extfieldsofstudy: Medicine Biology

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Duroc boars have lower progeny mortality and lower fertility than Pietrain boars1

Abstract In pig production, Pietrain and Duroc lines are often used as terminal sire lines to produce crossbred slaughter pigs. The objective of this study was to identify the differences in paternal fertility and mortality during the suckling period of crossbred progeny from Pietrain and Duroc terminal sire lines. In total, 87 purebred Duroc boars and 68 purebred Pietrain boars were used as terminal sires to produce 1,823 crossbred Duroc litters (D-litters) and 1,705 crossbred Pietrain litters (P-litters) in two production herds. The sows were crosses between DanBred Landrace and Yorkshire (F1). All boars were kept at the same artificial insemination (AI) station, and all semen doses were produced in the same laboratory. The experiment was balanced according to herd, boars, and time, with approximately 13 sows from each herd mated to each boar within each breed. The results showed higher fertility expressed as litter size at birth in P-litters compared with D-litters led to 0.5 higher total number born (TNB) for P-litters (P = 0.0076). However, piglet mortality including number of stillborn piglets was lower in D-litters compared with P-litters (P < 0.0001), and 5 d after farrowing, the average litter size in P-litters ranged 0.4 below the litter size in D-litters (P < 0.027). At 21 d after birth, mean litter size in P- and D-litters were 14.5 and 14.9 piglets per litter, respectively (P < 0.015). This indicated that Pietrain progenies were weaker than Duroc progenies, and it was concluded that use of Duroc boars as the terminal sire line led to lower piglet mortality. In the two herds, the mean piglet mortality rate including still born piglets ranged from 19.5% to 23.6% and from 17.6% to 19.1% in P- and D-litters, respectively.

INTRODUCTION
In commercial pig production, the number of weaned pigs is a key factor to increase productivity and, therefore, selection for increased litter size has been a part of the breeding program in many female lines of different breeding companies. Selection strategy on the maternal genetic effect of litter size has resulted in a considerable increase in litter size (Sorensen et al., 2000;Su et al., 2007;Nielsen et al., 2013). The maternal genetic effect of fertility, litter size, and piglet survival has been considered in a number of quantitative genetic studies (Sorensen et al., 2000;Grandinson et al., 2002;Knol et al., Pedersen et al. 2002;Su et al., 2007Su et al., , 2008. Selection for the paternal effect on litter size has got less attention even though it has been shown to affect the number of piglets per litter significantly at birth and weaning (Rahnefeld and Swierstra, 1970). The heritability estimates from terminal sires were found to be 3-fold lower than for dam (van der Lende et al., 1999). Nevertheless production results showed that the differences in litter size vary by two piglets for the 10% top boars to the 10% bottom boars (Roca et al., 2015) and individual boars and genetic line of boars affects farrowing rate and total number of born piglets (Sonderman and Luebbe, 2008;Broekhuijse et al., 2012). It has been reported that the terminal sire influence on the genetic variances of litter size ranged from 2% to 5% (Hamann et al., 2004;Wolf and Wolfova, 2012). However, the effect on litter size and mortality during suckling period affected by the terminal sire lines used are unknown and have never been identified experimentally in production. Especially for use of Duroc or Pietrain, which are frequently used as terminal sires lines in commercial production of crossbred piglets, it is important to obtain a high number of weaned piglets and a low mortality rate. The object of the study was to identify the differences in paternal fertility and mortality during the suckling period of crossbred progeny from Pietrain and Duroc boar lines used as terminal sires.

Experimental Design
Sires from 87 purebred Duroc and 68 purebred Pietrain were used as terminal sires to produce 1,823 Duroc crossbred litters (D-litters) and 1,705 Pietrain crossbreed litters (P-litters) in two production herds denoted M and A. The litters were produced by 966 and 1,194 sows in two herds M and A, respectively. Numbers of litters, sows, boars, and the different traits of litter size in each herd are shown in Table 1. Some boars (13 Duroc and 8 Pietrain) were only used in one herd. The Duroc boars came from the DanBred Duroc population and were available for commercial production. The Pietrain boars were imported from Germany to Denmark and were all purebred boars from the German Piétrain line, available for the market for commercial production. To increase the genetic variances, boars that were full sib brothers were avoided. The Pietrain and Duroc boars were hosted at the Danish artificial insemination (AI) station Hatting-Viborg in two different sections and semen doses were produced according to Danish commercial standard of single sire doses (the semen doses contained a minimum of 1.5 billion motile sperm cells and was diluted in an ethylene-diamine-tetra-acetate extender). All used semen doses were produced and distributed from the same laboratory.
All sows were crossbreds between DanBred Landrace and DanBred Yorkshire (F1). Due to practical reasons first parity litters were not included in the study, and thereby the parities of the sows ranged from 2 to 7.
The experiment setup was a sire experiment designed to be balanced according to herd, boars, and farrowing group. Thereby, most of the sires were used in both herds (Table 1). Within a week after weaning of previous litters, the sows were randomly selected for boar breed and single sire mated with either Pietrain or Duroc boar. In herd A, a farrowing group of about 27 sows were inseminated each week with semen from about two boars from each sire line, and in herd M, a farrowing group of 54 sows were inseminated each second week with semen from about three boars from each sire line. The experimental inseminations were performed from 8 January 2014 to 9 September 2015. Every boar delivered semen doses over a period of about 5 wk and the Pietrain and Duroc boars had an equal production time. Several of the sows were included in the experiment with more than one litter in the experimental period. The proportion of sows that delivered 1, 2, 3, and 4 litters were 53%, 24%, 15%, and 8%, respectively.
During gestation, dead sows, repeated breeders, and sows having abortions were recorded. Farrowing rates were obtained as the ratio of farrowing sows in relation to mated sows.
At farrowing and within the first 12 h, the total number born (TNB) and number of still born piglets, including dead piglets in premature lifetime, were recorded. Within 1 d after birth and after colostrum intake, all piglets were earmarked using an individual identification number. In total, 57,977 piglets were earmarked. To avoid loss of identification, each pig was earmarked in both ears with two copies of same identification number. After Litter size of Duroc and Pietrain boars individual identification of the piglets, the litter size were equalized and each sow was assigned 13 or 14 piglets to nurse. The number of piglets assigned to each sow depended on management in the two herds. Equalizing the number of piglets per sow was done by moving piglets between litters (if needed), and it was allowed to do so during the whole suckling period. Furthermore, to avoid weak piglets, weight loss, or death of piglets, all commercial management tools were allowed, e.g., use of cross fostering. The movements of piglets to foster mother were not recorded and cross-fostering between offspring of the two sire lines might occur. All recordings of litter size at day 5 and at day 21 were recorded for the biological mother of each piglets and no information was recorded on the nurse sow (foster mother). During the suckling period, up to day 21 after farrowing, all dead piglets were recorded and assigned (according to the earmarks of piglets) to the biological mother and thereby also to the terminal sire of the piglets. For all dead piglets, the age of dead piglets was recorded. Litter size at 5 d after farrowing (LS5) of each biological mother was calculated as the TNB minus the number of stillborn and dead piglets up to day 5 after farrowing. Similar, the litter size at 21 d after farrowing (LS21) of each biological mother was calculated as the TNB minus the number of stillborn and dead piglets up to day 21 after farrowing. The mortality rate up to day 5 (MORT5) after farrowing was calculated as (TNB − LS5)/TNB in each litter and the mortality rate during suckling period up to day 21 (MORT21) was calculated as (TNB -LS21)/TNB in each litter. The difference in number of litter recordings of TNB and LS5 in Table 1 reflect either missing observations for TNB or differences in recorded number of piglet born alive and the number of earmarks assigned to each biological mother.

