=0.98) and soil water content. The result of model validation with independent data series also showed that the result of soil water content is desirable except in dry treatment, and the corrolation coeficient between simulated and measured crop yield (R2=0.98) was acceptable.