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Author Names: Sarah Price (BSc (Hons) Equine Science) and Rachel Collins
Retraining racehorses is proven to be popular within the equine industry, especially due to known good performance in eventing due to factors such as conformation and training methods enabling strengthening of the hors’es psychological and physiological state from a young age. This research aimed to determine whether the effects of the racing career have an impact on the success in eventing post retraining. Retrospective data was analysed from online competition records to determine whether length of racing career, the number and type of races completed or the time between racing and eventing careers affected the highest level completed, Foundation Points (FP) or Grading Points (GP) achieved during eventing careers. Of the racehorses foaled in 2000 that had competed in racing (n=3869), 2.8% (n=107) had completed a minimum one event after their racing career, suggesting high wastage rates. Negative correlations were found between the number of flat races and total FP achieved (p=0.018), FP achieved at BE100 level (p=0.036) alongside time between racing and eventing careers and highest eventing level completed (p<0.001) or FP achieved at BE100 level (p=0.029). Significant positive correlations were found between the number of jump races and total FP (p=0.001), or FP achieved at BE100 level (p=0.036). Horses with longer careers achieved more FP at BE100 level (p=0.040), those completing more jump races achieved more FP (p=0.045) and FP at BE100 level (p=0.034) and those with shorter time between racing and eventing careers (p=0.024) reached higher eventing levels. Overall, the findings suggest that horses who compete for a longer time, completing more races, especially jump racing, with shorter transitions into eventing, often achieve greater success during the eventing career. This research has created a baseline for more in-depth future research to expand upon this knowledge. Understanding which horses are successful when retraining from racing to eventing could improve welfare and health of retired racehorses, alongside enhancing performance which could lead to improved Team Great Britain (GB) success.
1.1 Industry focus on retraining racehorses for eventing
Retraining Thoroughbreds following their racing career, especially to eventing, is anecdotally reported as common and successful within the equine industry (BHA, 2016; Crossman, 2010). The popular Retraining of Racehorses (RoR) eventing series was launched in 2000 to support the retraining of racehorses to other disciplines from grassroots to elite level (RoR, 2017; Crossman, 2010); registering more than 12,500 horses as active in disciplines including eventing (BHA, 2016). There is limited research into the wider application of retraining and competing at different levels, specifically regarding success and potential barriers to success for horses within the sport. To improve the chance of ex-racehorses having successful eventing careers alongside increasing progression to elite level to enhance Team Great Britain (GB) performance on national and international stages, future research would be beneficial.
Morscher (2010) found that Thoroughbred horses were preferred for eventing during long format as other horses did not have the required stamina and speed, especially for elite level. However, in the new short format, more than half of interviewed riders thought Irish Sport Horse crossed with Thoroughbred to be the most suitable breed, with pure Thoroughbreds the second favourite (Morscher, 2010). The continued desire for Thoroughbred horses shows that research to identify and expand upon best practice for eventing an ex-racehorse successfully as well as maintaining their health and welfare would be beneficial to industry.
Ex-racehorses have recently featured as a prominent part of Team GB, highlighted by ex-racehorse, Arctic Soul, being longlisted for the Rio de Janeiro Olympic Games 2016 after coming third at Badminton International CCI**** Horse Trials in the preceding months (BE, 2016a, BE, 2016b). The industry requires a baseline understanding of how ex-racehorses progress into eventing including the selection of elite horses, to expand knowledge of the links between the racing and eventing industries. Improved understanding of what makes a successful transition onwards from racing can enable riders, owners, and trainers to make the best possible decision when choosing competition horses, especially when selecting horses for specific reasons such as elite competition.
1.2 Thoroughbred wastage
Increased knowledge of how racing career longevity and associating factors impact the suitability of horses to transition to an eventing career, alongside reasons behind horse choice in industry could help to reduce wastage figures within the equine industry (Ducro et al., 2009; Burns et al., 2006). Limited progress has been made during the past twenty years in addressing the racehorse wastage issue, primarily due to the lack of purpose designed studies focusing on wastage and the relevant preventative measures, such as entering other appropriate disciplines (Verheyen, 2012). High wastage figures from the racing industry link to the competitive and profit-oriented nature of the sport, alongside large numbers of horses not achieving the desired results on the track (Stowe and Kibler, 2016; Mayberger, 2011). Introducing discipline specific research on suitability of retraining racehorses for eventing and patterns of success could begin to reduce wastage, especially considering improvement of suitable retirement periods for racehorses to promote long and successful future careers.
Wastage within the equine industry has many financial implications; as a largely underfunded industry it is important to increase understanding of the issue and reduce the occurrence (Ducro et al., 2009; Burns et al., 2006). The racing sector acquires more funding than any other part of the industry, meaning the effect of media and positive perception of the sport is of high priority (Collins et al., 2008). Controlling wastage to reduce the negative implications and perceptions regarding welfare of racehorses and the bad press surrounding the sport could in turn improve wider perceptions of the sport, and encourage further sponsorship and support (Collins et al., 2008). Seeing racehorses transition for a successful second career could benefit viewing figures, encouraging wider involvement in both racing and eventing.
Mayberger (2011) explains a potential cause for racehorse wastage during retraining to be associated with the levels of experience, commitment, and determination to become successful in their retrained career, compared to that of other horses of the same age and respective training experience. Research to develop understanding of which horses have potential for successful eventing careers could help to reduce some difficulties in training due to their natural suitability. Within eventing, dressage, show jumping and endurance horses, Sloet van Oldruitenborgh‐Oosterbaan et al., (2010) found 55% of careers ended due to being sold, often linked to unsatisfactory performance, which is known to make up a large portion of wastage within equestrian sport. It is important to understand both the horse’s suitability for eventing and the current equine market with regards to what are considered appealing traits according to owners, riders, and trainers within the sport to improve understanding of the reasons behind wastage versus success (Hennessy et al., 2008).
1.2.1 Strain during training the young racehorse
It has been suggested that competitive careers could end early due to strain during training the young skeleton causing injuries, or predisposition to injury further into ridden careers (Burns et al., 2006). However, it has also been evidenced that horses racing from younger ages had longer careers, consistent with human research demonstrating that exercising with a growing skeleton has beneficial effects on long-term fracture resistance (Verheyen, 2012). The differing evidence highlights the need for research specifically on longevity of racing career relating to the impact on future health and career potential, for example eventing retraining. This has not been investigated thoroughly leaving many trainers, owners, and riders without definitive guidance on the best practice for enhancing health, fitness, and performance of the ex-racehorse.
Thoroughbreds can start training from two years old and it has been found that within racing, those starting training younger had longer and more successful careers, highlighting the need for research to be continued, enabling further conclusions to be drawn in this area (Martig et al., 2014; Tanner et al., 2013; Velie et al., 2013). Training the young skeleton must be done gradually, allowing remodelling to take place, to ensure the bones are strengthened and not damaged to provide long term benefits to skeletal health (Martig et al., 2014). Exercising the young skeleton is considered to be protective to joints and enhance bone strength, which is potentially beneficial to horses undergoing two physically demanding careers where large forces will be taken through the limbs (Firth et al., 2011; Kawcak et al., 2010). Understanding whether longevity of career impacts the horse’s long term career is vital to prevent suffering and ensure suitable horses are selected for competitive careers.
Positive effects of exercising the young horse have been noted in all equestrian sports, although more heavily so in racing due to young horses being frequently exercised (Rogers et al., 2012). Proven benefits on the homogeneity, strength and quality of articular cartilage were found in Thoroughbred horses exercising at younger ages (Dykgraaf et al., 2008) and horses exercising at pasture showed signs of musculoskeletal disorders earlier than horses in structured training from 18 months old (Rogers et al., 2008). The benefits from early race training strengthening the musculoskeletal system could enable horses to train and compete for longer, potentially enabling higher levels of success to be reached in eventing.
Firth et al., (2011) also found significantly better bone strength and tolerance to increased loads in Thoroughbreds exposed to increased exercise prior to race training, which could reduce the risk of fractures, early breakdown or other musculoskeletal injuries during racing and eventing careers. Injuries involving bone were found to cause large portions of breakdown within racehorses (Martig et al., 2014), therefore training the young horse appropriately could benefit health, career longevity and potential success, although being mindful of the risks associated is also vital.
1.3 Retraining Thoroughbred horses
Thoroughbreds are physiologically capable of retraining for a successful eventing career due to natural suitability to the discipline, such as power and speed (Stowe and Kibler, 2016). International success is highlighted by horses such as Director General, winner of the RoR Elite Eventing Performance Award (RoR, 2016). Training throughout the racing and eventing career will emphasise speed and stamina; focusing training on low intensity canter exercise rather than high-speed exercise decreases the risk of wastage through skeletal injury and benefits joint health (Reed et al., 2013). Most racehorses are thought to retire at an age that allows them to pursue future careers successfully, due to the existing training and musculature providing a good basis for future training in the form of reducing injury risk via superior musculoskeletal strength and cardiorespiratory performance (Stowe and Kibler, 2016). Analysis of 17,962 competition results in dressage, show jumping, and eventing determined that horses competing in more than one discipline, especially those starting younger, had longer competitive careers (Braam et al., 2011), highlighting the possibility of increasing ridden and competitive careers through encouraging retraining after retirement from one area within equestrian sport, such as racing to promote good health and welfare.
Conditioning the racehorse for an eventing career should include an appropriate level of physiological and psychological stress to prepare them for the demands of competition, increasing the potential for success throughout the competing career (McKenzie, 2017). Thoroughbreds are likely to need longer in the habituation phase of retraining due to their high sensitivity, reactivity, and anxiety, which should be less present than in racing where the flight response is important (Lloyd et al., 2008). Assessing appropriate training prior to eventing is vital to ensure longevity of the career, enabling horses to be successful. Graf et al., (2013) explain that when sold, horses are often chosen for good temperament over physiological traits. This highlights the importance of investigating how racing career longevity could impact the duration, type and success of retraining required before achieving competition success in eventing, where limited research currently exists.
1.3.1 Physiological adaptations
All horses should be trained with a combination of schooling to develop neuromuscular coordination and conditioning for physiological and structural adaptation of the body systems, which can be transferred from racing to eventing (Castejon-Riber et al., 2017). However, it is important to consider the length of time required for the musculoskeletal system to adapt to the requirements of the new career to reduce the risk of injury and increase performance (RoR, 2018). It is vital to specifically train systems such as muscular, skeletal and cardiorespiratory to ensure the horse is physiologically strong and capable of carrying out the required work, to avoid strain that could lead to injury, poor performance or poor welfare (Castejon-Riber et al., 2017). Understanding the patterns between type of racing career and the period of retraining would aid production of training plans and further research into enhancing the Thoroughbred’s performance in both racing and eventing.
Skeletal muscle mass of Thoroughbreds is high at 53-57%, compared to 42% in ‘non-athletic’ breeds (Rivero and Hill, 2016). In response to training, protein constituents per muscle fibre increase creating an increased muscle mass, thus generating increased potential power output, creating higher performance potential (Rivero and Hill, 2016). Thoroughbreds have shown increases in muscle fibre type 2A in the forelimbs through training thus enabling enhanced locomotion and speed, however the hind limb is considered the key mechanism of propulsion, with the increase of type 2X fibres enhancing this it will also benefit the horse during the cross-country phase of eventing (Rivero and Hill, 2016; Kawai et al., 2009). Aerobic and anaerobic capacities can also be influenced by training, which should be enhanced through race training due to the goal of training the racehorse, giving horses an improved start to retraining for eventing (Jang et al., 2017).
1.4 Thoroughbred musculoskeletal health
Veterinary problems prevented 35.1% of horses from re-registering with British Eventing (BE) in a study conducted by O’Brien et al., (2005), highlighting the importance of understanding which horses are also not suitable for an eventing career post racing, to hopefully reduce this figure in the future. Horses that are older at their first race or have raced for over five years have demonstrated higher risk of injury (Martig et al., 2014; Ducro et al., 2009; Henley et al., 2006), showing the importance of understanding the interaction between factors in the racing career that could impact eventing performance. Increased injury risk is specified for horses competing in jump racing compared to flat racing, alongside an increased risk when running a different type of race for the first time (Henley et al., 2006).
It is also important to understand which horses are most likely to have experienced injuries during their careers to prevent horses being put under excessive strain during retraining. For example, horses in jump racing careers are older and due to the fatigue damage build-up throughout the longer ridden career they are at higher risk of injury, such as stress fractures due to high pressure through the distal limbs for longer periods (Martig et al., 2014). Superficial digital flexor tendonitis is common in racehorses due to pressures on the distal limb, however due to strain caused by the cross-country phase it is important to consider horses at higher risk, for example jump racing, and ensure training and future careers are considered appropriately (Witte et al., 2016). Musculoskeletal injury is considered the primary cause of wastage within sport horses, with conformational predisposition to lameness thought to impact heavily (Ducro et al., 2009).
It is known that Thoroughbreds make good event horses due to their athleticism and speed, however, considering conformation is important to reduce the risk of injury caused by strain to the limbs and back in particular during eventing (Dyson, 2002). Performing at elite level competition is thought to require stronger conformation, especially in the limbs and more specifically distal limbs (Ducro et al., 2009). Larger angles of the hock and stifle joints have been linked to improved jumping ability (Holmström and Philipsson, 1990), which could link horses with successful jump racing careers to possible eventing success due to natural potential. Ideally racehorses should be physically lightweight to reduce the strain on the body, especially the distal limb; the third metacarpal having smaller width has also been linked to sport horse ability, showing similarities in desired conformation (Holmström and Philipsson, 1990). Knowing that the horses are physiologically well matched could encourage those in industry to choose ex-racehorses for eventing, further evidence of potential for success could also increase appeal, therefore lessening wastage.
Conformational research in racehorses is often focused on development of young horses or injury risk, however significant conformational differences have been found between National Hunt (NH) and flat racehorses (Weller et al., 2006). NH horses presented significantly larger on average in neck, forelimb and pelvis length, tibia circumference and most notably so in front and hind hoof circumference compared to flat racehorses (Weller et al., 2006). It is important to ensure the larger hind hoof circumference does not negatively impact the horse if farriery is not suitable, as uneven hooves are considered undesirable due to the potential of limiting competition career longevity (Ducro et al., 2009). NH horses have larger frames, however, it is considered a possibility for this to be because of widespread anecdotal evidence that horses participating in jump racing should be bigger than flat racehorses, influencing the sales market (Weller et al., 2006). However, Weller et al., (2006) study used a sample of horses from only one yard, meaning the type of horses on that yard may be chosen specifically due to conformational preferences of the owners or trainers, potentially based on previous success rates, potentially impacting the results.
1.5 Research aim
The aim of this study is to determine whether the effects of the racing career have an impact on the success in eventing post retraining.
(1) To investigate whether the longevity of a racehorse’s career influences the level of competition reached through British Eventing post retraining.
- H0: The longevity of a racehorse’s career will have no association with the level of competition reached through British Eventing post retraining.
- H1: The longevity of a racehorse’s career will have an association with the level of competition reached through British Eventing post retraining.
(2) To investigate whether the longevity of a racehorse’s career influences the number of foundation and grading points accumulated through British Eventing post retraining.
- H0: The longevity of a racehorse’s career will have no association with the number of foundation and grading points accumulated through British Eventing post retraining.
- H1: The longevity of a racehorse’s career will have an association with the number of foundation and grading points accumulated through British Eventing post retraining.
(3) To investigate whether the type of races run influences success in eventing post retraining.
- H0: The longevity of a racehorse’s career will have no association with the level of competition reached through British Eventing post retraining.
- H1: The longevity of a racehorse’s career will have an association with the level of competition reached through British Eventing post retraining.
(4) To investigate whether the time between racing and eventing careers influences success in eventing post retraining.
- H0: The time between racing and eventing careers will have no association with the number of grading points accumulated through British Eventing post retraining.
- H1: The time between racing and eventing careers will have an association with the number of grading points accumulated through British Eventing post retraining.
2.1 Study design
To address the aim of the study, quantitative retrospective data from 2000 to 2017 were collected from Weatherbys, Weatherbys Point to Point, following approval to use the data, and open data from British Horseracing Authority (BHA), Racing Post and British Eventing (BE) online databases to gain full data on all aspects of the horses’ competition records, which has been proven as a performance parameter from previous research (BE, 2017; BHA, 2017; Kearsley et al., 2008; Olsson et al., 2008; Racing Post, 2017; Weatherbys, 2017; Weatherbys Point to Point, 2017). Data were recorded and coded in Microsoft Office Excel 2016, before data analysis was undertaken on IBM SPSS Statistics 24; all data were kept confidential and securely in accordance with The Data Protection Act (1998).
Data obtained from Weatherbys included a list of every horse foaled in 2000 in Great Britain that had successfully completed one or more race (n=3869), the names were checked against the BE database to compare full name, dam and sire of the horse to determine whether they had competed in affiliated eventing. Horses that had competition records for racing and eventing then had data collected for the date of first and last race completed, number of runs and the type of races run collected from BHA, Racing Post and Weatherbys Point to Point. Data collection from British Eventing included the date of first event completed, highest competition level finished, number of BE Foundation Points (FP) and the level at which they were achieved (BE90 or BE100) alongside the number of BE Grading Points (GP) and the level at which they were achieved (Novice, CIC*, CCI*, Intermediate, CIC**, CCI**, Advanced, CIC***, CCI*** or CCI****) (BE, 2018).
The population of racehorses foaled in 2000 in Great Britain (GB) that completed minimum one race (n=3869) was gained from Weatherbys by homogeneous sampling (Etikan, 2016). Inclusion and exclusion criteria were enforced to ensure data was relevant and accurate for analysis to limit the impact of extraneous variables on the results (Smith, 2010). For example, large variations of age could affect the data available regarding career length, retraining and success due to incomplete databases, or horses having unfinished careers. The inclusion criteria consisted of horses foaled in 2000 that had completed a minimum of one competition in racing and affiliated eventing, to enable full competition career data to be gathered. The exclusion criteria specified horses that had not competed in both disciplines or competed in eventing prior to competing in racing.
The sample of horses foaled in 2000 was chosen as BE and The Fédération Equestre Internationale (FEI) changed the competition format from 2004 onwards by removing the roads and tracks phase, significantly changing the athletic demand of the sport (BE, 2009a). Some horses may have been more successful in one version of the sport than the other due to individual variation, therefore standardising age removed the impact of this when determining eventing success. Horses are not able to compete with BE until four years old for four-year-old classes comprising of a different format without a cross country phase, or five years old for all other standard competition, which was recorded during this study (BE, 2009b). All horses in the sample were competing in the same format regardless of the start and end dates of their eventing career. Horses that were foaled in earlier years could have competed in the previous or current format if they evented at a younger age. Horses foaled in more recent years could be at the start of their eventing career and not yet reached peak success, both of which would have influenced the results.
2.3 Data analysis
To assess the quantitative data collected, descriptive statistics including mean, standard deviation (SD), median and range were used to create an overview of the data, before inferential statistics were used to assess the research objectives relating to correlations and differences found (Foster et al., 2015) through the following statistical tests. Kolmogorov Smirnov Test (KS Test) for normality was carried out on all data to determine whether it was parametric (p>0.05) or non-parametric (p<0.05), and therefore which test must follow (Dytham, 2011).
Spearman’s Rho test for correlation was carried out on all variables as they were non-parametric following the KS test (Field, 2013), to determine the significance, strength, and direction of the correlation and whether to accept or reject the null hypotheses (Dytham, 2011). A significance output (p-value) of less than 0.05 indicated a relationship between the two variables; the correlation coefficient, between -1 (negative correlation) and +1 (positive correlation), determined the direction and strength of the relationship between the two variables (Elliott and Woodward, 2016; Dytham, 2011). Effect size is determined by the correlation strength; strong linear relationships were indicated by a correlation coefficient of +/- ≥0.51, moderate relationships at +/-0.31-0.50, weak relationships at +/-0.10-0.30 and no correlation +/-0.10 (Elliott and Woodward, 2016).
To test for difference following the KS test, the Kruskall Wallis Test was carried out on the non-parametric variables (Field, 2013). The output determined the significance, whether a difference was found, if so where the difference was found and therefore whether to accept or reject the null hypotheses (Field, 2013). The significance values of each test were noted, with p<0.05 showing significance; however, the adjusted significance values were taken as the true significance as the data had undergone the Bonferroni correction for multiple tests via pairwise comparisons (Field, 2018). The post-hoc tests in the form of pairwise comparisons compared all different combinations of the groups to provide the adjusted significance values and determine if there is significance between any groups, where the significance lies and to what extent (Field, 2018). Adjusted significance values of p<0.05 demonstrated a significant difference in the distribution of data across the specified categories (Dytham, 2011).
3.1 Descriptive statistics
Of the 3869 racehorses that made it to the track, 3.6% (142 of 3869) registered with BE and 81.6% of these (116 of 142) completed a minimum of one eventing competition. Horses that competed in eventing before their racing career were excluded from the study (n=8), alongside those only competing in Jumping and Style (JAS) competitions (n=1), leaving 107 horses valid for analysis. The racing career length median was 12 months (IQR=24) with a range of <1-71; see figure 1. The median number of racing runs was 6 (IQR=7.5) with a range of 1-46; throughout the horses’ careers, 17.8% (19 of 107) ran both flat and jump races, 29.9% (32 of 107) only ran flat races and 52.3% (56 of 107) only ran jump races; see figure 2.
Figure 1: Length of horses’ racing careers (months)
Figure 2: Type of races completed during racing careers.
The time between racing and eventing careers had a range of 2-124 months, with a median of 24 (IQR=24). The highest BE level completed was predominately BE100 at 40.2% (43 of 107), followed by 32.7% at BE90 (35 of 107), 18.7% at Novice (20 of 107), 3.7% at BE80(T) (4 of 107), 1.9% at CIC* (2 of 107), 1.9% at Intermediate (2 of 107) and finally 0.9% at CCI** (1 of 107); see figure 3
Figure 3: Highest British Eventing (BE) level completed during eventing careers.
30.8% (33 of 107) of horses achieved FP and 11.2% (12 of 107) of horses achieved GP throughout their eventing careers; 24.2% (8 of 33) of horses with FP achieved points at both BE90 and BE100 levels, whilst 45.5% (15 of 33) of achieved points only at BE90 level and 30.3% (10 of 33) achieved points only at BE100 level; see figure 4. 8.3% (1 of 12) of horses with GP achieved points at both Novice and CCI* levels, whereas 91.7% (11 of 12) achieved points only at Novice level; see figure 5.
Figure 4: Total Foundation Points (FP) achieved during eventing careers, across all horses.
Figure 5: Total Grading Points (GP) achieved during eventing careers, across all horses.
3.2 Testing for correlation with Spearman’s Rho
Testing for normality with KS test showed that all variables were significantly non-parametric (p<0.05). There was no significant correlation (p>0.05) between the highest eventing level completed and the length of racing career (months) or number of racing runs. No significant correlations were found (p>0.05) between racing career length (months) or number of racing runs and the total number of FP, FP at BE90 level, or FP at BE100 level, total GP, GP at Novice level or GP at CCI* achieved.
Weak negative correlations were found between the number of flat races completed and the total FP achieved (n=107, rs=-0.228, p=0.018) and FP achieved at BE100 level (n=107, rs=-0.203, p=0.036). A moderate positive correlation was found between the number of jump runs completed and the total FP achieved in eventing (n=107, rs=0.310, p=0.001) alongside a weak positive correlation between number of jump races completed and FP achieved at BE100 level (n=107, rs=0.203, p=0.036).
No significant correlations were found (p>0.05) between the number of flat or jump races completed and total GP achieved, GP at Novice level or GP at CCI* level. No significant correlations were found (p>0.05) between time between racing and eventing careers (months) and Total FP achieved, FP achieved at BE90 level, total GP achieved, GP achieved at Novice level or GP achieved at CCI* level.
3.3 Testing for difference with Kruskall Wallis
No significant difference was found (p=0.115) between the distribution of racing career length (categorised 6 monthly) across the highest BE level completed however the distribution of the number of racing runs (categorised per 6 runs) across the highest BE level completed was significantly different (X22=16.382 n=107 p=0.022), although there was no significant difference between groups (p>0.05) after post-hoc Bonferroni pairwise comparison.
No significant differences were found (p>0.05) between the distribution of racing career length (categorised 6 monthly) across total FP or FP at BE90 level achieved or between the distribution of number of runs (categorised per 6 runs) across total FP achieved. There was a significant difference between the distribution of number of racing runs (categorised per 6 runs) across FP achieved at BE90 level (X22=15.578 n=107 p=0.029) and across FP achieved at BE100 level (X22=14.167 n=107 p=0.048), although there was no significant difference between any of the groups (p>0.05).
The distribution of racing career length (categorised 6 monthly) across FP achieved at BE100 level was significantly different (X22=19.822 n=107 p=0.48), with horses racing for 49-54 months achieving significantly more FP at BE100 level than horses racing for 7-12 months (p=0.040), as shown in table 1.
Table 1: Significant findings after testing for difference with Kruskall Wallis and post-hoc Bonferroni correction between racing career length and eventing career success.
|Dependent Variable||Independent Variable||Group specific p value||Adjusted p value at post-hoc|
|Number of FP at BE100 level achieved||Racing career length (months)||p=0.048||p=0.040|
There was no significant difference (p>0.05) between the distribution of racing career length (categorised 6 monthly) across the total number of GP achieved or across the GP achieved at Novice level, or across GP achieved at CCI* level. There was also no significant difference (p>0.05) between the distribution of number of racing runs (categorised per 6 runs) across the total number of GP achieved, or across GP achieved at Novice level.
The distribution of number of racing runs (categorised per 6 runs) across GP achieved at CCI* level was significantly different (X22=20.400 n=107 p=0.005). Horses racing for 25-30 months achieved significantly more GP at CCI* level than horses racing for £6 months (a) (p<0.001), horses racing for 7-12 months (b) (p=0.001), and horses racing for 13-15 months (c) (p=0.011), as shown in table 2.
Table 2: Significant findings after testing for difference with Kruskall Wallis and post-hoc Bonferroni correction between number of racing runs and eventing career success.
|Dependent Variable||Independent Variable||Group specific p value||Adjusted p value at post-hoc|
|Number of GP at CCI* level achieved (a)||Number of racing runs||p<0.001||p<0.001|
|Number of GP at CCI* level achieved (b)||Number of racing runs||p<0.001||p=0.001|
|Number of GP at CCI* level achieved (c)||Number of racing runs||p<0.001||p=0.011|
There was no significant difference (p>0.05) between the distribution of types of races completed across highest BE level completed, or FP achieved at BE90 level, total GP achieved, GP achieved at Novice level or GP achieved at CCI* level. The distribution of the types of races completed across total FP achieved was significantly different (X22=8.786 n=107 p=0.012); horses competing in jump racing only achieved significantly more FP than those competing in both flat and jump racing (p=0.045). The distribution of the types of races completed across FP at BE100 level was significantly different (X22=7.198 n=107 p=0.027); horses competing in jump racing only achieved significantly more FP at BE100 level than those competing in both flat and jump racing (p=0.034) as seen in table 3.
Table 3: Significant findings after testing for difference with Kruskall Wallis and post-hoc Bonferroni correction between type of races completed and eventing career success.
|Dependent Variable||Independent Variable||Group specific p value||Adjusted p value at post-hoc|
|Total number of FP achieved||Types of races completed (flat only, jump only, flat and jump)||p=0.012||p=0.045|
|Number of FP achieved at BE100 level||Types of races completed (flat only, jump only, flat and jump)||p=0.027||p=0.034|
There was no significant difference (p>0.05) between the distribution of time between racing and eventing careers across total FP achieved, or FP at BE90 level, FP at BE100 level, total GP achieved, GP achieved at Novice level or GP achieved at CCI* level. The distribution of the time between racing and eventing careers across highest BE level completed was significantly different (X22=19.582 n=107 p=0.003); horses with 13-24 months between the racing and eventing careers reached a significantly higher level than those with over 73 months between careers (p=0.024) as seen in table 4.
