Eight genotypes of Brassica oleracea var. capitata (cabbage) consisting three commercial cultivars namely, Rubra, YR gold, and Ohgane and five inbred lines namely, BN3383, BN4059, BN4072, BN4098, and BN4303 were obtained from Asia Seed Co., Ltd., Seoul, South Korea (Supplementary Figure S1). The genotypes were chosen based on their diverse pedigree records and contrasting morphological properties and prior reputation of differential glucosinolate profiles (Robin et al., 2016). Seeds were germinated and seedlings were raised in moistened garden soil mix at 25 ± 1°C, 65% relative humidity, 80–120 μmol m^-2s^-1 light intensity and 16 h photoperiod in a controlled plant growth room. After 4 weeks when seedlings attained a height to withstand transplantation shock, seedlings were transferred to 30 cm × 25 cm × 15 cm garden pots filled with a mixture of 1:3 sand/commercial potting mix 25 cm apart in glasshouse and maintained by light irrigation, standardized NPK fertilization, and without using pesticides before collecting leaf samples for glucosinlate profiling, in vitro feeding tests and insect imposition for glass-house feeding test at 3 months of total age.

Diamondback moth, Plutella xylostella L., were collected from nearby natural field using sweep nets and poolers and were harbored on Brassica napus plants inside a 2.5 m² makeshift net cage in glass-house (20–25°C temperature, 60–70% RH, ambient light level, natural photoperiod) prior to being used to assess their preferential feeding behavior on cabbage genotypes. For in vitro feeding assessment, the eggs were allowed to hatch and the neonate larvae were reared in plastic boxes in controlled growth room and fed with fresh B. napus leaves until they reached fourth instar stage.

Three months old cabbage plants grown in the glass-house were transferred to four net cages (tents) where adult DBM were being reared on B. napus plants. The insect population inside the net was approximately 50 adult m^-2. One plant from each of the eight varieties were planted 25 cm apart and randomly in a net cage. Four net cages thus represent four replications set with randomized complete block design (tent experiment). Insect herbivory in each cabbage genotypes were measured in terms of percent leaf area damaged by DBM larvae at 2, 4, 7, and 13 days after insect introduction using a visual score of 1–9 where ‘1’ and ‘9’ being scored for least and maximum area damaged as shown in Figure 1. The higher scores thus indicate a higher feeding preference of adult Diamondback moth on the cabbage genotypes. The percent leaf area damaged by the DBM larvae was measured based on visual estimation using eight replications per genotypes.

Infestation and damage by insects were also tested in vitro using two different procedures namely, multiple-choice feeding test and two-choice feeding test. For multiple-choice feeding test, leaf disks of 1.5 cm diameter were cut out from each genotypes and placed equally apart in square plastic petri-dishes (12 cm × 12 cm × 2 cm). Eight fourth instar larvae were released in the center of the petri-dish and their feeding preferences were measured in terms of number of larvae on and in the close vicinity of the leaf disk of each of the eight genotypes at 30 min, 1, 3, and 6 h and percentage of leaf area damaged at 6 and 12 h of release of larvae (Figure 2). For two-choice feeding test, insect herbivory of each genotype was tested separately against the control genotype, BN4303. Two leaf disks of each test genotype were placed alternately against two leaf disks of control genotype in one plastic petri-dish. Four larvae were released in the center of each petri-dish and their preferential feeding behavior was assessed based on similar parameters of multiple-choice in vitro procedures as described above. Data of both multiple- and two-choice feeding tests were recorded from eight replications. The genotype BN4303 was chosen as control genotype due to its least preference (least damage occurred) to DBM as per glass-house and in vitro multiple-choice feeding test.

