جستجوی مقالات مرتبط با کلیدواژه « مدل سازی » در نشریات گروه « زراعت »

Crop simulation models provide the possibility to study the effect of agronomic management practices on agricultural production activities in a given location. The models are mechanistic and process-oriented that are able to simulate different agricultural systems (including arid and semi-arid areas such as Iran) through different sub-models (biological, environmental, managerial, and economic sub-models) connected with the main engine of the models. The APSIM-Barley model can be used to evaluate the management practices on barley crop. The current research was carried out to evaluate the APSIM-Barley model in relation to simulating the growth, development, and yield of three barley cultivars (Azaran, Jolgeh, and Bahman) under different conditions of nitrogen and irrigation supplies.

 In order to evaluate the APSIM-Barley model regarding simulating and quantifying the phenological stages, biomass, and grain yield of different barley cultivars, some independent experiment datasets were used. For crop model calibration, an experiment was conducted in a factorial split plot design at the Agricultural Research Center of Hamedan in 2019. The main factor consisted of three irrigation levels (30-40, 60-70, and 90-100% of the field capacity) and sub-factors included cultivar levels (Azran, Jolgeh, and Bahman as early, mid, and late cultivars, respectively) and three nitrogen levels (zero, medium, and optimum). For model validation, another series of independent datasets in different years consisting of published articles and research projects were used. For assessing the efficiency of the crop model and comparing the simulated and measured values, R2, nRMSE, d-index, and MBE indices were used and OriginPro software was considerd for all statistical analysis and drawing of figures.

 The simulation results of APSIM-Barley model in the calibration step showed that nRMSE for days to flowering and maturity was 2.43 and 2.4%, respectively.  Also, under full water and nitrogen conditions, nRMSE for the leaf area index of Azaran, Jolgeh and Bahman cultivars was 13.5, 14.1, and 10.7%, respectively, and under severe water and nitrogen stresses, it was 16.5, 18.6, and 26.5% respectively. At this step, and nRMSE and d-index for biomass and grain yield were 24.9% and 0.98 and 15.2% and 0.96, respectively.  In model validation step, nRMSE, d-index, R2, MBE were 15.02%, 0.96, 0.82 and 0.34 t ha-1, respectively, for grain yield.

In general, the results showed that the APSIM-Barley model was able to simulate the growth, development, and grain yield of different barley cultivars with acceptable accuracy under different water and nitrogen management conditions in different years and regions. Therefore, the APSIM-Barley model can be used as a reliable tool in future studies such as climate change assessment, yield gap analysis, agricultural zoning, and etc. by other researchers.

Saffron is the most expensive spice in the world. The economic value of saffron is due to the existence of apocarotenoids in its stigma. Therefore, the isolation and characterization of genes involving in apocarotenoids metabolism is on particular importance. In this research, beacause the importance of CsUGT gene in crocin biosynthesis, it was isolated, cloned the E.coli strain DH5α. The full length gene was sequenced and registered in the NCBI. In order to characterize CsUGT gene, first the protein sequence was obtained, and then the physical and chemical characteristics and physiology of the CsUGT protein were analyzed by Protparam, SOPMA, ProtScale, Pfam, ProtComp, SignalP, TMHMM and ChloroP servers and tools. Also, using the Swiss-Model server, the 3D structure of this protein was investigated and Ramachandran diagram was drawn to validate the 3D drawn model structure. According to the results, CsUGT protein with 462 amino acids has the conserved sequence of glycosyltransferase family proteins and was identified as a polar protein, stable at high temperatures and without hydrophobic domain. CsUGT protein has no peptide signal or binding signals and has a cytoplasmic location. This research made it possible to isolate the CsGTS gene of saffron and optimized the conditions of its transfer into a vector. In addition, the results of CsUGT protein structure analysis provide the basis for future functional studies and can also provide valuable information regarding the behavior and reaction of this enzyme in the synthesis of saffron apocarotenoids. In addition, these results can be useful in the future programs of Iranian saffron genetic modification.

