stepwise regression

This study was conducted to evaluate the relationship between leaf iron and some easily-available soil properties in citrus orchards in the southern region of Kerman province by artificial neural network modeling and stepwise regression. For this purpose, 40 orchards were selected from the study area and the physical and chemical properties of soil and iron in the plant leaves were measured. Using artificial neural network in different models with different data from soil properties as input and leaf iron as output, the ability of these models to predict leaf iron concentration was evaluated. The results showed artificial neural network with variables of organic carbon, pH, clay, phosphorus, TNV and electrical conductivity with explanation coefficient of 0.86 and 0.81 and root mean square error (RMSE) of 14.60 and 20.13 mg.kg-1 for data Training and testing were the best models in estimating leaf iron. Comparison of regression and neural network models in the test data showed that the neural network had a higher accuracy with an explanation coefficient of 0.81 than stepwise regression with an explanation coefficient of 0.2. The amount of RMSE in the neural network also improved and increased from 27.72 mg.kg-1 in the stepwise regression model to 20.13 mg.kg-1 in the neural network. Artificial neural networks have been able to predict the iron in plant leaves based on the easily-available properties of the soil, so that by choosing organic carbon as the input of the first model to the best model by selecting organic carbon, pH, clay, phosphorus, TNV and electrical conductivity, model accuracy increased.

Recognition of effective Parameters in sediment Production has an important role in understanding the phenomenon of erosion and its consequences and is one of the important principles of optimal management of a watershed. In the study of the relationship between watershed environmental and hydro geomorphological parameters with erosion and sediment, the quantity and amount of sediment has been considered so far, while in this research, has been studied the relationship between important climatic parameters including average annual rainfall, average annual temperature, altitude, slope, aspect and geological factor (formation) with sediment organic carbon as an important parameter and qualitative indicator in some of the Ardabil province watersheds. 98 samples of sediment after collecting from the study area, were analyzed in the laboratory by Walkley - Black method to extract the amount of organic carbon, then performed statistical analyze on samples using stepwise regression and partial least squares regression (PLSR) models. Findings showed that among the hydrogeomorphological parameters, slope and formation with regression coefficients of - 0.293 and -0.078, respectively, were identified as factors affecting the sediment organic carbon, so that both parameters have an inverse correlation with the sediment organic carbon and aspect parameter is more effective than the formation and output of both regression models is almost the same. Therefore, sediment organic carbon, in addition to the quantity and amount of sediments, can be considered as an important indicator for studying the degradation processes, watershed erosion and loss of soil organic carbon.

In this study, spatial-temporal variation of dust deposition rate in the western and eastern half of Tehran and its climatic parameters affecting were studied. At 34 points in the city, dust samples were collected by glass traps from the roof, for twelve months, and the climatic data were obtained and analyzed from relevant organizations. The highest deposition rate is in the western half of the city and its total amount has varied from 54.52 to 121.21 g/m2/y. In both halves of the city, summer has the highest dust deposition rate and its central areas have the highest amount. There were significant positive correlations between dust deposition rate with temperature and medium wind speed, and there were significant negative correlations between dust deposition rate with rainfall and relative humidity in all months, which justifies the high dust deposition rate in the dry seasons of the year. The results of stepwise regression showed that rainfall was the most important factor affecting the dust deposition rate in both halves of the city. The city of Tehran has a special geographical location the presence of mountains like a barrier has prevented dust from leaving the city and the air inlet corridor of Tehran has faced problems due to the expansion of building construction and high-rise building. Other factors affecting the rate of dust deposition in this city, in addition to the distance from the main source of dust production, atmospheric parameters can be mentioned the existence of barren lands around the city, vegetation cover, construction operations, and traffic.

