جستجوی مقالات مرتبط با کلیدواژه « مدل های رگرسیونی » در نشریات گروه « کشاورزی »

For an accurate irrigation schedule, the daily soil water depletion should be estimated during the crop growth period. Soil water depletion is dependent on daily evapotranspiration. In this research, daily evapotranspiration of S.C 704 maize was measured in mini-lysimeters. Estimation of daily evapotranspiration was done by continuous measurement of soil moisture. Leaves stomatal resistance was measured daily, by AP4 Porometer device. Soil water allowable depletion was determined in four growth stages of initial (C1), development (C2), mid (C3), and late (C4), based on the leaves stomatal resistance response. At each growth stage, when leaves stomatal resistance increased relative to the control crops, readily available water was ending and the time was right for new irrigation. The main variables included growth stage effect on crop evapotranspiration and water depletion coefficient, which was investigated in a completely randomized basic design, with three replications. Regression functions (models) were used for simulation of allowable soil water depletion coefficient (P) based on the daily evapotranspiration (ETc). The models were calibrated by daily data at initial and development stages, and were evaluated by daily data in mid and late stages. The FAO-56 linear model was compared with the models introduced in this research. The results showed that maize ETc (S.C 704) in initial, development, mid, and late stages was in the range of 1.5-4.5, 3.9 -7.1, 1.4 -7.5, and 0.2 -2.1 mm.d-1, respectively. The allowable soil water depletion in the mentioned stages was calculated as 0.45, 0.66, 0.61 and 0.7, respectively. Different sensitivity in crop growth stages caused readily available water limit not to be constant during growth period. The ETc increase caused a decrease in P, and decrease in ETc increased P. Linear, exponential, logarithmic, polynomial, power, and FAO-56 linear functions were investigated. Polynomial function with statistical indices of RMSE=0.00035, NRMSE=0.054, ME=0.0008, CRM=-0/000005, R2=0.999 and EF=0.999, was the optimal model in estimation of P coefficient. The reason for weak performance of FAO-56 model was the constant limit for readily available water and mean ETc rate in the growing season. Therefore, the FAO-56 model was modified. The research result was to estimate the soil water allowable depletion coefficient (by using ETc), without daily measurement of soil moisture. This method will be useful in irrigation scheduling, especially those with short intervals.

Reports indicate the invasion of two species of Ivy-leaved morning-glory (Ipomoea hederaceae Jacq) and Asian spider flower (Cleome viscosa L.) to summer crops fields such as soybeans in Golestan province. Considering the importance of knowing the cardinal temperatures of germination in the models for predicting the presence of weeds and consequently designing correct management strategies on these plants, this experiment was conducted to study the germination behavior and to compare the cardinal temperatures of germination for two species of Ivy-leaved morning-glory and Asian spider flower under two conditions of constant and alternating temperatures.

In order to study the effects of constant and alternating temperature on the percent, rate and estimating cardinal temperature of germination for Ivy-leaved morning glory and Asian spider flower, two separate experiments as a Completely Randomized Design with four replications were carried out at the seed laboratory of Gorgan University of Agricultural Sciences and Natural Resources. Studied temperature treatments on Ivy-leaved morning glory included the constant temperatures of 10,15,17,20,25,30,35 and 40 oC and alternating temperatures of 12.5:7.5,10:15, 12.5:20, 15:25, 20:30, 25:37.5, 30:42.5 and 35:45 oC. Temperature treatments on Asian spider flower included the constant temperatures of 15, 20, 23, 25, 28, 30, 35, 40, 45 and 50 o C and alternating temperatures of 15:20, 20:25, 20:30, 25:30, 25:35, 30:40, 30:45, 30:50 and 40:50 oC.

Results indicated that alternating temperatures had a positive effect on the germination of Ivy-leaved morning glory, which increased from 78% at constant temperatures to 94% at alternative temperatures. However, the maximum germination percentage of Asian spiderflower at the alternative temperature of 20:30 was 79%, which had no significant difference with a constant temperature of 30 with 84% germination. The base temperature for seed germination of Ivy-leaved morning glory at alternating temperatures (10.09 oC) was a little lower than that of constant temperatures (11.25 oC). But in Asian spider flower, the base temperature at alternating temperatures (17.57 oC) was more than that of constant temperatures (15.43 o C).