Table 4: Significant findings after testing for difference with Kruskall Wallis and post-hoc Bonferroni correction between the time between racing and eventing careers and eventing career success.
|Dependent Variable||Independent Variable||Group specific p value||Adjusted p value at post-hoc|
|Highest BE level completed||Time between racing and eventing careers (months)||p=0.003||p=0.024|
5194 Thoroughbreds were foaled in 2000 (IFHA, 2018), however this project found that only 3869 of these horses made it to the track with 107 competing in eventing following their racing career, suggesting a high wastage rate. Lack of ability was found to be a large contributor to wastage (More, 1999), which could explain the loss of horses prior to racing and eventing careers if potential is not shown during training. Awareness of where these horses end up and reasons behind unsuccessfulness is largely unknown, due to issues in record keeping as highlighted by Collins et al., (2008) when examining the Irish equine industry, learning that only Weatherbys births were truly regulated.
4.1 Racing career length
Horses racing for 49-54 months achieved significantly more FP at BE100 level than those racing for 7-12 months (p=0.040) suggesting that horses with longer careers are more inclined to succeed at this level. Benefits to musculoskeletal health have been noted due to early and prolonged exercise in horses (Rogers et al., 2012); creating a robust horse through maintenance of appropriate training may aid the length and potential for success in future careers. Knight and Thomson (2011) found that horses racing as two-year-olds earned significantly more prize money throughout their careers (p<0.001), had significantly longer careers (p<0.001) and completed more races (p<0.001) than horses starting racing older. Knight and Thomson (2011) found that horses beginning training at younger ages, thus enabling longer racing careers to follow, were chosen due to demonstrating high athletic ability and trainability. Such qualities are also sought after in the event horse, highlighting a possible explanation for horses with longer careers achieving significantly more FP at BE100 level.
Vigre et al., (2002) found that horses with the same trainer for more than three months had a reduced risk of lameness, as it takes time for the trainer to create optimal training plans to enhance both performance and musculoskeletal health. Horses that are moved on in less time may not be given opportunity to show their full potential which could be a reason for fewer horses succeeding in eventing, with only 30.8% (33 of 107) achieving FP and 11.2% (12/107) achieving GP and no horses exceeding CCI** level. Sobczynska (2007) found that Thoroughbreds with experienced professional trainers had significantly longer racing careers than those trained by amateurs; professional training may increase positive effects such as musculoskeletal development and therefore speed and stamina, meaning improved knowledge as to why professionals are not commonly training ex-racehorses would be beneficial.
Sobczynska (2007) found horses at smaller yards to more commonly be trained by their owners and not professionals; extrapolating this research to eventing suggests that training could have a large impact upon their success and career longevity, potentially explaining why no significant differences were found other than FP BE100. Caston and Burzette (2018) found 77.5% of horses were reported in eventing training with amateur riders from an online survey, suggesting that ambitions for elite competition and associated training may be reduced, potentially impacting the eventing level reached and GP achieved. Caston and Burzette (2018) respondents were predominantly Thoroughbred horses at 44.6%, echoing thoughts that these horses remain popular within eventing. However, it is important to note that findings could be influenced by response rates in professional riders, as only 3% respondents competed at intermediate or advanced level. Understanding the differences between horse choice in amateur and professionals could improve knowledge on why increased points were only noted in FP BE100
4.2 Number of racing runs
Significant differences were found between the distribution of number of racing runs across the FP achieved at BE90 level (p=0.029) and FP achieved at BE100 level (p=0.048). The increase in significance compared to that found across career length could be due to the increased information of how much training and competing the horse undertook during their racing career. The intensity of runs within the horse’s career show the true physiological demands of the career, therefore number of races may be a more representative measure than career length on its own (Castejon-Riber et al., 2017). By assessing the number of runs horses have carried out it was shown that those with increased runs, presented increased success in eventing.
Significantly more GP at CCI* level were achieved by horses running 25-30 races than those running £6 races (p<0.001), 7-12 races (p=0.001) or 13-15 races (p=0.011). It was found that only one horse achieved points at CCI* level meaning the significant differences found may not represent the wider population due to not having enough horses (Etikan et al., 2016). To draw valid conclusions within this area, investigating cohorts of horse with elite eventing results could provide more detailed and representative investigations into the impact of racing career on eventing success, providing better application to industry.
There was a significant difference between the overall distribution of the number of racing runs and the highest BE level completed (p=0.022), however no differences were found between groups. 95.3% of horses’ highest eventing level completed was novice or below, potentially influencing these results as there could be significant differences between number of racing runs compared to higher eventing levels which cannot be fully appreciated with this study population. Levels above novice consist of increased height (≥1.10-1.30m), number (≥28-45) and technicality of jumping efforts, increased speed from £475m/min to ≥520m/min alongside increased technicality required for dressage and show jumping such as the introduction of lateral work and trebles (BE, 2018). Horses capable of competing in these levels require natural ability for the sport, alongside superior musculoskeletal health and training, which can also be impacted by who is training the horse and the time available for development (Sobczynska, 2007), highlighting the importance to understand more on industry practice within training for specific levels of success.
4.3 Types of races completed
Negative correlations were found between the number of flat races completed and FP achieved (p=0.018) or FP at BE100 level (p=0.036). Positive correlations were found between the number of jump races completed and total FP achieved (p=0.001) or FP at BE100 level (p=0.036). Horses competing in only jump racing compared to flat and jump achieved significantly more FP (p=0.045) and FP at BE100 level (p=0.034). This suggests that horses training specifically for jump racing could find large benefit throughout the retraining process due to physiological capabilities and coordination, thus indicating that horses completing more jump races show proclivity for higher success rates at levels up to BE100. Horse completing jump racing tend to be older, therefore may not have the time to do detailed enough training to achieve success at higher levels, including those involving GP (Castejon-Riber et al., 2017; Stowe and Kibler, 2016).
Ducro et al., (2009) highlight that elite sport horses require strong conformation to ensure musculoskeletal health and performance, with the large hock and stifle angles to aid jumping, jump race horses are well suited to the biomechanical principles of eventing (Holmström and Philipsson, 1990). Horses with desirable jumping conformation should ideally compete in just jump racing due to natural ability, allowing jump training to progress when retraining for eventing (Ducro et al., 2009). However, if horses compete in flat racing first, it will add extra strain to the distal limbs, increasing injury risk prior to two strenuous jumping careers (Henley et al., 2006), potentially why these horses achieved less FP and FP at BE100 (p<0.05).
Hausberger et al., (2011) found that personality was affected by the type of work the horse was doing, expression became different in the horses, which is important to consider when looking at the number of horses undertaking flat racing compared to jump racing. Horses start flat racing at a younger age, and can train for jump racing as well, although horses that do not suit flat racing may be immediately retried, although a jump career could improve behaviour and performance (Hausberger et al., 2011). As increased jump racing shows a trend of increased success, it is important to explore whether the training involved in jump racing or physiological characteristics are giving horses the advantage.
4.4 Time between racing and eventing careers
Negative correlations were found concerning the time between racing and eventing careers and the highest eventing level reached (p<0.001), alongside FP achieved at BE100 level (p=0.029). Alongside this, horses with 13-24months between racing and eventing careers reached a significantly higher eventing level than those with 73months or more (p=0.024), suggesting that horses with shorter time between the racing and eventing careers have increased likelihood of achieving greater levels of eventing success.
Conversely, horses that have small breaks between racing and eventing careers may not have had enough time for conditioning and retraining, meaning they are not physiologically and psychologically prepared for a long and successful eventing career (Lloyd et al., 2008). Thomson et al., (2014) found that more horses left competitive careers due to unsuitable behaviour than any other reason, highlighting the importance of suitable training and habituation. This could explain why no significant differences were found regarding GP achievement, as horses may require different degrees of training and development to succeed at elite level.
McKenzie, (2017) suggests that short transitions between racing and eventing are common and often successful, as demonstrated with these findings. Training the horse for the high intensity cross country phase should focus more on optimising aerobic capacity and speed which can typically be done over short periods, especially as these horses will already have such qualities from race training (Jang et al., 2017; McKenzie, 2017). McKenzie (2017) explains that lengthier periods of training are required to perfect the discipline and technicality required during phases such as dressage. Horses with shorter periods between racing and eventing careers could be missing this increased precision within their performance, potentially reducing success rates with regards to GP achieved and reaching elite level eventing. It is important to consider that horses within this sample did not exceed levels of CCI**, therefore assessing these correlations and differences with horses at top level such as Advanced/CCI**** would improve application to industry with regards of choosing elite event horses.
Negative correlations between time between racing and eventing careers and highest level achieved or FP at BE100 level could signify that horses with lengthier retraining periods have experienced periods out of work, meaning the horse is not capable of reaching high success levels in eventing due to injury rehabilitation and enduring weakness. Racehorses with superficial digital flexor tendonitis were found to have long periods off work, on average 18 months, however the time to competition fitness could be extensive (Witte et al., 2016), elongating the time between racing and eventing careers. During time out of training, bone mineral density (BMD) has been proven to decrease 0.45% in 12 weeks, highlighting issues with reduction of bone strength and increased injury risk upon return to work, especially in those with lengthy periods out of work before returning to strenuous work. (Yamada et al., 2015; Porr et al., 1998).
4.5 Applications to industry
Understanding that horses with longer careers have shown success in eventing can help to improve preventative measures for common injuries and strains seen in both disciplines, to improve career longevity and success alongside reducing wastage. Knowledge on the links between jump racing and eventing success can also aid in horse selection to promote strong conformation and ability, ensuring horses are in a career they can excel in to promote performance, especially at higher levels. The retraining period has been shown to be of importance to eventing success, with shorter periods enabling greater success, potentially due to the horses maintaining training and physiological prowess. Promoting good practice of retraining to ensure physiological benefits from race training are maintained could improve welfare of horses alongside performance. Learning more about why horses are proving successful at lower levels involving FP, but not at higher levels with GP would be useful to improve access for horses to reach the higher levels, improving team GB performance and appealing to wider variety of horse owners.
Limitations within this research link to the nature of retrospective data usage, through lack of access or insufficient and incomplete datasets, meaning study design is important to get the most out of the data accessible (Saunders et al., 2012). However, using databases from Weatherbys and national governing bodies reduced the impact of this due to higher standardisation and regulations. This research focused on creating an understanding of baseline patterns within the existing population to enable further exploration into specific areas with prospective or qualitative research. It is not possible to say that horses could not have exceeded the highest eventing level or number of FP and GP achieved during their career as there are many reasons for progression to stop, even where natural talent and good performance is present. Quantitative research based on purely competition records does not enable full comprehension into why horses did not progress further, for example due to reaching peak performance or injury, rider change or financial factors.
Horses could be exported prior to, during, or after racing, which cannot be controlled, leaving gaps in the data as the horses quickly become untraceable due to insufficient regulations and record keeping (Collins et al., 2008). These horses could have competed in other countries in racing and eventing, which could impact the industry-wide perception of retraining racehorses for eventing due to data potentially being incorrect regarding the longevity and success of their competitive careers. It is also possible that ex-racehorses eventing with BE have competed in racing abroad prior to retraining, where full data is not acquirable, potentially impacting data analysis. Horses may transition to unaffiliated rather than BE due to costs, perception of less competitive pressure and more enjoyment-based compared to more intense competition. Accurate and consistent information also cannot be compiled from unaffiliated eventing due to no overarching standardisation in recording and publishing results (Collins et al., 2008).
4.7 Future research recommendations
Using prospective research to investigate cohorts from other years could improve knowledge on the careers and success rates post racing retirement. Factors such as the financial crisis, which impacted the UK greatly (Hodson and Mabbett, 2009), could influence the number of horses competing at that time. Therefore, determining why horses are not competing through qualitative research could benefit the industry, alongside investigating a variety of time periods. Thirkell and Hyland (2017) explain that quantitative research prevents greater depth of information being collected, therefore now there is information that factors of the racing career longevity and retraining impact some factors of eventing success, it would be recommended to investigate further with qualitative research, enabling more in-depth conclusions to be drawn.
Horses do not reach the racetrack for varying reasons, for instance poor performance or interruptions to training; some horses are also bred with the intention of other purposes despite the full Thoroughbred bloodlines (Bolwell et al., 2013); it would be interesting to investigate the path of these horses to investigate further where the Thoroughbred event horses start off. There is also little knowledge of what horses do between competition record data, therefore establishing methods of gathering more in-depth data via qualitative research following up with horses throughout the retraining period would be beneficial. Information on the length of time in training for racing following the final race and before the first event would give a better indication on the factors relating to training that impact success in eventing, alongside whether time out of training is truly beneficial or detrimental to the horse’s health and performance.
The research aim was to determine whether the racing career impacts the success of eventing careers post retraining. Overall, horses who competed for a longer time, completed more races, especially jump racing, and had shorter transitions into eventing achieved significantly greater success regarding competition level reached, FP or GP achieved (p<0.05). High wastage rates were found from birth of Thoroughbred foals (n=5194) to racing (n=3869) and finally eventing (n=107), investigating further why those that did not successfully achieve racing or eventing careers could improve knowledge on how to improve training and management strategies to promote good performance in a larger range of horses.
Horses with longer racing careers achieved significantly more FP at BE100 level (p=0.040) suggesting that the physiological conditioning through prolonged training could have lasting benefits to the horse through the eventing career. Underlying conformation, similar demands and training style may contribute to increased jump racing runs resulting in significantly more FP (p=0.045) and FP at BE100 level (p=0.034) being achieved. Horses with shorter periods between racing and eventing careers achieved significantly higher levels in eventing (p=0.003), highlighting the importance of gaining knowledge of horses’ activities throughout their career, to appreciate whether longer periods out of competition equates to injury, therefore potentially limiting future performance. Research to develop this knowledge regarding current success rates of ex-racehorses in eventing, involving qualitative and prospective studies could improve understanding of the interaction between careers further, to promote good welfare, health and performance of Thoroughbred horses.
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Microbiological assessment of canine drinking water: the impact of construction material on the quantity and species of bacteria present in water bowls
Author Names: Coralie Wright (BSc (Hons) Bioveterinary Science) and Aisling Carroll
The number of pet dogs (Canis lupus familiaris) in the common household is continually rising. The increasingly close contact between humans and cohabitant pets is leading to concerns regarding bacterial transmission of zoonoses. The dog water bowl has been identified as the third most contaminated item within the household, suggesting that it is able to act as a fomite for bacterial transmission, particularly where young or immunocompromised individuals are present. Studies in livestock have identified that water trough construction material influences bacterial count; however no similar research has been conducted for dog water bowls. The objectives of the current study were to identify which dog bowl material – plastic, ceramic or stainless steel – harbours the most bacteria over a 14 day period and whether the species identified varies between bowl materials. The study took place over 6 weeks. A sample of 6 medium sized (10-25kg) dogs aged 2-7 (mean= 3.8 ± 1.95) was used. All dogs were clinically healthy, housed individually and located within a rural environment. All bowls were purchased brand new and sterilised prior to a two week sampling period. On day 0, day 7 and day 14 swabs were taken from each bowl and 10-fold serial dilutions were conducted on blood agar. The cultured bacteria were subjected to biochemical testing and the most prominent bacteria from day 14 were further identified using PCR. A significant difference was identified for all bowl materials when comparing total CFU/ml between day 0 and day 7 and day 0 and day 14 (p<0.05). No significant difference was identified between total CFU/ml and bowl material (P>0.05), however descriptive statistics suggest that the plastic bowl material maintains the highest bacterial count after 14 days. Several medically important bacteria were identified from the bowls, including MRSA and Salmonella, with the majority of species being identified from the ceramic bowl. This could suggest that harmful bacteria may be able to develop biofilms more successfully on ceramic materials. Further research is required to identify the most suitable or alternative materials for dog water bowls.
The domestic dog (Canis lupus familiaris) has lived alongside humans for at least 15,000 years (Frantz et al., 2016; Wang et al., 2016; Shannon et al., 2015). Now one of humankind’s closest companions, the number of dogs kept within households is increasing, with an estimated population of 8.5 million in the UK (PFMA, 2018; Damborg et al., 2016; Wang et al., 2016; da Costa, Loureiro and Matos, 2013; Buma et al., 2006). Increasingly close contact between humans and their dogs allows favourable conditions for bacterial transmission both directly and indirectly (Damborg et al., 2016; Chomel and Sun, 2011; Deplazes et al., 2011; Bowman and Lucio-Forster, 2010; Fèvre et al., 2006; Guardabassi, Schwartz and Lloyd, 2004). Dogs spend a considerable amount of time outdoors, where they come into contact with a plethora of bacterial pathogens through contaminated soil, water, faeces and other animals (Lambertini et al., 2016). Additionally, the canine skin and oral microbiome is naturally colonised by a diverse range of bacteria (Hoffmann et al., 2014; Dewhirst et al., 2012; Elliot et al., 2005). Sixty percent of emerging pathogens are considered zoonotic (Dula and Pal, 2017; Cutler, Fooks and van der Poel, 2010), emphasising the importance of minimising the risk of bacterial transmission between humans, particularly the young and immunocompromised, and their cohabitant pets (Damborg et al., 2016; Moyaert et al., 2006). Lambertini et al. (2016) state that contact between owners and their dog occurs multiple times a day and can also be mediated by surfaces. The dog water bowl has been identified by Donofrio et al. (2012) as the third most contaminated item in the household, suggesting it is able to act as a fomite in bacterial transmission. There is currently no research exploring the most suitable material for dog water bowls with regards to bacterial load and species.
1.1 Development of Biofilms
Water is essential for life and almost every canine bodily function (Kenssington, 2014; Jéquier and Constant, 2010; Aspinall, 2012); however, the visual purity of water can disguise the proliferation of microorganisms (Folorunso, Kayode and Onibon, 2014). Bacterial biofilms can develop on many abiotic surfaces, particularly those in contact with non-sterile water (Flemming, 2011; Morita et al., 2011; Fuster-Valls et al., 2008). Livestock water troughs have been previously identified as reservoirs for bacterial growth and biofilm formation (Folorunso, Kayode and Onibon, 2014; Watson et al., 2012; Cook, Britt and Bolster, 2010; Avery et al., 2008; Sargeant et al., 2003; McGee et al., 2002; LeJeune et al., 2001a; LeJeune et al., 2001b); however research on dog water quality is incredibly limited. Biofilm formation in dog water bowls could serve as temporary or long term habitats for hygienically important bacteria (Wingender and Flemming, 2011). In addition to continuous re-infection of bacteria to dogs via ingestion of contaminated water, it is suggested that biofilms are responsible for 65% of all bacterial infections in humans (Rowson and Townsend, 2016). When washing or refilling a water bowl, humans may come into contact with bacterial biofilms and subsequently transfer pathogens from their hands to household surfaces (Lambertini et al., 2016). This is cause for concern as cohabitant pets often reside within the kitchen, which may ultimately lead to ingestion of contaminant bacteria (Lambertini et al., 2016; Behravesh et al., 2010).
Bacteria in biofilms exhibit increased resistance and tolerance to antimicrobials and environmental stressors (Ciofu et al., 2017; Wall and Mah, 2017; Rowson and Townsend, 2016; Steenackers et al., 2012), suggesting that they may be able to persist for longer within dog water bowls. This is concerning where zoonotic transfer is possible because of the shared antimicrobials in both human and veterinary medicine (Rendle and Page, 2018; Pomba et al., 2017; da Costa, Loureiro and Matos, 2013; Moyaert et al., 2006; Guardabassi, Loeber and Jacobson, 2004). This highlights the importance of minimising the risk of zoonotic spread from water bowls by identifying the most suitable material.
1.2 Influence of Materials on Bacterial Adhesion
The strength of bacterial adhesion depends on the organism’s surface properties and the material being colonised (Ribeiro, Monteiro and Ferraz, 2012; Van Houdt and Michiels, 2010; Faille et al., 2002). Material properties, including surface roughness and hydrophobicity influence the ability of bacterial cells to adhere to the surface and therefore determine the hygienic status of the material (Gharechahi, Moosavi and Forghani, 2012; Van Houdt and Michiels, 2010). A study by LeJeune et al. (2001a) into the hygiene of cattle water troughs identified a significant difference in bacterial coliform count between troughs constructed of different materials, with metal having lower counts than concrete and plastic. This study collected samples of water from each trough, however it is thought that 95% of the bacteria present in water systems are located at the surfaces and only 5% can be identified in the water phase (Flemming, Percival and Walker, 2002). Therefore, these findings may not accurately represent material influence on water quality, despite the large sample used by LeJeune et al. (2001a). Cook, Britt and Bolster (2010) examined decay rates of biofilms on different materials, artificially inoculated with Mycobacterium avium subsp. Paratuberculosis (Map), within glass water troughs. They identified that biofilm decay rates were fastest on stainless steel materials, followed closely by plastic. No significant difference was identified between stainless steel and plastic, however these findings are in agreement with the results of LeJeune et al. (2001a). Despite this, they only examined decay rates for Map and these results are therefore not representative for all bacterial species and their specific adherence to these materials as described by Van Houdt and Michiels (2010) and Faille et al. (2002).
Donofrio et al. (2012) identified high levels of bacteria within dog water bowls; however no specific links between bowl material and heterotrophic plate counts (HPC) were made. Samples of 26 items were tested, in 22 households, and the data revealed that sponge materials had the highest HPC, followed by porcelain, stainless steel and plastic. Dog bowls are commonly constructed of stainless steel, plastic and ceramic. These materials may permit heavy growth of bacterial pathogens as they lack antimicrobial effects (Fatoba et al., 2014). The results of this study suggest that these materials can harbour bacteria in high volumes and thus research specifically applied to bacterial load in dog water bowls constructed with these materials and the species present in them would be beneficial. Furthermore, identification of the most hygienic dog bowl material could allow for evidence-based pet ownership guidelines to be developed and thus reduce zoonotic transmission (Damborg et al., 2016; Leonard, 2014; Moyaert et al., 2006).
1.3 Dogs as a Reservoir of Zoonotic Transmission
There is substantial research which provides evidence of dogs acting as a reservoir of both zoonotic and resistant bacteria (Schwartz, Loeffler and Kadlec, 2017; Damborg et al., 2016; Chomel and Sun, 2013; Martins et al., 2013; Buma et al., 2006; Guardabassi, Loeber and Jacobson, 2004; Guardabassi, Schwartz and Lloyd, 2004). Several hygienically important bacteria have been isolated from dog mouths including Pasteurella spp., Staphylococcus spp., Bacillus spp. and many more (Zambori et al., 2013; Dewhirst et al., 2012; Elliot et al., 2005); therefore these species may potentially be present in dog water bowls.
A pathogen of major importance, Methicillin-resistant Staphylococcus aureus (MRSA), is of growing concern due to increased reports of zoonotic transmission (Han, Yang and Park, 2016; Loeffler et al., 2011; Abbott et al., 2010; Aklilu et al., 2010; Rutland et al., 2009). MRSA can potentially lead to mild or life-threatening skin infections in both humans and animals and it is highly resistant (Pantosti, 2012; Catry et al., 2010; Weese, 2010). Balen et al. (2013) conducted a year-long MRSA surveillance study in a veterinary hospital and discovered that some water bowls were contaminated for 2 consecutive months with the same pulsotype of MRSA, which was also detected on doors and floor surfaces. This study was conducted within a veterinary hospital with a large number of animals present and thus these findings cannot be extrapolated to household environments (Balen et al., 2013). However, the results demonstrate how bacteria, such as MRSA, can persist within a water bowl and can be spread throughout the environment – acting as a possible reservoir of bacterial transmission. Additionally, many studies have identified MRSA strain sharing amongst humans and their cohabitant pets (Faires, Tater and Weese, 2009; Sing, Tuschak and Hörmansdorfer, 2008; Boost, O’donoghue and Siu, 2007; Weese, 2006). However, cross-sectional studies of MRSA colonisation cannot identify direction of transmission and they do not eliminate common source infection (Weese, 2010).
Another microorganism with high zoonotic potential is Salmonella spp., which causes salmonellosis, a severe, common enteric disease in both humans and animals (Lowdon et al., 2015; Verma, Sinha and Singh, 2011). Dogs are thought to be asymptomatic carriers of salmonella and have been shown to act as a vector for bacterial contamination throughout the household (Lambertini et al., 2016; Hoelzer, Switt and Wiedmann, 2011; Bagcigil et al., 2007; Cherry et al., 2004; Sato et al., 2000). Additionally, recent studies have also identified human salmonella outbreaks caused by feeding dogs both dry and raw diets (CDC, 2012; Behravesh et al., 2010; Lenz et al., 2009; CDC, 2008). Weese and Rousseau (2006) state that food bowls can act as a source of infection for humans and animals if not sufficiently disinfected. They analysed the ability of salmonella to persist within both plastic and stainless steel food bowls, however no significant difference was determined when comparing overall persistence of salmonella and bowl material. A small sample size of 10 bowls was used, which significantly limits the statistical power of this study (Kylie et al., 2017). The presence of bacteria in food bowls may lead to cross contamination to water bowls, which are typically kept within the same area (Lambertini et al., 2016).
Current research suggests that animal drinking systems have the potential to act as a reservoir for multiple bacteria and that material does influence bacterial quantities (Folorunso, Kayode and Onibon, 2014; Martins et al., 2013; Donofrio et al., 2012; Watson et al., 2012; Cook, Britt and Bolster, 2010; Avery et al., 2008; LeJeune et al., 2001a). Weese, Rousseau and Arroyo (2005) state that pathogen survival in dog water bowls has not been adequately evaluated. Furthermore, to the author’s knowledge, there is no current research evaluating the influence of water bowl material on bacterial count and species present.
1.4 Aims and Objectives
The aim of the current study was to contribute to the limited existing literature by identifying whether the material of a dog’s water bowl – ceramic, plastic or stainless steel – and the length of use influences the quantity of bacteria present. Additionally, it was investigated whether the species identified from the water bowls varied between the three materials.
- To determine whether there was a difference in bacterial count with different bowl materials.
Null hypothesis: There will be no significant difference between bacterial count with different bowl materials.
Alternative hypothesis: There will be a significant difference between bacterial count with different bowl materials.
- To identify whether the length of use of the water bowl influences the bacterial count.
Null hypothesis: There will be no significant effect of the length of use of the water bowl on the bacterial count.
Alternative hypothesis: There will be a significant effect of the length of use of the water bowl on the bacterial count.
- To qualitatively determine, by observation, whether there is a difference in the species identified between bowl materials.
2.0 Materials and Methods
2.1 Pilot Study
A two week pilot study was conducted in order to eliminate any problems that may be encountered during the six week data collection period. It was acknowledged that the use of nutrient broth to transport the samples encouraged growth and increased contamination risk, and was thus rendering the bacterial counts inaccurate. To rectify this, phosphate buffer saline (PBS) solution was used as the transport medium, similar to that of Donofrio et al. (2012), as this does not encourage bacterial growth.
2.2 Bowl Sterilisation
The three construction materials tested were plastic, stainless steel and ceramic. All bowls were purchased brand new and sterilised in the same way. Gloves were worn throughout the process to prevent contamination. Following adapted methods of Jensen et al. (2013), a sterile basin was filled with boiling water, Dettol and washing up liquid. Each bowl was submerged, scrubbed gently, rinsed thoroughly and shaken dry. Bowls were immediately placed into a sterile autoclave bag and sealed until required.
2.3 Agar Preparation
Prior to each two week sampling period, Blood Agar Base (Oxoid, UK, CM0055) was prepared following the manufacturer’s instructions. This was autoclaved at 121oC, 103 kPa for 15 minutes in order to eliminate microbial contamination (Choi, Rodrigez and Sobsey, 2014). Once the agar had cooled to approximately 50-60oC, defibrinated horse blood was added aseptically at a 5% concentration and approximately 20ml of the solution was poured into petri dishes – in an aseptic environment – and allowed to set. Blood agar was used to culture the bacteria due to it being non-selective, rich in nutrients and able to grow a variety of bacteria (Dortet et al., 2014).
2.4 Broth Preparation
Prior to each two week sampling period MRVP broth (Merck, UK, V964212) and Nutrient Broth (Oxoid, UK, CM0001) was produced according to the manufacturer’s guidelines. Once the MRVP broth had dissolved, 10ml was syringed into individual test tubes and sealed with a lid. The test tubes and nutrient broth were autoclaved at 121oC, 103 kPa for 15 minutes and kept in the fridge until required.
2.5 Sample Collection
A sample of 6 dogs (5 female and 1 male), classified as medium weight (10-25kg) were used in this study (Vicente and Hammond, 2017). The age of the subjects ranged from 2-7 years (mean= 3.8 ± 1.95), and subjects were of various breeds (3 Cockapoos, 1 Border terrier, 1 Springer spaniel and 1 Golden retriever). All dogs were clinically healthy, fed a dry diet and had similar lifestyles. For example, all subjects were walked twice daily, housed individually within a rural location and no other pets were present in the household.
