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.

In this study the possibility of using fuzzy inference system efficiency, creating a bridge between meteorological, plant parameters, and Daily Yield, and comparing the accuracy of Daily Yield using these systems were investigated. After analyzing the different models and different combinations of Daily meteorological data, seven models for estimating Daily Yield were presented. For these models, the calculated Yield from AQUACROP model was considered as a base and the efficiency of other models was evluated using statistical methods such as root mean squared error, error of the mean deviation, coefficient of determination, Jacovides (t) and Sabbagh et al. (R2/t) criteria. An experiment was carried out during the 2014-2015 growing season in the Agricultural Research and Education Center of Khorasane Razavi province using a randomized complete block design with a split plot arrangement and four replications. This experiment was including of three irrigation levels treatments as the main plot and three method of planting treatments (transplanting 20-days, transplanting 30-days and direct seeded) as subplots. From the available data, 75 percent was used for training the model and the rest of 25 percent was utilized for the testing purposes. The results derived from the fuzzy models with different input parameters as compared with AQUACROP model showed that fuzzy systems were very well able to estimate the Daily Yield. Fuzzy model so that the highest correlation with the 9 input variables (r=0. 98) had in mind and evaluate other parameters, the model with 2 parameters, match very well with the AQUACROP model had stage training. In the test phase, training phase was very similar results and the model with the second phase of harvest index and canopy cover will get the best match. According to the results of this study it can be concluded that fuzzy model approach is an appropriate method to estimate the Daily Yield.

Todays, dynamic models are supposed as the most important tools in erosion and sediment phenomena due to their complexities and existence of many affecting factors. Towards, the present study was conducted in the Kojour watershed for Daily sediment modeling using Daily rainfall, discharge and sediment during 2007 to 2010. The modeling process was carried out all data and the monthly and seasonally classification data in linear and nonlinear models. The results indicated that Daily linear and non-linear models did not indicate a suitable model. The monthly and seasonally classification of the data led to achievement of better models with determination coefficient significant at 5 percent level and relative error less than 40 percent as compared with those obtained from no classification. It was also found out that Daily sediment of Kojour watershed was affected by discharge occurred event day and before four days. The discharge occurred event day is the most effective factor in 80% selected models in the study watershed. The nonlinear models were better estimation than linear models in July, September, December and March and autumn but linear models were better than nonlinear models in other months and seasons.

Background: Buffalo reactivity during milking affects milking procedures, milk Yield, and quality. Aims: This study evaluated the influence of milking reactivity on the Yield, composition, somatic cell count, pH, and electrical resistance of milk in Jaffarabadi buffaloes. Methods: A 1-4 point scale, based on leg movement, was used to assess the milking reactivity of buffaloes (n=40). Based on the milking reactivity score, animals were classified into four groups: reactivity score-1 (RS1), reactivity score-2 (RS2), reactivity score-3 (RS3), and reactivity score-4 (RS4). The influence of milking reactivity on Yield, composition, somatic cell count, pH, and electrical resistance of milk was observed. Results: Buffaloes with RS1 and RS2 produced significantly (P≤0.001) higher Daily milk Yield, 6% fat-corrected Yield, solid-corrected Yield, and energy-corrected Yield than the RS3+4 group. Milking reactivity score did not influence milk fat, protein, lactose, ash, solid-not-fat, total solids content, and the fat: protein ratio. However, Daily Yield of milk fat (P<0.001), protein (P=0.001), lactose (P=0.001), ash (P=0.002), solid-not-fat (P=0.001), and total solids (P<0.001) were significantly higher in buffaloes in the RS1 and RS2 groups than in the RS3+4 group. Milk somatic cell count and somatic cell score were not influenced by milking reactivity score (P>0.05). Milk density and pH did not differ significantly (P>0.05) between groups. However, the electrical resistance of milk in the RS1 group was significantly (P<0.05) higher than in the RS2 and RS3+4 groups. Conclusion: Milking reactivity influences Daily milk Yield, milk component Yield, and electrical resistance, but not milk composition in Jaffarabadi buffaloes.