Studies of climate change impacts on water resources need to conversion of projection of climate variable pattern from coarser scales to a suitable scale. Downscaling processes improves projection of General Circulation Models (GCMs) significantly. In this study is assessed performance of Artificial Neural Network and Support Vector Regression. Observation rainfall was collected for 1961-1990 from Birjand synoptic station. Also value of 26 predictors from CanESM2 output in Assessment Report Five (AR5) was extracted. Predictor selection was performed by Stepwise regression. Model skill was evaluated using indices R2, RMSE and NSE. Also for better analysis using of various tests such as uncertainty assessment, reproduction of descriptive statistics, dry and wet spells and monthly rainfall. Results of assessment indices showed that estimation performances of both methods in daily rainfall are relatively suitable. Value of R2, RMSE and NSE are achieved 0.48, 1.5 mm and 0.47 for SVR with Polynomial kernel function in best case respectively. Results of skill model in estimation of descriptive statistics indicated that SVR with Polynomial kernel function outperforming others. Comparison of observed and downscaled monthly rainfall illustrates that SVR performs better than ANN in winter season. Also both methods have overestimation in summer months. Results of identification of dry days sowed that performance of both models is well and same. In this case ANN identified 96% dry days correctly. Results of identification of wet days showed that SVR outperforms ANN. Assessment of skill method for estimation of dry spells length indicated that both methods have more efficiently in long spells than short

Type and source of fertilizer in fruit trees nutrition play an important role in increasing yield and fruit quality, shelf-life prolonging and reducing waste in harvested fruit. Evaluation of the possibility of integrated use of organic and inorganic fertilizers or gradual replacement of chemical fertilizers with organic manures is necessary in nutrition of kiwi vines, due to environmental issues resulted due to application of chemical fertilizers as well as increasing market value of organic fruits. Studies in this regard, especially comparison between organic manure and chemical fertilizer effects in kiwi fruit production is very limited. Therefore,this research was performed to investigate the short-term effects of four types of organic fertilizers as compared to chemical fertilizers over the amount of nutrients in leaves and fruits in Kiwi- Hayward variety.
This short-term field research was conducted in a randomized complete block design with six treatments including (azola, azocompost,vermicompost, cow manure, chemical fertilizer and control treatments) and four replications inHorticultural Science Research Institute, Citrus and Subtropical Fruits Research Center in Ramsar.Fertilizers were mixed with topsoil in canopy,weed control and drop irrigation was performed. The amount of nutrients, including nitrogen, phosphorus, potassium, calcium and magnesium in kiwi leaves and fruits were measured. In addition, relationship between nutrients in leaves and fruit yield was examined. The correlation analysis between mineral elements and the characteristics of qualitative and quantitative fruit firmness, dry matter, total soluble solids, titratable acidity and ratio of soluble solids to titratable acidity was performed. Stepwise regression equation between treatments with nitrogen, phosphorus, potassium and calcium was written on kiwifruit leaves and fruit. In addition, stepwise regression, the overall equation between yield and nutrient content of kiwifruit were reported.
Result and The results showed that concentration of nitrogen, phosphorus, potassium, calcium and magnesium in leaves and fruits in Kiwi trees are higher than control treatment and the amounts of these elements in leaves and fruit werein optimum condition. Fertilization increased the amount of nitrogen, phosphorus, potassium, calcium and magnesium in leaves and fruit of kiwifruit trees in fertilizer treatments, compared to the control. Azocompost treatment had the highest amount of nitrogen and calcium in leaves and fruit compared to other treatments. Azocompost and vermicompost treatments had the highest potassium content in the leaf. As well as cow manure, vermicompost and Azocompost treatments resulted in the highest amount of potassium in fruit. Increasing theamount of nutrients in the leaves of kiwifruit had a direct impact. The correlation between the amount of elements in the leaves and fruit showed a direct and significant relationship between nitrogen, phosphorus and potassium leaves and nitrogen, phosphorus and potassium in fruits. Increasing of nutrients in kiwifruit leaves had a direct and positive impact on fruits. In stepwise regression equation there was a significant relationship between the yield andamounts of nitrogen, phosphorus and potassium and kiwi fruit trees, and by increasing of these elements and yield wasincreased. Correlation analysis between minerals and firmness in kiwifruit showed that there are negative correlations between the nitrogen content in mature fruit and firmness at harvest time at probability level 5 percent. In addition, positive and significant correlation between the calcium content in mature fruit and firmness, as well as between the percentage of dry matter and phosphorus and potassium contents of mature fruit at harvest time was obtained. Results showed that a significant negative correlation was between soluble solids with calcium and between titratable acidity with phosphorus, and of soluble solids ratio to titratable acidity with nitrogen and phosphorus the mature fruit at harvest mature fruit.
In general, according to these research results we can say that use of organic fertilizers like chemical fertilizers in kiwifriut orchards increasethenutrient amounts in leaves and fruit of kiwifruit. In addition,there was a significant positive correlation between quantitative and qualitative characteristics of kiwifruit and its leaf and fruit nutrients. Nitrogen, phosphorus and potassium have significant positive effect in increasing fruit yield.Chemical fertilizers leaching and environmental problems caused by the consumption of these fertilizers, motivated to useof organic fertilizers, such as vermicompost and azocompost in kiwifruit orchards, in order to produce fruit in north of the country.