The most important factor in the occurrence of such different responses to constant and alternating temperatures in Ivy-leaved morning glory and Asian spider flower is their adaptation to environmental conditions and their survival in highly degraded agricultural environments. These two weeds are warm-season plants and their seeds enter the soil seed bank in autumn. The presence of dormancy (regardless of its type) in the seeds of these two plants prevents the germination of their seeds in winter; and the experience of alternating temperatures at this time prepares the seeds for germination in the spring. The base temperature of Ivy-leaved morning glory was not much different under the constant and alternating temperature conditions. But the base temperature of Asian spider flower was higher under alternating temperatures compared with constant temperatures.

The lead-time and accuracy of the precipitation forecasts have a substantial influence on the flood forecast and warning systems. The application of Ensemble Precipitation Forecasts (EPFs) derived from numerical precipitation models has been developed due to their impact on increasing flood lead-time. This research aims to improve the skill of numerical precipitation models using post-processing techniques. In this regard, EPFs of three meteorological models, e.g., NCEP, UKMO, and KMA, were extracted for sex precipitation events leading to flood in the Dez river basin during 2013-2019. The statistical approaches and data-driven model were applied to post-process the EPFs. For this purpose, the raw forecast of every single model was corrected using linear and power regression models. Then, the corrected output of single models was combined using the proposed model of Group Method of Data Handling (GMDH). The results indicated that Power Regression Model (PRM) outperformed the linear models to correct raw forecasts. After correction of models' output, more accurate results were obtained by NCEP and UKMO models. Moreover, the Multi-Model Ensemble (MME) system constructed by the GMDH model (MME_GMDH) had a great effect on the skill of numerical precipitation models, so that the Nash–Sutcliffe and normalized error (NRMSE) efficiency criteria for MME_GMDH respectively were improved on average 23% and 11% in comparison with the PRM. A comparative assessment of the discrimination capability of MME with single ensemble models using ROC curve at the thresholds of 2.5 and 10 mm represented a higher discrimination ability by MME_GMDH for both thresholds. Post-processed EPFs exert as a reliable input to the hydrological models for extreme events forecast.

For irrigation planning, parameters such as actual crop water needs (transpiration) and water losses (evaporation) are considered. In this research, for management of deficit irrigation, the amounts of maize evapotranspiration components were simulated under water stress conditions. Water stress was applied by reducing the soil water, relative to the readily available water. Four treatments were defined as depletion of the available soil water by 40% (I0), 55% (I1), 70% (I2), and 85% (I3). The amounts of maize evapotranspiration and its components (transpiration and evaporation rates separately) were measured in a mini-lysimeter. The seasonal total values of evapotranspiration and components of transpiration and evaporation were equal to 443, 319 and 124 mm (I0), 401, 282 and 119 mm (I1), 303, 211 and 92 mm (I2), and 201, 127 and 74 mm (I3), respectively. Soil water deficiency reduced the evapotranspiration and its components relative to the normal conditions (treatment I0). Reduction of evaporation losses was favorable point in this deficit irrigation method (long irrigation interval). Transpiration and evaporation values were simulated based on the evapotranspiration data (in I0), evapotranspiration stress coefficient (Ks), and crop growth stage sensitivity (Kpi). For this purpose, we used the linear, exponential, logarithmic, polynomial, and power functions as the regression models. By using the actual data, unknown coefficients in the functions were estimated by SPSS software and regression models were generated. Statistical analyses showed that the linear function (R2= 0.91) and polynomial function (R2= 0.874) were the optimal models for estimation of transpiration and evaporation components (under water stress conditions), respectively. The actual water requirement of crop and evaporation losses can be estimated more accurately by separate estimation of evapotranspiration components. This would provide a suitable criterion for irrigation planning and calculation of water use efficiency.