Statistical Analysis
Litter size and mortality recorded up to day 21 after farrowing were analyzed using a univariate linear mixed model, i.e., in which y pbhig denotes the values of either TNB, LS5, LS21, MORT5, or MORT21 recorded for each litter; µ denotes the overall mean of the trait, par p is the fixed effect of parity p, breed b is the fixed effect of sire line b (Duroc or Pietrain), herd h is the fixed herd effect, (breed × herd) bh is the herd and breed interaction effect, is the random group effect of farrowing group g, and e pbhig ∼ N(0, σ 2 e ) is the random residual effect. The sire effect s i was nested within the breed effect breed b , and the group effect g g was nested within herd effect herd h . This implies that significance of breed effect and interaction between breed and herd effect were tested against the sire effect. All random effects were assumed to be independent and independent of fixed effect levels.
The model above was also extended to include two different variances, σ 2 s,b related to each of the two sire lines, i.e., s i ∼ N(0, σ 2 s,b ). Farrowing rate was analyzed as a binary trait using generalized linear mixed model of the form: in which for each litter, pbhig, an underlying liability of farrowing was assumed. A probit link function f (y pbhig ) = Φ −1 y(y pbhig ) was introduced and y pbhig is the probability of success, and Ф is the cumulative Gaussian distribution. Success was obtained for sows that farrowed and failure was obtained for inseminated sows that failed to farrow. In the model above, μ is the overall mean, par p is the effect of parity p, breed b is the effect of sire line b (Duroc or Pietrain), herd h is the herd effect, s i is the sire effect, g g is the effect of farrowing group g. Significance of the breed effect and interaction between breed and herd effect were tested against the sire effect.
For all dead piglets, the age of dead was analyzed using a univariate linear mixed model, i.e., in which y pbhijgk was the age of dead of piglet k in the litter within the sire i and the dam j. The other effects are designated as in the models above.
All models were applied to data by using PROC MIXED or PROC GLIMMIX in SAS Enterprise Guide 7.1.

RESULTS
Across the two herds, the two terminal sire lines obtained the same mean farrowing rate of 92% whether the sows were inseminated with Duroc semen or Pietrain semen (Table 2). When Pietrain boars were used as the terminal sire line, the TNB per litter was significantly higher (18.7) than when Duroc boars (18.2) were used (P = 0.0076; Table 2). The Pietrain boars produced 0.5 more TNB than Duroc boars. However, the higher TNB of the Pietrain sires (18.7) did not result in higher number of piglets at day 5 after farrowing (14.8) compared with Duroc (18.2 and 15.2). The litter size at day 5 was slightly significant higher in Duroc (P = 0.027), and at day 21, the litter size of Pietrain progeny litters (14.5) was significant less than litter size in Duroc progeny litters (14.9, P = 0.015).
Regardless of the sire line used, the significance of the herd effects for the traits of TNB, LS5, and LS21 shows different production levels in herds A and M (Table 2, P < 0.0001 in all three traits).
The traits of MORT5 and MORT21 show that significantly more piglets of Pietrain progeny died (P < 0.0001) and the interaction between breed and herd was significant on day 5 after farrowing (P = 0.0004). The interaction between breed and herd was also significant for MORT21 showing different mortality rates in the two herds M and A (P = 0.0048; Table 2). In herd A, the mortality in P-litters took place earlier than in D-litters: the mean ages of death were 3.2 and 3.9, respectively. In herd M, the opposite pattern was observed, and the mean ages of death were 4.6 and 4.2 for P-litters and D-litters, respectively ( Table 2). The reduction in litter size during the suckling period shows that during the first day after farrowing, the mortality of Pietrain piglets were higher than for Duroc piglets (Figure 1). The following suckling days up to 20 d after farrowing, the difference in mortality between Pietrain and Duroc progeny vanished.
The lower mortality in D-litters reflected by higher survival rate was linear for all levels of TNB and ranged from 12 to 26 TNB (Figure 2). The survival rate in D-litters was about 3% higher than in P-litters ( Figure 2). As indicated in Figure 2, survival in P-litters for the small litters with 14 TNB was about 85%, while survival in Duroc was about 88%. In large litters with 24 TNB, the survival in Pietrain and Duroc was 73% and 76%, respectively ( Figure 2). Although the Pietrain sires produced a higher number of piglets at birth and the survival rate thereby might be lower, Figure 2 however shows lower survival rate of Pietrain progeny than for Duroc progeny, independent of TNB.
At sire level, there was no clear relation between predicted boar levels of TNB and MORT21 (Figure 3). It shows that the higher mortality rate in the Pietrain progeny was independent of TNB. Nonsignificance is indicated by "ns".
Litter size of Duroc and Pietrain boars However, Figure 3 also indicates three extreme (outlier) boars. Two Duroc boars showed low levels of predicted TNB and one Pietrain boar showed a high predicted level of MORT21. The extreme boars increase the variances especially for TNB. The highest variance in litter size was obtained at birth for TNB obtaining 12.50 (Table 3). After birth, the variances were decreased to 10.18 for LS5 and 10.13 for LS21. The decrease in variances might relate to the decrease in mean litter size during the suckling period ( Table 2). The highest variance due to sire effect was obtained also at birth for the TNB at 0.66 covered 5.3% of the total variance. After birth and during the suckling period, the sire effect decreased more than the total variances, and at day 21, the sire variation accounted for 3.8% of the total variation in litter size (LS21 ; Table 3). During the same period, the variance of environmental farrowing group effect increased from 0.2% to 1.4%. However, the variance affected by the farrowing group was still of minor level compared with the residual variance that cover most of the environmental variance in litter size.
Using the model which allows for individual variances within Pietrain boars and within Duroc boars (i.e., sire variances i ∼ N(0, σ 2  s,b )), the variance of TNB between boars were found to be higher for Duroc (1.11) and lower for Pietrain (0.10) ( Table 3). The higher variation in TNB between Duroc boars compared with the variance between Pietrain boars was clearly shown in the probability plot of the boars (Figure 4). In the probability plot, the boars are ranked from the lowest to the highest predicted level of TNB, and if the predicted boar levels follow a trend line, then they will be Gaussian distributed, which was assumed in the models. Boars which deviate from the trend line deviate from the expected Gaussian distribution and increase the sire variance. Variations between Pietrain boars have a minor effect on TNB; however, for Duroc, a much larger variation between boars in TNB was found. The two lowest ranked Duroc boars obtain a litter size about two piglets below the expected mean level for TNB and the two second lowest Duroc boars ranked 0.5 piglets below the expected mean level for TNB (Figure 4). Except from extreme Duroc boars, the probability plot runs two parallel trend lines indicating Gaussian distribution of both boar lines. The two lowest ranked Duroc boars were already shown to have low litter size in Figure 3. After birth, the difference in variances between Pietrain and Duroc sire lines decreased to 0.30 and 0.23 for LS5 and 0.30 and 0.19 for LS21, respectively (Table 3).

