More than half of the agricultural lands in arid and semi-arid climates are rainfed. Many climatic variables affect the yield of rainfed crops, among them rainfall is the most important variable. The aim of the present study is to determine the yield functions of rainfed wheat in Tabriz, Sarab and Maragheh stations located in the east of Lake Urmia basin, considering the changes of climatic variables during different stages of rainfed wheat growth. In order to model the yield using Multivariate regression method, some precipitation variables such as, number of effective precipitation events, vegetation precipitation deficit, reference precipitation and evapotranspiration deficit in rainfed conditions during six stages of rainfed wheat growth including germination; End of germination until the beginning of flowering; Flowering stage; Finishing flowering until the seeds start to fill; Seed filling stage and whole growing season were used. In general, based on the obtained results, precipitation fluctuations have the greatest effect on wheat yield. Therefore, identifying the precipitation regime and analyzing its characteristics is important for assessing yield fluctuations of rainfed crops. Among the growth stages, the fluctuation of the proposed traits in the whole growth season has a greater role in determining yield functions. Yield functions were determined using variables that had a significant correlation with yield. For this purpose, 22-year and 3-year data were used for calibration and validation, respectively. The results of the model efficiency coefficient and normalized root mean square error indicated better effeciency of Enter method in the Sarab (EF=0. 55 and NRMSE=0. 19) and the Moderate accuracy of Stepwise method in estimating the rainfed wheat yield in Maragheh and Tabriz. In Maragheh and Tabriz, the Stepwise method with average relative error values of 21% and 15. 6%, respectively, and in Sarab, the Enter method with an average relative error of 16. 5% had better results in yield fitting.

Background & Aim: One of the basic assumptions in simple linear regression models is the statistical independence of observations. Sometimes this assumption is not true for study subject and consequently the use of general regression models may not be appropriate. In this case, one of the leading methods is the use of multilevel models. The present study utilizes Multivariate logistic regression model using a multilevel model to exhibit the chance of having elbow, wrist and knee disorders over the past year based on elbow, wrist and disorders during the past week. Methods & Materials: This study is a cross-sectional study that was carried out from April 2015 to May 2016 in Mobarakeh Steel Company, Isfahan. The study population includes 300 male employees of Mobarakeh Steel Company, with a mean age of 41. 40± 8. 17 years and an average working experience of 16. 0± 7. 66 years. Data were analyzed using SPSS (version 24) and MLwiN software. Results: Based on this study, results obtained from single variable and multivariable regression were different. Conclusion: Based on this study, it can be suggested that multivariable regression cause a better and more accurate deduction compared to single variable method.

Given the importance of wheat in human nutrition and its cultivation in large-area under rainfed in Iran, this study was aimed to evaluate the efficiency of artificial neural networks and linear multiple regression models to predict biomass and grain yields of wheat (cv. Sardari), in two-year study. In two stations (Koohrang and Ardal), 202 sampling points were selectedin the various hillslopes includes summit, shoulder, back slope, foot slope and toe slope. Atthe harvesting stage, the soil and plant samples were collected. Primary and secondary terrain attributes were extracted from digital elevation models, and meteorological data were used in two regions. Topography, 54 different soil characteristics, rainfall and management as the inputs as well as biomass and grain yields were considered as the outputs of both models. Artificial neural networks and multiple linear regression models, respectively, accounted for 84% and 15% of variations (R2) in grain yield prediction, and 76% and 6% in prediction of biomass yield. The root mean square error (RMSE) of the models also were equal to 0.033 and 0.092 to predict grain yield, and 0.037 and 0.102 to predict the biomass based on artificial neural network and multiple linear regression models, respectively. The results showed a better ability of artificial neural networks in comparison with multiple linear regression to estimate grain and biomass yields of wheat in the target areas.