Soil properties determination such as soil texture is an important tool for proper management, optimized and sustainable use of soil. The aim of this research is determination the soil texture, geometric mean and standard deviation of soil particles using images of MODIS sensor in the period of 2015-2016. After soil texture determination using hydrometer method, artificial neural network model have been used for soil properties determination using reflectance, thermal bands and indices of satellite images. The preprocessing is one the most important parts in the modeling process. In this research, the preprocessing of input parameters was based on the significance of correlation coefficient, using the constant number of input parameters and stepwise regression. Stepwise regression method has the minimum error which the RMSE decreasing rather to the significance correlation and constant input parameter methods for clay content determination was 22 and 18.6 percent, for sand determination 43.19 and 71.23 percent, for geometric mean determination 80.14 and 27.29 percent, for standard devotion determination 21.17 and 38.71 percent. Also, in the case of silt calculation, the RMSE decreasing rather to the constant input parameter method was 55.13 percent. The minimum of average criteria; RMSE, MAE and MRE for three different preprocessing methods was related to the sand particle, for example the average of MAE for clay, sand and silt was 1.74, 1,2 and 1,66 respectively. RMSE decreasing of sand, 27.77, using artificial neural networks indicted the better performance of method relative to the classical regression. Generally, the kind of input parameters and kind of modeling is important factors in the soil texture determination.

Iran has been considered as one of the arid and semi-arid regions of the world in terms of climatic conditions. Limited water resources and inappropriate management of them has fed the agricultural sector with significant challenges. Efficient usage of water in the field, requires accurate estimation of the plant’s water consumption. So far, many studies have been conducted in order to provide new methods for estimation of the reference evapotranspiration (ET0) using intelligent systems. In this study, in addition to evaluation of the efficiency of genetic programming (GP), other models are provided for estimation of evapotranspiration, which are using the minimum amount of meteorological variables. For this purpose, by use of the stepwise regression method, input variables of GP are selected among 7 meteorological variables (i.e., average air temperature, maximum air temperature, minimum air temperature, relative humidity, wind speed at two meters’ height, sunshine hours, and solar radiation). Moreover, eight conventional empirical models are used to compare the performance of empirical models with GP models in the estimation of reference evapotranspiration. In this study, the FAO Penman-Montieth method is considered as the reference method in evaluation of the performances of GP and empirical models. The obtained results show that the GP models have higher accuracy than empirical models. Finally for improving the performance of obtained results, the Bayesian model averaged method is used to combine the results of GP models and to determine their uncertainty bands.