DISCUSSION
This study shows the differences in levels of TNB and LS21 when using the two terminal sire lines in a cross breeding program in two commercial production herds. In both herds, a higher mortality rate was found for Pietrain progeny compared with Duroc progeny. The highest mortality rate was found in herd A and here the mortality rate decreased 4.5 units of percentage from 23.6% to 19.1% by use of Duroc compared with use of Pietrain as the terminal sire line (Table 2). In the other herd with a lower mortality, the mortality rate only decreased 1.9 units of percentage from 19.5% to 17.6%. The different effect on  mortality rate from the two sire lines in the two herds demonstrated the significant interaction between sire lines and herd. The mortalities until day 5 and until day 21 were significant higher in herd A compared with herd M (P = 0.0004 and P = 0.0048; Table  2). Thereby, the study clearly demonstrates how the choice of sire line have a practical impact on piglet mortality, and especially in a herd with high mortality, the differences between P-litters and D-litters were highest and in favor by use of the Duroc sire line.
The results in this study depend, to a certain extent, on the choice of sires within each sire line. The Duroc line was the DanBred Duroc line, available for commercial herds in Denmark. The Pietrain line was the German Piétrain and this line is among the most used in Germany. The Pietrain boars were transported to Denmark and placed in a Danish AI station where semen was collected and treated according to normal standards in Denmark. The main aim was to use boars reflecting the population average and most importantly to use boars that were unrelated to each other to get as much genetic variation as possible. However, it was not possible to find completely unrelated Pietrain boars when importing 68 different boars. This was easier in the selection of Duroc boars as these were selected arbitrary among the 260 boars at the AI station.
The farrowing rates in our study were about 92% and no difference in farrowing rates for the two sire lines were observed (Table 2). High farrowing rate was expected as no first parity sows were included in the experiment. Low farrowing rates has been observed in first parity sows (Koketsu et al., 1999). Farrowing rate is mostly associated to the sow lines and different lines have different level of farrowing rate; however, also the choice of sire within breed affects the farrowing rate (Sonderman and Luebbe, 2008;Broekhuijse et al., 2012;Roca et al., 2015). Sonderman and Luebbe (2008) reported that farrowing rates ranged from 71.8% to 92.2%, having the lowest rates for Landrace, Yorkshire, and Duroc and the highest rate for Meishan boars. Broekhuijse et al. (2012) found a variation in farrowing rate of 5.3%, which could be explained by the boar. Farrowing rate is also strongly affected by management (Koketsu et al., 1997;King et al., 1998), and the herds in this study were commercial pig producers having a high production level.
The proportion of service sire variances of TNB, LS5, and LS21 ranged between 0.038 and 0.053 with the highest variation at birth for TNB and the lowest variation at week 3 for LS21 (Table 3). Previous studies showed that the proportion of genetic sire variation ranged from 0 to 0.05 (van der Lende et al., 1999;Hamann et al., 2004;Wolf and Wolfova, 2012). However, the sire variances in this study were on a high level (0.053 on TNB) and it might be due to high fertility of the sows and the high number of TNB. Wolf and Wolfova (2012) showed service sire variance on 0.03 for TNB at about 12 piglets per litter. In our study, mean litter size ranged between 18.2 and 18.7 in D-litters and P-litters. All sows were crossbred sows of DanBred Landrace and Yorkshire and they all had  high female fertility and large phenotypic litter size, which increased the experimental variance of litter size, and thereby increased the sire fertility variance to be significant, and it also obtained a higher ratio compared with other studies. This study showed that Pietrain boars used as terminal sire resulted in higher TNB than for Duroc boars, and thereby the Pietrain boars are shown to be more fertile than Duroc boars. It indicates that Pietrain semen had higher fertilization ability than semen of Duroc boars. Previous studies have shown that boars of unknown breeds perform different with respect to TNB (Broekhuijse et al., 2012;Roca et al., 2015). One study found that 5.9% of the variation in TNB was boar or semen related (Broekhuijse et al., 2012). Roca et al. (2015) reported a variance of TNB ranging from 1.51 to 1.86 in boars from commercial AI station. The variation of TNB within D-litters was higher than for P-litters and the larger variation for Duroc was mostly affected by two Duroc sires (Figure 4). Two of those sires obtained mean levels lower than two piglets per litter below the expected mean of TNB ( Figure 4). Numerous studies have investigated the causes of low fertility on an individual level and semen quality in Duroc (Xu et al., 1998;Popwell and Flowers, 2004) and found that genetic defects like reciprocal chromosomal translocation (Kuokkanen and Mäkinen, 2008;Rodríguez et al., 2010;Quach et al., 2016) could be one reason for low litter size. Fertility problems caused by low semen quality can be minimized by finding the boars with defects of the semen by objective semen analyses like computer-assisted motility analysis (CASA). The CASA measurements correlate with field fertility results (Holt et al., 1997;Broekhuijse et al., 2012). However, boars with genetic defects like reciprocal chromosome translocation show no phenotypic signs (Kuokkanen and Mäkinen, 2008). Thereby, the TNB in litter size for each boar is the only reliable response of the male fertility. In this study, it was not possible to investigate why some Duroc boars had lower fertility. This study showed that Pietrain boars used as terminal lines resulted in higher TNB than use of Duroc boars. However, the LS5 and LS21 for D-litters were higher than for P-litters. Furthermore, in herd A, the Pietrain progeny died at a younger age than the Duroc progeny. The mean age of death of Pietrain and Duroc were 3.2 and 3.9 d, respectively, indicating that the Pietrain progenies were weaker at birth than Duroc progenies (Table 2). In herd M, the opposite pattern was observed, and the mean age of death were 4.6 and 4.2 d for Pietrain and Duroc progeny, respectively. The study shows that already at birth and in the first primal lifetime of piglets, the Duroc progenies were more robust with better vitality than Pietrain sire progenies (Figure 1). A large number of studies have shown that the sires affect the litter size (Chen et al., 2003;Hamann et al., 2004;Wolf and Wolfova, 2012). However, a comparison between different breeds of terminal sire lines has never been studied for piglet mortality or piglet survival. It was surprising that the significant difference in TNB (P = 0.0076) of 0.5 piglets at farrowing in benefit for Pietrain boars were equalized during the suckling period and ended up with slightly significant (P = 0.015) 0.4 more piglets per litter at day 21 produced by Duroc boars (Table 2). It shows that the mortality rate was larger in P-litters than in D-litters in the same time frame. The total mortality rate obtained as means of the mortality rates in all litters during the suckling period, including still born piglets, range from 19.1% to 23.6% in herd A and from 17.6% to 19.5% in herd M in Duroc and Pietrain progeny, respectively (Table 2). One reason could be that high TNB levels lead to lower average birth weight and thereby the causes of mortality increases due to weakness at birth, starvation, or crushing (Strange et al., 2013). The unfavorable relation between TNB and mortality has been shown in many studies (Högberg and Rydhmer, 2000;Damgaard et al., 2003). It is however important to mention that in these studies, only maternal effect of TNB was considered.
The higher piglet survival for progenies of Duroc was well demonstrated by all litter size levels in the experiments (Figure 2). The different piglet mortality rate shown for Pietrain and Duroc progeny was observed irrespective of litter size (TNB) and no relation between TNB and MORT 21 was found on boar level (Figure 3).
Low mortality rate is important for the commercial pig production to increase productivity and to increase animal welfare. The different survival rate of progeny from the two sire lines of Duroc and Pietrain showed that breeds might be different and that choice of breed can be a useful tool to increase animal welfare. Since the breeds were obtained from different breeding programs, it furthermore demonstrates that breeding can be a useful tool to increase animal welfare (Kanis et al., 2004).
In summary, the farrowing rates were on the same level for the two terminal sire lines obtaining a level at 92% for both breeds. Use of Pietrain boars as the terminal lines led to higher TNB than use of Duroc boars obtaining 18.7 and 18.2 TNB per litter, respectively. The Duroc progeny showed higher survival rate than Pietrain sire progeny. The LS5 and LS21 for D-litters were even higher than for P-litters. At day 21 after birth, the mean litter size in P-and D-litters were 14.5 and 14.9 piglets per litter, respectively. The mean mortality rate obtained as means of the mortality rates in all litters during the suckling period, including still born piglets, ranged from 19.5% to 23.6% and from 17.6% to 19.1% in P-and D-litters, respectively. The service sire variances of litter size, i.e., TNB, LS5, and LS21 ranged between 3.8% and 5.3% with the highest variation in TNB.