Crop simulation models are widely used in the analysis of cropping systems, climate change and crop management methods. It is a good tool for completing and developing the results of field trials to evaluate new cultivars and new management systems. The aim of this research was to simulate the phenological stages and yield of different bread wheat cultivars in climatic conditions of Gorgan city using DSSAT-Nwheat model.

In this study, in order to evaluate the performance of the DSSAT-Nwheat model, the data drived from a two-year experiment (Growing seasons 2019-2020 and 2021-2020), in which four new bread wheat cultivars were studied under seven sowing dates as split plot based on randomized complete block design (RCBD). Seven sowing dates (from 1 November to 31 December, 10-day intervals) were placed in main plots and four bread wheat genotypes (including Arman, Araz, Taktaz and N-93-9) were placed as subplots. The data derived from the first year and the second year were used for calibration and validation of the model, respectively. In addition to field data, daily meteorological data, management events, soil characteristics and geographical coordinates were provided to DSSAT 4.7 software. After determining the genetic coefficients of each genotype, the model was calibrated for different traits and subsequently the same coefficients were used to validate the model. Using statistical indices, the simulated values of the model were tested with the observed values.

The results showed that the phenological stages including day to anthesis and day to maturity were simulated with root mean squared error (RMSE) equal to four days, and normalized root mean square error (nRMSE) less than 3%. RMSE for grain yield and biological yield were 416 kg ha-1 and 1000 kg ha-1, respectively, and nRMSE values were between 7-8%. In water productivity based on grain yield and biological yield, nRMSE values were 6.21% and 7.53%, respectively, and RMSE values were 0.93 kg ha-1 mm-1 and 2.91 kg ha-1 mm-1, respectively. In all the simulated traits, the Willmott's agreement indices (d) and the coefficient of determination (R2) were in the acceptable range, which showed the proper performance of the DSSAT-Nwheat model for simulating these traits in different bread wheat cultivars.

The results of this study showed that the DSSAT-Nwheat model had proper performance for simulating phenological stages, grain yield, biological yield and water productivity in four cultivars including Araz, Arman, Taktaz and N-93-9. The nRMSE values for all studied traits were between 6-8%. The cultivars studied in this study are the latest cultivars released for the northern warm and humid agro-climatic zone, Iran, in the next few years, they will occupy a large area of wheat cultivation in Golestan province. Therefore, it seems that the results of this study can be used in the decisions of wheat cultivation systems, different effects of agricultural management and current and future climate change in Golestan province.

In order to investigate the variation in germination rate, cardinal temperature and hydrothermal time parameters of wild mustard seed in different water potentials were designed and implemented during the study in gorgan university of agricultural sciences and natural resources, in 2017 as a split plot done in a completely randomized design with three replications of the municipality. In this research, germination modeling was performed well. To describe the germination rate response to temperature in different potentials of water 0, -0.2, -0.4, -0.6, -0.8 MPa were used from the dent-like function. By applying different concentrations of gibberellic acid (100, 300, 500, 1000, 1500 and 2000 ppm), the process of germination changes in the studied temperatures was incremental, so that the highest germination percentage was related to gibberellic acid 2000 ppm. By reducing the potential at each level of gibberellic acid, the base temperature increased and the lower and upper optimum temperature and the ceiling temperature decreased. The hydrotime coefficient in control seeds decreased significantly with increasing temperature and the water potential was increased. The hydrotime coefficient and base water potential increased with decreasing gibberellic acid. The results showed that the coefficients obtained by the hydrothermal time model were of relatively high precision and the constant value of hydrothermal time, base water potential and base temperature in control seeds were 1173.83 MPa ºC h-1, -0.09 MPa and 3.07 °C and for gibberellic acid with concentration of 2000 ppm were 315.57 MPa ºC h-1, -0.50 MPa and -1.22 ºC, respectively. Changes in the base water potential for 50% of germination in gibberellic acid 1500 and 2000 ppm treatments had a two segmented trend up to 20 °C so that it -0.7 to -0.6 MPa and increased above 20 °C the maximum value at 30 °C.

