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

In the recent years, growth simulation models have been attracted for agricultural engineers in order to predict different crop yields. However, growth simulation of medicinal crops has been rarely studied. In current research, for the first time, the performance of SALTMED model in predicting peppermint yield under different deficit irrigation and salinity conditions was studied. Treatments of water deficit included (100% irrigation requirement (as control), 20 and 40% water deficit) and salinity treatments (control, 2, 3 and 4 dS/m). Calibration of model conducted using control treatment data. After insuring the results, validation of model conducted using other treatments data. Results indicated that model accuracy for predicting peppermint yield was relatively good. The coefficient of determination of model during calibration and validation were 0.953 and 0.801. Studying the separate results showed that the model predicted maximum yield of water deficit levels with higher accuracy than salinity levels. Maximum simulated yield of 20 and 40% water deficit levels were 2.75 and 2.83 ton per hectare while the yield of 2, 3 and 4 dS/m obtained as 2.1, 2.06 and 2.05 ton per hectare.

  Potential production condition is defined as situation where water and nutrients are supplied in ample with no damage from weeds, pests and diseases. Under this situation potential yield (Yp) which is defined by solar radiation, temperature, CO2 concentration and genotypic characteristics of the crop species could be achieved. Closing the gap between Yp and actual yield (Ya) harvested by farmers is considered as the main challenge of agronomic sciences all around the world. Crop simulation models provide a powerful tool for prediction of Yp at regional and national scales. However, the accuracy of these models is highly dependent on the quality of input data which are usually not fully available even in developed countries. Simplified models designed based on few simple equations may be considered as an alternative where accurate data sets are lacking. In this research, Yp of wheat and sugar beet were estimated using some simple methods and the results were compared with those of complex simulation models under the same climatic and management conditions in Khorasan province, north east of Iran. Potential yields of wheat and sugar beet were estimated by three simple calculation methods including FAO method (FAO, 1981), modified FAO, FAO-M (Versteeg and van Keulen, 1986) and RUE-based method (Nonhebel, 1997) at three different regions (Torbat, Mashhad and Neishabor) in Khorasan province. In these methods total above ground dry matter (DM) is calculated using two equations and Yp is estimated as the product of DM and harvest index with minimum weather data requirements. In addition, Yp for both crops and at the same locations was predicted using LINTUL and SUCROS simulation models which were previously calibrated for yield estimation of wheat and sugar beet at regional level. The accuracy of predicted potential yields by five methods was compared statistically against the measured yields obtained from the field experiments and high yielding farms at the studied regions. Mean observed yield of wheat over the three studied regions was 7.18 t ha-1 and the estimated potential yield in the same regions using two simulation models and three simplified models was ranged between 6.92 and 7.63 t ha-1. Prediction error for the simulation models was 1.39 and for simple methods less than 5% (4.64%). Relative root mean squared error (RMSEn) for the simple methods was higher than that of complex models. However, it was between 7.11 and 10.16 % of the mean measured wheat yield which could be statistically considered as reasonable. Calculated values of modeling efficiency (ME) were positive and higher than 0.60 except for FAO-M method (ME=0.48). Measured sugar beet yield averaged over regions was 82.5 t ha-1 and estimated potential yield by different methods was between 89 and 91 t ha-1. Simple calculation methods had lower accuracy in predicting sugar beet yield compared to simulation models but RMSEnof simple methods never exceeded 11.5% showing statistically acceptable prediction. Cross validation indicated significant correlation between the results of three simple methods and that of complex simulation models. Results of this study showed that potential yields of different crops can be estimated accurately using simple calculation methods with minimum weather data requirements. Such methods may be used as an alternative for agroecological zoning and yield gap analysis at regional level where calibrated simulation models and complete daily weather data sets are not available

Quantitative information regarding biomass partitioning in chickpea (Cicer arietinum L.) is limited or inconclusive. The objective of this study was to predict biomass partitioning to seed based on harvest index in chickpea crops grown under well-watered conditions. A field experiments, with 3 sowing dates and 4 plant densities, was conducted in Gorgan during 2004-2005. Crop biomass and phenological stages were measured between emergence and maturity. Density increase and delay in sowing date led to each phonological stages period decrease. Harvest index increased in seed filling period linearly. The effect of density on harvest index linear increase was not significant but there was different between sowing date. The time of beginning harvest index linear increase, decreased from 26 to 8 days from first sowing date to third one, but there was any difference in plant density. therefore From obtained coefficient in this research we can use in chickpea growth simulation models.