Bowls were placed in the kitchen by the back door in all households and the same position was used for all three construction materials. All owners were informed to thoroughly wash their hands before and after filling the dog bowl, and asked not to rinse, wash or disinfect the bowl. Samples were always collected at the same time in the morning. Gloves were worn throughout sample collections, sterile cotton swabs were used and all swabs were conducted by the same person (Donofrio et al., 2012), in the same manner – rubbed three times around the inner circumference of the bowl and once over the inner base, before being placed into 3ml sterile PBS. Samples were kept on ice and transported to the laboratory within two hours.
For the controls, on day 0 the bowl was filled with tap water, emptied and immediately swabbed, before being re-filled and placed in the selected position. Samples were collected again at day 7 and day 14.
2.6 Enumeration of Bacteria
2.6.1 Serial Dilution and Bacterial Count
Using modified methods of Folorunso, Kayode and Onibon (2014), Donofrio et al. (2010), LeJeune et al. (2001a) and LeJeune et al. (2001b), spread plate 10 fold serial dilutions were performed for each sample from 10-1 to 10-8. All serial dilutions were conducted within a biosafety cabinet to minimise contamination risk. The original sample was vortexed for 15 seconds using a Mini Vortex Mixer (Fisher Scientific, UK); 100μl aliquots were aseptically withdrawn using a micropipette and transferred into 900μl of sterile PBS (10-1). The solution was pipetted in and out to ensure thorough mixing. A 100μl aliquot was then aseptically withdrawn from the 10-1 solution and transferred into 10-2. This process was repeated until 10-8 was reached. A 100μl aliquot of each dilution from 10-3 to 10-8 was inoculated onto blood agar and spread using an L-shaped spreader. All plates were incubated at 37oC for 18-24 hours (Folorunso, Kayode and Onibon 2014).
Following incubation, total colony forming units (CFU) were counted and recorded. This was performed manually which is considered the gold standard approach (Davey, 2011; Clark et al., 2010). An average total CFU was calculated from the dilutions, for each sample, for each bowl type. Similar to LeJuene et al. (2001a) Plates with less than 30 colonies, or more than 300 colonies were excluded from the results as they are considered not statistically viable (Sutton, 2011; Rawling et al., 2009).
2.7 Isolation of Pure Bacteria
In a biosafety cabinet, the serial dilution plates were observed for individual, varying colonies. All colonies, from each sample, with a morphological difference were individually collected using a sterile, plastic inoculation loop and then streaked, using the four quadrant streaking method, onto blood agar plates to produce pure colonies. Plates were incubated at 37oC for 24 hours (Folorunso, Kayode and Onibon 2014).
2.8 Bacterial Identification
Pure bacterial samples were identified by their morphological characteristics, gram stain, and biochemical test results, similar to the methods of Folorunso, Kayode and Onibon (2014). The catalase, oxidase, Voges-Proskauer and methyl red test were conducted and interpreted following methods presented in literature (Sawain et al., 2018; Cobos-Trigueros et al., 2017; den Bakker et al., 2014; Hemraj, Diksha and Aveneet, 2013).
2.9 Freezing of Bacterial Isolates
The pure bacterial isolates from day 14, which were in the late log phase, were frozen at -80oC until required for polymerase chain reaction (PCR) (Kataoka et al, 2013).
2.10 Identification by Polymerase Chain Reaction
Following biochemical test results, further identification for the presence of potential medically important bacteria, isolated from day 14, was carried out using PCR.
2.10.1 DNA Extraction
The frozen samples were thawed at room temperature. Following 15 seconds of vortexing, using a Mini Vortex Mixer (Fisher Scientific, UK), a 100μl aliquot of each sample was added to an individual, labelled blood agar plate and spread using an inoculation ‘L-shaped’ spreader. The plates were incubated at 37oC for 48 hours. After incubation, DNA was extracted following the guidelines of Dashti et al. (2009).
2.10.2 PCR and Primers
The specific primers (Thermo Fisher Scientific, UK) selected for PCR and expected amplicon product size (bp) are displayed in table 1.
All primers were centrifuged for 30 seconds, hydrated using sterile distilled water according to manufacturer’s guidelines and stored at -20oC until required.
Table 1: A table to show the forward and reverse primer sequences used and the size of the amplicon product (bp)
|Organism/ gene||Primer Sequence
|Size of amplicon product (bp)||Reference|
|E.coli O157:H7/ eaeA (Forward)||AAG CGA CTG AGG TCA CT||450||(Holland et al., 2000; Louie et al., 1994)|
|E.coli O157:H7/ eaeA
|ACG CTG CTC ACT AGA TGT|
|MRSA/ mecA (Forward)||AAA ATC GAT GGT AAA GGT TGG C||533||(Bühlmann et al., 2008)|
|MRSA/ mecA (Reverse)||AGT TCT GGA GTA CCG GAT TTG C|
|Pasteurella multocida/ kmt
|TGC CAC TTG AAA TGG GAA ATG||168||(Król et al., 2011)|
|Pasteurella multocida/ kmt
|AAT AAC GTC CAA TCA GTT GCG|
|Pasteurella canis/ sod A (Forward)||GTA AAT AAT GCA AAT GCG G||186|
|Pasteurella canis/ sod A
|GCC TTG CAA AGT AGT AC|
|Staphylococcus species/ TstaG422
|GGC CGT GTT GAA CGT GGT CAA ATC A||370||(Martineau et al., 2001)|
|Staphylococcus species/ Tstag765
|TIA CCA TTT CAG TAC CTT CTG GTA A|
|Bacillus species/ p8FPL
|AGT TTG GAT CCT GGC TCA G||78||(Fernández-No et al., 2011)|
|Bacillus species/ p806R
|GGA CTA CCA GGG TAT CTA AT|
|Salmonella species/ ttr
|CTC ACC AGG AGA TTA CAA CAT GG||94||(Gwida and Al-Ashmawy, 2014)|
|Salmonella species/ ttr
|AGC TCA GAC CAA AAG TGA CCA TC|
A QuantiTect® Probe PCR Kit was used to prepare samples for PCR. A total volume of 20μl was used for amplification. Following the protocol, 10μl of QuantiTect® Probe PCR 2x Master Mix, 0.1μl of specific forward primer, 0.1μl of specific reverse primer and 4.8μl of RNase-free water were added aseptically to each sample tube. A 5μl aliquot of the DNA template was added to each tube. A blank was created using 5μl of RNase-free water instead of template DNA. Aseptic techniques were used throughout to eliminate contamination. All PCR was run using a Techne TC-3000 thermocycler (VWR, UK).
2.10.3 Gel Electrophoresis
A 50x stock solution of Tris acetate EDTA (TAE) was prepared and diluted to a 1x concentration by adding 980ml of distilled water to 20ml of 50x TAE. Each amplified DNA fragment required a different percentage of agarose gel for analysis. The appropriate weight of Electran® Agarose DNA grade (VWR,Belgium) was added to 150ml of TAE in a conical flask and stirred until dissolved. The solution was heated for 2 minutes and then heated gently until the solution was clear. After slight cooling, the gel was poured into a horizontal gel tank and combs were added. Once the gel had set, the combs were removed gently to produce the wells. TAE x1 was poured over the gel until it was covered.
Using a micropipette, 5μl of 100bp DNA marker, containing loading dye, was added to 2μl of 10x GelRed and 6μl of this solution was added to the first and last well. A 5μl aliquot of DNA sample was mixed with 2μl of 10x GelRed and 1μl of 6x loading dye (bromophenol blue) and then 6μl was added to the remaining wells. All runs of gel electrophoresis were examined by UV light using the UVP gel Doc-it reading system. Observations were made for clear bands at the specific amplicon product size (bp), displayed in table 1.
2.11 Statistical analysis
All statistical analysis was carried out using SPSS software (IBM SPSS, version 24, USA). Tests were conducted on the average total CFU/ml for each sample, for each bowl type. Descriptive statistics including mean and standard deviation were calculated. The Kolmogorov Smirnoff test was conducted to determine normality. A Kruskal Wallis was used to determine whether there is a significant difference between the average total CFU/ml for each bowl construction material. A Mann Whitney or independent T test, depending on normality, was used to determine if there is a significant difference in average total CFU/ml between day 0 and day 7, day 7 and day 14, and day 0 and day 14.
2.12 Ethical considerations
Recent studies have found evidence of dogs having the ability to remember the physical properties of objects (Kundey et al., 2010; Pattison et al., 2010). Additionally, exposure to novel surroundings has been shown to cause dogs stress (Rooney, Gaines and Bradshaw, 2007). Changing the material of a dog’s bowl could induce stress. If any signs of stress or adversity to drinking were displayed, owners were advised to remove their dog from the study.
Owners provided informed consent for each subject via the completion of a participant consent form. All data provided was held in accordance of The Data Protection Act (1998). The researcher and owners had the right to withdraw a subject at any time, for any reason, until the point of data analysis.
3.1 Bacterial Enumeration
For each bowl material on each day, an average total CFU/ml was calculated using all samples. The average total CFU/ml for each bowl material over the two week period is presented in table 2 and figure 1. No bacterial growth was observed from the initial control samples taken on day 0. Bacteria were cultured from all three bowl materials on day 7 and day 14. However, for the plastic bowl material, two of the six samples showed no growth on both day 7 and day 14. On day 7, the stainless steel bowl had the highest bacterial count of 250.33 ± 236.47 CFU/ml x 106. However, although all counts were lower after 14 days, the plastic bowl had the highest remaining bacterial count with 43.26 ± 62.40 CFU/ml x 106. The ceramic bowl had the lowest bacterial count throughout all test days of the study.
Table 2: A table to show the average total CFU/ml ± standard deviation (SD) (1.0 x 106) for each bowl material over time.
|Bowl material||Average total ± SD CFU/ml (1.0 x 106)|
|Day 0||Day 7||Day 14|
|Ceramic||0.00 ± 0.00||18.56 ± 40.50||2.20 ± 2.65|
|Plastic||0.00 ± 0.00||92.28 ± 191.21||43.26 ± 62.40|
|Stainless steel||0.00 ± 0.00||250.33 ± 236.40||10.95 ± 16.96|
The Kolmogorov Smirnoff test revealed the data to be mostly non-parametric (P<0.05). The Kruskal Wallis identified no significant difference when comparing bowl material and total CFU/ml (P>0.05); therefore the null hypothesis that there is no significant difference between bacterial count and bowl material must be retained. When testing for difference between day and average total CFU/ml, the Mann Whitney U test revealed a significant difference between day 0 and day 7 and between day 0 and day 14 for all bowl materials (P<0.05); thus the null hypothesis that there is no significant difference between the length of use of the water bowl and the bacterial count can be rejected. However, no significant difference in total CFU/ml was identified for all bowl materials between day 7 and day 14 (P>0.05).
Figure 1: A bar chart presenting the average total CFU/ml (1.0 x 106) over time.
3.2 Bacterial Identification
3.2.1 Biochemical test results
If bacterial characteristics identified on day 7 were identified again on day 14, the suspected genus was used for the selection of specific PCR primers, which are displayed in table 1. The colony morphology and biochemical test results for these isolates are presented in table 3. The results for the Voges-Proskauer tests are considered anomalies as no colour change was observed for this test for all isolates on day 7 and day 14.
Table 3: A table to show the colony morphology, biochemical test results and possible genus for all isolates that appeared in samples at both day 7 and day 14.
|Colony morphology||Biochemical test||Possible genus|
3.2.2 Polymerase Chain Reaction Results
Following the biochemical test results, all day 14 samples were subjected to PCR for identification of seven hygienically important bacteria, presented in table 1. The results for PCR are presented in table 4. The E.coli O157:H7 primer identified one isolate from the ceramic bowl material, displayed by the faint amplification at 450 base pairs (bp). The MRSA primer detected seven samples with positive amplification at 533bp. From the seven positive isolates, three originated from the stainless steel bowl and four from the ceramic bowl. The primer for the genus of staphylococcus only displayed 3 positive amplifications at 370bp, two from the stainless steel bowl and one from ceramic. No clear bands were present at 168bp for identification of Pasteurella multocida. Two samples, one from both the stainless steel and plastic bowl were identified as positive for Pasteurella canis, displayed by the bands at 186bp. Three ceramic and one plastic bowl displayed amplification at 78bp for detection of Bacillus species. Five Salmonella species were identified by the presence of faint bands at 94bp. One of the positive isolates originated from the plastic bowl and the remaining four from the ceramic.
Table 4: A table to show the results from PCR. S1 – S6 represents the stainless steel bowl samples, C1 – C6 the ceramic bowl samples and P1-P5 the plastic bowl samples. ‘+’ represents the presence of the specific amplicon product.
|– Control (blank)||–||–||–||–||–||–||–|
At present, dogs are the most popular pet in the UK and concerns of zoonotic disease transmission are rising due to their increasingly close contact with owners (PMFA, 2018; Damborg et al., 2016; Wang et al., 2016; da Costa, Loureiro and Matos, 2013; Buma et al., 2006). Dog water bowls have the potential to act as a reservoir of zoonotic bacteria, similar to the findings in livestock water troughs (Folorunso, Kayode and Onibon, 2014; Watson et al., 2012; Cook, Britt and Bolster, 2010; Avery et al., 2008; Sargeant et al., 2003; McGee et al., 2002; LeJeune et al., 2001a; LeJeune et al., 2001b). The current study aimed to explore the effects of material, and length of use, on the quantity and species of bacteria present in dog water bowls. The results indicate that bowl material may influence bacterial count and the species isolated.
4.1 Bacterial Enumeration
4.1.1 Influence of bowl material on bacterial count
On day 7, there was no significant difference identified between bowl material and average total CFU/ml (P>0.05). Although not significantly different, the descriptive statistics suggest that the stainless steel bowl had the highest bacterial count, followed by plastic and then ceramic. Stainless steel is considered a hydrophilic material with a high surface energy and negative charge (Hamadi et al., 2014; Hočevar et al., 2014; Di Bonaventura et al., 2008; Sinde and Carballo, 2000; An and Friedman, 1998). Bacteria frequently attach in higher numbers to hydrophobic materials, such as plastic (Hočevar et al., 2014; Di Bonaventura et al., 2008; Sinde and Carballo, 2000); however Gebhardt et al. (2012) state that cell growth on hydrophobic surfaces is slower than on hydrophilic surfaces. Additionally, Flint, Brooks and Bremer (2000) examined the bacterial adhesion of two streptococcus species to stainless steel, glass and other metal coupons and concluded that negatively charged surfaces attracted more bacteria than positively charged surfaces. This study examined two bacterial species and therefore is not representative of all bacterial species adhesion mechanisms (Greene et al., 2016). Despite this, these findings may suggest why stainless steel had a higher bacterial count than plastic on day 7.
Similarly, on day 14, no significant difference was identified between bowl material and average total CFU/ml (P>0.05). In contrast to day 7, the plastic bowl had the highest bacterial count. Plastic materials are known to degrade rapidly and surface scratches and wear increase bacterial adhesion (Bohinc et al., 2014; Crawford et al., 2012; Verran et al., 2008). An investigation over a longer time period may be beneficial. Cook, Britt and Bolster (2010) examined bacterial decay rate of Map over one year and identified that plastic disks have slower bacterial decay rates than stainless steel disks, although not significantly different. This study was conducted in controlled laboratory conditions, and may not be applicable in vivo (Sorrentino et al., 2018; Cook, Britt and Bolster, 2010). Despite this, LeJeune et al. (2001a) observed metal water troughs to have significantly lower bacterial counts than plastic water troughs over 8 months. A large sample of 467 troughs was studied, suggesting this data to be reliable with high statistical power (Halsey et al., 2015). The lack of statistical significance found in the current study is likely to be due to the small sample used and larger samples should be considered in future research (Halsey et al., 2015).
The ceramic bowl obtained the lowest bacterial count on both day 7 and day 14, although it was not significantly different from the other materials. There have been several reports that ceramic materials exhibit lower bacterial adhesion than other materials due to its higher hydrophilicity (Hofs et al., 2011; Eick et al., 2004). Sorrentino et al. (2018) studied the bacterial adhesion of S.aureus and S.epidermis to ceramic, metal and plastic disks and identified that ceramic materials had a significantly lower bacterial adhesion, slower biofilm development and thinner biofilm formation than the other materials (P<0.05). Additionally, the plastic material showed the highest bacterial adhesion, in line with the findings of this study. Similarly, Fatoba et al. (2014) placed various materials into individual liquid cultures of Bacillus, E.coli and S.aureus and conducted bacterial counts after various time periods. Their findings suggest that the ceramic material obtained lower bacterial counts, followed by plastic and then stainless steel when immersed in E.coli. However, all three materials had very similar counts for both Bacillus and S.aureus. Although there is a body of evidence that suggests ceramic materials obtain lower bacterial counts – similar to the findings of the current study – some studies demonstrate that adhesion is bacteria dependent (Fatoba et al., 2014; Ribeiro, Monteiro and Ferraz, 2012; Hofs et al., 2011; Van Houdt and Michiels, 2010; Eick et al., 2004; Faille et al., 2002). This highlights the need for further research in this area.
Although there was no significant difference identified between bowl material and average total CFU/ml in the current study, descriptive statistics and the above studies support the findings that the ceramic bowl has lower bacterial adhesion. However, further research is required to identify if there is a more hygienic bowl material. Additionally, Crawford et al. (2012) state that it is imperative to have a comprehensive characterisation of the specific surface properties when studying bacterial adhesion to materials and therefore thorough examination of the materials should be implemented in future research.
4.1.2 Influence of length of use on bacterial count
A significant difference was identified for all bowl materials when comparing average total CFU/ml between both day 0 and day 7, and day 0 and day 14 (P<0.05). Bacterial count increased significantly between days 0 and 7. This demonstrates that when bowls are refilled without cleaning, they produce a favourable environment for bacterial proliferation (Folorunso, Kayode and Obinon, 2014). This is in agreement with the findings of Folorunso, Kayode and Onibon (2014) who studied the bacterial count in poultry water troughs at three farms and found that the bacterial load progressively increases over 7 days.
Interestingly, the bacterial counts reduced for all bowl materials on day 14; however they were not significantly different from day 7. This suggests that high quantities of bacteria can persist within dog bowl biofilms. Many environmental factors, such as competition and nutrient availability, influence bacterial survival and adhesion – which may suggest why the bacterial counts slightly reduce (Crawford et al., 2012; Di Bonaventura et al., 2008; Katsikogianni and Missirlis, 2004). Many cells aggregated as part of a biofilm inhibit their motility, preventing them from searching for optimal environments when nutrient depletion occurs, ultimately causing cell death (Tuson and Weibel, 2013). Furthermore, the bacteria may have been removed by the dog or upon refilling. Additionally, the swab taken from the bowl surfaces on day 7 is likely to have removed and disrupted bacteria present in the biofilm, reducing the count on day 14. This represents a major limitation of the current study which may be overcome in future by conducting swabs on day 7 and then beginning a new 14 day sampling period with no day 7 swab. Despite this, the lack of significant difference between day 7 and day 14 highlights the importance of daily cleaning regimes as high levels of bacteria in dog drinking water could significantly impact their health and welfare and increase transmission to human cohabitants – particularly immunocompromised individuals (Damborg et al., 2016; Folorunso, Kayode and Obinon, 2014; Weese et al., 2010). Furthermore, this study demonstrates that the water bowl acts as a fomite; therefore it may be a vector of diseases with high veterinary importance.
4.2 Bacterial Identification
4.2.1 Biochemical tests
Bacteria with the same biochemical characteristics isolated from both day 7 and day 14 were identified to the genus level using biochemical tests and morphological characteristics (Vaseekaran, Balakumar and Arasaratnam, 2010). The isolates were identified as E.coli, Salmonella, Staphylococcus, Pasteurella and Bacillus, according to biochemical results presented in literature (Folorunso, Kayode and Onibon, 2014; Patnaik, Prasad and Ganguly, 2014; Verma et al., 2013; Vaseekaran, Balakumar and Arasaratnam, 2010). The limited number of biochemical tests used in this study meant that accurate identification was difficult (Janda and Abbott, 2002). Additionally, the Voges-Proskauer test results were identified as anomalies and excluded from the results. Biochemical tests have low specificity and interpretation of can be difficult if untrained (vanVeen, Claas and Kuijper, 2010). Furthermore, many diverse species can share the same biochemical characteristics, causing unreliability (Srinivasan et al., 2015; Heikens et al., 2005). Despite this, they allowed for selection of appropriate primers for PCR identification. Accurate identification of microorganisms is fundamental in order to implement suitable infection control and improve general hygiene (Nomura, 2015; Pahlow et al., 2015; Srinivasan et al., 2015).
4.2.2 Polymerase Chain Reaction
PCR provides a highly sensitive and specific method for bacterial species identification and avoids the limitations of biochemical tests (Pahlow et al., 2015, Cherkaoui et al., 2010; Vasoo, Stevens and Singh, 2009). Interestingly, the bacteria tested for by PCR were most frequently isolated from the ceramic bowl, followed by stainless steel and then plastic, suggesting an influence of bowl material on species.
E.coli O157:H7 was identified in one sample for the ceramic bowl material. The genus E.coli was identified in many bowls from the biochemical tests; however many species of E.coli are commensal and are therefore typically harmless (Chandran et al., 2017; Odonkor and Ampofo, 2013). E.coli O157:H7 is a highly pathogenic serotype of E.coli often found on the intestinal tract of many animals; therefore dogs are likely to come into contact with contaminated faecal matter when on walks (Lambertini et al., 2016; Mendonça et al., 2012). The presence of E.coli O157:H7 in one ceramic water bowl is concerning as infection can cause bloody diarrhoea, hemorrhagic colitis and fatal haemolytic uremic syndrome and it is consequently a major threat to public health (Albanese et al., 2018; Gould et al., 2016; Li et al., 2014).
Staphylococcus spp. was confirmed by PCR to be present in two stainless steel bowls and one ceramic bowl. The low presence of Staphylococcus is surprising as S.aureus has been previously isolated from dog water bowls (Donofrio et al., 2012). Furthermore, a study by Bean and Wigmore (2013) examined 117 healthy dogs for the presence of S.aureus and S.pseudointermedius and concluded that the mouth and perineum were the most common sites for detection of these bacteria. This suggests that these species are likely to be present within water bowls. However, this study was conducted in Australia and thus cannot be generalised to the worldwide dog population (Jaeger et al., 2010). Despite this, studies in Denmark have also suggested the mouth and perineum as the area’s most highly colonised with S.pseudointermedius (Paul et al., 2012). Additionally, studies assessing the microbiology of canine bite wounds have identified Staphylococcus spp. as one of the most commonly isolated bacteria (Abrahamian and Goldstein, 2011; Thomas and Brook, 2011; Talan et al., 1999). These studies suggest that Staphylococcus spp. are highly prevalent in dog mouths. The low presence of these species in this study may be as a result of an unsuitable primer, as it was designed for qPCR, which was not performed in this study (Martineau et al., 2001); however the positive control amplified with a very clear band.
Interestingly, seven isolates of MRSA were identified, three in the stainless steel bowl and four in the ceramic. This may suggest that false negative or false positive results were obtained due to the discrepancies between the Staphylococcus and MRSA findings. The clearest amplicon band for MRSA was seen in sample C5, which was negative in the Staphylococcus PCR. This suggests the error is in the Staphylococcus primer where minor modifications were made to the cycling conditions and this may have impacted the efficacy. Despite this, the identification of MRSA in the water bowl is concerning as it can be transferred to humans and cause life threatening illness (Faires, Tater and Weese, 2009; Sing, Tuschak and Hörmansdorfer, 2008; Boost, O’donoghue and Siu, 2007; Weese, 2006). Mafu et al. (2013) state that there is limited knowledge about the adhesion of MRSA to surfaces and the current study suggests it preferentially adheres to hydrophilic materials. Alternatively, the other bowls may not have been contaminated with the bacterium. Balen et al. (2013) showed that MRSA can survive within a dog water bowl for 2 months; suggesting the need for a suitable cleaning regime that is effective against this highly resistant bacteria.
Salmonella species were confirmed in four ceramic bowls and one plastic bowl. Salmonella has hydrophobic cell properties and has been previously reported to preferentially adhere to hydrophobic surfaces, such as plastic (Veluz, Pitchiah and Alvarado, 2012; Chia et al., 2009; Chia et al., 2008). This does not agree with the findings of the current study; however, Oliveira et al. (2006) suggest that adherence is entirely strain dependent. Furthermore, the bacterium may not have been introduced into all bowls. Although only 9% of human infections are attributable to animal contact, Salmonella results in severe gastrointestinal disorders (Lowdon et al., 2015; Verma, Sinha and Singh, 2011; Majowicz et al., 2010). It can also lead to abdominal pain, weakness and fever in dogs (Marks et al., 2011). Identifying water bowls as a vector for Salmonella transmission may lead to a reduction of infections if owners’ education is increased.
PCR identified Bacillus in three ceramic bowls and one plastic bowl. The primer identified B.cereus, B.licheniformis and B.subtilis and although it is not clear which exact species were isolated from these bowls, all of these species can be pathogenic (Fernández-No et al., 2011). Bacillus species are often found within soil, suggesting that dogs are likely to come into contact with it on walks (Lambertini et al., 2016; Earl, Losick and Kolter, 2008). Identification of Bacillus species is concerning as they develop spores; this increases their resistance to cleaning procedures and Faille et al. (2002) found that removal was harder on hydrophobic materials.
Pasteurella canis was identified in one stainless steel bowl and one plastic bowl. Pasteruella multocida was not identified. Pasteurella spp. are responsible for several soft tissue infections, occasional respiratory tract infections and in rare cases, meningitis (Faceira et al., 2017; Zambori et al., 2013).The low presence of these species was not expected as Pasteurella species were identified frequently from the biochemical tests and are also frequently isolated from dog mouths and bites (Faceira et al., 2017; Oehler et al., 2009; Talan et al., 1999). This may suggest PCR errors were encountered, for example, the heat lysis method of DNA extraction may not have been successful and thus there was no DNA present to be amplified. Additionally, the ladder did not migrate clearly for P.multocida which may suggest that the gel did not run correctly.
The limited sample used within this experiment does not allow for findings to be extrapolated to all dogs. In future, a larger sample, including a larger variety of breeds should be used. Additionally, the findings of this study are only applicable to dogs in a rural location of Devon and geographical studies should be considered to identify the difference between urban and rural housed dogs.
Although great care was taken to ensure all components of the PCR were managed correctly, the high number of samples meant that maintaining the reagents at 4oC was difficult. Prolonged exposure to higher temperatures can denature the Taq polymerase enzyme, reducing the sensitivity of PCR (Peters et al., 2004). In addition, non-specific activation of Taq polymerase can cause non-specific amplification (Ashrafi, Yee and Paul, 2009). Furthermore, the lack of positive controls throughout limited the ability to compare the amplifications to a known positive amplicon product.
4.4 Future Research
Although not tested in this study, Fatabo et al. (2014) demonstrated that copper and brass materials had antimicrobial and bactericidal effects against bacteria over time. Additionally, Mehtar et al. (2008) discovered that copper surfaces decreased bacterial loads from 107 CFU/ ml to below detectable limits within 180 minutes. However, these studies are conducted in laboratory settings and may not be representative of the antimicrobial properties of copper in heterogeneous bacterial environments (Gould et al., 2009). Despite this, the antimicrobial properties in these materials should be studied in vivo for the development of antimicrobial dog water bowls, reducing bacterial count and viability. This would subsequently improve dog welfare and health and reduce the risk of zoonotic transmission.
Additionally, due to the high level of antimicrobial resistance in biofilm bacteria, antimicrobial susceptibility testing should be carried out in order to identify effective disinfectants against the bacteria isolated from dog water bowls (Ciofu et al., 2017; Wall and Mah, 2017; Rowson and Townsend, 2016; Steenackers et al., 2012; Reller et al., 2009). This would allow for evidence-based cleaning routines to be developed (Leonard, 2014), improving both owner and dog health and welfare.