Climate variations are one of the most important factors affecting water resources. Severe floods and runoff increase the risk of the vulnerability of hydraulic structures. The failure of an embankment dam causes extensive financial, human, and environmental damages. Overtopping and internal erosion are the main causes of the embankment failure. More than 46% of the embankment failures around the world are attributed to overtopping. The breach process during this event is generally divided into two parts: the breach initiation stage; and the breach development stage. In the initial stage, the outflow discharge from the dam is not considerable and includes a small stream along the channel, while in the breach development stage, the outflow and erosion processes are significant. On the other hand, about 48% of failures and accidents affecting embankment dams are related to piping. In an embankment dam failure, accurate determination of flow, time, and breach characteristics along with the analysis of hydrograph components can play an important role in reducing the financial losses and fatality. Several equations have been established using literature data and field observations to calculate the peak outflow discharge (Qp) as a function of the height of water above the breach (Hw) and stored volume above the breach (Vw) (Costa 1985; Gupta and Singh 2012; Hooshyaripor et al. 2014; MacDonald and Langridge‐Monopolis 1984; Pierce et al. 2010). The failure time (tf) has been investigated by various researchers. The tf equations were obtained as a function of the average breach width (Bave) (USBR, 1988), Hw, or their combination (Von Thun & Gillette, 1990). Therefore, the height of breach (Hb) and Vw are also considered as important parameters (Froehlich, 2008). On the other hand, Hb is found to be an important factor affecting the hydraulic and breach characteristics (Dhiman & Patra, 2019; Wang et al., 2020). In this research, physical models with different geometrical and mechanical properties have been used to investigate the mechanisms of erosion and breach evolution. Therefore, several experimental models were constructed and tested in the laboratory flume at three heights of 0.3, 0.4, and 0.5 m. Therefore, five different soil combinations were used and studied. The breach hydrograph components were analyzed, and the breach characteristics were examined as well as the hydraulic characteristics. The BREACH mathematical model is one of the most common models used to evaluate the breach parameters and output hydrographs in embankment dam failures. This model is developed by the National Weather Service (NWS) and is based on the principles of hydraulics, hydrology, and geotechnics. In overtopping failures, the flow over the crest is calculated by the broad-crest weir formula. While the flow into the pipe is simulated by the orifice equation. The results of the model are compared with the observations of some historical failures, which indicates the higher accuracy among the existing mathematical models. In the present study, the output hydrograph components of the experimental models were also compared with the historical data obtained from the BREACH mathematical model. Gene Expression Programming (GEP) was employed as one of the artificial intelligence methods along with the nonlinear regression to obtain a suitable relationship between the input parameters of the models. In order to evaluate the efficiency of the models, three statistical indices, including the root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and coefficient of determination (R2) are used for performance assessment of the proposed equations in the present study. To develop new equations for the determination of Qp, there are several input variables, that were not introduced before. Therefore, multiple combinations of datasets including historical, experimental, and the hypothetical failure of real dams have been used in this study. Based on observations, the values of tf are highly correlated with hydraulic and breach characteristics. Therefore, new relations have been proposed based on those parameters. On the other hand, the newly introduced equations related to the Hb can lead to more accurate assessment of the breach process in the overtopping and piping failures. According to statistical analyses, the values of R2 for the proposed equations of Qp, obtained from GEP, as well as nonlinear regression, were 0.84 and 0.84, respectively, based on the breach parameters. The values of the recent coefficient for the development of tf relations obtained from the regressions were 0.87 and 0.88, respectively, according to the hydraulic and breach characteristics. By application of GEP for the determination of Hb in both overtopping and piping failure cases, the values of R2 were 0.99 and 0.99, respectively, as a function of the embankment and hydraulic characteristics. Soil gradations play an important role in increasing the erosion rate and reducing the tf. Therefore, breach formation in coarse-grained particles will take less time to evolve due to its lower shear stresses compared to fine-grained soils. Similarly, the Bave has a significant effect on the breach output hydrograph and its components. The Bave variations based on Hw defined in this study, could cover a wide range of embankment dam failures.