For profiling constitutive individual glucosinolate compounds in the leaves of eight cabbage genotypes, a slightly adapted version of the HPLC procedures used by Robin et al. (2016) and Yi et al. (2016) were used in this study, which in brief is described here. Methanol treated and frozen leaf tissues (10 g) were grinded to a fine powder and incubated initially at 70°C for 10 min and then at room temperature for 1 h. The structural components and proteins of the tissues were removed by centrifuging the samples at 14000 rpm for 10 min at 4°C. The supernatant was used to conduct an anion-exchange chromatography. This centrifugation and chromatography were repeated three times and the supernatants were pooled which represented the crude extract of glucosinolates. This crude glucosinolates were then desulfated by mixing 0.5 mL 50 mM barium acetate and 0.5 mL 50 mM lead acetate; centrifuging at 2,000 × g for 10 min; loading into a 0.5 M sodium acetate pre-equilibrated DEAE-Sephadex column and adding 250 μL aryl sulfatase to the column. After running the desulfation process for 16 h the desulfated glucosinolates were eluted with 1 mL of distilled water which was further purified by centrifugation at 20,000 × g for 4 min at 4°C and filtration through a PTFE filter (13 mm, 0.2 μm, Advantec, Pleasanton, CA, United States). The HPLC analysis of the purified desulfo-glucosinolate samples was conducted in a Waters 2695 HPLC system (Waters, Milford, MA, United States) with a C18 column (Zorbax Eclipse XBD C18, 4.6 mm × 150 mm, Agilent Technologies, Palo Alto, CA, United States) using acetonitrile and water as mobile phase solvents. The purified desulfo-glucosinolates were detected using a PDA 996UV-visible detector at a wavelength of 229 nm and quantified using the standard curve of sinigrin. The glucosinolate profiling was concluded by identifying individual glucosinolate molecules (Supplementary Table S4) using Mass spectrometry analysis (HPLC/MS, Agilent 1200 series, Agilent Technologies).

Depending on the type and corresponding distribution of the data, combinations of significance tests and post hoc tests were performed. Normality of data was tested following Anderson-Darling test in MINITAB v. 17 statistical packages (Minitab Inc., State College, PA, United States). Glucosinolate contents were analyzed using one-way ANOVA and Tukey’s pairwise comparisons in MINITAB v. 17 statistical packages. Glass-house feedings scores were analyzed using Non-parametric Mood’s Median test and post hoc Bonferroni’s pairwise comparisons were done for each of the time-points independently using Minitab software packages (v. 17.0). The number of larvae per leaf disk was analyzed using generalized linear model with Poisson distribution using SPSS software packages (v. 24.0). Both genotypic difference and time-point difference were considered as fixed effect. Percent leaf area damage was analyzed using two-way ANOVA and Tukey’s pairwise comparisons using Minitab software packages (v. 17.0). The traits such as glucosinolate content, number of larvae per leaf disk and percent leaf damage were presented as mean ± SD whereas glass-house feeding scores were presented as median and range. The detailed and trait-wise procedure of the statistical analyses are given in Supplementary Table S1. Normality and deviance of data, degrees of freedom, test statistic, and p-value of statistical significance of different traits are given in Supplementary Table S2.

Principal component analysis (PCA) was performed using the standardized (mean subtracted from the variable and then divided by the standard deviation) data of individual glucosinolate contents and the glass-house feeding scores at day 7 and 13 and both multiple- and two-choice in vitro feeding scores (of 12 h) as set of variables using MINITAB v. 17 statistical packages (Minitab Inc., State College, PA, United States).

HPLC analysis of the leaves of three commercial cultivars namely, Rubra, YR gold, and Ohgane and five inbred lines namely, BN3383, BN4059, BN4072, BN4098, and BN4303 detected in total 11 different glucosinolates of which seven were aliphatic glucosinolates namely, sinigrin, glucoiberin, glucoiberverin, glucoraphanin, gluconapin, glucoerucin, and progoitrin and four were indolic glucosinolates namely, glucobrassicin, neoglucobrassicin, 4-methoxyglucobrassicin and 4-hydroxyglucobrassicin (Figure 3). Statistically significant differences were observed between the genotypes for individual and total glucosinolate contents (p < 0.01 for all) (Figure 3). Among these 11 glucosinolate compounds, 4-methoxyglucobrassicin was found in all eight genotypes; in traces (0.2–0.8 μmol g^-1 DW) in content (Figure 3). The aliphatic glucosinolate sinigrin was found in all genotypes except BN3383 with the highest (30 μmol g^-1 DW) and the lowest (0.3 μmol g^-1 DW) were being found in BN4059 and Ohgane, respectively.