Gully erosion is an important type of soil erosion worldwide with respect to sediment production and land degradation that also occurs in different parts of Iran (12, 19, 26). Topography and soil properties of gully's drainage catchment have important effects on gully's development (17). Various studies reported the effect of soil on gully formation (22, 25). The objective of this study is investigate hydrologic process of gully forming by topography index and determining of surface and subsurface soil properties effect on gully extension in 2 year and select the most important of this properties.
In order to determine the main hydrological process that shapes the gully's shapes three regions of Ardabil province were selected including Orta Dagh, Mola Ahmad and Sarcham, and topographic threshold of gully forming was determined as where S and A are the slope and area of gully's drainage catchment, respectively and a and b are the regional coefficients. Furthermore to investigate the effect of soil properties on gully's development in each region, a set of similar gullies were selected and the changes of volume and cross section area for gullies in four points along its length were measured as indicator of gully development during a two years time. 19 different soil properties at surface and subsurface layer of gullies and at drainage area were measured and their effects on gully development were investigated. At the end, stepwise regression models were introduced to predict the value of gully development and to determine the main factors of gully development in each catchment. Precision of regression models determined with R2 and RMSE.
The results showed that in Sarcham catchment b power of topographic threshold was positive, therefore piping and tunnel erosion seems to be main reason for gully forming but in two other catchments the values of b power was negative therefore runoff may have caused gully formation. Correlation analysis between soil properties and gully's development revealed that in Sarcham catchment erodibility and presence of soluble materials in subsurface layers of soil had a strong relation with gully development. While in other two catchments, erodibility of surface and subsurface layers of soil had similar effects on gully development. In general, application of regression models that used total data set of surface as well as subsurface as independent variable for prediction of cross sectional area changes showed higher precision. For prediction of changes of gully volume, total properties of drainage area were used as independent variables. The precision of this model in Orta Dagh and Sarcham was high but in Mola Ahmad was low. The results of stepwise regression model demonstrated that in Orta Dagh catchment, MWD, very fine sand, clay contents in surface layer, gypsum, calcium carbonate, dispersible clay in subsurface layer, percentage of plant cover and CROSS index in drainage area of gully, in Mola Ahmad catchment, organic carbon and bulk density at surface layer, dispersible clay content and SAR in subsurface layer, and mean slope of gully drainage area and in Sarcham catchment, bulk density and dispersible clay content in surface layer, bulk density, silt and sand contents in subsurface layer, and curve number (CN) and Miller shape index of gully drainage area had more meaningful effect on gully development.
Used topography index had careful for determining of gully formation hydrological process. In Sarcham gully's extension related to soluble material in subsurface soil but in two other region gully's extension related to erodibility of surface and subsurface soil. Between properties of gully catchment properties that effected on runoff volume had main effect on gully's extension.

One way to decreases the damage caused by cavitation in spillways is aeration flow using aerators. The required air flow of aerator is one of the most important factors in their design. In this study, to estimate the required air flow of spillway aerators four methods were applied including of stepwise regression, Pfister empirical method, neural network (based on Levenberg- Marquardt algorithm) and the combination of fuzzy-neural (ANFIS). In order to perform of modeling, 914 experimental data on physical model of Clyde Dam spillway and 12 data of Azad Dam related to conducted tests by Water Research Center on Azad dam hydraulic model were gathered. However, the performance and error of these methods were investigated after calculating the required air flow of aerators. The results showed that the combination of fuzzy-neural has the best performance with a root mean square error (RMSE) and correlation coefficient (R) about 0.0194 and 0.968, respectively. In addition, artificial neural network, stepwise regression and Pfister empirical methods had a root mean square error equal to 0.0538, 0.0596 and 1.98, respectively.

