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Crop Science 41:748-754 (2001)
© 2001 Crop Science Society of America

CROP PHYSIOLOGY & METABOLISM

Seed Number as a Function of Growth. A Comparative Study in Soybean, Sunflower, and Maize

Claudia R.C. Vega*, Fernando H. Andrade, Víctor O. Sadras, Sergio A. Uhart and Oscar R. Valentinuz

Unidad Integrada INTA Balcarce - Facultad de Ciencias Agrarias UNMP. CC 276, 7620 Balcarce, Argentina

* Corresponding author (clavega{at}mdp.edu.ar)


   ABSTRACT
TOP
 NOTES
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 CONCLUSION
 REFERENCES
 
Seed number, the main yield component of cereals and oil-seed species, strongly depends on the physiological status of the crop during a critical period for seed set. Using a comparative approach including three species with contrasting reproductive strategies, we investigated the relationship between seed number per plant (SNP) and plant growth rate during the critical period for seed set (PGRC). Indeterminate soybean [Glycine max (L.) Merr.], sunflower (Helianthus annuus L.), and maize (Zea mays L.) crops were grown under a wide range of plant densities to generate contrasting availability of resources per plant. Growth of individual plants was estimated by a novel, nondestructive method based on relationships between actual shoot dry matter and morphometric variables, including stem diameter, plant height, and dimensions of reproductive structures. Seed number per plant ranged from 0 to 890 in soybean, 0 to 4096 in sunflower, and 0 to 1348 in maize and PGRC (g d-1) from 0.01 to 4.3 in soybean, 0.3 to 17.6 in sunflower, and 0.4 to 12.3 in maize. Our study showed that (i) the relationship between SNP and PGRC was linear in soybean, reflecting the reproductive plasticity of this species, and curvilinear in sunflower and maize, reflecting morphogenetic restrictions to generate reproductive sinks under favorable growing conditions; (ii) the PGRC threshold below which no seed was set varied among species, being negligible in soybean, close to 0.35 g d-1 in sunflower, and 1 g d-1 in maize. Quantitative relationships between seed number and plant growth rate during the critical period of seed set could be useful for crop modeling.


   INTRODUCTION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 CONCLUSION
 REFERENCES
 
SEED NUMBER, the main yield component of cereals and oil-seed crops, is strongly dependent on genotype, environmental and management factors (Egli, 1998). In soybean, sunflower, and maize, seed number depends on the sequential processes of flower morphogenesis and seed set. The former controls potential seed number and has a relatively low energy cost. In general, even in very productive environments, flower number exceeds the potential capacity of plants to set seeds (Stephenson, 1981). In contrast, seed set is very sensitive to the physiological status of crops during critical windows of time that are species dependent. Despite some uncertainty about the actual beginning and the end of this critical period, it is generally accepted that it brackets flowering in maize (Earley et al., 1967; Aluko and Fischer, 1988) and sunflower (Cantagallo et al., 1997). In indeterminate soybean, the critical period for seed set extends from flowering to beginning or middle seed filling (Board and Tan, 1995; Jiang and Egli, 1995; Egli, 1998).

Growth rate during the window of time critical for seed set has been used to quantify the physiological status of crops as affected by genotype, environment, and their interaction. Further, empirical evidence supports the association between seed number and crop growth (Hawkins and Cooper, 1981; Egli and Yu, 1991). Controversy exists, however, on the actual shape of the relationship between SNP and PGRC in maize (Tollenaar et al., 1992; Kiniry et al., 1997; Ritchie and Wei, 2000) and no reports have been found regarding the SNP-PGRC relationship in sunflower. Importantly, most research related to seed set has dealt with single plant species. In this work, a comparative approach is used to highlight common pathways and contrasts in the way in which seed set is determined in major crop species.

Sunflower, soybean, and maize are three annual species with contrasting reproductive strategies. Our hypothesis is, therefore, that the putative relationship between seed number and plant growth rate during the critical period for seed number determination is not unique but strongly depends on the reproductive strategies of the species. Because plant-to-plant variation is an important feature of plant populations (Hara, 1986), we propose a novel analysis of the SNP-PGRC relationship at the level of interacting individuals within crops. To assess growth of individuals during the critical period for seed set and relate it to SNP at harvest, we developed an original, nondestructive method to quantify PGRC. The study of individual plants under a comparative approach including contrasting species is a powerful tool to improve our understanding of seed number determination.