The main objective of this research was to compare of accuracy of three widely used simulation methods including mathematical thin0layer models, artificial neural networks (ANN), and adaptive neuro-fuzzy inference system (ANFIS) in estimation of instantaneous moisture ratio of microwave power dried potato slices. To predict the moisture ratio, seven mathematical models were used. Furthermore, based on the experimental data, microwave power, samples thickness and process time, and the moisture ratio were considered as inputs and output of artificial neural networks and adaptive neuro-fuzzy inference system, respectively. Designing of neural networks was performed based on multi-layer feed-forward back-propagation (MFFBP) and cascade forward back-propagation (CFBP) structures, linear (Lin), sigmoid hyperbolic tangent (Tan) and logarithmic (Log) threshold functions, and Levenberg-Marquardt (LM) and Bayesian Regularization (BR) training algorithms. For simulation by adaptive neuro-fuzzy inference system, Takagi-Sugeno fuzzy system was selected, structure of the fuzzy inference system (FIS) created by grid partitioning method, and the membership functions in fuzzy logic toolbar of MATLAB software used. Among the studied modeling methods, Midilli model, CFBP network with 3-10-10-1 topology, LM training algorithm and Tan-Tan-Lin function, and ANFIS model with sigmoid membership function in input and 3×3×4 fuzzy rules were found as the best models. Based on the obtained results, all the three modeling methods were capable t estimate the instantaneous moisture ratio with desirable accuracy. However, showing the coefficient of determination of 0.9997 and mean square error of 4.53×10-5, ANFIS model had the better performance in estimation of the experimental data.

Eryngo (Eryngium caucasicum Tratvu.)is an endemic plant used as a leafy herb that its seed germination behavior has not been well studied. Research on its domestication and propagation can enhance its maintenance and commercialization. Therefore, a laboratory experiment was carried out with eight constant temperatures ranging from 0-35 °C (0, 5, 10, 15, 20, 25, 30 and 35 °C with three replicates in a completely randomized design at Agriculture Faculty, Ferdowsi University of Mashhad, Iran in 2016. Two regression models, including Intersected Lines Model and Quadratic Polynomial Model were applied to germination data to evaluate cardinal temperatures and R2 value was used as criteria for comparison between the models. Results showed that seed germination occurred at temperature of 10-25 °C. Optimum germination uniformity was obtaned at temperature of 10 to 20 °C. The temperature of 15 °C with highst germination percentage (88.33 %), germination energy (92.5 %), time to 90 % germination and germination uniformity (7.17 day) was found as the optimum temperature for seed germination. Quadratic Polynomial Model was found as a better model to evaluate eryngo seed germination. The temperature of 0.85, 15.33 and 29.81 °C were respectively estimated as minimum, optimum and maximum temperatures for eryngo seed germination. It is concluded that the data can be used to predict germination of eryngo response to temperature and management of the plant in agriculture as a new leafy herb.

The aim of this study was to investigate the effect of finisher diet nutrient density and slaughter age on energy and protein retention efficiency of broiler chickens. Three hundred 23-day-old Cobb-500 male broiler chickens were assigned in a 5×2 factorial arrangement of completely randomize design with 10 treatments, 6 replicates and 5 birds each. The experimental treatments included five nutrient levels of finisher diets (102.5, 100, 97.5, 95, and 92.5% levels of nutrient composition recommendations stated by the Cobb-500 Commercial Management Guide) and two slaughtered ages (38 and 46 days of age). As increased dietary nutrient density significantly and l inearlyincreased weight gain, feed efficiency, energy and protein retention efficiency and decreased maintenance energy requirements per unit of weight gain (P<0.05). By increasing slaughter age from d38 to d46, above indices significantly deteriorated (P<0.05). The highest residual feed intake belonged to birds fed the finisher diet with 97.5% of strain recommendation nutrients level. Dietary nutrient density level for optimal weight gain, feed efficiency, and energy and protein retention efficiency by linear broken line models were estimated 101.7, 98.7, 97.7 and 99.5% of strain recommendation, respectively. Whereas, these values were 2.3-5.5 percent less than those estimated by the quadratic broken-line model. As a conclusion, residual feed intake methodology can be a viable alternative to measure dietary energy efficiency. Formulation broiler finisher diet with nutrients concentration lowers than 97.5% of strain recommendation is not suitable.