A field experiment was conducted at the research field of the University of Lorestan in 2011 as a randomized complete block design with three replications to estimate genetic coefficients of some maize (Zea mays L.) cultivars. Treatments include six maize cultivars (T.V.C.767 and S.C.704 from late maturing group, T N.S640 and Maxima from mid-maturing group, and Koppany and D.C.370 from early maturing group). Results showed that there were significant differences among cultivars in terms of stem dry weight, maximum number of kernel per ear, thermal time from the flag leaf appearance to flowering, thermal time from flowering to maturity, phyllochron interval, grain weight, maximum plant height and minimum growth degree days during vegetative period. The highest (649.2) and lowest (350.6) maximum number of kernel per ear belonged to cultivars S.C.704 and D.C.370, respectively. Also, the highest and lowest stem dry weight, phyllochron interval and maximum plant height belonged to cultivars S.C.704 and D.C.370, respectively. Among genetic coefficients, the minimum growth degree days required for vegetative growth and the maximum number of kernel per ear had the greatest correlation with grain yield (r=0.72 and r=0.84, respectively). Overall, the results portrayed that the estimated genetic coefficients of the cultivars are not identical in different models and varied in a defined range.

Many numerical and analytical models have been developed for estimation of soil water distribution in order to increase water use efficiency in drip irrigation. Accurate solution of well-known soil water equation، Richard’s equation، in these models cause more accurate estimation of soil wetting front. The purpose of this study was to evaluate finite difference and finite element methods to numerical solution of Richard’s equation for simulating soil water flow around dripper via comparing HYDRUS-2D and SEEP/W numerical models. Experiments were carried out to collect required data to investigate the advance of moisture front inside a Plexiglas box filled with a silt loam soil in central laboratory of water researches in University of Tehran. Wetting front advance at different time intervals were plotted on the transparent Plexiglas box walls. The wetting front around the emitters، for pressures 1. 5 and 2. 2 meters (equivalent to 4. 5 and 6. 3 liters per hour، respectively)، were measured. Comparison of two simulation models، HYDRUS-2DandSEEP/W، showed that HYDRUS-2D model (finite difference solution method) with higher determination coefficient and lower root mean square error coefficient had better performance to simulate wetted area dimensions for both surface and subsurface drip irrigation.

Accurate prediction of crop phenology is important in crop simulation models. The objective of this work was to quantify the occurrence of phenological stages in cotton and to construct a simple phenology model. Required parameters of model were obtained from a serially sown field experiment (22 April, 5 May, 24 May, 13 June, 9 July, and 7 August in 2006). The results showed that none of developmental stages is sensitive to photoperiod (on based phonological stages reaction to GDD), so only temperature was taken into account. Also, there was no difference between hourly, 3-hourly and daily temperature data. Therefore, average daily temperature can be used in predicting developmental stages. For independent test of the model, data from different field experiments that were carried out at Hashem-Abad and Karkandeh stations were used. Root mean square errors (RMSE) between simulated and experimental data were 1.8 d for emergence, 7.2 d for squaring, 5.5 d for flowering, and 7.8 d for open boll. Therefore, this model can be used to predict development stages in cotton simulation model in non-stress conditions.

The propose of this study was evaluation of CropSyst Model on growth simulation and yield stimation of canola under different irrigation treatments. Canola (Talaye) was sown under 5 treatment and 4 replicants in completed randomized block designs at the college of Agriculture, Shiraz Universityduring 2007-2008. During the growth season dry mass and LAI was meseaured frequently, then model was calibrated by the resukts. Obtained results showed very good esyimation by the simulation model, as the correlation coefficient square between dry mass and LAI in defferent treatments were more than 0.99 and 0.95, respectively. Also correlation coeficient between meseaured and simulated crop yeild was 0.96, that shows the accuracy of model in simulation on crop parameter. Also the model was valuated by independent data. Obtained results showed the accuracy of model simulation in dry matter as the correlation cofficient was achieved 0.9. Also LAI simulation in all treatments had good reults, as generaly is reliable in canola investigations.