Predicting crop yield is an important factor for efficient planning and management. In the present study, a simple mathematical model is developed for predicting the yield of autumn-sown sugar beet in Khuzestan region. In this model, the logistic function was used, in which the root yield and white sugar yield is defined as a function of irrigation, rainfall and evaporation from the pan. In order to evaluate the model, data of two-year trials conducted under five treatments of drip irrigation water based on percentage of water requirement (25, 50, 75, 100 and 125%) at Safiabad Agricultural Research and Education and Natural Resources Center, Dezful, were used. Results showed that the model was able to estimate root yield and white sugar yield with good accuracy so that the normalized root mean squares error (NRMSE) for estimating root yield in the calibration and validation stage were 9.4 and 13.0%, respectively. Also, the value of NRMSE for estimating white sugar yield in both calibration and validation years was less than 10% and equal to 6.8 and 9.8%, respectively. The model is able to have good accuracy during the growing season and estimate the values of root yield and sugar yield during different days after planting (DAP) with an error of less than 20%. Considering that the model presented in the current study is an experimental model, it is recommended to first recalibrate the model, and then use it in regions with different climatic conditions.

Germination is considered the first and most important stage of establishment and consequently, successful competition which is influenced by genetic and environmental factors. Among the environmental factors influencing the germination, temperature and light are the most important ones. Using different models, the germination response of seeds to temperature can be quantified; therefore, this study was performed to investigate the effect of temperature on germination and to quantify the germination response of Buckwheat seed (Fagopyrum esculentum Moenc) to temperature using nonlinear regression models and thermal-time model.

The seeds were germinated in 4 replications of 25 seeds under 8 constant temperature treatments (5, 10, 15, 20, 25, 30, 35 and 40 ° C). Using a three-parameter logistic model, Buckwheat seed germination was quantified at different temperature levels and the percentage and time to reach 50% germination were obtained. Four nonlinear regression models and a thermal-time model were used to quantify the response of Buckwheat seed germination rate to temperature. To compare the models and determine the most appropriate model, the root mean square error index (RMSE), coefficient of determination (R2), coefficient of variation (CV) and standard error (SE) were used for the observed germination rate versus the predicted germination rate.

The results indicated that temperature affected the seedling length, normal seedling percentage, seed vigor and the germination rate as well as germination percentage. Also, the results showed that germination characteristics increased with increasing temperature up to 20 and 25 °C. Comparison of the three models based on the root mean square error (RMSE) of germination time, the coefficient of determination (R2), CV and SE, the best model to determine the cardinal temperatures of Fagopyrum esculentum was the dent-like model. The results of thermal-time model showed that the base temperature of Fagopyrum esculentum seeds was 4.01 ° C and the thermal-time coefficient was 1242.6 h° C.

Utilization of non-linear regression models (segmented, dent-like and beta) and thermal-time model to quantify the germination response of Fagopyrum esculentum response to different temperatures led to acceptable results. Therefore, germination rate and percentage may be predicted using the outputs of these models at different temperatures.

Fusarium head blight (FHB) is one of the most important diseases of wheat that is a suitable disease for forecasting. In order to determine the statistical relationship among disease occurrence and weather and crop variables, this study was conducted in Golestan, Mazandaran and Ardabil provinces during 3 years (crop years 2016-17, 2017-18 and 2018-19). Crop characteristics and disease severity were recorded in wheat fields around meteorological stations. Nine weather data was received from the stations as daily data and converted to 30 periodic variables. Integrating flowering start date and anthesis length in different years and regions calculated as 11 time and meteorological calendar variables and used in statistical analyses. Results of correlation tests of these variables with disease showed that the best window for disease prediction was B020 (20 days before anthesis) and ATX (average of maximum temperature), RD (number of rainy days), DD (number of dry days), ASHTR (average of special hydrothermal ratio) and NP80 (number of days with rain and relative humidity > 80%) showed the highest correlation with disease. Results of logistic regression and discriminant analysis for the selected variables as one, two and three showed that ATX was the best individual variable, and the best combination of two variables was ATX + NP80 and adding ASHTR to them did not cause significant increase in prediction efficacy. Therefore, logistic regression and discriminant analysis model of ATX and NP80 in 20 days before anthesis, introduced as the best model for forecasting wheat FHB.