In Mazandaran Province the performance of different drainage systems is essential for Canola cultivation as a second crop in paddies. For this purpose an experiment was conducted in Sari agricultural Sciences and Natural Resources University as base, in 2017-2018 cropping season. In different canola growth stage intervals, eight sampling were established under randomized complete block design with three replications in subsurface drainage systems and surface (Control) in order to determinate leaf area (LA), leaf area index (LAI), numbers of leaves (SLN) and leaf dry weight (LDW). Then Statistical analysis was carried for study of drainage depth and space on the mentioned growth index and mathematical relationship between leaf area and its dry weight with the evaluation of linear and nonlinear regression models was determined in different drainage systems. Maximum of LA in all treatments occurred about 90 day after planting. Mathematical relations between LA and LDW were described by linear and power model is suitable as a correlation coefficient was 0.98. Among the mathematical functions, reverse polynomial functions with degrees 4 and 5, logarithmic equations and inverse logarithmic equations with degrees 3, 4, and 5 showed better results than other mathematical functions The relations obtained in this experiment can be used in simulation models of winter canola growth under water table control conditions.

Leaf area is a key indicator for the growth and production of plant products and also determines the efficiency of light consumption. Therefore, the study of diversity and also the estimation of leaf area in different mint ecotypes is particular importance. One of the common methods for estimating leaf area is regression analysis, the leaf area as independent variable, and leaf length and width as dependent variable. In this study, leaf area of ​​18 mint ecotypes with different models of ANFIS, artificial neural network (MLP and RBF), linear and nonlinear regressions using two inputs of leaf length and width in four levels of salinity stress (control, 2.5, 5 and 7.5 dS/m) were estimated in two levels of harvesting. The results showed that there is a high correlation between length and width with leaf area, so that the width correlation with leaf area was greater than leaf length. Among the regression models in both harvest levels and at all levels of salinity stress, the NLR model is presented as the best and most accurate model. ANFIS model was harvested in both stages and at all levels of stress was more accurate than other models and less error rate was obtained. Also, the results of cluster analysis showed that there is a good variation between ecotypes. In addition, the mean comparison between different cluster analysis groups showed that the highest amount of length, width and leaf area was taken in both stages and at all levels of salinity stress was related to E18.

In this study, to analyze the pattern of soil temperature changes in the depths of  5, 10, 20, 30, 50 and 100 cm under the influence of temperature (minimum, average and maximum) and humidity (minimum, average and maximum) the Pearson and regression methods were used for Taleghaan synoptic station during the period of 2008 to 2016 . The results showed that soil temperature had the highest correlation coefficient with air temperature while the lowest correlation was found with air humidity time series. So that the maximum coefficient of determination of air temperature and humidity was in a depth of 5 cm whereas the lowest was found in a depth of 100 cm. In order to estimate the deep soil temperature using temperature and humidity variables, the regression models were fitted by 70% to 30% of data for calibration and validation stages, respectively. So to evaluate the accuracy of the calibration and validation stages the NSE and RMSE error criteria were used. In general, the results indicated that the presented regression models had very good and acceptable performance to estimate soil temperature and also with increasing soil depths, correlation coefficient has decreased while error evaluation criteria has increased.

