In order to investigate the possibility of quinoa producing in Garmsar, Iran, a factorial experiment conducted in randomized complete block design with three replications in 2018-2019 growing season at Garmsar Agricultural Research Station. The factors were planting date at three levels (March 6th, April 1st and April 6th) and the three genotypes of quinoa (Q26, Q29 and Titicaca). Results showed that the effect of planting date was significant for all studied traits except the harvest index. Also, all studied traits were significantly different in all genotypes. Titicaca planted on March 6th had the highest YIELD (2276 kg.ha-1).The grain YIELD and YIELD components decreased with the delaying the planting date. Compared to early plantings, Latest date, April 6th, led to reduction of all traits, especially grain YIELD (about 50%). The results of simple phenotypic correlation between the studied traits showed that grain YIELD per hectare had the highest correlation with plant YIELD (0.877) and then with leaf area index (0.832), panicle weight (0.815) and number of branches per plant (0.745) that was significant at the 1% probability level.

Identifying the constraining factors of production and YIELD gap is very important. Therefore; this research was performed to identify the production constraining factors of local rice cultivars. All management practices from nursery preparation to harvesting stages for 100 paddy fields of local rice cultivars were recorded through field studies, in Sari, from 2015-2016. In the CPA, the actual and calculated potential YIELD were 4495 and 5703 kg/ha, respectively and the gap was 1221 kg/ha. The YIELD gap caused by number of top-dressing variables was 324 kg/ha, equal to 27% of the total YIELD gap. The YIELD gap related to previous year of legumes cultivation was 218 kg ha-1, equal to 18% of the total YIELD variation. Among the 10 variables entered in the CPA model, the effects of top-dress fertilizer application and its application frequency and foliar application were remarkable, which could compensate a significant part of the YIELD gap (444 kg/ha, 37% of total) in the farmers’ fields by managing these variables. According to boundary line analysis (BLA) finding, actual YIELD mean on the basis of optimal limit related to 12 variables under study was 5369 kg/ha, with 881 kg/ha YIELD gap . Mean relative YIELD and relative YIELD gap for 12 variables (transplanting date, seedling age, number of seedlings per hill, planting density, nitrogen and phosphorous per hectare, nitrogen before transplanting, harvesting date, lodging problem, pest problem, diseases problem and weeds problem) were 83.64 and 16.35 kg/ha, respectively. Based on the finding, it can be stated that the model precision is appropriate and can be applied for both estimation of the quantity of YIELD gap and determining the portion of each restricting YIELD variables.

Harvest index is one of the important parameters in estimation of modules of seed YIELD prediction. Since, this parameter undergoes changes under various conditions. Therefore, this research was aimed to prepare relations to estimate it. for this purpuse, a compound analysis experiment with randomized complete block design with four replications was conducted at the Gorgan University of Agricultural Sciences and Natural Resources Univercity Research Farm in 2005. The treatments were four wheat varieties (Koohdasht, Shirudi, Tajan, Zagros) and six different sowing dates (14 December, 20 Jan, 20 Feb, 20 March, 16 Apr, 17 May). The results indicated that a significant difference was obvious in different sawing dates in respect to the slope of Harvest Index line (dHI/dt). These changes were attributed to mean temperature, pre-Grain filling accumulated biomass, and fraction of post-anthesis (fG) producted dry matter. Therefore, based on obtained results and evaluated models, fG was the best optian for SIMULATION of dHI/dt and Harvest Index, in this respect, RMSE of dHI/dt for all varities by fG was 0.25 percentage per day which was the lowest one. Therefore, these relations can be used to evaluate the slope of Harvest Index line, or to model grain YIELD in crop SIMULATION models.

Water and nitrogen are the most important parameters affecting the YIELD of different plants such as corn. The aim of this study was to investigate the response of field corn to different levels of water and nitrogen and simulating with Aquacrop model. Water treatments were included control (I100), water stress in vegetative growth, flowering and grain filling stages, each of them had two levels 80% and 60% of water requirement and application of nitrogen fertilizer in two levels of 220 (N100) and 110 (N50) Kg N/ha. Field results showed that various stages of crop growth was affected by water and nitrogen stresses. The results of analysis of variance showed that various water and nitrogen treatments had a significant effect on above-ground biomass and grain YIELD at 1%. AquaCrop model was able to accurately simulate canopy cover, biomass and grain YIELD during the growth season. SIMULATION results showed that calibrated AquaCrop model can be used to a wide range of Iran.