provenance: AgriculturalAndFoodSciences,2019-2020-Agricultural And Food Sciences-2019.jsonl.gz.gz

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METHODS OF POTASSIUM CONTENTS EVALUATION IN THE SUBSTRATE SOLUTION AND GERBERA LEAVES

Several authors report that potassium (K) is the nutrient absorbed in greater amounts by gerbera plants. Thus, objective of determining the concentration of Kin the solution of substrates for growing potted gerbera, quantified by the Cardy Horiba C-131 and an atomic absorption spectrophotometer, correlating them with each other and with the plant leaf content. The experiment was carried out in a greenhouse. The experimental design was in randomized complete blocks using a 5x2 factorial scheme (5 substrates/2 cultivars) and four replications. Cherry and Red gerbera cultivar seedlings with four final leaves were transplanted into pots, filled with the substrates and acclimated for 30 days. After acclimation, the K content in the substrate solution extracted by the “PourThru” methodology was evaluated every two weeks using the Cardy Horiba C-131 portable ion meter and an atomic absorption spectrophotometer, as well as the K content in the leaves of gerbera plants. The solution of the commercial substrate had higher concentrations of K in relation to the others in the two forms of measurement, which is directly related to the high initial contents of the nutrient in this substrate. This is directly related to the high initial K contents in the substrate. Greater values of K were obtained for the cultivar Red, both in the solution and in the leaves. The K concentration of the substrate solution cultured with gerbera plants quantified by the Cardy Horiba C-131 and atomic absorption spectrophotometry correlated significantly with each other and showed a low correlation with the content of this nutrient in the plant tissue.