The exciting growth of various sciences and technologies and the complexity of decision-making in recent decades, have marked new ways for humanity to use information systems and artificial intelligence, accurately and quickly and provide a solution for its time-consuming scientific and technical predictions and calculations. This study was conducted to evaluate the predictive outcome of two stepwise regression models and the M5 decision tree model under the influence of different water and soil properties on saffron flower and stigma yield in 2019 in Sabzevar saffron fields (located at longitude "57.43" Latitude "36.12") and was performed in the laboratory of the Faculty of Plant Production of Gorgan University of Agriculture Sciences and Natural Resources. In April, after the end of the saffron growing season, soil samples from a depth of zero to 30 cm and 12 irrigation water samples from 69 saffron farms were prepared and transferred to the laboratory to analysis and measuring the physical and chemical properties of soil samples. Around 13 parameters including pH, acidity, and percentage of soil components, soil elements, etc. and acidity, bicarbonate, etc. were measured in soil and water samples, respectively. Flowers were collected at the time of flower emergence from the specified areas of the fields and the desired measurements were made. The results showed that, as the soil and water analysis is relatively expensive, the M5 decision tree model has more accuracy due to the speed and lower cost than the regression model. So that, in the result of predicting the stepwise regression model, in the most ideal case and entering all the measured parameters, dry stigma weight and flower weight with correlations of 70 and 74%, respectively, and the error value is 0.23 RMSE and RMSE 16.38 were predicted. While the M5 decision tree model with lower parameters had a high capability to predict flower weight and dry stigma weight. It estimated the weight of dry stigma and flower weight with 90% correlation and error value equal to RMSE = 0.12 and RMSE = 9.4 at the end of modeling for the study area. Therefore, the M5 decision tree method is recommended in evaluating and predicting various factors on saffron yield.

Purple nutsedge (Cyperus rotundus L.) is one of the problematic weeds worldwide prevalent in tropical and subtropical regions. Tubers are major tools through which purple nutsedge is propagated, whereas its seeds have a low ability to germinate. Therefore, evaluation of the response of tubers against environmental agents is great of importance to know the germination and emergence time. Germination, in turn, is mostly affected by temperature, among other environmental factors. Various models that are recognized as the Thermal Time model have been introduced to describe the seed germination pattern against temperature. Since predicting the emergence of reproductive organs through the modeling is great of importance for improving the control strategies; the present study was carried out to investigate the response of tuber sprouting of purple nutsedge (Cyperus rotundus) against temperature using thermal time models.

The experiment was carried out as a randomized complete block design with three replications in a germinator. Each replicate was placed on a separate shelf. For each replicate, 15 tubers were placed inside a 20 cm Petri dish on a filter paper and then 100 ml of water was added. The experiment was performed separately for constant temperatures of 10, 15, 20, 25, 30, 35, and 40 °C in absolute darkness. To analyze the data as modeling, five thermal time models were evaluated based on the statistical distributions of normal, Weibull, Gumble, logistic and log logistic. Indices such as R2, RMSE, RMSE%, and AICc were used to evaluate the models.