The current study contributes to the limited research on dog water bowl microbiology and begins to determine the impact of bowl material on the bacteria present. The findings demonstrate how ceramic, plastic and stainless steel water bowls allow for proliferation of bacteria – a health risk for owners and their dogs. No significant difference was identified between the bowl material and the bacterial count over two weeks, although descriptive statistics indicate that differences might be present, suggesting that further research involving a larger sample size is required. However, ceramic bowls appear to reduce bacterial proliferation more than stainless steel and plastic bowls. Additionally, other materials, such as copper, should be studied due to their antimicrobial properties. This would allow for identification of a more hygienic bowl that could reduce the risk of zoonotic transmission.
The length of use influences the bacterial count in all three bowl types, which demonstrates the need for daily cleaning regimes and increased owner awareness on the impacts of bowl hygiene. A study over a longer time period would be beneficial in order to identify the bowl material that minimises bacterial load. This would lead to improvements in both human and canine health.
PCR accurately identified multiple bacterial species, many of which have pathogenic potential. This presents the dog water bowl as a possible vector in disease transmission, both to humans and other animals. The frequent observation of MRSA and Salmonella isolated from the bowls is concerning due to their infectious nature for both humans and animals. However, this identifies an area where good hygiene implementation could reduce the number of infections from these bacteria and ultimately improve dog welfare. The ceramic bowl isolated the highest number of harmful bacteria, despite having the lowest bacterial count. This suggests that these bacteria may be able to adhere more easily to ceramic materials, demonstrating how bowl material may impact the species present in the bowl. Ultimately, more research is required before suitable conclusions can be drawn.
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Welcome to Hartpury: How does a university attract new first-year students through marketing communication strategies – with specific reference to open days
Author Names: Anastasia Noble (BA (Hons) Equine Business Management) and Mike Green
The use of marketing strategies as a tool to recruit potential undergraduate students is prevalent within higher education institutes. The ever-rising competition amongst universities to fill their places requires their marketers to implement precise and targeted marketing approaches to promote their open days. The application of strategic marketing needs to be employed through the use of the marketing mix, integrated marketing communications mix and marketing segmentation. Hartpury University Centre uses these tools to engage with prospective students at open days and increase the demand for their services. The study followed an inductive approach by using a mixed-method strategy in an explanatory sequential design. The main results were taken from one focus group with six participants formulated of third-year sports undergraduate students; it was further backed up with quantitative data from a questionnaire given out to prospective sports undergraduate students on an open day. A sample of 50 respondents were invited to take part in the questionnaire to represent the target audience on the open day. The research collected from both data sets followed a thematic analysis. The findings concluded that the main motivation for students to apply for Hartpury University Centre were the vocational opportunities, and the campus facilities. Additionally, the data showed the most effective marketing communication strategies to recruit prospective students were word-of-mouth marketing and personal selling through student interaction on open days. Further study targeting larger higher education institutes with a different demographic than Hartpury University Centre would be valuable.
The UK Higher Education industry has declined in the last three years, with the overall application rate of people applying standing at 559,000 – a 0.9% reduction from January 2017 (UCAS, 2018). However, higher education is still in demand and is deemed to increase an individual’s development and long-term economic growth (King, 1995). Therefore, the importance of considering effective marketing strategies, such as, McCarthy’s 4Ps Marketing Mix, Marketing Communication Mix and Marketing Segmentation, is required to increase the application rate. This is a key factor to ensure that there is brand awareness and motivate students to apply for university.
Marketing strategies for higher education institutes differ from other industries since they are involved in service marketing, which offers intangible components that cannot be ‘felt’ or ‘tasted’ before purchase (Moogan, 2011). Mazzarol (1998) focused on the nature of services as ‘people based’ and emphasised the importance of relationships with consumers. Students now increasingly view higher education as a ‘service’ because they directly pay for their education like a consumer, (Higgins et al, 2002).
The development of the smartphone has changed the way marketers communicate with their customers who are increasingly consuming information and making purchasing decisions online (Kietzmann et al, 2012). The combination of the Internet and traditional media allows the application of hybrid media, which increases interaction between users and media products by merging the new and old media techniques thus simplifying a user’s access (Louho et al, 2006). The use of social media for marketing is the most engaging type of public relations, being interactive and involving real time content (Bîja, and Balaş, 2014). By taking a hybrid media approach to marketing it will allow universities to create an effective bond with the parents and the students with the brand (Moor, 2003).
Hartpury University Centre is a specialised higher education institution and has students working towards degrees in the fields of animal, agriculture, equine, sport and veterinary nursing (Hartpury College, 2018b). The contrast between the overall decline in applications to higher education to Hartpury University Centre’s own application data is surprising as the university saw a 5% increase, more than 1,650 students, applying to their 2018/19 degrees (Hartpury College, 2017). The rising number of young adults applying for higher education, has increased pressure on marketers to establish effective communication strategies for their target consumers.
The development of the study relates to the limited research available on the motivation of students choosing to go to university based on marketing strategies. By specifically utilising a specialised institution, such as Hartpury University Centre, this will create an understanding of what strategies are used, and evaluate the motivations of students choosing exclusive courses compared to other universities. By establishing clear aims and objectives, it will provide clarity to the reader and ensure that the study is current and relevant.
1.2 Dissertation Concept
This study aims to understand what attracted both prospective sports undergraduate students, and actual third-year sports students to apply and study at Hartpury University Centre, and additionally, to investigate what marketing communication strategies were effective in recruiting these students. The project will use a mixed-method approach on participants at the Hartpury University Centre. It will be conducted through a questionnaire and focus group to investigate how the university creates brand awareness for their open days through particular marketing strategies.
1.3 Research Aims and Objectives
To understand how Hartpury University Centre, achieves their recruitment targets of attracting new undergraduate students through their integrated marketing communication strategies.
- To understand the effectiveness of open days as a marketing communication strategy.
- To identify which current marketing strategies are effective in creating brand awareness.
- To understand the motivations of students in choosing Hartpury University Centre for undergraduate study.
2.0 Literature Review
In this chapter, the existing literature around strategic marketing, integrated marketing communications mix and marketing segmentation will be examined. This will highlight the deficiencies in existing literature currently available surrounding the effectiveness of university open days.
2.1 Strategic Marketing
Marketing is no longer a strategy but a requirement and a company necessity. According to CIM (2015), marketing is “the management process responsible for identifying, anticipating and satisfying customer requirements profitably”. Grönroos (1997) follows a similar definition, however, he looks at the relationships between customers, parties and how to create a profit from these interactions. Marketing is a valuable asset for businesses because it allows them to set objectives and aims to reach a target (Kotler and Armstrong, 2016). Marketers influence consumers, through an exchange between the company and consumer, whilst Blythe (2013) believes that consumers co-create value – however, they do not act a certain way for marketers.
To ensure a company is meeting its profit potential Ansoff (1957) created four strategies – market penetration, market development, product development and diversification – to allow a business to continue growing. According to Lynch and Smith (2007), the matrix identifies options the company has for its products to see whether it has the potential to diversify, or the possibility to withdraw. If a company is a small-to-medium enterprise (SME), it is suggested that it should avoid direct competition with larger firms because the firm may have limited resources or skills, and instead should develop closer customer relationships (Storey and Sykes, 1996). For continued growth and to be able to compete with other firms, organisations must choose product development and market development (Perry, 1987). However, in a study based on SME food producers by Watts, Cope and Hulme (1998), there was limited support for Perry’s (1987) hypothesis on product and market development being the favourable growth strategies. The respondents preferred to engage with new customers with their existing product range, rather than new products with existing customers.
2.2 The Marketing Mix
Ivy (2008) describes the marketing mix as a tool which allows an institution to generate a reaction from numerous target markets, and increase the need for their service. The original marketing mix was formed by McCarthy (1960) which consisted of the 4Ps – Price, Product, Place and Promotion. It is a useful framework to create long-term and short-term strategies for companies (Palmer, 2004). The usefulness of the marketing mix depends on the resources the firm can offer, and each element impacts the others when only one element is focused on (Low and Kok, 1997). Therefore, organisations must choose the best combinations of elements to achieve objectives and set marketing targets to complete (Felicia, 2014). Rafiq and Ahmed (1995) found that there is dissatisfaction towards the 4Ps framework, with 84% of UK’s Marketing Education Group (MEG) feeling it was a deficient marketing tool. However, MEG respondents had been using a modified version of the 4Ps. The study indicated that 51% of respondents thought that the marketing mix was unsuitable for services, not-for-profit organisations and industrial marketing because it would be problematic (Rafiq and Ahmed, 1995). The reason why the 4Ps were considered problematic for services, is because it can become unusable and challenging to use when applied to their marketing strategies.
The original 4Ps founded by McCarthy (1960) did not provide enough information for certain industries, specifically service firms, therefore, the 7Ps was developed. The 7Ps involves Price, Product, Place, Promotion, People, Process and Physical Evidence (Shirahada and Kosaka, 2012). According to Bitner and Booms (1981), the elements included in the 7Ps vary both in reliability and precision; Product and Physical Evidence are the most precise, while Process is the least accurate element in the mix. The addition of expanding the 4Ps to the 7Ps was well received by 58% respondents involved in Rafiq and Ahmed’s (1995) study – showing that a significant proportion of the group thought the framework was more useful than the 4Ps, and more of them were using the 7Ps. Conversely, a weakness of the 7Ps framework is that it is more complicated to use, and some marketers believe that the new variables can be incorporated into the original 4Ps.
2.3 Integrated Marketing Communications Mix
The marketing communications mix is a blend of advertising, sales promotion, personal selling, public relations and direct and digital marketing (Kotler and Armstrong, 2016). Niculescu (2000) grouped each promotional element into either direct or indirect action and communication or stimulation objectives – which allows marketers to recognise different promotional tools. However, Lendrevie and Lindon (1997), decided to group the elements into four categories: “promotional tools, other tools of marketing with powerful promotional content, the organisation and its staff, and sources outside the organisation”. The formation of the promotional mix made by a company must be conducted if they want to outperform their competition by using the elements of the mix. By using these elements, a company is able to achieve their strategic objectives by reaching their target audience, their purpose and the resources available to promote their products (Matei, 2014).
Mintzberg (1973) found that managers spend nearly 80% communicating with colleagues and customers. For efficient communication, the receiver must understand the message from the sender. To create a successful company, effective communication is required between managers and employees to ensure that a company will produce a creative workforce (Spaho, 2012). The Communication Model starts by having the sender encode a message which is received creating a response and gives feedback to the sender, whilst having interference by different types of ‘noises’ (Copley, 2014). Anand and Shachar (2007) believe that there is noisy communication within marketing and the receiver cannot understand the message the sender is conveying. The miscommunication within marketing means that senders do not have control over what the consumers see, therefore, they should provide more information to overcome that problem.
Figure 1: The Elements in the Communication Process (Kotler and Armstrong, 2016).
When a communicator chooses the channel of communication there are two types – personal and non-personal. The personal communication channel involves the word-of-mouth influence focusing on associates, friends and family (Kotler and Armstrong, 2016). According to Buttle (1998), word-of-mouth marketing is a major influence on “what people know, feel and do” and it is more influential on the consumer’s behaviour than other marketing communication tools. Day (1971) found this method of communication reliable, flexible, and nine times as effective as advertising because it raises awareness and is more important than traditional advertising. Traditional advertising is decreasing in effectiveness, especially in the younger demographic groups, and word-of-mouth is currently creating a “buzz” and becoming the new promotional tool (Keller and Berry, 2003). Word-of-Mouth endorsement can be described as free advertising as the corporation are not paying receivers to provide feedback about the corporation. Word-of-Mouth feedback may be uttered either before or after a purchase – which is an important source of pre-purchase information because it provides a positive testimonial of the company to increase brand awareness. A limitation of using word-of-mouth is a company may receive negative feedback from customers – for example, a dissatisfied customer is likely to tell twice as many people as a satisfied customer (Technical Assistance Research Program (TARP), 1986). Desatnick (1987) also found that if a customer is dissatisfied, they would tell at least another nine other people.
Digital and social media marketing are becoming the new face of advertising evolving with more prominence in companies’ marketing strategies (Lamberton and Stephen, 2016). Both elements are involved in the hybrid communications model. Hybrid media is described by Lynne et al (2014), as the “strategic use of traditional and new media marketing communications tools to communicate the message in a more effective way”. Eid and El-Gohary (2013), discovered that Internet marketing and e-mail marketing are the most commonly used and most successful e-marketing tools for small businesses. This indicates that digital and social media are fantastic tools to cost effectively ‘reach’ both an international audience or highly targeted market segment using less time, effort and money. Nonetheless, small businesses are capable enough to use digital and social media marketing to promote their brand, engage with customers and become sustainable (Taneja and Toombs, 2014). Another concept within hybrid communication is consumer-to-consumer (C2C) which is defined as an e-commerce model where transactions are conducted electronically from one consumer to another (Leonard, 2011). By enabling consumers to have easy access, e-commerce is making the supply and demand of products more efficient in comparison to traditional C2C commerce (Yrjölä et al, 2017). A limitation of C2C for a company is how and why it is being used, and what motivates the consumer to engage with C2C e-commerce (Chu, 2013). A concern of hybrid media is the effect appears to be small and indirect, however, it impacts the attitudes towards the brands and “feeling” component of attitudes towards the brand (Aylesworth, Goodstein and Kalra, 1999).
2.4 Marketing Segmentation
Kotler and Armstrong (2016) describe market segmentation as “dividing a market into smaller segments of buyers with distinct needs, characteristics, or behaviours that might require separate marketing strategies or mixes”. Marketing segmentation plays a key role in consumer diversity, as businesses are starting to recognise the products and services their competition offer to consumers (Martin, 2011).
Wind (1978) identified two approaches to market segmentation – post hoc and a priori – the latter is important for a firm’s marketing strategy since the firm chooses the variable(s) of interest and then classifies the consumers according to that specification. Hoek et al (1996) found by following the a priori approach, that it cannot guarantee all segment members will respond equally to the variable(s) of interest. This is considered to be a traditional approach to segmentation (Ahmad, 2003). Hence, a company must include a marketing mix and consumer responses to receive the required results.
Market segmentation is significant, however, Cahill (1997) argues that organisations have over-segmented markets and therefore some products should not be segmented as it will be expensive. Wright’s (1996) study found that empirical results from two detergent sales show the failure of segmentation and it did not lead to higher sales, as no specific groups of customers preferred a particular brand. This shows the unpredictability of using segmentation, highlighting that it depends on what market it is being used on and if the theory is being applied correctly.
Martin (2011) explains that market segmentation is comprised of four segments – geographical, demographical, behaviour and psychographic. These four segments are effective for businesses to develop communication strategies and gain interest from desired consumers. Martin (2011) clarifies that geographical segmentation signifies a market divided by location and if the consumers in the region have similar wants and needs. In a study looking at the genotypes and geographical segmentation of Arabica coffee with Self-Organising Maps (SOM), it was able to predict the quality of the beverage through segmenting the different locations and the compounds in the coffee beans (Link et al, 2014). This technique is feasible for the agriculture industry, although, it is not practical for other industries because it needs to be applied to clustered problems and is only widely used elsewhere in finance, natural science and linguistics (Kohonen, 2013). The limitation of only using a geographical segmentation is the segmentation base of consumers is quite limited and it can be restricted by physical and/or economic considerations (McDonald and Dunbar, 2012).
In the wine-related lifestyle (WRL), a suitable segmentation method is a priori approach for demographic variables, as their responses would be split into homogenous sub-groups (Bruwer and Li, 2007). The limitations of demographic segmentation, certainly in WRL, is it lacks the predictability of consumer behaviours and it is inadequate to describe information of the wine target segments (Wedel and Kamakura, 2000).
Kotler and Armstrong (2016) describes psychographic segmentation as buyers in divided segments based on their lifestyles. In order to measure psychographic segments, organisations must conduct empirical research on buyers by classifying their lifestyle and capturing the psychographic data (Ahmad, 2003). Wells and Gubar (1966) created a life-cycle to allow consumers to be divided into nine linear stages allowing a company to understand which consumer to target when creating a product or launching new products to a new industry. The restrictions of using psychographic segmentation in a business is the huge scope of psychographic information, as there are a variety of different aspects involved (Kaže and Škapars, 2011).
Blythe (2013) defines behavioural segmentation as “dividing up a potential marketing according to the behaviour of its members”. By separating consumers into behaviour categories, it is a reliable and useful way of segmenting. Sampson (1992) formed three groups that could divide buyers who are looking for benefits when purchasing products; functionality seekers, image seekers and pleasure seekers. The limitation of behavioural segmentation is a company does not understand why a customer is buying a product, and therefore needs another marketing model to understand the behaviours of their customers (Dietrich, Rundle-Thiele and Kubacki, 2017).
Figure 2: Major Segmentation Variables for Consumer Markets (Kotler and Armstrong, 2016)
The purpose of the methodology is to explore the various techniques and methods used, and aims to comprehend the different philosophies to fulfil the study. Crotty (1998) depicts methodology as the process which chooses certain methods and links the chosen method back to the desired results. Therefore, at the end of this chapter, the researcher should be able to find which chosen method answers the aims and objectives of this study.
3.1 Research Philosophy
It is advised by Klenke (2016) to understand the philosophical foundations of the study, as it is not possible to conduct a thorough study without understanding the philosophical assumptions, research design and research strategy. Khin et al (2011), explains that once the researcher appreciates the philosophical stances, the ‘journey’ towards collecting data will become instinctive and it will enhance the study. Philosophy assists with removing obstacles that are hindering the study in progressing further and act as an under-labourer to find knowledge (Bhaskar, 1978; Locke, 1996).
Wisker (2008) describes a research paradigm as a “set of beliefs about how elements of research fit together, how we can enquire of it and make meaning”. The main paradigm of inquiry in this study is interpretivism (epistemology). This will be discussed in further detail together with the researcher’s philosophical stance that supports this study.
3.1.1 Philosophical Stance and Paradigms of Inquiry
The different terms, ontology and epistemology, are the various philosophies that are involved to ensure that the correct philosophy is chosen, and the study is correctly researched. Bryman and Bell (2015), describes ontology as a theory that focuses on the nature of social individuals and can be studied objectively (objectivism) or subjectively (subjectivism). Ontology questions the assumptions of researchers regarding how they think the world operates and particular views (Saunders, Lewis and Thornhill, 2012). If the study followed an ontological philosophy, objectivism implies that a social phenomenon shows the researcher external facts that is beyond our reach (Bryman, 2015).
Conversely, epistemology is the philosophical study of knowledge embedded in the theoretical perspective within the study’s design (Crotty, 1998); and it enquires into researching the nature of reality and the nature of the social world (Hitchcock and Hughes, 1995) – it therefore entails in knowing, i.e. how we know what we know. The study will be primarily following a qualitative approach; therefore, it will be following an interpretivist paradigm since the researcher wanted to be fully involved with the subjects that are being observed (Burns and Burns, 2008). The use of interpretivism allows the research process to move effortlessly between the participants and researcher – as it is an adaptable study (Burns and Burns, 2008). The two paradigms are linked to the qualitative method; hence, there will be no large sample sizes, as that would be leaning towards a positivism stance in a quantitative method (Klenke, 2016). By adopting this interpretivist stance, the researcher may find surprising findings from the social context studied, since the term is all focused on the study of the social world (Bryman, 2015).
The study will be conducted through an interpretivist epistemological approach. The focus group held by the researcher with six participants will be conducted, which will provide the researcher with qualitative data that will generate a number of key themes to answer one of the research’s objectives.
3.2 Research Approach
3.2.1 Research Methods
This study will be following a methodological triangulation strategy, also known as a mixed-method strategy. Mixed-method research has two main characteristics – it involves the collection and analysis of both quantitative and qualitative data in an epistemological approach (Creswell and Plano Clark, 2011). Greene, Caracelli and Graham (1989) highlighted three main reasons why a researcher would choose to do a combination of quantitative and qualitative, expansion, initiation and development. By choosing the mixed-method approach, the study will be following an explanatory sequential design, where the quantitative data findings will be used to expand and follow up the results of the qualitative data (Creswell, 2014).
The study will be primarily using the qualitative method for the research data collection. Jacob (1987) describes the traditional qualitative research as a holistic ethnography, ecological psychology and cognitive anthropology approach. According to Hogan, Dolan and Donnelly (2009), qualitative research is an approach based around people’s words and actions when a researcher is looking at their culture and behaviour. Quantitative research involves numerical data while qualitative data remains in words, and the researcher can make direct observations through a focus group. Consequently, qualitative researchers do not carry out hypotheses at the beginning of their studies compared to quantitative researchers (Gay, Mills and Airasian, 2006). Therefore, this method is suitable for the focus group in this study, which will give reliable data. Using a focus group supports Hays and Singh’s (2012) theory that utilising qualitative research can investigate the intended phenomena in a natural setting where the information can be obtained easily and directly observed in the form of words (Morrow, 2007). Hence, the appropriate reasoning for choosing the qualitative method is to discover the perceptions of Hartpury University Centre from students.
The use of an open and close-ended questionnaire creates an empirical approach to achieve the quantitative research objectives (Cottrell and McKenzie, 2011); hence, the data is represented as numbers and statistics (Burns and Burns, 2008). Haverkamp and Young (2007), explain that quantitative designs are linear which leads to the development of theories, opposing qualitative designs that begin with experiences. According to Cottrell and McKenzie (2011), this is a traditional form of research because it is more reliable, and the researcher does not interfere with the subjects. A questionnaire needs a large sample size to allow for more dependable data to be gathered (Patton 1990, p. 169). Hence, the reason why the research is following an explanatory sequential design; as the analysed results from the questionnaires are explained further within the focus group (Watkins and Gioia, 2015).
3.2.2 Inductive Approach
The researcher has utilised an inductive approach to develop a principle from the data by creating awareness from the information collected (Cottrell and McKenzie, 2011, p. 6 – 7). According to Creswell (1994, p. 19 – 20), the process of qualitative research involving the inductive approach is that the researcher builds a foundation of aspects from abstractions, concepts and theories from the collected data. Conversely, a deductive approach comes from a general theory and narrows down to a particular statement (Bergdahl and Berterö, 2015). The inductive approach is suitable for this study, as models should emerge from the research (Gray, 2013).
3.3 Research Strategy
3.3.1 Sampling Method
Saris and Gallhofer (2007), describe sampling as a “procedure which selects a limited number of units from a population in order to describe the population”. The population of interest for the study are sports students – prospective undergraduate students and third-year students – either considering applying for a place or who are already studying at Hartpury University Centre. The first population, prospective students, are able to provide the data needed to understand why open days are effective. The latter population, were selected since they would be able to give an opinion of what specifically motivated them to choose Hartpury University Centre.
The study follows an explanatory sequential design; hence, the questionnaires were administered prior to the focus group. The quantitative research approach, was conducted on three Hartpury University Centre open days to gather data from prospective undergraduate sports students. The sampling method chosen for the questionnaires was convenience sampling, also known as opportunity sampling, as it allows the researcher to select accessible subjects to do the study (Marshall, 1996). By following this method of sampling, the participants involved in the questionnaire were easily accessible and in the proximity of the researcher as they were attending an open day at the university (Schwandt, 2001). Marshall (1996) clarifies that it is a more thoughtful and justified approach to selecting participants, however, it is the least rigorous sampling technique. By having easy access to the different participants, the researcher was able to identify which qualifying candidates would be suitable for the survey through face-to-face contact, for example, all of the prospective sports students (Teddlie and Yu, 2007; Saunders, Lewis and Thornhill, 2016). Patton (1990) explains this sampling method allows for a larger sample size because the respondents will be readily available for the researcher, therefore, they will help to elaborate the data needed for the focus group (Creswell and Plano Clark, 2011).
The qualitative approach followed the sampling method of purposive sampling to select its participants. Purposive sampling is a non-probability form of sampling where the researcher needs participants relevant to the study (Bryman, 2015). Hood (2007) created a generic inductive qualitative model, also known as generic purposive sampling, based on open-ended questions and emphasizes on the generation of theories. The main feature is the researcher handpicks the cases to be included based on their judgement of the required characteristics (Cohen, Manion and Morrison, 2018). According to Teddlie and Yu (2007), it provides a greater depth, however, less breadth to the study than a probability sampling method. Consequently, the focus group must stay homogenous in forms of background, not attitudes, to avoid disruption within the group – hence, the reason why the chosen participants will be third-year sports students (Morgan, 1988; Murphy, Cockburn and Murphy, 1992).
Based on the data needed to answer the research question, it was decided to conduct questionnaires with prospective undergraduate sports students on a Hartpury University Centre open day. The questionnaires were distributed at three different open days at Hartpury University Centre – one in October, November and December (Hartpury College, 2018c). This allowed the researcher to obtain the desired number of respondents to successfully answer the questionnaire and assist with the study. The questionnaire was self-administered to remove potential bias in responses(Brace, 2008). The questionnaire allowed respondents to read each question independently (Bryman, 2015), and was more convenient and a quicker way to answer questions. Therefore, the questionnaires were administered in a quick ‘delivery and collection’ fashion (Saunders, Lewis and Thornhill, 2009).
The questionnaire featured nine questions, which comprised personal information, age, gender, religion and current situation, and questions about the open day, what course were they interested in, and what aspect of the open day did they find helpful. Over the three open days, prospective students completed 50 questionnaires. According to Patton (1990), sample size gives the study a purpose of inquiry and what information will be useful, hence, the decision to visit as many open days as possible to get as many responses as possible.
3.3.3 Focus Group
Due to the data needed for the research and the philosophical stance of the research question, a focus group with participants would be the most effective route to gather the needed data. According to Green and Hart (1999), the venue impacts on the data collected as the formality of the group varies with the formality of the setting the discussion is set in. Hence, the chosen venue was at Hartpury University Centre in a room free from interruptions and background noises that may otherwise spoil the audio recording.
There was careful consideration in the group composition when selecting participants for the focus group to encourage a discussion. Therefore, each participant was checked prior to the focus group on how they found Hartpury University Centre, for example, an international student and a student who went through UCAS clearing. By using a pre-existing social group, it provides a more ‘natural’ setting for the discussion; however, there may be impacts of over-disclosure (Bloor et al, 2001). Additionally, using a pre-existing social group brings interacting comments and encourages participants to share experiences, which challenges any discrepancies in the data (Kitzinger, 1994). The most common source of failure in focus group research is ensuring attendance; however, it can be higher if the group consists of a pre-existing social group (Morgan, 1995). To guarantee the participants would attend, there were reminder phone calls on the day of the focus group to remind them of the established meeting venue and time.
It is advised to have a focus group between six to eight participants as an optimum size to ensure that people have enough time to express their views (Morgan, 1995). The study had six participants consisting of two female and four male respondents. Due to the size of the group, the audio recording was strategically placed to ensure that all could be adequately recorded. Before the focus group started, the researcher experimented with the recorder by asking each participant to identify themselves and then checking the audibility. The discussion was recorded on two audio recording devices to ensure there were no pitfalls during the study and to help with the transcription later.
The whole discussion lasted 15 minutes with an average of 13 questions, including probing questions asked to the members. The questions were based on Hartpury University Centre and improvements to the open days. Each question was well received and, as previously mentioned by Morgan (1995), the group size allowed each participant to express their views. Once the focus group was finished, it was transcribed in full which is more complex than other qualitative methods (Bloor et al, 2001). According to Silverman (1993, p. 124), there cannot be a perfect transcript of an audio recording because people do not speak in neat planned sentences.
3.4 Research Design
3.4.1 Reliability and Validity
The reliability of the data is to guarantee that the measurement of the data yields the same answer as the very start of the study; and validity is to ensure what you are observing and ‘measuring’ is correct (Kirk and Miller, 1986). LeCompte and Goetz (1982) explains that there are two types of reliability and validity – internal and external. The prolonged participation of the applicants involved in the questionnaires and focus group allowed the researcher to certify that there was a high level of similarity.
The reliability of the data collection for the questionnaires was achieved through attendance and the notion that there is consistency with the questionnaires through the test-retest method (Charles, 1995). If the study were to be repeated, the research instrument would show consistency and conclude it is a reliable and valid procedure.
The internal validity of the focus group was achieved by recording on two digital machines, and stored on a password-protected computer meeting the requirements of the Data Protection Act (1998). By carrying out the focus group, it ensured whether the researcher should accept the quantitative data, and trust the quality of the validity of the answers from the participants from the focus group. There is a low external reliability in the immediate results because there were subjective and individual beliefs within the responses from the focus group. Additionally, there is a bias in the focus group regarding the researcher because of the personal knowledge and involvement relating to Hartpury University Centre open days.