Introduction
Gerbera is an herbaceous plant, perennial, with capitulum inflorescence, belonging to the Asteraceae family and original from South Africa (Mercurio, 2002). It's internationally known as cut flower, flower in vase and garden lining (Jeong et al., 2009). Recently introduced in Brazil, it still needs several studies that contribute for its productive quality .
The species has been more largely studied as cut flower, there being only little available information about cultivation techniques as flower in vase and, thereby, the management of the production factors is realized several times in an empirical way by farmers (Ludwig et al., 2010), mainly fertilization (Guerrero et al., 2012).
Potassium (K) is an essential nutrient for plant growth and development (Schachtman e Shin, 2007). Its importance in photosynthesis, osmoregulation, enzymatic activation, protein synthesis and maintenance of the anion/cation equilibrium in plants is well documented (Marschner, 1986;Bhandal and Malik, 1988;Zhao et al., 2001;Kanai et al., 2007). Potassium deficiency can result in negative impacts, such as reduction of photosynthesis, transpiration (Degl'innocenti et al., 2009;Kanai et al., 2011) and sucrose transport from leaf to consumer organs such as flowers (White and Karley, 2010), and leaf area reduction (Zhao et al., 2001;Gerardeaux et al., 2010). These impacts, integrated, can result in even bigger losses in floriculture due to possible damages in the agricultural products appearance, considerably depreciating it and compromising its commercialization.
The most demanded macronutrient by Gerbera plants is K, being essential its supply, mainly in the flowering stage (Mercurio, 2002;Ludwig et al., 2008;Guerrero et al., 2012;Guerrero et al., 2016). It becomes important to study methods of K content determination in Gerbera plants, due to its high importance in species growth and development. In this context, quick tests are potential alternatives (Mota et al., 2014).
Short cycle plants cultivated in reduced volume vases, being the case of Gerbera, need frequent nutritional supply to result in a recommended aspect for commercialization. For the production success, it's important to realize the nutritional evaluation in the most agile way possible. To test the substrate solution is a precise way to monitor the real rhizosphere nutrition, thus helping in the fertilization program adaptation (Yao et al., 2008).
Knowing quick and practical methodologies of ionic concentration determination of the substrate solution is important to make less onerous the verification of the available nutrients for plants. Among the available methods for solution obtaining, stands out the "PourThru", that's based in the solution volume displacement, in order to obtain samples of available nutrients for plants in the lixiviated solution (Cavins et al., 2000). It's a nondestructive method and there is the possibility of realization even in the greenhouse.
Allied to this, there is the perspective of quick tests utilization with good accuracy through the use of portable measurers, resulting in a relevant time and costs reduction for the nutritional monitoring of the applied nutritive solution. According to Mota et al. (2014), quick tests present high correlation with nitrogen and K amounts in Gerbera leaves, presenting the advantages of practicality and determination speed, making easy the substrate fertility monitoring by farmers and technicians, and making it easier to take eventual decisions on fertilization adjustments.
Thus, the present work was conducted with the aim of determining the K concentration in substrate solution in Gerbera cultivation in vases, quantified by Cardy Horiba C-131 and atomic absorption spectrophotometry, and to correlate them with each other and with the leaf content.

Material and Methods
Experiment was conducted in greenhouse covered in arch with transparent plastic of 150 µm of thickness, laterals of white grille, transparent plastic curtain and paved with concrete, with total area of 168 m² (7.0 x 24.0 m) and 2.6 m of height. Average temperature and relative air humidity inside greenhouse were 23 °C and 63%, respectively, during experimental period.
The experimental design used was randomized blocks, applying factorial schedule 5x2 (5 substrates and 2 cultivars) with 4 reps. The 5 substrates used were elaborated with different formulations (Table 1) composed by red soil (RS) from Oxisol; pine bark (PB); granulated coconut fiber (GCF); mixed coconut fiber (MCF); composition 1 (C1) corresponding to 40% pine bark + 30% vermiculite + 30% carbonized rice hull; composition 2 (C2) with 75% pine bark + 25% pine aciculae and the commercial substrate with 70% pine bark + 15% peat + 15% vermiculite. Substrates were selected through chemical and physical analyses previously realized. Chemical characteristics of electrical conductivity (EC) and pH 1:5 (Brasil, 2007) and macronutrients and micronutrients 1:1,5 (Sonneveld and Elderen, 1994), as well as the physical characteristics of density (Brasil, 2007) and water retention (De Boodt and Verdonck, 1972), C/N relation and organic matter (Raij et al., 2001) of substrates are presented in Table 2. It was used Gerbera seedlings (Gerbera jamesonii L) with four definitive leaves, from Cherry and Red cultivars, belonging to series "Dark Eyes" from Sakata® company. They were planted in vases with capacity for 1 L (11.5 cm of depth, 13 cm of superior basis and 9 cm of inferior basis), being previously fulfilled according to the humid density of each used substrate, until total vase volume, allowing particle accommodation.
Plants were acclimated during 30 days, disposed on wooden bench (1.2 m of width, 12.0 m of length and 0.8 m of height) under removable thermo-reflective mesh (Aluminet®) disposed in the superior part of the greenhouse, keeping luminous intensity close to 25,000 lx. Finished the acclimation, vases were spaced 25 cm by 25 cm and distributed on two wooden benches, under maximum luminous intensity of 50,000 lx. Experimental evaluation started immediately after acclimation period, being the results presented in days after acclimation (DAA).
Fertigation management was based on the daily weight of the vases. Necessary amount for water reposition was stablished by difference between the obtained mass in vase capacity (100% of available water) and the minimum retention capacity, or permanent wilting point. From these measurements, it was assessed the corresponding mass to 50% and 25% of the available water in the vase.
Fortnightly, substrate solution was collected using "PourThru" methodology (Cavins et al., 2000), adapted by Ludwig et al. (2017), to quantify K content. For this determination, it was used the Cardy Horiba C-131 portable ions measurer and atomic absorption spectrophotometry, being the values expressed in mg L -1 . New Gerbera leaves, completely expanded, were collected at the same period and in the same vases in which it was applied the above mentioned method. Leaves were washed, dried in drying oven with forced ventilation at 65 °C until constant weight, milled in miller type "Wiley" and analyzed for K content, following Malavolta et al. (1997) methodology.
Obtained results were submitted to variance analysis by F test and means compared by Tukey test at 5%, when significant, using the Sisvar statistical program (Ferreira, 2011). Correlation was stablished by the Pearson method between K contents obtained by different methodologies.