The results showed that all models predicted the germination response of purple nutsedge tuber with high accuracy (R2 = 0.95). A comparison of models based on AICc values showed significant superiority of the Gumble model over other models. According to this index, there was no difference between logistic and log logistic models with normal. Among the models, Weibull was identified as the most inappropriate model. Different models estimated the final germination (Gmax) between 0.93 to 0.94 (93 to 94%). The base temperature was estimated through different models from 7.10 to 7.47 °C. Among the models, the model based on the Gumble distribution proved the skew to the right of the thermal time and Tm. According to the Gumble model, the thermal time parameters required to reach 50% germination (θT (50)) equals 123.8 ° C day and the maximum temperature for germination at 50% probability (Tc (50)) was estimated to be 46.10 ° C.

the thermal time model based on the Gumble probability distribution was most plausible among the models. Also, a distributed right skewness related to the thermal time and Tm was proved through the Gumble model. The parameters obtained from the Gumble model can be used to predict the sprouting of purple nutsedge tubers. 

Thermal time models were evaluated for prediction of tuber sprouting of purple nutsedge. The thermal time model based on the Gumble distribution was superior over the normal distribution. Thermal time and Tm for tuber sprouting of purple nutsedge were distributed as right skewness.

Saffron is the most valuable spice known in the world. Crocin, picrocrocin and safranal are known as saffron color, taste and aroma indices, respectively. Drying is one of the most important steps affecting the final quality of saffron in terms of crocin, picrocrocin and safranal content. In this study, the efficiency of drying process by oven method based on important variables such as temperature, time and thickness of saffron layer was modeled by artificial neural network method. Modeling results of saffron drying process by oven method showed that if crocin changes under momentum learning rule and tangent transfer function with 8 neurons and 25, 55 and 20% of the data were used for evaluation and test training respectively; the coefficient has the highest correlation coefficient (0.914). Whereas for the changes of picrocrocin, the Levenberg learning law and the tangent transfer function in the number of neurons, 12 designed the best networks with 50, 25 and 25% of data for training, evaluation and testing, respectively (R = 0.986). Safranal changes were also predicted by the Levenberg learning law and Sigmoid transfer function in neuron number 8 with 35, 45 and 20% of the data for training, evaluation and testing with appropriate correlation coefficient of 0.981 and predicted by its network.

Nowadays, saffron is considered as a strategic medicinal plant in Iran. Apocarotenoids are also known as economic compound in saffron stigmas. Therefore, isolation and functional analysis of genes involving in carotenoid metbolism in saffron has a great importance. In this study, regarding the role of CsCCD1 gene in saffron apocarotenoids biosynthesis, it was targeted for studying the genomic structure of Iranian saffron CCD1. ProtParam, SOPMA, ProtScale, Pfam, ProtComp, SignalP, TMHMM, TargetP and ChloroPwre are the softwares used for studying the physicochemical and physiological charachterizations of CCD1 protein. Swiss-Model server was used for investigate the 3D structure of CCD1 protein followed by Ramachandran plotting for structural validation of 3D model. The results of phylogenetic analysis showed that amino acid structure of CsCCD1 protein have the most similarity to CaCCD. It is also found that CsCCD1 protein has no signal sequence and transmembrane domains. In addition, the results of this study demonstrated that CCD1 protein belongs to carotenoid oxidase family and it is stable In vitro. The results of this study can provide valuable information on the behavior and response of the CCD1 enzyme in the pathway for the synthesis of apocarotenoids in saffron, and these results can be useful in future protein engineering programs.

Saffron is one of the most expensive spices and natural colors used in various food, pharmaceutical and cosmetic industries. In recent years, a family of enzymes that digest carotenoid substrates into double bonds are identified and introduced in plants. This family is of enzymes Carotenoid Cleavage Dioxygenase (CCD) enzymes. In this study, two isoforms of this gene were cloned and sequenced due to the importance of CCD genes in biosynthesis of apocarotenoids. Bioinformatics analyses including phylogenetic relationships and protein structures were evaluated. 3D modeling of these proteins was done by homologous modeling and using the Swiss Model database after selecting the appropriate pattern. The Ramachandran plot was drawn in order to validate the structure of the 3D model. The results show that the two CCD4a and CCD4b isoforms have both exons and one intron. In silico analysis, the physicochemical properties of CsCCD4a and CsCCD4b proteins also show that the proteins derived from these two isoforms are similar in terms of molecular weight, amino acids, isoelectric points, aliphatic index, instability index and solubility. The results of study of 3D structures resulted in proposal of similar structures for two isoforms. The results of this study can provide valuable information on the behavior and response of CCD4 enzyme in the pathway for synthesis of apricotines in saffron, and these results can be useful in future protein engineering programs.

