ozone. Values are least squared means for n=3 ± standard error. ......................................50 Figure 2. Photosynthetic rate of two soybean cultivars treated with either charcoal filter or 70ppb ozone. Measurements were made at three different leaf positions. There were two experiments, the first one is represented with (A) and the second (B). Values are least squared means for n=3 ± standard error ....................................................................51 Figure 3. Stomatal conductance (gs) of two soybeangenotypes in CF and 70ppb ozone treatments. (A) is the stomatal conductance in the first experiment and (B) is that of the second experiment. Values are least squared means for n=3 ± standard error. .................52 Figure 4. Internal CO 2 (Ci) concentration of soybean leaves at three different leaf
Transcriptomics, enabled by advances in DNA sequencing and computation, is a powerful tool to identify gene expression differences and correlations with genetic/developmental cues or environmental conditions. Detailed studies have generated “transcrip- tomic atlases” for soybean gene expression [11–15]. However, studies have ignored soybean seed germina- tion, in favor of seed development or vegetative tissues (typically leaves or roots). In this study, we examined three soybean seed germination stages: (1) dry, mature seed; (2) imbibed seed; and (3) germinated seed and contrasted two soybeangenotypes which differ in their tolerance to the impact of elevated temperature on seed quality, using seed produced in two environments differing in abiotic stress: (A) a lower temperature, Midwest location; and (B) the high temperature condi- tions of the ESPS.
The more common response in crops plants is that as the VPD increases (atmospheric air becomes drier) transpiration increases linearly causing plans loose large amount of water (Sadok and Sinclair, 2009). The limited-transpiration trait has been identified in soybeangenotypes and they therefore expend less soil water under deficit condition and they use water more efficiently (Sinclair et al., 2008). Genotype PI 416937 has limited transpiration rates when the vapor pressure deficit (VPD) of the atmosphere exceeds about 2 kPa (Fletcher et al., 2007). Breakpoint values for PI 471938 were similar under nitrogen stress and recovery phases with values greater than two. These results however, are not consistent with the measurements of Fletcher et al. (2007) on greenhouse grown plants of PI 471938. It was shown that there was no expression of an increasing limitation on plant transpiration at high vapor pressure deficit. This difference could be due to the different soil type used in the studies. In the present study sandy soil was used; lower water retention capacity of sandy soil might cause closer of stomata and limitation of transpiration rate under higher VPD in PI 471938.
Soybean is an important oil seed crop. Pakistan is fronting the scarcity of edible oil because it is grown in a limited area. The present study was conducted for yield and quality improvement of the crop in the research site of the Department of Plant Breeding and Genetics, PirMehr Ali Shah Arid Agriculture University, Rawalpindi. The five genotypes of the soybean were treated with the Chemical mutagens viz. Sodium Azide and physical mutagens viz. gamma rays at different doses, to create genetic variation in the crop. For the current experiment the genotypic, phenotypic and environmental correlation was also determined to specify the correlation among yield related attributes. The result showed that germination percentage for the soybeangenotypes, in control was 40.33% (Table 2), while germination percentage for the genotypes in which the physical mutagens applied were high as compared to control. The results showed that with the rise in concentration of
Joint analysis of variance (Table 2) revealed a significant effect of genotypes (G), environments (E), and GxE interaction (P ≤ 0.01), which indicates contrasts between the environments and the occurrence of differential genotype performance over the environments. The presence of GxE interaction can be attributed to predictable factors such as soil type, pest and disease management, and unpredictable factors such as precipitation, temperature, and humidity in each environment. This can be confirmed by observing the characteristics of each environment (Table 1), which show differences regarding altitude, latitude, and longitude, as well as the climatic effects of precipitation and temperature provided by the different sowing times in the same place. Similar results were obtained by Carvalho et al. (2002), Silva and Duarte (2006), Rangel et al. (2007), and Peluzio et al. (2008), which also observed the presence of a GxE interaction for soybean yield in different regions of Brazil. The existence of the GxE interaction suggests the need to use adaptability and stability analysis since edaphoclimatic factors are the ones that most influence the adaptability and stability of soybeangenotypes.