Saffron is one of the most important economic plants in the Khorasan province. Awareness of soil quality in agricultural lands is essential for the best management of lands and for obtaining maximum economic benefit. In general, plant growth is a function of environmental factors especially chemical and physical properties of soil (20). It has been demonstrated that there was a positive and high correlation between soil organic matter and saffron yield. Increasing the yield of saffron due to organic matter is probably due to soil nutrient, especially phosphorous and nitrogen and also improvement of soil physical quality (6, 28, 29). The yield of saffron in soils with high nitrogen as a result of vegetative growth is high (8). Shahandeh (6) found that most of the variation of saffron yield depends on soil properties. Due to the economic importance of saffron and the role of soil properties on saffron yield, this research was conducted to find the relationship between saffron yield and some soil physical and chemical properties, and to determine the contribution of soil properties that have the greatest impact on saffron yield in the Ghayenat area. This research was performed in 30 saffron fields (30 soil samples) of the Ghayenat area (longitude 59° 10΄ 10.37˝ - 59° 11΄ 38.41˝ and latitude 33° 43΄ 35.08˝ - 33΄ 44΄ 02.78˝), which is located in the Khrasan province of Iran. In this research, 21 soil properties were regarded as the total data set (TDS). Then the principal component analysis (PCA) was used to determine the most important soil properties affecting saffron yield as a minimum data set (MDS) and the stepwise regression to estimate saffron yield. To estimate the yield of saffron in stepwise regression method, saffron yield was considered as a dependent variable and soil physical and chemical properties were considered to be independent variables. According to the PCA method, among the 21 studied properties, 7 out of them including calcium, iron, zinc contents, sand, calcium carbonate equivalent percent, mean weight diameter of aggregates (MWD) and manganese (Mn) had the higher Eigenvalues. Therefore, the above properties were introduced as the most important soil properties in saffron fields. Calcium carbonate had the negative effect on the availability of micro-nutrients (26). Christensen et al. (15) found that by increasing the calcium carbonate in soil due to high pH and formation of insoluble components, the uptake of micro-nutrients is especially limited. The results of stepwise regression method (equation 1) showed that soil acidity (pH), zinc content, bulk density, MWD, iron content, salinity (EC), organic carbon and available potassium in soil were the most important properties that affect the yield of saffron, so that the determination coefficient (R2) of the regression model was high (Table 2) and it can explain 74% of the variation of saffron yield. Y = 6924.51 – 1187.31 pH – 89.65 EC + 71.6 Fe – 826.02 Zn + 471.55 OC, + 5490.96 K + 1353.56 BD + 752.82 MWD (1) where Y: saffron yield (kgha-1), pH: soil acidity, EC: electoral conductivity (dSm-1), Fe: iron concentration (mgkg-1), Zn: zinc concentration (mgkg-1), OC: organic carbon (%), K: soil potassium (%), BD: soil bulk density (Mgm-3), and MWD: mean weight diameter of aggregates (MM). Based on the absolute values of standard ß in the regression model (Table 3), pH value and then after Zn concentration had the most effect on saffron yield. In general, responses of different plants to soil pH is varied, and saffron grows satisfactory in pH = 7.8 (5). Soil pH influences the uptake of soil nutrients by plants (15), so that this parameter had the most effect on saffron yield and by increasing the soil pH, the yield of saffron decreases. According to the regression model, Zn concentration was the second parameter in saffron yield. Zn has the important role in structure of plant enzymes (30). After these 2 parameters, Bd, MWD, Fe concentration, EC, Organic carbon and K concentration in soil had more effect on saffron yield (Table 3). According to both PCA and regression methods, the concentration of iron and zinc and MWD were determined as the important and effective soil properties on saffron yield in the Ghayenat area. In addition, soil pH in stepwise regression method and calcium carbonate in PCA method were determined as the effective properties on saffron yield. Therefore, it is suggested that the parameters of Zn, Fe, and MWD along with soil pH and calcium carbonate which were regarded individually in two methods, were considered as the most soil properties in saffron yield.

Cation exchange capacity (CEC) is one of the main factors in monitoring and management of soil quality in order to achieve sustainable production. Although CEC can be measured directly, these measurements are difficult, time-consuming and costly, especially for aridisols. One alternative method is the use of pedotransfer functions (PTF), in which CEC of soils is estimated through available soil information. A dataset including 1141 data points was used for development and accuracy test of functions and another independent data set with 232 data points was used for validation of the PTFs. The results showed that, grouping the soils based on clay and organic carbon contents are generally reduced the coefficient of variation of clay, organic carbon and CEC. In general, the grouping of soils declined the correlation of clay and organic carbon with CEC. In soils with organic carbon contents of less than 0.5%, CEC was predicted with reasonable accuracy using only the clay. Generally, grouping the soils enhanced the accuracy of the PTFs, while in most cases decreased their reliability. However grouping the soils based on their clay content, except for soils with clay content ≥ 35%, was more effective in improving the accuracy and reliability of the functions. Clay (except for soil with clay content of ≥ 35%) was the most influential factor in predicting the CEC of the soils, so that including the other variables were not significantly improve the accuracy and reliability of the functions.