   MATERIALS AND METHODS
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 CONCLUSION
 REFERENCES
 
Crop Management and Treatments
Soybean, sunflower, and maize crops were grown on deep (>=1.5 m) Typical Argiudolls at Balcarce, Argentina, (37°45' S, 58°18' W; elevation 130 m) between 1994 and 1999 (Table 1). All crops were fertilized with 35 kg P ha-1 before sowing, and with 150 kg N ha-1 at the 6-leaf stage in maize and 4-leaf stage in sunflower. Soybean seed was inoculated with Bradyrhizobium japonicum . Irrigation was provided to keep water content above 50% of maximum soil available water. Weeds and insects were adequately controlled. Each species was grown in a different section of the same field. Plant density treatments were arranged within each crop species in a randomized block design with three (Season I) or four replications (Season II). Distance between rows was 0.7 m in all three species, except for soybean in Season II, when 0.35 m between rows was used. Target plant densities in maize and sunflower (Table 1) were achieved by hand sowing and thinning to one plant per hill. Thickly sown soybean was thinned to the appropriate density at the 2-leaf stage.


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  		Table 1. Summary of treatments in field experiments at Balcarce.

 
Measurements
Shoot dry matter of individual plants was quantified at the beginning and end of the critical period for seed set. We used a nondestructive method based on allometric relationships between selected plant variables and actual shoot dry matter obtained in conventional plant samplings (see below). Plant samples were taken 15 d before and 15 d after flowering in maize and sunflower. In soybean, samples were taken at full flowering and at the middle of seed filling. Sampling strategies varied between seasons. In the first season, three plant densities were established and large samplings were taken to exploit variation within treatments as well as variation among treatments. In the second, we increased the number of treatments and took fewer plants per treatment (Table 1).

Allometric Relationships
We measured basal stem diameter and, when visible, diameter and length of ears in maize and head diameter in sunflower. In Season II, stem diameter in soybean was measured at the height of the internode associated with the first trifoliate leaf and the number of branches and height of the main stem were also included as variables. Immediately after taking these measurements, shoots were harvested, separated into leaf blade and petiole, stem and reproductive structures, and oven dried at 65°C to constant weight to determine dry matter. Samples included three plants per replicate at low density and five plants per replicate at high and intermediate densities (Table 2). Relationships between shoot dry matter and morphometric variables were evaluated using regression models summarized in Table 2.


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  		Table 2. Relationships between shoot dry matter (S) and morphometric variables. Subscripts 0 and 1 indicate the beginning and the end of the critical period for seed set, respectively. SD: stem diameter; HD: head diameter; ED: diameter of apical ear; EL: length of the apical ear; BN: branch number; H: stem length. All models were significant at P < 0.0001.

 
Plant Growth Rate and Seed Number
We tagged a density-dependent number of plants (Table 1) to determine (i) shoot dry matter at the beginning (S0) and end (S1) of the critical period for seed set, and (ii) seed number at physiological maturity. Shoot dry matter per plant, S0 and S1, was estimated for each plant on the basis of nondestructive measurements and the models in Table 2, and PGRC calculated as (S1-S0)/d between samplings. In maize and soybean, we counted all the seed on each individual plant. In sunflower, we counted the seed in subsamples from about a quarter of the head and estimated total seed number as a function of total seed mass and the ratio between seed number and mass from the subsamples. In maize, the number of fertile ears per plant was also recorded.

We investigated the relationship between SNP and PGRC using linear and nonlinear models. In maize and sunflower, several nonlinear functions adequately fitted the data (Andrade et al., 1999). Of them, we chose hyperbolic functions because they are statistically sound and include biologically meaningful parameters (Edmeades and Daynard, 1979; Tollenaar et al., 1992; Vega et al., 2000). The selected models were as follows.

