The objective of this study was developing a simple mechanistic MODEL for bambara groundnut to predict CROP performance at both potential and water limited conditions. An agrometerological MODEL "BAMunt" driven by solar radiation, temperature, rainfall and atmospheric humidity was developed to simulate the growth and development of the bambara groundnut at a range of locations. When water is not limiting, BAMunt predicts light-limited growth as a result of intercepted radiation and conversion of radiation into dry matter. A separate module calculates the depth of the root front and the extractable water amount to predict water-limited growth. Our results show that the MODEL predictions are reasonably close for both light and water limited production. In most cases, simulated pod yield was similar to observed yield which ranged from 2.6 to 28.4 gm-2. Similar results obtained in comparison between observed and simulated shoot weight with range of 78.8 to 535.8 gm-2. The reasonable agreement obtained showed that the MODEL could be a good starting point to provide a generic MODEL for a range of underutilized CROPs.

Plant development can be defined as a programmed qualitative change in plant form, which leads plant to maturity, and researchers call it as phasic development or phenology. Recognizing the timing of occurring each development stage is necessary for managing system in order to yield increment. The timing of occurring development stages depend on climate, genotype specifications and sowing date then determination of these times in different regions is difficult and it is only possible through the using of CROP SIMULATION MODELs which can predict the timing of occurrence each development stage by integrating effective factors. The MODEL was constructed based on linear equation of plant temperature response. In order to MODEL evaluation two experiments were carried out in agricultural and natural resources research center of Khuzistan in 2003-2004 and 2004-2005 CROPping years. Wheat development stages were determined based on Kirby and Appleyard’s scale by stereoscopic microscope and required GDD for each development stage as well. The constructed MODEL was calibrated and run for SIMULATION. Comparison of simulated and observed data showed that the MODEL can strongly predict wheat development stages.

There are two approaches for simulating memory as well as learning in artificial intelligence; the functionalistic approach and the cognitive approach. The necessary condition to put the second approach into account is to provide a MODEL of brain activity that contains a quite good congruence with observational facts such as mistakes and forgotten experiences. Given that human memory has a solid core that includes the components of our identity, our family and our hometown, the major and determinative events of our lives, and the countless repeated and accepted facts of our culture, the more we go to the peripheral spots the data becomes flimsier and more easily exposed to oblivion. It was essential to propose a MODEL in which the topographical differences are quite distinguishable. In our proposed MODEL, we have translated this topographical situation into quantities, which are attributed to the nodes. The result is an edge-weighted graph with mass-based values on the nodes which demonstrates the importance of each atomic proposition, as a truth, for an intelligent being. Furthermore, it dynamically develops and modifies, and in successive phases, it changes the mass of the nodes and weight of the edges depending on gathered inputs from the environment.

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.

The greatest limitations to agricultural development and production in Iran are scarcity of water resources, ineffective irrigation scheduling and wasteful water usage. Avenues discussed for optimum water use include improving water productivity by improving irrigation scheduling, farm water management and water profitability. The present study evaluated current water use and ways to improve winter wheat productivity by varying field scale water usage in the Abshar irrigation network in Esfahan province, Iran.Optimal irrigation depth and scheduling, and yield function for winter wheat using current water management tactics were determined. The availability of water, rotational water rights laws and field data were combined using the AquaCROP SIMULATION MODEL for field scale CROP growth. CROP yield and winter wheat productivity were then simulated and compared to field results. Field research indicated that 800 mm of water is applied annually for winter wheat and CROP yield averages 5000 kg per hectare. Improvements in water management and productivity based on different irrigation schedules (changes in depth and time) and water quantity schemes and the effect on water balance and CROP yield were investigated. The baseline scheme used current conditions as a reference for other schemes. The results showed that eliminating the second, third and seventh irrigations from the schedule decreased the quantity of water applied by 38%r and yield by 4%, which produced a 45% increase in water productivity over the baseline scheme. However, improving agronomic management and decreasing the first irrigation depth by 50% (from 200 to 100 mm) also produced a slight variation in CROP yield. Increasing the water applied to the optimal depth increased water productivity, but increasing the water applied to greater than optimal levels had no significant effect on yield and decreased water productivity. The results showed that proper irrigation scheduling using the AquaCROP MODEL in combination with improved agronomic management decreased the quantity of water applied during irrigation 38%, increased CROP yield by 16% and water productivity by 79%.

This study was carried out using AquaCROP plant growth MODEL to simulate potato CROP yield in irrigation levels of 100, 80 and 65 percent of plant water requirement under climate change conditions in Shahrekord region. The data of two years of 2013 and 2014 were used to calibrate and validate the AquaCROP plant growth MODEL. In order to investigate the effect of climate change on product yield, the output data of the HadCM3 general circulation MODEL under two scenarios A2 and B1 for the periods 2011-2030 and 2046-2065 are downscaled using LARS-WG MODEL and used as inputs of the growth MODEL. Based on the results, the SIMULATION of potato yields with the AquaCROP plant growth MODEL was carried out with high precision so that the average difference between observed and simulated values in calibration and validation stage was 0. 56 and 0. 68 ton/ha, respectively. Also, the mean absolute error (MAE) for simulating minimum temperature, maximum temperature, sunshine hours and precipitation with LARS-WG MODEL was 0. 05, 0. 03, 0. 01 and 0. 16, respectively which reflects the good performance of the MODEL in producing climate data for the future periods. SIMULATION results showed that the potato yield for the periods 2011-2030 and 2046-2065 under the A2 scenarios was 15. 8 and 24. 5 percent, respectively, and under B1 scenario, 11. 8 and 22. 4 percent increase compared to the base period. Also, for I80 and I65 treatments, the yield value decreases in both the future periods and the two scenarios campared to the base period.