Introduction One of the most important planning tools for timely supply of crops, especially the strategic wheat product, is to predict the performance of this product before harvest, which can be very important in planning for itself. Combining the results of observations and ground measurements with remote sensing techniques can be widely applied in all agricultural sectors and facilitate the access to precision farming. Agricultural products have always been associated with the risk of fluctuations in the climate and changes in international markets, although this risk is never completely eliminated, but it can be understood by identifying the various parameters affecting plant growth and estimating the amount of the product Before harvesting, they minimize them. The forecast of rainfed wheat yields as a strategic product, with the Earth's population reaching 7 billion now. Materials and methods Field data includes biomass and net weight of wheat produced per farm in kilograms. These data are obtained by direct field surveys during harvesting. The GPS was used to determine the total area of the land, and considering the time zone of the crop, the Landsat-8 satellite time series was used from mid-February to late May in the studied years. After performing the necessary pre-processing on the images, the images were classified using a multi-timed classification. Initially, both NDVI and LAI indexes were obtained for all images in each ENVI environment every 5 years. Finally, the phenolic curves of both indices for each plot of land were fitted for each year from the studied years, which cultivated the wheat field, and the time of each phonological step was obtained for the studied area. Results In order to evaluate the overall accuracy of the classification, the Kappa coefficient and overall accuracy for the classes defined separately were calculated using the classification error matrix. According to the phenological diagrams, the parameters of the area under the charts of both indicators were calculated for all lands. According to the phenological diagrams, the parameters of the area under the charts of both indicators were calculated for all lands. For this reason regression relations and determination coefficients (R2) between indices and wheat and biomass were created. For this reason regression relations and determination coefficients (R2) between indices and wheat and biomass were created. In this study, both indicators were affected by the multivariable regression of the product estimation, and the highest coefficient of determination was obtained for each of the indices alone. From the 5 phonological stages, the inflorescence stage with R2=0. 65 has the highest correlation coefficient. Discussion and conclusion From the obtained coefficients, we conclude that GLAI or green leaf area index (absorption) has a higher coefficient than NDVI. GLAI, which represents the main part of the photosynthesis of the plant (leaf), which is the main factor in the production process in the plant, certainly has a greater impact on the plant's production process. This leads to the preference of this index for the NDVI for estimation, but since the main goal of the paper is to obtain a Multivariate regression relationship, we can do this in addition to the effect of both the desired index, the coefficient of determination and continuity For each of the indicators, we increase individually and make estimates in the region more accurately. With the involvement of both indicators in our relationship, we obtained a significant coefficient, especially for biomass with R2=0. 865, which ensures that our prediction values are close to real values and that the program On the basis of this estimate, the probability of success will be high. From the study of phonological stages with wheat yield, we also conclude that, firstly, the entire phonological stages have less regurgitation coefficients than the phonological graphs of the two vegetation indexes, which means the whole diagram of wheat growth stages relative to the phenological periods on these graphs Have more ability to estimate the yield of wheat. Two of the five phonological stages studied, the inflorescence formation stage with R2=0. 65 the highest correlation coefficient. This step in time is about the peak of the graph or the maximum value of the phonological graph.

A combination of plant residues and increased crop density would play an important role in enhancing crop growth through weed suppression in organic agriculture. In order to attain the best level of wheat residue amount and sunflower density, a factorial experiment based on RCBD with three replications was conducted at research field of the Faculty of Agriculture of Birjand University (Amirabaad campus) in 2008 growing season. Treatments were 5 different rates of wheat residue including 0, 250, 1250, 3750 and 5000 kg ha-1 and 3 sunflower densities including 50000, 70000 and 90000 plants ha-1. Results showed that the maximum crop growth as well as the lowest weed growth was observed at the density of 70000 plants ha-1 and 3000 kg ha-1 of wheat residue. Increasing crop density and residue amount to the levels above these limits would have no effect, or even in some cases reduce crop growth.

In this research، the multi-variable linear regression relationship has been developed among the important factors influencing unemployment rate. The seasonal data is from 1394 to 1394، which is compiled from reliable economic data bases of country. Independent variables are: net foreign assets of the banking system (billion rials)، net debt of the public sector to the banking system (billion rials)، liquidity in terms of its constituent parts (billion rials)، rate of dollar (rials)، economic participation rate، average inflation rate، The average annual interest rate on state-owned banks، the percentage of jobseekers (65-15). The results indicate that there is a negative and significant relationship between unemployment rate and average inflation rate and economic participation rate، while the net debt of the public sector has had a positive and significant relationship with the banking system and unemployment rate. The greatest negative effects on unemployment rate are the rate of economic participation and the greatest positive impact on the unemployment rate is the net debt of the public sector to the banking system.