For soybean,

(Model 1)

For sunflower and maize,

(Model 2)

In Model 2, GT is the growth rate threshold for seed set. In Model 1, the threshold is calculated as -{alpha}1 ß1-1. For comparisons among species, GT was normalized by expressing it as a fraction of average PGRC at the most common plant density in commercial crops, i.e., 30 plants m-2 in soybean, 5.6 plants m-2 in sunflower, and 8.5 plants m-2 in maize. In soybean, normalized GT was calculated on the basis of the average PGRC measured in sterile plants. Parameters {alpha}2 and ß2 are defined in Table 3.


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  		Table 3. Parameters (± s.e.) of Model 2 fitted to seed number per plant (SNP) and plant growth rate during the critical period for seed set (PGRc) in individuals of sunflower and maize.

 
Additionally, we calculated independent estimates of SNP and PGRc on the basis of averages (hereafter referred as conventional plant sampling). Average plant growth rate during the critical period was assessed as the quotient between crop growth rate and plant density. Samples to determine crop growth rate (g d-1 m-2) were taken at the beginning and at the end of the critical period; consisted of three (low density) or five plants (high and intermediate densities) in sunflower and maize and 1-m row in soybean and were processed as described above. At physiological maturity, grain yield was estimated by harvesting 7 m2 (soybean) and 10 m2 (sunflower and maize). Weight per seed was determined in two 500-seed samples per replicate. Seed number per plant was estimated as the quotient between yield and weight per seed and plant density.

Potential Seed Number per Plant
Average potential seed number per plant (PSNP) was determined for each plant density treatment and crop in Season I. In sunflower, PSNP was estimated as the sum of filled seeds, empty seeds, and sterile florets. In maize, PSNP of the uppermost ear was calculated as the sum of numbers of kernels and infertile florets. In sunflower and maize, we took two to three plants per replicate. In soybean, total number of flowers was estimated on the basis of periodic flower counts of 10 randomly selected and tagged individual plants. The PSNP in soybean was calculated as the product of total number of flowers and average number of seeds per pod. The fertility ratio was defined as SNP PSNP-1.


   RESULTS
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 CONCLUSION
 REFERENCES
 
Estimates of Shoot Dry Matter
Morphometric variables accounted for most of the variation in shoot dry matter (Table 2). The relationship between shoot dry matter and morphometric variables was stronger at the end of the critical period of grain set, when both stem diameter and size of reproductive structures were used as independent variables, than at the beginning, when only stem diameter was used. In soybean, measurement of stem diameter at the height of the second internode and the inclusion of branch number significantly improved the R2 in Season II. The strong association between actual shoot dry matter at physiological maturity and shoot dry matter estimated with morphometric variables during the critical period further supports the reliability of the method (0.79 < r2 < 0.94; P < 0.0001).

Relationship between SNP and PGRC on the Basis of Averages
Figure 1 shows the relationship between average plant growth rate and average seed number per plant in Season I and compares plant growth rate calculated with estimated and actual shoot dry matter (conventional plant sampling) at the beginning and the end of the critical period for seed set. The agreement between these two independent methods to estimate SNP and PGRc reinforces the reliability of our nondestructive approach.



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  		Fig. 1. Relationship between average seed number per plant at maturity and average plant growth rate during the critical period for seed set in Season I. Data obtained from nondestructive measurements (NDS) and from conventional plant sampling (CS). In maize, seed number per plant includes seeds in the uppermost and in the subapical ear. Bars indicate standard error of the mean.

 
Data based on individual plants allowed us to analyze plant-to-plant variation. In all three species, the coefficient of variation (CV) of both PGRC and SNP increased with plant density (Fig. 2). Variation in PGRC was greatest in soybean and lowest in maize. In soybean and sunflower, variation in seed number paralleled the variation in plant growth rate. In contrast, maize plants at the lowest and highest densities showed larger variation in seed number than in plant growth rate.



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  		Fig. 2. Coefficient of variation of average seed number per plant at maturity and average plant growth rate during the critical period for seed set as a function of plant density in Season I. Number 2 indicates commercial densities (29.8, 5.8, and 8.5 in soybean, sunflower and, maize, respectively). Numbers 1 and 3 indicate low and high densities, respectively (Table 1).