Background and Objectives: The unceasing growth in demand for water in the industrial sector, drinking water and reduction in the amount of water available for agricultural sector has led to a reduction of water usage in rice, which threatens its production. CROPs SIMULATION MODELs can be used to carry out various studies such as selection of suitable cultivar and plant, determining the best agricultural management and production capacity of the area. The purpose of this study was to investigate the ORYZA2000 accuracy in simulating grain and biomass yields, and studying water balance and productivity of rice affected by irrigation and planting dates. Materials and Methods: In order to evaluate the ORYZA2000 MODEL and investigate the productivity of rice production under irrigation management and planting date, a split plot experiment based on a complete randomized block design with three replications was carried out on a local (Hashemi) cultivar in the years of 2016 and 2017 in the Rice Research Institute of Iran, Rasht. Irrigation interval was considered as the main factor at 4 levels including full flooding, 5, 10 and 15 days irrigation intervals and transplanting date was assigned to subplot at three levels (April 21th, May 11th and May 31th). Evaluation of simulated and observed values of grain yield and biological yield was conducted based on coefficient of determination, T-test, root mean square error (RMSE) and normalized root mean square error (RMSEn). In this research, the water balance equation throughout the growing season was considered which its components included irrigation, rainfall, actual evaporation, actual transpiration, leakage and deep penetration and changes in the water stored in the root development zone. Water productivity was investigated based on the grain yield of rice for transpiration, evapotranspiration, irrigation and total precipitation and irrigation. Results: The results of this study revealed that normalized root means square error of the grain yield and biological yield were determined 8% and 6%, respectively. Also, the results showed that among water managements, flooding irrigation and 15-day irrigation interval had the highest water productivity regarding transpiration and evapotranspiration and the amount of input water and irrigation, respectively. Among the planting dates, the planting date of May, 11th had the highest water productivity based on transpiration, evapotranspiration and planting date of April, 21th, had the highest water productivity based on irrigation and irrigation and rainfall. In these conditions, the planting date of April, 11th and May, 21th, with an average of 136 and 116 millimeters, had the highest and lowest water reserves, respectively. The highest amount of water saving during the two years of experiment was observed in irrigation intervals of 10 and 15 days (145 and 143 mm, respectively) and the lowest was recorded in the flood treatment (92 mm). Conclusion: Considering paddy and biomass yield of rice, water productivity and water consumption, five days irrigation treatment had the best paddy and biomass yield in April 21st planting date. This treatment was the best treatment in terms of productivity and rice production, with 9% reduction in water use and 6% reduction in paddy yield of rice. According to the present study, the ORYZA2000 MODEL can be used to support the results of experiments under irrigation management conditions and planting dates.

WOFOST is a CROP growth SIMULATION MODEL which can simulate CROP growth and yield components of annual CROPs dynamically in the various stages of growth period. Some parts of the MODEL outputs (CROP yield and CROP water requirements) can be used in irrigation planning and management of water resources. In this study, a field research was carried out in a randomized complete block design with three irrigation treatments (Fully Irrigation, 20% and 40% deficit irrigation) in the research farm of Jihad-e-Agriculture and Natural Resources Research Center, located in Mahidasht, Kermanshah. The grain maize (variety 704C) was cultivated in the May 2010. The required phonological stages (emergence, flowering and maturity) for the CROP growth MODEL were observed in the field and then thermal time (degree days) required for each of the phonological stages were calculated based on daily weather data. Dry weights of leaf, stem and storage organ as well as LAI were measured eight times during the growing season. The CROP parameters of the MODEL were calibrated based on the measured data. The MODEL outputs in the potential situation were compared with the measured data using statistical indicators including the root mean square error (RMSE), coefficient of efficiency (E), index of agreement (d), the maximum error (ME) and the coefficient of residual mass (CRM). The value of these indices were 1174, 0.96, 0.99, 16.67 & -0.02, respectively, for total biomass at the harvest time.

One of the main constraints in predicting a CROP yield mostly under rainfed conditions is the final yield temporal and spatial variability. This is mainly due to considering a constant value for a parameter within a MODEL while in reality it usually changes at both time and location. In this study we used the Markov Chain Monte Carlo (MCMC) approach together with a simple CROP MODEL for chickpea yield prediction under both rainfed and irrigated conditions to see if there is any possibility to simulate the chickpea production when there are sparse and not enough available data. The CROP MODEL was run for the great Khorasan province and SIMULATION results were compared with historical observed CROP yield data (21 years) obtained by Ministry of Agriculture and various field experiments on chickpea within Khorasan. For MODEL results verification Root Mean Square Difference (RMSD) was employed. MODEL results were acceptable at both irrigated and rainfed conditions which in turn indicated the high capability of the very simple CROP MODEL when linked with MCMC technique. Such a package would be able to analyze the chickpea production throughout the Khorasan chickpea production systems.