Results and Discussion
Potassium content obtained in substrates solutions, using the portable measurer Cardy Horiba C-131 (Table 3) and with the atomic absorption spectrophotometer (Table  4), varied significantly in all analyzed epochs.   Average K contents obtained by Cardy Horiba C-131 didn't show interaction of cultivars with substrates, while K content obtained by atomic absorption spectrophotometry showed this interaction in evaluated dates, except for 29 DAA. The highest concentrations were found in solution of the substrate 5 (commercial), which relates directly with the most elevated total content determined in substrate 5, corresponding to 176.25 mg L-1 (Table 2).  The initial substrate fertilization is a fundamental factor for the nutrient management supplied during the culture cycle and, specifically, for the cultivation in commercial substrate (Table 2), for which the nutritive solution can be reformulated, reducing the K concentrations. The validity and consistency of this affirmation are evident when analyzed the leaf content of K ( Table 5) that didn't show significant increase in plants conducted in this substrate, compared to the others. In research with substrates for citrus rootstock production, Schäfer et al. (2006) verified the variation in results that different commercial substrates can result, when evaluating two commercial substrates. Red Cherry In reproductive stage, evaluations about K content in nutritive solution demonstrated that K concentration was elevated, and even with the EC management of the nutritive solution, its values in the commercial substrate were much higher than the others (Table 3 and 4), reaffirming the importance of the adequate initial fertilization to prevent problems by excess of nutrients. Specifically for the Gerbera cultivars Red and Cherry, it wasn't registered excessive absorptions that could result in visual symptoms of plant toxicity, but yes, huge amounts of daily applied K in fertigation and not used by plants.
In spite of the higher K concentration in substrate solution were found in substrates in which Red variety was cultivated, in most part of evaluations with both methodologies, leaf content of this cultivar was higher ( Table 5). The higher content, however, is due to the lower average dry leaf mass production (Table 5) from Red cultivar plants (11.76 g) in relation with that from Cherry (13.38 g), resulting in bigger K content. These data lead to infer that the substrate evaluation must come together with the vegetal tissue analysis, for the correct comprehension of the interaction between substrate and plant.
The K contents in Gerbera leaves are variable in literature. In study developed by Damasceno et al. (2011), K content in Gerbera leaves cultivated in vases and fertigated with treated effluents from domestic sewage, with and without mineral fertilization, varied from 39.11 to 40.23 g kg -1 , with no visual symptoms of deficiency of this nutrient in leaves. According to Mercurio (2002), the adequate K content for Gerbera is found in the range between 30.6 e 36.4 g kg -1 , being these values inferior to the observed (Table 5). However, this nutrients requirement difference among Gerbera cultivars, and among vase and cut plants, contribute to the observed variation, being that Mota et al. (2014), in work with Gerbera of vase, Cherry cultivar, registered average K content of 43 and 38 g kg -1 , at 28 and 56 days after spacing, respectively. The utilization of the nutritional referential standard for Gerbera production can induce to errors in fertilization management, being that visual injuries in floriculture are extremely harmful in comparison with other crops. Therefore, it becomes relevant the determination of the ideal nutrient ranges for each type of production, cultivar, or cultivars with similar characteristics.
The average relation between the obtained values by Cardy Horiba C-131 and by atomic absorption spectrophotometry is showed in Image 1. It's observed that the majority of the values are situated between 100 and 200 mg L -1 . Both methodologies presented K values very close to each other, even when the contents were higher. Correlation between K content from substrate solution measured with Cardy Horiba C-131 and atomic absorption spectrophotometry was highly significant (r²: 0.95**), validating the determination methodologies. Evaluating tomato plants, Folegati et al. (2005) also registered high correlation between K contents measured with the portable ion measurer Horiba (CIM) and with flame photometry, highlighting that the use of portable measurer is adequate to evaluate the ionic concentration in soil or substrate solution, giving quick and precise measurements for the fertigation management. The use of portable extractors is viable, with acceptable precision, speeding up the nutritional disequilibrium diagnose and helping with the fertigation management (Silva et al., 2003).
The portable measurers are being used for different purposes and are obtaining success in its objective. In vines, there was significate correlation between K content in the chemical analysis of the petiole with the K content in the petiole sap obtained with the help of a portable measurer (Tecchio et al., 2011). In Gerbera plants, quick tests showed high correlation with total N and K (Mota et al., 2014).
Low significate correlations were registered between leaf nutrient content and substrate solution quantified by Cardy Horiba C-131 (r²: 0.38*) and from vegetal tissue and substrate solution quantified by atomic absorption spectrophotometry (r²: 0.39*). Mota et al. (2014), however, obtained high correlation (r²: 0.72) between K from extractive solution with Cardy Horiba C-131 and K content in aerial part of Cherry cultivar. The differences between the results can be explained by the initial K values in substrate, mainly in substrates 2 and 5 ( Table 2) that previously provided a huge part of the plant nutrient demand, impairing the correlation between the obtained by "PourThru" method and the one found in vegetal tissue. Other factors can jeopardize the reading fidelity through the portable measurers, such as the K forms applied (Kallenbach, 2000) and the interference of other ions, which can affect the possible correlations with contents found in plants by the traditional methods, there being the necessity of equipment calibration at each measuring. For vines, Tecchio et al. (2011) concluded being possible the use of portable measurers of N and K, provided that there are studies to improve the calibration of them. Thus, the use of portable measurers is a viable alternative to determine the K content in substrates solution, being an easier and quicker method than the standard one. However, there is still the necessity of new studies with other portable measurers in Gerbera plants.

Conclusion
The K concentration of the substrate solution from Gerbera cultivated in vase, quantified by Cardy Horiba C-131 and by atomic absorption spectrophotometry, correlated positive and significantly with each other, but showed low correlation with the leaf K content.

Author Contribution
F.L. 0000-0001-6641-9996 , A.C.G. and G.A.F. 0000-0001-9498-0676 : conception and design of the research, conducting of the research, obtaining data, analyze and interpretation of data, statistical analysis, write of manuscript; D.M.F.: advisor, conception and design of the research, analyze and interpretation of data, write and critically analyses of manuscript; V.P.: data analyze, write and critically analyses of manuscript.


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Eﬀect of HEDTA, CDTA, and EGTA Chelates on Remediation of Pb- and Zn-Polluted Soil Using Sunﬂower and Canola Plants: A Case Study; Soil Around Bama Pb and Zn Mine

Background: The soil heavy metal pollution caused by human activities is one of the main environmental problems. Therefore, it is necessary to use appropriate methods to remediate such soils. Objectives: The current study aimed at investigating the eﬀect of Hydroxyl Ethylene Diamine Tetra Acetic Acid (HEDTA), Trans-1,2-Cyclohexylene Dinitrilo Tetra Acetic Acid (CDTA), and Ethylene Glycol-bis ( β -Aminoethyl Ether)-N,N,N’,N’-Tetra Acetic Acid (EGTA) chelates on Pb and Zn removal eﬃciency in a Pb and Zn contaminated soil. Methods: Thisresearchwasperformedasafactorialexperimentinarandomizedcompleteblockdesignwithfourlevelsof chelate applicationrate(0,1.5,3,and4.5mM/kgsoil),twolevelsof chelatesapplicationtimes(oneortwoweeksbeforeharvesting),andplant types (sunﬂower or canola). At the end of the experiment, Pb and Zn concentrations in the soil and plant samples were measured and the least signiﬁcant diﬀerence (LSD) test was employed to determine the diﬀerences between the means. Results: The eﬀectiveness of studied chelates on the availability of Zn and Pb in soil was in the descending order as HEDTA > CDTA > EGTA. Application of 4.5 mM/kg soil of CDTA chelate under sunﬂower and canola cultivation caused a signiﬁcant increase in the availability of Zn and Pb in soil compared to 1.5 mM/kg by 12.2% and 13.3%, respectively. Plant type also had a signiﬁcant eﬀect on increasing the availability of Pb and Zn in soil. Conclusions: Planttype,applicationrate,andchelatestypeareimportantfactorsonremediationof heavymetalsincontaminated soil. However, the role of physicochemical properties of soil on its heavy metal availability cannot be ignored. The current study aimed at investigating the eﬀect of type, time, and application rate of chelates on Pb and Zn uptake by canola and sunﬂower in a heavy-metal-polluted soilnearBamaPbandZnMineintheSouthwestof Isfahan, Iran.