 
Relationship between SNP and PGRC on the Basis of Individuals
Data from individual plants showed a wide range of both plant growth rate and seed number (Fig. 3). Plant growth rates (g d-1) varied between 0.01 and 4.3 in soybean; 0.3 and 17.6 in sunflower, and 0.4 and 12.3 in maize. Seed number per plant ranged from 0 to 890 in soybean, 0 to 4096 in sunflower, and 0 to 1348 in maize.



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  		Fig. 3. Seed number per plant and plant growth rate during the critical period for seed set in soybean, sunflower, and maize individuals during two growing seasons at Balcarce. In Season I, symbols indicate plant densities (PD). Normal densities (D) were 29.8, 5.8, and 8.5 p m-2 in soybean, sunflower, and maize, respectively. Low PD was 0.25D and high PD was 2D. In maize, each point represents seed number in the uppermost ear of nonprolific and prolific plants, except when otherwise indicated. In maize, the fitted curve did not include seed from the second ear in prolific plants. Parameters of the fitted relationships are in Table 3.

 
In all three species, the number of seeds set by each individual was closely associated with its growth rate during the critical period. The relationship between SNP and PGRC was best described by a linear model in soybean and by hyperbolic models in sunflower and maize (Fig. 3, Tables 3 and 4). In maize, the model was fitted to SNP for the uppermost ear in prolific and nonprolific plants. In soybean, nonlinear models had slightly higher R2 but poorer distribution of residuals than the linear models in Table 4. In sunflower and maize, the relationship between SNP and PGRC revealed significant PGRC thresholds for seed set (GT). These thresholds were close to 0.35 g d-1 in sunflower and close to 1 g d-1 in maize (P < 0.0001; Table 3). In contrast, the threshold in soybean was not significantly different from zero (Table 4). Moreover, calculated thresholds were negative, a biologically meaningless result which would imply the setting of seed in plants with zero growth rate. Clearly, statistics did not have enough resolution to detect growth thresholds in soybean.


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  		Table 4. Parameters (± s.e.) of the linear Model 1 fitted to seed number per plant (SNP) and plant growth rate during the critical period for seed set (PGRc) for individuals of soybean. Regressions were significant at P < 0.0001.

 
Average PGRC in plants without seeds were 0.08 ± 0.067 (Season I) and 0.01 ± 0.006 (Season II) g d-1 in soybean, 0.49 ± 0.044 (Season I) and 0.25 ± 0.052 (Season II) g d-1 in sunflower, and 1.11 ± 0.460 (Season I) and 1.02 ± 0.200 (Season II) g d-1 in maize. These values correlated with reported GT in Table 3. Normalized thresholds to account for differences in plant growth rate among species ranked soybean (8.8 ± 5.63) < sunflower (11.0 ± 2.84) < maize (25.8 ± 1.76) (average for two seasons). Maize presented the highest GT and the largest proportion of sterile plants at high density, i.e., 26% compared with 7% in sunflower and 3% in soybean (values for Season I).

For hybrids used in this work, SNP did not significantly increase when PGRC was greater than 4 g d-1 in maize (considering the uppermost ear only) or 7 g d-1 in sunflower. This indicated a restriction in reproductive output that was not observed in soybean (Fig. 3).

Some maize plants produced a second seed-bearing ear (Fig. 3). Growth thresholds for prolificacy were close to 6 g d-1 in Season I (DK 636) and to 5 g d-1 in Season II (DK 639). In both seasons, about 50% of plants that grew at PGRC greater than the threshold for prolificacy set a second fertile ear.

Average potential seed number per plant (PSNP) decreased 70% in soybean, 50% in sunflower, and 11% in maize from low to high density. The fall in PGRC associated with increasing plant density decreased the fertility ratio, SNP PSNP-1, from 0.74 to 0.44 in soybean, from 0.80 to 0.47 in sunflower and from 0.93 to 0.26 in maize.