Contrastingly, glucoraphanin, and gluconapin were found only in two genotypes Rubra and YR Gold. Except glucoiberin, glucoiberverin, and glucobrassicin, all of the rest of the 11 glucosinolates were detected in the genotypes, Rubra and YR Gold. The genotypes, YR Gold contained the highest (157.6 μmol g^-1 DW) total glucosinolate followed by BN4059 (49.4 μmol g^-1 DW) and Rubra (46.8 μmol g^-1 DW) which was characterized largely by the higher concentration of 4-hydroxyglucobrassicin (87.6 μmol g^-1 DW) and progoitrin (24.8 μmol g^-1 DW) in YR Gold, sinigrin in BN4059 (30.0 μmol g^-1 DW) and 4-hydroxyglucobrassicin in Rubra (26.1 μmol g^-1 DW). The genotype Ohgane (4.9 μmol g^-1 DW) followed by BN3383 (7.8 μmol g^-1 DW) contained the lowest total glucosinolate (Figure 3).

Feeding scores in glass-house experiments increased with the increase in the duration of larval feeding in all genotypes (Figure 4). The genotypic differences were significant in each of the time points (p < 0.01) for feeding scores as determined by the Non-parametric Mood’s Median test (Figure 4). Data revealed that genotypic differences became greater at progressively later time-points compared to earlier time-points. Feeding scores of genotype BN4303 and BN4072 were not significantly different at 2 days after infestation, while they became significantly different at the subsequent time-points (Figure 4). At 2 days of larval feeding, the genotypes BN4303, BN4059, and BN4072 were least damaged (Figure 4 and Supplementary Table S3). Among these three initially least damaged genotypes, BN4303 outstands from the rest of the eight genotypes as the most resistant genotype with its lowest feeding score (3 on a scale of 1–9) making it statistically different form the rest of the genotypes between 4 and 13 days of larval feeding. The other two genotypes, BN4059 and BN4072 also appeared as less damaged with the feeding scores of 5 and 6 (median values), respectively, at 13 days of larval feeding (Figure 4 and Supplementary Table S3). Notably, all three commercial cultivars, Rubra, YR Gold, and Ohgane appeared as the most damaged by DBM larvae with Rubra and Ohgane showing the consistent signs of maximum damage during the entire larval feeding duration (Figure 4 and Supplementary Table S3).

In in vitro multiple-choice feeding test, cut leaf disks of all eight genotypes were placed equally apart giving the eight fourth instar DBM larva to preferentially choose from and feed on (Figure 2). The percentage of damaged area per leaf disk varied significantly (p < 0.01) but the number of larvae per leaf disk did not show any statistically significant variation across different time-points (p = 1.00). The genotypic responses, however, varied significantly in terms of both the number of larvae per leaf disk and % leaf area damaged (p < 0.01 for both) (Figure 5). The most resistant genotype BN4303 strikingly varied from the four most susceptible genotypes Rubra, Ohgane, YR Gold, and BN3383 in terms of both number of larvae per leaf disk and % leaf area damaged (Figure 5). The least number of larvae fed on (or visited) BN4303 leaf disks within first 3 h and hence, the area of leaf disks were found to be least damaged (only 5%) within the first 6 h (data not shown) on that genotype (Figure 5). BN4098 was the second least visited genotype by DBM larvae within first 3 h causing 26.2% damage to the leaf disk at 12 h of infestation (Figure 5). DBM larvae seem to instantly prefer the leaf disks of Rubra and YR Gold as two larvae fed on (or visited) within first half an hour causing a final leaf damage of 33 and 46 percent at 12 h of larval infestation (Figure 5). During the entire period of infestation, the maximum number of larvae fed on the leaf disks of BN3383 and hence, inflicted maximum damage (53.3%) at 12 h of infestation (Figure 5). Overall, the findings corroborate with that of the glass-house feeding tests as both test identified BN4303, BN4372 as least preferred and BN3383, Rubra, YR Gold, and Ohgane as most preferred genotypes. There were significant variations in the numbers of larvae visited at different time points within genotypes for most of the cabbage lines (p < 0.05) except BN4072 and BN4059 (Figure 5). The most susceptible genotype Rubra had no larvae visitors at the final studied time point (6 h) whereas it was heavily infested at the initial two time points (p < 0.01). In contrast, two resistant genotypes BN4303 and BN4098 had no larvae visitors at the initial time points (30 min and 1 h) whereas a few larvae visited these genotypes at the later time points (p < 0.01). This may be because the most susceptible genotypes were heavily damaged initially leaving less leaf area to feed on. This forced the larvae to move on and start feeding the intact leaves of the comparatively less preferred genotypes.