Soil organic carbon (SOC) storage estimation with high accuracy is very important due to having such prominent role corresponding to climate change and global warming reduction. Therefore، the current study was carried out in the beech mixed-stands forest of reserved compartment in Glandrood forests، in the northern parts of Iran. Stepwise regression method was used in order to present the SOC pool weight modeling considering the prominent soil physic-chemical properties at 3 different soil depths (0-10، 10-30، and 30-50 cm). The results of ANOVAs showed that exchangeable calcium content، lime and clay percentage (P < 0. 05) and also carbon content، soil organic matter، nitrogen and C/N ratio (P < 0. 01) were significantly different among the three soil depths. Moreover، on average، the total soil carbon stored considered as SOC pool weight in the ecosystem equaled 543. 87 ± 22. 07 t. C ha-1. According to the stepwise regression analysis C/N ratio was the effective content as explanatory variable to predict SOC pool (R2adj = 0. 44; SEE = 4. 8). In the following models، P، N and clay percentage، respectively، were the effective contents which improved significantly SOC pool prediction (R2adj = 0. 64-0. 83; SEE = 3. 8-2. 6). C/N ratio، P، N and clay were included 46. 3، 22. 41، 14. 71 and 9. 44 percent respectively with respect to the variations of SOC pool. Regression analysis indicated that introducing electrical conductivity، pH، and Mg as input variables in the following steps did not ameliorate the precision of SOC pool prediction clearly (R2adj = 0. 85-0. 89; SEE = 2. 5-2. 1). Furthermore، during the modeling and according to the collinearity diagnostic test، the result indicated that the maximum variance inflation factor of the models was less than 10 (VIF < 10)، validating them for application.

Rainfall and mean temperature are the most important meteorological parameters in determining distribution of other meteorological variables and also in climatic classifications. In addition, they have undeniable effects on human, natural and especially agricultural activities. Therefore knowing the value, variation and prediction of these parameters could result in precise management in different sectors. This research was done for the assessment of teleconnection indices, which affect the two mentioned parameters in Mashhad during the period of 1956-2010. The stepwise regression model with limited input was used for training and also evaluation data to derive monthly and seasonal prediction models. The statistical results showed that these models have the suitable performance to predict the rainfall and temperature parameters. In general, this performance was more appropriate for monthly models than seasonal ones, and also for temperature than rainfall.

Factor and principal component analyses are widely used in different sciences especially in agricultural science. To determine the factors that create variation between potato cultivars, in normal (non-stress) and water deficit (stress) conditions, two experiments were conducted in the form of randomized complete block design with three replications in summer 2002. Stepwise regression analysis showed that in normal conditions, stem length, number of stems/plant and leaflet width contributed significantly to yield. In stress condition, other than stem length and number of leaves/main stem, leaflet length also entered the model. As is evident, stem length had a detrimental effect on tuber yield in both stress and non-stress conditions. So, this trait could be used as an important criterion for the selection of high yielding genotypes. Principal component analysis revealed that number of stem, leaf length and leaf width were important traits creating variability between potato cultivars, especially number of stem that had high coefficients in the first principal for both environments. Factor analysis distinguished two factors in normal environment named leaf surface and structural attitude factors, and also two factors in stress environment called photosynthetic surface and structural attitude. Therefore, these factors should be intervened and attended to in breeding programs.

This research was conducted in order to determine the relationship between grain yield and yield components, using 30 common bean varieties in a randomized complete block design with four replications at the Agricultural filed faculty of Agriculture,Tehran university In this study 18 traits were assessed on 10 random plants from each plot. The result showed that there were significant differences among varieties in terms of trait under study, indicating the existence of genetic variation among varieties. Also results showed that the grain yield had a positive and significant genotypic correlation with number of seed/pod, pod weight, number of pod/plant, biological yield, days to flowering and maturity. Stepwise regression analysis showed that the maximum variation in grain yield could be attributed to the number pod/plant, number seed/plant, 100 seed weight and pod length. The results of path analysis showed that the highest direct effect, being positive, was related to number seed/plant and the lowest direct effect, which was related to number pod/plant. Factor analysis resulted in three factors that accounted for 78/7% of total variation. The first factor accounted for 38.39% of total variation and was designated as yield and yield component factor. This factor is comprised of pod weight, biological yield, grain yield and number of pod/plant traits.