soybean is one of the most important oily plants in the world. More than 50 percent of the world's oilseed production is concentrated on soybean. The purpose of this study was to evaluate and apply a simple model of soybean growth and yield in eastern Mazandaran provinc. Different aspects of plant growth in the model are as follows including phonological development, leaf area changes, and the production and distribution of dry matter. Grain yield, biological yield and harvest index were simulated using different scenarios. The results showed that the regression coefficients of the observed grain yield and harvest index versus simulated values based on 95% confidence intervals were not significantly different with the coefficients of line 1: 1 (a = 0 and b = 1). The values of R2 for grain yield and harvest index were 0.96 and 0.80 respectively. Despite R2 of linear regressed line between observed yield biological versus predicted values was 0.96 but bias frome the 1:1 line was high. Therefore, it can be said that the model has the ability to predict grain yield and harvest index in the environmental conditions of Mazandaran, but it can not be suitable for soybean biological yield in Mazandaran.

A major component in a crop growth model is leaf area development, which has crucial influence on photosynthesis and transpiration. Leaf area development involves the appearance of new leaves, expansion of the newly emerged leaves and senescence of old leaves. Modeling leaf growth has been extensively studied in many crops including cereals. Methods of predicting leaf area development are diverse from those dealing with the individual component processes of leaf growth, viz., leaf appearance, leaf expansion and leaf death to the models predicting leaf growth at the whole plant or whole crop levels. The concept of leaf lifetime is used in some crop simulations models to quantify the aging of the leaves after reaching thermal time to a certain amount. There is very little information about wheat aging time in the field, and most estimates of leaf lifetime are related to this observation that says on the main stem of wheat, at least 3 to 5 green leaves remains until pollination; one leaf is in the early stages of development, another leaf is completely developed and one to three leaves are aging. Quantitative information regarding leaf area development in wheat especially in environmental conditions with high temperatures for the purpose of crop modeling is scarce. Furthermore, genotypic variations have not been evaluated. Therefore, the goal of this research was to determine parameters related to leaf lifetime in wheat cultivars in warm environmental conditions.

The aim of this study was to quantify leaf lifetime of 15 different wheat cultivars. Two field experiments with 15 wheat cultivars (Atrak, Bayat, Chamran, Chenab, Dez, Ineia, Kavir, Marvdasht, Shiraz, S78-18, Yavaroos and shova-Mald) were conducted at the research farm of the Islamic Azad University of Ramhormoz Branch, south-western of Iran during 2008-9 and 2009-10 using a randomized complete block design with four replications. To determine leaf lifetime, a logistic model (Amax/[(1+exp)-a(x-b)]) was used in two stages. At first phase, changes in total plant leaf number versus growing degree days was determined, then, changes in plant senesced leaf number versus growing degree days were investigated.

The results indicate that the average of leaf lifetime based on growing degree days was 468.8 C dᵒ. This conclusion shows at optimum condition in terms of temperature, on average, a leaf lasts 468.8 C dᵒ. The average of phyllochron (the interval time between the sequential emergence of leaves on the main stem of a plant) was 84 C dᵒ in studied cultivars, upon which, the average of leaf lifetime in cultivars was 5.5 phyllochron. Hence, knowing the differences among hybrids in leaf area attributes may be useful in plant breeding, crop management and in wheat growth modeling.