In in vitro two-choice feeding test, the cut leaf disks of each of the seven genotypes were tested against the leaf disks of control genotype BN4303 (most resistant as per both glass-house and in vitro multiple-choice feeding tests) separately to determine the preferential (comparative) feeding behavior of DBM larva (Figure 2). Genotypic differences for number of larvae and percent leaf area damaged across different time-points were statistically significant (p < 0.01) (Figure 6). Number of larvae visitors per leaf disk were significantly higher and percent insect damage was significantly lower in all seven test genotypes compared to control genotype BN4303 (p < 0.01). Only the genotype Ohgane differed significantly from the rest of the six test genotypes in terms of mean number of larvae per leaf disk (Figure 6). For percent leaf area damaged, the genotypes Rubra and YR Gold were more severely damaged compared to other six test genotypes (Figure 6). Like the previous two tests, insects seem to prefer Rubra and YR Gold compared to other genotypes over control genotype BN4303 as evident by higher number of larva visitors that causing almost the complete damage of the leaf disks (82 and 80% area damaged, respectively) during different time points of entire feeding period (p < 0.01, Figure 6). The highest number of larvae visited YR gold at 30 min and BN4098 at 1 h compared to all other genotypes (p < 0.01, Figure 6). Genotype BN3383 which appeared as one of the susceptible genotypes in the previous two tests was also found to be visited by more number of larvae at 1 h compared to control, Ohgane, Rubra, BN4059, and BN4072 (p < 0.01, Figure 6). The use of BN4303 as control genotype was justified as the leaf disk of this genotype was only damaged to a maximum of 25% (range 10–25%) against any of the rest seven genotypes separately, whereas the damage in test genotypes ranged from 37 to 82% during the 12 h of larval feeding (Figure 6).

Principal component analysis of the contents of 11 individual glucosinolates and feeding test data extracted major contrast between individual glucosinolates and insect preference toward particular cabbage genotypes. The first three PCs explained 81.5% of the total variation in the datasets (Table 1). PC1 accounted for 48.32% of the total variation which is largely manifested by higher positive coefficients for feedings scores or percent leaf damage, 4-hydroxyglucobrassicin, glucoerucin, glucoraphanin, and progoitrin versus lower negative co-efficients for glucobrassicin, glucoiberin, and glucoiberverin (Table 1).

In PC1, the traits such as the contents of 4-hydroxyglucobrassicin, glucoerucin, glucoraphanin, glucobrassicin, and glass-house feeding score at 7 days; and in PC2, the traits such as the contents of progoitrin, sinigrin and glass-house feeding score at 13 days contributed the most in explaining the overall variability of the entire datasets (Table 1). The PC1 clearly separated YR Gold and Rubra (two susceptible genotypes as per feeding tests results) from rest of the genotypes and placed the two most resistant genotypes, BN4303 and BN4059 to the furthest opposite quadrant in PCA-biplot (Figure 7). This is also evident from the higher mean PC scores in opposite direction in these resistant (+3.61 and +1.71 in YR Gold and Rubra, respectively) and susceptible (-4.66 and -4.00 in BN4059 and BN4303, respectively) genotypes (Table 1).

PC2 explained 20.3% of total variation which is largely dominated by higher coefficients (and also higher contents) of aliphatic progoitrin, sinigrin, glucoerucin and indolic 4-hydroxyglucobrassicin and lower co-efficients (and also lower contents) of glass-house feeding scores (at 7 and 13 days), neoglucobrassicin and gluconapin (Table 1). PC2 clearly separated YR Gold, BN4059, and BN4303 from Rubra and BN3383 as evident by their differential location in PCA-biplot (Figure 7) and by their contrasting mean PC scores (Table 1).

Glucosinolates (GSL) were quantified in the middle-aged leaves of eight different cabbage genotypes as DBM larvae mainly feeds on leaves; and younger leaves contain higher amount of glucosinolates compared to older leaves (Lambdon et al., 2003; Lambdon and Hassall, 2005). The amount of individual aliphatic-, indolic-, and total- glucosinolate contents varied constitutively across plant genotypes (Figure 3). Among the known 120 glucosinolate compounds in various plant species (Kliebenstein et al., 2001), only 11 were detected. No single genotype contained all these 11 glucosinolates and their contents ranged from traces to too high amounts. Besides the previous report of two aliphatic compounds (sinigrin and glucoiberin) and one indolic/ compound (glucobrassicin) dominating the glucosinolate patterns (Velasco et al., 2007; Cartea et al., 2008), this study found 4-hydroxyglucobrassicin at higher concentrations in few of the studied genotypes (Figure 3). This diversity in glucosinolate profiles in these eight genotypes is reasonable as the genotypes were developed from different parents (as per Asia Seed Company that provided these materials) and varies in terms of morphological features including variation in leaf size, shape, pigmentation and wax deposition etc. (Figure 1). The diversity of genotypes in glucosinolate contents and profiles rendered ideal materials for studying the preferential feeding of DBM.