Safflower (Carthamus tinctoriusL.) is annual and oil crop belong to Aster-aceae family that planted all over the world (Isigigure et al., 1995). Seed germination is a complex physiological process and one of the most important part of plant life cycle that affected by environmental factors. Temperature and moisture are the most important of these factors. Seed planting in optimum temperature resulted in maximum germination, uniformity and seedling establishment. Knowing minimum, maximum and optimum tem-perature of seed germination is necessary to appointment suitable planting date. In the oth-er way, environmental factors for seeding are not always favorable for seed germination. Osmotic stress is the most important factor that reduces growth and crop yield. Most re-ports showed that decline of osmotic potential cause a change cardinal temperature of seed germination. Hence the aim of this study is determination of cardinal temperature of safflower and the effects of different levels of temperature and osmotic potential on seed germination characteristics. The experiment was conducted as factorial in a completely ran-domized design with four replications, in agronomy laboratory, agricultural college, the University of Guilan in 2015. Experimental factors were including temperature (5, 10, 15, 20, 25, 30 and 35 ̊C) and osmotic potential (0, -0.2, -0.4, -0.6 and -0.8 MPa). Different osmotic potential prepared based on Michael and Kaufman (1973) method using with PEG 6000 and double distilled water. Germination percentage, germination rate and vigor index was calculated in the end of germination period (14 days). To estimation the cardinal tem-perature and needed biological hours for germination, five nonlinear regression models (polynomial quadratic, segmented, beta, dent like and curvelinear) were used. RMSe, R2, r and coefficient regression (a, b) related to amount of observed and predicted germination rate were used for finding the best model. Choosing the best model was conducted in two stages. At First, a model selected that amounts of parameters a, b were not significant dif-ferences with zero and one respectively. In the second stage, the model that had maximum R2 and minimum RMSe introduced as the selected model. The results showed that, minimum (%4) and maximum (%92) germination per-centage were observed in interactions of -0.8 MPa with 30 ̊C and 15 ̊C with control (dis-tilled water) respectively. Overall, the seed germination rate decreased with declining os-motic potential. The temperature levels of 25 and 35 ̊C had maximum and minimum ger-mination rate in all osmotic potential levels respectively. The treatment of osmotic poten-tial in comparison to the temperature on vigor index had the worse effect, nonetheless in all temperature levels reducing osmotic potential, was showed significant decrease. Based on model parameters measurement, only segmented and quadratic models had suitable fitness. Although segmented model had better fitness than quadratic model for safflower germination rate to temperature response. The preferable regression model estimated min-imum seed germination temperature (2.2 to 4.2 ̊C) optimum temperature (23.12 to 23.98 C) and maximum temperature (42.2 to 43.89 C). Decreasing in osmotic potential from control until -0.8 MPa causes decreasing minimum temperature in both models. The opti-mum temperature in -0.8 MPa osmotic potential increased nearly one degree in segmented model but in quadratic model this temperature is declined 3.25 ̊C degree. Also the effect of decreasing osmotic potential on maximum temperature was much observable than its effects on minimum and optimum temperature. So that maximum temperature in -0.8 MPa osmotic potential comparison control (zero MPa) in segmented model 9 ̊C and in quadratic model 6.3 ̊C was decreased. Overall, safflower seeds, protected their germination parame-ters suitably in sub optimal temperature in comparison with supra optimal temperature (in the same osmotic potential levels). Extreme decreasing of these parameters in high tem-perature (35 ̊C) expression the susceptibility of this crop to high temperature. Expect preferable regression models and estimated parameters (minimum, maximum and optimum temperature) in preparation and assessment of seedling appear-ance models in field and determination of planting date in every area are used. Safflower is a tolerant plant to drought stress under suboptimal temperature, but is very sensitive at higher than 30̊C temperature. Therefore, it is a good candidate to be planted in suitable planting date even relatively low soil moisture condition.

Evolution of resistance in weeds leads to induction of fitness cost. Five winter wild oat biotypes resistant to haloxyfop R methyl ester collected from Kalaleh Township located in Golestan province were evaluated along with the susceptible biotype in terms of variation in cardinal temperatures in the following study. The experiment was conducted at laboratories of Gorgan University of Agricultural Sciences and Natural Resources as completely randomized design with 4 replications each consisting of 25 seeds. Petri dishes containing seeds were incubated and monitored continuously at temperatures 5-40 °C (at 5°C intervals) following a 72 hours vernalization at 4°C. Non-linear regression models were used to quantify germination response to temperature. No notable difference was observed among winter wild oat biotypes and beta model at 50 percentile was the most suitable model to describe cardinal temperatures. Base, optimum and ceiling temperatures were estimated between -4.21 to -5.91, 23.37-23.94 and 36.99-37.54 °C, respectively. Thermal tolerance range of susceptible and resistant biotypes were not significantly different. Susceptible and resistant biotypes showed similar response. Lack of fitness cost regarding cardinal temperatures demonstrates that similar non-chemical management practices including early sowing may be implemented to control resistant and susceptible biotypes germination and emergence

