SOYBEAN OIL YIELD АS AFFECTED BY THE GROWING LOCALITY IN AGRO-CLIMATIC DIVERGENT YEARS

The subject of this study are two-year results of the oil yield of six NS soybean genotypes, 0 and I maturity group (MG) at two growing localities (Rimski Sancevi and Sombor in Serbia). Sombor had higher oil yield than Rimski Sancevi (by 119 kg ha -1 , i.e. 15.97%). In the locality of Sombor, in 2010, of the oil yields were statistically significantly higher (1.088 kg ha -1 ) compared to 2009 (640 kg ha -1 ), which is higher by 448 kg ha -1 or 70% of the average oil yield in 2009. The average oil yield, for all tested genotypes at both locations was 805 kg ha -1 , and ranged from 745 kg ha -1 (Rimski Sancevi) to 864 kg ha -1 (Sombor). At both sites significantly higher oil yield was recorded in 2010 compared to 2009. The highest average oil yield at both sites was achieved growing genotype Sava (840 kg ha -1 ). Sava had highest oil yield (887 kg ha -1 ) and Balkan (902 kg ha -1 ) in locality of Sombor. On average for both genotypes and growing localities I MG had higher average oil yield of 29 kg ha -1 ( 3.67%) than 0 MG genotypes. Realizing the potential for soybean productivity depends on genetic factors, the cultural practice implemented, meteorological conditions and the growing localities.


INTRODUCTION
Global importance of soybean (Glycine max.(L.) Merr.) is continually growing, with soybean planted areas reaching almost 113 million ha in 2013 (FAO).Areas and yields have had a growing tendency (and hence higher production) in recent years, in our country and abroad.For the production of soybean in addition to high yield, technological quality of grain is also essential (Popovic, 2010;Miladinovic et al., 2008;Popovic et al., 2014;Glamoclija et al., 2015).
According to these indicators, soybean is the most important industrial plant worldwide, both as a basic source of protein nutrients, and as the most important source of plant oil, Miladinovic et al., 2008.Both protein and oil contents are in part determined by additive gene action, with heritability values ranging from medium to high (Rodrigues et al, 2014).The oil fraction of soybean represents 20% of the seed dry mass and is primarily (95%) used for edible oils.The remaining soybean fractions are used to create a variety of industrial products, such as fatty acids, soaps and biodiesel (http://www.soyatech.com/soy_facts.htm).Soybean oil contents approximately 11, 4, 23, 54, and 8 % palmitic (16:0), stearic (18:0), oleic (18:1), linoleic (18:2) and linolenic (18:3) acid, respectively.The amounts and relative proportions of each fatty acid are important factors, as they affect the flavour, stability, and nutritional value of the oil (Katan et al., 1995).For example, saturated acids have been shown to increase low density lipoprotein (LDL) cholesterol levels as well as the risk for coronary heart disease (Wilson, 2004), and high levels of polyunsaturated fatty acids can cause rancidity and undesirable odours.In particular, oleic acid is less susceptible to oxidation during storage and frying.Therefore, decreasing the levels of saturated (16:0 and 18:0) and polyunsaturated fatty acids (18:2 and 18:3) and increasing the levels of monounsaturated acids (18:1) has been the goal of many studies aimed at improving edible soybean soils (Priolli et al, 2015).
In plant populations, variation in the expression of a quantitative trait is due to both genetic and environmental variability and an interaction between the two.Variation due to genotype by environmental interaction (G x E) that stems from differences in ranking of genotypes among environments reduces heritability and makes it difficult to obtain good estimates of genotypic breeding value.Given that such interactions occur, the plant breeder is faced with decision: which environments should be used for testing and how many are necessary for adequate genotypic evaluation.The two questions are linked because often the number of necessary environments is dependent upon the kind of environments chosen.A related approach to this problem is to study genotype response to environment and in so doing characterize genotypes according to their performance under a given set of environmental conditions (Miladinovic et al, 2008;Popovic et al., 2015).
Environmental variation can be considered as a continuum from predictable to unpredictable (Allard and Bradshaw, 1964).Predictable variation results from those conditions which are controlled in some way (greenhouse, irrigated).Unpredictable variation is usually weather related (Miladinovic et al, 2008;Popovic et al, 2014).
The aim of this study was to determine the productivity soybean oil yield of varieties in the regions of Sombor and Rimski Sancevi-Novi Sad (Serbia), in agro-climatic divergent years.