With larger seed, higher soluble sugar content, lower oil percentage and higher protein content, vegetable soybean is increasingly popular. Edible quality of vegetable soybean was higher at fresh pod stage with higher soluble sugar concentration and better waxy (Tsou & Hong, 1991, Zhang et al., 2017). In order to add value to the soybean crop, soybean breeders are developing specialty soybeans with desired quality attributes for specific applications (Zhang et al., 2017). The seed size of matured vegetable soybean was generally larger than commercial soybeans, which could also be used as a special soybean types for soy food production. However, few studies assessed the nutritional value of vegetable soybean between fresh pod stage and mature stage. Previous studies only focused on the nutrition composition of fresh vegetable soybean seeds (Primomo et al., 2002), and no comprehensive research has been carried out to assess if fresh seeds are more nutritious. In this study, a field experiment was conducted and five vegetable soybeangenotypes were used to evaluate the nutritional compositions of fresh and matured seeds including protein, soluble sugar, oil, fatty acid, free amino
Several authors (Sadowsky et al. 1987, Reliæ and Sariæ 1988, Mrkovaèki et al. 1992, Hungria and Bihrer 2000) have studied the differences between Bradyrhizobium japonicum strains regarding their effectiveness with dif- ferent soybeangenotypes and their results have shown the differences of nodulation, yield, and total N accumu- lated in grains.
In the present investigation, the accumulation pattern of major fatty acids during seed development in five Indian soybeangenotypes (viz. NRC 37, Pb1, Shilajeet, JS 335 and LSb1) at various developmental stages was studied. The observations revealed intervarietal variation in accumulation of different fatty acids except linolenic acid content. Varieties with similar fatty acid composition in mature seeds exhibited different accumulation patterns. The results indicated that total variation for fatty acid composition depended largely on the degree of desaturation from oleic acid to linoleic acid. This may be attributed to the lower activity of fatty acid desaturase that converts oleic to linoleic acid. Latent genetic variation for fatty acid composition observed in developing soybean seeds of different varieties may be exploited by the plant breeders for developing varieties with desirable fatty acid composition.
Calculation of observed average length of stomata on soybeangenotypes indicates that generally at field capacity moisture content and water content of 80-100% FC all genotypes were not significantly different, whereas the water content of 60- 80% FC and 40-60% FC showed consistently longer stomata in genotype 3 and 6 are than in the other genotypes with values of 23 µmand 18 µm, respectively (Table 5). This is consistent with study of Lestari (2006) which states that the genetic character of stomata that determined the level of plant adaptation to a dry environment and lower stomatal density are a potential genetic character to increase tolerance to water deficit. Dickison (2000) that ionization radiation can cause a change in the palisade cells, spongy cells or an increase or decrease in the vascular tissue, any changes in anatomy are generally accompanied by changes in physiological activity.
the analysis of canonical variables (Cruz et al., 2012). The analysis of canonical variables was performed for the traits TGW, GY, GW10P, PH, NP1G, NP2G, and NP3Gassessed in 11 soybeangenotypes grown during the 2013/2014 agricultural year. The variation of genotypes was expressed through the first two canonical variables, which CV1 was responsible for 68.70% and CV2 for 16.31% of the total variation. Graphically expressed scores through the canonical variables CV1 and CV2 are exhibited in Figure 1, with the formation of four groups. Thus, the first group was composed by the genotypes 1 (NS 5445 IPRO) and 2 (NS 6211 RR); the second group was composed by the genotype 9 (CD 2611 IPRO), which presents early maturity cycle, indeterminate growth habit and belongs to the maturity group 6.1; the third group was composed by the genotype 10 (CD 2585 RR), which presentssuper-early cycle, indeterminate growth habit, maturity group 5.8, and it is indicated for high and cold regions;andthe fourth group was composed by the genotypes 3 (TEC 6029 IPRO), 4 (TEC S 13/03 RR), 5 (6458RSF IPRO), 6 (6160RSF IPRO), 7 (DON MARIO 5.8i), 8 (DON MARIO 5.9i), and 11 (TMG 7161 RR).