 Based on the results, there were no significant differences between wheat cultivars in terms of parameters related to leaf lifetime on stem. The relationships presented in this study describe leaf lifetime under well-watered condition and reflect the effects of carbon and nitrogen availability and remobilization under these conditions. However, they do not account for the effects of shortage of carbon, nitrogen or water on leaf development. Other relationships are required to predict these effects.

Development of germplasm resources through importing new genotypes could improve the efficiency of safflower (Carthamus tinctorius L.) breeding programs. During 2014-2015 season, 82 imported safflower genotypes were evaluated in randomized complete block design at the research station of ORDC (Oilseeds Research & Development Company) in Sari. In this plan, six important quantitative traits related to grain yield were recorded for all genotypes. For modeling of stepwise regression PCA (Principal components analysis) were analyzed by PROC MIXED, PROC GLM and PROC IML methods, respectively. The results of PCA showed that grain yield and thousand grain weight components were correlation in linear combination. It was found 68% of the total variation justified with the three first components. The regression coefficients were significant for grain number per head and thousand grain weights at 1% and 5%, respectively indicating that these two mentioned traits had an effective role on increasing grain yield. The number of grain per pod and thousand grain weights remained in the model of Stepwise regression and for showed a signification with grain yield. Therefore, for increasing grain yield in breeding programs and producing high potential genotypes, the studied traits would be of great importance.

Seedling emergence probably is the single most important phenological event that influences the success of an annual plant. The main objective of this study was to develop a seedling emergence model for green bean and maize and select a best-fitted model associated with sowing depth. A factorial experiment based on completely randomized design was conducted in 2015 at Research Farm of Mohaghegh Ardabili University, to quantify the response of seedling emergence to sowing depth. Treatments were four sowing depths (2, 4, 6 and 8 cm) in three replications. The results indicated that the percentage emergences of both species in the first two levels of sowing depth (2 and 4 cm) were high, but at deeper levels, seedling emergence were suffering a severe loss. Emergence indicators (MED, ERI, D50%) showed that seedling emergence of bean was greater than maize. For two species, an increase in pre-emergence mortality with increasing depth was observed. So that the highest germinated seeds mortality occurred at 8 cm depth. However, in probit fitted curves for each dataset, the rates of increasing between plants varied. The rate of emergence varied between plants and based on values of statistical criteria, because of less parameters number in linear probit model, it was showed suitable to fit model. Therefore, these models may provide a better basis for broad practical application in crop management.

Yield gap analysis is useful method for prioritization agricultural researches and production to reduce yield constraints. To identify options for increasing chickpea yield, the SSM-chickpea model was parameterized and evaluated to analyze yield potentials, water limited yields and yield gaps for 12 regions representing major chickpea-growing areas of Razavi Khorasan province. For model parameterization, a field experiment was conducted in a randomized complete design with 4 replications in the research field of the Ferdowsi University of Mashhad (36. 15° N, 56. 28° E). The chickpea cultivar ILC482 was used in this experiment. Also, irrigation levels were as (full irrigation, supplemental irrigation at floweringand supplemental irrigation at both flowering and podding). Besides the above experiment, data obtained from a large number of field experiments involving varying seasons and management practices at diverse regions in Iran were also used for model evaluation. The evaluation of model indicated that the model predicted potential yield reasonably well. The results of running the model under different irrigation and sowing dates scenarios (19 February, March 25 , 4 April and 21 April ) showed that optimum sowing date is 19 February in most locations, the highest and lowest potential yield obtained in Taibad and Mashhad with an average yield of 2736 and 2306 Kg ha-1 respectively. Furthermore, in all of irrigation levels and the optimal sowing dates, the highest and lowest yield was observed in Quchan and Taibad respectively. The highest and lowest yield gap between the potential yield and irrigation levels were observed in Taibad and Mashhad respectively. Overall, the results indicated that supplemental irrigation at flowering and podding both reduced yield gap which is strategy in semi-arid areas with low precipitation.