The research follows the ethical inquiry of descriptive ethics which describes ‘what’, and ‘how’ people do the things they do, and asks empirical questions (Sugarman and Sulmasy, 2010). The ethical concerns that are inherent in this study are to guarantee that the participants from the focus group and questionnaires remain confidential and are protected from harm – to protect all who gave information to the study. To safeguard the data, it will be protected by the Data Protection Act of 1998, and the audio from the focus group will be stored securely within a password protected computer. The researcher will follow the Data Protection Act to guarantee that the information is used fairly and lawfully (Government UK, 1998).
In order to address the qualitative approach, the questionnaire answered by prospective sports students will ask for their voluntary and informed consent to take part in the study. This is to ensure there is minimal risk towards the participants and for the participants to understand the project (Rudestam and Newton, 2015). As it is an open day, there will be under 18s – therefore, at the end of the questionnaire it will ask for a parent’s signature for their consent. To avoid any negative consequences from under 18 participants, the study will be following the Children Act 1989 – “an Act to reform the law relating to children” (Government UK, 1989).
The focus group involves third-year students who are aged over 18 and in full time education at the university. To overcome the issue of receiving consent by the prospective students and the focus group participants, the questionnaire will have a statement asking for their consent, and the focus group will sign a consent form. The consent form given to the focus group participants explains what the study entails, the risks, the benefits, what they would have to do and the final consent form to sign if they agree to participate. During the transcribing, the researcher will ensure there is confidentiality of participants’ identities by creating pseudonyms of P1, P2 etc. (Creswell, 2014; Frankfort-Nachmias and Nachmias, 1992).
3.6 Data Analysis
Descriptive statistics are helpful to analyse data because they describe the characteristics of the sample in the project, and addresses the specific research questions (Pallant, 2013). The categorical variables, such as gender and region, will be shown using frequency distribution. The ordinal variables, such as how they heard about Hartpury University, will also be shown using frequency distribution. To ensure the necessary data is gathered, the equipment required is a paper questionnaire to hand out to respondents. The data will then be entered into Microsoft Excel, calculated and formatted into the required frequency tables.
The focus group’s epistemological stance will be following a thematic analysis, which extracts key themes from the study’s data. However, it is a diffuse approach as it defines few core themes in data (Bryman, 2015). Tuckett (2005) defines a theme as a recurring idea or topic discovered within a certain text. When the researcher is searching for themes, it is recommended to look for repetition as it is the most common way to identify themes; and similarities and differences in the transcripts through the way the interviewees discuss the different topics (Ryan and Bernard, 2003). By applying Miles and Huberman’s (1994) thematic conceptual matrix to the data, it will be following the inductive approach of the study and identifying key themes from the discussion. By using a thematic approach to analyse the data, it will help the researcher to understand the feedback from the focus group, and answer the aims and objectives of the study.
In this chapter, the data collected will be presented from the above chosen methods. The researcher will use a thematic approach to process the data to recognise key themes within the focus group. Similarly, the questionnaire uses descriptive statistics and the data will be grouped in frequency tables. The data will be analysed from both the focus group and questionnaire, be linked back to the objectives of the study and become the fundamentals for the discussion. There was one focus group and 50 questionnaires that were successfully collected.
4.1 To understand the effectiveness of open days as a marketing communication strategy.
4.1.1 University ‘Interaction’
From the thematic analysis of the focus group transcript, a reoccurring theme was the interaction with current students that undergraduates were exposed to at Hartpury University Centre open days. The current students involved in the open days partook in subject talks – depending on what course they were currently studying. This is a tool used at Hartpury University Centre to allow for prospective students to engage and ask questions to present students. Participant 2 states the following: ‘… I found it very helpful to speak to the students’.
Participant 4 also stated that it was ‘very good’ to have met current students because it ‘massively influenced my decision’ to come to Hartpury University Centre. Therefore, the use of subject talks and tours involving present students were helpful to involve prospective students too, as Participant 4 says, ‘getting used to the university environment’ was very beneficial.
Another aspect used at Hartpury University Centre are welcome talks – these are designed to inform and help the prospective students to make the right decision on what course to choose and whether it is the right university for them. This is seen in Participant 5’s statement: ‘When we were all sat in MDC1 and they told us all about the different classes and modules they had going on – it helped me to decide on what I wanted to do’.
Consequently, this shows that some participants preferred the engagement of students, while others preferred more in-depth information on their chosen course. This shows that the different aspects used on the open days are beneficial to different prospective students.
The frequency table from the questionnaire, shows similar results to the focus group as the prospective students found the subject talks and tours the most helpful. However, the welcome talk was the least helpful out of the four aspects available on the Hartpury University Centre open day. Surprisingly, no one in the focus group praised the value or usefulness of the information zone.
Table 1: A table to show which aspects prospective undergraduate students found helpful at the Hartpury University Centre open day.
4.2 Identify which current marketing strategies are effective in creating brand awareness.
4.2.1 Campus ‘Environment’
The second theme was the atmosphere found on Hartpury’s campus and its facilities. The most common way people heard about Hartpury University Centre was through family or friends. The questionnaire found that over 29 people found Hartpury University Centre through family or friends; following with others already doing BTEC or A-Levels at Hartpury College or one respondent stating: ‘I found Hartpury because my dog came here for hydrotherapy at the Cotswold Dog Spa’.
Another respondent claimed that they found Hartpury University Centre since: ‘I competed with my horse at one of the unaffiliated events’.
This shows that the world class events and facilities at Hartpury University Centre attract people who are not originally looking to further their education at the university, but personal activities such as equestrian events are also a factor.
Four out of the six participants in the focus group found Hartpury University Centre through word of mouth – Participants 2 and 5 heard it from their riding instructors. The other two participants found Hartpury University Centre on the Internet.
Surprisingly, the old media techniques – emails, magazines, television – had the lowest results. The growing social media accounts of Hartpury University Centre did not attract much attention from the prospective students despite the popularity of them.
Table 2: A table to show how prospective undergraduate students heard about Hartpury University Centre.
The participants in the focus group agreed that the unique selling point of Hartpury University Centre is the campus and the facilities. Participant 4 explains that: ‘… the charm of Hartpury is like having a look at the facilities, and just being immersed in the sort of campus environment’.
Participant 2, who studies BSc (Hons) Equine Sports Science, states that Hartpury University Centre is the largest equine college in the world and follows on by saying: ‘… there is nowhere that can compare to Hartpury with the facilities wise and in terms of the events and stuff with equestrianism’.
Additionally, an advantage of Hartpury University Centre’s lectures is that the sizes are smaller than larger universities. Participant 1 found it beneficial as it built the fundamentals for second and third year getting to know your lecturers’ well in first year: ‘… in first year, especially on the Foundation (Sport Business Management) course we had classes of about five to six students which was quite good because we had quite more a hand on approach to our lecturers’.
4.3 To understand the motivations of students in choosing Hartpury University Centre for undergraduate study.
4.3.1 Vocational Opportunities
The third theme found in the results which answers objective three, was the opportunities the courses gave students after graduation and what attracted students to the course.
Strength and Conditioning was the second most popular at the open day. The Sports Business Management degree was the fourth liked course. However, Equine and Nutrition were the least preferred courses over the three open days. Sports Coaching was the most popular degree which correlates with the high amount of people who apply for this degree.
Table 3: A table to show what sport course area prospective students were interested in.
The overall opinion from the focus group on why they chose their degree was because of the career opportunities after graduation. It was also, how Participant 1 explained: ‘… it sounded the most enjoyable course’.
In one comment made by Participant 3: ‘I think the Business Management degree just has a plethora of options to pursue once you are done’.
Both Participant 1 and 3 study BA (Hons) Sports Business Management and, therefore, have similar views that the degree would give them more opportunities afterwards. Similarly, Participant 4 and 6 study BSc (Hons) Strength and Conditioning and explain: ‘It was the course that gave me the most options once I graduate from university, different career paths I could have gone down’ (Participant 6) and ‘I chose my course because it really spoke to what I enjoy and the practicality of it, and actually getting out there and enjoying what you do’ (Participant 4).
Both Participant 2 and 5 said similar comments about their course, BSc (Hons) Equine Sports Science, although not because of the opportunities it gave them after graduation. They chose it because they were indecisive on what area to go into. Participant 5 said: ‘I picked it because I wanted to, I couldn’t decide whether to do sport or equine and it combines both together quite nicely’.
From the 50 respondents, over half of them answered that they were interested in that course because of the vocational opportunities that come with the degree. Nearly 22 respondents decided to choose that degree because they had done previous study within the field and wanted to expand on that knowledge. The three open day attendees who were interested in the course because of a friend’s recommendation explained that one had a friend already studying that degree at Hartpury University Centre.
Table 4: A table to show why the prospective students were interested in their chosen course.
Concluding on the theme of vocational opportunities, it is a real selling point for Hartpury University Centre as other universities do not offer the same degrees. As Participant 6 described: ‘They don’t do your sort of typical “old-fashioned degrees”. So, the degrees they offer are really vocational and they really send you down a certain career path whether that be business, if you’re into that side of things, or more practical hands-on side of things in the field’.
Therefore, as Participant 6 summarises, the degrees taught at Hartpury University Centre send undergraduate students down the certain career paths with the unique vocational courses. This recaps that both prospective and undergraduate students are conscious of choosing a degree that will provide vocational opportunities after three years at university.
4.4 To understand the marketing opportunities for Hartpury University Centre.
4.4.1 University ‘Culture’
A theme that came up within the focus group was the university culture and marketing opportunities. A few comments from the questionnaires are: ‘They should add the modules that you would do which would be in your course in the prospectus’ and ‘There should be more subject lecturers and sports people at the open day’.
Moving away from the open day questionnaires, the focus group participants made statements on how Hartpury University Centre could improve as a university for future undergraduate students. The recommendations ranged from the Hartpury University Centre website to university’s campus. Participant 6 wanted the university to help with the final year of study: ‘I would like to see a little bit more of help in transition from university, the contact time seems to drop off significantly in third year…but a little assistance finishing off that last year of university’.
However, Participant 6 also stated: ‘I would definitely look at the employment conversion rates six months post-graduation because I think one thing that Hartpury does well is sets you up for a career path’.
Furthermore, Hartpury University Centre does not actively promote these statistics, unless an applicant actively searches for it on their website or in their prospectuses, and the university should ensure increased promotion for prospective students. Furthermore, Participant 1 believes the ‘new Hartpury website is pretty poor’ and the respondent gave a suggestion: ‘If you’re a student thinking of coming, navigating around that website is not the best. I do not know if they could do a bit more of a virtual kind of tour of Hartpury on the website because it’s a different campus compared to other unis’.
The virtual tour came across as a good recommendation among all of participants in the focus group. Participant 5 said: ‘Yeah, especially as we have the international students like Participant 3 who could not come to an open day’.
The main recommendation following those suggestions was the university culture. Participant 3 found: ‘I think first year living on campus gets really boring, when I think about other universities having a bit more stuff going on’.
This came with a proposal from Participant 6 suggesting they should increase the awareness of the Student Union (SU). The Student Union is available to receive feedback on the university experience and to develop change for students. The suggestion from Participant 6 is: ‘I feel like the development of the SU would sort a more relaxed atmosphere during the daytime. Be nice to have bit of a buzz around the SU, brightening it up and making it a bit bigger’.
This came with criticism that there is not enough space on campus for non-residential students to socialise during the day. Despite the size of the campus, the buildings take a fraction of the space of the land it owns. Both Participant 1 and 2 gave the proposal for the university to expand: ‘On campus, we don’t have a common room or anything. There’s just the coffee shop’ (Participant 3) and ‘People just usually go to the library, it’s a social atmosphere more than a working atmosphere, because there is nowhere really elsewhere to go as a university student to have a chat, you know, with your mates and everything’ (Participant 2).
In terms of the learning and standard of teaching, there were no recommendations – however, in the BA (Hons) Sports Business Management degree they integrate an Institute of Leadership and Management (ILM) course. Participant 3 wishes for the university to review what courses they integrate into degrees: ‘I think, for my course they could build better links with professional development institutions like the CFA (Chartered Financial Analyst Institute) and ACCA (Association of Chartered Certified Accountants). We do have the ILM but that’s not enough I think’.
Another recommendation is the size of lectures as compared to other universities Hartpury has very small classes. However, Participant 1 believes they should try to keep the classes smaller over the three years, since it only lasts in first year: ‘…in first year, especially on the Foundation (Sports Business Management) course we had classes of about five to six students which was quite good because we had more of a hands-on approach to our lecturers and we could have more of a chat with them… I wish that sort of happened a bit more during second and third year but I suppose as the classes get bigger, it can’t really happen, but it was nice’.
The final recommendation on the teaching aspect was the launch of their Masters degrees. Surprisingly, when asked if any of them would study a Masters at Hartpury University, no one would. They all agreed that a Masters creates industry distinction, however, Participant 2 said: ‘I don’t think I would do an Equine Masters, because there’s not very much in terms of the equine industry. The science is not respected enough for a Masters to be like worth it at the moment – but maybe in the future’.
This chapter aims to discuss and compare the themes found in the previous chapter with the study’s objectives. Being an inductive study, the researcher will evaluate the collected data and results from the focus group and questionnaires, alongside relevant literature discussed previously in the literature review. The findings here will lead to an overall result for the research and determine if the topic needs future examination.
5.1 To understand the effectiveness of open days as a marketing communication strategy.
A key finding from the research was that all participants found the interaction with current students on open days as a ‘well-received’ marketing communication strategy highlighting the benefits of a ‘personal touch’. This communication is useful because listeners must concentrate on the information provided which enhances the comprehension given to them (Daly and Vangelisti, 2003). This indicates that Hartpury University Centre should involve students as part of their open day planning to keep both parents and prospective students engaged with the campus and degree choices available.
The advantage of having a ‘main’ rather than multiple campus locations is it allows for a central point of study. Baldry (1999) explains that the physical environment for teaching affects the qualities of the work experience suggesting vocational opportunities and the campus environment were the main reasons students choose to study at Hartpury University Centre. The campus covers 360 hectares, an ideal size for a student who wants a university feel, however, not too overwhelming (Hartpury College, 2018a). According to Hajrasouliha (2017), a ‘well-designed’ campus requires a “mixed, compact, well-connected, well-structured, inhabited, green campus in an urbanised setting”. Hartpury University Centre is able to occupy residential students and the focus group found that all of the respondents described the location as, ‘nowhere that can compare’. By holding an open day, it allows a student to see all of the features that Hartpury University Centre can offer creating an effective marketing strategy.
Hartpury University Centre provides sports students with exceptional facilities from pitches to sport performance laboratories (Hartpury College, 2018d). Côté et al (2016) found that providing athletes with modified sports facilities allowed them to develop an integrated relationship with their activities and settings that creates a positive sports experience and facilitates personal development through sport. Burton et al (2011) suggests a modern sports environment produces a self-determination climate for the athletes, and creates positive social relationships (Coakley, 1980). According to Palma et al (2013), it is important for a host destination to provide cultural facilities and venues to attract and prolong the stay of the personnel. Therefore, it is important to hold events both indoors and outdoors to increase attendance (Silberberg, 1995).
One of the aspects that allows prospective students to visualise and ‘get used to the university environment’ was through campus tours. McGunagle (1997) reinforces that a campus tour is a vital part of an open day as students and their families can obtain knowledge of the university. A Customer Relationship Management (CRM) strategy creates a company-customer relationship and enhances the performance of the university, improving the relationship of prospective and current students (Rigo et al, 2016). CRM is a consistent strategy for a university to personalise and tailor open days, managing all of the aspects a company uses when interacting with their customers (Buttle and Maklan, 2015). Ambassadors and current undergraduate students contribute to the prospective student’s application decision through interaction on campus tours (Klein, 2004). Hartpury holds campus tours every 30 minutes to ensure that applicants and their families do not miss out. Qian and Yarnal (2010) found that guides were motivated to provide tours, offering visitors rich and personal information about the university. If the university selects undergraduate students who are passionate, it can influence decisions, and therefore increase students applying to the university (Dessoff, 1994). One participant in the focus group explained that ‘it massively influenced their decision’ to come to Hartpury especially being able to talk to the current students.
5.2 Identify which current marketing strategies are effective in creating brand awareness.
The research gathered from the questionnaires and focus group, highlighted key marketing techniques used to attract prospective students and create brand awareness. This concept is important in a specialised university, as brand image can develop name recognition within specialised agriculture, sports and equine fields (Christodoulides et al, 2015; Hoyer and Brown, 1990). Hartpury University Centre has successfully created awareness through a unique campus ‘environment’ and as the largest equine college in the world, alongside sporting excellence – for example, the Gloucester Academy and Hartpury RFC.
The main marketing strategies seen in promoting Hartpury University Centre were elements of the Marketing Mix, elements in the Integrated Marketing Communication Mix (IMC) and word-of-mouth. The marketing mix is used as a marketing tool to recruit students from various target markets (Ivy, 2008). The elements highlighted from the research are product, promotion, people and physical evidence. The elements of people and physical evidence were successfully integrated on the open days supported by Ivy (2008) who explains that staff, students, materials, buildings and facilities establish brand awareness. Hartpury University Centre’s unique selling points are the campus, facilities and its exclusive degrees. A respondent found their degrees were not ‘your typical “old-fashioned degrees”’, which enticed them to enrol. The promotional element helps develop and influence the purchasing attitude of potential customer’s (Munteanu, 2006). A promotional strategy at open days enables people to talk about experiences and the use of the website has contributed to the organisation as they continue to build their brand (Bîja and Balaş, 2014).
To create a popular brand, a company should follow the Integrated Marketing Communication (IMC) mix, as it comprises all of the components of the company and reinforces the brand’s message (Payne and Holt, 2001). This allows a university to segment their target consumers and recruit their students personally (Moore, 1999; Ebenkamp, 2000). To reiterate, IMC is a blend of personal selling, public relations, advertising, sales promotion and direct and digital marketing (Kotler and Armstrong, 2016). The results found that personal selling and digital marketing guaranteed strong brand awareness. Olariu (2016) describes personal selling as a direct face-to-face communication with the sole purpose of making a sale. This is a significant aspect on open days because there are campus tours led by student ambassadors, and university lecturers who are there to speak to applicants. This allows applicants to experience the campus environment and ‘the charm of Hartpury’. By using this promotional strategy, it remains effective in engaging with the consumer and ensuring awareness is still present in the target market. Hartpury University Centre also uses digital marketing through four social media pages. Direct and digital marketing is aimed at specific consumers with the end goal of creating a lasting relationship and gaining instantaneous responses (Kotler and Armstrong, 2016). It is currently the most efficient tool to reach potential customers and, therefore, the easiest influencer for brand awareness (Kannan, 2017). Kneblová (2009) found at Brno University of Technology that a university website was the most effective way to reach prospective students – however, this current study found word-of-mouth was the most successful to reach prospective sports students applying for Hartpury University Centre.
Word-of-Mouth (WoM) marketing is a form of viral marketing and has strong credibility as the information given to the consumers is subjective and persuasive (Phelps et al, 2004). It was the most effective to create brand knowledge as four out of six participants found Hartpury University Centre through WoM endorsements highlighting the campus environment as the main talking point. An advantage of using WoM is it is a free promotional tool and consumers are actively seeking information on the relevant brand (Leigh and Thompson, 2012). The efficiency of WoM allows a company to target consumers through multiple exposures e.g. competitions held at the Hartpury equestrian arena, and hydrotherapy for both dogs and horses, therefore creating brand awareness for Hartpury University as a centre of excellence.
5.3 To understand the motivations of students in choosing Hartpury University Centre for undergraduate study.
A significant finding in the study were the motivations of students choosing undergraduate degrees at Hartpury University Centre because of its vocational opportunities. The main attraction for the third-year students for choosing their courses was ‘it gave me a plethora of options to pursue’ and ‘it sounded the most enjoyable course’. According to Virtanen et al (2014), a vocational course has two aspects – theoretical and practical. The participants found Hartpury University Centre gave them the best preparation to enter the workplace because of the opportunities available to them.
The motivation behind a student’s choice is derived from the consumer behaviour model (Alkaabi et al, 2017). Consumer behaviour is the actions people carry out to purchase a product or services (Blackwell et al, 2001). It is important for Hartpury University Centre to understand that the behaviour of the current students is vital for accurately targeting the correct students and creating relevant and appealing degrees. By correctly promoting open days, it motivates prospective students to attend a university open day and the open day allows for a consumer to become involved with the product (Reeve, 2008; Solomon, 2006). A sport open day’s consumer behaviour model is similar to a sport consumer behaviour model as they both involve three phases – input (external forces), internal processing (psychological forces) and outputs (Funk et al, 2016). A sports consumer becomes motivated to attend an event through word-of-mouth, attends the event and forms an attitudinal outcome based on the experience (Funk et al, 2016).
The main motivation for applying for university was for the vocational opportunities; however, the benefits of attending Hartpury University Centre are ‘the small classes which allow a “hands-on” approach with lecturers’. Graduating with a good degree, allows students to raise their future income due to the education obtained and improved abilities (Becker, 1964). Vocational opportunities are the deciding factor on the human capital a student will gain from their degree, since an employer will be willing to pay according to the contribution he believes can be made to the firm’s productivity (Blundell et al, 1999). It is common to attend university, as many jobs view it as a requirement (Trusty and Niles, 2004). One participant who described the courses at Hartpury as ‘really vocational’ noted this and that they align you with a certain career path.
The current study found that the main motivation for both prospective students and third-year students to apply for undergraduate degree courses were the world class facilities, campus and the vocational opportunities available at Hartpury University Centre. The campus facilities viewed on open days were considered the main attraction because there is ‘nowhere that can offer the same facilities’ as Hartpury University Centre. The specialised degrees were seen as particularly attractive by students to the university, as very few universities offer comparable vocational courses to those available at Hartpury University Centre.
The findings showed that the most successful technique to recruit prospective students was through personal selling by student interaction on open days, and word-of-mouth (WoM) marketing and endorsements. The research found that it was ‘very helpful’ to talk to current students as it allowed applicants to gain inside knowledge of the university, visualise the experience and provide greater understanding of their specific course. By allowing students to talk to prospective students, it facilitates Hartpury University Centre in reaching their target allocation for students applying for their undergraduate degrees. It was shown that the development of WoM marketing was established through people that had either studied at or visited Hartpury University Centre before, or knew someone who had visited previously. The creation of free WoM marketing was due to the unique selling point of Hartpury University Centre’s world class facilities and campus ‘environment’ – people who had heard about the university were already aware that it is the largest equine college in the world and famous for its sporting excellence.
To conclude, the key findings are not what the researcher originally assumed as it was thought that applicants had found Hartpury University Centre through direct and digital marketing channels – not word-of-mouth marketing. This was interesting as it was assumed that direct and digital marketing would be the primary channels for efficiently attracting and informing potential applicants about open days at Hartpury University Centre. In the study, it was established that the main motivation for students wanting to apply for Hartpury University Centre were the vocational opportunities the degrees gave them after graduation.
According to Denscombe (2010), constraints related to time and resources are common in any study. Time constraints only allowed the opportunity to collect data at three Hartpury University Centre open days, and to hold only one focus group. This meant that the sample size in the research was reduced and therefore, some viewpoints could have been missed. There was an absence of resources, since there was only one researcher – if there were a team of researchers more data would have been gathered on the open days, and more focus groups would have been held. Had there been additional time, it could have become a longitudinal study by observing the participants over a longer period of time, such as an entire year.
One limitation that hindered the research project was that it was held at a specific institute, Hartpury University Centre, to obtain the responses from the specific target market for the focus group and questionnaires. The selected university can only produce a certain set of results which will be unique to that institution compared to another university. Another constraint for the researcher was the limited literature review available on higher education institutes and the motivations of students wanting to apply for higher education.
6.2 Recommendations for Further Study
A recommendation for future research is to incorporate a similar methodology, however, the researcher should target a different higher education institute in a longitudinal study and sample students who are interested in more academic degrees. Further research could include different demographics, such as undergraduate and postgraduate students, and utilise separate male and female focus groups to allow different themes to be observed and compared. To ensure the reliability in results, the researcher should have a bigger sample size and more focus groups.
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Author Names: Georgina Lewis (BSc (Hons) Animal Behaviour and Welfare) and Dr Carrie Ijichi
Very few studies have explored the differences between dog breed groups and their cognitive abilities. Exploring canine cognition is important for improving both dog welfare and the dog-owner bond. It is crucial that dog owners understand a dog’s cognitive abilities and the level of stimulation a dog needs to meet their mental requirements. In doing so this may prevent behavioural problems developing which frequently result in the breakdown of the canine-owner relationship and dog relinquishment. The aim of the current study was to investigate whether cognitive abilities differ between the working dog group and the toy dog group. To assess the cognitive performance of toy and working dogs three cognitive tests were administered (object permanence test, quantity discrimination test and discriminative cues test). Latencies to complete each test and the number of correct choices made were measured, allowing a comparison to be made between the two breed groups and cognitive performance. It was hypothesised that there would be a significant difference between the two canine breed groups and their cognitive abilities, due to the working group completing the cognitive tests more successfully. Overall, no significant difference was found between the toy dog group and the working dog group in terms of their cognitive abilities. This suggests that working and toy dogs have similar cognitive abilities. No significant difference was found between the two breed groups and latencies taken to complete the tests or the number of correct choices made during the discriminative cues and quantity discrimination tests. A better understanding of the cognitive abilities of dogs may help owners understand their dog’s needs more appropriately, which may lead to a reduction in the number of dogs relinquished into shelters due to behavioural problems.
In the UK the estimated dog population is 8.5 million, with around 24% of households owning a pet dog (PFMA, 2015). It is accepted that dogs play an important part in society providing companionship to their owners, yet it has been estimated that around 5% of the population of owned dogs are relinquished to shelters yearly (Case, 2013; Westgarth et al, 2007). Although shelters aim to rehome as many unwanted dogs as possible, approximately 56% of dogs surrendered to kennels are euthanised (Case, 2013). The predominant cause of relinquishment is due to the breakdown of bonds between dogs and their owners (Case, 2013). Many factors can interfere with the success and length of the human-canine bond. These include; owners being unaware of their dog’s needs, unrealistic expectations and behavioural problems such as hyperactivity and destructive behaviours developing due to a lack of and/or inappropriate training and potentially inadequate environmental enrichment (Case, 2013; Previde and Valsecchi, 2004). Environmental enrichment can be utilised to improve an animal’s physical and psychological wellbeing (Newberry, 1995). Providing appropriate stimuli can provide opportunities for an animal to use its cognitive skills and may allow animals to perform motivated behaviours (Meehan and Mench, 2007). Cognition can be defined as the mental process by which an animal acquires, stores and acts on information from the environment (Broom and Fraser, 2015; Broom, 2010). Although studies have revealed that some species such as dogs, pigs and parrots have more complex cognitive abilities than previously expected, cognitive abilities of individuals within a species may differ (Broom, 2010). A better understanding of canine cognition, may help owners meet their dog’s needs more appropriately, leading to a reduction in dogs being relinquished due to behavioural problems.
It is apparent that dog owners overestimate a dog’s cognitive abilities. Howell et al (2013) conducted a survey involving 565 dog owners where participants were asked to indicate their perception of dog’s cognitive abilities. Howell et al (2013) revealed that most participants overestimated the cognitive capabilities of dogs. This demonstrates that owner’s expectations and perceptions are unrealistic, which may be due to a lack of understanding or knowledge regarding canine cognition amongst dog owners. However, this study was overrepresented by female dog owners (90.1%), which may have biased the results as it has been shown that men and women differ in the level of attachment to their pets (Payne, Bennett and McGreevy, 2015). Misinterpreting a dog’s abilities such as their level of cognition can often lead to poor welfare and can be responsible for the breakdown of canine-owner bonds (Payne, Bennett and McGreevy, 2015). For instance, owners may provide enrichment that does not stimulate a dog’s needs sufficiently or present a challenge that a dog cannot effectively solve, potentially leading to frustration (Meehan and Mench, 2007). Frustration is defined as an emotional state that arises when an animal is highly motivated to perform a behaviour but is unable to do so because of physical barriers or other constraints (Webster, 2011). This can result in abnormalities of physiology and behaviours which are indicative of poor welfare (Broom, 1991). Frustration is associated with behaviours such as chewing, pacing, barking and play bouncing, which are generally considered as undesirable, and may increase the risk of relinquishment (Shore, Burdsal and Douglas, 2008; Stephen and Ledger, 2005).