Notably UFU-002 genotype was the only one classified in Ideotype VI. The response pattern in this group considered maximum yields in favorable environments and the mean yield in unfavorable ones. This Ideotype was a modification of the original method proposed by Rocha et al. (2005) and it allowed a more biological sense to the analysis as the addition of intermediate ideotypes avoids comparisons with extreme genotypes . Preceding the graphical analysis of genotypes dispersion, a principal component analysis as shown in Table 6 was carried out. Based on values from Table 6, the first two principal components could explain 62.82% of the total variation. Similar values were obtained by Barros et al. , when evaluating the adaptability and stability of 29 soybeangenotypes in 6 environments. They reached 67% of total variation explained by the first two components.
Abstract: Gypsum and cow manure potential as ameliorant to increase crop production under salt stress or saline condition. This research aimed to learn the effect of gypsum and cow manure on the uptake of Na, K and the yield of soybeangenotypes under saline condition. This research conducted in green house Jatikerto Experimental Farm Faculty of Agriculture, Brawijaya University, from June to September 2014. The research was arranged in a split plot design. The main plot was soybeangenotypes consists of two saline susceptible varieties (G1 = Wilis and G2 = Tanggamus) and two saline tolerant genotypes (G3 = genotype IAC, 100/Bur//Malabar and G4 = genotype Argopuro//IAC, 100); sub plot was ameliorant application consists of A0 = without ameliorant; A1 = cow manure (20 t/ha); and A2 = gypsum (5 t/ha). The results of the research showed that Leaf Chlorophyll Index in susceptible varieties and tolerant genotypes were increased with ameliorant application. Accumulation of proline and K/Na ratio in susceptible varieties higher than tolerant genotypes. Ameliorant application on tolerant genotypes increased grain yield higher than susceptible varieties.
For studies of selection of soybeangenotypes tolerant to water deficit involving germination and vigor tests, several authors have observed the effectiveness of these tests in conditions of low osmotic potential simulated with polyethylene glycol (PEG), applying from the use in studies of genetic diversity, differentiation and grouping of soybeangenotypes most tolerant to water stress (Teixeira et al., 2008a,b); as well as to the relationship of genotype response during this test with its performance on the field, and subsequently with productivity (Kosturkova et al., 2008).
Eleven late maturing soybeangenotypes and two checks , were planted for evaluation in three replication on five locations for two consiquetive cropping seasons 2016-2017 in Ethiopia. All genotypes were obtained from IITA (International Institutes of Tropical Agriculture (Nigeria) in 2014/2015 as shown in Table1 except control varietys. The testing locations were; Pawe, Asossa (Benishangul Gumuze Regional States), Areka (SNNP) Regional State and Bako and Jimma (Oromia Regional States), located in Western, North Western and Southern parts of Ethiopia from 2016-2017.These locations represent the major Soybean growing areas of the country and are characterised by medium to long growing season with maximum rain fall . In all locations, genotypes were planted in Randomized. Completely Block Design (RCBD) in three replications with 2.4 m × 4m plots size at spacing of 1.5m, 60cm, 60cm and 5cm between block, plots, rows and plants respectively. In each season, experimental plots were kept free of weed following recommended agronomic practices with a fertilizer rate of 100kg/ha bases. Grain yield and Yield related traits. Data were collected on: Days to flowering (DF 50%), Days to maturity (DM 95%), Plant height (PH (cm)), Number of branch per plant (NBrch), Number of pod per plant (Npp), Number of seed per pod (NSpp), Hundred seed weight (HSW (g)), Seed moisture content (SMC (%)), Stand count at harvest (SCH ) and plot yield (g/plot). A combined analysis of variance to assess the significance of GEI was carried out before computing the yield and yield-stability statistics (YSi). Shukla’s Stability Variance and Kang’s Yield - Stability (Ysi) Statistics were calculated according to (Kang, 1993). All analysis were carried out using, SAS 9.3 version, R- version 3.1.2 (R-Core Team, 2014), Meta R (GEA-R). Following the detection of significant GEI, YSi statistics for 11 G were calculated as described by Kang (1993)
In vermiculite medium the growth rate and consequently accumulation of plant biomass was quite different than that obtained by soil treatments for both soybean varieties at 10 weeks except in case of that treatment included P1 671 when inoculated with CB 1809, since the obtained data were much close in both soil and vermiculite. That means that the host plant P1 671 proved to be more promiscuous to rhizobial strain CB 1809 in both used media soil and vermiculite in contrast to the pattern happened with Williams. These data are in agreement with those obtained by Abd El-Maksoud and Keyser (2010). Generally, in contrast to soil treatment Williams was almost equally productive with strain CB 1809 as with strain 123. As clearly seen, P1 671 was much more productive with strain1809 than with strain 123 (13.58 and 3.88 g/plant, respectively. The reaction between the host and the symbiont still continued even in case of competition between the two strains (12.81 g/plant). In other words, the data of competition treatment look like the data of Williams inoculated with strain 123 alone and P1 671 inoculated with CB 1809 alone. In this respect, the data obtained by Kosslak et al. (1983) indicated that interaction which occurred during the early period of infection between soybean host and its microsymbiote are perhaps the most critical for competition among R. japonicum strains. The role of the host in determining the outcome of competition among strains was highlighted by the different rate of biomass accumulation for both strains interacted with each host. These results suggested that the early events in infection and nodulation are important in competition among B. japonicum strains.