Introduction: Musculoskeletal problems are the common complaints of patients refer to internal medicine clinics and the pain is the most important of them. There are different physiotherapy methods for reduction of pain and action potential SIMULATION therapy (APS therapy) is one of newest methods. The aim of this study was to determine reduction of pain with APS therapy. Materials & Methods: In 47 patients with different musculoskeletal pain APS therapy performed in 6 days each for 16 minutes and with current of 0.7-1.2 mA. Pain (VAS), Global functional status (ACR) and relief of pain (VAS) before and after study compared.Results: APS therapy reduced pain (p<0.001) and increased Global functional status (p<0.001) and increased relief of pain (p<0.005) but there was not any correlation between this reduction of pain with APS therapy and educational status and past history of physiotherapy and duration of illness in these patients.Conclusion: APS therapy is a useful physiotherapy modality for reduction of pain in musculoskeletal problems.

A robust crop model can assist in genetic improvement and cultural management of the crop. The objectives of this study were to describe a wheat (Triticum aestivum L.) model and to report results of its evaluation. The model simulates phenological development, leaf development and senescence, crop mass production and partitioning, plant nitrogen balance, YIELD formation and soil water and nitrogen balances. The model includes responses of crop processes to environmental factors of solar radiation, temperature and nitrogen and water availability. Parameters are inputted in describing physiological processes so that these can be varied to define genotypic differences. The model uses a daily time step and readily available weather and soil information. The model was tested using independent data and indicated an acceptable performance for important crop attributes as compared to observed results including days to anthesis (CV=4.5%; r=0.98) and maturity (CV=5.6%; r=0.96), crop LAI (CV=11.8%; r=0.80) and dry mass at anthesis (CV=9.3%; r=0.72) and total crop mass (CV=9.5%; r=0.82) at maturity and grain YIELD (CV=8.4%; r=0.89). It was concluded that the model can be used in SIMULATION studies of wheat YIELD and its limitations in response to environmental conditions, management inputs and genetic factors.

In recent years, simulatiom modelling of YIELD has been the focus of attention for many researchers. Because, while reducing adminestrative costs, it can easily provide SIMULATION models of different situations. In this study, while a subroutine on SIMULATION of canola was added to CRPSM model, effect of different water treatments on canola was also investigated. In this research, canola (Talaye) under 5 irrigation treatments (full irrigation treatment during the growing period, water stress treatment at the spring re-growth stage, the flowering stage and pod formation, the grain formation stage and dry land treatment) was sown in complete randomized block designs at the college of Agriculture, Shiraz University during 2007-2008, and then the model was calibrated based on available information (soil-location-plant water). Review of statistical indicators between simulated and measured YIELD show high accuracy in the estimation of crop YIELD (R2=0.98) and soil water content. The result of model validation with independent data series also showed that the result of soil water content is desirable except in dry treatment, and the corrolation coeficient between simulated and measured crop YIELD (R2=0.98) was acceptable.

The investigation of plant production limiting factors requires extensive and costly research, so the use of models can be noteworthy. Practical models including Aquacrop and DSSAT are used to simulate plant production. In the first year, these models were calibrated for Khuzestan wheat farms in Ramseh pilot and in the second year for two pilots, Ramseh and Hamidiyeh. The normalized root mean square error, agreement index and model efficiency with DSSAT model for grain YIELD values in the first year were 0. 036, 0. 01, 0. 92 and 0. 76, respectively that Comparison of these values with statistical indices obtained from Aquacrop model indicates less error and more performance index for using DSSAT model. Also, the performance of the model, which is an indicator of the effectiveness in the selection of models, shows that the DSSAT is more efficient than the Aquacrop model. Mean root mean square error and normalized root mean square error of wheat biomass in the DSSAT model were determined to be 0. 23 and 0. 04 respectively that indicates the DSSAT model has higher efficiency and accuracy than the Aquacrop model in simulating grain YIELD and wheat biomass.