Insect herbivory is generally studied in common garden experiments that harness the natural field conditions to explore the feeding behaviors of various generalist and specialist insect species along with their natural enemies. Herbivory by a particular insect species is largely influenced by factors not limited to genotype (Bidart-Bouzat and Kliebenstein, 2008; Robin et al., 2016); leaf age and strata (Moreira et al., 2016) and host range (Zhang et al., 2012; Henniges-Janssen et al., 2014) along with potential influence of surrounding vegetation and insect population, biotic and abiotic stresses and environmental factors etc. (Hopkins et al., 2009; Grzywacz et al., 2010; Augusto and Amilton, 2014). We were particularly focused on interrogating the genotype specific feeding attributes of DBM larvae with regards to glucosinolate profiles and hence, opted for controlled experiments in both glass-house and in vitro conditions in a way that the non-genotypic factors can be kept to a minimum. Our tests identified BN4303, BN4059, and BN4072 as the least damaged (resistant) and BN3383, YR Gold and Rubra as most damaged (susceptible) genotypes (Figures 4, 5) in both field and laboratory tests. This results, however, should be viewed keeping the previous feeding experience of the insects in mind as the larvae were previously reared on B. napus leaves which may have an influence on the ability of insects to deal with plant toxins and their feeding preferences.

Glucosinolates, characteristic to Brassica and its crucifer relatives, are known to play differential role of both defense molecule (Mithen et al., 1995; Benderoth et al., 2006) and feeding and oviposition stimulants to a variety of insect pests and their predators (Li et al., 2000; Velasco et al., 2007; Müller et al., 2010). When the variable feeding preferences of DBM larvae were interrogated with the diverse glucosinolate profiles in eight cabbage genotypes, it became obvious that there is no linear association between the total glucosinolate contents and plant’s resistance against DBM. For example, despite having the least (4.9 μmol g^-1 DW) and the most (157.6 μmol g^-1 DW) total glucosinolate contents, respectively, the genotypes Ohgane and YR Gold didn’t show any obvious variation in insects feeding preference as both of the genotypes were severely damaged. On the other hand, the most resistant genotype BN4303 have comparatively less total glucosinolate (15.1 μmol g^-1 DW). This adds to the reports of contrasting roles played by total glucosinolates in insect resistance (Hopkins et al., 2009; Sarosh et al., 2010; Santolamazza-Carbone et al., 2014). Higher contents of total glucosinolates were reported to be positively associated with higher DBM damage in A. thaliana (Bidart-Bouzat and Kliebenstein, 2008), increased visit from cabbage butterflies (Pieris rapae) in oilseed rape (Giamoustaris and Mithen, 1995) and negatively associated with specialists flea beetles (Psylliodes convexior and Phyllotreta zimmermani) damage in A. thaliana (Mauricio, 1998). Lower contents of total glucosinolates were reported to be associated with higher and lower pupal mass of Pieris rapae and adult body mass of Pteromalus puparum, respectively, in wild cultivars and cultivated varieties of B. oleracea (Hopkins et al., 2009). In most of the cases, a generalized notion is thus developed regarding the influence of total glucosinolate, either positively or negatively, on various generalists, specialists and their natural enemies (Lankau, 2007; Arany et al., 2008; Kos et al., 2011). Contrary to this, a few cases reported no direct association between high total glucosinolates and DBM damage in B. juncea and A. thaliana (Bodnaryk, 1997; Li et al., 2000; Arany et al., 2008).

Additionally, despite a few previous reports of contrasting roles of two classes of glucosinolates namely, aliphatics and indolics in conferring resistance or susceptibility against insect herbivory (De Vos et al., 2008; Gols et al., 2008; Newton et al., 2009; Kos et al., 2011; Bohinc et al., 2013), our PCA-based approach didn’t recognize any definitive patterns in the contents of either of these two classes of glucosinolates and feeding preference of DBM larvae. This observation suggested that DBM’s feeding preference is not restricted to any particular classes of glucosinolates in our study. Together, all these results of laboratory and field experiments indicated that the insect–herbivore interaction is dynamically dependent on the genotype specific glucosinolate profiles as evident by its differential influence on the feeding behavior and damage levels of various generalist and specialist insects (Mauricio, 1998; Li et al., 2000; Barth and Jander, 2006; Bohinc et al., 2013).