Introduction The presence of broadleaf weeds not only reduces the aesthetic quality of the turfgrass, but more importantly they compete with desired turfgrass for water, nutrients, and light. Weed management after seeding is an important component to successfully establishing a healthy stand of cool season turfgrass. Weed seed germination is a key process because determines both the number of weeds that could potentially emerge and the timing of their appearance in the turfgrass. Temperature is of the most important factor regulating germination of non-dormant seeds in irrigated, annual agroecosystems at the beginning of the growth season where light, nutrients, and moisture are typically not growth limiting. Also prostrate knotweed (Polygonum aviculare L.) and annual sowthistle (Sonchus oleraceus L.) are two annual common broad leaf weeds in cool season turfgrass. Prostrate knotweed is very competitive in infertile and compacted soils and often invades turfgrasses along driveways, sidewalks, and beaten paths across lawns. Annual sowthistle is found in open habitats that include waste areas, turf, and roadsides. The aim of this study was to evaluate different nonlinear regression models to describe response of germination rate to different temperatures in perennial ryegrass (Lolium perenne L.), fescue (Festuca rubra L.), prostrate knotweed and annual sowthistle.
Materials and Methods This experiment was based on completely randomized design with 4 replications at Islamic Azad University in the laboratory of Science Research Branch in 2015. The seeds were treated with different temperatures (2, 5, 10, 15, 20, 25, 30, 35, 40 and 45oC) Twenty five seeds were placed in each petri dish for each species in per replication. Ten milliliters of distilled water were added to each petri dish and the filter papers were regularly moistened to ensure saturation throughout the germination tests. Petri dishes were placed in germinator with 16 hour day, 8 hour night and 60% relative humidity. Seeds were considered germinated when the radicle protruded at least 2 mm from the seed coat. Germinated and rotted seeds were counted and discarded at 24 hour intervals until no germination had occurred over four consecutive days. The germination percentage was obtained by dividing the number of germinated seeds at any time by the total number of seeds germinated multiplied by 100. Data from this experiment were first subjected to analysis of variance and means of treatments were compared using the least significant difference at the 5% level of probability. The following nonlinear regression models were used to quantify the response of the germination rate to temperature and to determine cardinal temperatures.
Results and Discussion The analysis of variance showed that temperature had a significant effect on all seed germination percentage and germination rates. Seed germination percentage and rate increased to a point with increasing the temperature. Germination model based on temperature can be used for the prediction of cardinal temperatures. Cardinal temperatures are required because a portion of the crop model is developed for prediction of the timing of germination. Non-linear regression models have been used to quantitatively describe development rate in many plants. Three regression models (Intersected-lines, Dent-like and Quadratic Polynomial) used to predict germination rate and cardinal temperature. Root mean square of error (RMSE) and R2 adjusted were used to find the appropriate model(s). Intersected-lines model was superior compared to other models in perennial ryegrass, fescue and annual sowthistle and Dent-like model was superior for prostrate knotweed. It was concluded that this model can be used to quantify response of turfgrass and common weeds of turfgrass germination to temperature and to obtain cardinal temperature of germination. Also base, optimum and maximum temperatures were for perennial ryegrass 4.12, 24.66 and 43.27oC; fescue 2.0, 24.86 and 43.48oC ; Prostrate knotweed 2.95, 19.94-22.21 and 44.97oC and annual sowthistle 2.0, 17.77 and 44.86oC, respectively.
Conclusions These results show that fescue germinated earliest among the studied species, because it had the lowest base temperature. In comparison to perennial ryegrass, using turfgrass seed with more fescue seed causes sooner turfgrass establishment and less weed competition. Because of their narrow leaves, young seedlings of prostrate knotweed grow upright and appear at first glance to be grass seedlings. With maturity, most plants grow prostrate, especially with traffic or mowing, so mechanical control of this species in turfgrass is impossible andit could be controlled with pre-emergence herbicide. Pre-emergence herbicides are applied prior to the germination of weeds; thus, predication of timing germination help us in decreasing prostrate knotweed competition with turfgrass and its proper management.