MATERIAL AND METHODS
Examination of six genotype productivity soybean oil yield during divergent examination years was on the experimental field of the Institute of Field and Vegetable Crops, Novi Sad, at the Rimski Šančevi and Agriculture Technical Service Sombor, experimental field Toplana; in Serbia, where experiments were performed during 2009 and 2010 on the chernozem meadow soil type.Four soybean varieties from the 0 maturity group (Galina, Valjevka, Becejka and Proteinka) were used as material, as well as two varieties of the I maturity group (Balkan and Sava), which are at the same time the current assortment (Popovic et al., 2012(Popovic et al., , 2013) ) in Republic of Serbia, tab.1.
Fields trials were designed as a randomized complete block design (Rimski Sancevi and Sombor) with 3 replications using plots of 10 m2 (Popovic et al., 2012(Popovic et al., , 2013)).Sowing was carried in the first half of April, with microexperiments planter on 50 cm row spacing.Microbiological preparation NS Nitragin was applied during sowing.Crop density was 500,000 plants per hectare for the 0 maturity group and 450,000 plants per hectare for the I maturity group.Crops were harvested mechanically on September 2009 and 2010 at localities of Rimski Sancevi and Sombor.Yield was measured after harvest and average samples were taken from each trial replicate to determine oil content in grain.Total oil content in grain (Popovic et al., 2012(Popovic et al., , 2013) ) was determined by infrared spectroscopy technique on the apparatus PERTEN DA 7000, NIR/VIS Spectrophotometer, employing non-destructive method.The data used for the calculation of the oil yield was total grain yield with accounting plot per replication and oil content percentage.Experimental data were analyzed by analytical statistics, using the statistics software package Statistica 12 for Windows.The significance of differences among the mean values of different factors studied in the paper was tested by adapted two-way ANOVA.All evaluations of significance were made on the basis of the LSD test at 5% and 1% significance levels.

Weather conditions
The data from Rimski Sancevi (Novi Sad) meteorological station was used for the analysis of weather conditions.The total amount of precipitation for the studied period was 478 mm and ranged from 271 mm (2009) to 684 mm (2010), Fig. 1.During 2009 average air temperature was 18.41ºC, which was 0.51°C higher (Popovic et al., 2012) than the average temperature in 2010, Fig. 1.The data from Sombor meteorological station was used for the analysis of weather conditions.The total amount of precipitation for the studied period was 476 mm and ranged from 258 mm (2009) to 694 mm (2010), Fig. 2.During 2009 average air temperature was 19.45ºC, which was 1.4°C higher (Popovic et al., 2013) than the average temperature in 2010, Fig. 2. In contrast to 2009, monthly precipitation distribution during the humid 2010 was more favorable and it reflected on soybean plants up growth and contributed to the achievement of higher oil yields.

Oil yield in soybean grain
Year and genotype had a statistically significant effect on the oil yield (p <0.01) at locality Rimski Sancevi.The oil yield average of all genotypes of soybean in 2009-2010, amounted to 745 kg ha -1 .The highest average of oil yield had genotype Sava (792 kg ha -1 ), followed by genotypes Valjevka (780 kg ha -1 ) and Becejka (753 kg ha -1 ), Tab. 2. Genotype Sava, at locality Rimski Sancevi, had significantly higher oil yield, statistically, compared to genotypes Galina, Proteinka and Balkan.Statistically significantly higher oil yield, had genotype Valjevka compared to genotypes Proteinka and the Balkan as well as genotype Becejka in relation to genotype Balkan.In 2010, of the oil yields were statistically very significantly higher (794 kg ha-1) compared to 2009 (697 kg ha-1), which is higher by 97 kg ha-1 or 13.92% compared to 2009, Tab. 2.
In the locality Sombor, year had statistically significant effect on the oil yield (p <0.01).In 2010, of the oil yields were statistically significantly higher (1.088 kg ha -1 ) compared to 2009 (640 kg ha -1 ), which is higher by 448 kg ha -1 or 70% of the average oil yield in 2009.The oil yield average for all soybean genotypes in 2009-2010 amounted to 864 kg ha -1 .The highest average oil yield genotype Balkan (902 kg ha -1 ) followed by genotype Sava (887 kg ha -1 ) and Becejka (886 kg ha -1 ), Table 2, Fig. 3.In the locality of Sombor genotype and interaction genotype x years had statistically significant effect on the oil yield, Table 2, graph.3.If we compare locations of growing, it is evident that the location of Sombor the oil yield was higher for 119 kg ha-1, or 15.98% compared to location of Rimski Sancevi, Novi Sad, Table 2, Graph.3.