compositions of soybean oils were affected depending on the boron dosages and showed differences among soybean cultivars. These differences can be probably due to cultivar, genetic factor, boron level, and soil- enzyme interactions. Under 2 mg B/kg supply, the increase in B concentration was associated with an increase in the protein and oleic acid content (except cv. Türksoy) and decrease in linoleic acids. This sug- gests that B application regulates the seed composition by influencing the fatty acid content and seed protein (Bellaloui et al. 2010). B applications resulted in a level significant increase in the oleic acid ratio of cv. 13935, leading to a 17.92% increase.
Experiments were performed on irrigated area at the Ab- sheron Experimental Station of the Research Institute of Crop Husbandry. Research targets include different soy- bean (Glycine max (L.) Merr.) genotypes contrasting in height, architectonics, duration of vegetation, producti- vity and other morpho-physiological traits, Rannaya-10, Bystritsa, Volna, VNIIMK-3895, Komsomolka, Provar, VNIIMK-9, Plamya, Biyson and Visokoroslaya-3 were used (Table 1). The genotypes were short-stemmed (40 - 55 cm), medium-stemmed (60 - 70 cm), and high-stemmed (80 - 115 cm) with low productivity (2 - 2.3 t·ha –1 ), me-
expensive energy, can contribute to better partitioning of assimilates between grains, favoring an increase in the size and for the weight of these grains, thus contributing to soybean yield.Study by Souza et al. (2014), point out the potential of soybean is due to leaf expansion, solar radiation interception, assimilation, conversion efficiency and entrainment of assimilates to reproductive structures. The Pearson correlation is revealed (r = 0.539) intermediate and positive towards thousand grain weight and grain yield. Phenotypic estimates of cause and effect in path analysis, are reliable due to the high value obtained in the coefficient of determination (0.859) and low residual effects (0.376). This way, highlights the importance of identifying the direct and indirect effects between the explanatory character and the main parameter in soybean with indeterminate growth habit. The method of Singh aims to determine the relative contribution of agronomic characters that influence the differentiation of genotypes (Table 2.). Thus, it is noteworthy that the dissimilarity of 10 soybeangenotypes is grounded in characters, number of legumes per node in branches with 57.50% contribution, followed by number of branches with 12.30% and number of nodes per branch 11.66%.However, studiesby Almeida et al. (2011), show that the variable hundred grain weight reveals 26.56% of contribution to the genetic similarity between the soybeangenotypes.
Dong D., Peng X., Yan X. (2004): Organic acid exudation induced by phosphorus deficiency and/or aluminium toxicity in two con- trasting soybeangenotypes. Physiologia Plantarum, 122: 190–199. Garland J.L., Mills A.L. (1991): Classification and characteriza- tion of heterotrophic microbial communities on the basis of patterns of community-level sole-carbon-source utilization. Applied and Environmental Microbiology, 57: 2351–2359. Jaeger C.H., III, Lindow S.E., Miller W., Clark E., Firestone M.K.