Dog breeds are also perceived to differ in their level of trainability, intelligence and obedience (Ley, Bennett and Coleman, 2009). Working dogs are described as dogs which have been bred to perform specific roles such as guarding and hunting (Turcsán, Kubinyi and Miklósi, 2011). Toy dogs are described as small breeds, with the main purpose being to provide companionship (Turcsán, Kubinyi and Miklósi, 2011). Ley, Bennett and Coleman (2009) found that dog owners rate working dogs higher on the training focus subscale in comparison to toy dogs. These perceptions seem reasonable considering the roles working dogs have been bred to perform such as search and rescue, herding and hunting and their long history working alongside humans (Serpell and Duffy, 2014; Ley, Bennett and Coleman, 2009). The assumption that working dogs are more intelligent and trainable in comparison to toy dogs should be tested. This could be achieved by conducting a series of cognitive tests to determine whether cognitive abilities do differ between toy and working dogs.
Although, much research into canine cognition has been addressed, breed group differences in relation to their cognitive abilities has received little attention from researchers. Serpell and Hsu (2005) investigated breed differences in trainability revealing that dogs bred for working were significantly more trainable in comparison to show bred dogs or toy breeds, indicating that working dogs have higher cognitive abilities (Serpell and Hsu, 2005). However, these results were based on subjective data collected from a self-report questionnaire, and secondly this study was primarily interested in breed differences and therefore did not explore breed group differences (Serpell and Hsu, 2005). To prevent subjectivity influencing the results, future studies should aim to collect objective data by conducting experimental studies to test the cognitive performance of canine breeds.
Research on canine cognition has shown that cognitive tests are reliable for measuring cognitive abilities and that dogs are capable of using human gestural cues, solving detour tasks and successfully completing quantity discrimination and object permanence tests (Bensky, Gosling and Sinn, 2013). Arden and Adams (2016) conducted a study to assess individual differences in cognitive abilities of 68 border collies. Arden and Adams (2016) achieved this by administering multiple cognitive tests, these included a detour test, quantity discrimination and discriminative cues test. Discriminative cues tests are designed to test a dog’s ability to use human gestures to infer the location of a hidden item (Lakatos et al, 2011). Quantity discrimination tests are used to assess whether a dog can discriminate between two different quantities (Arden and Adams, 2016). Although, Arden and Adams (2016) primarily explored within breed differences, the study revealed that there was in-breed variability in performance and that dogs which performed highly on one test tended to do well in the following tests.
Gagnon and Dore (1992) conducted a series of object permanence tests to assess the level of object permanence in dogs and to discover whether cognitive performance was consistent amongst various breeds. Object permanence tests involve presenting an object to a subject and then removing it from the subject’s view (Gagnon and Dore, 1992). This will test whether a dog can represent and locate an object that is no longer available to its immediate perception (Gagnon and Dore, 1992). Gagnon and Dore (1992) demonstrated that dogs can successfully complete visible displacement tasks and that the performance of sporting, terrier and working dogs was similar during displacement tasks. Considering existing literature, it is evident that further research needs to be conducted to discover whether cognitive abilities differ between canine breed groups.
Cognitive abilities of dogs are generally misunderstood by dog owners. The majority of dog owners overestimate the cognitive abilities of dogs and it is likely that most owners will have preconceived ideas regarding which breeds are the most intelligent (Howell et al, 2013; Ley, Bennett and Coleman, 2009). Very little research has investigated the differences between two canine breed groups and cognitive abilities. The current study aims to investigate whether cognitive abilities differ between the working dog group and the toy dog group, two dog groups which have very different roles in society (Turcsán, Kubinyi and Miklósi, 2011). This was achieved by running three cognitive tests (object permanence test, quantity discrimination test and discriminative cues test) selected and adapted from previous research. Latencies to complete each test and the number of correct choices made during the quantity discrimination and discriminative cues test were measured. Performance was then compared between the two breed groups to identify whether there was a significant difference in their cognitive abilities. Many studies have demonstrated that there are differences between dog breeds and their cognitive abilities (Bensky, Gosling and Sinn, 2013). Working dogs have been bred specifically to complete complex working roles such as hunting, unlike toy dogs whose main function is to provide companionship (Turcsán, Kubinyi and Miklósi, 2011; Serpell and Hsu, 2005). Therefore, it was hypothesised that there would be a significant difference between the two dog breed groups and their cognitive abilities, with the working group completing the cognitive tests more successfully.
2.1 Subjects and Site
A total of eighteen dogs participated in the present study (Table 1). Eleven dogs were female and seven were male. Subjects were aged between one and ten years (mean=4.77 ± 2.51). Dogs over the age of 10 were excluded from the study due to the potential of age-related cognitive dysfunction (Nagasawa et al, 2012). Dogs recruited for this study were from private households and were familiar with food treats and either tennis balls or squeezy toys which ensured that dogs would respond well to the cognitive tests that were food and game orientated. Subjects were sourced through posting adverts on social media and handing out adverts in veterinary practices, pet shops, canine groomers and dog walking parks where canine owners were likely to visit.
Tests were administered within two sessions and were conducted in a controlled room at Hartpury College where all sessions were videotaped. One experimenter and one research assistant participated during this study as well as a camera operator.
Table 1: Details of the subjects involved in this study.
|Subject||Breed group||Breed||Sex||Age (years)|
|5||Working||Husky Cross Collie||F||8|
|6||Working||Sprocker (Springer Spaniel Cross Cocker Spaniel)||F||4|
|10||Working||Springador (Springer Spaniel Cross Labrador).||F||6|
|15||Toy||Cavalier King Charles Spaniel||F||4|
|16||Toy||Cavalier King Charles Spaniel||M||10|
|17||Toy||Cavalier King Charles Spaniel||F||4|
2.2 Study Design
2.2.1 Pilot Study
An initial pilot study was conducted prior to the main study to assess the suitability of the tests involved. The pilot study took place in the same room as that used for the main study, and the same experimenter, handler and camera operator were involved. Two dogs (Bedlington Whippet and Jack Russell Terrier) aged two and nine completed the same three cognitive tests used in the main study. Results from the pilot study revealed that both dogs during the quantity discrimination test showed a side-bias for the baited plate on the right. This test consisted of three trials. During each trial a different pairing of food was presented (15ml vs 2.5ml, 7.5ml vs 1.2ml and 5ml vs 2.5ml), and the dog could choose a plate to go to. This test was then repeated using different pairings of food which were more visually distinguishable (7.5ml vs 1.2ml, 15ml vs 1.2ml and 15ml vs 2.5ml). When these measures were used, it resulted in both dogs performing more accurately and removed side bias, therefore these measures were used in the main study. The pilot study also revealed that both the gravy bones and wet food were good motivators for both dogs.
2.2.2 Main Study
Prior to testing in the main study, dogs were introduced to the experimental room with their owner. Subjects could explore the room for five minutes to become familiar with the experimenters, environment and objects, preventing distractions arising during testing. The familiarisation period is important in ensuring that factors that could affect a subject’s performance are limited and do not confound the results. During testing all participants (owners) and other subjects remained outside of the experimental room. Three tests; object permanence, quantity discrimination and discriminative cues test were all carried out in the same room in a random order. The number of trials per test was limited to minimise opportunities for trial and error learning which could improve performance (Pongracz et al, 2003). Once the first test was completed the subject then went on to complete the second and third test. In between each test dogs were given between 1-2 minutes to rest if necessary and use the water bowls provided. All tests were recorded using a Sony HRD-CX33OE video camera fixed on a tripod and timed using a stopwatch to ensure each dog received the same time to complete a test.
2.3 Object Permanence Test.
Squeezable toys (green frog) and tennis balls were used as target objects to gain the subject’s attention and maintain the subject’s motivation during the experiment. During the five-minute introduction to the experimental room, each subject was given the chance to play with the squeezable toy and ball. This allowed the experimenter to assess which item motivated the subject most and would therefore be the target object for that individual dog. The target object was manipulated by a 1.25 metre of transparent nylon thread.
Gagnon and Dore (1992) used three screens that functioned to hide the target object, however in the current study a single plywood screen was used to hide the object. This is because the current study was only concerned with whether dogs could successfully locate a displaced object. This could be assessed using a single visible displacement task which involves hiding the object in a single location, meaning only a single screen was required. Gagnon and Dore (1992) however required three screens in their study as they were interested in exploring what level of object permanence dogs had. To assess this Gagnon and Dore (1992) utilised numerous tests, including single visible and invisible tests and successive displacement tests which involved moving the target object multiple times behind a series of screens before the object is finally hidden. The screen consisted of three sides with no bottom or back panel (20cm wide x 40cm high x 12cm deep).
Subjects were placed 1.5 metres away from the screen and were restrained by the research assistant. This involved the research assistant holding the subject by its front shoulders which prevented the subject from moving towards the screen or target object. The experimenter attracted the subject’s attention towards the target object by moving the object via the nylon thread into the subject’s immediate visual field. Once the subject’s attention was on the target object, the object was hidden behind the screen and the experimenter stood motionless, 0.5 metres behind the screen. The subject was then released by the research assistant and allowed to search for the hidden object. If the subject successfully located the object it was given 10- 20 seconds to play with the toy as Gagnon and Dore (1992) found that a subject’s motivation to search for an object was increased if they were given the opportunity to play with the object after locating it.
A trial was successful if the subject grasped the object with its mouth, touched the object with its paw or placed its muzzle on the object. A trial was unsuccessful if the subject did not display one of the three mentioned responses and therefore the subject was given the full-time score of 60 seconds. Three consecutive trials were carried out, for each trial the subject had 60 seconds from when the subject was released to locate and respond to the displaced object. A maximum of 60 seconds was chosen as dogs can complete this test within 1-minute (Pongracz et al, 2001). A trial was ended and resumed from the beginning if the subject did not follow each step during the manipulation process of the object displacement.
Figure 1: Experimental set up for the object permanence test.
2.4 Quantity Discrimination Task
Two plates (20cm in diameter) were used during each trial, wet dog food was placed onto the centre of each plate. Four different sized spoons (1.2ml, 2.5ml, 7.5ml, 15ml) were used to measure out the wet dog food.
Quantity discrimination was tested by offering two quantities of food presented on two separate plates, then counting how many times the subject chose to go to the plate that held the larger amount of food. Subjects were brought to a marker (1.5 metres from the baited plates) and restrained by the research assistant who held the lead attached to the subject. This prevented the subject from moving towards either of the baited plates. The protocol of Arden and Adams (2016) was adapted such that instead of drawing various sized circles on to the plates and then filling the circle marked with wet dog food, the current study measured the wet dog food out using 4 different sized spoons (1.2ml, 2.5ml, 7.5ml, 15ml) which was then placed onto the centre of each plate. Using spoons was found to be a more precise method of measuring out the dog food as it ensured the accurate amount was presented on the plate each time. Also, using smaller volumes of food ensured that subjects still had an appetite and therefore motivation to complete the task.
Once the subject’s attention was directed towards the experimenter, the experimenter held two plates both containing a different amount of food. Both plates were shown to the subject and then placed on the floor 60cm apart and 0.5 metres in front of the experimenter. Once the plates were on the floor and the subject had seen the food, the subject which had been previously held on a lead was released by the research assistant allowing the subject to choose a plate to go to and eat. To prevent the research assistant subconsciously influencing the subject’s performance during the release, avoiding the “Clever Hans Effect”, the lead used previously to restrain the subject was simply dropped (Schmidjell et al, 2012). Once the subject had gone to a plate the other plate was removed immediately. This was repeated for three trials, during each trial a different pairing of measures were used (7.5ml vs 1.2ml, 15ml vs 1.2ml and 15ml vs 2.5ml). On average, it takes 2.4 seconds for a dog to complete the quantity discrimination task (Arden and Adam, 2016). Dogs in this study were therefore given 5 seconds to complete the task before the trial was ended. The number of times each dog correctly chose the plate with the larger quantity was measured as well as latency in seconds from the dog’s release to choice.
Figure 2: Experimental set up for the quantity discrimination task
2.5 Discriminative Cues Test
During training and testing the same experimenter and assistant were present. Two bowls (plastic tubs), both the same colour (white): 23.5cm in diameter and 9cm in height/depth functioned to hide the bait (piece of gravy bone).
The protocol described by Lakatos et al (2011) was followed and adapted to fit this study. During training the two bowls were placed in front of the experimenter, 1 metre apart. Subjects were positioned 2 metres away from the bowls where the subject could witness the hiding of the bait, which involved placing the bait into one of the two bowls. The subject was then released by the research assistant which allowed the subject to retrieve the treat from the bowl. This was repeated twice for each bowl to ensure that the subject knew that either bowl could contain food (Lakatos et al, 2011).
The position of subjects and bowls was the same as mentioned above, the main difference being that during testing the subject was prevented from observing the hiding of the bait. Subjects were positioned on a marker 2 metres from the two bowls set a metre apart. During training and testing subjects were restrained by the research assistant instead of the subject’s owner as described in Lakatos et al (2011). Owners of subjects were not present during testing to prevent them from influencing the subject’s behaviour which could lead to invalid results (Schmidjell et al, 2012). The research assistant restrained the subject gently by holding the free end of the lead that was attached to the subject. The experimenter picked up both bowls turned away from the subject and placed the food into one of the bowls. Both bowls were then returned to the allocated points at the same time and the experimenter stood 0.5 metres back from the middle line of both bowls to face the subject. It was essential that the experimenter stood equidistant from both bowls as Lakatos et al (2011) found that dogs showed a slight preference to choose the bowl that was closer to the experimenter. Therefore, if the experimenter stood closer to one bowl it could influence the subject’s decision and influence the results.
Once the subject’s attention was on the experimenter (sounds such as calling the subject’s name or clapping could be used to draw the dog’s attention) the visual cue was given. The pointing gesture (cue) used was momentary distal pointing: the experimenter pointed with an extended arm and index finger towards the correct location for 1-2 seconds before the arm was lowered beside the experimenter’s body. Once the arm was lowered the research assistant released the subject. To avoid the “Clever Hans Effect” the research assistant released the subject by letting go of the lead, which prevented unintentionally cueing the subject for instance through subliminally guiding the subject towards the correct direction (Schmidjell et al, 2012).
Subjects had up to 30 seconds to respond to the visual cue and make a choice (approach a bowl). If a subject did not react to the pointing gesture, it was repeated to a maximum of three times before the test was ended, resulting in the subject failing the test and receiving the full-time score of 30 seconds. Subjects could choose only one bowl, the bowl not chosen was removed as soon as the subject approached the other bowl. If the baited bowl was chosen, the subject could eat the contents. Test sessions consisted of 5 trials. The pointed side was presented in a pre-determined random order, to prevent bias towards one side a random letter generator app was utilised which offered 5 randomly allocated sides for each subject (Random Letter Sequence Generator, 2017). Food was not hidden in the same bowl more than twice in a row to avoid side bias (Szetei et al, 2003). Latency was measured in seconds from the dog’s release to choice and the number of times the dog went to the indicated side was measured.
Figure 3: Experimental set up for the discriminative cues test.
2.6 Ethical Note
Prior to commencement of the study, all participants were provided with a consent form that outlined details of the tests involved. Participants were asked to read and sign the consent form if they were happy to give permission to involve their dog in the study. All data provided by participants was held in accordance with the Data Protection Act (1998). All participants were able to remove their dog from the study at any point, up until the point of data analysis.
To minimise subjects becoming stressed and their welfare being impacted negatively, the following procedures were put in place. Subjects were familiarised with the experimental room prior to testing, the same handler and experimenter were involved, and the equipment used was safe and appropriate for small and large canine breeds. Stress was also monitored and upon any indication that a subject was becoming stressed (yawning, whining, trembling, constant head turning) the subject was either removed immediately from the experiment or put on a break before any further testing (Mariti et al, 2012). During two of the three tests food was involved and could potentially be consumed by the dogs if they completed a task successfully. As nutritional requirements vary from dog to dog, owners were provided with information regarding the food that would be involved. This ensured that the welfare of dogs with specific health or dietary requirements was not impacted. Water bowls were also provided within the experimental room to prevent subjects becoming dehydrated which could lead to dogs becoming anxious, potentially affecting their performance and welfare (Hatch, 2007).
2.7 Data Analysis
Data was statistically analysed using the SPSS statistical software package (IBM SPSS statistics for Windows, version 24.0. Armonk, NY: IBM Corp). To discover whether the data was parametric or nonparametric the Shapiro-Wilk test was run, revealing that all data was nonparametric. Therefore, the Mann-Whitney U test was employed. This test is suitable for running data on small sample sizes and compares the differences between two independent groups (Nachar, 2008). Mann-Whitney U was used to determine whether there was a significant difference between the two canine breed groups and the variables “time” taken to complete each of the three cognitive tests and “correct choices”, the number of correct choices made during the quantity discrimination and discriminative cues tests.
The Mann-Whitney U test revealed that there was a slight difference found between the two breed groups and the mean ranks for the object permanence test. To investigate this further, dogs were placed into one of two groups; those dogs which did not complete the test at all and those which completed the test at least once successfully. The Fishers exact test was employed to determine whether there was a significant difference between the two breed groups and whether they completed the object permanence test. This test was chosen due to the small sample size (N=18).
Mann-Whitney U test revealed that there was no significant difference between the two dog breed groups and time taken to complete the object permanence test (U=22, N1=12, N2=6, P=0.213; Figure 4). However, the mean rank was slightly higher for the toy dog group (M=11.83, IQR=43.20) in comparison to the working dog group (M=8.33, IQR=174.16). Fishers exact test revealed that there was no significant difference found between working and toy dog breeds and whether they completed the object permanence test (P=0.316; Table 2).
Figure 4: Comparison of the time taken to complete the object permanence test, (U=22, N1=12, N2=6, P=0.213).
Table 2: Results from the Fishers Exact Test: Comparison of successfully completed tests between working and toy dogs (P=0.316).
Mann-Whitney U test revealed that there was no significant difference found between the two dog breed groups and time taken to complete the discriminative cues test (U=32, N1=12, N2=6, P=0.75; Figure 5). There was no significant difference found between working dogs and toy dogs and the number of correct choices during the discriminative cues test (U=33.5, N1=12, N2=6, P=0.820; Figure 6).
Figure 5: Comparison of time taken to complete the discriminative cues test, (U=32, N1=12, N2=6, P=0.75).
Figure 6: Comparison between the two breed groups and the number of correct choices during the discriminative cues test, (U=33.5, N1=12, N2=6, P=0.820).
Mann-Whitney U test revealed that there was no significant difference between the two dog breed groups and time taken to complete the quantity discrimination test (U=31, N1=12, N2=6, P=0.682; Figure 7). No significant difference was found between the two canine breed groups and the number of correct choices during the quantity discrimination test (U=30.5, N1=12, N2=6, P=0.616; Figure 8).
Figure 7: Comparison of time taken to complete the quantity discrimination test, (U=31, N1=12, N2=6, P=0.682).
Figure 8: Comparison between the two breed groups and the number of correct choices during the quantity discrimination test, (U=30.5, N1=12, N2=6, P=0.616).
Behavioural problems are often the main cause for the breakdown of canine-owner relationships and have also been found to increase the risk of dog relinquishment (Case, 2013; Shore, Burdsal and Douglas, 2008). Providing adequate stimulation can help prevent or reduce undesirable behaviours, whilst increasing desirable behaviours and improving canine welfare (Herron, Kirby-Madden and Lord, 2014; Hubrecht, 1993). The aim of the current study was to discover whether cognitive abilities differ between the toy dog group and the working dog group. To investigate this, subjects completed three cognitive tests and the number of correct choices made and latencies to complete each test were measured. The results from the current study suggests that there was no significant difference between the two canine breed groups and cognitive performance.
From visual inspection of the graphs, it initially appears that the toy group took longer to complete the object permanence test and the discriminative cues test in comparison to the working dog group. However, this was not significantly different. The current study did not find a significant difference between the two dog breed groups and time taken to complete the three cognitive tests. Additionally, no significant difference was found between the two dog breed groups and the number of correct choices made during the quantity discrimination and discriminative cues tests. These results suggest that the working group and toy group have similar cognitive abilities. Therefore, these results do not support the hypothesis that there would be a significant difference between the working group and toy group in terms of their cognitive abilities. Furthermore, these results indicate that both breed groups can discriminate between two quantities, use human gestural cues to locate hidden items and understand object permanence to a similar level.
A potential explanation as to why the toy and working group completed all three cognitive tests within similar latencies and with a similar rate of correct choices is artificial selection. Artificial selection has been ongoing for approximately 14,000 years, which has given rise to a great variety of canine breeds (Turcsan, Kubinyi and Miklosi, 2011). Dogs have been selected for their behaviour, physical characteristics and perhaps even intelligence or trainability (Turcsán, Kubinyi and Miklósi, 2011). Toy breeds such as the Cavalier King Charles Spaniel have been recognised since the 16th century and are valued for their companionship (Serpell and Duffy, 2014; Coren, 2006). The results from the current study indicate that working and toy dogs have retained similar cognitive abilities. Intelligence may therefore be a trait which is valued by humans across all canine breeds, not only those selected for working roles. This might be a potential explanation as to why the toy group performed similarly to the working group in the current study. If intelligence was not appreciated by humans in toy breeds, it is expected that this trait would have been bred out.
King, Marston and Bennett (2009) conducted a questionnaire amongst Australian citizens to discover which characteristics were considered important in a dog. King, Marston and Bennett (2009) found that obedience was considered the most important characteristic. These findings support the theory that intelligence is valued in dogs regardless of breed group and supports our findings that working, and toy dogs have a similar level of intelligence. An obedient dog is one which is likely to be more trainable (King, Marston and Bennett, 2009). Therefore, an obedient dog may display fewer undesirable behaviours as owners may be able to teach their dog to behave appropriately (King, Marston and Bennett, 2009). This is likely to improve owner satisfaction and improve the human-canine relationship (King, Marston and Bennett, 2009). However, behavioural problems may still arise if a dog’s physical and mental requirements are not met (Landsberg, Hunthausen and Ackerman, 2012). According to the Kennel Club, the toy breeds involved within the current study, except for the Bichon Frise are described as intelligent and alert (The Kennel Club, 2009). These breed standards are clearly reflected in the current studies results, which show that toy dogs have similar cognitive abilities to working dogs. This is also consistent with breed rankings on perceived intelligence. Coren (2006) ranked the Papillion, a toy breed within the top ten breeds recognised as being the most intelligent and obedient. While Coren’s breed ranking is widely accepted, it is also criticised. Coren did not assess breed differences in trainability or performance but instead based rankings on the ratings of expert judges of trainability (Helton, 2010).
As the performance of the toy group and the working group was similar on all three cognitive tests, we would expect both breed groups to have similar levels of trainability. Serpell and Hsu (2005) investigated trainability between eleven common breeds. Data was collected from 1,563 canine owners who completed a canine behavioural assessment and research questionnaire (Serpell and Hsu, 2005). This study detected differences between breeds and trainability, revealing that toy breeds such as the Yorkshire Terrier ranked lowest in relation to trainability (Serpell and Hsu, 2005). Whereas working breeds such as the Labrador were ranked amongst the highest for trainability and therefore learning (Serpell and Hsu, 2005). The results from this study indicate that cognitive abilities do differ between working and toy breeds which is not in accordance with the current study’s findings. It must be noted that Serpell and Hsu (2005) relied upon self-rated measures and the opinions of dog owners to assess breed trainability which may have resulted in subjectivity, confounding the results. In comparison the results from the current study may be more reliable as an experimental design was employed. Each subject’s performance was assessed by measuring latency to complete each test and measuring the number of correct choices made during the quantity discrimination and discriminative cues tests. Therefore, the current studies results were not influenced by subjectivity as per self-reported measures.
However, a limitation of the current study that needs to be considered is the sample size utilised (N=18). This is a small sample size in comparison to existing studies that have investigated canine cognition (Arden and Adams, 2016; Ward and Smuts, 2007). There was also an overrepresentation of working dogs (N=12) in comparison to toy dogs (N=6) in the current study. Utilising a larger sample size with an equal representation of working and toy breeds may have resulted in a significant difference being found and would also allow the findings to be generalised. Furthermore, participants in the current study self-selected which may have resulted in a biased sample of only those participants interested in taking part or those who expected their dog to perform well. Consequently, the results may have been confounded and are unlikely to be representative of the wider population (Binfet and Passmore, 2016).
Though the current study suggests there to be no significant difference between the two breed groups and cognitive performance, few studies have found significant differences between breed groups and performance. Wobber et al (2009) conducted two experiments, the latter experiment investigated dogs’ (N=41) cue following abilities. Wobber et al (2009) found that working dogs (Shepherds and Huskies) historically bred for working purposes outperformed dogs typically bred for companionship such as the Toy Poodle and Basenji. This suggests that breeds traditionally bred to complete roles involving interactions with humans use human social cues more skilfully in comparison to non-working breeds bred for companionship. Although this study concluded that working breeds were more successful at using human gestural cues, the non-working group only consisted of two companion breeds, the Toy Poodle and Basenji, these two breeds alone cannot be used to represent all non-working breeds. Future studies should consider using multiple breeds per breed group alongside a larger sample size to strengthen their findings.
In comparison, Pongratz et al (2005) did not find any differences between dog breed groups and performance in a detour task. Although this study focussed on the breed groups; working, utility and hunting and therefore this is not completely relevant to the current study’s findings. The findings from this study indicate that cognitive abilities are independent of breed or breed group, suggesting that the findings from the current study are likely to be true (Pongratz et al, 2005). The differing results between these two studies might be due to the differences in their methodologies. Pongratz et al (2005) investigated cognitive abilities using a detour task conducted outside, while Wobber et al (2009) used a cue following task conducted within a novel indoor room. Whilst a vast amount of studies have explored canine cognition, many of these have only involved a single cognitive test to measure cognition unlike the current study. The current study involved three cognitive tests to assess cognitive abilities, increasing the reliability of the results. As the current study did not find a significant difference between the two breed groups and cognitive abilities across all three-cognitive tests, this indicates that the results found are likely to be valid.
Differently from toy breeds, working breeds have been selectively bred to optimise their performance in their working role (Turcsan, Kubinyi and Miklosi, 2011). This may have involved selecting for attentiveness or behavioural skills such as stalking in herding dogs (Turcsan, Kubinyi and Miklosi, 2011; Gacsi et al, 2009). Therefore, it would be expected that working breeds would outperform toy breeds in their cognitive abilities. However, the current study indicates otherwise, suggesting that toy breeds have similar cognitive abilities to working breeds as both group completed the cognitive tests within similar latencies and correct choices. These findings might be explained by selection pressures. Selection pressures have resulted in phenotypically different breeds, such as brachycephalic breeds (breeds with short noses) and dolichocephalic breeds (breeds with long noses) (Gacsi et al, 2009). Brachycephalic breeds such as the Cavalier King Charles tend to have more frontally positioned eyes, whereas dolichocephalic breeds such as sheepdogs tend to have more laterally located eyes (Gacsi et al, 2009). Gacsi et al (2009) recently demonstrated that brachycephalic breeds were more successful at using human gestural cues to locate hidden food items in comparison to dolichocephalic breeds. These results highlight the possibility that through selective breeding which has resulted in forward facing eyes, this may have also resulted in improved sensitivity or attentiveness (Helton and Helton, 2010). This is likely to improve or aid a dog’s performance during tasks, including those utilised in the current study. This theory has also been proposed by a few studies, suggesting that attention span can affect learning abilities (Gacsi et al, 2009; Range et al, 2008).
Factors such as training may have affected the results of the current study as this was not controlled for. Therefore, the effects of training cannot be ruled out. Marshall-Pescini et al (2008) discovered that training experiences affects the performance of dogs during problem solving. In comparison to untrained dogs (those with basic or no training), dogs which have received higher levels of training were more successful in problem solving tasks (Marshall-Pescini et al, 2008). This suggests that cognitive abilities are likely to be influenced by training experiences. However, Marshall-Pescini et al (2008) failed to put measures in place to minimise the effects of owner presence on a dog’s performance. Many studies have shown that owners can influence a dog’s decision (Schmidjell et al, 2012; Prato-Previde, Marshall-Pescini and Valsecchi, 2008). Although training experience was not controlled for in the current study, age was controlled. Nagasawa et al (2012) demonstrated that dogs over the age of ten have poorer cognitive performance in comparison to dogs younger than ten years. Therefore, dogs over the age of ten years were excluded from the current study, this ensured that a subject’s cognitive performance was not affected by age.
