Study Of Development Simulation Models In Sesame

It had the important theotetical significance and practical value that the research of crop development simulation model and which was used widely for agricultural informatization and digitalize. This study constructed leaf area index, champions league group light distribution, leaves of photosynthesis, group photosynthesis, respiration and dry matter accumulation process of sesame development simulation model, through analyzing quantitativly the relationship between the sesame development rhythm and environmental factor for the experiment result of planting date and seeding rate of different sesame varieties, based on study of sesame development characteristics and analysis of domestic and international advanced modeling theory and method, and the model was preliminary validated. The result showed that the model can better simulated the sesame development process, which had certain system, rationality, predictability and practicality.The sesame development period simulation model was builted in the development model based on the physiological development index, through the quantitative analysis of reaction of temperature and light sesame cycle. This study assumed that the simulation of various development by the development period physiological development index is accumulated, When the birth stage development index accumulative value equal to one, the show that the development stage finish, development index by the size of the daily temperature effect factor, light cycle effect factors and varieties parameters interactions decided together. By introducing varieties reaction model parameters different varieties of sesame seeds, so that every sesame variety of specific development period required physiological development time constant. The model is correct and inspection by using different place, year, variety, planting and density of the test data. The results show that sowing to seeding, to now for budding, from budding to seal a top and mature simulation value and forecast RMSE (root mean square difference) were1.19,2.35,3.29and2.85days. The results show that the model is the better machine rational and reliable.According to the different types of varieties of planting density test results, established the sesame leaf age and leaf area (LAI) refers to the simulation model. According to the different types of varieties of planting density test results, established the simulation model about the sesame leaf age correlated to leaf area (LAI). According to the relationship between sesame leaf age and main factors that influence temperature, constructing the sesame leaf age dynamic simulation model. The sesame leaf area index is changed into three stages:an exponential development, the maximum and the down, respectively simulation. First analysis to determine maximum leaf area index period that is flower age sesame leaf area index, leaf area index grow with the effective accumulative temperature (growing degree days GDD,℃) growing exponentially, the index equation simulation in this stage, after the leaf area index to the biggest in flower age, because of leafs covered for each other, leaf senescence and environmental factors, leaf area index fell. This stage because of the complex mechanism of leaf senescence, involving lots of parameters, and it is not easy to estimate budget, this research with development index modeling. Use different years, different processing experimental data on the model calibration and inspection. The results show that the model simulation value and observation value RMSE (root mean square difference) an average of1.05, leaf area index model simulation value with the observation value RMSE (root mean square difference) an average of0.43. The results shows that the model can well simulate the leaf born dynamic and LAI dynamic change in different conditions. Which shows good reliability and practicability.On the basis of the comprehensive existing research results, give the consideration both of the rational model machine and practical. Using Lambert-Beer’s law points to simulate the five layers sesame group light distribution, tends to simulate the leaves simple equation with photosynthesis, and using the gaussian integral method to calculate the amount that each layer of photosynthetic daily canopies of the total amount of assimilation. The model considered the biggest photosynthetic rate influence of carbon dioxide and temperature factors with taking into the breathing and maintain development breathing consumption. Under test of dry matter accumulation dynamic model using different varieties, sowing, seeding rate, RMSE average of503.54kg hm-2, and the result shows that the observation and simulation value is on the line, model machine rational and predictive is quite strong.