In order to investigate the genetic diversity of morphological traits in 121 genotypes of lima, red and pinto beans, and to study the relation between characters, and to get better understanding of factors affecting the interaction between characters by multivariate analysis, an experiment was conducted in 1997 as a simple lattice design at the Research Station of Agricultural College, Isfahan University of Technology. High variability was observed among genotypes for all characters studied. The phenotypic coefficients of variability were greater than the genotypic ones for all the traits. Among the characters studied seed yield, number of pods per lateral branches and main stem, length of lateral branches and main stem, number of nodes per lateral branches and main stem, 100-seed weight and number of lateral branches had the highest genotypic and phenotypic variability, while days to maturity had the lowest variability. Traits relative to lateral branches were more variable than the same traits in the main stem. Factor analysis for all the genotypes and, separately, for lima, red, pinto, determinate and indeterminate genotypes revealed 4 factors which justified more than 78.4 percent of the total variation. The results of the analysis based on all the genotypes showed that the first factor was mostly correlated to days to maturity, length of main stem and lateral branches, number of nodes per main stem and lateral branches, which was named “vegetative factor”. The second and third factors had the highest correlations with number of pods per main stem and lateral branches, number of seeds per pod in the main stem and branches and 100-seed weight, which were named “yield component factors”. The fourth factor showed the highest correlation with number of lateral branches and was named for this trait. The first and fourth factors were related to vegetative growth characteristics and physiological source. The second and third factors were related to physiological sink. On the basis of stepwise regression analysis, number of pods per lateral branches was the most important component of yield, and number of pods per lateral branches and main stem had the next following ranks. Number of pods per lateral branches and main stem had the highest relationships with yield.

In order to evaluate the relationship between SDS-sedimentation value and breadmaking quality of wheat (Triticum aestivum L.), glutenin subunits of different genotypes (foreign and Iranian cultivars) were analyzed by reverse-phase high performance liquid chromatography (RP-HPLC). SDS-sedimentation value was used as an indirect criterion for breadmaking quality. Correlation coefficients revealed a closer relationship between low molecular weight (LMW) glutenin subunits and variation in SDS-sedimentation value. Principal component analysis confirmed the presence of association between some of the glutenin subunits and SDS-sedimentation value. Based on the stepwise regression analysis, two LMW and four high molecular weight (HMW) peaks (subunits) were selected which accounted for 70.2 and 18.7% of variability in SDS sedimentation values, respectively. On the basis of the results of the stepwise regression analysis, a discriminant function was developed. The great efficiency of discriminant function in correct classification of completely different genotypes (Iranian landraces and cultivars) showed that the observed relationship between glutenin subunits and SDS-sedimentation value has a genetic basis and the effects of LMW and HMW glutenin subunits on SDS-sedimentation value are additive. Therefore, it seems that this method based on more protein components (rather than only on HMW glutenin subunits) can be used to predict breadmaking quality of wheat against many genetic backgrounds.

In order to study the genetic and environmental variability of morphological and phenological characteristics and also to gain a better understanding of the morphological basis of yield variation in soybean, an experiment was conducted at Research Farm, College of Agric., Isfahan University of Technology, in 1996. The experiment was arranged in three augmented designs with 285 lines and 5 control varieties. There were highly significant differences among genotypes for all the characteristics studied. The phenotypic coefficients of variability were greater than the genotypic ones for all the traits although the differences for most of them were small. The highest phenotypic and genotypic coefficients of variation, in order of magnitude, were obtained for number of pods per plant, days to flowering, plant height, height of the lowest pod, and number of lateral branches. Heritability estimates and the percent of genetic improvement for these traits and seed weight were high but for seed yield per plant were low. The lowest coefficients of variability were related to number of seeds per pod and days to germination. The results of stepwise regression analysis indicated that the maximum variation in seed yield could be attributed to the number of pods per plant, number of seeds per pod, and 100-seed weight. Factor analysis in addition to emphasizing the importance of yield components, resulted in four factors which determined 97.34% of yield variation. These factors which were indicators of physiological sink and source, with respect to the traits contained, were named as photosynthetic sink, plant architecture, fixed capital, and weight, respectively. In general, it was concluded that in order to improve soybean cultivars, selection should be in favor of plants with stronger structure, higher number of leaves, nods and pods per plant, and higher seed weights.