A further limitation of the current study is that hunger states of subjects were not considered during data collection. Assuming subjects differed in appetitive motivations due to being tested at various times throughout the day, our results which suggest that the two breed groups performed similarly may have been confounded. Future studies should consider asking owners not to feed their dogs for four hours prior testing. This might ensure that all dogs are highly motivated to complete the food orientated tests (Prato-Previde, Marshall-Pescini and Valsecchi, 2008).
Although the current study did not find a significant difference between the two canine breed groups and cognitive abilities, it would be beneficial to repeat this study with minor alterations. Future studies should involve a larger sample size to identify if the sample size used in the current study was too small to discover any significant differences. Arden and Adams (2016) conducted a study involving a sample size of 68 dogs. Future studies should aim to utilise larger sample sizes similar to that used by Arden and Adams (2016), this would enhance the reliability of the study’s findings. The current study only involved a selection of breeds to investigate breed group differences. Future studies should include all toy and working breeds to allow the findings to be generalised whilst strengthening the findings. Finally, since the current study found that the toy and working group could complete the three cognitive tests with similar latencies and rate of correct choices, future studies could implement cognitive tests that are more challenging to discover if there is a significant difference between the two breed groups and cognitive performance when using more advanced tasks. For instance, the current study tested for object permanence using single visible displacement, perhaps using invisible displacement or three successive displacements would have revealed a significant difference in cognitive abilities between the toy and working group (Gagnon and Dore, 1992).
The current study investigated whether there were differences between the toy dog group and the working dog group regarding their cognitive abilities. It was hypothesised that there would be a significant difference between the two dog breed groups and their cognitive abilities, due to the working group completing the cognitive tests more successfully. However, results from this study revealed no significant differences between the two dog breed groups and latency to complete the three cognitive tests or the number of correct choices made. Therefore, the current study suggests that dogs from the working dog group and the toy dog group have similar cognitive abilities. These findings may be useful in improving knowledge amongst dog owners. A better understanding of a dog’s cognitive abilities may allow owners to meet their dog’s mental needs more appropriately. This may lead to a reduced number of dogs surrendered into shelters because of broken or undeveloped canine-owner bonds due to behavioural problems developing.
The findings from the present study are in accordance with existing research which did not discover a difference between dog groups and cognitive performance. However, some studies have found differences between working and non-working breeds and their cognitive abilities. Due to this discordance between studies and their findings, more research is required to establish whether cognitive abilities are affected by breed group. Although the current study suggests there to be no difference between the two dog breed groups and their cognitive abilities, this study needs to be repeated with a larger sample size to confirm these findings, considering factors potentially associated with influencing cognitive performance such as training experience. Considering the current studies small sample size and the lack of supporting literature, the current study’s hypothesis cannot be accepted or rejected.
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The Effect of Vanvruddhi, an Organic Freshwater Plant-Based Biostimulant on Weight, Length and Colony Forming Unit of Radish (Raphanus Sativus L. cv. French Breakfast Radish)
Author Names: Alexandra Growden (BSc (Hons) Agriculture) and Patrick Tandy
The primary objective of the research was to investigate whether Vanvruddhi, an organic freshwater plant-based biostimulant affected weight, length and Colony Forming Unit (CFU) count of Radish. Six different treatment groups consisting of ten plants in each were tested; T1 – Control, T2 – Fertiliser, T3 – Fertiliser and Vanvruddhi granules, T4 – Fertiliser and Vanvruddhi liquid, T5 – Fertiliser, granules and liquid and T6 – Granules and liquid. Groups subject to fertiliser (nutrient analysis 6-3-6) received 5ml in 450ml water with a total of three applications. Groups subject to granule biostimulant received 0.08g/plant with a total of two applications. Groups subject to liquid biostimulant received 0.5ml in 50ml water with a total of two applications. Plants were harvested at 28 days and length and weight were measured. Soil samples were taken, tested using pour plate technique and measured for CFU count. Kruskal-Wallis H and Mann-Whitney U tests were performed to test for statistical significance. The results showed that groups T3, T5 and T6, subject to different biostimulant application methods all elicited significantly higher yields when compared with other groups at p ≤ 0.05 and Ucritical = 23. The groups subject to biostimulant granules only (T3) performed better than the group subject to biostimulant liquid only (T4). The group with the largest CFU count was the control group (T1), suggesting no significant difference between CFU and biostimulant eliciting plant growth. In conclusion, Vanvruddhi biostimulant was effective at increasing all measured parameters in experimental conditions of present study. Further research conducted over a longer time frame on a wider variety of agricultural and horticultural crops is suggested to investigate specific modes of action to better understand how biostimulants benefit plant growth.
The global population has been predicted to reach approximately 9.7 billion by 2050 (Food and Agriculture Organisation, 2009, United Nations, 2017), requiring food production to increase by at least 60% to meet global demand (Bijl et al., 2017, Canellas et al., 2015, Grafton et al., 2015, Keating et al., 2014, Ray et al., 2013). This increase in production will result in greater input requirements and is likely to place further strain on environmental resources (Acevedo et al., 2018, Le Mouël and Forslund, 2017, Owen et al., 2014, Vijay Anand et al., 2018).
Unsustainable farming practices have been cited in recent literature as key contributors to many environmental concerns such as climate change, water shortages, biodiversity loss, reduced soil fertility and erosion (Auserwald et al., 2018, Conijn et al., 2018, Dallimer et al., 2009, Duhan et al., 2017, Karimi et al., 2018, Moharana and Biswas, 2016, Zhang et al., 2015). Fertiliser use within the industry worldwide has contributed to the build-up of excess nitrogen (N) and phosphorus (P) in water bodies, which has been linked to the acceleration of eutrophication (Boeykens et al., 2017, Kube et al., 2018). The P reserves of numerous countries are predicted to be depleted in the next 100 years (Sattari et al., 2012, Walan et al., 2014), with the accumulation of surplus P from fertilisers in soils defined as “legacy P” (Bünemann, 2015, Chowdhury et al., 2016, Roberts and Johnston, 2015, Zhu et al., 2018). As a result, many countries are seeking to reduce fertiliser dosage, presenting an opportunity within the agricultural industry whereby intrinsic soil nutrient reserves can be used more efficiently (Condron et al., 2013, Jones et al., 2018, Khan et al., 2007, Owen et al., 2014, Sattari et al., 2012, Zhu et al., 2018).
Despite cited negative environmental impacts, agricultural production has contributed to the development of numerous landscapes and habitats which many species are now dependent upon for survival (Fraser et al., 2014, Global Harvest Initiative, 2017, Power, 2010). Furthermore, the agricultural industry remains essential for fostering social development and generating economic productivity within an increasing global population (Bianco, 2016, Donia et al., 2017, Dries et al., 2012). Therefore, yield growth rates must improve through better management practices and sustainable resource use, increasing food production to meet the global demand whilst enhancing environmental quality (Colla et al., 2017, Albacete et al., 2014, Sabiha et al., 2017).
In this context, biostimulants may contribute to food security by delivering a viable alternative to the use of non-renewable resources (Halpern et al., 2015, Sharma et al., 2017, Vijay Anand et al., 2018, Yakhin et al., 2017). This has the potential to improve the sustainability of production systems through optimising plant nutrient use and exploiting legacy P reserves, resulting in the reduction of fertilisers such as Diammonium Phosphate (DAP) (Colla and Rouphael, 2015, Ghosh et al., 2015, Jardin, 2015, Moharana and Biswas, 2016, Ondrasek et al., 2018). Alongside environmental benefits, fertiliser reduction will provide additional financial benefits to farmers, particularly due to the 11% rise in UK DAP prices reported at the end of 2017 following supply tightness and uncertainty (Agriculture and Horticulture Development Board, 2017a).
A proposal published by the European Commission (EC) in October 2017 defined a plant biostimulant as a “product containing any substance or microorganism stimulating plant nutrition processes independently of its nutrient content, or any combination of such substances and/or micro-organisms, with the sole aim of improving one or more characteristic of the plant or the plant rhizosphere” (EC, 2017). The proposal intends to revise regulation EC No 2003/2003 of the European Parliament to generate legislative action on biostimulants and create a single market for biostimulant products (EC, 2016). Plant biostimulants are currently estimated by the EC to account for only 3% of market value but have a strong market development potential and are gaining increasing consideration within crop management practices (EC, 2016, Frioni et al., 2018, Lokko et al., 2018). This expanding market is supported by the rising price of agricultural inputs and the growing consumer demand for products with minimal chemical use and environmental impact, encouraging farmers to seek more economic and sustainable alternatives (Brown and Saa, 2015, Finisterra do Paço et al., 2009, Groening et al., 2017, Kirstensson et al., 2017, Kocira et al., 2017, Rana and Paul, 2017).
Biostimulants are obtained from natural or biological sources (Biostimulant Coalition, 2018) and are usually either amino acid, marine bioactive or humic based (Kauffman et al., 2007, Lucini et al., 2015). They are often grouped as microbial (containing living organisms) or non-microbial (Bulgari et al., 2015), with Table 1 illustrating the most common product types in the UK.
Table 1: A Summary of the Most Common Biostimulant Product Types Used in the UK (AHDB, 2017b).
Understanding of biostimulants and their effective modes of action is continuously developing due to the complex nature and classification of contributing substances (Brown and Saa, 2015, Guinan et al., 2013, Rose et al., 2014, Van Oosten et al., 2017, Yakhin et al., 2017). However, whilst biostimulants contain a wide range of bioactive compounds that are still unknown, many studies have confirmed their repeated efficacy at eliciting plant growth (Frioni et al., 2018, Lucini et al., 2015, Sabir et al., 2014, Sharma et al., 2017). For example, Sabir et al., (2014) found that the application of a seaweed-based biostimulant in combination with nano-size fertiliser was able to increase the cluster size of grapes, and Polo and Mata (2018) concluded that application of an animal protein-based hydrolysate improved tomato yield. However, conflicting research such as that by Frioni et al., (2018) recorded yield and cluster size of grapes to be unaffected following foliar application of another seaweed-based biostimulant. This variability in results may be because biostimulants are not classified as plant growth regulators and their primary role is not to provide fertiliser action (Jardin, 2015, Yakhin et al., 2017). Instead, they optimise plant growth and yield through enhancing crop nutrient uptake and tolerance to abiotic and biotic stresses (Calvo et al., 2014, Nardi et al., 2016). This view is supported by Goni et al., (2018), who found the compositional complexity of three different biostimulants to affect drought tolerance in tomatoes in varying degrees. However, whilst this was attributed to the biostimulant’s ability to improve soil conditions or directly target plant physiology, the specific application types and modes of action responsible for these beneficial effects remain undefined, resulting in inconclusive evidence as to their actual efficacy (Bulgari et al., 2015, Guinan et al., 2013, Nardi et al., 2016, Owen et al., 2014, Van Oosten et al., 2017). Tejada et al., (2011) noted that the application of four biostimulants, particularly a hydrolysed poultry feather biostimulant, had positive effects on soil biochemical properties and microbial communities. Though this may suggest that biostimulants perform through improving soil conditions, research by Siwik-Ziomek and Szczepanek (2017) found biostimulant application increased Sulphur uptake in oilseed rape without affecting soil enzyme activity, suggesting that biostimulants may not need to affect soil conditions to elicit plant growth.
Due to this complex nature and varying literature, further research is required into individual biostimulant types and their effect on a wider variety of agricultural and horticultural crops (Brown and Saa, 2015, Van Oosten et al., 2017). Pot trials can provide the controlled conditions to investigate biostimulants as standalone products and in tandem with fertilisers, to gain a better understanding of how and under what conditions biostimulants facilitate growth. Furthermore, research into the relationship between biostimulant application and soil microbial activity may help to further determine how biostimulants elicit plant growth, providing scope for future research into the development of new products (Chen et al., 2002, Moharana and Biswas, 2016, Nardi et al., 2016). This is particularly important in providing scientific basis for efficacy, defining biostimulants varying modes of action to better benefit farmers and growers and contribute to the development of a single market for biostimulant products (Shubha et al., 2017).
Experimental research into the effects of organic freshwater plant-based biostimulant Vanvruddhi on radish (Raphanus Sativus L. cv. French Breakfast Radish) will contribute to an increasing body of research determining the benefit of biostimulants to crop production. Supplementary tests investigating Colony Forming Unit (CFU) counts will begin to investigate an area of limited research which may help to determine how biostimulants elicit plant growth. Present research will provide scope for future trials, contributing to the improvement and development of both existing and new products.
1.1 Aims and Objectives
This study aims to investigate the effects of organic freshwater plant-based biostimulant Vanvruddhi on radish (Raphanus Sativus L. cv. French Breakfast Radish). To achieve this the following objectives will be undertaken;
- The primary objective is to demonstrate if the application of biostimulant increases weight and length of radish.
- The secondary objective is to investigate which biostimulant treatment groups most increase weight and length.
- The tertiary objective is to investigate whether biostimulant use affects Colony Forming Unit (CFU) count.
H0 There is no significant difference between treatment groups in radish weight, length and Colony Forming Unit (CFU) count.
H1 There is a positive effect of treatment group and biostimulant use upon radish weight, length and Colony Forming Unit (CFU) count.
2.1 Growth Conditions and Experimental Design
In the summer of 2017 a single pot trial experiment was conducted in a glass greenhouse on a private estate in North Yorkshire, England (54°08’08.9″N 2°02’09.5″W). Daily temperature was maintained between 7°C and 24°C and nightly temperature >4°C. The greenhouse was not heated, and roof vents were used for passive ventilation and relative humidity ranged between 83 – 91%.
Two litre(2L) square plastic pots with a dimension of 20 x 20 centimetres used as described by Goni et al., (2018) were filled with 1.75L of top soil, with a texture of sandy loam, but manufacturers were unwilling to release chemical soil analysis. Raphanus Sativus L. cv. French Breakfast (Radish) was chosen due to its quick germination and rapid growth, oblong tap roots and aerial parts (Brickell, 2012). This provided material that could be easily measured and quantified, allowing for adequate data analysis (Ahmad et al., 2018, Ondrasek et al., 2018, Chung et al., 2017). Furthermore, different radish cultivars are relevant to the agricultural industry through their use as forage and cover or catch crops (AHDB Dairy, 2016, Price and Norsworthy, 2013). Seed manufacturers estimated harvest from 6 – 8 weeks of planting, with choice of cultivar enabling study to be done efficiently in optimal summer growing conditions whilst adhering to space and time constraints (EL-Sayed et al., 2014). Individual, uniform seeds were planted on July 12th in the centre of each pot at a depth of one quarter of an inch.
The experiment was conducted in a randomised complete block design (RCBD) as described by Arif et al., (2018) and Tejada et al., (2011), with six experimental treatment groups and ten replicates per group as described by Lucini et al., (2015). Pots were spaced next to each other with sides touching. Plants were watered daily by overhead irrigation in the early evening. Due to greenhouse conditions, limited pests and weeds were present, although any noted were manually removed and there was no incidence of disease.
2.2 Nutrient Management
Six groups consisting of ten plants in each group were grown (T1 – Control, T2 – Fertiliser, T3 – Fertiliser and Vanvruddhi granules, T4 – Fertiliser and Vanvruddhi liquid, T5 – Fertiliser, granules and liquid and T6 – Granules and liquid). The non-treated control group contributes to the validity of experiment through providing conditions whereby independent variables cannot influence results and the further five groups enable the product to be tested independently and in tandem with fertiliser to provide a greater understanding of biostimulants efficacy (Lavrakas, 2008). Independent variables (IV) were controlled and manipulated to determine the effect on the dependent variable (DV), collecting a longitudinal dataset (Saunders et al, 2016).
Fertiliser of the nutrient analysis 6-3-6 (N-P-K with trace elements) was applied to groups T2, T4 and T5 at a rate of 5ml in 450ml water. Fertiliser dilution was sprayed evenly using a plastic sprayer over each group of 10 plants. The first application was on 22nd July at cotyledon stage, then at weekly intervals of 29th July and 5th August, as recommended by manufacturer’s instructions (total applications of fertiliser = 3).
Vanvruddhi Biostimulant Granules were applied to groups T3, T5 and T6 at a rate of 0.08g/plant. The first application was on 12th July as a layer underneath the seed and then again on 22nd July at cotyledon stage on the surface of the soil around plant, as recommended by manufacturer’s instructions (total applications of granules = 2).
Vanvruddhi Biostimulant Natural Organic Liquid was applied to groups T4, T5 and T6 at a rate of 0.5ml in 50ml water sprayed evenly over 10 plants. The first application was on 22nd July at cotyledon stage and then again on the 6th August, as recommended by manufacturer’s instruction (total applications of liquid = 2).
2.3 Samples for Colony Forming Unit Counts
Ten gram (10g) samples of soil were collected from the centre of one of each pot from the six treatment groups, alongside a 10g composite control sample of soil before the experiment was conducted. Soil was stored at 4°C in sealed polypropylene tubes until used, to preserve soil biota, as described by Criado-Fornelio et al., (2017), Luo et al., (2017) and Tejada et al., (2011).
Five grams (5g) of each soil sample was diluted in 35ml Phosphate Buffered Saline (PBS), vortexed for 1 minute then left to settle for 15 minutes. Ten-fold serial dilutions up to 10-12 were performed and 1mL samples were cultured on Petri dishes containing 20mL of nutrient agar prepared by mixing agar powder with H2O as to manufacturer’s instructions, as described by Arif et al., (2018) and de Knegt et al., (2017).
Dilutions were plated using standard pour plate technique and incubated for 24 hours at 37°C as described by Obinna-Echem and Adjei-Duodu (2016).
2.4 Evaluated Parameters
Due to poor quality topsoil resulting in nutrient deficiency, plants had to be harvested at 28 days. Total plant growth was evaluated through measurement of length and weight as described by Sousa et al., (2017) and Rehman et al., (2018). Each plant was carefully uprooted, and roots submerged in water to remove excess soil before being patted dry with blotting paper and weighed replicating methods used by Çimrin et al., (2010) and Ertani et al., (2013). Plants were then measured for length from base of root to top of leaf and photographed against white background.
2.4.1 Colony Forming Unit Count
Plates with 25 – 250 colonies were used due to > 250 being considered Too Numerous to Count (TNTC) and < 25 to not have a statistically significant number of colonies (Jungck, 2012, Tortora et al., 2015).
The number of bacteria in 1mL of dilution was calculated using the standard Miles and Misra method (1938) = (no. of colonies x dilution factor) / volume of culture plate, as used by Obinna-Echem and Adjei-Duodu (2016) and expressed as colony forming units per mL of dilution (CFU mL-1), as described by Arif et al., (2018) and Boczek et al., (2014).
2.5 Statistical Analysis
Statistical analysis draws conclusions on null (H0) and alternative (H1) hypotheses, leading to data analysis that establishes causal relationship and allows for appropriate conclusions to be made as described by Saunders et al., (2016). A quantitative data collection method was chosen due to increased credibility through using quantifiable numbers and facts, providing reliable data for conclusions to be drawn and enabling generalisations to broader situations (Botti and Endacott, 2008, Bryman and Bell, 2011, Sheard, 2018).
Kolmogorov-Smirnov (K-S) test was used to determine data distribution. Data did not have the same variability; therefore, assumptions of normality were violated, and data were analysed via Kruskal-Wallis H test to determine differences (George and Mallery, 2016). Posthoc Mann-Whitney U tests compared differences between two individual groups to identify where significant differences lay.
The dependent variable was scored according to height in inches or weight in grams, with plants which failed to thrive (two in T1, one in T2 and T4) counted as viable data and input as “0.00”. Kolmogorov-Smirnov test revealed data did not meet assumptions for use of parametric tests as distribution was not normal. Because of this abnormal data distribution, Kruskal-Wallis H test could only reliably compare mean ranks and not median latencies, which was important to note for data interpretation (Field, 2014). Critical significance levels were as follows; p ≤ 0.05 and Ucritical = 23 (n1 = 10 and n2 = 10). Statistical analyses were all performed in the software package IBM SPSS Statistics ver. 24 (IBM, 2018).
The total weight of radish over all treatment groups averaged .85g (SD = 1.08). Figure 1 illustrates the mean weight in grams for each group, with standard error. Results show that plants in T5 treatment group receiving fertiliser, liquid and granular biostimulant, had the highest mean weight. Control group (T1), groups not receiving any biostimulant (T2) or only granular biostimulant (T4) showed similar lower mean weight, particularly expressed by parallel standard error bars.
Figure 1: Mean Whole Plant Weight of Radish Harvested at 28 Days Following Six Different Treatments; T1 = Control, T2 = Fertiliser, T3 = Fertiliser and Vanvruddhi Granule Biostimulant, T4 = Fertiliser and Vanvruddhi Liquid Biostimulant, T5 = Fertiliser, Vanvruddhi Liquid and Granule Biostimulants, T6 = Vanvruddhi Liquid and Granule Biostimulants.
The total length of radish over all treatment groups averaged 3.3 inches (SD = 2.12). Figure 2 illustrates the mean length in inches for each group, with standard error. Result show that plants in T5 treatment group receiving fertiliser, liquid and granular biostimulant, had the highest mean length. Control group (T1), groups not receiving biostimulant (T2) or only granular biostimulant (T4) showed similar lower mean length.
Figure 2: Mean Whole Plant Length of Radish Harvested at 28 Days Following Six Different Treatments; T1 = Control, T2 = Fertiliser, T3 = Fertiliser and Vanvruddhi Granule Biostimulant, T4 = Fertiliser and Vanvruddhi Liquid Biostimulant, T5 = Fertiliser, Vanvruddhi Liquid and Granule Biostimulants, T6 = Vanvruddhi Liquid and Granule Biostimulants.
3.2 Statistical analysis: Kruskall-Wallis H Tests
When df = 5, χ2 must be > 11.07 to reject H0, therefore Kruskal-Wallis H test showed that there was a statistically significant difference in plant weight between the different plant treatment groups, χ2(2) = 31.237, p = .001. Mean rank plant treatment scores are presented in Table 2, with results showing that plants in T5 treatment group receiving fertiliser, liquid and granular biostimulant, had the highest mean rank weight.
Kruskal-Wallis H test also showed that there was a statistically significant difference in plant length between the different plant treatment groups, χ2(2) = 32.416, p = .001. Mean rank plant treatment group scores are presented in Table 2, with results showing that plants in T5 treatment group receiving fertiliser, liquid and granular biostimulant, had the highest mean rank length.
Table 2: Mean Rank Scores of Plant Treatment Groups Following Kruskal-Wallis H Tests (T1 = Control, T2 = Fertiliser, T3 = Fertiliser and Vanvruddhi Granule Biostimulant, T4 = Fertiliser and Vanvruddhi Liquid Biostimulant, T5 = Fertiliser, Vanvruddhi Liquid and Granule Biostimulants, T6 = Vanvruddhi Liquid and Granule Biostimulants).
3.3 Statistical Analysis: Mann-Whitney U Tests
Due to the largest sample size of n = 10 for both treatment groups tested, critical levels for Mann-Whitney U tests are set at 23, therefore if U ≤ 23 then H0 can be rejected. All paired combinations between groups were subject to Mann-Whitney U two-tailed tests to determine differences between individual groups with Table 3 and 4 illustrating results.
Findings showed T3, T5 and T6 to have significantly higher final plant weight and length than all other groups at p ≤ .05 and U ≤ 23. T3, T5 and T6 all received biostimulant treatments, supporting H1 that biostimulant application positively effects plant length and weight. T4 also received biostimulant treatment in liquid form but was not shown to be statistically higher than T1, T2 or T3 in plant length or weight.
Table 3: Results from Mann-Whitney U Tests for Plant Weight (T1 = Control, T2 = Fertiliser, T3 = Fertiliser and Vanvruddhi Granule Biostimulant, T4 = Fertiliser and Vanvruddhi Liquid Biostimulant, T5 = Fertiliser, Vanvruddhi Liquid and Granule Biostimulants, T6 = Vanvruddhi Liquid and Granule Biostimulant
Table 4: Results from Mann-Whitney U Test for Plant Length (T1 = Control, T2 = Fertiliser, T3 = Fertiliser and Vanvruddhi Granule Biostimulant, T4 = Fertiliser and Vanvruddhi Liquid Biostimulant, T5 = Fertiliser, Vanvruddhi Liquid and Granule Biostimulants, T6 = Vanvruddhi Liquid and Granule Biostimulants).
|Plant Treatment Group #1||Plant Treatment Group #2||U||P|
|T1 (11.7)||T2 (9.2)||37.5||.339|
|T1 (6.85)||T3 (14.1)||13.5**||.006|
|T1 (11.2)||T4 (9.75)||42.5||.566|
|T1 (6.60)||T5 (14.4)||11.0**||.003*|
|T1 (6.05)||T6 (14.9)||5.50**||.001*|
|T2 (6.15)||T3 (14.8)||6.50**||.001*|
|T2 (9.50)||T4 (11.50)||40||.434|
|T2 (6.15)||T5 (14.85)||6.50**||.001*|
|T2 (5.80)||T6 (15.2)||3.00**||.000*|
|T3 (14.5)||T4 (6.50)||10.0**||.002*|
|T3 (9.85)||T5 (11.15)||43.5||.622|
|T3 (10.9)||T6 (10.0)||45.5||.733|
|T4 (6.45)||T5 (14.5)||9.50**||.002*|
|T4 (6.00)||T6 (15.00)||5.0**||.001*|
|T5 (11.6)||T6 (9.40)||39.0||.403|
Note. M in parentheses, N = 10 for all analyses groups
* = Significant at p ≤ .05 ** = Significant at U ≤ 23
3.4 Colony Forming Unit Tests
Results from supplementary research into CFU counts are displayed in Table 5, with T1 group containing the greatest number of bacteria per mL of dilution.
Table 5: Colony Forming Units (CFU) mL-1 for Six Different Treatment Groups (T1 = Control, T2 = Fertiliser, T3 = Fertiliser and Vanvruddhi Granule Biostimulant, T4 = Fertiliser and Vanvruddhi Liquid Biostimulant, T5 = Fertiliser, Vanvruddhi Liquid and Granule Biostimulants, T6 = Vanvruddhi Liquid and Granule Biostimulants).
Note. N = 10 for all analyses groups
a = Not viable due to no. of colonies <25
The aim of the study was to investigate the effects of Vanvruddhi organic seaweed-based biostimulant on length, weight and CFU of radish. Findings support H1, that there was a positive effect of biostimulant use upon radish yield. Groups subject to granular biostimulant (T3, T5 and T6) showed significantly increased weight and length when compared with other treatment groups (Figure 3).
Figure 3: Cross-Section of Radishes Harvested at 28 Days Subject to Six Different Treatment Groups (T1 = Control, T2 = Fertiliser, T3 = Fertiliser and Vanvruddhi Granule Biostimulant, T4 = Fertiliser and Vanvruddhi Liquid Biostimulant, T5 = Fertiliser, Vanvruddhi Liquid and Granule Biostimulants, T6 = Vanvruddhi Liquid and Granule Biostimulants) (Authors Own, 2017).
4.1 Effect of Biostimulant on Plant Weight and Length
Topsoil was chosen due to its high organic matter content and to enable better conformity within each pot as described by Merino-Martin et al., (2017). However, manufacturers were unwilling to release soil chemical analysis, raising concerns regarding soil nutrient profile. Consequently, this resulted in numerous radish displaying significantly delayed and impaired growth, with premature leaf senescence as described by Schippers et al., (2015), Fischer (2007) and Gregersen et al., (2013). Yellow leaf margins and pale green colouring were apparent in most plants, suggesting lack of chlorophyll production due to nitrogen (N) deficiency. Root formation was stunted, particularly in groups T1, T2 and T3, suggesting deficiencies in potassium (K) and phosphorus (P). Furthermore, purpling of the underside of leaves may also result from P deficiencies (Carmona et al., 2015, Kumar and Sharma, 2013, RB209, 2010).
