nafise seyednezhad; mahboobeh farzandi; H. Rezaee-Pazhand
Abstract
Introduction: The analysis of extreme events such as first frost dates are detrimental phenomena which influence in various branches of engineering, such as agriculture. The analysis and probability predicting of these events can decrease damage of agriculture, horticulture and the others. Furthermore, ...
Read More
Introduction: The analysis of extreme events such as first frost dates are detrimental phenomena which influence in various branches of engineering, such as agriculture. The analysis and probability predicting of these events can decrease damage of agriculture, horticulture and the others. Furthermore, this phenomenon can have a relation with other thermal indexes. The analyzing of first frost dates of all synoptic stations of Khorasan Razavi province is subject of this article. The frequency analysis applied to eight distributions. Then the relationship between first frost dates and thermal index were studied. Best relation was between minimum temperature and return periods of first frost dates.
Materials and Methods: The analyzing of first frost dates (origin is March 21) of all synoptic stations of Khorasan Razavi province is subject of this article. At first data of each station were screening. The basic properties such as homogeneity, randomness, stationary, independence and outliers must be tested. The eight distribution Normal, Gumbel type 1, Gamma 2-parameter, Log normal 2 or 3 parameters, Generalized Pareto, Generalized extreme values and Pearson Type 3 fitted to data and the parameters estimated with 7 methods by the name of the several types of Moments (5 methods), maximum likelihood and the maximum Entropy. The Kolmogorov – Smirnov goodness of fit test can be used to compare the best distribution. The return periods of first frost dates are major application in frequency analysis. There is maybe a relationship between periods and thermal index such as min, max and mean temperature. This relationship can be adapted by regression methods.
Results and Discussion: The statistical analysis for prediction probabilities and return periods of the first frost dates for all synoptic stations in Khorasan Razavi province and the relationship between annual temperature indicators and this phenomenon is the aim of this article. The origin date of this phenomenon is March 21. First, data were screened. Then basic hypothesis test were applied which including the Runtest (randomness), the Mann-Whitney test (homogeneity and jump), the Wald-Wolfowitz test (independence and stationary), the Grubbs and Beck test (detection Outliers) and the three sigma methods (Outlier). The results were: 1-The Sabzevar, Mashhad and Gonabad had lower Outliers that will not cause any problem in data analysis by their skewness. The first frost data of all station were without upper outlier. 2- The independence of all stations was accepted at the 10% level. 3-All stations were Randomness, Independence and homogeneous and lack of jump. Eight probability distributions (Normal, Gumbel type 1, 2-parameter gamma, 2 and 3 parameters log-normal, the generalized Pareto, the generalized extreme values and the Pearson type 3) were applied. The skewness coefficients for all stations were more than 0.1 so Normal distribution was rejected. Also the7 methods of estimation (five different methods of moments, maximum likelihood and maximum entropy methods) were used. The ks fit test was applied. The ks for some stations were closed together at several estimations methods. The results are as follows: GPA (4 times), PT3 (4 times), LN2 (4 times), GA2 (3 times). Generalized Pareto distribution had the best fitted to data (60% of cases compared to the other functions). The results significantly indicated that the occurrence of first frost on the first day of process is in place. The first frost in the period of 2 years at all stations, not occur earlier than Aban(October 28). The 100-year return period event does not occur earlier than first of Mehr(September 22). There is no significant relationship between first frost in the period of 2 years with other factors such as altitude, latitude, longitude, temperature and precipitation as well.
Conclusion: Date of the first fall frost is one of the unfavorite climate influences that cause reduction in crop products. The purpose of this paper is to analysis the frequency occurrence of first frost day in several Khorasan’s synoptic stations as study area. Screening and initial basic tests such as randomness homogenity, independence, etc. were done. Eight distribution function, namely Normal, Gumbel type 1, Gamma 2 parameters, Log normal 2 and 3 parameters, Generalized Pareto and Pearson type III were fitted to data with five probability distributions methods (Ordinary Moments, Maximum Likelihood method, Modified Moments, Probability Weighted Moment and Maximum Entropy). Goodness of fit test was Kolmogorove-Smirnov test. PWM and ModM methods revealed relatively superior results compared to the rest of methods. Generalized Pareto distribution had the best fitted to data (60% of cases compared to the other functions). The results significantly indicated that the occurrence of first frost on the first day of process is in place. The first frost in the period of 2 years at all stations, not occur earlier than Aban. The 100-year return period event does not occur earlier than first of Mehr. There is no significant relationship between first frost in the period of 2 years with other factors such as altitude, latitude, longitude, temperature and precipitation as well.