Background
Soil contamination with heavy metals is a major environmental problem that can threaten human health and ecosystems (1). Soil contamination with heavy metals is controversial, since heavy metals cannot be decomposed. Conventional methods are used to reduce soil contamination with heavy metals such as physical separation, chelate application, and electrochemical processes (2). Extraction and phytoremediation of soil heavy metals are considered as useful strategies due to their low cost and high efficiency; although the role of soil physicochemical properties on soil remediation cannot be ignored. Lead (Pb) and zinc (Zn) are toxic heavy metals that their accumulation in environment can damage human health (3). Thus finding suitable methods to remediate soil contaminated with heavy metals seems necessary.
Phytoremediation is an approach for heavy metals remediation that can extract heavy metals from soil and transfer them to the plant biomass (4). In this regard, it is necessary to select the plants with high biomass that can accumulate high amounts of heavy metals in their areal parts (5,6). On the other hand, chelate application can increase heavy metal availability by forming complex with heavy metals. Jian et al., suggested that application of EDTA chelates may be an effective strategy for phytoremediation with two Arundinaria bamboos in Pb-contaminated soil (7). However, the type and amount of chelates play an important role on increasing heavy metal concentration. Banaaraghi et al. also showed the role of EDTA (ethylene diamine tetra acetic acid) and EDDS (ethylene diamine-N, N'-disuccinic acid) chelate for enhanced corn phytoextraction of heavy metals from a contaminated soil (8). Thus, it is necessary to investigate the role of different chelates on Copyright © 2019, Jundishapur Journal of Health Sciences. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited. increasing heavy metal concentration in different plants constantly.

Objectives
The current study aimed at investigating the effect of type, time, and application rate of chelates on Pb and Zn uptake by canola and sunflower in a heavy-metal-polluted soil near Bama Pb and Zn Mine in the Southwest of Isfahan, Iran.

Treatments
The current study was conducted to investigate the effect of HEDTA (hydroxyl ethylene diamine tetra acetic acid), CDTA (trans-1,2-cyclohexylene dinitrilo tetra acetic acid), and EGTA (ethylene glycol-bis (β-aminoethyl ether)-N,N,N',N'-tetra acetic acid) chelate on Pb and Zn removal efficiency in a Pb and Zn contaminated soil near the Bama Pb and Zn Mine located in the Southwest of Isfahan. Treatments (48 treatments in three replication) included the application of 0 (M 0 ), 1.5 (M 1.5 ), 3 (M 3 ), and 4.5 (M 4.5 ) mM/kg soil of HEDTA, CDTA, and EGTA chelates (9) twice (one (T 1 ) or two (T 2 ) weeks before harvesting the plant) (10). The plants used in the current study were sunflower and canola.

Soil and Plant Analysis
Soil samples were taken from the fields around the Bama Mine (located in the 20 km of Southwest of Isfahan) and their physicochemical properties were analyzed (Table  1) (11). Then 5 kg pots were filled with soil, and canola (Brassica napus L.) and sunflower (Helianthus annuus L.) seeds were planted. Chelates were added to the soil at the mentioned rate 7 -8 weeks after planting. At the end of the experiment, the available concentrations of Pb and Zn in soil were measured according to the method described by Lindsay and Norvell (12). The Pb and Zn concentrations in plants were measured using atomic absorption spectroscopy (AAS) model 3030 after extraction with 3 mL of hot 14.4 M nitric acid (13). Soil pH and EC were measured in a 1:5 (W/V) solution. The organic carbon (OC) was measured according to the Walkley and Black method (14). The particle size analysis was also performed (15).

Translocation Factor
The translocation factor (TF) was calculated using the following formula (Equation 1) (16): (1) T F = C shoot Croot where, C shoot and C root are heavy metal concentrations in the shoot and root of the plant, respectively.

Statistical Analysis
The current study was conducted as a factorial experiment in the layout of randomized complete block design. The ANOVA was used for the statistical analysis of data. The least significant difference (LSD) test was also utilized to determine the differences between the means.

Results
Application of organic chelates had no significant effect on soil pH ( Figure 1). The effectiveness of different chelates on the availability of Pb and Zn in soil was in the descending order as HEDTA > CDTA > EGTA. Application of 4.5 mM/kg of soil HEDTA relative to EGTA chelate under sunflower cultivation (two weeks before plant harvest) resulted in increased availability of Pb and Zn in soil by 8% and 12.2%, respectively (Table 2). It should also be noted that the role of plant type on changing the availability of Pb and Zn in soil should not be ignored, since the application of 4.5 mM/kg of soil HEDTA chelate (two weeks before planting) in soil under the cultivation of sunflower caused a significant increase in the availability of Pb and Zn in soil in comparison with those of canola by 8.3% and 11.1%, respectively.
Regardless of the chelate type, chelate application rate was also effective in changing the availability of Pb and Zn in soil; therefore, application of 4.5 mM/kg of soil CDTA chelate could significantly increase the availability of Pb and Zn in the soil under cultivation of sunflower and canola compared to those of 1.5 mM/kg by 12.2% and 13.4% per kg of soil, respectively. In addition, application of the same amount of CDTA chelate resulted in increasing the availability of Pb and Zn in soil by 8.4% and 10.8%, respectively. According to the results of the current study, chelate application time had no significant effect on the availability of Zn and Pb in soil.