A major component in a crop growth model is leaf area development, which has crucial influence on photosynthesis and transpiration. Leaf area development involves the appearance of new leaves, expansion of the newly emerged leaves and senescence of old leaves. Modeling of the leaf growth has been extensively studied in many crops including cereals. Methods of predicting leaf area development are diverge from those dealing with the individual component processes of leaf growth viz. leaf appearance, leaf expansion and leaf death to the models predicting leaf growth at the whole plant or whole crop levels. Leaf appearance and expansion are most sensitive growth processes to environmental conditions and their dependence on temperature revealed in a range of cereals including wheat and barley and legumes including cowpea and soybean. Effects of temperature on leaf appearance rates are usually quantified using some form of thermal time. Air temperature above the canopy has most frequently been used to calculate thermal time. Genetic differences in senescence have also been reported among crop genotypes. Quantitative information regarding leaf area development in wheat especially in environmental conditions with high temperatures for the purpose of crop modeling is scarce. Furthermore, genotypic variations have not been evaluated. Therefore, the goal of this research was to determine parameters related to leaf production and senescence in wheat cultivars in warm environmental conditions.
The aim of this study was to quantify leaf production and senescence of 15 different wheat cultivars. Two field experiments with 15 wheat cultivars (Atrak, Bayat, Chamran, Chenab, Dez, Ineia, Kavir, Marvdasht, Shiraz, S78-18, Yavaroos and shova-Mald) were conducted at the research farm of the Islamic Azad University of Ramhormoz Branch, south-western of Iran in 2005-6 and 2006-7 using a randomized complete block design with four replications. The relationship between main stem leaf numbers (HS) versus degree-days was described using bi-linear regression model.
The results indicated that leaf production on main stem started with receiving 108.5 degree-days after sowing. The leaf appearance rate and along with it phyllochron had no significant across all data. Mean leaf appearance rate ranged from 0.0047 to 0.0082 leaf/˚Cd. At 13 out of 15 cultivars. The cession time (degree-days) of effective leaf production on main stem was not significantly different and ranged from 831.0 to 852.0 ˚C d. Leaf senescence on the main stem started when the main stem had about 4-6 leaves and proceeded at a rate of 0.065 % per each unit increase in degree-days. The relationships found in this study can be used in simulation models of wheat.
Based on results, there was no significant difference between wheat cultivars in terms of parameters related to leaf production and senescence on main stem except the time of cessation of the linear increase in leaf number on main stem. The relationships presented in this study describe leaf production and senescence under well-watered condition and reflect the effects of carbon and nitrogen availability and remobilization under these conditions. However, they do not account for the effects of shortage of carbon, nitrogen or water on leaf development. Other relationships are required to predict these effects.

This study was performed to yield prediction and statistical modeling of Safflower. Phenology, dry matter production and distribution and soil-water balance sub models should be studied in order to growth stages and yield prediction in agricultural crops. Parameters related to each sub model were estimated using data reported on different sowing dates during the years 2002-2015 in Isfahan region and the data reported by other researchers in other regions. Growth and yield changes were calculated by phenology, dry matter production and distribution using meteorological data from Isfahan region, and the safflower crop yield at the end of growing season was predicted. One of the model evaluation criteria is comparison of coefficient of linear regression between observed and predicted yield (a= 0.46 ± 0.073, b= 1.49 ± 0.18) with coefficient of line 1:1. In the field experiments the limit for Coefficient of variation (CV) is 20 to 25. Accuracy of the model was high, regarding to the coefficient of variation of predicted and observed grain yield (CV=8.89). R2 of grain yield was 0.75, which is indicating that predicted data are 70 percent likely match with observed data. Variation range for observed data was 1.2 to 4.61 tones per hectar and the mean was 2.9 tones and for the predicted data it was 1.94 to 3.62 tones per hectar and the mean was 2.78 Tones per hectar. In all cases, simulated yield compliance with observed yield.