   DISCUSSION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
CONCLUSION
 REFERENCES
 
Analysis of Individuals within Plant Communities
We developed a reliable method based on the combination of nondestructive and destructive plant sampling to estimate the growth rate of individual plants during the critical period for seed set (Table 2; Fig. 1). This approach allowed for (i) the investigation of the relationship between SNP and PGRC within ranges of PGRC and SNP much larger than the usually obtained with standard methods based on average plants (cf. Fig. 2 with Fig. 3) and (ii) the analysis of plant-to-plant variation.

Plant-to-plant variation of both growth rate and reproductive output was greatest in soybean, whereas variation in growth rate at intermediate and low plant densities was greater in sunflower than in maize (Fig. 2). This reflected a differential development of exploitation hierarchies for resources among individuals (Hara, 1986). A number of factors contribute to these differences: first, the greater extinction coefficient and the greater phenotypic plasticity of sunflower and soybean (Andrade et al., 2000) in relation to maize, a species with more erect leaves (Hara, 1986); second, a relatively earlier interaction among individuals in soybean owing to the comparatively higher plant density; third, the greater foraging capacity for light and nutrients in dicots than in monocots, derived from both shoot and root morphological features (Grime, 1988).

Increasing interference among neighboring plants increased the coefficient of variation of both SNP and PGRC in all three crops. In maize, at the highest density, however, the coefficient of variation was much greater for seed number than for growth rate; this was associated with a bimodal frequency distribution of SNP resulting from a large number of barren plants (data not shown). In soybean and sunflower, self thinning of the smallest plants may explain lack of comparable barrenness and bimodality in SNP (Hara, 1986; Vega, 1995, unpublished data). Most importantly, however, differences in barrenness among species were associated with differences in PGRC thresholds for seed set.

The Relationship between PGRC and SNP. Meaning and Applications
Notwithstanding the contrasting reproductive strategies of sunflower, soybean, and maize, the relationship between SNP and PGRC held for all three species (Fig 3; tables 3 and 4). The consistency of this highlights the value of PGRC as a variable that synthesizes the key determinants of seed set. Because of its role as an energy source in plants, carbon tends to integrate the allocation patterns of other resources and can therefore be used as a common currency (Reekie and Bazzaz, 1987; Egli, 1998). The relationship between SNP and PGRC, however, should not be taken as an indication of a causal effect. Importantly, factors other than carbohydrate availability, including mineral nutrients (Swank et al., 1982), water deficit (Westgate, 1994), and growth regulators (Carlson et al., 1987), can concurrently influence seed set. The SNP-PGRC relationship showed to be, however, useful in this comparison of species with contrasting reproductive strategies and may help understand environmental and genotype-related variation in seed set within a species (Tollenaar et al., 1992; Jiang and Egli, 1995; Echarte et al., 1998). It also may provide a method to estimate seed number in simulation models.

Comparative Physiology of Seed Number Determination
Our study showed that there is (i) a growth rate threshold during the critical period for seed set below which no seed is set, (ii) a substantial variation in this threshold among species, and (iii) a differential reproductive plasticity in response to increasing PGRC among species.

Thresholds of biomass per plant for reproduction have been described in the literature (Gardner and Gardner, 1983; Weiner, 1988; Vega et al., 2000). Interpretation of such thresholds, however, may be misleading as they are measured at harvest and are not necessarily associated with the actual situation of growth of plants during the critical period for seed set. In species as maize, for example, biomass accumulation in stems of barren plants continue during a relatively long period until photosynthesis is feed-back regulated (Dalla Valle, 1998). Growth thresholds during critical periods for seed set, hence, are more meaningful for understanding seed number determination; however, they have been scarcely mentioned and mostly in maize (Edmeades and Daynard, 1979; Tollenaar et al., 1992). Controversy, moreover, still remains regarding the existence of such growth thresholds for seed set (Tollenaar et al., 1992; Kiniry et al., 1997; Ritchie and Wei, 2000). We consider that no discussion about thresholds in this type of relationships is the result of lack or shortage of data at very low values of PGRC. In our work, the use of individuals, rather than average plants representing a treatment, allowed us to get a very ample range of PGRC and an adequate calculation of GT.