This study aimed to evaluate the non-linear regression model (Beta-original, Beta-modified, Segmented and Dent-like Models) to describe the germination rate of Arugula (Eruca sativa Mill.), temperature and water potential factorial a completely randomized design with four replications in Gorgan University of Agricultural Sciences and Natural Resources was performed in 1392. Treatments were temperature to 9 levels (2.5, 5, 10, 15, 20, 25, 30, 35 and 38 ° C) and water potential to 8 levels (0, -0.2, -0.4, -0.6, -0.8, -1.0, -1.2- and -1.4 Mpa). To quantify the response of germination rate to temperature and to determine cardinal temperatures for germination, Beta-original, and Beta-modified, Segmented and Dent-like models were used. The Beta-original model descript germination rate to temperature better than the others. According to this model the base temperature, optimum temperature and ceiling temperatures were 0.0, 30.13 and 38.50 0C respectively in non-stress conditions (zero potential). Changes of water potential had significantly effect on optimum and ceiling temperature but had no significantly effect on base temperature and biological hours.

Evapotranspiration is one of the most important parameters in the Planning and operation of reservoirs, designing of irrigation systems. The practical importance of accurate estimates of evaporation and the complexity of effect phenomenon, shows the use of new methods of data mining. In this study, the simulation of pan evaporation in Tabriz station using multiple regression models were investigated. Meteorological data, including maximum and minimum air temperature, dew point, maximum and minimum air relative humidity, number of sunshine hours and Daily wind speed during (1992-2012) were used in synoptic Tabriz stations. Various models of multiple linear regression and nonlinear one were derived for Tabriz station. The selected multiple linear regression model were tested by Ridge Regression method to be considered multi-collinearity among inputs in the model.Variance inflation factor, values for each variable were calculated. The results showed that all Variance inflation factor ,s had the value less than 10. In addition, the ratio for two- variable selected model was 3.34. Therefore, there was no multi-collinearity in the selected multiple linear regression model f (Tmin, n). Durbin-Watson statistic for the selected model was 1.45 that shows the reliability of the selected multiple linear regression model. RMSE and R2 values of the selected models (multiple linear regression and Non- Linear Regression) was calculated as 2.45 and 0.67 and 2.58 and 0.65, respectively. This results demonstrate the ability of regression techniques to estimate Pan evaporation in Tabriz station.

Access to weed distribution maps can be very useful in planning their management. Locating areas prone to infection with new entrant weeds is needy to knowledge of ecological characteristics of these plants such as cardinal temperatures, EC and pH. In order to investigate the effects of temperature on germination and determine the main germination temperatures (base temperature, optimum and ceiling for germination) of ivy-leaved morning glory, seeds incubated at 10, 15, 17, 20, 25, 30, 35 and 40 0C. To determine the cardinal temperatures, Original beta, Modified beta, segmented and dent like models were used. The results indicated that the original beta model was superior than the other models and base, optimum and ceiling temperatures for seed germination were 10, 25.91 and 41.22 0C respectively. The results showed that ivy-leaved morning glory has high tolerance ability to soil salinity. So, germination percent of this plant can be reduced by 50% in salinity of 45dS.m-1. In addition, germination present of these plants in acidic pH was higher than the alkaline pH. Distribution maps indicated that there were no limitations regarding temperatures and EC for germination and establishment of this weed through the Golestan Province. But regarding pH, there is a major in increasing distribution extent in the province. So that only a small section of Kalaleh and Galikesh lands are suitable in this regard.