Aida Mehrazar; Jaber Soltani; omid Rahmati
Abstract
Introduction: Limited water resources and its salinity uptrend has caused reducing water and soil quality and consequently reducing the crop production. Thus, use of saline water is the management strategies to decrease drought and water crisis. Furthermore, simulation models are valuable tools for improving ...
Read More
Introduction: Limited water resources and its salinity uptrend has caused reducing water and soil quality and consequently reducing the crop production. Thus, use of saline water is the management strategies to decrease drought and water crisis. Furthermore, simulation models are valuable tools for improving on-farm water management and study about the effects of water quality and quantity on crop yield.. The AquaCrop model has recently been developed by the FAO which has the ability to check the production process under different propositions. The initial version of the model was introduced for simulation of crop yield and soil water movement in 2007, that the effect of salinity on crop yield was not considered. Version 4 of the model was released in 2012 in which also considered the effects of salinity on crop yield and simulation of solute Transmission in soil profile.
Material and methods: In this project, evaluation of the AquaCrop model and its accuracy was studied in the simulating yield of maize under salt stress. This experiment was conducted in Karaj, on maize hybrid (Zea ma ys L) in a sandy soil for investigation of salinity stress on maize yield in 2011-2012. This experiment was conducted in form of randomized complete block design in four replications and five levels of salinity treatments including 0, 4.53, 9.06, 13.59 and 18.13 dS/m at the two times sampling. To evaluate the effect of different levels of salinity on the yield of maize was used Version 4 AquaCrop model and SAS ver 9.1 software .The model calibration was performed by comparing the results of the field studies and the results of simulations in the model. In calculating the yield under different scenarios of salt stress by using AquaCrop, the model needs climate data, soil data, vegetation data and information related to farm management. The effects of salinity on yield and some agronomic and physiological traits of hybrid maize (Shoot length, root length, dry weight and crop yield) under different levels of NaCl solution osmotic potential were also investigated by SAS ver 9.1 software. Data's mean comparisons were performed by Duncan's multiple range test. To assess the accuracy of AquaCrop Model for Simulation of the Maize Performance under Salt Stress used from Indicators RMSE, MAE, CRM, NSE, d and Er.
Results Discussion: The results of RMSE and MAE indices showed that AquaCrop model can simulate maize yield under the salinity stress. Accuracy decreased and crop yield prediction underestimated with increasing salinity from treatment 0 to 18.13 ds/m in the first and second harvest. The highest yield related to salinity treatment of 0 dS/m and the lowest yield related to salinity treatment 18.13 dS/m. yeild simulation error increased by increasing salinity, the highest and lowest error of yield simulation in model respectively related to salinity treatments 18.13 and 0 dS/m. The highest and lowest error was in the first harvest respectively 0.56 and 13.1 percent and in the second harvest respectively 0.42 and 21.79 percent, that in the comparison with the results of studies conducted by Steduto and colleagues on maize is not much different. The results comparison in the first and second harvest showed that soil salinity was increased by increasing irrigation number in second harvest, so the error in second harvest is greater than first harvest and the maximum error is related to treatment 18.13 ds/m in the second harvest 21.79 percent.The coefficient of determination R2 for the first and second harvest is respectively 0.850 and 0.834, that indicates a high correlation between yeild values of measured and predicted by the AquaCrop model. CRM index was negative and near zero in both harvest under Salinity different scenarios. According to CRM value, AquaCrop model was overestimated and the model was simulated maize yield under the salinity stress a little more than measured yield. The d statistic index value is close to unity, indicates that yield values in model is compatible with actual values. NSE index was calculated for the first and second harvest respectively 0.81 and 0.84, that is close to one and showed that the model has suitable performance in the yield simulation. Comparison of means by Duncan's multiple range test and analysis of variance in the software SAS ver 9.1 indicated Salinity has a very significant effect on all traits including shoot length, root length, dry weight and crop yield that all traits were decreased significantly by increasing salinity.
Conclusion: Comparison of the results of AquaCrop model and statistical analysis in software SAS ver 9.1 showed that maize yield was reduced with increasing salinity. According to index CRM, AquaCrop model was simulated maize yield under the salinity stress more than measured yield in farm. The results showed that the AquaCrop model simulated well maize yield in moderate and low stress, but accurately simulation slightly decreased in high stress. The results of this study was compared with other research and indicated that the error values of AquaCrop model in Karaj is not much different with the error values of other research.