Plants were subject to experimental conditions (see Materials and Methods) and evidence of other plants growing proved the greenhouse environment to be favourable. 10 additional seeds were planted on August 1st in field conditions to test for validity and all produced significant yield in 28 days without any additional inputs. Whilst low fertiliser nutrient analysis of 6-3-6 could be responsible for deficiencies, growth of radish planted outside without fertiliser application suggested otherwise. Therefore, the topsoil used was confirmed to be the cause of plant failure to thrive. This agrees with recent literature citing the increasing depleted nutritional status of soils and the effect on agricultural production, raising significant concerns regarding soil fertility in both agricultural land and commercial soils (Duhan et al., 2017, Moharana and Biswas, 2016, Zhang et al., 2015).
Despite these unfavourable growing conditions, plants from treatment groups receiving biostimulant (T3, T5 and T6) still showed significantly higher yields when compared to other groups. This is in accordance with previous reports such as that by Sharma et al., (2017), who found the use of two foliar-applied seaweed-based biostimulants to increase the yield of rice in India by 28 – 29% in fertility constrained laterite soils. Increased mineral content in plant biomass of treated plants suggested that the biostimulant either contributed to direct uptake or stimulated enhanced nutrient uptake from poor soil. Although, as with present study, specific mode of action remained undefined (Sharma et al., 2017). However, significant enhancement in yield over control was observed only in treatment groups subject to both fertiliser and one of either biostimulant. This is in contrast with present study, that showed treatment group receiving biostimulant granules and liquid only (T6) to have significantly higher length and weight when compared with control group (T1). This presents the novel concept that whilst biostimulants are not classified as plant fertilisers, they may have the potential to facilitate nutrient uptake and plant growth independently of additional fertiliser application. However, whilst T6 did show significant differences, the quality suggests the biostimulant together with fertiliser, particularly in group T5, produced the highest quality plant. This is evidenced by greener leaves, uniform plant structure and indication of bulbous edible root. Therefore, further research into biostimulants efficacy as a standalone product and effect on yield quality is required.
Further studies such as that by Ertani et al., (2013) support biostimulants’ ability to improve yield through enhancing plant tolerance to stress. Results recorded maize plant biomass to be improved under unfavourable saline solutions after application of a protein-hydrolysate based biostimulant. However, it was noted that research was conducted over a short time frame such as in present study and suggests the effect of biostimulants on plant growth in long-term experiments is required to investigate whether initial growth stimulation has negative effects on plants with a longer growth period (Ertani et al., 2013). Furthermore, whilst beneficial effects were attributed to biostimulant stimulating plant nitrogen metabolism and antioxidant systems, further research utilising methods that identify specific modes of action is suggested. Regardless of this limited understanding, findings from present study and supporting literature has positive implications for farmers to overcome soil constraints through increasing plant tolerance to abiotic stresses (Abbot et al., 2018).
4.2 Application Method Eliciting Most Growth
Plant groups subject to biostimulant granules (T3, T5 and T6) showed the most significant growth when compared with other treatment groups. Present research also suggests this can be attributed to the use of biostimulant instead of the nutritional value of fertiliser due to a low analysis of 6-3-6. Additionally, length and weight of plants in group T2, which received fertiliser application only, were significantly less than T3, T5 and T6.
Furthermore, the group subject to fertiliser, biostimulant granule and liquid (T5), had significantly higher yields than the group which received fertiliser and liquid biostimulant (T4), but not the group subject to fertiliser and granular biostimulant (T3). T4 did not show significant growth when compared with T3, T5 and T6. This suggests that granular biostimulant may be a more effective application method of eliciting plant growth.
This may be due to the concept that even if biostimulants are manufactured from the same raw material, the modes of action and efficacy can be significantly varied (Battacharyya et al., 2015). A recent study by Rouphael et al., (2017), investigating foliar application of a legume-derived protein hydrolysate biostimulant showed increased yield, photosynthesis and leaf nutritional status of two greenhouse tomato cultivars. Although application of biostimulant had to be administered at a higher dose than used in present study, of 5ml L-1 to have a significant effect. Similarly, research conducted by Goni et al., (2018) found three different foliar-applied seaweed-based biostimulants to offer varying levels of drought tolerance in tomatoes. Seaweed extract A, manufactured using a proprietary process at high temperatures and neutral pH, was shown to have the strongest effect on plant tolerance to drought stress. Sabir et al., (2014) found the combination of foliar applied seaweed-based biostimulant derived from Ascophyllum nodosum with nano-size fertiliser to achieve the greatest cluster weight in grapes during a study in Turkey. However, this contrasts with a recent study conducted in Italy by Frioni et al., (2018) investigating the effect of a biostimulant also derived from A. nodosum on grapevines, which found yield and cluster size to be unaffected following foliar application. These varying results could be attributed to the different cultivars and environments, for example, Sabir et al., (2014) used cv. ‘Narince’ grown in alkaline soil and Frioni et al., (2018) used cv. ‘Sangiovese’ grown in loamy soil. However, they may support the theory that different application methods produce different results, as in findings from present study suggesting that granular biostimulant elicits significantly greater radish growth (Battacharyya et al., 2015).
However, this view contrasts with findings by Polo and Mata (2018), who compared the effect of a foliar and irrigation applied animal protein-based hydrolysate with irrigation applied seaweed extract on tomatoes in Mexico. Research concluded that whilst both biostimulants were the most effective at improving all vegetative parameters when compared with the control group, the foliar and irrigation applied biostimulant elicited the most significant growth. Similarly, Lucini et al., (2015) explored the effect of a plant-derived protein-hydrolysate biostimulant on lettuce grown under saline conditions in a greenhouse in Italy. Treatment groups subject to both foliar and root application benefitted crop growth the most when compared with control groups, mitigating the effects of salt stress. However, biostimulant type was different and application was considerably more than present study at a concentration of 2.5 mL L-1. Whilst results vary, they may provide alternative insight into why treatment groups receiving biostimulant granule in present study performed significantly better than other groups; resulting from the combination of foliar and root application, rather than the efficacy of the granular biostimulant. Exact mechanisms of how granular biostimulant improved plant growth are unknown and beyond parameters of present study. However, factors such as these reinforce the requirement for a single market for biostimulants, defining individual products and their modes of action to better benefit farmers and growers (AHDB, 2017b, Jardin, 2015, Shubha et al., 2017).
4.3 Effect of Biostimulant on Colony Forming Unit Count
The concept that plants work with microbiota within their environments could provide further insight into why biostimulants are effective (Gillings and Holmes, 2004, Tejada et al., 2011, Vandenkoornhuyse, 2015). Biostimulants may have the ability to optimise existing relationships and recover degraded soils through contributing to organic matter and improving soil microbial populations (Besset-Manzoni et al., 2018, Duhan et al., 2017, Moharana and Biswas, 2016, Zhang et al., 2015). However, results from supplementary CFU counts in present study showed control group (T1) to have the largest number of CFU mL-1, suggesting no significant connection between increased CFU count and increased yield.
This contrasts with research by Tejada et al., (2011), who found that the application of four different types of protein-hydrolysates had positive effects on soil biological properties. Biostimulant application over a three-year period resulted in a higher stimulation of soil microbial communities and contributed to the establishment of ground cover and vegetative growth (Tejada et al., 2011). However, in the study by Siwik-Ziomek and Szczepanek (2017) on oilseed rape, whilst the seaweed biostimulant improved the use of S from fertiliser application it was found to have no effect on the activity of soil enzymes studied. This could suggest that there is no connection between biostimulants efficacy and increased soil microbial activity and that biostimulants are able to enhance nutrient uptake without improving soil biological properties as in present study (Siwik-Ziomek and Szczepanek, 2017). However, neither of the cited studies used CFU count, therefore inconclusive results in present study could result from the chosen method of enumeration. Furthermore, Tejada et al., (2011) conducted research over a longer period than present study, providing the length of time needed for soil biological properties to be altered (Eash et al., 2016, Osman, 2012). However, this is beyond the scope of present study and further research identifying beneficial microbiota could potentially result in greater insight into biostimulants effective modes of action and relationship to plant growth.
4.4 Limitations and Scope for Future Research
Poor topsoil resulted in delayed radish growth and failure to thrive, providing irregular data and abnormal distribution. Results could have been improved through using a higher quality topsoil and supplementing it with additional compost, vermiculite and/or perlite, such as described by Goni et al., (2018) and Lucini et al., (2015). Whilst research confirmed efficacy of biostimulant, improved overall radish growth would have contributed to the experiments’ validity by providing more quantifiable, high-quality data. Furthermore, whilst data provided insight into the effect of different treatment groups, specific methods utilising markers may provide a more scientific basis for efficacy. For example, Goni et al., (2018) investigated biostimulants ability to enhance drought tolerance in tomato plants by using the TAS14 gene as a marker. This kind of technology may help to provide a benchmark to better understand how and to what extent biostimulants elicit plant growth.
Additional limitations of experiment include the short time frame of 28 days. Studies conducted over a longer period such as three years in the study by Tejada et al., (2011), may contribute to understanding cumulative effects of biostimulant use such as contribution to organic matter, which are not captured in short time frame studies (Abbot et al., 2018). This may enable further insight into biostimulants’ effect on soil biological properties, especially when supported by additional tests investigating microbial communities of biostimulant treated soils (Calvo et al., 2014, Tejada et al., 2011). The present study showed no connection between increased plant growth after biostimulant use and CFU count. Methods such as that described by Tejada et al., (2011), who explored soil enzymatic activity through analysing phospholipid fatty acids may be more suitable for future research.
4.5 Dissemination Plan
Current research will be of relevance to the agricultural industry, particularly farmers and growers either already in organic systems, seeking alternatives to traditional inputs or desiring to use less dosage due to rising costs and environmental impacts (Auserwald et al., 2018, Conijn et al., 2018, Duhan et al., 2017, Karimi et al., 2018, Moharana and Biswas, 2016, Ondrasek et al., 2018, Owen et al., 2014). Farmers and growers can be accessed through research presented in a clear and concise manner in the form of an oral presentation at a conference such as the Cereals Event (Cereals Event, 2018) or the CropTec Show (CropTec Show, 2018). Furthermore, an article in a relevant magazine such as the Farmers Weekly (Farmers Weekly, 2018) or the Organic Farming Magazine (Soil Association, 2018) would provide an appropriate platform for findings to be made accessible to varying audiences. This will enable research to be communicated and easily replicated, benefitting the agricultural industry through improving crop production.
Additional audiences include researchers and scientists seeking to conduct further trials, potentially on a larger scale and with more funding available. Researchers can be accessed through seeking to publish present study in a relevant journal such as Frontiers in Plant Science (Frontiers, 2018) or Advances in Agriculture (Hindawi, 2018). Amendments to the current study would be required to make formatting suitable for various journals, and to revise content to ensure accuracy for publication.
Furthermore, research is of relevance to the biostimulant industry and will contribute to the body of research exploring biostimulants benefit to crop production. Representatives of the industry and those interested can be accessed through oral presentations with specific focus on scientific basis of efficacy and can be delivered in events such as the Biostimulants Europe Conference (European Biostimulants Industry Council, 2018) or AgBio: Innovate Europe (AgBio, 2018). This may further enable manufacturers of biostimulant products to approach companies of influence within the industry such as Agrii (Agrii, 2018), with the possibility of conducting larger scale trials with farmers, growers and agronomists from a variety of backgrounds.
Overall, dissemination of research will contribute to generating legislative action and developing a single market for biostimulant products. This will ensure the range of products available are adhering to appropriate standards, supporting the widespread use of biostimulants within the agricultural industry (Shubha et al., 2017).
In conclusion, application of organic freshwater plant-based biostimulant Vanvruddhi significantly increased the overall length and weight of radish. Groups subject to granular biostimulant showed significantly higher yields than groups receiving none or liquid biostimulant only. Whilst speculation into the specific modes of action were made, alongside supplementary research into CFU count, exact mechanisms are beyond the parameters of present study and remain unknown. Future studies exploring the effect of biostimulant and soil biology on a wider variety of agricultural and horticultural crops over a longer period is suggested. Overall, the benefit of biostimulants to the agricultural industry is apparent, with application enhancing plant growth and increasing tolerance to stress, resulting in improved yield and quality.
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1) The effect of Vanvruddhi, an organic freshwater plant-based biostimulant on weight, length and colony forming unit of radish (Raphanus sativus l. Cv. French breakfast radish): Alexandra Growden (BSc (Hons) Agriculture) and Patrick Tandy
2) Do cognitive abilities differ between the working dog group and the toy dog group? Georgina Lewis (BSc (Hons) Animal Behaviour and Welfare) and Dr Carrie Ijichi
3) Welcome to Hartpury: How does a university attract new first-year students through marketing communication strategies – with specific reference to open days: Anastasia Noble (BA (Hons) Equine Business Management) and Mike Green
4) The effect of a horse’s racing career on the success in eventing post retraining: Sarah Price (BSc (Hons) Equine Science) and Rachel Collins
5) Microbiological assessment of canine drinking water: the impact of construction material on the quantity and species of bacteria present in water bowls: Coralie Wright (BSc (Hons) Bioveterinary Science) and Aisling Carroll
Author Name: Heather Watts, BSc (Hons) Animal Behaviour and Welfare
Substantial research has supported the theory that children who abuse animals may become desensitised to the abuse, and therefore, are likely to move onto violence to humans in adulthood. This is termed ‘the graduation hypothesis’. However, much of this research is methodically limited and uses retrospective and cross-section approaches. More longitudinal studies are needed, with control groups, moving away from the focus on prison populations and self-report methods. This may improve accuracy of results and our understanding of juvenile animal cruelty and its relation to adult interpersonal violence. Alternative research has produced evidence in support of the ‘deviance generalisation hypothesis’, whereby violence to animals and violence to humans is generally related throughout the offenders’ lifetime. It is possible that results supporting the graduation hypothesis have produced ‘false positives’, as mainly the relationship between juvenile animal cruelty and adult interpersonal violence has been investigated, with general offending not included. Research into the methods of juvenile animal cruelty and their relation to adult interpersonal violence, has revealed bestiality as a risk factor for future human violence. However, the motivations for bestiality should be further investigated to confirm the risks to humans. Research into the motivations for animal cruelty and how they relate to future human violence needs more consideration of the full array of motives and social circumstances. Understanding juvenile animal cruelty and its relation to future interpersonal violence is of great interest to criminologists, sociologists, psychologists, legal scholars and social workers. Research may shape attitudes and laws, and reveal potential risk factors for future human violence.
Throughout history, those with violent tendencies towards animals, have been said to likely be a risk to humans also (Flynn, 2011). Animal cruelty is often related to many aggressive and antisocial behaviours, such as assault, rape and domestic violence (McPhedran, 2009). Interest has increased in the putative relationship between childhood animal cruelty and its progression onto adult interpersonal violence, often termed ‘the graduation hypothesis’ (Walters, 2013). This hypothesis postulates that abusing and killing animals provides an opportunity to experiment with abuse, desensitising the child, and serving as a stepping stone or starting point to later progress onto adult human violence (Walters, 2014). There are a wealth of case studies involving infamous murderers, who are said to have abused animals in their youth (Petersen and Farrington, 2007). Carroll Cole, Jeffrey Dahmer, Edmund Kemper, Henry Lee Lucas, and Arthur Shawcross are among the list of serial killers who abused and killed animals during their troubled childhoods (Wright and Hensley, 2003). Psychiatric interest began in the 1960’s (Macdonald, 1967) and much support for this hypothesis has arisen since the 1990’s (Patterson-Kane and Piper, 2009). However, conflicts exist in published research regarding the validity of the ‘link’ (Hensley, Tallichet, and Dutkiewicz, 2009). Research is largely underpinned by anthropocentric approaches, with animal abuse often labelled as a ‘red flag’ for interpersonal violence (Taylor and Signal, 2008). Critics suggest that rather than concentrating on abuse at a psychological or personal level, social and cultural levels should be more thoroughly investigated (Taylor and Signal, 2008). The treatment of animals and what is socially acceptable may vary between cultures (Flynn, 2011), therefore harming animals may not necessarily indicate underlying psychosis, if it is a societal norm (Patterson-Kane and Piper, 2009). Accurately understanding animal abuse and interpersonal violence is important to identify risk factors that may cause possible future violence, facilitating prevention (Lockwood and Arkow, 2016). Preventing animal cruelty in juveniles may also provide positive benefits to humans through reducing the likelihood of violence escalating to adult human violence (Petersen and Farrington, 2007).
2.0 The Graduation Hypothesis
Substantial research produced since the 1980’s has supported the graduation hypothesis (e.g. Hensley and Tallichet, 2004; Ressler et al., 1986; Wright and Hensley, 2003). However, it has been suggested that this research has not been supported by enough sound evidence (Ascione and Shapiro, 2009). For example, Wright and Hensley (2003) analysed 5 case studies of infamous serial murderers who abused animals in their youth to gather evidence for the graduation hypothesis. The validity of this can be questioned as there are also a vast number of serial killers that did not abuse animals as children (Arluke et al., 1999), and it is problematic to assume all those who harm animals will develop into serial killers (Patterson-Kane and Piper, 2009). A study by Simons, Wurtele, and Durham (2008) investigating the childhood experiences of child sexual abusers and rapists revealed that 38% of child sexual abusers participated in sexual activities with animals during their developmental period. In addition, 68% of rapists reported regular experiences of participating in animal cruelty during childhood (Simons, Wurtele, and Durham, 2008). However, the animal offences committed were combined with other detrimental developmental experiences, such as experiencing sexual and physical abuse, therefore it cannot be concluded that animal cruelty in childhood alone caused future interpersonal violence. The study was also reflective of adult sexual offenders only, hence the prevalence of juvenile animal abuse in the general population is unknown, as is the relation of juvenile animal abuse to other forms of non-sexual adult interpersonal violence. Hensley, Tallichet, and Dutkiewicz (2009) investigated the relationship between recurrent childhood animal cruelty and adult recurrent interpersonal violence using a survey sample of 180 male prisoners. The results indicated that those who participated in a higher number of childhood animal abuse acts were more likely as adults to have committed repeated interpersonal crimes. Demographic factors were not significant. However, the findings were taken from prison inmates, which are an extreme group, and not representative of the general population (Flynn, 2011). These kinds of participants may even be inclined to exaggerate answers to keep up a ‘tough’ persona, which may produce inaccurate results (Flynn, 2011). The reliability of self-report research can be questioned (Kimberlin and Winetrstein, 2008), especially involving subjects where people may not want to admit to harming an animal or human (Patterson-Kane and Piper, 2009). This may apply more to members of the general population who may wish to avoid criminal convictions. However, prison inmates may be given reduced sentences for good behaviour (Couture and Scheller, 2009), therefore, may not want to confess to extra crimes. The questionnaire received a 10% response rate, hence, may not be a fair representation of the prison population. The use of paper questionnaires may have excluded illiterate inmates, therefore, conducting interviews may be a more effective way to make sure that illiterate inmates are not excluded from the data collection (Hensley, Tallichet, and Dutkiewicz, 2009). The sample consisted of all male inmates, thus, gender differences in human-animal interactions were excluded. However, there is evidence that boys may commit more animal cruelty than girls (Herzog, 2007). The definition of cruelty, ‘how many times have you ever hurt or killed animals, other than hunting’ may also be problematic. A participant may have killed an animal as an act of mercy, or have worked in an abattoir, but this would be counted as abuse.
Walters (2014) tested the relationship between childhood animal cruelty and subsequent aggressive and non-aggressive offending. A larger sample size of 1,336 inmates, including 182 females was collected, compared to the Hensley, Tallichet, and Dutkiewicz (2009) study which had 180 males. The sample is still highly biased in favour of men though, which may not fully represent gender differences. The participants used for the study were the same participants from the pathways to resistance study (Mulvey, 2013) who were judged delinquent between the ages of 14-18. Participants were asked ‘Did you ever physically hurt an animal on purpose?’. Findings revealed that juvenile animal abuse was a precursor to non-aggressive and aggressive offending. This does not support the graduation hypothesis as violence to humans did not always follow, rather general offending. This is more in line with the ‘deviance generalisation’ hypothesis, whereby animal abuse and many other forms of general offending positively correlate, making no postulation of time-order, but based on the idea that those who abuse animals are likely to commit other forms of crime also (Arluke et al., 1999). Volant et al., (2008) state that it is more likely that human and animal violence are present throughout the offenders’ life time, rather than children ‘graduating up’. There was no evidence that age, sex, race and early onset of behavioural problems had an effect on the animal cruelty following offending relationship. The findings by Walters (2014) may be more accurate than the study by Hensley, Tallichet, and Dutkiewicz (2009), as prospective data was gathered from a longitudinal study. Hensley, Tallichet, and Dutkiewicz (2009) also only included interpersonal crimes in their study, and did not account for general offending as did Walters (2014), hence, the probability of the deviance generalisation hypothesis was excluded. This may have produced a false positive result for the graduation hypothesis. Merz-Perez, Heide, and Silverman, (2001) also investigated aggressive and non-aggressive offending in incarcerated individuals and the link to juvenile animal abuse. Their results differ to Walters (2014) such that they found that juvenile animal abuse was predictive of adult human violence, but not non-violent offending, thus supporting the graduation hypothesis. However, a small sample of 90 participants were used, which may make the results less accurate, compared to the 1,336 in the study by Walters (2014).
2.1 Childhood Animal Cruelty Methods and Adult Interpersonal Violence
Hensley and Tallichet (2009) investigated juvenile animal cruelty methods (drowned, hit or kicked, shot, choked, burned, and had sex) and their relation to adult interpersonal violence (assault, murder and rape), through a survey of 261 inmates. Results revealed that participants who had drowned or had sex with an animal, were more likely to have committed adult interpersonal violence. Hensley and Tallichet (2009) propose that this may be because these types of abuse are more ‘hands on’, and involve overpowering an animal, which may result in the same treatment of humans. Abuse such as bestiality may be severely underreported by juveniles (Schenk et al., 2014), which may be similar for adults, possibly understating results. However, as this is a retrospective study it may be flawed by recall bias, which could create inaccuracies in the results through incorrect recollections (Holoyda and Newman, 2016). Similarly, to Hensley, Tallichet, and Dutkiewicz (2009), the study used incarcerated individuals, and the questionnaire yielded a low response rate (10%) hence not providing a fair representation of the prison population. A prospective study, involving a group of juvenile animal abusers and a non-abusing control group and the outcomes of their adult behaviour may provide more accurate longitudinal research (Holoyda and Newman, 2016). Hensley, Tallichet and Dutkiewicz (2010) further investigated childhood bestiality and its link to adult interpersonal crime. A small sample size of 180 incarcerated individuals were used, with again only a 10% response rate to the survey, questioning the validity of the sample. Similarly, to the study by Hensley and Tallichet (2009), the results revealed that childhood bestialics were more likely to have committed interpersonal violence than those who were not. Hensley, Tallichet and Dutkiewicz (2010) suggest that this is due to lack of empathy and violent and sexual behaviours being joined during development, resulting in the enjoyment of overpowering victims. However, unlike Hensley and Tallichet (2009), drowning was not a significant predictor for future interpersonal violence. Hensley, Tallichet and Dutkiewicz (2010) added robbery and aggravated assault to violent history options, and this may have had an effect on results. Although race was accounted for, only two groups were presented ‘white’ or ‘other’. Cultural differences may have an impact on how animals are treated (Knight and Herzog, 2009), therefore, a broader option of ethnicities may have been more useful in determining if culture has an impact on results. The type of animal may also be a valid measure to record, relating to socially acceptable cultural differences (Patterson-Kane and Piper, 2009). The participants involved mostly had non-bestiality convictions, therefore, were not likely to have zoophilic tendencies, and may have been generally violent to humans, which may restrict the applicability of results (Holoyda and Newman, 2016). It is possible that those who have zoophilic tendencies, do not do so out of aggression, but sexual attraction, and wanting to express affection towards the animal (Holoyda and Newman, 2014). These ‘pure zoophiles’ may not necessarily pose a risk to humans (Holoyda and Newman, 2014). Using a classification scheme based on the motivations for bestiality, may help to elucidate the risks to humans, as those doing so out of violence or cruelty may be more of a threat (Holoyda and Newman, 2016). In 2011, Henderson, Hensley, and Tallichet, replicated the study by Hensley and Tallichet (2009) to further research animal cruelty methods and their link to adult interpersonal violence. The results revealed animal cruelty starting at a younger age, drowning animals and bestiality to be significant predictors for adult interpersonal violence, results consistent with their previous study (Hensley and Tallichet, 2009). This potentially provides more support for childhood bestiality being linked to adult interpersonal violence.
2.2 Childhood Animal Cruelty Motives and Adult Interpersonal Violence
Research into the motives for childhood animal cruelty linking to adult interpersonal crime has proved contradictory (Holoyda and Newman, 2016). Hensley and Tallichet (2008) conducted a study to determine if particular motives for animal cruelty had a link to later interpersonal violence. A sample of 261 inmates completed a survey on the types of violent interpersonal convictions obtained, and reasons for juvenile animal abuse (fun, anger, dislike, imitation). The results indicated that abusing animals for fun was significant for predicting adult interpersonal crime. Control, punishment, cultural prejudice, attempting to impress others, revenge, and displaced aggression are some of the common reasons for animal cruelty (Lockwood and Arkow, 2016), which were not included in the study by Hensley and Tallichet (2008). The motives for animal abuse may be far more complex, and depend on an array of circumstances (Lockwood and Arkow, 2016), therefore, four motivational categories do not seem robust enough to give this study meaningful results. Children from violent families that witness animal abuse (DeGue and DiLillo, 2009), and juveniles who are victims of, or carry out physical bullying are more likely to commit animal abuse themselves (Henry and Sanders, 2007). Animal abuse may have occurred through fear of the animal, or ignorance, such that possibly the child did not realise the animal could suffer (Pagani, Robustelli, and Ascione, 2010). Therefore, it would be beneficial to ask the participants’ personal history to make motives clearer. When investigating frequencies of crimes, rather than asking the inmates if they had been convicted of a crime, it may have been more beneficial to ask them if they had committed the crime, as they may not necessarily have been convicted. The presumption that inmates would also understand the legalistic terms included in the survey may also be problematic. In 2012, Overton, Hensley, and Tallichet replicated the study by Hensley and Tallichet (2008), investigating childhood animal cruelty motives and their link to adult interpersonal violence. The same survey was distributed to a different prison, robbery was added to the choice of violent crime and 180 inmates responded. The study produced contradictory results to Hensley and Tallichet (2008), with none of the motives being a significant predictor of adult interpersonal violence. Only recurrent childhood animal abuse was predictive of later recurrent violent crime. However, robbery was not defined to the participants; directly stealing possessions from an individual would be considered interpersonal violence, but stealing from a house or shop would not (Perreault and Brennan, 2010). Participants may have been included into the ‘robbery’ category, but possibly have not committed interpersonal crime, affecting the accuracy of results. It is also possible that the complexity of a full range of motives was not investigated thoroughly enough. Using a different prison and receiving contradictory results may highlight the need for a larger sample size when investigating these populations.
Research has supported the theory of childhood animal abuse being likely to lead onto adult human violence. However, some major methodological issues are present that are occurring throughout most of the studies. Small samples of male incarcerated individuals are repeatedly used, which are atypical of the prison population and certainly of the general population. Clearer definitions of animal abuse are needed, with investigation into personal social history and cultural factors. Future studies should move away from incarcerated participants and include those who may have abused animals but did not graduate to human violence. Self-report, cross-sectional and retrospective research may be limiting results and more longitudinal studies are needed. Most research is correlational in nature which does not help to identify the causation or time-order. Groups of juvenile animal abusers could be studied, with a control group of non-abusers, their adult behaviour could be recorded to see the likely progression onto human violence. It is possible that this kind of research has not been conducted due to the ethically sensitive methods involved, or has commenced but the results will not be available for a considerable time. Classification schemes looking at the motives for childhood bestiality are needed to realise the true threat to humans. The complexity of animal abuse motives during childhood may be the reason for contradictory results, and including detrimental social experiences to the participants may improve understanding. There is substantial evidence supporting that whenever humans are at risk, animals are too, and when animals are at risk, so are humans. It is possible that theories such as the deviance generalisation hypothesis are more sound. However, the graduation hypothesis should not be dismissed. Continuously replicating studies without improving on their weaknesses is not advancing research. Only once the major limitations are addressed, can we truly begin to understand and draw conclusions regarding the relationship between childhood animal cruelty and adult interpersonal violence.
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