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.

Introduction: Teeth loss and its consequences, such as mental discomfort, most often negatively affects one's health and well-being. Dental implants were introduced in an attempt to raise patients' satisfaction and improve health and quality of life in all dimensions. Hence, this study was performed to check the impact of implantation on the oral health indices in patients who were candidates for implant treatment in Isfahan city. Materials & Methods: In this analytical cross-sectional study, the lifestyle quality of 73 patients who were referred to Isfahan’, s Islamic Azad Dental University (Khorasgan) and its affiliated private centers, was assessed using the OIDP index before and after their implant treatment between 2019 and 2020. Also, data analysis was done using Independent ttest, Paired t-test and analysis of variance ANOVA test (α,= 0. 05). Results: In the current study, the most common problem and with the most effect on oral satisfaction quality were teeth lost and extractions, respectively. Majorly, the effects were evident on the eating habits (83. 6%), satisfaction of communication with others (68. 5%) and quality of smile (64. 4%). In the questionnaire, an attenuated score of OIDP was recorded, before the implant treatment compared to that of post-treatment, which was statistically meaningful (p value < 0. 05). Conclusion: According to the current study data, we can conclude that implant treatment can have a positive effect on the patients' lifestyle quality.

To day, the progressive of technology and access able various software and also to recognize in significant parameters of soil erosion and sediment Yield is provided in possibility of using different mathematical models. The models, demonstrate to create of hydrologicalevents through mathematical relationships between catchment's parameters. Whereas, the results of using some of models has been satisfied for this purpose. Therefore, using of these models is increased in the most watersheds through more than 40 models. Other side, as the mountainous catchments is very complicated, so simulation models are suitable for estimation of erosion and sediment Yield. One of the reliable models for above purpose, is known SWAT model. Where is applied in Amameh catchments. SWAT model has been modified. At present study, the SWAT model has calibrated by monthly and annually observed data, which is recorded in Amameh stations with monthly and Daily estimated data, which are obtained by model. In this study catchment's parameters are used as physical parameters for SWAT model. After calibration process, the model has been evaluated by some of sensitivity testing models such as, Nash - Sutcliff model and P-bias model. This process is shown which some of physical parameters are sensitive among the another parameters of the SWAT model such as, K (Soil erodibility parameters) and n (Roughness coefficient parameters). In final, the determine coefficient of observed value and estimated value is obtained, R2=0.74. This result can be obtained for unpaged catchments at same hydrological condition.

In recent decades, drastic land use changes in Golestan province caused to reduce a substantial amount of Hyrcanian forest. To investigate the changes, land cover maps produced using Landsat satellite imagery classification of sensors TM from 1984, 2012 and 2016 respectively used as input data in Land Change Modeler (LCM) to predict land cover changes in 2030. In order to assess the accuracy of modeling, statistics of relative performance characteristic (ROC), ratio Hits/False Alarms and figure of merit was used. In continue to investigate the role of land use changes in water Yield as one of ecosystem services was discussed. The results show the accuracy of artificial neural network with the ROC equal to 0. 949, the ratio Hits/False Alarms equal to 57 percent and the figure of merit is equal to 11 percent. Land use change modeling results showed that from 1984 to 2012, The most prominent changes were related to reduction of forest cover. This process modeling using artificial neural network showed, from 2016 to 2030 forest cover will be reduced about 30361 hectares. The results of water Yield study showed that runoff in the area, particularly in the East and North East area has increased. This increase in the amount of runoff occurred as a result of land use change on forest ecosystems to agriculture. Results of this study improve our understanding of hydrological consequences of land-use changes, and provide needed knowledge for effectively developing and managing land-use for sustainability and productivity in the Gorgan-rood watershed.