Yield prediction before harvesting is one of the tools in order to planning food production supply in future. Yield prediction was carried out for Wheat (Triticum aestivum) using different meteorological variables with agrometeorological indices in Hamedan district during 2003-04 and 2004-05. According to correlation coefficients, standard error of estimate as well as relative deviation of predicted yield from actual yield using different statistical models, the best subset of agrometeorological indices were selected including daily minimum temperature (Tmin), accumulated difference of maximum & minimum temperatures (TD), growing degree days (GDD), accumulated water vapour pressure deficit (VPD), sunshine hours (SH) & potential evapotranspiration (PET). Yield prediction was done two months in advance before harvesting time which was coincide with commencement of reproductive stage of wheat (27th of May). It revealed that in the final statistical models, 83% of wheat yield variability was accounted for variation in above agrometeorological indices.

Yield prediction before harvesting is one of the tools in order to make a plan for food production supply in future. Wheat yield prediction was carried out using different meteorological variables as well as agro meteorological indices in some regions in the western of the country including Sanandaj, Ghorveh, Bijar, Kermanshah and Kangavar for the years 2004-05 & 2005-06. On the basis of the highest correlation coefficients, the lowest standard error of estimate and relative deviation of predicted yield from actual yield using different statistical models, the best subset of agro meteorological indices were selected including daily minimum temperature, accumulated difference of maximum & minimum temperatures, accumulated photothermal units, sunshine hours, growing degree-days, total rainfall and accumulated heliothermal units. The results revealed that in Sanandaj district yield prediction was done using data related to second active vegetative stage of wheat (from 26th of March to 20th of May), for Ghorveh at reproductive stage (from 21st May to 19th of June), for Kermanshah at reproductive stage (from 30th April to 25th of May) and for Kangavar at second active vegetative stage (from 14th of March to 9th of May). In Bijar due to high values of relative deviation none of the models was used. The result showed that in Sanandaj and Ghorveh 68%, Kermanshah 91% and Kangavar 81% of wheat yield variability was accounted for variations in above agro meteorological indices.

Introduction: Agricultural production is affected by risks originated from weather and international markets. Although these risks could never completely been removed, we can minimize their effects by realizing the effective parameters in plant growth and crop yield and consequently by estimating the crop yield amount. Among these parameters, climate has a more significant role, especially in rainfed crops. Rainfed wheat is one of the major agricultural crops in Kurdistan province that includes most of the cultivated area. In 2006, Kurdistan province had %11.8 of the cultivated area which encompasses %13.67 of the rainfed wheat yield of the country. Regarding environmental outcomes, quite good prediction may be acquired by empirical fits of these crop-yield weather regression type models to real datasets. The aim of this paper is achieving higher accuracy revealed statistical models for rainfed wheat yield in different plant growth stages, regarding weather parameters and some specific agrometeorological indices. It is noticeable that non-weather parameters such as economic and management consideration to rainfed wheat yield were not considered in this study.Methodology: Therefore, in this study the prediction of rainfed wheat yield in Kurdistan province has been carried out, based on agrometeorological indices and climatological parameters. For this purpose Ranifed wheat yield data for Kurdistan province (34: 44? to 36:30? N? to 45:31? to 48:16?E) as well as its counties include Bijar, Sanandaj, Saghez, Ghorveh, Marivan and Divandareh, were obtained from Iran Aagriculture Ministry and also necessary weather parameters were obtained for all the weather stations in Kurdistan province from Iranian National Meteorological Organization for the period 1991-2006 (1993-2006 for Marivan station). Correlation and nearest neighboring methods were used for filling the missing data. Then linear stepwise regression models were developed for rainfed wheat yield data and independent parameters during 1991- 2003 years (1993-2003 for Marivan station). Stepwise regression method was chosen due to high amount of the independent parameters. The independent parameters in this study are 5 agrometeorological indices include; Growing Degree Days (GDD), Heliothermal Units (HTU), Photothermal Units (PTU), Vapor pressure deficit (VPD), Temperature Differences (TD) and 12 climatological parameters include; average maximum (Tmax) and minimum temperature (Tmin), absolute maximum (Tabs(max)) and minimum temperature (Tabs(min)), average (FF) and absolute (FFabs(max)) wind speed, relative humidity (RH), total (PET(total)) and average evapotranspiration (PET), sunshine hours (SH), total precipitation (R), rainy days (R(day)). Each daily amount of hese parameters has been extracted for six phenological phases of plant growing season from sowing to harvest. These stages are; the first stage of active vegetative before dormancy stage from November 7th to December 11th, dormancy stage from December 12th to March 15th, the second stage of active vegetative after dormancy stage from March 16th to May 10th, reproductive stage from May 11th to June 9th and maturity stage from June 10thto July 10th. In order to obtain the best models, regression models were calibrated for each rainfed wheat yield stage as well as the entire growing Season and that of the start of second stage of active vegetative after dormancy stage to the end of reproductive stage from March 16th to June 9th. Thus, 8 regression models were calculated for each study area. After entering The independent parameters in stepwise regression models the predictive parameters were chosen for each station and each phenological phase and based on R, R2, and SEOE the best models were chosen. Then crop yield for 2003-2006 is estimated, accordingly.Results and Discussion: The developed models show that 81, 70.2, 82.2, 71, 80, 90.6 and 65.6 percent of wheat yield variations is due to climatological parameters and agrometeorological indices for Baneh, Marivan, Divandareh, Bijar, Ghorveh, Saghez and Sanandaj provinces, respectively. In addition, the best phenological phase for predicting wheat yield for Bijar, Ghorveh, Saghez provinces are reproductive stage(May 11th to June 9th), for Baneh province is the second stage of active vegetative after dormancy phase(March 16th to May10th) and for Marivan is the dormancy phase (December 12th to March15th). For Sanandaj and Divandare district regression models are developed by using the data of all the growing season.Conclusion: Based on developed regression models for Kurdistan provinde in this study and the comparesion between these models and previous studies, it is obvious that with a combination of climatological parameters and agrometeorological indices and using stepwise regression models can predict higher amounts of rainfed wheat yield variation.