2
Jundishapur J Health Sci. 2019; 11(4):e92000.   Tables 3 and 4, the type and amount of applied chelates had a significant effect on root Pb and Zn concentration. The highest root Pb and Zn concentration belonged to the plants grown on the soil treated with 4.5 mM/kg of soil HEDTA chelate two weeks before harvesting, while the lowest was related to the plants cultivated in the soil without receiving chelate.
The application rate of HEDTA chelate had a significant effect on root Pb and Zn concentration; therefore, the use of 3 mM/kg of soil HEDTA chelate two weeks before harvesting resulted in an increase in root Pb and Zn concentrations compared to those of 1.5 mM/kg by 9% and 4%, respectively. The effect of chelate type on root Pb and Zn concentration was also significant; therefore, the highest and the lowest root Pb and Zn concentrations were observed when HEDTA and EGTA applied, respectively. The effect of chelate application time on root Pb and Zn concentrations was also significant ( Table 3).
Regardless of chelate type and its application time, plant type played a pivotal role in root and shoot Pb and Zn concentrations; so that the highest and lowest plant Pb and Zn concentrations belonged to sunflower and canola. The highest shoot Zn and Pb concentrations were observed in plants grown on the soil receiving 4.5 mg/kg of soil HEDTA chelate two weeks before plant harvesting (Tables  3 and 4), while the lowest values were in the soil without receiving chelate. The highest and lowest effects of the chelate type on shoot Pb and Zn concentrations were related to the use of HEDTA and EGTA chelate, respectively.
The highest Pb translocation factor (TF) value was observed in the soil treated with 4.5 mM/kg of soil HEDTA chelate two weeks before sunflower harvest (Table 5), while the lowest of that belonged to the soil without receiving any chelate. The Pb TF value was greater in the soil under cultivation of sunflower than canola. Based on the results of the current study, the time factor had no significant effect on Pb TF value. The same trend was observed for Zn TF value in the current study.

Discussion
Based on the results of the current study, chelate application can play an important role in increasing the availability of heavy metal in soil and, subsequently, uptake them by plant. However, the type and the amount of chelate application are important factors in changing the availability of heavy metal in soil; although, the role of soil physicochemical properties on the availability of soil heavy metals should not be ignored. The bond between organic compounds and heavy metals can prevent heavy metal precipitation and increase the solubility of that in the soil by the formation of organic complexes; although the increase of heavy metals absorption by the   plant may reduce the plant biomass (17,18). Thus, selecting plants that produce high biomass and are capable of heavy metal uptake is a useful method in phytoremedia-tion processes (19,20). Fatahi Kiasari et al., investigated the effect of sulfuric acid and EDTA chelate on shoot Pb concentration of sunflower and corn and concluded that using EDTA chelate had an important role in increasing plant Pb concentration and can remediate Pb-polluted soil that confirm the current study results clearly. They also reported that plant biomass significantly decreased with increasing shoot heavy metal concentration (21) that is similar to the current study results (data not shown). Mehmood et al. reported the similar results that were in agreement with those of the current study (22). Baghaie investigated the effect of EDDS chelate on increasing Cd phytoremediation efficiency by corn (Maxima Cv) in a soil treated with municipal waste compost and concluded that applying EDDS chelate had significant effect on remediation of Cdpolluted soil. However, the role of soil physicochemical properties in the available concentration of heavy metals was not investigated (17). The study by Abbas and Abdelhafez reported that using chelate had a significant relationship with increasing the concentration of heavy metals in the biomass of the aerial parts of corn plant (23). It is noteworthy that adding chelate to the soil may increase the solubility of heavy metal and, thereby, transferring it to the groundwater (24,25). Thereby, the role of chelate application on increasing heavy metal concentration in soil should be constantly investigated; although the concentration of heavy metals in soil is highly dependent on the soil physicochemical properties and the type and amount of contaminating element. As mentioned earlier, the highest and the lowest soil Pb and Zn availability and subsequent uptake by plant were related to the HEDTA and EGTA chelate, respectively, which is related to the strength of the bond between chelate and heavy metal. On the other hand, free chelate can enter the root and, subsequently, form metal complexes, which enhance metal transport to shoots (26,27). Chelating agents are a group of components that accelerate the release of heavy metals bonded to the solid parts of the soil and are not in the form that can be absorbed by plants due to the disruption of the solid and liquid soil phases' equilibrium and can increase the availability of heavy metals by forming bonds with heavy metals. Thereby, uptake of heavy metals by plants increases (28)(29)(30).
Although the type, the time, and the rate of chelate application to the soil may affect soil heavy metal availability, the results of the current study showed that the application time of chelate had no significant effect on the availability of heavy metal; perhaps it needs more time that requires further investigation.
In addition to the chelate chemical characteristics, plant physiological properties also played an important role in making changes in the TF value of heavy metals; therefore, the Pb and Zn TF values were greater in sunflower compared to canola. Plant root exudates are important sources of amino acids (AA) in soil solution. The con-centration of AA in the soil solution is often much higher than those of heavy metals. Therefore, AA can also form stable complexes with metal cations via their carboxylic and amine groups, and increase the solubility of heavy metals in soil (31).
It should be noted that in the studied treatments, the Pb TF values were < 1, indicating that the Pb accumulation was higher in the root (32). Although using chelate can help to remediate contaminated soil by increasing the availability of soil heavy metals and TF value, the type and the amount of chelate play an important role in Zn TF value, since the highest and lowest Zn TF values were related to HEDTA chelate and EGTA chelate, respectively. Jahanbakhshi et al. reported that plant root exudate can increase availability of heavy metal in soil and, thereby, increase phytoremediation efficiency (33). Askari et al. also reported similar results (34).
Since using HEDTA chelate had the greatest role in increasing the availability of Zn in soil, it can be concluded that chelate application can establish a strong bond with Zn to form a soluble complex that helps remediation of Znpolluted soil. According to Table 5, the TF value of Zn is > 1 suggesting that Zn was readily transported from roots to shoots. In this regard, Nikseresht et al. reported similar results that confirmed the current study findings clearly (35). Adesodun et al. assessed the phytoremediation potential of sunflowers for metal in Zn-contaminated soil and concluded that sunflower is a suitable plant to remediate Znpolluted soil due to its high TF value (36). Choram and Alizadeh investigated the effect of EDTA chelate on increasing the phytoremediation efficiency and concluded that high application of chelate can increase the TF value of heavy metal (37) that was similar to the current study results.

Conclusions
Based on the current study results, using HEDTA, CDTA, and EGTA chelate in the soil significantly increased the availability of Pb and Zn in soil. The greatest soil and plant Pb concentration was found with applying 4.5 mM/kg of soil HEDTA chelate. In addition, the chelate application caused a significant increase in Pb and Zn value. However, the TF value of Pb was < 1. According to the results of the current study, chelate application time had no significant effect on Pb and Zn TF values. It is necessary to consider the soil physicochemical properties due to their important roles in the concentration of heavy metal in soil in future studies. In addition, the effect of temperature and season on phytoremediation efficiency should be investigated in the field studies.