In an attempt to identify any underlying pattern hidden among the overall variability of feeding behaviors and glucosinolate contents in the studied eight genotypes we used PCA and observed a conspicuous contrasting profiles of certain glucosinolates between resistant and susceptible genotypes (Table 1 and Figure 7). Glucobrassicin, glucoiberin, and glucoiberverin were only detected in resistant genotypes BN4303, BN4059, and BN4072 whereas in susceptible genotypes BN3383, Rubra, and YR Gold these glucosinolates were completely absent indicating their potential role in conferring resistance against DBM (Supplementary Figure S2). Conversely, 4-hydroxyglucobrassicin, glucoerucin, glucoraphanin, progoitrin, and gluconapin were absent in these three resistant genotypes whereas those were present at relatively high amount in susceptible genotypes (Supplementary Figure S2). These results indicated the role of glucobrassicin, glucoiberin and glucoiberverin as potential repellents and 4-hydroxyglucobrassicin, glucoerucin, glucoraphanin, progoitrin and gluconapin as potential attractants for DBM. Based on these results, an apparent conclusion can be made that both the presence of these potential repellents and absence of these potential attractants is needed simultaneously to achieve non-preference and hence, resistance against DBM in these genotypes (and vice-versa, for susceptibility). However, it is not clear if the presence of these potential repellents alone, irrespective of the presence of the potential attractants, can make these genotypes resistant as no genotypes were found to contain the glucosinolates of both these groups. Further research is thus needed to validate this phenomenon.

Among the three potential repellents identified in this study, indolic glucobrassicin and aliphatic glucoiberin was negatively associated with insect richness, herbivory and larval fitness (Gols et al., 2008; Poelman et al., 2009; Kos et al., 2011; Santolamazza-Carbone et al., 2014, 2016). Reports on the role of the other glucosinolate, glucoiberverin in against insect herbivory is, however, scarce. Our approach identified glucoiberverin to be negatively associated with the feeding preference but the concentration of this glucosinolate is trivial compared to the amounts of other glucosinolates in each of the resistant genotypes. Nevertheless, glucoiberverin was not present in the susceptible genotypes at all. This indicates toward the possibility that glucoiberverin, even in trace amounts is needed for resistance in these genotypes.

Sinigrin was detected at a relatively high amount (in fact, it led the glucosinolate profiles) in all of our less preferred genotypes. However, unlike the contrasting groups of potential repellents and attractants, it was also present in susceptible genotypes such as Rubra and YR Gold, in comparatively lesser amounts though. This further adds to the diverse reports of sinigrin which was associated with reduced herbivory (Bodnaryk, 1997; Brian Traw and Dawson, 2002; Agrawal and Kurashige, 2003; Santolamazza-Carbone et al., 2014) in one hand and increased body weight and infestation of Mamestra brassicae larvae on young plants of B. oleracea var. acephala (Santolamazza-Carbone et al., 2016); increased visit from P. xylostella on B. juncea under laboratory conditions (Spencer et al., 1999) on the other hand, making it an important glucosinolate in insect–herbivore interaction.

All these further add to the dynamic roles of glucosinolates in plant–herbivore interactions (Tierens et al., 2001; Mewis et al., 2005; Hopkins et al., 2009; Santolamazza-Carbone et al., 2014) and points toward the fact that insect resistance should be viewed holistically taking other associated factors such as other secondary metabolites (Van Loon et al., 2002); further downstream breakdown products (Reed et al., 1989; Roessingh et al., 1997; Van Loon et al., 2002; Bezemer and Vandam, 2005; Renwick et al., 2006); plant physiological attributes (Sarosh et al., 2010); physical and biological context of both plant and insect population (Bidart-Bouzat and Kliebenstein, 2008) into account.

These findings can be extended to identify the best glucosinolate profiles for the prevalent insect species and their natural enemies of a particular area. This will be helpful in identifying the common glucosinolate determinants of resistance and in selective breeding of cultivars with specific glucosinolate profiles that may provide resistance against the prevalent insect species and their natural enemies of that particular area.