Isfahan district has been faced with limited water resources in recent years because of its special geographical location and highly dependent on the Zayandeh-rood River and groundwater for various uses, including agriculture. Meanwhile, according to available statistics, more than 90% of the country's water consumptions are allocated to the agricultural sector. Calculating effective rainfall, especially in arid and semi-arid regions that face with limited water resources is very important. In this study, effective rainfall in wheat cultivation in the 2015-2016 crop year was estimated by direct (field) method of Ramdas and obtained results from six empirical methods of Renfro, US Bureau of Reclamation (USBR), Evapotranspiration to precipitation ratio, US Soil Conservation Organization (SCS) method, FAO "(FAO/AGLW)", and percentage were compared with Ramdas technique by root-mean-square error (RMSE) tests, normalized root-mean-square error (NRMSE) and the mean absolute error (MAE). The results indicated that the ratio of evapotranspiration to precipitation and the method of the US Soil Conservation Organization (SCS) with RMSE and NRMSE 0. 31 and 0. 7, MAE 0. 11 and 0. 45 respectively could be recommended as empirical approaches in Kabootarabad region (Isfahan district).

Wind speed is one of the major parameters required for an estimation of evapotranspiration and determination of crop water requirements. Hence, several models and methods have been developed for a prediction of this needed climatic variable. In recent years, by development of soft computing tools, such intelligent systems as Artificial Neural Network (ANN) approach have been widely employed in agrometeorological studies. In this study, three types of four layers ANN models of different number of neurons were generated and utilized for a prediction of wind speed, using hourly data of Jiroft Agrometeorological Station, during a 6 month period, April to September, 2010. During these months winds are of higher speeds than those during the rest of the year. Statistical indices of RMSE, ME and EF (Efficiency Factor) were utilized for comparisons and as well for models'' evaluations. The results revealed that an ANN model with 20 neurons in each layer is of the most suitable performance in prediction of wind speed with the respective corresponding values of these indices as 1.1827, 0.6947 and 0.9246.

To investigate the response of corn to combined application of chemical fertilizers with rhizobacteria plant growth promoting, an experiment was conducted in 2017 at Research farm of Agricultural and Natural Resources Campus of Tehran University, Karaj, Iran, in a randomized complete block design with four replications. Four nutritional treatments including T1 (Control treatment without applying fertilizer), T2 (Just PGPRs), T3 (Use chemical fertilizers based on soil test) and T4 (T3 + PGPRs) were considered. According to the results, the highest total dry weight (3.9 kg/m2), crop growth rate (79.8 g.m-2.day-1), net assimilation rate (15.3 g.m-2.day-1) and grain yield (18.2 ton.ha-1) were observed in T4 treatment and T2 treatment produced the highest  leaf area index (5.3), leaf area duration (205.2) and specific leaf weight (78.5 g.m-2) . Also, the lowest value of all traits was observed in in T1 (control) treatment. The results showed that the presence of rhizobacteria plant growth promotioninduction in the corn nutrition program increased the growth and growth indices of the plant. Combined application of chemical fertilizers with rhizobacteria plant growth promoting resulted in the highest growth and final grain yield of corn.

In order to investigate the physiological and morphological traits of seven autumn sugar beet (Beta vulgaris L.) cultivars (Jolge, Palma, Giada, Monotunno, SBSI1, Suprema and PP8), under freezing stress, a study was carried out as a factorial experiment (7×10) based on randomized complete block design with three replications in college of Agriculture of Ferdowsi University of Mashhad. The plants at seedling stage were exposed to ten freezing temperatures (0, -2, -4, -6, -8,-10, -12, -14, -16 and -18oC). Then, electrolyte leakage percentage, yield of quantum efficiency (Fv/Fm), net photosynthesis, leaf number, leaf area, root length, root diameter and survival percentage indices were measured. Results showed that survival and electrolyte leakage percentage in Monotunno cultivar was 88 and 26 %, respectively, which showed superiority over other cultivars. The Minimum and maximum value of LT50su (-16.9 and -15.2oC) and also Fv/Fm (0.7 and 0.59) were observed in Monotunno and SBSI1 cultivars, respectively. Results indicated a strong and negative correlation between Electrolyte leakage (EL) and survival percentage (r = -0.65***) and also among EL with other traits, whereas survival percentage had a strong and positive correlation with leaf number (r = 0.88***) and root length (r=0.87***). A significantly positive correlation (r = 0.97***) between survival percentage and Fv/Fm ratio and also a negatively significant correlation between Fv/Fm and LT50su (r = -0.85***) and LT50el (r = -0.84***) showed the cultivars with high survival percentage and also low electrolyte leakage have high Fv/Fm compared with sensitive cultivars. In cold tolerant cultivars with reduction of EL% the LT50el and LT50su indices decreased significantly, but correlation between LT50el and LT50su was significantly positive (r= 0.75*).