Iranian Journal of Crop Sciences
Year:2000 | Volume:2 | Issue:1|Papers Count:8

Parallel to the increase in importance of oilseed rape, the need to rationalize a system for describing plant growth stages, has been enhanced. Most of the previous keys, have emphasized on a certain growth stage. Thus, it is not alwayseasy to link different "growth stage keys". In this study 6 genotypesof rapeseed (Brassica napus L.) and 2 genotypes of rape (Brassica campestris L.) were sown in a field experiment and by using frequent sampling, the life cycle of rapeseed was divided to seven main stages including germination and emergence, leaf production, stem extension, flower bud development, flowering, pod development and seed development. Each main stage was divided in to sub-stage sand the main and sub-stages were described. The use of this key in agriculture with some examples are also given.

The present research was carried out to determine the correlation among some yield related characters as well as their interactions of 25 advanced lines and cultivars of 6-rowed barleys. 19 Characters kernel yield, vegetative growth period, grain filling period, flag leaf area, flag leaf area duration, peduncle area, peduncle area duration, flag leaf sheath area, flag leaf sheath area duration, stem diameter, height of plant, head length, own length, number of kenel per head, head weight, 1000-kernel weight, straw yield and harvest index were measured on 10 random plants. Correlation between these characters were calculated. A stepwise regression analysis was also done for grain yield (dependent variable) and other characters. Finally, the correlation between the remaining characters in the model was divided into direct and indirect effects by path analysis. The results derived from correlation and path analysis indicated that kernel number per head with a direct effect 1.3607 was the greatest factor affecting kernel yield. The direct effect of number of head per unit area and kernel weight was positive. Correlation coefficient of 1000-kernel weight and number of kernel per head with kernel yield were positive and significant while correlation coefficient between grain yield and number of head per unit area were non significant but with a significant effect.

The efforts to increase the grain yield per unit area of wheat is unavailable due to limited area under cultivation. Macronutrients such as nitrogen can play an important role in increasing grain yield per unit area. N added to the soil might become unavailable to the crop due to leaching or volatilization. Foliar feeding of nitrogen appears to be important in improving both quantity and quality of wheat grain. To investigate the effect of foliar feeding of urea on grain yield and protein percent of two winter wheat cultivars, (Alvand and Barkat), a factorial field experiment was carried out using Randomized Complete Block Design with three replicates, during 1376-1377 (1997-1998) at Agricultural Service Center of Faridan in Isfahan province. Nitrogen was applied at booting stage in four levels (0, 10, 20, and 40 kg ha-1. The results showed that foliar feeding of urea at booting stage can significantly increase the grain yield. Such yield increase was mainly due to an increase in number of grain/ear. There was no significant change in mean kernel weight, and fertile shoots per unit area. Higher levels of urea spraying was associated with greater biological yield. It was shown that foliar feeding of urea at booting stage can significantly increase the grain protein percent of two winter wheat cultivars, Alvand and Barkat. The Barkat cultivar had a higher response in increasing protein percent than the Alvand cultivar.

Data analysis are usually based on a statistical model, which itself has some components. The most important components are, mathematical formula which relates the variable of interest to those factors which have influence on the value of that variable and controlled by the experimenter besides uncontrolled terms which are called nuisance factors. The second component are assumptions related to some factors which are important issues in data analysis. These assumptions are mainly concerned about the 1st and the 2nd moments of random factors as well as their distributions. Many data analysts do not consider these components and any result obtained without checking the validation of these components mayor may not be valid. This paper highlights these components and provide ways to check their validities.

To study the effects of different levels of drought stress on qualitative and quantitative characteristics of corn, sorghum and millet, an experiment was conducted at experimental farm in Qazvin. The design of the experiment was factorial in Randomize Complete Block Design. Three levels of stress factors without stress (A1), medium level (A2) and high level (A3). three different crop plants: corn (B1), sorghum (B2) and millet (B3) were employed. The index of stress was RWC for irrigation regime as 60-70% RWC for high stress, 80-85% for medium and over 95% for without stress. The results showed that the whole of traits are affected to drought stress though they were different in corn, sorghum and millet. Protein percent was increased but starch percent was decreased by drought. Dry matter yield and grain yield were decreased and growth rate was also decreased. The corn was more sensitive than sorghum and millet to water stress, so in dry regions sorghum and millet as a forage crops, is suggested.

In order to study the effect of plant densities on yield and yield components in four cultivars of bread wheat, a field experiment was conducted in College of Agriculture, Shahid Chamran University during 1997-98 year. Experiment was a factorial design based on RCB with four replications. Factors were cultivars (Falat, Atrak, Vee/nac and Maroon) and plant densities (200, 300, 400, 500 and 600 plant/m2). Results indicated that the effects of cultivar and plant density were significant in 1% level on the biological, grain and straw yield and harvest index(except effect of plant density on straw yield), but their interaction were not significant. Atrak and Maroon with 5728.8 and 4315.5 kg/ha had the highest and lowest grain yield, respectiveley. Biological yield increased By increasing plant density. The highest grain yield for Falat and Atrak cultivars were obtained at 500 plants/m2 , but in Vee/nac and Maroon at 600 plants/m2 gave the highest grain yield. The effects of cultivar and plant density were significant on number of spikes/m2, spikelets/spike, no. of grains/spike and grain weight (at 1% Level). Vee/noc had the highest number of spikes/m2 but Atrak had the highest number of spikelets/spike and no. of grains/spike. Maroon had the highest 1000 grain weight among tested cultivars. Number of spikes/m2 was the only yield component which was increased by increasing plant density.