Maize showed the highest GT for seed set, reflecting significant reductions of dry matter allocation to reproductive structures and developing kernels under conditions of low growth (Edmeades et al., 1979; Andrade et al., 1999). This may result from hierarchical patterns of distribution of assimilates within the plant (Edmeades et al., 1979). Stressful conditions during the critical period for seed set affect growth of ears and silks more than growth of other organs in maize (Hall et al., 1982; Otegui, 1997). In contrast, intermediate GT in sunflower and low GT in soybean may reflect a comparatively earlier and more stable allocation of dry matter to reproductive structures even under poor growing conditions.

Differences in abortion among species may additionally be related to other aspects of the floral biology, as the degree of synchrony in the pollination of florets, development of young seed, and the outcome of hierarchies among reproductive sinks (Stephenson, 1981; Bangerth, 1989; Lafitte and Edmeades, 1995). If flowers and young fruits compete for limited maternal resources, the proportion of pollinated flowers that set fruit decreases as the number of pollinated flowers increases (Stephenson, 1981). In comparison with sunflower and maize, soybean seems to adjust the potential number of reproductive sinks in an apparent balance with the availability of assimilates in the plant. Contrasting, poor growing conditions have comparatively little effect on the number of potential florets in maize (Ruget and Duburcq, 1983; Otegui, 1997). Hence, competition for limited assimilates among simultaneously developing seeds would be more accentuated in maize.

With increasing availability of resources and hence, plant growth, the relationship between SNP and PGRC tended to be linear in indeterminate soybean and curvilinear in sunflower and maize. Indeterminate soybean has a greater reproductive plasticity in response to increasing availability of resources than determinate plants with no (sunflower) or limited (maize) capacity to adjust the number of fertile reproductive sites (Loomis and Connor, 1996). In soybean, this plasticity reflects both a reproductive strategy primarily based on a pattern of allocation of meristems favoring branching and reproduction and a low dominance for assimilates among reproductive sites (Bonser and Aarssen, 1996; Sadras, 2000). A long critical period and a very marked modular structure, i.e., the production of rather independent metameric units that consist of a node-internode, functional racemes, and subtending leaf evenly distributed within the canopy, can account for a finer tuning between plant growth and setting of seed in soybean.

In contrast, the curvilinear relationships between SNP and PGRC for sunflower and for the uppermost ear in maize reflect a ceiling in reproductive plasticity. Strong apical dominance in maize and sunflower implies limited allocation to branch reproductive meristems (Sadras, 2000) despite the potential of maize plants to produce many ears. Lack of additional reproductive sinks may set more severe limitations to yield per plant, however, in nontillering and nonprolific maize than in sunflower because the latter has a greater ability to adjust flower and seed number per inflorescence and seed mass (Vega et al., 2000). In maize then, ear plasticity, prolificacy, and the threshold PGRC to set kernels in the second ear are features that correlate with a higher efficiency in SNP PGRC-1 in favorable environments. These features, nevertheless, are largely influenced by genetic and environmental factors (Otegui, 1995; Echarte et al., 1998).


   CONCLUSION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 CONCLUSION
REFERENCES
 
Consideration of interacting among individual plants within crops allowed us to develop strong relationships between seed number and growth rate during the critical period for seed set in species of contrasting growth habit, morphology, and physiology. Growth thresholds for seed set ranked soybean < sunflower < maize. The relationship between SNP and PGRC was linear in soybean, reflecting the high reproductive plasticity of this species, and curvilinear in sunflower and maize, reflecting ceilings in reproductive plasticity associated with morphogenetic restrictions in the production of additional reproductive sinks under favorable growing conditions. Prolific maize plants set more seed per unit PGRC than did nonprolific plants, highlighting the value of prolificacy for the adjustment to variable environments.


   NOTES
TOP
 NOTES
ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 CONCLUSION
 REFERENCES
 
This work was supported by INTA, UNMP, Fundación Antorchas, Dekalb S.A, and CONICET, the Research Council of Argentina. CRC Vega held a scholarship from CIC (Comisión de Investigaciones Científicas de la Provincia de Buenos Aires). F Andrade, V Sadras, and S Uhart are members of CONICET.

Received for publication June 5, 2000.


   REFERENCES
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 CONCLUSION
 REFERENCES
 




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