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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